knowledge-based variable selection: Topics by Science.gov

Sample records for knowledge-based variable selection

A review of covariate selection for non-experimental comparative effectiveness research.

PubMed

Sauer, Brian C; Brookhart, M Alan; Roy, Jason; VanderWeele, Tyler

2013-11-01

This paper addresses strategies for selecting variables for adjustment in non-experimental comparative effectiveness research and uses causal graphs to illustrate the causal network that relates treatment to outcome. Variables in the causal network take on multiple structural forms. Adjustment for a common cause pathway between treatment and outcome can remove confounding, whereas adjustment for other structural types may increase bias. For this reason, variable selection would ideally be based on an understanding of the causal network; however, the true causal network is rarely known. Therefore, we describe more practical variable selection approaches based on background knowledge when the causal structure is only partially known. These approaches include adjustment for all observed pretreatment variables thought to have some connection to the outcome, all known risk factors for the outcome, and all direct causes of the treatment or the outcome. Empirical approaches, such as forward and backward selection and automatic high-dimensional proxy adjustment, are also discussed. As there is a continuum between knowing and not knowing the causal, structural relations of variables, we recommend addressing variable selection in a practical way that involves a combination of background knowledge and empirical selection and that uses high-dimensional approaches. This empirical approach can be used to select from a set of a priori variables based on the researcher's knowledge to be included in the final analysis or to identify additional variables for consideration. This more limited use of empirically derived variables may reduce confounding while simultaneously reducing the risk of including variables that may increase bias. Copyright © 2013 John Wiley & Sons, Ltd.
A Review of Covariate Selection for Nonexperimental Comparative Effectiveness Research

PubMed Central

Sauer, Brian C.; Brookhart, Alan; Roy, Jason; Vanderweele, Tyler

2014-01-01

This paper addresses strategies for selecting variables for adjustment in non-experimental comparative effectiveness research (CER), and uses causal graphs to illustrate the causal network that relates treatment to outcome. Variables in the causal network take on multiple structural forms. Adjustment for on a common cause pathway between treatment and outcome can remove confounding, while adjustment for other structural types may increase bias. For this reason variable selection would ideally be based on an understanding of the causal network; however, the true causal network is rarely know. Therefore, we describe more practical variable selection approaches based on background knowledge when the causal structure is only partially known. These approaches include adjustment for all observed pretreatment variables thought to have some connection to the outcome, all known risk factors for the outcome, and all direct causes of the treatment or the outcome. Empirical approaches, such as forward and backward selection and automatic high-dimensional proxy adjustment, are also discussed. As there is a continuum between knowing and not knowing the causal, structural relations of variables, we recommend addressing variable selection in a practical way that involves a combination of background knowledge and empirical selection and that uses the high-dimensional approaches. This empirical approach can be used to select from a set of a priori variables based on the researcher’s knowledge to be included in the final analysis or to identify additional variables for consideration. This more limited use of empirically-derived variables may reduce confounding while simultaneously reducing the risk of including variables that may increase bias. PMID:24006330
Integrating biological knowledge into variable selection: an empirical Bayes approach with an application in cancer biology

PubMed Central

2012-01-01

Background An important question in the analysis of biochemical data is that of identifying subsets of molecular variables that may jointly influence a biological response. Statistical variable selection methods have been widely used for this purpose. In many settings, it may be important to incorporate ancillary biological information concerning the variables of interest. Pathway and network maps are one example of a source of such information. However, although ancillary information is increasingly available, it is not always clear how it should be used nor how it should be weighted in relation to primary data. Results We put forward an approach in which biological knowledge is incorporated using informative prior distributions over variable subsets, with prior information selected and weighted in an automated, objective manner using an empirical Bayes formulation. We employ continuous, linear models with interaction terms and exploit biochemically-motivated sparsity constraints to permit exact inference. We show an example of priors for pathway- and network-based information and illustrate our proposed method on both synthetic response data and by an application to cancer drug response data. Comparisons are also made to alternative Bayesian and frequentist penalised-likelihood methods for incorporating network-based information. Conclusions The empirical Bayes method proposed here can aid prior elicitation for Bayesian variable selection studies and help to guard against mis-specification of priors. Empirical Bayes, together with the proposed pathway-based priors, results in an approach with a competitive variable selection performance. In addition, the overall procedure is fast, deterministic, and has very few user-set parameters, yet is capable of capturing interplay between molecular players. The approach presented is general and readily applicable in any setting with multiple sources of biological prior knowledge. PMID:22578440
Knowledge Driven Variable Selection (KDVS) – a new approach to enrichment analysis of gene signatures obtained from high–throughput data

PubMed Central

2013-01-01

Background High–throughput (HT) technologies provide huge amount of gene expression data that can be used to identify biomarkers useful in the clinical practice. The most frequently used approaches first select a set of genes (i.e. gene signature) able to characterize differences between two or more phenotypical conditions, and then provide a functional assessment of the selected genes with an a posteriori enrichment analysis, based on biological knowledge. However, this approach comes with some drawbacks. First, gene selection procedure often requires tunable parameters that affect the outcome, typically producing many false hits. Second, a posteriori enrichment analysis is based on mapping between biological concepts and gene expression measurements, which is hard to compute because of constant changes in biological knowledge and genome analysis. Third, such mapping is typically used in the assessment of the coverage of gene signature by biological concepts, that is either score–based or requires tunable parameters as well, limiting its power. Results We present Knowledge Driven Variable Selection (KDVS), a framework that uses a priori biological knowledge in HT data analysis. The expression data matrix is transformed, according to prior knowledge, into smaller matrices, easier to analyze and to interpret from both computational and biological viewpoints. Therefore KDVS, unlike most approaches, does not exclude a priori any function or process potentially relevant for the biological question under investigation. Differently from the standard approach where gene selection and functional assessment are applied independently, KDVS embeds these two steps into a unified statistical framework, decreasing the variability derived from the threshold–dependent selection, the mapping to the biological concepts, and the signature coverage. We present three case studies to assess the usefulness of the method. Conclusions We showed that KDVS not only enables the selection of known biological functionalities with accuracy, but also identification of new ones. An efficient implementation of KDVS was devised to obtain results in a fast and robust way. Computing time is drastically reduced by the effective use of distributed resources. Finally, integrated visualization techniques immediately increase the interpretability of results. Overall, KDVS approach can be considered as a viable alternative to enrichment–based approaches. PMID:23302187
Improved knowledge diffusion model based on the collaboration hypernetwork

NASA Astrophysics Data System (ADS)

Wang, Jiang-Pan; Guo, Qiang; Yang, Guang-Yong; Liu, Jian-Guo

2015-06-01

The process for absorbing knowledge becomes an essential element for innovation in firms and in adapting to changes in the competitive environment. In this paper, we present an improved knowledge diffusion hypernetwork (IKDH) model based on the idea that knowledge will spread from the target node to all its neighbors in terms of the hyperedge and knowledge stock. We apply the average knowledge stock V(t) , the variable σ2(t) , and the variance coefficient c(t) to evaluate the performance of knowledge diffusion. By analyzing different knowledge diffusion ways, selection ways of the highly knowledgeable nodes, hypernetwork sizes and hypernetwork structures for the performance of knowledge diffusion, results show that the diffusion speed of IKDH model is 3.64 times faster than that of traditional knowledge diffusion (TKDH) model. Besides, it is three times faster to diffuse knowledge by randomly selecting "expert" nodes than that by selecting large-hyperdegree nodes as "expert" nodes. Furthermore, either the closer network structure or smaller network size results in the faster knowledge diffusion.
Variables selection methods in near-infrared spectroscopy.

PubMed

Xiaobo, Zou; Jiewen, Zhao; Povey, Malcolm J W; Holmes, Mel; Hanpin, Mao

2010-05-14

Near-infrared (NIR) spectroscopy has increasingly been adopted as an analytical tool in various fields, such as the petrochemical, pharmaceutical, environmental, clinical, agricultural, food and biomedical sectors during the past 15 years. A NIR spectrum of a sample is typically measured by modern scanning instruments at hundreds of equally spaced wavelengths. The large number of spectral variables in most data sets encountered in NIR spectral chemometrics often renders the prediction of a dependent variable unreliable. Recently, considerable effort has been directed towards developing and evaluating different procedures that objectively identify variables which contribute useful information and/or eliminate variables containing mostly noise. This review focuses on the variable selection methods in NIR spectroscopy. Selection methods include some classical approaches, such as manual approach (knowledge based selection), "Univariate" and "Sequential" selection methods; sophisticated methods such as successive projections algorithm (SPA) and uninformative variable elimination (UVE), elaborate search-based strategies such as simulated annealing (SA), artificial neural networks (ANN) and genetic algorithms (GAs) and interval base algorithms such as interval partial least squares (iPLS), windows PLS and iterative PLS. Wavelength selection with B-spline, Kalman filtering, Fisher's weights and Bayesian are also mentioned. Finally, the websites of some variable selection software and toolboxes for non-commercial use are given. Copyright 2010 Elsevier B.V. All rights reserved.
Model selection bias and Freedman's paradox

USGS Publications Warehouse

Lukacs, P.M.; Burnham, K.P.; Anderson, D.R.

2010-01-01

In situations where limited knowledge of a system exists and the ratio of data points to variables is small, variable selection methods can often be misleading. Freedman (Am Stat 37:152-155, 1983) demonstrated how common it is to select completely unrelated variables as highly "significant" when the number of data points is similar in magnitude to the number of variables. A new type of model averaging estimator based on model selection with Akaike's AIC is used with linear regression to investigate the problems of likely inclusion of spurious effects and model selection bias, the bias introduced while using the data to select a single seemingly "best" model from a (often large) set of models employing many predictor variables. The new model averaging estimator helps reduce these problems and provides confidence interval coverage at the nominal level while traditional stepwise selection has poor inferential properties. ?? The Institute of Statistical Mathematics, Tokyo 2009.
Variable selection for confounder control, flexible modeling and Collaborative Targeted Minimum Loss-based Estimation in causal inference

PubMed Central

Schnitzer, Mireille E.; Lok, Judith J.; Gruber, Susan

2015-01-01

This paper investigates the appropriateness of the integration of flexible propensity score modeling (nonparametric or machine learning approaches) in semiparametric models for the estimation of a causal quantity, such as the mean outcome under treatment. We begin with an overview of some of the issues involved in knowledge-based and statistical variable selection in causal inference and the potential pitfalls of automated selection based on the fit of the propensity score. Using a simple example, we directly show the consequences of adjusting for pure causes of the exposure when using inverse probability of treatment weighting (IPTW). Such variables are likely to be selected when using a naive approach to model selection for the propensity score. We describe how the method of Collaborative Targeted minimum loss-based estimation (C-TMLE; van der Laan and Gruber, 2010) capitalizes on the collaborative double robustness property of semiparametric efficient estimators to select covariates for the propensity score based on the error in the conditional outcome model. Finally, we compare several approaches to automated variable selection in low-and high-dimensional settings through a simulation study. From this simulation study, we conclude that using IPTW with flexible prediction for the propensity score can result in inferior estimation, while Targeted minimum loss-based estimation and C-TMLE may benefit from flexible prediction and remain robust to the presence of variables that are highly correlated with treatment. However, in our study, standard influence function-based methods for the variance underestimated the standard errors, resulting in poor coverage under certain data-generating scenarios. PMID:26226129
Variable Selection for Confounder Control, Flexible Modeling and Collaborative Targeted Minimum Loss-Based Estimation in Causal Inference.

PubMed

Schnitzer, Mireille E; Lok, Judith J; Gruber, Susan

2016-05-01

This paper investigates the appropriateness of the integration of flexible propensity score modeling (nonparametric or machine learning approaches) in semiparametric models for the estimation of a causal quantity, such as the mean outcome under treatment. We begin with an overview of some of the issues involved in knowledge-based and statistical variable selection in causal inference and the potential pitfalls of automated selection based on the fit of the propensity score. Using a simple example, we directly show the consequences of adjusting for pure causes of the exposure when using inverse probability of treatment weighting (IPTW). Such variables are likely to be selected when using a naive approach to model selection for the propensity score. We describe how the method of Collaborative Targeted minimum loss-based estimation (C-TMLE; van der Laan and Gruber, 2010 [27]) capitalizes on the collaborative double robustness property of semiparametric efficient estimators to select covariates for the propensity score based on the error in the conditional outcome model. Finally, we compare several approaches to automated variable selection in low- and high-dimensional settings through a simulation study. From this simulation study, we conclude that using IPTW with flexible prediction for the propensity score can result in inferior estimation, while Targeted minimum loss-based estimation and C-TMLE may benefit from flexible prediction and remain robust to the presence of variables that are highly correlated with treatment. However, in our study, standard influence function-based methods for the variance underestimated the standard errors, resulting in poor coverage under certain data-generating scenarios.
Resampling procedures to identify important SNPs using a consensus approach.

PubMed

Pardy, Christopher; Motyer, Allan; Wilson, Susan

2011-11-29

Our goal is to identify common single-nucleotide polymorphisms (SNPs) (minor allele frequency > 1%) that add predictive accuracy above that gained by knowledge of easily measured clinical variables. We take an algorithmic approach to predict each phenotypic variable using a combination of phenotypic and genotypic predictors. We perform our procedure on the first simulated replicate and then validate against the others. Our procedure performs well when predicting Q1 but is less successful for the other outcomes. We use resampling procedures where possible to guard against false positives and to improve generalizability. The approach is based on finding a consensus regarding important SNPs by applying random forests and the least absolute shrinkage and selection operator (LASSO) on multiple subsamples. Random forests are used first to discard unimportant predictors, narrowing our focus to roughly 100 important SNPs. A cross-validation LASSO is then used to further select variables. We combine these procedures to guarantee that cross-validation can be used to choose a shrinkage parameter for the LASSO. If the clinical variables were unavailable, this prefiltering step would be essential. We perform the SNP-based analyses simultaneously rather than one at a time to estimate SNP effects in the presence of other causal variants. We analyzed the first simulated replicate of Genetic Analysis Workshop 17 without knowledge of the true model. Post-conference knowledge of the simulation parameters allowed us to investigate the limitations of our approach. We found that many of the false positives we identified were substantially correlated with genuine causal SNPs.
The Relationship between Attitudes, Knowledge, and Demographic Variables of High School Teachers Regarding Food Irradiation

ERIC Educational Resources Information Center

Thompson, B. M.; Ribera, K. P.; Wingenbach, G. J.; Vestal, T. A.

2007-01-01

The purpose of this study was to use a validated instrument to determine the attitudes and knowledge of high school teachers regarding food irradiation, and to determine the correlations among their knowledge and attitudes and certain demographic variables. Knowledge and attitudes about food irradiation were measured in selected high school family…
Sustainability knowledge using “AKASA” model among architecture students from Klang Valley private universities, Malaysia.

NASA Astrophysics Data System (ADS)

Kuppusamy, Sivaraman; Faris Khamidi, Mohd; Sheng, Lee Xia; Salvi Mari, Tamil

2017-12-01

The study intend to investigate sustainability knowledge using “AKASA” model. This model comprises all the literacy level which is the awareness, knowledge, attitude, skills and action. 234 students from 5 selected private universities were surveyed using questionnaires. Students were specifically selected from year 2 and year 3 from private universities in Klang valley, Malaysia. The study intends to investigate the environmental literacy level specifically the knowledge variable. The parametric study was conducted with descriptive analysis and the results shows that the environmental knowledge is at high level compared to other environmental literacy variables among year 2, year 3 and combine year 2 and year 3.
An Investigation of Factors That Influence the Hypothesis Generation Ability of Students in School- Based Agricultural Education Programs When Troubleshooting Small Gasoline Engines

ERIC Educational Resources Information Center

Blackburn, J. Joey; Robinson, J. Shane

2017-01-01

The purpose of this study was to determine if selected factors influenced the ability of students in school-based agricultural education programs to generate a correct hypothesis when troubleshooting small gasoline engines. Variables of interest included students' cognitive style, age, GPA, and content knowledge in small gasoline engines. Kirton's…
Treatment Selection in Depression.

PubMed

Cohen, Zachary D; DeRubeis, Robert J

2018-05-07

Mental health researchers and clinicians have long sought answers to the question "What works for whom?" The goal of precision medicine is to provide evidence-based answers to this question. Treatment selection in depression aims to help each individual receive the treatment, among the available options, that is most likely to lead to a positive outcome for them. Although patient variables that are predictive of response to treatment have been identified, this knowledge has not yet translated into real-world treatment recommendations. The Personalized Advantage Index (PAI) and related approaches combine information obtained prior to the initiation of treatment into multivariable prediction models that can generate individualized predictions to help clinicians and patients select the right treatment. With increasing availability of advanced statistical modeling approaches, as well as novel predictive variables and big data, treatment selection models promise to contribute to improved outcomes in depression.
A Knowledge-Based Arrangement of Prototypical Neural Representation Prior to Experience Contributes to Selectivity in Upcoming Knowledge Acquisition.

PubMed

Kurashige, Hiroki; Yamashita, Yuichi; Hanakawa, Takashi; Honda, Manabu

2018-01-01

Knowledge acquisition is a process in which one actively selects a piece of information from the environment and assimilates it with prior knowledge. However, little is known about the neural mechanism underlying selectivity in knowledge acquisition. Here we executed a 2-day human experiment to investigate the involvement of characteristic spontaneous activity resembling a so-called "preplay" in selectivity in sentence comprehension, an instance of knowledge acquisition. On day 1, we presented 10 sentences (prior sentences) that were difficult to understand on their own. On the following day, we first measured the resting-state functional magnetic resonance imaging (fMRI). Then, we administered a sentence comprehension task using 20 new sentences (posterior sentences). The posterior sentences were also difficult to understand on their own, but some could be associated with prior sentences to facilitate their understanding. Next, we measured the posterior sentence-induced fMRI to identify the neural representation. From the resting-state fMRI, we extracted the appearances of activity patterns similar to the neural representations for posterior sentences. Importantly, the resting-state fMRI was measured before giving the posterior sentences, and thus such appearances could be considered as preplay-like or prototypical neural representations. We compared the intensities of such appearances with the understanding of posterior sentences. This gave a positive correlation between these two variables, but only if posterior sentences were associated with prior sentences. Additional analysis showed the contribution of the entorhinal cortex, rather than the hippocampus, to the correlation. The present study suggests that prior knowledge-based arrangement of neural activity before an experience contributes to the active selection of information to be learned. Such arrangement prior to an experience resembles preplay activity observed in the rodent brain. In terms of knowledge acquisition, the present study leads to a new view of the brain (or more precisely of the brain's knowledge) as an autopoietic system in which the brain (or knowledge) selects what it should learn by itself, arranges preplay-like activity as a position for the new information in advance, and actively reorganizes itself.
A Knowledge-Based Arrangement of Prototypical Neural Representation Prior to Experience Contributes to Selectivity in Upcoming Knowledge Acquisition

PubMed Central

Kurashige, Hiroki; Yamashita, Yuichi; Hanakawa, Takashi; Honda, Manabu

2018-01-01

Knowledge acquisition is a process in which one actively selects a piece of information from the environment and assimilates it with prior knowledge. However, little is known about the neural mechanism underlying selectivity in knowledge acquisition. Here we executed a 2-day human experiment to investigate the involvement of characteristic spontaneous activity resembling a so-called “preplay” in selectivity in sentence comprehension, an instance of knowledge acquisition. On day 1, we presented 10 sentences (prior sentences) that were difficult to understand on their own. On the following day, we first measured the resting-state functional magnetic resonance imaging (fMRI). Then, we administered a sentence comprehension task using 20 new sentences (posterior sentences). The posterior sentences were also difficult to understand on their own, but some could be associated with prior sentences to facilitate their understanding. Next, we measured the posterior sentence-induced fMRI to identify the neural representation. From the resting-state fMRI, we extracted the appearances of activity patterns similar to the neural representations for posterior sentences. Importantly, the resting-state fMRI was measured before giving the posterior sentences, and thus such appearances could be considered as preplay-like or prototypical neural representations. We compared the intensities of such appearances with the understanding of posterior sentences. This gave a positive correlation between these two variables, but only if posterior sentences were associated with prior sentences. Additional analysis showed the contribution of the entorhinal cortex, rather than the hippocampus, to the correlation. The present study suggests that prior knowledge-based arrangement of neural activity before an experience contributes to the active selection of information to be learned. Such arrangement prior to an experience resembles preplay activity observed in the rodent brain. In terms of knowledge acquisition, the present study leads to a new view of the brain (or more precisely of the brain’s knowledge) as an autopoietic system in which the brain (or knowledge) selects what it should learn by itself, arranges preplay-like activity as a position for the new information in advance, and actively reorganizes itself. PMID:29662446
Discovering New Variable Stars at Key Stage 3

ERIC Educational Resources Information Center

Chubb, Katy; Hood, Rosie; Wilson, Thomas; Holdship, Jonathan; Hutton, Sarah

2017-01-01

Details of the London pilot of the "Discovery Project" are presented, where university-based astronomers were given the chance to pass on some real and applied knowledge of astronomy to a group of selected secondary school pupils. It was aimed at students in Key Stage 3 of their education, allowing them to be involved in real…
Sparse Zero-Sum Games as Stable Functional Feature Selection

PubMed Central

Sokolovska, Nataliya; Teytaud, Olivier; Rizkalla, Salwa; Clément, Karine; Zucker, Jean-Daniel

2015-01-01

In large-scale systems biology applications, features are structured in hidden functional categories whose predictive power is identical. Feature selection, therefore, can lead not only to a problem with a reduced dimensionality, but also reveal some knowledge on functional classes of variables. In this contribution, we propose a framework based on a sparse zero-sum game which performs a stable functional feature selection. In particular, the approach is based on feature subsets ranking by a thresholding stochastic bandit. We provide a theoretical analysis of the introduced algorithm. We illustrate by experiments on both synthetic and real complex data that the proposed method is competitive from the predictive and stability viewpoints. PMID:26325268
SIGMA: A Knowledge-Based Simulation Tool Applied to Ecosystem Modeling

NASA Technical Reports Server (NTRS)

Dungan, Jennifer L.; Keller, Richard; Lawless, James G. (Technical Monitor)

1994-01-01

The need for better technology to facilitate building, sharing and reusing models is generally recognized within the ecosystem modeling community. The Scientists' Intelligent Graphical Modelling Assistant (SIGMA) creates an environment for model building, sharing and reuse which provides an alternative to more conventional approaches which too often yield poorly documented, awkwardly structured model code. The SIGMA interface presents the user a list of model quantities which can be selected for computation. Equations to calculate the model quantities may be chosen from an existing library of ecosystem modeling equations, or built using a specialized equation editor. Inputs for dim equations may be supplied by data or by calculation from other equations. Each variable and equation is expressed using ecological terminology and scientific units, and is documented with explanatory descriptions and optional literature citations. Automatic scientific unit conversion is supported and only physically-consistent equations are accepted by the system. The system uses knowledge-based semantic conditions to decide which equations in its library make sense to apply in a given situation, and supplies these to the user for selection. "Me equations and variables are graphically represented as a flow diagram which provides a complete summary of the model. Forest-BGC, a stand-level model that simulates photosynthesis and evapo-transpiration for conifer canopies, was originally implemented in Fortran and subsequenty re-implemented using SIGMA. The SIGMA version reproduces daily results and also provides a knowledge base which greatly facilitates inspection, modification and extension of Forest-BGC.
Projection of climatic suitability for Aedes albopictus Skuse (Culicidae) in Europe under climate change conditions

NASA Astrophysics Data System (ADS)

Fischer, Dominik; Thomas, Stephanie Margarete; Niemitz, Franziska; Reineking, Björn; Beierkuhnlein, Carl

2011-07-01

During the last decades the disease vector Aedes albopictus ( Ae. albopictus) has rapidly spread around the globe. The spread of this species raises serious public health concerns. Here, we model the present distribution and the future climatic suitability of Europe for this vector in the face of climate change. In order to achieve the most realistic current prediction and future projection, we compare the performance of four different modelling approaches, differentiated by the selection of climate variables (based on expert knowledge vs. statistical criteria) and by the geographical range of presence records (native range vs. global range). First, models of the native and global range were built with MaxEnt and were either based on (1) statistically selected climatic input variables or (2) input variables selected with expert knowledge from the literature. Native models show high model performance (AUC: 0.91-0.94) for the native range, but do not predict the European distribution well (AUC: 0.70-0.72). Models based on the global distribution of the species, however, were able to identify all regions where Ae. albopictus is currently established, including Europe (AUC: 0.89-0.91). In a second step, the modelled bioclimatic envelope of the global range was projected to future climatic conditions in Europe using two emission scenarios implemented in the regional climate model COSMO-CLM for three time periods 2011-2040, 2041-2070, and 2071-2100. For both global-driven models, the results indicate that climatically suitable areas for the establishment of Ae. albopictus will increase in western and central Europe already in 2011-2040 and with a temporal delay in eastern Europe. On the other hand, a decline in climatically suitable areas in southern Europe is pronounced in the Expert knowledge based model. Our projections appear unaffected by non-analogue climate, as this is not detected by Multivariate Environmental Similarity Surface analysis. The generated risk maps can aid in identifying suitable habitats for Ae. albopictus and hence support monitoring and control activities to avoid disease vector establishment.

Development and Validation of a Rubric for Diagnosing Students’ Experimental Design Knowledge and Difficulties

PubMed Central

Dasgupta, Annwesa P.; Anderson, Trevor R.

2014-01-01

It is essential to teach students about experimental design, as this facilitates their deeper understanding of how most biological knowledge was generated and gives them tools to perform their own investigations. Despite the importance of this area, surprisingly little is known about what students actually learn from designing biological experiments. In this paper, we describe a rubric for experimental design (RED) that can be used to measure knowledge of and diagnose difficulties with experimental design. The development and validation of the RED was informed by a literature review and empirical analysis of undergraduate biology students’ responses to three published assessments. Five areas of difficulty with experimental design were identified: the variable properties of an experimental subject; the manipulated variables; measurement of outcomes; accounting for variability; and the scope of inference appropriate for experimental findings. Our findings revealed that some difficulties, documented some 50 yr ago, still exist among our undergraduate students, while others remain poorly investigated. The RED shows great promise for diagnosing students’ experimental design knowledge in lecture settings, laboratory courses, research internships, and course-based undergraduate research experiences. It also shows potential for guiding the development and selection of assessment and instructional activities that foster experimental design. PMID:26086658
Discovering new variable stars at Key Stage 3

NASA Astrophysics Data System (ADS)

Chubb, Katy; Hood, Rosie; Wilson, Thomas; Holdship, Jonathan; Hutton, Sarah

2017-05-01

Details of the London pilot of the ‘Discovery Project’ are presented, where university-based astronomers were given the chance to pass on some real and applied knowledge of astronomy to a group of selected secondary school pupils. It was aimed at students in Key Stage 3 of their education, allowing them to be involved in real astronomical research at an early stage of their education, the chance to become the official discoverer of a new variable star, and to be listed in the International Variable Star Index database (The International Variable Star Index, Version 1.1, American Association of Variable Star Observers (AAVSO), 2016, http://aavso.org/vsx), all while learning and practising research-level skills. Future plans are discussed.
An Integrative Framework for Bayesian Variable Selection with Informative Priors for Identifying Genes and Pathways

PubMed Central

Ander, Bradley P.; Zhang, Xiaoshuai; Xue, Fuzhong; Sharp, Frank R.; Yang, Xiaowei

2013-01-01

The discovery of genetic or genomic markers plays a central role in the development of personalized medicine. A notable challenge exists when dealing with the high dimensionality of the data sets, as thousands of genes or millions of genetic variants are collected on a relatively small number of subjects. Traditional gene-wise selection methods using univariate analyses face difficulty to incorporate correlational, structural, or functional structures amongst the molecular measures. For microarray gene expression data, we first summarize solutions in dealing with ‘large p, small n’ problems, and then propose an integrative Bayesian variable selection (iBVS) framework for simultaneously identifying causal or marker genes and regulatory pathways. A novel partial least squares (PLS) g-prior for iBVS is developed to allow the incorporation of prior knowledge on gene-gene interactions or functional relationships. From the point view of systems biology, iBVS enables user to directly target the joint effects of multiple genes and pathways in a hierarchical modeling diagram to predict disease status or phenotype. The estimated posterior selection probabilities offer probabilitic and biological interpretations. Both simulated data and a set of microarray data in predicting stroke status are used in validating the performance of iBVS in a Probit model with binary outcomes. iBVS offers a general framework for effective discovery of various molecular biomarkers by combining data-based statistics and knowledge-based priors. Guidelines on making posterior inferences, determining Bayesian significance levels, and improving computational efficiencies are also discussed. PMID:23844055
An integrative framework for Bayesian variable selection with informative priors for identifying genes and pathways.

PubMed

Peng, Bin; Zhu, Dianwen; Ander, Bradley P; Zhang, Xiaoshuai; Xue, Fuzhong; Sharp, Frank R; Yang, Xiaowei

2013-01-01

The discovery of genetic or genomic markers plays a central role in the development of personalized medicine. A notable challenge exists when dealing with the high dimensionality of the data sets, as thousands of genes or millions of genetic variants are collected on a relatively small number of subjects. Traditional gene-wise selection methods using univariate analyses face difficulty to incorporate correlational, structural, or functional structures amongst the molecular measures. For microarray gene expression data, we first summarize solutions in dealing with 'large p, small n' problems, and then propose an integrative Bayesian variable selection (iBVS) framework for simultaneously identifying causal or marker genes and regulatory pathways. A novel partial least squares (PLS) g-prior for iBVS is developed to allow the incorporation of prior knowledge on gene-gene interactions or functional relationships. From the point view of systems biology, iBVS enables user to directly target the joint effects of multiple genes and pathways in a hierarchical modeling diagram to predict disease status or phenotype. The estimated posterior selection probabilities offer probabilitic and biological interpretations. Both simulated data and a set of microarray data in predicting stroke status are used in validating the performance of iBVS in a Probit model with binary outcomes. iBVS offers a general framework for effective discovery of various molecular biomarkers by combining data-based statistics and knowledge-based priors. Guidelines on making posterior inferences, determining Bayesian significance levels, and improving computational efficiencies are also discussed.
The impact of innovation intermediary on knowledge transfer

NASA Astrophysics Data System (ADS)

Lin, Min; Wei, Jun

2018-07-01

Many firms have opened up their innovation process and actively transfer knowledge with external partners in the market of technology. To reduce some of the market inefficiencies, more and more firms collaborate with innovation intermediaries. In light of the increasing importance of intermediary in the context of open innovation, we in this paper systematically investigate the effect of innovation intermediary on knowledge transfer and innovation process in networked systems. We find that the existence of innovation intermediary is conducive to the knowledge diffusion and facilitate the knowledge growth at system level. Interestingly, the scale of the innovation intermediary has little effect on the growth of knowledge. We further investigate the selection of intermediary members by comparing four selection strategies: random selection, initial knowledge level based selection, absorptive capability based selection, and innovative ability based selection. It is found that the selection strategy based on innovative ability outperforms all the other strategies in promoting the system knowledge growth. Our study provides a theoretical understanding of the impact of innovation intermediary on knowledge transfer and sheds light on the design and selection of innovation intermediary in open innovation.
Improving permafrost distribution modelling using feature selection algorithms

NASA Astrophysics Data System (ADS)

Deluigi, Nicola; Lambiel, Christophe; Kanevski, Mikhail

2016-04-01

The availability of an increasing number of spatial data on the occurrence of mountain permafrost allows the employment of machine learning (ML) classification algorithms for modelling the distribution of the phenomenon. One of the major problems when dealing with high-dimensional dataset is the number of input features (variables) involved. Application of ML classification algorithms to this large number of variables leads to the risk of overfitting, with the consequence of a poor generalization/prediction. For this reason, applying feature selection (FS) techniques helps simplifying the amount of factors required and improves the knowledge on adopted features and their relation with the studied phenomenon. Moreover, taking away irrelevant or redundant variables from the dataset effectively improves the quality of the ML prediction. This research deals with a comparative analysis of permafrost distribution models supported by FS variable importance assessment. The input dataset (dimension = 20-25, 10 m spatial resolution) was constructed using landcover maps, climate data and DEM derived variables (altitude, aspect, slope, terrain curvature, solar radiation, etc.). It was completed with permafrost evidences (geophysical and thermal data and rock glacier inventories) that serve as training permafrost data. Used FS algorithms informed about variables that appeared less statistically important for permafrost presence/absence. Three different algorithms were compared: Information Gain (IG), Correlation-based Feature Selection (CFS) and Random Forest (RF). IG is a filter technique that evaluates the worth of a predictor by measuring the information gain with respect to the permafrost presence/absence. Conversely, CFS is a wrapper technique that evaluates the worth of a subset of predictors by considering the individual predictive ability of each variable along with the degree of redundancy between them. Finally, RF is a ML algorithm that performs FS as part of its overall operation. It operates by constructing a large collection of decorrelated classification trees, and then predicts the permafrost occurrence through a majority vote. With the so-called out-of-bag (OOB) error estimate, the classification of permafrost data can be validated as well as the contribution of each predictor can be assessed. The performances of compared permafrost distribution models (computed on independent testing sets) increased with the application of FS algorithms on the original dataset and irrelevant or redundant variables were removed. As a consequence, the process provided faster and more cost-effective predictors and a better understanding of the underlying structures residing in permafrost data. Our work demonstrates the usefulness of a feature selection step prior to applying a machine learning algorithm. In fact, permafrost predictors could be ranked not only based on their heuristic and subjective importance (expert knowledge), but also based on their statistical relevance in relation of the permafrost distribution.
Comparing perceived and test-based knowledge of cancer risk and prevention among Hispanic and African Americans: an example of community participatory research.

PubMed

Jones, Loretta; Bazargan, Mohsen; Lucas-Wright, Anna; Vadgama, Jaydutt V; Vargas, Roberto; Smith, James; Otoukesh, Salman; Maxwell, Annette E

2013-01-01

Most theoretical formulations acknowledge that knowledge and awareness of cancer screening and prevention recommendations significantly influence health behaviors. This study compares perceived knowledge of cancer prevention and screening with test-based knowledge in a community sample. We also examine demographic variables and self-reported cancer screening and prevention behaviors as correlates of both knowledge scores, and consider whether cancer related knowledge can be accurately assessed using just a few, simple questions in a short and easy-to-complete survey. We used a community-partnered participatory research approach to develop our study aims and a survey. The study sample was composed of 180 predominantly African American and Hispanic community individuals who participated in a full-day cancer prevention and screening promotion conference in South Los Angeles, California, on July 2011. Participants completed a self-administered survey in English or Spanish at the beginning of the conference. Our data indicate that perceived and test-based knowledge scores are only moderately correlated. Perceived knowledge score shows a stronger association with demographic characteristics and other cancer related variables than the test-based score. Thirteen out of twenty variables that are examined in our study showed a statistically significant correlation with the perceived knowledge score, however, only four variables demonstrated a statistically significant correlation with the test-based knowledge score. Perceived knowledge of cancer prevention and screening was assessed with fewer items than test-based knowledge. Thus, using this assessment could potentially reduce respondent burden. However, our data demonstrate that perceived and test-based knowledge are separate constructs.
Relating brain signal variability to knowledge representation.

PubMed

Heisz, Jennifer J; Shedden, Judith M; McIntosh, Anthony R

2012-11-15

We assessed the hypothesis that brain signal variability is a reflection of functional network reconfiguration during memory processing. In the present experiments, we use multiscale entropy to capture the variability of human electroencephalogram (EEG) while manipulating the knowledge representation associated with faces stored in memory. Across two experiments, we observed increased variability as a function of greater knowledge representation. In Experiment 1, individuals with greater familiarity for a group of famous faces displayed more brain signal variability. In Experiment 2, brain signal variability increased with learning after multiple experimental exposures to previously unfamiliar faces. The results demonstrate that variability increases with face familiarity; cognitive processes during the perception of familiar stimuli may engage a broader network of regions, which manifests as higher complexity/variability in spatial and temporal domains. In addition, effects of repetition suppression on brain signal variability were observed, and the pattern of results is consistent with a selectivity model of neural adaptation. Crown Copyright © 2012. Published by Elsevier Inc. All rights reserved.
A Bayesian Theory of Sequential Causal Learning and Abstract Transfer.

PubMed

Lu, Hongjing; Rojas, Randall R; Beckers, Tom; Yuille, Alan L

2016-03-01

Two key research issues in the field of causal learning are how people acquire causal knowledge when observing data that are presented sequentially, and the level of abstraction at which learning takes place. Does sequential causal learning solely involve the acquisition of specific cause-effect links, or do learners also acquire knowledge about abstract causal constraints? Recent empirical studies have revealed that experience with one set of causal cues can dramatically alter subsequent learning and performance with entirely different cues, suggesting that learning involves abstract transfer, and such transfer effects involve sequential presentation of distinct sets of causal cues. It has been demonstrated that pre-training (or even post-training) can modulate classic causal learning phenomena such as forward and backward blocking. To account for these effects, we propose a Bayesian theory of sequential causal learning. The theory assumes that humans are able to consider and use several alternative causal generative models, each instantiating a different causal integration rule. Model selection is used to decide which integration rule to use in a given learning environment in order to infer causal knowledge from sequential data. Detailed computer simulations demonstrate that humans rely on the abstract characteristics of outcome variables (e.g., binary vs. continuous) to select a causal integration rule, which in turn alters causal learning in a variety of blocking and overshadowing paradigms. When the nature of the outcome variable is ambiguous, humans select the model that yields the best fit with the recent environment, and then apply it to subsequent learning tasks. Based on sequential patterns of cue-outcome co-occurrence, the theory can account for a range of phenomena in sequential causal learning, including various blocking effects, primacy effects in some experimental conditions, and apparently abstract transfer of causal knowledge. Copyright © 2015 Cognitive Science Society, Inc.
Expert systems for fault diagnosis in nuclear reactor control

NASA Astrophysics Data System (ADS)

Jalel, N. A.; Nicholson, H.

1990-11-01

An expert system for accident analysis and fault diagnosis for the Loss Of Fluid Test (LOFT) reactor, a small scale pressurized water reactor, was developed for a personal computer. The knowledge of the system is presented using a production rule approach with a backward chaining inference engine. The data base of the system includes simulated dependent state variables of the LOFT reactor model. Another system is designed to assist the operator in choosing the appropriate cooling mode and to diagnose the fault in the selected cooling system. The response tree, which is used to provide the link between a list of very specific accident sequences and a set of generic emergency procedures which help the operator in monitoring system status, and to differentiate between different accident sequences and select the correct procedures, is used to build the system knowledge base. Both systems are written in TURBO PROLOG language and can be run on an IBM PC compatible with 640k RAM, 40 Mbyte hard disk and color graphics.
Zoonotic importance of canine scabies and dermatophytosis in relation to knowledge level of dog owners.

PubMed

Raval, Heli S; Nayak, J B; Patel, B M; Bhadesiya, C M

2015-06-01

The present study was undertaken to understand the zoonotic importance of canine scabies and dermatophytosis with special reference to the knowledge level of dog owners in urban areas of Gujarat. The study was carried out in randomly selected 120 dog owners of 3 urban cities (viz., Ahmedabad, Anand and Vadodara) of Gujarat state, India. Dog owners (i.e., respondents) were subjected to a detailed interview regarding the zoonotic importance of canine scabies and dermatophytosis in dogs. Ex-post-facto research design was selected because of the independent variables of the selected respondent population for the study. The crucial method used in collecting data was a field survey to generate null hypothesis (Ho1). Available data was subjected to statistical analysis. The three independent variables, viz., extension contact (r=0.522**), mass-media exposure (r=0.205*) and management orientation (r=0.264**) had significant relationship with knowledge of dog owners about zoonotic diseases. Other independent variables, viz., education, experience in dog keeping and housing space were observed to have negative and non-significant relationship with knowledge of dog owners about zoonotic diseases. Extension contact, exposure to extension mass-media, management orientation and innovation proneness among dog owners of 3 urban cities of Gujarat state had significant relationship with knowledge of dog owners on zoonotic aspects of canine scabies and dermatophytosis. Data provided new insights on the present status of zoonotic disease-awareness, which would be an aid to plan preventive measures.
Effect of Education Based on Health Belief Model on the Behavior of Breast Cancer Screening in Women.

PubMed

Masoudiyekta, Leila; Rezaei-Bayatiyani, Hojat; Dashtbozorgi, Bahman; Gheibizadeh, Mahin; Malehi, Amal Saki; Moradi, Mehrnaz

2018-01-01

The purpose of this study was to determine the effect of education based on health belief model (HBM) on the behavior of breast cancer screening (bCS) in women. This quasi-experimental study was conducted on 226 women who were selected with cluster sampling method from those referred to Dezful health centers. Data collection tool was a researcher-made questionnaire. Demographic questionnaire bCS- scale, and the Knowledge about questionnaire, all given before and 3 months after the intervention. According to the findings of the study, there was a significant relationship between women's performance and variables of knowledge, perceived sensitivity, perceived benefits, perceived barriers, self-efficacy, and cues to action. Poor knowledge of women indicates a crucial need for formal educational programs to sensitize women regarding the importance of bCS. These educational programs should consider factors affecting bCS behaviors.
Profiles of the evaluators: impact of psychographic variables on the consumer-oriented quality assessment of mobile television

NASA Astrophysics Data System (ADS)

Jumisko-Pyykkö, Satu; Häkkinen, Jukka

2008-02-01

In the product development of services it is important to adjust mobile video quality according to the quality requirements of potential users. Therefore, a careful participant selection is very important. However, in the literature the details of participant selection are often handled without great detail. This is also reflected in the handling of experimental results, where the impact of psychographic factors on quality is rarely reported. As the user attributes potentially have a large effect to the results, we investigated the role of various psychographical variables on the subjective evaluation of audiovisual video quality in two different experiments. The studied variables were age, gender, education, professionalism, television consumption, experiences of different digital video qualities, and attitude towards technology. The results showed that quality evaluations were affected by almost all background factors. The most significant variables were age, professionalism, knowledge of digital quality features and attitude towards technology. The knowledge of these factors can be exploited in careful participant selection, which will in turn increase the validity of results as the subjective evaluations reflect better the requirements of potential users.
Blackboard architecture for medical image interpretation

NASA Astrophysics Data System (ADS)

Davis, Darryl N.; Taylor, Christopher J.

1991-06-01

There is a growing interest in using sophisticated knowledge-based systems for biomedical image interpretation. We present a principled attempt to use artificial intelligence methodologies in interpreting lateral skull x-ray images. Such radiographs are routinely used in cephalometric analysis to provide quantitative measurements useful to clinical orthodontists. Manual and interactive methods of analysis are known to be error prone and previous attempts to automate this analysis typically fail to capture the expertise and adaptability required to cope with the variability in biological structure and image quality. An integrated model-based system has been developed which makes use of a blackboard architecture and multiple knowledge sources. A model definition interface allows quantitative models, of feature appearance and location, to be built from examples as well as more qualitative modelling constructs. Visual task definition and blackboard control modules allow task-specific knowledge sources to act on information available to the blackboard in a hypothesise and test reasoning cycle. Further knowledge-based modules include object selection, location hypothesis, intelligent segmentation, and constraint propagation systems. Alternative solutions to given tasks are permitted.
Effectiveness of planned teaching program on knowledge regarding Alzheimer's disease among the family members of elderly in a selected urban community at Mangalore

PubMed Central

Rodrigues, Lavina; Mathias, Thereza

2016-01-01

Background: Alzheimer's disease is one of the debilitating chronic diseases among older persons. It is an irreversible condition that leads to progressive deterioration of cognitive, intellectual, physical, and psychosocial functions. The study was aimed to assess the knowledge of the family members of elderly regarding Alzheimer's disease in a selected urban community at Mangalore. Materials and Methods: A preexperimental research design of one group pretest and posttest with an evaluative approach was adopted for the study. A total of 50 family members of elderly who met the inclusion criteria were selected through purposive sampling technique. The researcher developed a planned teaching program on Alzheimer's disease, and structured knowledge questionnaire on Alzheimer's disease was used to collect the data. Results: Descriptive and inferential statistics was used to analyze the data. Analysis revealed that the mean posttest knowledge (20.78 ± 3.31) was higher than mean pretest knowledge scores (12.90 ± 2.43). Significance of difference between pretest and posttest was statistically tested using paired “t” test and it was found very highly significant (t = 40.85, P < 0.05). Majority of the variables showed no significant association between pretest and posttest knowledge score and with demographic variables. Conclusion: The findings revealed that the planned teaching program is an effective strategy for improving the knowledge of the subjects. PMID:26985104
Effectiveness of planned teaching program on knowledge regarding Alzheimer's disease among the family members of elderly in a selected urban community at Mangalore.

PubMed

Rodrigues, Lavina; Mathias, Thereza

2016-01-01

Alzheimer's disease is one of the debilitating chronic diseases among older persons. It is an irreversible condition that leads to progressive deterioration of cognitive, intellectual, physical, and psychosocial functions. The study was aimed to assess the knowledge of the family members of elderly regarding Alzheimer's disease in a selected urban community at Mangalore. A preexperimental research design of one group pretest and posttest with an evaluative approach was adopted for the study. A total of 50 family members of elderly who met the inclusion criteria were selected through purposive sampling technique. The researcher developed a planned teaching program on Alzheimer's disease, and structured knowledge questionnaire on Alzheimer's disease was used to collect the data. Descriptive and inferential statistics was used to analyze the data. Analysis revealed that the mean posttest knowledge (20.78 ± 3.31) was higher than mean pretest knowledge scores (12.90 ± 2.43). Significance of difference between pretest and posttest was statistically tested using paired "t" test and it was found very highly significant (t = 40.85, P < 0.05). Majority of the variables showed no significant association between pretest and posttest knowledge score and with demographic variables. The findings revealed that the planned teaching program is an effective strategy for improving the knowledge of the subjects.
What explains usage of mobile physician-rating apps? Results from a web-based questionnaire.

PubMed

Bidmon, Sonja; Terlutter, Ralf; Röttl, Johanna

2014-06-11

Consumers are increasingly accessing health-related information via mobile devices. Recently, several apps to rate and locate physicians have been released in the United States and Germany. However, knowledge about what kinds of variables explain usage of mobile physician-rating apps is still lacking. This study analyzes factors influencing the adoption of and willingness to pay for mobile physician-rating apps. A structural equation model was developed based on the Technology Acceptance Model and the literature on health-related information searches and usage of mobile apps. Relationships in the model were analyzed for moderating effects of physician-rating website (PRW) usage. A total of 1006 randomly selected German patients who had visited a general practitioner at least once in the 3 months before the beginning of the survey were randomly selected and surveyed. A total of 958 usable questionnaires were analyzed by partial least squares path modeling and moderator analyses. The suggested model yielded a high model fit. We found that perceived ease of use (PEOU) of the Internet to gain health-related information, the sociodemographic variables age and gender, and the psychographic variables digital literacy, feelings about the Internet and other Web-based applications in general, patients' value of health-related knowledgeability, as well as the information-seeking behavior variables regarding the amount of daily private Internet use for health-related information, frequency of using apps for health-related information in the past, and attitude toward PRWs significantly affected the adoption of mobile physician-rating apps. The sociodemographic variable age, but not gender, and the psychographic variables feelings about the Internet and other Web-based applications in general and patients' value of health-related knowledgeability, but not digital literacy, were significant predictors of willingness to pay. Frequency of using apps for health-related information in the past and attitude toward PRWs, but not the amount of daily Internet use for health-related information, were significant predictors of willingness to pay. The perceived usefulness of the Internet to gain health-related information and the amount of daily Internet use in general did not have any significant effect on both of the endogenous variables. The moderation analysis with the group comparisons for users and nonusers of PRWs revealed that the attitude toward PRWs had significantly more impact on the adoption and willingness to pay for mobile physician-rating apps in the nonuser group. Important variables that contribute to the adoption of a mobile physician-rating app and the willingness to pay for it were identified. The results of this study are important for researchers because they can provide important insights about the variables that influence the acceptance of apps that allow for ratings of physicians. They are also useful for creators of mobile physician-rating apps because they can help tailor mobile physician-rating apps to the consumers' characteristics and needs.
What Explains Usage of Mobile Physician-Rating Apps? Results From a Web-Based Questionnaire

PubMed Central

Terlutter, Ralf; Röttl, Johanna

2014-01-01

Background Consumers are increasingly accessing health-related information via mobile devices. Recently, several apps to rate and locate physicians have been released in the United States and Germany. However, knowledge about what kinds of variables explain usage of mobile physician-rating apps is still lacking. Objective This study analyzes factors influencing the adoption of and willingness to pay for mobile physician-rating apps. A structural equation model was developed based on the Technology Acceptance Model and the literature on health-related information searches and usage of mobile apps. Relationships in the model were analyzed for moderating effects of physician-rating website (PRW) usage. Methods A total of 1006 randomly selected German patients who had visited a general practitioner at least once in the 3 months before the beginning of the survey were randomly selected and surveyed. A total of 958 usable questionnaires were analyzed by partial least squares path modeling and moderator analyses. Results The suggested model yielded a high model fit. We found that perceived ease of use (PEOU) of the Internet to gain health-related information, the sociodemographic variables age and gender, and the psychographic variables digital literacy, feelings about the Internet and other Web-based applications in general, patients’ value of health-related knowledgeability, as well as the information-seeking behavior variables regarding the amount of daily private Internet use for health-related information, frequency of using apps for health-related information in the past, and attitude toward PRWs significantly affected the adoption of mobile physician-rating apps. The sociodemographic variable age, but not gender, and the psychographic variables feelings about the Internet and other Web-based applications in general and patients’ value of health-related knowledgeability, but not digital literacy, were significant predictors of willingness to pay. Frequency of using apps for health-related information in the past and attitude toward PRWs, but not the amount of daily Internet use for health-related information, were significant predictors of willingness to pay. The perceived usefulness of the Internet to gain health-related information and the amount of daily Internet use in general did not have any significant effect on both of the endogenous variables. The moderation analysis with the group comparisons for users and nonusers of PRWs revealed that the attitude toward PRWs had significantly more impact on the adoption and willingness to pay for mobile physician-rating apps in the nonuser group. Conclusions Important variables that contribute to the adoption of a mobile physician-rating app and the willingness to pay for it were identified. The results of this study are important for researchers because they can provide important insights about the variables that influence the acceptance of apps that allow for ratings of physicians. They are also useful for creators of mobile physician-rating apps because they can help tailor mobile physician-rating apps to the consumers’ characteristics and needs. PMID:24918859
Financial Knowledge and Best Practice Behavior

ERIC Educational Resources Information Center

Robb, Cliff A.; Woodyard, Ann S.

2011-01-01

The current research examines the relationship between personal financial knowledge (both objective and subjective), financial satisfaction, and selected demographic variables in terms of best practice financial behavior. Data are taken from the Financial Industry Regulatory Authority's (FINRA) National Financial Capability Study, a nationally…
Selecting Optimal Random Forest Predictive Models: A Case Study on Predicting the Spatial Distribution of Seabed Hardness

PubMed Central

Li, Jin; Tran, Maggie; Siwabessy, Justy

2016-01-01

Spatially continuous predictions of seabed hardness are important baseline environmental information for sustainable management of Australia’s marine jurisdiction. Seabed hardness is often inferred from multibeam backscatter data with unknown accuracy and can be inferred from underwater video footage at limited locations. In this study, we classified the seabed into four classes based on two new seabed hardness classification schemes (i.e., hard90 and hard70). We developed optimal predictive models to predict seabed hardness using random forest (RF) based on the point data of hardness classes and spatially continuous multibeam data. Five feature selection (FS) methods that are variable importance (VI), averaged variable importance (AVI), knowledge informed AVI (KIAVI), Boruta and regularized RF (RRF) were tested based on predictive accuracy. Effects of highly correlated, important and unimportant predictors on the accuracy of RF predictive models were examined. Finally, spatial predictions generated using the most accurate models were visually examined and analysed. This study confirmed that: 1) hard90 and hard70 are effective seabed hardness classification schemes; 2) seabed hardness of four classes can be predicted with a high degree of accuracy; 3) the typical approach used to pre-select predictive variables by excluding highly correlated variables needs to be re-examined; 4) the identification of the important and unimportant predictors provides useful guidelines for further improving predictive models; 5) FS methods select the most accurate predictive model(s) instead of the most parsimonious ones, and AVI and Boruta are recommended for future studies; and 6) RF is an effective modelling method with high predictive accuracy for multi-level categorical data and can be applied to ‘small p and large n’ problems in environmental sciences. Additionally, automated computational programs for AVI need to be developed to increase its computational efficiency and caution should be taken when applying filter FS methods in selecting predictive models. PMID:26890307

Selecting Optimal Random Forest Predictive Models: A Case Study on Predicting the Spatial Distribution of Seabed Hardness.

PubMed

Li, Jin; Tran, Maggie; Siwabessy, Justy

2016-01-01

Spatially continuous predictions of seabed hardness are important baseline environmental information for sustainable management of Australia's marine jurisdiction. Seabed hardness is often inferred from multibeam backscatter data with unknown accuracy and can be inferred from underwater video footage at limited locations. In this study, we classified the seabed into four classes based on two new seabed hardness classification schemes (i.e., hard90 and hard70). We developed optimal predictive models to predict seabed hardness using random forest (RF) based on the point data of hardness classes and spatially continuous multibeam data. Five feature selection (FS) methods that are variable importance (VI), averaged variable importance (AVI), knowledge informed AVI (KIAVI), Boruta and regularized RF (RRF) were tested based on predictive accuracy. Effects of highly correlated, important and unimportant predictors on the accuracy of RF predictive models were examined. Finally, spatial predictions generated using the most accurate models were visually examined and analysed. This study confirmed that: 1) hard90 and hard70 are effective seabed hardness classification schemes; 2) seabed hardness of four classes can be predicted with a high degree of accuracy; 3) the typical approach used to pre-select predictive variables by excluding highly correlated variables needs to be re-examined; 4) the identification of the important and unimportant predictors provides useful guidelines for further improving predictive models; 5) FS methods select the most accurate predictive model(s) instead of the most parsimonious ones, and AVI and Boruta are recommended for future studies; and 6) RF is an effective modelling method with high predictive accuracy for multi-level categorical data and can be applied to 'small p and large n' problems in environmental sciences. Additionally, automated computational programs for AVI need to be developed to increase its computational efficiency and caution should be taken when applying filter FS methods in selecting predictive models.
Zoonotic importance of canine scabies and dermatophytosis in relation to knowledge level of dog owners

PubMed Central

Raval, Heli S.; Nayak, J. B.; Patel, B. M.; Bhadesiya, C. M.

2015-01-01

Aim: The present study was undertaken to understand the zoonotic importance of canine scabies and dermatophytosis with special reference to the knowledge level of dog owners in urban areas of Gujarat. Materials and Methods: The study was carried out in randomly selected 120 dog owners of 3 urban cities (viz., Ahmedabad, Anand and Vadodara) of Gujarat state, India. Dog owners (i.e., respondents) were subjected to a detailed interview regarding the zoonotic importance of canine scabies and dermatophytosis in dogs. Ex-post-facto research design was selected because of the independent variables of the selected respondent population for the study. The crucial method used in collecting data was a field survey to generate null hypothesis (Ho1). Available data was subjected to statistical analysis. Results: The three independent variables, viz., extension contact (r=0.522**), mass-media exposure (r=0.205*) and management orientation (r=0.264**) had significant relationship with knowledge of dog owners about zoonotic diseases. Other independent variables, viz., education, experience in dog keeping and housing space were observed to have negative and non-significant relationship with knowledge of dog owners about zoonotic diseases. Conclusion: Extension contact, exposure to extension mass-media, management orientation and innovation proneness among dog owners of 3 urban cities of Gujarat state had significant relationship with knowledge of dog owners on zoonotic aspects of canine scabies and dermatophytosis. Data provided new insights on the present status of zoonotic disease-awareness, which would be an aid to plan preventive measures. PMID:27065644
Knowledge and Perceptions of Reproductive Health among Latinas

ERIC Educational Resources Information Center

Rojas-Guyler, Liliana; Price, Kimberly L. J.; Young, Kathleen; King, Keith A.

2010-01-01

Objectives: The purpose of this study was to assess potential relationships among reproductive health knowledge, preventive health behaviors, perceived severity and risk of breast cancer, cervical cancer, and sexually transmitted infections and selected demographical variables and characteristics related to acculturation among Latina immigrants.…
A Path Analysis of Latino Parental, Teenager and Cultural Variables in Teenagers' Sexual Attitudes, Norms, Self-Efficacy, and Sexual Intentions.

PubMed

Gaioso, Vanessa Pirani; Villarruel, Antonia Maria; Wilson, Lynda Anne; Azuero, Andres; Childs, Gwendolyn Denice; Davies, Susan Lane

2015-01-01

to test a theoretical model based on the Parent-Based Expansion of the Theory of Planned Behavior examining relation between selected parental, teenager and cultural variables and Latino teenagers' intentions to engage in sexual behavior. a cross-sectional correlational design based on a secondary data analysis of 130 Latino parent and teenager dyads. regression and path analysis procedures were used to test seven hypotheses and the results demonstrated partial support for the model. Parent familism and knowledge about sex were significantly associated with parents' attitudes toward sexual communication with their teenagers. Parent Latino acculturation was negatively associated with parents' self-efficacy toward sexual communication with their teenagers and positevely associated with parents' subjective norms toward sexual communication with their teenagers. Teenager knowledge about sex was significantly associated with higher levels of teenagers' attitudes and subjective norms about sexual communication with parents. Only the predictor of teenagers' attitudes toward having sex in the next 3 months was significantly associated with teenagers' intentions to have sex in the next 3 months. the results of this study provide important information to guide future research that can inform development of interventions to prevent risky teenager sexual behavior among Latinos.
The selection of construction sub-contractors using the fuzzy sets theory

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

Krzemiński, Michał

The paper presents the algorithm for the selection of sub-contractors. Main area of author’s interest is scheduling flow models. The ranking task aims at execution time as short as possible Brigades downtime should also be as small as possible. These targets are exposed to significant obsolescence. The criteria for selection of subcontractors will not be therefore time and cost, it is assumed that all those criteria be meet by sub-contractors. The decision should be made in regard to factors difficult to measure, to assess which is the perfect application of fuzzy sets theory. The paper will present a set ofmore » evaluation criteria, the part of the knowledge base and a description of the output variable.« less
Sexual Knowledge, Attitudes, and Behaviors of an Ethnically Diverse Sample of Community College Students in Metropolitan New York.

ERIC Educational Resources Information Center

DeMarzo, Jenine

This study investigated the association among select socio-cultural variables and sexual knowledge, attitudes, and behaviors with a diverse population of metropolitan New York community college students. The Sexual Knowledge, Attitude, and Behavior Test survey instrument was administered to 338 students between the ages of 17 and 26 in their…
A National Survey of Environmental Knowledge in High School Students: Levels of Knowledge and Related Variables.

ERIC Educational Resources Information Center

Gambro, John S.; Switzky, Harvey N.

The objectives of this study are to assess the current environmental knowledge base in a national probability sample of American high school students, and examine the distribution of environmental knowledge across several variables which have been found to be related to environmental knowledge in previous research (e.g. education and gender).…
Multiple Domains of Parental Secure Base Support During Childhood and Adolescence Contribute to Adolescents’ Representations of Attachment as a Secure Base Script

PubMed Central

Vaughn, Brian E.; Waters, Theodore E. A.; Steele, Ryan D.; Roisman, Glenn I.; Bost, Kelly K.; Truitt, Warren; Waters, Harriet S.; Booth-LaForce, Cathryn

2016-01-01

Although attachment theory claims that early attachment representations reflecting the quality of the child’s “lived experiences” are maintained across developmental transitions, evidence that has emerged over the last decade suggests that the association between early relationship quality and adolescents’ attachment representations is fairly modest in magnitude. We used aspects of parenting beyond sensitivity over childhood and adolescence and early security to predict adolescents’ scripted attachment representations. At age 18 years, 673 participants from the NICHD Study of Early Child Care and Youth Development (SECCYD) completed the Attachment Script Assessment (ASA) from which we derived an assessment of secure base script knowledge. Measures of secure base support from childhood through age 15 years (e.g., parental monitoring of child activity, father presence in the home) were selected as predictors and accounted for an additional 8% of the variance in secure base script knowledge scores above and beyond direct observations of sensitivity and early attachment status alone, suggesting that adolescents’ scripted attachment representations reflect multiple domains of parenting. Cognitive and demographic variables also significantly increased predicted variance in secure base script knowledge by 2% each. PMID:27032953
Optimization of Decision-Making for Spatial Sampling in the North China Plain, Based on Remote-Sensing a Priori Knowledge

NASA Astrophysics Data System (ADS)

Feng, J.; Bai, L.; Liu, S.; Su, X.; Hu, H.

2012-07-01

In this paper, the MODIS remote sensing data, featured with low-cost, high-timely and moderate/low spatial resolutions, in the North China Plain (NCP) as a study region were firstly used to carry out mixed-pixel spectral decomposition to extract an useful regionalized indicator parameter (RIP) (i.e., an available ratio, that is, fraction/percentage, of winter wheat planting area in each pixel as a regionalized indicator variable (RIV) of spatial sampling) from the initial selected indicators. Then, the RIV values were spatially analyzed, and the spatial structure characteristics (i.e., spatial correlation and variation) of the NCP were achieved, which were further processed to obtain the scalefitting, valid a priori knowledge or information of spatial sampling. Subsequently, founded upon an idea of rationally integrating probability-based and model-based sampling techniques and effectively utilizing the obtained a priori knowledge or information, the spatial sampling models and design schemes and their optimization and optimal selection were developed, as is a scientific basis of improving and optimizing the existing spatial sampling schemes of large-scale cropland remote sensing monitoring. Additionally, by the adaptive analysis and decision strategy the optimal local spatial prediction and gridded system of extrapolation results were able to excellently implement an adaptive report pattern of spatial sampling in accordance with report-covering units in order to satisfy the actual needs of sampling surveys.
Personal profile of medical students selected through a knowledge-based exam only: are we missing suitable students?

PubMed Central

Abbiati, Milena; Baroffio, Anne; Gerbase, Margaret W.

2016-01-01

Introduction A consistent body of literature highlights the importance of a broader approach to select medical school candidates both assessing cognitive capacity and individual characteristics. However, selection in a great number of medical schools worldwide is still based on knowledge exams, a procedure that might neglect students with needed personal characteristics for future medical practice. We investigated whether the personal profile of students selected through a knowledge-based exam differed from those not selected. Methods Students applying for medical school (N=311) completed questionnaires assessing motivations for becoming a doctor, learning approaches, personality traits, empathy, and coping styles. Selection was based on the results of MCQ tests. Principal component analysis was used to draw a profile of the students. Differences between selected and non-selected students were examined by Multivariate ANOVAs, and their impact on selection by logistic regression analysis. Results Students demonstrating a profile of diligence with higher conscientiousness, deep learning approach, and task-focused coping were more frequently selected (p=0.01). Other personal characteristics such as motivation, sociability, and empathy did not significantly differ, comparing selected and non-selected students. Conclusion Selection through a knowledge-based exam privileged diligent students. It did neither advantage nor preclude candidates with a more humane profile. PMID:27079886
Disease related knowledge and quality of life: a descriptive study focusing on hypertensive population in Pakistan.

PubMed

Saleem, Fahad; Hassali, Mohamed Azmi; Shafie, Asrul Akmal; Atif, Muhammad; Ul Haq, Noman; Aljadhey, Hisham

2012-07-01

This study aims to evaluate association between Health related quality of lifeand disease state knowledge among hypertensive population of Pakistan. A cross sectional descriptive study was undertaken with a representative cohort of hypertension patients. Using prevalence based sampling technique, a total of 385 hypertensive patients were selected from two public hospitals of Quetta city, Pakistan. Hypertension Fact Questionnaire (HFQ) and European Quality of Life scale (EQ-5D) were used for data collection. Statistical Package for the Social Sciences 16.0 was used to compute descriptive analysis of patients' demographic and disease related information. Categorical variables were described as percentages while continuous variables were expressed as mean ± standard deviation (SD). Spearman's rho correlation was used to identify the association between study variables. The mean (SD) age of the patients was 39.02 (6.59), with 68.8% males (n=265). The mean (SD) duration of hypertension was 3.01 (0.93) years. Forty percent (n=154) had bachelor degree with 34.8% (n=134) working in private sector. Almost forty one percent (n=140) had monthly income of more than 15000 Pakistan rupees per month with 75.1% (n=289) having urban residency. The mean EQ-5D descriptive score (0.46±0.28) and EQ-VAS score (63.97±6.62) indicated lower HRQoL in our study participants. Mean knowledge score was 8.03 ± 0.42. Correlation coefficient between HRQoL and knowledge was 0.208 (p< 0.001), indicating a week positive association. Results of this study highlight hypertension knowledge to be weakly associated with HRQoL suggesting that imparting knowledge to patients do not necessarily improve HRQoL. More attention should be given to identify individualized factors affecting HRQoL.
Kotai Antibody Builder: automated high-resolution structural modeling of antibodies.

PubMed

Yamashita, Kazuo; Ikeda, Kazuyoshi; Amada, Karlou; Liang, Shide; Tsuchiya, Yuko; Nakamura, Haruki; Shirai, Hiroki; Standley, Daron M

2014-11-15

Kotai Antibody Builder is a Web service for tertiary structural modeling of antibody variable regions. It consists of three main steps: hybrid template selection by sequence alignment and canonical rules, 3D rendering of alignments and CDR-H3 loop modeling. For the last step, in addition to rule-based heuristics used to build the initial model, a refinement option is available that uses fragment assembly followed by knowledge-based scoring. Using targets from the Second Antibody Modeling Assessment, we demonstrate that Kotai Antibody Builder generates models with an overall accuracy equal to that of the best-performing semi-automated predictors using expert knowledge. Kotai Antibody Builder is available at http://kotaiab.org standley@ifrec.osaka-u.ac.jp. © The Author 2014. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.
Incorporating abundance information and guiding variable selection for climate-based ensemble forecasting of species' distributional shifts.

PubMed

Tanner, Evan P; Papeş, Monica; Elmore, R Dwayne; Fuhlendorf, Samuel D; Davis, Craig A

2017-01-01

Ecological niche models (ENMs) have increasingly been used to estimate the potential effects of climate change on species' distributions worldwide. Recently, predictions of species abundance have also been obtained with such models, though knowledge about the climatic variables affecting species abundance is often lacking. To address this, we used a well-studied guild (temperate North American quail) and the Maxent modeling algorithm to compare model performance of three variable selection approaches: correlation/variable contribution (CVC), biological (i.e., variables known to affect species abundance), and random. We then applied the best approach to forecast potential distributions, under future climatic conditions, and analyze future potential distributions in light of available abundance data and presence-only occurrence data. To estimate species' distributional shifts we generated ensemble forecasts using four global circulation models, four representative concentration pathways, and two time periods (2050 and 2070). Furthermore, we present distributional shifts where 75%, 90%, and 100% of our ensemble models agreed. The CVC variable selection approach outperformed our biological approach for four of the six species. Model projections indicated species-specific effects of climate change on future distributions of temperate North American quail. The Gambel's quail (Callipepla gambelii) was the only species predicted to gain area in climatic suitability across all three scenarios of ensemble model agreement. Conversely, the scaled quail (Callipepla squamata) was the only species predicted to lose area in climatic suitability across all three scenarios of ensemble model agreement. Our models projected future loss of areas for the northern bobwhite (Colinus virginianus) and scaled quail in portions of their distributions which are currently areas of high abundance. Climatic variables that influence local abundance may not always scale up to influence species' distributions. Special attention should be given to selecting variables for ENMs, and tests of model performance should be used to validate the choice of variables.
Knowledge and experience sharing practices among health professionals in hospitals under the Addis Ababa health bureau, Ethiopia.

PubMed

Asemahagn, Mulusew Andualem

2014-09-24

Health professionals need updated health information from credible sources to improve their knowledge and provide evidence based health care services. Various types of medical errors have occurred in resource-limited countries because of poor knowledge and experience sharing practices among health professionals. The aim of this study was to assess knowledge-sharing practices and determinants among health professionals in Addis Ababa, Ethiopia. An institutional based cross-sectional study was conducted among 320 randomly selected health professionals from August12-25/2012. A pretested, self-administered questionnaire was used to collect data about different variables. Data entry and analysis were done using Epi-Info version 3.5.4 and SPSS version20 respectively. Descriptive statistics and multivariate regression analyses were applied to describe study objectives and identify the determinants of knowledge sharing practices respectively. Odds ratio at 95% CI was used to describe the strength of association between the study and outcome variables. Most of the respondents approved the need of knowledge and experience sharing practices in their routine activities. Nearly half, 152 (49.0%) of the study participants had knowledge and experience sharing practices. A majority, 219 (70.0%) of the respondents showed a willingness to share their knowledge and experiences. Trust on others' knowledge, motivation, supportive leadership, job satisfaction, awareness, willingness and resource allocation are the determinants of knowledge and experience sharing practices. Supportive leadership, resources, and trust on others' knowledge can enhance knowledge and experience sharing by OR = 3.12, 95% CI = [1.89 - 5.78], OR = 2.3, 95% CI = [1.61- 4.21] and OR = 2.78, 95% CI = [1.66 - 4.64] times compared with their counterparts respectively. Even though most of the respondents knew the importance of knowledge and experience sharing practices, only a limited number of respondents practiced it. Individual, organizational and resource related issues are the major determinants of low knowledge sharing practices. Improving management, proper resource allocation, motivating staffs, and accessing health information sources are important interventions to improve the problem in the study area.
Knowledge-based reconstruction for measurement of right ventricular volumes on cardiovascular magnetic resonance images in a mixed population.

PubMed

Pieterman, Elise D; Budde, Ricardo P J; Robbers-Visser, Daniëlle; van Domburg, Ron T; Helbing, Willem A

2017-09-01

Follow-up of right ventricular performance is important for patients with congenital heart disease. Cardiac magnetic resonance imaging is optimal for this purpose. However, observer-dependency of manual analysis of right ventricular volumes limit its use. Knowledge-based reconstruction is a new semiautomatic analysis tool that uses a database including knowledge of right ventricular shape in various congenital heart diseases. We evaluated whether knowledge-based reconstruction is a good alternative for conventional analysis. To assess the inter- and intra-observer variability and agreement of knowledge-based versus conventional analysis of magnetic resonance right ventricular volumes, analysis was done by two observers in a mixed group of 22 patients with congenital heart disease affecting right ventricular loading conditions (dextro-transposition of the great arteries and right ventricle to pulmonary artery conduit) and a group of 17 healthy children. We used Bland-Altman analysis and coefficient of variation. Comparison between the conventional method and the knowledge-based method showed a systematically higher volume for the latter group. We found an overestimation for end-diastolic volume (bias -40 ± 24 mL, r = .956), end-systolic volume (bias -34 ± 24 mL, r = .943), stroke volume (bias -6 ± 17 mL, r = .735) and an underestimation of ejection fraction (bias 7 ± 7%, r = .671) by knowledge-based reconstruction. The intra-observer variability of knowledge-based reconstruction varied with a coefficient of variation of 9% for end-diastolic volume and 22% for stroke volume. The same trend was noted for inter-observer variability. A systematic difference (overestimation) was noted for right ventricular size as assessed with knowledge-based reconstruction compared with conventional methods for analysis. Observer variability for the new method was comparable to what has been reported for the right ventricle in children and congenital heart disease with conventional analysis. © 2017 Wiley Periodicals, Inc.
Comparison of climate envelope models developed using expert-selected variables versus statistical selection

USGS Publications Warehouse

Brandt, Laura A.; Benscoter, Allison; Harvey, Rebecca G.; Speroterra, Carolina; Bucklin, David N.; Romañach, Stephanie; Watling, James I.; Mazzotti, Frank J.

2017-01-01

Climate envelope models are widely used to describe potential future distribution of species under different climate change scenarios. It is broadly recognized that there are both strengths and limitations to using climate envelope models and that outcomes are sensitive to initial assumptions, inputs, and modeling methods Selection of predictor variables, a central step in modeling, is one of the areas where different techniques can yield varying results. Selection of climate variables to use as predictors is often done using statistical approaches that develop correlations between occurrences and climate data. These approaches have received criticism in that they rely on the statistical properties of the data rather than directly incorporating biological information about species responses to temperature and precipitation. We evaluated and compared models and prediction maps for 15 threatened or endangered species in Florida based on two variable selection techniques: expert opinion and a statistical method. We compared model performance between these two approaches for contemporary predictions, and the spatial correlation, spatial overlap and area predicted for contemporary and future climate predictions. In general, experts identified more variables as being important than the statistical method and there was low overlap in the variable sets (<40%) between the two methods Despite these differences in variable sets (expert versus statistical), models had high performance metrics (>0.9 for area under the curve (AUC) and >0.7 for true skill statistic (TSS). Spatial overlap, which compares the spatial configuration between maps constructed using the different variable selection techniques, was only moderate overall (about 60%), with a great deal of variability across species. Difference in spatial overlap was even greater under future climate projections, indicating additional divergence of model outputs from different variable selection techniques. Our work is in agreement with other studies which have found that for broad-scale species distribution modeling, using statistical methods of variable selection is a useful first step, especially when there is a need to model a large number of species or expert knowledge of the species is limited. Expert input can then be used to refine models that seem unrealistic or for species that experts believe are particularly sensitive to change. It also emphasizes the importance of using multiple models to reduce uncertainty and improve map outputs for conservation planning. Where outputs overlap or show the same direction of change there is greater certainty in the predictions. Areas of disagreement can be used for learning by asking why the models do not agree, and may highlight areas where additional on-the-ground data collection could improve the models.
Input variable selection for data-driven models of Coriolis flowmeters for two-phase flow measurement

NASA Astrophysics Data System (ADS)

Wang, Lijuan; Yan, Yong; Wang, Xue; Wang, Tao

2017-03-01

Input variable selection is an essential step in the development of data-driven models for environmental, biological and industrial applications. Through input variable selection to eliminate the irrelevant or redundant variables, a suitable subset of variables is identified as the input of a model. Meanwhile, through input variable selection the complexity of the model structure is simplified and the computational efficiency is improved. This paper describes the procedures of the input variable selection for the data-driven models for the measurement of liquid mass flowrate and gas volume fraction under two-phase flow conditions using Coriolis flowmeters. Three advanced input variable selection methods, including partial mutual information (PMI), genetic algorithm-artificial neural network (GA-ANN) and tree-based iterative input selection (IIS) are applied in this study. Typical data-driven models incorporating support vector machine (SVM) are established individually based on the input candidates resulting from the selection methods. The validity of the selection outcomes is assessed through an output performance comparison of the SVM based data-driven models and sensitivity analysis. The validation and analysis results suggest that the input variables selected from the PMI algorithm provide more effective information for the models to measure liquid mass flowrate while the IIS algorithm provides a fewer but more effective variables for the models to predict gas volume fraction.
Variability of Neuronal Responses: Types and Functional Significance in Neuroplasticity and Neural Darwinism

PubMed Central

Chervyakov, Alexander V.; Sinitsyn, Dmitry O.; Piradov, Michael A.

2016-01-01

HIGHLIGHTS We suggest classifying variability of neuronal responses as follows: false (associated with a lack of knowledge about the influential factors), “genuine harmful” (noise), “genuine neutral” (synonyms, repeats), and “genuine useful” (the basis of neuroplasticity and learning).The genuine neutral variability is considered in terms of the phenomenon of degeneracy.Of particular importance is the genuine useful variability that is considered as a potential basis for neuroplasticity and learning. This type of variability is considered in terms of the neural Darwinism theory. In many cases, neural signals detected under the same external experimental conditions significantly change from trial to trial. The variability phenomenon, which complicates extraction of reproducible results and is ignored in many studies by averaging, has attracted attention of researchers in recent years. In this paper, we classify possible types of variability based on its functional significance and describe features of each type. We describe the key adaptive significance of variability at the neural network level and the degeneracy phenomenon that may be important for learning processes in connection with the principle of neuronal group selection. PMID:27932969
Variability of Neuronal Responses: Types and Functional Significance in Neuroplasticity and Neural Darwinism.

