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...

Impact of auditory selective attention on verbal short-term memory and vocabulary development.

PubMed

Majerus, Steve; Heiligenstein, Lucie; Gautherot, Nathalie; Poncelet, Martine; Van der Linden, Martial

2009-05-01

This study investigated the role of auditory selective attention capacities as a possible mediator of the well-established association between verbal short-term memory (STM) and vocabulary development. A total of 47 6- and 7-year-olds were administered verbal immediate serial recall and auditory attention tasks. Both task types probed processing of item and serial order information because recent studies have shown this distinction to be critical when exploring relations between STM and lexical development. Multiple regression and variance partitioning analyses highlighted two variables as determinants of vocabulary development: (a) a serial order processing variable shared by STM order recall and a selective attention task for sequence information and (b) an attentional variable shared by selective attention measures targeting item or sequence information. The current study highlights the need for integrative STM models, accounting for conjoined influences of attentional capacities and serial order processing capacities on STM performance and the establishment of the lexical language network.

Attitude towards the incorporation of the selective collection of biowaste in a municipal solid waste management system. A case study

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

Bernad-Beltrán, D.; Simó, A.; Bovea, M.D., E-mail: bovea@uji.es

Highlights: • Attitude towards incorporating biowaste selective collection is analysed. • Willingness to participate and to pay in biowaste selective collection is obtained. • Socioeconomic aspects affecting WtParticipate and WtPay are identified. - Abstract: European waste legislation has been encouraging for years the incorporation of selective collection systems for the biowaste fraction. European countries are therefore incorporating it into their current municipal solid waste management (MSWM) systems. However, this incorporation involves changes in the current waste management habits of households. In this paper, the attitude of the public towards the incorporation of selective collection of biowaste into an existing MSWMmore » system in a Spanish municipality is analysed. A semi-structured telephone interview was used to obtain information regarding aspects such as: level of participation in current waste collection systems, willingness to participate in selective collection of biowaste, reasons and barriers that affect participation, willingness to pay for the incorporation of the selective collection of biowaste and the socioeconomic characteristics of citizens who are willing to participate and pay for selective collection of biowaste. The results showed that approximately 81% of the respondents were willing to participate in selective collection of biowaste. This percentage would increase until 89% if the Town Council provided specific waste bins and bags, since the main barrier to participate in the new selective collection system is the need to use specific waste bin and bags for the separation of biowaste. A logit response model was applied to estimate the average willingness to pay, obtaining an estimated mean of 7.5% on top of the current waste management annual tax. The relationship of willingness to participate and willingness to pay for the implementation of this new selective collection with the socioeconomic variables (age, gender, size of the household, work, education and income) was analysed. Chi-square independence tests and binary logistic regression was used for willingness to participate, not being obtained any significant relationship. Chi-square independence tests, ordinal logistic regression and ordinary linear regression was applied for willingness to pay, obtaining statistically significant relationship for most of the socioeconomic variables.« less

Mathematical Model Of Variable-Polarity Plasma Arc Welding

NASA Technical Reports Server (NTRS)

Hung, R. J.

1996-01-01

Mathematical model of variable-polarity plasma arc (VPPA) welding process developed for use in predicting characteristics of welds and thus serves as guide for selection of process parameters. Parameters include welding electric currents in, and durations of, straight and reverse polarities; rates of flow of plasma and shielding gases; and sizes and relative positions of welding electrode, welding orifice, and workpiece.

Modelling the co-evolution of indirect genetic effects and inherited variability.

PubMed

Marjanovic, Jovana; Mulder, Han A; Rönnegård, Lars; Bijma, Piter

2018-03-28

When individuals interact, their phenotypes may be affected not only by their own genes but also by genes in their social partners. This phenomenon is known as Indirect Genetic Effects (IGEs). In aquaculture species and some plants, however, competition not only affects trait levels of individuals, but also inflates variability of trait values among individuals. In the field of quantitative genetics, the variability of trait values has been studied as a quantitative trait in itself, and is often referred to as inherited variability. Such studies, however, consider only the genetic effect of the focal individual on trait variability and do not make a connection to competition. Although the observed phenotypic relationship between competition and variability suggests an underlying genetic relationship, the current quantitative genetic models of IGE and inherited variability do not allow for such a relationship. The lack of quantitative genetic models that connect IGEs to inherited variability limits our understanding of the potential of variability to respond to selection, both in nature and agriculture. Models of trait levels, for example, show that IGEs may considerably change heritable variation in trait values. Currently, we lack the tools to investigate whether this result extends to variability of trait values. Here we present a model that integrates IGEs and inherited variability. In this model, the target phenotype, say growth rate, is a function of the genetic and environmental effects of the focal individual and of the difference in trait value between the social partner and the focal individual, multiplied by a regression coefficient. The regression coefficient is a genetic trait, which is a measure of cooperation; a negative value indicates competition, a positive value cooperation, and an increasing value due to selection indicates the evolution of cooperation. In contrast to the existing quantitative genetic models, our model allows for co-evolution of IGEs and variability, as the regression coefficient can respond to selection. Our simulations show that the model results in increased variability of body weight with increasing competition. When competition decreases, i.e., cooperation evolves, variability becomes significantly smaller. Hence, our model facilitates quantitative genetic studies on the relationship between IGEs and inherited variability. Moreover, our findings suggest that we may have been overlooking an entire level of genetic variation in variability, the one due to IGEs.

TRANPLAN and GIS support for agencies in Alabama

DOT National Transportation Integrated Search

2001-08-06

Travel demand models are computerized programs intended to forecast future roadway traffic volumes for a community based on selected socioeconomic variables and travel behavior algorithms. Software to operate these travel demand models is currently a...

Improved Variable Selection Algorithm Using a LASSO-Type Penalty, with an Application to Assessing Hepatitis B Infection Relevant Factors in Community Residents

PubMed Central

Guo, Pi; Zeng, Fangfang; Hu, Xiaomin; Zhang, Dingmei; Zhu, Shuming; Deng, Yu; Hao, Yuantao

2015-01-01

Objectives In epidemiological studies, it is important to identify independent associations between collective exposures and a health outcome. The current stepwise selection technique ignores stochastic errors and suffers from a lack of stability. The alternative LASSO-penalized regression model can be applied to detect significant predictors from a pool of candidate variables. However, this technique is prone to false positives and tends to create excessive biases. It remains challenging to develop robust variable selection methods and enhance predictability. Material and methods Two improved algorithms denoted the two-stage hybrid and bootstrap ranking procedures, both using a LASSO-type penalty, were developed for epidemiological association analysis. The performance of the proposed procedures and other methods including conventional LASSO, Bolasso, stepwise and stability selection models were evaluated using intensive simulation. In addition, methods were compared by using an empirical analysis based on large-scale survey data of hepatitis B infection-relevant factors among Guangdong residents. Results The proposed procedures produced comparable or less biased selection results when compared to conventional variable selection models. In total, the two newly proposed procedures were stable with respect to various scenarios of simulation, demonstrating a higher power and a lower false positive rate during variable selection than the compared methods. In empirical analysis, the proposed procedures yielding a sparse set of hepatitis B infection-relevant factors gave the best predictive performance and showed that the procedures were able to select a more stringent set of factors. The individual history of hepatitis B vaccination, family and individual history of hepatitis B infection were associated with hepatitis B infection in the studied residents according to the proposed procedures. Conclusions The newly proposed procedures improve the identification of significant variables and enable us to derive a new insight into epidemiological association analysis. PMID:26214802

Determination of Pre-Service Science Teachers' Level of Awareness of Environmental Ethics in Relation to Different Variables

ERIC Educational Resources Information Center

Keles, Özgül; Özer, Nilgün

2016-01-01

The purpose of the current study is to determine the pre-service science teachers' awareness levels of environmental ethics in relation to different variables. The sampling of the present study is comprised of 1,023 third and fourth year pre-service science teachers selected from 12 different universities in the spring term of 2013-2014 academic…

Sensitivity of solar-cell performance to atmospheric variables. 2: Dissimilar cells at several locations

NASA Technical Reports Server (NTRS)

Klucher, T. M.; Hart, R. E.

1976-01-01

Several solar cells having dissimilar spectral response curves and cell construction were measured at various locations in the United States to determine sensitivity of cell performance to atmospheric water vapor and turbidity. The locations selected represent a broad range of summer atmospheric conditions, from clear and dry to turbid and humid. Cell short circuit current under direct normal incidence sunlight, the intensity, water vapor and turbidity were measured. Regression equations were developed from the limited data base in order to provide a single method of prediction of cell current sensitivity to the atmospheric variables.

Analyses of the most influential factors for vibration monitoring of planetary power transmissions in pellet mills by adaptive neuro-fuzzy technique

NASA Astrophysics Data System (ADS)

Milovančević, Miloš; Nikolić, Vlastimir; Anđelković, Boban

2017-01-01

Vibration-based structural health monitoring is widely recognized as an attractive strategy for early damage detection in civil structures. Vibration monitoring and prediction is important for any system since it can save many unpredictable behaviors of the system. If the vibration monitoring is properly managed, that can ensure economic and safe operations. Potentials for further improvement of vibration monitoring lie in the improvement of current control strategies. One of the options is the introduction of model predictive control. Multistep ahead predictive models of vibration are a starting point for creating a successful model predictive strategy. For the purpose of this article, predictive models of are created for vibration monitoring of planetary power transmissions in pellet mills. The models were developed using the novel method based on ANFIS (adaptive neuro fuzzy inference system). The aim of this study is to investigate the potential of ANFIS for selecting the most relevant variables for predictive models of vibration monitoring of pellet mills power transmission. The vibration data are collected by PIC (Programmable Interface Controller) microcontrollers. The goal of the predictive vibration monitoring of planetary power transmissions in pellet mills is to indicate deterioration in the vibration of the power transmissions before the actual failure occurs. The ANFIS process for variable selection was implemented in order to detect the predominant variables affecting the prediction of vibration monitoring. It was also used to select the minimal input subset of variables from the initial set of input variables - current and lagged variables (up to 11 steps) of vibration. The obtained results could be used for simplification of predictive methods so as to avoid multiple input variables. It was preferable to used models with less inputs because of overfitting between training and testing data. While the obtained results are promising, further work is required in order to get results that could be directly applied in practice.

Annual Research Review: Embracing Not Erasing Contextual Variability in Children's Behavior--Theory and Utility in the Selection and Use of Methods and Informants in Developmental Psychopathology

ERIC Educational Resources Information Center

Dirks, Melanie A.; De Los Reyes, Andres; Briggs-Gowan, Margaret; Cella, David; Wakschlag, Lauren S.

2012-01-01

This paper examines the selection and use of multiple methods and informants for the assessment of disruptive behavior syndromes and attention deficit/hyperactivity disorder, providing a critical discussion of (a) the bidirectional linkages between theoretical models of childhood psychopathology and current assessment techniques; and (b) current…

A method for the dynamic management of genetic variability in dairy cattle

PubMed Central

Colleau, Jean-Jacques; Moureaux, Sophie; Briend, Michèle; Bechu, Jérôme

2004-01-01

According to the general approach developed in this paper, dynamic management of genetic variability in selected populations of dairy cattle is carried out for three simultaneous purposes: procreation of young bulls to be further progeny-tested, use of service bulls already selected and approval of recently progeny-tested bulls for use. At each step, the objective is to minimize the average pairwise relationship coefficient in the future population born from programmed matings and the existing population. As a common constraint, the average estimated breeding value of the new population, for a selection goal including many important traits, is set to a desired value. For the procreation of young bulls, breeding costs are additionally constrained. Optimization is fully analytical and directly considers matings. Corresponding algorithms are presented in detail. The efficiency of these procedures was tested on the current Norman population. Comparisons between optimized and real matings, clearly showed that optimization would have saved substantial genetic variability without reducing short-term genetic gains. PMID:15231230

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.

Couples at risk for transmission of alcoholism: protective influences.

PubMed

Bennett, L A; Wolin, S J; Reiss, D; Teitelbaum, M A

1987-03-01

A two-generation, sociocultural model of the transmission of alcoholism in families was operationalized and tested. Sixty-eight married children of alcoholic parents and their spouses were interviewed regarding dinner-time and holiday ritual practices in their families of origin, and heritage and ritual practices in the couples' current generation. Coders rated transcribed interviews along 14 theory-derived predictor variables, nine for the family of origin and five for the current nuclear family. Multiple regression analysis was applied in a two-step hierarchical method, with the dependent variable being transmission of alcoholism to the couple. The 14 predictor variables contributed significantly (p less than .01) to the couple's alcoholism outcome. A general theme of selective disengagement and reengagement for couples in families at risk for alcoholism recurrence is discussed.

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…

Between-Visit Variability in FEV1 as a Diagnostic Test for Asthma in Adults.

PubMed

Dean, Benjamin W; Birnie, Erin E; Whitmore, G A; Vandemheen, Katherine L; Boulet, Louis-Philippe; FitzGerald, J Mark; Ainslie, Martha; Gupta, Samir; Lemiere, Catherine; Field, Stephen K; McIvor, R Andrew; Hernandez, Paul; Mayers, Irvin; Aaron, Shawn D

2018-06-07

The reliability of using between-visit variation in FEV1 to diagnose asthma is understudied, and hence uncertain. To determine if FEV1 variability measured over recurrent visits is significantly associated with a diagnosis of current asthma. Randomly-selected adults (N=964) with a history of physician-diagnosed asthma were studied from 2005-2007 and from 2012-2016. A diagnosis of current asthma was confirmed in those participants who exhibited bronchial hyper-responsiveness to methacholine and/or acute worsening of asthma symptoms while being weaned off asthma medications. Regression analyses and receiver operating curves were used to evaluate the ability of between-visit FEV1 variability to diagnose asthma. A current diagnosis of asthma was confirmed in 584 of 964 participants (60%). Between-visit absolute variability in FEV1 was significantly greater in those in whom current asthma was confirmed, compared to those in whom current asthma was ruled out (7.3% vs 4.8%, mean difference between the 2 groups = 2.5%; 95% CI: 1.7-3.3%). However, a 12 percent and 200 mL between-visit variation in FEV1, which is the diagnostic threshold recommended by GINA, exhibited a sensitivity of only 0.17 and a specificity of 0.94 for confirming current asthma. A between-visit absolute variability in FEV1 ≥ 12% and 200 ml increased the pre-test probability of asthma from 60% to a post-test probability of 81%. A 12% and 200 mL between-visit variation in FEV1, if present, has reasonably good specificity for diagnosing asthma, but has poor sensitivity compared to bronchial challenge testing. Between-visit variability in FEV1 is a relatively unhelpful test to establish a diagnosis of asthma.

State observer for synchronous motors

DOEpatents

Lang, Jeffrey H.

1994-03-22

A state observer driven by measurements of phase voltages and currents for estimating the angular orientation of a rotor of a synchronous motor such as a variable reluctance motor (VRM). Phase voltages and currents are detected and serve as inputs to a state observer. The state observer includes a mathematical model of the electromechanical operation of the synchronous motor. The characteristics of the state observer are selected so that the observer estimates converge to the actual rotor angular orientation and velocity, winding phase flux linkages or currents.

Supersonic variable-cycle engines

NASA Technical Reports Server (NTRS)

Willis, E. A.; Welliver, A. D.

1976-01-01

The evolution and current status of selected recent variable cycle engine (VCE) studies are reviewed, and how the results were influenced by airplane requirements is described. Promising VCE concepts are described, their designs are simplified and the potential benefits in terms of aircraft performance are identified. This includes range, noise, emissions, and the time and effort it may require to ensure technical readiness of sufficient depth to satisfy reasonable economic, performance, and environmental constraints. A brief overview of closely related, ongoing technology programs in acoustics and exhaust emissions is also presented. Realistic technology advancements in critical areas combined with well matched aircraft and selected VCE concepts can lead to significantly improved economic and environmental performance relative to first generation SST predictions.

Neuropsychological Test Selection for Cognitive Impairment Classification: A Machine Learning Approach

PubMed Central

Williams, Jennifer A.; Schmitter-Edgecombe, Maureen; Cook, Diane J.

2016-01-01

Introduction Reducing the amount of testing required to accurately detect cognitive impairment is clinically relevant. The aim of this research was to determine the fewest number of clinical measures required to accurately classify participants as healthy older adult, mild cognitive impairment (MCI) or dementia using a suite of classification techniques. Methods Two variable selection machine learning models (i.e., naive Bayes, decision tree), a logistic regression, and two participant datasets (i.e., clinical diagnosis, clinical dementia rating; CDR) were explored. Participants classified using clinical diagnosis criteria included 52 individuals with dementia, 97 with MCI, and 161 cognitively healthy older adults. Participants classified using CDR included 154 individuals CDR = 0, 93 individuals with CDR = 0.5, and 25 individuals with CDR = 1.0+. Twenty-seven demographic, psychological, and neuropsychological variables were available for variable selection. Results No significant difference was observed between naive Bayes, decision tree, and logistic regression models for classification of both clinical diagnosis and CDR datasets. Participant classification (70.0 – 99.1%), geometric mean (60.9 – 98.1%), sensitivity (44.2 – 100%), and specificity (52.7 – 100%) were generally satisfactory. Unsurprisingly, the MCI/CDR = 0.5 participant group was the most challenging to classify. Through variable selection only 2 – 9 variables were required for classification and varied between datasets in a clinically meaningful way. Conclusions The current study results reveal that machine learning techniques can accurately classifying cognitive impairment and reduce the number of measures required for diagnosis. PMID:26332171

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.

Leaf growth dynamics in four plant species of the Patagonian Monte, Argentina.

PubMed

Campanella, M Victoria; Bertiller, Mónica B

2013-07-01

Studying plant responses to environmental variables is an elemental key to understand the functioning of arid ecosystems. We selected four dominant species of the two main life forms. The species selected were two evergreen shrubs: Larrea divaricata and Chuquiraga avellanedae and two perennial grasses: Nassella tenuis and Pappostipa speciosa. We registered leaf/shoot growth, leaf production and environmental variables (precipitation, air temperature, and volumetric soil water content at two depths) during summer-autumn and winter-spring periods. Multiple regressions were used to test the predictive power of the environmental variables. During the summer-autumn period, the strongest predictors of leaf/shoot growth and leaf production were the soil water content of the upper layer and air temperature while during the winter-spring period, the strongest predictor was air temperature. In conclusion, we found that the leaf/shoot growth and leaf production were associated with current environmental conditions, specially to soil water content and air temperature.

The Role of Ocean Currents in the Temperature Selection of Plankton: Insights from an Individual-Based Model

PubMed Central

Hellweger, Ferdi L.; van Sebille, Erik; Calfee, Benjamin C.; Chandler, Jeremy W.; Zinser, Erik R.; Swan, Brandon K.; Fredrick, Neil D.

2016-01-01

Biogeography studies that correlate the observed distribution of organisms to environmental variables are typically based on local conditions. However, in cases with substantial translocation, like planktonic organisms carried by ocean currents, selection may happen upstream and local environmental factors may not be representative of those that shaped the local population. Here we use an individual-based model of microbes in the global surface ocean to explore this effect for temperature. We simulate up to 25 million individual cells belonging to up to 50 species with different temperature optima. Microbes are moved around the globe based on a hydrodynamic model, and grow and die based on local temperature. We quantify the role of currents using the “advective temperature differential” metric, which is the optimum temperature of the most abundant species from the model with advection minus that from the model without advection. This differential depends on the location and can be up to 4°C. Poleward-flowing currents, like the Gulf Stream, generally experience cooling and the differential is positive. We apply our results to three global datasets. For observations of optimum growth temperature of phytoplankton, accounting for the effect of currents leads to a slightly better agreement with observations, but there is large variability and the improvement is not statistically significant. For observed Prochlorococcus ecotype ratios and metagenome nucleotide divergence, accounting for advection improves the correlation significantly, especially in areas with relatively strong poleward or equatorward currents. PMID:27907181

Climate Change Impacts at Department of Defense

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

Kotamarthi, Rao; Wang, Jiali; Zoebel, Zach

This project is aimed at providing the U.S. Department of Defense (DoD) with a comprehensive analysis of the uncertainty associated with generating climate projections at the regional scale that can be used by stakeholders and decision makers to quantify and plan for the impacts of future climate change at specific locations. The merits and limitations of commonly used downscaling models, ranging from simple to complex, are compared, and their appropriateness for application at installation scales is evaluated. Downscaled climate projections are generated at selected DoD installations using dynamic and statistical methods with an emphasis on generating probability distributions of climatemore » variables and their associated uncertainties. The sites selection and selection of variables and parameters for downscaling was based on a comprehensive understanding of the current and projected roles that weather and climate play in operating, maintaining, and planning DoD facilities and installations.« less

Digital mapping of soil properties in Canadian managed forests at 250 m of resolution using the k-nearest neighbor method

NASA Astrophysics Data System (ADS)

Mansuy, N. R.; Paré, D.; Thiffault, E.

2015-12-01

Large-scale mapping of soil properties is increasingly important for environmental resource management. Whileforested areas play critical environmental roles at local and global scales, forest soil maps are typically at lowresolution.The objective of this study was to generate continuous national maps of selected soil variables (C, N andsoil texture) for the Canadian managed forest landbase at 250 m resolution. We produced these maps using thekNN method with a training dataset of 538 ground-plots fromthe National Forest Inventory (NFI) across Canada,and 18 environmental predictor variables. The best predictor variables were selected (7 topographic and 5 climaticvariables) using the Least Absolute Shrinkage and Selection Operator method. On average, for all soil variables,topographic predictors explained 37% of the total variance versus 64% for the climatic predictors. Therelative root mean square error (RMSE%) calculated with the leave-one-out cross-validation method gave valuesranging between 22% and 99%, depending on the soil variables tested. RMSE values b 40% can be considered agood imputation in light of the low density of points used in this study. The study demonstrates strong capabilitiesfor mapping forest soil properties at 250m resolution, compared with the current Soil Landscape of CanadaSystem, which is largely oriented towards the agricultural landbase. The methodology used here can potentiallycontribute to the national and international need for spatially explicit soil information in resource managementscience.

Multiple dual mode counter-current chromatography with variable duration of alternating phase elution steps.

PubMed

Kostanyan, Artak E; Erastov, Andrey A; Shishilov, Oleg N

2014-06-20

The multiple dual mode (MDM) counter-current chromatography separation processes consist of a succession of two isocratic counter-current steps and are characterized by the shuttle (forward and back) transport of the sample in chromatographic columns. In this paper, the improved MDM method based on variable duration of alternating phase elution steps has been developed and validated. The MDM separation processes with variable duration of phase elution steps are analyzed. Basing on the cell model, analytical solutions are developed for impulse and non-impulse sample loading at the beginning of the column. Using the analytical solutions, a calculation program is presented to facilitate the simulation of MDM with variable duration of phase elution steps, which can be used to select optimal process conditions for the separation of a given feed mixture. Two options of the MDM separation are analyzed: 1 - with one-step solute elution: the separation is conducted so, that the sample is transferred forward and back with upper and lower phases inside the column until the desired separation of the components is reached, and then each individual component elutes entirely within one step; 2 - with multi-step solute elution, when the fractions of individual components are collected in over several steps. It is demonstrated that proper selection of the duration of individual cycles (phase flow times) can greatly increase the separation efficiency of CCC columns. Experiments were carried out using model mixtures of compounds from the GUESSmix with solvent systems hexane/ethyl acetate/methanol/water. The experimental results are compared to the predictions of the theory. A good agreement between theory and experiment has been demonstrated. Copyright © 2014 Elsevier B.V. All rights reserved.

Model-Averaged ℓ1 Regularization using Markov Chain Monte Carlo Model Composition

PubMed Central

Fraley, Chris; Percival, Daniel

2014-01-01

Bayesian Model Averaging (BMA) is an effective technique for addressing model uncertainty in variable selection problems. However, current BMA approaches have computational difficulty dealing with data in which there are many more measurements (variables) than samples. This paper presents a method for combining ℓ1 regularization and Markov chain Monte Carlo model composition techniques for BMA. By treating the ℓ1 regularization path as a model space, we propose a method to resolve the model uncertainty issues arising in model averaging from solution path point selection. We show that this method is computationally and empirically effective for regression and classification in high-dimensional datasets. We apply our technique in simulations, as well as to some applications that arise in genomics. PMID:25642001

A diagnostic approach to increase reusable dinnerware selection in a cafeteria.

PubMed

Manuel, Jennifer C; Sunseri, Mary Anne; Olson, Ryan; Scolari, Miranda

2007-01-01

The current project tested a diagnostic approach to selecting interventions to increase patron selection of reusable dinnerware in a cafeteria. An assessment survey, completed by a sample of 43 patrons, suggested that the primary causes of wasteful behavior were (a) environmental arrangement of dinnerware options and (b) competing motivational variables. A functional relation between environmental arrangement and reusable product selection was demonstrated in a reversal design. However, the largest effect occurred as function of a multicomponent intervention involving environmental arrangement, employee involvement, and personal spoken prompts with motivational signs. The results support the use of informant assessments when designing community interventions.

A Diagnostic Approach to Increase Reusable Dinnerware Selection in a Cafeteria

PubMed Central

Manuel, Jennifer C; Anne Sunseri, Mary; Olson, Ryan; Scolari, Miranda

2007-01-01

The current project tested a diagnostic approach to selecting interventions to increase patron selection of reusable dinnerware in a cafeteria. An assessment survey, completed by a sample of 43 patrons, suggested that the primary causes of wasteful behavior were (a) environmental arrangement of dinnerware options and (b) competing motivational variables. A functional relation between environmental arrangement and reusable product selection was demonstrated in a reversal design. However, the largest effect occurred as function of a multicomponent intervention involving environmental arrangement, employee involvement, and personal spoken prompts with motivational signs. The results support the use of informant assessments when designing community interventions. PMID:17624069

Risk models for post-endoscopic retrograde cholangiopancreatography pancreatitis (PEP): smoking and chronic liver disease are predictors of protection against PEP.

PubMed

DiMagno, Matthew J; Spaete, Joshua P; Ballard, Darren D; Wamsteker, Erik-Jan; Saini, Sameer D

2013-08-01

We investigated which variables independently associated with protection against or development of postendoscopic retrograde cholangiopancreatography (ERCP) pancreatitis (PEP) and severity of PEP. Subsequently, we derived predictive risk models for PEP. In a case-control design, 6505 patients had 8264 ERCPs, 211 patients had PEP, and 22 patients had severe PEP. We randomly selected 348 non-PEP controls. We examined 7 established- and 9 investigational variables. In univariate analysis, 7 variables predicted PEP: younger age, female sex, suspected sphincter of Oddi dysfunction (SOD), pancreatic sphincterotomy, moderate-difficult cannulation (MDC), pancreatic stent placement, and lower Charlson score. Protective variables were current smoking, former drinking, diabetes, and chronic liver disease (CLD, biliary/transplant complications). Multivariate analysis identified seven independent variables for PEP, three protective (current smoking, CLD-biliary, CLD-transplant/hepatectomy complications) and 4 predictive (younger age, suspected SOD, pancreatic sphincterotomy, MDC). Pre- and post-ERCP risk models of 7 variables have a C-statistic of 0.74. Removing age (seventh variable) did not significantly affect the predictive value (C-statistic of 0.73) and reduced model complexity. Severity of PEP did not associate with any variables by multivariate analysis. By using the newly identified protective variables with 3 predictive variables, we derived 2 risk models with a higher predictive value for PEP compared to prior studies.

Estimating the spatial scales of landscape effects on abundance

Treesearch

Richard Chandler; Jeffrey Hepinstall-Cymerman

2016-01-01

Spatial variation in abundance is influenced by local- and landscape-level environmental variables, but modeling landscape effects is challenging because the spatial scales of the relationships are unknown. Current approaches involve buffering survey locations with polygons of various sizes and using model selection to identify the best scale. The buffering...

EFFECTS OF CLIMATE VARIABILITY ON NITROGEN FLUXES FROM SELECTED WATERSHEDS ALONG THE US EAST COAST

EPA Science Inventory

To anticipate vulnerabilities of coastal ecosystems to global climate change, a better understanding is needed of factors affecting current and past nitrogen fluxes from watersheds to coastal systems. This study undertook a statistical examination of long-term data sets of nutrie...

Estimation and Model Selection for Finite Mixtures of Latent Interaction Models

ERIC Educational Resources Information Center

Hsu, Jui-Chen

2011-01-01

Latent interaction models and mixture models have received considerable attention in social science research recently, but little is known about how to handle if unobserved population heterogeneity exists in the endogenous latent variables of the nonlinear structural equation models. The current study estimates a mixture of latent interaction…

A predictive model for diagnosing bipolar disorder based on the clinical characteristics of major depressive episodes in Chinese population.

PubMed

Gan, Zhaoyu; Diao, Feici; Wei, Qinling; Wu, Xiaoli; Cheng, Minfeng; Guan, Nianhong; Zhang, Ming; Zhang, Jinbei

2011-11-01

A correct timely diagnosis of bipolar depression remains a big challenge for clinicians. This study aimed to develop a clinical characteristic based model to predict the diagnosis of bipolar disorder among patients with current major depressive episodes. A prospective study was carried out on 344 patients with current major depressive episodes, with 268 completing 1-year follow-up. Data were collected through structured interviews. Univariate binary logistic regression was conducted to select potential predictive variables among 19 initial variables, and then multivariate binary logistic regression was performed to analyze the combination of risk factors and build a predictive model. Receiver operating characteristic (ROC) curve was plotted. Of 19 initial variables, 13 variables were preliminarily selected, and then forward stepwise exercise produced a final model consisting of 6 variables: age at first onset, maximum duration of depressive episodes, somatalgia, hypersomnia, diurnal variation of mood, irritability. The correct prediction rate of this model was 78% (95%CI: 75%-86%) and the area under the ROC curve was 0.85 (95%CI: 0.80-0.90). The cut-off point for age at first onset was 28.5 years old, while the cut-off point for maximum duration of depressive episode was 7.5 months. The limitations of this study include small sample size, relatively short follow-up period and lack of treatment information. Our predictive models based on six clinical characteristics of major depressive episodes prove to be robust and can help differentiate bipolar depression from unipolar depression. Copyright © 2011 Elsevier B.V. All rights reserved.

Strategies for minimizing sample size for use in airborne LiDAR-based forest inventory

USGS Publications Warehouse

Junttila, Virpi; Finley, Andrew O.; Bradford, John B.; Kauranne, Tuomo

2013-01-01

Recently airborne Light Detection And Ranging (LiDAR) has emerged as a highly accurate remote sensing modality to be used in operational scale forest inventories. Inventories conducted with the help of LiDAR are most often model-based, i.e. they use variables derived from LiDAR point clouds as the predictive variables that are to be calibrated using field plots. The measurement of the necessary field plots is a time-consuming and statistically sensitive process. Because of this, current practice often presumes hundreds of plots to be collected. But since these plots are only used to calibrate regression models, it should be possible to minimize the number of plots needed by carefully selecting the plots to be measured. In the current study, we compare several systematic and random methods for calibration plot selection, with the specific aim that they be used in LiDAR based regression models for forest parameters, especially above-ground biomass. The primary criteria compared are based on both spatial representativity as well as on their coverage of the variability of the forest features measured. In the former case, it is important also to take into account spatial auto-correlation between the plots. The results indicate that choosing the plots in a way that ensures ample coverage of both spatial and feature space variability improves the performance of the corresponding models, and that adequate coverage of the variability in the feature space is the most important condition that should be met by the set of plots collected.

A discriminant function model for admission at undergraduate university level

NASA Astrophysics Data System (ADS)

Ali, Hamdi F.; Charbaji, Abdulrazzak; Hajj, Nada Kassim

1992-09-01

The study is aimed at predicting objective criteria based on a statistically tested model for admitting undergraduate students to Beirut University College. The University is faced with a dual problem of having to select only a fraction of an increasing number of applicants, and of trying to minimize the number of students placed on academic probation (currently 36 percent of new admissions). Out of 659 new students, a sample of 272 students (45 percent) were selected; these were all the students on the Dean's list and on academic probation. With academic performance as the dependent variable, the model included ten independent variables and their interactions. These variables included the type of high school, the language of instruction in high school, recommendations, sex, academic average in high school, score on the English Entrance Examination, the major in high school, and whether the major was originally applied for by the student. Discriminant analysis was used to evaluate the relative weight of the independent variables, and from the analysis three equations were developed, one for each academic division in the College. The predictive power of these equations was tested by using them to classify students not in the selected sample into successful and unsuccessful ones. Applicability of the model to other institutions of higher learning is discussed.

Dental Caries Status, Socio-Economic, Behavioral and Biological Variables among 12-Year-Old Palestinian School Children.

PubMed

Sgan-Cohen, H D; Bajali, M; Eskander, L; Steinberg, D; Zini, A

2015-01-01

There are currently inadequate data regarding the prevalence of dental caries and its associated variables, among Palestinian children. To determine the current prevalence of dental caries and related variables, among Palestinian children in East Jerusalem. A stratified sample of 286 East Jerusalem Palestinian children was selected, employing randomly chosen sixth grade clusters from three pre-selected socio-economic school groups. Dental caries was recorded according to WHO recommendations. Salivary flow, pH, buffer capacity and microbial parameters, were recorded according to previously employed methodologies. The mean level of caries experience, by DMFT, was 1.98 ± 2.05. This level was higher than those found among Israeli children, but lower than several other Middle Eastern countries. In uni-variate analysis, significant associations were revealed between caries and school categories, which indicated lower, middle and higher socio-economic position(SEP), mothers' employment, home densities, dental visits, tooth brushing, Streptococci mutans (SM), Lactobacilli (LB), and saliva pH. According to a linear logistic regression model, children learning in lower SEP schools, with higher SM levels and more acidic saliva, had a higher chance of experiencing dental caries. These findings should be considered in the planning of services and dental health care programs for Palestinian children.

Predictors of self-reported negative mood following a depressive mood induction procedure across previously depressed, currently anxious, and control individuals.

PubMed

Scherrer, Martin C; Dobson, Keith S; Quigley, Leanne

2014-09-01

This study identified and examined a set of potential predictors of self-reported negative mood following a depressive mood induction procedure (MIP) in a sample of previously depressed, clinically anxious, and control participants. The examined predictor variables were selected on the basis of previous research and theories of depression, and included symptoms of depression and anxiety, negative and positive affect, negative and positive automatic thoughts, dysfunctional beliefs, rumination, self-concept, and occurrence and perceived unpleasantness of recent negative events. The sample consisted of 33 previously depressed, 22 currently anxious, and 26 non-clinical control participants, recruited from community sources. Participant group status was confirmed through structured diagnostic interviews. Participants completed the Velten negative self-statement MIP as well as self-report questionnaires of affective, cognitive, and psychosocial variables selected as potential predictors of mood change. Symptoms of anxiety were associated with increased self-reported negative mood shift following the MIP in previously depressed participants, but not clinically anxious or control participants. Increased occurrence of recent negative events was a marginally significant predictor of negative mood shift for the previously depressed participants only. None of the other examined variables was significant predictors of MIP response for any of the participant groups. These results identify factors that may increase susceptibility to negative mood states in previously depressed individuals, with implications for theory and prevention of relapse to depression. The findings also identify a number of affective, cognitive, and psychosocial variables that do not appear to influence mood change following a depressive MIP in previously depressed, currently anxious, and control individuals. Limitations of the study and directions for future research are discussed. Current anxiety symptomatology was a significant predictor and occurrence of recent negative events was a marginally significant predictor of greater negative mood shift following the depressive mood induction for previously depressed individuals. None of the examined variables predicted change in mood following the depressive mood induction for currently anxious or control individuals. These results suggest that anxiety symptoms and experience with negative events may increase risk for experiencing depressive mood states among individuals with a vulnerability to depression. The generalizability of the present results to individuals with comorbid depression and anxiety is limited. Future research employing appropriate statistical approaches for confirmatory research is needed to test and confirm the present results. © 2014 The British Psychological Society.

Drift in ocean currents impacts intergenerational microbial exposure to temperature.

PubMed

Doblin, Martina A; van Sebille, Erik

2016-05-17

Microbes are the foundation of marine ecosystems [Falkowski PG, Fenchel T, Delong EF (2008) Science 320(5879):1034-1039]. Until now, the analytical framework for understanding the implications of ocean warming on microbes has not considered thermal exposure during transport in dynamic seascapes, implying that our current view of change for these critical organisms may be inaccurate. Here we show that upper-ocean microbes experience along-trajectory temperature variability up to 10 °C greater than seasonal fluctuations estimated in a static frame, and that this variability depends strongly on location. These findings demonstrate that drift in ocean currents can increase the thermal exposure of microbes and suggests that microbial populations with broad thermal tolerance will survive transport to distant regions of the ocean and invade new habitats. Our findings also suggest that advection has the capacity to influence microbial community assemblies, such that regions with strong currents and large thermal fluctuations select for communities with greatest plasticity and evolvability, and communities with narrow thermal performance are found where ocean currents are weak or along-trajectory temperature variation is low. Given that fluctuating environments select for individual plasticity in microbial lineages, and that physiological plasticity of ancestors can predict the magnitude of evolutionary responses of subsequent generations to environmental change [Schaum CE, Collins S (2014) Proc Biol Soc 281(1793):20141486], our findings suggest that microbial populations in the sub-Antarctic (∼40°S), North Pacific, and North Atlantic will have the most capacity to adapt to contemporary ocean warming.

Psychosocial correlates of substance use amongst secondary school students in south western Nigeria.

PubMed

Fatoye, F O

2003-03-01

To determine the psychosocial correlates of substance use among secondary school students in rural and urban communities in south western Nigeria. A cross-sectional survey of secondary school students using questionnaire eliciting substance use by students (WHO drug use questionnaire) and a well designed questionnaire on psychosocial variables. Six secondary schools selected from two local government areas in Ilesa, Osun State, South Western Nigeria. The study population comprised 600 randomly selected senior secondary school students from six schools. A total of 562 questionnaires were analysed. Current stimulant use was significantly associated with lower socio-economic status, coming from a polygamous family and self-rated poor academic performance. Current alcohol use was associated with being a male, polygamous family background, living alone or with friends, not being religious and self-rated poor academic performance. Current hypnosedatives use was commoner in students living alone or with friends and in those with self-rated poor academic performance. There was also significant positive relationship between current tobacco use and the male sex, not being religious and self-rated poor academic perfomance. Lifetime use of these substances had similar association with the psychosocial variables with slight differences. The similarity between the psychosocial correlates highlighted in this study and those reported in previous studies from other parts of Nigeria makes these observations useful enough for the planning of preventive strategies.

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.

The Use of Variable Q1 Isolation Windows Improves Selectivity in LC-SWATH-MS Acquisition.

PubMed

Zhang, Ying; Bilbao, Aivett; Bruderer, Tobias; Luban, Jeremy; Strambio-De-Castillia, Caterina; Lisacek, Frédérique; Hopfgartner, Gérard; Varesio, Emmanuel

2015-10-02

As tryptic peptides and metabolites are not equally distributed along the mass range, the probability of cross fragment ion interference is higher in certain windows when fixed Q1 SWATH windows are applied. We evaluated the benefits of utilizing variable Q1 SWATH windows with regards to selectivity improvement. Variable windows based on equalizing the distribution of either the precursor ion population (PIP) or the total ion current (TIC) within each window were generated by an in-house software, swathTUNER. These two variable Q1 SWATH window strategies outperformed, with respect to quantification and identification, the basic approach using a fixed window width (FIX) for proteomic profiling of human monocyte-derived dendritic cells (MDDCs). Thus, 13.8 and 8.4% additional peptide precursors, which resulted in 13.1 and 10.0% more proteins, were confidently identified by SWATH using the strategy PIP and TIC, respectively, in the MDDC proteomic sample. On the basis of the spectral library purity score, some improvement warranted by variable Q1 windows was also observed, albeit to a lesser extent, in the metabolomic profiling of human urine. We show that the novel concept of "scheduled SWATH" proposed here, which incorporates (i) variable isolation windows and (ii) precursor retention time segmentation further improves both peptide and metabolite identifications.

Current research in Spain on walnut for wood production

Treesearch

Neus Alet& #224; ; Neus NO-VALUE

2004-01-01

The Department of Mediterranean Trees at the Institut de Recerca i Tecnologia Agroalimentaries (IRTA) in Spain initiated a research program in 1993 to examine the variability among walnut species for wood production and to establish orchards with improved selections. The main objective of the programme is to obtain superior Persian walnut (Juglans regia...

Family Violence and Aggression and Their Associations with Psychosocial Functioning in Jamaican Adolescents

ERIC Educational Resources Information Center

Smith, Delores E.; Moore, Todd M.

2013-01-01

The purpose of the current study was to examine the relationships among selected family interaction variables and psychosocial outcomes in a sample of Jamaican adolescents. The authors hypothesized that adolescent psychosocial outcomes would be negatively associated with physical violence, verbal aggression would be more potent than physical…

Topic Sequence and Emphasis Variability of Selected Organic Chemistry Textbooks

ERIC Educational Resources Information Center

Houseknecht, Justin B.

2010-01-01

Textbook choice has a significant effect upon course success. Among the factors that influence this decision, two of the most important are material organization and emphasis. This paper examines the sequencing of 19 organic chemistry topics, 21 concepts and skills, and 7 biological topics within nine of the currently available organic textbooks.…

Microgenetic Patterns of Children's Multiplication Learning: Confirming the Overlapping Waves Model by Latent Growth Modeling

ERIC Educational Resources Information Center

van der Ven, Sanne H. G.; Boom, Jan; Kroesbergen, Evelyn H.; Leseman, Paul P. M.

2012-01-01

Variability in strategy selection is an important characteristic of learning new skills such as mathematical skills. Strategies gradually come and go during this development. In 1996, Siegler described this phenomenon as ''overlapping waves.'' In the current microgenetic study, we attempted to model these overlapping waves statistically. In…

Factors Influencing Successful Use of Information Retrieval Systems by Nurse Practitioner Students

PubMed Central

Rose, Linda; Crabtree, Katherine; Hersh, William

1998-01-01

This study examined whether a relationship exists between selected Nurse Practitioner students' attributes and successful information retrieval as demonstrated by correctly answering clinical questions using an information retrieval system (Medline). One predictor variable, attitude toward current computer technology, was significantly correlated (r =0.43, p ≤ .05) with successful literature searching.

CLINICAL APPLICATIONS OF CRYOTHERAPY AMONG SPORTS PHYSICAL THERAPISTS.

PubMed

Hawkins, Shawn W; Hawkins, Jeremy R

2016-02-01

Therapeutic modalities (TM) are used by sports physical therapists (SPT) but how they are used is unknown. To identify the current clinical use patterns for cryotherapy among SPT. Cross-sectional survey. All members (7283) of the Sports Physical Therapy Section of the APTA were recruited. A scenario-based survey using pre-participation management of an acute or sub-acute ankle sprain was developed. A Select Survey link was distributed via email to participants. Respondents selected a treatment approach based upon options provided. Follow-up questions were asked. The survey was available for two weeks with a follow-up email sent after one week. Question answers were the main outcome measures. Reliability: Cronbach's alpha=>0.9. The SPT response rate = 6.9% (503); responses came from 48 states. Survey results indicated great variability in respondents' approaches to the treatment of an acute and sub-acute ankle sprain. SPT applied cryotherapy with great variability and not always in accordance to the limited research on the TM. Continuing education, application of current research, and additional outcomes based research needs to remain a focus for clinicians. 3.

Variable-cycle engines for supersonic cruising aircraft

NASA Technical Reports Server (NTRS)

Willis, E. A.; Welliver, A. D.

1976-01-01

The paper reviews the evolution and current status of selected recent variable-cycle engine (VCE) studies and describes how the results are influenced by airplane requirements. The engine/airplane studies are intended to identify promising VCE concepts, simplify their designs and identify the potential benefits in terms of aircraft performance. This includes range, noise, emissions, and the time and effort it may require to ensure technical readiness of sufficient depth to satisfy reasonable economic, performance, and environmental constraints. A brief overview of closely-related, on-going technology programs in acoustics and exhaust emissions is presented. It is shown that realistic technology advancements in critical areas combined with well matched aircraft and selected VCE concepts can lead to significantly improved economic and environmental performance relative to first-generation SST predictions.

Water quality parameters of harbors of Charlotte Amalie, St. Thomas, Virgin Islands: Acquisition of in situ water data, intercorrelation of selected water parameters, and initial correlation of these in situ biological, chemical and physical data with ERTS-1 bulk CCT MSS band 5 data

NASA Technical Reports Server (NTRS)

Coulbourn, W. C.; Olsen, D. A. (Principal Investigator)

1973-01-01

The author has identified the following significant results. Remote sensing by the ERTS-1 satellite was compared with selected water quality parameters including pH, salinity, conductivity, dissolved oxygen, water depth, water temperature, turbidity, plankton concentration, current variables, chlorophylla, total carotenoids, and species diversity of the benthic community. Strong correlation between turbidity and MSS-sensed radiance was recorded and less strong correlations between the two plankton pigments and radiance. Turbidity and benthic species diversity were highly correlated furnishing an inferential tie between an easily sensed water quality variable and a sensitive indicator of average water quality conditions.

Datalist: A Value Added Service to Enable Easy Data Selection

NASA Technical Reports Server (NTRS)

Li, Angela; Hegde, Mahabaleshwa; Bryant, Keith; Seiler, Edward; Shie, Chung-Lin; Teng, William; Liu, Zhong; Hearty, Thomas; Shen, Suhung; Kempler, Steven;

Influences on the start, selection and duration of treatment with antibiotics in long-term care facilities

PubMed Central

Daneman, Nick; Campitelli, Michael A.; Giannakeas, Vasily; Morris, Andrew M.; Bell, Chaim M.; Maxwell, Colleen J.; Jeffs, Lianne; Austin, Peter C.; Bronskill, Susan E.

2017-01-01

BACKGROUND: Understanding the extent to which current antibiotic prescribing behaviour is influenced by clinicians’ historical patterns of practice will help target interventions to optimize antibiotic use in long-term care. Our objective was to evaluate whether clinicians’ historical prescribing behaviours influence the start, prolongation and class selection for treatment with antibiotics in residents of long-term care facilities. METHODS: We conducted a retrospective cohort study of all physicians who prescribed to residents in long-term care facilities in Ontario between Jan. 1 and Dec. 31, 2014. We examined variability in antibiotic prescribing among physicians for 3 measures: start of treatment with antibiotics, use of prolonged durations exceeding 7 days and selection of fluoroquinolones. Funnel plots with control limits were used to determine the extent of variation and characterize physicians as extreme low, low, average, high and extreme high prescribers for each tendency. Multivariable logistic regression was used to assess whether a clinician’s prescribing tendency in the previous year predicted current prescribing patterns, after accounting for residents’ demographics, comorbidity, functional status and indwelling devices. RESULTS: Among 1695 long-term care physicians, who prescribed for 93 132 residents, there was wide variability in the start of antibiotic treatment (median 45% of patients, interquartile range [IQR] 32%–55%), use of prolonged treatment durations (median 30% of antibiotic prescriptions, IQR 19%–46%) and selection of fluoroquinolones (median 27% of antibiotic prescriptions, IQR 18%–37%). Prescribing tendencies for antibiotics by physicians in 2014 correlated strongly with tendencies in the previous year. After controlling for individual resident characteristics, prior prescribing tendency was a significant predictor of current practice. INTERPRETATION: Physicians prescribing antibiotics exhibited individual, measurable and historical tendencies toward start of antibiotic treatment, use of prolonged treatment duration and class selection. Prescriber audit and feedback may be a promising tool to optimize antibiotic use in long-term care facilities. PMID:28652480

Influences on the start, selection and duration of treatment with antibiotics in long-term care facilities.

PubMed

Daneman, Nick; Campitelli, Michael A; Giannakeas, Vasily; Morris, Andrew M; Bell, Chaim M; Maxwell, Colleen J; Jeffs, Lianne; Austin, Peter C; Bronskill, Susan E

2017-06-26

Understanding the extent to which current antibiotic prescribing behaviour is influenced by clinicians' historical patterns of practice will help target interventions to optimize antibiotic use in long-term care. Our objective was to evaluate whether clinicians' historical prescribing behaviours influence the start, prolongation and class selection for treatment with antibiotics in residents of long-term care facilities. We conducted a retrospective cohort study of all physicians who prescribed to residents in long-term care facilities in Ontario between Jan. 1 and Dec. 31, 2014. We examined variability in antibiotic prescribing among physicians for 3 measures: start of treatment with antibiotics, use of prolonged durations exceeding 7 days and selection of fluoroquinolones. Funnel plots with control limits were used to determine the extent of variation and characterize physicians as extreme low, low, average, high and extreme high prescribers for each tendency. Multivariable logistic regression was used to assess whether a clinician's prescribing tendency in the previous year predicted current prescribing patterns, after accounting for residents' demographics, comorbidity, functional status and indwelling devices. Among 1695 long-term care physicians, who prescribed for 93 132 residents, there was wide variability in the start of antibiotic treatment (median 45% of patients, interquartile range [IQR] 32%-55%), use of prolonged treatment durations (median 30% of antibiotic prescriptions, IQR 19%-46%) and selection of fluoroquinolones (median 27% of antibiotic prescriptions, IQR 18%-37%). Prescribing tendencies for antibiotics by physicians in 2014 correlated strongly with tendencies in the previous year. After controlling for individual resident characteristics, prior prescribing tendency was a significant predictor of current practice. Physicians prescribing antibiotics exhibited individual, measurable and historical tendencies toward start of antibiotic treatment, use of prolonged treatment duration and class selection. Prescriber audit and feedback may be a promising tool to optimize antibiotic use in long-term care facilities. © 2017 Canadian Medical Association or its licensors.

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

Selecting minimum dataset soil variables using PLSR as a regressive multivariate method

NASA Astrophysics Data System (ADS)

Stellacci, Anna Maria; Armenise, Elena; Castellini, Mirko; Rossi, Roberta; Vitti, Carolina; Leogrande, Rita; De Benedetto, Daniela; Ferrara, Rossana M.; Vivaldi, Gaetano A.

2017-04-01

Long-term field experiments and science-based tools that characterize soil status (namely the soil quality indices, SQIs) assume a strategic role in assessing the effect of agronomic techniques and thus in improving soil management especially in marginal environments. Selecting key soil variables able to best represent soil status is a critical step for the calculation of SQIs. Current studies show the effectiveness of statistical methods for variable selection to extract relevant information deriving from multivariate datasets. Principal component analysis (PCA) has been mainly used, however supervised multivariate methods and regressive techniques are progressively being evaluated (Armenise et al., 2013; de Paul Obade et al., 2016; Pulido Moncada et al., 2014). The present study explores the effectiveness of partial least square regression (PLSR) in selecting critical soil variables, using a dataset comparing conventional tillage and sod-seeding on durum wheat. The results were compared to those obtained using PCA and stepwise discriminant analysis (SDA). The soil data derived from a long-term field experiment in Southern Italy. On samples collected in April 2015, the following set of variables was quantified: (i) chemical: total organic carbon and nitrogen (TOC and TN), alkali-extractable C (TEC and humic substances - HA-FA), water extractable N and organic C (WEN and WEOC), Olsen extractable P, exchangeable cations, pH and EC; (ii) physical: texture, dry bulk density (BD), macroporosity (Pmac), air capacity (AC), and relative field capacity (RFC); (iii) biological: carbon of the microbial biomass quantified with the fumigation-extraction method. PCA and SDA were previously applied to the multivariate dataset (Stellacci et al., 2016). PLSR was carried out on mean centered and variance scaled data of predictors (soil variables) and response (wheat yield) variables using the PLS procedure of SAS/STAT. In addition, variable importance for projection (VIP) statistics was used to quantitatively assess the predictors most relevant for response variable estimation and then for variable selection (Andersen and Bro, 2010). PCA and SDA returned TOC and RFC as influential variables both on the set of chemical and physical data analyzed separately as well as on the whole dataset (Stellacci et al., 2016). Highly weighted variables in PCA were also TEC, followed by K, and AC, followed by Pmac and BD, in the first PC (41.2% of total variance); Olsen P and HA-FA in the second PC (12.6%), Ca in the third (10.6%) component. Variables enabling maximum discrimination among treatments for SDA were WEOC, on the whole dataset, humic substances, followed by Olsen P, EC and clay, in the separate data analyses. The highest PLS-VIP statistics were recorded for Olsen P and Pmac, followed by TOC, TEC, pH and Mg for chemical variables and clay, RFC and AC for the physical variables. Results show that different methods may provide different ranking of the selected variables and the presence of a response variable, in regressive techniques, may affect variable selection. Further investigation with different response variables and with multi-year datasets would allow to better define advantages and limits of single or combined approaches. Acknowledgment The work was supported by the projects "BIOTILLAGE, approcci innovative per il miglioramento delle performances ambientali e produttive dei sistemi cerealicoli no-tillage", financed by PSR-Basilicata 2007-2013, and "DESERT, Low-cost water desalination and sensor technology compact module" financed by ERANET-WATERWORKS 2014. References Andersen C.M. and Bro R., 2010. Variable selection in regression - a tutorial. Journal of Chemometrics, 24 728-737. Armenise et al., 2013. Developing a soil quality index to compare soil fitness for agricultural use under different managements in the mediterranean environment. Soil and Tillage Research, 130:91-98. de Paul Obade et al., 2016. A standardized soil quality index for diverse field conditions. Sci. Total Env. 541:424-434. Pulido Moncada et al., 2014. Data-driven analysis of soil quality indicators using limited data. Geoderma, 235:271-278. Stellacci et al., 2016. Comparison of different multivariate methods to select key soil variables for soil quality indices computation. XLV Congress of the Italian Society of Agronomy (SIA), Sassari, 20-22 September 2016.

Epithelial–mesenchymal transition biomarkers and support vector machine guided model in preoperatively predicting regional lymph node metastasis for rectal cancer

PubMed Central

Fan, X-J; Wan, X-B; Huang, Y; Cai, H-M; Fu, X-H; Yang, Z-L; Chen, D-K; Song, S-X; Wu, P-H; Liu, Q; Wang, L; Wang, J-P

2012-01-01

Background: Current imaging modalities are inadequate in preoperatively predicting regional lymph node metastasis (RLNM) status in rectal cancer (RC). Here, we designed support vector machine (SVM) model to address this issue by integrating epithelial–mesenchymal-transition (EMT)-related biomarkers along with clinicopathological variables. Methods: Using tissue microarrays and immunohistochemistry, the EMT-related biomarkers expression was measured in 193 RC patients. Of which, 74 patients were assigned to the training set to select the robust variables for designing SVM model. The SVM model predictive value was validated in the testing set (119 patients). Results: In training set, eight variables, including six EMT-related biomarkers and two clinicopathological variables, were selected to devise SVM model. In testing set, we identified 63 patients with high risk to RLNM and 56 patients with low risk. The sensitivity, specificity and overall accuracy of SVM in predicting RLNM were 68.3%, 81.1% and 72.3%, respectively. Importantly, multivariate logistic regression analysis showed that SVM model was indeed an independent predictor of RLNM status (odds ratio, 11.536; 95% confidence interval, 4.113–32.361; P<0.0001). Conclusion: Our SVM-based model displayed moderately strong predictive power in defining the RLNM status in RC patients, providing an important approach to select RLNM high-risk subgroup for neoadjuvant chemoradiotherapy. PMID:22538975

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

Genome-Wide Association Analysis of Adaptation Using Environmentally Predicted Traits.

PubMed

van Heerwaarden, Joost; van Zanten, Martijn; Kruijer, Willem

2015-10-01

Current methods for studying the genetic basis of adaptation evaluate genetic associations with ecologically relevant traits or single environmental variables, under the implicit assumption that natural selection imposes correlations between phenotypes, environments and genotypes. In practice, observed trait and environmental data are manifestations of unknown selective forces and are only indirectly associated with adaptive genetic variation. In theory, improved estimation of these forces could enable more powerful detection of loci under selection. Here we present an approach in which we approximate adaptive variation by modeling phenotypes as a function of the environment and using the predicted trait in multivariate and univariate genome-wide association analysis (GWAS). Based on computer simulations and published flowering time data from the model plant Arabidopsis thaliana, we find that environmentally predicted traits lead to higher recovery of functional loci in multivariate GWAS and are more strongly correlated to allele frequencies at adaptive loci than individual environmental variables. Our results provide an example of the use of environmental data to obtain independent and meaningful information on adaptive genetic variation.

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.

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.

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…

Selected mesostructure properties in loblolly pine from Arkansas plantations

Treesearch

David E. Kretschmann; Steven M. Cramer; Roderic Lakes; Troy Schmidt

2006-01-01

Design properties of wood are currently established at the macroscale, assuming wood to be a homogeneous orthotropic material. The resulting variability from the use of such a simplified assumption has been handled by designing with lower percentile values and applying a number of factors to account for the wide statistical variation in properties. With managed...

Paying for Retirement: Sex Differences in Inclusion in Employer-Provided Retirement Plans

ERIC Educational Resources Information Center

Wright, Rosemary

2012-01-01

Purpose: This study examines sex differences among Baby Boom workers in the likelihood of coverage by an employer-provided retirement plan. Design and Methods: This study used a sample of Baby Boom workers drawn from the 2009 Current Population Survey. Independent variables were selected to replicate as closely as possible those in two 1995…

Economic values for growth and grade changes of sugar maple in the Lake States.

Treesearch

Richard M. Godman; Joseph J. Mendel

1978-01-01

Current and expected rates of value increase over a 10-year period were developed for sawtimber-size sugar maple based on variable growth rates, expected merchantable height changes, and butt log grade improvement. These economic guides, along with silvicultural considerations, provide a value basis for selecting trees during thinning and determining final harvest...

Drift in ocean currents impacts intergenerational microbial exposure to temperature

PubMed Central

Doblin, Martina A.; van Sebille, Erik

2016-01-01

Microbes are the foundation of marine ecosystems [Falkowski PG, Fenchel T, Delong EF (2008) Science 320(5879):1034–1039]. Until now, the analytical framework for understanding the implications of ocean warming on microbes has not considered thermal exposure during transport in dynamic seascapes, implying that our current view of change for these critical organisms may be inaccurate. Here we show that upper-ocean microbes experience along-trajectory temperature variability up to 10 °C greater than seasonal fluctuations estimated in a static frame, and that this variability depends strongly on location. These findings demonstrate that drift in ocean currents can increase the thermal exposure of microbes and suggests that microbial populations with broad thermal tolerance will survive transport to distant regions of the ocean and invade new habitats. Our findings also suggest that advection has the capacity to influence microbial community assemblies, such that regions with strong currents and large thermal fluctuations select for communities with greatest plasticity and evolvability, and communities with narrow thermal performance are found where ocean currents are weak or along-trajectory temperature variation is low. Given that fluctuating environments select for individual plasticity in microbial lineages, and that physiological plasticity of ancestors can predict the magnitude of evolutionary responses of subsequent generations to environmental change [Schaum CE, Collins S (2014) Proc Biol Soc 281(1793):20141486], our findings suggest that microbial populations in the sub-Antarctic (∼40°S), North Pacific, and North Atlantic will have the most capacity to adapt to contemporary ocean warming. PMID:27140608

Plastic and evolutionary responses of plankton to environmental change are influenced by drift in ocean currents

NASA Astrophysics Data System (ADS)

Doblin, M.; van Sebille, E.

2016-02-01

The analytical framework for understanding fluctuations in ocean habitats has typically involved a Eulerian view. However, for marine microbes, this framework does not take into account their transport in dynamic seascapes, implying that our current view of change for these critical organisms may be inaccurate. Using a modelling approach, we show that generations of upper ocean microbes experience along-trajectory temperature variability up to 10°C greater than seasonal fluctuations estimated in a static frame, and that this variability depends strongly on location. These findings demonstrate that drift in ocean currents contributes to environmental fluctuation experienced by microbes and suggests that microbial populations may be adapted to upstream rather than local conditions. In an empirical test, we demonstrate that microbes in a warm, poleward flowing western boundary current (East Australian Current) have a different thermal response curve to microbes in coastal water at the same latitude (p < 0.05). Our findings suggest that advection has the capacity to influence microbial community assemblies such that water masses with relatively small thermal fluctuations select for thermal specialists, and communities with broad temperature performance curves are found in locations where ocean currents are strong or along-trajectory temperature variation is high.

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.

Study on electrical current variations in electromembrane extraction process: Relation between extraction recovery and magnitude of electrical current.

PubMed

Rahmani, Turaj; Rahimi, Atyeh; Nojavan, Saeed

2016-01-15

This contribution presents an experimental approach to improve analytical performance of electromembrane extraction (EME) procedure, which is based on the scrutiny of current pattern under different extraction conditions such as using different organic solvents as supported liquid membrane, electrical potentials, pH values of donor and acceptor phases, variable extraction times, temperatures, stirring rates, different hollow fiber lengths and the addition of salts or organic solvents to the sample matrix. In this study, four basic drugs with different polarities were extracted under different conditions with the corresponding electrical current patterns compared against extraction recoveries. The extraction process was demonstrated in terms of EME-HPLC analyses of selected basic drugs. Comparing the obtained extraction recoveries with the electrical current patterns, most cases exhibited minimum recovery and repeatability at the highest investigated magnitude of electrical current. . It was further found that identical current patterns are associated with repeated extraction efficiencies. In other words, the pattern should be repeated for a successful extraction. The results showed completely different electrical currents under different extraction conditions, so that all variable parameters have contributions into the electrical current pattern. Finally, the current patterns of extractions from wastewater, plasma and urine samples were demonstrated. The results indicated an increase in the electrical current when extracting from complex matrices; this was seen to decrease the extraction efficiency. Copyright © 2015 Elsevier B.V. All rights reserved.

