Analysis of model development strategies: predicting ventral hernia recurrence.

PubMed

Holihan, Julie L; Li, Linda T; Askenasy, Erik P; Greenberg, Jacob A; Keith, Jerrod N; Martindale, Robert G; Roth, J Scott; Liang, Mike K

2016-11-01

There have been many attempts to identify variables associated with ventral hernia recurrence; however, it is unclear which statistical modeling approach results in models with greatest internal and external validity. We aim to assess the predictive accuracy of models developed using five common variable selection strategies to determine variables associated with hernia recurrence. Two multicenter ventral hernia databases were used. Database 1 was randomly split into "development" and "internal validation" cohorts. Database 2 was designated "external validation". The dependent variable for model development was hernia recurrence. Five variable selection strategies were used: (1) "clinical"-variables considered clinically relevant, (2) "selective stepwise"-all variables with a P value <0.20 were assessed in a step-backward model, (3) "liberal stepwise"-all variables were included and step-backward regression was performed, (4) "restrictive internal resampling," and (5) "liberal internal resampling." Variables were included with P < 0.05 for the Restrictive model and P < 0.10 for the Liberal model. A time-to-event analysis using Cox regression was performed using these strategies. The predictive accuracy of the developed models was tested on the internal and external validation cohorts using Harrell's C-statistic where C > 0.70 was considered "reasonable". The recurrence rate was 32.9% (n = 173/526; median/range follow-up, 20/1-58 mo) for the development cohort, 36.0% (n = 95/264, median/range follow-up 20/1-61 mo) for the internal validation cohort, and 12.7% (n = 155/1224, median/range follow-up 9/1-50 mo) for the external validation cohort. Internal validation demonstrated reasonable predictive accuracy (C-statistics = 0.772, 0.760, 0.767, 0.757, 0.763), while on external validation, predictive accuracy dipped precipitously (C-statistic = 0.561, 0.557, 0.562, 0.553, 0.560). Predictive accuracy was equally adequate on internal validation among models; however, on external validation, all five models failed to demonstrate utility. Future studies should report multiple variable selection techniques and demonstrate predictive accuracy on external data sets for model validation. Copyright © 2016 Elsevier Inc. All rights reserved.

Covariate Selection for Multilevel Models with Missing Data

PubMed Central

Marino, Miguel; Buxton, Orfeu M.; Li, Yi

2017-01-01

Missing covariate data hampers variable selection in multilevel regression settings. Current variable selection techniques for multiply-imputed data commonly address missingness in the predictors through list-wise deletion and stepwise-selection methods which are problematic. Moreover, most variable selection methods are developed for independent linear regression models and do not accommodate multilevel mixed effects regression models with incomplete covariate data. We develop a novel methodology that is able to perform covariate selection across multiply-imputed data for multilevel random effects models when missing data is present. Specifically, we propose to stack the multiply-imputed data sets from a multiple imputation procedure and to apply a group variable selection procedure through group lasso regularization to assess the overall impact of each predictor on the outcome across the imputed data sets. Simulations confirm the advantageous performance of the proposed method compared with the competing methods. We applied the method to reanalyze the Healthy Directions-Small Business cancer prevention study, which evaluated a behavioral intervention program targeting multiple risk-related behaviors in a working-class, multi-ethnic population. PMID:28239457

Socio-economic variables influencing mean age at marriage in Karnataka and Kerala.

PubMed

Prakasam, C P; Upadhyay, R B

1985-01-01

"In this paper an attempt was made to study the influence of certain socio-economic variables on the male and the female age at marriage in Karnataka and Kerala [India] for the year 1971. Step-wise regression method has been used to select the predictor variables influencing mean age at marriage. The results reveal that percent female literate...and percent female in labour force...are found to influence female mean age at marriage in Kerala, while the variables for Karnataka were percent female literate..., percent male literate..., and percent urban male population...." excerpt

Multiple linear regression analysis

NASA Technical Reports Server (NTRS)

Edwards, T. R.

1980-01-01

Program rapidly selects best-suited set of coefficients. User supplies only vectors of independent and dependent data and specifies confidence level required. Program uses stepwise statistical procedure for relating minimal set of variables to set of observations; final regression contains only most statistically significant coefficients. Program is written in FORTRAN IV for batch execution and has been implemented on NOVA 1200.

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.