PubMed

Chervyakov, Alexander V; Sinitsyn, Dmitry O; Piradov, Michael A

2016-01-01

HIGHLIGHTS We suggest classifying variability of neuronal responses as follows: false (associated with a lack of knowledge about the influential factors), "genuine harmful" (noise), "genuine neutral" (synonyms, repeats), and "genuine useful" (the basis of neuroplasticity and learning).The genuine neutral variability is considered in terms of the phenomenon of degeneracy.Of particular importance is the genuine useful variability that is considered as a potential basis for neuroplasticity and learning. This type of variability is considered in terms of the neural Darwinism theory. In many cases, neural signals detected under the same external experimental conditions significantly change from trial to trial. The variability phenomenon, which complicates extraction of reproducible results and is ignored in many studies by averaging, has attracted attention of researchers in recent years. In this paper, we classify possible types of variability based on its functional significance and describe features of each type. We describe the key adaptive significance of variability at the neural network level and the degeneracy phenomenon that may be important for learning processes in connection with the principle of neuronal group selection.
A Rapid Approach to Modeling Species-Habitat Relationships

NASA Technical Reports Server (NTRS)

Carter, Geoffrey M.; Breinger, David R.; Stolen, Eric D.

2005-01-01

A growing number of species require conservation or management efforts. Success of these activities requires knowledge of the species' occurrence pattern. Species-habitat models developed from GIS data sources are commonly used to predict species occurrence but commonly used data sources are often developed for purposes other than predicting species occurrence and are of inappropriate scale and the techniques used to extract predictor variables are often time consuming and cannot be repeated easily and thus cannot efficiently reflect changing conditions. We used digital orthophotographs and a grid cell classification scheme to develop an efficient technique to extract predictor variables. We combined our classification scheme with a priori hypothesis development using expert knowledge and a previously published habitat suitability index and used an objective model selection procedure to choose candidate models. We were able to classify a large area (57,000 ha) in a fraction of the time that would be required to map vegetation and were able to test models at varying scales using a windowing process. Interpretation of the selected models confirmed existing knowledge of factors important to Florida scrub-jay habitat occupancy. The potential uses and advantages of using a grid cell classification scheme in conjunction with expert knowledge or an habitat suitability index (HSI) and an objective model selection procedure are discussed.

Consumer health plan choice: current knowledge and future directions.

PubMed

Scanlon, D P; Chernew, M; Lave, J R

1997-01-01

A keystone of the competitive strategy in health insurance markets is the assumption that "consumers" can make informed choices based on the costs and quality of competing health plans, and that selection effects are not large. However, little is known about how individuals use information other than price in the decision making process. This review summarizes the state of knowledge about how individuals make choices among health plans and outlines an agenda for future research. We find that the existing literature on health plan choice is no longer sufficient given the widespread growth and acceptance of managed care, and the increased proportion of consumers' income now going toward the purchase of health plans. Instead, today's environment of health plan choice requires better understanding of how plan attributes other than price influence plan choice, how other variables such as health status interact with plan attributes in the decision making process, and how specific populations differ from one another in terms of the sensitivity of their health plan choices to these different types of variables.
The Roles of Attitudinal and Personality Variables in the Prediction of Environmental Behavior and Knowledge

ERIC Educational Resources Information Center

Arbuthnot, Jack

1977-01-01

This study explored the relationships among selected attitudinal and personality characteristics, attitudes toward environmental problems, and environmental knowledge and behavioral commitment of two diverse samples: 85 users of a recycling center and 60 conservative church members. Multiple regression analysis was utilized to determine the best…
Selecting predictors for discriminant analysis of species performance: an example from an amphibious softwater plant.

PubMed

Vanderhaeghe, F; Smolders, A J P; Roelofs, J G M; Hoffmann, M

2012-03-01

Selecting an appropriate variable subset in linear multivariate methods is an important methodological issue for ecologists. Interest often exists in obtaining general predictive capacity or in finding causal inferences from predictor variables. Because of a lack of solid knowledge on a studied phenomenon, scientists explore predictor variables in order to find the most meaningful (i.e. discriminating) ones. As an example, we modelled the response of the amphibious softwater plant Eleocharis multicaulis using canonical discriminant function analysis. We asked how variables can be selected through comparison of several methods: univariate Pearson chi-square screening, principal components analysis (PCA) and step-wise analysis, as well as combinations of some methods. We expected PCA to perform best. The selected methods were evaluated through fit and stability of the resulting discriminant functions and through correlations between these functions and the predictor variables. The chi-square subset, at P < 0.05, followed by a step-wise sub-selection, gave the best results. In contrast to expectations, PCA performed poorly, as so did step-wise analysis. The different chi-square subset methods all yielded ecologically meaningful variables, while probable noise variables were also selected by PCA and step-wise analysis. We advise against the simple use of PCA or step-wise discriminant analysis to obtain an ecologically meaningful variable subset; the former because it does not take into account the response variable, the latter because noise variables are likely to be selected. We suggest that univariate screening techniques are a worthwhile alternative for variable selection in ecology. © 2011 German Botanical Society and The Royal Botanical Society of the Netherlands.
Individualised training to address variability of radiologists' performance

NASA Astrophysics Data System (ADS)

Sun, Shanghua; Taylor, Paul; Wilkinson, Louise; Khoo, Lisanne

2008-03-01

Computer-based tools are increasingly used for training and the continuing professional development of radiologists. We propose an adaptive training system to support individualised learning in mammography, based on a set of real cases, which are annotated with educational content by experienced breast radiologists. The system has knowledge of the strengths and weakness of each radiologist's performance: each radiologist is assessed to compute a profile showing how they perform on different sets of cases, classified by type of abnormality, breast density, and perceptual difficulty. We also assess variability in cognitive aspects of image perception, classifying errors made by radiologists as errors of search, recognition or decision. This is a novel element in our approach. The profile is used to select cases to present to the radiologist. The intelligent and flexible presentation of these cases distinguishes our system from existing training tools. The training cases are organised and indexed by an ontology we have developed for breast radiologist training, which is consistent with the radiologists' profile. Hence, the training system is able to select appropriate cases to compose an individualised training path, addressing the variability of the radiologists' performance. A substantial part of the system, the ontology has been evaluated on a large number of cases, and the training system is under implementation for further evaluation.
The nature and use of prediction skills in a biological computer simulation

NASA Astrophysics Data System (ADS)

Lavoie, Derrick R.; Good, Ron

The primary goal of this study was to examine the science process skill of prediction using qualitative research methodology. The think-aloud interview, modeled after Ericsson and Simon (1984), let to the identification of 63 program exploration and prediction behaviors.The performance of seven formal and seven concrete operational high-school biology students were videotaped during a three-phase learning sequence on water pollution. Subjects explored the effects of five independent variables on two dependent variables over time using a computer-simulation program. Predictions were made concerning the effect of the independent variables upon dependent variables through time. Subjects were identified according to initial knowledge of the subject matter and success at solving three selected prediction problems.Successful predictors generally had high initial knowledge of the subject matter and were formal operational. Unsuccessful predictors generally had low initial knowledge and were concrete operational. High initial knowledge seemed to be more important to predictive success than stage of Piagetian cognitive development.Successful prediction behaviors involved systematic manipulation of the independent variables, note taking, identification and use of appropriate independent-dependent variable relationships, high interest and motivation, and in general, higher-level thinking skills. Behaviors characteristic of unsuccessful predictors were nonsystematic manipulation of independent variables, lack of motivation and persistence, misconceptions, and the identification and use of inappropriate independent-dependent variable relationships.
Variables Affecting Secondary School Students' Willingness to Eat Genetically Modified Food Crops

NASA Astrophysics Data System (ADS)

Maes, Jasmien; Bourgonjon, Jeroen; Gheysen, Godelieve; Valcke, Martin

2017-04-01

A large-scale cross-sectional study (N = 4002) was set up to determine Flemish secondary school students' willingness to eat genetically modified food (WTE) and to link students' WTE to previously identified key variables from research on the acceptance of genetic modification (GM). These variables include subjective and objective knowledge about genetics and biotechnology, perceived risks and benefits of GM food crops, trust in information from different sources about GM, and food neophobia. Differences between WTE-related variables based on students' grade level, educational track, and gender were analyzed. The students displayed a rather indecisive position toward GM food and scored weakly on a genetics and biotechnology knowledge test. WTE correlated most strongly with perceived benefits and subjective and objective knowledge. The results have clear implications for education, as they reiterate the need to strengthen students' scientific knowledge base and to introduce a GM-related debate at a much earlier stage in their school career.
Variables Affecting Secondary School Students' Willingness to Eat Genetically Modified Food Crops

NASA Astrophysics Data System (ADS)

Maes, Jasmien; Bourgonjon, Jeroen; Gheysen, Godelieve; Valcke, Martin

2018-06-01

A large-scale cross-sectional study ( N = 4002) was set up to determine Flemish secondary school students' willingness to eat genetically modified food (WTE) and to link students' WTE to previously identified key variables from research on the acceptance of genetic modification (GM). These variables include subjective and objective knowledge about genetics and biotechnology, perceived risks and benefits of GM food crops, trust in information from different sources about GM, and food neophobia. Differences between WTE-related variables based on students' grade level, educational track, and gender were analyzed. The students displayed a rather indecisive position toward GM food and scored weakly on a genetics and biotechnology knowledge test. WTE correlated most strongly with perceived benefits and subjective and objective knowledge. The results have clear implications for education, as they reiterate the need to strengthen students' scientific knowledge base and to introduce a GM-related debate at a much earlier stage in their school career.
A Path Analysis of Latino Parental, Teenager and Cultural Variables in Teenagers' Sexual Attitudes, Norms, Self-Efficacy, and Sexual Intentions1

PubMed Central

Gaioso, Vanessa Pirani; Villarruel, Antonia Maria; Wilson, Lynda Anne; Azuero, Andres; Childs, Gwendolyn Denice; Davies, Susan Lane

2015-01-01

OBJECTIVE: to test a theoretical model based on the Parent-Based Expansion of the Theory of Planned Behavior examining relation between selected parental, teenager and cultural variables and Latino teenagers' intentions to engage in sexual behavior. METHOD: a cross-sectional correlational design based on a secondary data analysis of 130 Latino parent and teenager dyads. RESULTS: regression and path analysis procedures were used to test seven hypotheses and the results demonstrated partial support for the model. Parent familism and knowledge about sex were significantly associated with parents' attitudes toward sexual communication with their teenagers. Parent Latino acculturation was negatively associated with parents' self-efficacy toward sexual communication with their teenagers and positevely associated with parents' subjective norms toward sexual communication with their teenagers. Teenager knowledge about sex was significantly associated with higher levels of teenagers' attitudes and subjective norms about sexual communication with parents. Only the predictor of teenagers' attitudes toward having sex in the next 3 months was significantly associated with teenagers' intentions to have sex in the next 3 months. CONCLUSION: the results of this study provide important information to guide future research that can inform development of interventions to prevent risky teenager sexual behavior among Latinos. PMID:26312635
Automatic Sleep Stage Determination by Multi-Valued Decision Making Based on Conditional Probability with Optimal Parameters

NASA Astrophysics Data System (ADS)

Wang, Bei; Sugi, Takenao; Wang, Xingyu; Nakamura, Masatoshi

Data for human sleep study may be affected by internal and external influences. The recorded sleep data contains complex and stochastic factors, which increase the difficulties for the computerized sleep stage determination techniques to be applied for clinical practice. The aim of this study is to develop an automatic sleep stage determination system which is optimized for variable sleep data. The main methodology includes two modules: expert knowledge database construction and automatic sleep stage determination. Visual inspection by a qualified clinician is utilized to obtain the probability density function of parameters during the learning process of expert knowledge database construction. Parameter selection is introduced in order to make the algorithm flexible. Automatic sleep stage determination is manipulated based on conditional probability. The result showed close agreement comparing with the visual inspection by clinician. The developed system can meet the customized requirements in hospitals and institutions.
Variables contributing to an excellent customer service management profile within the regulated electric utility industry: A comparison of self-concept with customer satisfaction for customer service management

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

Johnson, L.E.

1991-01-01

This research sought to address the relationship between self-concept and customer satisfaction: can customer satisfaction with a major electric utility be explained in terms of the self-reported, self-concept of the utility's managers The population to which the results of this study were generalized consisted of customer service managers in public electric utilities across the United States. In order to represent this population, a sample was selected consisting of customer service managers at a midwestern electric utility based in a large metropolitan area. Participants in this study were managers of four direct customer contact service organizations within six geographic division organizations.more » The methodology included comparisons of these four customer contact service organizations on twelve independent, self-concept variables and six customer satisfaction dependent variables using Analysis of Variance (ANOVA), Scheffe' tests, Chi-Square, and Stepwise multiple regression. The groups were found not to be significantly different and knowledge of the self-concept scores for managers will not increase the ability to predict customer satisfaction over no knowledge of self-concept scores.« less
Response to traumatic brain injury neurorehabilitation through an artificial intelligence and statistics hybrid knowledge discovery from databases methodology.

PubMed

Gibert, Karina; García-Rudolph, Alejandro; García-Molina, Alberto; Roig-Rovira, Teresa; Bernabeu, Montse; Tormos, José María

2008-01-01

Develop a classificatory tool to identify different populations of patients with Traumatic Brain Injury based on the characteristics of deficit and response to treatment. A KDD framework where first, descriptive statistics of every variable was done, data cleaning and selection of relevant variables. Then data was mined using a generalization of Clustering based on rules (CIBR), an hybrid AI and Statistics technique which combines inductive learning (AI) and clustering (Statistics). A prior Knowledge Base (KB) is considered to properly bias the clustering; semantic constraints implied by the KB hold in final clusters, guaranteeing interpretability of the resultis. A generalization (Exogenous Clustering based on rules, ECIBR) is presented, allowing to define the KB in terms of variables which will not be considered in the clustering process itself, to get more flexibility. Several tools as Class panel graph are introduced in the methodology to assist final interpretation. A set of 5 classes was recommended by the system and interpretation permitted profiles labeling. From the medical point of view, composition of classes is well corresponding with different patterns of increasing level of response to rehabilitation treatments. All the patients initially assessable conform a single group. Severe impaired patients are subdivided in four profiles which clearly distinct response patterns. Particularly interesting the partial response profile, where patients could not improve executive functions. Meaningful classes were obtained and, from a semantics point of view, the results were sensibly improved regarding classical clustering, according to our opinion that hybrid AI & Stats techniques are more powerful for KDD than pure ones.
A survey of variable selection methods in two Chinese epidemiology journals

PubMed Central

2010-01-01

Background Although much has been written on developing better procedures for variable selection, there is little research on how it is practiced in actual studies. This review surveys the variable selection methods reported in two high-ranking Chinese epidemiology journals. Methods Articles published in 2004, 2006, and 2008 in the Chinese Journal of Epidemiology and the Chinese Journal of Preventive Medicine were reviewed. Five categories of methods were identified whereby variables were selected using: A - bivariate analyses; B - multivariable analysis; e.g. stepwise or individual significance testing of model coefficients; C - first bivariate analyses, followed by multivariable analysis; D - bivariate analyses or multivariable analysis; and E - other criteria like prior knowledge or personal judgment. Results Among the 287 articles that reported using variable selection methods, 6%, 26%, 30%, 21%, and 17% were in categories A through E, respectively. One hundred sixty-three studies selected variables using bivariate analyses, 80% (130/163) via multiple significance testing at the 5% alpha-level. Of the 219 multivariable analyses, 97 (44%) used stepwise procedures, 89 (41%) tested individual regression coefficients, but 33 (15%) did not mention how variables were selected. Sixty percent (58/97) of the stepwise routines also did not specify the algorithm and/or significance levels. Conclusions The variable selection methods reported in the two journals were limited in variety, and details were often missing. Many studies still relied on problematic techniques like stepwise procedures and/or multiple testing of bivariate associations at the 0.05 alpha-level. These deficiencies should be rectified to safeguard the scientific validity of articles published in Chinese epidemiology journals. PMID:20920252
Bridging the guideline implementation gap: a systematic, document-centered approach to guideline implementation.

PubMed

Shiffman, Richard N; Michel, George; Essaihi, Abdelwaheb; Thornquist, Elizabeth

2004-01-01

A gap exists between the information contained in published clinical practice guidelines and the knowledge and information that are necessary to implement them. This work describes a process to systematize and make explicit the translation of document-based knowledge into workflow-integrated clinical decision support systems. This approach uses the Guideline Elements Model (GEM) to represent the guideline knowledge. Implementation requires a number of steps to translate the knowledge contained in guideline text into a computable format and to integrate the information into clinical workflow. The steps include: (1) selection of a guideline and specific recommendations for implementation, (2) markup of the guideline text, (3) atomization, (4) deabstraction and (5) disambiguation of recommendation concepts, (6) verification of rule set completeness, (7) addition of explanations, (8) building executable statements, (9) specification of origins of decision variables and insertions of recommended actions, (10) definition of action types and selection of associated beneficial services, (11) choice of interface components, and (12) creation of requirement specification. The authors illustrate these component processes using examples drawn from recent experience translating recommendations from the National Heart, Lung, and Blood Institute's guideline on management of chronic asthma into a workflow-integrated decision support system that operates within the Logician electronic health record system. Using the guideline document as a knowledge source promotes authentic translation of domain knowledge and reduces the overall complexity of the implementation task. From this framework, we believe that a better understanding of activities involved in guideline implementation will emerge.
Use of a web-based education program improves nurses' knowledge of breastfeeding.

PubMed

Deloian, Barbara J; Lewin, Linda Orkin; O'Connor, Mary E

2015-01-01

To evaluate the baseline knowledge and knowledge gained of nurses, nursing students, midwives, and nurse practitioners who completed Breastfeeding Basics, an online educational program. This study reports on an anonymous evaluation of an online breastfeeding education program developed and maintained to promote evidence-based breastfeeding practice. Included in the study were 3736 nurses, 728 nurse practitioners/midwives, and 3106 nursing students from the United States who completed ≥ one pretest or posttest on the Breastfeeding Basics website between April 1999 and December 31, 2011. Baseline scores were analyzed to determine if nurses' baseline knowledge varied by selected demographic variables such as age, gender, professional level, personal or partner breastfeeding experience, and whether they were required to complete the website for a job or school requirement and to determine knowledge gaps. Pretest and posttest scores on all modules and in specific questions with low pretest scores were compared as a measure of knowledge gained. Lower median pretest scores were found in student nurses (71%), males (71%), those required to take the course (75%), and those without personal breastfeeding experience (72%). The modules with the lowest median pretest scores were Anatomy/Physiology (67%), Growth and Development of the Breastfed Infant (67%), the Breastfeeding Couple (73%), and the Term Infant with Problems (60%). Posttest scores in all modules increased significantly (p < .001). Breastfeeding Basics was used by a large number of nurses and nursing students. Gaps exist in nurses' breastfeeding knowledge. Knowledge improved in all areas based on comparison of pretest and posttest scores. © 2015 AWHONN, the Association of Women's Health, Obstetric and Neonatal Nurses.
Development and Validation of a Rubric for Diagnosing Students' Experimental Design Knowledge and Difficulties.

PubMed

Dasgupta, Annwesa P; Anderson, Trevor R; Pelaez, Nancy

2014-01-01

It is essential to teach students about experimental design, as this facilitates their deeper understanding of how most biological knowledge was generated and gives them tools to perform their own investigations. Despite the importance of this area, surprisingly little is known about what students actually learn from designing biological experiments. In this paper, we describe a rubric for experimental design (RED) that can be used to measure knowledge of and diagnose difficulties with experimental design. The development and validation of the RED was informed by a literature review and empirical analysis of undergraduate biology students' responses to three published assessments. Five areas of difficulty with experimental design were identified: the variable properties of an experimental subject; the manipulated variables; measurement of outcomes; accounting for variability; and the scope of inference appropriate for experimental findings. Our findings revealed that some difficulties, documented some 50 yr ago, still exist among our undergraduate students, while others remain poorly investigated. The RED shows great promise for diagnosing students' experimental design knowledge in lecture settings, laboratory courses, research internships, and course-based undergraduate research experiences. It also shows potential for guiding the development and selection of assessment and instructional activities that foster experimental design. © 2014 A. P. Dasgupta et al. CBE—Life Sciences Education © 2014 The American Society for Cell Biology. This article is distributed by The American Society for Cell Biology under license from the author(s). It is available to the public under an Attribution–Noncommercial–Share Alike 3.0 Unported Creative Commons License (http://creativecommons.org/licenses/by-nc-sa/3.0).
Application variables and their influence on forest herbicide efficacy and selectivity: gaining understanding and control

Treesearch

James H. Miller

1998-01-01

Available research is reviewed on the interactions of application variables, herbicides, and species. Objectives of this review are to gain insights into why variation occurs with herbicide performance, how current knowledge might be applied to enhance efficacy and consistency, and research pathways that should foster integration of application-efficacy models. A...
The role of physician characteristics in clinical trial acceptance: testing pathways of influence.

PubMed

Curbow, Barbara; Fogarty, Linda A; McDonnell, Karen A; Chill, Julia; Scott, Lisa Benz

2006-03-01

Eight videotaped vignettes were developed that assessed the effects of three physician-related experimental variables (in a 2 x 2 x 2 factorial design) on clinical trial (CT) knowledge, video knowledge, information processing, CT beliefs, affective evaluations (attitudes), and CT acceptance. It was hypothesized that the physician variables (community versus academic-based affiliation, enthusiastic versus neutral presentation of the trial, and new versus previous relationship with the patient) would serve as communication cues that would interrupt message processing, leading to lower knowledge gain but more positive beliefs, attitudes, and CT acceptance. A total of 262 women (161 survivors and 101 controls) participated in the study. The manipulated variables primarily influenced the intermediary variables of post-test CT beliefs and satisfaction with information rather than knowledge or information processing. Multiple regression results indicated that CT acceptance was associated with positive post-CT beliefs, a lower level of information processing, satisfaction with information, and control status. Based on these results, CT acceptance does not appear to be based on a rational decision-making model; this has implications for both the ethics of informed consent and research conceptual models.
Propesticides and their use as agrochemicals.

PubMed

Jeschke, Peter

2016-02-01

The synthesis of propesticides is an important concept in design of modern agrochemicals with optimal efficacy, environmental safety, user friendliness and economic variability. Based on increasing knowledge of the biochemistry and genetics of major pest insects, weeds and agricultural pathogens, the search for selectivity has become an ever more important part of pesticide development and can be achieved by appropriate structural modifications of the active ingredient. Propesticides affect the absorption, distribution, metabolism and excretion parameters, which can lead to biological superiority of these modified active ingredients over their non-derivatised analogues. Various selected commercial propesticides testify to the successful utilisation of this concept in the design of agrochemicals. This review describes comprehensively the successful utilisation of propesticides and their role in syntheses of modern agrochemicals, exemplified by selected commercial products coming from different agrochemical areas. © 2015 Society of Chemical Industry.
Boosted structured additive regression for Escherichia coli fed-batch fermentation modeling.

PubMed

Melcher, Michael; Scharl, Theresa; Luchner, Markus; Striedner, Gerald; Leisch, Friedrich

2017-02-01

The quality of biopharmaceuticals and patients' safety are of highest priority and there are tremendous efforts to replace empirical production process designs by knowledge-based approaches. Main challenge in this context is that real-time access to process variables related to product quality and quantity is severely limited. To date comprehensive on- and offline monitoring platforms are used to generate process data sets that allow for development of mechanistic and/or data driven models for real-time prediction of these important quantities. Ultimate goal is to implement model based feed-back control loops that facilitate online control of product quality. In this contribution, we explore structured additive regression (STAR) models in combination with boosting as a variable selection tool for modeling the cell dry mass, product concentration, and optical density on the basis of online available process variables and two-dimensional fluorescence spectroscopic data. STAR models are powerful extensions of linear models allowing for inclusion of smooth effects or interactions between predictors. Boosting constructs the final model in a stepwise manner and provides a variable importance measure via predictor selection frequencies. Our results show that the cell dry mass can be modeled with a relative error of about ±3%, the optical density with ±6%, the soluble protein with ±16%, and the insoluble product with an accuracy of ±12%. Biotechnol. Bioeng. 2017;114: 321-334. © 2016 Wiley Periodicals, Inc. © 2016 Wiley Periodicals, Inc.
Professional values and competencies as explanatory factors for the use of evidence-based practice in nursing.

PubMed

Skela-Savič, Brigita; Hvalič-Touzery, Simona; Pesjak, Katja

2017-08-01

To establish the connection between values, competencies, selected job characteristics and evidence-based practice use. Nurses rarely apply evidence-based practice in everyday work. A recent body of research has looked at various variables explaining the use of evidence-based practice, but not values and competencies. A cross-sectional, non-experimental quantitative explorative research design. Standardized instruments were used (Nurse Professional Values Scale-R, Nurse Competence Scale, Evidence-Based Practice Beliefs and Implementation Scale). The sample included 780 nurses from 20 Slovenian hospitals. The data were collected in 2015. The study identifies two new variables contributing to a better understanding of beliefs on and implementation of evidence-based practice, thus broadening the existing research evidence. These are the values of activism and professionalism and competencies aimed at the development and professionalization of nursing. Values of caring, trust and justice and competencies expected in everyday practice do not influence the beliefs and implementation of evidence-based practice. Respondents ascribed less importance to values connected with activism and professionalism and competencies connected with the development of professionalism. Nurses agree that evidence-based practice is useful in their clinical work, but they lack the knowledge to implement it in practice. Evidence-based practice implementation in nursing practice is low. Study results stress the importance of increasing the knowledge and skills on professional values of activism and professionalism and competencies connected to nursing development. The study expands the current understanding of evidence-based practice use and provides invaluable insight for nursing managers, higher education managers and the national nursing association. © 2017 John Wiley & Sons Ltd.

Correlation between knowledge, attitude and practices on HIV and AIDS: cases from the Kathmandu Valley.

PubMed

Karki, T B

2014-01-01

This study investigated the correlation between knowledge, attitude and practices on HIV and AIDS in the context of Nepal.The study was conducted among the 404 respondents; selected from the transport workers, garment factory workers, brick factory workers and health workers. It was non-experimental cross sectional study based on descriptive as well as correlational research design. Simple random technique was used to select the respondents. Survey was conducted to collect the primary data and r value was used to analyze the correlation between variables. Finding shows that 391 (96.8%) respondents have heard about HIV and AIDS; among them 388 (95.8%) respondents were mentioned that they had knowledge of way of HIV transmission also. Total 50 out of 171 unmarried (29.2%) respondents had pre-marital sexual experience. It was found that only 71 (25.6%) respondents had used the condom during their first time sexual intercourse.There was significant association (p=.000) found between the knowledge on way of HIV transmission and occupation of respondents, similarly relationship found (r = .815, p = .000 (2-tailed) between marriage age and age of first time sexual intercourseof respondents. But there was no relationship (r = .097 and p =.106 (2-tailed) found between Knowledge on way of HIV transmission and sex with non-regular sex partners. Data showed that safer sex practices was low than the level of knowledge. The educational status of respondents shows the positive association with attitude towards the necessary to have knowledge of HIV and AIDS.
WE-E-BRE-05: Ensemble of Graphical Models for Predicting Radiation Pneumontis Risk

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

Lee, S; Ybarra, N; Jeyaseelan, K

Purpose: We propose a prior knowledge-based approach to construct an interaction graph of biological and dosimetric radiation pneumontis (RP) covariates for the purpose of developing a RP risk classifier. Methods: We recruited 59 NSCLC patients who received curative radiotherapy with minimum 6 month follow-up. 16 RP events was observed (CTCAE grade ≥2). Blood serum was collected from every patient before (pre-RT) and during RT (mid-RT). From each sample the concentration of the following five candidate biomarkers were taken as covariates: alpha-2-macroglobulin (α2M), angiotensin converting enzyme (ACE), transforming growth factor β (TGF-β), interleukin-6 (IL-6), and osteopontin (OPN). Dose-volumetric parameters were alsomore » included as covariates. The number of biological and dosimetric covariates was reduced by a variable selection scheme implemented by L1-regularized logistic regression (LASSO). Posterior probability distribution of interaction graphs between the selected variables was estimated from the data under the literature-based prior knowledge to weight more heavily the graphs that contain the expected associations. A graph ensemble was formed by averaging the most probable graphs weighted by their posterior, creating a Bayesian Network (BN)-based RP risk classifier. Results: The LASSO selected the following 7 RP covariates: (1) pre-RT concentration level of α2M, (2) α2M level mid- RT/pre-RT, (3) pre-RT IL6 level, (4) IL6 level mid-RT/pre-RT, (5) ACE mid-RT/pre-RT, (6) PTV volume, and (7) mean lung dose (MLD). The ensemble BN model achieved the maximum sensitivity/specificity of 81%/84% and outperformed univariate dosimetric predictors as shown by larger AUC values (0.78∼0.81) compared with MLD (0.61), V20 (0.65) and V30 (0.70). The ensembles obtained by incorporating the prior knowledge improved classification performance for the ensemble size 5∼50. Conclusion: We demonstrated a probabilistic ensemble method to detect robust associations between RP covariates and its potential to improve RP prediction accuracy. Our Bayesian approach to incorporate prior knowledge can enhance efficiency in searching of such associations from data. The authors acknowledge partial support by: 1) CREATE Medical Physics Research Training Network grant of the Natural Sciences and Engineering Research Council (Grant number: 432290) and 2) The Terry Fox Foundation Strategic Training Initiative for Excellence in Radiation Research for the 21st Century (EIRR21)« less
Canonical Measure of Correlation (CMC) and Canonical Measure of Distance (CMD) between sets of data. Part 3. Variable selection in classification.

PubMed

Ballabio, Davide; Consonni, Viviana; Mauri, Andrea; Todeschini, Roberto

2010-01-11

In multivariate regression and classification issues variable selection is an important procedure used to select an optimal subset of variables with the aim of producing more parsimonious and eventually more predictive models. Variable selection is often necessary when dealing with methodologies that produce thousands of variables, such as Quantitative Structure-Activity Relationships (QSARs) and highly dimensional analytical procedures. In this paper a novel method for variable selection for classification purposes is introduced. This method exploits the recently proposed Canonical Measure of Correlation between two sets of variables (CMC index). The CMC index is in this case calculated for two specific sets of variables, the former being comprised of the independent variables and the latter of the unfolded class matrix. The CMC values, calculated by considering one variable at a time, can be sorted and a ranking of the variables on the basis of their class discrimination capabilities results. Alternatively, CMC index can be calculated for all the possible combinations of variables and the variable subset with the maximal CMC can be selected, but this procedure is computationally more demanding and classification performance of the selected subset is not always the best one. The effectiveness of the CMC index in selecting variables with discriminative ability was compared with that of other well-known strategies for variable selection, such as the Wilks' Lambda, the VIP index based on the Partial Least Squares-Discriminant Analysis, and the selection provided by classification trees. A variable Forward Selection based on the CMC index was finally used in conjunction of Linear Discriminant Analysis. This approach was tested on several chemical data sets. Obtained results were encouraging.
"The Knowledge of" Counselors in Balqa Governorate: Behavior Modification Strategies in Light of Some of the Variables

ERIC Educational Resources Information Center

Al-basel, D-Nagham Mohammad Abu

2013-01-01

The present study aimed to identify the extent of knowledge of counselor behavior modification strategies. The current study sample consisted of (80) mentor and guide, were selected randomly from among all workers enrolled in regular public schools in the Balqa governorate represented the community study for the academic year 2012-2013. The study…
Variable Selection through Correlation Sifting

NASA Astrophysics Data System (ADS)

Huang, Jim C.; Jojic, Nebojsa

Many applications of computational biology require a variable selection procedure to sift through a large number of input variables and select some smaller number that influence a target variable of interest. For example, in virology, only some small number of viral protein fragments influence the nature of the immune response during viral infection. Due to the large number of variables to be considered, a brute-force search for the subset of variables is in general intractable. To approximate this, methods based on ℓ1-regularized linear regression have been proposed and have been found to be particularly successful. It is well understood however that such methods fail to choose the correct subset of variables if these are highly correlated with other "decoy" variables. We present a method for sifting through sets of highly correlated variables which leads to higher accuracy in selecting the correct variables. The main innovation is a filtering step that reduces correlations among variables to be selected, making the ℓ1-regularization effective for datasets on which many methods for variable selection fail. The filtering step changes both the values of the predictor variables and output values by projections onto components obtained through a computationally-inexpensive principal components analysis. In this paper we demonstrate the usefulness of our method on synthetic datasets and on novel applications in virology. These include HIV viral load analysis based on patients' HIV sequences and immune types, as well as the analysis of seasonal variation in influenza death rates based on the regions of the influenza genome that undergo diversifying selection in the previous season.
Variables that Correlate with Faculty Use of Research-Based Instructional Strategies

NASA Astrophysics Data System (ADS)

Henderson, Charles; Dancy, Melissa H.; Niewiadomska-Bugaj, Magdalena

2010-10-01

During the Fall of 2008 a web survey, designed to collect information about pedagogical knowledge and practices, was completed by a representative sample of 722 physics faculty across the United States (a 50.3% response rate). This paper examines how 20 predictor variables correlate with faculty knowledge about and use of research-based instructional strategies (RBIS). Profiles were developed for each of four faculty levels of knowledge about and use of RBIS. Logistic regression analysis was used to identify a subset of the variables that could predict group membership. Five significant predictor variables were identified. High levels of knowledge and use of RBIS were associated with the following characteristics: attendee of the physics and astronomy new faculty workshop, attendee of at least one talk or workshop related to teaching in the last two years, satisfaction with meeting instructional goals, regular reader of one or more journals related to teaching, and being female. High research productivity and large class sizes were not found to be barriers to use of at least some RBIS.
Towards an understanding of Internet-based problem shopping behaviour: The concept of online shopping addiction and its proposed predictors

PubMed Central

ROSE, SUSAN; DHANDAYUDHAM, ARUN

2014-01-01

Background: Compulsive and addictive forms of consumption and buying behaviour have been researched in both business and medical literature. Shopping enabled via the Internet now introduces new features to the shopping experience that translate to positive benefits for the shopper. Evidence now suggests that this new shopping experience may lead to problematic online shopping behaviour. This paper provides a theoretical review of the literature relevant to online shopping addiction (OSA). Based on this selective review, a conceptual model of OSA is presented. Method: The selective review of the literature draws on searches within databases relevant to both clinical and consumer behaviour literature including EBSCO, ABI Pro-Quest, Web of Science – Social Citations Index, Medline, PsycINFO and Pubmed. The article reviews current thinking on problematic, and specifically addictive, behaviour in relation to online shopping. Results: The review of the literature enables the extension of existing knowledge into the Internet-context. A conceptual model of OSA is developed with theoretical support provided for the inclusion of 7 predictor variables: low self-esteem, low self-regulation; negative emotional state; enjoyment; female gender; social anonymity and cognitive overload. The construct of OSA is defined and six component criteria of OSA are proposed based on established technological addiction criteria. Conclusions: Current Internet-based shopping experiences may trigger problematic behaviours which can be classified on a spectrum which at the extreme end incorporates OSA. The development of a conceptual model provides a basis for the future measurement and testing of proposed predictor variables and the outcome variable OSA. PMID:25215218
Towards an understanding of Internet-based problem shopping behaviour: The concept of online shopping addiction and its proposed predictors.

PubMed

Rose, Susan; Dhandayudham, Arun

2014-06-01

Compulsive and addictive forms of consumption and buying behaviour have been researched in both business and medical literature. Shopping enabled via the Internet now introduces new features to the shopping experience that translate to positive benefits for the shopper. Evidence now suggests that this new shopping experience may lead to problematic online shopping behaviour. This paper provides a theoretical review of the literature relevant to online shopping addiction (OSA). Based on this selective review, a conceptual model of OSA is presented. The selective review of the literature draws on searches within databases relevant to both clinical and consumer behaviour literature including EBSCO, ABI Pro-Quest, Web of Science - Social Citations Index, Medline, PsycINFO and Pubmed. The article reviews current thinking on problematic, and specifically addictive, behaviour in relation to online shopping. The review of the literature enables the extension of existing knowledge into the Internet-context. A conceptual model of OSA is developed with theoretical support provided for the inclusion of 7 predictor variables: low self-esteem, low self-regulation; negative emotional state; enjoyment; female gender; social anonymity and cognitive overload. The construct of OSA is defined and six component criteria of OSA are proposed based on established technological addiction criteria. Current Internet-based shopping experiences may trigger problematic behaviours which can be classified on a spectrum which at the extreme end incorporates OSA. The development of a conceptual model provides a basis for the future measurement and testing of proposed predictor variables and the outcome variable OSA.
Assessing risk based on uncertain avalanche activity patterns

NASA Astrophysics Data System (ADS)

Zeidler, Antonia; Fromm, Reinhard

2015-04-01

Avalanches may affect critical infrastructure and may cause great economic losses. The planning horizon of infrastructures, e.g. hydropower generation facilities, reaches well into the future. Based on the results of previous studies on the effect of changing meteorological parameters (precipitation, temperature) and the effect on avalanche activity we assume that there will be a change of the risk pattern in future. The decision makers need to understand what the future might bring to best formulate their mitigation strategies. Therefore, we explore a commercial risk software to calculate risk for the coming years that might help in decision processes. The software @risk, is known to many larger companies, and therefore we explore its capabilities to include avalanche risk simulations in order to guarantee a comparability of different risks. In a first step, we develop a model for a hydropower generation facility that reflects the problem of changing avalanche activity patterns in future by selecting relevant input parameters and assigning likely probability distributions. The uncertain input variables include the probability of avalanches affecting an object, the vulnerability of an object, the expected costs for repairing the object and the expected cost due to interruption. The crux is to find the distribution that best represents the input variables under changing meteorological conditions. Our focus is on including the uncertain probability of avalanches based on the analysis of past avalanche data and expert knowledge. In order to explore different likely outcomes we base the analysis on three different climate scenarios (likely, worst case, baseline). For some variables, it is possible to fit a distribution to historical data, whereas in cases where the past dataset is insufficient or not available the software allows to select from over 30 different distribution types. The Monte Carlo simulation uses the probability distribution of uncertain variables using all valid combinations of the values of input variables to simulate all possible outcomes. In our case the output is the expected risk (Euro/year) for each object (e.g. water intake) considered and the entire hydropower generation system. The output is again a distribution that is interpreted by the decision makers as the final strategy depends on the needs and requirements of the end-user, which may be driven by personal preferences. In this presentation, we will show a way on how we used the uncertain information on avalanche activity in future to subsequently use it in a commercial risk software and therefore bringing the knowledge of natural hazard experts to decision makers.
Resistance to Change: Reactions to Workplace Computerization.

ERIC Educational Resources Information Center

Gattiker, Urs E.; Larwood, Laurie

Although past research has suggested that computer acceptance and knowledge are two variables crucial in attaining desired profitability increases with computer-based technology, few studies have examined how these variables occur in organizational settings. A study was undertaken to examine acceptance of, and knowledge about, computer-based…
Semantic modeling of plastic deformation of polycrystalline rock

NASA Astrophysics Data System (ADS)

Babaie, Hassan A.; Davarpanah, Armita

2018-02-01

We have developed the first iteration of the Plastic Rock Deformation (PRD) ontology by modeling the semantics of a selected set of deformational processes and mechanisms that produce, reconfigure, displace, and/or consume the material components of inhomogeneous polycrystalline rocks. The PRD knowledge model also classifies and formalizes the properties (relations) that hold between instances of the dynamic physical and chemical processes and the rock components, the complex physio-chemical, mathematical, and informational concepts of the plastic rock deformation system, the measured or calculated laboratory testing conditions, experimental procedures and protocols, the state and system variables, and the empirical flow laws that define the inter-relationships among the variables. The ontology reuses classes and properties from several existing ontologies that are built for physics, chemistry, biology, and mathematics. With its flexible design, the PRD ontology is well positioned to incrementally develop into a model that more fully represents the knowledge of plastic deformation of polycrystalline rocks in the future. The domain ontology will be used to consistently annotate varied data and information related to the microstructures and the physical and chemical processes that produce them at different spatial and temporal scales in the laboratory and in the solid Earth. The PRDKB knowledge base, when built based on the ontology, will help the community of experimental structural geologists and metamorphic petrologists to coherently and uniformly distribute, discover, access, share, and use their data through automated reasoning and integration and query of heterogeneous experimental deformation data that originate from autonomous rock testing laboratories.
A GPU-Based Implementation of the Firefly Algorithm for Variable Selection in Multivariate Calibration Problems

PubMed Central

de Paula, Lauro C. M.; Soares, Anderson S.; de Lima, Telma W.; Delbem, Alexandre C. B.; Coelho, Clarimar J.; Filho, Arlindo R. G.

2014-01-01

Several variable selection algorithms in multivariate calibration can be accelerated using Graphics Processing Units (GPU). Among these algorithms, the Firefly Algorithm (FA) is a recent proposed metaheuristic that may be used for variable selection. This paper presents a GPU-based FA (FA-MLR) with multiobjective formulation for variable selection in multivariate calibration problems and compares it with some traditional sequential algorithms in the literature. The advantage of the proposed implementation is demonstrated in an example involving a relatively large number of variables. The results showed that the FA-MLR, in comparison with the traditional algorithms is a more suitable choice and a relevant contribution for the variable selection problem. Additionally, the results also demonstrated that the FA-MLR performed in a GPU can be five times faster than its sequential implementation. PMID:25493625
A GPU-Based Implementation of the Firefly Algorithm for Variable Selection in Multivariate Calibration Problems.

PubMed

de Paula, Lauro C M; Soares, Anderson S; de Lima, Telma W; Delbem, Alexandre C B; Coelho, Clarimar J; Filho, Arlindo R G

2014-01-01

Several variable selection algorithms in multivariate calibration can be accelerated using Graphics Processing Units (GPU). Among these algorithms, the Firefly Algorithm (FA) is a recent proposed metaheuristic that may be used for variable selection. This paper presents a GPU-based FA (FA-MLR) with multiobjective formulation for variable selection in multivariate calibration problems and compares it with some traditional sequential algorithms in the literature. The advantage of the proposed implementation is demonstrated in an example involving a relatively large number of variables. The results showed that the FA-MLR, in comparison with the traditional algorithms is a more suitable choice and a relevant contribution for the variable selection problem. Additionally, the results also demonstrated that the FA-MLR performed in a GPU can be five times faster than its sequential implementation.
Evaluation of an educational program on cervical cancer for rural women in Mangalore, Southern India.

PubMed

Mary, Bright; D'Sa, Juliana Linnette

2014-01-01

Cervical cancer is one of the leading causes of cancer in women worldwide. One way by which the incidence of this malignant disease can be minimized is by imparting knowledge through health education. This study aimed at developing an educational package on cervical cancer (EPCC) and determining its effectiveness in terms of significant increase in knowledge of rural women regarding cervical cancer. A one group pre-test, post-test design was adopted. Thirty rural women were selected using a convenient sampling method. Data were collected using a demographic questionnaire and a structured knowledge questionnaire developed by the researchers. The EPCC was designed for a duration of one hour and 10 minutes. The structured knowledge questionnaire was first administered as the pre-test, following which knowledge on cervical cancer was imparted using the EPCC. On the 8th day, the post-test was administered. Data were analyzed using descriptive and inferential statistics. The mean post-test knowledge score of the women regarding cervical cancer was significantly higher than that of their mean pre-test score, indicating that the EPCC was effective in improving the knowledge of rural women on cervical cancer. The association between pre-test knowledge scores and selected demo-graphic variables were computed using chi-square test showed that pre-test knowledge score of the women regarding cervical cancer was independent of all the socio-demographic variables. It was concluded that the EPCC is effective in improving the knowledge of women, regarding cervical cancer. Since the prevalence of cervical cancer is high, there is an immediate need to educate women on prevention of cervical cancer.
Awareness and Knowledge of Glaucoma among Workers in a Nigerian Tertiary Health Care Institution

PubMed Central

Komolafe, O. O.; Omolase, C. O.; Bekibele, C. O.; Ogunleye, O. A.; Komolafe, O. A.; Omotayo, F. O.

2013-01-01

Purpose: The aim of this study reports the level of awareness and knowledge of glaucoma among selected health care personnel at a health institution in southwestern Nigeria. Materials and Methods: Health personnel at the Federal Medical Centre, Owo, Nigeria, a tertiary health care institution were stratified into a clinical and an administrative directorate. One-hundred twenty participants were selected from each directorate by a random sampling technique. A structured questionnaire was used to collect sociodemographic data and data on the level of knowledge and awareness of glaucoma. Statistical analyses included the independent t-test and Pearson's chi-square test for categorical variables. Statistical significance was indicated by P < 0.05. Results: From the target population of 240 participants, 216 (98 males; 118 females) completed the questionnaire. The mean age of the participants was 35.07 ± 07 years. A total of 148 (68.6%) participants had heard of glaucoma comprising all participants from the clinical directorate and 28 participants from the administrative directorate. There was no statistically significant difference between the clinical and administrative directorates about the knowledge of the aspect of vision that is first affected by glaucoma, the painless nature of glaucoma among most Africans and the irreversible nature of glaucoma-related blindness (P > 0.05, all comparisons). Conclusion: There is the need to update the knowledge base of these workers if they are to be useful in propagating information of the irreversible blindness that could arise from delay in glaucoma diagnosis and treatment. PMID:23741136
[Application of characteristic NIR variables selection in portable detection of soluble solids content of apple by near infrared spectroscopy].

PubMed

Fan, Shu-Xiang; Huang, Wen-Qian; Li, Jiang-Bo; Guo, Zhi-Ming; Zhaq, Chun-Jiang

2014-10-01

In order to detect the soluble solids content(SSC)of apple conveniently and rapidly, a ring fiber probe and a portable spectrometer were applied to obtain the spectroscopy of apple. Different wavelength variable selection methods, including unin- formative variable elimination (UVE), competitive adaptive reweighted sampling (CARS) and genetic algorithm (GA) were pro- posed to select effective wavelength variables of the NIR spectroscopy of the SSC in apple based on PLS. The back interval LS- SVM (BiLS-SVM) and GA were used to select effective wavelength variables based on LS-SVM. Selected wavelength variables and full wavelength range were set as input variables of PLS model and LS-SVM model, respectively. The results indicated that PLS model built using GA-CARS on 50 characteristic variables selected from full-spectrum which had 1512 wavelengths achieved the optimal performance. The correlation coefficient (Rp) and root mean square error of prediction (RMSEP) for prediction sets were 0.962, 0.403°Brix respectively for SSC. The proposed method of GA-CARS could effectively simplify the portable detection model of SSC in apple based on near infrared spectroscopy and enhance the predictive precision. The study can provide a reference for the development of portable apple soluble solids content spectrometer.
Robust check loss-based variable selection of high-dimensional single-index varying-coefficient model

NASA Astrophysics Data System (ADS)

Song, Yunquan; Lin, Lu; Jian, Ling

2016-07-01

Single-index varying-coefficient model is an important mathematical modeling method to model nonlinear phenomena in science and engineering. In this paper, we develop a variable selection method for high-dimensional single-index varying-coefficient models using a shrinkage idea. The proposed procedure can simultaneously select significant nonparametric components and parametric components. Under defined regularity conditions, with appropriate selection of tuning parameters, the consistency of the variable selection procedure and the oracle property of the estimators are established. Moreover, due to the robustness of the check loss function to outliers in the finite samples, our proposed variable selection method is more robust than the ones based on the least squares criterion. Finally, the method is illustrated with numerical simulations.
Knowledge of safe motherhood among women in rural communities in northern Nigeria: implications for maternal mortality reduction

PubMed Central

2013-01-01

Background Most developed countries have made considerable progress in addressing maternal mortality, but it appears that countries with high maternal mortality burdens like Nigeria have made little progress in improving maternal health outcomes despite emphasis by the Millennium Development Goals (MDGs). Knowledge about safe motherhood practices could help reduce pregnancy related health risks. This study examines knowledge of safe motherhood among women in selected rural communities in northern Nigeria. Methods This was a cross-sectional study carried out in two states (Kaduna and Kano States) within northern Nigeria. Pretested, interviewer-administered questionnaires were applied by female data collectors to 540 randomly selected women who had recently delivered within the study site. Chi-square tests were used to determine possible association between variables during bivariate analysis. Variables significant in the bivariate analysis were subsequently entered into a multivariate logistic regression analysis. The degree of association was estimated by odds ratio (OR) and 95% confidence interval (CI) between knowledge of maternal danger signs and independent socio-demographic as well as obstetric history variables which indicated significance at p< 0.05. Results Over 90% of respondents in both states showed poor knowledge of the benefits of health facility delivery by a skilled birth attendant. More than 80% of respondents in both states displayed poor knowledge of the benefits of ANC visits. More than half of the respondents across both states had poor knowledge of maternal danger signs. According to multivariate regression analysis, ever attending school by a respondent increased the likelihood of knowing maternal danger signs by threefold (OR 2.63, 95% CI: 1.2-5.8) among respondents in Kaduna State. While attendance at ANC visits during most recent pregnancy increased the likelihood of knowing maternal danger signs by twofold among respondents in Kano State (OR 2.05, 95% CI: 1.1-3.9) and threefold among respondents in Kaduna State (OR 3.33, 95% CI: 1.6-7.2). Conclusion This study found generally poor knowledge about safe motherhood practices among female respondents within selected rural communities in northern Nigeria. Knowledge of safe pregnancy practices among some women in rural communities is strongly associated with attendance at ANC visits, being employed or acquiring some level of education. Increasing knowledge about safe motherhood practices should translate into safer pregnancy outcomes and subsequently lead to lower maternal mortality across the developing world. PMID:24160692
A Time-Series Water Level Forecasting Model Based on Imputation and Variable Selection Method.

PubMed

Yang, Jun-He; Cheng, Ching-Hsue; Chan, Chia-Pan

2017-01-01

Reservoirs are important for households and impact the national economy. This paper proposed a time-series forecasting model based on estimating a missing value followed by variable selection to forecast the reservoir's water level. This study collected data from the Taiwan Shimen Reservoir as well as daily atmospheric data from 2008 to 2015. The two datasets are concatenated into an integrated dataset based on ordering of the data as a research dataset. The proposed time-series forecasting model summarily has three foci. First, this study uses five imputation methods to directly delete the missing value. Second, we identified the key variable via factor analysis and then deleted the unimportant variables sequentially via the variable selection method. Finally, the proposed model uses a Random Forest to build the forecasting model of the reservoir's water level. This was done to compare with the listing method under the forecasting error. These experimental results indicate that the Random Forest forecasting model when applied to variable selection with full variables has better forecasting performance than the listing model. In addition, this experiment shows that the proposed variable selection can help determine five forecast methods used here to improve the forecasting capability.
Knowledge and perception about climate change and human health: findings from a baseline survey among vulnerable communities in Bangladesh.

PubMed

Kabir, Md Iqbal; Rahman, Md Bayzidur; Smith, Wayne; Lusha, Mirza Afreen Fatima; Azim, Syed; Milton, Abul Hasnat

2016-03-15

Bangladesh is one of the countries most vulnerable to climate change (CC). A basic understanding of public perception on vulnerability, attitude and the risk in relation to CC and health will provide strategic directions for government policy, adaptation strategies and development of community-based guidelines. The objective of this study was to collect community-based data on peoples' knowledge and perception about CC and its impact on health. In 2012, a cross-sectional survey was undertaken among 6720 households of 224 enumeration areas of rural villages geographically distributed in seven vulnerable districts of Bangladesh, with total population of 19,228,598. Thirty households were selected randomly from each enumeration area using the household listing provided by the Bangladesh Bureau of Statistics (BBS). Information was collected from all the 6720 research participants using a structured questionnaire. An observation checklist was used by the interviewers to collect household- and community-related information. In addition, we selected the head of each household as the eligible participant for an interview. Evidence of association between sociodemographic variables and knowledge of CC was explored by cross-tabulation and measured using chi-square tests. Logistic regression models were used to further explore the predictors of knowledge. The study revealed that the residents of the rural communities selected for this study largely come from a low socioeconomic background: only 9.6% had postsecondary education or higher, the majority worked as day labourer or farmer (60%), and only 10% earned a monthly income above BDT 12000 (equivalent to US $150 approx.). The majority of the participants (54.2%) had some knowledge about CC but 45.8% did not (p < 0.001). The majority of knowledgeable participants (n = 3645) felt excessive temperature as the change of climate (83.2%). Among all the respondents (n = 6720), 94.5% perceived change in climate and extreme weather events. Most of them (91.9%) observed change in rainfall patterns in the last 10 years, and 97.8% people think their health care expenditure increased after the extreme weather events. Age, educational qualification, monthly income, and occupation were significantly associated with the knowledge about climate change (p < 0.001). People with higher educational level or who live near a school were more knowledgeable about CC and its impact on health. The knowledge level about CC in our study group was average but the perception and awareness of CC related events and its impact on health was high. The most influential factor leading to understanding of CC and its impact on health was education. School-based intervention could be explored to increase peoples' knowledge about CC and necessary health adaptation at community level.

Computational methods in the development of a knowledge-based system for the prediction of solid catalyst performance.

PubMed

Procelewska, Joanna; Galilea, Javier Llamas; Clerc, Frederic; Farrusseng, David; Schüth, Ferdi

2007-01-01

The objective of this work is the construction of a correlation between characteristics of heterogeneous catalysts, encoded in a descriptor vector, and their experimentally measured performances in the propene oxidation reaction. In this paper the key issue in the modeling process, namely the selection of adequate input variables, is explored. Several data-driven feature selection strategies were applied in order to obtain an estimate of the differences in variance and information content of various attributes, furthermore to compare their relative importance. Quantitative property activity relationship techniques using probabilistic neural networks have been used for the creation of various semi-empirical models. Finally, a robust classification model, assigning selected attributes of solid compounds as input to an appropriate performance class in the model reaction was obtained. It has been evident that the mathematical support for the primary attributes set proposed by chemists can be highly desirable.
Knowledge of Oral Health Issues Among Low–Income Baltimore Adults: A Pilot Study

PubMed Central

Macek, Mark D.; Manski, Marion C.; Schneiderman, MaryAnn T.; Meakin, Sarah J.; Haynes, Don; Wells, William; Bauer–Leffler, Simon; Cotten, P. Ann; Parker, Ruth M.

2013-01-01

Purpose This pilot study documents conceptual knowledge of oral health among low–income adults in Baltimore. Methods Selected questions from the Baltimore Health Literacy and Oral Health Knowledge Project, a cross–sectional, population–based investigation of oral health literacy, were used for this analysis. Participants were asked questions during face–to–face interviews about basic oral health and the prevention and management of dental caries and periodontal diseases. Descriptive analyses included tests of association with selected socio–demographic variables (age, sex, education level, annual household income). Results The majority of respondents were African American women, 45 to 64 years of age, with 12 years of education and an income less than or equal to $25,000. Ninety–one percent of respondents knew that sugar caused dental caries, while 82% understood that the best way to prevent tooth decay was to brush and floss every day. Knowledge of oral hygiene practices and the prevention and management of gingivitis and periodontitis was mixed. Seventy–six percent understood that the best way to remove tartar was by a dental cleaning. However, only 15% knew how often to floss their teeth and only 21% knew that plaque was composed of germs. Conclusion Conceptual oral health knowledge is one component of oral health literacy. In turn, oral health literacy impacts communication. Practitioners should account for limited conceptual knowledge when they discuss oral health issues with their low–income and minority patients. If this is not accounted for, they will probably find that their oral hygiene education messages are being ignored and health promotion is being adversely affected. PMID:21396263
Bridging the Guideline Implementation Gap: A Systematic, Document-Centered Approach to Guideline Implementation

PubMed Central

Shiffman, Richard N.; Michel, George; Essaihi, Abdelwaheb; Thornquist, Elizabeth

2004-01-01

Objective: A gap exists between the information contained in published clinical practice guidelines and the knowledge and information that are necessary to implement them. This work describes a process to systematize and make explicit the translation of document-based knowledge into workflow-integrated clinical decision support systems. Design: This approach uses the Guideline Elements Model (GEM) to represent the guideline knowledge. Implementation requires a number of steps to translate the knowledge contained in guideline text into a computable format and to integrate the information into clinical workflow. The steps include: (1) selection of a guideline and specific recommendations for implementation, (2) markup of the guideline text, (3) atomization, (4) deabstraction and (5) disambiguation of recommendation concepts, (6) verification of rule set completeness, (7) addition of explanations, (8) building executable statements, (9) specification of origins of decision variables and insertions of recommended actions, (10) definition of action types and selection of associated beneficial services, (11) choice of interface components, and (12) creation of requirement specification. Results: The authors illustrate these component processes using examples drawn from recent experience translating recommendations from the National Heart, Lung, and Blood Institute's guideline on management of chronic asthma into a workflow-integrated decision support system that operates within the Logician electronic health record system. Conclusion: Using the guideline document as a knowledge source promotes authentic translation of domain knowledge and reduces the overall complexity of the implementation task. From this framework, we believe that a better understanding of activities involved in guideline implementation will emerge. PMID:15187061
Variable selection in subdistribution hazard frailty models with competing risks data

PubMed Central

Do Ha, Il; Lee, Minjung; Oh, Seungyoung; Jeong, Jong-Hyeon; Sylvester, Richard; Lee, Youngjo

2014-01-01

The proportional subdistribution hazards model (i.e. Fine-Gray model) has been widely used for analyzing univariate competing risks data. Recently, this model has been extended to clustered competing risks data via frailty. To the best of our knowledge, however, there has been no literature on variable selection method for such competing risks frailty models. In this paper, we propose a simple but unified procedure via a penalized h-likelihood (HL) for variable selection of fixed effects in a general class of subdistribution hazard frailty models, in which random effects may be shared or correlated. We consider three penalty functions (LASSO, SCAD and HL) in our variable selection procedure. We show that the proposed method can be easily implemented using a slight modification to existing h-likelihood estimation approaches. Numerical studies demonstrate that the proposed procedure using the HL penalty performs well, providing a higher probability of choosing the true model than LASSO and SCAD methods without losing prediction accuracy. The usefulness of the new method is illustrated using two actual data sets from multi-center clinical trials. PMID:25042872
Effectiveness of oral health education programs: A systematic review.

PubMed

Nakre, Priya Devadas; Harikiran, A G

2013-07-01

In recent years, attention has been drawn toward assessing the effectiveness of oral health education programs. This is in line with demand for evidence based research and will help to inform policy makers on how to allocate resources. (1) Collect and collate all information on oral health education programs. (2) Assess the programs based on various coding criteria. (3) Assess effectiveness of oral health education programs on oral health status and knowledge, attitude and practice. A search of all published articles in Medline was done using the keywords "oral health education, dental health education, oral health promotion". The resulting titles and abstracts provided the basis for initial decisions and selection of articles. Out of the primary list of articles, a total number of 40 articles were selected as they fulfilled the following inclusion criteria: (1). Articles on oral health programs with an oral health education component (2). Articles published after the year 1990 (3). Articles published in English. The full text of the articles was then obtained from either the internet or libraries of dental research colleges and hospitals in and around Bangalore. A set of important variables were identified and grouped under five headings to make them amenable for coding. The coding variables were then described under various subheadings to allow us to compare the chosen articles. Oral health education is effective in improving the knowledge attitude and practice of oral health and in reducing plaque, bleeding on probing of the gingiva and caries increment. This study identifies a few important variables which contribute to the effectiveness of the programs. There is an indication in this review that the most successful oral health programs are labor intensive, involve significant others and has received funding and additional support. A balance between inputs and outputs and health care resources available will determine if the program can be recommended for general use.
Selection of Construction Methods: A Knowledge-Based Approach

PubMed Central

Skibniewski, Miroslaw

2013-01-01

The appropriate selection of construction methods to be used during the execution of a construction project is a major determinant of high productivity, but sometimes this selection process is performed without the care and the systematic approach that it deserves, bringing negative consequences. This paper proposes a knowledge management approach that will enable the intelligent use of corporate experience and information and help to improve the selection of construction methods for a project. Then a knowledge-based system to support this decision-making process is proposed and described. To define and design the system, semistructured interviews were conducted within three construction companies with the purpose of studying the way that the method' selection process is carried out in practice and the knowledge associated with it. A prototype of a Construction Methods Knowledge System (CMKS) was developed and then validated with construction industry professionals. As a conclusion, the CMKS was perceived as a valuable tool for construction methods' selection, by helping companies to generate a corporate memory on this issue, reducing the reliance on individual knowledge and also the subjectivity of the decision-making process. The described benefits as provided by the system favor a better performance of construction projects. PMID:24453925
Speech-Language Pathologists' Knowledge of Genetics: Perceived Confidence, Attitudes, Knowledge Acquisition and Practice-Based Variables

ERIC Educational Resources Information Center

Tramontana, G. Michael; Blood, Ingrid M.; Blood, Gordon W.

2013-01-01

The purpose of this study was to determine (a) the general knowledge bases demonstrated by school-based speech-language pathologists (SLPs) in the area of genetics, (b) the confidence levels of SLPs in providing services to children and their families with genetic disorders/syndromes, (c) the attitudes of SLPs regarding genetics and communication…
The Effects Of Feedback And Selected Personality Variables On Aesthetic Judgment

ERIC Educational Resources Information Center

Stallings, William M.; And Others

1973-01-01

This present study is an attempt to investigate the extent to which knowledge of results in various forms (true, none, and false) may modify aesthetic judgment in "typical" (with respect to aesthetic judgment) students. (Author)
Variability and population genetic structure in Achyrocline flaccida (Weinm.) DC., a species with high value in folk medicine in South America.

PubMed

Rosa, Juliana da; Weber, Gabriela Gomes; Cardoso, Rafaela; Górski, Felipe; Da-Silva, Paulo Roberto

2017-01-01

Better knowledge of medicinal plant species and their conservation is an urgent need worldwide. Decision making for conservation strategies can be based on the knowledge of the variability and population genetic structure of the species and on the events that may influence these genetic parameters. Achyrocline flaccida (Weinm.) DC. is a native plant from the grassy fields of South America with high value in folk medicine. In spite of its importance, no genetic and conservation studies are available for the species. In this work, microsatellite and ISSR (inter-simple sequence repeat) markers were used to estimate the genetic variability and structure of seven populations of A. flaccida from southern Brazil. The microsatellite markers were inefficient in A. flaccida owing to a high number of null alleles. After the evaluation of 42 ISSR primers on one population, 10 were selected for further analysis of seven A. flaccida populations. The results of ISSR showed that the high number of exclusive absence of loci might contribute to the inter-population differentiation. Genetic variability of the species was high (Nei's diversity of 0.23 and Shannon diversity of 0.37). AMOVA indicated higher genetic variability within (64.7%) than among (33.96%) populations, and the variability was unevenly distributed (FST 0.33). Gene flow among populations ranged from 1.68 to 5.2 migrants per generation, with an average of 1.39. The results of PCoA and Bayesian analyses corroborated and indicated that the populations are structured. The observed genetic variability and population structure of A. flaccida are discussed in the context of the vegetation formation history in southern Brazil, as well as the possible anthropogenic effects. Additionally, we discuss the implications of the results in the conservation of the species.
Effect of age on variability in the production of text-based global inferences.

PubMed

Williams, Lynne J; Dunlop, Joseph P; Abdi, Hervé

2012-01-01

As we age, our differences in cognitive skills become more visible, an effect especially true for memory and problem solving skills (i.e., fluid intelligence). However, by contrast with fluid intelligence, few studies have examined variability in measures that rely on one's world knowledge (i.e., crystallized intelligence). The current study investigated whether age increased the variability in text based global inference generation--a measure of crystallized intelligence. Global inference generation requires the integration of textual information and world knowledge and can be expressed as a gist or lesson. Variability in generating two global inferences for a single text was examined in young-old (62 to 69 years), middle-old (70 to 76 years) and old-old (77 to 94 years) adults. The older two groups showed greater variability, with the middle elderly group being most variable. These findings suggest that variability may be a characteristic of both fluid and crystallized intelligence in aging.
Using education on irradiated foods to change behavior of Korean elementary, middle, and high school students

PubMed Central

Kim, Jaerok; Choi, Yoonseok

2014-01-01

BACKGROUND/OBJECTIVES Educational interventions targeted food selection perception, knowledge, attitude, and behavior. Education regarding irradiated food was intended to change food selection behavior specific to it. SUBJECTS AND METHODS There were 43 elementary students (35.0%), 45 middle school students (36.6%), and 35 high school students (28.5%). The first step was research design. Educational targets were selected and informed consent was obtained in step two. An initial survey was conducted as step three. Step four was a 45 minute-long theoretical educational intervention. Step five concluded with a survey and experiment on food selection behavior. RESULTS As a result of conducting a 45 minute-long education on the principles, actual state of usage, and pros and cons of irradiated food for elementary, middle, and high-school students in Korea, perception, knowledge, attitude, and behavior regarding the irradiated food was significantly higher after the education than before the education (P < 0.000). CONCLUSIONS The behavior of irradiated food selection shows high correlation with all variables of perception, knowledge, and attitude, and it is necessary to provide information of each level of change in perception, knowledge, and attitude in order to derive proper behavior change, which is the ultimate goal of the education. PMID:25324942
Supervised machine learning algorithms to diagnose stress for vehicle drivers based on physiological sensor signals.

PubMed

Barua, Shaibal; Begum, Shahina; Ahmed, Mobyen Uddin

2015-01-01

Machine learning algorithms play an important role in computer science research. Recent advancement in sensor data collection in clinical sciences lead to a complex, heterogeneous data processing, and analysis for patient diagnosis and prognosis. Diagnosis and treatment of patients based on manual analysis of these sensor data are difficult and time consuming. Therefore, development of Knowledge-based systems to support clinicians in decision-making is important. However, it is necessary to perform experimental work to compare performances of different machine learning methods to help to select appropriate method for a specific characteristic of data sets. This paper compares classification performance of three popular machine learning methods i.e., case-based reasoning, neutral networks and support vector machine to diagnose stress of vehicle drivers using finger temperature and heart rate variability. The experimental results show that case-based reasoning outperforms other two methods in terms of classification accuracy. Case-based reasoning has achieved 80% and 86% accuracy to classify stress using finger temperature and heart rate variability. On contrary, both neural network and support vector machine have achieved less than 80% accuracy by using both physiological signals.
Empirical Assessment of Spatial Prediction Methods for Location Cost Adjustment Factors

PubMed Central

Migliaccio, Giovanni C.; Guindani, Michele; D'Incognito, Maria; Zhang, Linlin

2014-01-01

In the feasibility stage, the correct prediction of construction costs ensures that budget requirements are met from the start of a project's lifecycle. A very common approach for performing quick-order-of-magnitude estimates is based on using Location Cost Adjustment Factors (LCAFs) that compute historically based costs by project location. Nowadays, numerous LCAF datasets are commercially available in North America, but, obviously, they do not include all locations. Hence, LCAFs for un-sampled locations need to be inferred through spatial interpolation or prediction methods. Currently, practitioners tend to select the value for a location using only one variable, namely the nearest linear-distance between two sites. However, construction costs could be affected by socio-economic variables as suggested by macroeconomic theories. Using a commonly used set of LCAFs, the City Cost Indexes (CCI) by RSMeans, and the socio-economic variables included in the ESRI Community Sourcebook, this article provides several contributions to the body of knowledge. First, the accuracy of various spatial prediction methods in estimating LCAF values for un-sampled locations was evaluated and assessed in respect to spatial interpolation methods. Two Regression-based prediction models were selected, a Global Regression Analysis and a Geographically-weighted regression analysis (GWR). Once these models were compared against interpolation methods, the results showed that GWR is the most appropriate way to model CCI as a function of multiple covariates. The outcome of GWR, for each covariate, was studied for all the 48 states in the contiguous US. As a direct consequence of spatial non-stationarity, it was possible to discuss the influence of each single covariate differently from state to state. In addition, the article includes a first attempt to determine if the observed variability in cost index values could be, at least partially explained by independent socio-economic variables. PMID:25018582
Identifying overarching excipient properties towards an in-depth understanding of process and product performance for continuous twin-screw wet granulation.

PubMed

Willecke, N; Szepes, A; Wunderlich, M; Remon, J P; Vervaet, C; De Beer, T

2017-04-30

The overall objective of this work is to understand how excipient characteristics influence the process and product performance for a continuous twin-screw wet granulation process. The knowledge gained through this study is intended to be used for a Quality by Design (QbD)-based formulation design approach and formulation optimization. A total of 9 preferred fillers and 9 preferred binders were selected for this study. The selected fillers and binders were extensively characterized regarding their physico-chemical and solid state properties using 21 material characterization techniques. Subsequently, principal component analysis (PCA) was performed on the data sets of filler and binder characteristics in order to reduce the variety of single characteristics to a limited number of overarching properties. Four principal components (PC) explained 98.4% of the overall variability in the fillers data set, while three principal components explained 93.4% of the overall variability in the data set of binders. Both PCA models allowed in-depth evaluation of similarities and differences in the excipient properties. Copyright © 2017. Published by Elsevier B.V.
Robust Variable Selection with Exponential Squared Loss.

PubMed

Wang, Xueqin; Jiang, Yunlu; Huang, Mian; Zhang, Heping

2013-04-01

Robust variable selection procedures through penalized regression have been gaining increased attention in the literature. They can be used to perform variable selection and are expected to yield robust estimates. However, to the best of our knowledge, the robustness of those penalized regression procedures has not been well characterized. In this paper, we propose a class of penalized robust regression estimators based on exponential squared loss. The motivation for this new procedure is that it enables us to characterize its robustness that has not been done for the existing procedures, while its performance is near optimal and superior to some recently developed methods. Specifically, under defined regularity conditions, our estimators are [Formula: see text] and possess the oracle property. Importantly, we show that our estimators can achieve the highest asymptotic breakdown point of 1/2 and that their influence functions are bounded with respect to the outliers in either the response or the covariate domain. We performed simulation studies to compare our proposed method with some recent methods, using the oracle method as the benchmark. We consider common sources of influential points. Our simulation studies reveal that our proposed method performs similarly to the oracle method in terms of the model error and the positive selection rate even in the presence of influential points. In contrast, other existing procedures have a much lower non-causal selection rate. Furthermore, we re-analyze the Boston Housing Price Dataset and the Plasma Beta-Carotene Level Dataset that are commonly used examples for regression diagnostics of influential points. Our analysis unravels the discrepancies of using our robust method versus the other penalized regression method, underscoring the importance of developing and applying robust penalized regression methods.
Robust Variable Selection with Exponential Squared Loss

PubMed Central

Wang, Xueqin; Jiang, Yunlu; Huang, Mian; Zhang, Heping

2013-01-01

Robust variable selection procedures through penalized regression have been gaining increased attention in the literature. They can be used to perform variable selection and are expected to yield robust estimates. However, to the best of our knowledge, the robustness of those penalized regression procedures has not been well characterized. In this paper, we propose a class of penalized robust regression estimators based on exponential squared loss. The motivation for this new procedure is that it enables us to characterize its robustness that has not been done for the existing procedures, while its performance is near optimal and superior to some recently developed methods. Specifically, under defined regularity conditions, our estimators are n-consistent and possess the oracle property. Importantly, we show that our estimators can achieve the highest asymptotic breakdown point of 1/2 and that their influence functions are bounded with respect to the outliers in either the response or the covariate domain. We performed simulation studies to compare our proposed method with some recent methods, using the oracle method as the benchmark. We consider common sources of influential points. Our simulation studies reveal that our proposed method performs similarly to the oracle method in terms of the model error and the positive selection rate even in the presence of influential points. In contrast, other existing procedures have a much lower non-causal selection rate. Furthermore, we re-analyze the Boston Housing Price Dataset and the Plasma Beta-Carotene Level Dataset that are commonly used examples for regression diagnostics of influential points. Our analysis unravels the discrepancies of using our robust method versus the other penalized regression method, underscoring the importance of developing and applying robust penalized regression methods. PMID:23913996
GWASinlps: Nonlocal prior based iterative SNP selection tool for genome-wide association studies.

PubMed

Sanyal, Nilotpal; Lo, Min-Tzu; Kauppi, Karolina; Djurovic, Srdjan; Andreassen, Ole A; Johnson, Valen E; Chen, Chi-Hua

2018-06-19

Multiple marker analysis of the genome-wide association study (GWAS) data has gained ample attention in recent years. However, because of the ultra high-dimensionality of GWAS data, such analysis is challenging. Frequently used penalized regression methods often lead to large number of false positives, whereas Bayesian methods are computationally very expensive. Motivated to ameliorate these issues simultaneously, we consider the novel approach of using nonlocal priors in an iterative variable selection framework. We develop a variable selection method, named, iterative nonlocal prior based selection for GWAS, or GWASinlps, that combines, in an iterative variable selection framework, the computational efficiency of the screen-and-select approach based on some association learning and the parsimonious uncertainty quantification provided by the use of nonlocal priors. The hallmark of our method is the introduction of 'structured screen-and-select' strategy, that considers hierarchical screening, which is not only based on response-predictor associations, but also based on response-response associations, and concatenates variable selection within that hierarchy. Extensive simulation studies with SNPs having realistic linkage disequilibrium structures demonstrate the advantages of our computationally efficient method compared to several frequentist and Bayesian variable selection methods, in terms of true positive rate, false discovery rate, mean squared error, and effect size estimation error. Further, we provide empirical power analysis useful for study design. Finally, a real GWAS data application was considered with human height as phenotype. An R-package for implementing the GWASinlps method is available at https://cran.r-project.org/web/packages/GWASinlps/index.html. Supplementary data are available at Bioinformatics online.
Exploring the Associations Among Nutrition, Science, and Mathematics Knowledge for an Integrative, Food-Based Curriculum.

PubMed

Stage, Virginia C; Kolasa, Kathryn M; Díaz, Sebastián R; Duffrin, Melani W

2018-01-01

Explore associations between nutrition, science, and mathematics knowledge to provide evidence that integrating food/nutrition education in the fourth-grade curriculum may support gains in academic knowledge. Secondary analysis of a quasi-experimental study. Sample included 438 students in 34 fourth-grade classrooms across North Carolina and Ohio; mean age 10 years old; gender (I = 53.2% female; C = 51.6% female). Dependent variable = post-test-nutrition knowledge; independent variables = baseline-nutrition knowledge, and post-test science and mathematics knowledge. Analyses included descriptive statistics and multiple linear regression. The hypothesized model predicted post-nutrition knowledge (F(437) = 149.4, p < .001; Adjusted R = .51). All independent variables were significant predictors with positive association. Science and mathematics knowledge were predictive of nutrition knowledge indicating use of an integrative science and mathematics curriculum to improve academic knowledge may also simultaneously improve nutrition knowledge among fourth-grade students. Teachers can benefit from integration by meeting multiple academic standards, efficiently using limited classroom time, and increasing nutrition education provided in the classroom. © 2018, American School Health Association.
eClims: An Extensible and Dynamic Integration Framework for Biomedical Information Systems.

PubMed

Savonnet, Marinette; Leclercq, Eric; Naubourg, Pierre

2016-11-01

Biomedical information systems (BIS) require consideration of three types of variability: data variability induced by new high throughput technologies, schema or model variability induced by large scale studies or new fields of research, and knowledge variability resulting from new discoveries. Beyond data heterogeneity, managing variabilities in the context of BIS requires extensible and dynamic integration process. In this paper, we focus on data and schema variabilities and we propose an integration framework based on ontologies, master data, and semantic annotations. The framework addresses issues related to: 1) collaborative work through a dynamic integration process; 2) variability among studies using an annotation mechanism; and 3) quality control over data and semantic annotations. Our approach relies on two levels of knowledge: BIS-related knowledge is modeled using an application ontology coupled with UML models that allow controlling data completeness and consistency, and domain knowledge is described by a domain ontology, which ensures data coherence. A system build with the eClims framework has been implemented and evaluated in the context of a proteomic platform.
UK medical selection: lottery or meritocracy?

PubMed

Harris, Benjamin H L; Walsh, Jason L; Lammy, Simon

2015-02-01

From senior school through to consultancy, a plethora of assessments shape medical careers. Multiple methods of assessment are used to discriminate between applicants. Medical selection in the UK appears to be moving increasingly towards non-knowledge-based testing at all career stages. We review the evidence for non-knowledge-based tests and discuss their perceived benefits. We raise the question: is the current use of non-knowledge-based tests within the UK at risk of undermining more robust measures of medical school and postgraduate performance? © 2015 Royal College of Physicians.

Variable Selection in the Presence of Missing Data: Imputation-based Methods.