Efficient Variable Selection Method for Exposure Variables on Binary Data

NASA Astrophysics Data System (ADS)

Ohno, Manabu; Tarumi, Tomoyuki

In this paper, we propose a new variable selection method for "robust" exposure variables. We define "robust" as property that the same variable can select among original data and perturbed data. There are few studies of effective for the selection method. The problem that selects exposure variables is almost the same as a problem that extracts correlation rules without robustness. [Brin 97] is suggested that correlation rules are possible to extract efficiently using chi-squared statistic of contingency table having monotone property on binary data. But the chi-squared value does not have monotone property, so it's is easy to judge the method to be not independent with an increase in the dimension though the variable set is completely independent, and the method is not usable in variable selection for robust exposure variables. We assume anti-monotone property for independent variables to select robust independent variables and use the apriori algorithm for it. The apriori algorithm is one of the algorithms which find association rules from the market basket data. The algorithm use anti-monotone property on the support which is defined by association rules. But independent property does not completely have anti-monotone property on the AIC of independent probability model, but the tendency to have anti-monotone property is strong. Therefore, selected variables with anti-monotone property on the AIC have robustness. Our method judges whether a certain variable is exposure variable for the independent variable using previous comparison of the AIC. Our numerical experiments show that our method can select robust exposure variables efficiently and precisely.

Hyperspectral imaging for predicting the allicin and soluble solid content of garlic with variable selection algorithms and chemometric models.

PubMed

Rahman, Anisur; Faqeerzada, Mohammad A; Cho, Byoung-Kwan

2018-03-14

Allicin and soluble solid content (SSC) in garlic is the responsible for its pungent flavor and odor. However, current conventional methods such as the use of high-pressure liquid chromatography and a refractometer have critical drawbacks in that they are time-consuming, labor-intensive and destructive procedures. The present study aimed to predict allicin and SSC in garlic using hyperspectral imaging in combination with variable selection algorithms and calibration models. Hyperspectral images of 100 garlic cloves were acquired that covered two spectral ranges, from which the mean spectra of each clove were extracted. The calibration models included partial least squares (PLS) and least squares-support vector machine (LS-SVM) regression, as well as different spectral pre-processing techniques, from which the highest performing spectral preprocessing technique and spectral range were selected. Then, variable selection methods, such as regression coefficients, variable importance in projection (VIP) and the successive projections algorithm (SPA), were evaluated for the selection of effective wavelengths (EWs). Furthermore, PLS and LS-SVM regression methods were applied to quantitatively predict the quality attributes of garlic using the selected EWs. Of the established models, the SPA-LS-SVM model obtained an Rpred2 of 0.90 and standard error of prediction (SEP) of 1.01% for SSC prediction, whereas the VIP-LS-SVM model produced the best result with an Rpred2 of 0.83 and SEP of 0.19 mg g -1 for allicin prediction in the range 1000-1700 nm. Furthermore, chemical images of garlic were developed using the best predictive model to facilitate visualization of the spatial distributions of allicin and SSC. The present study clearly demonstrates that hyperspectral imaging combined with an appropriate chemometrics method can potentially be employed as a fast, non-invasive method to predict the allicin and SSC in garlic. © 2018 Society of Chemical Industry. © 2018 Society of Chemical Industry.

Variable Selection with Prior Information for Generalized Linear Models via the Prior LASSO Method.

PubMed

Jiang, Yuan; He, Yunxiao; Zhang, Heping

LASSO is a popular statistical tool often used in conjunction with generalized linear models that can simultaneously select variables and estimate parameters. When there are many variables of interest, as in current biological and biomedical studies, the power of LASSO can be limited. Fortunately, so much biological and biomedical data have been collected and they may contain useful information about the importance of certain variables. This paper proposes an extension of LASSO, namely, prior LASSO (pLASSO), to incorporate that prior information into penalized generalized linear models. The goal is achieved by adding in the LASSO criterion function an additional measure of the discrepancy between the prior information and the model. For linear regression, the whole solution path of the pLASSO estimator can be found with a procedure similar to the Least Angle Regression (LARS). Asymptotic theories and simulation results show that pLASSO provides significant improvement over LASSO when the prior information is relatively accurate. When the prior information is less reliable, pLASSO shows great robustness to the misspecification. We illustrate the application of pLASSO using a real data set from a genome-wide association study.

An exploratory study of the association of acute posttraumatic stress, depression, and pain to cognitive functioning in mild traumatic brain injury.

PubMed

Massey, Jessica S; Meares, Susanne; Batchelor, Jennifer; Bryant, Richard A

2015-07-01

Few studies have examined whether psychological distress and pain affect cognitive functioning in the acute to subacute phase (up to 30 days postinjury) following mild traumatic brain injury (mTBI). The current study explored whether acute posttraumatic stress, depression, and pain were associated with performance on a task of selective and sustained attention completed under conditions of increasing cognitive demands (standard, auditory distraction, and dual-task), and on tests of working memory, memory, processing speed, reaction time (RT), and verbal fluency. At a mean of 2.87 days (SD = 2.32) postinjury, 50 adult mTBI participants, consecutive admissions to a Level 1 trauma hospital, completed neuropsychological tests and self-report measures of acute posttraumatic stress, depression, and pain. A series of canonical correlation analyses was used to explore the relationships of a common set of psychological variables to various sets of neuropsychological variables. Significant results were found on the task of selective and sustained attention. Strong relationships were found between psychological variables and speed (r(c) = .56, p = .02) and psychological variables and accuracy (r(c) = .68, p = .002). Pain and acute posttraumatic stress were associated with higher speed scores (reflecting more correctly marked targets) under standard conditions. Acute posttraumatic stress was associated with lower accuracy scores across all task conditions. Moderate but nonsignificant associations were found between psychological variables and most cognitive tasks. Acute posttraumatic stress and pain show strong associations with selective and sustained attention following mTBI. (c) 2015 APA, all rights reserved).

Multistage variable probability forest volume inventory. [the Defiance Unit of the Navajo Nation

NASA Technical Reports Server (NTRS)

Anderson, J. E. (Principal Investigator)

1979-01-01

An inventory scheme based on the use of computer processed LANDSAT MSS data was developed. Output from the inventory scheme provides an estimate of the standing net saw timber volume of a major timber species on a selected forested area of the Navajo Nation. Such estimates are based on the values of parameters currently used for scaled sawlog conversion to mill output. The multistage variable probability sampling appears capable of producing estimates which compare favorably with those produced using conventional techniques. In addition, the reduction in time, manpower, and overall costs lend it to numerous applications.

A Profile Approach to Child Care Quality, Quantity, and Type of Setting: Parent Selection of Infant Child Care Arrangements

ERIC Educational Resources Information Center

Sosinsky, Laura Stout; Kim, Se-Kang

2013-01-01

Building on prior variable-oriented research which demonstrates the independence of the associations of child care quality, quantity, and type of setting with family factors and child outcomes, the current study identifies four profiles of child care dimensions from the NICHD Study of Early Child Care and Youth Development. Profiles accounted for…

"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…

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.

Predicting Bobsled Pushing Ability from Various Combine Testing Events.

PubMed

Tomasevicz, Curtis L; Ransone, Jack W; Bach, Christopher W

2018-03-12

The requisite combination of speed, power, and strength necessary for a bobsled push athlete coupled with the difficulty in directly measuring pushing ability makes selecting effective push crews challenging. Current practices by USA Bobsled and Skeleton (USABS) utilize field combine testing to assess and identify specifically selected performance variables in an attempt to best predict push performance abilities. Combine data consisting of 11 physical performance variables were collected from 75 subjects across two winter Olympic qualification years (2009 and 2013). These variables were sprints of 15-, 30-, and 60 m, a flying 30 m sprint, a standing broad jump, a shot toss, squat, power clean, body mass, and dry-land brake and side bobsled pushes. Discriminant Analysis (DA) in addition to Principle Component Analysis (PCA) was used to investigate two cases (Case 1: Olympians vs. non-Olympians; Case 2: National Team vs. non-National Team). Using these 11 variables, DA led to a classification rule that proved capable of identifying Olympians from non-Olympians and National Team members from non-National Team members with 9.33% and 14.67% misclassification rates, respectively. The PCA was used to find similar test variables within the combine that provided redundant or useless data. After eliminating the unnecessary variables, DA on the new combinations showed that 8 (Case 1) and 20 (Case 2) other combinations with fewer performance variables yielded misclassification rates as low as 6.67% and 13.33% respectively. Utilizing fewer performance variables can allow governing bodies in many other sports to create more appropriate combine testing that maximize accuracy, while minimizing irrelevant and redundant strategies.

Investigating the Spectroscopic Variability of Magentically Active M Dwarfs In SDSS.

NASA Astrophysics Data System (ADS)

Ventura, Jean-Paul; Schmidt, Sarah J.; Cruz, Kelle; Rice, Emily; Cid, Aurora

2018-01-01

Magnetic activity, a wide range of observable phenomena produced in the outer atmospheres of stars is, currently, not well understood for M dwarfs. In higher mass stars, magnetic activity is powered by a dynamo process involving the differential rotation of a star’s inner regions. This process generates a magnetic field, heats up regions in the chromosphere and produces Hα emission line radiation from collisional excitation. Using spectroscopic data from the Sloan Digital Sky Survey (SDSS), I compare Hα emission line strengths for a subsample of 12,000 photometric variability selected M dwarfs from Pan-STARRS1 with those of a known non-variable sample. Presumably, the photometric variability originates from the occurrence of star spots at the stellar surface, which are the result of an intense magnetic field and associated chromospheric heating. We proceed with this work in order to test whether the photometric variability of the sample correlates with chromospheric Hα emission features. If not, we explore alternate reasons for that photometric variability (e.g. binarity or transiting planetary companions)

Transport variability of the Brazil Current from observations and a data assimilation model

NASA Astrophysics Data System (ADS)

Schmid, Claudia; Majumder, Sudip

2018-06-01

The Brazil Current transports from observations and the Hybrid Coordinate Model (HYCOM) model are analyzed to improve our understanding of the current's structure and variability. A time series of the observed transport is derived from a three-dimensional field of the velocity in the South Atlantic covering the years 1993 to 2015 (hereinafter called Argo & SSH). The mean transports of the Brazil Current increases from 3.8 ± 2.2 Sv (1 Sv is 106 m3 s-1) at 25° S to 13.9 ± 2.6 Sv at 32° S, which corresponds to a mean slope of 1.4 ± 0.4 Sv per degree. Transport estimates derived from HYCOM fields are somewhat higher (5.2 ± 2.7 and 18.7 ± 7.1 Sv at 25 and 32° S, respectively) than those from Argo & SSH, but these differences are small when compared with the standard deviations. Overall, the observed latitude dependence of the transport of the Brazil Current is in agreement with the wind-driven circulation in the super gyre of the subtropical South Atlantic. A mean annual cycle with highest (lowest) transports in austral summer (winter) is found to exist at selected latitudes (24, 35, and 38° S). The significance of this signal shrinks with increasing latitude (both in Argo & SSH and HYCOM), mainly due to mesoscale and interannual variability. Both Argo & SSH, as well as HYCOM, reveal interannual variability at 24 and 35° S that results in relatively large power at periods of 2 years or more in wavelet spectra. It is found that the interannual variability at 24° S is correlated with the South Atlantic Subtropical Dipole Mode (SASD), the Southern Annular Mode (SAM), and the Niño 3.4 index. Similarly, correlations between SAM and the Brazil Current transport are also found at 35° S. Further investigation of the variability reveals that the first and second mode of a coupled empirical orthogonal function of the meridional transport and the sea level pressure explain 36 and 15 % of the covariance, respectively. Overall, the results indicate that SAM, SASD, and El Niño-Southern Oscillation have an influence on the transport of the Brazil Current.

Can functional hologenomics aid tackling current challenges in plant breeding?

PubMed

Nogales, Amaia; Nobre, Tânia; Valadas, Vera; Ragonezi, Carla; Döring, Matthias; Polidoros, Alexios; Arnholdt-Schmitt, Birgit

2016-07-01

Molecular plant breeding usually overlooks the genetic variability that arises from the association of plants with endophytic microorganisms, when looking at agronomic interesting target traits. This source of variability can have crucial effects on the functionality of the organism considered as a whole (the holobiont), and therefore can be selectable in breeding programs. However, seeing the holobiont as a unit for selection and improvement in breeding programs requires novel approaches for genotyping and phenotyping. These should not focus just at the plant level, but also include the associated endophytes and their functional effects on the plant, to make effective desirable trait screenings. The present review intends to draw attention to a new research field on functional hologenomics that if associated with adequate phenotyping tools could greatly increase the efficiency of breeding programs. © The Author 2015. Published by Oxford University Press. All rights reserved. For permissions, please email: journals.permissions@oup.com.

Pharmacogenetics and outcome with antipsychotic drugs.

PubMed

Pouget, Jennie G; Shams, Tahireh A; Tiwari, Arun K; Müller, Daniel J

2014-12-01

Antipsychotic medications are the gold-standard treatment for schizophrenia, and are often prescribed for other mental conditions. However, the efficacy and side-effect profiles of these drugs are heterogeneous, with large interindividual variability. As a result, treatment selection remains a largely trial-and-error process, with many failed treatment regimens endured before finding a tolerable balance between symptom management and side effects. Much of the interindividual variability in response and side effects is due to genetic factors (heritability, h(2)~ 0.60-0.80). Pharmacogenetics is an emerging field that holds the potential to facilitate the selection of the best medication for a particular patient, based on his or her genetic information. In this review we discuss the most promising genetic markers of antipsychotic treatment outcomes, and present current translational research efforts that aim to bring these pharmacogenetic findings to the clinic in the near future.

Pharmacogenetics and outcome with antipsychotic drugs

PubMed Central

Pouget, Jennie G.; Shams, Tahireh A.; Tiwari, Arun K.; Müller, Daniel J.

2014-01-01

Antipsychotic medications are the gold-standard treatment for schizophrenia, and are often prescribed for other mental conditions. However, the efficacy and side-effect profiles of these drugs are heterogeneous, with large interindividual variability. As a result, treatment selection remains a largely trial-and-error process, with many failed treatment regimens endured before finding a tolerable balance between symptom management and side effects. Much of the interindividual variability in response and side effects is due to genetic factors (heritability, h2~ 0.60-0.80). Pharmacogenetics is an emerging field that holds the potential to facilitate the selection of the best medication for a particular patient, based on his or her genetic information. In this review we discuss the most promising genetic markers of antipsychotic treatment outcomes, and present current translational research efforts that aim to bring these pharmacogenetic findings to the clinic in the near future. PMID:25733959

Selective heart rate variability analysis to account for uterine activity during labor and improve classification of fetal distress.

PubMed

Warmerdam, G J J; Vullings, R; Van Laar, J O E H; Van der Hout-Van der Jagt, M B; Bergmans, J W M; Schmitt, L; Oei, S G

2016-08-01

Cardiotocography (CTG) is currently the most often used technique for detection of fetal distress. Unfortunately, CTG has a poor specificity. Recent studies suggest that, in addition to CTG, information on fetal distress can be obtained from analysis of fetal heart rate variability (HRV). However, uterine contractions can strongly influence fetal HRV. The aim of this study is therefore to investigate whether HRV analysis for detection of fetal distress can be improved by distinguishing contractions from rest periods. Our results from feature selection indicate that HRV features calculated separately during contractions or during rest periods are more informative on fetal distress than HRV features that are calculated over the entire fetal heart rate. Furthermore, classification performance improved from a geometric mean of 69.0% to 79.6% when including the contraction-dependent HRV features, in addition to HRV features calculated over the entire fetal heart rate.

Metocean design parameter estimation for fixed platform based on copula functions

NASA Astrophysics Data System (ADS)

Zhai, Jinjin; Yin, Qilin; Dong, Sheng

2017-08-01

Considering the dependent relationship among wave height, wind speed, and current velocity, we construct novel trivariate joint probability distributions via Archimedean copula functions. Total 30-year data of wave height, wind speed, and current velocity in the Bohai Sea are hindcast and sampled for case study. Four kinds of distributions, namely, Gumbel distribution, lognormal distribution, Weibull distribution, and Pearson Type III distribution, are candidate models for marginal distributions of wave height, wind speed, and current velocity. The Pearson Type III distribution is selected as the optimal model. Bivariate and trivariate probability distributions of these environmental conditions are established based on four bivariate and trivariate Archimedean copulas, namely, Clayton, Frank, Gumbel-Hougaard, and Ali-Mikhail-Haq copulas. These joint probability models can maximize marginal information and the dependence among the three variables. The design return values of these three variables can be obtained by three methods: univariate probability, conditional probability, and joint probability. The joint return periods of different load combinations are estimated by the proposed models. Platform responses (including base shear, overturning moment, and deck displacement) are further calculated. For the same return period, the design values of wave height, wind speed, and current velocity obtained by the conditional and joint probability models are much smaller than those by univariate probability. Considering the dependence among variables, the multivariate probability distributions provide close design parameters to actual sea state for ocean platform design.

Spacecraft with gradual acceleration of solar panels

NASA Technical Reports Server (NTRS)

Merhav, Tamir R. (Inventor); Festa, Michael T. (Inventor); Stetson, Jr., John B. (Inventor)

1996-01-01

A spacecraft (8) includes a movable appendage such as solar panels (12) operated by a stepping motor (28) driven by pulses (311). In order to reduce vibration andor attitude error, the drive pulses are generated by a clock down-counter (312) with variable count ratio. Predetermined desired clock ratios are stored in selectable memories (314a-d), and the selected ratio (R) is coupled to a comparator (330) together with the current ratio (C). An up-down counter (340) establishes the current count-down ratio by counting toward the desired ratio under the control of the comparator; thus, a step change of solar panel speed never occurs. When a direction change is commanded, a flag signal generator (350) disables the selectable memories, and enables a further store (360), which generates a count ratio representing a very slow solar panel rotational rate, so that the rotational rate always slows to a low value before direction is changed. The principles of the invention are applicable to any movable appendage.

Identification of solid state fermentation degree with FT-NIR spectroscopy: Comparison of wavelength variable selection methods of CARS and SCARS.

PubMed

Jiang, Hui; Zhang, Hang; Chen, Quansheng; Mei, Congli; Liu, Guohai

2015-01-01

The use of wavelength variable selection before partial least squares discriminant analysis (PLS-DA) for qualitative identification of solid state fermentation degree by FT-NIR spectroscopy technique was investigated in this study. Two wavelength variable selection methods including competitive adaptive reweighted sampling (CARS) and stability competitive adaptive reweighted sampling (SCARS) were employed to select the important wavelengths. PLS-DA was applied to calibrate identified model using selected wavelength variables by CARS and SCARS for identification of solid state fermentation degree. Experimental results showed that the number of selected wavelength variables by CARS and SCARS were 58 and 47, respectively, from the 1557 original wavelength variables. Compared with the results of full-spectrum PLS-DA, the two wavelength variable selection methods both could enhance the performance of identified models. Meanwhile, compared with CARS-PLS-DA model, the SCARS-PLS-DA model achieved better results with the identification rate of 91.43% in the validation process. The overall results sufficiently demonstrate the PLS-DA model constructed using selected wavelength variables by a proper wavelength variable method can be more accurate identification of solid state fermentation degree. Copyright © 2015 Elsevier B.V. All rights reserved.

Identification of solid state fermentation degree with FT-NIR spectroscopy: Comparison of wavelength variable selection methods of CARS and SCARS

NASA Astrophysics Data System (ADS)

Jiang, Hui; Zhang, Hang; Chen, Quansheng; Mei, Congli; Liu, Guohai

2015-10-01

The use of wavelength variable selection before partial least squares discriminant analysis (PLS-DA) for qualitative identification of solid state fermentation degree by FT-NIR spectroscopy technique was investigated in this study. Two wavelength variable selection methods including competitive adaptive reweighted sampling (CARS) and stability competitive adaptive reweighted sampling (SCARS) were employed to select the important wavelengths. PLS-DA was applied to calibrate identified model using selected wavelength variables by CARS and SCARS for identification of solid state fermentation degree. Experimental results showed that the number of selected wavelength variables by CARS and SCARS were 58 and 47, respectively, from the 1557 original wavelength variables. Compared with the results of full-spectrum PLS-DA, the two wavelength variable selection methods both could enhance the performance of identified models. Meanwhile, compared with CARS-PLS-DA model, the SCARS-PLS-DA model achieved better results with the identification rate of 91.43% in the validation process. The overall results sufficiently demonstrate the PLS-DA model constructed using selected wavelength variables by a proper wavelength variable method can be more accurate identification of solid state fermentation degree.

ARCAS (ACACIA Regional Climate-data Access System) -- a Web Access System for Climate Model Data Access, Visualization and Comparison

NASA Astrophysics Data System (ADS)

Hakkarinen, C.; Brown, D.; Callahan, J.; hankin, S.; de Koningh, M.; Middleton-Link, D.; Wigley, T.

2001-05-01

A Web-based access system to climate model output data sets for intercomparison and analysis has been produced, using the NOAA-PMEL developed Live Access Server software as host server and Ferret as the data serving and visualization engine. Called ARCAS ("ACACIA Regional Climate-data Access System"), and publicly accessible at http://dataserver.ucar.edu/arcas, the site currently serves climate model outputs from runs of the NCAR Climate System Model for the 21st century, for Business as Usual and Stabilization of Greenhouse Gas Emission scenarios. Users can select, download, and graphically display single variables or comparisons of two variables from either or both of the CSM model runs, averaged for monthly, seasonal, or annual time resolutions. The time length of the averaging period, and the geographical domain for download and display, are fully selectable by the user. A variety of arithmetic operations on the data variables can be computed "on-the-fly", as defined by the user. Expansions of the user-selectable options for defining analysis options, and for accessing other DOD-compatible ("Distributed Ocean Data System-compatible") data sets, residing at locations other than the NCAR hardware server on which ARCAS operates, are planned for this year. These expansions are designed to allow users quick and easy-to-operate web-based access to the largest possible selection of climate model output data sets available throughout the world.

A fast BK-type KCa current acts as a postsynaptic modulator of temporal selectivity for communication signals.

PubMed

Kohashi, Tsunehiko; Carlson, Bruce A

2014-01-01

Temporal patterns of spiking often convey behaviorally relevant information. Various synaptic mechanisms and intrinsic membrane properties can influence neuronal selectivity to temporal patterns of input. However, little is known about how synaptic mechanisms and intrinsic properties together determine the temporal selectivity of neuronal output. We tackled this question by recording from midbrain electrosensory neurons in mormyrid fish, in which the processing of temporal intervals between communication signals can be studied in a reduced in vitro preparation. Mormyrids communicate by varying interpulse intervals (IPIs) between electric pulses. Within the midbrain posterior exterolateral nucleus (ELp), the temporal patterns of afferent spike trains are filtered to establish single-neuron IPI tuning. We performed whole-cell recording from ELp neurons in a whole-brain preparation and examined the relationship between intrinsic excitability and IPI tuning. We found that spike frequency adaptation of ELp neurons was highly variable. Postsynaptic potentials (PSPs) of strongly adapting (phasic) neurons were more sharply tuned to IPIs than weakly adapting (tonic) neurons. Further, the synaptic filtering of IPIs by tonic neurons was more faithfully converted into variation in spiking output, particularly at short IPIs. Pharmacological manipulation under current- and voltage-clamp revealed that tonic firing is mediated by a fast, large-conductance Ca(2+)-activated K(+) (KCa) current (BK) that speeds up action potential repolarization. These results suggest that BK currents can shape the temporal filtering of sensory inputs by modifying both synaptic responses and PSP-to-spike conversion. Slow SK-type KCa currents have previously been implicated in temporal processing. Thus, both fast and slow KCa currents can fine-tune temporal selectivity.

Analysis of decentralized variable structure control for collective search by mobile robots

NASA Astrophysics Data System (ADS)

Goldsmith, Steven Y.; Feddema, John T.; Robinett, Rush D., III

1998-10-01

This paper presents an analysis of a decentralized coordination strategy for organizing and controlling a team of mobile robots performing collective search. The alpha- beta coordination strategy is a family of collective search algorithms that allow teams of communicating robots to implicitly coordinate their search activities through a division of labor based on self-selected roles. In an alpha- beta team, alpha agents are motivated to improve their status by exploring new regions of the search space. Beta agents are conservative, and rely on the alpha agents to provide advanced information on favorable regions of the search space. An agent selects its current role dynamically based on its current status value relative to the current status values of the other team members. Status is determined by some function of the agent's sensor readings, and is generally a measurement of source intensity at the agent's current location. Variations on the decision rules determining alpha and beta behavior produce different versions of the algorithm that lead to different global properties. The alpha-beta strategy is based on a simple finite-state machine that implements a form of Variable Structure Control (VSC). The VSC system changes the dynamics of the collective system by abruptly switching at defined states to alternative control laws. In VSC, Lyapunov's direct method is often used to design control surfaces which guide the system to a given goal. We introduce the alpha- beta algorithm and present an analysis of the equilibrium point and the global stability of the alpha-beta algorithm based on Lyapunov's method.

Genome-Wide Association Analysis of Adaptation Using Environmentally Predicted Traits

PubMed Central

van Zanten, Martijn

2015-01-01

Current methods for studying the genetic basis of adaptation evaluate genetic associations with ecologically relevant traits or single environmental variables, under the implicit assumption that natural selection imposes correlations between phenotypes, environments and genotypes. In practice, observed trait and environmental data are manifestations of unknown selective forces and are only indirectly associated with adaptive genetic variation. In theory, improved estimation of these forces could enable more powerful detection of loci under selection. Here we present an approach in which we approximate adaptive variation by modeling phenotypes as a function of the environment and using the predicted trait in multivariate and univariate genome-wide association analysis (GWAS). Based on computer simulations and published flowering time data from the model plant Arabidopsis thaliana, we find that environmentally predicted traits lead to higher recovery of functional loci in multivariate GWAS and are more strongly correlated to allele frequencies at adaptive loci than individual environmental variables. Our results provide an example of the use of environmental data to obtain independent and meaningful information on adaptive genetic variation. PMID:26496492

Utilizing multiple state variables to improve the dynamic range of analog switching in a memristor

NASA Astrophysics Data System (ADS)

Jeong, YeonJoo; Kim, Sungho; Lu, Wei D.

2015-10-01

Memristors and memristive systems have been extensively studied for data storage and computing applications such as neuromorphic systems. To act as synapses in neuromorphic systems, the memristor needs to exhibit analog resistive switching (RS) behavior with incremental conductance change. In this study, we show that the dynamic range of the analog RS behavior can be significantly enhanced in a tantalum-oxide-based memristor. By controlling different state variables enabled by different physical effects during the RS process, the gradual filament expansion stage can be selectively enhanced without strongly affecting the abrupt filament length growth stage. Detailed physics-based modeling further verified the observed experimental effects and revealed the roles of oxygen vacancy drift and diffusion processes, and how the diffusion process can be selectively enhanced during the filament expansion stage. These findings lead to more desirable and reliable memristor behaviors for analog computing applications. Additionally, the ability to selectively control different internal physical processes demonstrated in the current study provides guidance for continued device optimization of memristor devices in general.

Selection for sow longevity.

PubMed

Serenius, T; Stalder, K J

2006-04-01

Sow longevity plays an important role in economically efficient piglet production because sow longevity is related to the number of piglets produced during its productive lifetime; however, selection for sow longevity is not commonly practiced in any pig breeding program. There is relatively little scientific literature concerning the genetic parameters (genetic variation and genetic correlations) or methods available for breeding value estimation for effective selection for sow longevity. This paper summarizes the current knowledge about the genetics of sow longevity and discusses the available breeding value estimation methods for sow longevity traits. The studies in the literature clearly indicate that sow longevity is a complex trait, and even the definition of sow longevity is variable depending on the researcher and research objective. In general, the measures and analyses of sow longevity can be divided into 1) continuous traits (e.g., productive lifetime) analyzed with proportional hazard models; and 2) more simple binary traits such as stayability until some predetermined fixed parity. Most studies have concluded that sufficient genetic variation exists for effective selection on sow longevity, and heritability estimates have ranged between 0.02 and 0.25. Moreover, sow longevity has shown to be genetically associated with prolificacy and leg conformation traits. Variable results from previous research have led to a lack of consensus among swine breeders concerning the valid methodology of estimating breeding values for longevity traits. One can not deny the superiority of survival analysis in the modeling approach of longevity data; however, multiple-trait analyses are not possible using currently available survival analysis software. Less sophisticated approaches have the advantage of evaluating multiple traits simultaneously, and thus, can use the genetic associations between sow longevity and other traits. Additional research is needed to identify the most efficient selection methods for sow longevity. Future research needs to concentrate on multiple trait analysis of sow longevity traits. Moreover, because longevity is a fitness trait, the nonadditive genetic effects (e.g., dominance) may play important role in the inheritance of sow longevity. Currently, not a single estimate for dominance variance of sow longevity could be identified from the scientific literature.

Cholinergic enhancement reduces functional connectivity and BOLD variability in visual extrastriate cortex during selective attention.

PubMed

Ricciardi, Emiliano; Handjaras, Giacomo; Bernardi, Giulio; Pietrini, Pietro; Furey, Maura L

2013-01-01

Enhancing cholinergic function improves performance on various cognitive tasks and alters neural responses in task specific brain regions. We have hypothesized that the changes in neural activity observed during increased cholinergic function reflect an increase in neural efficiency that leads to improved task performance. The current study tested this hypothesis by assessing neural efficiency based on cholinergically-mediated effects on regional brain connectivity and BOLD signal variability. Nine subjects participated in a double-blind, placebo-controlled crossover fMRI study. Following an infusion of physostigmine (1 mg/h) or placebo, echo-planar imaging (EPI) was conducted as participants performed a selective attention task. During the task, two images comprised of superimposed pictures of faces and houses were presented. Subjects were instructed periodically to shift their attention from one stimulus component to the other and to perform a matching task using hand held response buttons. A control condition included phase-scrambled images of superimposed faces and houses that were presented in the same temporal and spatial manner as the attention task; participants were instructed to perform a matching task. Cholinergic enhancement improved performance during the selective attention task, with no change during the control task. Functional connectivity analyses showed that the strength of connectivity between ventral visual processing areas and task-related occipital, parietal and prefrontal regions reduced significantly during cholinergic enhancement, exclusively during the selective attention task. Physostigmine administration also reduced BOLD signal temporal variability relative to placebo throughout temporal and occipital visual processing areas, again during the selective attention task only. Together with the observed behavioral improvement, the decreases in connectivity strength throughout task-relevant regions and BOLD variability within stimulus processing regions support the hypothesis that cholinergic augmentation results in enhanced neural efficiency. This article is part of a Special Issue entitled 'Cognitive Enhancers'. Copyright © 2012 Elsevier Ltd. All rights reserved.

Cholinergic enhancement reduces functional connectivity and BOLD variability in visual extrastriate cortex during selective attention

PubMed Central

Ricciardi, Emiliano; Handjaras, Giacomo; Bernardi, Giulio; Pietrini, Pietro; Furey, Maura L.

2012-01-01

Enhancing cholinergic function improves performance on various cognitive tasks and alters neural responses in task specific brain regions. Previous findings by our group strongly suggested that the changes in neural activity observed during increased cholinergic function may reflect an increase in neural efficiency that leads to improved task performance. The current study was designed to assess the effects of cholinergic enhancement on regional brain connectivity and BOLD signal variability. Nine subjects participated in a double-blind, placebo-controlled crossover functional magnetic resonance imaging (fMRI) study. Following an infusion of physostigmine (1mg/hr) or placebo, echo-planar imaging (EPI) was conducted as participants performed a selective attention task. During the task, two images comprised of superimposed pictures of faces and houses were presented. Subjects were instructed periodically to shift their attention from one stimulus component to the other and to perform a matching task using hand held response buttons. A control condition included phase-scrambled images of superimposed faces and houses that were presented in the same temporal and spatial manner as the attention task; participants were instructed to perform a matching task. Cholinergic enhancement improved performance during the selective attention task, with no change during the control task. Functional connectivity analyses showed that the strength of connectivity between ventral visual processing areas and task-related occipital, parietal and prefrontal regions was reduced significantly during cholinergic enhancement, exclusively during the selective attention task. Cholinergic enhancement also reduced BOLD signal temporal variability relative to placebo throughout temporal and occipital visual processing areas, again during the selective attention task only. Together with the observed behavioral improvement, the decreases in connectivity strength throughout task-relevant regions and BOLD variability within stimulus processing regions provide further support to the hypothesis that cholinergic augmentation results in enhanced neural efficiency. PMID:22906685

The variability of software scoring of the CDMAM phantom associated with a limited number of images

NASA Astrophysics Data System (ADS)

Yang, Chang-Ying J.; Van Metter, Richard

2007-03-01

Software scoring approaches provide an attractive alternative to human evaluation of CDMAM images from digital mammography systems, particularly for annual quality control testing as recommended by the European Protocol for the Quality Control of the Physical and Technical Aspects of Mammography Screening (EPQCM). Methods for correlating CDCOM-based results with human observer performance have been proposed. A common feature of all methods is the use of a small number (at most eight) of CDMAM images to evaluate the system. This study focuses on the potential variability in the estimated system performance that is associated with these methods. Sets of 36 CDMAM images were acquired under carefully controlled conditions from three different digital mammography systems. The threshold visibility thickness (TVT) for each disk diameter was determined using previously reported post-analysis methods from the CDCOM scorings for a randomly selected group of eight images for one measurement trial. This random selection process was repeated 3000 times to estimate the variability in the resulting TVT values for each disk diameter. The results from using different post-analysis methods, different random selection strategies and different digital systems were compared. Additional variability of the 0.1 mm disk diameter was explored by comparing the results from two different image data sets acquired under the same conditions from the same system. The magnitude and the type of error estimated for experimental data was explained through modeling. The modeled results also suggest a limitation in the current phantom design for the 0.1 mm diameter disks. Through modeling, it was also found that, because of the binomial statistic nature of the CDMAM test, the true variability of the test could be underestimated by the commonly used method of random re-sampling.

Reducing Dropout in Treatment for Depression: Translating Dropout Predictors Into Individualized Treatment Recommendations.

PubMed

Zilcha-Mano, Sigal; Keefe, John R; Chui, Harold; Rubin, Avinadav; Barrett, Marna S; Barber, Jacques P

2016-12-01

Premature discontinuation of therapy is a widespread problem that hampers the delivery of mental health treatment. A high degree of variability has been found among rates of premature treatment discontinuation, suggesting that rates may differ depending on potential moderators. In the current study, our aim was to identify demographic and interpersonal variables that moderate the association between treatment assignment and dropout. Data from a randomized controlled trial conducted from November 2001 through June 2007 (N = 156) comparing supportive-expressive therapy, antidepressant medication, and placebo for the treatment of depression (based on DSM-IV criteria) were used. Twenty prerandomization variables were chosen based on previous literature. These variables were subjected to exploratory bootstrapped variable selection and included in the logistic regression models if they passed variable selection. Three variables were found to moderate the association between treatment assignment and dropout: age, pretreatment therapeutic alliance expectations, and the presence of vindictive tendencies in interpersonal relationships. When patients were divided into those randomly assigned to their optimal treatment and those assigned to their least optimal treatment, dropout rates in the optimal treatment group (24.4%) were significantly lower than those in the least optimal treatment group (47.4%; P = .03). Present findings suggest that a patient's age and pretreatment interpersonal characteristics predict the association between common depression treatments and dropout rate. If validated by further studies, these characteristics can assist in reducing dropout through targeted treatment assignment. Secondary analysis of data from ClinicalTrials.gov identifier: NCT00043550. © Copyright 2016 Physicians Postgraduate Press, Inc.

Classification of the European Union member states according to the relative level of sustainable development.

PubMed

Anna, Bluszcz

Nowadays methods of measurement and assessment of the level of sustained development at the international, national and regional level are a current research problem, which requires multi-dimensional analysis. The relative assessment of the sustainability level of the European Union member states and the comparative analysis of the position of Poland relative to other countries was the aim of the conducted studies in the article. EU member states were treated as objects in the multi-dimensional space. Dimensions of space were specified by ten diagnostic variables describing the sustainability level of UE countries in three dimensions, i.e., social, economic and environmental. Because the compiled statistical data were expressed in different units of measure, taxonomic methods were used for building an aggregated measure to assess the level of sustainable development of EU member states, which through normalisation of variables enabled the comparative analysis between countries. Methodology of studies consisted of eight stages, which included, among others: defining data matrices, calculating the variability coefficient for all variables, which variability coefficient was under 10 %, division of variables into stimulants and destimulants, selection of the method of variable normalisation, developing matrices of normalised data, selection of the formula and calculating the aggregated indicator of the relative level of sustainable development of the EU countries, calculating partial development indicators for three studies dimensions: social, economic and environmental and the classification of the EU countries according to the relative level of sustainable development. Statistical date were collected based on the Polish Central Statistical Office publication.

EGRET sources at intermediate galactic latitude

NASA Technical Reports Server (NTRS)

Halpern, Jules P. (Principal Investigator)

1996-01-01

This paper presents the abstracts of four papers (using ROSAT data) that are submitted to refereed journals during the current reporting period. The papers are: (1) Extreme x-ray variability in the narrow-line QSO PHL 1092; (2) The Geminga pulsar (soft x-ray variability and an EUVE observation); (3) a broad-band x-ray study of the geminga pulsar; and (4) Classification of IRAS-selected x-ray galaxies in the ROSAT all-sky survey. The abstracts of these papers are given in the next four sections of this report, and their status is given in the Appendix. Finally, two new projects (De-identifying a non-AGN and EGRET sources at intermediate galactic latitude) for which ROSAT data were recently received are currently being studied under this grant. A summary of work in progress on these new projects is given in the last two sections of this report.

High-frequency, long-duration water sampling in acid mine drainage studies: a short review of current methods and recent advances in automated water samplers

USGS Publications Warehouse

Chapin, Thomas

2015-01-01

Hand-collected grab samples are the most common water sampling method but using grab sampling to monitor temporally variable aquatic processes such as diel metal cycling or episodic events is rarely feasible or cost-effective. Currently available automated samplers are a proven, widely used technology and typically collect up to 24 samples during a deployment. However, these automated samplers are not well suited for long-term sampling in remote areas or in freezing conditions. There is a critical need for low-cost, long-duration, high-frequency water sampling technology to improve our understanding of the geochemical response to temporally variable processes. This review article will examine recent developments in automated water sampler technology and utilize selected field data from acid mine drainage studies to illustrate the utility of high-frequency, long-duration water sampling.

Dynamic Task Assignment and Path Planning of Multi-AUV System Based on an Improved Self-Organizing Map and Velocity Synthesis Method in Three-Dimensional Underwater Workspace.

PubMed

Zhu, Daqi; Huang, Huan; Yang, S X

2013-04-01

For a 3-D underwater workspace with a variable ocean current, an integrated multiple autonomous underwater vehicle (AUV) dynamic task assignment and path planning algorithm is proposed by combing the improved self-organizing map (SOM) neural network and a novel velocity synthesis approach. The goal is to control a team of AUVs to reach all appointed target locations for only one time on the premise of workload balance and energy sufficiency while guaranteeing the least total and individual consumption in the presence of the variable ocean current. First, the SOM neuron network is developed to assign a team of AUVs to achieve multiple target locations in 3-D ocean environment. The working process involves special definition of the initial neural weights of the SOM network, the rule to select the winner, the computation of the neighborhood function, and the method to update weights. Then, the velocity synthesis approach is applied to plan the shortest path for each AUV to visit the corresponding target in a dynamic environment subject to the ocean current being variable and targets being movable. Lastly, to demonstrate the effectiveness of the proposed approach, simulation results are given in this paper.

Resolving the Conflict Between Associative Overdominance and Background Selection

PubMed Central

Zhao, Lei; Charlesworth, Brian

2016-01-01

In small populations, genetic linkage between a polymorphic neutral locus and loci subject to selection, either against partially recessive mutations or in favor of heterozygotes, may result in an apparent selective advantage to heterozygotes at the neutral locus (associative overdominance) and a retardation of the rate of loss of variability by genetic drift at this locus. In large populations, selection against deleterious mutations has previously been shown to reduce variability at linked neutral loci (background selection). We describe analytical, numerical, and simulation studies that shed light on the conditions under which retardation vs. acceleration of loss of variability occurs at a neutral locus linked to a locus under selection. We consider a finite, randomly mating population initiated from an infinite population in equilibrium at a locus under selection. With mutation and selection, retardation occurs only when S, the product of twice the effective population size and the selection coefficient, is of order 1. With S >> 1, background selection always causes an acceleration of loss of variability. Apparent heterozygote advantage at the neutral locus is, however, always observed when mutations are partially recessive, even if there is an accelerated rate of loss of variability. With heterozygote advantage at the selected locus, loss of variability is nearly always retarded. The results shed light on experiments on the loss of variability at marker loci in laboratory populations and on the results of computer simulations of the effects of multiple selected loci on neutral variability. PMID:27182952

Characterization and evaluation of an aeolian-photovoltaic system in operation

NASA Astrophysics Data System (ADS)

Bonfatti, F.; Calzolari, P. U.; Cardinali, G. C.; Vivanti, G.; Zani, A.

Data management, analysis techniques and results of performance monitoring of a prototype combined photovoltaic (PV)-wind turbine farm power plant in northern Italy are reported. Emphasis is placed on the PV I-V characteristics and irradiance and cell temperatures. Automated instrumentation monitors and records meteorological data and generator variables such as voltages, currents, output, battery electrolyte temperature, etc. Analysis proceeds by automated selection of I-V data for specific intervals of the year when other variables can be treated as constants. The technique permits characterization of generator performance, adjusting the power plant set points for optimal output, and tracking performance degradation over time.

Environmental preferences of tuna and non-tuna species associated with drifting fish aggregating devices (DFADs) in the Atlantic Ocean, ascertained through fishers' echo-sounder buoys

NASA Astrophysics Data System (ADS)

Lopez, Jon; Moreno, Gala; Lennert-Cody, Cleridy; Maunder, Mark; Sancristobal, Igor; Caballero, Ainhoa; Dagorn, Laurent

2017-06-01

Understanding the relationship between environmental variables and pelagic species concentrations and dynamics is helpful to improve fishery management, especially in a changing environment. Drifting fish aggregating device (DFAD)-associated tuna and non-tuna biomass data from the fishers' echo-sounder buoys operating in the Atlantic Ocean have been modelled as functions of oceanographic (Sea Surface Temperature, Chlorophyll-a, Salinity, Sea Level Anomaly, Thermocline depth and gradient, Geostrophic current, Total Current, Depth) and DFAD variables (DFAD speed, bearing and soak time) using Generalized Additive Mixed Models (GAMMs). Biological interaction (presence of non-tuna species at DFADs) was also included in the tuna model, and found to be significant at this time scale. All variables were included in the analyses but only some of them were highly significant, and variable significance differed among fish groups. In general, most of the fish biomass distribution was explained by the ocean productivity and DFAD-variables. Indeed, this study revealed different environmental preferences for tunas and non-tuna species and suggested the existence of active habitat selection. This improved assessment of environmental and DFAD effects on tuna and non-tuna catchability in the purse seine tuna fishery will contribute to transfer of better scientific advice to regional tuna commissions for the management and conservation of exploited resources.

Stratifying FIA Ground Plots Using A 3-Year Old MRLC Forest Cover Map and Current TM Derived Variables Selected By "Decision Tree" Classification

Treesearch

Michael Hoppus; Stan Arner; Andrew Lister

2001-01-01

A reduction in variance for estimates of forest area and volume in the state of Connecticut was accomplished by stratifying FIA ground plots using raw, transformed and classified Landsat Thematic Mapper (TM) imagery. A US Geological Survey (USGS) Multi-Resolution Landscape Characterization (MRLC) vegetation cover map for Connecticut was used to produce a forest/non-...

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.

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

Urban Heat Wave Vulnerability Analysis Considering Climate Change

NASA Astrophysics Data System (ADS)

JE, M.; KIM, H.; Jung, S.

2017-12-01

Much attention has been paid to thermal environments in Seoul City in South Korea since 2016 when the worst heatwave in 22 years. It is necessary to provide a selective measure by singling out vulnerable regions in advance to cope with the heat wave-related damage. This study aims to analyze and categorize vulnerable regions of thermal environments in the Seoul and analyzes and discusses the factors and risk factors for each type. To do this, this study conducted the following processes: first, based on the analyzed various literature reviews, indices that can evaluate vulnerable regions of thermal environment are collated. The indices were divided into climate exposure index related to temperature, sensitivity index including demographic, social, and economic indices, and adaptation index related to urban environment and climate adaptation policy status. Second, significant variables were derived to evaluate a vulnerable region of thermal environment based on the summarized indices in the above. this study analyzed a relationship between the number of heat-related patients in Seoul and variables that affected the number using multi-variate statistical analysis to derive significant variables. Third, the importance of each variable was calculated quantitatively by integrating the statistical analysis results and analytic hierarchy process (AHP) method. Fourth, a distribution of data for each index was identified based on the selected variables and indices were normalized and overlapped. Fifth, For the climate exposure index, evaluations were conducted as same as the current vulnerability evaluation method by selecting future temperature of Seoul predicted through the representative concentration pathways (RCPs) climate change scenarios as an evaluation variable. The results of this study can be utilized as foundational data to establish a countermeasure against heatwave in Seoul. Although it is limited to control heatwave occurrences itself completely, improvements on environment for heatwave alleviation and response can be done. In particular, if vulnerable regions of heatwave can be identified and managed in advance, the study results are expected to be utilized as a basis of policy utilization in local communities accordingly.

Considering Future Potential Regarding Structural Diversity in Selection of Forest Reserves.

PubMed

Lundström, Johanna; Öhman, Karin; Rönnqvist, Mikael; Gustafsson, Lena

2016-01-01

A rich structural diversity in forests promotes biodiversity. Forests are dynamic and therefore it is crucial to consider future structural potential when selecting reserves, to make robust conservation decisions. We analyzed forests in boreal Sweden based on 17,599 National Forest Inventory (NFI) plots with the main aim to understand how effectiveness of reserves depends on the time dimension in the selection process, specifically by considering future structural diversity. In the study both the economic value and future values of 15 structural variables were simulated during a 100 year period. To get a net present structural value (NPSV), a single value covering both current and future values, we used four discounting alternatives: (1) only considering present values, (2) giving equal importance to values in each of the 100 years within the planning horizon, (3) applying an annual discount rate considering the risk that values could be lost, and (4) only considering the values in year 100. The four alternatives were evaluated in a reserve selection model under budget-constrained and area-constrained selections. When selecting young forests higher structural richness could be reached at a quarter of the cost over almost twice the area in a budget-constrained selection compared to an area-constrained selection. Our results point to the importance of considering future structural diversity in the selection of forest reserves and not as is done currently to base the selection on existing values. Targeting future values increases structural diversity and implies a relatively lower cost. Further, our results show that a re-orientation from old to young forests would imply savings while offering a more extensive reserve network with high structural qualities in the future. However, caution must be raised against a drastic reorientation of the current old-forest strategy since remnants of ancient forests will need to be prioritized due to their role for disturbance-sensitive species.

Mapping current and potential distribution of non-native Prosopis juliflora in the Afar region of Ethiopia

USGS Publications Warehouse

Wakie, Tewodros; Evangelista, Paul H.; Jarnevich, Catherine S.; Laituri, Melinda

2014-01-01

We used correlative models with species occurrence points, Moderate Resolution Imaging Spectroradiometer (MODIS) vegetation indices, and topo-climatic predictors to map the current distribution and potential habitat of invasive Prosopis juliflora in Afar, Ethiopia. Time-series of MODIS Enhanced Vegetation Indices (EVI) and Normalized Difference Vegetation Indices (NDVI) with 250 m2 spatial resolution were selected as remote sensing predictors for mapping distributions, while WorldClim bioclimatic products and generated topographic variables from the Shuttle Radar Topography Mission product (SRTM) were used to predict potential infestations. We ran Maxent models using non-correlated variables and the 143 species-occurrence points. Maxent generated probability surfaces were converted into binary maps using the 10-percentile logistic threshold values. Performances of models were evaluated using area under the receiver-operating characteristic (ROC) curve (AUC). Our results indicate that the extent of P. juliflora invasion is approximately 3,605 km2 in the Afar region (AUC = 0.94), while the potential habitat for future infestations is 5,024 km2 (AUC = 0.95). Our analyses demonstrate that time-series of MODIS vegetation indices and species occurrence points can be used with Maxent modeling software to map the current distribution of P. juliflora, while topo-climatic variables are good predictors of potential habitat in Ethiopia. Our results can quantify current and future infestations, and inform management and policy decisions for containing P. juliflora. Our methods can also be replicated for managing invasive species in other East African countries.

Roosting habitat use and selection by northern spotted owls during natal dispersal

USGS Publications Warehouse

Sovern, Stan G.; Forsman, Eric D.; Dugger, Catherine M.; Taylor, Margaret

2015-01-01

We studied habitat selection by northern spotted owls (Strix occidentalis caurina) during natal dispersal in Washington State, USA, at both the roost site and landscape scales. We used logistic regression to obtain parameters for an exponential resource selection function based on vegetation attributes in roost and random plots in 76 forest stands that were used for roosting. We used a similar analysis to evaluate selection of landscape habitat attributes based on 301 radio-telemetry relocations and random points within our study area. We found no evidence of within-stand selection for any of the variables examined, but 78% of roosts were in stands with at least some large (>50 cm dbh) trees. At the landscape scale, owls selected for stands with high canopy cover (>70%). Dispersing owls selected vegetation types that were more similar to habitat selected by adult owls than habitat that would result from following guidelines previously proposed to maintain dispersal habitat. Our analysis indicates that juvenile owls select stands for roosting that have greater canopy cover than is recommended in current agency guidelines.

Decision-making dynamics in parasitoids of Drosophila.

PubMed

Thiel, Andra; Hoffmeister, Thomas S

2009-01-01

Drosophilids and their associated parasitoids live in environments that vary in resource availability and quality within and between generations. The use of information to adapt behavior to the current environment is a key feature under such circumstances and Drosophila parasitic wasps are excellent model systems to study learning and information use. They are among the few parasitoid model species that have been tested in a wide array of situations. Moreover, several related species have been tested under similar conditions, allowing the analysis of within and between species variability, the effect of natural selection in a typical environment, the current physiological status, and previous experience of the individual. This holds for host habitat and host location as well as for host choice and search time allocation. Here, we review patterns of learning and memory, of information use and updating mechanisms, and we point out that information use itself is under strong selective pressure and thus, optimized by parasitic wasps.

Identification of Urinary Polyphenol Metabolite Patterns Associated with Polyphenol-Rich Food Intake in Adults from Four European Countries.

PubMed

Noh, Hwayoung; Freisling, Heinz; Assi, Nada; Zamora-Ros, Raul; Achaintre, David; Affret, Aurélie; Mancini, Francesca; Boutron-Ruault, Marie-Christine; Flögel, Anna; Boeing, Heiner; Kühn, Tilman; Schübel, Ruth; Trichopoulou, Antonia; Naska, Androniki; Kritikou, Maria; Palli, Domenico; Pala, Valeria; Tumino, Rosario; Ricceri, Fulvio; Santucci de Magistris, Maria; Cross, Amanda; Slimani, Nadia; Scalbert, Augustin; Ferrari, Pietro

2017-07-25

We identified urinary polyphenol metabolite patterns by a novel algorithm that combines dimension reduction and variable selection methods to explain polyphenol-rich food intake, and compared their respective performance with that of single biomarkers in the European Prospective Investigation into Cancer and Nutrition (EPIC) study. The study included 475 adults from four European countries (Germany, France, Italy, and Greece). Dietary intakes were assessed with 24-h dietary recalls (24-HDR) and dietary questionnaires (DQ). Thirty-four polyphenols were measured by ultra-performance liquid chromatography-electrospray ionization-tandem mass spectrometry (UPLC-ESI-MS-MS) in 24-h urine. Reduced rank regression-based variable importance in projection (RRR-VIP) and least absolute shrinkage and selection operator (LASSO) methods were used to select polyphenol metabolites. Reduced rank regression (RRR) was then used to identify patterns in these metabolites, maximizing the explained variability in intake of pre-selected polyphenol-rich foods. The performance of RRR models was evaluated using internal cross-validation to control for over-optimistic findings from over-fitting. High performance was observed for explaining recent intake (24-HDR) of red wine ( r = 0.65; AUC = 89.1%), coffee ( r = 0.51; AUC = 89.1%), and olives ( r = 0.35; AUC = 82.2%). These metabolite patterns performed better or equally well compared to single polyphenol biomarkers. Neither metabolite patterns nor single biomarkers performed well in explaining habitual intake (as reported in the DQ) of polyphenol-rich foods. This proposed strategy of biomarker pattern identification has the potential of expanding the currently still limited list of available dietary intake biomarkers.

Development of a Hospital Outcome Measure Intended for Use With Electronic Health Records: 30-Day Risk-standardized Mortality After Acute Myocardial Infarction.

PubMed

McNamara, Robert L; Wang, Yongfei; Partovian, Chohreh; Montague, Julia; Mody, Purav; Eddy, Elizabeth; Krumholz, Harlan M; Bernheim, Susannah M

2015-09-01

Electronic health records (EHRs) offer the opportunity to transform quality improvement by using clinical data for comparing hospital performance without the burden of chart abstraction. However, current performance measures using EHRs are lacking. With support from the Centers for Medicare & Medicaid Services (CMS), we developed an outcome measure of hospital risk-standardized 30-day mortality rates for patients with acute myocardial infarction for use with EHR data. As no appropriate source of EHR data are currently available, we merged clinical registry data from the Action Registry-Get With The Guidelines with claims data from CMS to develop the risk model (2009 data for development, 2010 data for validation). We selected candidate variables that could be feasibly extracted from current EHRs and do not require changes to standard clinical practice or data collection. We used logistic regression with stepwise selection and bootstrapping simulation for model development. The final risk model included 5 variables available on presentation: age, heart rate, systolic blood pressure, troponin ratio, and creatinine level. The area under the receiver operating characteristic curve was 0.78. Hospital risk-standardized mortality rates ranged from 9.6% to 13.1%, with a median of 10.7%. The odds of mortality for a high-mortality hospital (+1 SD) were 1.37 times those for a low-mortality hospital (-1 SD). This measure represents the first outcome measure endorsed by the National Quality Forum for public reporting of hospital quality based on clinical data in the EHR. By being compatible with current clinical practice and existing EHR systems, this measure is a model for future quality improvement measures.

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.

Quality assessment of data discrimination using self-organizing maps.

PubMed

Mekler, Alexey; Schwarz, Dmitri

2014-10-01

One of the important aspects of the data classification problem lies in making the most appropriate selection of features. The set of variables should be small and, at the same time, should provide reliable discrimination of the classes. The method for the discriminating power evaluation that enables a comparison between different sets of variables will be useful in the search for the set of variables. A new approach to feature selection is presented. Two methods of evaluation of the data discriminating power of a feature set are suggested. Both of the methods implement self-organizing maps (SOMs) and the newly introduced exponents of the degree of data clusterization on the SOM. The first method is based on the comparison of intraclass and interclass distances on the map. Another method concerns the evaluation of the relative number of best matching unit's (BMUs) nearest neighbors of the same class. Both methods make it possible to evaluate the discriminating power of a feature set in cases when this set provides nonlinear discrimination of the classes. Current algorithms in program code can be downloaded for free at http://mekler.narod.ru/Science/Articles_support.html, as well as the supporting data files. Copyright © 2014 Elsevier Inc. All rights reserved.

Shrinkage Estimation of Varying Covariate Effects Based On Quantile Regression

PubMed Central

Peng, Limin; Xu, Jinfeng; Kutner, Nancy

2013-01-01

Varying covariate effects often manifest meaningful heterogeneity in covariate-response associations. In this paper, we adopt a quantile regression model that assumes linearity at a continuous range of quantile levels as a tool to explore such data dynamics. The consideration of potential non-constancy of covariate effects necessitates a new perspective for variable selection, which, under the assumed quantile regression model, is to retain variables that have effects on all quantiles of interest as well as those that influence only part of quantiles considered. Current work on l1-penalized quantile regression either does not concern varying covariate effects or may not produce consistent variable selection in the presence of covariates with partial effects, a practical scenario of interest. In this work, we propose a shrinkage approach by adopting a novel uniform adaptive LASSO penalty. The new approach enjoys easy implementation without requiring smoothing. Moreover, it can consistently identify the true model (uniformly across quantiles) and achieve the oracle estimation efficiency. We further extend the proposed shrinkage method to the case where responses are subject to random right censoring. Numerical studies confirm the theoretical results and support the utility of our proposals. PMID:25332515

Optimal heavy tail estimation - Part 1: Order selection

NASA Astrophysics Data System (ADS)

Mudelsee, Manfred; Bermejo, Miguel A.

2017-12-01

The tail probability, P, of the distribution of a variable is important for risk analysis of extremes. Many variables in complex geophysical systems show heavy tails, where P decreases with the value, x, of a variable as a power law with a characteristic exponent, α. Accurate estimation of α on the basis of data is currently hindered by the problem of the selection of the order, that is, the number of largest x values to utilize for the estimation. This paper presents a new, widely applicable, data-adaptive order selector, which is based on computer simulations and brute force search. It is the first in a set of papers on optimal heavy tail estimation. The new selector outperforms competitors in a Monte Carlo experiment, where simulated data are generated from stable distributions and AR(1) serial dependence. We calculate error bars for the estimated α by means of simulations. We illustrate the method on an artificial time series. We apply it to an observed, hydrological time series from the River Elbe and find an estimated characteristic exponent of 1.48 ± 0.13. This result indicates finite mean but infinite variance of the statistical distribution of river runoff.

A Sensory Material Approach for Reducing Variability in Additively Manufactured Metal Parts.

PubMed

Franco, B E; Ma, J; Loveall, B; Tapia, G A; Karayagiz, K; Liu, J; Elwany, A; Arroyave, R; Karaman, I

2017-06-15

Despite the recent growth in interest for metal additive manufacturing (AM) in the biomedical and aerospace industries, variability in the performance, composition, and microstructure of AM parts remains a major impediment to its widespread adoption. The underlying physical mechanisms, which cause variability, as well as the scale and nature of variability are not well understood, and current methods are ineffective at capturing these details. Here, a Nickel-Titanium alloy is used as a sensory material in order to quantitatively, and rather rapidly, observe compositional and/or microstructural variability in selective laser melting manufactured parts; thereby providing a means to evaluate the role of process parameters on the variability. We perform detailed microstructural investigations using transmission electron microscopy at various locations to reveal the origins of microstructural variability in this sensory material. This approach helped reveal how reducing the distance between adjacent laser scans below a critical value greatly reduces both the in-sample and sample-to-sample variability. Microstructural investigations revealed that when the laser scan distance is wide, there is an inhomogeneity in subgrain size, precipitate distribution, and dislocation density in the microstructure, responsible for the observed variability. These results provide an important first step towards understanding the nature of variability in additively manufactured parts.

Selection for avian immune response: a commercial breeding company challenge.

PubMed

Fulton, J E

2004-04-01

Selection for immune function in the commercial breeding environment is a challenging proposition for commercial breeding companies. Immune response is only one of many traits that are under intensive selection, thus selection pressure needs to be carefully balanced across multiple traits. The selection environment (single bird cages, biosecure facilities, controlled environment) is a very different environment than the commercial production facilities (multiple bird cages, potential disease exposure, variable environment) in which birds are to produce. The testing of individual birds is difficult, time consuming, and expensive. It is essential that the results of any tests be relevant to actual disease or environmental challenge in the commercial environment. The use of genetic markers as indicators of immune function is being explored by breeding companies. Use of genetic markers would eliminate many of the limitations in enhancing immune function currently encountered by commercial breeding companies. Information on genetic markers would allow selection to proceed without subjecting breeding stock to disease conditions and could be done before production traits are measured. These markers could be candidate genes with known interaction or involvement with disease pathology or DNA markers that are closely linked to genetic regions that influence the immune response. The current major limitation to this approach is the paucity of mapped chicken immune response genes and the limited number of DNA markers mapped on the chicken genome. These limitations should be eliminated once the chicken genome is sequenced.

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.

Sensitivity study of Space Station Freedom operations cost and selected user resources

NASA Technical Reports Server (NTRS)

Accola, Anne; Fincannon, H. J.; Williams, Gregory J.; Meier, R. Timothy

1990-01-01

The results of sensitivity studies performed to estimate probable ranges for four key Space Station parameters using the Space Station Freedom's Model for Estimating Space Station Operations Cost (MESSOC) are discussed. The variables examined are grouped into five main categories: logistics, crew, design, space transportation system, and training. The modification of these variables implies programmatic decisions in areas such as orbital replacement unit (ORU) design, investment in repair capabilities, and crew operations policies. The model utilizes a wide range of algorithms and an extensive trial logistics data base to represent Space Station operations. The trial logistics data base consists largely of a collection of the ORUs that comprise the mature station, and their characteristics based on current engineering understanding of the Space Station. A nondimensional approach is used to examine the relative importance of variables on parameters.

Variable Neighborhood Search Heuristics for Selecting a Subset of Variables in Principal Component Analysis

ERIC Educational Resources Information Center

Brusco, Michael J.; Singh, Renu; Steinley, Douglas

2009-01-01

The selection of a subset of variables from a pool of candidates is an important problem in several areas of multivariate statistics. Within the context of principal component analysis (PCA), a number of authors have argued that subset selection is crucial for identifying those variables that are required for correct interpretation of the…

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.