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

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

Hydraulic geometry and streamflow of channels in the Piceance Basin, Rio Blanco and Garfield counties, Colorado

USGS Publications Warehouse

Elliott, J.G.; Cartier, K.D.

1986-01-01

The influence of streamflow and basin characteristics on channel geometry was investigated at 18 perennial and ephemeral stream reaches in the Piceance basin of northwestern Colorado. Results of stepwise multiple regression analyses indicated that the variabilities of mean bankfull depth (D) and bankfull cross-sectional flow area (Af) were predominantly a function of bankfull discharge (QB), and that most of the variability in channel slopes (S) could be explained by drainage area (DA). None of the independent variables selected for the study could account for a large part of the variability in bankfull channel width (W). (USGS)

Bootstrap investigation of the stability of a Cox regression model.

PubMed

Altman, D G; Andersen, P K

1989-07-01

We describe a bootstrap investigation of the stability of a Cox proportional hazards regression model resulting from the analysis of a clinical trial of azathioprine versus placebo in patients with primary biliary cirrhosis. We have considered stability to refer both to the choice of variables included in the model and, more importantly, to the predictive ability of the model. In stepwise Cox regression analyses of 100 bootstrap samples using 17 candidate variables, the most frequently selected variables were those selected in the original analysis, and no other important variable was identified. Thus there was no reason to doubt the model obtained in the original analysis. For each patient in the trial, bootstrap confidence intervals were constructed for the estimated probability of surviving two years. It is shown graphically that these intervals are markedly wider than those obtained from the original model.

Sex determination of the Acadian Flycatcher using discriminant analysis

USGS Publications Warehouse

Wilson, R.R.

1999-01-01

I used five morphometric variables from 114 individuals captured in Arkansas to develop a discriminant model to predict the sex of Acadian Flycatchers (Empidonax virescens). Stepwise discriminant function analyses selected wing chord and tail length as the most parsimonious subset of variables for discriminating sex. This two-variable model correctly classified 80% of females and 97% of males used to develop the model. Validation of the model using 19 individuals from Louisiana and Virginia resulted in 100% correct classification of males and females. This model provides criteria for sexing monomorphic Acadian Flycatchers during the breeding season and possibly during the winter.

The influence of attention deficits on functional recovery post stroke during the first 12 months after discharge from hospital.

PubMed

Hyndman, D; Pickering, R M; Ashburn, A

2008-06-01

Attention deficits have been linked to poor recovery after stroke and may predict outcome. We explored the influence of attention on functional recovery post stroke in the first 12 months after discharge from hospital. People with stroke completed measures of attention, balance, mobility and activities of daily living (ADL) ability at the point of discharge from hospital, and 6 and 12 months later. We used correlational analysis and stepwise linear regression to explore potential predictors of outcome. We recruited 122 men and women, mean age 70 years. At discharge, 56 (51%) had deficits of divided attention, 45 (37%) of sustained attention, 43 (36%) of auditory selective attention and 41 (37%) had visual selective attention deficits. Attention at discharge correlated with mobility, balance and ADL outcomes 12 months later. After controlling for the level of the outcome at discharge, correlations remained significant in only five of the 12 relationships. Stepwise linear regression revealed that the outcome measured at discharge, days until discharge and number of medications were better predictors of outcome: in no case was an attention variable at discharge selected as a predictor of outcome at 12 months. Although attention and function correlated significantly, this correlation was reduced after controlling for functional ability at discharge. Furthermore, side of lesion and the attention variables were not demonstrated as important predictors of outcome 12 months later.

A Latent-Variable Causal Model of Faculty Reputational Ratings.

ERIC Educational Resources Information Center

King, Suzanne; Wolfle, Lee M.

A reanalysis was conducted of Saunier's research (1985) on sources of variation in the National Research Council (NRC) reputational ratings of university faculty. Saunier conducted a stepwise regression analysis using 12 predictor variables. Due to problems with multicollinearity and because of the atheoretical nature of stepwise regression,…

Multivariate calibration on NIR data: development of a model for the rapid evaluation of ethanol content in bakery products.

PubMed

Bello, Alessandra; Bianchi, Federica; Careri, Maria; Giannetto, Marco; Mori, Giovanni; Musci, Marilena

2007-11-05

A new NIR method based on multivariate calibration for determination of ethanol in industrially packed wholemeal bread was developed and validated. GC-FID was used as reference method for the determination of actual ethanol concentration of different samples of wholemeal bread with proper content of added ethanol, ranging from 0 to 3.5% (w/w). Stepwise discriminant analysis was carried out on the NIR dataset, in order to reduce the number of original variables by selecting those that were able to discriminate between the samples of different ethanol concentrations. With the so selected variables a multivariate calibration model was then obtained by multiple linear regression. The prediction power of the linear model was optimized by a new "leave one out" method, so that the number of original variables resulted further reduced.