PubMed

Zhao, Yize; Long, Qi

2017-01-01

Variable selection plays an essential role in regression analysis as it identifies important variables that associated with outcomes and is known to improve predictive accuracy of resulting models. Variable selection methods have been widely investigated for fully observed data. However, in the presence of missing data, methods for variable selection need to be carefully designed to account for missing data mechanisms and statistical techniques used for handling missing data. Since imputation is arguably the most popular method for handling missing data due to its ease of use, statistical methods for variable selection that are combined with imputation are of particular interest. These methods, valid used under the assumptions of missing at random (MAR) and missing completely at random (MCAR), largely fall into three general strategies. The first strategy applies existing variable selection methods to each imputed dataset and then combine variable selection results across all imputed datasets. The second strategy applies existing variable selection methods to stacked imputed datasets. The third variable selection strategy combines resampling techniques such as bootstrap with imputation. Despite recent advances, this area remains under-developed and offers fertile ground for further research.
Selection of Variables in Cluster Analysis: An Empirical Comparison of Eight Procedures

ERIC Educational Resources Information Center

Steinley, Douglas; Brusco, Michael J.

2008-01-01

Eight different variable selection techniques for model-based and non-model-based clustering are evaluated across a wide range of cluster structures. It is shown that several methods have difficulties when non-informative variables (i.e., random noise) are included in the model. Furthermore, the distribution of the random noise greatly impacts the…
Socioscientific Issues-Based Instruction: An Investigation of Agriscience Students' Content Knowledge Based on Student Variables

ERIC Educational Resources Information Center

Shoulders, Catherine W.; Myers, Brian E.

2013-01-01

Numerous researchers in science education have reported student improvement in areas of scientific literacy resulting from socioscientific issues (SSI)-based instruction. The purpose of this study was to describe student agriscience content knowledge following a six-week SSI-based instructional unit focusing on the introduction of cultured meat…
Learning a common dictionary for subject-transfer decoding with resting calibration.

PubMed

Morioka, Hiroshi; Kanemura, Atsunori; Hirayama, Jun-ichiro; Shikauchi, Manabu; Ogawa, Takeshi; Ikeda, Shigeyuki; Kawanabe, Motoaki; Ishii, Shin

2015-05-01

Brain signals measured over a series of experiments have inherent variability because of different physical and mental conditions among multiple subjects and sessions. Such variability complicates the analysis of data from multiple subjects and sessions in a consistent way, and degrades the performance of subject-transfer decoding in a brain-machine interface (BMI). To accommodate the variability in brain signals, we propose 1) a method for extracting spatial bases (or a dictionary) shared by multiple subjects, by employing a signal-processing technique of dictionary learning modified to compensate for variations between subjects and sessions, and 2) an approach to subject-transfer decoding that uses the resting-state activity of a previously unseen target subject as calibration data for compensating for variations, eliminating the need for a standard calibration based on task sessions. Applying our methodology to a dataset of electroencephalography (EEG) recordings during a selective visual-spatial attention task from multiple subjects and sessions, where the variability compensation was essential for reducing the redundancy of the dictionary, we found that the extracted common brain activities were reasonable in the light of neuroscience knowledge. The applicability to subject-transfer decoding was confirmed by improved performance over existing decoding methods. These results suggest that analyzing multisubject brain activities on common bases by the proposed method enables information sharing across subjects with low-burden resting calibration, and is effective for practical use of BMI in variable environments. Copyright © 2015 Elsevier Inc. All rights reserved.
Body temperature variability (Part 1): a review of the history of body temperature and its variability due to site selection, biological rhythms, fitness, and aging.

PubMed

Kelly, Greg

2006-12-01

Body temperature is a complex, non-linear data point, subject to many sources of internal and external variation. While these sources of variation significantly complicate interpretation of temperature data, disregarding knowledge in favor of oversimplifying complex issues would represent a significant departure from practicing evidence-based medicine. Part 1 of this review outlines the historical work of Wunderlich on temperature and the origins of the concept that a healthy normal temperature is 98.6 degrees F (37.0 degrees C). Wunderlich's findings and methodology are reviewed and his results are contrasted with findings from modern clinical thermometry. Endogenous sources of temperature variability, including variations caused by site of measurement, circadian, menstrual, and annual biological rhythms, fitness, and aging are discussed. Part 2 will review the effects of exogenous masking agents - external factors in the environment, diet, or lifestyle that can influence body temperature, as well as temperature findings in disease states.
Exploring the Impact of Early Decisions in Variable Ordering for Constraint Satisfaction Problems.

PubMed

Ortiz-Bayliss, José Carlos; Amaya, Ivan; Conant-Pablos, Santiago Enrique; Terashima-Marín, Hugo

2018-01-01

When solving constraint satisfaction problems (CSPs), it is a common practice to rely on heuristics to decide which variable should be instantiated at each stage of the search. But, this ordering influences the search cost. Even so, and to the best of our knowledge, no earlier work has dealt with how first variable orderings affect the overall cost. In this paper, we explore the cost of finding high-quality orderings of variables within constraint satisfaction problems. We also study differences among the orderings produced by some commonly used heuristics and the way bad first decisions affect the search cost. One of the most important findings of this work confirms the paramount importance of first decisions. Another one is the evidence that many of the existing variable ordering heuristics fail to appropriately select the first variable to instantiate. Another one is the evidence that many of the existing variable ordering heuristics fail to appropriately select the first variable to instantiate. We propose a simple method to improve early decisions of heuristics. By using it, performance of heuristics increases.
Exploring the Impact of Early Decisions in Variable Ordering for Constraint Satisfaction Problems

PubMed Central

Amaya, Ivan

2018-01-01

When solving constraint satisfaction problems (CSPs), it is a common practice to rely on heuristics to decide which variable should be instantiated at each stage of the search. But, this ordering influences the search cost. Even so, and to the best of our knowledge, no earlier work has dealt with how first variable orderings affect the overall cost. In this paper, we explore the cost of finding high-quality orderings of variables within constraint satisfaction problems. We also study differences among the orderings produced by some commonly used heuristics and the way bad first decisions affect the search cost. One of the most important findings of this work confirms the paramount importance of first decisions. Another one is the evidence that many of the existing variable ordering heuristics fail to appropriately select the first variable to instantiate. Another one is the evidence that many of the existing variable ordering heuristics fail to appropriately select the first variable to instantiate. We propose a simple method to improve early decisions of heuristics. By using it, performance of heuristics increases. PMID:29681923
Using Multiple Lenses to Examine the Development of Beginning Biology Teachers' Pedagogical Content Knowledge for Teaching Natural Selection Simulations

NASA Astrophysics Data System (ADS)

Sickel, Aaron J.; Friedrichsen, Patricia

2018-02-01

Pedagogical content knowledge (PCK) has become a useful construct to examine science teacher learning. Yet, researchers conceptualize PCK development in different ways. The purpose of this longitudinal study was to use three analytic lenses to understand the development of three beginning biology teachers' PCK for teaching natural selection simulations. We observed three early-career biology teachers as they taught natural selection in their respective school contexts over two consecutive years. Data consisted of six interviews with each participant. Using the PCK model developed by Magnusson et al. (1999), we examined topic-specific PCK development utilizing three different lenses: (1) expansion of knowledge within an individual knowledge base, (2) integration of knowledge across knowledge bases, and (3) knowledge that explicitly addressed core concepts of natural selection. We found commonalities across the participants, yet each lens was also useful to understand the influence of different factors (e.g., orientation, subject matter preparation, and the idiosyncratic nature of teacher knowledge) on PCK development. This multi-angle approach provides implications for considering the quality of beginning science teachers' knowledge and future research on PCK development. We conclude with an argument that explicitly communicating lenses used to understand PCK development will help the research community compare analytic approaches and better understand the nature of science teacher learning.
Contingencies of Self-Worth and Psychological Distress in Iranian Patients Seeking Cosmetic Surgery: Integrative Self-Knowledge as Mediator.

PubMed

Valikhani, Ahmad; Goodarzi, Mohammad Ali

2017-08-01

Although previous studies have shown that people applying for cosmetic surgery experience high-intensity psychological distress, important variables that function as protective factors have rarely been the subject of study in this population. Therefore, this study aims to investigate the role of low and high self-knowledge in experiencing psychological distress and contingencies of self-worth to appearance and approval from others and to identify the mediatory role of the integrative self-knowledge in patients seeking cosmetic surgery. Eighty-eight patients seeking cosmetic surgery were selected and completed the contingencies of self-worth and integrative self-knowledge scales, as well as the depression, anxiety and stress scale. Data were analyzed using multivariate analysis of variance (MANOVA) and path analysis using 5000 bootstrap resampling. The results of MANOVA showed that patients seeking cosmetic surgery with high self-knowledge had lower levels of depression, anxiety and stress compared to patients with low self-knowledge. They also gained lower scores in contingencies of self-worth to appearance and approval from others. The results of path analysis indicated that self-knowledge is a complete mediator in the relationship between contingencies of self-worth to appearance and approval from others and psychological distress. Based on the results of this study, it can be concluded that self-knowledge as a protective factor plays a major role in relation to the psychological distress experienced by the patients seeking cosmetic surgery. In fact, by increasing self-knowledge among this group of patients, their psychological distress can be decreased. This journal requires that authors assign a level of evidence to each article. For a full description of these Evidence-Based Medicine ratings, please refer to the Table of Contents or the online Instructions to Authors www.springer.com/00266 .
Urban Waste Recycling Behavior: Antecedents of Participation in a Selective Collection Program

NASA Astrophysics Data System (ADS)

Garcés, Conchita; Lafuente, Alberto; Pedraja, Marta; Rivera, Pilar

2002-09-01

The aim of this study is to analyze the antecedents of urban waste recycling behavior. To achieve this goal, a concrete urban waste management program was chosen. The study focuses on the Selective Collection Program (SCP) in Zaragoza, a medium-sized city in northeastern Spain. The research starts with a conceptual model in which the variables that potentially affect recycling behavior can be classified into two groups: incentives and barriers. Moreover, the sociodemographic characteristics of the individuals are included in our study. Given that the proposed model requires specification of latent variables or constructs, the analysis is based on the Structural Equation Models (SEM) methodology. The results revealed that environmental awareness, knowledge of the environmental impact of urban waste, and the positive perception of management by local government exercise a positive effect on individual recycling behavior, while perceived personal difficulties (space and time availability) and distance to and from the container have a negative effect. As regards sociodemographic variables, this study found that annual family income sustains a negative relationship with recycling behavior, while age maintains a positive one. The results obtained clearly show the important role that the public authorities play, especially municipal governments, in achieving the waste recycling objectives established in accordance with international legislation.
Development of the Spacecraft Materials Selector Expert System

NASA Technical Reports Server (NTRS)

Pippin, H. G.

2000-01-01

A specific knowledge base to evaluate the on-orbit performance of selected materials on spacecraft is being developed under contract to the NASA SEE program. An artificial intelligence software package, the Boeing Expert System Tool (BEST), contains an inference engine used to operate knowledge bases constructed to selectively recall and distribute information about materials performance in space applications. This same system is used to make estimates of the environmental exposures expected for a given space flight. The performance capabilities of the Spacecraft Materials Selector (SMS) knowledge base are described in this paper. A case history for a planned flight experiment on ISS is shown as an example of the use of the SMS, and capabilities and limitations of the knowledge base are discussed.
Selected questions on biomechanical exposures for surveillance of upper-limb work-related musculoskeletal disorders

PubMed Central

Descatha, Alexis; Roquelaure, Yves; Evanoff, Bradley; Niedhammer, Isabelle; Chastang, Jean François; Mariot, Camille; Ha, Catherine; Imbernon, Ellen; Goldberg, Marcel; Leclerc, Annette

2007-01-01

Objective Questionnaires for assessment of biomechanical exposure are frequently used in surveillance programs, though few studies have evaluated which key questions are needed. We sought to reduce the number of variables on a surveillance questionnaire by identifying which variables best summarized biomechanical exposure in a survey of the French working population. Methods We used data from the 2002–2003 French experimental network of Upper-limb work-related musculoskeletal disorders (UWMSD), performed on 2685 subjects in which 37 variables assessing biomechanical exposures were available (divided into four ordinal categories, according to the task frequency or duration). Principal Component Analysis (PCA) with orthogonal rotation was performed on these variables. Variables closely associated with factors issued from PCA were retained, except those highly correlated to another variable (rho>0.70). In order to study the relevance of the final list of variables, correlations between a score based on retained variables (PCA score) and the exposure score suggested by the SALTSA group were calculated. The associations between the PCA score and the prevalence of UWMSD were also studied. In a final step, we added back to the list a few variables not retained by PCA, because of their established recognition as risk factors. Results According to the results of the PCA, seven interpretable factors were identified: posture exposures, repetitiveness, handling of heavy loads, distal biomechanical exposures, computer use, forklift operator specific task, and recovery time. Twenty variables strongly correlated with the factors obtained from PCA were retained. The PCA score was strongly correlated both with the SALTSA score and with UWMSD prevalence (p<0.0001). In the final step, six variables were reintegrated. Conclusion Twenty-six variables out of 37 were efficiently selected according to their ability to summarize major biomechanical constraints in a working population, with an approach combining statistical analyses and existing knowledge. PMID:17476519
An investigation of meaningful understanding and effectiveness of the implementation of Piagetian and Ausubelian theories in physics instruction

NASA Astrophysics Data System (ADS)

Williams, Karen Ann

One section of college students (N = 25) enrolled in an algebra-based physics course was selected for a Piagetian-based learning cycle (LC) treatment while a second section (N = 25) studied in an Ausubelian-based meaningful verbal reception learning treatment (MVRL). This study examined the students' overall (concept + problem solving + mental model) meaningful understanding of force, density/Archimedes Principle, and heat. Also examined were students' meaningful understanding as measured by conceptual questions, problems, and mental models. In addition, students' learning orientations were examined. There were no significant posttest differences between the LC and MVRL groups for students' meaningful understanding or learning orientation. Piagetian and Ausubelian theories explain meaningful understanding for each treatment. Students from each treatment increased their meaningful understanding. However, neither group altered their learning orientation. The results of meaningful understanding as measured by conceptual questions, problem solving, and mental models were mixed. Differences were attributed to the weaknesses and strengths of each treatment. This research also examined four variables (treatment, reasoning ability, learning orientation, and prior knowledge) to find which best predicted students' overall meaningful understanding of physics concepts. None of these variables were significant predictors at the.05 level. However, when the same variables were used to predict students' specific understanding (i.e. concept, problem solving, or mental model understanding), the results were mixed. For forces and density/Archimedes Principle, prior knowledge and reasoning ability significantly predicted students' conceptual understanding. For heat, however, reasoning ability was the only significant predictor of concept understanding. Reasoning ability and treatment were significant predictors of students' problem solving for heat and forces. For density/Archimedes Principle, treatment was the only significant predictor of students' problem solving. None of the variables were significant predictors of mental model understanding. This research suggested that Piaget and Ausubel used different terminology to describe learning yet these theories are similar. Further research is needed to validate this premise and validate the blending of the two theories.
Adaptive molecular evolution of the Major Histocompatibility Complex genes, DRA and DQA, in the genus Equus

PubMed Central

2011-01-01

Background Major Histocompatibility Complex (MHC) genes are central to vertebrate immune response and are believed to be under balancing selection by pathogens. This hypothesis has been supported by observations of extremely high polymorphism, elevated nonsynonymous to synonymous base pair substitution rates and trans-species polymorphisms at these loci. In equids, the organization and variability of this gene family has been described, however the full extent of diversity and selection is unknown. As selection is not expected to act uniformly on a functional gene, maximum likelihood codon-based models of selection that allow heterogeneity in selection across codon positions can be valuable for examining MHC gene evolution and the molecular basis for species adaptations. Results We investigated the evolution of two class II MHC genes of the Equine Lymphocyte Antigen (ELA), DRA and DQA, in the genus Equus with the addition of novel alleles identified in plains zebra (E. quagga, formerly E. burchelli). We found that both genes exhibited a high degree of polymorphism and inter-specific sharing of allele lineages. To our knowledge, DRA allelic diversity was discovered to be higher than has ever been observed in vertebrates. Evidence was also found to support a duplication of the DQA locus. Selection analyses, evaluated in terms of relative rates of nonsynonymous to synonymous mutations (dN/dS) averaged over the gene region, indicated that the majority of codon sites were conserved and under purifying selection (dN
Improvement in knowledge, social cognitive theory variables, and movement through stages of change after a community-based diabetes education program.

PubMed

Chapman-Novakofski, Karen; Karduck, Justine

2005-10-01

The objective of this program was to demonstrate the impact of a community-based diabetes education program. Participants were adults (N=239; mean age+/-standard deviation=63+/-10 years) with diabetes or caretakers. Community-based education incorporating Social Cognitive Theory and Stages of Change Theory included three group sessions focused on meal planning with cooking demonstrations. Knowledge and Social Cognitive Theory/Stages of Change variables were assessed pre- and postintervention. At posttest, significantly more (P<.05) used herbs in place of salt, cooked with olive or canola oils, used artificial sweeteners in baking (Stages of Change Theory), and were confident to change their diet and to prepare healthful meals. Knowledge of diabetes and nutrition increased (P<.05) and was a factor in postintervention belief in ability to use food labels and that meal planning was helpful. This community-based diabetes education intervention resulted in positive impacts on knowledge, health beliefs, and self-reported behaviors. Improvement in knowledge can be instrumental in moving individuals to an action or maintenance stage and in improving self-efficacy.
Detection of Nitrogen Content in Rubber Leaves Using Near-Infrared (NIR) Spectroscopy with Correlation-Based Successive Projections Algorithm (SPA).

PubMed

Tang, Rongnian; Chen, Xupeng; Li, Chuang

2018-05-01

Near-infrared spectroscopy is an efficient, low-cost technology that has potential as an accurate method in detecting the nitrogen content of natural rubber leaves. Successive projections algorithm (SPA) is a widely used variable selection method for multivariate calibration, which uses projection operations to select a variable subset with minimum multi-collinearity. However, due to the fluctuation of correlation between variables, high collinearity may still exist in non-adjacent variables of subset obtained by basic SPA. Based on analysis to the correlation matrix of the spectra data, this paper proposed a correlation-based SPA (CB-SPA) to apply the successive projections algorithm in regions with consistent correlation. The result shows that CB-SPA can select variable subsets with more valuable variables and less multi-collinearity. Meanwhile, models established by the CB-SPA subset outperform basic SPA subsets in predicting nitrogen content in terms of both cross-validation and external prediction. Moreover, CB-SPA is assured to be more efficient, for the time cost in its selection procedure is one-twelfth that of the basic SPA.
Graph-based impact analysis as a framework for incorporating practitioner knowledge in dairy herd health management.

PubMed

Krieger, M; Schwabenbauer, E-M; Hoischen-Taubner, S; Emanuelson, U; Sundrum, A

2018-03-01

Production diseases in dairy cows are multifactorial, which means they emerge from complex interactions between many different farm variables. Variables with a large impact on production diseases can be identified for groups of farms using statistical models, but these methods cannot be used to identify highly influential variables in individual farms. This, however, is necessary for herd health planning, because farm conditions and associated health problems vary largely between farms. The aim of this study was to rank variables according to their anticipated effect on production diseases on the farm level by applying a graph-based impact analysis on 192 European organic dairy farms. Direct impacts between 13 pre-defined variables were estimated for each farm during a round-table discussion attended by practitioners, that is farmer, veterinarian and herd advisor. Indirect impacts were elaborated through graph analysis taking into account impact strengths. Across farms, factors supposedly exerting the most influence on production diseases were 'feeding', 'hygiene' and 'treatment' (direct impacts), as well as 'knowledge and skills' and 'herd health monitoring' (indirect impacts). Factors strongly influenced by production diseases were 'milk performance', 'financial resources' and 'labour capacity' (directly and indirectly). Ranking of variables on the farm level revealed considerable differences between farms in terms of their most influential and most influenced farm factors. Consequently, very different strategies may be required to reduce production diseases in these farms. The method is based on perceptions and estimations and thus prone to errors. From our point of view, however, this weakness is clearly outweighed by the ability to assess and to analyse farm-specific relationships and thus to complement general knowledge with contextual knowledge. Therefore, we conclude that graph-based impact analysis represents a promising decision support tool for herd health planning. The next steps include testing the method using more specific and problem-oriented variables as well as evaluating its effectiveness.
Input variable selection and calibration data selection for storm water quality regression models.

PubMed

Sun, Siao; Bertrand-Krajewski, Jean-Luc

2013-01-01

Storm water quality models are useful tools in storm water management. Interest has been growing in analyzing existing data for developing models for urban storm water quality evaluations. It is important to select appropriate model inputs when many candidate explanatory variables are available. Model calibration and verification are essential steps in any storm water quality modeling. This study investigates input variable selection and calibration data selection in storm water quality regression models. The two selection problems are mutually interacted. A procedure is developed in order to fulfil the two selection tasks in order. The procedure firstly selects model input variables using a cross validation method. An appropriate number of variables are identified as model inputs to ensure that a model is neither overfitted nor underfitted. Based on the model input selection results, calibration data selection is studied. Uncertainty of model performances due to calibration data selection is investigated with a random selection method. An approach using the cluster method is applied in order to enhance model calibration practice based on the principle of selecting representative data for calibration. The comparison between results from the cluster selection method and random selection shows that the former can significantly improve performances of calibrated models. It is found that the information content in calibration data is important in addition to the size of calibration data.
Are general and strategic measures of organizational context and leadership associated with knowledge and attitudes toward evidence-based practices in public behavioral health settings? A cross-sectional observational study.

PubMed

Powell, Byron J; Mandell, David S; Hadley, Trevor R; Rubin, Ronnie M; Evans, Arthur C; Hurford, Matthew O; Beidas, Rinad S

2017-05-12

Examining the role of modifiable barriers and facilitators is a necessary step toward developing effective implementation strategies. This study examines whether both general (organizational culture, organizational climate, and transformational leadership) and strategic (implementation climate and implementation leadership) organizational-level factors predict therapist-level determinants of implementation (knowledge of and attitudes toward evidence-based practices). Within the context of a system-wide effort to increase the use of evidence-based practices (EBPs) and recovery-oriented care, we conducted an observational, cross-sectional study of 19 child-serving agencies in the City of Philadelphia, including 23 sites, 130 therapists, 36 supervisors, and 22 executive administrators. Organizational variables included characteristics such as EBP initiative participation, program size, and proportion of independent contractor therapists; general factors such as organizational culture and climate (Organizational Social Context Measurement System) and transformational leadership (Multifactor Leadership Questionnaire); and strategic factors such as implementation climate (Implementation Climate Scale) and implementation leadership (Implementation Leadership Scale). Therapist-level variables included demographics, attitudes toward EBPs (Evidence-Based Practice Attitudes Scale), and knowledge of EBPs (Knowledge of Evidence-Based Services Questionnaire). We used linear mixed-effects regression models to estimate the associations between the predictor (organizational characteristics, general and strategic factors) and dependent (knowledge of and attitudes toward EBPs) variables. Several variables were associated with therapists' knowledge of EBPs. Clinicians in organizations with more proficient cultures or higher levels of transformational leadership (idealized influence) had greater knowledge of EBPs; conversely, clinicians in organizations with more resistant cultures, more functional organizational climates, and implementation climates characterized by higher levels of financial reward for EBPs had less knowledge of EBPs. A number of organizational factors were associated with the therapists' attitudes toward EBPs. For example, more engaged organizational cultures, implementation climates characterized by higher levels of educational support, and more proactive implementation leadership were all associated with more positive attitudes toward EBPs. This study provides evidence for the importance of both general and strategic organizational determinants as predictors of knowledge of and attitudes toward EBPs. The findings highlight the need for longitudinal and mixed-methods studies that examine the influence of organizational factors on implementation.
Evaluating the effectiveness of a post-license education program for young novice drivers in Belgium.

PubMed

Brijs, Kris; Cuenen, Ariane; Brijs, Tom; Ruiter, Robert A C; Wets, Geert

2014-05-01

The disproportionately large number of traffic accidents of young novice drivers highlights the need for an effective driver education program. The Goals for Driving Education (GDE) matrix shows that driver education must target both lower and higher levels of driver competences. Research has indicated that current education programs do not emphasize enough the higher levels, for example awareness and insight. This has raised the importance of insight programs. On the Road (OtR), a Flemish post-license driver education program, is such an insight program that aims to target these higher levels. The program focus is on risky driving behavior like speeding and drink driving. In addition, the program addresses risk detection and risk-related knowledge. The goal of the study was to do an effect evaluation of this insight program at immediate post-test and 2 months follow-up. In addition, the study aimed to generalize the results of this program to comparable programs in order to make usable policy recommendations. A questionnaire based on the Theory of Planned Behavior (TPB) was used in order to measure participants' safety consciousness of speeding and drink driving. Moreover, we focused on risk detection and risk-related knowledge. Participants (N=366) were randomly assigned to a baseline-follow-up group or a post-test-follow-up group. Regarding speeding and driving, we found OtR to have little effect on the TPB variables. Regarding risk detection, we found no significant effect, even though participants clearly needed substantial improvement when stepping into the program. Regarding risk-related knowledge, the program did result in a significant improvement at post-test and follow-up. It is concluded that the current program format is a good starting point, but that it requires further attention to enhance high level driving skills. Program developers are encouraged to work in a more evidence-based manner when they select target variables and methods to influence these variables. Copyright © 2014 Elsevier Ltd. All rights reserved.

Diversified models for portfolio selection based on uncertain semivariance

NASA Astrophysics Data System (ADS)

Chen, Lin; Peng, Jin; Zhang, Bo; Rosyida, Isnaini

2017-02-01

Since the financial markets are complex, sometimes the future security returns are represented mainly based on experts' estimations due to lack of historical data. This paper proposes a semivariance method for diversified portfolio selection, in which the security returns are given subjective to experts' estimations and depicted as uncertain variables. In the paper, three properties of the semivariance of uncertain variables are verified. Based on the concept of semivariance of uncertain variables, two types of mean-semivariance diversified models for uncertain portfolio selection are proposed. Since the models are complex, a hybrid intelligent algorithm which is based on 99-method and genetic algorithm is designed to solve the models. In this hybrid intelligent algorithm, 99-method is applied to compute the expected value and semivariance of uncertain variables, and genetic algorithm is employed to seek the best allocation plan for portfolio selection. At last, several numerical examples are presented to illustrate the modelling idea and the effectiveness of the algorithm.
Knowledge of and Attitudes to Influenza Vaccination among Community Pharmacists in Catalonia (Spain). 2013-2014 Season: A Cross Sectional Study.

PubMed

Toledo, Diana; Soldevila, Núria; Guayta-Escolies, Rafel; Lozano, Pau; Rius, Pilar; Gascón, Pilar; Domínguez, Angela

2017-07-11

Annual recommendations on influenza seasonal vaccination include community pharmacists, who have low vaccination coverage. The aim of this study was to investigate the relationship between influenza vaccination in community pharmacists and their knowledge of and attitudes to vaccination. An online cross-sectional survey of community pharmacists in Catalonia, Spain, was conducted between September and November 2014. Sociodemographic, professional and clinical variables, the history of influenza vaccination and knowledge of and attitudes to influenza and seasonal influenza vaccination were collected. The survey response rate was 7.33% (506 out of 6906); responses from 463 community pharmacists were included in the final analyses. Analyses were performed using multivariable logistic regression models and stepwise backward selection method for variable selection. The influenza vaccination coverage in season 2013-2014 was 25.1%. There was an association between vaccination and correct knowledge of the virus responsible for epidemics (adjusted Odds Ratio (aOR) = 1.74; 95% CI 1.03-2.95), recommending vaccination in the postpartum (aOR = 3.63; 95% CI 2.01-6.55) and concern about infecting their clients (aOR = 5.27; 95% CI 1.88-14.76). In conclusion, community pharmacists have a very low influenza vaccination coverage, are not very willing to recommend vaccination to all their customers but they are concerned about infecting their clients.
Effect of Age on Variability in the Production of Text-Based Global Inferences

PubMed Central

Williams, Lynne J.; Dunlop, Joseph P.; Abdi, Hervé

2012-01-01

As we age, our differences in cognitive skills become more visible, an effect especially true for memory and problem solving skills (i.e., fluid intelligence). However, by contrast with fluid intelligence, few studies have examined variability in measures that rely on one’s world knowledge (i.e., crystallized intelligence). The current study investigated whether age increased the variability in text based global inference generation–a measure of crystallized intelligence. Global inference generation requires the integration of textual information and world knowledge and can be expressed as a gist or lesson. Variability in generating two global inferences for a single text was examined in young-old (62 to 69 years), middle-old (70 to 76 years) and old-old (77 to 94 years) adults. The older two groups showed greater variability, with the middle elderly group being most variable. These findings suggest that variability may be a characteristic of both fluid and crystallized intelligence in aging. PMID:22590523
Icelandic Physical Therapists' Attitudes Toward Adoption of New Knowledge and Evidence-Based Practice: Cross-Sectional Web-Based Survey.

PubMed

Arnadottir, Solveig A; Gudjonsdottir, Bjorg

2016-11-01

A positive attitude toward evidence-based practice (EBP) has been identified as an important factor in the effectiveness of the dissemination and implementation of EBP in real-world settings. The objectives of this study were: (1) to describe dimensions of Icelandic physical therapists' attitudes toward the adoption of new knowledge and EBP and (2) to explore the association between attitudes and selected personal and environmental factors. This study was a cross-sectional, Web-based survey of the total population of full members of the Icelandic Physiotherapy Association. The Evidence-Based Practice Attitude Scale (EBPAS) was used to survey attitudes toward EBP; the total EBPAS and its 4 subscales (requirements, appeal, openness, and divergence) were included. Linear regression was used to explore the association between the EBPAS and selected background variables. The response rate was 39.5% (N=211). The total EBPAS and all of its subscales reflected physical therapists' positive attitudes toward the adoption of new knowledge and EBP. Multivariable analysis revealed that being a woman was associated with more positive attitudes, as measured by the total EBPAS and the requirements, openness, and divergence subscales. Physical therapists with postprofessional education were more positive, as measured by the EBPAS openness subscale, and those working with at least 10 other physical therapists demonstrated more positive attitudes on the total EBPAS and the openness subscale. Because this was a cross-sectional survey, no causal inferences can be made, and there may have been unmeasured confounding factors. Potential nonresponse bias limits generalizability. The results expand understanding of the phenomenon of attitudes toward EBP. They reveal potentially modifiable dimensions of attitudes and the associated characteristics of physical therapists and their work environments. The findings encourage investigation of the effectiveness of strategies aimed at influencing various dimensions of attitudes toward EBP. © 2016 American Physical Therapy Association.
Spatial ability in secondary school students: intra-sex differences based on self-selection for physical education.

PubMed

Tlauka, Michael; Williams, Jennifer; Williamson, Paul

2008-08-01

Past research has demonstrated consistent sex differences with men typically outperforming women on tests of spatial ability. However, less is known about intra-sex effects. In the present study, two groups of female students (physical education and non-physical education secondary students) and two corresponding groups of male students explored a large-scale virtual shopping centre. In a battery of tasks, spatial knowledge of the shopping centre as well as mental rotation ability were tested. Additional variables considered were circulating testosterone levels, the ratio of 2D:4D digit length, and computer experience. The results revealed both sex and intra-sex differences in spatial ability. Variables related to virtual navigation and computer ability and experience were found to be the most powerful predictors of group membership. Our results suggest that in female and male secondary students, participation in physical education and spatial skill are related.
SVS: data and knowledge integration in computational biology.

PubMed

Zycinski, Grzegorz; Barla, Annalisa; Verri, Alessandro

2011-01-01

In this paper we present a framework for structured variable selection (SVS). The main concept of the proposed schema is to take a step towards the integration of two different aspects of data mining: database and machine learning perspective. The framework is flexible enough to use not only microarray data, but other high-throughput data of choice (e.g. from mass spectrometry, microarray, next generation sequencing). Moreover, the feature selection phase incorporates prior biological knowledge in a modular way from various repositories and is ready to host different statistical learning techniques. We present a proof of concept of SVS, illustrating some implementation details and describing current results on high-throughput microarray data.
The Use of Preenlistment Variables to Predict the Attrition of Navy Female Enlistees

DTIC Science & Technology

1979-09-01

individuals selecting a vocation. He grouped these needs into 17 areas such as information/knowledge, creativity/independence, risk, and belongingness . It was...216- 221. Porter, L. W., & Steers, R. M. Organizational, work, and personal factors in employee turnover and absenteeism. Psychological Bulletin
The Spacecraft Materials Selector: An Artificial Intelligence System for Preliminary Design Trade Studies, Materials Assessments, and Estimates of Environments Present

NASA Technical Reports Server (NTRS)

Pippin, H. G.; Woll, S. L. B.

2000-01-01

Institutions need ways to retain valuable information even as experienced individuals leave an organization. Modern electronic systems have enough capacity to retain large quantities of information that can mitigate the loss of experience. Performance information for long-term space applications is relatively scarce and specific information (typically held by a few individuals within a single project) is often rather narrowly distributed. Spacecraft operate under severe conditions and the consequences of hardware and/or system failures, in terms of cost, loss of information, and time required to replace the loss, are extreme. These risk factors place a premium on appropriate choice of materials and components for space applications. An expert system is a very cost-effective method for sharing valuable and scarce information about spacecraft performance. Boeing has an artificial intelligence software package, called the Boeing Expert System Tool (BEST), to construct and operate knowledge bases to selectively recall and distribute information about specific subjects. A specific knowledge base to evaluate the on-orbit performance of selected materials on spacecraft has been developed under contract to the NASA SEE program. The performance capabilities of the Spacecraft Materials Selector (SMS) knowledge base are described. The knowledge base is a backward-chaining, rule-based system. The user answers a sequence of questions, and the expert system provides estimates of optical and mechanical performance of selected materials under specific environmental conditions. The initial operating capability of the system will include data for Kapton, silverized Teflon, selected paints, silicone-based materials, and certain metals. For situations where a mission profile (launch date, orbital parameters, mission duration, spacecraft orientation) is not precisely defined, the knowledge base still attempts to provide qualitative observations about materials performance and likely exposures. Prior to the NASA contract, a knowledge base, the Spacecraft Environments Assistant (SEA,) was initially developed by Boeing to estimate the environmental factors important for a specific spacecraft mission profile. The NASA SEE program has funded specific enhancements to the capability of this knowledge base. The SEA qualitatively identifies over 25 environmental factors that may influence the performance of a spacecraft during its operational lifetime. For cases where sufficiently detailed answers are provided to questions asked by the knowledge base, atomic oxygen fluence levels, proton and/or electron fluence and dose levels, and solar exposure hours are calculated. The SMS knowledge base incorporates the previously developed SEA knowledge base. A case history for previous flight experiment will be shown as an example, and capabilities and limitations of the system will be discussed.
Development and preliminary evaluation of a practice-based learning and improvement tool for assessing resident competence and guiding curriculum development.

PubMed

Lawrence, Renée H; Tomolo, Anne M

2011-03-01

Although practice-based learning and improvement (PBLI) is now recognized as a fundamental and necessary skill set, we are still in need of tools that yield specific information about gaps in knowledge and application to help nurture the development of quality improvement (QI) skills in physicians in a proficient and proactive manner. We developed a questionnaire and coding system as an assessment tool to evaluate and provide feedback regarding PBLI self-efficacy, knowledge, and application skills for residency programs and related professional requirements. Five nationally recognized QI experts/leaders reviewed and completed our questionnaire. Through an iterative process, a coding system based on identifying key variables needed for ideal responses was developed to score project proposals. The coding system comprised 14 variables related to the QI projects, and an additional 30 variables related to the core knowledge concepts related to PBLI. A total of 86 residents completed the questionnaire, and 2 raters coded their open-ended responses. Interrater reliability was assessed by percentage agreement and Cohen κ for individual variables and Lin concordance correlation for total scores for knowledge and application. Discriminative validity (t test to compare known groups) and coefficient of reproducibility as an indicator of construct validity (item difficulty hierarchy) were also assessed. Interrater reliability estimates were good (percentage of agreements, above 90%; κ, above 0.4 for most variables; concordances for total scores were R = .88 for knowledge and R = .98 for application). Despite the residents' limited range of experiences in the group with prior PBLI exposure, our tool met our goal of differentiating between the 2 groups in our preliminary analyses. Correcting for chance agreement identified some variables that are potentially problematic. Although additional evaluation is needed, our tool may prove helpful and provide detailed information about trainees' progress and the curriculum.
Development and Preliminary Evaluation of a Practice-Based Learning and Improvement Tool for Assessing Resident Competence and Guiding Curriculum Development

PubMed Central

Lawrence, Renée H; Tomolo, Anne M

2011-01-01

Background Although practice-based learning and improvement (PBLI) is now recognized as a fundamental and necessary skill set, we are still in need of tools that yield specific information about gaps in knowledge and application to help nurture the development of quality improvement (QI) skills in physicians in a proficient and proactive manner. We developed a questionnaire and coding system as an assessment tool to evaluate and provide feedback regarding PBLI self-efficacy, knowledge, and application skills for residency programs and related professional requirements. Methods Five nationally recognized QI experts/leaders reviewed and completed our questionnaire. Through an iterative process, a coding system based on identifying key variables needed for ideal responses was developed to score project proposals. The coding system comprised 14 variables related to the QI projects, and an additional 30 variables related to the core knowledge concepts related to PBLI. A total of 86 residents completed the questionnaire, and 2 raters coded their open-ended responses. Interrater reliability was assessed by percentage agreement and Cohen κ for individual variables and Lin concordance correlation for total scores for knowledge and application. Discriminative validity (t test to compare known groups) and coefficient of reproducibility as an indicator of construct validity (item difficulty hierarchy) were also assessed. Results Interrater reliability estimates were good (percentage of agreements, above 90%; κ, above 0.4 for most variables; concordances for total scores were R = .88 for knowledge and R = .98 for application). Conclusion Despite the residents' limited range of experiences in the group with prior PBLI exposure, our tool met our goal of differentiating between the 2 groups in our preliminary analyses. Correcting for chance agreement identified some variables that are potentially problematic. Although additional evaluation is needed, our tool may prove helpful and provide detailed information about trainees' progress and the curriculum. PMID:22379522
Impact of communication on consumers' food choices.

PubMed

Verbeke, Wim

2008-08-01

Consumers' food choices and dietary behaviour can be markedly affected by communication and information. Whether the provided information is processed by the receiver, and thus becomes likely to be effective, depends on numerous factors. The role of selected determinants such as uncertainty, knowledge, involvement, health-related motives and trust, as well as message content variables, are discussed in the present paper based on previous empirical studies. The different studies indicate that: uncertainty about meat quality and safety does not automatically result in more active information search; subjective knowledge about fish is a better predictor of fish consumption than objective knowledge; high subjective knowledge about functional foods as a result of a low trusted information source such as mass media advertising leads to a lower probability of adopting these foods in the diet. Also, evidence of the stronger impact of negative news as compared with messages promoting positive outcomes of food choices is discussed. Finally, three audience-segmentation studies based on consumers' involvement with fresh meat, individuals' health-related-motive orientations and their use of and trust in fish information sources are presented. A clear message from these studies is that communication and information provision strategies targeted to a specific audience's needs, interests or motives stand a higher likelihood of being attended to and processed by the receiving audience, and therefore also stand a higher chance of yielding their envisaged impact in terms of food choice and dietary behaviour.
Ontology-guided data preparation for discovering genotype-phenotype relationships.

PubMed

Coulet, Adrien; Smaïl-Tabbone, Malika; Benlian, Pascale; Napoli, Amedeo; Devignes, Marie-Dominique

2008-04-25

Complexity and amount of post-genomic data constitute two major factors limiting the application of Knowledge Discovery in Databases (KDD) methods in life sciences. Bio-ontologies may nowadays play key roles in knowledge discovery in life science providing semantics to data and to extracted units, by taking advantage of the progress of Semantic Web technologies concerning the understanding and availability of tools for knowledge representation, extraction, and reasoning. This paper presents a method that exploits bio-ontologies for guiding data selection within the preparation step of the KDD process. We propose three scenarios in which domain knowledge and ontology elements such as subsumption, properties, class descriptions, are taken into account for data selection, before the data mining step. Each of these scenarios is illustrated within a case-study relative to the search of genotype-phenotype relationships in a familial hypercholesterolemia dataset. The guiding of data selection based on domain knowledge is analysed and shows a direct influence on the volume and significance of the data mining results. The method proposed in this paper is an efficient alternative to numerical methods for data selection based on domain knowledge. In turn, the results of this study may be reused in ontology modelling and data integration.
Classification and quantitation of milk powder by near-infrared spectroscopy and mutual information-based variable selection and partial least squares

NASA Astrophysics Data System (ADS)

Chen, Hui; Tan, Chao; Lin, Zan; Wu, Tong

2018-01-01

Milk is among the most popular nutrient source worldwide, which is of great interest due to its beneficial medicinal properties. The feasibility of the classification of milk powder samples with respect to their brands and the determination of protein concentration is investigated by NIR spectroscopy along with chemometrics. Two datasets were prepared for experiment. One contains 179 samples of four brands for classification and the other contains 30 samples for quantitative analysis. Principal component analysis (PCA) was used for exploratory analysis. Based on an effective model-independent variable selection method, i.e., minimal-redundancy maximal-relevance (MRMR), only 18 variables were selected to construct a partial least-square discriminant analysis (PLS-DA) model. On the test set, the PLS-DA model based on the selected variable set was compared with the full-spectrum PLS-DA model, both of which achieved 100% accuracy. In quantitative analysis, the partial least-square regression (PLSR) model constructed by the selected subset of 260 variables outperforms significantly the full-spectrum model. It seems that the combination of NIR spectroscopy, MRMR and PLS-DA or PLSR is a powerful tool for classifying different brands of milk and determining the protein content.
Enrichment assessment of multiple virtual screening strategies for Toll-like receptor 8 agonists based on a maximal unbiased benchmarking data set.

PubMed

Pei, Fen; Jin, Hongwei; Zhou, Xin; Xia, Jie; Sun, Lidan; Liu, Zhenming; Zhang, Liangren

2015-11-01

Toll-like receptor 8 agonists, which activate adaptive immune responses by inducing robust production of T-helper 1-polarizing cytokines, are promising candidates for vaccine adjuvants. As the binding site of toll-like receptor 8 is large and highly flexible, virtual screening by individual method has inevitable limitations; thus, a comprehensive comparison of different methods may provide insights into seeking effective strategy for the discovery of novel toll-like receptor 8 agonists. In this study, the performance of knowledge-based pharmacophore, shape-based 3D screening, and combined strategies was assessed against a maximum unbiased benchmarking data set containing 13 actives and 1302 decoys specialized for toll-like receptor 8 agonists. Prior structure-activity relationship knowledge was involved in knowledge-based pharmacophore generation, and a set of antagonists was innovatively used to verify the selectivity of the selected knowledge-based pharmacophore. The benchmarking data set was generated from our recently developed 'mubd-decoymaker' protocol. The enrichment assessment demonstrated a considerable performance through our selected three-layer virtual screening strategy: knowledge-based pharmacophore (Phar1) screening, shape-based 3D similarity search (Q4_combo), and then a Gold docking screening. This virtual screening strategy could be further employed to perform large-scale database screening and to discover novel toll-like receptor 8 agonists. © 2015 John Wiley & Sons A/S.
Defense Acquisitions: Assessments of Selected Weapon Programs

DTIC Science & Technology

2012-03-01

knowledge-based practices. As a result , most of these programs will carry technology, design, and production risks into subsequent phases of the...acquisition process that could result in cost growth or schedule delays. GAO also assessed the implementation of selected acquisition reforms and found...knowledge-based practices. As a result , most of these programs will carry technology, design, and production risks into subsequent phases of the
Analysis of a Moodle-Based Training Program about the Pedagogical Content Knowledge of Evolution Theory and Natural Selection

ERIC Educational Resources Information Center

Stasinakis, Panagiotis K.; Kalogiannnakis, Michail

2017-01-01

In this study we aim to find out whether a training program for secondary school science teachers which was organized based on the model of Pedagogical Content Knowledge (PCK), could improve their individual PCK for a specific scientific issue. The Evolution Theory (ET) and the Natural Selection (NS) were chosen as the scientific issues of…
Self-care resources and activity as predictors of quality of life in persons after myocardial infarction.

PubMed

Baas, Linda S

2004-01-01

An ex post facto correlational study was conducted to examine predictors of quality of life in persons 3 to 6 months after a myocardial infarction. Self-care resources, self-care knowledge (needs), activity level, and selected demographic variables were examined as predictor variables. A convenience sample of 86 subjects with a mean age of 61 years, was recruited for participation in this study. The study that explained 35% of the variance in quality of life included self-care resources available, activity level, and self-care needs. Modeling and Role Modeling Paradigm provided a useful explanation of how self-care resources and self-care knowledge can be applied to persons recovering from myocardial infarction.
Inquiry-Based Science and Technology Enrichment Program: Green Earth Enhanced with Inquiry and Technology

NASA Astrophysics Data System (ADS)

Kim, Hanna

2011-12-01

This study investigated the effectiveness of a guided inquiry integrated with technology, in terms of female middle-school students' attitudes toward science/scientists and content knowledge regarding selective science concepts (e.g., Greenhouse Effect, Air/Water Quality, Alternative Energy, and Human Health). Thirty-five female students who were entering eighth grade attended an intensive, 1-week Inquiry-Based Science and Technology Enrichment Program which used a main theme, "Green Earth Enhanced with Inquiry and Technology." We used pre- and post-attitude surveys, pre- and post-science content knowledge tests, and selective interviews to collect data and measure changes in students' attitudes and content knowledge. The study results indicated that at the post-intervention measures, participants significantly improved their attitudes toward science and science-related careers and increased their content knowledge of selected science concepts ( p < .05).
Presence and Potential Distribution of Aedes albopictus and Aedes japonicus japonicus (Diptera: Culicidae) in Slovenia.

PubMed

Kalan, Katja; Ivovic, Vladimir; Glasnovic, Peter; Buzan, Elena

2017-11-07

In Slovenia, two invasive mosquito species are present, Aedes albopictus (Skuse, 1895) (Diptera: Culicidae) and Aedes japonicus (Theobald, 1901) (Diptera: Culicidae). In this study, we examined their actual distribution and suitable habitats for new colonizations. Data from survey of species presence in 2013 and 2015, bioclimatic variables and altitude were used for the construction of predictive maps. We produced various models in Maxent software and tested two bioclimatic variable sets, WorldClim and CHELSA. For the variable selection of A. albopictus modeling we used statistical and expert knowledge-based approach, whereas for A. j. japonicus we used only a statistically based approach. The best performing models for both species were chosen according to AIC score-based evaluation. In 2 yr of sampling, A. albopictus was largely confined to the western half of Slovenia, whereas A. j. japonicus spread significantly and can be considered as an established species in a large part of the country. Comparison of models with WorldClim and CHELSA variables for both species showed models with CHELSA variables as a better tool for prediction. Finally, we validated the models performance in predicting distribution of species according to collected field data. Our study confirms that both species are co-occurring and are sympatric in a large part of the country area. The tested models could be used for future prevention of invasive mosquitoes spreading in other countries with similar bioclimatic conditions. © The Authors 2017. Published by Oxford University Press on behalf of Entomological Society of America. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.
The Adverse Drug Reactions from Patient Reports in Social Media Project: Five Major Challenges to Overcome to Operationalize Analysis and Efficiently Support Pharmacovigilance Process

PubMed Central

Dahamna, Badisse; Guillemin-Lanne, Sylvie; Darmoni, Stefan J; Faviez, Carole; Huot, Charles; Katsahian, Sandrine; Leroux, Vincent; Pereira, Suzanne; Richard, Christophe; Schück, Stéphane; Souvignet, Julien; Lillo-Le Louët, Agnès; Texier, Nathalie

2017-01-01

Background Adverse drug reactions (ADRs) are an important cause of morbidity and mortality. Classical Pharmacovigilance process is limited by underreporting which justifies the current interest in new knowledge sources such as social media. The Adverse Drug Reactions from Patient Reports in Social Media (ADR-PRISM) project aims to extract ADRs reported by patients in these media. We identified 5 major challenges to overcome to operationalize the analysis of patient posts: (1) variable quality of information on social media, (2) guarantee of data privacy, (3) response to pharmacovigilance expert expectations, (4) identification of relevant information within Web pages, and (5) robust and evolutive architecture. Objective This article aims to describe the current state of advancement of the ADR-PRISM project by focusing on the solutions we have chosen to address these 5 major challenges. Methods In this article, we propose methods and describe the advancement of this project on several aspects: (1) a quality driven approach for selecting relevant social media for the extraction of knowledge on potential ADRs, (2) an assessment of ethical issues and French regulation for the analysis of data on social media, (3) an analysis of pharmacovigilance expert requirements when reviewing patient posts on the Internet, (4) an extraction method based on natural language processing, pattern based matching, and selection of relevant medical concepts in reference terminologies, and (5) specifications of a component-based architecture for the monitoring system. Results Considering the 5 major challenges, we (1) selected a set of 21 validated criteria for selecting social media to support the extraction of potential ADRs, (2) proposed solutions to guarantee data privacy of patients posting on Internet, (3) took into account pharmacovigilance expert requirements with use case diagrams and scenarios, (4) built domain-specific knowledge resources embeding a lexicon, morphological rules, context rules, semantic rules, syntactic rules, and post-analysis processing, and (5) proposed a component-based architecture that allows storage of big data and accessibility to third-party applications through Web services. Conclusions We demonstrated the feasibility of implementing a component-based architecture that allows collection of patient posts on the Internet, near real-time processing of those posts including annotation, and storage in big data structures. In the next steps, we will evaluate the posts identified by the system in social media to clarify the interest and relevance of such approach to improve conventional pharmacovigilance processes based on spontaneous reporting. PMID:28935617

Evaluation of Selected Recycling Curricula: Educating the Green Citizen.

ERIC Educational Resources Information Center

Boerschig, Sally; De Young, Raymond

1993-01-01

Solid waste curricula from various programs around the country were reviewed using eight variables identified as predictors of conservation behavior. Scores demonstrated that solid waste curricula focus mainly on knowledge and include, to a lesser extent, attitude change and action strategies. Lists the 14 programs evaluated in the study. (MDH)
Evaluation of the Environmentalist Dimension of Ecotourism at the Dadia Forest Reserve (Greece)

NASA Astrophysics Data System (ADS)

Hovardas, Tasos; Poirazidis, Kostas

2006-11-01

Environmental education and financial support of nature conservation are considered among the primary components of the environmentalist dimension of ecotourism. The potential of environmental education calls for enhancing visitor environmental knowledge and prompting change of visitor behavior. The present study addressed the evaluation of the environmentalist dimension of ecotourism in the Dadia Forest Reserve. The first objective of the research was to study the influence of variables determined prior to the visit, namely, visitor and visit characteristics, visitor environmental information sources, visitor pro-environmental behavior, and visitation goals, on variables determined during the visit, that is, visitor participation in and satisfaction from ecotourism activities. The second objective was to study the effect of selected variables on visitor knowledge and behavior intentions. Visitor participation in and satisfaction from ecotourism activities were primarily controlled by visit characteristics. Levels of visitor knowledge were quite satisfactory; however, coherence among knowledge items was rather loose. On the other hand, behavior intention items revealed the highest possible degree of coherence, which should be attributed to the pervasive influence of visitor satisfaction from watching birds at the Bird Observatory within the Dadia Forest Reserve. Ecotourism activities, during which the main part of environmental knowledge was offered, exerted no effect on visitor knowledge; all the same, they significantly influenced visitor behavior intentions. Overall, visitor behavior intentions revealed a considerable potential of financing environmental protection and providing voluntary work in the frame of the environmental management of the Dadia Forest Reserve.
Evaluation of the environmentalist dimension of ecotourism at the Dadia Forest Reserve (Greece).

PubMed

Hovardas, Tasos; Poirazidis, Kostas

2006-11-01

Environmental education and financial support of nature conservation are considered among the primary components of the environmentalist dimension of ecotourism. The potential of environmental education calls for enhancing visitor environmental knowledge and prompting change of visitor behavior. The present study addressed the evaluation of the environmentalist dimension of ecotourism in the Dadia Forest Reserve. The first objective of the research was to study the influence of variables determined prior to the visit, namely, visitor and visit characteristics, visitor environmental information sources, visitor pro-environmental behavior, and visitation goals, on variables determined during the visit, that is, visitor participation in and satisfaction from ecotourism activities. The second objective was to study the effect of selected variables on visitor knowledge and behavior intentions. Visitor participation in and satisfaction from ecotourism activities were primarily controlled by visit characteristics. Levels of visitor knowledge were quite satisfactory; however, coherence among knowledge items was rather loose. On the other hand, behavior intention items revealed the highest possible degree of coherence, which should be attributed to the pervasive influence of visitor satisfaction from watching birds at the Bird Observatory within the Dadia Forest Reserve. Ecotourism activities, during which the main part of environmental knowledge was offered, exerted no effect on visitor knowledge; all the same, they significantly influenced visitor behavior intentions. Overall, visitor behavior intentions revealed a considerable potential of financing environmental protection and providing voluntary work in the frame of the environmental management of the Dadia Forest Reserve.
Logistic-based patient grouping for multi-disciplinary treatment.

PubMed

Maruşter, Laura; Weijters, Ton; de Vries, Geerhard; van den Bosch, Antal; Daelemans, Walter

2002-01-01

Present-day healthcare witnesses a growing demand for coordination of patient care. Coordination is needed especially in those cases in which hospitals have structured healthcare into specialty-oriented units, while a substantial portion of patient care is not limited to single units. From a logistic point of view, this multi-disciplinary patient care creates a tension between controlling the hospital's units, and the need for a control of the patient flow between units. A possible solution is the creation of new units in which different specialties work together for specific groups of patients. A first step in this solution is to identify the salient patient groups in need of multi-disciplinary care. Grouping techniques seem to offer a solution. However, most grouping approaches in medicine are driven by a search for pathophysiological homogeneity. In this paper, we present an alternative logistic-driven grouping approach. The starting point of our approach is a database with medical cases for 3,603 patients with peripheral arterial vascular (PAV) diseases. For these medical cases, six basic logistic variables (such as the number of visits to different specialist) are selected. Using these logistic variables, clustering techniques are used to group the medical cases in logistically homogeneous groups. In our approach, the quality of the resulting grouping is not measured by statistical significance, but by (i) the usefulness of the grouping for the creation of new multi-disciplinary units; (ii) how well patients can be selected for treatment in the new units. Given a priori knowledge of a patient (e.g. age, diagnosis), machine learning techniques are employed to induce rules that can be used for the selection of the patients eligible for treatment in the new units. In the paper, we describe the results of the above-proposed methodology for patients with PAV diseases. Two groupings and the accompanied classification rule sets are presented. One grouping is based on all the logistic variables, and another grouping is based on two latent factors found by applying factor analysis. On the basis of the experimental results, we can conclude that it is possible to search for medical logistic homogenous groups (i) that can be characterized by rules based on the aggregated logistic variables; (ii) for which we can formulate rules to predict to which cluster new patients belong.
Factors associated with knowledge about breastfeeding among female garment workers in Dhaka city.

PubMed

Afrose, Lucen; Banu, Bilkis; Ahmed, Kazi R; Khanom, Khurshida

2012-01-01

Knowledge about breastfeeding among women is very important for healthy children. The present study aims to determine the level of knowledge and factors associated with knowledge on breastfeeding among female garment workers in a selected garment factory in Dhaka city. A cross-sectional study was conducted among 200 female garment workers in the reproductive age group (15-49 years). Data were collected through a pre-tested questionnaire using the face-to-face interview method. Bivariate and multivariate analysis was done to determine the association between sociodemographic variables and knowledge on breastfeeding. The study showed that, overall the level of knowledge regarding breastfeeding is very poor (88%) among the study subjects. Most of the respondents have very poor knowledge regarding advantages of exclusive breastfeeding (89%) and breastfeeding (100%). In contrast, a majority have good knowledge on duration of exclusive breastfeeding (74%) and breastfeeding (66%). No significant association was found between the knowledge score of breastfeeding with remaining socio-demographic variables like age, marital status, family income and expenditure. Education is significantly (p<0.001) associated with a higher total knowledge score of breastfeeding. Women with secondary level of education had a significantly higher (p<0.001) level of total knowledge score than other categories (illiterate, primary and higher secondary) of education. A large proportion of female garment workers had inadequate knowledge regarding breastfeeding. It is also important that health education on breastfeeding is urgently provided to the female garments workers of Bangladesh.
Advances in variable selection methods II: Effect of variable selection method on classification of hydrologically similar watersheds in three Mid-Atlantic ecoregions

EPA Science Inventory

Hydrological flow predictions in ungauged and sparsely gauged watersheds use regionalization or classification of hydrologically similar watersheds to develop empirical relationships between hydrologic, climatic, and watershed variables. The watershed classifications may be based...
A Variable-Selection Heuristic for K-Means Clustering.

ERIC Educational Resources Information Center

Brusco, Michael J.; Cradit, J. Dennis

2001-01-01

Presents a variable selection heuristic for nonhierarchical (K-means) cluster analysis based on the adjusted Rand index for measuring cluster recovery. Subjected the heuristic to Monte Carlo testing across more than 2,200 datasets. Results indicate that the heuristic is extremely effective at eliminating masking variables. (SLD)
A particle swarm optimization variant with an inner variable learning strategy.

PubMed

Wu, Guohua; Pedrycz, Witold; Ma, Manhao; Qiu, Dishan; Li, Haifeng; Liu, Jin

2014-01-01

Although Particle Swarm Optimization (PSO) has demonstrated competitive performance in solving global optimization problems, it exhibits some limitations when dealing with optimization problems with high dimensionality and complex landscape. In this paper, we integrate some problem-oriented knowledge into the design of a certain PSO variant. The resulting novel PSO algorithm with an inner variable learning strategy (PSO-IVL) is particularly efficient for optimizing functions with symmetric variables. Symmetric variables of the optimized function have to satisfy a certain quantitative relation. Based on this knowledge, the inner variable learning (IVL) strategy helps the particle to inspect the relation among its inner variables, determine the exemplar variable for all other variables, and then make each variable learn from the exemplar variable in terms of their quantitative relations. In addition, we design a new trap detection and jumping out strategy to help particles escape from local optima. The trap detection operation is employed at the level of individual particles whereas the trap jumping out strategy is adaptive in its nature. Experimental simulations completed for some representative optimization functions demonstrate the excellent performance of PSO-IVL. The effectiveness of the PSO-IVL stresses a usefulness of augmenting evolutionary algorithms by problem-oriented domain knowledge.
Improving Critical Thinking Using a Web-Based Tutorial Environment.

PubMed

Wiesner, Stephen M; Walker, J D; Creeger, Craig R

2017-01-01

With a broad range of subject matter, students often struggle recognizing relationships between content in different subject areas. A scenario-based learning environment (SaBLE) has been developed to enhancing clinical reasoning and critical thinking among undergraduate students in a medical laboratory science program and help them integrate their new knowledge. SaBLE incorporates aspects of both cognitive theory and instructional design, including reduction of extraneous cognitive load, goal-based learning, feedback timing, and game theory. SaBLE is a website application that runs in most browsers and devices, and is used to develop randomly selected scenarios that challenge user thinking in almost any scenario-based instruction. User progress is recorded to allow comprehensive data analysis of changes in user performance. Participation is incentivized using a point system and digital badges or awards. SaBLE was deployed in one course with a total exposure for the treatment group of approximately 9 weeks. When assessing performance of SaBLE participants, and controlling for grade point average as a possible confounding variable, there was a statistically significant correlation between the number of SaBLE levels completed and performance on selected critical-thinking exam questions addressing unrelated content.
Relationships between digestive efficiency and metabolomic profiles of serum and intestinal contents in chickens.

PubMed

Beauclercq, Stéphane; Nadal-Desbarats, Lydie; Hennequet-Antier, Christelle; Gabriel, Irène; Tesseraud, Sophie; Calenge, Fanny; Le Bihan-Duval, Elisabeth; Mignon-Grasteau, Sandrine

2018-04-27

The increasing cost of conventional feedstuffs has bolstered interest in genetic selection for digestive efficiency (DE), a component of feed efficiency, assessed by apparent metabolisable energy corrected to zero nitrogen retention (AMEn). However, its measurement is time-consuming and constraining, and its relationship with metabolic efficiency poorly understood. To simplify selection for this trait, we searched for indirect metabolic biomarkers through an analysis of the serum metabolome using nuclear magnetic resonance ( 1 H NMR). A partial least squares (PLS) model including six amino acids and two derivatives from butyrate predicted 59% of AMEn variability. Moreover, to increase our knowledge of the molecular mechanisms controlling DE, we investigated 1 H NMR metabolomes of ileal, caecal, and serum contents by fitting canonical sparse PLS. This analysis revealed strong associations between metabolites and DE. Models based on the ileal, caecal, and serum metabolome respectively explained 77%, 78%, and 74% of the variability of AMEn and its constitutive components (utilisation of starch, lipids, and nitrogen). In our conditions, the metabolites presenting the strongest associations with AMEn were proline in the serum, fumarate in the ileum and glucose in caeca. This study shows that serum metabolomics offers new opportunities to predict chicken DE.
Can Dynamic Visualizations with Variable Control Enhance the Acquisition of Intuitive Knowledge?

NASA Astrophysics Data System (ADS)

Wichmann, Astrid; Timpe, Sebastian

2015-10-01

An important feature of inquiry learning is to take part in science practices including exploring variables and testing hypotheses. Computer-based dynamic visualizations have the potential to open up various exploration possibilities depending on the level of learner control. It is assumed that variable control, e.g., by changing parameters of a variable, leads to deeper processing (Chang and Linn 2013; de Jong and Njoo 1992; Nerdel 2003; Trey and Khan 2008). Variable control may be helpful, in particular, for acquiring intuitive knowledge (Swaak and de Jong 2001). However, it bares the risk of mental exhaustion and thus may have detrimental effects on knowledge acquisition (Sweller 1998). Students ( N = 118) from four chemistry classes followed inquiry cycles using the software Molecular Workbench (Xie and Tinker 2006). Variable control was varied across the conditions (1) No-Manipulation group and (2) Manipulation group. By adding a third condition, (3) Manipulation-Plus group, we tested whether adding an active hypothesis phase prepares students before changing parameters of a variable. As expected, students in the Manipulation group and Manipulation-Plus group performed better concerning intuitive knowledge ( d = 1.14) than students in the No-Manipulation group. On a descriptive level, results indicated higher cognitive effort in the Manipulation group and the Manipulation-Plus group than in the No-Manipulation group. Unexpectedly, students in the Manipulation-Plus group did not benefit from the active hypothesis phase (intuitive knowledge: d = .36). Findings show that students benefit from variable control. Furthermore, findings point toward the direction that variable control evokes desirable difficulties (Bjork and Linn 2006).
Never the twain shall meet? - a comparison of implementation science and policy implementation research

PubMed Central

2013-01-01

Background Many of society’s health problems require research-based knowledge acted on by healthcare practitioners together with implementation of political measures from governmental agencies. However, there has been limited knowledge exchange between implementation science and policy implementation research, which has been conducted since the early 1970s. Based on a narrative review of selective literature on implementation science and policy implementation research, the aim of this paper is to describe the characteristics of policy implementation research, analyze key similarities and differences between this field and implementation science, and discuss how knowledge assembled in policy implementation research could inform implementation science. Discussion Following a brief overview of policy implementation research, several aspects of the two fields were described and compared: the purpose and origins of the research; the characteristics of the research; the development and use of theory; determinants of change (independent variables); and the impact of implementation (dependent variables). The comparative analysis showed that there are many similarities between the two fields, yet there are also profound differences. Still, important learning may be derived from several aspects of policy implementation research, including issues related to the influence of the context of implementation and the values and norms of the implementers (the healthcare practitioners) on implementation processes. Relevant research on various associated policy topics, including The Advocacy Coalition Framework, Governance Theory, and Institutional Theory, may also contribute to improved understanding of the difficulties of implementing evidence in healthcare. Implementation science is at a relatively early stage of development, and advancement of the field would benefit from accounting for knowledge beyond the parameters of the immediate implementation science literature. Summary There are many common issues in policy implementation research and implementation science. Research in both fields deals with the challenges of translating intentions into desired changes. Important learning may be derived from several aspects of policy implementation research. PMID:23758952
Never the twain shall meet?--a comparison of implementation science and policy implementation research.

PubMed

Nilsen, Per; Ståhl, Christian; Roback, Kerstin; Cairney, Paul

2013-06-10

Many of society's health problems require research-based knowledge acted on by healthcare practitioners together with implementation of political measures from governmental agencies. However, there has been limited knowledge exchange between implementation science and policy implementation research, which has been conducted since the early 1970s. Based on a narrative review of selective literature on implementation science and policy implementation research, the aim of this paper is to describe the characteristics of policy implementation research, analyze key similarities and differences between this field and implementation science, and discuss how knowledge assembled in policy implementation research could inform implementation science. Following a brief overview of policy implementation research, several aspects of the two fields were described and compared: the purpose and origins of the research; the characteristics of the research; the development and use of theory; determinants of change (independent variables); and the impact of implementation (dependent variables). The comparative analysis showed that there are many similarities between the two fields, yet there are also profound differences. Still, important learning may be derived from several aspects of policy implementation research, including issues related to the influence of the context of implementation and the values and norms of the implementers (the healthcare practitioners) on implementation processes. Relevant research on various associated policy topics, including The Advocacy Coalition Framework, Governance Theory, and Institutional Theory, may also contribute to improved understanding of the difficulties of implementing evidence in healthcare. Implementation science is at a relatively early stage of development, and advancement of the field would benefit from accounting for knowledge beyond the parameters of the immediate implementation science literature. There are many common issues in policy implementation research and implementation science. Research in both fields deals with the challenges of translating intentions into desired changes. Important learning may be derived from several aspects of policy implementation research.
Refining Automatically Extracted Knowledge Bases Using Crowdsourcing.

PubMed

Li, Chunhua; Zhao, Pengpeng; Sheng, Victor S; Xian, Xuefeng; Wu, Jian; Cui, Zhiming

2017-01-01

Machine-constructed knowledge bases often contain noisy and inaccurate facts. There exists significant work in developing automated algorithms for knowledge base refinement. Automated approaches improve the quality of knowledge bases but are far from perfect. In this paper, we leverage crowdsourcing to improve the quality of automatically extracted knowledge bases. As human labelling is costly, an important research challenge is how we can use limited human resources to maximize the quality improvement for a knowledge base. To address this problem, we first introduce a concept of semantic constraints that can be used to detect potential errors and do inference among candidate facts. Then, based on semantic constraints, we propose rank-based and graph-based algorithms for crowdsourced knowledge refining, which judiciously select the most beneficial candidate facts to conduct crowdsourcing and prune unnecessary questions. Our experiments show that our method improves the quality of knowledge bases significantly and outperforms state-of-the-art automatic methods under a reasonable crowdsourcing cost.
Use of research-based instructional strategies in introductory physics: Where do faculty leave the innovation-decision process?

NASA Astrophysics Data System (ADS)

Henderson, Charles; Dancy, Melissa; Niewiadomska-Bugaj, Magdalena

2012-12-01

During the fall of 2008 a web survey, designed to collect information about pedagogical knowledge and practices, was completed by a representative sample of 722 physics faculty across the United States (50.3% response rate). This paper presents partial results to describe how 20 potential predictor variables correlate with faculty knowledge about and use of research-based instructional strategies (RBIS). The innovation-decision process was conceived of in terms of four stages: knowledge versus no knowledge, trial versus no trial, continuation versus discontinuation, and high versus low use. The largest losses occur at the continuation stage, with approximately 1/3 of faculty discontinuing use of all RBIS after trying one or more of these strategies. Nine of the predictor variables were statistically significant for at least one of these stages when controlling for other variables. Knowledge and/or use of RBIS are significantly correlated with reading teaching-related journals, attending talks and workshops related to teaching, attending the physics and astronomy new faculty workshop, having an interest in using more RBIS, being female, being satisfied with meeting instructional goals, and having a permanent, full-time position. The types of variables that are significant at each stage vary substantially. These results suggest that common dissemination strategies are good at creating knowledge about RBIS and motivation to try a RBIS, but more work is needed to support faculty during implementation and continued use of RBIS. Also, contrary to common assumptions, faculty age, institutional type, and percentage of job related to teaching were not found to be barriers to knowledge or use at any stage. High research productivity and large class sizes were not found to be barriers to use of at least some RBIS.
Evaluation of variable selection methods for random forests and omics data sets.

PubMed

Degenhardt, Frauke; Seifert, Stephan; Szymczak, Silke

2017-10-16

Machine learning methods and in particular random forests are promising approaches for prediction based on high dimensional omics data sets. They provide variable importance measures to rank predictors according to their predictive power. If building a prediction model is the main goal of a study, often a minimal set of variables with good prediction performance is selected. However, if the objective is the identification of involved variables to find active networks and pathways, approaches that aim to select all relevant variables should be preferred. We evaluated several variable selection procedures based on simulated data as well as publicly available experimental methylation and gene expression data. Our comparison included the Boruta algorithm, the Vita method, recurrent relative variable importance, a permutation approach and its parametric variant (Altmann) as well as recursive feature elimination (RFE). In our simulation studies, Boruta was the most powerful approach, followed closely by the Vita method. Both approaches demonstrated similar stability in variable selection, while Vita was the most robust approach under a pure null model without any predictor variables related to the outcome. In the analysis of the different experimental data sets, Vita demonstrated slightly better stability in variable selection and was less computationally intensive than Boruta.In conclusion, we recommend the Boruta and Vita approaches for the analysis of high-dimensional data sets. Vita is considerably faster than Boruta and thus more suitable for large data sets, but only Boruta can also be applied in low-dimensional settings. © The Author 2017. Published by Oxford University Press.
[Knowledge about sexuality in university students].

PubMed

Silva, P; Alvarado, R

1989-01-01

This study explores the level of sexual knowledge among chilean university students in 4 different professions, compares their responses and verifies them with selected socio-demographic variables. 813 university students were interviewed in 1st and 3rd year medical school, law and engineering from the University of Chile and in education, from the Superior Blas Canas Institute of Pedagogy. The group is equally divided between each of the 4 professions; 64.7% are men with 95.5% single and 84.7% are between 17.22; only 37.5% attended a mixed school; 73.1% are Catholic. The survey aimed to evaluate knowledge, attitude and practices (KAP) but this article only analyzes the attitudes of students through 6 variables: anatomy and physiology, pregnancy and delivery, contraception, venereal diseases, sources claimed by the interviewee to receive information and self-evaluation of actual levels of knowledge. Results demonstrated knowledge about anatomy and physiology, pregnancy and delivery and venereal diseases, but great disparity with contraception. Students are not learning about methods of contraception in school, possibly due to fear on the part of the faculty or their own lack of information. 3 factors influenced levels of knowledge: 1) formal education; 2) experience; and 3) personal interest. The highest results were from students of medicine with the lowest being students in engineering and education. Those that were in their 3rd year of school or married appeared more knowledgeable possibly due to more sexual experience and the need to prevent pregnancies. The females in all variables scored higher due to their own interest in preventing pregnancies, and because women are socialized in interpersonal relations and maternity issues. More than 1/2 the students gave themselves bad evaluations concerning their levels of sexual knowledge.
A web-based data visualization tool for the MIMIC-II database.

PubMed

Lee, Joon; Ribey, Evan; Wallace, James R

2016-02-04

Although MIMIC-II, a public intensive care database, has been recognized as an invaluable resource for many medical researchers worldwide, becoming a proficient MIMIC-II researcher requires knowledge of SQL programming and an understanding of the MIMIC-II database schema. These are challenging requirements especially for health researchers and clinicians who may have limited computer proficiency. In order to overcome this challenge, our objective was to create an interactive, web-based MIMIC-II data visualization tool that first-time MIMIC-II users can easily use to explore the database. The tool offers two main features: Explore and Compare. The Explore feature enables the user to select a patient cohort within MIMIC-II and visualize the distributions of various administrative, demographic, and clinical variables within the selected cohort. The Compare feature enables the user to select two patient cohorts and visually compare them with respect to a variety of variables. The tool is also helpful to experienced MIMIC-II researchers who can use it to substantially accelerate the cumbersome and time-consuming steps of writing SQL queries and manually visualizing extracted data. Any interested researcher can use the MIMIC-II data visualization tool for free to quickly and conveniently conduct a preliminary investigation on MIMIC-II with a few mouse clicks. Researchers can also use the tool to learn the characteristics of the MIMIC-II patients. Since it is still impossible to conduct multivariable regression inside the tool, future work includes adding analytics capabilities. Also, the next version of the tool will aim to utilize MIMIC-III which contains more data.
Pertinent parameters in photo-generation of electrons: Comparative study of anatase-based nano-TiO2 suspensions.

PubMed

Martel, D; Guerra, A; Turek, P; Weiss, J; Vileno, B

2016-04-01

In the field of solar fuel cells, the development of efficient photo-converting semiconductors remains a major challenge. A rational analysis of experimental photocatalytic results obtained with material in colloïdal suspensions is needed to access fundamental knowledge required to improve the design and properties of new materials. In this study, a simple system electron donor/nano-TiO2 is considered and examined via spin scavenging electron paramagnetic resonance as well as a panel of analytical techniques (composition, optical spectroscopy and dynamic light scattering) for selected type of nano-TiO2. Independent variables (pH, electron donor concentration and TiO2 amount) have been varied and interdependent variables (aggregate size, aggregate surface vs. volume and acid/base groups distribution) are discussed. This work shows that reliable understanding involves thoughtful combination of interdependent parameters, whereas the specific surface area seems not a pertinent parameter. The conclusion emphasizes the difficulty to identify the key features of the mechanisms governing photocatalytic properties in nano-TiO2. Copyright © 2016 Elsevier Inc. All rights reserved.
Variable Selection for Regression Models of Percentile Flows

NASA Astrophysics Data System (ADS)

Fouad, G.

2017-12-01

Percentile flows describe the flow magnitude equaled or exceeded for a given percent of time, and are widely used in water resource management. However, these statistics are normally unavailable since most basins are ungauged. Percentile flows of ungauged basins are often predicted using regression models based on readily observable basin characteristics, such as mean elevation. The number of these independent variables is too large to evaluate all possible models. A subset of models is typically evaluated using automatic procedures, like stepwise regression. This ignores a large variety of methods from the field of feature (variable) selection and physical understanding of percentile flows. A study of 918 basins in the United States was conducted to compare an automatic regression procedure to the following variable selection methods: (1) principal component analysis, (2) correlation analysis, (3) random forests, (4) genetic programming, (5) Bayesian networks, and (6) physical understanding. The automatic regression procedure only performed better than principal component analysis. Poor performance of the regression procedure was due to a commonly used filter for multicollinearity, which rejected the strongest models because they had cross-correlated independent variables. Multicollinearity did not decrease model performance in validation because of a representative set of calibration basins. Variable selection methods based strictly on predictive power (numbers 2-5 from above) performed similarly, likely indicating a limit to the predictive power of the variables. Similar performance was also reached using variables selected based on physical understanding, a finding that substantiates recent calls to emphasize physical understanding in modeling for predictions in ungauged basins. The strongest variables highlighted the importance of geology and land cover, whereas widely used topographic variables were the weakest predictors. Variables suffered from a high degree of multicollinearity, possibly illustrating the co-evolution of climatic and physiographic conditions. Given the ineffectiveness of many variables used here, future work should develop new variables that target specific processes associated with percentile flows.

Why do clinicians choose the therapies and techniques they do? Exploring clinical decision-making via treatment selections in dysphagia practice.

PubMed

McCurtin, Arlene; Healy, Chiara

2017-02-01

Speech-language pathologists (SLPs) are assumed to use evidence-based practice to inform treatment decisions. However, the reasoning underpinning treatment selections is not well known. Understanding why SLPs choose the treatments they do may be clarified by exploring the reasoning tied to specific treatments such as dysphagia interventions. An electronic survey methodology was utilised. Participants were accessed via the gatekeepers of two national dysphagia special interest groups representing adult and paediatric populations. Information was elicited on the dysphagia therapies and techniques used and on the reasoning for using/not using therapies. Data was analysed using descriptive and non-parametric statistics. The survey had a 74.8% response rate (n = 116). Consensus in both treatment selections and reasoning supporting treatment decisions was evident. Three favoured interventions (texture modification, thickening liquids, positioning changes) were identified. The reasoning supporting treatment choices centred primarily on client suitability and clinician knowledge. Knowledge reflected both absent knowledge (e.g. training) and accumulated knowledge (clinical experience). Dysphagia practice appears highly-defined, being characterised by group consensus regarding both preferred treatments and the reasoning underpinning treatment selections. Treatment selections are based on two core criteria: client suitability and the SLPs experience/knowledge. Explicit scientific reasoning is less influential than practice-centric influences.
Development of theory-based knowledge translation interventions to facilitate the implementation of evidence-based guidelines on the early management of adults with traumatic spinal cord injury.