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.

Temporal-spatial distribution of American bison (Bison bison) in a tallgrass prairie fire mosaic

USGS Publications Warehouse

Schuler, K.L.; Leslie, David M.; Shaw, J.H.; Maichak, E.J.

2006-01-01

Fire and bison (Bison bison) are thought to be historically responsible for shaping prairie vegetation in North America. Interactions between temporal-spatial distributions of bison and prescribed burning protocols are important in current restoration of tallgrass prairies. We examined dynamics of bison distribution in a patch-burned tallgrass prairie in the south-central United States relative to bison group size and composition, and burn age and temporal distribution. Bison formed larger mixed groups during summer and smaller sexually segregated groups the rest of the year, and bison selected dormant-season burn patches in the 1st posture growing season most often during spring and summer. Large bison herds selecting recently burned areas resulted in seasonally variable and concentrated grazing pressure that may substantially alter site-specific vegetation. These dynamics must be considered when reintroducing bison and fire into tallgrass prairie because variable outcomes of floral richness and structural complexity are likely depending on temporal-spatial distribution of bison. ?? 2006 American Society of Mammalogists.

QSPR model for bioconcentration factors of nonpolar organic compounds using molecular electronegativity distance vector descriptors.

PubMed

Qin, Li-Tang; Liu, Shu-Shen; Liu, Hai-Ling

2010-02-01

A five-variable model (model M2) was developed for the bioconcentration factors (BCFs) of nonpolar organic compounds (NPOCs) by using molecular electronegativity distance vector (MEDV) to characterize the structures of NPOCs and variable selection and modeling based on prediction (VSMP) to select the optimum descriptors. The estimated correlation coefficient (r (2)) and the leave-one-out cross-validation correlation coefficients (q (2)) of model M2 were 0.9271 and 0.9171, respectively. The model was externally validated by splitting the whole data set into a representative training set of 85 chemicals and a validation set of 29 chemicals. The results show that the main structural factors influencing the BCFs of NPOCs are -cCc, cCcc, -Cl, and -Br (where "-" refers to a single bond and "c" refers to a conjugated bond). The quantitative structure-property relationship (QSPR) model can effectively predict the BCFs of NPOCs, and the predictions of the model can also extend the current BCF database of experimental values.

Modification of selected South Carolina bridge-scour envelope curves

USGS Publications Warehouse

Benedict, Stephen T.; Caldwell, Andral W.

2012-01-01

Historic scour was investigated at 231 bridges in the Piedmont and Coastal Plain physiographic provinces of South Carolina by the U.S. Geological Survey in cooperation with the South Carolina Department of Transportation. These investigations led to the development of field-derived envelope curves that provided supplementary tools to assess the potential for scour at bridges in South Carolina for selected scour components that included clear-water abutment, contraction, and pier scour, and live-bed pier and contraction scour. The envelope curves consist of a single curve with one explanatory variable encompassing all of the measured field data for the respective scour components. In the current investigation, the clear-water abutment-scour and live-bed contraction-scour envelope curves were modified to include a family of curves that utilized two explanatory variables, providing a means to further refine the assessment of scour potential for those specific scour components. The modified envelope curves and guidance for their application are presented in this report.

Volume-based response evaluation with consensual lesion selection: a pilot study by using cloud solutions and comparison to RECIST 1.1.

PubMed

Oubel, Estanislao; Bonnard, Eric; Sueoka-Aragane, Naoko; Kobayashi, Naomi; Charbonnier, Colette; Yamamichi, Junta; Mizobe, Hideaki; Kimura, Shinya

2015-02-01

Lesion volume is considered as a promising alternative to Response Evaluation Criteria in Solid Tumors (RECIST) to make tumor measurements more accurate and consistent, which would enable an earlier detection of temporal changes. In this article, we report the results of a pilot study aiming at evaluating the effects of a consensual lesion selection on volume-based response (VBR) assessments. Eleven patients with lung computed tomography scans acquired at three time points were selected from Reference Image Database to Evaluate Response to therapy in lung cancer (RIDER) and proprietary databases. Images were analyzed according to RECIST 1.1 and VBR criteria by three readers working in different geographic locations. Cloud solutions were used to connect readers and carry out a consensus process on the selection of lesions used for computing response. Because there are not currently accepted thresholds for computing VBR, we have applied a set of thresholds based on measurement variability (-35% and +55%). The benefit of this consensus was measured in terms of multiobserver agreement by using Fleiss kappa (κfleiss) and corresponding standard errors (SE). VBR after consensual selection of target lesions allowed to obtain κfleiss = 0.85 (SE = 0.091), which increases up to 0.95 (SE = 0.092), if an extra consensus on new lesions is added. As a reference, the agreement when applying RECIST without consensus was κfleiss = 0.72 (SE = 0.088). These differences were found to be statistically significant according to a z-test. An agreement on the selection of lesions allows reducing the inter-reader variability when computing VBR. Cloud solutions showed to be an interesting and feasible strategy for standardizing response evaluations, reducing variability, and increasing consistency of results in multicenter clinical trials. Copyright © 2015 AUR. Published by Elsevier Inc. All rights reserved.

Definition study for variable cycle engine testbed engine and associated test program

NASA Technical Reports Server (NTRS)

Vdoviak, J. W.

1978-01-01

The product/study double bypass variable cycle engine (VCE) was updated to incorporate recent improvements. The effect of these improvements on mission range and noise levels was determined. This engine design was then compared with current existing high-technology core engines in order to define a subscale testbed configuration that simulated many of the critical technology features of the product/study VCE. Detailed preliminary program plans were then developed for the design, fabrication, and static test of the selected testbed engine configuration. These plans included estimated costs and schedules for the detail design, fabrication and test of the testbed engine and the definition of a test program, test plan, schedule, instrumentation, and test stand requirements.

Effects of HBV Genetic Variability on RNAi Strategies

PubMed Central

Panjaworayan, Nattanan; Brown, Chris M.

2011-01-01

RNAi strategies present promising antiviral strategies against HBV. RNAi strategies require base pairing between short RNAi effectors and targets in the HBV pregenome or other RNAs. Natural variation in HBV genotypes, quasispecies variation, or mutations selected by the RNAi strategy could potentially make these strategies less effective. However, current and proposed antiviral strategies against HBV are being, or could be, designed to avoid this. This would involve simultaneous targeting of multiple regions of the genome, or regions in which variation or mutation is not tolerated. RNAi strategies against single genotypes or against variable regions of the genome would need to have significant other advantages to be part of robust therapies. PMID:21760994

Optical hybrid quantum teleportation and its applications

NASA Astrophysics Data System (ADS)

Takeda, Shuntaro; Okada, Masanori; Furusawa, Akira

2017-08-01

Quantum teleportation, a transfer protocol of quantum states, is the essence of many sophisticated quantum information protocols. There have been two complementary approaches to optical quantum teleportation: discrete variables (DVs) and continuous variables (CVs). However, both approaches have pros and cons. Here we take a "hybrid" approach to overcome the current limitations: CV quantum teleportation of DVs. This approach enabled the first realization of deterministic quantum teleportation of photonic qubits without post-selection. We also applied the hybrid scheme to several experiments, including entanglement swapping between DVs and CVs, conditional CV teleportation of single photons, and CV teleportation of qutrits. We are now aiming at universal, scalable, and fault-tolerant quantum computing based on these hybrid technologies.

Exploring Job Satisfaction of Nursing Faculty: Theoretical Approaches.

PubMed

Wang, Yingchen; Liesveld, Judy

2015-01-01

The Future of Nursing report identified the shortage of nursing faculty as 1 of the barriers to nursing education. In light of this, it is becoming increasingly important to understand the work-life of nursing faculty. The current research focused on job satisfaction of nursing faculty from 4 theoretical perspectives: human capital theory, which emphasizes the expected monetary and nonmonetary returns for any career choices; structural theory, which emphasizes the impact of institutional features on job satisfaction; positive extrinsic environment by self-determination theory, which asserts that a positive extrinsic environment promotes competency and effective outcomes at work; and psychological theory, which emphasizes the proposed relationship between job performance and satisfaction. In addition to the measures for human capital theory, institutional variables (from structural theory and self-determination theory), and productivity measures (from psychological theory), the authors also selected sets of variables for personal characteristics to investigate their effects on job satisfaction. The results indicated that variables related to human capital theory, especially salary, contributed the most to job satisfaction, followed by those related to institutional variables. Personal variables and productivity variables as a whole contributed as well. The only other variable with marginal significance was faculty's perception of institutional support for teaching. Published by Elsevier Inc.

Perspectives on Current Issues Is ``Anthropic Selection'' Science?

NASA Astrophysics Data System (ADS)

Larson, Ronald G.

2007-01-01

I argue that there are strong reasons for resisting as a principle of science the concept of “anthropic selection.” This concept asserts that the existence of “observers” in a universe can be used as a condition that selects physical laws and constants necessary for intelligent life from different laws or physical constants prevailing in a vast number of other universes, to thereby explain why the properties of our universe are conducive to intelligent life. My reasons for limiting “anthropic selection” to the realm of speculation rather than permitting it to creep into mainstream science include our inability to estimate the probabilities of emergence of “observers” in a universe, the lack of testability through direct observation of the assumed high variability of the constants of nature, the lack of a clear definition of an “observer,” and the arbitrariness in how and to what questions anthropic selection is applied.

Drugs to Treat Systemic Lupus Erythematosus: Relationship between Current Use and Cardiovascular Risk Factors

PubMed Central

Rho, Young Hee; Oeser, Annette; Chung, Cecilia P; Morrow, Jason D; Stein, C Michael

2008-01-01

Objectives Cardiovascular risk is increased in patients with systemic lupus erythematosus (SLE). Drugs used to treat SLE can modify traditional cardiovascular risk factors. We examined the effect of selected drugs used in the treatment of SLE on cardiovascular risk factors. Methods We compared systolic and diastolic blood pressure, serum lipid concentrations, glucose, homocysteine, and urinary F2-isoprostane concentrations in 99 patients with lupus who were either current users or non-users of systemic corticosteroids, antimalarials, non-steroidal anti-inflammatory drugs (NSAIDs), COX-2 selective NSAIDs, azathioprine, and methotrexate. Multivariable adjustment was done with linear regression modeling using sex, age and disease activity (SLEDAI) as controlling variables. Results Serum triglyceride concentrations were higher (135.1 ± 61.4 vs. 95.3 ± 47.5 mg/dL, adjusted P = 0.003) in patients receiving corticosteroids. Homocysteine concentrations were marginally higher in patients receiving methotrexate (adjusted P = 0.08). Current use of either NSAIDs or COX-2 inhibitors was not associated with increased cardiovascular risk factors. Current hydroxychloroquine use was not associated with significant alterations in lipid profiles. Conclusions In a non-random sample of patients with SLE, current corticosteroid use was associated with increased triglyceride concentrations, but other drugs had little effect on traditional cardiovascular risk factors. PMID:20157365

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.

Did we choose the best one? A new site selection approach based on exposure and uptake potential for waste incineration.

PubMed

Demirarslan, K Onur; Korucu, M Kemal; Karademir, Aykan

2016-08-01

Ecological problems arising after the construction and operation of a waste incineration plant generally originate from incorrect decisions made during the selection of the location of the plant. The main objective of this study is to investigate how the selection method for the location of a new municipal waste incineration plant can be improved by using a dispersion modelling approach supported by geographical information systems and multi-criteria decision analysis. Considering this aim, the appropriateness of the current location of an existent plant was assessed by applying a pollution dispersion model. Using this procedure, the site ranking for a total of 90 candidate locations and the site of the existing incinerator were determined by a new location selection practice and the current place of the plant was evaluated by ANOVA and Tukey tests. This ranking, made without the use of modelling approaches, was re-evaluated based on the modelling of various variables, including the concentration of pollutants, population and population density, demography, temporality of meteorological data, pollutant type, risk formation type by CALPUFF and re-ranking the results. The findings clearly indicate the impropriety of the location of the current plant, as the pollution distribution model showed that its location was the fourth-worst choice among 91 possibilities. It was concluded that the location selection procedures for waste incinerators should benefit from the improvements obtained by the articulation of pollution dispersion studies combined with the population density data to obtain the most suitable location. © The Author(s) 2016.

Selection Practices of Group Leaders: A National Survey.

ERIC Educational Resources Information Center

Riva, Maria T.; Lippert, Laurel; Tackett, M. Jan

2000-01-01

Study surveys the selection practices of group leaders. Explores methods of selection, variables used to make selection decisions, and the types of selection errors that leaders have experienced. Results suggest that group leaders use clinical judgment to make selection decisions and endorse using some specific variables in selection. (Contains 22…

Unbiased split variable selection for random survival forests using maximally selected rank statistics.

PubMed

Wright, Marvin N; Dankowski, Theresa; Ziegler, Andreas

2017-04-15

The most popular approach for analyzing survival data is the Cox regression model. The Cox model may, however, be misspecified, and its proportionality assumption may not always be fulfilled. An alternative approach for survival prediction is random forests for survival outcomes. The standard split criterion for random survival forests is the log-rank test statistic, which favors splitting variables with many possible split points. Conditional inference forests avoid this split variable selection bias. However, linear rank statistics are utilized by default in conditional inference forests to select the optimal splitting variable, which cannot detect non-linear effects in the independent variables. An alternative is to use maximally selected rank statistics for the split point selection. As in conditional inference forests, splitting variables are compared on the p-value scale. However, instead of the conditional Monte-Carlo approach used in conditional inference forests, p-value approximations are employed. We describe several p-value approximations and the implementation of the proposed random forest approach. A simulation study demonstrates that unbiased split variable selection is possible. However, there is a trade-off between unbiased split variable selection and runtime. In benchmark studies of prediction performance on simulated and real datasets, the new method performs better than random survival forests if informative dichotomous variables are combined with uninformative variables with more categories and better than conditional inference forests if non-linear covariate effects are included. In a runtime comparison, the method proves to be computationally faster than both alternatives, if a simple p-value approximation is used. Copyright © 2017 John Wiley & Sons, Ltd. Copyright © 2017 John Wiley & Sons, Ltd.

Recent developments in clopidogrel pharmacology and their relation to clinical outcomes.

PubMed

Gurbel, Paul A; Antonino, Mark J; Tantry, Udaya S

2009-08-01

Oral antiplatelet therapy with clopidogrel and aspirin is an important and widely prescribed strategy to prevent ischemic events in patients with cardiovascular diseases. However, the occurrence of thrombotic events including stent thrombosis is still high (> 10%). Current practice guidelines are mainly based on large-scale trials focusing on clinical endpoints and 'one size fits all' strategies of treating all patients with the same clopidogrel doses. Pharmacodynamic studies have demonstrated that the latter strategy is associated with wide response variability where a substantial percentage of patients show nonresponsivenes. Translational research studies have established the relation between clopidogrel nonresponsivenes or high on-treatment platelet reactivity to adverse clinical events, thereby establishing clopidogrel nonresponsivenes as an important emerging clinical entity. Clopidogrel response variability is primarily a pharmacokinetic phenomenon associated with insufficient active metabolite generation that is secondary to i) limited intestinal absorption affected by an ABCB1 gene polymorphism; ii) functional variability in P450 isoenzyme activity; and iii) a genetic polymorphism of CYP450 isoenzymes. Personalized antiplatelet treatment with higher clopidogrel doses in selected patients or with newer more potent P2Y(12) receptor blockers based on individual platelet function measurement can overcome some of the limitations of current clopidogrel treatment.

Palliative care and prognosis in COPD: a systematic review with a validation cohort.

PubMed

Almagro, Pere; Yun, Sergi; Sangil, Ana; Rodríguez-Carballeira, Mónica; Marine, Meritxell; Landete, Pedro; Soler-Cataluña, Juan José; Soriano, Joan B; Miravitlles, Marc

2017-01-01

Current recommendations to consider initiation of palliative care (PC) in COPD patients are often based on an expected poor prognosis. However, this approach is not evidence-based, and which and when COPD patients should start PC is controversial. We aimed to assess whether current suggested recommendations for initiating PC were sufficiently reliable. We identified prognostic variables proposed in the literature for initiating PC; then, we ascertained their relationship with 1-year mortality, and finally, we validated their utility in our cohort of 697 patients hospitalized for COPD exacerbation. From 24 articles of 499 screened, we selected 20 variables and retrieved 48 original articles in which we were able to calculate the relationship between each of them and 1-year mortality. The number of studies where 1-year mortality was detailed for these variables ranged from 9 for previous hospitalizations or FEV 1 ≤30% to none for albumin ≤25 mg/dL. The percentage of 1-year mortality in the literature for these variables ranged from 5% to 60%. In the validation cohort study, the prevalence of these proposed variables ranged from 8% to 64%; only 10 of the 18 variables analyzed in our cohort reached statistical significance with Cox regression analysis, and none overcame an area under the curve ≥0.7. We conclude that none of the suggested criteria for initiating PC based on an expected poor vital prognosis in COPD patients in the short or medium term offers sufficient reliability, and consequently, they should be avoided as exclusive criteria for considering PC or at least critically appraised.

Palliative care and prognosis in COPD: a systematic review with a validation cohort

PubMed Central

Almagro, Pere; Yun, Sergi; Sangil, Ana; Rodríguez-Carballeira, Mónica; Marine, Meritxell; Landete, Pedro; Soler-Cataluña, Juan José; Soriano, Joan B; Miravitlles, Marc

2017-01-01

Current recommendations to consider initiation of palliative care (PC) in COPD patients are often based on an expected poor prognosis. However, this approach is not evidence-based, and which and when COPD patients should start PC is controversial. We aimed to assess whether current suggested recommendations for initiating PC were sufficiently reliable. We identified prognostic variables proposed in the literature for initiating PC; then, we ascertained their relationship with 1-year mortality, and finally, we validated their utility in our cohort of 697 patients hospitalized for COPD exacerbation. From 24 articles of 499 screened, we selected 20 variables and retrieved 48 original articles in which we were able to calculate the relationship between each of them and 1-year mortality. The number of studies where 1-year mortality was detailed for these variables ranged from 9 for previous hospitalizations or FEV1 ≤30% to none for albumin ≤25 mg/dL. The percentage of 1-year mortality in the literature for these variables ranged from 5% to 60%. In the validation cohort study, the prevalence of these proposed variables ranged from 8% to 64%; only 10 of the 18 variables analyzed in our cohort reached statistical significance with Cox regression analysis, and none overcame an area under the curve ≥0.7. We conclude that none of the suggested criteria for initiating PC based on an expected poor vital prognosis in COPD patients in the short or medium term offers sufficient reliability, and consequently, they should be avoided as exclusive criteria for considering PC or at least critically appraised. PMID:28652724

Analysis of Decentralized Variable Structure Control for Collective Search by Mobile Robots

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

Feddema, J.; Goldsmith, S.; Robinett, R.

1998-11-04

This paper presents an analysis of a decentralized coordination strategy for organizing and controlling a team of mobile robots performing collective search. The alpha-beta coordination strategy is a family of collective search algorithms that allow teams of communicating robots to implicitly coordinate their search activities through a division of labor based on self-selected roIes. In an alpha-beta team. alpha agents are motivated to improve their status by exploring new regions of the search space. Beta a~ents are conservative, and reiy on the alpha agents to provide advanced information on favorable regions of the search space. An agent selects its currentmore » role dynamically based on its current status value relative to the current status values of the other team members. Status is determined by some function of the agent's sensor readings, and is generally a measurement of source intensity at the agent's current location. Variations on the decision rules determining alpha and beta behavior produce different versions of the algorithm that lead to different global properties. The alpha-beta strategy is based on a simple finite-state machine that implements a form of Variable Structure Control (VSC). The VSC system changes the dynamics of the collective system by abruptly switching at defined states to alternative control laws . In VSC, Lyapunov's direct method is often used to design control surfaces which guide the system to a given goal. We introduce the alpha-beta aIgorithm and present an analysis of the equilibrium point and the global stability of the alpha-beta algorithm based on Lyapunov's method.« less

A synthesis of the theories and concepts of early human evolution.

PubMed

Maslin, Mark A; Shultz, Susanne; Trauth, Martin H

2015-03-05

Current evidence suggests that many of the major events in hominin evolution occurred in East Africa. Hence, over the past two decades, there has been intensive work undertaken to understand African palaeoclimate and tectonics in order to put together a coherent picture of how the environment of Africa has varied over the past 10 Myr. A new consensus is emerging that suggests the unusual geology and climate of East Africa created a complex, environmentally very variable setting. This new understanding of East African climate has led to the pulsed climate variability hypothesis that suggests the long-term drying trend in East Africa was punctuated by episodes of short alternating periods of extreme humidity and aridity which may have driven hominin speciation, encephalization and dispersals out of Africa. This hypothesis is unique as it provides a conceptual framework within which other evolutionary theories can be examined: first, at macro-scale comparing phylogenetic gradualism and punctuated equilibrium; second, at a more focused level of human evolution comparing allopatric speciation, aridity hypothesis, turnover pulse hypothesis, variability selection hypothesis, Red Queen hypothesis and sympatric speciation based on sexual selection. It is proposed that each one of these mechanisms may have been acting on hominins during these short periods of climate variability, which then produce a range of different traits that led to the emergence of new species. In the case of Homo erectus (sensu lato), it is not just brain size that changes but life history (shortened inter-birth intervals, delayed development), body size and dimorphism, shoulder morphology to allow thrown projectiles, adaptation to long-distance running, ecological flexibility and social behaviour. The future of evolutionary research should be to create evidence-based meta-narratives, which encompass multiple mechanisms that select for different traits leading ultimately to speciation.

A synthesis of the theories and concepts of early human evolution

PubMed Central

Maslin, Mark A.; Shultz, Susanne; Trauth, Martin H.

2015-01-01

Current evidence suggests that many of the major events in hominin evolution occurred in East Africa. Hence, over the past two decades, there has been intensive work undertaken to understand African palaeoclimate and tectonics in order to put together a coherent picture of how the environment of Africa has varied over the past 10 Myr. A new consensus is emerging that suggests the unusual geology and climate of East Africa created a complex, environmentally very variable setting. This new understanding of East African climate has led to the pulsed climate variability hypothesis that suggests the long-term drying trend in East Africa was punctuated by episodes of short alternating periods of extreme humidity and aridity which may have driven hominin speciation, encephalization and dispersals out of Africa. This hypothesis is unique as it provides a conceptual framework within which other evolutionary theories can be examined: first, at macro-scale comparing phylogenetic gradualism and punctuated equilibrium; second, at a more focused level of human evolution comparing allopatric speciation, aridity hypothesis, turnover pulse hypothesis, variability selection hypothesis, Red Queen hypothesis and sympatric speciation based on sexual selection. It is proposed that each one of these mechanisms may have been acting on hominins during these short periods of climate variability, which then produce a range of different traits that led to the emergence of new species. In the case of Homo erectus (sensu lato), it is not just brain size that changes but life history (shortened inter-birth intervals, delayed development), body size and dimorphism, shoulder morphology to allow thrown projectiles, adaptation to long-distance running, ecological flexibility and social behaviour. The future of evolutionary research should be to create evidence-based meta-narratives, which encompass multiple mechanisms that select for different traits leading ultimately to speciation. PMID:25602068

The Effects of Music Salience on the Gait Performance of Young Adults.

PubMed

de Bruin, Natalie; Kempster, Cody; Doucette, Angelica; Doan, Jon B; Hu, Bin; Brown, Lesley A

2015-01-01

The presence of a rhythmic beat in the form of a metronome tone or beat-accentuated original music can modulate gait performance; however, it has yet to be determined whether gait modulation can be achieved using commercially available music. The current study investigated the effects of commercially available music on the walking of healthy young adults. Specific aims were (a) to determine whether commercially available music can be used to influence gait (i.e., gait velocity, stride length, cadence, stride time variability), (b) to establish the effect of music salience on gait (i.e., gait velocity, stride length, cadence, stride time variability), and (c) to examine whether music tempi differentially effected gait (i.e., gait velocity, stride length, cadence, stride time variability). Twenty-five participants walked the length of an unobstructed walkway while listening to music. Music selections differed with respect to the salience or the tempo of the music. The genre of music and artists were self-selected by participants. Listening to music while walking was an enjoyable activity that influenced gait. Specifically, salient music selections increased measures of cadence, velocity, and stride length; in contrast, gait was unaltered by the presence of non-salient music. Music tempo did not differentially affect gait performance (gait velocity, stride length, cadence, stride time variability) in these participants. Gait performance was differentially influenced by music salience. These results have implications for clinicians considering the use of commercially available music as an alternative to the traditional rhythmic auditory cues used in rehabilitation programs. © the American Music Therapy Association 2015. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

A variant of sparse partial least squares for variable selection and data exploration.

PubMed

Olson Hunt, Megan J; Weissfeld, Lisa; Boudreau, Robert M; Aizenstein, Howard; Newman, Anne B; Simonsick, Eleanor M; Van Domelen, Dane R; Thomas, Fridtjof; Yaffe, Kristine; Rosano, Caterina

2014-01-01

When data are sparse and/or predictors multicollinear, current implementation of sparse partial least squares (SPLS) does not give estimates for non-selected predictors nor provide a measure of inference. In response, an approach termed "all-possible" SPLS is proposed, which fits a SPLS model for all tuning parameter values across a set grid. Noted is the percentage of time a given predictor is chosen, as well as the average non-zero parameter estimate. Using a "large" number of multicollinear predictors, simulation confirmed variables not associated with the outcome were least likely to be chosen as sparsity increased across the grid of tuning parameters, while the opposite was true for those strongly associated. Lastly, variables with a weak association were chosen more often than those with no association, but less often than those with a strong relationship to the outcome. Similarly, predictors most strongly related to the outcome had the largest average parameter estimate magnitude, followed by those with a weak relationship, followed by those with no relationship. Across two independent studies regarding the relationship between volumetric MRI measures and a cognitive test score, this method confirmed a priori hypotheses about which brain regions would be selected most often and have the largest average parameter estimates. In conclusion, the percentage of time a predictor is chosen is a useful measure for ordering the strength of the relationship between the independent and dependent variables, serving as a form of inference. The average parameter estimates give further insight regarding the direction and strength of association. As a result, all-possible SPLS gives more information than the dichotomous output of traditional SPLS, making it useful when undertaking data exploration and hypothesis generation for a large number of potential predictors.

Selecting Populations for Non-Analogous Climate Conditions Using Universal Response Functions: The Case of Douglas-Fir in Central Europe.

PubMed

Chakraborty, Debojyoti; Wang, Tongli; Andre, Konrad; Konnert, Monika; Lexer, Manfred J; Matulla, Christoph; Schueler, Silvio

2015-01-01

Identifying populations within tree species potentially adapted to future climatic conditions is an important requirement for reforestation and assisted migration programmes. Such populations can be identified either by empirical response functions based on correlations of quantitative traits with climate variables or by climate envelope models that compare the climate of seed sources and potential growing areas. In the present study, we analyzed the intraspecific variation in climate growth response of Douglas-fir planted within the non-analogous climate conditions of Central and continental Europe. With data from 50 common garden trials, we developed Universal Response Functions (URF) for tree height and mean basal area and compared the growth performance of the selected best performing populations with that of populations identified through a climate envelope approach. Climate variables of the trial location were found to be stronger predictors of growth performance than climate variables of the population origin. Although the precipitation regime of the population sources varied strongly none of the precipitation related climate variables of population origin was found to be significant within the models. Overall, the URFs explained more than 88% of variation in growth performance. Populations identified by the URF models originate from western Cascades and coastal areas of Washington and Oregon and show significantly higher growth performance than populations identified by the climate envelope approach under both current and climate change scenarios. The URFs predict decreasing growth performance at low and middle elevations of the case study area, but increasing growth performance on high elevation sites. Our analysis suggests that population recommendations based on empirical approaches should be preferred and population selections by climate envelope models without considering climatic constrains of growth performance should be carefully appraised before transferring populations to planting locations with novel or dissimilar climate.

CORRELATION PURSUIT: FORWARD STEPWISE VARIABLE SELECTION FOR INDEX MODELS

PubMed Central

Zhong, Wenxuan; Zhang, Tingting; Zhu, Yu; Liu, Jun S.

2012-01-01

In this article, a stepwise procedure, correlation pursuit (COP), is developed for variable selection under the sufficient dimension reduction framework, in which the response variable Y is influenced by the predictors X1, X2, …, Xp through an unknown function of a few linear combinations of them. Unlike linear stepwise regression, COP does not impose a special form of relationship (such as linear) between the response variable and the predictor variables. The COP procedure selects variables that attain the maximum correlation between the transformed response and the linear combination of the variables. Various asymptotic properties of the COP procedure are established, and in particular, its variable selection performance under diverging number of predictors and sample size has been investigated. The excellent empirical performance of the COP procedure in comparison with existing methods are demonstrated by both extensive simulation studies and a real example in functional genomics. PMID:23243388

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.

Group Variable Selection Via Convex Log-Exp-Sum Penalty with Application to a Breast Cancer Survivor Study

PubMed Central

Geng, Zhigeng; Wang, Sijian; Yu, Menggang; Monahan, Patrick O.; Champion, Victoria; Wahba, Grace

2017-01-01

Summary In many scientific and engineering applications, covariates are naturally grouped. When the group structures are available among covariates, people are usually interested in identifying both important groups and important variables within the selected groups. Among existing successful group variable selection methods, some methods fail to conduct the within group selection. Some methods are able to conduct both group and within group selection, but the corresponding objective functions are non-convex. Such a non-convexity may require extra numerical effort. In this article, we propose a novel Log-Exp-Sum(LES) penalty for group variable selection. The LES penalty is strictly convex. It can identify important groups as well as select important variables within the group. We develop an efficient group-level coordinate descent algorithm to fit the model. We also derive non-asymptotic error bounds and asymptotic group selection consistency for our method in the high-dimensional setting where the number of covariates can be much larger than the sample size. Numerical results demonstrate the good performance of our method in both variable selection and prediction. We applied the proposed method to an American Cancer Society breast cancer survivor dataset. The findings are clinically meaningful and may help design intervention programs to improve the qualify of life for breast cancer survivors. PMID:25257196

A cost effective 5΄ selective single cell transcriptome profiling approach with improved UMI design

PubMed Central

Arguel, Marie-Jeanne; LeBrigand, Kevin; Paquet, Agnès; Ruiz García, Sandra; Zaragosi, Laure-Emmanuelle; Waldmann, Rainer

2017-01-01

Abstract Single cell RNA sequencing approaches are instrumental in studies of cell-to-cell variability. 5΄ selective transcriptome profiling approaches allow simultaneous definition of the transcription start size and have advantages over 3΄ selective approaches which just provide internal sequences close to the 3΄ end. The only currently existing 5΄ selective approach requires costly and labor intensive fragmentation and cell barcoding after cDNA amplification. We developed an optimized 5΄ selective workflow where all the cell indexing is done prior to fragmentation. With our protocol, cell indexing can be performed in the Fluidigm C1 microfluidic device, resulting in a significant reduction of cost and labor. We also designed optimized unique molecular identifiers that show less sequence bias and vulnerability towards sequencing errors resulting in an improved accuracy of molecule counting. We provide comprehensive experimental workflows for Illumina and Ion Proton sequencers that allow single cell sequencing in a cost range comparable to qPCR assays. PMID:27940562

Evaluation of training programs and entry-level qualifications for nuclear-power-plant control-room personnel based on the systems approach to training

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

Haas, P M; Selby, D L; Hanley, M J

1983-09-01

This report summarizes results of research sponsored by the US Nuclear Regulatory Commission (NRC) Office of Nuclear Regulatory Research to initiate the use of the Systems Approach to Training in the evaluation of training programs and entry level qualifications for nuclear power plant (NPP) personnel. Variables (performance shaping factors) of potential importance to personnel selection and training are identified, and research to more rigorously define an operationally useful taxonomy of those variables is recommended. A high-level model of the Systems Approach to Training for use in the nuclear industry, which could serve as a model for NRC evaluation of industrymore » programs, is presented. The model is consistent with current publically stated NRC policy, with the approach being followed by the Institute for Nuclear Power Operations, and with current training technology. Checklists to be used by NRC evaluators to assess training programs for NPP control-room personnel are proposed which are based on this model.« less

Guidelines for the standardization of preanalytic variables for blood-based biomarker studies in Alzheimer’s disease research

PubMed Central

Gupta, Veer; Henriksen, Kim; Edwards, Melissa; Jeromin, Andreas; Lista, Simone; Bazenet, Chantal; Soares, Holly; Lovestone, Simon; Hampel, Harald; Montine, Thomas; Blennow, Kaj; Foroud, Tatiana; Carrillo, Maria; Graff-Radford, Neill; Laske, Christoph; Breteler, Monique; Shaw, Leslie; Trojanowski, John Q.; Schupf, Nicole; Rissman, Robert A.; Fagan, Anne M.; Oberoi, Pankaj; Umek, Robert; Weiner, Michael W.; Grammas, Paula; Posner, Holly; Martins, Ralph

2015-01-01

The lack of readily available biomarkers is a significant hindrance towards progressing to effective therapeutic and preventative strategies for Alzheimer’s disease (AD). Blood-based biomarkers have potential to overcome access and cost barriers and greatly facilitate advanced neuroimaging and cerebrospinal fluid biomarker approaches. Despite the fact that preanalytical processing is the largest source of variability in laboratory testing, there are no currently available standardized preanalytical guidelines. The current international working group provides the initial starting point for such guidelines for standardized operating procedures (SOPs). It is anticipated that these guidelines will be updated as additional research findings become available. The statement provides (1) a synopsis of selected preanalytical methods utilized in many international AD cohort studies, (2) initial draft guidelines/SOPs for preanalytical methods, and (3) a list of required methodological information and protocols to be made available for publications in the field in order to foster cross-validation across cohorts and laboratories. PMID:25282381

Alternating high and low climate variability: The context of natural selection and speciation in Plio-Pleistocene hominin evolution.

PubMed

Potts, Richard; Faith, J Tyler

2015-10-01

Interaction of orbital insolation cycles defines a predictive model of alternating phases of high- and low-climate variability for tropical East Africa over the past 5 million years. This model, which is described in terms of climate variability stages, implies repeated increases in landscape/resource instability and intervening periods of stability in East Africa. It predicts eight prolonged (>192 kyr) eras of intensified habitat instability (high variability stages) in which hominin evolutionary innovations are likely to have occurred, potentially by variability selection. The prediction that repeated shifts toward high climate variability affected paleoenvironments and evolution is tested in three ways. In the first test, deep-sea records of northeast African terrigenous dust flux (Sites 721/722) and eastern Mediterranean sapropels (Site 967A) show increased and decreased variability in concert with predicted shifts in climate variability. These regional measurements of climate dynamics are complemented by stratigraphic observations in five basins with lengthy stratigraphic and paleoenvironmental records: the mid-Pleistocene Olorgesailie Basin, the Plio-Pleistocene Turkana and Olduvai Basins, and the Pliocene Tugen Hills sequence and Hadar Basin--all of which show that highly variable landscapes inhabited by hominin populations were indeed concentrated in predicted stages of prolonged high climate variability. Second, stringent null-model tests demonstrate a significant association of currently known first and last appearance datums (FADs and LADs) of the major hominin lineages, suites of technological behaviors, and dispersal events with the predicted intervals of prolonged high climate variability. Palynological study in the Nihewan Basin, China, provides a third test, which shows the occupation of highly diverse habitats in eastern Asia, consistent with the predicted increase in adaptability in dispersing Oldowan hominins. Integration of fossil, archeological, sedimentary, and paleolandscape evidence illustrates the potential influence of prolonged high variability on the origin and spread of critical adaptations and lineages in the evolution of Homo. The growing body of data concerning environmental dynamics supports the idea that the evolution of adaptability in response to climate and overall ecological instability represents a unifying theme in hominin evolutionary history. Published by Elsevier Ltd.

Assessing the accuracy and stability of variable selection methods for random forest modeling in ecology.

PubMed

Fox, Eric W; Hill, Ryan A; Leibowitz, Scott G; Olsen, Anthony R; Thornbrugh, Darren J; Weber, Marc H

2017-07-01

Random forest (RF) modeling has emerged as an important statistical learning method in ecology due to its exceptional predictive performance. However, for large and complex ecological data sets, there is limited guidance on variable selection methods for RF modeling. Typically, either a preselected set of predictor variables are used or stepwise procedures are employed which iteratively remove variables according to their importance measures. This paper investigates the application of variable selection methods to RF models for predicting probable biological stream condition. Our motivating data set consists of the good/poor condition of n = 1365 stream survey sites from the 2008/2009 National Rivers and Stream Assessment, and a large set (p = 212) of landscape features from the StreamCat data set as potential predictors. We compare two types of RF models: a full variable set model with all 212 predictors and a reduced variable set model selected using a backward elimination approach. We assess model accuracy using RF's internal out-of-bag estimate, and a cross-validation procedure with validation folds external to the variable selection process. We also assess the stability of the spatial predictions generated by the RF models to changes in the number of predictors and argue that model selection needs to consider both accuracy and stability. The results suggest that RF modeling is robust to the inclusion of many variables of moderate to low importance. We found no substantial improvement in cross-validated accuracy as a result of variable reduction. Moreover, the backward elimination procedure tended to select too few variables and exhibited numerous issues such as upwardly biased out-of-bag accuracy estimates and instabilities in the spatial predictions. We use simulations to further support and generalize results from the analysis of real data. A main purpose of this work is to elucidate issues of model selection bias and instability to ecologists interested in using RF to develop predictive models with large environmental data sets.

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.

Mitigating Insider Sabotage and Espionage: A Review of the United States Air Force’s Current Posture

DTIC Science & Technology

2009-03-01

published on ins ider threat, to include the variables that come into play and historical case studies. Existing insider threat models are discussed ...problem, including the initial development of a logical da ta mod el and a system dynamics model. This chapter also discusses the selection of the...Finally, Chapter V provides a summary of the research along with a discussion of its conclusions and impact. Recommendations for future research

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.

CAP waveform estimation from the measured electrical bioimpedance values: Patient's heart rate variability analysis.

PubMed

Krivoshei, A; Uuetoa, H; Min, M; Annus, P; Uuetoa, T; Lamp, J

2015-08-01

The paper presents analysis of the generic transfer function (TF) between Electrical Bioimpedance (EBI) measured non-invasively on the wrist and Central Aortic Pressure (CAP) invasively measured at the aortic root. Influence of the Heart Rate (HR) variations on the generic TF and on reconstructed CAP waveforms is investigated. The HR variation analysis is provided on a single patient data to exclude inter-patient influences at the current research stage. A new approach for the generic TF estimating from a data ensemble is presented as well. Moreover, an influence of the cardiac period beginning point selection is analyzed and empirically optimal solution for its selection is proposed.

[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.

Assessing the accuracy and stability of variable selection ...

EPA Pesticide Factsheets

Random forest (RF) modeling has emerged as an important statistical learning method in ecology due to its exceptional predictive performance. However, for large and complex ecological datasets there is limited guidance on variable selection methods for RF modeling. Typically, either a preselected set of predictor variables are used, or stepwise procedures are employed which iteratively add/remove variables according to their importance measures. This paper investigates the application of variable selection methods to RF models for predicting probable biological stream condition. Our motivating dataset consists of the good/poor condition of n=1365 stream survey sites from the 2008/2009 National Rivers and Stream Assessment, and a large set (p=212) of landscape features from the StreamCat dataset. Two types of RF models are compared: a full variable set model with all 212 predictors, and a reduced variable set model selected using a backwards elimination approach. We assess model accuracy using RF's internal out-of-bag estimate, and a cross-validation procedure with validation folds external to the variable selection process. We also assess the stability of the spatial predictions generated by the RF models to changes in the number of predictors, and argue that model selection needs to consider both accuracy and stability. The results suggest that RF modeling is robust to the inclusion of many variables of moderate to low importance. We found no substanti

Gas engine heat pump cycle analysis. Volume 1: Model description and generic analysis

NASA Astrophysics Data System (ADS)

Fischer, R. D.

1986-10-01

The task has prepared performance and cost information to assist in evaluating the selection of high voltage alternating current components, values for component design variables, and system configurations and operating strategy. A steady-state computer model for performance simulation of engine-driven and electrically driven heat pumps was prepared and effectively used for parametric and seasonal performance analyses. Parametric analysis showed the effect of variables associated with design of recuperators, brine coils, domestic hot water heat exchanger, compressor size, engine efficiency, insulation on exhaust and brine piping. Seasonal performance data were prepared for residential and commercial units in six cities with system configurations closely related to existing or contemplated hardware of the five GRI engine contractors. Similar data were prepared for an advanced variable-speed electric unit for comparison purposes. The effect of domestic hot water production on operating costs was determined. Four fan-operating strategies and two brine loop configurations were explored.

Development of toughened epoxy polymers for high performance composite and ablative applications

NASA Technical Reports Server (NTRS)

Allen, V. R.

1982-01-01

A survey of current procedures for the assessment of state of cure in epoxy polymers and for the evaluation of polymer toughness as related to nature of the crosslinking agent was made to facilitate a cause-effect study of the chemical modification of epoxy polymers. Various conformations of sample morphology were examined to identify testing variables and to establish optimum conditions for the selected physical test methods. Dynamic viscoelasticity testing was examined in conjunction with chemical analyses to allow observation of the extent of the curing reaction with size of the crosslinking agent the primary variable. Specifically the aims of the project were twofold: (1) to consider the experimental variables associated with development of "extent of cure" analysis, and (2) to assess methodology of fracture energy determination and to prescribe a meaningful and reproducible procedure. The following is separated into two categories for ease of presentation.

Variegated clonality and rapid emergence of new molecular lesions in xenografts of acute lymphoblastic leukemia are associated with drug resistance.

PubMed

Nowak, Daniel; Liem, Natalia L M; Mossner, Maximilian; Klaumünzer, Marion; Papa, Rachael A; Nowak, Verena; Jann, Johann C; Akagi, Tadayuki; Kawamata, Norihiko; Okamoto, Ryoko; Thoennissen, Nils H; Kato, Motohiro; Sanada, Masashi; Hofmann, Wolf-Karsten; Ogawa, Seishi; Marshall, Glenn M; Lock, Richard B; Koeffler, H Phillip

2015-01-01

The use of genome-wide copy-number analysis and massive parallel sequencing has revolutionized the understanding of the clonal architecture of pediatric acute lymphoblastic leukemia (ALL) by demonstrating that this disease is composed of highly variable clonal ancestries following the rules of Darwinian selection. The current study aimed to analyze the molecular composition of childhood ALL biopsies and patient-derived xenografts with particular emphasis on mechanisms associated with acquired chemoresistance. Genomic DNA from seven primary pediatric ALL patient samples, 29 serially passaged xenografts, and six in vivo selected chemoresistant xenografts were analyzed with 250K single-nucleotide polymorphism arrays. Copy-number analysis of non-drug-selected xenografts confirmed a highly variable molecular pattern of variegated subclones. Whereas primary patient samples from initial diagnosis displayed a mean of 5.7 copy-number alterations per sample, serially passaged xenografts contained a mean of 8.2 and chemoresistant xenografts a mean of 10.5 copy-number alterations per sample, respectively. Resistance to cytarabine was explained by a new homozygous deletion of the DCK gene, whereas methotrexate resistance was associated with monoallelic deletion of FPGS and mutation of the remaining allele. This study demonstrates that selecting for chemoresistance in xenografted human ALL cells can reveal novel mechanisms associated with drug resistance. Copyright © 2015 ISEH - International Society for Experimental Hematology. Published by Elsevier Inc. All rights reserved.

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/.

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

A New Variable Weighting and Selection Procedure for K-Means Cluster Analysis

ERIC Educational Resources Information Center

Steinley, Douglas; Brusco, Michael J.

2008-01-01

A variance-to-range ratio variable weighting procedure is proposed. We show how this weighting method is theoretically grounded in the inherent variability found in data exhibiting cluster structure. In addition, a variable selection procedure is proposed to operate in conjunction with the variable weighting technique. The performances of these…

Plastic flies: the regulation and evolution of trait variability in Drosophila.

PubMed

Shingleton, Alexander W; Tang, Hui Yuan

2012-01-01

Individuals within species and populations vary. Such variation arises through environmental and genetic factors and ensures that no two individuals are identical. However, it is clear that not all traits show the same degree of intraspecific variation. Some traits, in particular secondary sexual characteristics used by males to compete for and attract females, are extremely variable among individuals in a population. Other traits, for example brain size in mammals, are not. Recent research has begun to explore the possibility that the extent of phenotypic variation (here referred to as "variability") may be a character itself and subject to natural selection. While these studies support the concept of variability as an evolvable trait, controversy remains over what precisely the trait is. At the heart of this controversy is the fact that there are very few examples of developmental mechanisms that regulate trait variability in response to any source of variation, be it environmental or genetic. Here, we describe a recent study from our laboratory that identifies such a mechanism. We then place the study in the context of current research on the regulation of trait variability, and discuss the implications for our understanding of the developmental regulation and evolution of phenotypic variation.

Variability and Predictability of Land-Atmosphere Interactions: Observational and Modeling Studies

NASA Technical Reports Server (NTRS)

Roads, John; Oglesby, Robert; Marshall, Susan; Robertson, Franklin R.

2002-01-01

The overall goal of this project is to increase our understanding of seasonal to interannual variability and predictability of atmosphere-land interactions. The project objectives are to: 1. Document the low frequency variability in land surface features and associated water and energy cycles from general circulation models (GCMs), observations and reanalysis products. 2. Determine what relatively wet and dry years have in common on a region-by-region basis and then examine the physical mechanisms that may account for a significant portion of the variability. 3. Develop GCM experiments to examine the hypothesis that better knowledge of the land surface enhances long range predictability. This investigation is aimed at evaluating and predicting seasonal to interannual variability for selected regions emphasizing the role of land-atmosphere interactions. Of particular interest are the relationships between large, regional and local scales and how they interact to account for seasonal and interannual variability, including extreme events such as droughts and floods. North and South America, including the Global Energy and Water Cycle Experiment Continental International Project (GEWEX GCIP), MacKenzie, and LBA basins, are currently being emphasized. We plan to ultimately generalize and synthesize to other land regions across the globe, especially those pertinent to other GEWEX projects.

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

Methodological development for selection of significant predictors explaining fatal road accidents.

PubMed

Dadashova, Bahar; Arenas-Ramírez, Blanca; Mira-McWilliams, José; Aparicio-Izquierdo, Francisco

2016-05-01

Identification of the most relevant factors for explaining road accident occurrence is an important issue in road safety research, particularly for future decision-making processes in transport policy. However model selection for this particular purpose is still an ongoing research. In this paper we propose a methodological development for model selection which addresses both explanatory variable and adequate model selection issues. A variable selection procedure, TIM (two-input model) method is carried out by combining neural network design and statistical approaches. The error structure of the fitted model is assumed to follow an autoregressive process. All models are estimated using Markov Chain Monte Carlo method where the model parameters are assigned non-informative prior distributions. The final model is built using the results of the variable selection. For the application of the proposed methodology the number of fatal accidents in Spain during 2000-2011 was used. This indicator has experienced the maximum reduction internationally during the indicated years thus making it an interesting time series from a road safety policy perspective. Hence the identification of the variables that have affected this reduction is of particular interest for future decision making. The results of the variable selection process show that the selected variables are main subjects of road safety policy measures. Published by Elsevier Ltd.

A tripolar current-steering stimulator ASIC for field shaping in deep brain stimulation.

PubMed

Valente, Virgilio; Demosthenous, Andreas; Bayford, Richard

2012-06-01

A significant problem with clinical deep brain stimulation (DBS) is the high variability of its efficacy and the frequency of side effects, related to the spreading of current beyond the anatomical target area. This is the result of the lack of control that current DBS systems offer on the shaping of the electric potential distribution around the electrode. This paper presents a stimulator ASIC with a tripolar current-steering output stage, aiming at achieving more selectivity and field shaping than current DBS systems. The ASIC was fabricated in a 0.35-μ m CMOS technology occupying a core area of 0.71 mm(2). It consists of three current sourcing/sinking channels. It is capable of generating square and exponential-decay biphasic current pulses with five different time constants up to 28 ms and delivering up to 1.85 mA of cathodic current, in steps of 4 μA, from a 12 V power supply. Field shaping was validated by mapping the potential distribution when injecting current pulses through a multicontact DBS electrode in saline.

VARSEDIG: an algorithm for morphometric characters selection and statistical validation in morphological taxonomy.

PubMed

Guisande, Cástor; Vari, Richard P; Heine, Jürgen; García-Roselló, Emilio; González-Dacosta, Jacinto; Perez-Schofield, Baltasar J García; González-Vilas, Luis; Pelayo-Villamil, Patricia

2016-09-12

We present and discuss VARSEDIG, an algorithm which identifies the morphometric features that significantly discriminate two taxa and validates the morphological distinctness between them via a Monte-Carlo test. VARSEDIG is freely available as a function of the RWizard application PlotsR (http://www.ipez.es/RWizard) and as R package on CRAN. The variables selected by VARSEDIG with the overlap method were very similar to those selected by logistic regression and discriminant analysis, but overcomes some shortcomings of these methods. VARSEDIG is, therefore, a good alternative by comparison to current classical classification methods for identifying morphometric features that significantly discriminate a taxon and for validating its morphological distinctness from other taxa. As a demonstration of the potential of VARSEDIG for this purpose, we analyze morphological discrimination among some species of the Neotropical freshwater family Characidae.

Current Literature Review of Registered Nurses' Competency in the Global Community.

PubMed

Liu, Ying; Aungsuroch, Yupin

2018-03-01

In order to enhance international standards of nursing service, this article aims to analyze the English full-text peer-reviewed published articles from the past 10 years that describe contemporary registered nurses' (RNs') competency in the global community. An integrative review of literature was conducted between June 2016 and January 2017. A systematic search was completed using four databases (Science Direct, Scopus, Web of Science, and the Cumulative Index to Nursing and Allied Health Literature) that covered the years between 2007 and 2017, and used the key words nurs * OR (staff nurs * ) OR (register nurs * ) AND competenc * AND international OR global. Ultimately, 32 studies meeting inclusion and exclusion criteria were selected for analysis. Nursing competency trended towards definitions using a holistic lens and behavior statements reflecting the skills, knowledge, attitudes, and judgment required for effective performance in the nursing profession. By using inductive content analysis, 11 components emerged. Additionally, six instruments were found to measure generalist RNs' competencies across countries. The variables related to generalist nursing competency included sociodemographic variables, professional-related variables, and work environment variables. This review provides the research evidence for updating definitions, components, measurements, and variables related to RNs' competency in the global community. Further research should consider cross-cultural validation of instruments and influencing factors related to nursing competency. The components and measurements identified in this review can be used by nursing administrators to select or evaluate qualified nurses. The multivariables related to nursing competency can assistant hospital administrators to recognize and find effective ways to improve nursing competency. © 2018 Sigma Theta Tau International.

BATEMANATER: a computer program to estimate and bootstrap mating system variables based on Bateman's principles.

PubMed

Jones, Adam G

2015-11-01

Bateman's principles continue to play a major role in the characterization of genetic mating systems in natural populations. The modern manifestations of Bateman's ideas include the opportunity for sexual selection (i.e. I(s) - the variance in relative mating success), the opportunity for selection (i.e. I - the variance in relative reproductive success) and the Bateman gradient (i.e. β(ss) - the slope of the least-squares regression of reproductive success on mating success). These variables serve as the foundation for one convenient approach for the quantification of mating systems. However, their estimation presents at least two challenges, which I address here with a new Windows-based computer software package called BATEMANATER. The first challenge is that confidence intervals for these variables are not easy to calculate. BATEMANATER solves this problem using a bootstrapping approach. The second, more serious, problem is that direct estimates of mating system variables from open populations will typically be biased if some potential progeny or adults are missing from the analysed sample. BATEMANATER addresses this problem using a maximum-likelihood approach to estimate mating system variables from incompletely sampled breeding populations. The current version of BATEMANATER addresses the problem for systems in which progeny can be collected in groups of half- or full-siblings, as would occur when eggs are laid in discrete masses or offspring occur in pregnant females. BATEMANATER has a user-friendly graphical interface and thus represents a new, convenient tool for the characterization and comparison of genetic mating systems. © 2015 John Wiley & Sons Ltd.

Substance use Among University Undergraduates: A Study of Pattern and Beliefs in Ile-Ife.

PubMed

Fatoye, F O

2007-03-01

To determine the prevalence of substance use and to investigate the relationship between psychosocial variables and current use of psychoactive substances among university undergraduates. A cross-sectional survey of randomly selected undergraduates of the Obafemi Awolowo University, Ile Ife was carried out using the WHO drug use questionnaire. Alcohol, stimulants, hypnosedatives, tobacco and cannabis with current use prevalence rates of 20.2 %, 17.7%, 9.8%, 9.0% and 8.2 % respectively were the most commonly used substances. Inhalants/organic solvents, cocaine, heroin, hallucinogens and pethidine/morphine with current use rates of 3.2%, 2.6%, 2.2%, 1.0% and 0.5% respectively belonged to the 'low - use' category. However, these rates were marginally higher than most previous findings. Five variables (sex, polygamy, living outside the university campus, poor mental health and study difficulty), were significantly associated with the use of many of the substances. Also, perceived harmfulness was observed to be a possible deterrent to substance use and most users were engaged in the use of cheap and easily available substances. The observations are largely similar to those from other locations in Nigeria and may therefore be useful in preventive programmes.

Optimizing cropland cover for stable food production in Sub-Saharan Africa using simulated yield and Modern Portfolio Theory

NASA Astrophysics Data System (ADS)

Bodin, P.; Olin, S.; Pugh, T. A. M.; Arneth, A.

2014-12-01

Food security can be defined as stable access to food of good nutritional quality. In Sub Saharan Africa access to food is strongly linked to local food production and the capacity to generate enough calories to sustain the local population. Therefore it is important in these regions to generate not only sufficiently high yields but also to reduce interannual variability in food production. Traditionally, climate impact simulation studies have focused on factors that underlie maximum productivity ignoring the variability in yield. By using Modern Portfolio Theory, a method stemming from economics, we here calculate optimum current and future crop selection that maintain current yield while minimizing variance, vs. maintaining variance while maximizing yield. Based on simulated yield using the LPJ-GUESS dynamic vegetation model, the results show that current cropland distribution for many crops is close to these optimum distributions. Even so, the optimizations displayed substantial potential to either increase food production and/or to decrease its variance regionally. Our approach can also be seen as a method to create future scenarios for the sown areas of crops in regions where local food production is important for food security.

Selective block of late Na+ current by local anaesthetics in rat large sensory neurones

PubMed Central

Baker, Mark D

2000-01-01

The actions of lignocaine and benzocaine on transient and late Na+ current generated by large diameter (⩾50 μm) adult rat dorsal root ganglion neurones, were studied using patch-clamp techniques.Both drugs blocked whole-cell late Na+ current in a concentration-dependent manner. At 200 ms following the onset of a clamp step from −110 to −40 mV, the apparent K for block of late Na+ current by lignocaine was 57.8±15 μM (mean±s.e.mean, n=4). The value for benzocaine was 24.9±3.3 μM, (mean±s.e.mean, n=3).The effect of lignocaine on transient current, in randomly selected neurones, appeared variable (n=8, half-block from ∼50 to 400 μM). Half-block by benzocaine was not attained, but both whole-cell (n=11) and patch data suggested a high apparent K,>250 μM. Transient current always remained after late current was blocked.The voltage-dependence of residual late current steady-state inactivation was not shifted by 20 μM benzocaine (n=3), whereas 200 μM benzocaine shifted the voltage-dependence of transient current steady-state inactivation by −18.7±5.9 mV (mean±s.e.mean, n=4).In current-clamp, benzocaine (250 μM) could block subthreshold, voltage-dependent inward current, increasing the threshold for eliciting action potentials, without preventing their generation (n=2).Block of late Na+ current by systemic local anaesthetic may play a part in preventing ectopic impulse generation in sensory neurones. PMID:10780966

An analysis of the relationship of seven selected variables to State Board Test Pool Examination performance of the University of Tennessee, Knoxville, College of Nursing.

PubMed

Sharp, T G

1984-02-01

The study was designed to determine whether any one of seven selected variables or a combination of the variables is predictive of performance on the State Board Test Pool Examination. The selected variables studied were: high school grade point average (HSGPA), The University of Tennessee, Knoxville, College of Nursing grade point average (GPA), and American College Test Assessment (ACT) standard scores (English, ENG; mathematics, MA; social studies, SS; natural sciences, NSC; composite, COMP). Data utilized were from graduates of the baccalaureate program of The University of Tennessee, Knoxville, College of Nursing from 1974 through 1979. The sample of 322 was selected from a total population of 572. The Statistical Analysis System (SAS) was designed to accomplish analysis of the predictive relationship of each of the seven selected variables to State Board Test Pool Examination performance (result of pass or fail), a stepwise discriminant analysis was designed for determining the predictive relationship of the strongest combination of the independent variables to overall State Board Test Pool Examination performance (result of pass or fail), and stepwise multiple regression analysis was designed to determine the strongest predictive combination of selected variables for each of the five subexams of the State Board Test Pool Examination. The selected variables were each found to be predictive of SBTPE performance (result of pass or fail). The strongest combination for predicting SBTPE performance (result of pass or fail) was found to be GPA, MA, and NSC.

Reliability of Current Biokinetic and Dosimetric Models for Radionuclides: A Pilot Study

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

Leggett, Richard Wayne; Eckerman, Keith F; Meck, Robert A.

2008-10-01

This report describes the results of a pilot study of the reliability of the biokinetic and dosimetric models currently used by the U.S. Nuclear Regulatory Commission (NRC) as predictors of dose per unit internal or external exposure to radionuclides. The study examines the feasibility of critically evaluating the accuracy of these models for a comprehensive set of radionuclides of concern to the NRC. Each critical evaluation would include: identification of discrepancies between the models and current databases; characterization of uncertainties in model predictions of dose per unit intake or unit external exposure; characterization of variability in dose per unit intakemore » or unit external exposure; and evaluation of prospects for development of more accurate models. Uncertainty refers here to the level of knowledge of a central value for a population, and variability refers to quantitative differences between different members of a population. This pilot study provides a critical assessment of models for selected radionuclides representing different levels of knowledge of dose per unit exposure. The main conclusions of this study are as follows: (1) To optimize the use of available NRC resources, the full study should focus on radionuclides most frequently encountered in the workplace or environment. A list of 50 radionuclides is proposed. (2) The reliability of a dose coefficient for inhalation or ingestion of a radionuclide (i.e., an estimate of dose per unit intake) may depend strongly on the specific application. Multiple characterizations of the uncertainty in a dose coefficient for inhalation or ingestion of a radionuclide may be needed for different forms of the radionuclide and different levels of information of that form available to the dose analyst. (3) A meaningful characterization of variability in dose per unit intake of a radionuclide requires detailed information on the biokinetics of the radionuclide and hence is not feasible for many infrequently studied radionuclides. (4) The biokinetics of a radionuclide in the human body typically represents the greatest source of uncertainty or variability in dose per unit intake. (5) Characterization of uncertainty in dose per unit exposure is generally a more straightforward problem for external exposure than for intake of a radionuclide. (6) For many radionuclides the most important outcome of a large-scale critical evaluation of databases and biokinetic models for radionuclides is expected to be the improvement of current models. Many of the current models do not fully or accurately reflect available radiobiological or physiological information, either because the models are outdated or because they were based on selective or uncritical use of data or inadequate model structures. In such cases the models should be replaced with physiologically realistic models that incorporate a wider spectrum of information.« less

Exact tests using two correlated binomial variables in contemporary cancer clinical trials.

PubMed

Yu, Jihnhee; Kepner, James L; Iyer, Renuka

2009-12-01

New therapy strategies for the treatment of cancer are rapidly emerging because of recent technology advances in genetics and molecular biology. Although newer targeted therapies can improve survival without measurable changes in tumor size, clinical trial conduct has remained nearly unchanged. When potentially efficacious therapies are tested, current clinical trial design and analysis methods may not be suitable for detecting therapeutic effects. We propose an exact method with respect to testing cytostatic cancer treatment using correlated bivariate binomial random variables to simultaneously assess two primary outcomes. The method is easy to implement. It does not increase the sample size over that of the univariate exact test and in most cases reduces the sample size required. Sample size calculations are provided for selected designs.

Enhanced spectral efficiency using bandwidth switchable SAW filtering for mobile satellite communications systems

NASA Technical Reports Server (NTRS)

Peach, Robert; Malarky, Alastair

1990-01-01

Currently proposed mobile satellite communications systems require a high degree of flexibility in assignment of spectral capacity to different geographic locations. Conventionally this results in poor spectral efficiency which may be overcome by the use of bandwidth switchable filtering. Surface acoustic wave (SAW) technology makes it possible to provide banks of filters whose responses may be contiguously combined to form variable bandwidth filters with constant amplitude and phase responses across the entire band. The high selectivity possible with SAW filters, combined with the variable bandwidth capability, makes it possible to achieve spectral efficiencies over the allocated bandwidths of greater than 90 percent, while retaining full system flexibility. Bandwidth switchable SAW filtering (BSSF) achieves these gains with a negligible increase in hardware complexity.