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.

Modeling Governance KB with CATPCA to Overcome Multicollinearity in the Logistic Regression

NASA Astrophysics Data System (ADS)

Khikmah, L.; Wijayanto, H.; Syafitri, U. D.

2017-04-01

The problem often encounters in logistic regression modeling are multicollinearity problems. Data that have multicollinearity between explanatory variables with the result in the estimation of parameters to be bias. Besides, the multicollinearity will result in error in the classification. In general, to overcome multicollinearity in regression used stepwise regression. They are also another method to overcome multicollinearity which involves all variable for prediction. That is Principal Component Analysis (PCA). However, classical PCA in only for numeric data. Its data are categorical, one method to solve the problems is Categorical Principal Component Analysis (CATPCA). Data were used in this research were a part of data Demographic and Population Survey Indonesia (IDHS) 2012. This research focuses on the characteristic of women of using the contraceptive methods. Classification results evaluated using Area Under Curve (AUC) values. The higher the AUC value, the better. Based on AUC values, the classification of the contraceptive method using stepwise method (58.66%) is better than the logistic regression model (57.39%) and CATPCA (57.39%). Evaluation of the results of logistic regression using sensitivity, shows the opposite where CATPCA method (99.79%) is better than logistic regression method (92.43%) and stepwise (92.05%). Therefore in this study focuses on major class classification (using a contraceptive method), then the selected model is CATPCA because it can raise the level of the major class model accuracy.

QSPR models for half-wave reduction potential of steroids: a comparative study between feature selection and feature extraction from subsets of or entire set of descriptors.

PubMed

Hemmateenejad, Bahram; Yazdani, Mahdieh

2009-02-16

Steroids are widely distributed in nature and are found in plants, animals, and fungi in abundance. A data set consists of a diverse set of steroids have been used to develop quantitative structure-electrochemistry relationship (QSER) models for their half-wave reduction potential. Modeling was established by means of multiple linear regression (MLR) and principle component regression (PCR) analyses. In MLR analysis, the QSPR models were constructed by first grouping descriptors and then stepwise selection of variables from each group (MLR1) and stepwise selection of predictor variables from the pool of all calculated descriptors (MLR2). Similar procedure was used in PCR analysis so that the principal components (or features) were extracted from different group of descriptors (PCR1) and from entire set of descriptors (PCR2). The resulted models were evaluated using cross-validation, chance correlation, application to prediction reduction potential of some test samples and accessing applicability domain. Both MLR approaches represented accurate results however the QSPR model found by MLR1 was statistically more significant. PCR1 approach produced a model as accurate as MLR approaches whereas less accurate results were obtained by PCR2 approach. In overall, the correlation coefficients of cross-validation and prediction of the QSPR models resulted from MLR1, MLR2 and PCR1 approaches were higher than 90%, which show the high ability of the models to predict reduction potential of the studied steroids.

Associations between dietary and lifestyle risk factors and colorectal cancer in the Scottish population.

PubMed

Theodoratou, Evropi; Farrington, Susan M; Tenesa, Albert; McNeill, Geraldine; Cetnarskyj, Roseanne; Korakakis, Emmanouil; Din, Farhat V N; Porteous, Mary E; Dunlop, Malcolm G; Campbell, Harry

2014-01-01

Colorectal cancer (CRC) accounts for 9.7% of all cancer cases and for 8% of all cancer-related deaths. Established risk factors include personal or family history of CRC as well as lifestyle and dietary factors. We investigated the relationship between CRC and demographic, lifestyle, food and nutrient risk factors through a case-control study that included 2062 patients and 2776 controls from Scotland. Forward and backward stepwise regression was applied and the stability of the models was assessed in 1000 bootstrap samples. The variables that were automatically selected to be included by the forward or backward stepwise regression and whose selection was verified by bootstrap sampling in the current study were family history, dietary energy, 'high-energy snack foods', eggs, juice, sugar-sweetened beverages and white fish (associated with an increased CRC risk) and NSAIDs, coffee and magnesium (associated with a decreased CRC risk). Application of forward and backward stepwise regression in this CRC study identified some already established as well as some novel potential risk factors. Bootstrap findings suggest that examination of the stability of regression models by bootstrap sampling is useful in the interpretation of study findings. 'High-energy snack foods' and high-energy drinks (including sugar-sweetened beverages and fruit juices) as risk factors for CRC have not been reported previously and merit further investigation as such snacks and beverages are important contributors in European and North American diets.