PubMed

Bérubé, Mélanie; Albert, Martin; Chauny, Jean-Marc; Contandriopoulos, Damien; DuSablon, Anne; Lacroix, Sébastien; Gagné, Annick; Laflamme, Élise; Boutin, Nathalie; Delisle, Stéphane; Pauzé, Anne-Marie; MacThiong, Jean-Marc

2015-12-01

Optimal, early management following a spinal cord injury (SCI) can limit individuals' disabilities and costs related to their care. Several knowledge syntheses were recently published to guide health care professionals with regard to early interventions in SCI patients. However, no knowledge translation (KT) intervention, selected according to a behaviour change theory, has been proposed to facilitate the use of SCI guidelines in an acute care setting. To develop theory-informed KT interventions to promote the application of evidence-based recommendations on the acute care management of SCI patients. The first four phases of the knowledge-to-action model were used to establish the study design. Knowledge selection was based on the Grading of Recommendations Assessment, Development and Evaluation system. Knowledge adaptation to the local context was sourced from the ADAPTE process. The theoretical domains framework oriented the selection and development of the interventions based on an assessment of barriers and enablers to knowledge application. Twenty-nine recommendations were chosen and operationalized in measurable clinical indicators. Barriers related to knowledge, skills, perceived capacities, beliefs about consequences, social influences, and the environmental context and resources theoretical domains were identified. The mapping of behaviour change techniques associated with those barriers led to the development of an online educational curriculum, interdisciplinary clinical pathways as well as policies and procedures. This research project allowed us developing KT interventions according to a thorough behavioural change methodology. Exposure to the generated interventions will support health care professionals in providing the best care to SCI patients. © 2015 John Wiley & Sons, Ltd.
An efficient swarm intelligence approach to feature selection based on invasive weed optimization: Application to multivariate calibration and classification using spectroscopic data

NASA Astrophysics Data System (ADS)

Sheykhizadeh, Saheleh; Naseri, Abdolhossein

2018-04-01

Variable selection plays a key role in classification and multivariate calibration. Variable selection methods are aimed at choosing a set of variables, from a large pool of available predictors, relevant to the analyte concentrations estimation, or to achieve better classification results. Many variable selection techniques have now been introduced among which, those which are based on the methodologies of swarm intelligence optimization have been more respected during a few last decades since they are mainly inspired by nature. In this work, a simple and new variable selection algorithm is proposed according to the invasive weed optimization (IWO) concept. IWO is considered a bio-inspired metaheuristic mimicking the weeds ecological behavior in colonizing as well as finding an appropriate place for growth and reproduction; it has been shown to be very adaptive and powerful to environmental changes. In this paper, the first application of IWO, as a very simple and powerful method, to variable selection is reported using different experimental datasets including FTIR and NIR data, so as to undertake classification and multivariate calibration tasks. Accordingly, invasive weed optimization - linear discrimination analysis (IWO-LDA) and invasive weed optimization- partial least squares (IWO-PLS) are introduced for multivariate classification and calibration, respectively.
An efficient swarm intelligence approach to feature selection based on invasive weed optimization: Application to multivariate calibration and classification using spectroscopic data.

PubMed

Sheykhizadeh, Saheleh; Naseri, Abdolhossein

2018-04-05

Variable selection plays a key role in classification and multivariate calibration. Variable selection methods are aimed at choosing a set of variables, from a large pool of available predictors, relevant to the analyte concentrations estimation, or to achieve better classification results. Many variable selection techniques have now been introduced among which, those which are based on the methodologies of swarm intelligence optimization have been more respected during a few last decades since they are mainly inspired by nature. In this work, a simple and new variable selection algorithm is proposed according to the invasive weed optimization (IWO) concept. IWO is considered a bio-inspired metaheuristic mimicking the weeds ecological behavior in colonizing as well as finding an appropriate place for growth and reproduction; it has been shown to be very adaptive and powerful to environmental changes. In this paper, the first application of IWO, as a very simple and powerful method, to variable selection is reported using different experimental datasets including FTIR and NIR data, so as to undertake classification and multivariate calibration tasks. Accordingly, invasive weed optimization - linear discrimination analysis (IWO-LDA) and invasive weed optimization- partial least squares (IWO-PLS) are introduced for multivariate classification and calibration, respectively. Copyright © 2018 Elsevier B.V. All rights reserved.
Pilot testing model to uncover industrial symbiosis in Brazilian industrial clusters.

PubMed

Saraceni, Adriana Valélia; Resende, Luis Mauricio; de Andrade Júnior, Pedro Paulo; Pontes, Joseane

2017-04-01

The main objective of this study was to create a pilot model to uncover industrial symbiosis practices in Brazilian industrial clusters. For this purpose, a systematic revision was conducted in journals selected from two categories of the ISI Web of Knowledge: Engineering, Environmental and Engineering, Industrial. After an in-depth revision of literature, results allowed the creation of an analysis structure. A methodology based on fuzzy logic was applied and used to attribute the weights of industrial symbiosis variables. It was thus possible to extract the intensity indicators of the interrelations required to analyse the development level of each correlation between the variables. Determination of variables and their weights initially resulted in a framework for the theory of industrial symbiosis assessments. Research results allowed the creation of a pilot model that could precisely identify the loopholes or development levels in each sphere. Ontology charts for data analysis were also generated. This study contributes to science by presenting the foundations for building an instrument that enables application and compilation of the pilot model, in order to identify opportunity to symbiotic development, which derives from "uncovering" existing symbioses.
Ethiopian health care professionals' knowledge, attitude, and interests toward pharmacogenomics.

PubMed

Abdela, Ousman Abubeker; Bhagavathula, Akshaya Srikanth; Gebreyohannes, Eyob Alemayehu; Tegegn, Henok Getachew

2017-01-01

Pharmacogenomics is a field of science which studies the impact of inheritance on individual variation in medication therapy response. We assessed healthcare professionals' knowledge, attitude, and interest toward pharmacogenomics. A cross-sectional survey was conducted using a 32-item questionnaire among physicians, nurses, and pharmacists who were working at the University of Gondar Referral and Teaching Hospital in northwest Ethiopia. Descriptive statistics was applied, and the categorical variables were summarized as frequency and percentages. An analysis of variance (ANOVA) test was performed to compare mean scores among health professionals. A p -value of <0.05 was considered as statistically significant. Of 292 health professionals who responded, the majority were male (60%) and the mean age of study participants was 27.00 (±4.85 SD) years. The mean knowledge scores of all participants, pharmacists, physicians, and nurses were 2.343±1.109, 2.671±1.059, 2.375±1.093, and 2.173±1.110, respectively. Based on the ANOVA test, a statistically significant difference was noted in mean knowledge score between pharmacists and nurses ( p =0.002). More than two-thirds (67.33%) of nurses, 42.86% of pharmacists, and 40.27% of physicians who participated did not know that genetic variations can account for as much as 95% of the variability in drug disposition and effects. The ability to accurately apply their knowledge to drug therapy selection, dosing, or monitoring parameter was reported by 35.3% of the participants. More than two-thirds (69.2%) of participants thought that pharmacogenomic testing will allow the identification of the right drug with less side effects. Most of the participants (83.2%) also requested to have training on pharmacogenomics. Participants showed limited knowledge, but they had positive attitude toward pharmacogenomics. Educational programs focusing on pharmacogenomic testing and its clinical application need to be emphasized.
Multiple Influences of Semantic Memory on Sentence Processing: Distinct Effects of Semantic Relatedness on Violations of Real-World Event/State Knowledge and Animacy Selection Restrictions

ERIC Educational Resources Information Center

Paczynski, Martin; Kuperberg, Gina R.

2012-01-01

We aimed to determine whether semantic relatedness between an incoming word and its preceding context can override expectations based on two types of stored knowledge: real-world knowledge about the specific events and states conveyed by a verb, and the verb's broader selection restrictions on the animacy of its argument. We recorded event-related…
Firmness prediction in Prunus persica 'Calrico' peaches by visible/short-wave near infrared spectroscopy and acoustic measurements using optimised linear and non-linear chemometric models.

PubMed

Lafuente, Victoria; Herrera, Luis J; Pérez, María del Mar; Val, Jesús; Negueruela, Ignacio

2015-08-15

In this work, near infrared spectroscopy (NIR) and an acoustic measure (AWETA) (two non-destructive methods) were applied in Prunus persica fruit 'Calrico' (n = 260) to predict Magness-Taylor (MT) firmness. Separate and combined use of these measures was evaluated and compared using partial least squares (PLS) and least squares support vector machine (LS-SVM) regression methods. Also, a mutual-information-based variable selection method, seeking to find the most significant variables to produce optimal accuracy of the regression models, was applied to a joint set of variables (NIR wavelengths and AWETA measure). The newly proposed combined NIR-AWETA model gave good values of the determination coefficient (R(2)) for PLS and LS-SVM methods (0.77 and 0.78, respectively), improving the reliability of MT firmness prediction in comparison with separate NIR and AWETA predictions. The three variables selected by the variable selection method (AWETA measure plus NIR wavelengths 675 and 697 nm) achieved R(2) values 0.76 and 0.77, PLS and LS-SVM. These results indicated that the proposed mutual-information-based variable selection algorithm was a powerful tool for the selection of the most relevant variables. © 2014 Society of Chemical Industry.
Temporal and Spatial Variability of Surface Motor Activation Zones in Hemiplegic Patients During Functional Electrical Stimulation Therapy Sessions.

PubMed

Malešević, Jovana; Štrbac, Matija; Isaković, Milica; Kojić, Vladimir; Konstantinović, Ljubica; Vidaković, Aleksandra; Dedijer Dujović, Suzana; Kostić, Miloš; Keller, Thierry

2017-11-01

The goal of this study was to investigate surface motor activation zones and their temporal variability using an advanced multi-pad functional electrical stimulation system. With this system motor responses are elicited through concurrent activation of electrode matrix pads collectively termed "virtual electrodes" (VEs) with appropriate stimulation parameters. We observed VEs used to produce selective wrist, finger, and thumb extension movements in 20 therapy sessions of 12 hemiplegic stroke patients. The VEs which produce these three selective movements were created manually on the ergonomic multi-pad electrode by experienced clinicians based on visual inspection of the muscle responses. Individual results indicated that changes in VE configuration were required each session for all patients and that overlap in joint movements was evident between some VEs. However, by analyzing group data, we defined the probability distribution over the electrode surface for the three VEs of interest. Furthermore, through Bayesian logic we obtained preferred stimulation zones that are in accordance with our previously reported heuristically obtained results. We have also analyzed the number of active pads and stimulation amplitudes for these three VEs. Presented results provide a basis for an automated electrode calibration algorithm built on a priori knowledge or the starting point for manual selection of stimulation points. © 2017 International Center for Artificial Organs and Transplantation and Wiley Periodicals, Inc.
Effectiveness of touch and feel (TAF) technique on first aid measures for visually challenged.

PubMed

Mary, Helen; Sasikalaz, D; Venkatesan, Latha

2013-01-01

There is a common perception that a blind person cannot even help his own self. In order to challenge that view, a workshop for visually-impaired people to develop the skills to be independent and productive members of society was conceived. An experimental study was conducted at National Institute of Visually Handicapped, Chennai with the objective to assess the effectiveness of Touch and Feel (TAF) technique on first aid measures for the visually challenged. Total 25 visually challenged people were selected by non-probability purposive sampling technique and data was collected using demographic variable and structured knowledge questionnaire. The score obtained was categorised into three levels: inadequate (0-8), moderately adequate (8 - 17), adequate (17 -25). The study revealed that most of the visually challenged (40%) had inadequate knowledge, and 56 percent had moderately adequate and only few (4%) had adequate knowledge in the pre-test, whereas most (68%) of them had adequate knowledge in the post-test which is statistically significant at p < 0.000 with t-value 6.779. This proves that TAF technique was effective for the visually challenged. There was no association between the demographic variables and their level of knowledge regarding first aid.
Predictors of Placement Stability at the State Level: The Use of Logistic Regression to Inform Practice

ERIC Educational Resources Information Center

Courtney, Jon R.; Prophet, Retta

2011-01-01

Placement instability is often associated with a number of negative outcomes for children. To gain state level contextual knowledge of factors associated with placement stability/instability, logistic regression was applied to selected variables from the New Mexico Adoption and Foster Care Administrative Reporting System dataset. Predictors…
A data mining approach to optimize pellets manufacturing process based on a decision tree algorithm.

PubMed

Ronowicz, Joanna; Thommes, Markus; Kleinebudde, Peter; Krysiński, Jerzy

2015-06-20

The present study is focused on the thorough analysis of cause-effect relationships between pellet formulation characteristics (pellet composition as well as process parameters) and the selected quality attribute of the final product. The shape using the aspect ratio value expressed the quality of pellets. A data matrix for chemometric analysis consisted of 224 pellet formulations performed by means of eight different active pharmaceutical ingredients and several various excipients, using different extrusion/spheronization process conditions. The data set contained 14 input variables (both formulation and process variables) and one output variable (pellet aspect ratio). A tree regression algorithm consistent with the Quality by Design concept was applied to obtain deeper understanding and knowledge of formulation and process parameters affecting the final pellet sphericity. The clear interpretable set of decision rules were generated. The spehronization speed, spheronization time, number of holes and water content of extrudate have been recognized as the key factors influencing pellet aspect ratio. The most spherical pellets were achieved by using a large number of holes during extrusion, a high spheronizer speed and longer time of spheronization. The described data mining approach enhances knowledge about pelletization process and simultaneously facilitates searching for the optimal process conditions which are necessary to achieve ideal spherical pellets, resulting in good flow characteristics. This data mining approach can be taken into consideration by industrial formulation scientists to support rational decision making in the field of pellets technology. Copyright © 2015 Elsevier B.V. All rights reserved.
Climate Change Impact Assessment of Food- and Waterborne Diseases.

PubMed

Semenza, Jan C; Herbst, Susanne; Rechenburg, Andrea; Suk, Jonathan E; Höser, Christoph; Schreiber, Christiane; Kistemann, Thomas

2012-04-01

The PubMed and ScienceDirect bibliographic databases were searched for the period of 1998-2009 to evaluate the impact of climatic and environmental determinants on food- and waterborne diseases. The authors assessed 1,642 short and concise sentences (key facts), which were extracted from 722 relevant articles and stored in a climate change knowledge base. Key facts pertaining to temperature, precipitation, water, and food for 6 selected pathogens were scrutinized, evaluated, and compiled according to exposure pathways. These key facts (corresponding to approximately 50,000 words) were mapped to 275 terminology terms identified in the literature, which generated 6,341 connections. These relationships were plotted on semantic network maps to examine the interconnections between variables. The risk of campylobacteriosis is associated with mean weekly temperatures, although this link is shown more strongly in the literature relating to salmonellosis. Irregular and severe rain events are associated with Cryptosporidium sp. outbreaks, while noncholera Vibrio sp. displays increased growth rates in coastal waters during hot summers. In contrast, for Norovirus and Listeria sp. the association with climatic variables was relatively weak, but much stronger for food determinants. Electronic data mining to assess the impact of climate change on food- and waterborne diseases assured a methodical appraisal of the field. This climate change knowledge base can support national climate change vulnerability, impact, and adaptation assessments and facilitate the management of future threats from infectious diseases. In the light of diminishing resources for public health this approach can help balance different climate change adaptation options.
Climate Change Impact Assessment of Food- and Waterborne Diseases

PubMed Central

Semenza, Jan C.; Herbst, Susanne; Rechenburg, Andrea; Suk, Jonathan E.; Höser, Christoph; Schreiber, Christiane; Kistemann, Thomas

2011-01-01

The PubMed and ScienceDirect bibliographic databases were searched for the period of 1998–2009 to evaluate the impact of climatic and environmental determinants on food- and waterborne diseases. The authors assessed 1,642 short and concise sentences (key facts), which were extracted from 722 relevant articles and stored in a climate change knowledge base. Key facts pertaining to temperature, precipitation, water, and food for 6 selected pathogens were scrutinized, evaluated, and compiled according to exposure pathways. These key facts (corresponding to approximately 50,000 words) were mapped to 275 terminology terms identified in the literature, which generated 6,341 connections. These relationships were plotted on semantic network maps to examine the interconnections between variables. The risk of campylobacteriosis is associated with mean weekly temperatures, although this link is shown more strongly in the literature relating to salmonellosis. Irregular and severe rain events are associated with Cryptosporidium sp. outbreaks, while noncholera Vibrio sp. displays increased growth rates in coastal waters during hot summers. In contrast, for Norovirus and Listeria sp. the association with climatic variables was relatively weak, but much stronger for food determinants. Electronic data mining to assess the impact of climate change on food- and waterborne diseases assured a methodical appraisal of the field. This climate change knowledge base can support national climate change vulnerability, impact, and adaptation assessments and facilitate the management of future threats from infectious diseases. In the light of diminishing resources for public health this approach can help balance different climate change adaptation options. PMID:24808720
NASA/DOD Aerospace Knowledge Diffusion Research Project. Report 6: The relationship between the use of US government technical reports by US aerospace engineers and scientists and selected institutional and sociometric variables. Ph.D. Thesis - Indiana Univ., Nov. 1990 No. 6

NASA Technical Reports Server (NTRS)

Pinelli, Thomas E.

1991-01-01

The relationship between the use of U.S. government technical reports by U.S. aerospace engineers and scientists and selected institutional and sociometric variables was investigated. The methodology used for this study was survey research. Data were collected by means of a self-administered mail questionnaire. The approximately 34,000 members of the American Institute of Aeronautics and Astronauts (AIAA) served as the study population. The response rate for the survey was 70 percent. A dependent relationship was found to exist between the use of U.S. government technical reports and three of the institutional variables (academic preparation, years of professional aerospace work experience, and technical discipline). The use of U.S. government technical reports was found to be independent of all of the sociometric variables. The institutional variables best explain the use of U.S. government technical reports by U.S. aerospace engineers and scientists.
Assessment of fine-scale resource selection and spatially explicit habitat suitability modelling for a re-introduced tiger (Panthera tigris) population in central India

PubMed Central

Sarkar, Mriganka Shekhar; Johnson, Jeyaraj A.; Sen, Subharanjan

2017-01-01

Background Large carnivores influence ecosystem functions at various scales. Thus, their local extinction is not only a species-specific conservation concern, but also reflects on the overall habitat quality and ecosystem value. Species-habitat relationships at fine scale reflect the individuals’ ability to procure resources and negotiate intraspecific competition. Such fine scale habitat choices are more pronounced in large carnivores such as tiger (Panthera tigris), which exhibits competitive exclusion in habitat and mate selection strategies. Although landscape level policies and conservation strategies are increasingly promoted for tiger conservation, specific management interventions require knowledge of the habitat correlates at fine scale. Methods We studied nine radio-collared individuals of a successfully reintroduced tiger population in Panna Tiger Reserve, central India, focussing on the species-habitat relationship at fine scales. With 16 eco-geographical variables, we performed Manly’s selection ratio and K-select analyses to define population-level and individual-level variation in resource selection, respectively. We analysed the data obtained during the exploratory period of six tigers and during the settled period of eight tigers separately, and compared the consequent results. We further used the settled period characteristics to model and map habitat suitability based on the Mahalanobis D2 method and the Boyce index. Results There was a clear difference in habitat selection by tigers between the exploratory and the settled period. During the exploratory period, tigers selected dense canopy and bamboo forests, but also spent time near villages and relocated village sites. However, settled tigers predominantly selected bamboo forests in complex terrain, riverine forests and teak-mixed forest, and totally avoided human settlements and agriculture areas. There were individual variations in habitat selection between exploratory and settled periods. Based on threshold limits of habitat selection by the Boyce Index, we established that 83% of core and 47% of buffer areas are now suitable habitats for tiger in this reserve. Discussion Tiger management often focuses on large-scale measures, but this study for the first time highlights the behaviour and fine-scale individual-specific habitat selection strategies. Such knowledge is vital for management of critical tiger habitats and specifically for the success of reintroduction programs. Our spatially explicit habitat suitability map provides a baseline for conservation planning and optimizing carrying capacity of the tiger population in this reserve. PMID:29114438
Assessment of fine-scale resource selection and spatially explicit habitat suitability modelling for a re-introduced tiger (Panthera tigris) population in central India.

PubMed

Sarkar, Mriganka Shekhar; Krishnamurthy, Ramesh; Johnson, Jeyaraj A; Sen, Subharanjan; Saha, Goutam Kumar

2017-01-01

Large carnivores influence ecosystem functions at various scales. Thus, their local extinction is not only a species-specific conservation concern, but also reflects on the overall habitat quality and ecosystem value. Species-habitat relationships at fine scale reflect the individuals' ability to procure resources and negotiate intraspecific competition. Such fine scale habitat choices are more pronounced in large carnivores such as tiger ( Panthera tigris ), which exhibits competitive exclusion in habitat and mate selection strategies. Although landscape level policies and conservation strategies are increasingly promoted for tiger conservation, specific management interventions require knowledge of the habitat correlates at fine scale. We studied nine radio-collared individuals of a successfully reintroduced tiger population in Panna Tiger Reserve, central India, focussing on the species-habitat relationship at fine scales. With 16 eco-geographical variables, we performed Manly's selection ratio and K-select analyses to define population-level and individual-level variation in resource selection, respectively. We analysed the data obtained during the exploratory period of six tigers and during the settled period of eight tigers separately, and compared the consequent results. We further used the settled period characteristics to model and map habitat suitability based on the Mahalanobis D 2 method and the Boyce index. There was a clear difference in habitat selection by tigers between the exploratory and the settled period. During the exploratory period, tigers selected dense canopy and bamboo forests, but also spent time near villages and relocated village sites. However, settled tigers predominantly selected bamboo forests in complex terrain, riverine forests and teak-mixed forest, and totally avoided human settlements and agriculture areas. There were individual variations in habitat selection between exploratory and settled periods. Based on threshold limits of habitat selection by the Boyce Index, we established that 83% of core and 47% of buffer areas are now suitable habitats for tiger in this reserve. Tiger management often focuses on large-scale measures, but this study for the first time highlights the behaviour and fine-scale individual-specific habitat selection strategies. Such knowledge is vital for management of critical tiger habitats and specifically for the success of reintroduction programs. Our spatially explicit habitat suitability map provides a baseline for conservation planning and optimizing carrying capacity of the tiger population in this reserve.
Predictors of Start of Different Antidepressants in Patient Charts among Patients with Depression

PubMed Central

Kim, Hyungjin Myra; Zivin, Kara; Choe, Hae Mi; Stano, Clare M.; Ganoczy, Dara; Walters, Heather; Valenstein, Marcia

2016-01-01

Background In usual psychiatric care, antidepressant treatments are selected based on physician and patient preferences rather than being randomly allocated, resulting in spurious associations between these treatments and outcome studies. Objectives To identify factors recorded in electronic medical chart progress notes predictive of antidepressant selection among patients who had received a depression diagnosis. Methods This retrospective study sample consisted of 556 randomly selected Veterans Health Administration (VHA) patients diagnosed with depression from April 1, 1999 to September 30, 2004, stratified by the antidepressant agent, geographic region, gender, and year of depression cohort entry. Predictors were obtained from administrative data, and additional variables were abstracted from electronic medical chart notes in the year prior to the start of the antidepressant in five categories: clinical symptoms and diagnoses, substance use, life stressors, behavioral/ideation measures (e.g., suicide attempts), and treatments received. Multinomial logistic regression analysis was used to assess the predictors associated with different antidepressant prescribing, and adjusted relative risk ratios (RRR) are reported. Results Of the administrative data-based variables, gender, age, illicit drug abuse or dependence, and number of psychiatric medications in prior year were significantly associated with antidepressant selection. After adjusting for administrative data-based variables, sleep problems (RRR = 2.47) or marital issues (RRR = 2.64) identified in the charts were significantly associated with prescribing mirtazapine rather than sertraline; however, no other chart-based variables showed a significant association or an association with a large magnitude. Conclusion Some chart data-based variables were predictive of antidepressant selection, but we neither found many nor found them highly predictive of antidepressant selection in patients treated for depression. PMID:25943003
Using a knowledge-based planning solution to select patients for proton therapy.

PubMed

Delaney, Alexander R; Dahele, Max; Tol, Jim P; Kuijper, Ingrid T; Slotman, Ben J; Verbakel, Wilko F A R

2017-08-01

Patient selection for proton therapy by comparing proton/photon treatment plans is time-consuming and prone to bias. RapidPlan™, a knowledge-based-planning solution, uses plan-libraries to model and predict organ-at-risk (OAR) dose-volume-histograms (DVHs). We investigated whether RapidPlan, utilizing an algorithm based only on photon beam characteristics, could generate proton DVH-predictions and whether these could correctly identify patients for proton therapy. Model PROT and Model PHOT comprised 30 head-and-neck cancer proton and photon plans, respectively. Proton and photon knowledge-based-plans (KBPs) were made for ten evaluation-patients. DVH-prediction accuracy was analyzed by comparing predicted-vs-achieved mean OAR doses. KBPs and manual plans were compared using salivary gland and swallowing muscle mean doses. For illustration, patients were selected for protons if predicted Model PHOT mean dose minus predicted Model PROT mean dose (ΔPrediction) for combined OARs was ≥6Gy, and benchmarked using achieved KBP doses. Achieved and predicted Model PROT /Model PHOT mean dose R 2 was 0.95/0.98. Generally, achieved mean dose for Model PHOT /Model PROT KBPs was respectively lower/higher than predicted. Comparing Model PROT /Model PHOT KBPs with manual plans, salivary and swallowing mean doses increased/decreased by <2Gy, on average. ΔPrediction≥6Gy correctly selected 4 of 5 patients for protons. Knowledge-based DVH-predictions can provide efficient, patient-specific selection for protons. A proton-specific RapidPlan-solution could improve results. Copyright © 2017 Elsevier B.V. All rights reserved.
Development of a Spacecraft Materials Selector Expert System

NASA Technical Reports Server (NTRS)

Pippin, G.; Kauffman, W. (Technical Monitor)

2002-01-01

This report contains a description of the knowledge base tool and examples of its use. A downloadable version of the Spacecraft Materials Selector (SMS) knowledge base is available through the NASA Space Environments and Effects Program. The "Spacecraft Materials Selector" knowledge base is part of an electronic expert system. The expert system consists of an inference engine that contains the "decision-making" code and the knowledge base that contains the selected body of information. The inference engine is a software package previously developed at Boeing, called the Boeing Expert System Tool (BEST) kit.

Comparison of Feature Selection Techniques in Machine Learning for Anatomical Brain MRI in Dementia.

PubMed

Tohka, Jussi; Moradi, Elaheh; Huttunen, Heikki

2016-07-01

We present a comparative split-half resampling analysis of various data driven feature selection and classification methods for the whole brain voxel-based classification analysis of anatomical magnetic resonance images. We compared support vector machines (SVMs), with or without filter based feature selection, several embedded feature selection methods and stability selection. While comparisons of the accuracy of various classification methods have been reported previously, the variability of the out-of-training sample classification accuracy and the set of selected features due to independent training and test sets have not been previously addressed in a brain imaging context. We studied two classification problems: 1) Alzheimer's disease (AD) vs. normal control (NC) and 2) mild cognitive impairment (MCI) vs. NC classification. In AD vs. NC classification, the variability in the test accuracy due to the subject sample did not vary between different methods and exceeded the variability due to different classifiers. In MCI vs. NC classification, particularly with a large training set, embedded feature selection methods outperformed SVM-based ones with the difference in the test accuracy exceeding the test accuracy variability due to the subject sample. The filter and embedded methods produced divergent feature patterns for MCI vs. NC classification that suggests the utility of the embedded feature selection for this problem when linked with the good generalization performance. The stability of the feature sets was strongly correlated with the number of features selected, weakly correlated with the stability of classification accuracy, and uncorrelated with the average classification accuracy.
A family of variable step-size affine projection adaptive filter algorithms using statistics of channel impulse response

NASA Astrophysics Data System (ADS)

Shams Esfand Abadi, Mohammad; AbbasZadeh Arani, Seyed Ali Asghar

2011-12-01

This paper extends the recently introduced variable step-size (VSS) approach to the family of adaptive filter algorithms. This method uses prior knowledge of the channel impulse response statistic. Accordingly, optimal step-size vector is obtained by minimizing the mean-square deviation (MSD). The presented algorithms are the VSS affine projection algorithm (VSS-APA), the VSS selective partial update NLMS (VSS-SPU-NLMS), the VSS-SPU-APA, and the VSS selective regressor APA (VSS-SR-APA). In VSS-SPU adaptive algorithms the filter coefficients are partially updated which reduce the computational complexity. In VSS-SR-APA, the optimal selection of input regressors is performed during the adaptation. The presented algorithms have good convergence speed, low steady state mean square error (MSE), and low computational complexity features. We demonstrate the good performance of the proposed algorithms through several simulations in system identification scenario.
Investigating Knowledge Management Status among Faculty Members of Kerman University of Medical Sciences based on the Nonaka Model in 2015

PubMed Central

Vali, Leila; Izadi, Azar; Jahani, Yunes; Okhovati, Maryam

2016-01-01

Introduction Education and research are two major functions of universities, which require proper and systematic exploitation of available knowledge and information. Therefore, it is necessary to investigate the knowledge management status in an education system by considering the function of faculty members in creation and dissemination of knowledge. This study was conducted to investigate the knowledge management status among faculty members of the Kerman University of Medical Sciences based on the Nonaka and Takeuchi models in 2015. Methods This was a descriptive-analytical and cross-sectional study. It was conducted on 165 faculty members at the Kerman University of Medical Sciences, who were selected from seven faculties as weighted using a random stratified sampling method. The Nonaka and Takeuchi knowledge management questionnaire consists of 26 questions in four dimensions of socialization, externalization, internalization, and combination. Scoring of questions was conducted using the five-point Likert scale. To analyze data, independent t-test, one-way ANOVA, Pearson correlation coefficients, and the Kruskal-Wallis test were employed. Results The four dimensions in the Nonaka and Takeuchi model are based on optimal indicators (3.5), dimensions of combination, and externalization with an average of 3.3 were found in higher ranks and internalization and socialization had averages of 3.1 and 3. According to the findings of this study, the average knowledge management among faculty members of the Kerman University of Medical Sciences was estimated to be 3.1, with a bit difference compared to the average. According to the results of t-tests, there was no significant relationship between gender and various dimensions of knowledge management (p>0.05). The findings of Kruskal-Wallis showed that there is no significant relationship between variables of age, academic rank, and type of faculty with regard to dimensions of knowledge management (p>0.05). In addition, according to the results of Pearson tests, there is no significant relation between employment history and dimensions of knowledge management (p>0.05). Conclusion Considering the function and importance of knowledge management in education and research organizations including universities, it is recommended to pay comprehensive attention to establishment of knowledge management and knowledge sharing in universities and provide the required background to from research teams and communication networks inside and outside universities. PMID:27757183
Investigating Knowledge Management Status among Faculty Members of Kerman University of Medical Sciences based on the Nonaka Model in 2015.

PubMed

Vali, Leila; Izadi, Azar; Jahani, Yunes; Okhovati, Maryam

2016-08-01

Education and research are two major functions of universities, which require proper and systematic exploitation of available knowledge and information. Therefore, it is necessary to investigate the knowledge management status in an education system by considering the function of faculty members in creation and dissemination of knowledge. This study was conducted to investigate the knowledge management status among faculty members of the Kerman University of Medical Sciences based on the Nonaka and Takeuchi models in 2015. This was a descriptive-analytical and cross-sectional study. It was conducted on 165 faculty members at the Kerman University of Medical Sciences, who were selected from seven faculties as weighted using a random stratified sampling method. The Nonaka and Takeuchi knowledge management questionnaire consists of 26 questions in four dimensions of socialization, externalization, internalization, and combination. Scoring of questions was conducted using the five-point Likert scale. To analyze data, independent t-test, one-way ANOVA, Pearson correlation coefficients, and the Kruskal-Wallis test were employed. The four dimensions in the Nonaka and Takeuchi model are based on optimal indicators (3.5), dimensions of combination, and externalization with an average of 3.3 were found in higher ranks and internalization and socialization had averages of 3.1 and 3. According to the findings of this study, the average knowledge management among faculty members of the Kerman University of Medical Sciences was estimated to be 3.1, with a bit difference compared to the average. According to the results of t-tests, there was no significant relationship between gender and various dimensions of knowledge management (p>0.05). The findings of Kruskal-Wallis showed that there is no significant relationship between variables of age, academic rank, and type of faculty with regard to dimensions of knowledge management (p>0.05). In addition, according to the results of Pearson tests, there is no significant relation between employment history and dimensions of knowledge management (p>0.05). Considering the function and importance of knowledge management in education and research organizations including universities, it is recommended to pay comprehensive attention to establishment of knowledge management and knowledge sharing in universities and provide the required background to from research teams and communication networks inside and outside universities.
Socio-economic factors explain differences in public health-related variables among women in Bangladesh: a cross-sectional study.

PubMed

Khan, Md Mobarak H; Kraemer, Alexander

2008-07-23

Worldwide one billion people are living in slum communities and experts projected that this number would double by 2030. Slum populations, which are increasing at an alarming rate in Bangladesh mainly due to rural-urban migration, are often neglected and characterized by poverty, poor housing, overcrowding, poor environment, and high prevalence of communicable diseases. Unfortunately, comparisons between women living in slums and those not living in slums are very limited in Bangladesh. The objectives of the study were to examine the association of living in slums (dichotomized as slum versus non-slum) with selected public health-related variables among women, first without adjusting for the influence of other factors and then in the presence of socio-economic variables. Secondary data was used in this study. 120 women living in slums (as cases) and 480 age-matched women living in other areas (as controls) were extracted from the Bangladesh Demographic and Health Survey 2004. Many socio-economic and demographic variables were analysed. SPSS was used to perform simple as well as multiple analyses. P-values based on t-test and Wald test were also reported to show the significance level. Unadjusted results indicated that a significantly higher percent of women living in slums came from country side, had a poorer status by household characteristics, had less access to mass media, and had less education than women not living in slums. Mean BMI, knowledge of AIDS indicated by ever heard about AIDS, knowledge of avoiding AIDS by condom use, receiving adequate antenatal visits (4 or more) during the last pregnancy, and safe delivery practices assisted by skilled sources were significantly lower among women living in slums than those women living in other areas. However, all the unadjusted significant associations with the variable slum were greatly attenuated and became insignificant (expect safe delivery practices) when some socio-economic variables namely childhood place of residence, a composite variable of household characteristics, a composite variable of mass media access, and education were inserted into the multiple regression models. Taken together, childhood place of residence, the composite variable of mass media access, and education were the strongest predictors for the health related outcomes. Reporting unadjusted findings of public health variables in women from slums versus non-slums can be misleading due to confounding factors. Our findings suggest that an association of childhood place of residence, mass media access and public health education should be considered before making any inference based on slum versus non-slum comparisons.
Sampling in ecology and evolution - bridging the gap between theory and practice

USGS Publications Warehouse

Albert, C.H.; Yoccoz, N.G.; Edwards, T.C.; Graham, C.H.; Zimmermann, N.E.; Thuiller, W.

2010-01-01

Sampling is a key issue for answering most ecological and evolutionary questions. The importance of developing a rigorous sampling design tailored to specific questions has already been discussed in the ecological and sampling literature and has provided useful tools and recommendations to sample and analyse ecological data. However, sampling issues are often difficult to overcome in ecological studies due to apparent inconsistencies between theory and practice, often leading to the implementation of simplified sampling designs that suffer from unknown biases. Moreover, we believe that classical sampling principles which are based on estimation of means and variances are insufficient to fully address many ecological questions that rely on estimating relationships between a response and a set of predictor variables over time and space. Our objective is thus to highlight the importance of selecting an appropriate sampling space and an appropriate sampling design. We also emphasize the importance of using prior knowledge of the study system to estimate models or complex parameters and thus better understand ecological patterns and processes generating these patterns. Using a semi-virtual simulation study as an illustration we reveal how the selection of the space (e.g. geographic, climatic), in which the sampling is designed, influences the patterns that can be ultimately detected. We also demonstrate the inefficiency of common sampling designs to reveal response curves between ecological variables and climatic gradients. Further, we show that response-surface methodology, which has rarely been used in ecology, is much more efficient than more traditional methods. Finally, we discuss the use of prior knowledge, simulation studies and model-based designs in defining appropriate sampling designs. We conclude by a call for development of methods to unbiasedly estimate nonlinear ecologically relevant parameters, in order to make inferences while fulfilling requirements of both sampling theory and field work logistics. ?? 2010 The Authors.
Dress Nicer = Know More? Young Children’s Knowledge Attribution and Selective Learning Based on How Others Dress

PubMed Central

McDonald, Kyla P.; Ma, Lili

2015-01-01

This research explored whether children judge the knowledge state of others and selectively learn novel information from them based on how they dress. The results indicated that 4- and 6-year-olds identified a formally dressed individual as more knowledgeable about new things in general than a casually dressed one (Study 1). Moreover, children displayed an overall preference to seek help from a formally dressed individual rather than a casually dressed one when learning about novel objects and animals (Study 2). These findings are discussed in relation to the halo effect, and may have important implications for child educators regarding how instructor dress might influence young students’ knowledge attribution and learning preferences. PMID:26636980
Adult Bronchoscopy Training

PubMed Central

Wahidi, Momen M.; Read, Charles A.; Buckley, John D.; Addrizzo-Harris, Doreen J.; Shah, Pallav L.; Herth, Felix J. F.; de Hoyos Parra, Alberto; Ornelas, Joseph; Yarmus, Lonny; Silvestri, Gerard A.

2015-01-01

BACKGROUND: The determination of competency of trainees in programs performing bronchoscopy is quite variable. Some programs provide didactic lectures with hands-on supervision, other programs incorporate advanced simulation centers, whereas others have a checklist approach. Although no single method has been proven best, the variability alone suggests that outcomes are variable. Program directors and certifying bodies need guidance to create standards for training programs. Little well-developed literature on the topic exists. METHODS: To provide credible and trustworthy guidance, rigorous methodology has been applied to create this bronchoscopy consensus training statement. All panelists were vetted and approved by the CHEST Guidelines Oversight Committee. Each topic group drafted questions in a PICO (population, intervention, comparator, outcome) format. MEDLINE data through PubMed and the Cochrane Library were systematically searched. Manual searches also supplemented the searches. All gathered references were screened for consideration based on inclusion criteria, and all statements were designated as an Ungraded Consensus-Based Statement. RESULTS: We suggest that professional societies move from a volume-based certification system to skill acquisition and knowledge-based competency assessment for trainees. Bronchoscopy training programs should incorporate multiple tools, including simulation. We suggest that ongoing quality and process improvement systems be introduced and that certifying agencies move from a volume-based certification system to skill acquisition and knowledge-based competency assessment for trainees. We also suggest that assessment of skill maintenance and improvement in practice be evaluated regularly with ongoing quality and process improvement systems after initial skill acquisition. CONCLUSIONS: The current methods used for bronchoscopy competency in training programs are variable. We suggest that professional societies and certifying agencies move from a volume- based certification system to a standardized skill acquisition and knowledge-based competency assessment for pulmonary and thoracic surgery trainees. PMID:25674901
Symbolic dynamics marker of heart rate variability combined with clinical variables enhance obstructive sleep apnea screening

NASA Astrophysics Data System (ADS)

Ravelo-García, A. G.; Saavedra-Santana, P.; Juliá-Serdá, G.; Navarro-Mesa, J. L.; Navarro-Esteva, J.; Álvarez-López, X.; Gapelyuk, A.; Penzel, T.; Wessel, N.

2014-06-01

Many sleep centres try to perform a reduced portable test in order to decrease the number of overnight polysomnographies that are expensive, time-consuming, and disturbing. With some limitations, heart rate variability (HRV) has been useful in this task. The aim of this investigation was to evaluate if inclusion of symbolic dynamics variables to a logistic regression model integrating clinical and physical variables, can improve the detection of subjects for further polysomnographies. To our knowledge, this is the first contribution that innovates in that strategy. A group of 133 patients has been referred to the sleep center for suspected sleep apnea. Clinical assessment of the patients consisted of a sleep related questionnaire and a physical examination. The clinical variables related to apnea and selected in the statistical model were age (p < 10-3), neck circumference (p < 10-3), score on a questionnaire scale intended to quantify daytime sleepiness (p < 10-3), and intensity of snoring (p < 10-3). The validation of this model demonstrated an increase in classification performance when a variable based on non-linear dynamics of HRV (p < 0.01) was used additionally to the other variables. For diagnostic rule based only on clinical and physical variables, the corresponding area under the receiver operating characteristic (ROC) curve was 0.907 (95% confidence interval (CI) = 0.848, 0.967), (sensitivity 87.10% and specificity 80%). For the model including the average of a symbolic dynamic variable, the area under the ROC curve was increased to 0.941 (95% = 0.897, 0.985), (sensitivity 88.71% and specificity 82.86%). In conclusion, symbolic dynamics, coupled with significant clinical and physical variables can help to prioritize polysomnographies in patients with a high probability of apnea. In addition, the processing of the HRV is a well established low cost and robust technique.
Reconciling resource utilization and resource selection functions

USGS Publications Warehouse

Hooten, Mevin B.; Hanks, Ephraim M.; Johnson, Devin S.; Alldredge, Mat W.

2013-01-01

Summary: 1. Analyses based on utilization distributions (UDs) have been ubiquitous in animal space use studies, largely because they are computationally straightforward and relatively easy to employ. Conventional applications of resource utilization functions (RUFs) suggest that estimates of UDs can be used as response variables in a regression involving spatial covariates of interest. 2. It has been claimed that contemporary implementations of RUFs can yield inference about resource selection, although to our knowledge, an explicit connection has not been described. 3. We explore the relationships between RUFs and resource selection functions from a hueristic and simulation perspective. We investigate several sources of potential bias in the estimation of resource selection coefficients using RUFs (e.g. the spatial covariance modelling that is often used in RUF analyses). 4. Our findings illustrate that RUFs can, in fact, serve as approximations to RSFs and are capable of providing inference about resource selection, but only with some modification and under specific circumstances. 5. Using real telemetry data as an example, we provide guidance on which methods for estimating resource selection may be more appropriate and in which situations. In general, if telemetry data are assumed to arise as a point process, then RSF methods may be preferable to RUFs; however, modified RUFs may provide less biased parameter estimates when the data are subject to location error.
Knowledge, attitude and practice for cervical cancer prevention and control among women of childbearing age in Hossana Town, Hadiya zone, Southern Ethiopia: Community-based cross-sectional study.

PubMed

Aweke, Yitagesu Habtu; Ayanto, Samuel Yohannes; Ersado, Tariku Laelago

2017-01-01

Cervical cancer is the second most common female cancer which Ethiopia put a strategic goal to reduce its incidence and mortality by 2020. Lack of knowledge and poor attitude towards the disease and risk factors can affect screening practice and development of preventive behavior for cervical cancer. The aim of this study was to assess knowledge, attitude, practices and factors for each domain for cervical cancer among women of child bearing age in Hossana town, Southern, Ethiopia. Community based cross sectional study was carried out in June 2015. A total of 583 participants were selected using systematic random sampling technique. Pretested structured interviewer administered questionnaire was used to gather the data. Data were entered in to Epi Info software version 3.5.4 and exported to SPSS version 16 for descriptive and logistic regression analysis. Two hundred seventy (46.3%) of the respondents had poor comprehensive knowledge. Only 58 (9.9%) of participants had been screed for the cervical cancer before the survey. Two hundred three (34.8%) of participants had negative attitude towards selected proxy variables. Not having health seeking behavior for cervical cancer [AOR: 5.45, 95% CI: (1.18, 30.58), P <0.031], had not ever received information about cervical cancer and its prevention [AOR: 2.63, 95%CI: (1.78,8.84), P < 0.018] and not actively seeking health information about cervical cancer [AOR: 6.25, (95%CI: (1.26, 31.06) P < 0.025] were significantly associated factors with poor knowledge. Poor knowledge score was associated with poor attitude [AOR: 56.51, 95%CI: (23.76, 134.37), P <0.001]. Had not ever received information about the disease from any source [AOR: 45.24, (95%CI: (11.47, 178.54), P <0.001] was significantly associated factor with not to be screened for the disease. This study highlighted the importance of awareness creation, increasing knowledge, promoting active searching for health information and experiences of receiving information from any information sources regarding cervical cancer. Therefore, it will be essential to integrate cervical cancer prevention strategies with other reproductive health services at all level of health care delivery system.
Knowledge, attitude and practice for cervical cancer prevention and control among women of childbearing age in Hossana Town, Hadiya zone, Southern Ethiopia: Community-based cross-sectional study

PubMed Central

Ersado, Tariku Laelago

2017-01-01

Background Cervical cancer is the second most common female cancer which Ethiopia put a strategic goal to reduce its incidence and mortality by 2020. Lack of knowledge and poor attitude towards the disease and risk factors can affect screening practice and development of preventive behavior for cervical cancer. The aim of this study was to assess knowledge, attitude, practices and factors for each domain for cervical cancer among women of child bearing age in Hossana town, Southern, Ethiopia. Methods Community based cross sectional study was carried out in June 2015. A total of 583 participants were selected using systematic random sampling technique. Pretested structured interviewer administered questionnaire was used to gather the data. Data were entered in to Epi Info software version 3.5.4 and exported to SPSS version 16 for descriptive and logistic regression analysis. Results Two hundred seventy (46.3%) of the respondents had poor comprehensive knowledge. Only 58 (9.9%) of participants had been screed for the cervical cancer before the survey. Two hundred three (34.8%) of participants had negative attitude towards selected proxy variables. Not having health seeking behavior for cervical cancer [AOR: 5.45, 95% CI: (1.18, 30.58), P <0.031], had not ever received information about cervical cancer and its prevention [AOR: 2.63, 95%CI: (1.78,8.84), P < 0.018] and not actively seeking health information about cervical cancer [AOR: 6.25, (95%CI: (1.26, 31.06) P < 0.025] were significantly associated factors with poor knowledge. Poor knowledge score was associated with poor attitude [AOR: 56.51, 95%CI: (23.76, 134.37), P <0.001]. Had not ever received information about the disease from any source [AOR: 45.24, (95%CI: (11.47, 178.54), P <0.001] was significantly associated factor with not to be screened for the disease. Conclusion This study highlighted the importance of awareness creation, increasing knowledge, promoting active searching for health information and experiences of receiving information from any information sources regarding cervical cancer. Therefore, it will be essential to integrate cervical cancer prevention strategies with other reproductive health services at all level of health care delivery system. PMID:28742851
Refining Automatically Extracted Knowledge Bases Using Crowdsourcing

PubMed Central

Xian, Xuefeng; Cui, Zhiming

2017-01-01

Machine-constructed knowledge bases often contain noisy and inaccurate facts. There exists significant work in developing automated algorithms for knowledge base refinement. Automated approaches improve the quality of knowledge bases but are far from perfect. In this paper, we leverage crowdsourcing to improve the quality of automatically extracted knowledge bases. As human labelling is costly, an important research challenge is how we can use limited human resources to maximize the quality improvement for a knowledge base. To address this problem, we first introduce a concept of semantic constraints that can be used to detect potential errors and do inference among candidate facts. Then, based on semantic constraints, we propose rank-based and graph-based algorithms for crowdsourced knowledge refining, which judiciously select the most beneficial candidate facts to conduct crowdsourcing and prune unnecessary questions. Our experiments show that our method improves the quality of knowledge bases significantly and outperforms state-of-the-art automatic methods under a reasonable crowdsourcing cost. PMID:28588611
Socioeconomic factors and effect of evidence-based patient information about primary prevention of type 2 diabetes mellitus--are there interactions?

PubMed

Genz, Jutta; Haastert, Burkhard; Müller, Hardy; Verheyen, Frank; Cole, Dennis; Rathmann, Wolfgang; Nowotny, Bettina; Roden, Michael; Giani, Guido; Ohmann, Christian; Icks, Andrea

2014-08-18

Having shown in a recent randomized controlled trial that evidence-based patient information (EBPI) significantly increased knowledge on primary prevention of diabetes compared to standard patient information, we now investigated interaction between socioeconomic status (SES) and the effect of an EBPI. 1,120 visitors (aged 40-70 years, without known diabetes) to the "Techniker Krankenkasse" and the "German Diabetes Center" websites were randomized. The intervention group received a newly developed on-line EBPI, the control group standard on-line information. The primary outcome measure was knowledge, classified as "good/average/poor". We analyzed associations of knowledge with socioeconomic variables (education, vocational training, employment, subjective social status) combined with intervention effect including interactions, adjusted for possible confounding by knowledge before intervention, self-reported blood glucose measurements, blood pressure, blood lipid levels, age and gender. Logistic regression models were fitted to the subpopulation (n = 647) with complete values in these variables.Education (high vs. low) was significantly associated with knowledge (good vs. average/poor); however, there was no significant interaction between education and intervention. After adjustment, the other socioeconomic variables were not significantly associated with knowledge. Socioeconomic variables did not significantly change the effect of the intervention. There was a tendency towards a lower effect where lower educated individuals were concerned. Possibly the power was too low to detect interaction effects. Larger studies using SES-specific designs are needed to clarify the effect of SES. We suggest considering the socioeconomic status when evaluating a decision aid, e.g. an EBPI, to ensure its effectiveness not only in higher socioeconomic groups. Current Controlled Trials ISRCTN22060616 (Date assigned: 12 September 2008).
Kindergarten predictors of second versus eighth grade reading comprehension impairments.

PubMed

Adlof, Suzanne M; Catts, Hugh W; Lee, Jaehoon

2010-01-01

Multiple studies have shown that kindergarten measures of phonological awareness and alphabet knowledge are good predictors of reading achievement in the primary grades. However, less attention has been given to the early predictors of later reading achievement. This study used a modified best-subsets variable-selection technique to examine kindergarten predictors of early versus later reading comprehension impairments. Participants included 433 children involved in a longitudinal study of language and reading development. The kindergarten test battery assessed various language skills in addition to phonological awareness, alphabet knowledge, naming speed, and nonverbal cognitive ability. Reading comprehension was assessed in second and eighth grades. Results indicated that different combinations of variables were required to optimally predict second versus eighth grade reading impairments. Although some variables effectively predicted reading impairments in both grades, their relative contributions shifted over time. These results are discussed in light of the changing nature of reading comprehension over time. Further research will help to improve the early identification of later reading disabilities.
Can climate variability information constrain a hydrological model for an ungauged Costa Rican catchment?

NASA Astrophysics Data System (ADS)

Quesada-Montano, Beatriz; Westerberg, Ida K.; Fuentes-Andino, Diana; Hidalgo-Leon, Hugo; Halldin, Sven

2017-04-01

Long-term hydrological data are key to understanding catchment behaviour and for decision making within water management and planning. Given the lack of observed data in many regions worldwide, hydrological models are an alternative for reproducing historical streamflow series. Additional types of information - to locally observed discharge - can be used to constrain model parameter uncertainty for ungauged catchments. Climate variability exerts a strong influence on streamflow variability on long and short time scales, in particular in the Central-American region. We therefore explored the use of climate variability knowledge to constrain the simulated discharge uncertainty of a conceptual hydrological model applied to a Costa Rican catchment, assumed to be ungauged. To reduce model uncertainty we first rejected parameter relationships that disagreed with our understanding of the system. We then assessed how well climate-based constraints applied at long-term, inter-annual and intra-annual time scales could constrain model uncertainty. Finally, we compared the climate-based constraints to a constraint on low-flow statistics based on information obtained from global maps. We evaluated our method in terms of the ability of the model to reproduce the observed hydrograph and the active catchment processes in terms of two efficiency measures, a statistical consistency measure, a spread measure and 17 hydrological signatures. We found that climate variability knowledge was useful for reducing model uncertainty, in particular, unrealistic representation of deep groundwater processes. The constraints based on global maps of low-flow statistics provided more constraining information than those based on climate variability, but the latter rejected slow rainfall-runoff representations that the low flow statistics did not reject. The use of such knowledge, together with information on low-flow statistics and constraints on parameter relationships showed to be useful to constrain model uncertainty for an - assumed to be - ungauged basin. This shows that our method is promising for reconstructing long-term flow data for ungauged catchments on the Pacific side of Central America, and that similar methods can be developed for ungauged basins in other regions where climate variability exerts a strong control on streamflow variability.
Quantifying Variability of Avian Colours: Are Signalling Traits More Variable?

PubMed Central

Delhey, Kaspar; Peters, Anne

2008-01-01

Background Increased variability in sexually selected ornaments, a key assumption of evolutionary theory, is thought to be maintained through condition-dependence. Condition-dependent handicap models of sexual selection predict that (a) sexually selected traits show amplified variability compared to equivalent non-sexually selected traits, and since males are usually the sexually selected sex, that (b) males are more variable than females, and (c) sexually dimorphic traits more variable than monomorphic ones. So far these predictions have only been tested for metric traits. Surprisingly, they have not been examined for bright coloration, one of the most prominent sexual traits. This omission stems from computational difficulties: different types of colours are quantified on different scales precluding the use of coefficients of variation. Methodology/Principal Findings Based on physiological models of avian colour vision we develop an index to quantify the degree of discriminable colour variation as it can be perceived by conspecifics. A comparison of variability in ornamental and non-ornamental colours in six bird species confirmed (a) that those coloured patches that are sexually selected or act as indicators of quality show increased chromatic variability. However, we found no support for (b) that males generally show higher levels of variability than females, or (c) that sexual dichromatism per se is associated with increased variability. Conclusions/Significance We show that it is currently possible to realistically estimate variability of animal colours as perceived by them, something difficult to achieve with other traits. Increased variability of known sexually-selected/quality-indicating colours in the studied species, provides support to the predictions borne from sexual selection theory but the lack of increased overall variability in males or dimorphic colours in general indicates that sexual differences might not always be shaped by similar selective forces. PMID:18301766
Knowledge of sickle cell disease and haemoglobin electrophoresis: a survey of students of a tertiary institution.

PubMed

Alao, O O; Araoye, M; Ojabo, C

2009-01-01

Sickle Cell Disease (SCD) is the commonest genetic disease worldwide. Of the sickle cell control strategies, premarital genetic counselling is increasingly practised in many countries of the world. Knowledge of the citizenry of a nation about SCD constitutes an important variable that influences the acceptability, practice and success of premarital genetic counselling. A study of students of Benue State University, Makurdi was carried out to determine their current level of knowledge. A cross sectional study involving 300 students of Benue State University, Makurdi; selected by a multistage stratified sampling technique, using self administered structured questionnaire, was carried out. Virtually all study respondents had at one time or the other heard about sickle cell disease. Based on the criteria used for knowledge scoring, less than half of the students (48%) demonstrated good knowledge. Overall Mean Score Knowledge (MSK) was 4.65 +/- 1.65. MSK was 4.58 +/- 1.66 and 4.74 +/- 1.64 for males and females respectively; there was no statistically significant difference (P > 0.05). However, having an affected relative suffering from sickle cell disease significantly influenced level of knowledge among study respondents (P < 0.05), but was not significantly associated with respondents knowing their haemoglobin phenotype. Only 141 students (47%) knew their haemoglobin phenotype. Level of knowledge about SCD did not significantly increase with age. Also, sex and religion did not significantly influence level of knowledge. The knowledge about SCD was poor and only a few knew their haemoglobin phenotype. If sickle cell disease control strategies must yield any significant results, more education about SCD, especially among students in tertiary institutions in Nigeria is recommended. The use of persons with SCD as peer educators/counsellors should be explored.
Expert and Knowledge Based Systems.

ERIC Educational Resources Information Center

Demaid, Adrian; Edwards, Lyndon

1987-01-01

Discusses the nature and current state of knowledge-based systems and expert systems. Describes an expert system from the viewpoints of a computer programmer and an applications expert. Addresses concerns related to materials selection and forecasts future developments in the teaching of materials engineering. (ML)
Ada as an implementation language for knowledge based systems

NASA Technical Reports Server (NTRS)

Rochowiak, Daniel

1990-01-01

Debates about the selection of programming languages often produce cultural collisions that are not easily resolved. This is especially true in the case of Ada and knowledge based programming. The construction of programming tools provides a desirable alternative for resolving the conflict.

System and method for knowledge based matching of users in a network

DOEpatents

Verspoor, Cornelia Maria [Santa Fe, NM; Sims, Benjamin Hayden [Los Alamos, NM; Ambrosiano, John Joseph [Los Alamos, NM; Cleland, Timothy James [Los Alamos, NM

2011-04-26

A knowledge-based system and methods to matchmaking and social network extension are disclosed. The system is configured to allow users to specify knowledge profiles, which are collections of concepts that indicate a certain topic or area of interest selected from an. The system utilizes the knowledge model as the semantic space within which to compare similarities in user interests. The knowledge model is hierarchical so that indications of interest in specific concepts automatically imply interest in more general concept. Similarity measures between profiles may then be calculated based on suitable distance formulas within this space.
Effect of Selected Variables on Funding State Compensatory and Regular Education in Texas

ERIC Educational Resources Information Center

Wiesman, Karen Wheeler

2009-01-01

Funding public schools has been an ongoing struggle since the inception of the United States. Beginning with Jefferson's "A General Diffusion of Knowledge" that charged the states with properly funding public schools, to the current day legal battles that continue in states across the Union, America struggles with finding a solution to…
The Effects of an Environmental Studies Course on Selected Variables Related To Environmentally Responsible Behavior.

ERIC Educational Resources Information Center

Smith-Sebasto, N. J.

1995-01-01

Reports that students completing an environmental studies course displayed significant gains when compared with students not completing such a course. These gains were made in acquiring a more internally-oriented locus of control of reinforcement for environmentally responsible behavior, a higher perception of their knowledge of and skill in using…
The Effects of Feedback and Selected Personality Variables on Aesthetic Judgment.

ERIC Educational Resources Information Center

West, Charles K.; And Others

This study is an attempt to investigate the extent of which knowledge of results in various forms (true, none, and false) may modify aesthetic judgment. Seventy-two graduate students were administered an aesthetic judgment test of fifty items. On half of the test, twenty-four subjects received correct feedback and twenty-four received false…
The Impact of Selected Educational Factors on the Academic Achievement of Secondary Students

ERIC Educational Resources Information Center

Epps, Bernethia Mechelle

2010-01-01

The purpose of this study was to examine the impact of related educational factors on the mathematics and science achievement of secondary students. The researcher compared the variables of instructional design, economic status and retention against the exit level scores on the mathematics and science Texas Assessment of Knowledge and Skills…
Student reactions to problem-based learning in photonics technician education

NASA Astrophysics Data System (ADS)

Massa, Nicholas M.; Donnelly, Judith; Hanes, Fenna

2014-07-01

Problem-based learning (PBL) is an instructional approach in which students learn problem-solving and teamwork skills by collaboratively solving complex real-world problems. Research shows that PBL improves student knowledge and retention, motivation, problem-solving skills, and the ability to skillfully apply knowledge in new and novel situations. One of the challenges faced by students accustomed to traditional didactic methods, however, is acclimating to the PBL process in which problem parameters are often ill-defined and ambiguous, often leading to frustration and disengagement with the learning process. To address this problem, the New England Board of Higher Education (NEBHE), funded by the National Science Foundation Advanced Technological Education (NSF-ATE) program, has created and field tested a comprehensive series of industry-based multimedia PBL "Challenges" designed to scaffold the development of students' problem solving and critical thinking skills. In this paper, we present the results of a pilot study conducted to examine student reactions to the PBL Challenges in photonics technician education. During the fall 2012 semester, students (n=12) in two associate degree level photonics courses engaged in PBL using the PBL Challenges. Qualitative and quantitative methods were used to assess student motivation, self-efficacy, critical thinking, metacognitive self-regulation, and peer learning using selected scales from the Motivated Strategies for Learning Questionnaire (MSLQ). Results showed positive gains in all variables. Follow-up focus group interviews yielded positive themes supporting the effectiveness of PBL in developing the knowledge, skills and attitudes of photonics technicians.
A Theory of the Measurement of Knowledge Content, Access, and Learning.

ERIC Educational Resources Information Center

Pirolli, Peter; Wilson, Mark

1998-01-01

An approach to the measurement of knowledge content, knowledge access, and knowledge learning is developed. First a theoretical view of cognition is described, and then a class of measurement models, based on Rasch modeling, is presented. Knowledge access and content are viewed as determining the observable actions selected by an agent to achieve…
The relationship between knowledge of leadership and knowledge management practices in the food industry in Kurdistan province, Iran.

PubMed

Jad, Seyyed Mohammad Moosavi; Geravandi, Sahar; Mohammadi, Mohammad Javad; Alizadeh, Rashin; Sarvarian, Mohammad; Rastegarimehr, Babak; Afkar, Abolhasan; Yari, Ahmad Reza; Momtazan, Mahboobeh; Valipour, Aliasghar; Mahboubi, Mohammad; Karimyan, Azimeh; Mazraehkar, Alireza; Nejad, Ali Soleimani; Mohammadi, Hafez

2017-12-01

The aim of this study was to identify the relationship between the knowledge of leadership and knowledge management practices. This research strategy, in terms of quantity, procedure and obtain information, is descriptive and correlational. Statistical population, consist of all employees of a food industry in Kurdistan province of Iran, who were engaged in 2016 and their total number is about 1800 people. 316 employees in the Kurdistan food industry (Kurdistan FI) were selected, using Cochran formula. Non-random method and valid questions (standard) for measurement of the data are used. Reliability and validity were confirmed. Statistical analysis of the data was carried out, using SPSS 16. The statistical analysis of collected data showed the relationship between knowledge-oriented of leadership and knowledge management activities as mediator variables. The results of the data and test hypotheses suggest that knowledge management activities play an important role in the functioning of product innovation and the results showed that the activities of Knowledge Management (knowledge transfer, storage knowledge, application of knowledge, creation of knowledge) on performance of product innovation.
Occurrence of pharmaceutical compounds in wastewater process streams in Dublin, Ireland.

PubMed

Lacey, Clair; Basha, Shaik; Morrissey, Anne; Tobin, John M

2012-01-01

The aim of this work is to establish baseline levels of pharmaceuticals in three wastewater treatment plant (WWTP) streams in the greater Dublin region to assess the removal efficiency of the selected WWTPs and to investigate the existence of any seasonal variability. Twenty compounds including several classes of antibiotics, acidic and basic pharmaceuticals, and prescribed medications were selected for investigation using a combination of membrane filtration, solid phase extraction (SPE) cleanup, and liquid chromatography-electrospray ionization tandem mass spectrometry. Fourteen of the selected compounds were found in the samples. Increased effluent concentrations, compared to influent concentrations, for a number of compounds (carbamazepine, clotrimazole, propranolol, nimesulide, furosemide, mefenamic acid, diclofenac, metoprolol, and gemfibrozil) were observed. The detected concentrations were generally below toxicity levels and based on current knowledge are unlikely to pose any threat to aquatic species. Mefenamic acid concentrations detected in both Leixlip and Swords effluents may potentially exert ecotoxicological effects with maximum risk quotients (i.e., ratio of predicted exposure concentration to predicted no effect concentration) of 4.04 and 1.33, respectively.
Predictors of knowledge about tuberculosis: results from SANHANES I, a national, cross-sectional household survey in South Africa.

PubMed

Naidoo, Pamela; Simbayi, Leickness; Labadarios, Demetre; Ntsepe, Yoliswa; Bikitsha, Nwabisa; Khan, Gadija; Sewpaul, Ronel; Moyo, Sizulu; Rehle, Thomas

2016-03-18

South Africa is one of the 22 high tuberculosis burden countries that contribute 80% of the global tuberculosis cases. Tuberculosis is infectious and due to its rapid and easy transmission route poses a threat to population health. Considering the importance of social and psychological factors in influencing health outcomes, appraising knowledge and awareness of tuberculosis, remain vital for effective tuberculosis control. The main aim of this study was to investigate the factors that predict knowledge about tuberculosis among 18-64 year old adults in South Africa. A cross-sectional survey method was used. Multi-stage disproportionate, stratified cluster sampling was used to select households within enumeration areas stratified by province and locality type. Based on the Human Sciences Research Council 2007 master sample, 500 Enumerator Areas representative of the socio-demographic profile of South Africa were identified and a random sample of 20 households was randomly selected from each Enumerator Area, yielding an overall sample of 10,000 households. The tuberculosis module contained in the South African National Health And Nutrition Examination Survey I was the only module that examined the social determinants of an infectious disease. This module was questionnaire-based with no biomarkers obtained to screen for the presence of tuberculosis disease among the participants. Data was collected by administering a researcher developed individual level questionnaire. Simple and multiple linear regression was used to determine the independent variables associated with tuberculosis knowledge. Half the sample (52.6%) was female and the majority of the respondents were black African (76.5%). More than two thirds (68.0%) resided in urban areas, 56.9% did not complete high school and half were not in formal employment. Significant predictors of tuberculosis knowledge were race, sex, completion of high school, being in employment, having a diagnosis of the disease in ones' life-time and learning about tuberculosis from television, brochures, health workers, and teachers. To reduce the burden of tuberculosis in South Africa, media campaigns targeting both rural and urban communities should include conveying accurate information about the disease. Policy makers should also address structural barriers that vulnerable communities face.
Design and development of food safety knowledge and attitude scales for consumer food safety education.

PubMed

Medeiros, Lydia C; Hillers, Virginia N; Chen, Gang; Bergmann, Verna; Kendall, Patricia; Schroeder, Mary

2004-11-01

The objective of this study was to design and develop food safety knowledge and attitude scales based on food-handling guidelines developed by a national panel of food safety experts. Knowledge (n=43) and attitude (n=49) questions were developed and pilot-tested with a variety of consumer groups. Final questions were selected based on item analysis and on validity and reliability statistical tests. Knowledge questions were tested in Washington State with participants in low-income nutrition education programs (pretest/posttest n=58, test/retest n=19) and college students (pretest/posttest n=34). Attitude questions were tested in Ohio with nutrition education program participants (n=30) and college students (non-nutrition majors n=138, nutrition majors n=57). Item analysis, paired sample t tests, Pearson's correlation coefficients, and Cronbach's alpha were used. Reliability and validity tests of individual items and the question sets were used to reduce the scales to 18 knowledge questions and 10 attitude questions. The knowledge and attitude scales covered topics ranked as important by a national panel of experts and met most validity and reliability standards. The 18-item knowledge questionnaire had instructional sensitivity (mean score increase of more than three points after instruction), internal reliability (Cronbach's alpha >.75), and produced similar results in test-retest without intervention (coefficient of stability=.81). Knowledge of correct procedures for hand washing and avoiding cross-contamination was widespread before instruction. Knowledge was limited regarding avoiding food preparation while ill, cooking hamburgers, high-risk foods, and whether cooked rice and potatoes could be stored at room temperature. The 10-item attitude scale had an appropriate range of responses (item difficulty) and produced similar results in test-retest ( P
Identification of Genes Involved in Breast Cancer Metastasis by Integrating Protein-Protein Interaction Information with Expression Data.

PubMed

Tian, Xin; Xin, Mingyuan; Luo, Jian; Liu, Mingyao; Jiang, Zhenran

2017-02-01

The selection of relevant genes for breast cancer metastasis is critical for the treatment and prognosis of cancer patients. Although much effort has been devoted to the gene selection procedures by use of different statistical analysis methods or computational techniques, the interpretation of the variables in the resulting survival models has been limited so far. This article proposes a new Random Forest (RF)-based algorithm to identify important variables highly related with breast cancer metastasis, which is based on the important scores of two variable selection algorithms, including the mean decrease Gini (MDG) criteria of Random Forest and the GeneRank algorithm with protein-protein interaction (PPI) information. The new gene selection algorithm can be called PPIRF. The improved prediction accuracy fully illustrated the reliability and high interpretability of gene list selected by the PPIRF approach.
DOE Office of Scientific and Technical Information (OSTI.GOV)

Mendell, Mark J.

This report briefly summarizes, based on recent review articles and selected more recent research reports, current scientific knowledge on two topics: assessing unhealthy levels of indoor D/M in homes and remediating home dampness-related problems to protect health. Based on a comparison of current scientific knowledge to that required to support effective, evidence-based, health-protective policies on home D/M, gaps in knowledge are highlighted, prior questions and research questions specified, and necessary research activities and approaches recommended.
Application of artificial intelligence (AI) concepts to the development of space flight parts approval model

NASA Technical Reports Server (NTRS)

Krishnan, G. S.

1997-01-01

A cost effective model which uses the artificial intelligence techniques in the selection and approval of parts is presented. The knowledge which is acquired from the specialists for different part types are represented in a knowledge base in the form of rules and objects. The parts information is stored separately in a data base and is isolated from the knowledge base. Validation, verification and performance issues are highlighted.
Application of Advanced Process Control techniques to a pusher type reheating furnace

NASA Astrophysics Data System (ADS)

Zanoli, S. M.; Pepe, C.; Barboni, L.

2015-11-01

In this paper an Advanced Process Control system aimed at controlling and optimizing a pusher type reheating furnace located in an Italian steel plant is proposed. The designed controller replaced the previous control system, based on PID controllers manually conducted by process operators. A two-layer Model Predictive Control architecture has been adopted that, exploiting a chemical, physical and economic modelling of the process, overcomes the limitations of plant operators’ mental model and knowledge. In addition, an ad hoc decoupling strategy has been implemented, allowing the selection of the manipulated variables to be used for the control of each single process variable. Finally, in order to improve the system flexibility and resilience, the controller has been equipped with a supervision module. A profitable trade-off between conflicting specifications, e.g. safety, quality and production constraints, energy saving and pollution impact, has been guaranteed. Simulation tests and real plant results demonstrated the soundness and the reliability of the proposed system.
ISLE: Intelligent Selection of Loop Electronics. A CLIPS/C++/INGRES integrated application

NASA Technical Reports Server (NTRS)

Fischer, Lynn; Cary, Judson; Currie, Andrew

1990-01-01

The Intelligent Selection of Loop Electronics (ISLE) system is an integrated knowledge-based system that is used to configure, evaluate, and rank possible network carrier equipment known as Digital Loop Carrier (DLC), which will be used to meet the demands of forecasted telephone services. Determining the best carrier systems and carrier architectures, while minimizing the cost, meeting corporate policies and addressing area service demands, has become a formidable task. Network planners and engineers use the ISLE system to assist them in this task of selecting and configuring the appropriate loop electronics equipment for future telephone services. The ISLE application is an integrated system consisting of a knowledge base, implemented in CLIPS (a planner application), C++, and an object database created from existing INGRES database information. The embedibility, performance, and portability of CLIPS provided us with a tool with which to capture, clarify, and refine corporate knowledge and distribute this knowledge within a larger functional system to network planners and engineers throughout U S WEST.
Manufacturing process and material selection in concurrent collaborative design of MEMS devices

NASA Astrophysics Data System (ADS)

Zha, Xuan F.; Du, H.

2003-09-01

In this paper we present knowledge of an intensive approach and system for selecting suitable manufacturing processes and materials for microelectromechanical systems (MEMS) devices in concurrent collaborative design environment. In the paper, fundamental issues on MEMS manufacturing process and material selection such as concurrent design framework, manufacturing process and material hierarchies, and selection strategy are first addressed. Then, a fuzzy decision support scheme for a multi-criteria decision-making problem is proposed for estimating, ranking and selecting possible manufacturing processes, materials and their combinations. A Web-based prototype advisory system for the MEMS manufacturing process and material selection, WebMEMS-MASS, is developed based on the client-knowledge server architecture and framework to help the designer find good processes and materials for MEMS devices. The system, as one of the important parts of an advanced simulation and modeling tool for MEMS design, is a concept level process and material selection tool, which can be used as a standalone application or a Java applet via the Web. The running sessions of the system are inter-linked with webpages of tutorials and reference pages to explain the facets, fabrication processes and material choices, and calculations and reasoning in selection are performed using process capability and material property data from a remote Web-based database and interactive knowledge base that can be maintained and updated via the Internet. The use of the developed system including operation scenario, use support, and integration with an MEMS collaborative design system is presented. Finally, an illustration example is provided.
Image Mining in Remote Sensing for Coastal Wetlands Mapping: from Pixel Based to Object Based Approach

NASA Astrophysics Data System (ADS)

Farda, N. M.; Danoedoro, P.; Hartono; Harjoko, A.

2016-11-01

The availably of remote sensing image data is numerous now, and with a large amount of data it makes “knowledge gap” in extraction of selected information, especially coastal wetlands. Coastal wetlands provide ecosystem services essential to people and the environment. The aim of this research is to extract coastal wetlands information from satellite data using pixel based and object based image mining approach. Landsat MSS, Landsat 5 TM, Landsat 7 ETM+, and Landsat 8 OLI images located in Segara Anakan lagoon are selected to represent data at various multi temporal images. The input for image mining are visible and near infrared bands, PCA band, invers PCA bands, mean shift segmentation bands, bare soil index, vegetation index, wetness index, elevation from SRTM and ASTER GDEM, and GLCM (Harralick) or variability texture. There is three methods were applied to extract coastal wetlands using image mining: pixel based - Decision Tree C4.5, pixel based - Back Propagation Neural Network, and object based - Mean Shift segmentation and Decision Tree C4.5. The results show that remote sensing image mining can be used to map coastal wetlands ecosystem. Decision Tree C4.5 can be mapped with highest accuracy (0.75 overall kappa). The availability of remote sensing image mining for mapping coastal wetlands is very important to provide better understanding about their spatiotemporal coastal wetlands dynamics distribution.
An Ensemble Successive Project Algorithm for Liquor Detection Using Near Infrared Sensor.

PubMed

Qu, Fangfang; Ren, Dong; Wang, Jihua; Zhang, Zhong; Lu, Na; Meng, Lei

2016-01-11

Spectral analysis technique based on near infrared (NIR) sensor is a powerful tool for complex information processing and high precision recognition, and it has been widely applied to quality analysis and online inspection of agricultural products. This paper proposes a new method to address the instability of small sample sizes in the successive projections algorithm (SPA) as well as the lack of association between selected variables and the analyte. The proposed method is an evaluated bootstrap ensemble SPA method (EBSPA) based on a variable evaluation index (EI) for variable selection, and is applied to the quantitative prediction of alcohol concentrations in liquor using NIR sensor. In the experiment, the proposed EBSPA with three kinds of modeling methods are established to test their performance. In addition, the proposed EBSPA combined with partial least square is compared with other state-of-the-art variable selection methods. The results show that the proposed method can solve the defects of SPA and it has the best generalization performance and stability. Furthermore, the physical meaning of the selected variables from the near infrared sensor data is clear, which can effectively reduce the variables and improve their prediction accuracy.
The Adverse Drug Reactions from Patient Reports in Social Media Project: Five Major Challenges to Overcome to Operationalize Analysis and Efficiently Support Pharmacovigilance Process.