Carbon Dioxide Methanation for Human Exploration of Mars: A Look at Catalyst Longevity and Activity Using Supported Ruthenium

NASA Technical Reports Server (NTRS)

Petersen, Elspeth M.; Meier, Anne J.; Tessonnier, Jean-Philippe

2018-01-01

Overarching Purpose: To design a carbon dioxide methanation/Sabatier reaction catalyst able to withstand variable conditions including fluctuations in bed temperature and feed flow rates for 480 days of remote operation to produce seven tons of methane. Current Study Purpose: Examine supported Ruthenium as a carbon dioxide methanation catalyst to determine the effects support properties have on the active phase by studying activity and selectivity. Objective: The remote operation of the Mars ISRU (In Situ Resources Utilization) lander to produce rocket fuel prior to crew arrival on the planet to power an ascent vehicle. Constraints: Long-term operation (480 days); Variable conditions: Feed gas flow rates, Feed gas flow ratios, Reactor bed temperature.

Learning and Study Strategies Inventory subtests and factors as predictors of National Board of Chiropractic Examiners Part 1 examination performance.

PubMed

Schutz, Christine M; Dalton, Leanne; Tepe, Rodger E

2013-01-01

This study was designed to extend research on the relationship between chiropractic students' learning and study strategies and national board examination performance. Sixty-nine first trimester chiropractic students self-administered the Learning and Study Strategies Inventory (LASSI). Linear trends tests (for continuous variables) and Mantel-Haenszel trend tests (for categorical variables) were utilized to determine if the 10 LASSI subtests and 3 factors predicted low, medium and high levels of National Board of Chiropractic Examiners (NBCE) Part 1 scores. Multiple regression was performed to predict overall mean NBCE examination scores using the 3 LASSI factors as predictor variables. Four LASSI subtests (Anxiety, Concentration, Selecting Main Ideas, Test Strategies) and one factor (Goal Orientation) were significantly associated with NBCE examination levels. One factor (Goal Orientation) was a significant predictor of overall mean NBCE examination performance. Learning and study strategies are predictive of NBCE Part 1 examination performance in chiropractic students. The current study found LASSI subtests Anxiety, Concentration, Selecting Main Ideas, and Test Strategies, and the Goal-Orientation factor to be significant predictors of NBCE scores. The LASSI may be useful to educators in preparing students for academic success. Further research is warranted to explore the effects of learning and study strategies training on GPA and NBCE performance.

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.

A non-linear data mining parameter selection algorithm for continuous variables

PubMed Central

Razavi, Marianne; Brady, Sean

2017-01-01

In this article, we propose a new data mining algorithm, by which one can both capture the non-linearity in data and also find the best subset model. To produce an enhanced subset of the original variables, a preferred selection method should have the potential of adding a supplementary level of regression analysis that would capture complex relationships in the data via mathematical transformation of the predictors and exploration of synergistic effects of combined variables. The method that we present here has the potential to produce an optimal subset of variables, rendering the overall process of model selection more efficient. This algorithm introduces interpretable parameters by transforming the original inputs and also a faithful fit to the data. The core objective of this paper is to introduce a new estimation technique for the classical least square regression framework. This new automatic variable transformation and model selection method could offer an optimal and stable model that minimizes the mean square error and variability, while combining all possible subset selection methodology with the inclusion variable transformations and interactions. Moreover, this method controls multicollinearity, leading to an optimal set of explanatory variables. PMID:29131829

A Selective Encryption Algorithm Based on AES for Medical Information.

PubMed

Oh, Ju-Young; Yang, Dong-Il; Chon, Ki-Hwan

2010-03-01

The transmission of medical information is currently a daily routine. Medical information needs efficient, robust and secure encryption modes, but cryptography is primarily a computationally intensive process. Towards this direction, we design a selective encryption scheme for critical data transmission. We expand the advandced encrytion stanard (AES)-Rijndael with five criteria: the first is the compression of plain data, the second is the variable size of the block, the third is the selectable round, the fourth is the optimization of software implementation and the fifth is the selective function of the whole routine. We have tested our selective encryption scheme by C(++) and it was compiled with Code::Blocks using a MinGW GCC compiler. The experimental results showed that our selective encryption scheme achieves a faster execution speed of encryption/decryption. In future work, we intend to use resource optimization to enhance the round operations, such as SubByte/InvSubByte, by exploiting similarities between encryption and decryption. As encryption schemes become more widely used, the concept of hardware and software co-design is also a growing new area of interest.

A Selective Encryption Algorithm Based on AES for Medical Information

PubMed Central

Oh, Ju-Young; Chon, Ki-Hwan

2010-01-01

Objectives The transmission of medical information is currently a daily routine. Medical information needs efficient, robust and secure encryption modes, but cryptography is primarily a computationally intensive process. Towards this direction, we design a selective encryption scheme for critical data transmission. Methods We expand the advandced encrytion stanard (AES)-Rijndael with five criteria: the first is the compression of plain data, the second is the variable size of the block, the third is the selectable round, the fourth is the optimization of software implementation and the fifth is the selective function of the whole routine. We have tested our selective encryption scheme by C++ and it was compiled with Code::Blocks using a MinGW GCC compiler. Results The experimental results showed that our selective encryption scheme achieves a faster execution speed of encryption/decryption. In future work, we intend to use resource optimization to enhance the round operations, such as SubByte/InvSubByte, by exploiting similarities between encryption and decryption. Conclusions As encryption schemes become more widely used, the concept of hardware and software co-design is also a growing new area of interest. PMID:21818420

Multilevel analysis of school anti-smoking education and current cigarette use among South African students

PubMed Central

Talley, Brandon; Masyn, Katherine; Chandora, Rachna; Vivolo-Kantor, Alana

2017-01-01

Introduction South Africa (SA) implemented the Global Youth Tobacco Survey (GYTS) four times between 1999 and 2011. Data from the four surveys indicated that downward trends in cigarette use among students may have stalled. Understanding the effect of school anti-smoking education on current smoking among students within schools and variability across schools may provide important insights into policies aimed at preventing or reducing tobacco use among students. The objective was to assess the student- and school-level effects of students' exposure to school anti-smoking education on current cigarette use among the study population using the most recent wave of GYTS data in SA (2011). Methods An analytic sample of students 13-15 years of age was selected (n=3,068) from the SA GYTS 2011. A taxonomy of two-level logistic regression models was fit to assess the relationship of various tobacco use, control, and exposure predictor variables on current cigarette smoking among the study population. Results At the student-level in the full model, secondhand smoke (SHS) exposure, peer smoking, and ownership of a promotional item were significantly associated with higher risk of current smoking. At the school-level in the full model, average exposure to peer smoking was associated with significant increases in the prevalence of current cigarette use, while average family anti-smoking education was significantly associated with decreases in the outcome variable. School anti-smoking education was not a statistically significant predictor at the student- or school-levels. Conclusion in this study, exposure to school anti-smoking education had no association with current cigarette smoking among the study population. Consistent with previous studies, having peers that smoked was highly associated with a student being a current smoker. Interestingly, at the school-level in the multilevel analysis, schools with higher rates of average family anti-smoking education had lower prevalence of current smoking. This finding has potential implications for tobacco control in SA, particularly if the school-level, family-centered protective effect can be operationalized as a prevention tool in the country's tobacco control program. PMID:28451015

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

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.

AGN Variability in the GOODS Fields

NASA Astrophysics Data System (ADS)

Sarajedini, Vicki

2007-07-01

Variability is a proven method to identify intrinsically faint active nuclei in galaxies found in deep HST surveys. We propose to extend our short-term variability study of the GOODS fields to include the more recent epochs obtained via supernovae searchers, increasing the overall time baseline from 6 months to 2.5 years. Based on typical AGN lightcurves, we expect to detect 70% more AGN by including these more recent epochs. Variable-detected AGN samples complement current X-ray and mid-IR surveys for AGN by providing unambigous evidence of nuclear activity. Additionallty, a significant number of variable nuclei are not associated with X-ray or mid-IR sources and would thus go undetected. With the increased time baseline, we will be able to construct the structure function {variability amplitude vs. time} for low-luminosity AGN to z 1. The inclusion of the longer time interval will allow for better descrimination among the various models describing the nature of AGN variability. The variability survey will be compared against spectroscopically selected AGN from the Team Keck Redshift Survey of the GOODS-N and the upcoming Flamingos-II NIR survey of the GOODS-S. The high-resolution ACS images will be used to separate the AGN from the host galaxy light and study the morphology, size and environment of the host galaxy. These studies will address questions concerning the nature of low-luminosity AGN evolution and variability at z 1.

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.

Comparisons of Selected Student and Teacher Variables in All-Girls and Coeducational Physical Education Environments

ERIC Educational Resources Information Center

Derry, Julie A.; Phillips, D. Allen

2004-01-01

The purpose of this study was to investigate selected student and teacher variables and compare the differences between these variables for female students and female teachers in coeducation and single-sex physical education classes. Eighteen female teachers and intact classes were selected; 9 teachers from coeducation and 9 teachers from…

Petroleomics by electrospray ionization FT-ICR mass spectrometry coupled to partial least squares with variable selection methods: prediction of the total acid number of crude oils.

PubMed

Terra, Luciana A; Filgueiras, Paulo R; Tose, Lílian V; Romão, Wanderson; de Souza, Douglas D; de Castro, Eustáquio V R; de Oliveira, Mirela S L; Dias, Júlio C M; Poppi, Ronei J

2014-10-07

Negative-ion mode electrospray ionization, ESI(-), with Fourier transform ion cyclotron resonance mass spectrometry (FT-ICR MS) was coupled to a Partial Least Squares (PLS) regression and variable selection methods to estimate the total acid number (TAN) of Brazilian crude oil samples. Generally, ESI(-)-FT-ICR mass spectra present a power of resolution of ca. 500,000 and a mass accuracy less than 1 ppm, producing a data matrix containing over 5700 variables per sample. These variables correspond to heteroatom-containing species detected as deprotonated molecules, [M - H](-) ions, which are identified primarily as naphthenic acids, phenols and carbazole analog species. The TAN values for all samples ranged from 0.06 to 3.61 mg of KOH g(-1). To facilitate the spectral interpretation, three methods of variable selection were studied: variable importance in the projection (VIP), interval partial least squares (iPLS) and elimination of uninformative variables (UVE). The UVE method seems to be more appropriate for selecting important variables, reducing the dimension of the variables to 183 and producing a root mean square error of prediction of 0.32 mg of KOH g(-1). By reducing the size of the data, it was possible to relate the selected variables with their corresponding molecular formulas, thus identifying the main chemical species responsible for the TAN values.

A Selective Overview of Variable Selection in High Dimensional Feature Space

PubMed Central

Fan, Jianqing

2010-01-01

High dimensional statistical problems arise from diverse fields of scientific research and technological development. Variable selection plays a pivotal role in contemporary statistical learning and scientific discoveries. The traditional idea of best subset selection methods, which can be regarded as a specific form of penalized likelihood, is computationally too expensive for many modern statistical applications. Other forms of penalized likelihood methods have been successfully developed over the last decade to cope with high dimensionality. They have been widely applied for simultaneously selecting important variables and estimating their effects in high dimensional statistical inference. In this article, we present a brief account of the recent developments of theory, methods, and implementations for high dimensional variable selection. What limits of the dimensionality such methods can handle, what the role of penalty functions is, and what the statistical properties are rapidly drive the advances of the field. The properties of non-concave penalized likelihood and its roles in high dimensional statistical modeling are emphasized. We also review some recent advances in ultra-high dimensional variable selection, with emphasis on independence screening and two-scale methods. PMID:21572976

Nudging at the checkout counter - A longitudinal study of the effect of a food repositioning nudge on healthy food choice.

PubMed

Van Gestel, L C; Kroese, F M; De Ridder, D T D

2018-06-01

Objective The current study is a longitudinal conceptual replication and aimed to investigate the effect of a food repositioning nudge on healthy food choice in a kiosk. Design During eight weeks, sales data were collected. The former four weeks formed the baseline phase and the latter four weeks formed the nudge phase where healthy food products were repositioned at the checkout counter display, while unhealthy alternatives remained available elsewhere in the store. Main Outcome Measures The main variable of interest was the proportion of healthy food products (selected to be repositioned) sold per day. Also exit interviews were administered to gather individual level data about purchases, and awareness and opinions of the nudge. Results Results showed that the proportion of selected healthy food products in total food sales was higher in all four nudge weeks than in all four baseline weeks. Individual level data showed that more customers had bought a selected healthy food product in the nudge phase and that customers generally approved of the nudge. Conclusion The current study strengthened the empirical evidence base of repositioning healthy food products as an effective and well-accepted nudge.

Identification of spectral regions for the quantification of red wine tannins with fourier transform mid-infrared spectroscopy.

PubMed

Jensen, Jacob S; Egebo, Max; Meyer, Anne S

2008-05-28

Accomplishment of fast tannin measurements is receiving increased interest as tannins are important for the mouthfeel and color properties of red wines. Fourier transform mid-infrared spectroscopy allows fast measurement of different wine components, but quantification of tannins is difficult due to interferences from spectral responses of other wine components. Four different variable selection tools were investigated for the identification of the most important spectral regions which would allow quantification of tannins from the spectra using partial least-squares regression. The study included the development of a new variable selection tool, iterative backward elimination of changeable size intervals PLS. The spectral regions identified by the different variable selection methods were not identical, but all included two regions (1485-1425 and 1060-995 cm(-1)), which therefore were concluded to be particularly important for tannin quantification. The spectral regions identified from the variable selection methods were used to develop calibration models. All four variable selection methods identified regions that allowed an improved quantitative prediction of tannins (RMSEP = 69-79 mg of CE/L; r = 0.93-0.94) as compared to a calibration model developed using all variables (RMSEP = 115 mg of CE/L; r = 0.87). Only minor differences in the performance of the variable selection methods were observed.

New Variable Porosity Flow Diverter (VPOD) Stent Design for Treatment of Cerebrovascular Aneurysms

PubMed Central

Ionita, Ciprian; Baier, Robert; Rudin, Stephen

2012-01-01

Using flow diverting Stents for intracranial aneurysm repair has been an area of recent active research. While current commercial flow diverting stents rely on a dense mesh of braided coils for flow diversion, our group has been developing a method to selectively occlude the aneurysm neck, without endangering nearby perforator vessels. In this paper, we present a new method of fabricating the low porosity patch, a key element of such asymmetric vascular stents (AVS). PMID:22254507

Comparisons of rational engineering correlations of thermophoretically-augmented particle mass transfer with STAN5-predictions for developing boundary layers

NASA Technical Reports Server (NTRS)

Gokoglu, S. A.; Rosner, D. E.

1984-01-01

Modification of the code STAN5 to properly include thermophoretic mass transport, and examination of selected test cases developing boundary layers which include variable properties, viscous dissipation, transition to turbulence and transpiration cooling. Under conditions representative of current and projected GT operation, local application of St(M)/St(M),o correlations evidently provides accurate and economical engineering design predictions, especially for suspended particles characterized by Schmidt numbers outside of the heavy vapor range.

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.

No difference in variability of unique hue selections and binary hue selections.

PubMed

Bosten, J M; Lawrance-Owen, A J

2014-04-01

If unique hues have special status in phenomenological experience as perceptually pure, it seems reasonable to assume that they are represented more precisely by the visual system than are other colors. Following the method of Malkoc et al. (J. Opt. Soc. Am. A22, 2154 [2005]), we gathered unique and binary hue selections from 50 subjects. For these subjects we repeated the measurements in two separate sessions, allowing us to measure test-retest reliabilities (0.52≤ρ≤0.78; p≪0.01). We quantified the within-individual variability for selections of each hue. Adjusting for the differences in variability intrinsic to different regions of chromaticity space, we compared the within-individual variability for unique hues to that for binary hues. Surprisingly, we found that selections of unique hues did not show consistently lower variability than selections of binary hues. We repeated hue measurements in a single session for an independent sample of 58 subjects, using a different relative scaling of the cardinal axes of MacLeod-Boynton chromaticity space. Again, we found no consistent difference in adjusted within-individual variability for selections of unique and binary hues. Our finding does not depend on the particular scaling chosen for the Y axis of MacLeod-Boynton chromaticity space.

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.

Variable screening via quantile partial correlation

PubMed Central

Ma, Shujie; Tsai, Chih-Ling

2016-01-01

In quantile linear regression with ultra-high dimensional data, we propose an algorithm for screening all candidate variables and subsequently selecting relevant predictors. Specifically, we first employ quantile partial correlation for screening, and then we apply the extended Bayesian information criterion (EBIC) for best subset selection. Our proposed method can successfully select predictors when the variables are highly correlated, and it can also identify variables that make a contribution to the conditional quantiles but are marginally uncorrelated or weakly correlated with the response. Theoretical results show that the proposed algorithm can yield the sure screening set. By controlling the false selection rate, model selection consistency can be achieved theoretically. In practice, we proposed using EBIC for best subset selection so that the resulting model is screening consistent. Simulation studies demonstrate that the proposed algorithm performs well, and an empirical example is presented. PMID:28943683

Selection of key ambient particulate variables for epidemiological studies - applying cluster and heatmap analyses as tools for data reduction.

PubMed

Gu, Jianwei; Pitz, Mike; Breitner, Susanne; Birmili, Wolfram; von Klot, Stephanie; Schneider, Alexandra; Soentgen, Jens; Reller, Armin; Peters, Annette; Cyrys, Josef

2012-10-01

The success of epidemiological studies depends on the use of appropriate exposure variables. The purpose of this study is to extract a relatively small selection of variables characterizing ambient particulate matter from a large measurement data set. The original data set comprised a total of 96 particulate matter variables that have been continuously measured since 2004 at an urban background aerosol monitoring site in the city of Augsburg, Germany. Many of the original variables were derived from measured particle size distribution (PSD) across the particle diameter range 3 nm to 10 μm, including size-segregated particle number concentration, particle length concentration, particle surface concentration and particle mass concentration. The data set was complemented by integral aerosol variables. These variables were measured by independent instruments, including black carbon, sulfate, particle active surface concentration and particle length concentration. It is obvious that such a large number of measured variables cannot be used in health effect analyses simultaneously. The aim of this study is a pre-screening and a selection of the key variables that will be used as input in forthcoming epidemiological studies. In this study, we present two methods of parameter selection and apply them to data from a two-year period from 2007 to 2008. We used the agglomerative hierarchical cluster method to find groups of similar variables. In total, we selected 15 key variables from 9 clusters which are recommended for epidemiological analyses. We also applied a two-dimensional visualization technique called "heatmap" analysis to the Spearman correlation matrix. 12 key variables were selected using this method. Moreover, the positive matrix factorization (PMF) method was applied to the PSD data to characterize the possible particle sources. Correlations between the variables and PMF factors were used to interpret the meaning of the cluster and the heatmap analyses. Copyright © 2012 Elsevier B.V. All rights reserved.

The role of multidimensional attentional abilities in academic skills of children with ADHD.

PubMed

Preston, Andrew S; Heaton, Shelley C; McCann, Sarah J; Watson, William D; Selke, Gregg

2009-01-01

Despite reports of academic difficulties in children with attention-deficit/hyperactivity disorder (ADHD), little is known about the relationship between performance on tests of academic achievement and measures of attention. The current study assessed intellectual ability, parent-reported inattention, academic achievement, and attention in 45 children (ages 7-15) diagnosed with ADHD. Hierarchical regressions were performed with selective, sustained, and attentional control/switching domains of the Test of Everyday Attention for Children as predictor variables and with performance on the Wechsler Individual Achievement Test-Second Edition as dependent variables. It was hypothesized that sustained attention and attentional control/switching would predict performance on achievement tests. Results demonstrate that attentional control/ switching accounted for a significant amount of variance in all academic areas (reading, math, and spelling), even after accounting for verbal IQ and parent-reported inattention. Sustained attention predicted variance only in math, whereas selective attention did not account for variance in any achievement domain. Therefore, attentional control/switching, which involves components of executive functions, plays an important role in academic performance.

Identification and ranking of environmental threats with ecosystem vulnerability distributions.

PubMed

Zijp, Michiel C; Huijbregts, Mark A J; Schipper, Aafke M; Mulder, Christian; Posthuma, Leo

2017-08-24

Responses of ecosystems to human-induced stress vary in space and time, because both stressors and ecosystem vulnerabilities vary in space and time. Presently, ecosystem impact assessments mainly take into account variation in stressors, without considering variation in ecosystem vulnerability. We developed a method to address ecosystem vulnerability variation by quantifying ecosystem vulnerability distributions (EVDs) based on monitoring data of local species compositions and environmental conditions. The method incorporates spatial variation of both abiotic and biotic variables to quantify variation in responses among species and ecosystems. We show that EVDs can be derived based on a selection of locations, existing monitoring data and a selected impact boundary, and can be used in stressor identification and ranking for a region. A case study on Ohio's freshwater ecosystems, with freshwater fish as target species group, showed that physical habitat impairment and nutrient loads ranked highest as current stressors, with species losses higher than 5% for at least 6% of the locations. EVDs complement existing approaches of stressor assessment and management, which typically account only for variability in stressors, by accounting for variation in the vulnerability of the responding ecosystems.

Bayesian Group Bridge for Bi-level Variable Selection.

PubMed

Mallick, Himel; Yi, Nengjun

2017-06-01

A Bayesian bi-level variable selection method (BAGB: Bayesian Analysis of Group Bridge) is developed for regularized regression and classification. This new development is motivated by grouped data, where generic variables can be divided into multiple groups, with variables in the same group being mechanistically related or statistically correlated. As an alternative to frequentist group variable selection methods, BAGB incorporates structural information among predictors through a group-wise shrinkage prior. Posterior computation proceeds via an efficient MCMC algorithm. In addition to the usual ease-of-interpretation of hierarchical linear models, the Bayesian formulation produces valid standard errors, a feature that is notably absent in the frequentist framework. Empirical evidence of the attractiveness of the method is illustrated by extensive Monte Carlo simulations and real data analysis. Finally, several extensions of this new approach are presented, providing a unified framework for bi-level variable selection in general models with flexible penalties.

Using the NANA toolkit at home to predict older adults' future depression.

PubMed

Andrews, J A; Harrison, R F; Brown, L J E; MacLean, L M; Hwang, F; Smith, T; Williams, E A; Timon, C; Adlam, T; Khadra, H; Astell, A J

2017-04-15

Depression is currently underdiagnosed among older adults. As part of the Novel Assessment of Nutrition and Aging (NANA) validation study, 40 older adults self-reported their mood using a touchscreen computer over three, one-week periods. Here, we demonstrate the potential of these data to predict future depression status. We analysed data from the NANA validation study using a machine learning approach. We applied the least absolute shrinkage and selection operator with a logistic model to averages of six measures of mood, with depression status according to the Geriatric Depression Scale 10 weeks later as the outcome variable. We tested multiple values of the selection parameter in order to produce a model with low deviance. We used a cross-validation framework to avoid overspecialisation, and receiver operating characteristic (ROC) curve analysis to determine the quality of the fitted model. The model we report contained coefficients for two variables: sadness and tiredness, as well as a constant. The cross-validated area under the ROC curve for this model was 0.88 (CI: 0.69-0.97). While results are based on a small sample, the methodology for the selection of variables appears suitable for the problem at hand, suggesting promise for a wider study and ultimate deployment with older adults at increased risk of depression. We have identified self-reported scales of sadness and tiredness as sensitive measures which have the potential to predict future depression status in older adults, partially addressing the problem of underdiagnosis. Copyright © 2017 The Authors. Published by Elsevier B.V. All rights reserved.

Environmental variability and acoustic signals: a multi-level approach in songbirds.

PubMed

Medina, Iliana; Francis, Clinton D

2012-12-23

Among songbirds, growing evidence suggests that acoustic adaptation of song traits occurs in response to habitat features. Despite extensive study, most research supporting acoustic adaptation has only considered acoustic traits averaged for species or populations, overlooking intraindividual variation of song traits, which may facilitate effective communication in heterogeneous and variable environments. Fewer studies have explicitly incorporated sexual selection, which, if strong, may favour variation across environments. Here, we evaluate the prevalence of acoustic adaptation among 44 species of songbirds by determining how environmental variability and sexual selection intensity are associated with song variability (intraindividual and intraspecific) and short-term song complexity. We show that variability in precipitation can explain short-term song complexity among taxonomically diverse songbirds, and that precipitation seasonality and the intensity of sexual selection are related to intraindividual song variation. Our results link song complexity to environmental variability, something previously found for mockingbirds (Family Mimidae). Perhaps more importantly, our results illustrate that individual variation in song traits may be shaped by both environmental variability and strength of sexual selection.

An evaluation of plastic surgery resident selection factors.

PubMed

Liang, Fan; Rudnicki, Pamela A; Prince, Noah H; Lipsitz, Stuart; May, James W; Guo, Lifei

2015-01-01

Our purpose was to provide a metric by which evaluation criteria are prioritized during resident selection. In this study, we assessed which residency applicant qualities are deemed important by members of the American Association of Plastic Surgeons (AAPS). A survey was distributed to all 580 AAPS members, and 295 responded to rate the importance of resident metrics, including measures of competency and personal characteristics. Demographic information, background training, and interaction with residents were also noted. Using SAS v9.2 (SAS Institute, Cary, NC), outcomes were analyzed across demographic groups with column trend exact (CTE) test for ordinal variables, Mantel-Haenszel trend test for interval variables, and Fisher exact test for discrete variables. Regarding competency metrics, letters of recommendation from known sources is the most important factor, whereas letters from unknown sources ranks the lowest. Character evaluations identified honesty as the most desirable trait; dishonesty was the most despised. Across demographic groups, academic surgeons and program directors value letters from known sources more than nonacademicians or nonprogram directors (CTE p = 0.005 and 0.002, respectively). Academicians and current program directors regard research more highly than their counterparts do (CTE p = 0.022 and 0.022, respectively). Currently, practicing surgeons, academicians, and program directors value hard work more than others (CTE p = 0.008, 0.033, and 0.029, respectively). Program directors emphasize maturity and patient commitment and are less tolerant of narcissism (CTE p = 0.002, 0.005, and 0.003, respectively). Lastly, academic surgeons and program directors look more favorably upon strong team players (CTE p < 0.00001 and p = 0.008, respectively), but less so over time (Mantel-Haenszel trend p = 0.006). We have examined applicant metrics that were deemed important by AAPS members and assessed their demographic interpretation. We hope this article provides a framework for plastic surgery resident selection and a guide for applicants to ascertain which qualities are highly regarded by programs. Although these attributes are highly desirable, future studies could identify if they are predictive of successful and productive plastic surgery residencies and careers. Copyright © 2014 Association of Program Directors in Surgery. Published by Elsevier Inc. All rights reserved.

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.

Distribution of Reynolds stress carried by mesoscale variability in the Antarctic Circumpolar Current

NASA Technical Reports Server (NTRS)

Johnson, Thomas J.; Stewart, Robert H.; Shum, C. K.; Tapley, Byron D.

1992-01-01

Satellite altimeter data collected by the Geosat Exact Repeat Mission were used to investigate turbulent stress resulting from the variability of surface geostrophic currents in the Antarctic Circumpolar Current. The altimeter measured sea level along the subsatellite track. The variability of the along-track slope of sea level is directly proportional to the variability of surface geostrophic currents in the cross-track direction. Because the grid of crossover points is dense at high latitudes, the satellite data could be used for mapping the temporal and spatial variability of the current. Two and a half years of data were used to compute the statistical structure of the variability. The statistics included the probability distribution functions for each component of the current, the time-lagged autocorrelation functions of the variability, and the Reynolds stress produced by the variability. The results demonstrate that stress is correlated with bathymetry. In some areas the distribution of negative stress indicate that eddies contribute to an acceleration of the mean flow, strengthening the hypothesis that baroclinic instability makes important contributions to strong oceanic currents.

Functional molecular markers (EST-SSR) in the full-sib reciprocal recurrent selection program of maize (Zea mays L.).

PubMed

Galvão, K S C; Ramos, H C C; Santos, P H A D; Entringer, G C; Vettorazzi, J C F; Pereira, M G

2015-07-03

This study aimed to improve grain yield in the full-sib reciprocal recurrent selection program of maize from the North Fluminense State University. In the current phase of the program, the goal is to maintain, or even increase, the genetic variability within and among populations, in order to increase heterosis of the 13th cycle of reciprocal recurrent selection. Microsatellite expressed sequence tags (EST-SSRs) were used as a tool to assist the maximization step of genetic variability, targeting the functional genome. Eighty S1 progenies of the 13th recur-rent selection cycle, 40 from each population (CIMMYT and Piranão), were analyzed using 20 EST-SSR loci. Genetic diversity, observed heterozygosity, information content of polymorphism, and inbreeding co-efficient were estimated. Subsequently, analysis of genetic dissimilarity, molecular variance, and a graphical dispersion of genotypes were conducted. The number of alleles in the CIMMYT population ranged from 1 to 6, while in the Piranão population the range was from 2 to 8, with a mean of 3.65 and 4.35, respectively. As evidenced by the number of alleles, the Shannon index showed greater diversity for the Piranão population (1.04) in relation to the CIMMYT population (0.89). The genic SSR markers were effective in clustering genotypes into their respective populations before selection and an increase in the variation between populations after selection was observed. The results indicate that the study populations have expressive genetic diversity, which cor-responds to the functional genome, indicating that this strategy may contribute to genetic gain, especially in association with the grain yield of future hybrids.

Fecundity selection on ornamental plumage colour differs between ages and sexes and varies over small spatial scales.

PubMed

Parker, T H; Wilkin, T A; Barr, I R; Sheldon, B C; Rowe, L; Griffith, S C

2011-07-01

Avian plumage colours are some of the most conspicuous sexual ornaments, and yet standardized selection gradients for plumage colour have rarely been quantified. We examined patterns of fecundity selection on plumage colour in blue tits (Cyanistes caeruleus L.). When not accounting for environmental heterogeneity, we detected relatively few cases of selection. We found significant disruptive selection on adult male crown colour and yearling female chest colour and marginally nonsignificant positive linear selection on adult female crown colour. We discovered no new significant selection gradients with canonical rotation of the matrix of nonlinear selection. Next, using a long-term data set, we identified territory-level environmental variables that predicted fecundity to determine whether these variables influenced patterns of plumage selection. The first of these variables, the density of oaks within 50 m of the nest, influenced selection gradients only for yearling males. The second variable, an inverse function of nesting density, interacted with a subset of plumage selection gradients for yearling males and adult females, although the strength and direction of selection did not vary predictably with population density across these analyses. Overall, fecundity selection on plumage colour in blue tits appeared rare and inconsistent among sexes and age classes. © 2011 The Authors. Journal of Evolutionary Biology © 2011 European Society For Evolutionary Biology.

Cardiac vagal tone is correlated with selective attention to neutral distractors under load.

PubMed

Park, Gewnhi; Vasey, Michael W; Van Bavel, Jay J; Thayer, Julian F

2013-04-01

We examined whether cardiac vagal tone (indexed by heart rate variability, HRV) was associated with the functioning of selective attention under load. Participants were instructed to detect a target letter among letter strings superimposed on either fearful or neutral distractor faces. Under low load, when letter strings consisted of six target letters, there was no difference between people with high and low HRV on task performance. Under high load, when letter strings consisted of one target letter and five nontarget letters, people with high HRV were faster in trials with neutral distractors, but not with fearful distractors. However, people with low HRV were slower in trials with both fearful and neutral distractors. The current research suggests cardiac vagal tone is associated with successful control of selective attention critical for goal-directed behavior, and its impact is greater when fewer cognitive resources are available. Copyright © 2013 Society for Psychophysiological Research.

Peptide-based protein capture agents with high affinity, selectivity, and stability as antibody replacements in biodetection assays

NASA Astrophysics Data System (ADS)

Coppock, Matthew B.; Farrow, Blake; Warner, Candice; Finch, Amethist S.; Lai, Bert; Sarkes, Deborah A.; Heath, James R.; Stratis-Cullum, Dimitra

2014-05-01

Current biodetection assays that employ monoclonal antibodies as primary capture agents exhibit limited fieldability, shelf life, and performance due to batch-to-batch production variability and restricted thermal stability. In order to improve upon the detection of biological threats in fieldable assays and systems for the Army, we are investigating protein catalyzed capture (PCC) agents as drop-in replacements for the existing antibody technology through iterative in situ click chemistry. The PCC agent oligopeptides are developed against known protein epitopes and can be mass produced using robotic methods. In this work, a PCC agent under development will be discussed. The performance, including affinity, selectivity, and stability of the capture agent technology, is analyzed by immunoprecipitation, western blotting, and ELISA experiments. The oligopeptide demonstrates superb selectivity coupled with high affinity through multi-ligand design, and improved thermal, chemical, and biochemical stability due to non-natural amino acid PCC agent design.

Dynamic optimization of open-loop input signals for ramp-up current profiles in tokamak plasmas

NASA Astrophysics Data System (ADS)

Ren, Zhigang; Xu, Chao; Lin, Qun; Loxton, Ryan; Teo, Kok Lay

2016-03-01

Establishing a good current spatial profile in tokamak fusion reactors is crucial to effective steady-state operation. The evolution of the current spatial profile is related to the evolution of the poloidal magnetic flux, which can be modeled in the normalized cylindrical coordinates using a parabolic partial differential equation (PDE) called the magnetic diffusion equation. In this paper, we consider the dynamic optimization problem of attaining the best possible current spatial profile during the ramp-up phase of the tokamak. We first use the Galerkin method to obtain a finite-dimensional ordinary differential equation (ODE) model based on the original magnetic diffusion PDE. Then, we combine the control parameterization method with a novel time-scaling transformation to obtain an approximate optimal parameter selection problem, which can be solved using gradient-based optimization techniques such as sequential quadratic programming (SQP). This control parameterization approach involves approximating the tokamak input signals by piecewise-linear functions whose slopes and break-points are decision variables to be optimized. We show that the gradient of the objective function with respect to the decision variables can be computed by solving an auxiliary dynamic system governing the state sensitivity matrix. Finally, we conclude the paper with simulation results for an example problem based on experimental data from the DIII-D tokamak in San Diego, California.

Process for applying control variables having fractal structures

DOEpatents

Bullock, IV, Jonathan S.; Lawson, Roger L.

1996-01-01

A process and apparatus for the application of a control variable having a fractal structure to a body or process. The process of the present invention comprises the steps of generating a control variable having a fractal structure and applying the control variable to a body or process reacting in accordance with the control variable. The process is applicable to electroforming where first, second and successive pulsed-currents are applied to cause the deposition of material onto a substrate, such that the first pulsed-current, the second pulsed-current, and successive pulsed currents form a fractal pulsed-current waveform.

Process for applying control variables having fractal structures

DOEpatents

Bullock, J.S. IV; Lawson, R.L.

1996-01-23

A process and apparatus are disclosed for the application of a control variable having a fractal structure to a body or process. The process of the present invention comprises the steps of generating a control variable having a fractal structure and applying the control variable to a body or process reacting in accordance with the control variable. The process is applicable to electroforming where first, second and successive pulsed-currents are applied to cause the deposition of material onto a substrate, such that the first pulsed-current, the second pulsed-current, and successive pulsed currents form a fractal pulsed-current waveform. 3 figs.

Quantification of Peptides from Immunoglobulin Constant and Variable Regions by Liquid Chromatography-Multiple Reaction Monitoring Mass Spectrometry for Assessment of Multiple Myeloma Patients

PubMed Central

Remily-Wood, Elizabeth R.; Benson, Kaaron; Baz, Rachid C.; Chen, Y. Ann; Hussein, Mohamad; Hartley-Brown, Monique A.; Sprung, Robert W.; Perez, Brianna; Liu, Richard Z.; Yoder, Sean; Teer, Jamie; Eschrich, Steven A.; Koomen, John M.

2014-01-01

Purpose Quantitative mass spectrometry assays for immunoglobulins (Igs) are compared with existing clinical methods in samples from patients with plasma cell dyscrasias, e.g. multiple myeloma. Experimental design Using LC-MS/MS data, Ig constant region peptides and transitions were selected for liquid chromatography-multiple reaction monitoring mass spectrometry (LC-MRM). Quantitative assays were used to assess Igs in serum from 83 patients. Results LC-MRM assays quantify serum levels of Igs and their isoforms (IgG1–4, IgA1–2, IgM, IgD, and IgE, as well as kappa(κ) and lambda(λ) light chains). LC-MRM quantification has been applied to single samples from a patient cohort and a longitudinal study of an IgE patient undergoing treatment, to enable comparison with existing clinical methods. Proof-of-concept data for defining and monitoring variable region peptides are provided using the H929 multiple myeloma cell line and two MM patients. Conclusions and Clinical Relevance LC-MRM assays targeting constant region peptides determine the type and isoform of the involved immunoglobulin and quantify its expression; the LC-MRM approach has improved sensitivity compared with the current clinical method, but slightly higher interassay variability. Detection of variable region peptides is a promising way to improve Ig quantification, which could produce a dramatic increase in sensitivity over existing methods, and could further complement current clinical techniques. PMID:24723328

Quantification of peptides from immunoglobulin constant and variable regions by LC-MRM MS for assessment of multiple myeloma patients.

PubMed

Remily-Wood, Elizabeth R; Benson, Kaaron; Baz, Rachid C; Chen, Y Ann; Hussein, Mohamad; Hartley-Brown, Monique A; Sprung, Robert W; Perez, Brianna; Liu, Richard Z; Yoder, Sean J; Teer, Jamie K; Eschrich, Steven A; Koomen, John M

2014-10-01

Quantitative MS assays for Igs are compared with existing clinical methods in samples from patients with plasma cell dyscrasias, for example, multiple myeloma (MM). Using LC-MS/MS data, Ig constant region peptides, and transitions were selected for LC-MRM MS. Quantitative assays were used to assess Igs in serum from 83 patients. RNA sequencing and peptide-based LC-MRM are used to define peptides for quantification of the disease-specific Ig. LC-MRM assays quantify serum levels of Igs and their isoforms (IgG1-4, IgA1-2, IgM, IgD, and IgE, as well as kappa (κ) and lambda (λ) light chains). LC-MRM quantification has been applied to single samples from a patient cohort and a longitudinal study of an IgE patient undergoing treatment, to enable comparison with existing clinical methods. Proof-of-concept data for defining and monitoring variable region peptides are provided using the H929 MM cell line and two MM patients. LC-MRM assays targeting constant region peptides determine the type and isoform of the involved Ig and quantify its expression; the LC-MRM approach has improved sensitivity compared with the current clinical method, but slightly higher inter-assay variability. Detection of variable region peptides is a promising way to improve Ig quantification, which could produce a dramatic increase in sensitivity over existing methods, and could further complement current clinical techniques. © 2014 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Nottingham Prognostic Index Plus (NPI+): a modern clinical decision making tool in breast cancer.

PubMed

Rakha, E A; Soria, D; Green, A R; Lemetre, C; Powe, D G; Nolan, C C; Garibaldi, J M; Ball, G; Ellis, I O

2014-04-02

Current management of breast cancer (BC) relies on risk stratification based on well-defined clinicopathologic factors. Global gene expression profiling studies have demonstrated that BC comprises distinct molecular classes with clinical relevance. In this study, we hypothesised that molecular features of BC are a key driver of tumour behaviour and when coupled with a novel and bespoke application of established clinicopathologic prognostic variables can predict both clinical outcome and relevant therapeutic options more accurately than existing methods. In the current study, a comprehensive panel of biomarkers with relevance to BC was applied to a large and well-characterised series of BC, using immunohistochemistry and different multivariate clustering techniques, to identify the key molecular classes. Subsequently, each class was further stratified using a set of well-defined prognostic clinicopathologic variables. These variables were combined in formulae to prognostically stratify different molecular classes, collectively known as the Nottingham Prognostic Index Plus (NPI+). The NPI+ was then used to predict outcome in the different molecular classes. Seven core molecular classes were identified using a selective panel of 10 biomarkers. Incorporation of clinicopathologic variables in a second-stage analysis resulted in identification of distinct prognostic groups within each molecular class (NPI+). Outcome analysis showed that using the bespoke NPI formulae for each biological BC class provides improved patient outcome stratification superior to the traditional NPI. This study provides proof-of-principle evidence for the use of NPI+ in supporting improved individualised clinical decision making.

Variable Selection for Nonparametric Quantile Regression via Smoothing Spline AN OVA

PubMed Central

Lin, Chen-Yen; Bondell, Howard; Zhang, Hao Helen; Zou, Hui

2014-01-01

Quantile regression provides a more thorough view of the effect of covariates on a response. Nonparametric quantile regression has become a viable alternative to avoid restrictive parametric assumption. The problem of variable selection for quantile regression is challenging, since important variables can influence various quantiles in different ways. We tackle the problem via regularization in the context of smoothing spline ANOVA models. The proposed sparse nonparametric quantile regression (SNQR) can identify important variables and provide flexible estimates for quantiles. Our numerical study suggests the promising performance of the new procedure in variable selection and function estimation. Supplementary materials for this article are available online. PMID:24554792

An international delphi survey for the definition of the variables for the development of new classification criteria for periodic fever aphtous stomatitis pharingitis cervical adenitis (PFAPA).

PubMed

Vanoni, Federica; Federici, Silvia; Antón, Jordi; Barron, Karyl S; Brogan, Paul; De Benedetti, Fabrizio; Dedeoglu, Fatma; Demirkaya, Erkan; Hentgen, Veronique; Kallinich, Tilmann; Laxer, Ronald; Russo, Ricardo; Toplak, Natasa; Uziel, Yosef; Martini, Alberto; Ruperto, Nicolino; Gattorno, Marco; Hofer, Michael

2018-04-18

Diagnosis of Periodic fever, aphthous stomatitis, pharyngitis and cervical adenitis (PFAPA) is currently based on a set of criteria proposed in 1999 modified from Marshall's criteria. Nevertheless no validated evidence based set of classification criteria for PFAPA has been established so far. The aim of this study was to identify candidate classification criteria PFAPA syndrome using international consensus formation through a Delphi questionnaire survey. A first open-ended questionnaire was sent to adult and pediatric clinicians/researchers, asking to identify the variables thought most likely to be helpful and relevant for the diagnosis of PFAPA. In a second survey, respondents were asked to select, from the list of variables coming from the first survey, the 10 features that they felt were most important, and to rank them in descending order from most important to least important. The response rate to the first and second Delphi was respectively 109/124 (88%) and 141/162 (87%). The number of participants that completed the first and second Delphi was 69/124 (56%) and 110/162 (68%). From the first Delphi we obtained a list of 92 variables, of which 62 were selected in the second Delphi. Variables reaching the top five position of the rank were regular periodicity, aphthous stomatitis, response to corticosteroids, cervical adenitis, and well-being between flares. Our process led to identification of features that were felt to be the most important as candidate classification criteria for PFAPA by a large sample of international rheumatologists. The performance of these items will be tested further in the next phase of the study, through analysis of real patient data.

To pair or not to pair: Sources of social variability with white-faced saki monkeys (Pithecia pithecia) as a case study.

PubMed

Thompson, Cynthia L

2016-05-01

Intraspecific variability in social systems is gaining increased recognition in primatology. Many primate species display variability in pair-living social organizations through incorporating extra adults into the group. While numerous models exist to explain primate pair-living, our tools to assess how and why variation in this trait occurs are currently limited. Here I outline an approach which: (i) utilizes conceptual models to identify the selective forces driving pair-living; (ii) outlines novel possible causes for variability in social organization; and (iii) conducts a holistic species-level analysis of social behavior to determine the factors contributing to variation in pair-living. A case study on white-faced sakis (Pithecia pithecia) is used to exemplify this approach. This species lives in either male-female pairs or groups incorporating "extra" adult males and/or females. Various conceptual models of pair-living suggest that high same-sex aggression toward extra-group individuals is a key component of the white-faced saki social system. Variable pair-living in white-faced sakis likely represents alternative strategies to achieve competency in this competition, in which animals experience conflicting selection pressures between achieving successful group defense and maintaining sole reproductive access to mates. Additionally, independent decisions by individuals may generate social variation by preventing other animals from adopting a social organization that maximizes fitness. White-faced saki inter-individual relationships and demographic patterns also lend conciliatory support to this conclusion. By utilizing both model-level and species-level approaches, with a consideration for potential sources of variation, researchers can gain insight into the factors generating variation in pair-living social organizations. © 2014 The Authors. American Journal of Primatology published by Wiley Periodicals, Inc.

Face-Likeness and Image Variability Drive Responses in Human Face-Selective Ventral Regions

PubMed Central

Davidenko, Nicolas; Remus, David A.; Grill-Spector, Kalanit

2012-01-01

The human ventral visual stream contains regions that respond selectively to faces over objects. However, it is unknown whether responses in these regions correlate with how face-like stimuli appear. Here, we use parameterized face silhouettes to manipulate the perceived face-likeness of stimuli and measure responses in face- and object-selective ventral regions with high-resolution fMRI. We first use “concentric hyper-sphere” (CH) sampling to define face silhouettes at different distances from the prototype face. Observers rate the stimuli as progressively more face-like the closer they are to the prototype face. Paradoxically, responses in both face- and object-selective regions decrease as face-likeness ratings increase. Because CH sampling produces blocks of stimuli whose variability is negatively correlated with face-likeness, this effect may be driven by more adaptation during high face-likeness (low-variability) blocks than during low face-likeness (high-variability) blocks. We tested this hypothesis by measuring responses to matched-variability (MV) blocks of stimuli with similar face-likeness ratings as with CH sampling. Critically, under MV sampling, we find a face-specific effect: responses in face-selective regions gradually increase with perceived face-likeness, but responses in object-selective regions are unchanged. Our studies provide novel evidence that face-selective responses correlate with the perceived face-likeness of stimuli, but this effect is revealed only when image variability is controlled across conditions. Finally, our data show that variability is a powerful factor that drives responses across the ventral stream. This indicates that controlling variability across conditions should be a critical tool in future neuroimaging studies of face and object representation. PMID:21823208

Uniting statistical and individual-based approaches for animal movement modelling.

PubMed

Latombe, Guillaume; Parrott, Lael; Basille, Mathieu; Fortin, Daniel

2014-01-01

The dynamic nature of their internal states and the environment directly shape animals' spatial behaviours and give rise to emergent properties at broader scales in natural systems. However, integrating these dynamic features into habitat selection studies remains challenging, due to practically impossible field work to access internal states and the inability of current statistical models to produce dynamic outputs. To address these issues, we developed a robust method, which combines statistical and individual-based modelling. Using a statistical technique for forward modelling of the IBM has the advantage of being faster for parameterization than a pure inverse modelling technique and allows for robust selection of parameters. Using GPS locations from caribou monitored in Québec, caribou movements were modelled based on generative mechanisms accounting for dynamic variables at a low level of emergence. These variables were accessed by replicating real individuals' movements in parallel sub-models, and movement parameters were then empirically parameterized using Step Selection Functions. The final IBM model was validated using both k-fold cross-validation and emergent patterns validation and was tested for two different scenarios, with varying hardwood encroachment. Our results highlighted a functional response in habitat selection, which suggests that our method was able to capture the complexity of the natural system, and adequately provided projections on future possible states of the system in response to different management plans. This is especially relevant for testing the long-term impact of scenarios corresponding to environmental configurations that have yet to be observed in real systems.

Uniting Statistical and Individual-Based Approaches for Animal Movement Modelling

PubMed Central

Latombe, Guillaume; Parrott, Lael; Basille, Mathieu; Fortin, Daniel

2014-01-01

The dynamic nature of their internal states and the environment directly shape animals' spatial behaviours and give rise to emergent properties at broader scales in natural systems. However, integrating these dynamic features into habitat selection studies remains challenging, due to practically impossible field work to access internal states and the inability of current statistical models to produce dynamic outputs. To address these issues, we developed a robust method, which combines statistical and individual-based modelling. Using a statistical technique for forward modelling of the IBM has the advantage of being faster for parameterization than a pure inverse modelling technique and allows for robust selection of parameters. Using GPS locations from caribou monitored in Québec, caribou movements were modelled based on generative mechanisms accounting for dynamic variables at a low level of emergence. These variables were accessed by replicating real individuals' movements in parallel sub-models, and movement parameters were then empirically parameterized using Step Selection Functions. The final IBM model was validated using both k-fold cross-validation and emergent patterns validation and was tested for two different scenarios, with varying hardwood encroachment. Our results highlighted a functional response in habitat selection, which suggests that our method was able to capture the complexity of the natural system, and adequately provided projections on future possible states of the system in response to different management plans. This is especially relevant for testing the long-term impact of scenarios corresponding to environmental configurations that have yet to be observed in real systems. PMID:24979047

Threats to the Internal Validity of Experimental and Quasi-Experimental Research in Healthcare.

PubMed

Flannelly, Kevin J; Flannelly, Laura T; Jankowski, Katherine R B

2018-01-01

The article defines, describes, and discusses the seven threats to the internal validity of experiments discussed by Donald T. Campbell in his classic 1957 article: history, maturation, testing, instrument decay, statistical regression, selection, and mortality. These concepts are said to be threats to the internal validity of experiments because they pose alternate explanations for the apparent causal relationship between the independent variable and dependent variable of an experiment if they are not adequately controlled. A series of simple diagrams illustrate three pre-experimental designs and three true experimental designs discussed by Campbell in 1957 and several quasi-experimental designs described in his book written with Julian C. Stanley in 1966. The current article explains why each design controls for or fails to control for these seven threats to internal validity.

Predictors of Outcomes in Autism Early Intervention: Why Don’t We Know More?

PubMed Central

Vivanti, Giacomo; Prior, Margot; Williams, Katrina; Dissanayake, Cheryl

2014-01-01

Response to early intervention programs in autism is variable. However, the factors associated with positive versus poor treatment outcomes remain unknown. Hence the issue of which intervention/s should be chosen for an individual child remains a common dilemma. We argue that lack of knowledge on “what works for whom and why” in autism reflects a number of issues in current approaches to outcomes research, and we provide recommendations to address these limitations. These include: a theory-driven selection of putative predictors; the inclusion of proximal measures that are directly relevant to the learning mechanisms demanded by the specific educational strategies; the consideration of family characteristics. Moreover, all data on associations between predictor and outcome variables should be reported in treatment studies. PMID:24999470

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

Dayman, Ken J; Ade, Brian J; Weber, Charles F

High-dimensional, nonlinear function estimation using large datasets is a current area of interest in the machine learning community, and applications may be found throughout the analytical sciences, where ever-growing datasets are making more information available to the analyst. In this paper, we leverage the existing relevance vector machine, a sparse Bayesian version of the well-studied support vector machine, and expand the method to include integrated feature selection and automatic function shaping. These innovations produce an algorithm that is able to distinguish variables that are useful for making predictions of a response from variables that are unrelated or confusing. We testmore » the technology using synthetic data, conduct initial performance studies, and develop a model capable of making position-independent predictions of the coreaveraged burnup using a single specimen drawn randomly from a nuclear reactor core.« less

Smallholder agriculture in India and adaptation to current and future climate variability and climate change

NASA Astrophysics Data System (ADS)

Murari, K. K.; Jayaraman, T.

2014-12-01

Modeling studies have indicated that global warming, in many regions, will increase the exposure of major crops to rainfall and temperature stress, leading to lower crop yields. Climate variability alone has a potential to decrease yield to an extent comparable to or greater than yield reductions expected due to rising temperature. For India, where agriculture is important, both in terms of food security as well as a source of livelihoods to a majority of its population, climate variability and climate change are subjects of serious concern. There is however a need to distinguish the impact of current climate variability and climate change on Indian agriculture, especially in relation to their socioeconomic impact. This differentiation is difficult to determine due to the secular trend of increasing production and yield of the past several decades. The current research in this aspect is in an initial stage and requires a multi-disciplinary effort. In this study, we assess the potential differential impacts of environmental stress and shock across different socioeconomic strata of the rural population, using village level survey data. The survey data from eight selected villages, based on the Project on Agrarian Relations in India conducted by the Foundation for Agrarian Studies, indicated that income from crop production of the top 20 households (based on the extent of operational land holding, employment of hired labour and asset holdings) is a multiple of the mean income of the village. In sharp contrast, the income of the bottom 20 households is a fraction of the mean and sometimes negative, indicating a net loss from crop production. The considerable differentials in output and incomes suggest that small and marginal farmers are far more susceptible to climate variability and climate change than the other sections. Climate change is effectively an immediate threat to small and marginal farmers, which is driven essentially by socioeconomic conditions. The impact of climate variability on smallholder agriculture in the present can therefore provide important insights into the nature of its vulnerability to future climate change.

New insights into time series analysis. II - Non-correlated observations

NASA Astrophysics Data System (ADS)

Ferreira Lopes, C. E.; Cross, N. J. G.

2017-08-01

Context. Statistical parameters are used to draw conclusions in a vast number of fields such as finance, weather, industrial, and science. These parameters are also used to identify variability patterns on photometric data to select non-stochastic variations that are indicative of astrophysical effects. New, more efficient, selection methods are mandatory to analyze the huge amount of astronomical data. Aims: We seek to improve the current methods used to select non-stochastic variations on non-correlated data. Methods: We used standard and new data-mining parameters to analyze non-correlated data to find the best way to discriminate between stochastic and non-stochastic variations. A new approach that includes a modified Strateva function was performed to select non-stochastic variations. Monte Carlo simulations and public time-domain data were used to estimate its accuracy and performance. Results: We introduce 16 modified statistical parameters covering different features of statistical distribution such as average, dispersion, and shape parameters. Many dispersion and shape parameters are unbound parameters, I.e. equations that do not require the calculation of average. Unbound parameters are computed with single loop and hence decreasing running time. Moreover, the majority of these parameters have lower errors than previous parameters, which is mainly observed for distributions with few measurements. A set of non-correlated variability indices, sample size corrections, and a new noise model along with tests of different apertures and cut-offs on the data (BAS approach) are introduced. The number of mis-selections are reduced by about 520% using a single waveband and 1200% combining all wavebands. On the other hand, the even-mean also improves the correlated indices introduced in Paper I. The mis-selection rate is reduced by about 18% if the even-mean is used instead of the mean to compute the correlated indices in the WFCAM database. Even-statistics allows us to improve the effectiveness of both correlated and non-correlated indices. Conclusions: The selection of non-stochastic variations is improved by non-correlated indices. The even-averages provide a better estimation of mean and median for almost all statistical distributions analyzed. The correlated variability indices, which are proposed in the first paper of this series, are also improved if the even-mean is used. The even-parameters will also be useful for classifying light curves in the last step of this project. We consider that the first step of this project, where we set new techniques and methods that provide a huge improvement on the efficiency of selection of variable stars, is now complete. Many of these techniques may be useful for a large number of fields. Next, we will commence a new step of this project regarding the analysis of period search methods.

Spectral irradiance variations: comparison between observations and the SATIRE model on solar rotation time scales

NASA Astrophysics Data System (ADS)

Unruh, Y. C.; Krivova, N. A.; Solanki, S. K.; Harder, J. W.; Kopp, G.

2008-07-01

Aims: We test the reliability of the observed and calculated spectral irradiance variations between 200 and 1600 nm over a time span of three solar rotations in 2004. Methods: We compare our model calculations to spectral irradiance observations taken with SORCE/SIM, SoHO/VIRGO, and UARS/SUSIM. The calculations assume LTE and are based on the SATIRE (Spectral And Total Irradiance REconstruction) model. We analyse the variability as a function of wavelength and present time series in a number of selected wavelength regions covering the UV to the NIR. We also show the facular and spot contributions to the total calculated variability. Results: In most wavelength regions, the variability agrees well between all sets of observations and the model calculations. The model does particularly well between 400 and 1300 nm, but fails below 220 nm, as well as for some of the strong NUV lines. Our calculations clearly show the shift from faculae-dominated variability in the NUV to spot-dominated variability above approximately 400 nm. We also discuss some of the remaining problems, such as the low sensitivity of SUSIM and SORCE for wavelengths between approximately 310 and 350 nm, where currently the model calculations still provide the best estimates of solar variability.

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.

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

Design of a High Temperature Radiator for the Variable Specific Impulse Magnetoplasma Rocket

NASA Technical Reports Server (NTRS)

Sheth, Rubik B.; Ungar, Eugene K.; Chambliss, Joe P.

2012-01-01

The Variable Specific Impulse Magnetoplasma Rocket (VASIMR), currently under development by Ad Astra Rocket Company (Webster, TX), is a unique propulsion system that could change the way space propulsion is performed. VASIMR's efficiency, when compared to that of a conventional chemical rocket, reduces the propellant needed for exploration missions by a factor of 10. Currently plans include flight tests of a 200 kW VASIMR system, titled VF-200, on the International Space Station (ISS). The VF-200 will consist of two 100 kW thruster units packaged together in one engine bus. Each thruster core generates 27 kW of waste heat during its 15 minute firing time. The rocket core will be maintained between 283 and 573 K by a pumped thermal control loop. The design of a high temperature radiator is a unique challenge for the vehicle design. This paper will discuss the path taken to develop a steady state and transient-based radiator design. The paper will describe the radiator design option selected for the VASIMR thermal control system for use on ISS, and how the system relates to future exploration vehicles.

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.

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.

Exhaustive Search for Sparse Variable Selection in Linear Regression

NASA Astrophysics Data System (ADS)

Igarashi, Yasuhiko; Takenaka, Hikaru; Nakanishi-Ohno, Yoshinori; Uemura, Makoto; Ikeda, Shiro; Okada, Masato

2018-04-01

We propose a K-sparse exhaustive search (ES-K) method and a K-sparse approximate exhaustive search method (AES-K) for selecting variables in linear regression. With these methods, K-sparse combinations of variables are tested exhaustively assuming that the optimal combination of explanatory variables is K-sparse. By collecting the results of exhaustively computing ES-K, various approximate methods for selecting sparse variables can be summarized as density of states. With this density of states, we can compare different methods for selecting sparse variables such as relaxation and sampling. For large problems where the combinatorial explosion of explanatory variables is crucial, the AES-K method enables density of states to be effectively reconstructed by using the replica-exchange Monte Carlo method and the multiple histogram method. Applying the ES-K and AES-K methods to type Ia supernova data, we confirmed the conventional understanding in astronomy when an appropriate K is given beforehand. However, we found the difficulty to determine K from the data. Using virtual measurement and analysis, we argue that this is caused by data shortage.

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.

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.

Projecting climate effects on birds and reptiles of the Southwestern United States

USGS Publications Warehouse

van Riper, Charles; Hatten, James R.; Giermakowski, J. Tomasz; Mattson, David; Holmes, Jennifer A.; Johnson, Matthew J.; Nowak, Erika M.; Ironside, Kirsten; Peters, Michael; Heinrich, Paul; Cole, K.L.; Truettner, C.; Schwalbe, Cecil R.

2014-01-01

We modeled the current and future breeding ranges of seven bird and five reptile species in the Southwestern United States with sets of landscape, biotic (plant), and climatic global circulation model (GCM) variables. For modeling purposes, we used PRISM data to characterize the climate of the Western United States between 1980 and 2009 (baseline for birds) and between 1940 and 2009 (baseline for reptiles). In contrast, we used a pre-selected set of GCMs that are known to be good predictors of southwestern climate (five individual and one ensemble GCM), for the A1B emission scenario, to characterize future climatic conditions in three time periods (2010–39; 2040–69; and, 2070–99). Our modeling approach relied on conceptual models for each target species to inform selection of candidate explanatory variables and to interpret the ecological meaning of developed probabilistic distribution models. We employed logistic regression and maximum entropy modeling techniques to create a set of probabilistic models for each target species. We considered climatic, landscape, and plant variables when developing and testing our probabilistic models. Climatic variables included the maximum and minimum mean monthly and seasonal temperature and precipitation for three time periods. Landscape features included terrain ruggedness and insolation. We also considered plant species distributions as candidate explanatory variables where prior ecological knowledge implicated a strong association between a plant and animal species. Projected changes in range varied widely among species, from major losses to major gains. Breeding bird ranges exhibited greater expansions and contractions than did reptile species. We project range losses for Williamson’s sapsucker and pygmy nuthatch of a magnitude that could move these two species close to extinction within the next century. Although both species currently have a relatively limited distribution, they can be locally common, and neither are presently considered candidates for prospective endangerment. We project range losses of over 40 percent, from its current extent of occurrence, for the plateau striped whiptail, Arizona black rattlesnake, and common lesser earless lizard. Currently, these reptile species are thought to be common or at least locally abundant throughout their ranges. The total contribution of plants in each distribution model was very small, but models that contained at least one plant always outperformed models with only physical variables (climatic or landscape). The magnitude of change in projected range increased further into the future, especially when a plant was in the model. Among bird species, those that had the strongest association with a landscape feature during the breeding season, such as terrain ruggedness and insolation, exhibited the smallest contractions in projected breeding range in the future. In contrast, bird species that had weak associations with landscape features, but strong climatic associations, suffered the greatest breeding range contractions. Thus, landscape effects appeared to buffer some of the negative effects of climate change for some species. Among bird species, magnitude of change in projected breeding range was positively related to the annual average temperature of their baseline distribution, thus species with the warmest breeding ranges exhibited the greatest changes in future breeding ranges. This pattern was not evident for reptiles, but might exist if additional species were included in the model. Our results provide managers with a series of projected range maps that will enable scientists, concerned citizens, and wildlife managers to identify what the potential effects of climate change will be on bird and reptile distributions in the Western United States. We hope that our results can be used in proactive ways to mitigate some of the potential effects of climate change on selected species.

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.