[Winter wheat yield gap between field blocks based on comparative performance analysis].

PubMed

Chen, Jian; Wang, Zhong-Yi; Li, Liang-Tao; Zhang, Ke-Feng; Yu, Zhen-Rong

2008-09-01

Based on a two-year household survey data, the yield gap of winter wheat in Quzhou County of Hebei Province, China in 2003-2004 was studied through comparative performance analysis (CPA). The results showed that there was a greater yield gap (from 4.2 to 7.9 t x hm(-2)) between field blocks, with a variation coefficient of 0.14. Through stepwise forward linear multiple regression, it was found that the yield model with 8 selected variables could explain 63% variability of winter wheat yield. Among the variables selected, soil salinity, soil fertility, and irrigation water quality were the most important limiting factors, accounting for 52% of the total yield gap. Crop variety was another important limiting factor, accounting for 14%; while planting date, fertilizer type, disease and pest, and water press accounted for 7%, 14%, 10%, and 3%, respectively. Therefore, besides soil and climate conditions, management practices occupied the majority of yield variability in Quzhou County, suggesting that the yield gap could be reduced significantly through optimum field management.

Determination of benzo[a]pyrene in cigarette mainstream smoke by using mid-infrared spectroscopy associated with a novel chemometric algorithm.

PubMed

Zhang, Yan; Zou, Hong-Yan; Shi, Pei; Yang, Qin; Tang, Li-Juan; Jiang, Jian-Hui; Wu, Hai-Long; Yu, Ru-Qin

2016-01-01

Determination of benzo[a]pyrene (BaP) in cigarette smoke can be very important for the tobacco quality control and the assessment of its harm to human health. In this study, mid-infrared spectroscopy (MIR) coupled to chemometric algorithm (DPSO-WPT-PLS), which was based on the wavelet packet transform (WPT), discrete particle swarm optimization algorithm (DPSO) and partial least squares regression (PLS), was used to quantify harmful ingredient benzo[a]pyrene in the cigarette mainstream smoke with promising result. Furthermore, the proposed method provided better performance compared to several other chemometric models, i.e., PLS, radial basis function-based PLS (RBF-PLS), PLS with stepwise regression variable selection (Stepwise-PLS) as well as WPT-PLS with informative wavelet coefficients selected by correlation coefficient test (rtest-WPT-PLS). It can be expected that the proposed strategy could become a new effective, rapid quantitative analysis technique in analyzing the harmful ingredient BaP in cigarette mainstream smoke. Copyright © 2015 Elsevier B.V. All rights reserved.

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.

Complex interaction between symptoms, social factors, and gender in social functioning in a community-dwelling sample of schizophrenia.

PubMed

Vila-Rodriguez, F; Ochoa, S; Autonell, J; Usall, J; Haro, J M

2011-12-01

Social functioning (SF) is the ultimate target aimed in treatment plans in schizophrenia, thus it is critical to know what are the factors that determine SF. Gender is a well-established variable influencing SF, yet it is not known how social variables and symptoms interact in schizophrenia patients. Furthermore, it remains unclear whether the interaction between social variables and symptoms is different in men compared to women. Our aim is to test whether social variables are better predictors of SF in community-dwelled individuals with schizophrenia, and whether men and women differ in how symptoms and social variables interact to impact SF. Community-dwelling individuals with schizophrenia (N = 231) were randomly selected from a register. Participants were assessed with symptom measures (PANSS), performance-based social scale (LSP), objective social and demographic variables. Stratification by gender and stepwise multivariate regression analyses by gender were used to find the best-fitting models that predict SF in both gender. Men had poorer SF than women in spite of showing similar symptom scores. On stepwise regression analyses, gender was the main variable explaining SF, with a significant contribution by disorganized and excitatory symptoms. Age of onset made a less marked, yet significant, contribution to explain SF. When the sample was stratified by gender, disorganized symptoms and 'Income' variable entered the model and accounted for a 30.8% of the SF variance in women. On the other hand, positive and disorganized symptoms entered the model and accounted for a 36.1% of the SF variance in men. Community-dwelling men and women with schizophrenia differ in the constellation of variables associated with SF. Symptom scores still account for most of the variance in SF in both genders.