PubMed

Bousquet, Cedric; Dahamna, Badisse; Guillemin-Lanne, Sylvie; Darmoni, Stefan J; Faviez, Carole; Huot, Charles; Katsahian, Sandrine; Leroux, Vincent; Pereira, Suzanne; Richard, Christophe; Schück, Stéphane; Souvignet, Julien; Lillo-Le Louët, Agnès; Texier, Nathalie

2017-09-21

Adverse drug reactions (ADRs) are an important cause of morbidity and mortality. Classical Pharmacovigilance process is limited by underreporting which justifies the current interest in new knowledge sources such as social media. The Adverse Drug Reactions from Patient Reports in Social Media (ADR-PRISM) project aims to extract ADRs reported by patients in these media. We identified 5 major challenges to overcome to operationalize the analysis of patient posts: (1) variable quality of information on social media, (2) guarantee of data privacy, (3) response to pharmacovigilance expert expectations, (4) identification of relevant information within Web pages, and (5) robust and evolutive architecture. This article aims to describe the current state of advancement of the ADR-PRISM project by focusing on the solutions we have chosen to address these 5 major challenges. In this article, we propose methods and describe the advancement of this project on several aspects: (1) a quality driven approach for selecting relevant social media for the extraction of knowledge on potential ADRs, (2) an assessment of ethical issues and French regulation for the analysis of data on social media, (3) an analysis of pharmacovigilance expert requirements when reviewing patient posts on the Internet, (4) an extraction method based on natural language processing, pattern based matching, and selection of relevant medical concepts in reference terminologies, and (5) specifications of a component-based architecture for the monitoring system. Considering the 5 major challenges, we (1) selected a set of 21 validated criteria for selecting social media to support the extraction of potential ADRs, (2) proposed solutions to guarantee data privacy of patients posting on Internet, (3) took into account pharmacovigilance expert requirements with use case diagrams and scenarios, (4) built domain-specific knowledge resources embeding a lexicon, morphological rules, context rules, semantic rules, syntactic rules, and post-analysis processing, and (5) proposed a component-based architecture that allows storage of big data and accessibility to third-party applications through Web services. We demonstrated the feasibility of implementing a component-based architecture that allows collection of patient posts on the Internet, near real-time processing of those posts including annotation, and storage in big data structures. In the next steps, we will evaluate the posts identified by the system in social media to clarify the interest and relevance of such approach to improve conventional pharmacovigilance processes based on spontaneous reporting. ©Cedric Bousquet, Badisse Dahamna, Sylvie Guillemin-Lanne, Stefan J Darmoni, Carole Faviez, Charles Huot, Sandrine Katsahian, Vincent Leroux, Suzanne Pereira, Christophe Richard, Stéphane Schück, Julien Souvignet, Agnès Lillo-Le Louët, Nathalie Texier. Originally published in JMIR Research Protocols (http://www.researchprotocols.org), 21.09.2017.

Event-based Plausibility Immediately Influences On-line Language Comprehension

PubMed Central

Matsuki, Kazunaga; Chow, Tracy; Hare, Mary; Elman, Jeffrey L.; Scheepers, Christoph; McRae, Ken

2011-01-01

In some theories of sentence comprehension, linguistically-relevant lexical knowledge such as selectional restrictions is privileged in terms of the time-course of its access and influence. We examined whether event knowledge computed by combining multiple concepts can rapidly influence language understanding even in the absence of selectional restriction violations. Specifically, we investigated whether instruments can combine with actions to influence comprehension of ensuing patients. Instrument-verb-patient triplets were created in a norming study designed to tap directly into event knowledge. In self-paced reading (Experiment 1), participants were faster to read patient nouns such as hair when they were typical of the instrument-action pair (Donna used the shampoo to wash vs. the hose to wash). Experiment 2 showed that these results were not due to direct instrument-patient relations. Experiment 3 replicated Experiment 1 using eyetracking, with effects of event typicality observed in first fixation and gaze durations on the patient noun. This research demonstrates that conceptual event-based expectations are computed and used rapidly and dynamically during on-line language comprehension. We discuss relationships among plausibility and predictability, as well as their implications. We conclude that selectional restrictions may be best considered as event-based conceptual knowledge, rather than lexical-grammatical knowledge. PMID:21517222
An individual differences approach to semantic cognition: Divergent effects of age on representation, retrieval and selection.

PubMed

Hoffman, Paul

2018-05-25

Semantic cognition refers to the appropriate use of acquired knowledge about the world. This requires representation of knowledge as well as control processes which ensure that currently-relevant aspects of knowledge are retrieved and selected. Although these abilities can be impaired selectively following brain damage, the relationship between them in healthy individuals is unclear. It is also commonly assumed that semantic cognition is preserved in later life, because older people have greater reserves of knowledge. However, this claim overlooks the possibility of decline in semantic control processes. Here, semantic cognition was assessed in 100 young and older adults. Despite having a broader knowledge base, older people showed specific impairments in semantic control, performing more poorly than young people when selecting among competing semantic representations. Conversely, they showed preserved controlled retrieval of less salient information from the semantic store. Breadth of semantic knowledge was positively correlated with controlled retrieval but was unrelated to semantic selection ability, which was instead correlated with non-semantic executive function. These findings indicate that three distinct elements contribute to semantic cognition: semantic representations that accumulate throughout the lifespan, processes for controlled retrieval of less salient semantic information, which appear age-invariant, and mechanisms for selecting task-relevant aspects of semantic knowledge, which decline with age and may relate more closely to domain-general executive control.
Can Dynamic Visualizations with Variable Control Enhance the Acquisition of Intuitive Knowledge?

ERIC Educational Resources Information Center

Wichmann, Astrid; Timpe, Sebastian

2015-01-01

An important feature of inquiry learning is to take part in science practices including exploring variables and testing hypotheses. Computer-based dynamic visualizations have the potential to open up various exploration possibilities depending on the level of learner control. It is assumed that variable control, e.g., by changing parameters of a…
An evaluation of unsupervised and supervised learning algorithms for clustering landscape types in the United States

USGS Publications Warehouse

Wendel, Jochen; Buttenfield, Barbara P.; Stanislawski, Larry V.

2016-01-01

Knowledge of landscape type can inform cartographic generalization of hydrographic features, because landscape characteristics provide an important geographic context that affects variation in channel geometry, flow pattern, and network configuration. Landscape types are characterized by expansive spatial gradients, lacking abrupt changes between adjacent classes; and as having a limited number of outliers that might confound classification. The US Geological Survey (USGS) is exploring methods to automate generalization of features in the National Hydrography Data set (NHD), to associate specific sequences of processing operations and parameters with specific landscape characteristics, thus obviating manual selection of a unique processing strategy for every NHD watershed unit. A chronology of methods to delineate physiographic regions for the United States is described, including a recent maximum likelihood classification based on seven input variables. This research compares unsupervised and supervised algorithms applied to these seven input variables, to evaluate and possibly refine the recent classification. Evaluation metrics for unsupervised methods include the Davies–Bouldin index, the Silhouette index, and the Dunn index as well as quantization and topographic error metrics. Cross validation and misclassification rate analysis are used to evaluate supervised classification methods. The paper reports the comparative analysis and its impact on the selection of landscape regions. The compared solutions show problems in areas of high landscape diversity. There is some indication that additional input variables, additional classes, or more sophisticated methods can refine the existing classification.
Knowledge about Danger Signs of Obstetric Complications and Associated Factors among Postnatal Mothers of Mechekel District Health Centers, East Gojjam Zone, Northwest Ethiopia, 2014.

PubMed

Amenu, Gedefa; Mulaw, Zerfu; Seyoum, Tewodros; Bayu, Hinsermu

2016-01-01

Background. Developing countries like Ethiopia contributed highest level of maternal mortality due to obstetric complications. Women awareness of obstetric danger sign to recognize complications to seek medical care early is the first intervention in an effort to decrease maternal death. Objective. To assess knowledge about danger signs of obstetric complications and associated factors among postnatal mothers at Mechekel district health centers, East Gojjam zone, Northwest Ethiopia, 2014. Methods. An institution based cross-sectional study was conducted from August to October, 2014, in Mechekel district health centers. Systematic random sampling was used to select four hundred eleven study participants. A pretested structured questionnaire was used to collect data. Data were entered to Epi Info version 3.5.3 and exported to SPSS 20.0 for further analysis. Descriptive and summary statistics were done. Logistic regression analyses were used to see the association of different variables. Odds ratios and 95% confidence interval were computed to determine the presence and strength of association. Results. According to this study, 55.1% participants were knowledgeable about danger signs of obstetric complications. Maternal and husband educational level ((AOR = 1.977, 95% CI: 1.052, 3.716) and (AOR = 3.163, 95% CI: 1.860, 5.3770), resp.), family monthly income ≥ 1500 (AOR = 2.954, 95% CI: 1.289, 6.770), being multipara (AOR = 7.463, 95% CI: 1.301, 12.800), ANC follow-up during last pregnancy (AOR = 2.184, 95% CI: 1.137, 4.196), and place of last delivery (AOR = 1.955, 95% CI: 1.214, 3.150) were variables found to be significantly associated with women's knowledge on danger signs of obstetric complications. Conclusion. Significant proportion of respondents were not knowledgeable about obstetric danger signs and factors like educational status, place of last delivery, and antenatal follow-up were found to be associated.
Optimization and design of ibuprofen-loaded nanostructured lipid carriers using a hybrid-design approach for ocular drug delivery

NASA Astrophysics Data System (ADS)

Rathod, Vishal

The objective of the present project was to develop the Ibuprofen-loaded Nanostructured Lipid Carrier (IBU-NLCs) for topical ocular delivery based on substantial pre-formulation screening of the components and understanding the interplay between the formulation and process variables. The BCS Class II drug: Ibuprofen was selected as the model drug for the current study. IBU-NLCs were prepared by melt emulsification and ultrasonication technique. Extensive pre-formulation studies were performed to screen the lipid components (solid and liquid) based on drug's solubility and affinity as well as components compatibility. The results from DSC & XRD assisted in selecting the most suitable ratio to be utilized for future studies. DynasanRTM 114 was selected as the solid lipid & MiglyolRTM 840 was selected as the liquid lipid based on preliminary lipid screening. The ratio of 6:4 was predicted to be the best based on its crystallinity index and the thermal events. As there are many variables involved for further optimization of the formulation, a single design approach is not always adequate. A hybrid-design approach was applied by employing the Plackett Burman design (PBD) for preliminary screening of 7 critical variables, followed by Box-Behnken design (BBD), a sub-type of response surface methodology (RSM) design using 2 relatively significant variables from the former design and incorporating Surfactant/Co-surfactant ratio as the third variable. Comparatively, KolliphorRTM HS15 demonstrated lower Mean Particle Size (PS) & Polydispersity Index (PDI) and KolliphorRTM P188 resulted in Zeta Potential (ZP) < -20 mV during the surfactant screening & stability studies. Hence, Surfactant/Cosurfactant ratio was employed as the third variable to understand its synergistic effect on the response variables. We selected PS, PDI, and ZP as critical response variables in the PBD since they significantly influence the stability & performance of NLCs. Formulations prepared using BBD were further characterized and evaluated concerning PS, PDI, ZP and Entrapment Efficiency (EE) to identify the multi-factor interactions between selected formulation variables. In vitro release studies were performed using Spectra/por dialysis membrane on Franz diffusion cell and Phosphate Saline buffer (7.4 pH) as the medium. Samples for assay, EE, Loading Capacity (LC), Solubility studies & in-vitro release were filtered using Amicon 50K and analyzed via UPLC system (Waters) at a detection wavelength of 220 nm. Significant variables were selected through PBD, and the third variable was incorporated based on surfactant screening & stability studies for the next design. Assay of the BBD based formulations was found to be within 95-104% of the theoretically calculated values. Further studies were investigated for PS, PDI, ZP & EE. PS was found to be in the range of 103-194 nm with PDI ranging from 0.118 to 0.265. The ZP and EE were observed to be in the range of -22.2 to -11 mV & 90 to 98.7 % respectively. Drug release of 30% was observed from the optimized formulation in the first 6 hr of in-vitro studies, and the drug release showed a sustained release of ibuprofen thereafter over several hours. These values also confirm that the production method, and all other selected variables, effectively promoted the incorporation of ibuprofen in NLC. Quality by Design (QbD) approach was successfully implemented in developing a robust ophthalmic formulation with superior physicochemical and morphometric properties. NLCs as the nanocarrier demonstrated promising perspective for topical delivery of poorly water-soluble drugs.
Computer Aided Program Synthesis.

DTIC Science & Technology

1980-01-01

Representations 18 .2 Refinements and Reductions 18.:2.3 Dependenc ies 20 3.3 The Programming Knowledge Base 21 3.4 Linguistic Knowledge 22 3.5...strategy selection knowledge, i.e. knowledge representing a context sensitive discrimination among alternate methods; and knowledge of logical...program, each supplying his expertise. The client describes his task to the consultant and supplies answers and explanations to the consultant’s
Knowledge diffusion in the collaboration hypernetwork

NASA Astrophysics Data System (ADS)

Yang, Guang-Yong; Hu, Zhao-Long; Liu, Jian-Guo

2015-02-01

As knowledge constitutes a primary productive force, it is important to understand the performance of knowledge diffusion. In this paper, we present a knowledge diffusion model based on the local-world non-uniform hypernetwork, which introduces the preferential diffusion mechanism and the knowledge absorptive capability αj, where αj is correlated with the hyperdegree dH(j) of node j. At each time step, we randomly select a node i as the sender; a receiver node is selected from the set of nodes that the sender i has published with previously, with probability proportional to the number of papers they have published together. Applying the average knowledge stock V bar(t) , the variance σ2(t) and the variance coefficient c(t) of knowledge stock to measure the growth and diffusion of knowledge and the adequacy of knowledge diffusion, we have made 3 groups of comparative experiments to investigate how different network structures, hypernetwork sizes and knowledge evolution mechanisms affect the knowledge diffusion, respectively. As the diffusion mechanisms based on the hypernetwork combine with the hyperdegree of node, the hypernetwork is more suitable for investigating the performance of knowledge diffusion. Therefore, the proposed model could be helpful for deeply understanding the process of the knowledge diffusion in the collaboration hypernetwork.
A decision tool for selecting trench cap designs

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

Paige, G.B.; Stone, J.J.; Lane, L.J.

1995-12-31

A computer based prototype decision support system (PDSS) is being developed to assist the risk manager in selecting an appropriate trench cap design for waste disposal sites. The selection of the {open_quote}best{close_quote} design among feasible alternatives requires consideration of multiple and often conflicting objectives. The methodology used in the selection process consists of: selecting and parameterizing decision variables using data, simulation models, or expert opinion; selecting feasible trench cap design alternatives; ordering the decision variables and ranking the design alternatives. The decision model is based on multi-objective decision theory and uses a unique approach to order the decision variables andmore » rank the design alternatives. Trench cap designs are evaluated based on federal regulations, hydrologic performance, cover stability and cost. Four trench cap designs, which were monitored for a four year period at Hill Air Force Base in Utah, are used to demonstrate the application of the PDSS and evaluate the results of the decision model. The results of the PDSS, using both data and simulations, illustrate the relative advantages of each of the cap designs and which cap is the {open_quotes}best{close_quotes} alternative for a given set of criteria and a particular importance order of those decision criteria.« less
Knowledge diffusion of dynamical network in terms of interaction frequency.

PubMed

Liu, Jian-Guo; Zhou, Qing; Guo, Qiang; Yang, Zhen-Hua; Xie, Fei; Han, Jing-Ti

2017-09-07

In this paper, we present a knowledge diffusion (SKD) model for dynamic networks by taking into account the interaction frequency which always used to measure the social closeness. A set of agents, which are initially interconnected to form a random network, either exchange knowledge with their neighbors or move toward a new location through an edge-rewiring procedure. The activity of knowledge exchange between agents is determined by a knowledge transfer rule that the target node would preferentially select one neighbor node to transfer knowledge with probability p according to their interaction frequency instead of the knowledge distance, otherwise, the target node would build a new link with its second-order neighbor preferentially or select one node in the system randomly with probability 1 - p. The simulation results show that, comparing with the Null model defined by the random selection mechanism and the traditional knowledge diffusion (TKD) model driven by knowledge distance, the knowledge would spread more fast based on SKD driven by interaction frequency. In particular, the network structure of SKD would evolve as an assortative one, which is a fundamental feature of social networks. This work would be helpful for deeply understanding the coevolution of the knowledge diffusion and network structure.
Hubby and Lewontin on Protein Variation in Natural Populations: When Molecular Genetics Came to the Rescue of Population Genetics.

PubMed

Charlesworth, Brian; Charlesworth, Deborah; Coyne, Jerry A; Langley, Charles H

2016-08-01

The 1966 GENETICS papers by John Hubby and Richard Lewontin were a landmark in the study of genome-wide levels of variability. They used the technique of gel electrophoresis of enzymes and proteins to study variation in natural populations of Drosophila pseudoobscura, at a set of loci that had been chosen purely for technical convenience, without prior knowledge of their levels of variability. Together with the independent study of human populations by Harry Harris, this seminal study provided the first relatively unbiased picture of the extent of genetic variability in protein sequences within populations, revealing that many genes had surprisingly high levels of diversity. These papers stimulated a large research program that found similarly high electrophoretic variability in many different species and led to statistical tools for interpreting the data in terms of population genetics processes such as genetic drift, balancing and purifying selection, and the effects of selection on linked variants. The current use of whole-genome sequences in studies of variation is the direct descendant of this pioneering work. Copyright © 2016 by the Genetics Society of America.
Artificial Intelligence Based Selection of Optimal Cutting Tool and Process Parameters for Effective Turning and Milling Operations

NASA Astrophysics Data System (ADS)

Saranya, Kunaparaju; John Rozario Jegaraj, J.; Ramesh Kumar, Katta; Venkateshwara Rao, Ghanta

2016-06-01

With the increased trend in automation of modern manufacturing industry, the human intervention in routine, repetitive and data specific activities of manufacturing is greatly reduced. In this paper, an attempt has been made to reduce the human intervention in selection of optimal cutting tool and process parameters for metal cutting applications, using Artificial Intelligence techniques. Generally, the selection of appropriate cutting tool and parameters in metal cutting is carried out by experienced technician/cutting tool expert based on his knowledge base or extensive search from huge cutting tool database. The present proposed approach replaces the existing practice of physical search for tools from the databooks/tool catalogues with intelligent knowledge-based selection system. This system employs artificial intelligence based techniques such as artificial neural networks, fuzzy logic and genetic algorithm for decision making and optimization. This intelligence based optimal tool selection strategy is developed using Mathworks Matlab Version 7.11.0 and implemented. The cutting tool database was obtained from the tool catalogues of different tool manufacturers. This paper discusses in detail, the methodology and strategies employed for selection of appropriate cutting tool and optimization of process parameters based on multi-objective optimization criteria considering material removal rate, tool life and tool cost.
Kernel-PCA data integration with enhanced interpretability

PubMed Central

2014-01-01

Background Nowadays, combining the different sources of information to improve the biological knowledge available is a challenge in bioinformatics. One of the most powerful methods for integrating heterogeneous data types are kernel-based methods. Kernel-based data integration approaches consist of two basic steps: firstly the right kernel is chosen for each data set; secondly the kernels from the different data sources are combined to give a complete representation of the available data for a given statistical task. Results We analyze the integration of data from several sources of information using kernel PCA, from the point of view of reducing dimensionality. Moreover, we improve the interpretability of kernel PCA by adding to the plot the representation of the input variables that belong to any dataset. In particular, for each input variable or linear combination of input variables, we can represent the direction of maximum growth locally, which allows us to identify those samples with higher/lower values of the variables analyzed. Conclusions The integration of different datasets and the simultaneous representation of samples and variables together give us a better understanding of biological knowledge. PMID:25032747
Stabilometric parameters are affected by anthropometry and foot placement.

PubMed

Chiari, Lorenzo; Rocchi, Laura; Cappello, Angelo

2002-01-01

To recognize and quantify the influence of biomechanical factors, namely anthropometry and foot placement, on the more common measures of stabilometric performance, including new-generation stochastic parameters. Fifty normal-bodied young adults were selected in order to cover a sufficiently wide range of anthropometric properties. They were allowed to choose their preferred side-by-side foot position and their quiet stance was recorded with eyes open and closed by a force platform. biomechanical factors are known to influence postural stability but their impact on stabilometric parameters has not been extensively explored yet. Principal component analysis was used for feature selection among several biomechanical factors. A collection of 55 stabilometric parameters from the literature was estimated from the center-of-pressure time series. Linear relations between stabilometric parameters and selected biomechanical factors were investigated by robust regression techniques. The feature selection process returned height, weight, maximum foot width, base-of-support area, and foot opening angle as the relevant biomechanical variables. Only eleven out of the 55 stabilometric parameters were completely immune from a linear dependence on these variables. The remaining parameters showed a moderate to high dependence that was strengthened upon eye closure. For these parameters, a normalization procedure was proposed, to remove what can well be considered, in clinical investigations, a spurious source of between-subject variability. Care should be taken when quantifying postural sway through stabilometric parameters. It is suggested as a good practice to include some anthropometric measurements in the experimental protocol, and to standardize or trace foot position. Although the role of anthropometry and foot placement has been investigated in specific studies, there are no studies in the literature that systematically explore the relationship between such BF and stabilometric parameters. This knowledge may contribute to better defining the experimental protocol and improving the functional evaluation of postural sway for clinical purposes, e.g. by removing through normalization the spurious effects of body properties and foot position on postural performance.
Optimization techniques for integrating spatial data

USGS Publications Warehouse

Herzfeld, U.C.; Merriam, D.F.

1995-01-01

Two optimization techniques ta predict a spatial variable from any number of related spatial variables are presented. The applicability of the two different methods for petroleum-resource assessment is tested in a mature oil province of the Midcontinent (USA). The information on petroleum productivity, usually not directly accessible, is related indirectly to geological, geophysical, petrographical, and other observable data. This paper presents two approaches based on construction of a multivariate spatial model from the available data to determine a relationship for prediction. In the first approach, the variables are combined into a spatial model by an algebraic map-comparison/integration technique. Optimal weights for the map comparison function are determined by the Nelder-Mead downhill simplex algorithm in multidimensions. Geologic knowledge is necessary to provide a first guess of weights to start the automatization, because the solution is not unique. In the second approach, active set optimization for linear prediction of the target under positivity constraints is applied. Here, the procedure seems to select one variable from each data type (structure, isopachous, and petrophysical) eliminating data redundancy. Automating the determination of optimum combinations of different variables by applying optimization techniques is a valuable extension of the algebraic map-comparison/integration approach to analyzing spatial data. Because of the capability of handling multivariate data sets and partial retention of geographical information, the approaches can be useful in mineral-resource exploration. ?? 1995 International Association for Mathematical Geology.
Testing of technology readiness index model based on exploratory factor analysis approach

NASA Astrophysics Data System (ADS)

Ariani, AF; Napitupulu, D.; Jati, RK; Kadar, JA; Syafrullah, M.

2018-04-01

SMEs readiness in using ICT will determine the adoption of ICT in the future. This study aims to evaluate the model of technology readiness in order to apply the technology on SMEs. The model is tested to find if TRI model is relevant to measure ICT adoption, especially for SMEs in Indonesia. The research method used in this paper is survey to a group of SMEs in South Tangerang. The survey measures the readiness to adopt ICT based on four variables which is Optimism, Innovativeness, Discomfort, and Insecurity. Each variable contains several indicators to make sure the variable is measured thoroughly. The data collected through survey is analysed using factor analysis methodwith the help of SPSS software. The result of this study shows that TRI model gives more descendants on some indicators and variables. This result can be caused by SMEs owners’ knowledge is not homogeneous about either the technology that they are used, knowledge or the type of their business.
Knowledge-base browsing: an application of hybrid distributed/local connectionist networks

NASA Astrophysics Data System (ADS)

Samad, Tariq; Israel, Peggy

1990-08-01

We describe a knowledge base browser based on a connectionist (or neural network) architecture that employs both distributed and local representations. The distributed representations are used for input and output thereby enabling associative noise-tolerant interaction with the environment. Internally all representations are fully local. This simplifies weight assignment and facilitates network configuration for specific applications. In our browser concepts and relations in a knowledge base are represented using " microfeatures. " The microfeatures can encode semantic attributes structural features contextual information etc. Desired portions of the knowledge base can then be associatively retrieved based on a structured cue. An ordered list of partial matches is presented to the user for selection. Microfeatures can also be used as " bookmarks" they can be placed dynamically at appropriate points in the knowledge base and subsequently used as retrieval cues. A proof-of-concept system has been implemented for an internally developed Honeywell-proprietary knowledge acquisition tool. 1.
Using variable rate models to identify genes under selection in sequence pairs: their validity and limitations for EST sequences.

PubMed

Church, Sheri A; Livingstone, Kevin; Lai, Zhao; Kozik, Alexander; Knapp, Steven J; Michelmore, Richard W; Rieseberg, Loren H

2007-02-01

Using likelihood-based variable selection models, we determined if positive selection was acting on 523 EST sequence pairs from two lineages of sunflower and lettuce. Variable rate models are generally not used for comparisons of sequence pairs due to the limited information and the inaccuracy of estimates of specific substitution rates. However, previous studies have shown that the likelihood ratio test (LRT) is reliable for detecting positive selection, even with low numbers of sequences. These analyses identified 56 genes that show a signature of selection, of which 75% were not identified by simpler models that average selection across codons. Subsequent mapping studies in sunflower show four of five of the positively selected genes identified by these methods mapped to domestication QTLs. We discuss the validity and limitations of using variable rate models for comparisons of sequence pairs, as well as the limitations of using ESTs for identification of positively selected genes.
Ambulatory orthopaedic surgery patients' knowledge with internet-based education.

PubMed

Heikkinen, Katja; Leino-Kilpi, H; Salanterä, S

2012-01-01

There is a growing need for patient education and an evaluation of its outcomes. The aim of this study was to compare ambulatory orthopaedic surgery patients' knowledge with Internet-based education and face-to-face education with a nurse. The following hypothesis was proposed: Internet-based patient education (experiment) is as effective as face-to-face education with a nurse (control) in increasing patients' level of knowledge and sufficiency of knowledge. In addition, the correlations of demographic variables were tested. The patients were randomized to either an experiment group (n = 72) or a control group (n = 75). Empirical data were collected with two instruments. Patients in both groups showed improvement in their knowledge during their care. Patients in the experiment group improved their knowledge level significantly more in total than those patients in the control group. There were no differences in patients' sufficiency of knowledge between the groups. Knowledge was correlated especially with patients' age, gender and earlier ambulatory surgeries. As a conclusion, positive results concerning patients' knowledge could be achieved with the Internet-based education. The Internet is a viable method in ambulatory care.
Assessing Local Knowledge Use in Agroforestry Management with Cognitive Maps

NASA Astrophysics Data System (ADS)

Isaac, Marney E.; Dawoe, Evans; Sieciechowicz, Krystyna

2009-06-01

Small-holder farmers often develop adaptable agroforestry management techniques to improve and diversify crop production. In the cocoa growing region of Ghana, local knowledge on such farm management holds a noteworthy role in the overall farm development. The documentation and analysis of such knowledge use in cocoa agroforests may afford an applicable framework to determine mechanisms driving farmer preference and indicators in farm management. This study employed 12 in-depth farmer interviews regarding variables in farm management as a unit of analysis and utilized cognitive mapping as a qualitative method of analysis. Our objectives were (1) to illustrate and describe agroforestry management variables and associated farm practices, (2) to determine the scope of decision making of individual farmers, and (3) to investigate the suitability of cognitive mapping as a tool for assessing local knowledge use. Results from the cognitive maps revealed an average of 16 ± 3 variables and 19 ± 3 links between management variables in the farmer cognitive maps. Farmer use of advantageous ecological processes was highly central to farm management (48% of all variables), particularly manipulation of organic matter, shade and food crop establishment, and maintenance of a tree stratum as the most common, highly linked variables. Over 85% of variables included bidirectional arrows, interpreted as farm management practices dominated by controllable factors, insofar as farmers indicated an ability to alter most farm characteristics. Local knowledge use on cocoa production revealed detailed indicators for site evaluation, thus affecting farm preparation and management. Our findings suggest that amid multisourced information under conditions of uncertainty, strategies for adaptable agroforestry management should integrate existing and localized management frameworks and that cognitive mapping provides a tool-based approach to advance such a management support system.

Assessing local knowledge use in agroforestry management with cognitive maps.

PubMed

Isaac, Marney E; Dawoe, Evans; Sieciechowicz, Krystyna

2009-06-01

Small-holder farmers often develop adaptable agroforestry management techniques to improve and diversify crop production. In the cocoa growing region of Ghana, local knowledge on such farm management holds a noteworthy role in the overall farm development. The documentation and analysis of such knowledge use in cocoa agroforests may afford an applicable framework to determine mechanisms driving farmer preference and indicators in farm management. This study employed 12 in-depth farmer interviews regarding variables in farm management as a unit of analysis and utilized cognitive mapping as a qualitative method of analysis. Our objectives were (1) to illustrate and describe agroforestry management variables and associated farm practices, (2) to determine the scope of decision making of individual farmers, and (3) to investigate the suitability of cognitive mapping as a tool for assessing local knowledge use. Results from the cognitive maps revealed an average of 16 +/- 3 variables and 19 +/- 3 links between management variables in the farmer cognitive maps. Farmer use of advantageous ecological processes was highly central to farm management (48% of all variables), particularly manipulation of organic matter, shade and food crop establishment, and maintenance of a tree stratum as the most common, highly linked variables. Over 85% of variables included bidirectional arrows, interpreted as farm management practices dominated by controllable factors, insofar as farmers indicated an ability to alter most farm characteristics. Local knowledge use on cocoa production revealed detailed indicators for site evaluation, thus affecting farm preparation and management. Our findings suggest that amid multisourced information under conditions of uncertainty, strategies for adaptable agroforestry management should integrate existing and localized management frameworks and that cognitive mapping provides a tool-based approach to advance such a management support system.
Perceptual and associative knowledge in category specific impairment of semantic memory: a study of two cases.

PubMed

Laiacona, M; Barbarotto, R; Capitani, E

1993-12-01

We report two head-injured patients whose knowledge of living things was selectively disrupted. Their semantic knowledge was tested with naming and verbal comprehension tasks and a verbal questionnaire. In all of them there was consistent evidence that knowledge of living things was impaired and that of non-living things was relatively preserved. The living things deficit emerged irrespective of whether the question tapped associative or perceptual knowledge or required visual or non visual information. In all tasks the category effect was still significant after the influence on the performance of the following variables was partialled out: word frequency, concept familiarity, prototypicality, name agreement, image agreement and visual complexity. In the verbal questionnaire dissociations were still significant even after adjustment for the difficulty of questions for normals, that had proven greater for living things. Besides diffuse brain damage, both patients presented with a left posterior temporo-parietal lesion.
A novel variable selection approach that iteratively optimizes variable space using weighted binary matrix sampling.

PubMed

Deng, Bai-chuan; Yun, Yong-huan; Liang, Yi-zeng; Yi, Lun-zhao

2014-10-07

In this study, a new optimization algorithm called the Variable Iterative Space Shrinkage Approach (VISSA) that is based on the idea of model population analysis (MPA) is proposed for variable selection. Unlike most of the existing optimization methods for variable selection, VISSA statistically evaluates the performance of variable space in each step of optimization. Weighted binary matrix sampling (WBMS) is proposed to generate sub-models that span the variable subspace. Two rules are highlighted during the optimization procedure. First, the variable space shrinks in each step. Second, the new variable space outperforms the previous one. The second rule, which is rarely satisfied in most of the existing methods, is the core of the VISSA strategy. Compared with some promising variable selection methods such as competitive adaptive reweighted sampling (CARS), Monte Carlo uninformative variable elimination (MCUVE) and iteratively retaining informative variables (IRIV), VISSA showed better prediction ability for the calibration of NIR data. In addition, VISSA is user-friendly; only a few insensitive parameters are needed, and the program terminates automatically without any additional conditions. The Matlab codes for implementing VISSA are freely available on the website: https://sourceforge.net/projects/multivariateanalysis/files/VISSA/.
Research design.

PubMed

Toledo-Pereyra, Luis H

2012-10-01

The development of a good research design permits us to obtain the best research data possible. From the experimental question to the research hypothesis and data collection variables, we can begin to consider the optimal research design. Details pertaining to the selection of the research design are considered within and very much in relation with the knowledge of the researcher and the support of his research group.
Using variable importance measures to identify a small set of single nucleotide polymorphisms capable of predicting heading date in perennial ryegrass

USDA-ARS?s Scientific Manuscript database

Prior knowledge on heading date enables the selection of parents for synthetic cultivars that are well-matched with respect to heading date, which is necessary to ensure plants put together will successfully cross with each other. Heading date of individual plants can be determined directly, which h...
A bioinformatics approach for precision medicine off-label drug drug selection among triple negative breast cancer patients

PubMed Central

Cheng, Lijun; Schneider, Bryan P

2016-01-01

Background Cancer has been extensively characterized on the basis of genomics. The integration of genetic information about cancers with data on how the cancers respond to target based therapy to help to optimum cancer treatment. Objective The increasing usage of sequencing technology in cancer research and clinical practice has enormously advanced our understanding of cancer mechanisms. The cancer precision medicine is becoming a reality. Although off-label drug usage is a common practice in treating cancer, it suffers from the lack of knowledge base for proper cancer drug selections. This eminent need has become even more apparent considering the upcoming genomics data. Methods In this paper, a personalized medicine knowledge base is constructed by integrating various cancer drugs, drug-target database, and knowledge sources for the proper cancer drugs and their target selections. Based on the knowledge base, a bioinformatics approach for cancer drugs selection in precision medicine is developed. It integrates personal molecular profile data, including copy number variation, mutation, and gene expression. Results By analyzing the 85 triple negative breast cancer (TNBC) patient data in the Cancer Genome Altar, we have shown that 71.7% of the TNBC patients have FDA approved drug targets, and 51.7% of the patients have more than one drug target. Sixty-five drug targets are identified as TNBC treatment targets and 85 candidate drugs are recommended. Many existing TNBC candidate targets, such as Poly (ADP-Ribose) Polymerase 1 (PARP1), Cell division protein kinase 6 (CDK6), epidermal growth factor receptor, etc., were identified. On the other hand, we found some additional targets that are not yet fully investigated in the TNBC, such as Gamma-Glutamyl Hydrolase (GGH), Thymidylate Synthetase (TYMS), Protein Tyrosine Kinase 6 (PTK6), Topoisomerase (DNA) I, Mitochondrial (TOP1MT), Smoothened, Frizzled Class Receptor (SMO), etc. Our additional analysis of target and drug selection strategy is also fully supported by the drug screening data on TNBC cell lines in the Cancer Cell Line Encyclopedia. Conclusions The proposed bioinformatics approach lays a foundation for cancer precision medicine. It supplies much needed knowledge base for the off-label cancer drug usage in clinics. PMID:27107440
Contrasting habitat selection amongst cephalopods in the Mediterranean Sea: When the environment makes the difference.

PubMed

Lauria, V; Garofalo, G; Gristina, M; Fiorentino, F

2016-08-01

Conservation of fish habitat requires a deeper knowledge of how species distribution patterns are related to environmental factors. Habitat suitability modelling is an essential tool to quantify species' realised niches and understand species-environment relationships. Cephalopods are important players in the marine food web and a significant resource for fisheries; they are also very sensitive to environmental changes. Here a time series of fishery-independent data (1998-2011) was used to construct habitat suitability models and investigate the influence of environmental variables on four commercial cephalopods: Todaropsis eblanae, Illex coindetii, Eledone moschata and Eledone cirrhosa, in the central Mediterranean Sea. The main environmental predictors of cephalopod habitat suitability were depth, seafloor morphology, chlorophyll-a concentration, sea surface temperature and surface salinity. Predictive maps highlighted contrasting habitat selection amongst species. This study identifies areas where the important commercial species of cephalopods are concentrated and provides significant information for a future spatial based approach to fisheries management in the Mediterranean Sea. Copyright © 2016 Elsevier Ltd. All rights reserved.
20 CFR 627.422 - Selection of service providers.

Code of Federal Regulations, 2011 CFR

2011-04-01

... operational controls; and (7) The technical skills to perform the work. (e) In selecting service providers to... community-based organizations (section 107(a)). These community-based organizations, including women's organizations with knowledge about or experience in nontraditional training for women, shall be organizations...
Habitat classification modeling with incomplete data: Pushing the habitat envelope

USGS Publications Warehouse

Zarnetske, P.L.; Edwards, T.C.; Moisen, Gretchen G.

2007-01-01

Habitat classification models (HCMs) are invaluable tools for species conservation, land-use planning, reserve design, and metapopulation assessments, particularly at broad spatial scales. However, species occurrence data are often lacking and typically limited to presence points at broad scales. This lack of absence data precludes the use of many statistical techniques for HCMs. One option is to generate pseudo-absence points so that the many available statistical modeling tools can be used. Traditional techniques generate pseudoabsence points at random across broadly defined species ranges, often failing to include biological knowledge concerning the species-habitat relationship. We incorporated biological knowledge of the species-habitat relationship into pseudo-absence points by creating habitat envelopes that constrain the region from which points were randomly selected. We define a habitat envelope as an ecological representation of a species, or species feature's (e.g., nest) observed distribution (i.e., realized niche) based on a single attribute, or the spatial intersection of multiple attributes. We created HCMs for Northern Goshawk (Accipiter gentilis atricapillus) nest habitat during the breeding season across Utah forests with extant nest presence points and ecologically based pseudo-absence points using logistic regression. Predictor variables were derived from 30-m USDA Landfire and 250-m Forest Inventory and Analysis (FIA) map products. These habitat-envelope-based models were then compared to null envelope models which use traditional practices for generating pseudo-absences. Models were assessed for fit and predictive capability using metrics such as kappa, thresholdindependent receiver operating characteristic (ROC) plots, adjusted deviance (Dadj2), and cross-validation, and were also assessed for ecological relevance. For all cases, habitat envelope-based models outperformed null envelope models and were more ecologically relevant, suggesting that incorporating biological knowledge into pseudo-absence point generation is a powerful tool for species habitat assessments. Furthermore, given some a priori knowledge of the species-habitat relationship, ecologically based pseudo-absence points can be applied to any species, ecosystem, data resolution, and spatial extent. ?? 2007 by the Ecological Society of America.
Foraging Behaviour in Magellanic Woodpeckers Is Consistent with a Multi-Scale Assessment of Tree Quality

PubMed Central

Vergara, Pablo M.; Soto, Gerardo E.; Rodewald, Amanda D.; Meneses, Luis O.; Pérez-Hernández, Christian G.

2016-01-01

Theoretical models predict that animals should make foraging decisions after assessing the quality of available habitat, but most models fail to consider the spatio-temporal scales at which animals perceive habitat availability. We tested three foraging strategies that explain how Magellanic woodpeckers (Campephilus magellanicus) assess the relative quality of trees: 1) Woodpeckers with local knowledge select trees based on the available trees in the immediate vicinity. 2) Woodpeckers lacking local knowledge select trees based on their availability at previously visited locations. 3) Woodpeckers using information from long-term memory select trees based on knowledge about trees available within the entire landscape. We observed foraging woodpeckers and used a Brownian Bridge Movement Model to identify trees available to woodpeckers along foraging routes. Woodpeckers selected trees with a later decay stage than available trees. Selection models indicated that preferences of Magellanic woodpeckers were based on clusters of trees near the most recently visited trees, thus suggesting that woodpeckers use visual cues from neighboring trees. In a second analysis, Cox’s proportional hazards models showed that woodpeckers used information consolidated across broader spatial scales to adjust tree residence times. Specifically, woodpeckers spent more time at trees with larger diameters and in a more advanced stage of decay than trees available along their routes. These results suggest that Magellanic woodpeckers make foraging decisions based on the relative quality of trees that they perceive and memorize information at different spatio-temporal scales. PMID:27416115
Foraging Behaviour in Magellanic Woodpeckers Is Consistent with a Multi-Scale Assessment of Tree Quality.

PubMed

Vergara, Pablo M; Soto, Gerardo E; Moreira-Arce, Darío; Rodewald, Amanda D; Meneses, Luis O; Pérez-Hernández, Christian G

2016-01-01

Theoretical models predict that animals should make foraging decisions after assessing the quality of available habitat, but most models fail to consider the spatio-temporal scales at which animals perceive habitat availability. We tested three foraging strategies that explain how Magellanic woodpeckers (Campephilus magellanicus) assess the relative quality of trees: 1) Woodpeckers with local knowledge select trees based on the available trees in the immediate vicinity. 2) Woodpeckers lacking local knowledge select trees based on their availability at previously visited locations. 3) Woodpeckers using information from long-term memory select trees based on knowledge about trees available within the entire landscape. We observed foraging woodpeckers and used a Brownian Bridge Movement Model to identify trees available to woodpeckers along foraging routes. Woodpeckers selected trees with a later decay stage than available trees. Selection models indicated that preferences of Magellanic woodpeckers were based on clusters of trees near the most recently visited trees, thus suggesting that woodpeckers use visual cues from neighboring trees. In a second analysis, Cox's proportional hazards models showed that woodpeckers used information consolidated across broader spatial scales to adjust tree residence times. Specifically, woodpeckers spent more time at trees with larger diameters and in a more advanced stage of decay than trees available along their routes. These results suggest that Magellanic woodpeckers make foraging decisions based on the relative quality of trees that they perceive and memorize information at different spatio-temporal scales.
Household survey analysis of the impact of comprehensive strategies to improve the expanded programme on immunisation at the county level in western China, 2006–2010

PubMed Central

Zhou, Yuqing; Xing, Yi; Liang, Xiaofeng; Yue, Chenyan; Zhu, Xu; Hipgrave, David

2016-01-01

Objective To evaluate interventions to improve routine vaccination coverage and caregiver knowledge in China's remote west, where routine immunisation is relatively weak. Design Prospective pre–post (2006–2010) evaluation in project counties; retrospective comparison based on 2004 administrative data at baseline and surveyed post-intervention (2010) data in selected non-project counties. Setting Four project counties and one non-project county in each of four provinces. Participants 3390 children in project counties at baseline, and 3299 in project and 830 in non-project counties post-intervention; and 3279 caregivers at baseline, and 3389 in project and 830 in non-project counties post-intervention. Intervention Multicomponent inexpensive knowledge-strengthening and service-strengthening and innovative, multisectoral engagement. Data collection Standard 30-cluster household surveys of vaccine coverage and caregiver interviews pre-intervention and post-intervention in each project county. Similar surveys in one non-project county selected by local authorities in each province post-intervention. Administrative data on vaccination coverage in non-project counties at baseline. Primary outcome measures Changes in vaccine coverage between baseline and project completion (2010); comparative caregiver knowledge in all counties in 2010. Analysis Crude (χ2) analysis of changes and differences in vaccination coverage and related knowledge. Multiple logistic regression to assess associations with timely coverage. Results Timely coverage of four routine vaccines increased by 21% (p<0.001) and hepatitis B (HepB) birth dose by 35% (p<0.001) over baseline in project counties. Comparison with non-project counties revealed secular improvement in most provinces, except new vaccine coverage was mostly higher in project counties. Ethnicity, province, birthplace, vaccination site, dual-parental out-migration and parental knowledge had significant associations with coverage. Knowledge increased for all variables but one in project counties (highest p<0.05) and was substantially higher than in non-project counties (p<0.01). Conclusions Comprehensive but inexpensive strategies improved vaccination coverage and caretaker knowledge in western China. Establishing multisectoral leadership, involving the education sector and including immunisation in public-sector performance standards, are affordable and effective interventions. PMID:26966053
Novel harmonic regularization approach for variable selection in Cox's proportional hazards model.

PubMed

Chu, Ge-Jin; Liang, Yong; Wang, Jia-Xuan

2014-01-01

Variable selection is an important issue in regression and a number of variable selection methods have been proposed involving nonconvex penalty functions. In this paper, we investigate a novel harmonic regularization method, which can approximate nonconvex Lq (1/2 < q < 1) regularizations, to select key risk factors in the Cox's proportional hazards model using microarray gene expression data. The harmonic regularization method can be efficiently solved using our proposed direct path seeking approach, which can produce solutions that closely approximate those for the convex loss function and the nonconvex regularization. Simulation results based on the artificial datasets and four real microarray gene expression datasets, such as real diffuse large B-cell lymphoma (DCBCL), the lung cancer, and the AML datasets, show that the harmonic regularization method can be more accurate for variable selection than existing Lasso series methods.
Microbes in Mascara: Hypothesis-Driven Research in a Nonmajor Biology Lab †

PubMed Central

Burleson, Kathryn M.; Martinez-Vaz, Betsy M.

2011-01-01

In this laboratory exercise, students were taught concepts of microbiology and scientific process through an everyday activity — cosmetic use. The students’ goals for the lab were to develop a hypothesis regarding microbial contamination in cosmetics, learn techniques to culture and differentiate microorganisms from cosmetics, and propose best practices in cosmetics use based on their findings. Prior to the lab, students took a pretest to assess their knowledge of scientific hypotheses, microbiology, and cosmetic safety. In the first week, students were introduced to microbiological concepts and methodologies, and cosmetic terminology and safety. Students completed a hypothesis-writing exercise before formulating and testing their own hypotheses regarding cosmetic contamination. Students provided a cosmetic of their own and, in consultation with their lab group, chose one product for testing. Samples were serially diluted and plated on a variety of selective media. In the second week, students analyzed their plates to determine the presence and diversity of microbes and if their hypotheses were supported. Students completed a worksheet of their results and were given a posttest to assess their knowledge. Average test scores improved from 5.2 (pretest) to 7.8 (posttest), with p-values < 0.0001. Seventy-nine percent (79%) of students correctly identified hypotheses that were not falsifiable or lacked variables, and 89% of students improved their scores on questions concerning safe cosmetic use. Ninety-one percent (91%) of students demonstrated increased knowledge of microbial concepts and methods. Based on our results, this lab is an easy, yet effective, way to enhance knowledge of scientific concepts for nonmajors, while maintaining relevance to everyday life. PMID:23653761
The Mediating Roles of Internal Context Variables in the Relationship between Distributed Leadership Perceptions and Continuous Change Behaviours of Public School Teachers

ERIC Educational Resources Information Center

Kondakci, Yasar; Zayim, Merve; Beycioglu, Kadir; Sincar, Mehmet; Ugurlu, Celal T

2016-01-01

This study aims at building a theoretical base for continuous change in education and using this base to test the mediating roles of two key contextual variables, knowledge sharing and trust, in the relationship between the distributed leadership perceptions and continuous change behaviours of teachers. Data were collected from 687 public school…
Parent Knowledge and Attitudes About School-Based Hepatitis B Immunization Programs.

ERIC Educational Resources Information Center

Middleman, Amy B.; Guajardo, Andrea D.; Sunwoo, Edward; Sansaricq, Kim M.

2002-01-01

Surveyed parents of students in the Houston Independent School District to determine preferences regarding immunization clinic site and preferred consent procedures for a Hepatitis B immunization program. Results indicated a significant lack of parent knowledge regarding the Hepatitis B virus. Demographic variables influenced parents' knowledge…
Genetic variability in Jatropha curcas L. from diallel crossing.

PubMed

Ribeiro, D O; Silva-Mann, R; Alvares-Carvalho, S V; Souza, E M S; Vasconcelos, M C; Blank, A F

2017-05-18

Physic nut (Jatropha curcas L.) presents high oilseed yield and low production cost. However, technical-scientific knowledge on this crop is still limited. This study aimed to evaluate and estimate the genetic variability of hybrids obtained from dialell crossing. Genetic variability was carried out using ISSR molecular markers. For genetic variability, nine primers were used, and six were selected with 80.7% polymorphism. Genetic similarity was obtained using the NTSYS pc. 2.1 software, and cluster analysis was obtained by the UPGMA method. Mean genetic similarity was 58.4% among hybrids; the most divergent pair was H1 and H10 and the most similar pair was H9 and H10. ISSR PCR markers provided a quick and highly informative system for DNA fingerprinting, and also allowed establishing genetic relationships of Jatropha hybrids.
Variable selection in discrete survival models including heterogeneity.

PubMed

Groll, Andreas; Tutz, Gerhard

2017-04-01

Several variable selection procedures are available for continuous time-to-event data. However, if time is measured in a discrete way and therefore many ties occur models for continuous time are inadequate. We propose penalized likelihood methods that perform efficient variable selection in discrete survival modeling with explicit modeling of the heterogeneity in the population. The method is based on a combination of ridge and lasso type penalties that are tailored to the case of discrete survival. The performance is studied in simulation studies and an application to the birth of the first child.
[Cognitive capacity in advanced age: initial results of the Berlin Aging Study].

PubMed

Lindenberger, U; Baltes, P B

1995-01-01

This study reports data on intellectual functioning in old and very old age from the Berlin Aging Study (N = 516; age range = 70-103 years; mean age = 85 years). A psychometric battery of 14 tests was used to assess five cognitive abilities: reasoning, memory, and perceptual speed from the broad fluid-mechanical as well as knowledge and fluency from the broad crystallized-pragmatic domains. Cognitive abilities had a negative linear relationship with age, with more pronounced age-based reductions in fluid-mechanical than crystallized-pragmatic abilities. At the same time, ability intercorrelations formed a highly positive manifold, and did not follow the fluid-crystallized distinction. Interindividual variability was of about equal magnitude across the entire age range studied. There was, however, no evidence for substantial sex differences. As to origins of individual differences, indicators of sensory and sensorimotor functioning were more powerful predictors of intellectual functioning than cultural-biographical variables, and the two sets of predictors were, consistent with theoretical expectations, differentially related to measures of fluid-mechanical (perceptual speed) and crystallized pragmatic (knowledge) functioning. Results, in general indicative of sizeable and general losses with age, are consistent with the view that aging-induced biological influences are a prominent source of individual differences in intellectual functioning in old and very old age. Longitudinal follow-ups are underway to examine the role of cohort effects, selective mortality, and interindividual differences in change trajectories.
Knowledge, attitude and practices regarding HIV/AIDS among adult fishermen in coastal areas of Karachi

PubMed Central

2014-01-01

Background Migrant populations are at high risk of Human Immuno Deficiency Virus infection (HIV) and Acquired Immunodeficiency Syndrome (AIDS). Studies of HIV/AIDS knowledge, attitudes and practices among fishermen in developing countries have shown gaps in knowledge and fear of contagion with ambivalent attitudes towards HIV/AIDS and inconsistent universal precautions adherence. The aim of this study was to determine the knowledge, attitude and practices regarding HIV/AIDS among adult fishermen in a coastal area of Karachi, Pakistan. Methods Community based cross sectional study was conducted among fishermen in coastal area of Karachi from June to September 2012. A total of 297 adult fishermen were selected by using simple random sampling technique from different sectors of coastal village. Data were collected using a structured validated questionnaire. The frequency distribution of both dependent and independent variables were worked out. Comparisons of knowledge, attitude and practices regarding HIV/AIDS by socio-demographic characteristics were made using logistic regression. Results Out of 297 fishermen, majority had in-appropriate knowledge (93.6%), negative attitude (75.8%) and less adherent sexual practices (91.6%). In univariate analysis, lower education and higher income were significantly associated (OR 2.25, 95% CI, 1.11, 4.55), (OR = 3.04 CI 1.03-9.02, p value 0.04) with negative attitude and un-safe practices towards HIV/AIDS respectively, whereas no significant association of socio-economic characteristics with knowledge, attitude and practices were observed in multivariate analysis. Conclusions This study suggests that fishermen had very poor knowledge, negative attitudes towards HIV and AIDS and had unsafe sexual practices which suggest that they lack the basic understanding of HIV/AIDS infection. Extensive health education campaign should be provided to the vulnerable sections of the society for the control of HIV/AIDS. PMID:24886122

Expert Knowledge-Based Automatic Sleep Stage Determination by Multi-Valued Decision Making Method

NASA Astrophysics Data System (ADS)

Wang, Bei; Sugi, Takenao; Kawana, Fusae; Wang, Xingyu; Nakamura, Masatoshi

In this study, an expert knowledge-based automatic sleep stage determination system working on a multi-valued decision making method is developed. Visual inspection by a qualified clinician is adopted to obtain the expert knowledge database. The expert knowledge database consists of probability density functions of parameters for various sleep stages. Sleep stages are determined automatically according to the conditional probability. Totally, four subjects were participated. The automatic sleep stage determination results showed close agreements with the visual inspection on sleep stages of awake, REM (rapid eye movement), light sleep and deep sleep. The constructed expert knowledge database reflects the distributions of characteristic parameters which can be adaptive to variable sleep data in hospitals. The developed automatic determination technique based on expert knowledge of visual inspection can be an assistant tool enabling further inspection of sleep disorder cases for clinical practice.
The Use of Research-Based Instructional Strategies in Introductory Physics: Where do Faculty Leave the Innovation-Decision Process?

NASA Astrophysics Data System (ADS)

Henderson, Charles; Dancy, Melissa; Niewiadomska-Bugaj, Magdalena

2013-03-01

During the Fall of 2008 a web survey was completed by a representative sample of 722 United States physics faculty. In this talk we will briefly present summary statistics to describe faculty knowledge about and use of 24 specific research-based instructional strategies (RBIS). We will then analyze the results based on a four stage model of the innovation-decision process: knowledge, trial, continuation, and high use. The largest losses occur at the continuation stage, with approximately 1/3 of faculty discontinuing use of all RBIS after trying one or more of these strategies. These results suggest that common dissemination strategies are good at creating knowledge about RBIS and motivation to try a RBIS, but more work is needed to support faculty during implementation and continued use of RBIS. Based on a logistic regression analysis, only nine of the 20 potential predictor variables measured were statistically significant when controlling for other variables. Faculty age, institutional type, and percentage of job related to teaching were not found to be correlated with knowledge or use at any stage. High research productivity and large class sizes were not found to be barriers to use of at least some RBIS. Supported by NSF #0715698.
Knowledge Management System Model for Learning Organisations

ERIC Educational Resources Information Center

Amin, Yousif; Monamad, Roshayu

2017-01-01

Based on the literature of knowledge management (KM), this paper reports on the progress of developing a new knowledge management system (KMS) model with components architecture that are distributed over the widely-recognised socio-technical system (STS) aspects to guide developers for selecting the most applicable components to support their KM…
Stigma and its correlates in patients with schizophrenia attending a general hospital psychiatric unit

PubMed Central

Singh, Aakanksha; Mattoo, Surendra K.; Grover, Sandeep

2016-01-01

Background: Very few studies from India have studied stigma experienced by patients with schizophrenia. Aim of the Study: To study stigma in patients with schizophrenia (in the form of internalized stigma, perceived stigma and social-participation-restriction stigma) and its relationship with specified demographic and clinical variables (demographic variables, clinical profile, level of psychopathology, knowledge about illness, and insight). Materials and Methods: Selected by purposive random sampling, 100 patients with schizophrenia in remission were evaluated on internalized stigma of mental illness scale (ISMIS), explanatory model interview catalog stigma scale, participation scale (P-scale), positive and negative syndrome scale for schizophrenia, global assessment of functioning scale, scale to assess unawareness of mental disorder, and knowledge of mental illness scale. Results: On ISMIS scale, 81% patients experienced alienation and 45% exhibited stigma resistance. Stereotype endorsement was seen in 26% patients, discrimination experience was faced by 21% patients, and only 16% patients had social withdrawal. Overall, 29% participants had internalized stigma when total ISMIS score was taken into consideration. On P-scale, 67% patients experienced significant restriction, with a majority reporting moderate to mild restriction. In terms of associations between stigma and sociodemographic variables, no consistent correlations emerged, except for those who were not on paid job, had higher participation restriction. Of the clinical variables, level of functioning was the only consistent predictor of stigma. While better knowledge about the disorder was associated with lower level of stigma, there was no association between stigma and insight. Conclusion: Significant proportion of patients with schizophrenia experience stigma and stigma is associated with lower level of functioning and better knowledge about illness is associated with lower level of stigma. PMID:28066007
Knowledge of the Relationships between Oral Health, Diabetes, Body Mass Index and Lifestyle among Students at the Kuwait University Health Sciences Center, Kuwait.

PubMed

Ali, Dena A

2016-01-01

To evaluate the level of knowledge regarding the relationships between oral health, diabetes, body mass index (BMI; obesity) and lifestyle among students of the Health Sciences Center (HSC), Kuwait, and to explore any possible correlation between students' oral health knowledge, BMI and lifestyle choices. A stratified random sample was proportionally selected according to the size of each faculty from the 1,799 students. The questionnaire was divided into 3 sections (i.e. demographics, evaluation of oral health knowledge in relation to diabetes, and evaluation of diabetes knowledge in relation to lifestyle) and distributed to 532 students. Oral health knowledge was categorized as limited, reasonable or knowledgeable. Lifestyle was classified as healthy or nonhealthy. The BMI was calculated as weight (kg) divided by the square of the height (m). ANOVA and χ2 tests were used to test for differences between independent variables. A Pearson correlation coefficient test was used to assess correlations. p < 0.05 was considered statistically significant. Of the 532 questionnaires, 498 (93.6%) were completed. The mean knowledge score was 47.7 ± 25.2; of the 498 students, 235 (47.3%) had a BMI within the normal range, 184 (37.0%) were pre-obese and 67 (13.5%) were obese. Of the 498 students, 244 (49%) had a healthy lifestyle. There was no correlation between oral health knowledge and the other variables; however, there was a correlation between lifestyle and obesity. In this study, the majority of the students had limited knowledge of oral health in association with diabetes and lifestyle. More than half of the students fell in the pre-obese/obese range. © 2015 S. Karger AG, Basel.
Actor groups, related needs, and challenges at the climate downscaling interface

NASA Astrophysics Data System (ADS)

Rössler, Ole; Benestad, Rasmus; Diamando, Vlachogannis; Heike, Hübener; Kanamaru, Hideki; Pagé, Christian; Margarida Cardoso, Rita; Soares, Pedro; Maraun, Douglas; Kreienkamp, Frank; Christodoulides, Paul; Fischer, Andreas; Szabo, Peter

2016-04-01

At the climate downscaling interface, numerous downscaling techniques and different philosophies compete on being the best method in their specific terms. Thereby, it remains unclear to what extent and for which purpose these downscaling techniques are valid or even the most appropriate choice. A common validation framework that compares all the different available methods was missing so far. The initiative VALUE closes this gap with such a common validation framework. An essential part of a validation framework for downscaling techniques is the definition of appropriate validation measures. The selection of validation measures should consider the needs of the stakeholder: some might need a temporal or spatial average of a certain variable, others might need temporal or spatial distributions of some variables, still others might need extremes for the variables of interest or even inter-variable dependencies. Hence, a close interaction of climate data providers and climate data users is necessary. Thus, the challenge in formulating a common validation framework mirrors also the challenges between the climate data providers and the impact assessment community. This poster elaborates the issues and challenges at the downscaling interface as it is seen within the VALUE community. It suggests three different actor groups: one group consisting of the climate data providers, the other two groups being climate data users (impact modellers and societal users). Hence, the downscaling interface faces classical transdisciplinary challenges. We depict a graphical illustration of actors involved and their interactions. In addition, we identified four different types of issues that need to be considered: i.e. data based, knowledge based, communication based, and structural issues. They all may, individually or jointly, hinder an optimal exchange of data and information between the actor groups at the downscaling interface. Finally, some possible ways to tackle these issues are discussed.
Knowledge, attitude and practice GAP in family planning usage: an analysis of selected cities of Uttar Pradesh.

PubMed

Singh, Anjali; Singh, K K; Verma, Prashant

2016-01-01

The GAP between the knowledge of contraception and its actual practice is well recognized in the literature of family welfare studies. The present study assessed the relation between the level of knowledge and practice of contraception among the women and sought to explore the reasons behind the Knowledge, Attitude, and Practice - GAP (KAP GAP) regarding contraceptive users in six cities of Uttar Pradesh. Present analysis based on 17,643 currently married women aged 15 to 49. A Bivariate analysis ( χ 2 test) and a multivariable logistic regression were performed for the study. The highest percentages of respondents (women) were in the age group 35-49 (40-45 %) in all the districts considered. Knowledge of contraceptives was almost universal; tubal ligation and pill were the commonly known methods. Information about the contraceptive methods was mostly obtained through the husband. In the present study, there was a highly significant association ( p < 0.01) of age group, educational status of respondents, the number of living children, the wealth of the respondent, media exposure and husband's education with the variable KAP GAP for all six cities. Health concern issues in all the districts were the most prominent reason for not using contraception. There differences in the socioeconomic and demographic factors exist, which lead to KAP GAP in the family planning (FP) usages. Therefore, in designing effective family planning programme, there is a need to understand the various factors which influence the practice of contraception.
Efficient Results in Semantic Interoperability for Health Care. Findings from the Section on Knowledge Representation and Management.

PubMed

Soualmia, L F; Charlet, J

2016-11-10

To summarize excellent current research in the field of Knowledge Representation and Management (KRM) within the health and medical care domain. We provide a synopsis of the 2016 IMIA selected articles as well as a related synthetic overview of the current and future field activities. A first step of the selection was performed through MEDLINE querying with a list of MeSH descriptors completed by a list of terms adapted to the KRM section. The second step of the selection was completed by the two section editors who separately evaluated the set of 1,432 articles. The third step of the selection consisted of a collective work that merged the evaluation results to retain 15 articles for peer-review. The selection and evaluation process of this Yearbook's section on Knowledge Representation and Management has yielded four excellent and interesting articles regarding semantic interoperability for health care by gathering heterogeneous sources (knowledge and data) and auditing ontologies. In the first article, the authors present a solution based on standards and Semantic Web technologies to access distributed and heterogeneous datasets in the domain of breast cancer clinical trials. The second article describes a knowledge-based recommendation system that relies on ontologies and Semantic Web rules in the context of chronic diseases dietary. The third article is related to concept-recognition and text-mining to derive common human diseases model and a phenotypic network of common diseases. In the fourth article, the authors highlight the need for auditing the SNOMED CT. They propose to use a crowdbased method for ontology engineering. The current research activities further illustrate the continuous convergence of Knowledge Representation and Medical Informatics, with a focus this year on dedicated tools and methods to advance clinical care by proposing solutions to cope with the problem of semantic interoperability. Indeed, there is a need for powerful tools able to manage and interpret complex, large-scale and distributed datasets and knowledge bases, but also a need for user-friendly tools developed for the clinicians in their daily practice.
Negotiation Performance: Antecedents, Outcomes, and Training Recommendations

DTIC Science & Technology

2011-10-01

Tutorial Cognitive Apprenticeships Instructional Conversations Independent Programmed Instruction Computer-Based Instruction I Rr La...procedural knowledge, as well as the more distal antecedents of individual difference variables (e.g., cognitive ability , personality) and psychological...individual difference variables (e.g., cognitive ability , personality) and psychological processes (e.g., cognitive , motivational, and emotional). This
Autonomous mental development with selective attention, object perception, and knowledge representation

NASA Astrophysics Data System (ADS)

Ban, Sang-Woo; Lee, Minho

2008-04-01

Knowledge-based clustering and autonomous mental development remains a high priority research topic, among which the learning techniques of neural networks are used to achieve optimal performance. In this paper, we present a new framework that can automatically generate a relevance map from sensory data that can represent knowledge regarding objects and infer new knowledge about novel objects. The proposed model is based on understating of the visual what pathway in our brain. A stereo saliency map model can selectively decide salient object areas by additionally considering local symmetry feature. The incremental object perception model makes clusters for the construction of an ontology map in the color and form domains in order to perceive an arbitrary object, which is implemented by the growing fuzzy topology adaptive resonant theory (GFTART) network. Log-polar transformed color and form features for a selected object are used as inputs of the GFTART. The clustered information is relevant to describe specific objects, and the proposed model can automatically infer an unknown object by using the learned information. Experimental results with real data have demonstrated the validity of this approach.
Automatic variable selection method and a comparison for quantitative analysis in laser-induced breakdown spectroscopy

NASA Astrophysics Data System (ADS)

Duan, Fajie; Fu, Xiao; Jiang, Jiajia; Huang, Tingting; Ma, Ling; Zhang, Cong

2018-05-01

In this work, an automatic variable selection method for quantitative analysis of soil samples using laser-induced breakdown spectroscopy (LIBS) is proposed, which is based on full spectrum correction (FSC) and modified iterative predictor weighting-partial least squares (mIPW-PLS). The method features automatic selection without artificial processes. To illustrate the feasibility and effectiveness of the method, a comparison with genetic algorithm (GA) and successive projections algorithm (SPA) for different elements (copper, barium and chromium) detection in soil was implemented. The experimental results showed that all the three methods could accomplish variable selection effectively, among which FSC-mIPW-PLS required significantly shorter computation time (12 s approximately for 40,000 initial variables) than the others. Moreover, improved quantification models were got with variable selection approaches. The root mean square errors of prediction (RMSEP) of models utilizing the new method were 27.47 (copper), 37.15 (barium) and 39.70 (chromium) mg/kg, which showed comparable prediction effect with GA and SPA.
Book Selection, Collection Development, and Bounded Rationality.

ERIC Educational Resources Information Center

Schwartz, Charles A.

1989-01-01

Reviews previously proposed schemes of classical rationality in book selection, describes new approaches to rational choice behavior, and presents a model of book selection based on bounded rationality in a garbage can decision process. The role of tacit knowledge and symbolic content in the selection process are also discussed. (102 references)…
Integrative Bayesian variable selection with gene-based informative priors for genome-wide association studies.

PubMed

Zhang, Xiaoshuai; Xue, Fuzhong; Liu, Hong; Zhu, Dianwen; Peng, Bin; Wiemels, Joseph L; Yang, Xiaowei

2014-12-10

Genome-wide Association Studies (GWAS) are typically designed to identify phenotype-associated single nucleotide polymorphisms (SNPs) individually using univariate analysis methods. Though providing valuable insights into genetic risks of common diseases, the genetic variants identified by GWAS generally account for only a small proportion of the total heritability for complex diseases. To solve this "missing heritability" problem, we implemented a strategy called integrative Bayesian Variable Selection (iBVS), which is based on a hierarchical model that incorporates an informative prior by considering the gene interrelationship as a network. It was applied here to both simulated and real data sets. Simulation studies indicated that the iBVS method was advantageous in its performance with highest AUC in both variable selection and outcome prediction, when compared to Stepwise and LASSO based strategies. In an analysis of a leprosy case-control study, iBVS selected 94 SNPs as predictors, while LASSO selected 100 SNPs. The Stepwise regression yielded a more parsimonious model with only 3 SNPs. The prediction results demonstrated that the iBVS method had comparable performance with that of LASSO, but better than Stepwise strategies. The proposed iBVS strategy is a novel and valid method for Genome-wide Association Studies, with the additional advantage in that it produces more interpretable posterior probabilities for each variable unlike LASSO and other penalized regression methods.
An assessment of environmental literacy and analysis of predictors of responsible environmental behavior held by secondary teachers in Hualien County of Taiwan

NASA Astrophysics Data System (ADS)

Hsu, Shih-Jang

The major purpose of this study was to determine the relative contribution of nine variables in predicting teachers' responsible environmental behavior (REB). The theoretic framework of this study was based on the Hines model, the Hungerford and Volk model, and the environmental literacy framework proposed by Environmental Literacy Assessment Consortium. A nine-page instrument was administered by mailed questionnaire to 300 randomly selected secondary teachers in Hualien County of Taiwan with a 78.7% response rate. Correlation and stepwise multiple regression analyses were conducted. The following conclusions were drawn: (1) For all the respondents, all the nine environmental literacy variables were significant correlates of REB. These correlates included: perceived knowledge of environmental action strategies (KNOW; r =.46), intention to act (IA; r =.46), perceived skill in using environmental action strategies (SKILL; r =.45), perceived knowledge of environmental problems and issues (KISSU; r =.34), environmental sensitivity (r =.28), environmental responsibility (r =.27), perceived knowledge of ecology and environmental science (r =.27), locus of control (r =.27), and environmental attitudes (r =.21). (2) When only the nine environmental literacy variables were considered, the most parsimonious set of predictors of REB for all the teachers included: (a) KNOW, (Rsp2 =.2116); (b) IA, (Rsp2 =.0916); and (c) SKILL, (Rsp2 =.0205). For the urban teachers, the most parsimonious set of predictors included: (a) IA (Rsp2 =.2559); (b) SKILL (Rsp2.0926); and (c) environmental responsibility (Rsp2 =.0219). For the rural teachers, the most parsimonious set of predictors included: (a) KNOW (Rsp2 =.1872); (b) IA (Rsp2 =.0816); and (c) KISSU (Rsp2 =.0318). (3) When the environmental literacy variables as well as demographic and experience variables were considered, the most parsimonious set of predictors for all the teachers included: (a) KNOW, (Rsp2 =.2834); (b) IA, (Rsp2 =.0696); (c) area of residence, (Rsp2 =.0174); and (d) SKILL, (Rsp2 =.0163). For the urban teachers, the most parsimonious set of predictors included: (a) IA (Rsp2 =.3199); (b) SKILL (Rsp2 =.0840); (c) major sources of environmental information (Rsp2 =.0432); and (d) membership in environmental organizations, (Rsp2 =.0240). Implications for environmental education program development and instructional practice were presented. Recommendations for further research were also provided.
Effectiveness of Key Knowledge Spreader Identification in Online Communities of Practice: A Simulation Study from Network Perspective

ERIC Educational Resources Information Center

Cao, Yu

2017-01-01

With the rapid development of online communities of practice (CoPs), how to identify key knowledge spreader (KKS) in online CoPs has grown up to be a hot issue. In this paper, we construct a network with variable clustering based on Holme-Kim model to represent CoPs, a simple dynamics of knowledge sharing is considered. Kendall's Tau coefficient…
Sugars in peach fruit: a breeding perspective

PubMed Central

Cirilli, Marco; Bassi, Daniele; Ciacciulli, Angelo

2016-01-01

The last decade has been characterized by a decrease in peach (Prunus persica) fruit consumption in many countries, foremost due to unsatisfactory quality. The sugar content is one of the most important quality traits perceived by consumers, and the development of novel peach cultivars with sugar-enhanced content is a primary objective of breeding programs to revert the market inertia. Nevertheless, the progress reachable through classical phenotypic selection is limited by the narrow genetic bases of peach breeding material and by the complex quantitative nature of the trait, which is deeply affected by environmental conditions and agronomical management. The development of molecular markers applicable in MAS or MAB has become an essential strategy to boost the selection efficiency. Despite the enormous advances in ‘omics’ sciences, providing powerful tools for plant genotyping, the identification of the genetic bases of sugar-related traits is hindered by the lack of adequate phenotyping methods that are able to address strong within-plant variability. This review provides an overview of the current knowledge of the metabolic pathways and physiological mechanisms regulating sugar accumulation in peach fruit, the main advances in phenotyping approaches and genetic background, and finally addressing new research priorities and prospective for breeders. PMID:26816618
Consultant-Client Relationship and Knowledge Transfer in Small- and Medium-Sized Enterprises Change Processes.

PubMed

Martinez, Luis F; Ferreira, Aristides I; Can, Amina B

2016-04-01

Based on Szulanski's knowledge transfer model, this study examined how the communicational, motivational, and sharing of understanding variables influenced knowledge transfer and change processes in small- and medium-sized enterprises, particularly under projects developed by funded programs. The sample comprised 144 entrepreneurs, mostly male (65.3%) and mostly ages 35 to 45 years (40.3%), who filled an online questionnaire measuring the variables of "sharing of understanding," "motivation," "communication encoding competencies," "source credibility," "knowledge transfer," and "organizational change." Data were collected between 2011 and 2012 and measured the relationship between clients and consultants working in a Portuguese small- and medium-sized enterprise-oriented action learning program. To test the hypotheses, structural equation modeling was conducted to identify the antecedents of sharing of understanding, motivational, and communicational variables, which were positively correlated with the knowledge transfer between consultants and clients. This transfer was also positively correlated with organizational change. Overall, the study provides important considerations for practitioners and academicians and establishes new avenues for future studies concerning the issues of consultant-client relationship and the efficacy of Government-funded programs designed to improve performance of small- and medium-sized enterprises. © The Author(s) 2016.
A fast chaos-based image encryption scheme with a dynamic state variables selection mechanism

NASA Astrophysics Data System (ADS)

Chen, Jun-xin; Zhu, Zhi-liang; Fu, Chong; Yu, Hai; Zhang, Li-bo

2015-03-01

In recent years, a variety of chaos-based image cryptosystems have been investigated to meet the increasing demand for real-time secure image transmission. Most of them are based on permutation-diffusion architecture, in which permutation and diffusion are two independent procedures with fixed control parameters. This property results in two flaws. (1) At least two chaotic state variables are required for encrypting one plain pixel, in permutation and diffusion stages respectively. Chaotic state variables produced with high computation complexity are not sufficiently used. (2) The key stream solely depends on the secret key, and hence the cryptosystem is vulnerable against known/chosen-plaintext attacks. In this paper, a fast chaos-based image encryption scheme with a dynamic state variables selection mechanism is proposed to enhance the security and promote the efficiency of chaos-based image cryptosystems. Experimental simulations and extensive cryptanalysis have been carried out and the results prove the superior security and high efficiency of the scheme.
Novel Harmonic Regularization Approach for Variable Selection in Cox's Proportional Hazards Model

PubMed Central

Chu, Ge-Jin; Liang, Yong; Wang, Jia-Xuan

2014-01-01

Variable selection is an important issue in regression and a number of variable selection methods have been proposed involving nonconvex penalty functions. In this paper, we investigate a novel harmonic regularization method, which can approximate nonconvex Lq (1/2 < q < 1) regularizations, to select key risk factors in the Cox's proportional hazards model using microarray gene expression data. The harmonic regularization method can be efficiently solved using our proposed direct path seeking approach, which can produce solutions that closely approximate those for the convex loss function and the nonconvex regularization. Simulation results based on the artificial datasets and four real microarray gene expression datasets, such as real diffuse large B-cell lymphoma (DCBCL), the lung cancer, and the AML datasets, show that the harmonic regularization method can be more accurate for variable selection than existing Lasso series methods. PMID:25506389
Examining the Moderating Effect of Disability Status on the Relationship between Trauma Symptomatology and Select Career Variables

ERIC Educational Resources Information Center

Strauser, David R.; Lustig, Daniel C.; Uruk, Aye Ciftci

2006-01-01

In the current study, the authors examined whether the influence of trauma symptomatology on select career variables differs based on disability status. A total of 131 college students and 81 individuals with disabilities completed the "Career Thoughts Inventory," "My Vocational Situation," "Developmental Work Personality…

Characterizing the Optical Variability of Bright Blazars: Variability-Based Selection of Fermi Active Galactic Nuclei

DTIC Science & Technology

2012-11-20

10′. We do not apply cosmological redshift corrections here for blazar selection. Similar to the conclusions drawn from Figure 4, there is clear...effects. For example, the observed blazar characteristic damping timescale τblz,obs (after correcting for cosmological redshift) should be shortened in
Rank-based estimation in the {ell}1-regularized partly linear model for censored outcomes with application to integrated analyses of clinical predictors and gene expression data.

PubMed

Johnson, Brent A

2009-10-01

We consider estimation and variable selection in the partial linear model for censored data. The partial linear model for censored data is a direct extension of the accelerated failure time model, the latter of which is a very important alternative model to the proportional hazards model. We extend rank-based lasso-type estimators to a model that may contain nonlinear effects. Variable selection in such partial linear model has direct application to high-dimensional survival analyses that attempt to adjust for clinical predictors. In the microarray setting, previous methods can adjust for other clinical predictors by assuming that clinical and gene expression data enter the model linearly in the same fashion. Here, we select important variables after adjusting for prognostic clinical variables but the clinical effects are assumed nonlinear. Our estimator is based on stratification and can be extended naturally to account for multiple nonlinear effects. We illustrate the utility of our method through simulation studies and application to the Wisconsin prognostic breast cancer data set.
Furthering Our Understanding of Therapist Knowledge and Attitudinal Measurement in Youth Community Mental Health.