Where the wild things are: Predicting hotspots of seabird aggregations in the California Current System

USGS Publications Warehouse

Nur, N.; Jahncke, J.; Herzog, M.P.; Howar, J.; Hyrenbach, K.D.; Zamon, J.E.; Ainley, D.G.; Wiens, J.A.; Morgan, K.; Balance, L.T.; Stralberg, D.

2011-01-01

Marine Protected Areas (MPAs) provide an important tool for conservation of marine ecosystems. To be most effective, these areas should be strategically located in a manner that supports ecosystem function. To inform marine spatial planning and support strategic establishment of MPAs within the California Current System, we identified areas predicted to support multispecies aggregations of seabirds ("hotspot????). We developed habitat-association models for 16 species using information from at-sea observations collected over an 11-year period (1997-2008), bathymetric data, and remotely sensed oceanographic data for an area from north of Vancouver Island, Canada, to the USA/Mexico border and seaward 600 km from the coast. This approach enabled us to predict distribution and abundance of seabirds even in areas of few or no surveys. We developed single-species predictive models using a machine-learning algorithm: bagged decision trees. Single-species predictions were then combined to identify potential hotspots of seabird aggregation, using three criteria: (1) overall abundance among species, (2) importance of specific areas ("core area????) to individual species, and (3) predicted persistence of hotspots across years. Model predictions were applied to the entire California Current for four seasons (represented by February, May, July, and October) in each of 11 years. Overall, bathymetric variables were often important predictive variables, whereas oceanographic variables derived from remotely sensed data were generally less important. Predicted hotspots often aligned with currently protected areas (e.g., National Marine Sanctuaries), but we also identified potential hotspots in Northern California/Southern Oregon (from Cape Mendocino to Heceta Bank), Southern California (adjacent to the Channel Islands), and adjacent to Vancouver Island, British Columbia, that are not currently included in protected areas. Prioritization and identification of multispecies hotspots will depend on which group of species is of highest management priority. Modeling hotspots at a broad spatial scale can contribute to MPA site selection, particularly if complemented by fine-scale information for focal areas. ?? 2011 by the Ecological Society of America.

AC/DC current ratio in a current superimposition variable flux reluctance machine

NASA Astrophysics Data System (ADS)

Kohara, Akira; Hirata, Katsuhiro; Niguchi, Noboru; Takahara, Kazuaki

2018-05-01

We have proposed a current superimposition variable flux reluctance machine for traction motors. The torque-speed characteristics of this machine can be controlled by increasing or decreasing the DC current. In this paper, we discuss an AC/DC current ratio in the current superimposition variable flux reluctance machine. The structure and control method are described, and the characteristics are computed using FEA in several AC/DC ratios.

Predicting cognitive function from clinical measures of physical function and health status in older adults.

PubMed

Bolandzadeh, Niousha; Kording, Konrad; Salowitz, Nicole; Davis, Jennifer C; Hsu, Liang; Chan, Alison; Sharma, Devika; Blohm, Gunnar; Liu-Ambrose, Teresa

2015-01-01

Current research suggests that the neuropathology of dementia-including brain changes leading to memory impairment and cognitive decline-is evident years before the onset of this disease. Older adults with cognitive decline have reduced functional independence and quality of life, and are at greater risk for developing dementia. Therefore, identifying biomarkers that can be easily assessed within the clinical setting and predict cognitive decline is important. Early recognition of cognitive decline could promote timely implementation of preventive strategies. We included 89 community-dwelling adults aged 70 years and older in our study, and collected 32 measures of physical function, health status and cognitive function at baseline. We utilized an L1-L2 regularized regression model (elastic net) to identify which of the 32 baseline measures were strongly predictive of cognitive function after one year. We built three linear regression models: 1) based on baseline cognitive function, 2) based on variables consistently selected in every cross-validation loop, and 3) a full model based on all the 32 variables. Each of these models was carefully tested with nested cross-validation. Our model with the six variables consistently selected in every cross-validation loop had a mean squared prediction error of 7.47. This number was smaller than that of the full model (115.33) and the model with baseline cognitive function (7.98). Our model explained 47% of the variance in cognitive function after one year. We built a parsimonious model based on a selected set of six physical function and health status measures strongly predictive of cognitive function after one year. In addition to reducing the complexity of the model without changing the model significantly, our model with the top variables improved the mean prediction error and R-squared. These six physical function and health status measures can be easily implemented in a clinical setting.

Selecting Populations for Non-Analogous Climate Conditions Using Universal Response Functions: The Case of Douglas-Fir in Central Europe

PubMed Central

Chakraborty, Debojyoti; Wang, Tongli; Andre, Konrad; Konnert, Monika; Lexer, Manfred J.; Matulla, Christoph; Schueler, Silvio

2015-01-01

Identifying populations within tree species potentially adapted to future climatic conditions is an important requirement for reforestation and assisted migration programmes. Such populations can be identified either by empirical response functions based on correlations of quantitative traits with climate variables or by climate envelope models that compare the climate of seed sources and potential growing areas. In the present study, we analyzed the intraspecific variation in climate growth response of Douglas-fir planted within the non-analogous climate conditions of Central and continental Europe. With data from 50 common garden trials, we developed Universal Response Functions (URF) for tree height and mean basal area and compared the growth performance of the selected best performing populations with that of populations identified through a climate envelope approach. Climate variables of the trial location were found to be stronger predictors of growth performance than climate variables of the population origin. Although the precipitation regime of the population sources varied strongly none of the precipitation related climate variables of population origin was found to be significant within the models. Overall, the URFs explained more than 88% of variation in growth performance. Populations identified by the URF models originate from western Cascades and coastal areas of Washington and Oregon and show significantly higher growth performance than populations identified by the climate envelope approach under both current and climate change scenarios. The URFs predict decreasing growth performance at low and middle elevations of the case study area, but increasing growth performance on high elevation sites. Our analysis suggests that population recommendations based on empirical approaches should be preferred and population selections by climate envelope models without considering climatic constrains of growth performance should be carefully appraised before transferring populations to planting locations with novel or dissimilar climate. PMID:26288363

Physiologic correlates to background noise acceptance

NASA Astrophysics Data System (ADS)

Tampas, Joanna; Harkrider, Ashley; Nabelek, Anna

2004-05-01

Acceptance of background noise can be evaluated by having listeners indicate the highest background noise level (BNL) they are willing to accept while following the words of a story presented at their most comfortable listening level (MCL). The difference between the selected MCL and BNL is termed the acceptable noise level (ANL). One of the consistent findings in previous studies of ANL is large intersubject variability in acceptance of background noise. This variability is not related to age, gender, hearing sensitivity, personality, type of background noise, or speech perception in noise performance. The purpose of the current experiment was to determine if individual differences in physiological activity measured from the peripheral and central auditory systems of young female adults with normal hearing can account for the variability observed in ANL. Correlations between ANL and various physiological responses, including spontaneous, click-evoked, and distortion-product otoacoustic emissions, auditory brainstem and middle latency evoked potentials, and electroencephalography will be presented. Results may increase understanding of the regions of the auditory system that contribute to individual noise acceptance.

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)

Is the technical performance of young soccer players influenced by hormonal status, sexual maturity, anthropometric profile, and physical performance?

PubMed

Moreira, Alexandre; Massa, Marcelo; Thiengo, Carlos R; Rodrigues Lopes, Rafael Alan; Lima, Marcelo R; Vaeyens, Roel; Barbosa, Wesley P; Aoki, Marcelo S

2017-12-01

The aim of this study was to examine the influence of hormonal status, anthropometric profile, sexual maturity level, and physical performance on the technical abilities of 40 young male soccer players during small-sided games (SSGs). Anthropometric profiling, saliva sampling, sexual maturity assessment (Tanner scale), and physical performance tests (Yo-Yo and vertical jumps) were conducted two weeks prior to the SSGs. Salivary testosterone was determined by the enzyme-linked immunosorbent assay method. Technical performance was determined by the frequency of actions during SSGs. Principal component analyses identified four technical actions of importance: total number of passes, effectiveness, goal attempts, and total tackles. A multivariate canonical correlation analysis was then employed to verify the prediction of a multiple dependent variables set (composed of four technical actions) from an independent set of variables, composed of testosterone concentration, stage of pubic hair and genitalia development, vertical jumps and Yo-Yo performance. A moderate-to-large relationship between the technical performance set and the independent set was observed. The canonical correlation was 0.75 with a canonical R 2 of 0.45. The highest structure coefficient in the technical performance set was observed for tackles (0.77), while testosterone presented the highest structure coefficient (0.75) for the variables of the independent set. The current data suggest that the selected independent set of variables might be useful in predicting SSG performance in young soccer players. Coaches should be aware that physical development plays a key role in technical performance to avoid decision-making mistakes during the selection of young players.

Composition variability of spent mushroom compost in Ireland.

PubMed

Jordan, S N; Mullen, G J; Murphy, M C

2008-01-01

Spent mushroom compost (SMC) has proven to be an attractive material for improving soil structure in tilled soils and increasing dry matter production in grassland soils, owing to its high organic matter content and availability of essential plant nutrients. Because of this, it is important to identify the variability in composition of SMC in order to evaluate its merit as a fertilizer/soil conditioner. For this reason, a study was carried out involving the analysis of SMC samples obtained from five mushroom growers using compost from each of the 13 mushroom composting yards currently operating in both Northern Ireland (5 yd) and the Republic of Ireland (8 yd). The selected parameters measured include dry matter, organic matter, total N, P and K, C/N ratio; plant-available P and K, pH, EC, total Ca, Mg, Na, Cu, Zn, Fe, Mn, Cd, Cr, Ni, Pb; and cellulose, hemicellulose and lignin constituents. Yield of mushroom data were also collected from the selected growers. There were significant differences (P<0.05) within two compost production yards for some parameters, therefore, for the most part, the uniformity of SMC within each yard is relatively consistent. However, significant differences (P<0.05) were evident when comparing SMC obtained from growers supplied with compost from Northern Ireland and the Republic of Ireland independently, particularly among total and available phosphorus and potassium values. The results obtained show that, while SMC has fertilizer merit, its variability of composition must be taken into account when assessing this value. The variability of composition is also of particular interest in the context of recent emphasis on plant nutrient management in agriculture.

Potential benefits of genomic selection on genetic gain of small ruminant breeding programs.

PubMed

Shumbusho, F; Raoul, J; Astruc, J M; Palhiere, I; Elsen, J M

2013-08-01

In conventional small ruminant breeding programs, only pedigree and phenotype records are used to make selection decisions but prospects of including genomic information are now under consideration. The objective of this study was to assess the potential benefits of genomic selection on the genetic gain in French sheep and goat breeding designs of today. Traditional and genomic scenarios were modeled with deterministic methods for 3 breeding programs. The models included decisional variables related to male selection candidates, progeny testing capacity, and economic weights that were optimized to maximize annual genetic gain (AGG) of i) a meat sheep breeding program that improved a meat trait of heritability (h(2)) = 0.30 and a maternal trait of h(2) = 0.09 and ii) dairy sheep and goat breeding programs that improved a milk trait of h(2) = 0.30. Values of ±0.20 of genetic correlation between meat and maternal traits were considered to study their effects on AGG. The Bulmer effect was accounted for and the results presented here are the averages of AGG after 10 generations of selection. Results showed that current traditional breeding programs provide an AGG of 0.095 genetic standard deviation (σa) for meat and 0.061 σa for maternal trait in meat breed and 0.147 σa and 0.120 σa in sheep and goat dairy breeds, respectively. By optimizing decisional variables, the AGG with traditional selection methods increased to 0.139 σa for meat and 0.096 σa for maternal traits in meat breeding programs and to 0.174 σa and 0.183 σa in dairy sheep and goat breeding programs, respectively. With a medium-sized reference population (nref) of 2,000 individuals, the best genomic scenarios gave an AGG that was 17.9% greater than with traditional selection methods with optimized values of decisional variables for combined meat and maternal traits in meat sheep, 51.7% in dairy sheep, and 26.2% in dairy goats. The superiority of genomic schemes increased with the size of the reference population and genomic selection gave the best results when nref > 1,000 individuals for dairy breeds and nref > 2,000 individuals for meat breed. Genetic correlation between meat and maternal traits had a large impact on the genetic gain of both traits. Changes in AGG due to correlation were greatest for low heritable maternal traits. As a general rule, AGG was increased both by optimizing selection designs and including genomic information.

Evaluation of alternative model selection criteria in the analysis of unimodal response curves using CART

USGS Publications Warehouse

Ribic, C.A.; Miller, T.W.

1998-01-01

We investigated CART performance with a unimodal response curve for one continuous response and four continuous explanatory variables, where two variables were important (ie directly related to the response) and the other two were not. We explored performance under three relationship strengths and two explanatory variable conditions: equal importance and one variable four times as important as the other. We compared CART variable selection performance using three tree-selection rules ('minimum risk', 'minimum risk complexity', 'one standard error') to stepwise polynomial ordinary least squares (OLS) under four sample size conditions. The one-standard-error and minimum-risk-complexity methods performed about as well as stepwise OLS with large sample sizes when the relationship was strong. With weaker relationships, equally important explanatory variables and larger sample sizes, the one-standard-error and minimum-risk-complexity rules performed better than stepwise OLS. With weaker relationships and explanatory variables of unequal importance, tree-structured methods did not perform as well as stepwise OLS. Comparing performance within tree-structured methods, with a strong relationship and equally important explanatory variables, the one-standard-error-rule was more likely to choose the correct model than were the other tree-selection rules 1) with weaker relationships and equally important explanatory variables; and 2) under all relationship strengths when explanatory variables were of unequal importance and sample sizes were lower.

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.

Corrosion of Ceramic Materials

NASA Technical Reports Server (NTRS)

Opila, Elizabeth J.; Jacobson, Nathan S.

1999-01-01

Non-oxide ceramics are promising materials for a range of high temperature applications. Selected current and future applications are listed. In all such applications, the ceramics are exposed to high temperature gases. Therefore it is critical to understand the response of these materials to their environment. The variables to be considered here include both the type of ceramic and the environment to which it is exposed. Non-oxide ceramics include borides, nitrides, and carbides. Most high temperature corrosion environments contain oxygen and hence the emphasis of this chapter will be on oxidation processes.

Left Atrial Appendage Closure for Stroke Prevention: Devices, Techniques, and Efficacy.

PubMed

Iskandar, Sandia; Vacek, James; Lavu, Madhav; Lakkireddy, Dhanunjaya

2016-05-01

Left atrial appendage closure can be performed either surgically or percutaneously. Surgical approaches include direct suture, excision and suture, stapling, and clipping. Percutaneous approaches include endocardial, epicardial, and hybrid endocardial-epicardial techniques. Left atrial appendage anatomy is highly variable and complex; therefore, preprocedural imaging is crucial to determine device selection and sizing, which contribute to procedural success and reduction of complications. Currently, the WATCHMAN is the only device that is approved for left atrial appendage closure in the United States. Copyright © 2016 Elsevier Inc. All rights reserved.

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

Fusing Data Mining, Machine Learning and Traditional Statistics to Detect Biomarkers Associated with Depression

PubMed Central

Dipnall, Joanna F.

2016-01-01

Background Atheoretical large-scale data mining techniques using machine learning algorithms have promise in the analysis of large epidemiological datasets. This study illustrates the use of a hybrid methodology for variable selection that took account of missing data and complex survey design to identify key biomarkers associated with depression from a large epidemiological study. Methods The study used a three-step methodology amalgamating multiple imputation, a machine learning boosted regression algorithm and logistic regression, to identify key biomarkers associated with depression in the National Health and Nutrition Examination Study (2009–2010). Depression was measured using the Patient Health Questionnaire-9 and 67 biomarkers were analysed. Covariates in this study included gender, age, race, smoking, food security, Poverty Income Ratio, Body Mass Index, physical activity, alcohol use, medical conditions and medications. The final imputed weighted multiple logistic regression model included possible confounders and moderators. Results After the creation of 20 imputation data sets from multiple chained regression sequences, machine learning boosted regression initially identified 21 biomarkers associated with depression. Using traditional logistic regression methods, including controlling for possible confounders and moderators, a final set of three biomarkers were selected. The final three biomarkers from the novel hybrid variable selection methodology were red cell distribution width (OR 1.15; 95% CI 1.01, 1.30), serum glucose (OR 1.01; 95% CI 1.00, 1.01) and total bilirubin (OR 0.12; 95% CI 0.05, 0.28). Significant interactions were found between total bilirubin with Mexican American/Hispanic group (p = 0.016), and current smokers (p<0.001). Conclusion The systematic use of a hybrid methodology for variable selection, fusing data mining techniques using a machine learning algorithm with traditional statistical modelling, accounted for missing data and complex survey sampling methodology and was demonstrated to be a useful tool for detecting three biomarkers associated with depression for future hypothesis generation: red cell distribution width, serum glucose and total bilirubin. PMID:26848571

Seasonal Habitat Use by Greater Sage-Grouse (Centrocercus urophasianus) on a Landscape with Low Density Oil and Gas Development.

PubMed

Rice, Mindy B; Rossi, Liza G; Apa, Anthony D

2016-01-01

Fragmentation of the sagebrush (Artemisia spp.) ecosystem has led to concern about a variety of sagebrush obligates including the greater sage-grouse (Centrocercus urophasianus). Given the increase of energy development within greater sage-grouse habitats, mapping seasonal habitats in pre-development populations is critical. The North Park population in Colorado is one of the largest and most stable in the state and provides a unique case study for investigating resource selection at a relatively low level of energy development compared to other populations both within and outside the state. We used locations from 117 radio-marked female greater sage-grouse in North Park, Colorado to develop seasonal resource selection models. We then added energy development variables to the base models at both a landscape and local scale to determine if energy variables improved the fit of the seasonal models. The base models for breeding and winter resource selection predicted greater use in large expanses of sagebrush whereas the base summer model predicted greater use along the edge of riparian areas. Energy development variables did not improve the winter or the summer models at either scale of analysis, but distance to oil/gas roads slightly improved model fit at both scales in the breeding season, albeit in opposite ways. At the landscape scale, greater sage-grouse were closer to oil/gas roads whereas they were further from oil/gas roads at the local scale during the breeding season. Although we found limited effects from low level energy development in the breeding season, the scale of analysis can influence the interpretation of effects. The lack of strong effects from energy development may be indicative that energy development at current levels are not impacting greater sage-grouse in North Park. Our baseline seasonal resource selection maps can be used for conservation to help identify ways of minimizing the effects of energy development.

Fusing Data Mining, Machine Learning and Traditional Statistics to Detect Biomarkers Associated with Depression.

PubMed

Dipnall, Joanna F; Pasco, Julie A; Berk, Michael; Williams, Lana J; Dodd, Seetal; Jacka, Felice N; Meyer, Denny

2016-01-01

Atheoretical large-scale data mining techniques using machine learning algorithms have promise in the analysis of large epidemiological datasets. This study illustrates the use of a hybrid methodology for variable selection that took account of missing data and complex survey design to identify key biomarkers associated with depression from a large epidemiological study. The study used a three-step methodology amalgamating multiple imputation, a machine learning boosted regression algorithm and logistic regression, to identify key biomarkers associated with depression in the National Health and Nutrition Examination Study (2009-2010). Depression was measured using the Patient Health Questionnaire-9 and 67 biomarkers were analysed. Covariates in this study included gender, age, race, smoking, food security, Poverty Income Ratio, Body Mass Index, physical activity, alcohol use, medical conditions and medications. The final imputed weighted multiple logistic regression model included possible confounders and moderators. After the creation of 20 imputation data sets from multiple chained regression sequences, machine learning boosted regression initially identified 21 biomarkers associated with depression. Using traditional logistic regression methods, including controlling for possible confounders and moderators, a final set of three biomarkers were selected. The final three biomarkers from the novel hybrid variable selection methodology were red cell distribution width (OR 1.15; 95% CI 1.01, 1.30), serum glucose (OR 1.01; 95% CI 1.00, 1.01) and total bilirubin (OR 0.12; 95% CI 0.05, 0.28). Significant interactions were found between total bilirubin with Mexican American/Hispanic group (p = 0.016), and current smokers (p<0.001). The systematic use of a hybrid methodology for variable selection, fusing data mining techniques using a machine learning algorithm with traditional statistical modelling, accounted for missing data and complex survey sampling methodology and was demonstrated to be a useful tool for detecting three biomarkers associated with depression for future hypothesis generation: red cell distribution width, serum glucose and total bilirubin.

Multi-omics facilitated variable selection in Cox-regression model for cancer prognosis prediction.

PubMed

Liu, Cong; Wang, Xujun; Genchev, Georgi Z; Lu, Hui

2017-07-15

New developments in high-throughput genomic technologies have enabled the measurement of diverse types of omics biomarkers in a cost-efficient and clinically-feasible manner. Developing computational methods and tools for analysis and translation of such genomic data into clinically-relevant information is an ongoing and active area of investigation. For example, several studies have utilized an unsupervised learning framework to cluster patients by integrating omics data. Despite such recent advances, predicting cancer prognosis using integrated omics biomarkers remains a challenge. There is also a shortage of computational tools for predicting cancer prognosis by using supervised learning methods. The current standard approach is to fit a Cox regression model by concatenating the different types of omics data in a linear manner, while penalty could be added for feature selection. A more powerful approach, however, would be to incorporate data by considering relationships among omics datatypes. Here we developed two methods: a SKI-Cox method and a wLASSO-Cox method to incorporate the association among different types of omics data. Both methods fit the Cox proportional hazards model and predict a risk score based on mRNA expression profiles. SKI-Cox borrows the information generated by these additional types of omics data to guide variable selection, while wLASSO-Cox incorporates this information as a penalty factor during model fitting. We show that SKI-Cox and wLASSO-Cox models select more true variables than a LASSO-Cox model in simulation studies. We assess the performance of SKI-Cox and wLASSO-Cox using TCGA glioblastoma multiforme and lung adenocarcinoma data. In each case, mRNA expression, methylation, and copy number variation data are integrated to predict the overall survival time of cancer patients. Our methods achieve better performance in predicting patients' survival in glioblastoma and lung adenocarcinoma. Copyright © 2017. Published by Elsevier Inc.

A new approach used to explore associations of current Ambrosia pollen levels with current and past meteorological elements

NASA Astrophysics Data System (ADS)

Matyasovszky, István; Makra, László; Csépe, Zoltán; Deák, Áron József; Pál-Molnár, Elemér; Fülöp, Andrea; Tusnády, Gábor

2015-09-01

The paper examines the sensitivity of daily airborne Ambrosia (ragweed) pollen levels of a current pollen season not only on daily values of meteorological variables during this season but also on the past meteorological conditions. The results obtained from a 19-year data set including daily ragweed pollen counts and ten daily meteorological variables are evaluated with special focus on the interactions between the phyto-physiological processes and the meteorological elements. Instead of a Pearson correlation measuring the strength of the linear relationship between two random variables, a generalised correlation that measures every kind of relationship between random vectors was used. These latter correlations between arrays of daily values of the ten meteorological elements and the array of daily ragweed pollen concentrations during the current pollen season were calculated. For the current pollen season, the six most important variables are two temperature variables (mean and minimum temperatures), two humidity variables (dew point depression and rainfall) and two variables characterising the mixing of the air (wind speed and the height of the planetary boundary layer). The six most important meteorological variables before the current pollen season contain four temperature variables (mean, maximum, minimum temperatures and soil temperature) and two variables that characterise large-scale weather patterns (sea level pressure and the height of the planetary boundary layer). Key periods of the past meteorological variables before the current pollen season have been identified. The importance of this kind of analysis is that a knowledge of the past meteorological conditions may contribute to a better prediction of the upcoming pollen season.

A new approach used to explore associations of current Ambrosia pollen levels with current and past meteorological elements.

PubMed

Matyasovszky, István; Makra, László; Csépe, Zoltán; Deák, Áron József; Pál-Molnár, Elemér; Fülöp, Andrea; Tusnády, Gábor

2015-09-01

The paper examines the sensitivity of daily airborne Ambrosia (ragweed) pollen levels of a current pollen season not only on daily values of meteorological variables during this season but also on the past meteorological conditions. The results obtained from a 19-year data set including daily ragweed pollen counts and ten daily meteorological variables are evaluated with special focus on the interactions between the phyto-physiological processes and the meteorological elements. Instead of a Pearson correlation measuring the strength of the linear relationship between two random variables, a generalised correlation that measures every kind of relationship between random vectors was used. These latter correlations between arrays of daily values of the ten meteorological elements and the array of daily ragweed pollen concentrations during the current pollen season were calculated. For the current pollen season, the six most important variables are two temperature variables (mean and minimum temperatures), two humidity variables (dew point depression and rainfall) and two variables characterising the mixing of the air (wind speed and the height of the planetary boundary layer). The six most important meteorological variables before the current pollen season contain four temperature variables (mean, maximum, minimum temperatures and soil temperature) and two variables that characterise large-scale weather patterns (sea level pressure and the height of the planetary boundary layer). Key periods of the past meteorological variables before the current pollen season have been identified. The importance of this kind of analysis is that a knowledge of the past meteorological conditions may contribute to a better prediction of the upcoming pollen season.

Does self-selection affect samples' representativeness in online surveys? An investigation in online video game research.

PubMed

Khazaal, Yasser; van Singer, Mathias; Chatton, Anne; Achab, Sophia; Zullino, Daniele; Rothen, Stephane; Khan, Riaz; Billieux, Joel; Thorens, Gabriel

2014-07-07

The number of medical studies performed through online surveys has increased dramatically in recent years. Despite their numerous advantages (eg, sample size, facilitated access to individuals presenting stigmatizing issues), selection bias may exist in online surveys. However, evidence on the representativeness of self-selected samples in online studies is patchy. Our objective was to explore the representativeness of a self-selected sample of online gamers using online players' virtual characters (avatars). All avatars belonged to individuals playing World of Warcraft (WoW), currently the most widely used online game. Avatars' characteristics were defined using various games' scores, reported on the WoW's official website, and two self-selected samples from previous studies were compared with a randomly selected sample of avatars. We used scores linked to 1240 avatars (762 from the self-selected samples and 478 from the random sample). The two self-selected samples of avatars had higher scores on most of the assessed variables (except for guild membership and exploration). Furthermore, some guilds were overrepresented in the self-selected samples. Our results suggest that more proficient players or players more involved in the game may be more likely to participate in online surveys. Caution is needed in the interpretation of studies based on online surveys that used a self-selection recruitment procedure. Epidemiological evidence on the reduced representativeness of sample of online surveys is warranted.

ESS++: a C++ objected-oriented algorithm for Bayesian stochastic search model exploration

PubMed Central

Bottolo, Leonardo; Langley, Sarah R.; Petretto, Enrico; Tiret, Laurence; Tregouet, David; Richardson, Sylvia

2011-01-01

Summary: ESS++ is a C++ implementation of a fully Bayesian variable selection approach for single and multiple response linear regression. ESS++ works well both when the number of observations is larger than the number of predictors and in the ‘large p, small n’ case. In the current version, ESS++ can handle several hundred observations, thousands of predictors and a few responses simultaneously. The core engine of ESS++ for the selection of relevant predictors is based on Evolutionary Monte Carlo. Our implementation is open source, allowing community-based alterations and improvements. Availability: C++ source code and documentation including compilation instructions are available under GNU licence at http://bgx.org.uk/software/ESS.html. Contact: l.bottolo@imperial.ac.uk Supplementary information: Supplementary data are available at Bioinformatics online. PMID:21233165

Current modeling practice may lead to falsely high benchmark dose estimates.

PubMed

Ringblom, Joakim; Johanson, Gunnar; Öberg, Mattias

2014-07-01

Benchmark dose (BMD) modeling is increasingly used as the preferred approach to define the point-of-departure for health risk assessment of chemicals. As data are inherently variable, there is always a risk to select a model that defines a lower confidence bound of the BMD (BMDL) that, contrary to expected, exceeds the true BMD. The aim of this study was to investigate how often and under what circumstances such anomalies occur under current modeling practice. Continuous data were generated from a realistic dose-effect curve by Monte Carlo simulations using four dose groups and a set of five different dose placement scenarios, group sizes between 5 and 50 animals and coefficients of variations of 5-15%. The BMD calculations were conducted using nested exponential models, as most BMD software use nested approaches. "Non-protective" BMDLs (higher than true BMD) were frequently observed, in some scenarios reaching 80%. The phenomenon was mainly related to the selection of the non-sigmoidal exponential model (Effect=a·e(b)(·dose)). In conclusion, non-sigmoid models should be used with caution as it may underestimate the risk, illustrating that awareness of the model selection process and sound identification of the point-of-departure is vital for health risk assessment. Copyright © 2014 The Authors. Published by Elsevier Inc. All rights reserved.

Using Bayesian variable selection to analyze regular resolution IV two-level fractional factorial designs

DOE PAGES

Chipman, Hugh A.; Hamada, Michael S.

2016-06-02

Regular two-level fractional factorial designs have complete aliasing in which the associated columns of multiple effects are identical. Here, we show how Bayesian variable selection can be used to analyze experiments that use such designs. In addition to sparsity and hierarchy, Bayesian variable selection naturally incorporates heredity . This prior information is used to identify the most likely combinations of active terms. We also demonstrate the method on simulated and real experiments.

Variable Selection in Logistic Regression.

DTIC Science & Technology

1987-06-01

23 %. AUTIOR(.) S. CONTRACT OR GRANT NUMBE Rf.i %Z. D. Bai, P. R. Krishnaiah and . C. Zhao F49620-85- C-0008 " PERFORMING ORGANIZATION NAME AND AOORESS...d I7 IOK-TK- d 7 -I0 7’ VARIABLE SELECTION IN LOGISTIC REGRESSION Z. D. Bai, P. R. Krishnaiah and L. C. Zhao Center for Multivariate Analysis...University of Pittsburgh Center for Multivariate Analysis University of Pittsburgh Y !I VARIABLE SELECTION IN LOGISTIC REGRESSION Z- 0. Bai, P. R. Krishnaiah

Using Bayesian variable selection to analyze regular resolution IV two-level fractional factorial designs

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

Chipman, Hugh A.; Hamada, Michael S.

Regular two-level fractional factorial designs have complete aliasing in which the associated columns of multiple effects are identical. Here, we show how Bayesian variable selection can be used to analyze experiments that use such designs. In addition to sparsity and hierarchy, Bayesian variable selection naturally incorporates heredity . This prior information is used to identify the most likely combinations of active terms. We also demonstrate the method on simulated and real experiments.

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

Estimation of the barrier layer thickness in the Indian Ocean using Aquarius Salinity

NASA Astrophysics Data System (ADS)

Felton, Clifford S.; Subrahmanyam, Bulusu; Murty, V. S. N.; Shriver, Jay F.

2014-07-01

Monthly barrier layer thickness (BLT) estimates are derived from satellite measurements using a multilinear regression model (MRM) within the Indian Ocean. Sea surface salinity (SSS) from the recently launched Soil Moisture and Ocean Salinity (SMOS) and Aquarius SAC-D salinity missions are utilized to estimate the BLT. The MRM relates BLT to sea surface salinity (SSS), sea surface temperature (SST), and sea surface height anomalies (SSHA). Three regions where the BLT variability is most rigorous are selected to evaluate the performance of the MRM for 2012; the Southeast Arabian Sea (SEAS), Bay of Bengal (BoB), and Eastern Equatorial Indian Ocean (EEIO). The MRM derived BLT estimates are compared to gridded Argo and Hybrid Coordinate Ocean Model (HYCOM) BLTs. It is shown that different mechanisms are important for sustaining the BLT variability in each of the selected regions. Sensitivity tests show that SSS is the primary driver of the BLT within the MRM. Results suggest that salinity measurements obtained from Aquarius and SMOS can be useful for tracking and predicting the BLT in the Indian Ocean. Largest MRM errors occur along coastlines and near islands where land contamination skews the satellite SSS retrievals. The BLT evolution during 2012, as well as the advantages and disadvantages of the current model are discussed. BLT estimations using HYCOM simulations display large errors that are related to model layer structure and the selected BLT methodology.

Maintenance of Genetic Variability under Strong Stabilizing Selection: A Two-Locus Model

PubMed Central

Gavrilets, S.; Hastings, A.

1993-01-01

We study a two locus model with additive contributions to the phenotype to explore the relationship between stabilizing selection and recombination. We show that if the double heterozygote has the optimum phenotype and the contributions of the loci to the trait are different, then any symmetric stabilizing selection fitness function can maintain genetic variability provided selection is sufficiently strong relative to linkage. We present results of a detailed analysis of the quadratic fitness function which show that selection need not be extremely strong relative to recombination for the polymorphic equilibria to be stable. At these polymorphic equilibria the mean value of the trait, in general, is not equal to the optimum phenotype, there exists a large level of negative linkage disequilibrium which ``hides'' additive genetic variance, and different equilibria can be stable simultaneously. We analyze dependence of different characteristics of these equilibria on the location of optimum phenotype, on the difference in allelic effect, and on the strength of selection relative to recombination. Our overall result that stabilizing selection does not necessarily eliminate genetic variability is compatible with some experimental results where the lines subject to strong stabilizing selection did not have significant reductions in genetic variability. PMID:8514145

Heart rate variability in patients with systemic lupus erythematosus: a systematic review and methodological considerations.

PubMed

Matusik, P S; Matusik, P T; Stein, P K

2018-07-01

Aim The aim of this review was to summarize current knowledge about the scientific findings and potential clinical utility of heart rate variability measures in patients with systemic lupus erythematosus. Methods PubMed, Embase and Scopus databases were searched for the terms associated with systemic lupus erythematosus and heart rate variability, including controlled vocabulary, when appropriate. Articles published in English and available in full text were considered. Finally, 11 publications were selected, according to the systematic review protocol and were analyzed. Results In general, heart rate variability, measured in the time and frequency domains, was reported to be decreased in patients with systemic lupus erythematosus compared with controls. In some systemic lupus erythematosus studies, heart rate variability was found to correlate with inflammatory markers and albumin levels. A novel heart rate variability measure, heart rate turbulence onset, was shown to be increased, while heart rate turbulence slope was decreased in systemic lupus erythematosus patients. Reports of associations of changes in heart rate variability parameters with increasing systemic lupus erythematosus activity were inconsistent, showing decreasing heart rate variability or no relationship. However, the low/high frequency ratio was, in some studies, reported to increase with increasing disease activity or to be inversely correlated with albumin levels. Conclusions Patients with systemic lupus erythematosus have abnormal heart rate variability, which reflects cardiac autonomic dysfunction and may be related to inflammatory cytokines but not necessarily to disease activity. Thus measurement of heart rate variability could be a useful clinical tool for monitoring autonomic dysfunction in systemic lupus erythematosus, and may potentially provide prognostic information.

Do bioclimate variables improve performance of climate envelope models?

USGS Publications Warehouse

Watling, James I.; Romañach, Stephanie S.; Bucklin, David N.; Speroterra, Carolina; Brandt, Laura A.; Pearlstine, Leonard G.; Mazzotti, Frank J.

2012-01-01

Climate envelope models are widely used to forecast potential effects of climate change on species distributions. A key issue in climate envelope modeling is the selection of predictor variables that most directly influence species. To determine whether model performance and spatial predictions were related to the selection of predictor variables, we compared models using bioclimate variables with models constructed from monthly climate data for twelve terrestrial vertebrate species in the southeastern USA using two different algorithms (random forests or generalized linear models), and two model selection techniques (using uncorrelated predictors or a subset of user-defined biologically relevant predictor variables). There were no differences in performance between models created with bioclimate or monthly variables, but one metric of model performance was significantly greater using the random forest algorithm compared with generalized linear models. Spatial predictions between maps using bioclimate and monthly variables were very consistent using the random forest algorithm with uncorrelated predictors, whereas we observed greater variability in predictions using generalized linear models.

Bayesian inference for the genetic control of water deficit tolerance in spring wheat by stochastic search variable selection.

PubMed

Safari, Parviz; Danyali, Syyedeh Fatemeh; Rahimi, Mehdi

2018-06-02

Drought is the main abiotic stress seriously influencing wheat production. Information about the inheritance of drought tolerance is necessary to determine the most appropriate strategy to develop tolerant cultivars and populations. In this study, generation means analysis to identify the genetic effects controlling grain yield inheritance in water deficit and normal conditions was considered as a model selection problem in a Bayesian framework. Stochastic search variable selection (SSVS) was applied to identify the most important genetic effects and the best fitted models using different generations obtained from two crosses applying two water regimes in two growing seasons. The SSVS is used to evaluate the effect of each variable on the dependent variable via posterior variable inclusion probabilities. The model with the highest posterior probability is selected as the best model. In this study, the grain yield was controlled by the main effects (additive and non-additive effects) and epistatic. The results demonstrate that breeding methods such as recurrent selection and subsequent pedigree method and hybrid production can be useful to improve grain yield.

Metabolic effects of exercise on childhood obesity: a current view

PubMed Central

Paes, Santiago Tavares; Marins, João Carlos Bouzas; Andreazzi, Ana Eliza

2015-01-01

OBJECTIVE: To review the current literature concerning the effects of physical exercise on several metabolic variables related to childhood obesity. DATA SOURCE: A search was performed in Pubmed/MEDLINE and Web of Science databases. The keywords used were as follows: Obesity, Children Obesity, Childhood Obesity, Exercise and Physical Activity. The online search was based on studies published in English, from April 2010 to December 2013. DATA SYNTHESIS: Search queries returned 88,393 studies based on the aforementioned keywords; 4,561 studies were selected by crossing chosen keywords. After applying inclusion criteria, four studies were selected from 182 eligible titles. Most studies found that aerobic and resistance training improves body composition, lipid profile and metabolic and inflammatory status of obese children and adolescents; however, the magnitude of these effects is associated with the type, intensity and duration of practice. CONCLUSIONS: Regardless of the type, physical exercise promotes positive adaptations to childhood obesity, mainly acting to restore cellular and cardiovascular homeostasis, to improve body composition, and to activate metabolism; therefore, physical exercise acts as a co-factor in fighting obesity. PMID:25662015

[Indicators of the persistent pro-inflammatory activation of the immune system in depression].

PubMed

Cubała, Wiesław Jerzy; Godlewska, Beata; Trzonkowski, Piotr; Landowski, Jerzy

2006-01-01

The aetiology of depression remains tentative. Current hypotheses on the aetiology of the depressive disorder tend to integrate monoaminoergic, neuroendocrine and immunological concepts of depression. A number of research papers emphasise the altered hormonal and immune status of patients with depression with pronounced cytokine level variations. Those studies tend to link the variable course of depression in relation to the altered proinflammatory activity of the immune system. The results of the studies on the activity of the selected elements of the immune system are ambiguous indicating both increased and decreased activities of its selected elements. However, a number of basic and psychopharmacological studies support the hypothesis of the increased proinflammatory activity of the immune system in the course of depression which is the foundation for the immunological hypothesis of depression. The aim of this paper is to review the functional abnormalities that are observed in depression focusing on the monoaminoergic deficiency and increased immune activation as well as endocrine dysregulation. This paper puts together and discusses current studies related to this subject with a detailed insight into interactions involving nervous, endocrine and immune systems.

Paying for retirement: sex differences in inclusion in employer-provided retirement plans.

PubMed

Wright, Rosemary

2012-04-01

This study examines sex differences among Baby Boom workers in the likelihood of coverage by an employer-provided retirement plan. This study used a sample of Baby Boom workers drawn from the 2009 Current Population Survey. Independent variables were selected to replicate as closely as possible those in two 1995 studies of retired workers and pension plans. Three new variables were added to reflect major social and economic shifts since 1995. Logistic regression was performed to analyze the effect of the independent variables on the likelihood of retirement plan coverage. In this cohort, the proportions of men and women included in employer-provided retirement plans were almost the same. The overall odds of women being included in a plan were only slightly less than even and in certain cases were significantly higher than the odds for men. Predictors of inclusion that were most important for both women and men were minority status, employment in a core industry or in a government position, educational level, and marital status. Although a much larger group of workers is included in retirement plans than in previous studies, and Baby Boom women are less disadvantaged in this regard than women in earlier studies, minority and immigrant workers continue to be disadvantaged, and the security of government retirement plans may be weakening with current economic difficulties.

Satisfaction with physical and social surroundings and the habit of smoking cigarettes in the metropolitan area of Belo Horizonte, Brazil.

PubMed

Souza, Ricardo Alexandre de; Oliveira, Cláudia Di Lorenzo; Lima-Costa, Maria Fernanda; Proietti, Fernando Augusto

2014-01-01

The objective of this study was to examine the association between individual satisfaction with social and physical surroundings and the habit of smoking cigarettes. Data from the Health Survey of Adults from the metropolitan area of Belo Horizonte, Minas Gerais, Brazil, were used. Based on a probability sample, participants (n = 12,299) were selected among residents aged 20 years old or more. The response variable was the smoking habit and the explanatory variable of interest was the neighborhood perception. Potential confounding variables included demographic characteristics, health behaviors and other indicators of socioeconomic position. The prevalence of current smokers, former smokers and never smokers were 20.8, 14.1 and 65.1%, respectively; 74.4 and 25.5% of the participants were categorized as being more satisfied and less satisfied with the neighborhood, respectively. Compared to those who never smoked, former smokers (adjusted odds ratio = 1.40, 95% confidence interval 1.20 - 1.62) and current smokers (adjusted odds ratio = 1.17, 95% confidence interval 1.03 - 1.34) were less satisfied with the neighborhood compared to those who never smoked. The results of this study indicate there is an independent association between the smoking habit and a less satisfying neighborhood perception in the metropolitan region of Belo Horizonte, which does not depend on individual characteristics, traditionally reported as being associated with smoking.

Jury Selection in Child Sex Abuse Trials: A Case Analysis

ERIC Educational Resources Information Center

Cramer, Robert J.; Adams, Desiree D.; Brodsky, Stanley L.

2009-01-01

Child sex abuse cases have been the target of considerable psycho-legal research. The present paper offers an analysis of psychological constructs for jury selection in child sex abuse cases from the defense perspective. The authors specifically delineate general and case-specific jury selection variables. General variables include…

The Effects of Variability and Risk in Selection Utility Analysis: An Empirical Comparison.

ERIC Educational Resources Information Center

Rich, Joseph R.; Boudreau, John W.

1987-01-01

Investigated utility estimate variability for the selection utility of using the Programmer Aptitude Test to select computer programmers. Comparison of Monte Carlo results to other risk assessment approaches (sensitivity analysis, break-even analysis, algebraic derivation of the distribtion) suggests that distribution information provided by Monte…

The influence of genetic and environmental factors in estimations of current body size, desired body size, and body dissatisfaction.

PubMed

Wade, T D; Bulik, C M; Heath, A C; Martin, N G; Eaves, L J

2001-08-01

The objective was to investigate the genetic epidemiology of figural stimuli. Standard figural stimuli were available from 5,325 complete twin pairs: 1,751 (32.9%) were monozygotic females, 1,068 (20.1%) were dizygotic females, 752 (14.1%) were monozygotic males, 495 (9.3%) were dizygotic males, and 1,259 (23.6%) were dizygotic male-female pairs. Univariate twin analyses were used to examine the influences on the individual variation in current body size and ideal body size. These data were analysed separately for men and women in each of five age groups. A factorial analysis of variance, with polychoric correlations between twin pairs as the dependent variable, and age, sex, zygosity, and the three interaction terms (age x sex, age x zygosity, sex x zygosity) as independent variables, was used to examine trends across the whole data set. Results showed genetic influences had the largest impact on the individual variation in current body size measures, whereas non-shared environmental influences were associated with the majority of individual variation in ideal body size. There was a significant main effect of zygosity (heritability) in predicting polychoric correlations for current body size and body dissatisfaction. There was a significant main effect of gender and zygosity in predicting ideal body size, with a gender x zygosity interaction. In common with BMI, heritability is important in influencing the estimation of current body size. Selection of desired body size for both men and women is more strongly influenced by environmental factors.

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.

Developing a spatial-statistical model and map of historical malaria prevalence in Botswana using a staged variable selection procedure

PubMed Central

Craig, Marlies H; Sharp, Brian L; Mabaso, Musawenkosi LH; Kleinschmidt, Immo

2007-01-01

Background Several malaria risk maps have been developed in recent years, many from the prevalence of infection data collated by the MARA (Mapping Malaria Risk in Africa) project, and using various environmental data sets as predictors. Variable selection is a major obstacle due to analytical problems caused by over-fitting, confounding and non-independence in the data. Testing and comparing every combination of explanatory variables in a Bayesian spatial framework remains unfeasible for most researchers. The aim of this study was to develop a malaria risk map using a systematic and practicable variable selection process for spatial analysis and mapping of historical malaria risk in Botswana. Results Of 50 potential explanatory variables from eight environmental data themes, 42 were significantly associated with malaria prevalence in univariate logistic regression and were ranked by the Akaike Information Criterion. Those correlated with higher-ranking relatives of the same environmental theme, were temporarily excluded. The remaining 14 candidates were ranked by selection frequency after running automated step-wise selection procedures on 1000 bootstrap samples drawn from the data. A non-spatial multiple-variable model was developed through step-wise inclusion in order of selection frequency. Previously excluded variables were then re-evaluated for inclusion, using further step-wise bootstrap procedures, resulting in the exclusion of another variable. Finally a Bayesian geo-statistical model using Markov Chain Monte Carlo simulation was fitted to the data, resulting in a final model of three predictor variables, namely summer rainfall, mean annual temperature and altitude. Each was independently and significantly associated with malaria prevalence after allowing for spatial correlation. This model was used to predict malaria prevalence at unobserved locations, producing a smooth risk map for the whole country. Conclusion We have produced a highly plausible and parsimonious model of historical malaria risk for Botswana from point-referenced data from a 1961/2 prevalence survey of malaria infection in 1–14 year old children. After starting with a list of 50 potential variables we ended with three highly plausible predictors, by applying a systematic and repeatable staged variable selection procedure that included a spatial analysis, which has application for other environmentally determined infectious diseases. All this was accomplished using general-purpose statistical software. PMID:17892584

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.

Definition study of a Variable Cycle Experimental Engine (VCEE) and associated test program and test plan

NASA Technical Reports Server (NTRS)

Allan, R. D.

1978-01-01

The Definition Study of a Variable Cycle Experimental Engine (VCEE) and Associated Test Program and Test Plan, was initiated to identify the most cost effective program for a follow-on to the AST Test Bed Program. The VCEE Study defined various subscale VCE's based on different available core engine components, and a full scale VCEE utilizing current technology. The cycles were selected, preliminary design accomplished and program plans and engineering costs developed for several program options. In addition to the VCEE program plans and options, a limited effort was applied to identifying programs that could logically be accomplished on the AST Test Bed Program VCE to extend the usefulness of this test hardware. Component programs were provided that could be accomplished prior to the start of a VCEE program.

High-speed peak matching algorithm for retention time alignment of gas chromatographic data for chemometric analysis

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

Johnson, Kevin J.; Wright, Bob W.; Jarman, Kristin H.

2003-05-09

A rapid retention time alignment algorithm was developed as a preprocessing utility to be used prior to chemometric analysis of large datasets of diesel fuel gas chromatographic profiles. Retention time variation from chromatogram-to-chromatogram has been a significant impediment against the use of chemometric techniques in the analysis of chromatographic data due to the inability of current multivariate techniques to correctly model information that shifts from variable to variable within a dataset. The algorithm developed is shown to increase the efficacy of pattern recognition methods applied to a set of diesel fuel chromatograms by retaining chemical selectivity while reducing chromatogram-to-chromatogram retentionmore » time variations and to do so on a time scale that makes analysis of large sets of chromatographic data practical.« less

Predicting the effects of magnesium oxide nanoparticles and temperature on the thermal conductivity of water using artificial neural network and experimental data

NASA Astrophysics Data System (ADS)

Afrand, Masoud; Hemmat Esfe, Mohammad; Abedini, Ehsan; Teimouri, Hamid

2017-03-01

The current paper first presents an empirical correlation based on experimental results for estimating thermal conductivity enhancement of MgO-water nanofluid using curve fitting method. Then, artificial neural networks (ANNs) with various numbers of neurons have been assessed by considering temperature and MgO volume fraction as the inputs variables and thermal conductivity enhancement as the output variable to select the most appropriate and optimized network. Results indicated that the network with 7 neurons had minimum error. Eventually, the output of artificial neural network was compared with the results of the proposed empirical correlation and those of the experiments. Comparisons revealed that ANN modeling was more accurate than curve-fitting method in the predicting the thermal conductivity enhancement of the nanofluid.

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.

Crucial nesting habitat for gunnison sage-grouse: A spatially explicit hierarchical approach

USGS Publications Warehouse

Aldridge, Cameron L.; Saher, D.J.; Childers, T.M.; Stahlnecker, K.E.; Bowen, Z.H.

2012-01-01

Gunnison sage-grouse (Centrocercus minimus) is a species of special concern and is currently considered a candidate species under Endangered Species Act. Careful management is therefore required to ensure that suitable habitat is maintained, particularly because much of the species' current distribution is faced with exurban development pressures. We assessed hierarchical nest site selection patterns of Gunnison sage-grouse inhabiting the western portion of the Gunnison Basin, Colorado, USA, at multiple spatial scales, using logistic regression-based resource selection functions. Models were selected using Akaike Information Criterion corrected for small sample sizes (AIC c) and predictive surfaces were generated using model averaged relative probabilities. Landscape-scale factors that had the most influence on nest site selection included the proportion of sagebrush cover >5%, mean productivity, and density of 2 wheel-drive roads. The landscape-scale predictive surface captured 97% of known Gunnison sage-grouse nests within the top 5 of 10 prediction bins, implicating 57% of the basin as crucial nesting habitat. Crucial habitat identified by the landscape model was used to define the extent for patch-scale modeling efforts. Patch-scale variables that had the greatest influence on nest site selection were the proportion of big sagebrush cover >10%, distance to residential development, distance to high volume paved roads, and mean productivity. This model accurately predicted independent nest locations. The unique hierarchical structure of our models more accurately captures the nested nature of habitat selection, and allowed for increased discrimination within larger landscapes of suitable habitat. We extrapolated the landscape-scale model to the entire Gunnison Basin because of conservation concerns for this species. We believe this predictive surface is a valuable tool which can be incorporated into land use and conservation planning as well the assessment of future land-use scenarios. ?? 2011 The Wildlife Society.

Spatial and Temporal Distribution of Tropospheric Clouds and Aerosols Observed by MODIS Onboard the Terra and Aqua Satellites

NASA Technical Reports Server (NTRS)

King, Michael D.; Platnick, Steven; Remer, Lorraine A.; Kaufman, Yoram J.

2004-01-01

Remote sensing of cloud and aerosol optical properties is routinely obtained using the Moderate Resolution Imaging Spectroradiometer (MODIS) onboard the Terra and Aqua satellites. Techniques that are being used to enhance our ability to characterize the global distribution of cloud and aerosol properties include well-calibrated multispectral radiometers that rely on visible, near-infrared, and thermal infrared channels. The availability of thermal channels to aid in cloud screening for aerosol properties is an important additional piece of information that has not always been incorporated into sensor designs. In this paper, we describe the radiative properties of clouds as currently determined from satellites (cloud fraction, optical thickness, cloud top pressure, and cloud effective radius), and highlight the global and regional cloud microphysical properties currently available for assessing climate variability and forcing. These include the latitudinal distribution of cloud optical and radiative properties of both liquid water and ice clouds, as well as joint histograms of cloud optical thickness and effective radius for selected geographical locations around the world. In addition, we will illustrate the radiative and microphysical properties of aerosol particles that are currently available from space-based observations, and show selected cases in which aerosol particles are observed to modify the cloud optical properties.

An Investigation of Selected Readiness Variables As Predictors of Reading Achievement at Second Grade Level.

ERIC Educational Resources Information Center

Seals, Caryl Neman

This study was designed to determine the relationship of selected readiness variables to achievement in reading at the second grade level. The readiness variables were environment, mathematics, letters and sounds, aural comprehension, visual perception, auditory perception, vocabulary and concepts, word meaning, listening, matching, alphabet,…

Curve fitting and modeling with splines using statistical variable selection techniques

NASA Technical Reports Server (NTRS)

Smith, P. L.

1982-01-01

The successful application of statistical variable selection techniques to fit splines is demonstrated. Major emphasis is given to knot selection, but order determination is also discussed. Two FORTRAN backward elimination programs, using the B-spline basis, were developed. The program for knot elimination is compared in detail with two other spline-fitting methods and several statistical software packages. An example is also given for the two-variable case using a tensor product basis, with a theoretical discussion of the difficulties of their use.

Fitting multidimensional splines using statistical variable selection techniques

NASA Technical Reports Server (NTRS)

Smith, P. L.

1982-01-01

This report demonstrates the successful application of statistical variable selection techniques to fit splines. Major emphasis is given to knot selection, but order determination is also discussed. Two FORTRAN backward elimination programs using the B-spline basis were developed, and the one for knot elimination is compared in detail with two other spline-fitting methods and several statistical software packages. An example is also given for the two-variable case using a tensor product basis, with a theoretical discussion of the difficulties of their use.

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.

How to evade a coevolving brood parasite: egg discrimination versus egg variability as host defences.

PubMed

Spottiswoode, Claire N; Stevens, Martin

2011-12-07

Arms races between avian brood parasites and their hosts often result in parasitic mimicry of host eggs, to evade rejection. Once egg mimicry has evolved, host defences could escalate in two ways: (i) hosts could improve their level of egg discrimination; and (ii) negative frequency-dependent selection could generate increased variation in egg appearance (polymorphism) among individuals. Proficiency in one defence might reduce selection on the other, while a combination of the two should enable successful rejection of parasitic eggs. We compared three highly variable host species of the Afrotropical cuckoo finch Anomalospiza imberbis, using egg rejection experiments and modelling of avian colour and pattern vision. We show that each differed in their level of polymorphism, in the visual cues they used to reject foreign eggs, and in their degree of discrimination. The most polymorphic host had the crudest discrimination, whereas the least polymorphic was most discriminating. The third species, not currently parasitized, was intermediate for both defences. A model simulating parasitic laying and host rejection behaviour based on the field experiments showed that the two host strategies result in approximately the same fitness advantage to hosts. Thus, neither strategy is superior, but rather they reflect alternative potential evolutionary trajectories.

A uniquely adaptable pore is consistent with NALCN being an ion sensor

PubMed Central

Senatore, Adriano; Spafford, J. David

2013-01-01

NALCN is an intriguing, orphan ion channel among the 4x6TM family of related voltage-gated cation channels, sharing a common architecture of four homologous domains consisting of six transmembrane helices, separated by three cytoplasmic linkers and delimited by N and C-terminal ends. NALCN is one of the shortest 4x6TM family members, lacking much of the variation that provides the diverse palate of gating features, and tissue specific adaptations of sodium and calcium channels. NALCN’s most distinctive feature is that that it possesses a highly adaptable pore with a calcium-like EEEE selectivity filter in radially symmetrical animals and a more sodium-like EEKE or EKEE selectivity filter in bilaterally symmetrical animals including vertebrates. Two lineages of animals evolved alternative calcium-like EEEE and sodium-like EEKE / EKEE pores, spliced to regulate NALCN functions in differing cellular environments, such as muscle (heart and skeletal) and secretory tissue (brain and glands), respectively. A highly adaptable pore in an otherwise conserved ion channel in the 4x6TM channel family is not consistent with a role for NALCN in directly gating a significant ion conductance that can be either sodium ions or calcium ions. NALCN was proposed to be an expressible Gd3+-sensitive, NMDG+-impermeant, non-selective and ohmic leak conductance in HEK-293T cells, but we were unable to distinguish these reported currents from leaky patch currents (ILP) in control HEK-293T cells. We suggest that NALCN functions as a sensor for the much larger UNC80/UNC79 complex, in a manner consistent with the coupling mechanism known for other weakly or non-conducting 4x6TM channel sensor proteins such as Nax or Cav1.1. We propose that NALCN serves as a variable sensor that responds to calcium or sodium ion flux, depending on whether the total cellular current density is generated more from calcium-selective or sodium-selective channels. PMID:23442378

A uniquely adaptable pore is consistent with NALCN being an ion sensor.

PubMed

Senatore, Adriano; Spafford, J David

2013-01-01

NALCN is an intriguing, orphan ion channel among the 4x6TM family of related voltage-gated cation channels, sharing a common architecture of four homologous domains consisting of six transmembrane helices, separated by three cytoplasmic linkers and delimited by N and C-terminal ends. NALCN is one of the shortest 4x6TM family members, lacking much of the variation that provides the diverse palate of gating features, and tissue specific adaptations of sodium and calcium channels. NALCN's most distinctive feature is that that it possesses a highly adaptable pore with a calcium-like EEEE selectivity filter in radially symmetrical animals and a more sodium-like EEKE or EKEE selectivity filter in bilaterally symmetrical animals including vertebrates. Two lineages of animals evolved alternative calcium-like EEEE and sodium-like EEKE / EKEE pores, spliced to regulate NALCN functions in differing cellular environments, such as muscle (heart and skeletal) and secretory tissue (brain and glands), respectively. A highly adaptable pore in an otherwise conserved ion channel in the 4x6TM channel family is not consistent with a role for NALCN in directly gating a significant ion conductance that can be either sodium ions or calcium ions. NALCN was proposed to be an expressible Gd ( 3+) -sensitive, NMDG (+) -impermeant, non-selective and ohmic leak conductance in HEK-293T cells, but we were unable to distinguish these reported currents from leaky patch currents (ILP) in control HEK-293T cells. We suggest that NALCN functions as a sensor for the much larger UNC80/UNC79 complex, in a manner consistent with the coupling mechanism known for other weakly or non-conducting 4x6TM channel sensor proteins such as Nax or Cav 1.1. We propose that NALCN serves as a variable sensor that responds to calcium or sodium ion flux, depending on whether the total cellular current density is generated more from calcium-selective or sodium-selective channels.

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.

A comparison of regression methods for model selection in individual-based landscape genetic analysis.

PubMed

Shirk, Andrew J; Landguth, Erin L; Cushman, Samuel A

2018-01-01

Anthropogenic migration barriers fragment many populations and limit the ability of species to respond to climate-induced biome shifts. Conservation actions designed to conserve habitat connectivity and mitigate barriers are needed to unite fragmented populations into larger, more viable metapopulations, and to allow species to track their climate envelope over time. Landscape genetic analysis provides an empirical means to infer landscape factors influencing gene flow and thereby inform such conservation actions. However, there are currently many methods available for model selection in landscape genetics, and considerable uncertainty as to which provide the greatest accuracy in identifying the true landscape model influencing gene flow among competing alternative hypotheses. In this study, we used population genetic simulations to evaluate the performance of seven regression-based model selection methods on a broad array of landscapes that varied by the number and type of variables contributing to resistance, the magnitude and cohesion of resistance, as well as the functional relationship between variables and resistance. We also assessed the effect of transformations designed to linearize the relationship between genetic and landscape distances. We found that linear mixed effects models had the highest accuracy in every way we evaluated model performance; however, other methods also performed well in many circumstances, particularly when landscape resistance was high and the correlation among competing hypotheses was limited. Our results provide guidance for which regression-based model selection methods provide the most accurate inferences in landscape genetic analysis and thereby best inform connectivity conservation actions. Published 2017. This article is a U.S. Government work and is in the public domain in the USA.

Superconducting fault current-limiter with variable shunt impedance

DOEpatents

Llambes, Juan Carlos H; Xiong, Xuming

2013-11-19

A superconducting fault current-limiter is provided, including a superconducting element configured to resistively or inductively limit a fault current, and one or more variable-impedance shunts electrically coupled in parallel with the superconducting element. The variable-impedance shunt(s) is configured to present a first impedance during a superconducting state of the superconducting element and a second impedance during a normal resistive state of the superconducting element. The superconducting element transitions from the superconducting state to the normal resistive state responsive to the fault current, and responsive thereto, the variable-impedance shunt(s) transitions from the first to the second impedance. The second impedance of the variable-impedance shunt(s) is a lower impedance than the first impedance, which facilitates current flow through the variable-impedance shunt(s) during a recovery transition of the superconducting element from the normal resistive state to the superconducting state, and thus, facilitates recovery of the superconducting element under load.

A valuable approach to the use of electronic medical data in primary care research: Panning for gold.

PubMed

Barnett, Stephen; Henderson, Joan; Hodgkins, Adam; Harrison, Christopher; Ghosh, Abhijeet; Dijkmans-Hadley, Bridget; Britt, Helena; Bonney, Andrew

2017-05-01

Electronic medical data (EMD) from electronic health records of general practice computer systems have enormous research potential, yet many variables are unreliable. The aim of this study was to compare selected data variables from general practice EMD with a reliable, representative national dataset (Bettering the Evaluation and Care of Health (BEACH)) in order to validate their use for primary care research. EMD variables were compared with encounter data from the nationally representative BEACH program using χ 2 tests and robust 95% confidence intervals to test their validity (measure what they reportedly measure). The variables focused on for this study were patient age, sex, smoking status and medications prescribed at the visit. The EMD sample from six general practices in the Illawarra region of New South Wales, Australia, yielded data on 196,515 patient encounters. Details of 90,553 encounters were recorded in the 2013 BEACH dataset from 924 general practitioners. No significant differences in patient age ( p = 0.36) or sex ( p = 0.39) were found. EMD had a lower rate of current smokers and higher average scripts per visit, but similar prescribing distribution patterns. Validating EMD variables offers avenues for improving primary care delivery and measuring outcomes of care to inform clinical practice and health policy.

A Selective Review of Group Selection in High-Dimensional Models

PubMed Central

Huang, Jian; Breheny, Patrick; Ma, Shuangge

2013-01-01

Grouping structures arise naturally in many statistical modeling problems. Several methods have been proposed for variable selection that respect grouping structure in variables. Examples include the group LASSO and several concave group selection methods. In this article, we give a selective review of group selection concerning methodological developments, theoretical properties and computational algorithms. We pay particular attention to group selection methods involving concave penalties. We address both group selection and bi-level selection methods. We describe several applications of these methods in nonparametric additive models, semiparametric regression, seemingly unrelated regressions, genomic data analysis and genome wide association studies. We also highlight some issues that require further study. PMID:24174707

A hybrid machine learning model to estimate nitrate contamination of production zone groundwater in the Central Valley, California

NASA Astrophysics Data System (ADS)

Ransom, K.; Nolan, B. T.; Faunt, C. C.; Bell, A.; Gronberg, J.; Traum, J.; Wheeler, D. C.; Rosecrans, C.; Belitz, K.; Eberts, S.; Harter, T.

2016-12-01

A hybrid, non-linear, machine learning statistical model was developed within a statistical learning framework to predict nitrate contamination of groundwater to depths of approximately 500 m below ground surface in the Central Valley, California. A database of 213 predictor variables representing well characteristics, historical and current field and county scale nitrogen mass balance, historical and current landuse, oxidation/reduction conditions, groundwater flow, climate, soil characteristics, depth to groundwater, and groundwater age were assigned to over 6,000 private supply and public supply wells measured previously for nitrate and located throughout the study area. The machine learning method, gradient boosting machine (GBM) was used to screen predictor variables and rank them in order of importance in relation to the groundwater nitrate measurements. The top five most important predictor variables included oxidation/reduction characteristics, historical field scale nitrogen mass balance, climate, and depth to 60 year old water. Twenty-two variables were selected for the final model and final model errors for log-transformed hold-out data were R squared of 0.45 and root mean square error (RMSE) of 1.124. Modeled mean groundwater age was tested separately for error improvement in the model and when included decreased model RMSE by 0.5% compared to the same model without age and by 0.20% compared to the model with all 213 variables. 1D and 2D partial plots were examined to determine how variables behave individually and interact in the model. Some variables behaved as expected: log nitrate decreased with increasing probability of anoxic conditions and depth to 60 year old water, generally decreased with increasing natural landuse surrounding wells and increasing mean groundwater age, generally increased with increased minimum depth to high water table and with increased base flow index value. Other variables exhibited much more erratic or noisy behavior in the model making them more difficult to interpret but highlighting the usefulness of the non-linear machine learning method. 2D interaction plots show probability of anoxic groundwater conditions largely control estimated nitrate concentrations compared to the other predictors.

How the risky features of previous selection affect subsequent decision-making: evidence from behavioral and fMRI measures.

PubMed

Dong, Guangheng; Zhang, Yifen; Xu, Jiaojing; Lin, Xiao; Du, Xiaoxia

2015-01-01

Human decision making is rarely conducted in temporal isolation. It is often biased and affected by environmental variables, particularly prior selections. In this study, we used a task that simulates a real gambling process to explore the effect of the risky features of a previous selection on subsequent decision making. Compared with decision making after an advantageous risk-taking situation (Risk_Adv), that after a disadvantageous risk-taking situation (Risk_Disadv) is associated with a longer response time (RT, the time spent in making decisions) and higher brain activations in the caudate and the dorsolateral prefrontal cortex (DLPFC). Compared with decisions after Risk_Adv, those after Risk_Disadv in loss trials are associated with higher brain activations in the left superior temporal gyrus (STG) and the precuneus. Brain activity and relevant RTs significantly correlated. Overall, people who experience disadvantageous risk-taking selections tend to focus on current decision making and engage cognitive endeavors in value evaluation and in the regulation of their risk-taking behaviors during decision making.

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

The MIXR sample or: how I learned to stop worrying and love multiwavelength catalogue cross-correlations

NASA Astrophysics Data System (ADS)

Mingo, Beatriz; Watson, Mike; Stewart, Gordon; Rosen, Simon; Blain, Andrew; Hardcastle, Martin; Mateos, Silvia; Carrera, Francisco; Ruiz, Angel; Pineau, Francois-Xavier

2016-08-01

We cross-match 3XMM, WISE and FIRST/NVSS to create the largest-to-date mid-IR, X-ray, and radio (MIXR) sample of galaxies and AGN. We use MIXR to triage sources and efficiently and accurately pre-classify them as star-forming galaxies or AGN, and to highlight bias and shortcomings in current AGN sample selection methods, paving the way for the next generation of instruments. Our results highlight key questions in AGN science, such as the need for a re-definition of the radio-loud/radio-quiet classification, and our observed lack of correlation between the kinetic (jet) and radiative (luminosity) output in AGN, which has dramatic potential consequences on our current understanding of AGN accretion, variability and feedback.

In vitro screening for population variability in toxicity of pesticide-containing mixtures

PubMed Central

Abdo, Nour; Wetmore, Barbara A.; Chappell, Grace A.; Shea, Damian; Wright, Fred A.; Rusyna, Ivan

2016-01-01

Population-based human in vitro models offer exceptional opportunities for evaluating the potential hazard and mode of action of chemicals, as well as variability in responses to toxic insults among individuals. This study was designed to test the hypothesis that comparative population genomics with efficient in vitro experimental design can be used for evaluation of the potential for hazard, mode of action, and the extent of population variability in responses to chemical mixtures. We selected 146 lymphoblast cell lines from 4 ancestrally and geographically diverse human populations based on the availability of genome sequence and basal RNA-seq data. Cells were exposed to two pesticide mixtures – an environmental surface water sample comprised primarily of organochlorine pesticides and a laboratory-prepared mixture of 36 currently used pesticides – in concentration response and evaluated for cytotoxicity. On average, the two mixtures exhibited a similar range of in vitro cytotoxicity and showed considerable inter-individual variability across screened cell lines. However, when in vitroto-in vivo extrapolation (IVIVE) coupled with reverse dosimetry was employed to convert the in vitro cytotoxic concentrations to oral equivalent doses and compared to the upper bound of predicted human exposure, we found that a nominally more cytotoxic chlorinated pesticide mixture is expected to have greater margin of safety (more than 5 orders of magnitude) as compared to the current use pesticide mixture (less than 2 orders of magnitude) due primarily to differences in exposure predictions. Multivariate genome-wide association mapping revealed an association between the toxicity of current use pesticide mixture and a polymorphism in rs1947825 in C17orf54. We conclude that a combination of in vitro human population-based cytotoxicity screening followed by dosimetric adjustment and comparative population genomics analyses enables quantitative evaluation of human health hazard from complex environmental mixtures. Additionally, such an approach yields testable hypotheses regarding potential toxicity mechanisms. PMID:26386728

A hybrid machine learning model to predict and visualize nitrate concentration throughout the Central Valley aquifer, California, USA

USGS Publications Warehouse

Ransom, Katherine M.; Nolan, Bernard T.; Traum, Jonathan A.; Faunt, Claudia; Bell, Andrew M.; Gronberg, Jo Ann M.; Wheeler, David C.; Zamora, Celia; Jurgens, Bryant; Schwarz, Gregory E.; Belitz, Kenneth; Eberts, Sandra; Kourakos, George; Harter, Thomas

2017-01-01

Intense demand for water in the Central Valley of California and related increases in groundwater nitrate concentration threaten the sustainability of the groundwater resource. To assess contamination risk in the region, we developed a hybrid, non-linear, machine learning model within a statistical learning framework to predict nitrate contamination of groundwater to depths of approximately 500 m below ground surface. A database of 145 predictor variables representing well characteristics, historical and current field and landscape-scale nitrogen mass balances, historical and current land use, oxidation/reduction conditions, groundwater flow, climate, soil characteristics, depth to groundwater, and groundwater age were assigned to over 6000 private supply and public supply wells measured previously for nitrate and located throughout the study area. The boosted regression tree (BRT) method was used to screen and rank variables to predict nitrate concentration at the depths of domestic and public well supplies. The novel approach included as predictor variables outputs from existing physically based models of the Central Valley. The top five most important predictor variables included two oxidation/reduction variables (probability of manganese concentration to exceed 50 ppb and probability of dissolved oxygen concentration to be below 0.5 ppm), field-scale adjusted unsaturated zone nitrogen input for the 1975 time period, average difference between precipitation and evapotranspiration during the years 1971–2000, and 1992 total landscape nitrogen input. Twenty-five variables were selected for the final model for log-transformed nitrate. In general, increasing probability of anoxic conditions and increasing precipitation relative to potential evapotranspiration had a corresponding decrease in nitrate concentration predictions. Conversely, increasing 1975 unsaturated zone nitrogen leaching flux and 1992 total landscape nitrogen input had an increasing relative impact on nitrate predictions. Three-dimensional visualization indicates that nitrate predictions depend on the probability of anoxic conditions and other factors, and that nitrate predictions generally decreased with increasing groundwater age.

Selecting the process variables for filament winding

NASA Technical Reports Server (NTRS)

Calius, E.; Springer, G. S.

1986-01-01

A model is described which can be used to determine the appropriate values of the process variables for filament winding cylinders. The process variables which can be selected by the model include the winding speed, fiber tension, initial resin degree of cure, and the temperatures applied during winding, curing, and post-curing. The effects of these process variables on the properties of the cylinder during and after manufacture are illustrated by a numerical example.

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

IRAS variables as galactic structure tracers - Classification of the bright variables

NASA Technical Reports Server (NTRS)

Allen, L. E.; Kleinmann, S. G.; Weinberg, M. D.

1993-01-01

The characteristics of the 'bright infrared variables' (BIRVs), a sample consisting of the 300 brightest stars in the IRAS Point Source Catalog with IRAS variability index VAR of 98 or greater, are investigated with the purpose of establishing which of IRAS variables are AGB stars (e.g., oxygen-rich Miras and carbon stars, as was assumed by Weinberg (1992)). Results of the analysis of optical, infrared, and microwave spectroscopy of these stars indicate that, out of 88 stars in the BIRV sample identified with cataloged variables, 86 can be classified as Miras. Results of a similar analysis performed for a color-selected sample of stars, using the color limits employed by Habing (1988) to select AGB stars, showed that, out of 52 percent of classified stars, 38 percent are non-AGB stars, including H II regions, planetary nebulae, supergiants, and young stellar objects, indicating that studies using color-selected samples are subject to misinterpretation.

Patient selection, echocardiographic screening and treatment strategies for interventional tricuspid repair using the edge-to-edge repair technique.

PubMed

Hausleiter, Jörg; Braun, Daniel; Orban, Mathias; Latib, Azeem; Lurz, Philipp; Boekstegers, Peter; von Bardeleben, Ralph Stephan; Kowalski, Marek; Hahn, Rebecca T; Maisano, Francesco; Hagl, Christian; Massberg, Steffen; Nabauer, Michael

2018-04-24

Severe tricuspid regurgitation (TR) has long been neglected despite its well known association with mortality. While surgical mortality rates remain high in isolated tricuspid valve surgery, interventional TR repair is rapidly evolving as an alternative to cardiac surgery in selected patients at high surgical risk. Currently, interventional edge-to-edge repair is the most frequently applied technique for TR repair even though the device has not been developed for this particular indication. Due to the inherent differences in tricuspid and mitral valve anatomy and pathology, percutaneous repair of the tricuspid valve is challenging due to a variety of factors including the complexity and variability of tricuspid valve anatomy, echocardiographic visibility of the valve leaflets, and device steering to the tricuspid valve. Furthermore, it remains to be clarified which patients are suitable for a percutaneous tricuspid repair and which features predict a successful procedure. On the basis of the available experience, we describe criteria for patient selection including morphological valve features, a standardized process for echocardiographic screening, and a strategy for clip placement. These criteria will help to achieve standardization of valve assessment and the procedural approach, and to further develop interventional tricuspid valve repair using either currently available devices or dedicated tricuspid edge-to-edge repair devices in the future. In summary, this manuscript will provide guidance for patient selection and echocardiographic screening when considering edge-to-edge repair for severe TR.

Effect of aging on dynamic postural stability and variability during a multi-directional lean and reach object transportation task.

PubMed

Huntley, Andrew H; Zettel, John L; Vallis, Lori Ann

2016-01-01

A "reach and transport object" task that represents common activities of daily living may provide improved insight into dynamic postural stability and movement variability deficits in older adults compared to previous lean to reach and functional reach tests. Healthy young and older, community dwelling adults performed three same elevation object transport tasks and two multiple elevation object transport tasks under two self-selected speeds, self-paced and fast-paced. Dynamic postural stability and movement variability was quantified by whole-body center of mass motion. Older adults demonstrated significant decrements in frontal plane stability during the multiple elevation tasks while exhibiting the same movement variability as their younger counterparts, regardless of task speed. Interestingly, older adults did not exhibit a tradeoff in maneuverability in favour of maintaining stability throughout the tasks, as has previously been reported. In conclusion, the multi-planar, ecologically relevant tasks employed in the current study were specific enough to elucidate decrements in dynamic stability, and thus may be useful for assessing fall risk in older adults with suspected postural instability. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

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.

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…

Determination of main fruits in adulterated nectars by ATR-FTIR spectroscopy combined with multivariate calibration and variable selection methods.

PubMed

Miaw, Carolina Sheng Whei; Assis, Camila; Silva, Alessandro Rangel Carolino Sales; Cunha, Maria Luísa; Sena, Marcelo Martins; de Souza, Scheilla Vitorino Carvalho

2018-07-15

Grape, orange, peach and passion fruit nectars were formulated and adulterated by dilution with syrup, apple and cashew juices at 10 levels for each adulterant. Attenuated total reflectance Fourier transform mid infrared (ATR-FTIR) spectra were obtained. Partial least squares (PLS) multivariate calibration models allied to different variable selection methods, such as interval partial least squares (iPLS), ordered predictors selection (OPS) and genetic algorithm (GA), were used to quantify the main fruits. PLS improved by iPLS-OPS variable selection showed the highest predictive capacity to quantify the main fruit contents. The selected variables in the final models varied from 72 to 100; the root mean square errors of prediction were estimated from 0.5 to 2.6%; the correlation coefficients of prediction ranged from 0.948 to 0.990; and, the mean relative errors of prediction varied from 3.0 to 6.7%. All of the developed models were validated. Copyright © 2018 Elsevier Ltd. All rights reserved.

Effects of selected design variables on three ramp, external compression inlet performance. [boundary layer control bypasses, and mass flow rate

NASA Technical Reports Server (NTRS)

Kamman, J. H.; Hall, C. L.

1975-01-01

Two inlet performance tests and one inlet/airframe drag test were conducted in 1969 at the NASA-Ames Research Center. The basic inlet system was two-dimensional, three ramp (overhead), external compression, with variable capture area. The data from these tests were analyzed to show the effects of selected design variables on the performance of this type of inlet system. The inlet design variables investigated include inlet bleed, bypass, operating mass flow ratio, inlet geometry, and variable capture area.

Towards the development of laboratory methods for studying drinking games: Initial findings, methodological considerations, and future directions

PubMed Central

Silvestri, Mark M.; Lewis, Jennifer M.; Borsari, Brian; Correia, Christopher J.

2014-01-01

Background Drinking games are prevalent among college students and are associated with increased alcohol use and negative alcohol-related consequences. There has been substantial growth in research on drinking games. However, the majority of published studies rely on retrospective self-reports of behavior and very few studies have made use of laboratory procedures to systematically observe drinking game behavior. Objectives The current paper draws on the authors’ experiences designing and implementing methods for the study of drinking games in the laboratory. Results The paper addressed the following key design features: (a) drinking game selection; (b) beverage selection; (c) standardizing game play; (d) selection of dependent and independent variables; and (e) creating a realistic drinking game environment. Conclusions The goal of this methodological review paper is to encourage other researchers to pursue laboratory research on drinking game behavior. Use of laboratory-based methodologies will facilitate a better understanding of the dynamics of risky drinking and inform prevention and intervention efforts. PMID:25192209

Missing-value estimation using linear and non-linear regression with Bayesian gene selection.

PubMed

Zhou, Xiaobo; Wang, Xiaodong; Dougherty, Edward R

2003-11-22

Data from microarray experiments are usually in the form of large matrices of expression levels of genes under different experimental conditions. Owing to various reasons, there are frequently missing values. Estimating these missing values is important because they affect downstream analysis, such as clustering, classification and network design. Several methods of missing-value estimation are in use. The problem has two parts: (1) selection of genes for estimation and (2) design of an estimation rule. We propose Bayesian variable selection to obtain genes to be used for estimation, and employ both linear and nonlinear regression for the estimation rule itself. Fast implementation issues for these methods are discussed, including the use of QR decomposition for parameter estimation. The proposed methods are tested on data sets arising from hereditary breast cancer and small round blue-cell tumors. The results compare very favorably with currently used methods based on the normalized root-mean-square error. The appendix is available from http://gspsnap.tamu.edu/gspweb/zxb/missing_zxb/ (user: gspweb; passwd: gsplab).

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.

Current Directions in Mediation Analysis

PubMed Central

MacKinnon, David P.; Fairchild, Amanda J.

2010-01-01

Mediating variables continue to play an important role in psychological theory and research. A mediating variable transmits the effect of an antecedent variable on to a dependent variable, thereby providing more detailed understanding of relations among variables. Methods to assess mediation have been an active area of research for the last two decades. This paper describes the current state of methods to investigate mediating variables. PMID:20157637

Welfare recipients’ involvement with child protective services after welfare reform

PubMed Central

Nam, Yunju; Meezan, William; Danziger, Sandra K.

2007-01-01

Objective This study identifies factors associated with child protective services (CPS) involvement among current and former welfare recipients after welfare reform legislation was passed in the US in 1996. Method Data come from the Women’s Employment Study, a longitudinal study of randomly selected welfare recipients living in a Michigan city in 1997 (N = 541). In order to identify risk factors for CPS involvement among current and former welfare recipients, multinomial logit analyses with 29 independent variables were employed on a trichotomous dependent variable: no CPS involvement, investigation only, and supervision by CPS after investigation. Results The relationship between work and involvement with CPS differs by work experience prior to welfare reform. As the percentage of months working after welfare reform increased, the risk of being investigated by CPS declined among those with prior work experience but the risk increased among those without prior work experience. However, work variables were not significant predictors of supervision by CPS after an initial investigation. Further, race, cohabitation, childhood welfare receipt, having a learning disability, having a large number of children, being newly divorced, living in a high problem neighborhood, and being convicted of a crime were associated with one’s probability of being either investigated or supervised by CPS. Conclusions These findings suggest that employment could have increased the stress levels of current or former welfare recipients without prior work experience to the point where they were prone to minor child rearing mistakes that resulted in a CPS investigation, but were not severe enough to warrant opening the case for supervision. Supports should be provided to welfare mothers who are prone to involvement with CPS; expansions in the childcare subsidy and a reduction or delay in work requirements might also help these families. PMID:17116329

Welfare recipients' involvement with child protective services after welfare reform.

PubMed

Nam, Yunju; Meezan, William; Danziger, Sandra K

2006-11-01

This study identifies factors associated with child protective services (CPS) involvement among current and former welfare recipients after welfare reform legislation was passed in the US in 1996. Data come from the Women's Employment Study, a longitudinal study of randomly selected welfare recipients living in a Michigan city in 1997 (N=541). In order to identify risk factors for CPS involvement among current and former welfare recipients, multinomial logit analyses with 29 independent variables were employed on a trichotomous dependent variable: no CPS involvement, investigation only, and supervision by CPS after investigation. The relationship between work and involvement with CPS differs by work experience prior to welfare reform. As the percentage of months working after welfare reform increased, the risk of being investigated by CPS declined among those with prior work experience but the risk increased among those without prior work experience. However, work variables were not significant predictors of supervision by CPS after an initial investigation. Further, race, cohabitation, childhood welfare receipt, having a learning disability, having a large number of children, being newly divorced, living in a high problem neighborhood, and being convicted of a crime were associated with one's probability of being either investigated or supervised by CPS. These findings suggest that employment could have increased the stress levels of current or former welfare recipients without prior work experience to the point where they were prone to minor child rearing mistakes that resulted in a CPS investigation, but were not severe enough to warrant opening the case for supervision. Supports should be provided to welfare mothers who are prone to involvement with CPS; expansions in the childcare subsidy and a reduction or delay in work requirements might also help these families.

Monitoring Climate Variability and Change in Northern Alaska: Updates to the U.S. Geological Survey (USGS) Climate and Permafrost Monitoring Network

NASA Astrophysics Data System (ADS)

Urban, F. E.; Clow, G. D.; Meares, D. C.

2004-12-01

Observations of long-term climate and surficial geological processes are sparse in most of the Arctic, despite the fact that this region is highly sensitive to climate change. Instrumental networks that monitor the interplay of climatic variability and geological/cryospheric processes are a necessity for documenting and understanding climate change. Improvements to the spatial coverage and temporal scale of Arctic climate data are in progress. The USGS, in collaboration with The Bureau of Land Management (BLM) and The Fish and Wildlife Service (FWS) currently maintains two types of monitoring networks in northern Alaska: (1) A 15 site network of continuously operating active-layer and climate monitoring stations, and (2) a 21 element array of deep bore-holes in which the thermal state of deep permafrost is monitored. Here, we focus on the USGS Alaska Active Layer and Climate Monitoring Network (AK-CLIM). These 15 stations are deployed in longitudinal transects that span Alaska north of the Brooks Range, (11 in The National Petroleum Reserve Alaska, (NPRA), and 4 in The Arctic National Wildlife Refuge (ANWR)). An informative overview and update of the USGS AK-CLIM network is presented, including insight to current data, processing and analysis software, and plans for data telemetry. Data collection began in 1998 and parameters currently measured include air temperature, soil temperatures (5-120 cm), snow depth, incoming and reflected short-wave radiation, soil moisture (15 cm), wind speed and direction. Custom processing and analysis software has been written that calculates additional parameters such as active layer thaw depth, thawing-degree-days, albedo, cloudiness, and duration of seasonal snow cover. Data from selected AK-CLIM stations are now temporally sufficient to begin identifying trends, anomalies, and inter-annual variability in the climate of northern Alaska.

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.

A global map of suitability for coastal Vibrio cholerae under current and future climate conditions.

PubMed

Escobar, Luis E; Ryan, Sadie J; Stewart-Ibarra, Anna M; Finkelstein, Julia L; King, Christine A; Qiao, Huijie; Polhemus, Mark E

2015-09-01

Vibrio cholerae is a globally distributed water-borne pathogen that causes severe diarrheal disease and mortality, with current outbreaks as part of the seventh pandemic. Further understanding of the role of environmental factors in potential pathogen distribution and corresponding V. cholerae disease transmission over time and space is urgently needed to target surveillance of cholera and other climate and water-sensitive diseases. We used an ecological niche model (ENM) to identify environmental variables associated with V. cholerae presence in marine environments, to project a global model of V. cholerae distribution in ocean waters under current and future climate scenarios. We generated an ENM using published reports of V. cholerae in seawater and freely available remotely sensed imagery. Models indicated that factors associated with V. cholerae presence included chlorophyll-a, pH, and sea surface temperature (SST), with chlorophyll-a demonstrating the greatest explanatory power from variables selected for model calibration. We identified specific geographic areas for potential V. cholerae distribution. Coastal Bangladesh, where cholera is endemic, was found to be environmentally similar to coastal areas in Latin America. In a conservative climate change scenario, we observed a predicted increase in areas with environmental conditions suitable for V. cholerae. Findings highlight the potential for vulnerability maps to inform cholera surveillance, early warning systems, and disease prevention and control. Copyright © 2015 The Authors. Published by Elsevier B.V. All rights reserved.

Altered trait variability in response to size-selective mortality.

PubMed

Uusi-Heikkilä, Silva; Lindström, Kai; Parre, Noora; Arlinghaus, Robert; Alós, Josep; Kuparinen, Anna

2016-09-01

Changes in trait variability owing to size-selective harvesting have received little attention in comparison with changes in mean trait values, perhaps because of the expectation that phenotypic variability should generally be eroded by directional selection typical for fishing and hunting. We show, however, that directional selection, in particular for large body size, leads to increased body-size variation in experimentally harvested zebrafish (Danio rerio) populations exposed to two alternative feeding environments: ad libitum and temporarily restricted food availability. Trait variation may influence population adaptivity, stability and resilience. Therefore, rather than exerting selection pressures that favour small individuals, our results stress the importance of protecting large ones, as they can harbour a great amount of variation within a population, to manage fish stocks sustainably. © 2016 The Author(s).

From Metabonomics to Pharmacometabonomics: The Role of Metabolic Profiling in Personalized Medicine

PubMed Central

Everett, Jeremy R.

2016-01-01

Variable patient responses to drugs are a key issue for medicine and for drug discovery and development. Personalized medicine, that is the selection of medicines for subgroups of patients so as to maximize drug efficacy and minimize toxicity, is a key goal of twenty-first century healthcare. Currently, most personalized medicine paradigms rely on clinical judgment based on the patient's history, and on the analysis of the patients' genome to predict drug effects i.e., pharmacogenomics. However, variability in patient responses to drugs is dependent upon many environmental factors to which human genomics is essentially blind. A new paradigm for predicting drug responses based on individual pre-dose metabolite profiles has emerged in the past decade: pharmacometabonomics, which is defined as “the prediction of the outcome (for example, efficacy or toxicity) of a drug or xenobiotic intervention in an individual based on a mathematical model of pre-intervention metabolite signatures.” The new pharmacometabonomics paradigm is complementary to pharmacogenomics but has the advantage of being sensitive to environmental as well as genomic factors. This review will chart the discovery and development of pharmacometabonomics, and provide examples of its current utility and possible future developments. PMID:27660611

Comparison of atmospheric concentrations of currently used pesticides between urban and rural areas during intensive application period in Alsace (France) by using XAD-2® based passive samplers.

PubMed

Liaud, Celine; Schwartz, Jean-Jacques; Millet, Maurice

2017-07-03

XAD-2® passive samplers (PAS) have been exposed simultaneously for 14 days on two sites, one rural and one urban, situated in Alsace (East of France) during intensive pesticides application in agriculture (between March and September). PAS have been extracted and analyzed for current-used pesticides and lindane with an analytical method coupling accelerated solvent extraction (ASE), solid-phase microextraction (SPME) and GC/MS/MS. Results show the detection of pesticides is linked to the period of application and spatial and temporal variabilities can be observed with these PAS during the selected sampling period. The spatial and temporal variability is comparable to the one previously observed by comparing data obtained with PAS with data from Hi.-Vol. samplers in an urban area. Sampling rates were calculated for some pesticides and values are comparable to the data already available in the literature. From these sampling rates, concentrations in ng m -3 of pesticides in PAS have been calculated and are in the same order of magnitude as those obtained with Hi.Vol. sampling during the same period of time.

Transferability of optimally-selected climate models in the quantification of climate change impacts on hydrology

NASA Astrophysics Data System (ADS)

Chen, Jie; Brissette, François P.; Lucas-Picher, Philippe

2016-11-01

Given the ever increasing number of climate change simulations being carried out, it has become impractical to use all of them to cover the uncertainty of climate change impacts. Various methods have been proposed to optimally select subsets of a large ensemble of climate simulations for impact studies. However, the behaviour of optimally-selected subsets of climate simulations for climate change impacts is unknown, since the transfer process from climate projections to the impact study world is usually highly non-linear. Consequently, this study investigates the transferability of optimally-selected subsets of climate simulations in the case of hydrological impacts. Two different methods were used for the optimal selection of subsets of climate scenarios, and both were found to be capable of adequately representing the spread of selected climate model variables contained in the original large ensemble. However, in both cases, the optimal subsets had limited transferability to hydrological impacts. To capture a similar variability in the impact model world, many more simulations have to be used than those that are needed to simply cover variability from the climate model variables' perspective. Overall, both optimal subset selection methods were better than random selection when small subsets were selected from a large ensemble for impact studies. However, as the number of selected simulations increased, random selection often performed better than the two optimal methods. To ensure adequate uncertainty coverage, the results of this study imply that selecting as many climate change simulations as possible is the best avenue. Where this was not possible, the two optimal methods were found to perform adequately.

Robustness of Two Formulas to Correct Pearson Correlation for Restriction of Range

ERIC Educational Resources Information Center

tran, minh

2011-01-01

Many research studies involving Pearson correlations are conducted in settings where one of the two variables has a restricted range in the sample. For example, this situation occurs when tests are used for selecting candidates for employment or university admission. Often after selection, there is interest in correlating the selection variable,…

Clustering Words to Match Conditions: An Algorithm for Stimuli Selection in Factorial Designs

ERIC Educational Resources Information Center

Guasch, Marc; Haro, Juan; Boada, Roger

2017-01-01

With the increasing refinement of language processing models and the new discoveries about which variables can modulate these processes, stimuli selection for experiments with a factorial design is becoming a tough task. Selecting sets of words that differ in one variable, while matching these same words into dozens of other confounding variables…

Selection of relevant input variables in storm water quality modeling by multiobjective evolutionary polynomial regression paradigm

NASA Astrophysics Data System (ADS)

Creaco, E.; Berardi, L.; Sun, Siao; Giustolisi, O.; Savic, D.

2016-04-01

The growing availability of field data, from information and communication technologies (ICTs) in "smart" urban infrastructures, allows data modeling to understand complex phenomena and to support management decisions. Among the analyzed phenomena, those related to storm water quality modeling have recently been gaining interest in the scientific literature. Nonetheless, the large amount of available data poses the problem of selecting relevant variables to describe a phenomenon and enable robust data modeling. This paper presents a procedure for the selection of relevant input variables using the multiobjective evolutionary polynomial regression (EPR-MOGA) paradigm. The procedure is based on scrutinizing the explanatory variables that appear inside the set of EPR-MOGA symbolic model expressions of increasing complexity and goodness of fit to target output. The strategy also enables the selection to be validated by engineering judgement. In such context, the multiple case study extension of EPR-MOGA, called MCS-EPR-MOGA, is adopted. The application of the proposed procedure to modeling storm water quality parameters in two French catchments shows that it was able to significantly reduce the number of explanatory variables for successive analyses. Finally, the EPR-MOGA models obtained after the input selection are compared with those obtained by using the same technique without benefitting from input selection and with those obtained in previous works where other data-modeling techniques were used on the same data. The comparison highlights the effectiveness of both EPR-MOGA and the input selection procedure.

Cross-validation pitfalls when selecting and assessing regression and classification models.

PubMed

Krstajic, Damjan; Buturovic, Ljubomir J; Leahy, David E; Thomas, Simon

2014-03-29

We address the problem of selecting and assessing classification and regression models using cross-validation. Current state-of-the-art methods can yield models with high variance, rendering them unsuitable for a number of practical applications including QSAR. In this paper we describe and evaluate best practices which improve reliability and increase confidence in selected models. A key operational component of the proposed methods is cloud computing which enables routine use of previously infeasible approaches. We describe in detail an algorithm for repeated grid-search V-fold cross-validation for parameter tuning in classification and regression, and we define a repeated nested cross-validation algorithm for model assessment. As regards variable selection and parameter tuning we define two algorithms (repeated grid-search cross-validation and double cross-validation), and provide arguments for using the repeated grid-search in the general case. We show results of our algorithms on seven QSAR datasets. The variation of the prediction performance, which is the result of choosing different splits of the dataset in V-fold cross-validation, needs to be taken into account when selecting and assessing classification and regression models. We demonstrate the importance of repeating cross-validation when selecting an optimal model, as well as the importance of repeating nested cross-validation when assessing a prediction error.

The validity of the Gait Variability Index for individuals with mild to moderate Parkinson's disease.

PubMed

Rennie, Linda; Dietrichs, Espen; Moe-Nilssen, Rolf; Opheim, Arve; Franzén, Erika

2017-05-01

Increased step-to-step variability is a feature of gait in individuals with Parkinson's disease (PD) and is associated with increased disease severity and reductions in balance and mobility. The Gait Variability Index (GVI) quantifies gait variability in spatiotemporal variables where a score ≥100 indicates a similar level of gait variability as the control group, and lower scores denote increased gait variability. The study aim was to explore mean GVI score and investigate construct validity of the index for individuals with mild to moderate PD. 100 (57 males) subjects with idiopathic PD, Hoehn & Yahr 2 (n=44) and 3, and ≥60 years were included. Data on disease severity, dynamic balance, mobility and spatiotemporal gait parameters at self-selected speed (GAITRite) was collected. The results showed a mean overall GVI: 97.5 (SD 11.7) and mean GVI for the most affected side: 94.5 (SD 10.6). The associations between the GVI and Mini- BESTest and TUG were low (r=0.33 and 0.42) and the GVI could not distinguish between Hoehn & Yahr 2 and 3 (AUC=0.529, SE=0.058, p=0.622). The mean GVI was similar to previously reported values for older adults, contrary to consistent reports of increased gait variability in PD compared to healthy peers. Therefore, the validity of the GVI could not be confirmed for individuals with mild to moderate PD in its current form due to low associations with validated tests for functional balance and mobility and poor discriminatory ability. Future work should aim to establish which spatiotemporal variables are most informative regarding gait variability in individuals with PD. Copyright © 2017 Elsevier B.V. All rights reserved.

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.

Variable selection and model choice in geoadditive regression models.

PubMed

Kneib, Thomas; Hothorn, Torsten; Tutz, Gerhard

2009-06-01

Model choice and variable selection are issues of major concern in practical regression analyses, arising in many biometric applications such as habitat suitability analyses, where the aim is to identify the influence of potentially many environmental conditions on certain species. We describe regression models for breeding bird communities that facilitate both model choice and variable selection, by a boosting algorithm that works within a class of geoadditive regression models comprising spatial effects, nonparametric effects of continuous covariates, interaction surfaces, and varying coefficients. The major modeling components are penalized splines and their bivariate tensor product extensions. All smooth model terms are represented as the sum of a parametric component and a smooth component with one degree of freedom to obtain a fair comparison between the model terms. A generic representation of the geoadditive model allows us to devise a general boosting algorithm that automatically performs model choice and variable selection.

Does Self-Selection Affect Samples’ Representativeness in Online Surveys? An Investigation in Online Video Game Research

PubMed Central

van Singer, Mathias; Chatton, Anne; Achab, Sophia; Zullino, Daniele; Rothen, Stephane; Khan, Riaz; Billieux, Joel; Thorens, Gabriel

2014-01-01

Background The number of medical studies performed through online surveys has increased dramatically in recent years. Despite their numerous advantages (eg, sample size, facilitated access to individuals presenting stigmatizing issues), selection bias may exist in online surveys. However, evidence on the representativeness of self-selected samples in online studies is patchy. Objective Our objective was to explore the representativeness of a self-selected sample of online gamers using online players’ virtual characters (avatars). Methods All avatars belonged to individuals playing World of Warcraft (WoW), currently the most widely used online game. Avatars’ characteristics were defined using various games’ scores, reported on the WoW’s official website, and two self-selected samples from previous studies were compared with a randomly selected sample of avatars. Results We used scores linked to 1240 avatars (762 from the self-selected samples and 478 from the random sample). The two self-selected samples of avatars had higher scores on most of the assessed variables (except for guild membership and exploration). Furthermore, some guilds were overrepresented in the self-selected samples. Conclusions Our results suggest that more proficient players or players more involved in the game may be more likely to participate in online surveys. Caution is needed in the interpretation of studies based on online surveys that used a self-selection recruitment procedure. Epidemiological evidence on the reduced representativeness of sample of online surveys is warranted. PMID:25001007

Mechanisms of pro-arrhythmic abnormalities in ventricular repolarisation and anti-arrhythmic therapies in human hypertrophic cardiomyopathy.

PubMed

Passini, Elisa; Mincholé, Ana; Coppini, Raffaele; Cerbai, Elisabetta; Rodriguez, Blanca; Severi, Stefano; Bueno-Orovio, Alfonso

2016-07-01

Hypertrophic cardiomyopathy (HCM) is a cause of sudden arrhythmic death, but the understanding of its pro-arrhythmic mechanisms and an effective pharmacological treatment are lacking. HCM electrophysiological remodelling includes both increased inward and reduced outward currents, but their role in promoting repolarisation abnormalities remains unknown. The goal of this study is to identify key ionic mechanisms driving repolarisation abnormalities in human HCM, and to evaluate anti-arrhythmic effects of single and multichannel inward current blocks. Experimental ionic current, action potential (AP) and Ca(2+)-transient (CaT) recordings were used to construct populations of human non-diseased and HCM AP models (n=9118), accounting for inter-subject variability. Simulations were conducted for several degrees of selective and combined inward current block. Simulated HCM cardiomyocytes exhibited prolonged AP and CaT, diastolic Ca(2+) overload and decreased CaT amplitude, in agreement with experiments. Repolarisation abnormalities in HCM models were consistently driven by L-type Ca(2+) current (ICaL) re-activation, and ICaL block was the most effective intervention to normalise repolarisation and diastolic Ca(2+), but compromised CaT amplitude. Late Na(+) current (INaL) block partially abolished repolarisation abnormalities, with small impact on CaT. Na(+)/Ca(2+) exchanger (INCX) block effectively restored repolarisation and CaT amplitude, but increased Ca(2+) overload. Multichannel block increased efficacy in normalising repolarisation, AP biomarkers and CaT amplitude compared to selective block. Experimentally-calibrated populations of human AP models identify ICaL re-activation as the key mechanism for repolarisation abnormalities in HCM, and combined INCX, INaL and ICaL block as effective anti-arrhythmic therapies also able to partially reverse the HCM electrophysiological phenotype. Copyright © 2015 The Authors. Published by Elsevier Ltd.. All rights reserved.

Method of operating a thermoelectric generator

DOEpatents

Reynolds, Michael G; Cowgill, Joshua D

2013-11-05

A method for operating a thermoelectric generator supplying a variable-load component includes commanding the variable-load component to operate at a first output and determining a first load current and a first load voltage to the variable-load component while operating at the commanded first output. The method also includes commanding the variable-load component to operate at a second output and determining a second load current and a second load voltage to the variable-load component while operating at the commanded second output. The method includes calculating a maximum power output of the thermoelectric generator from the determined first load current and voltage and the determined second load current and voltage, and commanding the variable-load component to operate at a third output. The commanded third output is configured to draw the calculated maximum power output from the thermoelectric generator.

System and method for optimal load and source scheduling in context aware homes

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

Shetty, Pradeep; Foslien Graber, Wendy; Mangsuli, Purnaprajna R.

A controller for controlling energy consumption in a home includes a constraints engine to define variables for multiple appliances in the home corresponding to various home modes and persona of an occupant of the home. A modeling engine models multiple paths of energy utilization of the multiple appliances to place the home into a desired state from a current context. An optimal scheduler receives the multiple paths of energy utilization and generates a schedule as a function of the multiple paths and a selected persona to place the home in a desired state.

NASA Langley Atmospheric Science Data Center Toolsets for Airborne Data (TAD): Common Variable Naming Schema

NASA Astrophysics Data System (ADS)

Chen, G.; Early, A. B.; Peeters, M. C.

2014-12-01

NASA has conducted airborne tropospheric chemistry studies for about three decades. These field campaigns have generated a great wealth of observations, which are characterized by a wide range of trace gases and aerosol properties. The airborne observational data have often been used in assessment and validation of models and satellite instruments. One particular issue is a lack of consistent variable naming across field campaigns, which makes cross-mission data discovery difficult. The ASDC Toolset for Airborne Data (TAD) is being designed to meet the user community needs for manipulating aircraft data for scientific research on climate change and air quality relevant issues. As part of this effort, a common naming system was developed to provide a link between variables from different aircraft field studies. This system covers all current and past airborne in-situ measurements housed at the ASDC, as well as select NOAA missions. The TAD common variable naming system consists of 6 categories and 3 sub-levels. The top-level category is primarily defined by the physical characteristics of the measurement: e.g., aerosol, cloud, trace gases. The sub-levels were designed to organize the variables according to nature of measurement (e.g., aerosol microphysical and optical properties) or chemical structures (e.g., carbon compound). The development of the TAD common variable naming system was in consultation with staff from the Global Change Master Directory (GCMD) and referenced/expanded the existing Climate and Forecast (CF) variable naming conventions. The detailed structure of the TAD common variable naming convention and its application in TAD development will be presented.

Music training relates to the development of neural mechanisms of selective auditory attention.

PubMed

Strait, Dana L; Slater, Jessica; O'Connell, Samantha; Kraus, Nina

2015-04-01

Selective attention decreases trial-to-trial variability in cortical auditory-evoked activity. This effect increases over the course of maturation, potentially reflecting the gradual development of selective attention and inhibitory control. Work in adults indicates that music training may alter the development of this neural response characteristic, especially over brain regions associated with executive control: in adult musicians, attention decreases variability in auditory-evoked responses recorded over prefrontal cortex to a greater extent than in nonmusicians. We aimed to determine whether this musician-associated effect emerges during childhood, when selective attention and inhibitory control are under development. We compared cortical auditory-evoked variability to attended and ignored speech streams in musicians and nonmusicians across three age groups: preschoolers, school-aged children and young adults. Results reveal that childhood music training is associated with reduced auditory-evoked response variability recorded over prefrontal cortex during selective auditory attention in school-aged child and adult musicians. Preschoolers, on the other hand, demonstrate no impact of selective attention on cortical response variability and no musician distinctions. This finding is consistent with the gradual emergence of attention during this period and may suggest no pre-existing differences in this attention-related cortical metric between children who undergo music training and those who do not. Copyright © 2015 The Authors. Published by Elsevier Ltd.. All rights reserved.

Classification of debtor credit status and determination amount of credit risk by using linier discriminant function

NASA Astrophysics Data System (ADS)

Aidi, Muhammad Nur; Sari, Resty Indah

2012-05-01

A decision of credit that given by bank or another creditur must have a risk and it called credit risk. Credit risk is an investor's risk of loss arising from a borrower who does not make payments as promised. The substantial of credit risk can lead to losses for the banks and the debtor. To minimize this problem need a further study to identify a potential new customer before the decision given. Identification of debtor can using various approaches analysis, one of them is by using discriminant analysis. Discriminant analysis in this study are used to classify whether belonging to the debtor's good credit or bad credit. The result of this study are two discriminant functions that can identify new debtor. Before step built the discriminant function, selection of explanatory variables should be done. Purpose of selection independent variable is to choose the variable that can discriminate the group maximally. Selection variables in this study using different test, for categoric variable selection of variable using proportion chi-square test, and stepwise discriminant for numeric variable. The result of this study are two discriminant functions that can identify new debtor. The selected variables that can discriminating two groups of debtor maximally are status of existing checking account, credit history, credit amount, installment rate in percentage of disposable income, sex, age in year, other installment plans, and number of people being liable to provide maintenance. This classification produce a classification accuracy rate is good enough, that is equal to 74,70%. Debtor classification using discriminant analysis has risk level that is small enough, and it ranged beetwen 14,992% and 17,608%. Based on that credit risk rate, using discriminant analysis on the classification of credit status can be used effectively.

Mesoscale Convective Complex versus Non-Mesoscale Convective Complex Thunderstorms: A Comparison of Selected Meteorological Variables.

DTIC Science & Technology

1986-08-01

mean square errors for selected variables . . 34 8. Variable range and mean value for MCC and non-MCC cases . . 36 9. Alpha ( a ) levels at which the...Table 9. For each variable, the a level is listed at which the two mean values are determined to be significantly 38 Table 9. Alpha ( a ) levels at...vorticity advection None 700 mb vertical velocity forecast .20 different. These a levels express the probability of erroneously con- cluding that the

Magnetically Controlled Variable Transformer

NASA Technical Reports Server (NTRS)

Kleiner, Charles T.

1994-01-01

Improved variable-transformer circuit, output voltage and current of which controlled by use of relatively small current supplied at relatively low power to control windings on its magnetic cores. Transformer circuits of this type called "magnetic amplifiers" because ratio between controlled output power and power driving control current of such circuit large. This ratio - power gain - can be as large as 100 in present circuit. Variable-transformer circuit offers advantages of efficiency, safety, and controllability over some prior variable-transformer circuits.

Mental Status Documentation: Information Quality and Data Processes

PubMed Central

Weir, Charlene; Gibson, Bryan; Taft, Teresa; Slager, Stacey; Lewis, Lacey; Staggers, Nancy

2016-01-01

Delirium is a fluctuating disturbance of cognition and/or consciousness associated with poor outcomes. Caring for patients with delirium requires integration of disparate information across clinicians, settings and time. The goal of this project was to characterize the information processes involved in nurses’ assessment, documentation, decisionmaking and communication regarding patients’ mental status in the inpatient setting. VA nurse managers of medical wards (n=18) were systematically selected across the US. A semi-structured telephone interview focused on current assessment, documentation, and communication processes, as well as clinical and administrative decision-making was conducted, audio-recorded and transcribed. A thematic analytic approach was used. Five themes emerged: 1) Fuzzy Concepts, 2) Grey Data, 3) Process Variability 4) Context is Critical and 5) Goal Conflict. This project describes the vague and variable information processes related to delirium and mental status that undermine effective risk, prevention, identification, communication and mitigation of harm. PMID:28269919

Mental Status Documentation: Information Quality and Data Processes.

PubMed

Weir, Charlene; Gibson, Bryan; Taft, Teresa; Slager, Stacey; Lewis, Lacey; Staggers, Nancy

2016-01-01

Delirium is a fluctuating disturbance of cognition and/or consciousness associated with poor outcomes. Caring for patients with delirium requires integration of disparate information across clinicians, settings and time. The goal of this project was to characterize the information processes involved in nurses' assessment, documentation, decisionmaking and communication regarding patients' mental status in the inpatient setting. VA nurse managers of medical wards (n=18) were systematically selected across the US. A semi-structured telephone interview focused on current assessment, documentation, and communication processes, as well as clinical and administrative decision-making was conducted, audio-recorded and transcribed. A thematic analytic approach was used. Five themes emerged: 1) Fuzzy Concepts, 2) Grey Data, 3) Process Variability 4) Context is Critical and 5) Goal Conflict. This project describes the vague and variable information processes related to delirium and mental status that undermine effective risk, prevention, identification, communication and mitigation of harm.

Multidisciplinary Optimization of a Transport Aircraft Wing using Particle Swarm Optimization

NASA Technical Reports Server (NTRS)

Sobieszczanski-Sobieski, Jaroslaw; Venter, Gerhard

2002-01-01

The purpose of this paper is to demonstrate the application of particle swarm optimization to a realistic multidisciplinary optimization test problem. The paper's new contributions to multidisciplinary optimization is the application of a new algorithm for dealing with the unique challenges associated with multidisciplinary optimization problems, and recommendations as to the utility of the algorithm in future multidisciplinary optimization applications. The selected example is a bi-level optimization problem that demonstrates severe numerical noise and has a combination of continuous and truly discrete design variables. The use of traditional gradient-based optimization algorithms is thus not practical. The numerical results presented indicate that the particle swarm optimization algorithm is able to reliably find the optimum design for the problem presented here. The algorithm is capable of dealing with the unique challenges posed by multidisciplinary optimization as well as the numerical noise and truly discrete variables present in the current example problem.

Prospects and applicability of wave energy for South Africa

NASA Astrophysics Data System (ADS)

Lavidas, George; Venugopal, Vengatesan

2018-03-01

Renewable energy offers significant opportunities for electricity diversification. South Africa belongs to the group of developing nations and encompasses a lot of potential for renewable energy developments. Currently, the majority of its electricity production originates from fossil fuels; however, incorporation of clean coal technologies will aid in reaching the assigned targets. This study offers a long-term wave power quantification analysis with a numerical wave model. The investigation includes long-term resource assessment in the region, variability, seasonal and monthly wave energy content. Locations with high-energy content but low variability pose an opportunity that can contribute in the alleviation of energy poverty. Application of wave converters depends on the combination of complex terms. The study presents resource levels and the joint distributions, which indicate suitability for converter selection. Depending on the region of interest, these characteristics change. Thus, this resource assessment adds knowledge on wave power and optimal consideration for wave energy applicability.

Mentally disordered women in jail: who receives services?

PubMed Central

Teplin, L A; Abram, K M; McClelland, G M

1997-01-01

OBJECTIVES: Many jail inmates have severe psychiatric disorders (e.g., schizophrenia, major affective disorders). The courts have mandated that detainees have a constitutional right to treatment. We investigated what proportion of female jail detainees needed mental health services, what proportion received services, and what variables predicted who received services. METHODS: Trained interviewers administered a psychiatric evaluation (the NIMH Diagnostic Interview Schedule) to 1272 randomly selected female jail detainees during jail intake in a large Midwestern city. Project staff then documented whether women subsequently received services, using records and case files. RESULTS: Of the women who needed services, 23.5% received them while they were in jail. Type of disorder, treatment history, and socio-demographic variables all affected the odds of a mentally ill woman's receiving services. CONCLUSIONS: Correctional health care is a growing national public health problem. The magnitude of mental health service needs far exceeds current resources. PMID:9146439

The value of item response theory in clinical assessment: a review.

PubMed

Thomas, Michael L

2011-09-01

Item response theory (IRT) and related latent variable models represent modern psychometric theory, the successor to classical test theory in psychological assessment. Although IRT has become prevalent in the measurement of ability and achievement, its contributions to clinical domains have been less extensive. Applications of IRT to clinical assessment are reviewed to appraise its current and potential value. Benefits of IRT include comprehensive analyses and reduction of measurement error, creation of computer adaptive tests, meaningful scaling of latent variables, objective calibration and equating, evaluation of test and item bias, greater accuracy in the assessment of change due to therapeutic intervention, and evaluation of model and person fit. The theory may soon reinvent the manner in which tests are selected, developed, and scored. Although challenges remain to the widespread implementation of IRT, its application to clinical assessment holds great promise. Recommendations for research, test development, and clinical practice are provided.

Effects of the chemical environment on the spectroscopic properties of clays: Applications for Mars

NASA Technical Reports Server (NTRS)

Bishop, Janice L.; Pieters, Carle M.

1992-01-01

Laboratory studies of Mars soil analogs pose unique problems, since soils interact readily with their environment and exhibit variable characteristics depending on the environment. We have performed a series of experiments focusing on the spectral properties of clays and how they vary as a function of composition and environment, including examination of fundamental as well as overtone absorptions, that occur in the mid- and near-IR, respectively. Smectite clays have been selected in our laboratory experiments as a primary surface analog for Mars because of their compatibility with results of the Viking biology experiments, their stability under current martian conditions, and their compatibility with reflectance spectra of Mars. We prepared a number of monoionic montmorillonites in order to examine the influence of cations on the water molecules in the clay interlayer region. Moessbauer spectra of several montmorillonites with variable amounts of interlayer iron confirm the presence of ferrihydrite.

Predicting punching acceleration from selected strength and power variables in elite karate athletes: a multiple regression analysis.

PubMed

Loturco, Irineu; Artioli, Guilherme Giannini; Kobal, Ronaldo; Gil, Saulo; Franchini, Emerson

2014-07-01

This study investigated the relationship between punching acceleration and selected strength and power variables in 19 professional karate athletes from the Brazilian National Team (9 men and 10 women; age, 23 ± 3 years; height, 1.71 ± 0.09 m; and body mass [BM], 67.34 ± 13.44 kg). Punching acceleration was assessed under 4 different conditions in a randomized order: (a) fixed distance aiming to attain maximum speed (FS), (b) fixed distance aiming to attain maximum impact (FI), (c) self-selected distance aiming to attain maximum speed, and (d) self-selected distance aiming to attain maximum impact. The selected strength and power variables were as follows: maximal dynamic strength in bench press and squat-machine, squat and countermovement jump height, mean propulsive power in bench throw and jump squat, and mean propulsive velocity in jump squat with 40% of BM. Upper- and lower-body power and maximal dynamic strength variables were positively correlated to punch acceleration in all conditions. Multiple regression analysis also revealed predictive variables: relative mean propulsive power in squat jump (W·kg-1), and maximal dynamic strength 1 repetition maximum in both bench press and squat-machine exercises. An impact-oriented instruction and a self-selected distance to start the movement seem to be crucial to reach the highest acceleration during punching execution. This investigation, while demonstrating strong correlations between punching acceleration and strength-power variables, also provides important information for coaches, especially for designing better training strategies to improve punching speed.

Not accounting for interindividual variability can mask habitat selection patterns: a case study on black bears.

PubMed

Lesmerises, Rémi; St-Laurent, Martin-Hugues

2017-11-01

Habitat selection studies conducted at the population scale commonly aim to describe general patterns that could improve our understanding of the limiting factors in species-habitat relationships. Researchers often consider interindividual variation in selection patterns to control for its effects and avoid pseudoreplication by using mixed-effect models that include individuals as random factors. Here, we highlight common pitfalls and possible misinterpretations of this strategy by describing habitat selection of 21 black bears Ursus americanus. We used Bayesian mixed-effect models and compared results obtained when using random intercept (i.e., population level) versus calculating individual coefficients for each independent variable (i.e., individual level). We then related interindividual variability to individual characteristics (i.e., age, sex, reproductive status, body condition) in a multivariate analysis. The assumption of comparable behavior among individuals was verified only in 40% of the cases in our seasonal best models. Indeed, we found strong and opposite responses among sampled bears and individual coefficients were linked to individual characteristics. For some covariates, contrasted responses canceled each other out at the population level. In other cases, interindividual variability was concealed by the composition of our sample, with the majority of the bears (e.g., old individuals and bears in good physical condition) driving the population response (e.g., selection of young forest cuts). Our results stress the need to consider interindividual variability to avoid misinterpretation and uninformative results, especially for a flexible and opportunistic species. This study helps to identify some ecological drivers of interindividual variability in bear habitat selection patterns.

Multi-point Adjoint-Based Design of Tilt-Rotors in a Noninertial Reference Frame

NASA Technical Reports Server (NTRS)

Jones, William T.; Nielsen, Eric J.; Lee-Rausch, Elizabeth M.; Acree, Cecil W.

2014-01-01

Optimization of tilt-rotor systems requires the consideration of performance at multiple design points. In the current study, an adjoint-based optimization of a tilt-rotor blade is considered. The optimization seeks to simultaneously maximize the rotorcraft figure of merit in hover and the propulsive efficiency in airplane-mode for a tilt-rotor system. The design is subject to minimum thrust constraints imposed at each design point. The rotor flowfields at each design point are cast as steady-state problems in a noninertial reference frame. Geometric design variables used in the study to control blade shape include: thickness, camber, twist, and taper represented by as many as 123 separate design variables. Performance weighting of each operational mode is considered in the formulation of the composite objective function, and a build up of increasing geometric degrees of freedom is used to isolate the impact of selected design variables. In all cases considered, the resulting designs successfully increase both the hover figure of merit and the airplane-mode propulsive efficiency for a rotor designed with classical techniques.

Inter-rater reliability of output measures for a posture matching assessment approach: a pilot study with food service workers.

PubMed

Cann, A P; Connolly, M; Ruuska, R; MacNeil, M; Birmingham, T B; Vandervoort, A A; Callaghan, J P

2008-04-01

Despite the ongoing health problem of repetitive strain injuries, there are few tools currently available for ergonomic applications evaluating cumulative loading that have well-documented evidence of reliability and validity. The purpose of this study was to determine the inter-rater reliability of a posture matching based analysis tool (3DMatch, University of Waterloo) for predicting cumulative and peak spinal loads. A total of 30 food service workers were each videotaped for a 1-h period while performing typical work activities and a single work task was randomly selected from each for analysis by two raters. Inter-rater reliability was determined using intraclass correlation coefficients (ICC) model 2,1 and standard errors of measurement for cumulative and peak spinal and shoulder loading variables across all subjects. Overall, 85.5% of variables had moderate to excellent inter-rater reliability, with ICCs ranging from 0.30-0.99 for all cumulative and peak loading variables. 3DMatch was found to be a reliable ergonomic tool when more than one rater is involved.

Algorithms for Discovery of Multiple Markov Boundaries

PubMed Central

Statnikov, Alexander; Lytkin, Nikita I.; Lemeire, Jan; Aliferis, Constantin F.

2013-01-01

Algorithms for Markov boundary discovery from data constitute an important recent development in machine learning, primarily because they offer a principled solution to the variable/feature selection problem and give insight on local causal structure. Over the last decade many sound algorithms have been proposed to identify a single Markov boundary of the response variable. Even though faithful distributions and, more broadly, distributions that satisfy the intersection property always have a single Markov boundary, other distributions/data sets may have multiple Markov boundaries of the response variable. The latter distributions/data sets are common in practical data-analytic applications, and there are several reasons why it is important to induce multiple Markov boundaries from such data. However, there are currently no sound and efficient algorithms that can accomplish this task. This paper describes a family of algorithms TIE* that can discover all Markov boundaries in a distribution. The broad applicability as well as efficiency of the new algorithmic family is demonstrated in an extensive benchmarking study that involved comparison with 26 state-of-the-art algorithms/variants in 15 data sets from a diversity of application domains. PMID:25285052

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

Planetarium instructional efficacy: A research synthesis

NASA Astrophysics Data System (ADS)

Brazell, Bruce D.

The purpose of the current study was to explore the instructional effectiveness of the planetarium in astronomy education using meta-analysis. A review of the literature revealed 46 studies related to planetarium efficacy. However, only 19 of the studies satisfied selection criteria for inclusion in the meta-analysis. Selected studies were then subjected to coding procedures, which extracted information such as subject characteristics, experimental design, and outcome measures. From these data, 24 effect sizes were calculated in the area of student achievement and five effect sizes were determined in the area of student attitudes using reported statistical information. Mean effect sizes were calculated for both the achievement and the attitude distributions. Additionally, each effect size distribution was subjected to homogeneity analysis. The attitude distribution was found to be homogeneous with a mean effect size of -0.09, which was not significant, p = .2535. The achievement distribution was found to be heterogeneous with a statistically significant mean effect size of +0.28, p < .05. Since the achievement distribution was heterogeneous, the analog to the ANOVA procedure was employed to explore variability in this distribution in terms of the coded variables. The analog to the ANOVA procedure revealed that the variability introduced by the coded variables did not fully explain the variability in the achievement distribution beyond subject-level sampling error under a fixed effects model. Therefore, a random effects model analysis was performed which resulted in a mean effect size of +0.18, which was not significant, p = .2363. However, a large random effect variance component was determined indicating that the differences between studies were systematic and yet to be revealed. The findings of this meta-analysis showed that the planetarium has been an effective instructional tool in astronomy education in terms of student achievement. However, the meta-analysis revealed that the planetarium has not been a very effective tool for improving student attitudes towards astronomy.

Theory and design of variable conductance heat pipes

NASA Technical Reports Server (NTRS)

Marcus, B. D.

1972-01-01

A comprehensive review and analysis of all aspects of heat pipe technology pertinent to the design of self-controlled, variable conductance devices for spacecraft thermal control is presented. Subjects considered include hydrostatics, hydrodynamics, heat transfer into and out of the pipe, fluid selection, materials compatibility and variable conductance control techniques. The report includes a selected bibliography of pertinent literature, analytical formulations of various models and theories describing variable conductance heat pipe behavior, and the results of numerous experiments on the steady state and transient performance of gas controlled variable conductance heat pipes. Also included is a discussion of VCHP design techniques.

Comparing Selections of Environmental Variables for Ecological Studies: A Focus on Terrain Attributes.

PubMed

Lecours, Vincent; Brown, Craig J; Devillers, Rodolphe; Lucieer, Vanessa L; Edinger, Evan N

2016-01-01

Selecting appropriate environmental variables is a key step in ecology. Terrain attributes (e.g. slope, rugosity) are routinely used as abiotic surrogates of species distribution and to produce habitat maps that can be used in decision-making for conservation or management. Selecting appropriate terrain attributes for ecological studies may be a challenging process that can lead users to select a subjective, potentially sub-optimal combination of attributes for their applications. The objective of this paper is to assess the impacts of subjectively selecting terrain attributes for ecological applications by comparing the performance of different combinations of terrain attributes in the production of habitat maps and species distribution models. Seven different selections of terrain attributes, alone or in combination with other environmental variables, were used to map benthic habitats of German Bank (off Nova Scotia, Canada). 29 maps of potential habitats based on unsupervised classifications of biophysical characteristics of German Bank were produced, and 29 species distribution models of sea scallops were generated using MaxEnt. The performances of the 58 maps were quantified and compared to evaluate the effectiveness of the various combinations of environmental variables. One of the combinations of terrain attributes-recommended in a related study and that includes a measure of relative position, slope, two measures of orientation, topographic mean and a measure of rugosity-yielded better results than the other selections for both methodologies, confirming that they together best describe terrain properties. Important differences in performance (up to 47% in accuracy measurement) and spatial outputs (up to 58% in spatial distribution of habitats) highlighted the importance of carefully selecting variables for ecological applications. This paper demonstrates that making a subjective choice of variables may reduce map accuracy and produce maps that do not adequately represent habitats and species distributions, thus having important implications when these maps are used for decision-making.

User Values in the Selection of Information Services. Final Report.

ERIC Educational Resources Information Center

Hall, Homer J.

The value systems by which the users of purchased information services select which one to use or buy are found to differ sharply among different user populations, but the variable found to control them is a function of use, not of the user as an individual. Selection variables are summarized in a matrix of user values and interaction effects. The…

Social Determinants of Health and Tobacco Use in Five Low and Middle-Income Countries - Results from the Global Adult Tobacco Survey (GATS), 2011 - 2012.