PubMed

Okamura, Kelsie H; Hee, Puanani J; Jackson, David; Nakamura, Brad J

2018-02-19

Examining therapist evidence-based practice (EBP) knowledge seems an important step for supporting successful implementation. Advances in implementation science suggest a distinction between practice specific (i.e., knowing which practices are derived from the evidence base) and EBP process (i.e., integrating research evidence, clinical experience, client characteristics, and monitoring outcomes) knowledge. An examination of how these knowledge types are measured and relate to attitudes appears warranted. In our sample of 58 youth community therapists, both practice specific and EBP process knowledge accounted for EBP attitude scores, which varied by therapist demographic variables. Implications for measurement of therapist constructs and future research in identifying therapist predictors of EBP use and youth clinical improvement are discussed.
Paternity and Nested-within-Family Marker Assisted Selection in Space Planted Red Clover Nurseries

USDA-ARS?s Scientific Manuscript database

Presented is a cost effective marker assisted selection methodology that utilizes individual plant phenotypes, seed production based knowledge of maternity, molecular marker determined paternity, and nested within halfsib family linkage relationships. Combining all above listed components, selection...
Validating a Web-based Diabetes Education Program in continuing nursing education: knowledge and competency change and user perceptions on usability and quality

PubMed Central

2014-01-01

Background Nurses as the members of health care professionals need to improve their knowledge and competencies particularly in diabetes mellitus through continuing nursing education programs. E-learning is an indirect method of training that can meet nurses’ educational needs. This study is aimed at validating a web-based diabetes education program through measurement of nurses’ knowledge and clinical competency in diabetes and nurses’ perception about its usability and quality. Methods This Quasi-experimental research was conducted on a single group of 31 nurses employed in hospitals affiliated with Shiraz University of Medical Sciences. We used a 125 MCQ knowledge test and Objective Structured Clinical Exam (OSCE) to measure knowledge and clinical competency of nurses in diabetes before and after intervention. A Learning Management System (LMS) was designed to provide educational content in the form of 12 multimedia electronic modules, interactive tests; a forum and learning activities. Nurses were trained for two months in this system after which the post-test was administered. Each nurse completed two questionnaires for measurement of their perceptions on usability and quality. We used descriptive statistics for demographic and descriptive data analysis. Paired t-test was used to compare pre- and post-data using SPSS. Results The findings showed significant differences in knowledge scores (p < 0.001), total score of clinical competencies (p < 0.001), and all ten assessed clinical competencies. The range of ratings given by participants varied on the six usability variables of Web-based training (2.96-4.23 from 5) and eight quality variables of Web-based training (3.58-4.37 from 5). Conclusion Web-based education increased nurses’ knowledge and competencies in diabetes. They positively evaluated Web-based learning usability and quality. It is hoped that this course will have a positive clinical outcomes. PMID:26086025
Validating a Web-based Diabetes Education Program in continuing nursing education: knowledge and competency change and user perceptions on usability and quality.

PubMed

Moattari, Marzieh; Moosavinasab, Elham; Dabbaghmanesh, Mohammad Hossein; ZarifSanaiey, Nahid

2014-01-01

Nurses as the members of health care professionals need to improve their knowledge and competencies particularly in diabetes mellitus through continuing nursing education programs. E-learning is an indirect method of training that can meet nurses' educational needs. This study is aimed at validating a web-based diabetes education program through measurement of nurses' knowledge and clinical competency in diabetes and nurses' perception about its usability and quality. This Quasi-experimental research was conducted on a single group of 31 nurses employed in hospitals affiliated with Shiraz University of Medical Sciences. We used a 125 MCQ knowledge test and Objective Structured Clinical Exam (OSCE) to measure knowledge and clinical competency of nurses in diabetes before and after intervention. A Learning Management System (LMS) was designed to provide educational content in the form of 12 multimedia electronic modules, interactive tests; a forum and learning activities. Nurses were trained for two months in this system after which the post-test was administered. Each nurse completed two questionnaires for measurement of their perceptions on usability and quality. We used descriptive statistics for demographic and descriptive data analysis. Paired t-test was used to compare pre- and post-data using SPSS. The findings showed significant differences in knowledge scores (p < 0.001), total score of clinical competencies (p < 0.001), and all ten assessed clinical competencies. The range of ratings given by participants varied on the six usability variables of Web-based training (2.96-4.23 from 5) and eight quality variables of Web-based training (3.58-4.37 from 5). Web-based education increased nurses' knowledge and competencies in diabetes. They positively evaluated Web-based learning usability and quality. It is hoped that this course will have a positive clinical outcomes.
Prevalence of photoprotection and its associated factors in risk group for skin cancer in Teresina, Piauí*

PubMed Central

Cardoso, Fernanda Ayres de Morais e Silva; Mesquita, Gerardo Vasconcelos; Campelo, Viriato; Martins, Maria do Carmo de Carvalho e; Almeida, Camila Aparecida Pinheiro Landim; Rabelo, Regina Silva; Rocha, Amanda Eugênia Almeida; dos Santos, Jadson Lener Oliveira

2017-01-01

Background The incidence of skin cancer has increased worldwide, particularly melanoma rates, which had a mean development of 2.6 % a year in the last 10 years. The agreement on the relation between long-term or chronic exposure to the sun and the emergence of these neoplasias has made several workers who perform activities exposed to solar radiation to form a risk group for the development of skin cancer, community health agents included. OBJECTIVES To analyze the prevalence of sunscreen-use-related factors to skin cancer in a labor risk group. METHODOLOGY Cross-sectional study with community health agents selected through simple random sampling. After collecting data using semi-structured interviews, a descriptive analysis was performed for the qualitative variables, bivariate analysis was employed for checking the association between sunscreen use and sociodemographic, occupational and knowledge about skin variables, and multivariate analysis was conducted to check independent variables associated to sunscreen use. A 5% significance level was used. Results Of 261 health gents selected, 243 were able to participate in the study. The prevalence rate of sunscreen use was 34.2% (95% CI: 28.2-40.2). Factors associated with sunscreen use were female sex, advanced age, use of sunscreen in situations when the skin got burnt, knowledge of the negative effects of the sun on the skin and skin cancer history. Conclusions The prevalence found reveals that there is a need for implementing educational strategies in health services regarding photoprotection. PMID:28538880
Understanding Knowledge Sharing between IT Professionals--An Integration of Social Cognitive and Social Exchange Theory

ERIC Educational Resources Information Center

Tsai, Ming-Tien; Cheng, Nai-Chang

2012-01-01

The research includes various constructs based on social exchange theory and social cognitive theory. This study mainly explored the relationships among organisational justice, trust, commitment and knowledge-sharing cognition and verified their mediating effects through two variables of trust and commitment. A survey utilising a questionnaire was…
Teacher Preparation Practices in Kenya and the 21st Century Learning: A Moral Obligation

ERIC Educational Resources Information Center

Kafwa, Nabwire Opata; Gaudience, Obondo; Kisaka, Sella Terrie

2015-01-01

Teacher preparation practices are indices used to measure quality teacher besides other variables. Whereas the current teacher preparation is test scores based inclining to cognitive knowledge, a good teacher preparation practices is a holistic development in nature oriented towards character, skills and knowledge. To embed teacher preparation in…
Verbal Knowledge, Working Memory, and Processing Speed as Predictors of Verbal Learning in Older Adults

ERIC Educational Resources Information Center

Rast, Philippe

2011-01-01

The present study aimed at modeling individual differences in a verbal learning task by means of a latent structured growth curve approach based on an exponential function that yielded 3 parameters: initial recall, learning rate, and asymptotic performance. Three cognitive variables--speed of information processing, verbal knowledge, working…
A Project to Enhance Superintendents' Knowledge and Application of Characteristics of High Quality Teachers

ERIC Educational Resources Information Center

Pummill, Bret L.; Edson, Jerry C.; Loftin, Michelle M.; Robinson, Matthew A.

2011-01-01

This report describes a problem based learning project focusing on superintendents' knowledge of the characteristics of high quality teachers. Current research findings offer evidence teacher quality is an important school variable related to student achievement. School district leaders are faced with the problem of identifying the characteristics…
Promoting Access: The Role of Organization and Awareness Factors. Elementary Subjects Center Series No. 1.

ERIC Educational Resources Information Center

Prawat, Richard S.

This review presents a framework that accommodates static and dynamic approaches to comprehension of students' transfer of knowledge and skill. The review focuses on three sets of variables considered fundamental to students' ability to access intellectual resources in potentially relevant situations: knowledge bases, strategies, and dispositions.…
Web-video-mining-supported workflow modeling for laparoscopic surgeries.

PubMed

Liu, Rui; Zhang, Xiaoli; Zhang, Hao

2016-11-01

As quality assurance is of strong concern in advanced surgeries, intelligent surgical systems are expected to have knowledge such as the knowledge of the surgical workflow model (SWM) to support their intuitive cooperation with surgeons. For generating a robust and reliable SWM, a large amount of training data is required. However, training data collected by physically recording surgery operations is often limited and data collection is time-consuming and labor-intensive, severely influencing knowledge scalability of the surgical systems. The objective of this research is to solve the knowledge scalability problem in surgical workflow modeling with a low cost and labor efficient way. A novel web-video-mining-supported surgical workflow modeling (webSWM) method is developed. A novel video quality analysis method based on topic analysis and sentiment analysis techniques is developed to select high-quality videos from abundant and noisy web videos. A statistical learning method is then used to build the workflow model based on the selected videos. To test the effectiveness of the webSWM method, 250 web videos were mined to generate a surgical workflow for the robotic cholecystectomy surgery. The generated workflow was evaluated by 4 web-retrieved videos and 4 operation-room-recorded videos, respectively. The evaluation results (video selection consistency n-index ≥0.60; surgical workflow matching degree ≥0.84) proved the effectiveness of the webSWM method in generating robust and reliable SWM knowledge by mining web videos. With the webSWM method, abundant web videos were selected and a reliable SWM was modeled in a short time with low labor cost. Satisfied performances in mining web videos and learning surgery-related knowledge show that the webSWM method is promising in scaling knowledge for intelligent surgical systems. Copyright © 2016 Elsevier B.V. All rights reserved.
Teacher Quality Indicators as Predictors of Instructional Assessment Practices in Science Classrooms in Secondary Schools in Barbados

NASA Astrophysics Data System (ADS)

Ogunkola, Babalola J.; Archer-Bradshaw, Ramona E.

2013-02-01

This study investigated the self-reported instructional assessment practices of a selected sample of secondary school science teachers in Barbados. The study sought to determine if there were statistically significant differences in the instructional assessment practices of teachers based on their sex and teacher quality (teaching experience, professional qualification and teacher academic qualification). It also sought to determine the extent to which each of these four selected variables individually and jointly affected the teachers' report of their instructional assessment practices. A sample of 55 science teachers from nine secondary schools in Barbados was randomly selected to participate in this study. Data was collected by means of a survey and was analyzed using the means and standard deviations of the instructional assessment practices scores and linear, multiple and binary logistic regression. The results of the study were such that the majority of the sample reported good overall instructional assessment practices while only a few participants reported moderate assessment practices. The instructional assessment practices in the area of student knowledge were mostly moderate as indicated by the sample. There were no statistically significant differences between or among the mean scores of the teachers' reported instructional assessment practices based on sex ( t = 0.10; df = 53; p = 0.992), teaching experience ( F[4,50] = 1.766; p = 0.150), the level of professional qualification (F[3,45] = 0.2117; p = 0.111) or the level of academic qualification (F[2,52] = 0.504; p = 0.607). The independent variables (teacher sex, teaching experience, teacher professional qualification or teacher academic qualification) were not significant predictors of the instructional assessment practices scores. However, teacher sex was a significant predictor of the teachers' report of good instructional assessment practices. The study also found that the joint effect of the variables teacher sex, teaching experience, teacher professional qualification and teacher academic qualification was not significant in predicting the instructional assessment practices scores of the science teachers. However, the joint effect of these variables was statistically significant ( X 2 = 18.482; df = 10; p = 0.047) in predicting the teachers' reported use of good instructional assessment practices. The best predictor of teachers' report of good instructional assessment practices, though not statistically significant, was the diploma in education professional qualification.
VARIABLE SELECTION FOR REGRESSION MODELS WITH MISSING DATA

PubMed Central

Garcia, Ramon I.; Ibrahim, Joseph G.; Zhu, Hongtu

2009-01-01

We consider the variable selection problem for a class of statistical models with missing data, including missing covariate and/or response data. We investigate the smoothly clipped absolute deviation penalty (SCAD) and adaptive LASSO and propose a unified model selection and estimation procedure for use in the presence of missing data. We develop a computationally attractive algorithm for simultaneously optimizing the penalized likelihood function and estimating the penalty parameters. Particularly, we propose to use a model selection criterion, called the ICQ statistic, for selecting the penalty parameters. We show that the variable selection procedure based on ICQ automatically and consistently selects the important covariates and leads to efficient estimates with oracle properties. The methodology is very general and can be applied to numerous situations involving missing data, from covariates missing at random in arbitrary regression models to nonignorably missing longitudinal responses and/or covariates. Simulations are given to demonstrate the methodology and examine the finite sample performance of the variable selection procedures. Melanoma data from a cancer clinical trial is presented to illustrate the proposed methodology. PMID:20336190
Dynamic Educational e-Content Selection Using Multiple Criteria in Web-Based Personalized Learning Environments.

ERIC Educational Resources Information Center

Manouselis, Nikos; Sampson, Demetrios

This paper focuses on the way a multi-criteria decision making methodology is applied in the case of agent-based selection of offered learning objects. The problem of selection is modeled as a decision making one, with the decision variables being the learner model and the learning objects' educational description. In this way, selection of…
The neural representation of social status in the extended face-processing network.

PubMed

Koski, Jessica E; Collins, Jessica A; Olson, Ingrid R

2017-12-01

Social status is a salient cue that shapes our perceptions of other people and ultimately guides our social interactions. Despite the pervasive influence of status on social behavior, how information about the status of others is represented in the brain remains unclear. Here, we tested the hypothesis that social status information is embedded in our neural representations of other individuals. Participants learned to associate faces with names, job titles that varied in associated status, and explicit markers of reputational status (star ratings). Trained stimuli were presented in an functional magnetic resonance imaging experiment where participants performed a target detection task orthogonal to the variable of interest. A network of face-selective brain regions extending from the occipital lobe to the orbitofrontal cortex was localized and served as regions of interest. Using multivoxel pattern analysis, we found that face-selective voxels in the lateral orbitofrontal cortex - a region involved in social and nonsocial valuation, could decode faces based on their status. Similar effects were observed with two different status manipulations - one based on stored semantic knowledge (e.g., different careers) and one based on learned reputation (e.g., star ranking). These data suggest that a face-selective region of the lateral orbitofrontal cortex may contribute to the perception of social status, potentially underlying the preferential attention and favorable biases humans display toward high-status individuals. © 2017 Federation of European Neuroscience Societies and John Wiley & Sons Ltd.
Relationship among Food-Safety Knowledge, Beliefs, and Risk-Reduction Behavior in University Students in Japan

ERIC Educational Resources Information Center

Takeda, Sayaka; Akamatsu, Rie; Horiguchi, Itsuko; Marui, Eiji

2011-01-01

Objective: To identify whether university students who have both food-safety knowledge and beliefs perform risk-reduction behaviors. Design: Cross-sectional research using a questionnaire that included food-safety knowledge, perceptions, risk-reduction behavior, stages for the selection of safer food based on the Transtheoretical Model, and…
Evaluation of Residential Consumers Knowledge of Wireless Network Security and Its Correlation with Identity Theft

ERIC Educational Resources Information Center

Kpaduwa, Fidelis Iheanyi

2010-01-01

This current quantitative correlational research study evaluated the residential consumers' knowledge of wireless network security and its relationship with identity theft. Data analysis was based on a sample of 254 randomly selected students. All the study participants completed a survey questionnaire designed to measure their knowledge of…
Knowledge modeling tool for evidence-based design.

PubMed

Durmisevic, Sanja; Ciftcioglu, Ozer

2010-01-01

The aim of this study is to take evidence-based design (EBD) to the next level by activating available knowledge, integrating new knowledge, and combining them for more efficient use by the planning and design community. This article outlines a framework for a performance-based measurement tool that can provide the necessary decision support during the design or evaluation of a healthcare environment by estimating the overall design performance of multiple variables. New knowledge in EBD adds continuously to complexity (the "information explosion"), and it becomes impossible to consider all aspects (design features) at the same time, much less their impact on final building performance. How can existing knowledge and the information explosion in healthcare-specifically the domain of EBD-be rendered manageable? Is it feasible to create a computational model that considers many design features and deals with them in an integrated way, rather than one at a time? The found evidence is structured and readied for computation through a "fuzzification" process. The weights are calculated using an analytical hierarchy process. Actual knowledge modeling is accomplished through a fuzzy neural tree structure. The impact of all inputs on the outcome-in this case, patient recovery-is calculated using sensitivity analysis. Finally, the added value of the model is discussed using a hypothetical case study of a patient room. The proposed model can deal with the complexities of various aspects and the relationships among variables in a coordinated way, allowing existing and new pieces of evidence to be integrated in a knowledge tree structure that facilitates understanding of the effects of various design interventions on overall design performance.

Analysis of influencing factors on public perception in contaminated site management: Simulation by structural equation modeling at four sites in China.

PubMed

Li, Xiaonuo; Chen, Weiping; Cundy, Andrew B; Chang, Andrew C; Jiao, Wentao

2018-03-15

Public perception towards contaminated site management, a not readily quantifiable latent parameter, was linked through structural equation modeling in this paper to 22 measurable/observable manifest variables associated with the extent of information dissemination and public knowledge of soil pollution, attitude towards remediation policies, and participation in risk mitigation processes. Data obtained through a survey of 412 community residents at four remediation sites in China were employed in the model validation. The outcomes showed that public perception towards contaminated site management might be explained through selected measurable parameters in five categories, namely information disclosure, knowledge of soil pollution, expectations of remediation and redevelopment outcomes, public participation, and site policy, along with their interactions. Among these, information dissemination and attitude towards management policies exhibited significant influence in promoting positive public perception. Based on these examples, responsible agencies therefore should focus on public accessibility to reliable information, and encourage public inputs into policies for contaminated site management, in order to gain public confidence during remediation and regeneration projects. Copyright © 2018 Elsevier Ltd. All rights reserved.
Complement Coercion: The Joint Effects of Type and Typicality.

PubMed

Zarcone, Alessandra; McRae, Ken; Lenci, Alessandro; Padó, Sebastian

2017-01-01

Complement coercion ( begin a book → reading ) involves a type clash between an event-selecting verb and an entity-denoting object, triggering a covert event ( reading ). Two main factors involved in complement coercion have been investigated: the semantic type of the object (event vs. entity), and the typicality of the covert event ( the author began a book → writing ). In previous research, reading times have been measured at the object. However, the influence of the typicality of the subject-object combination on processing an aspectual verb such as begin has not been studied. Using a self-paced reading study, we manipulated semantic type and subject-object typicality, exploiting German word order to measure reading times at the aspectual verb. These variables interacted at the target verb. We conclude that both type and typicality probabilistically guide expectations about upcoming input. These results are compatible with an expectation-based view of complement coercion and language comprehension more generally in which there is rapid interaction between what is typically viewed as linguistic knowledge, and what is typically viewed as domain general knowledge about how the world works.
Complement Coercion: The Joint Effects of Type and Typicality

PubMed Central

Zarcone, Alessandra; McRae, Ken; Lenci, Alessandro; Padó, Sebastian

2017-01-01

Complement coercion (begin a book →reading) involves a type clash between an event-selecting verb and an entity-denoting object, triggering a covert event (reading). Two main factors involved in complement coercion have been investigated: the semantic type of the object (event vs. entity), and the typicality of the covert event (the author began a book →writing). In previous research, reading times have been measured at the object. However, the influence of the typicality of the subject–object combination on processing an aspectual verb such as begin has not been studied. Using a self-paced reading study, we manipulated semantic type and subject–object typicality, exploiting German word order to measure reading times at the aspectual verb. These variables interacted at the target verb. We conclude that both type and typicality probabilistically guide expectations about upcoming input. These results are compatible with an expectation-based view of complement coercion and language comprehension more generally in which there is rapid interaction between what is typically viewed as linguistic knowledge, and what is typically viewed as domain general knowledge about how the world works. PMID:29225585
Punishment induced behavioural and neurophysiological variability reveals dopamine-dependent selection of kinematic movement parameters

PubMed Central

Galea, Joseph M.; Ruge, Diane; Buijink, Arthur; Bestmann, Sven; Rothwell, John C.

2013-01-01

Action selection describes the high-level process which selects between competing movements. In animals, behavioural variability is critical for the motor exploration required to select the action which optimizes reward and minimizes cost/punishment, and is guided by dopamine (DA). The aim of this study was to test in humans whether low-level movement parameters are affected by punishment and reward in ways similar to high-level action selection. Moreover, we addressed the proposed dependence of behavioural and neurophysiological variability on DA, and whether this may underpin the exploration of kinematic parameters. Participants performed an out-and-back index finger movement and were instructed that monetary reward and punishment were based on its maximal acceleration (MA). In fact, the feedback was not contingent on the participant’s behaviour but pre-determined. Blocks highly-biased towards punishment were associated with increased MA variability relative to blocks with either reward or without feedback. This increase in behavioural variability was positively correlated with neurophysiological variability, as measured by changes in cortico-spinal excitability with transcranial magnetic stimulation over the primary motor cortex. Following the administration of a DA-antagonist, the variability associated with punishment diminished and the correlation between behavioural and neurophysiological variability no longer existed. Similar changes in variability were not observed when participants executed a pre-determined MA, nor did DA influence resting neurophysiological variability. Thus, under conditions of punishment, DA-dependent processes influence the selection of low-level movement parameters. We propose that the enhanced behavioural variability reflects the exploration of kinematic parameters for less punishing, or conversely more rewarding, outcomes. PMID:23447607
Analysis of the GRNs Inference by Using Tsallis Entropy and a Feature Selection Approach

NASA Astrophysics Data System (ADS)

Lopes, Fabrício M.; de Oliveira, Evaldo A.; Cesar, Roberto M.

An important problem in the bioinformatics field is to understand how genes are regulated and interact through gene networks. This knowledge can be helpful for many applications, such as disease treatment design and drugs creation purposes. For this reason, it is very important to uncover the functional relationship among genes and then to construct the gene regulatory network (GRN) from temporal expression data. However, this task usually involves data with a large number of variables and small number of observations. In this way, there is a strong motivation to use pattern recognition and dimensionality reduction approaches. In particular, feature selection is specially important in order to select the most important predictor genes that can explain some phenomena associated with the target genes. This work presents a first study about the sensibility of entropy methods regarding the entropy functional form, applied to the problem of topology recovery of GRNs. The generalized entropy proposed by Tsallis is used to study this sensibility. The inference process is based on a feature selection approach, which is applied to simulated temporal expression data generated by an artificial gene network (AGN) model. The inferred GRNs are validated in terms of global network measures. Some interesting conclusions can be drawn from the experimental results, as reported for the first time in the present paper.
Machine learning for toxicity characterization of organic chemical emissions using USEtox database: Learning the structure of the input space.

PubMed

Marvuglia, Antonino; Kanevski, Mikhail; Benetto, Enrico

2015-10-01

Toxicity characterization of chemical emissions in Life Cycle Assessment (LCA) is a complex task which usually proceeds via multimedia (fate, exposure and effect) models attached to models of dose-response relationships to assess the effects on target. Different models and approaches do exist, but all require a vast amount of data on the properties of the chemical compounds being assessed, which are hard to collect or hardly publicly available (especially for thousands of less common or newly developed chemicals), therefore hampering in practice the assessment in LCA. An example is USEtox, a consensual model for the characterization of human toxicity and freshwater ecotoxicity. This paper places itself in a line of research aiming at providing a methodology to reduce the number of input parameters necessary to run multimedia fate models, focusing in particular to the application of the USEtox toxicity model. By focusing on USEtox, in this paper two main goals are pursued: 1) performing an extensive exploratory analysis (using dimensionality reduction techniques) of the input space constituted by the substance-specific properties at the aim of detecting particular patterns in the data manifold and estimating the dimension of the subspace in which the data manifold actually lies; and 2) exploring the application of a set of linear models, based on partial least squares (PLS) regression, as well as a nonlinear model (general regression neural network--GRNN) in the seek for an automatic selection strategy of the most informative variables according to the modelled output (USEtox factor). After extensive analysis, the intrinsic dimension of the input manifold has been identified between three and four. The variables selected as most informative may vary according to the output modelled and the model used, but for the toxicity factors modelled in this paper the input variables selected as most informative are coherent with prior expectations based on scientific knowledge of toxicity factors modelling. Thus the outcomes of the analysis are promising for the future application of the approach to other portions of the model, affected by important data gaps, e.g., to the calculation of human health effect factors. Copyright © 2015. Published by Elsevier Ltd.
A Research Agenda on Assessing and Remediating Home Dampness and Mold to Reduce Dampness-Related Health Effects

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

Mendell, Mark J.

2015-06-01

This report briefly summarizes, based on recent review articles and selected more recent research reports, current scientific knowledge on two topics: assessing unhealthy levels of indoor D/M in homes and remediating home dampness-related problems to protect health. Based on a comparison of current scientific knowledge to that required to support effective, evidence-based, health-protective policies on home D/M, gaps in knowledge are highlighted, prior questions and research questions specified, and necessary research activities and approaches recommended.
Ontology-Based Gap Analysis for Technology Selection: A Knowledge Management Framework for the Support of Equipment Purchasing Processes

NASA Astrophysics Data System (ADS)

Macris, Aristomenis M.; Georgakellos, Dimitrios A.

Technology selection decisions such as equipment purchasing and supplier selection are decisions of strategic importance to companies. The nature of these decisions usually is complex, unstructured and thus, difficult to be captured in a way that will be efficiently reusable. Knowledge reusability is of paramount importance since it enables users participate actively in process design/redesign activities stimulated by the changing technology selection environment. This paper addresses the technology selection problem through an ontology-based approach that captures and makes reusable the equipment purchasing process and assists in identifying (a) the specifications requested by the users' organization, (b) those offered by various candidate vendors' organizations and (c) in performing specifications gap analysis as a prerequisite for effective and efficient technology selection. This approach has practical appeal, operational simplicity, and the potential for both immediate and long-term strategic impact. An example from the iron and steel industry is also presented to illustrate the approach.
Progress and challenges in the application of artificial intelligence to computational fluid dynamics

NASA Technical Reports Server (NTRS)

Andrews, Alison E.

1987-01-01

An approach to analyzing CFD knowledge-based systems is proposed which is based, in part, on the concept of knowledge-level analysis. Consideration is given to the expert cooling fan design system, the PAN AIR knowledge system, grid adaptation, and expert zonal grid generation. These AI/CFD systems demonstrate that current AI technology can be successfully applied to well-formulated problems that are solved by means of classification or selection of preenumerated solutions.
Unraveling the development of scientific literacy: Domain-specific inquiry support in a system of cognitive and social interactions

NASA Astrophysics Data System (ADS)

Tabak, Iris Ellen

The goal of this dissertation was to study how to harness technological tools in service of establishing a climate of inquiry in science classrooms. The research is a design experiment drawing on sociocultural and cognitive theory. As part of the BGuILE project, I developed software to support observational research of natural selection, and a complementary high school unit on evolution. Focusing on urban schools, I employed interpretive methods to examine learning as it unfolds in the classroom. I present design principles for realizing a climate of inquiry in technology-infused classrooms. This research contributes to technology design, teaching practice and educational and cognitive research. My pedagogical approach, Domain-Specific Strategic Support (DSSS), helps students analyze and synthesize primary data by making experts' considerations of content knowledge explicit. Students query data by constructing questions from a selection of comparison and variable types that are privileged in the domain. Students organize their data according to evidence categories that comprise a natural selection argument. I compared the inquiry process of contrastive cases: an honor group, a regular group and a lower track group. DSSS enabled students at different achievement levels to set up systematic comparisons, and construct empirically-based explanations. Prior knowledge and inquiry experience influenced spontaneous strategy use. Teacher guidance compensated for lack of experience, and enabled regular level students to employ strategies as frequently as honor students. I extend earlier research by proposing a taxonomy of both general and domain-specific reflective inquiry strategies. I argue that software, teacher and curriculum work in concert to sustain a climate of inquiry. Teachers help realize the potential that technological tools invite. Teachers reinforce software supports by encouraging students utilize technological tools, and by modeling their use. They also establish classroom norms that reflect scientific values. Discussions at the computer allow teachers to provide just-in-time guidance on inquiry actions. Whole class discussions afford sharing insights across groups, and relating finding to normative knowledge. Pretest to posttest improvements in both conceptual and strategic knowledge suggest that DSSS helps reconcile the tension that can exist between content and process goals in inquiry settings.
Invited review: study design considerations for clinical research in veterinary radiology and radiation oncology.

PubMed

Scrivani, Peter V; Erb, Hollis N

2013-01-01

High quality clinical research is essential for advancing knowledge in the areas of veterinary radiology and radiation oncology. Types of clinical research studies may include experimental studies, method-comparison studies, and patient-based studies. Experimental studies explore issues relative to pathophysiology, patient safety, and treatment efficacy. Method-comparison studies evaluate agreement between techniques or between observers. Patient-based studies investigate naturally acquired disease and focus on questions asked in clinical practice that relate to individuals or populations (e.g., risk, accuracy, or prognosis). Careful preplanning and study design are essential in order to achieve valid results. A key point to planning studies is ensuring that the design is tailored to the study objectives. Good design includes a comprehensive literature review, asking suitable questions, selecting the proper sample population, collecting the appropriate data, performing the correct statistical analyses, and drawing conclusions supported by the available evidence. Most study designs are classified by whether they are experimental or observational, longitudinal or cross-sectional, and prospective or retrospective. Additional features (e.g., controlled, randomized, or blinded) may be described that address bias. Two related challenging aspects of study design are defining an important research question and selecting an appropriate sample population. The sample population should represent the target population as much as possible. Furthermore, when comparing groups, it is important that the groups are as alike to each other as possible except for the variables of interest. Medical images are well suited for clinical research because imaging signs are categorical or numerical variables that might be predictors or outcomes of diseases or treatments. © 2013 Veterinary Radiology & Ultrasound.
The Knowledge-Based Reasoning of Physical Education Teachers: A Comparison between Groups with Different Expertise

ERIC Educational Resources Information Center

Reuker, Sabine

2017-01-01

The study addresses professional vision, including the abilities of selective attention and knowledge-based reasoning. This article focuses on the latter ability. Groups with different sport-specific and pedagogical expertise (n = 60) were compared according to their observation and interpretation of sport activities in a four-field design. The…
Developing a Knowledge Base for Educational Leadership and Management in East Asia

ERIC Educational Resources Information Center

Hallinger, Philip

2011-01-01

The role of school leadership in educational reform has reached the status of a truism, and led to major changes in school leader recruitment, selection, training and appraisal. While similar policy trends are evident in East Asia, the empirical knowledge base underlying these measures is distorted and lacking in validation. This paper begins by…
Effect of Instructional Strategy on Critical Thinking and Content Knowledge: Using Problem-Based Learning in the Secondary Classroom

ERIC Educational Resources Information Center

Burris, Scott; Garton, Bryan L.

2007-01-01

The purpose of the study was to determine the effect of problem-based learning (PBL) on critical thinking ability and content knowledge among selected secondary agriculture students in Missouri. The study employed a quasi-experimental, non-equivalent comparison group design. The treatment consisted of two instructional strategies: problem-based…
An Intelligent Learning Diagnosis System for Web-Based Thematic Learning Platform

ERIC Educational Resources Information Center

Huang, Chenn-Jung; Liu, Ming-Chou; Chu, San-Shine; Cheng, Chih-Lun

2007-01-01

This work proposes an intelligent learning diagnosis system that supports a Web-based thematic learning model, which aims to cultivate learners' ability of knowledge integration by giving the learners the opportunities to select the learning topics that they are interested, and gain knowledge on the specific topics by surfing on the Internet to…
The importance of immune gene variability (MHC) in evolutionary ecology and conservation

PubMed Central

Sommer, Simone

2005-01-01

Genetic studies have typically inferred the effects of human impact by documenting patterns of genetic differentiation and levels of genetic diversity among potentially isolated populations using selective neutral markers such as mitochondrial control region sequences, microsatellites or single nucleotide polymorphism (SNPs). However, evolutionary relevant and adaptive processes within and between populations can only be reflected by coding genes. In vertebrates, growing evidence suggests that genetic diversity is particularly important at the level of the major histocompatibility complex (MHC). MHC variants influence many important biological traits, including immune recognition, susceptibility to infectious and autoimmune diseases, individual odours, mating preferences, kin recognition, cooperation and pregnancy outcome. These diverse functions and characteristics place genes of the MHC among the best candidates for studies of mechanisms and significance of molecular adaptation in vertebrates. MHC variability is believed to be maintained by pathogen-driven selection, mediated either through heterozygote advantage or frequency-dependent selection. Up to now, most of our knowledge has derived from studies in humans or from model organisms under experimental, laboratory conditions. Empirical support for selective mechanisms in free-ranging animal populations in their natural environment is rare. In this review, I first introduce general information about the structure and function of MHC genes, as well as current hypotheses and concepts concerning the role of selection in the maintenance of MHC polymorphism. The evolutionary forces acting on the genetic diversity in coding and non-coding markers are compared. Then, I summarise empirical support for the functional importance of MHC variability in parasite resistance with emphasis on the evidence derived from free-ranging animal populations investigated in their natural habitat. Finally, I discuss the importance of adaptive genetic variability with respect to human impact and conservation, and implications for future studies. PMID:16242022
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.)
Comparing Strategic Knowledge Gaps for Human Mars Settlement vs. Exploration

NASA Astrophysics Data System (ADS)

Mackenzie, B. A.

2012-06-01

We list knowledge needed to establish a permanent Mars base, compared that for round-trip human exploration missions. Topics include: site selection, reliable access to water, long term effects of contaminations, and in-situ materials production.
Toward a Knowledge Base for School Learning. Publication Series #93-5a.

ERIC Educational Resources Information Center

Wang, M. C.; And Others

The study explores the relative effects of a wide range of variables that influence learning, and whether three methods--content analysis, expert ratings, and meta-analysis--agree on whether and how strongly these variables influence learning, using the educational research literature and an expert survey. The presence of an emergent knowledge…
On the mid-infrared variability of candidate eruptive variables (exors): A comparison between Spitzer and WISE data

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

Antoniucci, S.; Giannini, T.; Li Causi, G.

2014-02-10

Aiming to statistically study the variability in the mid-IR of young stellar objects, we have compared the 3.6, 4.5, and 24 μm Spitzer fluxes of 1478 sources belonging to the C2D (Cores to Disks) legacy program with the WISE fluxes at 3.4, 4.6, and 22 μm. From this comparison, we have selected a robust sample of 34 variable sources. Their variations were classified per spectral Class (according to the widely accepted scheme of Class I/flat/II/III protostars), and per star forming region. On average, the number of variable sources decreases with increasing Class and is definitely higher in Perseus and Ophiuchusmore » than in Chamaeleon and Lupus. According to the paradigm Class ≡ Evolution, the photometric variability can be considered to be a feature more pronounced in less evolved protostars, and, as such, related to accretion processes. Moreover, our statistical findings agree with the current knowledge of star formation activity in different regions. The 34 selected variables were further investigated for similarities with known young eruptive variables, namely the EXors. In particular, we analyzed (1) the shape of the spectral energy distribution, (2) the IR excess over the stellar photosphere, (3) magnitude versus color variations, and (4) output parameters of model fitting. This first systematic search for EXors ends up with 11 bona fide candidates that can be considered as suitable targets for monitoring or future investigations.« less

Linking energy behaviour, attitude and habits with environmental predisposition and knowledge

NASA Astrophysics Data System (ADS)

Pothitou, Mary; Varga, Liz; Kolios, Athanasios J.; Gu, Sai

2017-04-01

The purpose of this paper is to present and discuss the findings of an empirical study that compares individuals' environmental predisposition and knowledge with their energy behaviour, attitude and habits. Additionally, the study attempts to correlate education level and household income with the above variables. The statistical analysis reveals significant correlations between environmental predisposition and knowledge and elements of individuals' energy attitudes, habits and behaviour. An unanticipated outcome from the principal component analysis was that household income, and to a lesser extent gender, is associated with energy-saving habits and behaviours. On further investigation, household income was found to be correlated with knowledge of greenhouse gas emissions and the number of laptops and electric showers owned per household. The study sample comprises 68 employees of an educational institution, which was selected as the first phase of research aiming to compare energy-saving behaviour at home and in the workplace.
Barriers to the implementation of practice guidelines in managing patients with nonvariceal upper gastrointestinal bleeding: A qualitative approach

PubMed Central

Hayes, Sean M; Murray, Suzanne; Dupuis, Martin; Dawes, Martin; Hawes, Ian A; Barkun, Alan N

2010-01-01

BACKGROUND/OBJECTIVE: Guidelines for the management of patients with nonvariceal upper gastrointestinal bleeding (NVUGIB) are inconsistently applied by health care providers, potentially resulting in suboptimal care and patient outcomes. A needs assessment was performed to assess health care providers’ barriers to the implementation of these guidelines in Canada. METHODS: Semistructured telephone interviews were conducted by trained research personnel with 22 selectively sampled health care professionals actively treating and managing NVUGIB patients, including emergency room physicians (ER), intensivists (ICU), gastroenterologists (GI), gastroenterology nurses and hospital administrators. Participants were chosen from a representative sample of six Canadian community- and academic-based hospitals that participated in a national Canadian audit on the management of NVUGIB. RESULTS: Participants reported substantive gaps in the implementation of NVUGIB guidelines that included the following: lack of knowledge of the specifics of the NVUGIB guidelines (ER, ICU, nurses); limited belief in the value of guidelines, especially in areas where evidence is lacking (ER, ICU); limited belief in the value of available tools to support implementation of guidelines (GI); lack of knowledge of the roles and responsibilities of health care professions and disciplines, and lack of effective collaboration skills (ER, ICU and GI); variability of knowledge and skills of health care professionals within professions (eg, variability of nurses’ knowledge and skills in endoscopic procedures); and perceived overuse of intravenous proton pump inhibitor treatment, with limited concern regarding cost or side effect implications (all participants). CONCLUSIONS: In the present study population, ER, ICU and nurses did not adhere to NVUGIB guidelines because they were neither aware of nor familiar with them, whereas the GI lack of adherence to NVUGIB guidelines was influenced more by attitudinal and contextual barriers. These findings can guide the design of multifaceted educational and behavioural interventions when attempting to effectively disseminate existing guidelines, and for guideline implementation into practice. PMID:20485702
Extending (Q)SARs to incorporate proprietary knowledge for regulatory purposes: A case study using aromatic amine mutagenicity.

PubMed

Ahlberg, Ernst; Amberg, Alexander; Beilke, Lisa D; Bower, David; Cross, Kevin P; Custer, Laura; Ford, Kevin A; Van Gompel, Jacky; Harvey, James; Honma, Masamitsu; Jolly, Robert; Joossens, Elisabeth; Kemper, Raymond A; Kenyon, Michelle; Kruhlak, Naomi; Kuhnke, Lara; Leavitt, Penny; Naven, Russell; Neilan, Claire; Quigley, Donald P; Shuey, Dana; Spirkl, Hans-Peter; Stavitskaya, Lidiya; Teasdale, Andrew; White, Angela; Wichard, Joerg; Zwickl, Craig; Myatt, Glenn J

2016-06-01

Statistical-based and expert rule-based models built using public domain mutagenicity knowledge and data are routinely used for computational (Q)SAR assessments of pharmaceutical impurities in line with the approach recommended in the ICH M7 guideline. Knowledge from proprietary corporate mutagenicity databases could be used to increase the predictive performance for selected chemical classes as well as expand the applicability domain of these (Q)SAR models. This paper outlines a mechanism for sharing knowledge without the release of proprietary data. Primary aromatic amine mutagenicity was selected as a case study because this chemical class is often encountered in pharmaceutical impurity analysis and mutagenicity of aromatic amines is currently difficult to predict. As part of this analysis, a series of aromatic amine substructures were defined and the number of mutagenic and non-mutagenic examples for each chemical substructure calculated across a series of public and proprietary mutagenicity databases. This information was pooled across all sources to identify structural classes that activate or deactivate aromatic amine mutagenicity. This structure activity knowledge, in combination with newly released primary aromatic amine data, was incorporated into Leadscope's expert rule-based and statistical-based (Q)SAR models where increased predictive performance was demonstrated. Copyright © 2016 Elsevier Inc. All rights reserved.
Manipulating measurement scales in medical statistical analysis and data mining: A review of methodologies

PubMed Central

Marateb, Hamid Reza; Mansourian, Marjan; Adibi, Peyman; Farina, Dario

2014-01-01

Background: selecting the correct statistical test and data mining method depends highly on the measurement scale of data, type of variables, and purpose of the analysis. Different measurement scales are studied in details and statistical comparison, modeling, and data mining methods are studied based upon using several medical examples. We have presented two ordinal–variables clustering examples, as more challenging variable in analysis, using Wisconsin Breast Cancer Data (WBCD). Ordinal-to-Interval scale conversion example: a breast cancer database of nine 10-level ordinal variables for 683 patients was analyzed by two ordinal-scale clustering methods. The performance of the clustering methods was assessed by comparison with the gold standard groups of malignant and benign cases that had been identified by clinical tests. Results: the sensitivity and accuracy of the two clustering methods were 98% and 96%, respectively. Their specificity was comparable. Conclusion: by using appropriate clustering algorithm based on the measurement scale of the variables in the study, high performance is granted. Moreover, descriptive and inferential statistics in addition to modeling approach must be selected based on the scale of the variables. PMID:24672565
Protein construct storage: Bayesian variable selection and prediction with mixtures.

PubMed

Clyde, M A; Parmigiani, G

1998-07-01

Determining optimal conditions for protein storage while maintaining a high level of protein activity is an important question in pharmaceutical research. A designed experiment based on a space-filling design was conducted to understand the effects of factors affecting protein storage and to establish optimal storage conditions. Different model-selection strategies to identify important factors may lead to very different answers about optimal conditions. Uncertainty about which factors are important, or model uncertainty, can be a critical issue in decision-making. We use Bayesian variable selection methods for linear models to identify important variables in the protein storage data, while accounting for model uncertainty. We also use the Bayesian framework to build predictions based on a large family of models, rather than an individual model, and to evaluate the probability that certain candidate storage conditions are optimal.
Order Selection for General Expression of Nonlinear Autoregressive Model Based on Multivariate Stepwise Regression

NASA Astrophysics Data System (ADS)

Shi, Jinfei; Zhu, Songqing; Chen, Ruwen

2017-12-01

An order selection method based on multiple stepwise regressions is proposed for General Expression of Nonlinear Autoregressive model which converts the model order problem into the variable selection of multiple linear regression equation. The partial autocorrelation function is adopted to define the linear term in GNAR model. The result is set as the initial model, and then the nonlinear terms are introduced gradually. Statistics are chosen to study the improvements of both the new introduced and originally existed variables for the model characteristics, which are adopted to determine the model variables to retain or eliminate. So the optimal model is obtained through data fitting effect measurement or significance test. The simulation and classic time-series data experiment results show that the method proposed is simple, reliable and can be applied to practical engineering.
An integrated, quality by design (QbD) approach for design, development and optimization of orally disintegrating tablet formulation of carbamazepine.

PubMed

Mishra, Saurabh M; Rohera, Bhagwan D

2017-11-01

The objective of the present study was to design and develop a formulation for orally disintegrating tablets (ODTs) of carbamazepine using quality by design principles. The target product profile (TPP) and quality target product profile (QTPP) of ODTs were identified. Risk assessment was carried out by leveraging prior knowledge and experience to define the criticality of factors based on their impact by Ishikawa fishbone diagram and preliminary hazard analysis tool. Box-Behnken response surface methodology was used to study the effect of critical factors on various attributes of ODTs. The independent factors selected were compression pressure (X 1 ), concentration of sublimating agent (volatile material) (X 2 ), disintegrant concentration (X 3 ) and the responses were tablet crushing strength, tablet porosity, disintegration time, water absorption time, tablet friability and drug dissolution. ANOVA and lack of fit test illustrated that selected independent variables had significant effect on the response variables, and excellent correlation was observed between actual and predicted values. Optimization by desirability function indicated that compression pressure, X 1 (1534 lbs), ammonium bicarbonate concentration, X 2 (7.68%) and Kollidon ® CL-SF concentration, X 3 (6%) were optimum to prepare ODT formulation of carbamazepine of desired attributes complying with QTPP. Thus, in the present study, a high level of assurance was established for ODT product quality and performance.
A bioinformatics approach for precision medicine off-label drug drug selection among triple negative breast cancer patients.

PubMed

Cheng, Lijun; Schneider, Bryan P; Li, Lang

2016-07-01

Cancer has been extensively characterized on the basis of genomics. The integration of genetic information about cancers with data on how the cancers respond to target based therapy to help to optimum cancer treatment. The increasing usage of sequencing technology in cancer research and clinical practice has enormously advanced our understanding of cancer mechanisms. The cancer precision medicine is becoming a reality. Although off-label drug usage is a common practice in treating cancer, it suffers from the lack of knowledge base for proper cancer drug selections. This eminent need has become even more apparent considering the upcoming genomics data. In this paper, a personalized medicine knowledge base is constructed by integrating various cancer drugs, drug-target database, and knowledge sources for the proper cancer drugs and their target selections. Based on the knowledge base, a bioinformatics approach for cancer drugs selection in precision medicine is developed. It integrates personal molecular profile data, including copy number variation, mutation, and gene expression. By analyzing the 85 triple negative breast cancer (TNBC) patient data in the Cancer Genome Altar, we have shown that 71.7% of the TNBC patients have FDA approved drug targets, and 51.7% of the patients have more than one drug target. Sixty-five drug targets are identified as TNBC treatment targets and 85 candidate drugs are recommended. Many existing TNBC candidate targets, such as Poly (ADP-Ribose) Polymerase 1 (PARP1), Cell division protein kinase 6 (CDK6), epidermal growth factor receptor, etc., were identified. On the other hand, we found some additional targets that are not yet fully investigated in the TNBC, such as Gamma-Glutamyl Hydrolase (GGH), Thymidylate Synthetase (TYMS), Protein Tyrosine Kinase 6 (PTK6), Topoisomerase (DNA) I, Mitochondrial (TOP1MT), Smoothened, Frizzled Class Receptor (SMO), etc. Our additional analysis of target and drug selection strategy is also fully supported by the drug screening data on TNBC cell lines in the Cancer Cell Line Encyclopedia. The proposed bioinformatics approach lays a foundation for cancer precision medicine. It supplies much needed knowledge base for the off-label cancer drug usage in clinics. © The Author 2016. Published by Oxford University Press on behalf of the American Medical Informatics Association. All rights reserved. For Permissions, please email: journals.permissions@oup.com.
Structure identification in fuzzy inference using reinforcement learning

NASA Technical Reports Server (NTRS)

Berenji, Hamid R.; Khedkar, Pratap

1993-01-01

In our previous work on the GARIC architecture, we have shown that the system can start with surface structure of the knowledge base (i.e., the linguistic expression of the rules) and learn the deep structure (i.e., the fuzzy membership functions of the labels used in the rules) by using reinforcement learning. Assuming the surface structure, GARIC refines the fuzzy membership functions used in the consequents of the rules using a gradient descent procedure. This hybrid fuzzy logic and reinforcement learning approach can learn to balance a cart-pole system and to backup a truck to its docking location after a few trials. In this paper, we discuss how to do structure identification using reinforcement learning in fuzzy inference systems. This involves identifying both surface as well as deep structure of the knowledge base. The term set of fuzzy linguistic labels used in describing the values of each control variable must be derived. In this process, splitting a label refers to creating new labels which are more granular than the original label and merging two labels creates a more general label. Splitting and merging of labels directly transform the structure of the action selection network used in GARIC by increasing or decreasing the number of hidden layer nodes.
Predictors of Full Enteral Feeding Achievement in Very Low Birth Weight Infants

PubMed Central

Corvaglia, Luigi; Fantini, Maria Pia; Aceti, Arianna; Gibertoni, Dino; Rucci, Paola; Baronciani, Dante; Faldella, Giacomo

2014-01-01

Background To elucidate the role of prenatal, neonatal and early postnatal variables in influencing the achievement of full enteral feeding (FEF) in very low birth weight (VLBW) infants and to determine whether neonatal intensive care units (NICUs) differ in this outcome. Methods Population-based retrospective cohort study using data on 1,864 VLBW infants drawn from the “Emilia-Romagna Perinatal Network” Registry from 2004 to 2009. The outcome of interest was time to FEF achievement. Eleven prenatal, neonatal and early postnatal variables and the study NICUs were selected as potential predictors of time to FEF. Parametric survival analysis was used to model time to FEF as a function of the predictors. Marginal effects were used to obtain adjusted estimates of median time to FEF for specific subgroups of infants. Results Lower gestational age, exclusive formula feeding, higher CRIB II score, maternal hypertension, cesarean delivery, SGA and PDA predicted delayed FEF. NICUs proved to be heterogeneous in terms of FEF achievement. Newborns with PDA had a 4.2 days longer predicted median time to FEF compared to those without PDA; newborns exclusively formula-fed had a 1.4 days longer time to FEF compared to those fed human milk. Conclusions The results of our study suggest that time to FEF is influenced by clinical variables and NICU-specific practices. Knowledge of the variables associated with delayed/earlier FEF achievement could help in improving specific aspects of routine clinical management of VLBW infants and to reduce practice variability. PMID:24647523
Perspectives on knowledge in engineering design

NASA Technical Reports Server (NTRS)

Rasdorf, W. J.

1985-01-01

Various perspectives are given of the knowledge currently used in engineering design, specifically dealing with knowledge-based expert systems (KBES). Constructing an expert system often reveals inconsistencies in domain knowledge while formalizing it. The types of domain knowledge (facts, procedures, judgments, and control) differ from the classes of that knowledge (creative, innovative, and routine). The feasible tasks for expert systems can be determined based on these types and classes of knowledge. Interpretive tasks require reasoning about a task in light of the knowledge available, where generative tasks create potential solutions to be tested against constraints. Only after classifying the domain by type and level can the engineer select a knowledge-engineering tool for the domain being considered. The critical features to be weighed after classification are knowledge representation techniques, control strategies, interface requirements, compatibility with traditional systems, and economic considerations.
Using Data Mining for Wine Quality Assessment

NASA Astrophysics Data System (ADS)

Cortez, Paulo; Teixeira, Juliana; Cerdeira, António; Almeida, Fernando; Matos, Telmo; Reis, José

Certification and quality assessment are crucial issues within the wine industry. Currently, wine quality is mostly assessed by physicochemical (e.g alcohol levels) and sensory (e.g. human expert evaluation) tests. In this paper, we propose a data mining approach to predict wine preferences that is based on easily available analytical tests at the certification step. A large dataset is considered with white vinho verde samples from the Minho region of Portugal. Wine quality is modeled under a regression approach, which preserves the order of the grades. Explanatory knowledge is given in terms of a sensitivity analysis, which measures the response changes when a given input variable is varied through its domain. Three regression techniques were applied, under a computationally efficient procedure that performs simultaneous variable and model selection and that is guided by the sensitivity analysis. The support vector machine achieved promising results, outperforming the multiple regression and neural network methods. Such model is useful for understanding how physicochemical tests affect the sensory preferences. Moreover, it can support the wine expert evaluations and ultimately improve the production.
A quantitative analysis of statistical power identifies obesity endpoints for improved in vivo preclinical study design

PubMed Central

Selimkhanov, Jangir; Thompson, W. Clayton; Guo, Juen; Hall, Kevin D.; Musante, Cynthia J.

2017-01-01

The design of well-powered in vivo preclinical studies is a key element in building knowledge of disease physiology for the purpose of identifying and effectively testing potential anti-obesity drug targets. However, as a result of the complexity of the obese phenotype, there is limited understanding of the variability within and between study animals of macroscopic endpoints such as food intake and body composition. This, combined with limitations inherent in the measurement of certain endpoints, presents challenges to study design that can have significant consequences for an anti-obesity program. Here, we analyze a large, longitudinal study of mouse food intake and body composition during diet perturbation to quantify the variability and interaction of key metabolic endpoints. To demonstrate how conclusions can change as a function of study size, we show that a simulated pre-clinical study properly powered for one endpoint may lead to false conclusions based on secondary endpoints. We then propose guidelines for endpoint selection and study size estimation under different conditions to facilitate proper power calculation for a more successful in vivo study design. PMID:28392555
A Strategic Approach to Medical Care for Exploration Missions

NASA Technical Reports Server (NTRS)

Canga, Michael A.; Shah, Ronak V.; Mindock, Jennifer A.; Antonsen, Erik L.

2016-01-01

Exploration missions will present significant new challenges to crew health, including effects of variable gravity environments, limited communication with Earth-based personnel for diagnosis and consultation for medical events, limited resupply, and limited ability for crew return. Providing health care capabilities for exploration class missions will require system trades be performed to identify a minimum set of requirements and crosscutting capabilities, which can be used in design of exploration medical systems. Medical data, information, and knowledge collected during current space missions must be catalogued and put in formats that facilitate querying and analysis. These data are used to inform the medical research and development program through analysis of risk trade studies between medical care capabilities and system constraints such as mass, power, volume, and training. Medical capability as a quantifiable variable is proposed as a surrogate risk metric and explored for trade space analysis that can improve communication between the medical and engineering approaches to mission design. The resulting medical system design approach selected will inform NASA mission architecture, vehicle, and subsystem design for the next generation of spacecraft.
Talent identification and selection in elite youth football: An Australian context.

PubMed

O'Connor, Donna; Larkin, Paul; Mark Williams, A

2016-10-01

We identified the perceptual-cognitive skills and player history variables that differentiate players selected or not selected into an elite youth football (i.e. soccer) programme in Australia. A sample of elite youth male football players (n = 127) completed an adapted participation history questionnaire and video-based assessments of perceptual-cognitive skills. Following data collection, 22 of these players were offered a full-time scholarship for enrolment at an elite player residential programme. Participants selected for the scholarship programme recorded superior performance on the combined perceptual-cognitive skills tests compared to the non-selected group. There were no significant between group differences on the player history variables. Stepwise discriminant function analysis identified four predictor variables that resulted in the best categorization of selected and non-selected players (i.e. recent match-play performance, region, number of other sports participated, combined perceptual-cognitive performance). The effectiveness of the discriminant function is reflected by 93.7% of players being correctly classified, with the four variables accounting for 57.6% of the variance. Our discriminating model for selection may provide a greater understanding of the factors that influence elite youth talent selection and identification.
Selection and Validation of a Multilocus Variable-Number Tandem-Repeat Analysis Panel for Typing Shigella spp.▿ †

PubMed Central

Gorgé, Olivier; Lopez, Stéphanie; Hilaire, Valérie; Lisanti, Olivier; Ramisse, Vincent; Vergnaud, Gilles

2008-01-01

The Shigella genus has historically been separated into four species, based on biochemical assays. The classification within each species relies on serotyping. Recently, genome sequencing and DNA assays, in particular the multilocus sequence typing (MLST) approach, greatly improved the current knowledge of the origin and phylogenetic evolution of Shigella spp. The Shigella and Escherichia genera are now considered to belong to a unique genomospecies. Multilocus variable-number tandem-repeat (VNTR) analysis (MLVA) provides valuable polymorphic markers for genotyping and performing phylogenetic analyses of highly homogeneous bacterial pathogens. Here, we assess the capability of MLVA for Shigella typing. Thirty-two potentially polymorphic VNTRs were selected by analyzing in silico five Shigella genomic sequences and subsequently evaluated. Eventually, a panel of 15 VNTRs was selected (i.e., MLVA15 analysis). MLVA15 analysis of 78 strains or genome sequences of Shigella spp. and 11 strains or genome sequences of Escherichia coli distinguished 83 genotypes. Shigella population cluster analysis gave consistent results compared to MLST. MLVA15 analysis showed capabilities for E. coli typing, providing classification among pathogenic and nonpathogenic E. coli strains included in the study. The resulting data can be queried on our genotyping webpage (http://mlva.u-psud.fr). The MLVA15 assay is rapid, highly discriminatory, and reproducible for Shigella and Escherichia strains, suggesting that it could significantly contribute to epidemiological trace-back analysis of Shigella infections and pathogenic Escherichia outbreaks. Typing was performed on strains obtained mostly from collections. Further studies should include strains of much more diverse origins, including all pathogenic E. coli types. PMID:18216214
Early Formulation of Training Programs for Cost Effectiveness Analysis

DTIC Science & Technology

1978-07-01

training approaches. viii Although the method and media variables aid training program selection de- cisions, a technique is also required to monitor...fact that personnel must still be taught certain prerequisite skills and knowledges before they can begin to use the actual equipment, this approach...often difficult to identify causal relations. Good summaries have been produced, e.g., Meister, 1976,4 however, and are a great aid in pull- ing
NASA/DoD Aerospace Knowledge Diffusion Research Project. Report Number 6. The Relationship between the Use of U.S. Government Technical Reports by U.S. Aerospace Engineers and Scientists and Selected Institutional and Sociometric Variables

DTIC Science & Technology

1991-01-01

Peter R.; James D. Schriner; Bettie F. Farace ; and Richard V. Farace . The Assessment of NASA Technical Information. NASA CR-181367. Washington, DC... Farace ; and Richard V. Farace . The Assessment of NASA Technical Information. NASA CR-181367. Washington, DC: National Aeronautics and Space
Informative Top-k Retrieval for Advanced Skill Management

NASA Astrophysics Data System (ADS)

Colucci, Simona; di Noia, Tommaso; Ragone, Azzurra; Ruta, Michele; Straccia, Umberto; Tinelli, Eufemia

The paper presents a knowledge-based framework for skills and talent management based on an advanced matchmaking between profiles of candidates and available job positions. Interestingly, informative content of top-k retrieval is enriched through semantic capabilities. The proposed approach allows to: (1) express a requested profile in terms of both hard constraints and soft ones; (2) provide a ranking function based also on qualitative attributes of a profile; (3) explain the resulting outcomes (given a job request, a motivation for the obtained score of each selected profile is provided). Top-k retrieval allows to select most promising candidates according to an ontology formalizing the domain knowledge. Such a knowledge is further exploited to provide a semantic-based explanation of missing or conflicting features in retrieved profiles. They also indicate additional profile characteristics emerging by the retrieval procedure for a further request refinement. A concrete case study followed by an exhaustive experimental campaign is reported to prove the approach effectiveness.
How has problem based learning fared in Pakistan?

PubMed

Mahmud, Waqas; Hyder, Omar

2012-10-01

To conduct a systematic review of primary research in undergraduate medical education in Pakistan in order to evaluate PBL programs, examine outcomes and competencies influenced by PBL, and compare them with conventional learning (lecture based learning, LBL). Qualitative content analysis. Rawalpindi Medical College, Rawalpindi, from June 2010 - February 2011. Literature was searched using online resources. Studies evaluating outcomes influenced by PBL, or comparing PBL with lecture based learning (LBL) were selected. Due to heterogeneity, a qualitative content analysis was performed in which studies were classified according to the methods of assessment; results were then summarized by outcome and frequencies were calculated. Eleven studies were included. Apart from knowledge acquisition, students gave high ratings to PBL in selected outcomes, alone, and in comparison with LBL. There was a disagreement among results of studies that evaluated knowledge acquisition alone. Based on student perceptions, PBL has many advantages. However, the results of this review are limited due to heterogeneity and methodological weakness of studies, specially the studies that compared exam scores to assess knowledge acquisition.

Secondary Teachers’ Mathematics-related Beliefs and Knowledge about Mathematical Problem-solving

NASA Astrophysics Data System (ADS)

E Siswono, T. Y.; Kohar, A. W.; Hartono, S.

2017-02-01

This study investigates secondary teachers’ belief about the three mathematics-related beliefs, i.e. nature of mathematics, teaching mathematics, learning mathematics, and knowledge about mathematical problem solving. Data were gathered through a set of task-based semi-structured interviews of three selected teachers with different philosophical views of teaching mathematics, i.e. instrumental, platonist, and problem solving. Those teachers were selected from an interview using a belief-related task from purposively selected teachers in Surabaya and Sidoarjo. While the interviews about knowledge examine teachers’ problem solving content and pedagogical knowledge, the interviews about beliefs examine their views on several cases extracted from each of such mathematics-related beliefs. Analysis included the categorization and comparison on each of beliefs and knowledge as well as their interaction. Results indicate that all the teachers did not show a high consistency in responding views of their mathematics-related beliefs, while they showed weaknesses primarily on problem solving content knowledge. Findings also point out that teachers’ beliefs have a strong relationship with teachers’ knowledge about problem solving. In particular, the instrumental teacher’s beliefs were consistent with his insufficient knowledge about problem-solving, while both platonist and problem-solving teacher’s beliefs were consistent with their sufficient knowledge of either content or pedagogical problem solving.
Healthcare waste management: qualitative and quantitative appraisal of nurses in a tertiary care hospital of India.

PubMed

Shivalli, Siddharudha; Sanklapur, Vasudha

2014-01-01

The nurse's role in healthcare waste management is crucial. (1) To appraise nurses quantitatively and qualitatively regarding healthcare waste management; (2) to elicit the determinants of knowledge and attitudes of healthcare waste management. A cross-sectional study was undertaken at a tertiary care hospital of Mangalore, India. Self-administered pretested questionnaire and "nonparticipatory observation" were used for quantitative and qualitative appraisals. Percentage knowledge score was calculated based on their total knowledge score. Nurses' knowledge was categorized as excellent (>70%), good (50-70%), and poor (<50%). Chi square test was applied to judge the association of study variables with their attitudes and knowledge. Out of 100 nurses 47 had excellent knowledge (>70% score). Most (86%) expressed the need of refresher training. No study variable displayed significant association (P > 0.05) with knowledge. Apt segregation practices were followed except in casualty. Patients and entourages misinterpreted the colored containers. Nurses' knowledge and healthcare waste management practices were not satisfactory. There is a need of refresher trainings at optimum intervals to ensure sustainability and further improvement. Educating patients and their entourages and display of segregation information board in local language are recommended.
Providing written language services in the schools: the time is now.

PubMed

Fallon, Karen A; Katz, Lauren A

2011-01-01

The current study was conducted to investigate the provision of written language services by school-based speech-language pathologists (SLPs). Specifically, the study examined SLPs' knowledge, attitudes, and collaborative practices in the area of written language services as well as the variables that impact provision of these services. Public school-based SLPs from across the country were solicited for participation in an online, Web-based survey. Data from 645 full-time SLPs from 49 states were evaluated using descriptive statistics and logistic regression. Many school-based SLPs reported not providing any services in the area of written language to students with written language weaknesses. Knowledge, attitudes, and collaborative practices were mixed. A logistic regression revealed three variables likely to predict high levels of service provision in the area of written language. Data from the current study revealed that many struggling readers and writers on school-based SLPs' caseloads are not receiving services from their SLPs. Implications for SLPs' preservice preparation, continuing education, and doctoral preparation are discussed.
New knowledge-based genetic algorithm for excavator boom structural optimization

NASA Astrophysics Data System (ADS)

Hua, Haiyan; Lin, Shuwen

2014-03-01

Due to the insufficiency of utilizing knowledge to guide the complex optimal searching, existing genetic algorithms fail to effectively solve excavator boom structural optimization problem. To improve the optimization efficiency and quality, a new knowledge-based real-coded genetic algorithm is proposed. A dual evolution mechanism combining knowledge evolution with genetic algorithm is established to extract, handle and utilize the shallow and deep implicit constraint knowledge to guide the optimal searching of genetic algorithm circularly. Based on this dual evolution mechanism, knowledge evolution and population evolution can be connected by knowledge influence operators to improve the configurability of knowledge and genetic operators. Then, the new knowledge-based selection operator, crossover operator and mutation operator are proposed to integrate the optimal process knowledge and domain culture to guide the excavator boom structural optimization. Eight kinds of testing algorithms, which include different genetic operators, are taken as examples to solve the structural optimization of a medium-sized excavator boom. By comparing the results of optimization, it is shown that the algorithm including all the new knowledge-based genetic operators can more remarkably improve the evolutionary rate and searching ability than other testing algorithms, which demonstrates the effectiveness of knowledge for guiding optimal searching. The proposed knowledge-based genetic algorithm by combining multi-level knowledge evolution with numerical optimization provides a new effective method for solving the complex engineering optimization problem.
Sparse representation based biomarker selection for schizophrenia with integrated analysis of fMRI and SNPs.

PubMed

Cao, Hongbao; Duan, Junbo; Lin, Dongdong; Shugart, Yin Yao; Calhoun, Vince; Wang, Yu-Ping

2014-11-15

Integrative analysis of multiple data types can take advantage of their complementary information and therefore may provide higher power to identify potential biomarkers that would be missed using individual data analysis. Due to different natures of diverse data modality, data integration is challenging. Here we address the data integration problem by developing a generalized sparse model (GSM) using weighting factors to integrate multi-modality data for biomarker selection. As an example, we applied the GSM model to a joint analysis of two types of schizophrenia data sets: 759,075 SNPs and 153,594 functional magnetic resonance imaging (fMRI) voxels in 208 subjects (92 cases/116 controls). To solve this small-sample-large-variable problem, we developed a novel sparse representation based variable selection (SRVS) algorithm, with the primary aim to identify biomarkers associated with schizophrenia. To validate the effectiveness of the selected variables, we performed multivariate classification followed by a ten-fold cross validation. We compared our proposed SRVS algorithm with an earlier sparse model based variable selection algorithm for integrated analysis. In addition, we compared with the traditional statistics method for uni-variant data analysis (Chi-squared test for SNP data and ANOVA for fMRI data). Results showed that our proposed SRVS method can identify novel biomarkers that show stronger capability in distinguishing schizophrenia patients from healthy controls. Moreover, better classification ratios were achieved using biomarkers from both types of data, suggesting the importance of integrative analysis. Copyright © 2014 Elsevier Inc. All rights reserved.
Construction of nested maximin designs based on successive local enumeration and modified novel global harmony search algorithm

NASA Astrophysics Data System (ADS)

Yi, Jin; Li, Xinyu; Xiao, Mi; Xu, Junnan; Zhang, Lin

2017-01-01

Engineering design often involves different types of simulation, which results in expensive computational costs. Variable fidelity approximation-based design optimization approaches can realize effective simulation and efficiency optimization of the design space using approximation models with different levels of fidelity and have been widely used in different fields. As the foundations of variable fidelity approximation models, the selection of sample points of variable-fidelity approximation, called nested designs, is essential. In this article a novel nested maximin Latin hypercube design is constructed based on successive local enumeration and a modified novel global harmony search algorithm. In the proposed nested designs, successive local enumeration is employed to select sample points for a low-fidelity model, whereas the modified novel global harmony search algorithm is employed to select sample points for a high-fidelity model. A comparative study with multiple criteria and an engineering application are employed to verify the efficiency of the proposed nested designs approach.
Gender and occupation wise knowledge, Awareness and prevention of tuberculosis among people of district Muzaffarabad AJ & K.

PubMed

Abbasi, Atif; Rafique, Muhammad; Saghir, Amir; Abbas, Kamran; Shaheen, Shabnum; Abdullah, Farooq

2016-11-01

To assess the awareness about the spread and control of tuberculosis as well as to investigate the gender and occupation wise differences among people regarding knowledge and attitude towards tuberculosis in the State of AJ & K. A cross-sectional descriptive study was conducted in district Muzaffarabad and a sample of 4000 respondents was selected by using stratified random sampling technique. The stratification was done with respect to gender and occupation. The occupation wise classification includes households, labors, and shop keepers, government employers, under graduate students of social and natural sciences, medical students and doctors. A close ended structured questionnaire was developed to collect the data and data were analyzed by using SPSS (Statistical Package for Social Sciences). Chi-Square test was used for association and Logistic Regression model was used to find out the most significant risk factors with gender. Majority of the males were more aware of tuberculosis than females regarding different aspects related to tuberculosis. The respondents from household, labors and shopkeepers have less awareness and knowledge than those who belong to other professions. The doctors and medical students have almost 100% awareness and knowledge of tuberculosis. It was examined that all the variables were associated with gender except threat, curable and transmissible. Only three variables mentioned above showed non- significant result, while all other variables were strongly associated with gender. Males were found more aware about TB than females. Moreover, the literate people were more conscious concerning the prevalence and threats of the disease.
Effects of paired-object affordance in search tasks across the adult lifespan.

PubMed

Wulff, Melanie; Stainton, Alexandra; Rotshtein, Pia

2016-06-01

The study investigated the processes underlying the retrieval of action information about functional object pairs, focusing on the contribution of procedural and semantic knowledge. We further assessed whether the retrieval of action knowledge is affected by task demands and age. The contribution of procedural knowledge was examined by the way objects were selected, specifically whether active objects were selected before passive objects. The contribution of semantic knowledge was examined by manipulating the relation between targets and distracters. A touchscreen-based search task was used testing young, middle-aged, and elderly participants. Participants had to select by touching two targets among distracters using two search tasks. In an explicit action search task, participants had to select two objects which afforded a mutual action (e.g., functional pair: hammer-nail). Implicit affordance perception was tested using a visual color-matching search task; participants had to select two objects with the same colored frame. In both tasks, half of the colored targets also afforded an action. Overall, middle-aged participants performed better than young and elderly participants, specifically in the action task. Across participants in the action task, accuracy was increased when the distracters were semantically unrelated to the functional pair, while the opposite pattern was observed in the color task. This effect was enhanced with increased age. In the action task all participants utilized procedural knowledge, i.e., selected the active object before the passive object. This result supports the dual-route account from vision to action. Semantic knowledge contributed to both the action and the color task, but procedural knowledge associated with the direct route was primarily retrieved when the task was action-relevant. Across the adulthood lifespan, the data show inverted U-shaped effects of age on the retrieval of action knowledge. Age also linearly increased the involvement of the indirect (semantic) route and the integration of information of the direct and the indirect routes in selection processes. Copyright © 2016 Elsevier Inc. All rights reserved.
Conceptualization of an R&D Based Learning-to-Innovate Model for Science Education

NASA Astrophysics Data System (ADS)

Lai, Oiki Sylvia

The purpose of this research was to conceptualize an R & D based learning-to-innovate (LTI) model. The problem to be addressed was the lack of a theoretical L TI model, which would inform science pedagogy. The absorptive capacity (ACAP) lens was adopted to untangle the R & D LTI phenomenon into four learning processes: problem-solving via knowledge acquisition, incremental improvement via knowledge participation, scientific discovery via knowledge creation, and product design via knowledge productivity. The four knowledge factors were the latent factors and each factor had seven manifest elements as measured variables. The key objectives of the non experimental quantitative survey were to measure the relative importance of the identified elements and to explore the underlining structure of the variables. A questionnaire had been prepared, and was administered to more than 155 R & D professionals from four sectors - business, academic, government, and nonprofit. The results showed that every identified element was important to the R & D professionals, in terms of improving the related type of innovation. The most important elements were highlighted to serve as building blocks for elaboration. In search for patterns of the data matrix, exploratory factor analysis (EF A) was performed. Principal component analysis was the first phase of EF A to extract factors; while maximum likelihood estimation (MLE) was used to estimate the model. EF A yielded the finding of two aspects in each kind of knowledge. Logical names were assigned to represent the nature of the subsets: problem and knowledge under knowledge acquisition, planning and participation under knowledge participation, exploration and discovery under knowledge creation, and construction and invention under knowledge productivity. These two constructs, within each kind of knowledge, added structure to the vague R & D based LTI model. The research questions and hypotheses testing were addressed using correlation analysis. The alternative hypotheses that there were positive relationships between knowledge factors and their corresponding types of innovation were accepted. In-depth study of each process is recommended in both research and application. Experimental tests are needed, in order to ultimately present the LTI model to enhance the scientific knowledge absorptive capacity of the learners to facilitate their innovation performance.
Fine-scale habitat modeling of a top marine predator: do prey data improve predictive capacity?

PubMed

Torres, Leigh G; Read, Andrew J; Halpin, Patrick

2008-10-01

Predators and prey assort themselves relative to each other, the availability of resources and refuges, and the temporal and spatial scale of their interaction. Predictive models of predator distributions often rely on these relationships by incorporating data on environmental variability and prey availability to determine predator habitat selection patterns. This approach to predictive modeling holds true in marine systems where observations of predators are logistically difficult, emphasizing the need for accurate models. In this paper, we ask whether including prey distribution data in fine-scale predictive models of bottlenose dolphin (Tursiops truncatus) habitat selection in Florida Bay, Florida, U.S.A., improves predictive capacity. Environmental characteristics are often used as predictor variables in habitat models of top marine predators with the assumption that they act as proxies of prey distribution. We examine the validity of this assumption by comparing the response of dolphin distribution and fish catch rates to the same environmental variables. Next, the predictive capacities of four models, with and without prey distribution data, are tested to determine whether dolphin habitat selection can be predicted without recourse to describing the distribution of their prey. The final analysis determines the accuracy of predictive maps of dolphin distribution produced by modeling areas of high fish catch based on significant environmental characteristics. We use spatial analysis and independent data sets to train and test the models. Our results indicate that, due to high habitat heterogeneity and the spatial variability of prey patches, fine-scale models of dolphin habitat selection in coastal habitats will be more successful if environmental variables are used as predictor variables of predator distributions rather than relying on prey data as explanatory variables. However, predictive modeling of prey distribution as the response variable based on environmental variability did produce high predictive performance of dolphin habitat selection, particularly foraging habitat.
Statistical synthesis of contextual knowledge to increase the effectiveness of theory-based behaviour change interventions.

PubMed

Hanbury, Andria; Thompson, Carl; Mannion, Russell

2011-07-01

Tailored implementation strategies targeting health professionals' adoption of evidence-based recommendations are currently being developed. Research has focused on how to select an appropriate theoretical base, how to use that theoretical base to explore the local context, and how to translate theoretical constructs associated with the key factors found to influence innovation adoption into feasible and tailored implementation strategies. The reasons why an intervention is thought not to have worked are often cited as being: inappropriate choice of theoretical base; unsystematic development of the implementation strategies; and a poor evidence base to guide the process. One area of implementation research that is commonly overlooked is how to synthesize the data collected in a local context in order to identify what factors to target with the implementation strategies. This is suggested to be a critical process in the development of a theory-based intervention. The potential of multilevel modelling techniques to synthesize data collected at different hierarchical levels, for example, individual attitudes and team level variables, is discussed. Future research is needed to explore further the potential of multilevel modelling for synthesizing contextual data in implementation studies, as well as techniques for synthesizing qualitative and quantitative data.
Dynamics relationship between stock prices and economic variables in Malaysia

NASA Astrophysics Data System (ADS)

Chun, Ooi Po; Arsad, Zainudin; Huen, Tan Bee

2014-07-01

Knowledge on linkages between stock prices and macroeconomic variables are essential in the formulation of effective monetary policy. This study investigates the relationship between stock prices in Malaysia (KLCI) with four selected macroeconomic variables, namely industrial production index (IPI), quasi money supply (MS2), real exchange rate (REXR) and 3-month Treasury bill (TRB). The variables used in this study are monthly data from 1996 to 2012. Vector error correction (VEC) model and Kalman filter (KF) technique are utilized to assess the impact of macroeconomic variables on the stock prices. The results from the cointegration test revealed that the stock prices and macroeconomic variables are cointegrated. Different from the constant estimate from the static VEC model, the KF estimates noticeably exhibit time-varying attributes over the entire sample period. The varying estimates of the impact coefficients should be better reflect the changing economic environment. Surprisingly, IPI is negatively related to the KLCI with the estimates of the impact slowly increase and become positive in recent years. TRB is found to be generally negatively related to the KLCI with the impact fluctuating along the constant estimate of the VEC model. The KF estimates for REXR and MS2 show a mixture of positive and negative impact on the KLCI. The coefficients of error correction term (ECT) are negative in majority of the sample period, signifying the stock prices responded to stabilize any short term deviation in the economic system. The findings from the KF model indicate that any implication that is based on the usual static model may lead to authorities implementing less appropriate policies.
A Semiautomated Framework for Integrating Expert Knowledge into Disease Marker Identification

DOE PAGES

Wang, Jing; Webb-Robertson, Bobbie-Jo M.; Matzke, Melissa M.; ...

2013-01-01

Background . The availability of large complex data sets generated by high throughput technologies has enabled the recent proliferation of disease biomarker studies. However, a recurring problem in deriving biological information from large data sets is how to best incorporate expert knowledge into the biomarker selection process. Objective . To develop a generalizable framework that can incorporate expert knowledge into data-driven processes in a semiautomated way while providing a metric for optimization in a biomarker selection scheme. Methods . The framework was implemented as a pipeline consisting of five components for the identification of signatures from integrated clustering (ISIC). Expertmore » knowledge was integrated into the biomarker identification process using the combination of two distinct approaches; a distance-based clustering approach and an expert knowledge-driven functional selection. Results . The utility of the developed framework ISIC was demonstrated on proteomics data from a study of chronic obstructive pulmonary disease (COPD). Biomarker candidates were identified in a mouse model using ISIC and validated in a study of a human cohort. Conclusions . Expert knowledge can be introduced into a biomarker discovery process in different ways to enhance the robustness of selected marker candidates. Developing strategies for extracting orthogonal and robust features from large data sets increases the chances of success in biomarker identification.« less
A Semiautomated Framework for Integrating Expert Knowledge into Disease Marker Identification

PubMed Central

Wang, Jing; Webb-Robertson, Bobbie-Jo M.; Matzke, Melissa M.; Varnum, Susan M.; Brown, Joseph N.; Riensche, Roderick M.; Adkins, Joshua N.; Jacobs, Jon M.; Hoidal, John R.; Scholand, Mary Beth; Pounds, Joel G.; Blackburn, Michael R.; Rodland, Karin D.; McDermott, Jason E.