PubMed

Tee, Guat Hiong; Aris, Tahir; Rarick, James; Irimie, Sorina

2016-01-01

Tobacco consumption continues to be the leading cause of preventable deaths globally. The objective of this study was to examine the associaton of selected socio-demographic variables with current tobacco use in five countries that participated in the Phase II Global Adult Tobacco Survey in 2011 - 2012. We analysed internationally comparable representative household survey data from 33,482 respondents aged ≥ 15 years in Indonesia, Malaysia, Romania, Argentina and Nigeria for determinants of tobacco use within each country. Socio-demographic variables analysed included gender, age, residency, education, wealth index and awareness of smoking health consequences. Current tobacco use was defined as smoking or use of smokeless tobacco daily or occasionally. The overall prevalence of tobacco use varied from 5.5% in Nigeria to 35.7% in Indonesia and was significantly higher among males than females in all five countries. Odds ratios for current tobacco use were significantly higher among males for all countries [with the greatest odds among Indonesian men (OR=67.4, 95% CI: 51.2-88.7)] and among urban dwellers in Romania. The odds of current tobacco use decreased as age increased for all countries except Nigeria where. The reverse was true for Argentina and Nigeria. Significant trends for decreasing tobacco use with increasing educational levels and wealth index were seen in Indonesia, Malaysia and Romania. Significant negative associations between current tobacco use and awareness of adverse health consequences of smoking were found in all countries except Argentina. Males and the socially and economically disadvantaged populations are at the greatest risk of tobacco use. Tobacco control interventions maybe tailored to this segment of population and incorporate educational interventions to increase knowledge of adverse health consequences of smoking.

Evaluation of two selection tests for recruitment into radiology specialty training.

PubMed

Patterson, Fiona; Knight, Alec; McKnight, Liam; Booth, Thomas C

2016-07-11

This study evaluated whether two selection tests previously validated for primary care General Practice (GP) trainee selection could provide a valid shortlisting selection method for entry into specialty training for the secondary care specialty of radiology. We conducted a retrospective analysis of data from radiology applicants who also applied to UK GP specialty training or Core Medical Training. The psychometric properties of the two selection tests, a clinical problem solving (CPS) test and situational judgement test (SJT), were analysed to evaluate their reliability. Predictive validity of the tests was analysed by comparing them with the current radiology selection assessments, and the licensure examination results taken after the first stage of training (Fellowship of the Royal College of Radiologists (FRCR) Part 1). The internal reliability of the two selection tests in the radiology applicant sample was good (α ≥ 0.80). The average correlation with radiology shortlisting selection scores was r = 0.26 for the CPS (with p < 0.05 in 5 of 11 shortlisting centres), r = 0.15 for the SJT (with p < 0.05 in 2 of 11 shortlisting centres) and r = 0.25 (with p < 0.05 in 5 of 11 shortlisting centres) for the two tests combined. The CPS test scores significantly correlated with performance in both components of the FRCR Part 1 examinations (r = 0.5 anatomy; r = 0.4 physics; p < 0.05 for both). The SJT did not correlate with either component of the examination. The current CPS test may be an appropriate selection method for shortlisting in radiology but would benefit from further refinement for use in radiology to ensure that the test specification is relevant. The evidence on whether the SJT may be appropriate for shortlisting in radiology is limited. However, these results may be expected to some extent since the SJT is designed to measure non-academic attributes. Further validation work (e.g. with non-academic outcome variables) is required to evaluate whether an SJT will add value in recruitment for radiology specialty training and will further inform construct validity of SJTs as a selection methodology.

Choice of Control Variables in Variational Data Assimilation and Its Analysis and Forecast Impact

NASA Astrophysics Data System (ADS)

Xie, Yuanfu; Sun, Jenny; Fang, Wei-ting

2014-05-01

Choice of control variables directly impacts the analysis qualify of a variational data assimilation and its forecasts. A theory on selecting control variables for wind and moisture field is introduced for 3DVAR or 4DVAR. For a good control variable selection, Parseval's theory is applied to 3-4DVAR and the behavior of different control variables is illustrated in physical and Fourier space in terms of minimization condition, meteorological dynamic scales and practical implementation. The computational and meteorological benefits will be discussed. Numerical experiments have been performed using WRF-DA for wind control variables and CRTM for moisture control variables. It is evident of the WRF forecast improvement and faster convergence of CRTM satellite data assimilation.

Eliminating Survivor Bias in Two-stage Instrumental Variable Estimators.

PubMed

Vansteelandt, Stijn; Walter, Stefan; Tchetgen Tchetgen, Eric

2018-07-01

Mendelian randomization studies commonly focus on elderly populations. This makes the instrumental variables analysis of such studies sensitive to survivor bias, a type of selection bias. A particular concern is that the instrumental variable conditions, even when valid for the source population, may be violated for the selective population of individuals who survive the onset of the study. This is potentially very damaging because Mendelian randomization studies are known to be sensitive to bias due to even minor violations of the instrumental variable conditions. Interestingly, the instrumental variable conditions continue to hold within certain risk sets of individuals who are still alive at a given age when the instrument and unmeasured confounders exert additive effects on the exposure, and moreover, the exposure and unmeasured confounders exert additive effects on the hazard of death. In this article, we will exploit this property to derive a two-stage instrumental variable estimator for the effect of exposure on mortality, which is insulated against the above described selection bias under these additivity assumptions.

Spectroscopic follow-up of variability-selected active galactic nuclei in the Chandra Deep Field South

NASA Astrophysics Data System (ADS)

Boutsia, K.; Leibundgut, B.; Trevese, D.; Vagnetti, F.

2009-04-01

Context: Supermassive black holes with masses of 10^5-109 M⊙ are believed to inhabit most, if not all, nuclear regions of galaxies, and both observational evidence and theoretical models suggest a scenario where galaxy and black hole evolution are tightly related. Luminous AGNs are usually selected by their non-stellar colours or their X-ray emission. Colour selection cannot be used to select low-luminosity AGNs, since their emission is dominated by the host galaxy. Objects with low X-ray to optical ratio escape even the deepest X-ray surveys performed so far. In a previous study we presented a sample of candidates selected through optical variability in the Chandra Deep Field South, where repeated optical observations were performed in the framework of the STRESS supernova survey. Aims: The analysis is devoted to breaking down the sample in AGNs, starburst galaxies, and low-ionisation narrow-emission line objects, to providing new information about the possible dependence of the emission mechanisms on nuclear luminosity and black-hole mass, and eventually studying the evolution in cosmic time of the different populations. Methods: We obtained new optical spectroscopy for a sample of variability selected candidates with the ESO NTT telescope. We analysed the new spectra, together with those existing in the literature and studied the distribution of the objects in U-B and B-V colours, optical and X-ray luminosity, and variability amplitude. Results: A large fraction (17/27) of the observed candidates are broad-line luminous AGNs, confirming the efficiency of variability in detecting quasars. We detect: i) extended objects which would have escaped the colour selection and ii) objects of very low X-ray to optical ratio, in a few cases without any X-ray detection at all. Several objects resulted to be narrow-emission line galaxies where variability indicates nuclear activity, while no emission lines were detected in others. Some of these galaxies have variability and X-ray to optical ratio close to active galactic nuclei, while others have much lower variability and X-ray to optical ratio. This result can be explained by the dilution of the nuclear light due to the host galaxy. Conclusions: Our results demonstrate the effectiveness of supernova search programmes to detect large samples of low-luminosity AGNs. A sizable fraction of the AGN in our variability sample had escaped X-ray detection (5/47) and/or colour selection (9/48). Spectroscopic follow-up to fainter flux limits is strongly encouraged. Based on observations collected at the European Southern Observatory, Chile, 080.B-0187(A).

The performance of integrated transconductance amplifiers as variable current sources for bio-electric impedance measurements.

PubMed

Smith, D N

1992-01-01

Multiple applied current impedance measurement systems require numbers of current sources which operate simultaneously at the same frequency and within the same phase but at variable amplitudes. Investigations into the performance of some integrated operational transconductance amplifiers as variable current sources are described. Measurements of breakthrough, non-linearity and common-mode output levels for LM13600, NE5517 and CA3280 were carried out. The effects of such errors on the overall performance and stability of multiple current systems when driving floating loads are considered.

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.

Vulnerability to Substance Abuse and the Risk of Suicide in Students of Region 12 of Islamic Azad University

PubMed Central

Monirpoor, Nader; Khoosfi, Helen; Gholamy Zarch, Morteza; Tamaddonfard, Mohsen; Tabatabaei Mir, Seyed Farzad; Mohammad Alipour, Maryam; Karimi, Yasamin

2014-01-01

Background: Substance abuse prevalence and the number of suicides among university students is less than public population; however the sensitivity of society regarding the occurrence of such damages among students puts special emphasis on appraising these variables. More than 30% of Iranian students study in Islamic Azad University. Objectives: The current research aimed to appraise the vulnerability of substance abuse and the risk of suicide in students of region 12 of Islamic Azad University. Patients and Methods: In the current study, 1053 students (606 boys and 447 girls) with the average age of 22.55 years were selected through stratified sampling from Karaj, Takestan, Qazvin and Qom branches of Islamic Azad University. In order to assess the variables, Mental Health Worksheet of Central Counseling Office of the Ministry Science, Research and Technology was utilized. Results: Average, standard deviation, minimum and maximum scores in substance abuse vulnerability of the students in region 12 were measured as 36.28, 14.68, 11.22 and 92.87; and the same for risk of suicide were 31.29, 15.61, 7.93 and 96.30, respectively. Students in Qom branch were significantly less vulnerable to substance abuse and less exposed to the risk of suicide than their peers in Karaj, Qazvin and Takestan branches. Conclusions: Less significant possibility of substance abuse and risk of suicide in students of Qom branch in comparison with other branches could be due to numerous variables particularly their religious attitudes. Nevertheless the average of these variables among the students of region 12 were higher than the reported scores of their peers in the state universities which reflects the serious need for precise assessments and providing preventive services and mental health interventions. PMID:25032157

Vulnerability to substance abuse and the risk of suicide in students of region 12 of islamic azad university.

PubMed

Monirpoor, Nader; Khoosfi, Helen; Gholamy Zarch, Morteza; Tamaddonfard, Mohsen; Tabatabaei Mir, Seyed Farzad; Mohammad Alipour, Maryam; Karimi, Yasamin

2014-06-01

Substance abuse prevalence and the number of suicides among university students is less than public population; however the sensitivity of society regarding the occurrence of such damages among students puts special emphasis on appraising these variables. More than 30% of Iranian students study in Islamic Azad University. The current research aimed to appraise the vulnerability of substance abuse and the risk of suicide in students of region 12 of Islamic Azad University. In the current study, 1053 students (606 boys and 447 girls) with the average age of 22.55 years were selected through stratified sampling from Karaj, Takestan, Qazvin and Qom branches of Islamic Azad University. In order to assess the variables, Mental Health Worksheet of Central Counseling Office of the Ministry Science, Research and Technology was utilized. Average, standard deviation, minimum and maximum scores in substance abuse vulnerability of the students in region 12 were measured as 36.28, 14.68, 11.22 and 92.87; and the same for risk of suicide were 31.29, 15.61, 7.93 and 96.30, respectively. Students in Qom branch were significantly less vulnerable to substance abuse and less exposed to the risk of suicide than their peers in Karaj, Qazvin and Takestan branches. Less significant possibility of substance abuse and risk of suicide in students of Qom branch in comparison with other branches could be due to numerous variables particularly their religious attitudes. Nevertheless the average of these variables among the students of region 12 were higher than the reported scores of their peers in the state universities which reflects the serious need for precise assessments and providing preventive services and mental health interventions.

Seroepidemiology of pertussis among elementary school children in northern Taiwan.

PubMed

Kuo, Ching-Chia; Huang, Yhu-Chering; Hsieh, Yu-Chia; Huang, Ya-Ling; Huang, Yu-Chiau; Hung, Yung-Tai

2017-06-01

Pertussis has been considered a vaccine-preventable "childhood disease", but a shift in age distribution has been reported worldwide. We conducted a seroepidemiological study in 2013 in Taiwan to elucidate the seroprevalence of pertussis among elementary school children. With a multilevel randomized method, which included 14 variables (4 population variables, 4 socio-educational variables, and 6 medical facilities' variables), the 29 executive districts of New Taipei City, Taiwan, were categorized into five strata. From each stratum, the number of school children as well as the number of elementary schools were proportionally selected. Enzyme immunoassay was applied for pertussis immunoglobulin-G measurement. A total of 936 children from 14 schools were recruited. Most participants (98.89%) received at least three doses of acellular diphtheria-tetanus-pertussis vaccine. The overall seropositive rate for pertussis was 33.97%. The seropositive rate was highest for students in Grade 1 (49.36%) and then declined with time, except for Grade 6 students. Students from Grade 1 to Grade 4 had a significant higher seropositive rate (37.18% vs. 27.56%, p = 0.002) than those from Grade 5 to Grade 6, but a lower geometric mean titer (18.71 NovaTec Unit/mL vs. 20.04 NovaTec Unit/mL, p = 0.20). For the class grades, geometric mean titers were positively correlated with seroprevalence (p < 0.005). Currently, almost one-third of elementary school children in Taiwan were seropositive for pertussis, a rate lower than expected. Seroprevalence declined with increasing class grades except for Grade 6. The current national immunization program may not provide adequate protection for children against pertussis. Copyright © 2015. Published by Elsevier B.V.

A structural equation modeling approach to understanding pathways that connect socioeconomic status and smoking.

PubMed

Martinez, Sydney A; Beebe, Laura A; Thompson, David M; Wagener, Theodore L; Terrell, Deirdra R; Campbell, Janis E

2018-01-01

The inverse association between socioeconomic status and smoking is well established, yet the mechanisms that drive this relationship are unclear. We developed and tested four theoretical models of the pathways that link socioeconomic status to current smoking prevalence using a structural equation modeling (SEM) approach. Using data from the 2013 National Health Interview Survey, we selected four indicator variables (poverty ratio, personal earnings, educational attainment, and employment status) that we hypothesize underlie a latent variable, socioeconomic status. We measured direct, indirect, and total effects of socioeconomic status on smoking on four pathways through four latent variables representing social cohesion, financial strain, sleep disturbance, and psychological distress. Results of the model indicated that the probability of being a smoker decreased by 26% of a standard deviation for every one standard deviation increase in socioeconomic status. The direct effects of socioeconomic status on smoking accounted for the majority of the total effects, but the overall model also included significant indirect effects. Of the four mediators, sleep disturbance and psychological distress had the largest total effects on current smoking. We explored the use of structural equation modeling in epidemiology to quantify effects of socioeconomic status on smoking through four social and psychological factors to identify potential targets for interventions. A better understanding of the complex relationship between socioeconomic status and smoking is critical as we continue to reduce the burden of tobacco and eliminate health disparities related to smoking.

A structural equation modeling approach to understanding pathways that connect socioeconomic status and smoking

PubMed Central

Beebe, Laura A.; Thompson, David M.; Wagener, Theodore L.; Terrell, Deirdra R.; Campbell, Janis E.

2018-01-01

The inverse association between socioeconomic status and smoking is well established, yet the mechanisms that drive this relationship are unclear. We developed and tested four theoretical models of the pathways that link socioeconomic status to current smoking prevalence using a structural equation modeling (SEM) approach. Using data from the 2013 National Health Interview Survey, we selected four indicator variables (poverty ratio, personal earnings, educational attainment, and employment status) that we hypothesize underlie a latent variable, socioeconomic status. We measured direct, indirect, and total effects of socioeconomic status on smoking on four pathways through four latent variables representing social cohesion, financial strain, sleep disturbance, and psychological distress. Results of the model indicated that the probability of being a smoker decreased by 26% of a standard deviation for every one standard deviation increase in socioeconomic status. The direct effects of socioeconomic status on smoking accounted for the majority of the total effects, but the overall model also included significant indirect effects. Of the four mediators, sleep disturbance and psychological distress had the largest total effects on current smoking. We explored the use of structural equation modeling in epidemiology to quantify effects of socioeconomic status on smoking through four social and psychological factors to identify potential targets for interventions. A better understanding of the complex relationship between socioeconomic status and smoking is critical as we continue to reduce the burden of tobacco and eliminate health disparities related to smoking. PMID:29408939

Modelling Ecuador's rainfall distribution according to geographical characteristics.

NASA Astrophysics Data System (ADS)

Tobar, Vladimiro; Wyseure, Guido

2017-04-01

It is known that rainfall is affected by terrain characteristics and some studies had focussed on its distribution over complex terrain. Ecuador's temporal and spatial rainfall distribution is affected by its location on the ITCZ, the marine currents in the Pacific, the Amazon rainforest, and the Andes mountain range. Although all these factors are important, we think that the latter one may hold a key for modelling spatial and temporal distribution of rainfall. The study considered 30 years of monthly data from 319 rainfall stations having at least 10 years of data available. The relatively low density of stations and their location in accessible sites near to main roads or rivers, leave large and important areas ungauged, making it not appropriate to rely on traditional interpolation techniques to estimate regional rainfall for water balance. The aim of this research was to come up with a useful model for seasonal rainfall distribution in Ecuador based on geographical characteristics to allow its spatial generalization. The target for modelling was the seasonal rainfall, characterized by nine percentiles for each one of the 12 months of the year that results in 108 response variables, later on reduced to four principal components comprising 94% of the total variability. Predictor variables for the model were: geographic coordinates, elevation, main wind effects from the Amazon and Coast, Valley and Hill indexes, and average and maximum elevation above the selected rainfall station to the east and to the west, for each one of 18 directions (50-135°, by 5°) adding up to 79 predictors. A multiple linear regression model by the Elastic-net algorithm with cross-validation was applied for each one of the PC as response to select the most important ones from the 79 predictor variables. The Elastic-net algorithm deals well with collinearity problems, while allowing variable selection in a blended approach between the Ridge and Lasso regression. The model fitting produced explained variances of 59%, 81%, 49% and 17% for PC1, PC2, PC3 and PC4, respectively, backing up the hypothesis of good correlation between geographical characteristics and seasonal rainfall patterns (comprised in the four principal components). With the obtained coefficients from the regression, the 108 rainfall percentiles for each station were back estimated giving very good results when compared with the original ones, with an overall 60% explained variance.

Combining clinical and angiographic variables for estimating risk of target lesion revascularization after drug eluting stent placement.

PubMed

Stolker, Joshua M; Cohen, David J; Kennedy, Kevin F; Pencina, Michael J; Arnold, Suzanne V; Kleiman, Neal S; Spertus, John A

Drug-eluting stents (DES) reduce restenosis but require prolonged antiplatelet therapy, when compared with bare metal stents. Ideally, the patient should be involved in this risk:benefit assessment prior to selecting DES, to maximize the benefits and cost-effectiveness of care, and to improve medication adherence. However, accurate estimation of restenosis risk may require angiographic factors identified at cardiac catheterization. In a large PCI registry, we used logistic regression to identify clinical and angiographic predictors of clinically-driven target lesion revascularization (TLR) over the first year after stent placement. Discrimination c-statistic and integrated discrimination improvement (IDI) were used to calculate the incremental utility of angiographic variables when added to clinical predictors. Of 8501 PCI patients, TLR occurred in 4.5%. After adjusting for DES use, clinical TLR predictors were younger age, female sex, diabetes, prior PCI, and prior bypass surgery (model c-statistic 0.630). Angiographic predictors were vein graft PCI, in-stent restenosis lesion, longer stent length, and smaller stent diameter (c-statistic 0.650). After adding angiographic factors to the clinical model, c-statistic improved to 0.680 and the average separation in TLR risk among patients with and without TLR improved by 1% (IDI=0.010, 95% CI 0.009-0.014), primarily driven by those experiencing TLR (from 5.9% to 6.9% absolute risk). Among unselected PCI patients, the incidence of clinically-indicated TLR is <5% at 1-year, and standard clinical variables only moderately discriminate who will and will not experience TLR. Angiographic variables significantly improve TLR risk assessment, suggesting that stent selection may be best performed after coronary anatomy has been delineated. Although several recent studies have challenged traditional expectations regarding the duration of dual antiplatelet therapy, current guidelines recommend at least 6 to 12months of treatment after implantation of a drug eluting stent, with a shorter course for bare metal stents. Stent selection ideally should involve input from the patient receiving these stents, but multiple studies have suggested that angiographic factors - obtained after the patient has received sedation during the diagnostic catheterization - are important predictors of repeat revascularization. In this analysis from a large registry of patients receiving coronary stents, angiographic characteristics were found to significantly improve risk assessment for target lesion revascularization, when added to clinical variables alone. Copyright © 2016 Elsevier Inc. All rights reserved.

Selection of latent variables for multiple mixed-outcome models

PubMed Central

ZHOU, LING; LIN, HUAZHEN; SONG, XINYUAN; LI, YI

2014-01-01

Latent variable models have been widely used for modeling the dependence structure of multiple outcomes data. However, the formulation of a latent variable model is often unknown a priori, the misspecification will distort the dependence structure and lead to unreliable model inference. Moreover, multiple outcomes with varying types present enormous analytical challenges. In this paper, we present a class of general latent variable models that can accommodate mixed types of outcomes. We propose a novel selection approach that simultaneously selects latent variables and estimates parameters. We show that the proposed estimator is consistent, asymptotically normal and has the oracle property. The practical utility of the methods is confirmed via simulations as well as an application to the analysis of the World Values Survey, a global research project that explores peoples’ values and beliefs and the social and personal characteristics that might influence them. PMID:27642219

Variable selection for zero-inflated and overdispersed data with application to health care demand in Germany

PubMed Central

Wang, Zhu; Shuangge, Ma; Wang, Ching-Yun

2017-01-01

In health services and outcome research, count outcomes are frequently encountered and often have a large proportion of zeros. The zero-inflated negative binomial (ZINB) regression model has important applications for this type of data. With many possible candidate risk factors, this paper proposes new variable selection methods for the ZINB model. We consider maximum likelihood function plus a penalty including the least absolute shrinkage and selection operator (LASSO), smoothly clipped absolute deviation (SCAD) and minimax concave penalty (MCP). An EM (expectation-maximization) algorithm is proposed for estimating the model parameters and conducting variable selection simultaneously. This algorithm consists of estimating penalized weighted negative binomial models and penalized logistic models via the coordinated descent algorithm. Furthermore, statistical properties including the standard error formulae are provided. A simulation study shows that the new algorithm not only has more accurate or at least comparable estimation, also is more robust than the traditional stepwise variable selection. The proposed methods are applied to analyze the health care demand in Germany using an open-source R package mpath. PMID:26059498

Agricultural disturbance response models for invertebrate and algal metrics from streams at two spatial scales within the U.S.

USGS Publications Warehouse

Waite, Ian R.

2014-01-01

As part of the USGS study of nutrient enrichment of streams in agricultural regions throughout the United States, about 30 sites within each of eight study areas were selected to capture a gradient of nutrient conditions. The objective was to develop watershed disturbance predictive models for macroinvertebrate and algal metrics at national and three regional landscape scales to obtain a better understanding of important explanatory variables. Explanatory variables in models were generated from landscape data, habitat, and chemistry. Instream nutrient concentration and variables assessing the amount of disturbance to the riparian zone (e.g., percent row crops or percent agriculture) were selected as most important explanatory variable in almost all boosted regression tree models regardless of landscape scale or assemblage. Frequently, TN and TP concentration and riparian agricultural land use variables showed a threshold type response at relatively low values to biotic metrics modeled. Some measure of habitat condition was also commonly selected in the final invertebrate models, though the variable(s) varied across regions. Results suggest national models tended to account for more general landscape/climate differences, while regional models incorporated both broad landscape scale and more specific local-scale variables.

Influence of olfactory and visual cover on nest site selection and nest success for grassland-nesting birds.

PubMed

Fogarty, Dillon T; Elmore, R Dwayne; Fuhlendorf, Samuel D; Loss, Scott R

2017-08-01

Habitat selection by animals is influenced by and mitigates the effects of predation and environmental extremes. For birds, nest site selection is crucial to offspring production because nests are exposed to extreme weather and predation pressure. Predators that forage using olfaction often dominate nest predator communities; therefore, factors that influence olfactory detection (e.g., airflow and weather variables, including turbulence and moisture) should influence nest site selection and survival. However, few studies have assessed the importance of olfactory cover for habitat selection and survival. We assessed whether ground-nesting birds select nest sites based on visual and/or olfactory cover. Additionally, we assessed the importance of visual cover and airflow and weather variables associated with olfactory cover in influencing nest survival. In managed grasslands in Oklahoma, USA, we monitored nests of Northern Bobwhite ( Colinus virginianus ), Eastern Meadowlark ( Sturnella magna ), and Grasshopper Sparrow ( Ammodramus savannarum ) during 2015 and 2016. To assess nest site selection, we compared cover variables between nests and random points. To assess factors influencing nest survival, we used visual cover and olfactory-related measurements (i.e., airflow and weather variables) to model daily nest survival. For nest site selection, nest sites had greater overhead visual cover than random points, but no other significant differences were found. Weather variables hypothesized to influence olfactory detection, specifically precipitation and relative humidity, were the best predictors of and were positively related to daily nest survival. Selection for overhead cover likely contributed to mitigation of thermal extremes and possibly reduced detectability of nests. For daily nest survival, we hypothesize that major nest predators focused on prey other than the monitored species' nests during high moisture conditions, thus increasing nest survival on these days. Our study highlights how mechanistic approaches to studying cover informs which dimensions are perceived and selected by animals and which dimensions confer fitness-related benefits.

Investigating EMIC Wave Dynamics with RAM-SCB-E

NASA Astrophysics Data System (ADS)

Jordanova, V. K.; Fu, X.; Henderson, M. G.; Morley, S.; Welling, D. T.; Yu, Y.

2017-12-01

The distribution of ring current ions and electrons in the inner magnetosphere depends strongly on their transport in realistic electric (E) and magnetic (B) fields and concurrent energization or loss. To investigate the high variability of energetic particle (H+, He+, O+, and electron) fluxes during storms selected by the GEM Surface Charging Challenge, we use our kinetic ring current model (RAM) two-way coupled with a 3-D magnetic field code (SCB). This model was just extended to include electric field calculations, making it a unique, fully self-consistent, anisotropic ring current-atmosphere interactions model, RAM-SCB-E. Recently we investigated electromagnetic ion cyclotron (EMIC) instability in a local plasma using both linear theory and nonlinear hybrid simulations and derived a scaling formula that relates the saturation EMIC wave amplitude to initial plasma conditions. Global dynamic EMIC wave maps obtained with our RAM-SCB-E model using this scaling will be presented and compared with statistical models. These plasma waves can affect significantly both ion and electron precipitation into the atmosphere and the subsequent patterns of ionospheric conductance, as well as the global ring current dynamics.

Life-history strategies associated with local population variability confer regional stability.

PubMed

Pribil, Stanislav; Houlahan, Jeff E

2003-07-07

A widely held ecological tenet is that, at the local scale, populations of K-selected species (i.e. low fecundity, long lifespan and large body size) will be less variable than populations of r-selected species (i.e. high fecundity, short lifespan and small body size). We examined the relationship between long-term population trends and life-history attributes for 185 bird species in the Czech Republic and found that, at regional spatial scales and over moderate temporal scales (100-120 years), K-selected bird species were more likely to show both large increases and decreases in population size than r-selected species. We conclude that life-history attributes commonly associated with variable populations at the local scale, confer stability at the regional scale.

Current superimposition variable flux reluctance motor with 8 salient poles

NASA Astrophysics Data System (ADS)

Takahara, Kazuaki; Hirata, Katsuhiro; Niguchi, Noboru; Kohara, Akira

2017-12-01

We propose a current superimposition variable flux reluctance motor for a traction motor of electric vehicles and hybrid electric vehicles, which consists of 10 salient poles in the rotor and 12 slots in the stator. However, iron losses of this motor in high rotation speed ranges is large because the number of salient poles is large. In this paper, we propose a current superimposition variable flux reluctance motor that consists of 8 salient poles and 12 slots. The characteristics of the 10-pole-12-slot and 8-pole-12-slot current superimposition variable flux reluctance motors are compared using finite element analysis under vector control.

Volume 19, Issue8 (December 2004)Articles in the Current Issue:Research ArticleTowards automation of palynology 1: analysis of pollen shape and ornamentation using simple geometric measures, derived from scanning electron microscope images

NASA Astrophysics Data System (ADS)

Treloar, W. J.; Taylor, G. E.; Flenley, J. R.

2004-12-01

This is the first of a series of papers on the theme of automated pollen analysis. The automation of pollen analysis could result in numerous advantages for the reconstruction of past environments, with larger data sets made practical, objectivity and fine resolution sampling. There are also applications in apiculture and medicine. Previous work on the classification of pollen using texture measures has been successful with small numbers of pollen taxa. However, as the number of pollen taxa to be identified increases, more features may be required to achieve a successful classification. This paper describes the use of simple geometric measures to augment the texture measures. The feasibility of this new approach is tested using scanning electron microscope (SEM) images of 12 taxa of fresh pollen taken from reference material collected on Henderson Island, Polynesia. Pollen images were captured directly from a SEM connected to a PC. A threshold grey-level was set and binary images were then generated. Pollen edges were then located and the boundaries were traced using a chain coding system. A number of simple geometric variables were calculated directly from the chain code of the pollen and a variable selection procedure was used to choose the optimal subset to be used for classification. The efficiency of these variables was tested using a leave-one-out classification procedure. The system successfully split the original 12 taxa sample into five sub-samples containing no more than six pollen taxa each. The further subdivision of echinate pollen types was then attempted with a subset of four pollen taxa. A set of difference codes was constructed for a range of displacements along the chain code. From these difference codes probability variables were calculated. A variable selection procedure was again used to choose the optimal subset of probabilities that may be used for classification. The efficiency of these variables was again tested using a leave-one-out classification procedure. The proportion of correctly classified pollen ranged from 81% to 100% depending on the subset of variables used. The best set of variables had an overall classification rate averaging at about 95%. This is comparable with the classification rates from the earlier texture analysis work for other types of pollen. Copyright

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.

Improved Sparse Multi-Class SVM and Its Application for Gene Selection in Cancer Classification

PubMed Central

Huang, Lingkang; Zhang, Hao Helen; Zeng, Zhao-Bang; Bushel, Pierre R.

2013-01-01

Background Microarray techniques provide promising tools for cancer diagnosis using gene expression profiles. However, molecular diagnosis based on high-throughput platforms presents great challenges due to the overwhelming number of variables versus the small sample size and the complex nature of multi-type tumors. Support vector machines (SVMs) have shown superior performance in cancer classification due to their ability to handle high dimensional low sample size data. The multi-class SVM algorithm of Crammer and Singer provides a natural framework for multi-class learning. Despite its effective performance, the procedure utilizes all variables without selection. In this paper, we propose to improve the procedure by imposing shrinkage penalties in learning to enforce solution sparsity. Results The original multi-class SVM of Crammer and Singer is effective for multi-class classification but does not conduct variable selection. We improved the method by introducing soft-thresholding type penalties to incorporate variable selection into multi-class classification for high dimensional data. The new methods were applied to simulated data and two cancer gene expression data sets. The results demonstrate that the new methods can select a small number of genes for building accurate multi-class classification rules. Furthermore, the important genes selected by the methods overlap significantly, suggesting general agreement among different variable selection schemes. Conclusions High accuracy and sparsity make the new methods attractive for cancer diagnostics with gene expression data and defining targets of therapeutic intervention. Availability: The source MATLAB code are available from http://math.arizona.edu/~hzhang/software.html. PMID:23966761

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.

Detection methods and performance criteria for genetically modified organisms.

PubMed

Bertheau, Yves; Diolez, Annick; Kobilinsky, André; Magin, Kimberly

2002-01-01

Detection methods for genetically modified organisms (GMOs) are necessary for many applications, from seed purity assessment to compliance of food labeling in several countries. Numerous analytical methods are currently used or under development to support these needs. The currently used methods are bioassays and protein- and DNA-based detection protocols. To avoid discrepancy of results between such largely different methods and, for instance, the potential resulting legal actions, compatibility of the methods is urgently needed. Performance criteria of methods allow evaluation against a common standard. The more-common performance criteria for detection methods are precision, accuracy, sensitivity, and specificity, which together specifically address other terms used to describe the performance of a method, such as applicability, selectivity, calibration, trueness, precision, recovery, operating range, limit of quantitation, limit of detection, and ruggedness. Performance criteria should provide objective tools to accept or reject specific methods, to validate them, to ensure compatibility between validated methods, and be used on a routine basis to reject data outside an acceptable range of variability. When selecting a method of detection, it is also important to consider its applicability, its field of applications, and its limitations, by including factors such as its ability to detect the target analyte in a given matrix, the duration of the analyses, its cost effectiveness, and the necessary sample sizes for testing. Thus, the current GMO detection methods should be evaluated against a common set of performance criteria.

Reliable and accurate point-based prediction of cumulative infiltration using soil readily available characteristics: A comparison between GMDH, ANN, and MLR

NASA Astrophysics Data System (ADS)

Rahmati, Mehdi

2017-08-01

Developing accurate and reliable pedo-transfer functions (PTFs) to predict soil non-readily available characteristics is one of the most concerned topic in soil science and selecting more appropriate predictors is a crucial factor in PTFs' development. Group method of data handling (GMDH), which finds an approximate relationship between a set of input and output variables, not only provide an explicit procedure to select the most essential PTF input variables, but also results in more accurate and reliable estimates than other mostly applied methodologies. Therefore, the current research was aimed to apply GMDH in comparison with multivariate linear regression (MLR) and artificial neural network (ANN) to develop several PTFs to predict soil cumulative infiltration point-basely at specific time intervals (0.5-45 min) using soil readily available characteristics (RACs). In this regard, soil infiltration curves as well as several soil RACs including soil primary particles (clay (CC), silt (Si), and sand (Sa)), saturated hydraulic conductivity (Ks), bulk (Db) and particle (Dp) densities, organic carbon (OC), wet-aggregate stability (WAS), electrical conductivity (EC), and soil antecedent (θi) and field saturated (θfs) water contents were measured at 134 different points in Lighvan watershed, northwest of Iran. Then, applying GMDH, MLR, and ANN methodologies, several PTFs have been developed to predict cumulative infiltrations using two sets of selected soil RACs including and excluding Ks. According to the test data, results showed that developed PTFs by GMDH and MLR procedures using all soil RACs including Ks resulted in more accurate (with E values of 0.673-0.963) and reliable (with CV values lower than 11 percent) predictions of cumulative infiltrations at different specific time steps. In contrast, ANN procedure had lower accuracy (with E values of 0.356-0.890) and reliability (with CV values up to 50 percent) compared to GMDH and MLR. The results also revealed that Ks exclusion from input variables list caused around 30 percent decrease in PTFs accuracy for all applied procedures. However, it seems that Ks exclusion resulted in more practical PTFs especially in the case of GMDH network applying input variables which are less time consuming than Ks. In general, it is concluded that GMDH provides more accurate and reliable estimates of cumulative infiltration (a non-readily available characteristic of soil) with a minimum set of input variables (2-4 input variables) and can be promising strategy to model soil infiltration combining the advantages of ANN and MLR methodologies.

[Metabolic effects of exercise on childhood obesity: a current view].

PubMed

Paes, Santiago Tavares; Marins, João Carlos Bouzas; Andreazzi, Ana Eliza

2015-01-01

To review the current literature concerning the effects of physical exercise on several metabolic variables related to childhood obesity. A search was performed in Pubmed/Medline and Web of Science databases. The keywords used were as follows: Obesity, Children Obesity, Childhood Obesity, Exercise and Physical Activity. The online search was based on studies published in English, from April 2010 to December 2013. Search queries returned 88,393 studies based on the aforementioned keywords; 4,561 studies were selected by crossing chosen keywords. After applying inclusion criteria, four studies were selected from 182 eligible titles. Most studies have found that aerobic and resistance training improves body composition, lipid profile and metabolic and inflammatory status of obese children and adolescents; however, the magnitude of the effects is associated with the type, intensity and duration of practice. Regardless of type, physical exercise promotes positive adaptations to childhood obesity, mainly acting to restore cellular and cardiovascular homeostasis, to improve body composition, and to activate metabolism; therefore, physical exercise acts as a co-factor in combating obesity. Copyright © 2014 Associação de Pediatria de São Paulo. Publicado por Elsevier Editora Ltda. All rights reserved.

[Theoretical and methodological uses of research in Social and Human Sciences in Health].

PubMed

Deslandes, Suely Ferreira; Iriart, Jorge Alberto Bernstein

2012-12-01

The current article aims to map and critically reflect on the current theoretical and methodological uses of research in the subfield of social and human sciences in health. A convenience sample was used to select three Brazilian public health journals. Based on a reading of 1,128 abstracts published from 2009 to 2010, 266 articles were selected that presented the empirical base of research stemming from social and human sciences in health. The sample was classified thematically as "theoretical/ methodological reference", "study type/ methodological design", "analytical categories", "data production techniques", and "analytical procedures". We analyze the sample's emic categories, drawing on the authors' literal statements. All the classifications and respective variables were tabulated in Excel. Most of the articles were self-described as qualitative and used more than one data production technique. There was a wide variety of theoretical references, in contrast with the almost total predominance of a single type of data analysis (content analysis). In several cases, important gaps were identified in expounding the study methodology and instrumental use of the qualitative research techniques and methods. However, the review did highlight some new objects of study and innovations in theoretical and methodological approaches.

Talent identification and specialization in sport: an overview of some unanswered questions.

PubMed

Gonçalves C, E B; Rama L, M L; Figueiredo, António B

2012-12-01

The theory of deliberate practice postulates that experts are always made, not born. This theory translated to the youth-sport domain means that if athletes want to be high-level performers, they need to deliberately engage in practice during the specialization years, spending time wisely and always focusing on tasks that challenge current performance. Sport organizations in several countries around the world created specialized training centers where selected young talents practice under the supervision of experienced coaches in order to become professional athletes and integrate onto youth national teams. Early specialization and accurate observation by expert coaches or scouts remain the only tools to find a potential excellent athlete among a great number of participants. In the current study, the authors present 2 of the problems raised by talent search and the risks of such a search. Growth and maturation are important concepts to better understand the identification, selection, and development processes of young athletes. However, the literature suggests that sport-promoting strategies are being maintained despite the increased demands in the anthropometric characteristics of professional players and demands of actual professional soccer competitions. On the other hand, identifying biological variables that can predict performance is almost impossible.

Variability in Non-Target Terrestrial Plant Studies Should Inform Endpoint Selection.

PubMed

Staveley, J P; Green, J W; Nusz, J; Edwards, D; Henry, K; Kern, M; Deines, A M; Brain, R; Glenn, B; Ehresman, N; Kung, T; Ralston-Hooper, K; Kee, F; McMaster, S

2018-05-04

Inherent variability in Non-Target Terrestrial Plant (NTTP) testing of pesticides creates challenges for using and interpreting these data for risk assessment. Standardized NTTP testing protocols were initially designed to calculate the application rate causing a 25% effect (ER25, used in the U.S.) or a 50% effect (ER50, used in Europe) for various measures based on the observed dose-response. More recently, the requirement to generate a no-observed-effect rate (NOER), or, in the absence of a NOER, the rate causing a 5% effect (ER05), has raised questions about the inherent variability in, and statistical detectability of, these tests. Statistically significant differences observed between test and control groups may be a product of this inherent variability and may not represent biological relevance. Attempting to derive an ER05 and the associated risk assessment conclusions drawn from these values can overestimate risk. To address these concerns, we evaluated historical data from approximately 100 seedling emergence and vegetative vigor guideline studies on pesticides to assess the variability of control results across studies for each plant species, examined potential causes for the variation in control results, and defined the minimum percent effect that can be reliably detected. The results indicate that with current test design and implementation, the ER05 cannot be reliably estimated. This article is protected by copyright. All rights reserved. This article is protected by copyright. All rights reserved.

Hydrological excitation of polar motion by different variables of the GLDAS models

NASA Astrophysics Data System (ADS)

Wińska, Małgorzata; Nastula, Jolanta

Continental hydrological loading, by land water, snow, and ice, is an element that is strongly needed for a full understanding of the excitation of polar motion. In this study we compute different estimations of hydrological excitation functions of polar motion (Hydrological Angular Momentum - HAM) using various variables from the Global Land Data Assimilation System (GLDAS) models of land hydrosphere. The main aim of this study is to show the influence of different variables for example: total evapotranspiration, runoff, snowmelt, soil moisture to polar motion excitations in annual and short term scale. In our consideration we employ several realizations of the GLDAS model as: GLDAS Common Land Model (CLM), GLDAS Mosaic Model, GLDAS National Centers for Environmental Prediction/Oregon State University/Air Force/Hydrologic Research Lab Model (Noah), GLDAS Variable Infiltration Capacity (VIC) Model. Hydrological excitation functions of polar motion, both global and regional, are determined by using selected variables of these GLDAS realizations. First we compare a timing, spectra and phase diagrams of different regional and global HAMs with each other. Next, we estimate, the hydrological signal in geodetically observed polar motion excitation by subtracting the atmospheric -- AAM (pressure + wind) and oceanic -- OAM (bottom pressure + currents) contributions. Finally, the hydrological excitations are compared to these hydrological signal in observed polar motion excitation series. The results help us understand which variables of considered hydrological models are the most important for the polar motion excitation and how well we can close polar motion excitation budget in the seasonal and inter-annual spectral ranges.

Alternative Regression Equations for Estimation of Annual Peak-Streamflow Frequency for Undeveloped Watersheds in Texas using PRESS Minimization

USGS Publications Warehouse

Asquith, William H.; Thompson, David B.

2008-01-01

The U.S. Geological Survey, in cooperation with the Texas Department of Transportation and in partnership with Texas Tech University, investigated a refinement of the regional regression method and developed alternative equations for estimation of peak-streamflow frequency for undeveloped watersheds in Texas. A common model for estimation of peak-streamflow frequency is based on the regional regression method. The current (2008) regional regression equations for 11 regions of Texas are based on log10 transformations of all regression variables (drainage area, main-channel slope, and watershed shape). Exclusive use of log10-transformation does not fully linearize the relations between the variables. As a result, some systematic bias remains in the current equations. The bias results in overestimation of peak streamflow for both the smallest and largest watersheds. The bias increases with increasing recurrence interval. The primary source of the bias is the discernible curvilinear relation in log10 space between peak streamflow and drainage area. Bias is demonstrated by selected residual plots with superimposed LOWESS trend lines. To address the bias, a statistical framework based on minimization of the PRESS statistic through power transformation of drainage area is described and implemented, and the resulting regression equations are reported. Compared to log10-exclusive equations, the equations derived from PRESS minimization have PRESS statistics and residual standard errors less than the log10 exclusive equations. Selected residual plots for the PRESS-minimized equations are presented to demonstrate that systematic bias in regional regression equations for peak-streamflow frequency estimation in Texas can be reduced. Because the overall error is similar to the error associated with previous equations and because the bias is reduced, the PRESS-minimized equations reported here provide alternative equations for peak-streamflow frequency estimation.

A further examination of word frequency and age-of-acquisition effects in English lexical decision task performance: The role of frequency trajectory.

PubMed

Juhasz, Barbara J; Yap, Melvin J; Raoul, Akila; Kaye, Micaela

2018-04-23

Word frequency is an important predictor of lexical-decision task performance. The current study further examined the role of this variable by exploring the influence of frequency trajectory. Frequency trajectory is measured by how often a word occurs in childhood relative to adulthood. Past research on the role of this variable in word recognition has produced equivocal results. In the current study, words were selected based on their frequencies in Grade 1 (child frequency) and Grade 13 (college frequency). In Experiment 1, four frequency trajectory conditions were factorially examined in a lexical-decision task with English words: high-to-high (world), high-to-low (uncle), low-to-high (brain) and low-to-low (opera). an interaction between Grade 1 and college frequency demonstrated that words in the low-to-high condition were processed significantly faster and more accurately than words in the low-to-low condition, whereas the high-to-high and high-to-low conditions did not differ significantly. In Experiment 2, an advantage for words with an increasing frequency trajectory was also supported in regression analyses on both lexical decision and naming times for 3,039 items selected from the English Lexicon Project (Balota et al., 2007). This was replicated in Experiment 3, based on a regression analysis of 2,680 words from the British Lexicon Project (BLP; Keuleers, Lacey, Rastle, & Brysbaert, 2012). In all analyses, rated age-of-acquisition also significantly impacted word recognition. Together, the results suggest that the age at which a word is initially learned as well as its frequency trajectory across childhood impact performance in the lexical-decision task. (PsycINFO Database Record (c) 2018 APA, all rights reserved).

Radio variability in complete samples of extragalactic radio sources at 1.4 GHz

NASA Astrophysics Data System (ADS)

Rys, S.; Machalski, J.

1990-09-01

Complete samples of extragalactic radio sources obtained in 1970-1975 and the sky survey of Condon and Broderick (1983) were used to select sources variable at 1.4 GHz, and to investigate the characteristics of variability in the whole population of sources at this frequency. The radio structures, radio spectral types, and optical identifications of the selected variables are discussed. Only compact flat-spectrum sources vary at 1.4 GHz, and all but four are identified with QSOs, BL Lacs, or other (unconfirmed spectroscopically) stellar objects. No correlation of degree of variability at 1.4 GHz with Galactic latitude or variability at 408 MHz has been found, suggesting that most of the 1.4-GHz variability is intrinsic and not caused by refractive scintillations. Numerical models of the variability have been computed.

Investigating the Effect of Recruitment Variability on Length-Based Recruitment Indices for Antarctic Krill Using an Individual-Based Population Dynamics Model

PubMed Central

Thanassekos, Stéphane; Cox, Martin J.; Reid, Keith

2014-01-01

Antarctic krill (Euphausia superba; herein krill) is monitored as part of an on-going fisheries observer program that collects length-frequency data. A krill feedback management programme is currently being developed, and as part of this development, the utility of data-derived indices describing population level processes is being assessed. To date, however, little work has been carried out on the selection of optimum recruitment indices and it has not been possible to assess the performance of length-based recruitment indices across a range of recruitment variability. Neither has there been an assessment of uncertainty in the relationship between an index and the actual level of recruitment. Thus, until now, it has not been possible to take into account recruitment index uncertainty in krill stock management or when investigating relationships between recruitment and environmental drivers. Using length-frequency samples from a simulated population – where recruitment is known – the performance of six potential length-based recruitment indices is assessed, by exploring the index-to-recruitment relationship under increasing levels of recruitment variability (from ±10% to ±100% around a mean annual recruitment). The annual minimum of the proportion of individuals smaller than 40 mm (F40 min, %) was selected because it had the most robust index-to-recruitment relationship across differing levels of recruitment variability. The relationship was curvilinear and best described by a power law. Model uncertainty was described using the 95% prediction intervals, which were used to calculate coverage probabilities and assess model performance. Despite being the optimum recruitment index, the performance of F40 min degraded under high (>50%) recruitment variability. Due to the persistence of cohorts in the population over several years, the inclusion of F40 min values from preceding years in the relationship used to estimate recruitment in a given year improved its accuracy (mean bias reduction of 8.3% when including three F40 min values under a recruitment variability of 60%). PMID:25470296

Delay correlation analysis and representation for vital complaint VHDL models

DOEpatents

Rich, Marvin J.; Misra, Ashutosh

2004-11-09

A method and system unbind a rise/fall tuple of a VHDL generic variable and create rise time and fall time generics of each generic variable that are independent of each other. Then, according to a predetermined correlation policy, the method and system collect delay values in a VHDL standard delay file, sort the delay values, remove duplicate delay values, group the delay values into correlation sets, and output an analysis file. The correlation policy may include collecting all generic variables in a VHDL standard delay file, selecting each generic variable, and performing reductions on the set of delay values associated with each selected generic variable.

The Use of Scale-Dependent Precision to Increase Forecast Accuracy in Earth System Modelling

NASA Astrophysics Data System (ADS)

Thornes, Tobias; Duben, Peter; Palmer, Tim

2016-04-01

At the current pace of development, it may be decades before the 'exa-scale' computers needed to resolve individual convective clouds in weather and climate models become available to forecasters, and such machines will incur very high power demands. But the resolution could be improved today by switching to more efficient, 'inexact' hardware with which variables can be represented in 'reduced precision'. Currently, all numbers in our models are represented as double-precision floating points - each requiring 64 bits of memory - to minimise rounding errors, regardless of spatial scale. Yet observational and modelling constraints mean that values of atmospheric variables are inevitably known less precisely on smaller scales, suggesting that this may be a waste of computer resources. More accurate forecasts might therefore be obtained by taking a scale-selective approach whereby the precision of variables is gradually decreased at smaller spatial scales to optimise the overall efficiency of the model. To study the effect of reducing precision to different levels on multiple spatial scales, we here introduce a new model atmosphere developed by extending the Lorenz '96 idealised system to encompass three tiers of variables - which represent large-, medium- and small-scale features - for the first time. In this chaotic but computationally tractable system, the 'true' state can be defined by explicitly resolving all three tiers. The abilities of low resolution (single-tier) double-precision models and similar-cost high resolution (two-tier) models in mixed-precision to produce accurate forecasts of this 'truth' are compared. The high resolution models outperform the low resolution ones even when small-scale variables are resolved in half-precision (16 bits). This suggests that using scale-dependent levels of precision in more complicated real-world Earth System models could allow forecasts to be made at higher resolution and with improved accuracy. If adopted, this new paradigm would represent a revolution in numerical modelling that could be of great benefit to the world.

How well can body size represent effects of the environment on demographic rates? Disentangling correlated explanatory variables.

PubMed

Brooks, Mollie E; Mugabo, Marianne; Rodgers, Gwendolen M; Benton, Timothy G; Ozgul, Arpat

2016-03-01

Demographic rates are shaped by the interaction of past and current environments that individuals in a population experience. Past environments shape individual states via selection and plasticity, and fitness-related traits (e.g. individual size) are commonly used in demographic analyses to represent the effect of past environments on demographic rates. We quantified how well the size of individuals captures the effects of a population's past and current environments on demographic rates in a well-studied experimental system of soil mites. We decomposed these interrelated sources of variation with a novel method of multiple regression that is useful for understanding nonlinear relationships between responses and multicollinear explanatory variables. We graphically present the results using area-proportional Venn diagrams. Our novel method was developed by combining existing methods and expanding upon them. We showed that the strength of size as a proxy for the past environment varied widely among vital rates. For instance, in this organism with an income breeding life history, the environment had more effect on reproduction than individual size, but with substantial overlap indicating that size encompassed some of the effects of the past environment on fecundity. This demonstrates that the strength of size as a proxy for the past environment can vary widely among life-history processes within a species, and this variation should be taken into consideration in trait-based demographic or individual-based approaches that focus on phenotypic traits as state variables. Furthermore, the strength of a proxy will depend on what state variable(s) and what demographic rate is being examined; that is, different measures of body size (e.g. length, volume, mass, fat stores) will be better or worse proxies for various life-history processes. © 2016 The Authors. Journal of Animal Ecology © 2016 British Ecological Society.

Developing approaches for linear mixed modeling in landscape genetics through landscape-directed dispersal simulations

USGS Publications Warehouse

Row, Jeffrey R.; Knick, Steven T.; Oyler-McCance, Sara J.; Lougheed, Stephen C.; Fedy, Bradley C.

2017-01-01

Dispersal can impact population dynamics and geographic variation, and thus, genetic approaches that can establish which landscape factors influence population connectivity have ecological and evolutionary importance. Mixed models that account for the error structure of pairwise datasets are increasingly used to compare models relating genetic differentiation to pairwise measures of landscape resistance. A model selection framework based on information criteria metrics or explained variance may help disentangle the ecological and landscape factors influencing genetic structure, yet there are currently no consensus for the best protocols. Here, we develop landscape-directed simulations and test a series of replicates that emulate independent empirical datasets of two species with different life history characteristics (greater sage-grouse; eastern foxsnake). We determined that in our simulated scenarios, AIC and BIC were the best model selection indices and that marginal R2 values were biased toward more complex models. The model coefficients for landscape variables generally reflected the underlying dispersal model with confidence intervals that did not overlap with zero across the entire model set. When we controlled for geographic distance, variables not in the underlying dispersal models (i.e., nontrue) typically overlapped zero. Our study helps establish methods for using linear mixed models to identify the features underlying patterns of dispersal across a variety of landscapes.

Design and Testing of a Variable Pressure Regulator for the Constellation Space Suit

NASA Technical Reports Server (NTRS)

Gill, Larry; Campbell, Colin

2008-01-01

The next generation space suit requires additional capabilities for controlling and adjusting internal pressure than previous design suits. Next generation suit pressures will range from slight pressure, for astronaut prebreath comfort, to hyperbaric pressure levels for emergency medical treatment. Carleton was awarded a contract in 2008 to design and build a proof of concept bench top demonstrator regulator having five setpoints which are selectable using input electronic signaling. Although the basic regulator architecture is very similar to the existing SOP regulator used in the current EMU, the major difference is the electrical selectivity of multiple setpoints rather than the mechanical On/Off feature found on the SOP regulator. The concept regulator employs a linear actuator stepper motor combination to provide variable compression to a custom design main regulator spring. This concept allows for a continuously adjustable outlet pressures from 8.2 psid (maximum) down to "firm" zero thus effectively allowing it to serve as a shutoff valve. This paper details the regulator design and presents test results on regulation band width, command set point accuracy; slue rate and regulation stability, particularly when the set point is being slued. Projections for a flight configuration version are also offered for performance, architectural layout and weight.

Dementia training programmes for staff working in general hospital settings - a systematic review of the literature.

PubMed

Scerri, Anthony; Innes, Anthea; Scerri, Charles

2017-08-01

Although literature describing and evaluating training programmes in hospital settings increased in recent years, there are no reviews that summarise these programmes. This review sought to address this, by collecting the current evidence on dementia training programmes directed to staff working in general hospitals. Literature from five databases were searched, based on a number of inclusion criteria. The selected studies were summarised and data was extracted and compared using narrative synthesis based on a set of pre-defined categories. Methodological quality was assessed. Fourteen peer-reviewed studies were identified with the majority being pre-test post-test investigations. No randomised controlled trials were found. Methodological quality was variable with selection bias being the major limitation. There was a great variability in the development and mode of delivery although, interdisciplinary ward based, tailor-made, short sessions using experiential and active learning were the most utilised. The majority of the studies mainly evaluated learning, with few studies evaluating changes in staff behaviour/practices and patients' outcomes. This review indicates that high quality studies are needed that especially evaluate staff behaviours and patient outcomes and their sustainability over time. It also highlights measures that could be used to develop and deliver training programmes in hospital settings.

Establishing the "Fit" between the Patient and the Therapy: The Role of Patient Gender in Selecting Psychological Therapy for Distressing Voices.

PubMed

Hayward, Mark; Slater, Luke; Berry, Katherine; Perona-Garcelán, Salvador

2016-01-01

The experience of hearing distressing voices has recently attracted much attention in the literature on psychological therapies. A new "wave" of therapies is considering voice hearing experiences within a relational framework. However, such therapies may have limited impact if they do not precisely target key psychological variables within the voice hearing experience and/or ensure there is a "fit" between the profile of the hearer and the therapy (the so-called "What works for whom" debate). Gender is one aspect of both the voice and the hearer (and the interaction between the two) that may be influential when selecting an appropriate therapy, and is an issue that has thus far received little attention within the literature. The existing literature suggests that some differences in voice hearing experience are evident between the genders. Furthermore, studies exploring interpersonal relating in men and women more generally suggest differences within intimate relationships in terms of distancing and emotionality. The current study utilized data from four published studies to explore the extent to which these gender differences in social relating may extend to relating within the voice hearing experience. The findings suggest a role for gender as a variable that can be considered when identifying an appropriate psychological therapy for a given hearer.

Variable thickness double-refracting plate

DOEpatents

Hadeishi, Tetsuo

1976-01-01

This invention provides an A.C., cyclic, current-controlled, phase retardation plate that uses a magnetic clamp to produce stress birefringence. It was developed for an Isotope-Zeeman Atomic Absorption Spectrometer that uses polarization modulation to effect automatic background correction in atomic absorption trace-element measurements. To this end, the phase retardation plate of the invention is a variable thickness, photoelastic, double-refracting plate that is alternately stressed and released by the magnetic clamp selectively to modulate specific components selected from the group consisting of circularly and plane polarized Zeeman components that are produced in a dc magnetic field so that they correspond respectively to Zeeman reference and transmission-probe absorption components. The polarization modulation changes the phase of these polarized Zeeman components, designated as .sigma. reference and .pi. absorption components, so that every half cycle the components change from a transmission mode to a mode in which the .pi. component is blocked and the .sigma. components are transmitted. Thus, the Zeeman absorption component, which corresponds in amplitude to the amount of the trace element to be measured in a sample, is alternately transmitted and blocked by a linear polarizer, while the circularly polarized reference components are continuously transmitted thereby. The result is a sinusoidally varying output light amplitude whose average corresponds to the amount of the trace element present in the sample.

Hidden diversity within the lizard genus Liolaemus: Genetic vs morphological divergence in the L. rothi complex (Squamata:Liolaeminae).

PubMed

Olave, Melisa; Avila, Luciano J; Sites, Jack W; Morando, Mariana

2017-02-01

Currently, Liolaemus is the second most species-rich reptile genus in the world (257 species), and predictions of its real diversity suggest that it may be the most diverse genus. Originally, Liolaemus species were described as widely distributed and morphologically variable taxa, but extensive sampling in previously unexplored geographic areas, coupled with molecular and more extensive morphological studies, have discovered an unexpectedly high number of previously undetected species. Here, we study the level of molecular vs. morphological divergence within the L. rothi complex, combining a total of 14 loci (2 mitochondrial and 12 nuclear loci) for 97 individuals, as well as morphological data (nine morphometric and 15 color pattern variables), that represent all six described species of the L. rothi complex, plus two candidate species. We use the multi-coalescent species delimitation program iBPP and resolve strong differences in molecular divergence; and each species is inferred as an independent lineage supported by high posterior probabilities. However, morphological differences are not that clear, and our modeling of morphological characters suggests differential selection pressures implying some level of morphological stasis. We discuss the role of natural selection on phenotypic traits, which may be an important factor in "hiding" the real diversity of the genus. Copyright © 2016. Published by Elsevier Inc.

Dry and wet arc track propagation resistance testing

NASA Technical Reports Server (NTRS)

Beach, Rex

1995-01-01

The wet arc-propagation resistance test for wire insulation provides an assessment of the ability of an insulation to prevent damage in an electrical environment. Results of an arc-propagation test may vary slightly due to the method of arc initiation; therefore a standard test method must be selected to evaluate the general arc-propagation resistance characteristics of an insulation. This test method initiates an arc by dripping salt water over pre-damaged wires which creates a conductive path between the wires. The power supply, test current, circuit resistances, and other variables are optimized for testing 20 guage wires. The use of other wire sizes may require modifications to the test variables. The dry arc-propagation resistance test for wire insulation also provides an assessment of the ability of an insulation to prevent damage in an electrical arc environment. In service, electrical arcs may originate form a variety of factors including insulation deterioration, faulty installation, and chafing. Here too, a standard test method must be selected to evaluate the general arc-propagation resistance characteristics of an insulation. This test method initiates an arc with a vibrating blade. The test also evaluates the ability of the insulation to prevent further arc-propagation when the electrical arc is re-energized.

Selecting sagebrush seed sources for restoration in a variable climate: ecophysiological variation among genotypes

USGS Publications Warehouse

Germino, Matthew J.

2012-01-01

Big sagebrush (Artemisia tridentata) communities dominate a large fraction of the United States and provide critical habitat for a number of wildlife species of concern. Loss of big sagebrush due to fire followed by poor restoration success continues to reduce ecological potential of this ecosystem type, particularly in the Great Basin. Choice of appropriate seed sources for restoration efforts is currently unguided due to knowledge gaps on genetic variation and local adaptation as they relate to a changing landscape. We are assessing ecophysiological responses of big sagebrush to climate variation, comparing plants that germinated from ~20 geographically distinct populations of each of the three subspecies of big sagebrush. Seedlings were previously planted into common gardens by US Forest Service collaborators Drs. B. Richardson and N. Shaw, (USFS Rocky Mountain Research Station, Provo, Utah and Boise, Idaho) as part of the Great Basin Native Plant Selection and Increase Project. Seed sources spanned all states in the conterminous Western United States. Germination, establishment, growth and ecophysiological responses are being linked to genomics and foliar palatability. New information is being produced to aid choice of appropriate seed sources by Bureau of Land Management and USFS field offices when they are planning seed acquisitions for emergency post-fire rehabilitation projects while considering climate variability and wildlife needs.

Elastic-net regularization approaches for genome-wide association studies of rheumatoid arthritis.

PubMed

Cho, Seoae; Kim, Haseong; Oh, Sohee; Kim, Kyunga; Park, Taesung

2009-12-15

The current trend in genome-wide association studies is to identify regions where the true disease-causing genes may lie by evaluating thousands of single-nucleotide polymorphisms (SNPs) across the whole genome. However, many challenges exist in detecting disease-causing genes among the thousands of SNPs. Examples include multicollinearity and multiple testing issues, especially when a large number of correlated SNPs are simultaneously tested. Multicollinearity can often occur when predictor variables in a multiple regression model are highly correlated, and can cause imprecise estimation of association. In this study, we propose a simple stepwise procedure that identifies disease-causing SNPs simultaneously by employing elastic-net regularization, a variable selection method that allows one to address multicollinearity. At Step 1, the single-marker association analysis was conducted to screen SNPs. At Step 2, the multiple-marker association was scanned based on the elastic-net regularization. The proposed approach was applied to the rheumatoid arthritis (RA) case-control data set of Genetic Analysis Workshop 16. While the selected SNPs at the screening step are located mostly on chromosome 6, the elastic-net approach identified putative RA-related SNPs on other chromosomes in an increased proportion. For some of those putative RA-related SNPs, we identified the interactions with sex, a well known factor affecting RA susceptibility.

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.

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.

Neural correlates of the divergence of instrumental probability distributions.

PubMed

Liljeholm, Mimi; Wang, Shuo; Zhang, June; O'Doherty, John P

2013-07-24

Flexible action selection requires knowledge about how alternative actions impact the environment: a "cognitive map" of instrumental contingencies. Reinforcement learning theories formalize this map as a set of stochastic relationships between actions and states, such that for any given action considered in a current state, a probability distribution is specified over possible outcome states. Here, we show that activity in the human inferior parietal lobule correlates with the divergence of such outcome distributions-a measure that reflects whether discrimination between alternative actions increases the controllability of the future-and, further, that this effect is dissociable from those of other information theoretic and motivational variables, such as outcome entropy, action values, and outcome utilities. Our results suggest that, although ultimately combined with reward estimates to generate action values, outcome probability distributions associated with alternative actions may be contrasted independently of valence computations, to narrow the scope of the action selection problem.

Black chrome solar selective coating

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

Pettit, R.B.; Sowell, R.R.

1980-01-01

Electrodeposited black chrome solar selective coatings have frequently experienced thermal stability problems when heated to temperatures above 250/sup 0/C (480/sup 0/F) in air. By reducing the trivalent chromium concentration in the standard black chrome plating bath, coatings on nickel substrates are obtained which are stable for thousands of hours at 350/sup 0/C (660/sup 0/F) and for hundreds of hours at 400/sup 0/C (750/sup 0/F). These results have been obtained consistently on a laboratory scale, but difficulty in reproducing the results has been encountered in a production environment. A current study of the effects of known plating variables on the opticalmore » properties and thermal stability of coatings is aimed at establishing an acceptable range for each plating parameter. A preliminary process specification for electroplating mild steel substrates with a stable black chrome coating is presented.« less

Neural Correlates of the Divergence of Instrumental Probability Distributions

PubMed Central

Wang, Shuo; Zhang, June; O'Doherty, John P.

2013-01-01

Flexible action selection requires knowledge about how alternative actions impact the environment: a “cognitive map” of instrumental contingencies. Reinforcement learning theories formalize this map as a set of stochastic relationships between actions and states, such that for any given action considered in a current state, a probability distribution is specified over possible outcome states. Here, we show that activity in the human inferior parietal lobule correlates with the divergence of such outcome distributions–a measure that reflects whether discrimination between alternative actions increases the controllability of the future–and, further, that this effect is dissociable from those of other information theoretic and motivational variables, such as outcome entropy, action values, and outcome utilities. Our results suggest that, although ultimately combined with reward estimates to generate action values, outcome probability distributions associated with alternative actions may be contrasted independently of valence computations, to narrow the scope of the action selection problem. PMID:23884955

A Theoretical Approach for Selecting Elementary School Environmental Variables.

ERIC Educational Resources Information Center

Sinclair, Robert L.

To determine specific environmental variables of the elementary school is the purpose of this study. Stable characteristics of intelligence and achievement were selected because they were considered useful for generating salient environmental counterparts likely to exist in elementary institutions. Achievement motivation, language development, and…

Selective Use of Optical Variables to Control Forward Speed

NASA Technical Reports Server (NTRS)

Johnson, Walter W.; Awe, Cynthia A.; Hart, Sandra G. (Technical Monitor)

1994-01-01

Previous work on the perception and control of simulated vehicle speed has examined the contributions of optical flow rate (angular visual speed) and texture, or edge rate (frequency of passing terrain objects or markings) on the perception and control of forward speed. However, these studies have not examined the ability to selectively use edge rate or flow rate. The two studies reported here show that subjects found it very difficult to arbitrarily direct attention to one or the other of these variables; but that the ability to selectively use these variables is linked to the visual contextual information about the relative validity (linkage with speed) of the two variables. The selectivity also resulted in different velocity adaptation levels for events in which flow rate and edge rate specified forward speed. Finally, the role of visual context in directing attention was further buttressed by the finding that the incorrect perception of changes in ground texture density tended to be coupled with incorrect perceptions of changes in forward speed.

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.

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.

Metacommunity composition of web-spiders in a fragmented neotropical forest: relative importance of environmental and spatial effects.

PubMed

Baldissera, Ronei; Rodrigues, Everton N L; Hartz, Sandra M

2012-01-01

The distribution of beta diversity is shaped by factors linked to environmental and spatial control. The relative importance of both processes in structuring spider metacommunities has not yet been investigated in the Atlantic Forest. The variance explained by purely environmental, spatially structured environmental, and purely spatial components was compared for a metacommunity of web spiders. The study was carried out in 16 patches of Atlantic Forest in southern Brazil. Field work was done in one landscape mosaic representing a slight gradient of urbanization. Environmental variables encompassed plot- and patch-level measurements and a climatic matrix, while principal coordinates of neighbor matrices (PCNMs) acted as spatial variables. A forward selection procedure was carried out to select environmental and spatial variables influencing web-spider beta diversity. Variation partitioning was used to estimate the contribution of pure environmental and pure spatial effects and their shared influence on beta-diversity patterns, and to estimate the relative importance of selected environmental variables. Three environmental variables (bush density, land use in the surroundings of patches, and shape of patches) and two spatial variables were selected by forward selection procedures. Variation partitioning revealed that 15% of the variation of beta diversity was explained by a combination of environmental and PCNM variables. Most of this variation (12%) corresponded to pure environmental and spatially environmental structure. The data indicated that (1) spatial legacy was not important in explaining the web-spider beta diversity; (2) environmental predictors explained a significant portion of the variation in web-spider composition; (3) one-third of environmental variation was due to a spatial structure that jointly explains variation in species distributions. We were able to detect important factors related to matrix management influencing the web-spider beta-diversity patterns, which are probably linked to historical deforestation events.

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

Genomic analysis and selected molecular pathways in rare cancers

NASA Astrophysics Data System (ADS)

Liu, Stephen V.; Lenkiewicz, Elizabeth; Evers, Lisa; Holley, Tara; Kiefer, Jeffrey; Ruiz, Christian; Glatz, Katharina; Bubendorf, Lukas; Demeure, Michael J.; Eng, Cathy; Ramanathan, Ramesh K.; Von Hoff, Daniel D.; Barrett, Michael T.

2012-12-01

It is widely accepted that many cancers arise as a result of an acquired genomic instability and the subsequent evolution of tumor cells with variable patterns of selected and background aberrations. The presence and behaviors of distinct neoplastic cell populations within a patient's tumor may underlie multiple clinical phenotypes in cancers. A goal of many current cancer genome studies is the identification of recurring selected driver events that can be advanced for the development of personalized therapies. Unfortunately, in the majority of rare tumors, this type of analysis can be particularly challenging. Large series of specimens for analysis are simply not available, allowing recurring patterns to remain hidden. In this paper, we highlight the use of DNA content-based flow sorting to identify and isolate DNA-diploid and DNA-aneuploid populations from tumor biopsies as a strategy to comprehensively study the genomic composition and behaviors of individual cancers in a series of rare solid tumors: intrahepatic cholangiocarcinoma, anal carcinoma, adrenal leiomyosarcoma, and pancreatic neuroendocrine tumors. We propose that the identification of highly selected genomic events in distinct tumor populations within each tumor can identify candidate driver events that can facilitate the development of novel, personalized treatment strategies for patients with cancer.

Genomic analysis and selected molecular pathways in rare cancers.

PubMed

Liu, Stephen V; Lenkiewicz, Elizabeth; Evers, Lisa; Holley, Tara; Kiefer, Jeffrey; Ruiz, Christian; Glatz, Katharina; Bubendorf, Lukas; Demeure, Michael J; Eng, Cathy; Ramanathan, Ramesh K; Von Hoff, Daniel D; Barrett, Michael T

2012-12-01

It is widely accepted that many cancers arise as a result of an acquired genomic instability and the subsequent evolution of tumor cells with variable patterns of selected and background aberrations. The presence and behaviors of distinct neoplastic cell populations within a patient's tumor may underlie multiple clinical phenotypes in cancers. A goal of many current cancer genome studies is the identification of recurring selected driver events that can be advanced for the development of personalized therapies. Unfortunately, in the majority of rare tumors, this type of analysis can be particularly challenging. Large series of specimens for analysis are simply not available, allowing recurring patterns to remain hidden. In this paper, we highlight the use of DNA content-based flow sorting to identify and isolate DNA-diploid and DNA-aneuploid populations from tumor biopsies as a strategy to comprehensively study the genomic composition and behaviors of individual cancers in a series of rare solid tumors: intrahepatic cholangiocarcinoma, anal carcinoma, adrenal leiomyosarcoma, and pancreatic neuroendocrine tumors. We propose that the identification of highly selected genomic events in distinct tumor populations within each tumor can identify candidate driver events that can facilitate the development of novel, personalized treatment strategies for patients with cancer.

Less or more hemodynamic monitoring in critically ill patients.

PubMed

Jozwiak, Mathieu; Monnet, Xavier; Teboul, Jean-Louis

2018-06-07

Hemodynamic investigations are required in patients with shock to identify the type of shock, to select the most appropriate treatments and to assess the patient's response to the selected therapy. We discuss how to select the most appropriate hemodynamic monitoring techniques in patients with shock as well as the future of hemodynamic monitoring. Over the last decades, the hemodynamic monitoring techniques have evolved from intermittent toward continuous and real-time measurements and from invasive toward less-invasive approaches. In patients with shock, current guidelines recommend the echocardiography as the preferred modality for the initial hemodynamic evaluation. In patients with shock nonresponsive to initial therapy and/or in the most complex patients, it is recommended to monitor the cardiac output and to use advanced hemodynamic monitoring techniques. They also provide other useful variables that are useful for managing the most complex cases. Uncalibrated and noninvasive cardiac output monitors are not reliable enough in the intensive care setting. The use of echocardiography should be initially encouraged in patients with shock to identify the type of shock and to select the most appropriate therapy. The use of more invasive hemodynamic monitoring techniques should be discussed on an individualized basis.

New GES DISC Services Shortening the Path in Science Data Discovery

NASA Technical Reports Server (NTRS)

Li, Angela; Shie, Chung-Lin; Petrenko, Maksym; Hegde, Mahabaleshwa; Teng, William; Liu, Zhong; Bryant, Keith; Shen, Suhung; Hearty, Thomas; Wei, Jennifer;

Antiarrhythmic effect of IKr activation in a cellular model of LQT3.

PubMed

Diness, Jonas Goldin; Hansen, Rie Schultz; Nissen, Jakob Dahl; Jespersen, Thomas; Grunnet, Morten

2009-01-01

Long QT syndrome type 3 (LQT3) is an inherited cardiac disorder caused by gain-of-function mutations in the cardiac voltage-gated sodium channel, Na(v)1.5. LQT3 is associated with the polymorphic ventricular tachycardia torsades de pointes (TdP), which can lead to syncope and sudden cardiac death. The sea anemone toxin ATX-II has been shown to inhibit the inactivation of Na(v)1.5, thereby closely mimicking the underlying cause of LQT3 in patients. The hypothesis for this study was that activation of the I(Kr) current could counteract the proarrhythmic effects of ATX-II. Two different activators of I(Kr), NS3623 and mallotoxin (MTX), were used in patch clamp studies of ventricular cardiac myocytes acutely isolated from guinea pig to test the effects of selective I(Kr) activation alone and in the presence of ATX-II. Action potentials were elicited at 1 Hz by current injection and the cells were kept at 32 degrees C to 35 degrees C. NS3623 significantly shortened action potential duration at 90% repolarization (APD(90)) compared with controls in a dose-dependent manner. Furthermore, it reduced triangulation, which is potentially antiarrhythmic. Application of ATX-II (10 nM) was proarrhythmic, causing a profound increase of APD(90) as well as early afterdepolarizations and increased beat-to-beat variability. Two independent I(Kr) activators attenuated the proarrhythmic effects of ATX-II. NS3623 did not affect the late sodium current (I(NaL)) in the presence of ATX-II. Thus, the antiarrhythmic effect of NS3623 is likely to be caused by selective I(Kr) activation. The present data show the antiarrhythmic potential of selective I(Kr) activation in a cellular model of the LQT3 syndrome.

Effect of field view size and lighting on unique-hue selection using Natural Color System object colors.

PubMed

Shamey, Renzo; Zubair, Muhammad; Cheema, Hammad

2015-08-01

The aim of this study was twofold, first to determine the effect of field view size and second of illumination conditions on the selection of unique hue samples (UHs: R, Y, G and B) from two rotatable trays, each containing forty highly chromatic Natural Color System (NCS) samples, on one tray corresponding to 1.4° and on the other to 5.7° field of view size. UH selections were made by 25 color-normal observers who repeated assessments three times with a gap of at least 24h between trials. Observers separately assessed UHs under four illumination conditions simulating illuminants D65, A, F2 and F11. An apparent hue shift (statistically significant for UR) was noted for UH selections at 5.7° field of view compared to those at 1.4°. Observers' overall variability was found to be higher for UH stimuli selections at the larger field of view. Intra-observer variability was found to be approximately 18.7% of inter-observer variability in selection of samples for both sample sizes. The highest intra-observer variability was under simulated illuminant D65, followed by A, F11, and F2. Copyright © 2015 Elsevier Ltd. All rights reserved.

Detecting most influencing courses on students grades using block PCA

NASA Astrophysics Data System (ADS)

Othman, Osama H.; Gebril, Rami Salah

2014-12-01

One of the modern solutions adopted in dealing with the problem of large number of variables in statistical analyses is the Block Principal Component Analysis (Block PCA). This modified technique can be used to reduce the vertical dimension (variables) of the data matrix Xn×p by selecting a smaller number of variables, (say m) containing most of the statistical information. These selected variables can then be employed in further investigations and analyses. Block PCA is an adapted multistage technique of the original PCA. It involves the application of Cluster Analysis (CA) and variable selection throughout sub principal components scores (PC's). The application of Block PCA in this paper is a modified version of the original work of Liu et al (2002). The main objective was to apply PCA on each group of variables, (established using cluster analysis), instead of involving the whole large pack of variables which was proved to be unreliable. In this work, the Block PCA is used to reduce the size of a huge data matrix ((n = 41) × (p = 251)) consisting of Grade Point Average (GPA) of the students in 251 courses (variables) in the faculty of science in Benghazi University. In other words, we are constructing a smaller analytical data matrix of the GPA's of the students with less variables containing most variation (statistical information) in the original database. By applying the Block PCA, (12) courses were found to `absorb' most of the variation or influence from the original data matrix, and hence worth to be keep for future statistical exploring and analytical studies. In addition, the course Independent Study (Math.) was found to be the most influencing course on students GPA among the 12 selected courses.

Assessing the influence of watershed characteristics on chlorophyll a in waterbodies at global and regional scales

USGS Publications Warehouse

Woelmer, Whitney; Kao, Yu-Chun; Bunnell, David B.; Deines, Andrew M.; Bennion, David; Rogers, Mark W.; Brooks, Colin N.; Sayers, Michael J.; Banach, David M.; Grimm, Amanda G.; Shuchman, Robert A.

2016-01-01

Prediction of primary production of lentic water bodies (i.e., lakes and reservoirs) is valuable to researchers and resource managers alike, but is very rarely done at the global scale. With the development of remote sensing technologies, it is now feasible to gather large amounts of data across the world, including understudied and remote regions. To determine which factors were most important in explaining the variation of chlorophyll a (Chl-a), an indicator of primary production in water bodies, at global and regional scales, we first developed a geospatial database of 227 water bodies and watersheds with corresponding Chl-a, nutrient, hydrogeomorphic, and climate data. Then we used a generalized additive modeling approach and developed model selection criteria to select models that most parsimoniously related Chl-a to predictor variables for all 227 water bodies and for 51 lakes in the Laurentian Great Lakes region in the data set. Our best global model contained two hydrogeomorphic variables (water body surface area and the ratio of watershed to water body surface area) and a climate variable (average temperature in the warmest model selection criteria to select models that most parsimoniously related Chl-a to predictor variables quarter) and explained ~ 30% of variation in Chl-a. Our regional model contained one hydrogeomorphic variable (flow accumulation) and the same climate variable, but explained substantially more variation (58%). Our results indicate that a regional approach to watershed modeling may be more informative to predicting Chl-a, and that nearly a third of global variability in Chl-a may be explained using hydrogeomorphic and climate variables.

A Multi-Scale Distribution Model for Non-Equilibrium Populations Suggests Resource Limitation in an Endangered Rodent

PubMed Central

Bean, William T.; Stafford, Robert; Butterfield, H. Scott; Brashares, Justin S.

2014-01-01

Species distributions are known to be limited by biotic and abiotic factors at multiple temporal and spatial scales. Species distribution models, however, frequently assume a population at equilibrium in both time and space. Studies of habitat selection have repeatedly shown the difficulty of estimating resource selection if the scale or extent of analysis is incorrect. Here, we present a multi-step approach to estimate the realized and potential distribution of the endangered giant kangaroo rat. First, we estimate the potential distribution by modeling suitability at a range-wide scale using static bioclimatic variables. We then examine annual changes in extent at a population-level. We define “available” habitat based on the total suitable potential distribution at the range-wide scale. Then, within the available habitat, model changes in population extent driven by multiple measures of resource availability. By modeling distributions for a population with robust estimates of population extent through time, and ecologically relevant predictor variables, we improved the predictive ability of SDMs, as well as revealed an unanticipated relationship between population extent and precipitation at multiple scales. At a range-wide scale, the best model indicated the giant kangaroo rat was limited to areas that received little to no precipitation in the summer months. In contrast, the best model for shorter time scales showed a positive relation with resource abundance, driven by precipitation, in the current and previous year. These results suggest that the distribution of the giant kangaroo rat was limited to the wettest parts of the drier areas within the study region. This multi-step approach reinforces the differing relationship species may have with environmental variables at different scales, provides a novel method for defining “available” habitat in habitat selection studies, and suggests a way to create distribution models at spatial and temporal scales relevant to theoretical and applied ecologists. PMID:25237807

Assessing the accuracy and stability of variable selection methods for random forest modeling in ecology

EPA Science Inventory

Random forest (RF) modeling has emerged as an important statistical learning method in ecology due to its exceptional predictive performance. However, for large and complex ecological datasets there is limited guidance on variable selection methods for RF modeling. Typically, e...

Impact of donor- and collection-related variables on product quality in ex utero cord blood banking.

PubMed

Askari, Sabeen; Miller, John; Chrysler, Gayl; McCullough, Jeffrey

2005-02-01

Optimizing product quality is a current focus in cord blood banking. This study evaluates the role of selected donor- and collection-related variables. Retrospective review was performed of cord blood units (CBUs) collected ex utero between February 1, 2000, and February 28, 2002. Preprocessing volume and total nucleated cell (TNC) counts and postprocessing CD34 cell counts were used as product quality indicators. Of 2084 CBUs, volume determinations and TNC counts were performed on 1628 and CD34+ counts on 1124 CBUs. Mean volume and TNC and CD34+ counts were 85.2 mL, 118.9 x 10(7), and 5.2 x 10(6), respectively. In univariate analysis, placental weight of greater than 500 g and meconium in amniotic fluid correlated with better volume and TNC and CD34+ counts. Greater than 40 weeks' gestation predicted enhanced volume and TNC count. Cesarean section, two- versus one-person collection, and not greater than 5 minutes between placental delivery and collection produced superior volume. Increased TNC count was also seen in Caucasian women, primigravidae, female newborns, and collection duration of more than 5 minutes. A time between delivery of newborn and placenta of not greater than 10 minutes predicted better volume and CD34+ count. By regression analysis, collection within not greater than 5 minutes of placental delivery produced superior volume and TNC count. Donor selection and collection technique modifications may improve product quality. TNC count appears to be more affected by different variables than CD34+ count.

Risk assessment of metal vapor arcing

NASA Technical Reports Server (NTRS)

Hill, Monika C. (Inventor); Leidecker, Henning W. (Inventor)

2009-01-01

A method for assessing metal vapor arcing risk for a component is provided. The method comprises acquiring a current variable value associated with an operation of the component; comparing the current variable value with a threshold value for the variable; evaluating compared variable data to determine the metal vapor arcing risk in the component; and generating a risk assessment status for the component.

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.

Climate variability slows evolutionary responses of Colias butterflies to recent climate change.

PubMed

Kingsolver, Joel G; Buckley, Lauren B

2015-03-07

How does recent climate warming and climate variability alter fitness, phenotypic selection and evolution in natural populations? We combine biophysical, demographic and evolutionary models with recent climate data to address this question for the subalpine and alpine butterfly, Colias meadii, in the southern Rocky Mountains. We focus on predicting patterns of selection and evolution for a key thermoregulatory trait, melanin (solar absorptivity) on the posterior ventral hindwings, which affects patterns of body temperature, flight activity, adult and egg survival, and reproductive success in Colias. Both mean annual summer temperatures and thermal variability within summers have increased during the past 60 years at subalpine and alpine sites. At the subalpine site, predicted directional selection on wing absorptivity has shifted from generally positive (favouring increased wing melanin) to generally negative during the past 60 years, but there is substantial variation among years in the predicted magnitude and direction of selection and the optimal absorptivity. The predicted magnitude of directional selection at the alpine site declined during the past 60 years and varies substantially among years, but selection has generally been positive at this site. Predicted evolutionary responses to mean climate warming at the subalpine site since 1980 is small, because of the variability in selection and asymmetry of the fitness function. At both sites, the predicted effects of adaptive evolution on mean population fitness are much smaller than the fluctuations in mean fitness due to climate variability among years. Our analyses suggest that variation in climate within and among years may strongly limit evolutionary responses of ectotherms to mean climate warming in these habitats. © 2015 The Author(s) Published by the Royal Society. All rights reserved.

Space Technology 5 Multipoint Observations of Temporal and Spatial Variability of Field-Aligned Currents

NASA Technical Reports Server (NTRS)

Le, G.; Wang, Y.; Slavin, J. A.; Strangeway, R. L.

2009-01-01

Space Technology 5 (ST5) is a constellation mission consisting of three microsatellites. It provides the first multipoint magnetic field measurements in low Earth orbit, which enables us to separate spatial and temporal variations. In this paper, we present a study of the temporal variability of field-aligned currents using the ST5 data. We examine the field-aligned current observations during and after a geomagnetic storm and compare the magnetic field profiles at the three spacecraft. The multipoint data demonstrate that mesoscale current structures, commonly embedded within large-scale current sheets, are very dynamic with highly variable current density and/or polarity in approx.10 min time scales. On the other hand, the data also show that the time scales for the currents to be relatively stable are approx.1 min for mesoscale currents and approx.10 min for large-scale currents. These temporal features are very likely associated with dynamic variations of their charge carriers (mainly electrons) as they respond to the variations of the parallel electric field in auroral acceleration region. The characteristic time scales for the temporal variability of mesoscale field-aligned currents are found to be consistent with those of auroral parallel electric field.

Variable mechanical ventilation

PubMed Central

Fontela, Paula Caitano; Prestes, Renata Bernardy; Forgiarini Jr., Luiz Alberto; Friedman, Gilberto

2017-01-01

Objective To review the literature on the use of variable mechanical ventilation and the main outcomes of this technique. Methods Search, selection, and analysis of all original articles on variable ventilation, without restriction on the period of publication and language, available in the electronic databases LILACS, MEDLINE®, and PubMed, by searching the terms "variable ventilation" OR "noisy ventilation" OR "biologically variable ventilation". Results A total of 36 studies were selected. Of these, 24 were original studies, including 21 experimental studies and three clinical studies. Conclusion Several experimental studies reported the beneficial effects of distinct variable ventilation strategies on lung function using different models of lung injury and healthy lungs. Variable ventilation seems to be a viable strategy for improving gas exchange and respiratory mechanics and preventing lung injury associated with mechanical ventilation. However, further clinical studies are necessary to assess the potential of variable ventilation strategies for the clinical improvement of patients undergoing mechanical ventilation. PMID:28444076

Mating tactics determine patterns of condition dependence in a dimorphic horned beetle.

PubMed

Knell, Robert J; Simmons, Leigh W

2010-08-07

The persistence of genetic variability in performance traits such as strength is surprising given the directional selection that such traits experience, which should cause the fixation of the best genetic variants. One possible explanation is 'genic capture' which is usually considered as a candidate mechanism for the maintenance of high genetic variability in sexual signalling traits. This states that if a trait is 'condition dependent', with expression being strongly influenced by the bearer's overall viability, then genetic variability can be maintained via mutation-selection balance. Using a species of dimorphic beetle with males that gain matings either by fighting or by 'sneaking', we tested the prediction of strong condition dependence for strength, walking speed and testes mass. Strength was strongly condition dependent only in those beetles that fight for access to females. Walking speed, with less of an obvious selective advantage, showed no condition dependence, and testes mass was more condition dependent in sneaks, which engage in higher levels of sperm competition. Within a species, therefore, condition dependent expression varies between morphs, and corresponds to the specific selection pressures experienced by that morph. These results support genic capture as a general explanation for the maintenance of genetic variability in traits under directional selection.

Variable selection for zero-inflated and overdispersed data with application to health care demand in Germany.

PubMed

Wang, Zhu; Ma, Shuangge; Wang, Ching-Yun

2015-09-01

In health services and outcome research, count outcomes are frequently encountered and often have a large proportion of zeros. The zero-inflated negative binomial (ZINB) regression model has important applications for this type of data. With many possible candidate risk factors, this paper proposes new variable selection methods for the ZINB model. We consider maximum likelihood function plus a penalty including the least absolute shrinkage and selection operator (LASSO), smoothly clipped absolute deviation (SCAD), and minimax concave penalty (MCP). An EM (expectation-maximization) algorithm is proposed for estimating the model parameters and conducting variable selection simultaneously. This algorithm consists of estimating penalized weighted negative binomial models and penalized logistic models via the coordinated descent algorithm. Furthermore, statistical properties including the standard error formulae are provided. A simulation study shows that the new algorithm not only has more accurate or at least comparable estimation, but also is more robust than the traditional stepwise variable selection. The proposed methods are applied to analyze the health care demand in Germany using the open-source R package mpath. © 2015 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Sparse PLS discriminant analysis: biologically relevant feature selection and graphical displays for multiclass problems.

PubMed

Lê Cao, Kim-Anh; Boitard, Simon; Besse, Philippe

2011-06-22

Variable selection on high throughput biological data, such as gene expression or single nucleotide polymorphisms (SNPs), becomes inevitable to select relevant information and, therefore, to better characterize diseases or assess genetic structure. There are different ways to perform variable selection in large data sets. Statistical tests are commonly used to identify differentially expressed features for explanatory purposes, whereas Machine Learning wrapper approaches can be used for predictive purposes. In the case of multiple highly correlated variables, another option is to use multivariate exploratory approaches to give more insight into cell biology, biological pathways or complex traits. A simple extension of a sparse PLS exploratory approach is proposed to perform variable selection in a multiclass classification framework. sPLS-DA has a classification performance similar to other wrapper or sparse discriminant analysis approaches on public microarray and SNP data sets. More importantly, sPLS-DA is clearly competitive in terms of computational efficiency and superior in terms of interpretability of the results via valuable graphical outputs. sPLS-DA is available in the R package mixOmics, which is dedicated to the analysis of large biological data sets.

From Metaphors to Formalism: A Heuristic Approach to Holistic Assessments of Ecosystem Health.

PubMed

Fock, Heino O; Kraus, Gerd

2016-01-01

Environmental policies employ metaphoric objectives such as ecosystem health, resilience and sustainable provision of ecosystem services, which influence corresponding sustainability assessments by means of normative settings such as assumptions on system description, indicator selection, aggregation of information and target setting. A heuristic approach is developed for sustainability assessments to avoid ambiguity and applications to the EU Marine Strategy Framework Directive (MSFD) and OSPAR assessments are presented. For MSFD, nineteen different assessment procedures have been proposed, but at present no agreed assessment procedure is available. The heuristic assessment framework is a functional-holistic approach comprising an ex-ante/ex-post assessment framework with specifically defined normative and systemic dimensions (EAEPNS). The outer normative dimension defines the ex-ante/ex-post framework, of which the latter branch delivers one measure of ecosystem health based on indicators and the former allows to account for the multi-dimensional nature of sustainability (social, economic, ecological) in terms of modeling approaches. For MSFD, the ex-ante/ex-post framework replaces the current distinction between assessments based on pressure and state descriptors. The ex-ante and the ex-post branch each comprise an inner normative and a systemic dimension. The inner normative dimension in the ex-post branch considers additive utility models and likelihood functions to standardize variables normalized with Bayesian modeling. Likelihood functions allow precautionary target setting. The ex-post systemic dimension considers a posteriori indicator selection by means of analysis of indicator space to avoid redundant indicator information as opposed to a priori indicator selection in deconstructive-structural approaches. Indicator information is expressed in terms of ecosystem variability by means of multivariate analysis procedures. The application to the OSPAR assessment for the southern North Sea showed, that with the selected 36 indicators 48% of ecosystem variability could be explained. Tools for the ex-ante branch are risk and ecosystem models with the capability to analyze trade-offs, generating model output for each of the pressure chains to allow for a phasing-out of human pressures. The Bayesian measure of ecosystem health is sensitive to trends in environmental features, but robust to ecosystem variability in line with state space models. The combination of the ex-ante and ex-post branch is essential to evaluate ecosystem resilience and to adopt adaptive management. Based on requirements of the heuristic approach, three possible developments of this concept can be envisioned, i.e. a governance driven approach built upon participatory processes, a science driven functional-holistic approach requiring extensive monitoring to analyze complete ecosystem variability, and an approach with emphasis on ex-ante modeling and ex-post assessment of well-studied subsystems.

From Metaphors to Formalism: A Heuristic Approach to Holistic Assessments of Ecosystem Health

PubMed Central

Kraus, Gerd

2016-01-01

Environmental policies employ metaphoric objectives such as ecosystem health, resilience and sustainable provision of ecosystem services, which influence corresponding sustainability assessments by means of normative settings such as assumptions on system description, indicator selection, aggregation of information and target setting. A heuristic approach is developed for sustainability assessments to avoid ambiguity and applications to the EU Marine Strategy Framework Directive (MSFD) and OSPAR assessments are presented. For MSFD, nineteen different assessment procedures have been proposed, but at present no agreed assessment procedure is available. The heuristic assessment framework is a functional-holistic approach comprising an ex-ante/ex-post assessment framework with specifically defined normative and systemic dimensions (EAEPNS). The outer normative dimension defines the ex-ante/ex-post framework, of which the latter branch delivers one measure of ecosystem health based on indicators and the former allows to account for the multi-dimensional nature of sustainability (social, economic, ecological) in terms of modeling approaches. For MSFD, the ex-ante/ex-post framework replaces the current distinction between assessments based on pressure and state descriptors. The ex-ante and the ex-post branch each comprise an inner normative and a systemic dimension. The inner normative dimension in the ex-post branch considers additive utility models and likelihood functions to standardize variables normalized with Bayesian modeling. Likelihood functions allow precautionary target setting. The ex-post systemic dimension considers a posteriori indicator selection by means of analysis of indicator space to avoid redundant indicator information as opposed to a priori indicator selection in deconstructive-structural approaches. Indicator information is expressed in terms of ecosystem variability by means of multivariate analysis procedures. The application to the OSPAR assessment for the southern North Sea showed, that with the selected 36 indicators 48% of ecosystem variability could be explained. Tools for the ex-ante branch are risk and ecosystem models with the capability to analyze trade-offs, generating model output for each of the pressure chains to allow for a phasing-out of human pressures. The Bayesian measure of ecosystem health is sensitive to trends in environmental features, but robust to ecosystem variability in line with state space models. The combination of the ex-ante and ex-post branch is essential to evaluate ecosystem resilience and to adopt adaptive management. Based on requirements of the heuristic approach, three possible developments of this concept can be envisioned, i.e. a governance driven approach built upon participatory processes, a science driven functional-holistic approach requiring extensive monitoring to analyze complete ecosystem variability, and an approach with emphasis on ex-ante modeling and ex-post assessment of well-studied subsystems. PMID:27509185

The sea surface currents as a potential factor in the estimation and monitoring of wave energy potential

NASA Astrophysics Data System (ADS)

Zodiatis, George; Galanis, George; Nikolaidis, Andreas; Stylianoy, Stavros; Liakatas, Aristotelis

2015-04-01

The use of wave energy as an alternative renewable is receiving attention the last years under the shadow of the economic crisis in Europe and in the light of the promising corresponding potential especially for countries with extended coastline. Monitoring and studying the corresponding resources is further supported by a number of critical advantages of wave energy compared to other renewable forms, like the reduced variability and the easier adaptation to the general grid, especially when is jointly approached with wind power. Within the framework, a number of countries worldwide have launched research and development projects and a significant number of corresponding studies have been presented the last decades. However, in most of them the impact of wave-sea surface currents interaction on the wave energy potential has not been taken into account neglecting in this way a factor of potential importance. The present work aims at filling this gap for a sea area with increased scientific and economic interest, the Eastern Mediterranean Sea. Based on a combination of high resolution numerical modeling approach with advanced statistical tools, a detailed analysis is proposed for the quantification of the impact of sea surface currents, which produced from downscaling the MyOcean-FO regional data, to wave energy potential. The results although spatially sensitive, as expected, prove beyond any doubt that the wave- sea surface currents interaction should be taken into account for similar resource analysis and site selection approaches since the percentage of impact to the available wave power may reach or even exceed 20% at selected areas.

Stepping towards causation: do built environments or neighborhood and travel preferences explain physical activity, driving, and obesity?

PubMed

Frank, Lawrence Douglas; Saelens, Brian E; Powell, Ken E; Chapman, James E

2007-11-01

Evidence documents associations between neighborhood design and active and sedentary forms of travel. Most studies compare travel patterns for people located in different types of neighborhoods at one point in time adjusting for demographics. Most fail to account for either underlying neighborhood selection factors (reasons for choosing a neighborhood) or preferences (neighborhoods that are preferred) that impact neighborhood selection and behavior. Known as self-selection, this issue makes it difficult to evaluate causation among built form, behavior, and associated outcomes and to know how much more walking and less driving could occur through creating environments conducive to active transport. The current study controls for neighborhood selection and preference and isolates the effect of the built environment on walking, car use, and obesity. Separate analyses were conducted among 2056 persons in the Atlanta, USA based Strategies for Metropolitan Atlanta's Regional Transportation and Air Quality (SMARTRAQ) travel survey on selection factors and 1466 persons in the SMARTRAQ community preference sub-survey. A significant proportion of the population are "mismatched" and do not live in their preferred neighborhood type. Factors influencing neighborhood selection and individual preferences, and current neighborhood walkability explained vehicle travel distance after controlling for demographic variables. Individuals who preferred and lived in a walkable neighborhood walked most (33.9% walked) and drove 25.8 miles per day on average. Individuals that preferred and lived in car dependent neighborhoods drove the most (43 miles per day) and walked the least (3.3%). Individuals that do not prefer a walkable environment walked little and show no change in obesity prevalence regardless of where they live. About half as many participants were obese (11.7%) who prefer and live in walkable environments than participants who prefer car dependent environments (21.6%). Findings suggest that creating walkable environments may result in higher levels of physical activity and less driving and in slightly lower obesity prevalence for those preferring walkability.

Principal Selection Decisions Made by Teachers: The Influence of Principal Candidate Experience

ERIC Educational Resources Information Center

Winter, Paul A.; Jaeger, Mary Grace

2004-01-01

Public school teachers (N = 189) role-played as members of school councils making principal selection decisions by rating simulated candidates for principal vacancies. The independent variables were principal candidate job experience, candidate person characteristics, and teacher school level. The dependent variable was teacher rating of the job…

Role Appropriateness of Educational Fields: Bias in Selection.

ERIC Educational Resources Information Center

Smith, Elizabeth P.; And Others

Bias towards women exists in the selection of applicants to professional and other positions. This research investigated the effects of two rater variables--sex and attitude toward women--and three applicant variables--sex, field (engineering-dietetics), and attributes--(feminine-masculine) upon ratings of competency and personal charm. Analyses…

Variable selection with random forest: Balancing stability, performance, and interpretation in ecological and environmental modeling

EPA Science Inventory

Random forest (RF) is popular in ecological and environmental modeling, in part, because of its insensitivity to correlated predictors and resistance to overfitting. Although variable selection has been proposed to improve both performance and interpretation of RF models, it is u...

Quantifying interindividual variability and asymmetry of face-selective regions: a probabilistic functional atlas.

PubMed

Zhen, Zonglei; Yang, Zetian; Huang, Lijie; Kong, Xiang-Zhen; Wang, Xu; Dang, Xiaobin; Huang, Yangyue; Song, Yiying; Liu, Jia

2015-06-01

Face-selective regions (FSRs) are among the most widely studied functional regions in the human brain. However, individual variability of the FSRs has not been well quantified. Here we use functional magnetic resonance imaging (fMRI) to localize the FSRs and quantify their spatial and functional variabilities in 202 healthy adults. The occipital face area (OFA), posterior and anterior fusiform face areas (pFFA and aFFA), posterior continuation of the superior temporal sulcus (pcSTS), and posterior and anterior STS (pSTS and aSTS) were delineated for each individual with a semi-automated procedure. A probabilistic atlas was constructed to characterize their interindividual variability, revealing that the FSRs were highly variable in location and extent across subjects. The variability of FSRs was further quantified on both functional (i.e., face selectivity) and spatial (i.e., volume, location of peak activation, and anatomical location) features. Considerable interindividual variability and rightward asymmetry were found in all FSRs on these features. Taken together, our work presents the first effort to characterize comprehensively the variability of FSRs in a large sample of healthy subjects, and invites future work on the origin of the variability and its relation to individual differences in behavioral performance. Moreover, the probabilistic functional atlas will provide an adequate spatial reference for mapping the face network. Copyright © 2015 Elsevier Inc. All rights reserved.

[Variable selection methods combined with local linear embedding theory used for optimization of near infrared spectral quantitative models].

PubMed

Hao, Yong; Sun, Xu-Dong; Yang, Qiang

2012-12-01

Variables selection strategy combined with local linear embedding (LLE) was introduced for the analysis of complex samples by using near infrared spectroscopy (NIRS). Three methods include Monte Carlo uninformation variable elimination (MCUVE), successive projections algorithm (SPA) and MCUVE connected with SPA were used for eliminating redundancy spectral variables. Partial least squares regression (PLSR) and LLE-PLSR were used for modeling complex samples. The results shown that MCUVE can both extract effective informative variables and improve the precision of models. Compared with PLSR models, LLE-PLSR models can achieve more accurate analysis results. MCUVE combined with LLE-PLSR is an effective modeling method for NIRS quantitative analysis.

A data-driven approach to modeling physical fatigue in the workplace using wearable sensors.

PubMed

Sedighi Maman, Zahra; Alamdar Yazdi, Mohammad Ali; Cavuoto, Lora A; Megahed, Fadel M

2017-11-01

Wearable sensors are currently being used to manage fatigue in professional athletics, transportation and mining industries. In manufacturing, physical fatigue is a challenging ergonomic/safety "issue" since it lowers productivity and increases the incidence of accidents. Therefore, physical fatigue must be managed. There are two main goals for this study. First, we examine the use of wearable sensors to detect physical fatigue occurrence in simulated manufacturing tasks. The second goal is to estimate the physical fatigue level over time. In order to achieve these goals, sensory data were recorded for eight healthy participants. Penalized logistic and multiple linear regression models were used for physical fatigue detection and level estimation, respectively. Important features from the five sensors locations were selected using Least Absolute Shrinkage and Selection Operator (LASSO), a popular variable selection methodology. The results show that the LASSO model performed well for both physical fatigue detection and modeling. The modeling approach is not participant and/or workload regime specific and thus can be adopted for other applications. Copyright © 2017 Elsevier Ltd. All rights reserved.

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.

Data compression using adaptive transform coding. Appendix 1: Item 1. Ph.D. Thesis

NASA Technical Reports Server (NTRS)

Rost, Martin Christopher

1988-01-01

Adaptive low-rate source coders are described in this dissertation. These coders adapt by adjusting the complexity of the coder to match the local coding difficulty of the image. This is accomplished by using a threshold driven maximum distortion criterion to select the specific coder used. The different coders are built using variable blocksized transform techniques, and the threshold criterion selects small transform blocks to code the more difficult regions and larger blocks to code the less complex regions. A theoretical framework is constructed from which the study of these coders can be explored. An algorithm for selecting the optimal bit allocation for the quantization of transform coefficients is developed. The bit allocation algorithm is more fully developed, and can be used to achieve more accurate bit assignments than the algorithms currently used in the literature. Some upper and lower bounds for the bit-allocation distortion-rate function are developed. An obtainable distortion-rate function is developed for a particular scalar quantizer mixing method that can be used to code transform coefficients at any rate.

Time-of-day effects in implicit racial in-group preferences are likely selection effects, not circadian rhythms

PubMed Central

2016-01-01

Time-of-day effects in human psychological functioning have been known of since the 1800s. However, outside of research specifically focused on the quantification of circadian rhythms, their study has largely been neglected. Moves toward online data collection now mean that psychological investigations take place around the clock, which affords researchers the ability to easily study time-of-day effects. Recent analyses have shown, for instance, that implicit attitudes have time-of-day effects. The plausibility that these effects indicate circadian rhythms rather than selection effects is considered in the current study. There was little evidence that the time-of-day effects in implicit attitudes shifted appropriately with factors known to influence the time of circadian rhythms. Moreover, even variables that cannot logically show circadian rhythms demonstrated stronger time-of-day effects than did implicit attitudes. Taken together, these results suggest that time-of-day effects in implicit attitudes are more likely to represent processes of selection rather than circadian rhythms, but do not rule out the latter possibility. PMID:27114886

Time-of-day effects in implicit racial in-group preferences are likely selection effects, not circadian rhythms.

PubMed

Schofield, Timothy P

2016-01-01

Time-of-day effects in human psychological functioning have been known of since the 1800s. However, outside of research specifically focused on the quantification of circadian rhythms, their study has largely been neglected. Moves toward online data collection now mean that psychological investigations take place around the clock, which affords researchers the ability to easily study time-of-day effects. Recent analyses have shown, for instance, that implicit attitudes have time-of-day effects. The plausibility that these effects indicate circadian rhythms rather than selection effects is considered in the current study. There was little evidence that the time-of-day effects in implicit attitudes shifted appropriately with factors known to influence the time of circadian rhythms. Moreover, even variables that cannot logically show circadian rhythms demonstrated stronger time-of-day effects than did implicit attitudes. Taken together, these results suggest that time-of-day effects in implicit attitudes are more likely to represent processes of selection rather than circadian rhythms, but do not rule out the latter possibility.

The challenges of modelling antibody repertoire dynamics in HIV infection

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

Luo, Shishi; Perelson, Alan S.

Antibody affinity maturation by somatic hypermutation of B-cell immunoglobulin variable region genes has been studied for decades in various model systems using well-defined antigens. While much is known about the molecular details of the process, our understanding of the selective forces that generate affinity maturation are less well developed, particularly in the case of a co-evolving pathogen such as HIV. Despite this gap in understanding, high-throughput antibody sequence data are increasingly being collected to investigate the evolutionary trajectories of antibody lineages in HIV-infected individuals. Here, we review what is known in controlled experimental systems about the mechanisms underlying antibody selectionmore » and compare this to the observed temporal patterns of antibody evolution in HIV infection. In addition, we describe how our current understanding of antibody selection mechanisms leaves questions about antibody dynamics in HIV infection unanswered. Without a mechanistic understanding of antibody selection in the context of a co-evolving viral population, modelling and analysis of antibody sequences in HIV-infected individuals will be limited in their interpretation and predictive ability.« less

Selecting, Acquiring, and Using Small Molecule Libraries for High-Throughput Screening

PubMed Central

Dandapani, Sivaraman; Rosse, Gerard; Southall, Noel; Salvino, Joseph M.; Thomas, Craig J.

2015-01-01

The selection, acquisition and use of high quality small molecule libraries for screening is an essential aspect of drug discovery and chemical biology programs. Screening libraries continue to evolve as researchers gain a greater appreciation of the suitability of small molecules for specific biological targets, processes and environments. The decisions surrounding the make-up of any given small molecule library is informed by a multitude of variables and opinions vary on best-practices. The fitness of any collection relies upon upfront filtering to avoiding problematic compounds, assess appropriate physicochemical properties, install the ideal level of structural uniqueness and determine the desired extent of molecular complexity. These criteria are under constant evaluation and revision as academic and industrial organizations seek out collections that yield ever improving results from their screening portfolios. Practical questions including cost, compound management, screening sophistication and assay objective also play a significant role in the choice of library composition. This overview attempts to offer advice to all organizations engaged in small molecule screening based upon current best practices and theoretical considerations in library selection and acquisition. PMID:26705509

Selecting, Acquiring, and Using Small Molecule Libraries for High-Throughput Screening.

PubMed

Dandapani, Sivaraman; Rosse, Gerard; Southall, Noel; Salvino, Joseph M; Thomas, Craig J

The selection, acquisition and use of high quality small molecule libraries for screening is an essential aspect of drug discovery and chemical biology programs. Screening libraries continue to evolve as researchers gain a greater appreciation of the suitability of small molecules for specific biological targets, processes and environments. The decisions surrounding the make-up of any given small molecule library is informed by a multitude of variables and opinions vary on best-practices. The fitness of any collection relies upon upfront filtering to avoiding problematic compounds, assess appropriate physicochemical properties, install the ideal level of structural uniqueness and determine the desired extent of molecular complexity. These criteria are under constant evaluation and revision as academic and industrial organizations seek out collections that yield ever improving results from their screening portfolios. Practical questions including cost, compound management, screening sophistication and assay objective also play a significant role in the choice of library composition. This overview attempts to offer advice to all organizations engaged in small molecule screening based upon current best practices and theoretical considerations in library selection and acquisition.

The challenges of modelling antibody repertoire dynamics in HIV infection

DOE PAGES

Luo, Shishi; Perelson, Alan S.

2015-07-20

Antibody affinity maturation by somatic hypermutation of B-cell immunoglobulin variable region genes has been studied for decades in various model systems using well-defined antigens. While much is known about the molecular details of the process, our understanding of the selective forces that generate affinity maturation are less well developed, particularly in the case of a co-evolving pathogen such as HIV. Despite this gap in understanding, high-throughput antibody sequence data are increasingly being collected to investigate the evolutionary trajectories of antibody lineages in HIV-infected individuals. Here, we review what is known in controlled experimental systems about the mechanisms underlying antibody selectionmore » and compare this to the observed temporal patterns of antibody evolution in HIV infection. In addition, we describe how our current understanding of antibody selection mechanisms leaves questions about antibody dynamics in HIV infection unanswered. Without a mechanistic understanding of antibody selection in the context of a co-evolving viral population, modelling and analysis of antibody sequences in HIV-infected individuals will be limited in their interpretation and predictive ability.« less

Sq Currents and Neutral Winds

NASA Astrophysics Data System (ADS)

Yamazaki, Y.

2015-12-01

The relationship between ionospheric dynamo currents and neutral winds is examined using the Thermosphere Ionosphere Mesosphere Electrodynamic General Circulation Model (TIME-GCM). The simulation is run for May and June 2009 with variable neutral winds but with constant solar and magnetospheric energy inputs, which ensures that day-to-day changes in the solar quiet (Sq) current system arise only from lower atmospheric forcing. The intensity and focus position of the simulated Sq current system exhibit large day-to-day variability, as is also seen in ground magnetometer data. We show how the day-to-day variation of the Sq current system relate to variable winds at various altitudes, latitudes, and longitudes.

Selection of Optimal Auxiliary Soil Nutrient Variables for Cokriging Interpolation

PubMed Central

Song, Genxin; Zhang, Jing; Wang, Ke

2014-01-01

In order to explore the selection of the best auxiliary variables (BAVs) when using the Cokriging method for soil attribute interpolation, this paper investigated the selection of BAVs from terrain parameters, soil trace elements, and soil nutrient attributes when applying Cokriging interpolation to soil nutrients (organic matter, total N, available P, and available K). In total, 670 soil samples were collected in Fuyang, and the nutrient and trace element attributes of the soil samples were determined. Based on the spatial autocorrelation of soil attributes, the Digital Elevation Model (DEM) data for Fuyang was combined to explore the coordinate relationship among terrain parameters, trace elements, and soil nutrient attributes. Variables with a high correlation to soil nutrient attributes were selected as BAVs for Cokriging interpolation of soil nutrients, and variables with poor correlation were selected as poor auxiliary variables (PAVs). The results of Cokriging interpolations using BAVs and PAVs were then compared. The results indicated that Cokriging interpolation with BAVs yielded more accurate results than Cokriging interpolation with PAVs (the mean absolute error of BAV interpolation results for organic matter, total N, available P, and available K were 0.020, 0.002, 7.616, and 12.4702, respectively, and the mean absolute error of PAV interpolation results were 0.052, 0.037, 15.619, and 0.037, respectively). The results indicated that Cokriging interpolation with BAVs can significantly improve the accuracy of Cokriging interpolation for soil nutrient attributes. This study provides meaningful guidance and reference for the selection of auxiliary parameters for the application of Cokriging interpolation to soil nutrient attributes. PMID:24927129

Seasonal and Interannual Variability of the Brazil - Malvinas Front: an Altimetry Perspective

NASA Astrophysics Data System (ADS)

Saraceno, M.; Valla, D.; Pelegrí, J. L.; Piola, A. R.

2016-02-01

The Brazil and Malvinas Confluence in the Southwestern Atlantic is one of the most energetic regions of the world ocean. Using recent measurements of sub-surface velocity currents, collected along 2348 nautical miles with a vessel mounted acoustic Doppler profiler onboard R/V BIO Hespérides, we validate geostrophic velocities derived from gridded fields of sea surface height (SSH). A remarkable correspondence between in-situ surface hydrographic data collected from the vessel and satellite sea surface temperature (SST), color and altimetry data allows selecting a specific SSH contour to track the position of the Brazil-Malvinas front. We then use 22 years of SSH data distributed by AVISO to show that the Brazil-Malvinas front shows a NS orientation in winter and a NE-SW orientation in summer, in good agreement with results based on the analysis of SST gradients. Furthermore, a clear southward migration of the front during the 22 year period is observed. The migration is associated with the southward shift of the South Atlantic high-pressure system that is in turn related to large climate changes in the southern portion of the South American continent. The seasonal variability in the orientation of the front is related to the Brazil and Malvinas encountering currents.

Non-destructive technique for determining the viability of soybean (Glycine max) seeds using FT-NIR spectroscopy.

PubMed

Kusumaningrum, Dewi; Lee, Hoonsoo; Lohumi, Santosh; Mo, Changyeun; Kim, Moon S; Cho, Byoung-Kwan

2018-03-01

The viability of seeds is important for determining their quality. A high-quality seed is one that has a high capability of germination that is necessary to ensure high productivity. Hence, developing technology for the detection of seed viability is a high priority in agriculture. Fourier transform near-infrared (FT-NIR) spectroscopy is one of the most popular devices among other vibrational spectroscopies. This study aims to use FT-NIR spectroscopy to determine the viability of soybean seeds. Viable and artificial ageing seeds as non-viable soybeans were used in this research. The FT-NIR spectra of soybean seeds were collected and analysed using a partial least-squares discriminant analysis (PLS-DA) to classify viable and non-viable soybean seeds. Moreover, the variable importance in projection (VIP) method for variable selection combined with the PLS-DA was employed. The most effective wavelengths were selected by the VIP method, which selected 146 optimal variables from the full set of 1557 variables. The results demonstrated that the FT-NIR spectral analysis with the PLS-DA method that uses all variables or the selected variables showed good performance based on the high value of prediction accuracy for soybean viability with an accuracy close to 100%. Hence, FT-NIR techniques with a chemometric analysis have the potential for rapidly measuring soybean seed viability. © 2017 Society of Chemical Industry. © 2017 Society of Chemical Industry.

LQTA-QSAR: a new 4D-QSAR methodology.

PubMed

Martins, João Paulo A; Barbosa, Euzébio G; Pasqualoto, Kerly F M; Ferreira, Márcia M C

2009-06-01

A novel 4D-QSAR approach which makes use of the molecular dynamics (MD) trajectories and topology information retrieved from the GROMACS package is presented in this study. This new methodology, named LQTA-QSAR (LQTA, Laboratório de Quimiometria Teórica e Aplicada), has a module (LQTAgrid) that calculates intermolecular interaction energies at each grid point considering probes and all aligned conformations resulting from MD simulations. These interaction energies are the independent variables or descriptors employed in a QSAR analysis. The comparison of the proposed methodology to other 4D-QSAR and CoMFA formalisms was performed using a set of forty-seven glycogen phosphorylase b inhibitors (data set 1) and a set of forty-four MAP p38 kinase inhibitors (data set 2). The QSAR models for both data sets were built using the ordered predictor selection (OPS) algorithm for variable selection. Model validation was carried out applying y-randomization and leave-N-out cross-validation in addition to the external validation. PLS models for data set 1 and 2 provided the following statistics: q(2) = 0.72, r(2) = 0.81 for 12 variables selected and 2 latent variables and q(2) = 0.82, r(2) = 0.90 for 10 variables selected and 5 latent variables, respectively. Visualization of the descriptors in 3D space was successfully interpreted from the chemical point of view, supporting the applicability of this new approach in rational drug design.

Development and Testing of a Coupled Ocean-atmosphere Mesoscale Ensemble Prediction System

DTIC Science & Technology

2011-06-28

wind, temperature, and moisture variables, while the oceanographic ET is derived from ocean current, temperature, and salinity variables. Estimates of...wind, temperature, and moisture variables while the oceanographic ET is derived from ocean current temperature, and salinity variables. Estimates of...uncertainty in the model. Rigorously accurate ensemble methods for describing the distribution of future states given past information include particle

Psychopathology among senior secondary school students in Ilesa, south western Nigeria.

PubMed

Fatoye, F O; Morakinyo, O

2003-09-01

The prevalence rate of psychopathology and the relationship between psychopathology and some socio-demographic variables and consolidated current drug use were studied in 600 randomly selected senior secondary school students in Ilesa, south-western Nigeria. The 30-item version of the General Health Questionnaire and the WHO student drug use questionnaire were administered for the study. The findings revealed that the prevalence of psychopathology among the study population was 39.5%. There were significant positive associations between psychopathology and belonging to low socio-economic status, coming from a polygamous family and self-rated poor academic performance. The results also showed that although psychopathology was commoner amongst respondents who were engaged in current use of psychoactive substances than those who were not, the difference was not significant. The implications of these findings within the context of the limitations of the study and the importance of effective preventive and therapeutic student mental health services are discussed.

Torque ripple reduction of brushless DC motor based on adaptive input-output feedback linearization.

PubMed

Shirvani Boroujeni, M; Markadeh, G R Arab; Soltani, J

2017-09-01

Torque ripple reduction of Brushless DC Motors (BLDCs) is an interesting subject in variable speed AC drives. In this paper at first, a mathematical expression for torque ripple harmonics is obtained. Then for a non-ideal BLDC motor with known harmonic contents of back-EMF, calculation of desired reference current amplitudes, which are required to eliminate some selected harmonics of torque ripple, are reviewed. In order to inject the reference harmonic currents to the motor windings, an Adaptive Input-Output Feedback Linearization (AIOFBL) control is proposed, which generates the reference voltages for three phases voltage source inverter in stationary reference frame. Experimental results are presented to show the capability and validity of the proposed control method and are compared with the vector control in Multi-Reference Frame (MRF) and Pseudo-Vector Control (P-VC) method results. Copyright © 2017 ISA. Published by Elsevier Ltd. All rights reserved.

The importance of scale-dependent ravine characteristics on breeding-site selection by the Burrowing Parrot, Cyanoliseus patagonus

PubMed Central

Rios, Rodrigo S.; Vargas-Rodriguez, Renzo; Novoa-Jerez, Jose-Enrique; Squeo, Francisco A.

2017-01-01

In birds, the environmental variables and intrinsic characteristics of the nest have important fitness consequences through its influence on the selection of nesting sites. However, the extent to which these variables interact with variables that operate at the landscape scale, and whether there is a hierarchy among the different scales that influences nest-site selection, is unknown. This interaction could be crucial in burrowing birds, which depend heavily on the availability of suitable nesting locations. One representative of this group is the burrowing parrot, Cyanoliseus patagonus that breeds on specific ravines and forms large breeding colonies. At a particular site, breeding aggregations require the concentration of adequate environmental elements for cavity nesting, which are provided by within ravine characteristics. Therefore, intrinsic ravine characteristics should be more important in determining nest site selection compared to landscape level characteristics. Here, we assess this hypothesis by comparing the importance of ravine characteristics operating at different scales on nest-site selection and their interrelation with reproductive success. We quantified 12 characteristics of 105 ravines in their reproductive habitat. For each ravine we quantified morphological variables, distance to resources and disturbance as well as nest number and egg production in order to compare selected and non-selected ravines and determine the interrelationship among variables in explaining ravine differences. In addition, the number of nests and egg production for each reproductive ravine was related to ravine characteristics to assess their relation to reproductive success. We found significant differences between non-reproductive and reproductive ravines in both intrinsic and extrinsic characteristics. The multidimensional environmental gradient of variation between ravines, however, shows that differences are mainly related to intrinsic morphological characteristics followed by extrinsic variables associated to human disturbance. Likewise, within reproductive ravines, intrinsic characteristics are more strongly related to the number of nests. The probability of producing eggs, however, was related only to distance to roads and human settlements. Patterns suggest that C. patagonus mainly selects nesting sites based on intrinsic morphological characteristics of ravines. Scale differences in the importance of ravine characteristics could be a consequence of the particular orography of the breeding habitat. The arrangement of resources is associated to the location of the gullies rather than to individual ravines, determining the spatial availability and disposition of resources and disturbances. Thus, nest selection is influenced by intrinsic characteristics that maximize the fitness of individuals. Scaling in nest-selection is discussed under an optimality approach that partitions patch selection based on foraging theory. PMID:28462019

Sexual selection on morphological and physiological traits and fluctuating asymmetry in the black scavenger fly Sepsis cynipsea.

PubMed

Blanckenhorn, W U; Kraushaar, U R S; Teuschl, Y; Reim, C

2004-05-01

Previous univariate studies of the fly Sepsis cynipsea (Diptera: Sepsidae) have demonstrated spatiotemporally variable and consequently overall weak sexual selection favouring large male size, which is nevertheless stronger on average than fecundity selection favouring larger females. To identify specific target(s) of selection on body size and additional traits possibly affecting mating success, two multivariate field studies of sexual selection were conducted. In one study using seasonal replicates from three populations, we assessed 15 morphological traits. No clear targets of sexual selection on male size could be detected, perhaps because spatiotemporal variation in selection was again strong. In particular, there was no (current) selection on male abdomen length or fore coxa length, the only traits for which S. cynipsea males are not smaller than females. Interestingly, copulating males had a consistently shorter fore femur base, a secondary sexual trait, and a wider clasper (hypopygium) gap, an external genital trait. In a second study using daily and seasonal replicates from one population, we included physiological measures of energy reserves (lipids, glucose, glycogen), in addition to hind tibia length and fluctuating asymmetry (FA) of all pairs of legs. This study again confirmed the mating advantage of large males, and additionally suggests independent positive influences of lipids (the long-term energy stores), with effects of glucose and glycogen (the short-term energy stores) tending to be negative. FA of paired traits was not associated with male mating success. Our study suggests that inclusion of physiological measures and genital traits in phenomenological studies of selection, which is rare, would be fruitful in other species.

Fish habitat selection in a large hydropeaking river: Strong individual and temporal variations revealed by telemetry.

PubMed

Capra, Hervé; Plichard, Laura; Bergé, Julien; Pella, Hervé; Ovidio, Michaël; McNeil, Eric; Lamouroux, Nicolas

2017-02-01

Modeling individual fish habitat selection in highly variable environments such as hydropeaking rivers is required for guiding efficient management decisions. We analyzed fish microhabitat selection in the heterogeneous hydraulic and thermal conditions (modeled in two-dimensions) of a reach of the large hydropeaking Rhône River locally warmed by the cooling system of a nuclear power plant. We used modern fixed acoustic telemetry techniques to survey 18 fish individuals (five barbels, six catfishes, seven chubs) signaling their position every 3s over a three-month period. Fish habitat selection depended on combinations of current microhabitat hydraulics (e.g. velocity, depth), past microhabitat hydraulics (e.g. dewatering risk or maximum velocities during the past 15days) and to a lesser extent substrate and temperature. Mixed-effects habitat selection models indicated that individual effects were often stronger than specific effects. In the Rhône, fish individuals appear to memorize spatial and temporal environmental changes and to adopt a "least constraining" habitat selection. Avoiding fast-flowing midstream habitats, fish generally live along the banks in areas where the dewatering risk is high. When discharge decreases, however, they select higher velocities but avoid both dewatering areas and very fast-flowing midstream habitats. Although consistent with the available knowledge on static fish habitat selection, our quantitative results demonstrate temporal variations in habitat selection, depending on individual behavior and environmental history. Their generality could be further tested using comparative experiments in different environmental configurations. Copyright © 2016 Elsevier B.V. All rights reserved.