2013-01-01

Background. The availability of large complex data sets generated by high throughput technologies has enabled the recent proliferation of disease biomarker studies. However, a recurring problem in deriving biological information from large data sets is how to best incorporate expert knowledge into the biomarker selection process. Objective. To develop a generalizable framework that can incorporate expert knowledge into data-driven processes in a semiautomated way while providing a metric for optimization in a biomarker selection scheme. Methods. The framework was implemented as a pipeline consisting of five components for the identification of signatures from integrated clustering (ISIC). Expert knowledge was integrated into the biomarker identification process using the combination of two distinct approaches; a distance-based clustering approach and an expert knowledge-driven functional selection. Results. The utility of the developed framework ISIC was demonstrated on proteomics data from a study of chronic obstructive pulmonary disease (COPD). Biomarker candidates were identified in a mouse model using ISIC and validated in a study of a human cohort. Conclusions. Expert knowledge can be introduced into a biomarker discovery process in different ways to enhance the robustness of selected marker candidates. Developing strategies for extracting orthogonal and robust features from large data sets increases the chances of success in biomarker identification. PMID:24223463
A Semiautomated Framework for Integrating Expert Knowledge into Disease Marker Identification

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

Wang, Jing; Webb-Robertson, Bobbie-Jo M.; Matzke, Melissa M.

2013-10-01

Background. The availability of large complex data sets generated by high throughput technologies has enabled the recent proliferation of disease biomarker studies. However, a recurring problem in deriving biological information from large data sets is how to best incorporate expert knowledge into the biomarker selection process. Objective. To develop a generalizable framework that can incorporate expert knowledge into data-driven processes in a semiautomated way while providing a metric for optimization in a biomarker selection scheme. Methods. The framework was implemented as a pipeline consisting of five components for the identification of signatures from integrated clustering (ISIC). Expert knowledge was integratedmore » into the biomarker identification process using the combination of two distinct approaches; a distance-based clustering approach and an expert knowledge-driven functional selection. Results. The utility of the developed framework ISIC was demonstrated on proteomics data from a study of chronic obstructive pulmonary disease (COPD). Biomarker candidates were identified in a mouse model using ISIC and validated in a study of a human cohort. Conclusions. Expert knowledge can be introduced into a biomarker discovery process in different ways to enhance the robustness of selected marker candidates. Developing strategies for extracting orthogonal and robust features from large data sets increases the chances of success in biomarker identification.« less
An improved distance-to-dose correlation for predicting bladder and rectum dose-volumes in knowledge-based VMAT planning for prostate cancer

NASA Astrophysics Data System (ADS)

Wall, Phillip D. H.; Carver, Robert L.; Fontenot, Jonas D.

2018-01-01

The overlap volume histogram (OVH) is an anatomical metric commonly used to quantify the geometric relationship between an organ at risk (OAR) and target volume when predicting expected dose-volumes in knowledge-based planning (KBP). This work investigated the influence of additional variables contributing to variations in the assumed linear DVH-OVH correlation for the bladder and rectum in VMAT plans of prostate patients, with the goal of increasing prediction accuracy and achievability of knowledge-based planning methods. VMAT plans were retrospectively generated for 124 prostate patients using multi-criteria optimization. DVHs quantified patient dosimetric data while OVHs quantified patient anatomical information. The DVH-OVH correlations were calculated for fractional bladder and rectum volumes of 30, 50, 65, and 80%. Correlations between potential influencing factors and dose were quantified using the Pearson product-moment correlation coefficient (R). Factors analyzed included the derivative of the OVH, prescribed dose, PTV volume, bladder volume, rectum volume, and in-field OAR volume. Out of the selected factors, only the in-field bladder volume (mean R = 0.86) showed a strong correlation with bladder doses. Similarly, only the in-field rectal volume (mean R = 0.76) showed a strong correlation with rectal doses. Therefore, an OVH formalism accounting for in-field OAR volumes was developed to determine the extent to which it improved the DVH-OVH correlation. Including the in-field factor improved the DVH-OVH correlation, with the mean R values over the fractional volumes studied improving from -0.79 to -0.85 and -0.82 to -0.86 for the bladder and rectum, respectively. A re-planning study was performed on 31 randomly selected database patients to verify the increased accuracy of KBP dose predictions by accounting for bladder and rectum volume within treatment fields. The in-field OVH led to significantly more precise and fewer unachievable KBP predictions, especially for lower bladder and rectum dose-volumes.
Spatial Variability of Sources and Mixing State of Atmospheric Particles in a Metropolitan Area.

PubMed

Ye, Qing; Gu, Peishi; Li, Hugh Z; Robinson, Ellis S; Lipsky, Eric; Kaltsonoudis, Christos; Lee, Alex K Y; Apte, Joshua S; Robinson, Allen L; Sullivan, Ryan C; Presto, Albert A; Donahue, Neil M

2018-05-30

Characterizing intracity variations of atmospheric particulate matter has mostly relied on fixed-site monitoring and quantifying variability in terms of different bulk aerosol species. In this study, we performed ground-based mobile measurements using a single-particle mass spectrometer to study spatial patterns of source-specific particles and the evolution of particle mixing state in 21 areas in the metropolitan area of Pittsburgh, PA. We selected sampling areas based on traffic density and restaurant density with each area ranging from 0.2 to 2 km 2 . Organics dominate particle composition in all of the areas we sampled while the sources of organics differ. The contribution of particles from traffic and restaurant cooking varies greatly on the neighborhood scale. We also investigate how primary and aged components in particles mix across the urban scale. Lastly we quantify and map the particle mixing state for all areas we sampled and discuss the overall pattern of mixing state evolution and its implications. We find that in the upwind and downwind of the urban areas, particles are more internally mixed while in the city center, particle mixing state shows large spatial heterogeneity that is mostly driven by emissions. This study is to our knowledge, the first study to perform fine spatial scale mapping of particle mixing state using ground-based mobile measurement and single-particle mass spectrometry.
Variable Cycle Engine Technology Program Planning and Definition Study

NASA Technical Reports Server (NTRS)

Westmoreland, J. S.; Stern, A. M.

1978-01-01

The variable stream control engine, VSCE-502B, was selected as the base engine, with the inverted flow engine concept selected as a backup. Critical component technologies were identified, and technology programs were formulated. Several engine configurations were defined on a preliminary basis to serve as demonstration vehicles for the various technologies. The different configurations present compromises in cost, technical risk, and technology return. Plans for possible variably cycle engine technology programs were formulated by synthesizing the technology requirements with the different demonstrator configurations.
SVM-RFE based feature selection and Taguchi parameters optimization for multiclass SVM classifier.

PubMed

Huang, Mei-Ling; Hung, Yung-Hsiang; Lee, W M; Li, R K; Jiang, Bo-Ru

2014-01-01

Recently, support vector machine (SVM) has excellent performance on classification and prediction and is widely used on disease diagnosis or medical assistance. However, SVM only functions well on two-group classification problems. This study combines feature selection and SVM recursive feature elimination (SVM-RFE) to investigate the classification accuracy of multiclass problems for Dermatology and Zoo databases. Dermatology dataset contains 33 feature variables, 1 class variable, and 366 testing instances; and the Zoo dataset contains 16 feature variables, 1 class variable, and 101 testing instances. The feature variables in the two datasets were sorted in descending order by explanatory power, and different feature sets were selected by SVM-RFE to explore classification accuracy. Meanwhile, Taguchi method was jointly combined with SVM classifier in order to optimize parameters C and γ to increase classification accuracy for multiclass classification. The experimental results show that the classification accuracy can be more than 95% after SVM-RFE feature selection and Taguchi parameter optimization for Dermatology and Zoo databases.
SVM-RFE Based Feature Selection and Taguchi Parameters Optimization for Multiclass SVM Classifier

PubMed Central

Huang, Mei-Ling; Hung, Yung-Hsiang; Lee, W. M.; Li, R. K.; Jiang, Bo-Ru

2014-01-01

Recently, support vector machine (SVM) has excellent performance on classification and prediction and is widely used on disease diagnosis or medical assistance. However, SVM only functions well on two-group classification problems. This study combines feature selection and SVM recursive feature elimination (SVM-RFE) to investigate the classification accuracy of multiclass problems for Dermatology and Zoo databases. Dermatology dataset contains 33 feature variables, 1 class variable, and 366 testing instances; and the Zoo dataset contains 16 feature variables, 1 class variable, and 101 testing instances. The feature variables in the two datasets were sorted in descending order by explanatory power, and different feature sets were selected by SVM-RFE to explore classification accuracy. Meanwhile, Taguchi method was jointly combined with SVM classifier in order to optimize parameters C and γ to increase classification accuracy for multiclass classification. The experimental results show that the classification accuracy can be more than 95% after SVM-RFE feature selection and Taguchi parameter optimization for Dermatology and Zoo databases. PMID:25295306

Effects of selected socio-demographic characteristics on nutrition knowledge and eating behavior of elementary students in two provinces in China.

PubMed

Qian, Ling; Zhang, Fan; Newman, Ian M; Shell, Duane F; Du, Weijing

2017-07-14

National and international child health surveys have indicated an increase in childhood obesity in China. The increase has been attributed to a rising standard of living, increasing availability of unhealthy foods, and a lack of knowledge about healthy diet. The objective of this study was to assess the effect of selected socio-demographic characteristics on the BMI, nutrition knowledge, and eating behavior of elementary school children. Multistage stratified cluster sampling was used. Information on demographics, nutrition knowledge, and eating behavior was gathered by means of questionnaires. The schools' doctors provided the height and weight data. The study was set in one economically advantaged and one economically disadvantaged province in China. The participants were Grade 3 students, ages 8-10 years (N = 3922). A cluster analysis identified four socio-demographic variables distinguished by parental education and family living arrangement. A one-way ANOVA compared differences among the clusters in BMI, child nutrition knowledge, and child eating behavior. Students in the cluster with lowest parent education level had the lowest nutrition knowledge scores and eating behavior scores. There was no significant benefit from college education versus high school education of parents in the other three clusters. BMI was not affected by parent education level. The nutrition status of elementary school age children will benefit most by increasing the general level of education for those adults who are presently least educated.
A Study of Building a Resource-Based Learning Environment with the Inquiry Learning Approach: Knowledge of Family Trees

ERIC Educational Resources Information Center

Kong, Siu Cheung; So, Wing Mui Winnie

2008-01-01

This study aims to provide teachers with ways and means to facilitate learners to develop nomenclature knowledge of family trees through the establishment of resource-based learning environments (RBLEs). It discusses the design of an RBLE in the classroom by selecting an appropriate context with the assistance of computer-mediated learning…
Measuring New Environmental Paradigm Based on Students' Knowledge about Ecosystem and Locus of Control

ERIC Educational Resources Information Center

Putrawan, I. Made

2015-01-01

This research is aimed at obtaining information related to instrument development of Students' New Environmental Paradigm (NEP) based on their knowledge about ecosystem and Locus of Control (LOC). A survey method has been carried out by selecting senior high school students randomly with n = 362 (first stage 2013) and n = 722 (2014). Data analysed…
The Influence of Trust on Knowledge Donating and Collecting: An Examination of Malaysian Universities

ERIC Educational Resources Information Center

Goh, See-Kwong; Sandhu, Manjit-Singh

2014-01-01

The purpose of this research is to examine the influence of affect-based trust and cognition-based trust on knowledge sharing behaviour by adopting the theory of planned behaviour in selected universities in Malaysia. The research adopted survey method and a total of 545 participants from 30 universities. Multiple regression was used to assess the…
Knowledge synthesis with maps of neural connectivity.

PubMed

Tallis, Marcelo; Thompson, Richard; Russ, Thomas A; Burns, Gully A P C

2011-01-01

This paper describes software for neuroanatomical knowledge synthesis based on neural connectivity data. This software supports a mature methodology developed since the early 1990s. Over this time, the Swanson laboratory at USC has generated an account of the neural connectivity of the sub-structures of the hypothalamus, amygdala, septum, hippocampus, and bed nucleus of the stria terminalis. This is based on neuroanatomical data maps drawn into a standard brain atlas by experts. In earlier work, we presented an application for visualizing and comparing anatomical macro connections using the Swanson third edition atlas as a framework for accurate registration. Here we describe major improvements to the NeuARt application based on the incorporation of a knowledge representation of experimental design. We also present improvements in the interface and features of the data mapping components within a unified web-application. As a step toward developing an accurate sub-regional account of neural connectivity, we provide navigational access between the data maps and a semantic representation of area-to-area connections that they support. We do so based on an approach called "Knowledge Engineering from Experimental Design" (KEfED) model that is based on experimental variables. We have extended the underlying KEfED representation of tract-tracing experiments by incorporating the definition of a neuronanatomical data map as a measurement variable in the study design. This paper describes the software design of a web-application that allows anatomical data sets to be described within a standard experimental context and thus indexed by non-spatial experimental design features.
Reengineering of MeSH thesauri for term selection to optimize literature retrieval and knowledge reconstruction in support of stem cell research.

PubMed

Su, Yan; Andrews, James; Huang, Hong; Wang, Yue; Kong, Liangliang; Cannon, Peter; Xu, Ping

2016-05-23

PubMed is a widely used database for scientists to find biomedical-related literature. Due to the complexity of the selected research subject and its interdisciplinary nature, as well as the exponential growth in the number of disparate pieces of biomedical literature, it is an overwhelming challenge for scientists to define the right search strategies and quickly locate all related information. Specialized subsets and groupings of controlled vocabularies, such as Medical Subject Headings (MeSH), can enhance information retrieval in specialized domains, such as stem cell research. There is a need to develop effective search strategies and convenient solutions for knowledge organization in stem cell research. The understanding of the interrelationships between these MeSH terms also facilitates the building of knowledge organization systems in related subject fields. This study collected empirical data for MeSH-related terms from stem cell literature and developed a novel approach that uses both automation and expert-selection to create a set of terms that supports enhanced retrieval. The selected MeSH terms were reconstructed into a classified thesaurus that can guide researchers towards a successful search and knowledge organization of stem cell literature. First, 4253 MeSH terms were harvested from a sample of 5527 stem cell related research papers from the PubMed database. Next, unrelated terms were filtered out based on term frequency and specificity. Precision and recall measures were used to help identify additional valuable terms, which were mostly non-MeSH terms. The study identified 15 terms that specifically referred to stem cell research for information retrieval, which would yield a higher precision (97.7 %) and recall (94.4 %) rates in comparison to other approaches. In addition, 128 root MeSH terms were selected to conduct knowledge organization of stem cell research in categories of anatomy, disease, and others. This study presented a novel strategy and procedure to reengineer term selections of the MeSH thesaurus for literature retrieval and knowledge organization using stem cell research as a case. It could help scientists to select their own search terms and build up a thesaurus-based knowledge organization system in interested and interdisciplinary research subject areas.
Multivariate fault isolation of batch processes via variable selection in partial least squares discriminant analysis.

PubMed

Yan, Zhengbing; Kuang, Te-Hui; Yao, Yuan

2017-09-01

In recent years, multivariate statistical monitoring of batch processes has become a popular research topic, wherein multivariate fault isolation is an important step aiming at the identification of the faulty variables contributing most to the detected process abnormality. Although contribution plots have been commonly used in statistical fault isolation, such methods suffer from the smearing effect between correlated variables. In particular, in batch process monitoring, the high autocorrelations and cross-correlations that exist in variable trajectories make the smearing effect unavoidable. To address such a problem, a variable selection-based fault isolation method is proposed in this research, which transforms the fault isolation problem into a variable selection problem in partial least squares discriminant analysis and solves it by calculating a sparse partial least squares model. As different from the traditional methods, the proposed method emphasizes the relative importance of each process variable. Such information may help process engineers in conducting root-cause diagnosis. Copyright © 2017 ISA. Published by Elsevier Ltd. All rights reserved.
Purposeful Variable Selection and Stratification to Impute Missing FAST Data in Trauma Research

PubMed Central

Fuchs, Paul A.; del Junco, Deborah J.; Fox, Erin E.; Holcomb, John B.; Rahbar, Mohammad H.; Wade, Charles A.; Alarcon, Louis H.; Brasel, Karen J.; Bulger, Eileen M.; Cohen, Mitchell J.; Myers, John G.; Muskat, Peter; Phelan, Herb A.; Schreiber, Martin A.; Cotton, Bryan A.

2013-01-01

Background The Focused Assessment with Sonography for Trauma (FAST) exam is an important variable in many retrospective trauma studies. The purpose of this study was to devise an imputation method to overcome missing data for the FAST exam. Due to variability in patients’ injuries and trauma care, these data are unlikely to be missing completely at random (MCAR), raising concern for validity when analyses exclude patients with missing values. Methods Imputation was conducted under a less restrictive, more plausible missing at random (MAR) assumption. Patients with missing FAST exams had available data on alternate, clinically relevant elements that were strongly associated with FAST results in complete cases, especially when considered jointly. Subjects with missing data (32.7%) were divided into eight mutually exclusive groups based on selected variables that both described the injury and were associated with missing FAST values. Additional variables were selected within each group to classify missing FAST values as positive or negative, and correct FAST exam classification based on these variables was determined for patients with non-missing FAST values. Results Severe head/neck injury (odds ratio, OR=2.04), severe extremity injury (OR=4.03), severe abdominal injury (OR=1.94), no injury (OR=1.94), other abdominal injury (OR=0.47), other head/neck injury (OR=0.57) and other extremity injury (OR=0.45) groups had significant ORs for missing data; the other group odds ratio was not significant (OR=0.84). All 407 missing FAST values were imputed, with 109 classified as positive. Correct classification of non-missing FAST results using the alternate variables was 87.2%. Conclusions Purposeful imputation for missing FAST exams based on interactions among selected variables assessed by simple stratification may be a useful adjunct to sensitivity analysis in the evaluation of imputation strategies under different missing data mechanisms. This approach has the potential for widespread application in clinical and translational research and validation is warranted. Level of Evidence Level II Prognostic or Epidemiological PMID:23778515
Impact of Genomics Platform and Statistical Filtering on Transcriptional Benchmark Doses (BMD) and Multiple Approaches for Selection of Chemical Point of Departure (PoD)

PubMed Central

Webster, A. Francina; Chepelev, Nikolai; Gagné, Rémi; Kuo, Byron; Recio, Leslie; Williams, Andrew; Yauk, Carole L.

2015-01-01

Many regulatory agencies are exploring ways to integrate toxicogenomic data into their chemical risk assessments. The major challenge lies in determining how to distill the complex data produced by high-content, multi-dose gene expression studies into quantitative information. It has been proposed that benchmark dose (BMD) values derived from toxicogenomics data be used as point of departure (PoD) values in chemical risk assessments. However, there is limited information regarding which genomics platforms are most suitable and how to select appropriate PoD values. In this study, we compared BMD values modeled from RNA sequencing-, microarray-, and qPCR-derived gene expression data from a single study, and explored multiple approaches for selecting a single PoD from these data. The strategies evaluated include several that do not require prior mechanistic knowledge of the compound for selection of the PoD, thus providing approaches for assessing data-poor chemicals. We used RNA extracted from the livers of female mice exposed to non-carcinogenic (0, 2 mg/kg/day, mkd) and carcinogenic (4, 8 mkd) doses of furan for 21 days. We show that transcriptional BMD values were consistent across technologies and highly predictive of the two-year cancer bioassay-based PoD. We also demonstrate that filtering data based on statistically significant changes in gene expression prior to BMD modeling creates more conservative BMD values. Taken together, this case study on mice exposed to furan demonstrates that high-content toxicogenomics studies produce robust data for BMD modelling that are minimally affected by inter-technology variability and highly predictive of cancer-based PoD doses. PMID:26313361
Effects of a Culturally Adapted HIV Prevention Intervention in Haitian Youth

PubMed Central

Malow, Robert M.; Stein, Judith A.; McMahon, Robert C.; Dévieux, Jessy G.; Rosenberg, Rhonda; Jean-Gilles, Michèle

2009-01-01

This study assessed the impact of an 8-week community-based translation of Becoming a Responsible Teen (BART), an HIV intervention that has been shown to be effective in other at-risk adolescent populations. A sample of Haitian adolescents living in the Miami area was randomized to a general health education control group (N = 101) or the BART intervention (N = 145), which is based on the information-motivation-behavior (IMB) model. Improvement in various IMB components (i.e., attitudinal, knowledge, and behavioral skills variables) related to condom use was assessed 1 month after the intervention. Longitudinal structural equation models using a mixture of latent and measured multi-item variables indicated that the intervention significantly and positively impacted all IMB variables tested in the model. These BART intervention-linked changes reflected greater knowledge, greater intentions to use condoms in the future, higher safer sex self-efficacy, an improved attitude about condom use and an enhanced ability to use condoms after the 8-week intervention. PMID:19286123
Interface induced spin-orbit interaction in silicon quantum dots and prospects of scalability

NASA Astrophysics Data System (ADS)

Ferdous, Rifat; Wai, Kok; Veldhorst, Menno; Hwang, Jason; Yang, Henry; Klimeck, Gerhard; Dzurak, Andrew; Rahman, Rajib

A scalable quantum computing architecture requires reproducibility over key qubit properties, like resonance frequency, coherence time etc. Randomness in these properties would necessitate individual knowledge of each qubit in a quantum computer. Spin qubits hosted in Silicon (Si) quantum dots (QD) is promising as a potential building block for a large-scale quantum computer, because of their longer coherence times. The Stark shift of the electron g-factor in these QDs has been used to selectively address multiple qubits. From atomistic tight-binding studies we investigated the effect of interface non-ideality on the Stark shift of the g-factor in a Si QD. We find that based on the location of a monoatomic step at the interface with respect to the dot center both the sign and magnitude of the Stark shift change. Thus the presence of interface steps in these devices will cause variability in electron g-factor and its Stark shift based on the location of the qubit. This behavior will also cause varying sensitivity to charge noise from one qubit to another, which will randomize the dephasing times T2*. This predicted device-to-device variability is experimentally observed recently in three qubits fabricated at a Si/Si02 interface, which validates the issues discussed.
High- and Low-Load Resistance Training: Interpretation and Practical Application of Current Research Findings.

PubMed

Fisher, James; Steele, James; Smith, Dave

2017-03-01

Our current state of knowledge regarding the load (lighter or heavier) lifted in resistance training programmes that will result in 'optimal' strength and hypertrophic adaptations is unclear. Despite this, position stands and recommendations are made based on, we propose, limited evidence to lift heavier weights. Here we discuss the state of evidence on the impact of load and how it, as a single variable, stimulates adaptations to take place and whether evidence for recommending heavier loads is available, well-defined, currently correctly interpreted or has been overlooked. Areas of discussion include electromyography amplitude, in vivo and in vitro methods of measuring hypertrophy, and motor schema and skill acquisition. The present piece clarifies to trainers and trainees the impact of these variables by discussing interpretation of synchronous and sequential motor unit recruitment and revisiting the size principle, poor agreement between whole-muscle cross-sectional area (CSA) and biopsy-determined changes in myofibril CSA, and neural adaptations around task specificity. Our opinion is that the practical implications of being able to self-select external load include reducing the need for specific facility memberships, motivating older persons or those who might be less confident using heavy loads, and allowing people to undertake home- or field-based resistance training intervention strategies that might ultimately improve exercise adherence.
Advanced supersonic propulsion system technology study, phase 2

NASA Technical Reports Server (NTRS)

Allan, R. D.

1975-01-01

Variable cycle engines were identified, based on the mixed-flow low-bypass-ratio augmented turbofan cycle, which has shown excellent range capability in the AST airplane. The best mixed-flow augmented turbofan engine was selected based on range in the AST Baseline Airplane. Selected variable cycle engine features were added to this best conventional baseline engine, and the Dual-Cycle VCE and Double-Bypass VCE were defined. The conventional mixed-flow turbofan and the Double-Bypass VCE were on the subjects of engine preliminary design studies to determine mechanical feasibility, confirm weight and dimensional estimates, and identify the necessary technology considered not yet available. Critical engine components were studied and incorporated into the variable cycle engine design.
Parental Perceptions of Life Context Variables for Involvement in Their Young Children's Education

ERIC Educational Resources Information Center

Tekin, Ali Kemal

2016-01-01

The purpose of this study was to discover Turkish parents' perceptions of life context variables, including personal knowledge and skills and personal time and energy for involvement activities in their young children's education. The scales used in this study were based on parents' self-report, and included: (1) Parental Perceptions of Personal…
Harmonize input selection for sediment transport prediction

NASA Astrophysics Data System (ADS)

Afan, Haitham Abdulmohsin; Keshtegar, Behrooz; Mohtar, Wan Hanna Melini Wan; El-Shafie, Ahmed

2017-09-01

In this paper, three modeling approaches using a Neural Network (NN), Response Surface Method (RSM) and response surface method basis Global Harmony Search (GHS) are applied to predict the daily time series suspended sediment load. Generally, the input variables for forecasting the suspended sediment load are manually selected based on the maximum correlations of input variables in the modeling approaches based on NN and RSM. The RSM is improved to select the input variables by using the errors terms of training data based on the GHS, namely as response surface method and global harmony search (RSM-GHS) modeling method. The second-order polynomial function with cross terms is applied to calibrate the time series suspended sediment load with three, four and five input variables in the proposed RSM-GHS. The linear, square and cross corrections of twenty input variables of antecedent values of suspended sediment load and water discharge are investigated to achieve the best predictions of the RSM based on the GHS method. The performances of the NN, RSM and proposed RSM-GHS including both accuracy and simplicity are compared through several comparative predicted and error statistics. The results illustrated that the proposed RSM-GHS is as uncomplicated as the RSM but performed better, where fewer errors and better correlation was observed (R = 0.95, MAE = 18.09 (ton/day), RMSE = 25.16 (ton/day)) compared to the ANN (R = 0.91, MAE = 20.17 (ton/day), RMSE = 33.09 (ton/day)) and RSM (R = 0.91, MAE = 20.06 (ton/day), RMSE = 31.92 (ton/day)) for all types of input variables.
Adoption of innovations by specialised nurses: personal, work and organisational characteristics.

PubMed

van der Weide, Marian; Smits, Jeroen

2004-04-01

To gain insight in the factors that influence the adoption of professional information by specialised nurses, we studied the effects of individual, work and organisational characteristics on the extent to which continence nurses gained knowledge and made use of a book on nursing diagnosis and interventions for patients with urinary incontinence, which they received as a present. Subjects were all members of the Dutch Association of Continence Nurses. Data collection took place via a postal questionnaire with closed questions. In total, 109 valid questionnaires (78%) were received back. Stepwise selected ordered logit models were estimated with reading the book and knowledge and use of five selected parts of it as dependent variables and individual, work and organisational characteristics as independent variables. The most important factors found to promote reading of the book and taking knowledge of the parts of it were a personal characteristic of the nurses called "information directedness" (or eagerness to acquire professional information from other sources), the presence of an "innovative atmosphere" at the department, and "relevance" of the information for daily nursing practice. The most important factors found to promote the use of the book are (again) information directedness, working at a (relatively) small department and having experience with nursing diagnosis. Results suggest that nurses differ in the degree to which they are open to innovations and that information directedness might be a useful indicator of this characteristic. In addition, the degree of innovativeness of the atmosphere at the department and the relevance of the innovation for nursing practice are important factors influencing the success or failure of innovations in nursing practice.
Roost site selection by ring-billed and herring gulls

USGS Publications Warehouse

Clark, Daniel E.; DeStefano, Stephen; MacKenzie, Kenneth G.; Koenen, Kiana K. G.; Whitney, Jillian J.

2016-01-01

Gulls (Larus spp.) commonly roost in large numbers on inland and coastal waters, yet there is little information on how or where gulls choose sites for roosting. Roost site selection can lead to water quality degradation or aviation hazards when roosts are formed on water supply reservoirs or are close to airports. Harassment programs are frequently initiated to move or relocate roosting gulls but often have mixed results because gulls are reluctant to leave or keep returning. As such, knowledge of gull roost site selection and roosting ecology has applied and ecological importance. We used satellite telemetry and an information-theoretic approach to model seasonal roost selection of ring-billed (L. delawarensis) and herring gulls (L. argentatus) in Massachusetts, USA. Our results indicated that ring-billed gulls preferred freshwater roosts and will use a variety of rivers, lakes, and reservoirs. Herring gulls regularly roosted on fresh water but used salt water roosts more often than ring-billed gulls and also roosted on a variety of land habitats. Roost modeling showed that herring and ring-billed gulls selected inland fresh water roosts based on size of the water body and proximity to their last daytime location; they selected the largest roost closest to where they ended the day. Management strategies to reduce or eliminate roosting gulls could identify and try to eliminate other habitat variables (e.g., close-by foraging sites) that are attracting gulls before attempting to relocate or redistribute (e.g., through hazing programs) roosting birds.
Knowledge-Based Manufacturing and Structural Design for a High Speed Civil Transport

NASA Technical Reports Server (NTRS)

Marx, William J.; Mavris, Dimitri N.; Schrage, Daniel P.

1994-01-01

The aerospace industry is currently addressing the problem of integrating manufacturing and design. To address the difficulties associated with using many conventional procedural techniques and algorithms, one feasible way to integrate the two concepts is with the development of an appropriate Knowledge-Based System (KBS). The authors present their reasons for selecting a KBS to integrate design and manufacturing. A methodology for an aircraft producibility assessment is proposed, utilizing a KBS for manufacturing process selection, that addresses both procedural and heuristic aspects of designing and manufacturing of a High Speed Civil Transport (HSCT) wing. A cost model is discussed that would allow system level trades utilizing information describing the material characteristics as well as the manufacturing process selections. Statements of future work conclude the paper.
The Influence of Fisher Knowledge on the Susceptibility of Reef Fish Aggregations to Fishing

PubMed Central

Robinson, Jan; Cinner, Joshua E.; Graham, Nicholas A. J.

2014-01-01

Reef fishes that exhibit predictable aggregating behaviour are often considered vulnerable to overexploitation. However, fisher knowledge of this behaviour is often heterogeneous and, coupled with socioeconomic factors that constrain demand for or access to aggregated fish, will influence susceptibility to fishing. At two case study locations in Papua New Guinea, Ahus and Karkar islands, we conducted interview-based surveys to examine how local context influenced heterogeneity in knowledge of fish aggregations. We then explored the role of fisher knowledge in conferring susceptibility to fishing relative to socioeconomic drivers of fishing effort. Local heterogeneity in knowledge of aggregating behaviour differed between our case studies. At Ahus, variable access rights among fishers and genders to the main habitats were sources of heterogeneity in knowledge. By contrast, knowledge was more homogenous at Karkar and the sole source of variation was gear type. Differences between locations in the susceptibility of aggregations to fishing depended primarily on socioeconomic drivers of fishing effort rather than catchability. While Ahus fishers were knowledgeable of fish aggregations and used more selective gears, Karkar fishers were less constrained by tenure in their access to aggregation habitat. However, fishing effort was greater at Ahus and likely related to high dependency on fishing, greater access to provincial capital markets than Karkar and a weakening of customary management. Moreover, highly efficient fishing techniques have emerged at Ahus to exploit the non-reproductive aggregating behaviour of target species. Understanding how knowledge is structured within fishing communities and its relation to socioeconomic drivers of fishing effort is important if customary practices for conservation, such as tambu areas, are to be supported. The findings of this study call for a holistic approach to assessing the risks posed to reef fish aggregations by fishing, grounded in the principals of fisheries science and emerging social-ecological thinking. PMID:24646910
The influence of fisher knowledge on the susceptibility of reef fish aggregations to fishing.

PubMed

Robinson, Jan; Cinner, Joshua E; Graham, Nicholas A J

2014-01-01

Reef fishes that exhibit predictable aggregating behaviour are often considered vulnerable to overexploitation. However, fisher knowledge of this behaviour is often heterogeneous and, coupled with socioeconomic factors that constrain demand for or access to aggregated fish, will influence susceptibility to fishing. At two case study locations in Papua New Guinea, Ahus and Karkar islands, we conducted interview-based surveys to examine how local context influenced heterogeneity in knowledge of fish aggregations. We then explored the role of fisher knowledge in conferring susceptibility to fishing relative to socioeconomic drivers of fishing effort. Local heterogeneity in knowledge of aggregating behaviour differed between our case studies. At Ahus, variable access rights among fishers and genders to the main habitats were sources of heterogeneity in knowledge. By contrast, knowledge was more homogenous at Karkar and the sole source of variation was gear type. Differences between locations in the susceptibility of aggregations to fishing depended primarily on socioeconomic drivers of fishing effort rather than catchability. While Ahus fishers were knowledgeable of fish aggregations and used more selective gears, Karkar fishers were less constrained by tenure in their access to aggregation habitat. However, fishing effort was greater at Ahus and likely related to high dependency on fishing, greater access to provincial capital markets than Karkar and a weakening of customary management. Moreover, highly efficient fishing techniques have emerged at Ahus to exploit the non-reproductive aggregating behaviour of target species. Understanding how knowledge is structured within fishing communities and its relation to socioeconomic drivers of fishing effort is important if customary practices for conservation, such as tambu areas, are to be supported. The findings of this study call for a holistic approach to assessing the risks posed to reef fish aggregations by fishing, grounded in the principals of fisheries science and emerging social-ecological thinking.

Medication competency of nurses according to theoretical and drug calculation online exams: A descriptive correlational study.

PubMed

Sneck, Sami; Saarnio, Reetta; Isola, Arja; Boigu, Risto

2016-01-01

Medication administration is an important task of registered nurses. According to previous studies, nurses lack theoretical knowledge and drug calculation skills and knowledge-based mistakes do occur in clinical practice. Finnish health care organizations started to develop a systematic verification processes for medication competence at the end of the last decade. No studies have yet been made of nurses' theoretical knowledge and drug calculation skills according to these online exams. The aim of this study was to describe the medication competence of Finnish nurses according to theoretical and drug calculation exams. A descriptive correlation design was adopted. Participants and settings All nurses who participated in the online exam in three Finnish hospitals between 1.1.2009 and 31.05.2014 were selected to the study (n=2479). Quantitative methods like Pearson's chi-squared tests, analysis of variance (ANOVA) with post hoc Tukey tests and Pearson's correlation coefficient were used to test the existence of relationships between dependent and independent variables. The majority of nurses mastered the theoretical knowledge needed in medication administration, but 5% of the nurses struggled with passing the drug calculation exam. Theoretical knowledge and drug calculation skills were better in acute care units than in the other units and younger nurses achieved better results in both exams than their older colleagues. The differences found in this study were statistically significant, but not high. Nevertheless, even the tiniest deficiency in theoretical knowledge and drug calculation skills should be focused on. It is important to identify the nurses who struggle in the exams and to plan targeted educational interventions for supporting them. The next step is to study if verification of medication competence has an effect on patient safety. Copyright © 2015 Elsevier Ltd. All rights reserved.
Community models for wildlife impact assessment: a review of concepts and approaches

USGS Publications Warehouse

Schroeder, Richard L.

1987-01-01

The first two sections of this paper are concerned with defining and bounding communities, and describing those attributes of the community that are quantifiable and suitable for wildlife impact assessment purposes. Prior to the development or use of a community model, it is important to have a clear understanding of the concept of a community and a knowledge of the types of community attributes that can serve as outputs for the development of models. Clearly defined, unambiguous model outputs are essential for three reasons: (1) to ensure that the measured community attributes relate to the wildlife resource objectives of the study; (2) to allow testing of the outputs in experimental studies, to determine accuracy, and to allow for improvements based on such testing; and (3) to enable others to clearly understand the community attribute that has been measured. The third section of this paper described input variables that may be used to predict various community attributes. These input variables do not include direct measures of wildlife populations. Most impact assessments involve projects that result in drastic changes in habitat, such as changes in land use, vegetation, or available area. Therefore, the model input variables described in this section deal primarily with habitat related features. Several existing community models are described in the fourth section of this paper. A general description of each model is provided, including the nature of the input variables and the model output. The logic and assumptions of each model are discussed, along with data requirements needed to use the model. The fifth section provides guidance on the selection and development of community models. Identification of the community attribute that is of concern will determine the type of model most suitable for a particular application. This section provides guidelines on selected an existing model, as well as a discussion of the major steps to be followed in modifying an existing model or developing a new model. Considerations associated with the use of community models with the Habitat Evaluation Procedures are also discussed. The final section of the paper summarizes major findings of interest to field biologists and provides recommendations concerning the implementation of selected concepts in wildlife community analyses.
How Much Does AMH Really Vary in Normal Women?

PubMed Central

La Marca, Antonio; Grisendi, Valentina; Griesinger, Georg

2013-01-01

Anti-Mullerian Hormone (AMH) is an ovarian hormone expressed in growing follicles that have undergone recruitment from the primordial follicle pool but have not yet been selected for dominance. It is considered an accurate marker of ovarian reserve, able to reflect the size of the ovarian follicular pool of a woman of reproductive age. In comparison to other hormonal biomarkers such as serum FSH, low intra- and intermenstrual cycle variability have been proposed for AMH. This review summarizes the knowledge regarding within-subject variability, with particular attention on AMH intracycle variability. Moreover the impact of ethnicity, body mass index, and smoking behaviour on AMH interindividual variability will be reviewed. Finally changes in AMH serum levels in two conditions of ovarian quiescence, namely contraceptives use and pregnancy, will be discussed. The present review aims at guiding researchers and clinicians in interpreting AMH values and fluctuations in various research and clinical scenarios. PMID:24348558
Gendered knowledge and adaptive practices: Differentiation and change in Mwanga District, Tanzania.

PubMed

Smucker, Thomas A; Wangui, Elizabeth Edna

2016-12-01

We examine the wider social knowledge domain that complements technical and environmental knowledge in enabling adaptive practices through two case studies in Tanzania. We are concerned with knowledge production that is shaped by gendered exclusion from the main thrusts of planned adaptation, in the practice of irrigation in a dryland village and the adoption of fast-maturing seed varieties in a highland village. The findings draw on data from a household survey, community workshops, and key informant interviews. The largest challenge to effective adaptation is a lack of access to the social networks and institutions that allocate resources needed for adaptation. Results demonstrate the social differentiation of local knowledge, and how it is entwined with adaptive practices that emerge in relation to gendered mechanisms of access. We conclude that community-based adaptation can learn from engaging the broader social knowledge base in evaluating priorities for coping with greater climate variability.
Humidity: A review and primer on atmospheric moisture and human health.

PubMed

Davis, Robert E; McGregor, Glenn R; Enfield, Kyle B

2016-01-01

Research examining associations between weather and human health frequently includes the effects of atmospheric humidity. A large number of humidity variables have been developed for numerous purposes, but little guidance is available to health researchers regarding appropriate variable selection. We examine a suite of commonly used humidity variables and summarize both the medical and biometeorological literature on associations between humidity and human health. As an example of the importance of humidity variable selection, we correlate numerous hourly humidity variables to daily respiratory syncytial virus isolates in Singapore from 1992 to 1994. Most water-vapor mass based variables (specific humidity, absolute humidity, mixing ratio, dewpoint temperature, vapor pressure) exhibit comparable correlations. Variables that include a thermal component (relative humidity, dewpoint depression, saturation vapor pressure) exhibit strong diurnality and seasonality. Humidity variable selection must be dictated by the underlying research question. Despite being the most commonly used humidity variable, relative humidity should be used sparingly and avoided in cases when the proximity to saturation is not medically relevant. Care must be taken in averaging certain humidity variables daily or seasonally to avoid statistical biasing associated with variables that are inherently diurnal through their relationship to temperature. Copyright © 2015 Elsevier Inc. All rights reserved.
Validating a Model of Team Collaboration at the North American Aerospace Defense Command Using Selected Transcripts from September 11, 2001

DTIC Science & Technology

2008-06-01

1. Input........................................................................................ 21 2. Team Knowledge Base Construction...awareness. Team cognition differs from individual cognition. To effectively perform as a team, each member must share knowledge and understand his/her...sufficient to achieve situational awareness for decision-making or creation of a product. Knowledge interoperability is the identification, collection
Integrating knowledge-based multi-criteria evaluation techniques with GIS for landfill site selection: A case study using AHP

NASA Astrophysics Data System (ADS)

Fagbohun, B. J.; Aladejana, O. O.

2016-09-01

A major challenge in most growing urban areas of developing countries, without a pre-existing land use plan is the sustainable and efficient management of solid wastes. Siting a landfill is a complicated task because of several environmental regulations. This challenge gives birth to the need to develop efficient strategies for the selection of proper waste disposal sites in accordance with all existing environmental regulations. This paper presents a knowledge-based multi-criteria decision analysis using GIS for the selection of suitable landfill site in Ado-Ekiti, Nigeria. In order to identify suitable sites for landfill, seven factors - land use/cover, geology, river, soil, slope, lineament and roads - were taken into consideration. Each factor was classified and ranked based on prior knowledge about the area and existing guidelines. Weights for each factor were determined through pair-wise comparison using Saaty's 9 point scale and AHP. The integration of factors according to their weights using weighted index overlay analysis revealed that 39.23 km2 within the area was suitable to site a landfill. The resulting suitable area was classified as high suitability covering 6.47 km2 (16.49%), moderate suitability 25.48 km2 (64.95%) and low suitability 7.28 km2 (18.56%) based on their overall weights.
Application of a rule-based knowledge system using CLIPS for the taxonomy of selected Opuntia species

NASA Technical Reports Server (NTRS)

Heymans, Bart C.; Onema, Joel P.; Kuti, Joseph O.

1991-01-01

A rule based knowledge system was developed in CLIPS (C Language Integrated Production System) for identifying Opuntia species in the family Cactaceae, which contains approx. 1500 different species. This botanist expert tool system is capable of identifying selected Opuntia plants from the family level down to the species level when given some basic characteristics of the plants. Many plants are becoming of increasing importance because of their nutrition and human health potential, especially in the treatment of diabetes mellitus. The expert tool system described can be extremely useful in an unequivocal identification of many useful Opuntia species.
Teachers' Subject Matter Knowledge as a Teacher Qualification: A Synthesis of the Quantitative Literature on Students' Mathematics Achievement

ERIC Educational Resources Information Center

Ahn, Soyeon; Choi, Jinyoung

2004-01-01

The aim of this paper is to examine a variety of features of research that might account for mixed findings of the relationship between teachers' subject matter knowledge and student achievement based on meta-analytic technique. Of several variables that might contribute to inconsistencies and variations in study findings, three features of…
Development of Bilingual Phonological Awareness in Spanish-Speaking English Language Learners: The Roles of Vocabulary, Letter Knowledge, and Prior Phonological Awareness

ERIC Educational Resources Information Center

Anthony, Jason L.; Solari, Emily J.; Williams, Jeffrey M.; Schoger, Kimberly D.; Zhang, Zhou; Branum-Martin, Lee; Francis, David J.

2009-01-01

Theories concerning the development of phonological awareness place special emphasis on lexical and orthographic knowledge. Given the large degree of variability in preschool classrooms that house Spanish-speaking English language learners (ELL), this study controlled for classroom effects by removing classroom means and covariances based on 158…
Use of Research-Based Instructional Strategies in Introductory Physics: Where Do Faculty Leave the Innovation-Decision Process?

ERIC Educational Resources Information Center

Henderson, Charles; Dancy, Melissa; Niewiadomska-Bugaj, Magdalena

2012-01-01

During the fall of 2008 a web survey, designed to collect information about pedagogical knowledge and practices, was completed by a representative sample of 722 physics faculty across the United States (50.3% response rate). This paper presents partial results to describe how 20 potential predictor variables correlate with faculty knowledge about…
Positive Influence of Behavior Change Communication on Knowledge, Attitudes, and Practices for Visceral Leishmaniasis/Kala-azar in India.

PubMed

Srinivasan, Raghavan; Ahmad, Tanwir; Raghavan, Vidya; Kaushik, Manisha; Pathak, Ramakant

2018-03-21

Visceral leishmaniasis (VL) is endemic to 54 districts in 4 states of India. Poor awareness of the disease and inappropriate health-seeking behavior are major challenges to eliminating the disease. Between February 2016 and March 2017, we implemented a behavior change communication (BCC) intervention in 33 districts of Bihar, 4 districts of Jharkhand, and 3 districts of West Bengal using a mix of channels, including group and interpersonal communication, to improve knowledge, attitudes, and practices of communities, frontline health workers, and opinion leaders. We conducted an impact assessment in October 2016, after the second indoor residual spraying (IRS) round, in Bihar and Jharkhand to evaluate the effect of the BCC intervention. Villages in 10 districts of Bihar and 4 districts in Jharkhand were selected for inclusion in the assessment. Selected villages were categorized as either intervention or control based on where project activities were conducted. Households were randomly selected proportional to caste composition, and interviewers surveyed the head of the household on whether the house was sprayed during the last IRS round and on knowledge, attitudes, and practices related to VL. We interviewed 700 households in intervention villages and 350 households in control villages and conducted correlation analysis to explore the association between IRS refusal and socioeconomic variables, and tested for association between IRS refusal and exposure to BCC activities. Odds ratios (ORs) were calculated. We reached an estimated 3.3 million contacts in Bihar and Jharkhand through the intervention's BCC activities. IRS refusal rates were significantly lower in intervention households than control households (mean=7.95% vs. 24.45%, respectively; OR, 0.27; 95% confidence interval [CI], 0.11 to 0.62; P <.001). Households in intervention villages were more aware than those in control villages that VL is spread by sand flies (68.4% vs. 7.4%, respectively; P <.001) and of IRS as an effective control measure (82.3% vs. 41.7%, respectively; P <.001). A greater percentage of households in intervention villages than control villages indicated they would encourage a patient to go to primary health centers for diagnosis and treatment of VL (77.0% vs. 39.4%, respectively) and to encourage others to accept IRS (78.6% vs. 44.6%, respectively; P <.001). Households that were exposed to community-based BCC activities largely using group and interpersonal communication had better knowledge, attitudes, and practices related to VL, including acceptance of IRS as a preventive measure, than households not exposed. BCC activities are thus an important component of VL elimination strategies. © Srinivasan et al.
Characterizing the Optical Variability of Bright Blazars: Variability-based Selection of Fermi Active Galactic Nuclei

NASA Astrophysics Data System (ADS)

Ruan, John J.; Anderson, Scott F.; MacLeod, Chelsea L.; Becker, Andrew C.; Burnett, T. H.; Davenport, James R. A.; Ivezić, Željko; Kochanek, Christopher S.; Plotkin, Richard M.; Sesar, Branimir; Stuart, J. Scott

2012-11-01

We investigate the use of optical photometric variability to select and identify blazars in large-scale time-domain surveys, in part to aid in the identification of blazar counterparts to the ~30% of γ-ray sources in the Fermi 2FGL catalog still lacking reliable associations. Using data from the optical LINEAR asteroid survey, we characterize the optical variability of blazars by fitting a damped random walk model to individual light curves with two main model parameters, the characteristic timescales of variability τ, and driving amplitudes on short timescales \\hat{\\sigma }. Imposing cuts on minimum τ and \\hat{\\sigma } allows for blazar selection with high efficiency E and completeness C. To test the efficacy of this approach, we apply this method to optically variable LINEAR objects that fall within the several-arcminute error ellipses of γ-ray sources in the Fermi 2FGL catalog. Despite the extreme stellar contamination at the shallow depth of the LINEAR survey, we are able to recover previously associated optical counterparts to Fermi active galactic nuclei with E >= 88% and C = 88% in Fermi 95% confidence error ellipses having semimajor axis r < 8'. We find that the suggested radio counterpart to Fermi source 2FGL J1649.6+5238 has optical variability consistent with other γ-ray blazars and is likely to be the γ-ray source. Our results suggest that the variability of the non-thermal jet emission in blazars is stochastic in nature, with unique variability properties due to the effects of relativistic beaming. After correcting for beaming, we estimate that the characteristic timescale of blazar variability is ~3 years in the rest frame of the jet, in contrast with the ~320 day disk flux timescale observed in quasars. The variability-based selection method presented will be useful for blazar identification in time-domain optical surveys and is also a probe of jet physics.
Comprehensive assessment of variables affecting metabolic control in patients with type 2 diabetes mellitus in Jordan.

PubMed

Qteishat, Rola Reyad; Ghananim, Abdel Rahman Al

2016-01-01

The aim of the study was to identify variables affecting metabolic control among diabetic patients treated at diabetes and endocrine clinic in Jordan. A total of 200 patients were studied by using a cross sectional study design. Data were collected from patients' medical records, glycemic control tests and prestructured questionnaires about variables that were potentially important based on previous researches and clinical judgment: Adherence evaluation, Patients' knowledge about drug therapy and non-pharmacological therapy, Anxiety and depression, Beliefs about diabetes treatment (benefits and barriers of treatment), Knowledge about treatment goals, Knowledge about diabetes, Self efficacy, and Social support. The mean (±SD) age was 53.5 (±10.38) years and mean HbA1c was 8.4 (±1.95). In the multivariate analysis, education level, and self efficacy found to have significantly independent association with metabolic control (P<0.03). Adequate knowledge and high self efficacy was significant in patients with good metabolic control. Emphasizing the importance of continuous educational programs and improving the self efficacy as well, could warrant achieving good metabolic control. Copyright © 2015 Diabetes India. Published by Elsevier Ltd. All rights reserved.
Variable selection based cotton bollworm odor spectroscopic detection

NASA Astrophysics Data System (ADS)

Lü, Chengxu; Gai, Shasha; Luo, Min; Zhao, Bo

2016-10-01

Aiming at rapid automatic pest detection based efficient and targeting pesticide application and shooting the trouble of reflectance spectral signal covered and attenuated by the solid plant, the possibility of near infrared spectroscopy (NIRS) detection on cotton bollworm odor is studied. Three cotton bollworm odor samples and 3 blank air gas samples were prepared. Different concentrations of cotton bollworm odor were prepared by mixing the above gas samples, resulting a calibration group of 62 samples and a validation group of 31 samples. Spectral collection system includes light source, optical fiber, sample chamber, spectrometer. Spectra were pretreated by baseline correction, modeled with partial least squares (PLS), and optimized by genetic algorithm (GA) and competitive adaptive reweighted sampling (CARS). Minor counts differences are found among spectra of different cotton bollworm odor concentrations. PLS model of all the variables was built presenting RMSEV of 14 and RV2 of 0.89, its theory basis is insect volatilizes specific odor, including pheromone and allelochemics, which are used for intra-specific and inter-specific communication and could be detected by NIR spectroscopy. 28 sensitive variables are selected by GA, presenting the model performance of RMSEV of 14 and RV2 of 0.90. Comparably, 8 sensitive variables are selected by CARS, presenting the model performance of RMSEV of 13 and RV2 of 0.92. CARS model employs only 1.5% variables presenting smaller error than that of all variable. Odor gas based NIR technique shows the potential for cotton bollworm detection.
Sympatric speciation by sexual selection alone is unlikely.

PubMed

Arnegard, Matthew E; Kondrashov, Alexey S

2004-02-01

According to Darwin, sympatric speciation is driven by disruptive, frequency-dependent natural selection caused by competition for diverse resources. Recently, several authors have argued that disruptive sexual selection can also cause sympatric speciation. Here, we use hypergeometric phenotypic and individual-based genotypic models to explore sympatric speciation by sexual selection under a broad range of conditions. If variabilities of preference and display traits are each caused by more than one or two polymorphic loci, sympatric speciation requires rather strong sexual selection when females exert preferences for extreme male phenotypes. Under this kind of mate choice, speciation can occur only if initial distributions of preference and display are close to symmetric. Otherwise, the population rapidly loses variability. Thus, unless allele replacements at very few loci are enough for reproductive isolation, female preferences for extreme male displays are unlikely to drive sympatric speciation. By contrast, similarity-based female preferences that do not cause sexual selection are less destabilizing to the maintenance of genetic variability and may result in sympatric speciation across a broader range of initial conditions. Certain groups of African cichlids have served as the exclusive motivation for the hypothesis of sympatric speciation by sexual selection. Mate choice in these fishes appears to be driven by female preferences for extreme male phenotypes rather than similarity-based preferences, and the evolution of premating reproductive isolation commonly involves at least several genes. Therefore, differences in female preferences and male display in cichlids and other species of sympatric origin are more likely to have evolved as isolating mechanisms under disruptive natural selection.
An imbalance fault detection method based on data normalization and EMD for marine current turbines.

PubMed

Zhang, Milu; Wang, Tianzhen; Tang, Tianhao; Benbouzid, Mohamed; Diallo, Demba

2017-05-01

This paper proposes an imbalance fault detection method based on data normalization and Empirical Mode Decomposition (EMD) for variable speed direct-drive Marine Current Turbine (MCT) system. The method is based on the MCT stator current under the condition of wave and turbulence. The goal of this method is to extract blade imbalance fault feature, which is concealed by the supply frequency and the environment noise. First, a Generalized Likelihood Ratio Test (GLRT) detector is developed and the monitoring variable is selected by analyzing the relationship between the variables. Then, the selected monitoring variable is converted into a time series through data normalization, which makes the imbalance fault characteristic frequency into a constant. At the end, the monitoring variable is filtered out by EMD method to eliminate the effect of turbulence. The experiments show that the proposed method is robust against turbulence through comparing the different fault severities and the different turbulence intensities. Comparison with other methods, the experimental results indicate the feasibility and efficacy of the proposed method. Copyright © 2017 ISA. Published by Elsevier Ltd. All rights reserved.
Nutrition training improves health workers' nutrition knowledge and competence to manage child undernutrition: a systematic review.

PubMed

Sunguya, Bruno F; Poudel, Krishna C; Mlunde, Linda B; Urassa, David P; Yasuoka, Junko; Jimba, Masamine

2013-09-24

Medical and nursing education lack adequate practical nutrition training to fit the clinical reality that health workers face in their practices. Such a deficit creates health workers with poor nutrition knowledge and child undernutrition management practices. In-service nutrition training can help to fill this gap. However, no systematic review has examined its collective effectiveness. We thus conducted this study to examine the effectiveness of in-service nutrition training on health workers' nutrition knowledge, counseling skills, and child undernutrition management practices. We conducted a literature search on nutrition interventions from PubMed/MEDLINE, CINAHL, EMBASE, ISI Web of Knowledge, and World Health Organization regional databases. The outcome variables were nutrition knowledge, nutrition-counseling skills, and undernutrition management practices of health workers. Due to heterogeneity, we conducted only descriptive analyses. Out of 3910 retrieved articles, 25 were selected as eligible for the final analysis. A total of 18 studies evaluated health workers' nutrition knowledge and showed improvement after training. A total of 12 studies with nutrition counseling as the outcome variable also showed improvement among the trained health workers. Sixteen studies evaluated health workers' child undernutrition management practices. In all such studies, child undernutrition management practices and competence of health workers improved after the nutrition training intervention. In-service nutrition training improves quality of health workers by rendering them more knowledge and competence to manage nutrition-related conditions, especially child undernutrition. In-service nutrition training interventions can help to fill the gap created by the lack of adequate nutrition training in the existing medical and nursing education system. In this way, steps can be taken toward improving the overall nutritional status of the child population.
A method for simplifying the analysis of traffic accidents injury severity on two-lane highways using Bayesian networks.

PubMed

Mujalli, Randa Oqab; de Oña, Juan

2011-10-01

This study describes a method for reducing the number of variables frequently considered in modeling the severity of traffic accidents. The method's efficiency is assessed by constructing Bayesian networks (BN). It is based on a two stage selection process. Several variable selection algorithms, commonly used in data mining, are applied in order to select subsets of variables. BNs are built using the selected subsets and their performance is compared with the original BN (with all the variables) using five indicators. The BNs that improve the indicators' values are further analyzed for identifying the most significant variables (accident type, age, atmospheric factors, gender, lighting, number of injured, and occupant involved). A new BN is built using these variables, where the results of the indicators indicate, in most of the cases, a statistically significant improvement with respect to the original BN. It is possible to reduce the number of variables used to model traffic accidents injury severity through BNs without reducing the performance of the model. The study provides the safety analysts a methodology that could be used to minimize the number of variables used in order to determine efficiently the injury severity of traffic accidents without reducing the performance of the model. Copyright © 2011 Elsevier Ltd. All rights reserved.
Effects of peer education intervention on HIV/AIDS related sexual behaviors of secondary school students in Addis Ababa, Ethiopia: a quasi-experimental study.

PubMed

Menna, Takele; Ali, Ahmed; Worku, Alemayehu

2015-09-07

Worldwide, about 50% of all new cases of HIV occur in youth between age 15 and 24 years. Studies in various sub-Saharan African countries show that both out of school and in school adolescents and youth are engaged in risky sexual behaviors. School-based health education has been a cornerstone of youth-focused HIV prevention efforts since the early 1990s. In addition, peer-based interventions have become a common method to effect important health-related behavior changes and address the HIV/AIDS pandemic. Thus, the aim of this study was to evaluate efficacy of peer education on changing HIV related risky sexual behaviors among school youth in Addis Ababa, Ethiopia. A quasi experimental study with peer education intervention was conducted in purposively selected four secondary schools (two secondary schools for the intervention and other two for the control group) in Addis Ababa, Ethiopia. Five hundred sixty students from randomly selected sections of grade 11 were assessed through anonymous questionnaires conducted in pre- and post-intervention periods. Pertinent data on socio-demographic and sexual behavior related factors were collected. The statistical packages used for data entry and analysis were epi-info version 3.5.4 and SPSS version 20.0 respectively. Chi-square test and multivariable logistic regressions were used for testing association between peer education intervention and sexual behaviors of students. In addition to testing association between dependent and independent variables, multi-variable analysis was employed to control for the effects of confounding variables. When the pre and post intervention data of each group were compared, comprehensive Knowledge of HIV (P-Values =0.004) and willingness to go for HIV counseling and testing (P-value = 0.01) showed significant differences among intervention group students during post intervention period. Moreover, students in the intervention group were more likely to use condoms during post intervention period compared to students of the control group [AOR = 4.73 (95% CI (1.40-16.0)]. Despite short follow up period, students in the intervention group demonstrated positive changes in HIV related comprehensive knowledge and showed better interest to go for HIV testing in the near future. Furthermore, positive changes on risky sexual behaviors were reported from the intervention group. Implementing secondary school targeted peer education by allocating appropriate amounts of resources (money, man power, materials and time) could play significant role to prevent and control HIV/AIDS among school youth.

Molecular Classification Substitutes for the Prognostic Variables Stage, Age, and MYCN Status in Neuroblastoma Risk Assessment.

PubMed

Rosswog, Carolina; Schmidt, Rene; Oberthuer, André; Juraeva, Dilafruz; Brors, Benedikt; Engesser, Anne; Kahlert, Yvonne; Volland, Ruth; Bartenhagen, Christoph; Simon, Thorsten; Berthold, Frank; Hero, Barbara; Faldum, Andreas; Fischer, Matthias

2017-12-01

Current risk stratification systems for neuroblastoma patients consider clinical, histopathological, and genetic variables, and additional prognostic markers have been proposed in recent years. We here sought to select highly informative covariates in a multistep strategy based on consecutive Cox regression models, resulting in a risk score that integrates hazard ratios of prognostic variables. A cohort of 695 neuroblastoma patients was divided into a discovery set (n=75) for multigene predictor generation, a training set (n=411) for risk score development, and a validation set (n=209). Relevant prognostic variables were identified by stepwise multivariable L1-penalized least absolute shrinkage and selection operator (LASSO) Cox regression, followed by backward selection in multivariable Cox regression, and then integrated into a novel risk score. The variables stage, age, MYCN status, and two multigene predictors, NB-th24 and NB-th44, were selected as independent prognostic markers by LASSO Cox regression analysis. Following backward selection, only the multigene predictors were retained in the final model. Integration of these classifiers in a risk scoring system distinguished three patient subgroups that differed substantially in their outcome. The scoring system discriminated patients with diverging outcome in the validation cohort (5-year event-free survival, 84.9±3.4 vs 63.6±14.5 vs 31.0±5.4; P<.001), and its prognostic value was validated by multivariable analysis. We here propose a translational strategy for developing risk assessment systems based on hazard ratios of relevant prognostic variables. Our final neuroblastoma risk score comprised two multigene predictors only, supporting the notion that molecular properties of the tumor cells strongly impact clinical courses of neuroblastoma patients. Copyright © 2017 The Authors. Published by Elsevier Inc. All rights reserved.
The lack of knowledge about the voluntary reporting system of adverse drug reactions as a major cause of underreporting: direct survey among health professionals.

PubMed

Pérez García, Mariley; Figueras, Albert

2011-12-01

Underreporting suspected adverse drug reactions (ADRs) is one of the main problems that face the pharmacovigilance (PhV) systems based on yellow card schemes. To measure the knowledge level on the suspected ADR voluntary reporting system among physicians and pharmacists in Venezuela and to study its relationship with different variables. A population-based, anonymous, and self-administered questionnaire was addressed to health professionals along the national territory of Venezuela. An algorithm was developed to classify the knowledge level on the voluntary reporting system. Taken as a whole, the level of knowledge on the voluntary reporting system was "poor." Among the 515 participant physicians, 62.3% (95%CI = 58.0-66.5%) had a "poor" level of knowledge; PhV was associated with "control" of medicines use, and only 24.7% had ever reported a suspected ADR. "Medical specialty" was the only variable that showed a relationship with the knowledge level (p = 0.0041). Among the 78 participant pharmacists, 66.7% had a "poor" knowledge level (95%CI = 55.1-76.9%). The knowledge level on the voluntary reporting system among physicians and pharmacists in Venezuela is poor. These results strengthen the hypothesis that being unaware of the adverse effects of medicines and not knowing the existence of a PhV system is a major cause of underreporting. A careful study of the actual knowledge of PhV could be the basis to set up interventions specifically designed to overcome misleading concepts and to improve the reporting rate at a national level. Copyright © 2011 John Wiley & Sons, Ltd.
System Complexity Reduction via Feature Selection

ERIC Educational Resources Information Center

Deng, Houtao

2011-01-01

This dissertation transforms a set of system complexity reduction problems to feature selection problems. Three systems are considered: classification based on association rules, network structure learning, and time series classification. Furthermore, two variable importance measures are proposed to reduce the feature selection bias in tree…
A quality of care issue: appropriate use and efficacy knowledge of five contraceptive methods: views of men and women living in low socioeconomic settlements of Karachi, Pakistan.

PubMed

Fikree, Fariyal F; Saleem, Sarah; Sami, Neelofar

2005-09-01

To assess knowledge regarding availability, affordability, appropriate use and efficacy for five non-permanent contraceptive methods. Married Muslim women and men (500 each) were randomly selected from two low socioeconomic settlements in Karachi, Pakistan. Interviews to assess their knowledge on a range of contraceptive and abortion themes were conducted. Four hundred men and 357 women were selected from this larger sample based on their knowledge of condoms, withdrawal, oral pills, injectables and IUDs. Nearly half of the sampled men (56%) and women (48%) were contraceptive users. Knowledge regarding contraception, a specific method, its availability and affordability was high. Appropriate use knowledge for condoms was 73% among men (users 78%, non-users 60%; p-value < or = 0.001 ) and 5% among women. Efficacy knowledge was generally poor. Low knowledge levels regarding appropriate use and efficacy even among contraceptive users suggests, that quality of family planning services should not be limited to service delivery issues but extend to appropriate use and efficacy knowledge levels among clients.
Knowledge service decision making in business incubators based on the supernetwork model

NASA Astrophysics Data System (ADS)

Zhao, Liming; Zhang, Haihong; Wu, Wenqing

2017-08-01

As valuable resources for incubating firms, knowledge resources have received gradually increasing attention from all types of business incubators, and business incubators use a variety of knowledge services to stimulate rapid growth in incubating firms. Based on previous research, we generalize the knowledge transfer and knowledge networking services of two main forms of knowledge services and further divide knowledge transfer services into knowledge depth services and knowledge breadth services. Then, we construct the business incubators' knowledge supernetwork model, describe the evolution mechanism among heterogeneous agents and utilize a simulation to explore the performance variance of different business incubators' knowledge services. The simulation results show that knowledge stock increases faster when business incubators are able to provide knowledge services to more incubating firms and that the degree of discrepancy in the knowledge stock increases during the process of knowledge growth. Further, knowledge transfer services lead to greater differences in the knowledge structure, while knowledge networking services lead to smaller differences. Regarding the two types of knowledge transfer services, knowledge depth services are more conducive to knowledge growth than knowledge breadth services, but knowledge depth services lead to greater gaps in knowledge stocks and greater differences in knowledge structures. Overall, it is optimal for business incubators to select a single knowledge service or portfolio strategy based on the amount of time and energy expended on the two types of knowledge services.
Variable selection for marginal longitudinal generalized linear models.

PubMed

Cantoni, Eva; Flemming, Joanna Mills; Ronchetti, Elvezio

2005-06-01

Variable selection is an essential part of any statistical analysis and yet has been somewhat neglected in the context of longitudinal data analysis. In this article, we propose a generalized version of Mallows's C(p) (GC(p)) suitable for use with both parametric and nonparametric models. GC(p) provides an estimate of a measure of model's adequacy for prediction. We examine its performance with popular marginal longitudinal models (fitted using GEE) and contrast results with what is typically done in practice: variable selection based on Wald-type or score-type tests. An application to real data further demonstrates the merits of our approach while at the same time emphasizing some important robust features inherent to GC(p).
[Measurement of Water COD Based on UV-Vis Spectroscopy Technology].

PubMed

Wang, Xiao-ming; Zhang, Hai-liang; Luo, Wei; Liu, Xue-mei

2016-01-01

Ultraviolet/visible (UV/Vis) spectroscopy technology was used to measure water COD. A total of 135 water samples were collected from Zhejiang province. Raw spectra with 3 different pretreatment methods (Multiplicative Scatter Correction (MSC), Standard Normal Variate (SNV) and 1st Derivatives were compared to determine the optimal pretreatment method for analysis. Spectral variable selection is an important strategy in spectrum modeling analysis, because it tends to parsimonious data representation and can lead to multivariate models with better performance. In order to simply calibration models, the preprocessed spectra were then used to select sensitive wavelengths by competitive adaptive reweighted sampling (CARS), Random frog and Successive Genetic Algorithm (GA) methods. Different numbers of sensitive wavelengths were selected by different variable selection methods with SNV preprocessing method. Partial least squares (PLS) was used to build models with the full spectra, and Extreme Learning Machine (ELM) was applied to build models with the selected wavelength variables. The overall results showed that ELM model performed better than PLS model, and the ELM model with the selected wavelengths based on CARS obtained the best results with the determination coefficient (R2), RMSEP and RPD were 0.82, 14.48 and 2.34 for prediction set. The results indicated that it was feasible to use UV/Vis with characteristic wavelengths which were obtained by CARS variable selection method, combined with ELM calibration could apply for the rapid and accurate determination of COD in aquaculture water. Moreover, this study laid the foundation for further implementation of online analysis of aquaculture water and rapid determination of other water quality parameters.
Variables Affecting Student Motivation Based on Academic Publications

ERIC Educational Resources Information Center

Yilmaz, Ercan; Sahin, Mehmet; Turgut, Mehmet

2017-01-01

In this study, the variables having impact on the student motivation have been analyzed based on the articles, conference papers, master's theses and doctoral dissertations published in the years 2000-2017. A total of 165 research papers were selected for the research material and the data were collected through qualitative research techniques…
Impact of strong selection for the PrP major gene on genetic variability of four French sheep breeds (Open Access publication)

PubMed Central

Palhiere, Isabelle; Brochard, Mickaël; Moazami-Goudarzi, Katayoun; Laloë, Denis; Amigues, Yves; Bed'hom, Bertrand; Neuts, Étienne; Leymarie, Cyril; Pantano, Thais; Cribiu, Edmond Paul; Bibé, Bernard; Verrier, Étienne

2008-01-01

Effective selection on the PrP gene has been implemented since October 2001 in all French sheep breeds. After four years, the ARR "resistant" allele frequency increased by about 35% in young males. The aim of this study was to evaluate the impact of this strong selection on genetic variability. It is focussed on four French sheep breeds and based on the comparison of two groups of 94 animals within each breed: the first group of animals was born before the selection began, and the second, 3–4 years later. Genetic variability was assessed using genealogical and molecular data (29 microsatellite markers). The expected loss of genetic variability on the PrP gene was confirmed. Moreover, among the five markers located in the PrP region, only the three closest ones were affected. The evolution of the number of alleles, heterozygote deficiency within population, expected heterozygosity and the Reynolds distances agreed with the criteria from pedigree and pointed out that neutral genetic variability was not much affected. This trend depended on breed, i.e. on their initial states (population size, PrP frequencies) and on the selection strategies for improving scrapie resistance while carrying out selection for production traits. PMID:18990357
Selecting an Informative/Discriminating Multivariate Response for Inverse Prediction

DOE PAGES

Thomas, Edward V.; Lewis, John R.; Anderson-Cook, Christine M.; ...

2017-11-21

nverse prediction is important in a wide variety of scientific and engineering contexts. One might use inverse prediction to predict fundamental properties/characteristics of an object using measurements obtained from it. This can be accomplished by “inverting” parameterized forward models that relate the measurements (responses) to the properties/characteristics of interest. Sometimes forward models are science based; but often, forward models are empirically based, using the results of experimentation. For empirically-based forward models, it is important that the experiments provide a sound basis to develop accurate forward models in terms of the properties/characteristics (factors). While nature dictates the causal relationship between factorsmore » and responses, experimenters can influence control of the type, accuracy, and precision of forward models that can be constructed via selection of factors, factor levels, and the set of trials that are performed. Whether the forward models are based on science, experiments or both, researchers can influence the ability to perform inverse prediction by selecting informative response variables. By using an errors-in-variables framework for inverse prediction, this paper shows via simple analysis and examples how the capability of a multivariate response (with respect to being informative and discriminating) can vary depending on how well the various responses complement one another over the range of the factor-space of interest. Insights derived from this analysis could be useful for selecting a set of response variables among candidates in cases where the number of response variables that can be acquired is limited by difficulty, expense, and/or availability of material.« less
Selecting an Informative/Discriminating Multivariate Response for Inverse Prediction

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

Thomas, Edward V.; Lewis, John R.; Anderson-Cook, Christine M.

nverse prediction is important in a wide variety of scientific and engineering contexts. One might use inverse prediction to predict fundamental properties/characteristics of an object using measurements obtained from it. This can be accomplished by “inverting” parameterized forward models that relate the measurements (responses) to the properties/characteristics of interest. Sometimes forward models are science based; but often, forward models are empirically based, using the results of experimentation. For empirically-based forward models, it is important that the experiments provide a sound basis to develop accurate forward models in terms of the properties/characteristics (factors). While nature dictates the causal relationship between factorsmore » and responses, experimenters can influence control of the type, accuracy, and precision of forward models that can be constructed via selection of factors, factor levels, and the set of trials that are performed. Whether the forward models are based on science, experiments or both, researchers can influence the ability to perform inverse prediction by selecting informative response variables. By using an errors-in-variables framework for inverse prediction, this paper shows via simple analysis and examples how the capability of a multivariate response (with respect to being informative and discriminating) can vary depending on how well the various responses complement one another over the range of the factor-space of interest. Insights derived from this analysis could be useful for selecting a set of response variables among candidates in cases where the number of response variables that can be acquired is limited by difficulty, expense, and/or availability of material.« less
Cancer Related-Knowledge - Small Area Estimates

Cancer.gov

These model-based estimates are produced using statistical models that combine data from the Health Information National Trends Survey, and auxiliary variables obtained from relevant sources and borrow strength from other areas with similar characteristics.
Effect of Gender on the Knowledge of Medicinal Plants: Systematic Review and Meta-Analysis

PubMed Central

Torres-Avilez, Wendy; de Medeiros, Patrícia Muniz

2016-01-01

Knowledge of medicinal plants is not only one of the main components in the structure of knowledge in local medical systems but also one of the most studied resources. This study uses a systematic review and meta-analysis of a compilation of ethnobiological studies with a medicinal plant component and the variable of gender to evaluate whether there is a gender-based pattern in medicinal plant knowledge on different scales (national, continental, and global). In this study, three types of meta-analysis are conducted on different scales. We detect no significant differences on the global level; women and men have the same rich knowledge. On the national and continental levels, significant differences are observed in both directions (significant for men and for women), and a lack of significant differences in the knowledge of the genders is also observed. This finding demonstrates that there is no gender-based pattern for knowledge on different scales. PMID:27795730
Factors Associated with Hepatitis B Knowledge Among Vietnamese Americans: A Population-Based Survey.

PubMed

Chu, Janet N; Le, Phuoc V; Kennedy, Chris J; McPhee, Stephen J; Wong, Ching; Stewart, Susan L; Nguyen, Tung T

2017-08-01

Vietnamese Americans have high rates of hepatitis B virus (HBV) infection but low rates of knowledge and screening. A population-based survey conducted in 2011 of Vietnamese Americans in two geographic areas (n = 1666) was analyzed. The outcome variables were having heard of HBV and a score summarizing knowledge of HBV transmission. Most respondents (86.0%) had heard of HBV. Correct knowledge of transmission ranged from 59.5% for sex, 68.1% for sharing toothbrushes, 78.6% for during birth, and 85.0% for sharing needles. In multivariable analyses, factors associated with having heard of HBV and higher knowledge included Northern California residence, longer U.S. residence, higher education, family history of HBV, and discussing HBV with family/friends. Higher income was associated with having heard of HBV. English fluency and being U.S.-born were associated with higher knowledge. Interventions to increase knowledge of HBV transmission are needed to decrease this health disparity among Vietnamese Americans.
A veterinary anatomy tutoring system.

PubMed

Theodoropoulos, G; Loumos, V; Antonopoulos, J

1994-02-14

A veterinary anatomy tutoring system was developed by using Knowledge Pro, an object-oriented software development tool with hypermedia capabilities, and MS Access, a relational database. Communication between them is facilitated by using the Structured Query Language (SQL). The architecture of the system is based on knowledge sets, each of which covers four different descriptions of an organ, namely gross anatomy (general description), gross anatomy (comparative features), histology, and embryology, which constitute the knowledge units. These knowledge units are linked with three global variables that define the animals, the topographies, and the system to which this organ belongs, creating three data-bases. These three data-bases are interrelated through the organ field in order to establish a relational model. This system allows versatility in the student's navigation through the information space by offering different modes for information location and presentation. These include course mode, review mode, reference mode, dissection mode, and comparison mode. In addition, the system provides a self-evaluation mode.
Awareness, knowledge, and attitude of dentistry students in Kerman towards evidence-based dentistry

PubMed Central

Sarani, Arezoo; Sarani, Melika; Abdar, Mohammad Esmaeli; Abdar, Zahra Esmaeili

2016-01-01

Introduction Evidence-based care helps dentists provide quality dental services to patients, and such care is based on the use of reliable information about treatment and patient care from a large number of papers, books, and published textbooks. This study aimed to determine the knowledge, awareness, and attitude of dentistry students towards evidence-based dentistry. Methods In this cross-sectional study, all dentistry students who were studying in their sixth semester and higher in the Kerman School of Dentistry (n = 73) were studied. The data were analyzed using SPSS version 17 and the independent-samples t-tests and the ANOVA test. Results The means of the students’ knowledge, awareness, and attitude scores were 29.2 ± 10.8, 29.9 ± 8.12 and 44.5 ± 5.3, respectively. Among demographic variables, only the number of semesters showed a significant difference with knowledge, awareness, and attitude of dentistry students toward evidence-based dentistry (p = 0.001). Conclusion According to the results of this study, knowledge and awareness of dentistry students at Kerman University of Medical Sciences towards evidence-based dentistry were average and have a neutral attitude. Thus, providing necessary training in this regard will cause promoting the knowledge, awareness, and improved attitudes of dentistry students. PMID:27382446
Bridging the gap between formal and experience-based knowledge for context-aware laparoscopy.

PubMed

Katić, Darko; Schuck, Jürgen; Wekerle, Anna-Laura; Kenngott, Hannes; Müller-Stich, Beat Peter; Dillmann, Rüdiger; Speidel, Stefanie

2016-06-01

Computer assistance is increasingly common in surgery. However, the amount of information is bound to overload processing abilities of surgeons. We propose methods to recognize the current phase of a surgery for context-aware information filtering. The purpose is to select the most suitable subset of information for surgical situations which require special assistance. We combine formal knowledge, represented by an ontology, and experience-based knowledge, represented by training samples, to recognize phases. For this purpose, we have developed two different methods. Firstly, we use formal knowledge about possible phase transitions to create a composition of random forests. Secondly, we propose a method based on cultural optimization to infer formal rules from experience to recognize phases. The proposed methods are compared with a purely formal knowledge-based approach using rules and a purely experience-based one using regular random forests. The comparative evaluation on laparoscopic pancreas resections and adrenalectomies employs a consistent set of quality criteria on clean and noisy input. The rule-based approaches proved best with noisefree data. The random forest-based ones were more robust in the presence of noise. Formal and experience-based knowledge can be successfully combined for robust phase recognition.
The Effects of an Ethics Training Program on Attitude, Knowledge, and Transfer of Training of Office Professionals: A Treatment- and Control-Group Design

ERIC Educational Resources Information Center

Frisque, Deloise A.; Kolb, Judith A.

2008-01-01

This study examines the effects of ethics training on the attitudes, knowledge-based scores, and analysis of ethical dilemmas among office professionals. A treatment- and control-group design was used with variables of interest measured before, immediately after, and ninety days following completion of a six-hour ethics training workshop. A…
Examining the Feasibility and Impact of a Graduate Public Administration Course in Evidence-Informed Policy

ERIC Educational Resources Information Center

Ouimet, Mathieu; Lapointe, Luc; Léon, Grégory

2015-01-01

A pilot controlled before-and-after design was used to assess the impact of a new master-level course in evidence-informed policy making on students' basic knowledge in evidence-based practice. The primary outcome variable was the mean percentage of pre-post improvement on the knowledge test. In the treatment group, the mean percentage of pre-post…
Indigenous Knowledge and Sea Ice Science: What Can We Learn from Indigenous Ice Users?

NASA Astrophysics Data System (ADS)

Eicken, H.

2010-12-01

Drawing on examples mostly from Iñupiaq and Yup’ik sea-ice expertise in coastal Alaska, this contribution examines how local, indigenous knowledge (LIK) can inform and guide geophysical and biological sea-ice research. Part of the relevance of LIK derives from its linkage to sea-ice use and the services coastal communities derive from the ice cover. As a result, indigenous experts keep track of a broad range of sea-ice variables at a particular location. These observations are embedded into a broader worldview that speaks to both long-term variability or change and to the system of values associated with ice use. The contribution examines eight different contexts in which LIK in study site selection and assessment of a sampling campaign in the context of inter annual variability, the identification of rare or inconspicuous phenomena or events, the contribution by indigenous experts to hazard assessment and emergency response, the record of past and present climate embedded in LIK, and the value of holistic sea-ice knowledge in detecting subtle, intertwined patterns of environmental change. The relevance of local, indigenous sea-ice expertise in helping advance adaptation and responses to climate change as well as its potential role in guiding research questions and hypotheses are also examined. The challenges that may have to be overcome in creating an interface for exchange between indigenous experts and seaice researchers are considered. Promising approaches to overcome these challenges include cross-cultural, interdisciplinary education, and the fostering of Communities of Practice.

Artificial intelligence (AI)-based relational matching and multimodal medical image fusion: generalized 3D approaches

NASA Astrophysics Data System (ADS)

Vajdic, Stevan M.; Katz, Henry E.; Downing, Andrew R.; Brooks, Michael J.

1994-09-01

A 3D relational image matching/fusion algorithm is introduced. It is implemented in the domain of medical imaging and is based on Artificial Intelligence paradigms--in particular, knowledge base representation and tree search. The 2D reference and target images are selected from 3D sets and segmented into non-touching and non-overlapping regions, using iterative thresholding and/or knowledge about the anatomical shapes of human organs. Selected image region attributes are calculated. Region matches are obtained using a tree search, and the error is minimized by evaluating a `goodness' of matching function based on similarities of region attributes. Once the matched regions are found and the spline geometric transform is applied to regional centers of gravity, images are ready for fusion and visualization into a single 3D image of higher clarity.
Knowledge Synthesis with Maps of Neural Connectivity

PubMed Central

Tallis, Marcelo; Thompson, Richard; Russ, Thomas A.; Burns, Gully A. P. C.

2011-01-01

This paper describes software for neuroanatomical knowledge synthesis based on neural connectivity data. This software supports a mature methodology developed since the early 1990s. Over this time, the Swanson laboratory at USC has generated an account of the neural connectivity of the sub-structures of the hypothalamus, amygdala, septum, hippocampus, and bed nucleus of the stria terminalis. This is based on neuroanatomical data maps drawn into a standard brain atlas by experts. In earlier work, we presented an application for visualizing and comparing anatomical macro connections using the Swanson third edition atlas as a framework for accurate registration. Here we describe major improvements to the NeuARt application based on the incorporation of a knowledge representation of experimental design. We also present improvements in the interface and features of the data mapping components within a unified web-application. As a step toward developing an accurate sub-regional account of neural connectivity, we provide navigational access between the data maps and a semantic representation of area-to-area connections that they support. We do so based on an approach called “Knowledge Engineering from Experimental Design” (KEfED) model that is based on experimental variables. We have extended the underlying KEfED representation of tract-tracing experiments by incorporating the definition of a neuronanatomical data map as a measurement variable in the study design. This paper describes the software design of a web-application that allows anatomical data sets to be described within a standard experimental context and thus indexed by non-spatial experimental design features. PMID:22053155
A Permutation Approach for Selecting the Penalty Parameter in Penalized Model Selection

PubMed Central

Sabourin, Jeremy A; Valdar, William; Nobel, Andrew B

2015-01-01

Summary We describe a simple, computationally effcient, permutation-based procedure for selecting the penalty parameter in LASSO penalized regression. The procedure, permutation selection, is intended for applications where variable selection is the primary focus, and can be applied in a variety of structural settings, including that of generalized linear models. We briefly discuss connections between permutation selection and existing theory for the LASSO. In addition, we present a simulation study and an analysis of real biomedical data sets in which permutation selection is compared with selection based on the following: cross-validation (CV), the Bayesian information criterion (BIC), Scaled Sparse Linear Regression, and a selection method based on recently developed testing procedures for the LASSO. PMID:26243050
The Impact of Preparation: Conditions for Developing Professional Knowledge through Simulations

ERIC Educational Resources Information Center

Sjöberg, David; Karp, Staffan; Söderström, Tor

2015-01-01

This article examines simulations of critical incidents in police education by investigating how activities in the preparation phase influence participants' actions and thus the conditions for learning professional knowledge. The study is based on interviews in two stages (traditional and stimulated recall interviews) with six selected students…
Event-Based Plausibility Immediately Influences On-Line Language Comprehension

ERIC Educational Resources Information Center

Matsuki, Kazunaga; Chow, Tracy; Hare, Mary; Elman, Jeffrey L.; Scheepers, Christoph; McRae, Ken

2011-01-01

In some theories of sentence comprehension, linguistically relevant lexical knowledge, such as selectional restrictions, is privileged in terms of the time-course of its access and influence. We examined whether event knowledge computed by combining multiple concepts can rapidly influence language understanding even in the absence of selectional…
Evaluation of redundancy analysis to identify signatures of local adaptation.

PubMed

Capblancq, Thibaut; Luu, Keurcien; Blum, Michael G B; Bazin, Eric

2018-05-26

Ordination is a common tool in ecology that aims at representing complex biological information in a reduced space. In landscape genetics, ordination methods such as principal component analysis (PCA) have been used to detect adaptive variation based on genomic data. Taking advantage of environmental data in addition to genotype data, redundancy analysis (RDA) is another ordination approach that is useful to detect adaptive variation. This paper aims at proposing a test statistic based on RDA to search for loci under selection. We compare redundancy analysis to pcadapt, which is a nonconstrained ordination method, and to a latent factor mixed model (LFMM), which is a univariate genotype-environment association method. Individual-based simulations identify evolutionary scenarios where RDA genome scans have a greater statistical power than genome scans based on PCA. By constraining the analysis with environmental variables, RDA performs better than PCA in identifying adaptive variation when selection gradients are weakly correlated with population structure. Additionally, we show that if RDA and LFMM have a similar power to identify genetic markers associated with environmental variables, the RDA-based procedure has the advantage to identify the main selective gradients as a combination of environmental variables. To give a concrete illustration of RDA in population genomics, we apply this method to the detection of outliers and selective gradients on an SNP data set of Populus trichocarpa (Geraldes et al., 2013). The RDA-based approach identifies the main selective gradient contrasting southern and coastal populations to northern and continental populations in the northwestern American coast. This article is protected by copyright. All rights reserved. This article is protected by copyright. All rights reserved.
Validation of a Crowdsourcing Methodology for Developing a Knowledge Base of Related Problem-Medication Pairs.

PubMed

McCoy, A B; Wright, A; Krousel-Wood, M; Thomas, E J; McCoy, J A; Sittig, D F

2015-01-01

Clinical knowledge bases of problem-medication pairs are necessary for many informatics solutions that improve patient safety, such as clinical summarization. However, developing these knowledge bases can be challenging. We sought to validate a previously developed crowdsourcing approach for generating a knowledge base of problem-medication pairs in a large, non-university health care system with a widely used, commercially available electronic health record. We first retrieved medications and problems entered in the electronic health record by clinicians during routine care during a six month study period. Following the previously published approach, we calculated the link frequency and link ratio for each pair then identified a threshold cutoff for estimated problem-medication pair appropriateness through clinician review; problem-medication pairs meeting the threshold were included in the resulting knowledge base. We selected 50 medications and their gold standard indications to compare the resulting knowledge base to the pilot knowledge base developed previously and determine its recall and precision. The resulting knowledge base contained 26,912 pairs, had a recall of 62.3% and a precision of 87.5%, and outperformed the pilot knowledge base containing 11,167 pairs from the previous study, which had a recall of 46.9% and a precision of 83.3%. We validated the crowdsourcing approach for generating a knowledge base of problem-medication pairs in a large non-university health care system with a widely used, commercially available electronic health record, indicating that the approach may be generalizable across healthcare settings and clinical systems. Further research is necessary to better evaluate the knowledge, to compare crowdsourcing with other approaches, and to evaluate if incorporating the knowledge into electronic health records improves patient outcomes.
Validation of a Crowdsourcing Methodology for Developing a Knowledge Base of Related Problem-Medication Pairs

PubMed Central

Wright, A.; Krousel-Wood, M.; Thomas, E. J.; McCoy, J. A.; Sittig, D. F.

2015-01-01

Summary Background Clinical knowledge bases of problem-medication pairs are necessary for many informatics solutions that improve patient safety, such as clinical summarization. However, developing these knowledge bases can be challenging. Objective We sought to validate a previously developed crowdsourcing approach for generating a knowledge base of problem-medication pairs in a large, non-university health care system with a widely used, commercially available electronic health record. Methods We first retrieved medications and problems entered in the electronic health record by clinicians during routine care during a six month study period. Following the previously published approach, we calculated the link frequency and link ratio for each pair then identified a threshold cutoff for estimated problem-medication pair appropriateness through clinician review; problem-medication pairs meeting the threshold were included in the resulting knowledge base. We selected 50 medications and their gold standard indications to compare the resulting knowledge base to the pilot knowledge base developed previously and determine its recall and precision. Results The resulting knowledge base contained 26,912 pairs, had a recall of 62.3% and a precision of 87.5%, and outperformed the pilot knowledge base containing 11,167 pairs from the previous study, which had a recall of 46.9% and a precision of 83.3%. Conclusions We validated the crowdsourcing approach for generating a knowledge base of problem-medication pairs in a large non-university health care system with a widely used, commercially available electronic health record, indicating that the approach may be generalizable across healthcare settings and clinical systems. Further research is necessary to better evaluate the knowledge, to compare crowdsourcing with other approaches, and to evaluate if incorporating the knowledge into electronic health records improves patient outcomes. PMID:26171079
Cross-industry standard process for data mining is applicable to the lung cancer surgery domain, improving decision making as well as knowledge and quality management.

PubMed

Rivo, Eduardo; de la Fuente, Javier; Rivo, Ángel; García-Fontán, Eva; Cañizares, Miguel-Ángel; Gil, Pedro

2012-01-01

The aim of this study was to assess the applicability of knowledge discovery in database methodology, based upon data mining techniques, to the investigation of lung cancer surgery. According to CRISP 1.0 methodology, a data mining (DM) project was developed on a data warehouse containing records for 501 patients operated on for lung cancer with curative intention. The modelling technique was logistic regression. The finally selected model presented the following values: sensitivity 9.68%, specificity 100%, global precision 94.02%, positive predictive value 100% and negative predictive value 93.98% for a cut-off point set at 0.5. A receiver operating characteristic (ROC) curve was constructed. The area under the curve (CI 95%) was 0.817 (0.740- 0.893) (p < 0.05). Statistical association with perioperative mortality was found for the following variables [odds ratio (CI 95%)]: age over 70 [2.3822 (1.0338-5.4891)], heart disease [2.4875 (1.0089-6.1334)], peripheral arterial disease [5.7705 (1.9296-17.2570)], pneumonectomy [3.6199 (1.4939-8.7715)] and length of surgery (min) [1.0067 (1.0008-1.0126)]. The CRISP-DM process model is very suitable for lung cancer surgery analysis, improving decision making as well as knowledge and quality management.
Awareness, knowledge, and risks of zoonotic diseases among livestock farmers in Punjab.

PubMed

Hundal, Jaspal Singh; Sodhi, Simrinder Singh; Gupta, Aparna; Singh, Jaswinder; Chahal, Udeybir Singh

2016-02-01

The present study was conducted to assess the awareness, knowledge, and risks of zoonotic diseases among livestock farmers in Punjab. 250 livestock farmers were selected randomly and interviewed with a pretested questionnaire, which contained both open and close ended questions on different aspects of zoonotic diseases, i.e., awareness, knowledge, risks, etc. Knowledge scorecard was developed, and each correct answer was awarded one mark, and each incorrect answer was given zero mark. Respondents were categorized into low (mean - ½ standard deviation [SD]), moderate (mean ± ½ SD), and high knowledge (Mean + ½ SD) category based on the mean and SD. The information about independent variables viz., age, education, and herd size were collected with the help of structured schedule and scales. The data were analyzed by ANOVA, and results were prepared to assess awareness, knowledge, and risks of zoonotic diseases and its relation with independent variables. Majority of the respondents had age up to 40 years (70%), had their qualification from primary to higher secondary level (77.6%), and had their herd size up to 10 animals (79.6%). About 51.2% and 54.0% respondents had the history of abortion and retained placenta, respectively, at their farms. The respondents not only disposed off the infected placenta (35.6%), aborted fetus (39.6%), or feces (56.4%) from a diarrheic animal but also gave intrauterine medication (23.2%) bare-handedly. About 3.6-69.6% respondents consumed uncooked or unpasteurized animal products. About 84.8%, 46.0%, 32.8%, 4.61%, and 92.4% of livestock farmers were aware of zoonotic nature of rabies, brucellosis, tuberculosis, anthrax, and bird flu, respectively. The 55.6%, 67.2%, 52.0%, 64.0%, and 51.2% respondents were aware of the transmission of zoonotic diseases to human being through contaminated milk, meat, air, feed, or through contact with infected animals, respectively. The transmission of rabies through dog bite (98.4%), need of post-exposure vaccination (96.8%), and annual vaccination of dogs (78%) were well-known facts but only 47.2% livestock owners were aware of the occurrence of abortion due to brucellosis and availability of prophylactic vaccine (67.6%) against it as a preventive measure. About 69.2% respondents belonged to low to medium knowledge level categories, whereas 30.8% respondents had high knowledge (p<0.05) regarding different aspects of zoonotic diseases. Age, education, and herd size had no significant effect on the knowledge level and awareness of farmers toward zoonotic diseases. Therefore, from the present study, it may be concluded that there is a need to create awareness and improve knowledge of livestock farmers toward zoonotic diseases for its effective containment in Punjab.
Spatiotemporal Phylogenetic Analysis and Molecular Characterisation of Infectious Bursal Disease Viruses Based on the VP2 Hyper-Variable Region

PubMed Central

Dolz, Roser; Valle, Rosa; Perera, Carmen L.; Bertran, Kateri; Frías, Maria T.; Majó, Natàlia; Ganges, Llilianne; Pérez, Lester J.

2013-01-01

Background Infectious bursal disease is a highly contagious and acute viral disease caused by the infectious bursal disease virus (IBDV); it affects all major poultry producing areas of the world. The current study was designed to rigorously measure the global phylogeographic dynamics of IBDV strains to gain insight into viral population expansion as well as the emergence, spread and pattern of the geographical structure of very virulent IBDV (vvIBDV) strains. Methodology/Principal Findings Sequences of the hyper-variable region of the VP2 (HVR-VP2) gene from IBDV strains isolated from diverse geographic locations were obtained from the GenBank database; Cuban sequences were obtained in the current work. All sequences were analysed by Bayesian phylogeographic analysis, implemented in the Bayesian Evolutionary Analysis Sampling Trees (BEAST), Bayesian Tip-association Significance testing (BaTS) and Spatial Phylogenetic Reconstruction of Evolutionary Dynamics (SPREAD) software packages. Selection pressure on the HVR-VP2 was also assessed. The phylogeographic association-trait analysis showed that viruses sampled from individual countries tend to cluster together, suggesting a geographic pattern for IBDV strains. Spatial analysis from this study revealed that strains carrying sequences that were linked to increased virulence of IBDV appeared in Iran in 1981 and spread to Western Europe (Belgium) in 1987, Africa (Egypt) around 1990, East Asia (China and Japan) in 1993, the Caribbean Region (Cuba) by 1995 and South America (Brazil) around 2000. Selection pressure analysis showed that several codons in the HVR-VP2 region were under purifying selection. Conclusions/Significance To our knowledge, this work is the first study applying the Bayesian phylogeographic reconstruction approach to analyse the emergence and spread of vvIBDV strains worldwide. PMID:23805195
Factors that influence utilisation of HIV/AIDS prevention methods among university students residing at a selected university campus.

PubMed

Ndabarora, Eléazar; Mchunu, Gugu

2014-01-01

Various studies have reported that university students, who are mostly young people, rarely use existing HIV/AIDS preventive methods. Although studies have shown that young university students have a high degree of knowledge about HIV/AIDS and HIV modes of transmission, they are still not utilising the existing HIV prevention methods and still engage in risky sexual practices favourable to HIV. Some variables, such as awareness of existing HIV/AIDS prevention methods, have been associated with utilisation of such methods. The study aimed to explore factors that influence use of existing HIV/AIDS prevention methods among university students residing in a selected campus, using the Health Belief Model (HBM) as a theoretical framework. A quantitative research approach and an exploratory-descriptive design were used to describe perceived factors that influence utilisation by university students of HIV/AIDS prevention methods. A total of 335 students completed online and manual questionnaires. Study findings showed that the factors which influenced utilisation of HIV/AIDS prevention methods were mainly determined by awareness of the existing university-based HIV/AIDS prevention strategies. Most utilised prevention methods were voluntary counselling and testing services and free condoms. Perceived susceptibility and perceived threat of HIV/AIDS score was also found to correlate with HIV risk index score. Perceived susceptibility and perceived threat of HIV/AIDS showed correlation with self-efficacy on condoms and their utilisation. Most HBM variables were not predictors of utilisation of HIV/AIDS prevention methods among students. Intervention aiming to improve the utilisation of HIV/AIDS prevention methods among students at the selected university should focus on removing identified barriers, promoting HIV/AIDS prevention services and providing appropriate resources to implement such programmes.
Spatiotemporal Phylogenetic Analysis and Molecular Characterisation of Infectious Bursal Disease Viruses Based on the VP2 Hyper-Variable Region.

PubMed

Alfonso-Morales, Abdulahi; Martínez-Pérez, Orlando; Dolz, Roser; Valle, Rosa; Perera, Carmen L; Bertran, Kateri; Frías, Maria T; Majó, Natàlia; Ganges, Llilianne; Pérez, Lester J

2013-01-01

Infectious bursal disease is a highly contagious and acute viral disease caused by the infectious bursal disease virus (IBDV); it affects all major poultry producing areas of the world. The current study was designed to rigorously measure the global phylogeographic dynamics of IBDV strains to gain insight into viral population expansion as well as the emergence, spread and pattern of the geographical structure of very virulent IBDV (vvIBDV) strains. Sequences of the hyper-variable region of the VP2 (HVR-VP2) gene from IBDV strains isolated from diverse geographic locations were obtained from the GenBank database; Cuban sequences were obtained in the current work. All sequences were analysed by Bayesian phylogeographic analysis, implemented in the Bayesian Evolutionary Analysis Sampling Trees (BEAST), Bayesian Tip-association Significance testing (BaTS) and Spatial Phylogenetic Reconstruction of Evolutionary Dynamics (SPREAD) software packages. Selection pressure on the HVR-VP2 was also assessed. The phylogeographic association-trait analysis showed that viruses sampled from individual countries tend to cluster together, suggesting a geographic pattern for IBDV strains. Spatial analysis from this study revealed that strains carrying sequences that were linked to increased virulence of IBDV appeared in Iran in 1981 and spread to Western Europe (Belgium) in 1987, Africa (Egypt) around 1990, East Asia (China and Japan) in 1993, the Caribbean Region (Cuba) by 1995 and South America (Brazil) around 2000. Selection pressure analysis showed that several codons in the HVR-VP2 region were under purifying selection. To our knowledge, this work is the first study applying the Bayesian phylogeographic reconstruction approach to analyse the emergence and spread of vvIBDV strains worldwide.
Variable selection under multiple imputation using the bootstrap in a prognostic study

PubMed Central

Heymans, Martijn W; van Buuren, Stef; Knol, Dirk L; van Mechelen, Willem; de Vet, Henrica CW

2007-01-01

Background Missing data is a challenging problem in many prognostic studies. Multiple imputation (MI) accounts for imputation uncertainty that allows for adequate statistical testing. We developed and tested a methodology combining MI with bootstrapping techniques for studying prognostic variable selection. Method In our prospective cohort study we merged data from three different randomized controlled trials (RCTs) to assess prognostic variables for chronicity of low back pain. Among the outcome and prognostic variables data were missing in the range of 0 and 48.1%. We used four methods to investigate the influence of respectively sampling and imputation variation: MI only, bootstrap only, and two methods that combine MI and bootstrapping. Variables were selected based on the inclusion frequency of each prognostic variable, i.e. the proportion of times that the variable appeared in the model. The discriminative and calibrative abilities of prognostic models developed by the four methods were assessed at different inclusion levels. Results We found that the effect of imputation variation on the inclusion frequency was larger than the effect of sampling variation. When MI and bootstrapping were combined at the range of 0% (full model) to 90% of variable selection, bootstrap corrected c-index values of 0.70 to 0.71 and slope values of 0.64 to 0.86 were found. Conclusion We recommend to account for both imputation and sampling variation in sets of missing data. The new procedure of combining MI with bootstrapping for variable selection, results in multivariable prognostic models with good performance and is therefore attractive to apply on data sets with missing values. PMID:17629912
Collective feature selection to identify crucial epistatic variants.

PubMed

Verma, Shefali S; Lucas, Anastasia; Zhang, Xinyuan; Veturi, Yogasudha; Dudek, Scott; Li, Binglan; Li, Ruowang; Urbanowicz, Ryan; Moore, Jason H; Kim, Dokyoon; Ritchie, Marylyn D

2018-01-01

Machine learning methods have gained popularity and practicality in identifying linear and non-linear effects of variants associated with complex disease/traits. Detection of epistatic interactions still remains a challenge due to the large number of features and relatively small sample size as input, thus leading to the so-called "short fat data" problem. The efficiency of machine learning methods can be increased by limiting the number of input features. Thus, it is very important to perform variable selection before searching for epistasis. Many methods have been evaluated and proposed to perform feature selection, but no single method works best in all scenarios. We demonstrate this by conducting two separate simulation analyses to evaluate the proposed collective feature selection approach. Through our simulation study we propose a collective feature selection approach to select features that are in the "union" of the best performing methods. We explored various parametric, non-parametric, and data mining approaches to perform feature selection. We choose our top performing methods to select the union of the resulting variables based on a user-defined percentage of variants selected from each method to take to downstream analysis. Our simulation analysis shows that non-parametric data mining approaches, such as MDR, may work best under one simulation criteria for the high effect size (penetrance) datasets, while non-parametric methods designed for feature selection, such as Ranger and Gradient boosting, work best under other simulation criteria. Thus, using a collective approach proves to be more beneficial for selecting variables with epistatic effects also in low effect size datasets and different genetic architectures. Following this, we applied our proposed collective feature selection approach to select the top 1% of variables to identify potential interacting variables associated with Body Mass Index (BMI) in ~ 44,000 samples obtained from Geisinger's MyCode Community Health Initiative (on behalf of DiscovEHR collaboration). In this study, we were able to show that selecting variables using a collective feature selection approach could help in selecting true positive epistatic variables more frequently than applying any single method for feature selection via simulation studies. We were able to demonstrate the effectiveness of collective feature selection along with a comparison of many methods in our simulation analysis. We also applied our method to identify non-linear networks associated with obesity.
Integrative analysis of transcriptomic and metabolomic data via sparse canonical correlation analysis with incorporation of biological information.

PubMed

Safo, Sandra E; Li, Shuzhao; Long, Qi

2018-03-01

Integrative analysis of high dimensional omics data is becoming increasingly popular. At the same time, incorporating known functional relationships among variables in analysis of omics data has been shown to help elucidate underlying mechanisms for complex diseases. In this article, our goal is to assess association between transcriptomic and metabolomic data from a Predictive Health Institute (PHI) study that includes healthy adults at a high risk of developing cardiovascular diseases. Adopting a strategy that is both data-driven and knowledge-based, we develop statistical methods for sparse canonical correlation analysis (CCA) with incorporation of known biological information. Our proposed methods use prior network structural information among genes and among metabolites to guide selection of relevant genes and metabolites in sparse CCA, providing insight on the molecular underpinning of cardiovascular disease. Our simulations demonstrate that the structured sparse CCA methods outperform several existing sparse CCA methods in selecting relevant genes and metabolites when structural information is informative and are robust to mis-specified structural information. Our analysis of the PHI study reveals that a number of gene and metabolic pathways including some known to be associated with cardiovascular diseases are enriched in the set of genes and metabolites selected by our proposed approach. © 2017, The International Biometric Society.
Effectiveness of MMORPG-Based Instruction in Elementary English Education in Korea

ERIC Educational Resources Information Center

Suh, S.; Kim, S. W.; Kim, N. J.

2010-01-01

This study investigated the effectiveness of massive multiplayer online role-playing game (MMORPG)-based (massive multiplayer online role-playing game) instruction in elementary English education. The effectiveness of the MMORPG program was compared with face-to-face instruction and the independent variables (gender, prior knowledge, motivation…
Selectivity in reversed-phase separations: general influence of solvent type and mobile phase pH.

PubMed

Neue, Uwe D; Méndez, Alberto

2007-05-01

The influence of the mobile phase on retention is studied in this paper for a group of over 70 compounds with a broad range of multiple functional groups. We varied the pH of the mobile phase (pH 3, 7, and 10) and the organic modifier (methanol, acetonitrile (ACN), and tetrahydrofuran (THF)), using 15 different stationary phases. In this paper, we describe the overall retention and selectivity changes observed with these variables. We focus on the primary effects of solvent choice and pH. For example, transfer rules for solvent composition resulting in equivalent retention depend on the packing as well as on the type of analyte. Based on the retention patterns, one can calculate selectivity difference values for different variables. The selectivity difference is a measure of the importance of the different variables involved in method development. Selectivity changes specific to the type of analyte are described. The largest selectivity differences are obtained with pH changes.
Design enhancement tools in MSC/NASTRAN

NASA Technical Reports Server (NTRS)

Wallerstein, D. V.

1984-01-01

Design sensitivity is the calculation of derivatives of constraint functions with respect to design variables. While a knowledge of these derivatives is useful in its own right, the derivatives are required in many efficient optimization methods. Constraint derivatives are also required in some reanalysis methods. It is shown where the sensitivity coefficients fit into the scheme of a basic organization of an optimization procedure. The analyzer is to be taken as MSC/NASTRAN. The terminator program monitors the termination criteria and ends the optimization procedure when the criteria are satisfied. This program can reside in several plances: in the optimizer itself, in a user written code, or as part of the MSC/EOS (Engineering Operating System) MSC/EOS currently under development. Since several excellent optimization codes exist and since they require such very specialized technical knowledge, the optimizer under the new MSC/EOS is considered to be selected and supplied by the user to meet his specific needs and preferences. The one exception to this is a fully stressed design (FSD) based on simple scaling. The gradients are currently supplied by various design sensitivity options now existing in MSC/NASTRAN's design sensitivity analysis (DSA).
Children's selective trust decisions: rational competence and limiting performance factors.

PubMed

Hermes, Jonas; Behne, Tanya; Bich, Anna Elisa; Thielert, Christa; Rakoczy, Hannes

2018-03-01

Recent research has amply documented that even preschoolers learn selectively from others, preferring, for example, reliable over unreliable and competent over incompetent models. It remains unclear, however, what the cognitive foundations of such selective learning are, in particular, whether it builds on rational inferences or on less sophisticated processes. The current study, therefore, was designed to test directly the possibility that children are in principle capable of selective learning based on rational inference, yet revert to simpler strategies such as global impression formation under certain circumstances. Preschoolers (N = 75) were shown pairs of models that either differed in their degree of competence within one domain (strong vs. weak or knowledgeable vs. ignorant) or were both highly competent, but in different domains (e.g., strong vs. knowledgeable model). In the test trials, children chose between the models for strength- or knowledge-related tasks. The results suggest that, in fact, children are capable of rational inference-based selective trust: when both models were highly competent, children preferred the model with the competence most predictive and relevant for a given task. However, when choosing between two models that differed in competence on one dimension, children reverted to halo-style wide generalizations and preferred the competent models for both relevant and irrelevant tasks. These findings suggest that the rational strategies for selective learning, that children master in principle, can get masked by various performance factors. © 2017 John Wiley & Sons Ltd.

Pressure ulcer prevention and treatment knowledge of Jordanian nurses.

PubMed

Saleh, Mohammad Y N; Al-Hussami, Mahmoud; Anthony, Denis

2013-02-01

The aims of the study were to determine: (1) Jordanian nurses' level of knowledge of pressure ulcer prevention and treatment of hospitalized patients based on guidelines for pressure ulcer prevention and treatment. (2) Frequency of utilization of pressure ulcer prevention and treatment interventions in clinical practice. (3) Variables that are associated with nurses' utilization of pressure ulcer prevention and treatment interventions. Pressure ulcers are common and previous studies have shown education, knowledge and attitude affect implementation of interventions. A cross-sectional survey design was used to collect data from 460 nurses between June 2010 and November 2010. We used a questionnaire, which was informed by earlier work and guidelines, to collect data about nurses' knowledge and practice of pressure ulcer prevention and treatment. Knowledge and education show an association with implementation of prevention, and demographic variables do not. Similarly knowledge and type of hospital showed an association with implementing treatment. Of concern the use of "donuts" and massage are reported in use. Although pressure ulcer care is well known by nurses, inappropriate pressure ulcer interventions were reported in use. Copyright © 2013 Tissue Viability Society. Published by Elsevier Ltd. All rights reserved.
The Typicality Ranking Task: A New Method to Derive Typicality Judgments from Children.

PubMed

Djalal, Farah Mutiasari; Ameel, Eef; Storms, Gert

2016-01-01

An alternative method for deriving typicality judgments, applicable in young children that are not familiar with numerical values yet, is introduced, allowing researchers to study gradedness at younger ages in concept development. Contrary to the long tradition of using rating-based procedures to derive typicality judgments, we propose a method that is based on typicality ranking rather than rating, in which items are gradually sorted according to their typicality, and that requires a minimum of linguistic knowledge. The validity of the method is investigated and the method is compared to the traditional typicality rating measurement in a large empirical study with eight different semantic concepts. The results show that the typicality ranking task can be used to assess children's category knowledge and to evaluate how this knowledge evolves over time. Contrary to earlier held assumptions in studies on typicality in young children, our results also show that preference is not so much a confounding variable to be avoided, but that both variables are often significantly correlated in older children and even in adults.
The Typicality Ranking Task: A New Method to Derive Typicality Judgments from Children

PubMed Central

Ameel, Eef; Storms, Gert

2016-01-01

An alternative method for deriving typicality judgments, applicable in young children that are not familiar with numerical values yet, is introduced, allowing researchers to study gradedness at younger ages in concept development. Contrary to the long tradition of using rating-based procedures to derive typicality judgments, we propose a method that is based on typicality ranking rather than rating, in which items are gradually sorted according to their typicality, and that requires a minimum of linguistic knowledge. The validity of the method is investigated and the method is compared to the traditional typicality rating measurement in a large empirical study with eight different semantic concepts. The results show that the typicality ranking task can be used to assess children’s category knowledge and to evaluate how this knowledge evolves over time. Contrary to earlier held assumptions in studies on typicality in young children, our results also show that preference is not so much a confounding variable to be avoided, but that both variables are often significantly correlated in older children and even in adults. PMID:27322371
Variable selection in semiparametric cure models based on penalized likelihood, with application to breast cancer clinical trials.

PubMed

Liu, Xiang; Peng, Yingwei; Tu, Dongsheng; Liang, Hua

2012-10-30

Survival data with a sizable cure fraction are commonly encountered in cancer research. The semiparametric proportional hazards cure model has been recently used to analyze such data. As seen in the analysis of data from a breast cancer study, a variable selection approach is needed to identify important factors in predicting the cure status and risk of breast cancer recurrence. However, no specific variable selection method for the cure model is available. In this paper, we present a variable selection approach with penalized likelihood for the cure model. The estimation can be implemented easily by combining the computational methods for penalized logistic regression and the penalized Cox proportional hazards models with the expectation-maximization algorithm. We illustrate the proposed approach on data from a breast cancer study. We conducted Monte Carlo simulations to evaluate the performance of the proposed method. We used and compared different penalty functions in the simulation studies. Copyright © 2012 John Wiley & Sons, Ltd.
Measurement issues in the evaluation of chronic disease self-management programs.

PubMed

Nolte, Sandra; Elsworth, Gerald R; Newman, Stanton; Osborne, Richard H

2013-09-01

To provide an in-depth analysis of outcome measures used in the evaluation of chronic disease self-management programs consistent with the Stanford curricula. Based on a systematic review on self-management programs, effect sizes derived from reported outcome measures are categorized according to the quality of life appraisal model developed by Schwartz and Rapkin which classifies outcomes from performance-based measures (e.g., clinical outcomes) to evaluation-based measures (e.g., emotional well-being). The majority of outcomes assessed in self-management trials are based on evaluation-based methods. Overall, effects on knowledge--the only performance-based measure observed in selected trials--are generally medium to large. In contrast, substantially more inconsistent results are found for both perception- and evaluation-based measures that mostly range between nil and small positive effects. Effectiveness of self-management interventions and resulting recommendations for health policy makers are most frequently derived from highly variable evaluation-based measures, that is, types of outcomes that potentially carry a substantial amount of measurement error and/or bias such as response shift. Therefore, decisions regarding the value and efficacy of chronic disease self-management programs need to be interpreted with care. More research, especially qualitative studies, is needed to unravel cognitive processes and the role of response shift bias in the measurement of change.
Maternal nutritional knowledge and child nutritional status in the Volta region of Ghana.

PubMed

Appoh, Lily Yaa; Krekling, Sturla

2005-04-01

The relationship between mother's nutritional knowledge, maternal education, and child nutritional status (weight-for-age) was the subject of investigation in this study. The data were collected in Ghana on 55 well nourished and 55 malnourished mother-child pairs. A questionnaire designed to collect data on mother's knowledge and practices related to child care and nutrition was administered to the mothers. Data on mother's demographic and socio-economic characteristics as well as child anthropometric data were also collected. A nutrition knowledge score was calculated based on mother's responses to the nutrition related items. Bivariate analysis gave significant associations between child nutritional status and the following variables: time of initiating of breastfeeding, mother's knowledge of importance of colostrum and whether colostrum was given to child, age of introduction of supplementary food, and mother's knowledge about causes of kwashiorkor. The two groups also showed significant differences in their nutrition knowledge scores. Maternal formal education, and marital status were also found to be associated with child nutritional status in bivariate analyses. Further analysis with logistic regression revealed that maternal nutrition knowledge was independently associated with nutritional status after the effects of other significant variables were controlled for. Maternal education on the other hand was not found to be independently associated with nutritional status. These results imply that mother's practical knowledge about nutrition may be more important than formal maternal education for child nutrition outcome.
Correlates of pregnant women's gestational weight gain knowledge.

PubMed

Willcox, Jane Catherine; Ball, Kylie; Campbell, Karen Jane; Crawford, David Andrew; Wilkinson, Shelley Ann

2017-06-01

to investigate correlates of pregnant women's gestational weight gain (GWG) knowledge commensurate with GWG guidelines. cross sectional quantitative study. an Australian tertiary level maternity hospital. pregnant women (n=1032) following their first antenatal visit. survey to assess GWG knowledge and a range of potential correlates of knowledge including socio-economic characteristics, pregnancy characteristics (parity, gestation, pre-pregnancy BMI) and GWG information procurement and GWG attitudinal variables. participants (n=366; 35.4% response) averaged 32.5 years of age with 33% speaking a language other than English. One third of women reported GWG knowledge consistent with guidelines. Women overweight prior to pregnancy were less likely to underestimate appropriate GWG (RRR 0.23, 95% CI=0.09-0.59). Conversely, women in the overweight (RRR 8.80, 95% CI=4.02-19.25) and obese (RRR 19.62, 95% CI=8.03-48.00) categories were more likely to overestimate GWG recommendations, while tertiary educated women were less likely to overestimate GWG (RRR 0.28, 95% CI=0.10-0.79). No associations were found between GWG knowledge and pregnancy, GWG information source or attitudinal variables. the findings highlight women's lack of GWG knowledge and the role of pre-pregnancy body mass index and women's education as correlates of GWG knowledge. Women susceptible to poor GWG knowledge should be a priority target for individual and community-based education. Copyright © 2016 Elsevier Ltd. All rights reserved.
A study of masturbatory knowledge and attitudes and related factors among Taiwan adolescents.

PubMed

Wang, Rung-Jy; Huang, Yu; Lin, Yen-Chin

2007-09-01

The main purpose of this study was to investigate the relationship between masturbatory knowledge and masturbatory attitudes among Taiwan adolescents. This study was based on a structured questionnaire survey that used the Adolescent Masturbatory Knowledge Inventory (AMKI) and the Adolescent Negative Attitude toward Masturbation Inventory (ANAMI). Subjects were recruited from the third grade of high school and vocational school students aged 17-18 living in Kaohsiung (southern Taiwan) using stratified and cluster sampling approaches. Seven hundred and eighty questionnaires were sent out, with a 96.8% response rate. A total of 95.3% of male subjects and 30.3% of female subjects reported having masturbation experience. Masturbatory knowledge was significantly related to the variables "school system", "frequency of viewing pornographic media", "status of being sexually active", and "conversation about sex with friends". Masturbatory attitudes were significantly associated with the same variables as well as with gender and masturbatory behavior. Masturbatory attitudes were positively correlated with masturbatory knowledge. The school system explained 15.4% of masturbatory knowledge variance. Masturbatory knowledge, masturbatory behavior, frequency of viewing pornographic media and status of being sexually active explained 39.5% of masturbatory attitude variance. In conclusion, adolescents in Taiwan hold positive attitudes toward masturbation and reported having insufficient knowledge regarding masturbation. Results can assist school staffs and parents to gain a deeper understanding of adolescents' knowledge about and attitudes toward masturbation.
Using social cognitive theory to explain discretionary, "leisure-time" physical exercise among high school students.

PubMed

Winters, Eric R; Petosa, Rick L; Charlton, Thomas E

2003-06-01

To examine whether knowledge of high school students' actions of self-regulation, and perceptions of self-efficacy to overcome exercise barriers, social situation, and outcome expectation will predict non-school related moderate and vigorous physical exercise. High school students enrolled in introductory Physical Education courses completed questionnaires that targeted selected Social Cognitive Theory variables. They also self-reported their typical "leisure-time" exercise participation using a standardized questionnaire. Bivariate correlation statistic and hierarchical regression were conducted on reports of moderate and vigorous exercise frequency. Each predictor variable was significantly associated with measures of moderate and vigorous exercise frequency. All predictor variables were significant in the final regression model used to explain vigorous exercise. After controlling for the effects of gender, the psychosocial variables explained 29% of variance in vigorous exercise frequency. Three of four predictor variables were significant in the final regression equation used to explain moderate exercise. The final regression equation accounted for 11% of variance in moderate exercise frequency. Professionals who attempt to increase the prevalence of physical exercise through educational methods should focus on the psychosocial variables utilized in this study.
Constructing Proxy Variables to Measure Adult Learners' Time Management Strategies in LMS

ERIC Educational Resources Information Center

Jo, Il-Hyun; Kim, Dongho; Yoon, Meehyun

2015-01-01

This study describes the process of constructing proxy variables from recorded log data within a Learning Management System (LMS), which represents adult learners' time management strategies in an online course. Based on previous research, three variables of total login time, login frequency, and regularity of login interval were selected as…
Using Quality Management Methods in Knowledge-Based Organizations. An Approach to the Application of the Taguchi Method to the Process of Pressing Tappets into Anchors

NASA Astrophysics Data System (ADS)

Ţîţu, M. A.; Pop, A. B.; Ţîţu, Ș

2017-06-01

This paper presents a study on the modelling and optimization of certain variables by using the Taguchi Method with a view to modelling and optimizing the process of pressing tappets into anchors, process conducted in an organization that promotes knowledge-based management. The paper promotes practical concepts of the Taguchi Method and describes the way in which the objective functions are obtained and used during the modelling and optimization of the process of pressing tappets into the anchors.
Operator agency in process intervention: tampering versus application of tacit knowledge

NASA Astrophysics Data System (ADS)

Van Gestel, P.; Pons, D. J.; Pulakanam, V.

2015-09-01

Statistical process control (SPC) theory takes a negative view of adjustment of process settings, which is termed tampering. In contrast, quality and lean programmes actively encourage operators to acts of intervention and personal agency in the improvement of production outcomes. This creates a conflict that requires operator judgement: How does one differentiate between unnecessary tampering and needful intervention? Also, difficult is that operators apply tacit knowledge to such judgements. There is a need to determine where in a given production process the operators are applying tacit knowledge, and whether this is hindering or aiding quality outcomes. The work involved the conjoint application of systems engineering, statistics, and knowledge management principles, in the context of a case study. Systems engineering was used to create a functional model of a real plant. Actual plant data were analysed with the statistical methods of ANOVA, feature selection, and link analysis. This identified the variables to which the output quality was most sensitive. These key variables were mapped back to the functional model. Fieldwork was then directed to those areas to prospect for operator judgement activities. A natural conversational approach was used to determine where and how operators were applying judgement. This contrasts to the interrogative approach of conventional knowledge management. Data are presented for a case study of a meat rendering plant. The results identify specific areas where operators' tacit knowledge and mental model contribute to quality outcomes and untangles the motivations behind their agency. Also evident is how novice and expert operators apply their knowledge differently. Novices were focussed on meeting throughput objectives, and their incomplete understanding of the plant characteristics led them to inadvertently sacrifice quality in the pursuit of productivity in certain situations. Operators' responses to the plant are affected by their individual mental models of the plant, which differ between operators and have variable validity. Their behaviour is also affected by differing interpretations of how their personal agency should be applied to the achievement of production objectives. The methodology developed here is an integration of systems engineering, statistical analysis, and knowledge management. It shows how to determine where in a given production process the operator intervention is occurring, how it affects quality outcomes, and what tacit knowledge operators are using. It thereby assists the continuous quality improvement processes in a different way to SPC. A second contribution is the provision of a novel methodology for knowledge management, one that circumvents the usual codification barriers to knowledge management.
Function-Based Approach to Designing an Instructional Environment

ERIC Educational Resources Information Center

Park, Kristy; Pinkelman, Sarah

2017-01-01

Teachers are faced with the challenge of selecting interventions that are most likely to be effective and best matched to the function of problem behavior. This article will define aspects of the instructional environment and describe a decision-making logic to select environmental variables. A summary of commonly used function-based interventions…
The Effects of a Summer Inservice Program on Secondary Science Teachers' Stages of Concerns, Attitudes, and Knowledge of Selected STS Concepts and Its Impact on Students' Knowledge.

ERIC Educational Resources Information Center

Zielinski, Edward J.; Bernardo, John A.

This investigation was conducted to determine the effects of a 10-day summer workshop using the Concerns Based Adoption Model concerning science technology and society (STS) topics and methods of classroom implementation on the knowledge, attitudes, and stages of concerns of the participating secondary inservice teachers, as well as student…
DETECTING SELECTION IN NATURAL POPULATIONS: MAKING SENSE OF GENOME SCANS AND TOWARDS ALTERNATIVE SOLUTIONS

PubMed Central

Haasl, Ryan J.; Payseur, Bret A.

2016-01-01

Genomewide scans for natural selection (GWSS) have become increasingly common over the last 15 years due to increased availability of genome-scale genetic data. Here, we report a representative survey of GWSS from 1999 to present and find that (i) between 1999 and 2009, 35 of 49 (71%) GWSS focused on human, while from 2010 to present, only 38 of 83 (46%) of GWSS focused on human, indicating increased focus on nonmodel organisms; (ii) the large majority of GWSS incorporate interpopulation or interspecific comparisons using, for example FST, cross-population extended haplotype homozygosity or the ratio of nonsynonymous to synonymous substitutions; (iii) most GWSS focus on detection of directional selection rather than other modes such as balancing selection; and (iv) in human GWSS, there is a clear shift after 2004 from microsatellite markers to dense SNP data. A survey of GWSS meant to identify loci positively selected in response to severe hypoxic conditions support an approach to GWSS in which a list of a priori candidate genes based on potential selective pressures are used to filter the list of significant hits a posteriori. We also discuss four frequently ignored determinants of genomic heterogeneity that complicate GWSS: mutation, recombination, selection and the genetic architecture of adaptive traits. We recommend that GWSS methodology should better incorporate aspects of genomewide heterogeneity using empirical estimates of relevant parameters and/or realistic, whole-chromosome simulations to improve interpretation of GWSS results. Finally, we argue that knowledge of potential selective agents improves interpretation of GWSS results and that new methods focused on correlations between environmental variables and genetic variation can help automate this approach. PMID:26224644
Fifteen years of genomewide scans for selection: trends, lessons and unaddressed genetic sources of complication.

PubMed

Haasl, Ryan J; Payseur, Bret A

2016-01-01

Genomewide scans for natural selection (GWSS) have become increasingly common over the last 15 years due to increased availability of genome-scale genetic data. Here, we report a representative survey of GWSS from 1999 to present and find that (i) between 1999 and 2009, 35 of 49 (71%) GWSS focused on human, while from 2010 to present, only 38 of 83 (46%) of GWSS focused on human, indicating increased focus on nonmodel organisms; (ii) the large majority of GWSS incorporate interpopulation or interspecific comparisons using, for example F(ST), cross-population extended haplotype homozygosity or the ratio of nonsynonymous to synonymous substitutions; (iii) most GWSS focus on detection of directional selection rather than other modes such as balancing selection; and (iv) in human GWSS, there is a clear shift after 2004 from microsatellite markers to dense SNP data. A survey of GWSS meant to identify loci positively selected in response to severe hypoxic conditions support an approach to GWSS in which a list of a priori candidate genes based on potential selective pressures are used to filter the list of significant hits a posteriori. We also discuss four frequently ignored determinants of genomic heterogeneity that complicate GWSS: mutation, recombination, selection and the genetic architecture of adaptive traits. We recommend that GWSS methodology should better incorporate aspects of genomewide heterogeneity using empirical estimates of relevant parameters and/or realistic, whole-chromosome simulations to improve interpretation of GWSS results. Finally, we argue that knowledge of potential selective agents improves interpretation of GWSS results and that new methods focused on correlations between environmental variables and genetic variation can help automate this approach. © 2015 John Wiley & Sons Ltd.
Measurement of talent in team handball: the questionable use of motor and physical tests.

PubMed

Lidor, Ronnie; Falk, Bareket; Arnon, Michal; Cohen, Yoram; Segal, Gil; Lander, Yael

2005-05-01

Testing for selection is one of the most important fundamentals in any multistep sport program. In most ball games, coaches assess motor, physical, and technical skills on a regular basis in early stages of talent identification and development. However, selection processes are complex, are often unstructured, and lack clear-cut theory-based knowledge. For example, little is known about the relevance of the testing process to the final selection of the young prospects. The purpose of this study was to identify motor, physical, and skill variables that could provide coaches with relevant information in the selection process of young team handball players. In total, 405 players (12-13 years of age at the beginning of the testing period) were recommended by their coaches to undergo a battery of tests prior to selection to the Junior National Team. This number is the sum of all players participating in the different phases of the program. However, not all of them took part in each testing phase. The battery included physical measurements (height and weight), a 4 x 10-m running test, explosive power tests (medicine ball throw and standing long jump), speed tests (a 20-m sprint from a standing position and a 20-m sprint with a flying start), and a slalom dribbling test. Comparisons between those players eventually selected to the Junior National Team 2-3 years later with those not selected demonstrated that only the skill test served as a good indicator. In all other measurements, a wide overlap could be seen between the results of the selected and nonselected players. It is suggested that future studies investigate the usefulness of tests reflecting more specific physical ability and cognitive characteristics.
Neuromyths in Education: Prevalence and Predictors of Misconceptions among Teachers

PubMed Central

Dekker, Sanne; Lee, Nikki C.; Howard-Jones, Paul; Jolles, Jelle

2012-01-01

The OECD’s Brain and Learning project (2002) emphasized that many misconceptions about the brain exist among professionals in the field of education. Though these so-called “neuromyths” are loosely based on scientific facts, they may have adverse effects on educational practice. The present study investigated the prevalence and predictors of neuromyths among teachers in selected regions in the United Kingdom and the Netherlands. A large observational survey design was used to assess general knowledge of the brain and neuromyths. The sample comprised 242 primary and secondary school teachers who were interested in the neuroscience of learning. It would be of concern if neuromyths were found in this sample, as these teachers may want to use these incorrect interpretations of neuroscience findings in their teaching practice. Participants completed an online survey containing 32 statements about the brain and its influence on learning, of which 15 were neuromyths. Additional data was collected regarding background variables (e.g., age, sex, school type). Results showed that on average, teachers believed 49% of the neuromyths, particularly myths related to commercialized educational programs. Around 70% of the general knowledge statements were answered correctly. Teachers who read popular science magazines achieved higher scores on general knowledge questions. More general knowledge also predicted an increased belief in neuromyths. These findings suggest that teachers who are enthusiastic about the possible application of neuroscience findings in the classroom find it difficult to distinguish pseudoscience from scientific facts. Possessing greater general knowledge about the brain does not appear to protect teachers from believing in neuromyths. This demonstrates the need for enhanced interdisciplinary communication to reduce such misunderstandings in the future and establish a successful collaboration between neuroscience and education. PMID:23087664
Neuromyths in Education: Prevalence and Predictors of Misconceptions among Teachers.

PubMed

Dekker, Sanne; Lee, Nikki C; Howard-Jones, Paul; Jolles, Jelle

2012-01-01

The OECD's Brain and Learning project (2002) emphasized that many misconceptions about the brain exist among professionals in the field of education. Though these so-called "neuromyths" are loosely based on scientific facts, they may have adverse effects on educational practice. The present study investigated the prevalence and predictors of neuromyths among teachers in selected regions in the United Kingdom and the Netherlands. A large observational survey design was used to assess general knowledge of the brain and neuromyths. The sample comprised 242 primary and secondary school teachers who were interested in the neuroscience of learning. It would be of concern if neuromyths were found in this sample, as these teachers may want to use these incorrect interpretations of neuroscience findings in their teaching practice. Participants completed an online survey containing 32 statements about the brain and its influence on learning, of which 15 were neuromyths. Additional data was collected regarding background variables (e.g., age, sex, school type). Results showed that on average, teachers believed 49% of the neuromyths, particularly myths related to commercialized educational programs. Around 70% of the general knowledge statements were answered correctly. Teachers who read popular science magazines achieved higher scores on general knowledge questions. More general knowledge also predicted an increased belief in neuromyths. These findings suggest that teachers who are enthusiastic about the possible application of neuroscience findings in the classroom find it difficult to distinguish pseudoscience from scientific facts. Possessing greater general knowledge about the brain does not appear to protect teachers from believing in neuromyths. This demonstrates the need for enhanced interdisciplinary communication to reduce such misunderstandings in the future and establish a successful collaboration between neuroscience and education.
Hybrid forecasting of chaotic processes: Using machine learning in conjunction with a knowledge-based model

NASA Astrophysics Data System (ADS)

Pathak, Jaideep; Wikner, Alexander; Fussell, Rebeckah; Chandra, Sarthak; Hunt, Brian R.; Girvan, Michelle; Ott, Edward

2018-04-01

A model-based approach to forecasting chaotic dynamical systems utilizes knowledge of the mechanistic processes governing the dynamics to build an approximate mathematical model of the system. In contrast, machine learning techniques have demonstrated promising results for forecasting chaotic systems purely from past time series measurements of system state variables (training data), without prior knowledge of the system dynamics. The motivation for this paper is the potential of machine learning for filling in the gaps in our underlying mechanistic knowledge that cause widely-used knowledge-based models to be inaccurate. Thus, we here propose a general method that leverages the advantages of these two approaches by combining a knowledge-based model and a machine learning technique to build a hybrid forecasting scheme. Potential applications for such an approach are numerous (e.g., improving weather forecasting). We demonstrate and test the utility of this approach using a particular illustrative version of a machine learning known as reservoir computing, and we apply the resulting hybrid forecaster to a low-dimensional chaotic system, as well as to a high-dimensional spatiotemporal chaotic system. These tests yield extremely promising results in that our hybrid technique is able to accurately predict for a much longer period of time than either its machine-learning component or its model-based component alone.

Teaching topography-based and selection-based verbal behavior to developmentally disabled individuals: Some considerations

PubMed Central

Shafer, Esther

1993-01-01

Augmentative and alternative communication systems are widely recommended for nonvocal developmentally disabled individuals, with selection-based systems becoming increasingly popular. However, theoretical and experimental evidence suggests that topography-based communication systems are easier to learn. This paper discusses research relevant to the ease of acquisition of topography-based and selection-based systems. Additionally, current practices for choosing and designing communication systems are reviewed in order to investigate the extent to which links have been made with available theoretical and experimental knowledge. A stimulus equivalence model is proposed as a clearer direction for practitioners to follow when planning a communication training program. Suggestions for future research are also offered. PMID:22477085
Annual variability of PAH concentrations in the Potomac River watershed

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

Maher, I.L.; Foster, G.D.

1995-12-31

Dynamics of organic contaminant transport in a large river system is influenced by annual variability in organic contaminant concentrations. Surface runoff and groundwater input control the flow of river waters. They are also the two major inputs of contaminants to river waters. The annual variability of contaminant concentrations in rivers may or may not represent similar trends to the flow changes of river waters. The purpose of the research is to define the annual variability in concentrations of polycyclic aromatic hydrocarbons (PAH) in riverine environment. To accomplish this, from March 1992 to March 1995 samples of Potomac River water weremore » collected monthly or bimonthly downstream of the Chesapeake Bay fall line (Chain Bridge) during base flow and main storm flow hydrologic conditions. Concentrations of selected PAHs were measured in the dissolved phase and the particulate phase via GC/MS. The study of the annual variability of PAH concentrations will be performed through comparisons of PAH concentrations seasonally, annually, and through study of PAH concentration river discharge dependency and rainfall dependency. For selected PAHs monthly and annual loadings will be estimated based on their measured concentrations and average daily river discharge. The monthly loadings of selected PAHs will be compared by seasons and annually.« less
Mate choice theory and the mode of selection in sexual populations.

PubMed

Carson, Hampton L

2003-05-27

Indirect new data imply that mate and/or gamete choice are major selective forces driving genetic change in sexual populations. The system dictates nonrandom mating, an evolutionary process requiring both revised genetic theory and new data on heritability of characters underlying Darwinian fitness. Successfully reproducing individuals represent rare selections from among vigorous, competing survivors of preadult natural selection. Nonrandom mating has correlated demographic effects: reduced effective population size, inbreeding, low gene flow, and emphasis on deme structure. Characters involved in choice behavior at reproduction appear based on quantitative trait loci. This variability serves selection for fitness within the population, having only an incidental relationship to the origin of genetically based reproductive isolation between populations. The claim that extensive hybridization experiments with Drosophila indicate that selection favors a gradual progression of "isolating mechanisms" is flawed, because intra-group random mating is assumed. Over deep time, local sexual populations are strong, independent genetic systems that use rich fields of variable polygenic components of fitness. The sexual reproduction system thus particularizes, in small subspecific populations, the genetic basis of the grand adaptive sweep of selective evolutionary change, much as Darwin proposed.
C-fuzzy variable-branch decision tree with storage and classification error rate constraints

NASA Astrophysics Data System (ADS)

Yang, Shiueng-Bien

2009-10-01

The C-fuzzy decision tree (CFDT), which is based on the fuzzy C-means algorithm, has recently been proposed. The CFDT is grown by selecting the nodes to be split according to its classification error rate. However, the CFDT design does not consider the classification time taken to classify the input vector. Thus, the CFDT can be improved. We propose a new C-fuzzy variable-branch decision tree (CFVBDT) with storage and classification error rate constraints. The design of the CFVBDT consists of two phases-growing and pruning. The CFVBDT is grown by selecting the nodes to be split according to the classification error rate and the classification time in the decision tree. Additionally, the pruning method selects the nodes to prune based on the storage requirement and the classification time of the CFVBDT. Furthermore, the number of branches of each internal node is variable in the CFVBDT. Experimental results indicate that the proposed CFVBDT outperforms the CFDT and other methods.
Understanding morphological variability in a taxonomic context in Chilean diplomystids (Teleostei: Siluriformes), including the description of a new species

PubMed Central

2017-01-01

Following study of the external morphology and its unmatched variability throughout ontogeny and a re-examination of selected morphological characters based on many specimens of diplomystids from Central and South Chile, we revised and emended previous specific diagnoses and consider Diplomystes chilensis, D. nahuelbutaensis, D. camposensis, and Olivaichthys viedmensis (Baker River) to be valid species. Another group, previously identified as Diplomystes sp., D. spec., D. aff. chilensis, and D. cf. chilensis inhabiting rivers between Rapel and Itata Basins is given a new specific name (Diplomystes incognitus) and is diagnosed. An identification key to the Chilean species, including the new species, is presented. All specific diagnoses are based on external morphological characters, such as aspects of the skin, neuromast lines, and main lateral line, and position of the anus and urogenital pore, as well as certain osteological characters to facilitate the identification of these species that previously was based on many internal characters. Diplomystids below 150 mm standard length (SL) share a similar external morphology and body proportions that make identification difficult; however, specimens over 150 mm SL can be diagnosed by the position of the urogenital pore and anus, and a combination of external and internal morphological characters. According to current knowledge, diplomystid species have an allopatric distribution with each species apparently endemic to particular basins in continental Chile and one species (O. viedmensis) known only from one river in the Chilean Patagonia, but distributed extensively in southern Argentina. PMID:28224053
Anthropometry

NASA Technical Reports Server (NTRS)

Mcconville, J. T.; Laubach, L. L.

1978-01-01

Data on body-size measurement are presented to aid in spacecraft design. Tabulated dimensional anthropometric data on 59 variables for 12 selected populations are given. The variables chosen were those judged most relevant to the manned space program. A glossary of anatomical and anthropometric terms is included. Selected body dimensions of males and females from the potential astronaut population projected to the 1980-1990 time frame are given. Illustrations of drawing-board manikins based on those anticipated body sizes are included.
An empirical model to predict road dust emissions based on pavement and traffic characteristics.

PubMed

Padoan, Elio; Ajmone-Marsan, Franco; Querol, Xavier; Amato, Fulvio

2018-06-01

The relative impact of non-exhaust sources (i.e. road dust, tire wear, road wear and brake wear particles) on urban air quality is increasing. Among them, road dust resuspension has generally the highest impact on PM concentrations but its spatio-temporal variability has been rarely studied and modeled. Some recent studies attempted to observe and describe the time-variability but, as it is driven by traffic and meteorology, uncertainty remains on the seasonality of emissions. The knowledge gap on spatial variability is much wider, as several factors have been pointed out as responsible for road dust build-up: pavement characteristics, traffic intensity and speed, fleet composition, proximity to traffic lights, but also the presence of external sources. However, no parameterization is available as a function of these variables. We investigated mobile road dust smaller than 10 μm (MF10) in two cities with different climatic and traffic conditions (Barcelona and Turin), to explore MF10 seasonal variability and the relationship between MF10 and site characteristics (pavement macrotexture, traffic intensity and proximity to braking zone). Moreover, we provide the first estimates of emission factors in the Po Valley both in summer and winter conditions. Our results showed a good inverse relationship between MF10 and macro-texture, traffic intensity and distance from the nearest braking zone. We also found a clear seasonal effect of road dust emissions, with higher emission in summer, likely due to the lower pavement moisture. These results allowed building a simple empirical mode, predicting maximal dust loadings and, consequently, emission potential, based on the aforementioned data. This model will need to be scaled for meteorological effect, using methods accounting for weather and pavement moisture. This can significantly improve bottom-up emission inventory for spatial allocation of emissions and air quality management, to select those roads with higher emissions for mitigation measures. Copyright © 2017 Elsevier Ltd. All rights reserved.
The effect of the PROSPER partnership model on cultivating local stakeholder knowledge of evidence-based programs: a five-year longitudinal study of 28 communities.

PubMed

Crowley, D Max; Greenberg, Mark T; Feinberg, Mark E; Spoth, Richard L; Redmond, Cleve R

2012-02-01

A substantial challenge in improving public health is how to facilitate the local adoption of evidence-based interventions (EBIs). To do so, an important step is to build local stakeholders' knowledge and decision-making skills regarding the adoption and implementation of EBIs. One EBI delivery system, called PROSPER (PROmoting School-community-university Partnerships to Enhance Resilience), has effectively mobilized community prevention efforts, implemented prevention programming with quality, and consequently decreased youth substance abuse. While these results are encouraging, another objective is to increase local stakeholder knowledge of best practices for adoption, implementation and evaluation of EBIs. Using a mixed methods approach, we assessed local stakeholder knowledge of these best practices over 5 years, in 28 intervention and control communities. Results indicated that the PROSPER partnership model led to significant increases in expert knowledge regarding the selection, implementation, and evaluation of evidence-based interventions. Findings illustrate the limited programming knowledge possessed by members of local prevention efforts, the difficulty of complete knowledge transfer, and highlight one method for cultivating that knowledge.
Enhancing Knowledge Sharing Management Using BIM Technology in Construction

PubMed Central

Ho, Shih-Ping; Tserng, Hui-Ping

2013-01-01

Construction knowledge can be communicated and reused among project managers and jobsite engineers to alleviate problems on a construction jobsite and reduce the time and cost of solving problems related to constructability. This paper proposes a new methodology for the sharing of construction knowledge by using Building Information Modeling (BIM) technology. The main characteristics of BIM include illustrating 3D CAD-based presentations and keeping information in a digital format and facilitation of easy updating and transfer of information in the BIM environment. Using the BIM technology, project managers and engineers can gain knowledge related to BIM and obtain feedback provided by jobsite engineers for future reference. This study addresses the application of knowledge sharing management using BIM technology and proposes a BIM-based Knowledge Sharing Management (BIMKSM) system for project managers and engineers. The BIMKSM system is then applied in a selected case study of a construction project in Taiwan to demonstrate the effectiveness of sharing knowledge in the BIM environment. The results demonstrate that the BIMKSM system can be used as a visual BIM-based knowledge sharing management platform by utilizing the BIM technology. PMID:24723790
Enhancing knowledge sharing management using BIM technology in construction.

PubMed

Ho, Shih-Ping; Tserng, Hui-Ping; Jan, Shu-Hui

2013-01-01

Construction knowledge can be communicated and reused among project managers and jobsite engineers to alleviate problems on a construction jobsite and reduce the time and cost of solving problems related to constructability. This paper proposes a new methodology for the sharing of construction knowledge by using Building Information Modeling (BIM) technology. The main characteristics of BIM include illustrating 3D CAD-based presentations and keeping information in a digital format and facilitation of easy updating and transfer of information in the BIM environment. Using the BIM technology, project managers and engineers can gain knowledge related to BIM and obtain feedback provided by jobsite engineers for future reference. This study addresses the application of knowledge sharing management using BIM technology and proposes a BIM-based Knowledge Sharing Management (BIMKSM) system for project managers and engineers. The BIMKSM system is then applied in a selected case study of a construction project in Taiwan to demonstrate the effectiveness of sharing knowledge in the BIM environment. The results demonstrate that the BIMKSM system can be used as a visual BIM-based knowledge sharing management platform by utilizing the BIM technology.
A quantitative analysis of statistical power identifies obesity end points for improved in vivo preclinical study design.

PubMed

Selimkhanov, J; Thompson, W C; Guo, J; Hall, K D; Musante, C J

2017-08-01

The design of well-powered in vivo preclinical studies is a key element in building the knowledge of disease physiology for the purpose of identifying and effectively testing potential antiobesity drug targets. However, as a result of the complexity of the obese phenotype, there is limited understanding of the variability within and between study animals of macroscopic end points such as food intake and body composition. This, combined with limitations inherent in the measurement of certain end points, presents challenges to study design that can have significant consequences for an antiobesity program. Here, we analyze a large, longitudinal study of mouse food intake and body composition during diet perturbation to quantify the variability and interaction of the key metabolic end points. To demonstrate how conclusions can change as a function of study size, we show that a simulated preclinical study properly powered for one end point may lead to false conclusions based on secondary end points. We then propose the guidelines for end point selection and study size estimation under different conditions to facilitate proper power calculation for a more successful in vivo study design.
A Strategic Approach to Medical Care for Exploration Missions

NASA Technical Reports Server (NTRS)

Antonsen, E.; Canga, M.

2016-01-01

Exploration missions will present significant new challenges to crew health, including effects of variable gravity environments, limited communication with Earth-based personnel for diagnosis and consultation for medical events, limited resupply, and limited ability for crew return. Providing health care capabilities for exploration class missions will require system trades be performed to identify a minimum set of requirements and crosscutting capabilities which can be used in design of exploration medical systems. Current and future medical data, information, and knowledge must be cataloged and put in formats that facilitate querying and analysis. These data may then be used to inform the medical research and development program through analysis of risk trade studies between medical care capabilities and system constraints such as mass, power, volume, and training. These studies will be used to define a Medical Concept of Operations to facilitate stakeholder discussions on expected medical capability for exploration missions. Medical Capability as a quantifiable variable is proposed as a surrogate risk metric and explored for trade space analysis that can improve communication between the medical and engineering approaches to mission design. The resulting medical system approach selected will inform NASA mission architecture, vehicle, and subsystem design for the next generation of spacecraft.
Parametric regression model for survival data: Weibull regression model as an example

PubMed Central

2016-01-01

Weibull regression model is one of the most popular forms of parametric regression model that it provides estimate of baseline hazard function, as well as coefficients for covariates. Because of technical difficulties, Weibull regression model is seldom used in medical literature as compared to the semi-parametric proportional hazard model. To make clinical investigators familiar with Weibull regression model, this article introduces some basic knowledge on Weibull regression model and then illustrates how to fit the model with R software. The SurvRegCensCov package is useful in converting estimated coefficients to clinical relevant statistics such as hazard ratio (HR) and event time ratio (ETR). Model adequacy can be assessed by inspecting Kaplan-Meier curves stratified by categorical variable. The eha package provides an alternative method to model Weibull regression model. The check.dist() function helps to assess goodness-of-fit of the model. Variable selection is based on the importance of a covariate, which can be tested using anova() function. Alternatively, backward elimination starting from a full model is an efficient way for model development. Visualization of Weibull regression model after model development is interesting that it provides another way to report your findings. PMID:28149846
Parameter estimation and prediction for the course of a single epidemic outbreak of a plant disease.

PubMed

Kleczkowski, A; Gilligan, C A

2007-10-22

Many epidemics of plant diseases are characterized by large variability among individual outbreaks. However, individual epidemics often follow a well-defined trajectory which is much more predictable in the short term than the ensemble (collection) of potential epidemics. In this paper, we introduce a modelling framework that allows us to deal with individual replicated outbreaks, based upon a Bayesian hierarchical analysis. Information about 'similar' replicate epidemics can be incorporated into a hierarchical model, allowing both ensemble and individual parameters to be estimated. The model is used to analyse the data from a replicated experiment involving spread of Rhizoctonia solani on radish in the presence or absence of a biocontrol agent, Trichoderma viride. The rate of primary (soil-to-plant) infection is found to be the most variable factor determining the final size of epidemics. Breakdown of biological control in some replicates results in high levels of primary infection and increased variability. The model can be used to predict new outbreaks of disease based upon knowledge from a 'library' of previous epidemics and partial information about the current outbreak. We show that forecasting improves significantly with knowledge about the history of a particular epidemic, whereas the precision of hindcasting to identify the past course of the epidemic is largely independent of detailed knowledge of the epidemic trajectory. The results have important consequences for parameter estimation, inference and prediction for emerging epidemic outbreaks.
E-learning and nursing assessment skills and knowledge - An integrative review.

PubMed

McDonald, Ewan W; Boulton, Jessica L; Davis, Jacqueline L

2018-07-01

This review examines the current evidence on the effectiveness of digital technologies or e-based learning for enhancing the skills and knowledge of nursing students in nursing assessment. This integrative review identifies themes emerging from e-learning and 'nursing assessment' literature. Literature reviews have been undertaken in relation to digital learning and nursing education, including clinical skills, clinical case studies and the nurse-educator role. Whilst perceptions of digital learning are well covered, a gap in knowledge persists for understanding the effectiveness of e-learning on nursing assessment skills and knowledge. This is important as comprehensive assessment skills and knowledge are a key competency for newly qualified nurses. The MEDLINE, CINAHL, Cochrane Library and ProQuest Nursing and Allied Health Source electronic databases were searched for the period 2006 to 2016. Hand searching in bibliographies was also undertaken. Selection criteria for this review included: FINDINGS: Twenty articles met the selection criteria for this review, and five major themes for e-based learning were identified (a) students become self-evaluators; (b) blend and scaffold learning; (c) measurement of clinical reasoning; (d) mobile technology and Facebook are effective; and (e) training and preparation is vital. Although e-based learning programs provide a flexible teaching method, evidence suggests e-based learning alone does not exceed face-to-face patient simulation. This is particularly the case where nursing assessment learning is not scaffolded. This review demonstrates that e-based learning and traditional teaching methods used in conjunction with each other create a superior learning style. Copyright © 2018 Elsevier Ltd. All rights reserved.
Identifying the public's knowledge and intention to use human cloning in Greek urban areas.

PubMed

Tzamalouka, Georgia; Soultatou, Pelagia; Papadakaki, Maria; Chatzifotiou, Sevasti; Tarlatzis, Basil; El Chliaoutakis, Joannes

2005-02-01

The understanding of the public's knowledge on human cloning (HC) and its acceptability are considered important for the development of evidence-based policy making. The aim of this research study was to investigate the demographic and socioeconomic variables that affect the public's knowledge and intention to use HC in urban areas of Greece. Additionally, the possible association of religiousness with the knowledge and the intention to use HC were also investigated. Individual interviews were conducted with 1020 men and women of urban areas in Greece. Stratified random sampling was performed to select the respondents. Several scientists, experts in HC, evaluated the content of the instrument initially developed. The final questionnaire was consequently the result of a pilot study. Almost half of the respondents (51.5%) believed that "HC is a sort of in vitro fertilization" and 42.9% that "it has already been applied to human being." They were not aware that "the cloned fetus grows in the woman's uterus" (41.5%) and that "HC could regenerate human organs" (41.7%). The acceptability of human cloning for the cure of terminal diseases and transplantation need is very high (70.7% and 58.6%, respectively). The public's intention to have recourse to cloning on the grounds of "bringing" back to life a loved person or because of reproductive disorders was reported as desire by 35% and 32.5%, respectively. The occupational category (scientists, self-employed, and artists), the Intention to use HC, and the number of children are highly significant predictors of valid knowledge about HC. Low rates of church attendance appeared to relate with high reported Intention to use HC, and increasing scores of valid knowledge about HC increased the public's Intention to use HC. A number of specific demographic and socioeconomic characteristics and high scores of knowledge provide a persuasive justification in demonstrating intention toward HC. The current study suggests that these findings should receive further attention by policymakers and scientists within the Greek context.
Does Intensity Matter? Preschoolers' Print Knowledge Development within a Classroom-Based Intervention

ERIC Educational Resources Information Center

McGinty, Anita S.; Breit-Smith, Allison; Fan, Xitao; Justice, Laura M.; Kaderavek, Joan N.

2011-01-01

The present study examined the extent to which two dimensions of intervention intensity, ("dose frequency" and "dose") of a 30-week print-referencing intervention related to the print knowledge development of 367 randomly selected children from 55 preschool classrooms. "Dose frequency" refers to the number of intervention sessions implemented per…
Can History Succeed at School? Problems of Knowledge in the Australian History Curriculum

ERIC Educational Resources Information Center

Gilbert, Rob

2011-01-01

Successful curriculum development in any school subject requires a clear and established set of elements: agreed and widely appreciated goals; effective criteria for the selection of important knowledge content; and an explicit and well-integrated explanatory base for authentic problem-solving related to the subject goals. The article shows that…
On the use of variability time-scales as an early classifier of radio transients and variables

NASA Astrophysics Data System (ADS)

Pietka, M.; Staley, T. D.; Pretorius, M. L.; Fender, R. P.

2017-11-01

We have shown previously that a broad correlation between the peak radio luminosity and the variability time-scales, approximately L ∝ τ5, exists for variable synchrotron emitting sources and that different classes of astrophysical sources occupy different regions of luminosity and time-scale space. Based on those results, we investigate whether the most basic information available for a newly discovered radio variable or transient - their rise and/or decline rate - can be used to set initial constraints on the class of events from which they originate. We have analysed a sample of ≈800 synchrotron flares, selected from light curves of ≈90 sources observed at 5-8 GHz, representing a wide range of astrophysical phenomena, from flare stars to supermassive black holes. Selection of outbursts from the noisy radio light curves has been done automatically in order to ensure reproducibility of results. The distribution of rise/decline rates for the selected flares is modelled as a Gaussian probability distribution for each class of object, and further convolved with estimated areal density of that class in order to correct for the strong bias in our sample. We show in this way that comparing the measured variability time-scale of a radio transient/variable of unknown origin can provide an early, albeit approximate, classification of the object, and could form part of a suite of measurements used to provide early categorization of such events. Finally, we also discuss the effect scintillating sources will have on our ability to classify events based on their variability time-scales.
Individual differences in the joint effects of semantic priming and word frequency: The role of lexical integrity

PubMed Central

Yap, Melvin J.; Tse, Chi-Shing; Balota, David A.

2009-01-01

Word frequency and semantic priming effects are among the most robust effects in visual word recognition, and it has been generally assumed that these two variables produce interactive effects in lexical decision performance, with larger priming effects for low-frequency targets. The results from four lexical decision experiments indicate that the joint effects of semantic priming and word frequency are critically dependent upon differences in the vocabulary knowledge of the participants. Specifically, across two Universities, additive effects of the two variables were observed in participants with more vocabulary knowledge, while interactive effects were observed in participants with less vocabulary knowledge. These results are discussed with reference to Borowsky and Besner’s (1993) multistage account and Plaut and Booth’s (2000) single-mechanism model. In general, the findings are also consistent with a flexible lexical processing system that optimizes performance based on processing fluency and task demands. PMID:20161653

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