Impact of statistical learning methods on the predictive power of multivariate normal tissue complication probability models.

PubMed

Xu, Cheng-Jian; van der Schaaf, Arjen; Schilstra, Cornelis; Langendijk, Johannes A; van't Veld, Aart A

2012-03-15

To study the impact of different statistical learning methods on the prediction performance of multivariate normal tissue complication probability (NTCP) models. In this study, three learning methods, stepwise selection, least absolute shrinkage and selection operator (LASSO), and Bayesian model averaging (BMA), were used to build NTCP models of xerostomia following radiotherapy treatment for head and neck cancer. Performance of each learning method was evaluated by a repeated cross-validation scheme in order to obtain a fair comparison among methods. It was found that the LASSO and BMA methods produced models with significantly better predictive power than that of the stepwise selection method. Furthermore, the LASSO method yields an easily interpretable model as the stepwise method does, in contrast to the less intuitive BMA method. The commonly used stepwise selection method, which is simple to execute, may be insufficient for NTCP modeling. The LASSO method is recommended. Copyright © 2012 Elsevier Inc. All rights reserved.

Intractable Ménière's disease. Modelling of the treatment by means of statistical analysis.

PubMed

Sanchez-Ferrandiz, Noelia; Fernandez-Gonzalez, Secundino; Guillen-Grima, Francisco; Perez-Fernandez, Nicolas

2010-08-01

To evaluate the value of different variables of the clinical history, auditory and vestibular tests and handicap measurements to define intractable or disabling Ménière's disease. This is a prospective study with 212 patients of which 155 were treated with intratympanic gentamicin and considered to be suffering a medically intractable Ménière's disease. Age and sex adjustments were performed with the 11 variables selected. Discriminant analysis was performed either using the aforementioned variables or following the stepwise method. Different variables needed to be sex and/or age adjusted and both data were included in the discriminant function. Two different mathematical formulas were obtained and four models were analyzed. With the model selected, diagnostic accuracy is 77.7%, sensitivity is 94.9% and specificity is 52.8%. After discriminant analysis we found that the most informative variables were the number of vertigo spells, the speech discrimination score, the time constant of the VOR and a measure of handicap, the "dizziness index". Copyright 2009 Elsevier Ireland Ltd. All rights reserved.

Variability-aware compact modeling and statistical circuit validation on SRAM test array

NASA Astrophysics Data System (ADS)

Qiao, Ying; Spanos, Costas J.

2016-03-01

Variability modeling at the compact transistor model level can enable statistically optimized designs in view of limitations imposed by the fabrication technology. In this work we propose a variability-aware compact model characterization methodology based on stepwise parameter selection. Transistor I-V measurements are obtained from bit transistor accessible SRAM test array fabricated using a collaborating foundry's 28nm FDSOI technology. Our in-house customized Monte Carlo simulation bench can incorporate these statistical compact models; and simulation results on SRAM writability performance are very close to measurements in distribution estimation. Our proposed statistical compact model parameter extraction methodology also has the potential of predicting non-Gaussian behavior in statistical circuit performances through mixtures of Gaussian distributions.

Comparison of statistical methods for detection of serum lipid biomarkers for mesothelioma and asbestos exposure.

PubMed

Xu, Rengyi; Mesaros, Clementina; Weng, Liwei; Snyder, Nathaniel W; Vachani, Anil; Blair, Ian A; Hwang, Wei-Ting

2017-07-01

We compared three statistical methods in selecting a panel of serum lipid biomarkers for mesothelioma and asbestos exposure. Serum samples from mesothelioma, asbestos-exposed subjects and controls (40 per group) were analyzed. Three variable selection methods were considered: top-ranked predictors from univariate model, stepwise and least absolute shrinkage and selection operator. Crossed-validated area under the receiver operating characteristic curve was used to compare the prediction performance. Lipids with high crossed-validated area under the curve were identified. Lipid with mass-to-charge ratio of 372.31 was selected by all three methods comparing mesothelioma versus control. Lipids with mass-to-charge ratio of 1464.80 and 329.21 were selected by two models for asbestos exposure versus control. Different methods selected a similar set of serum lipids. Combining candidate biomarkers can improve prediction.

Testing Different Model Building Procedures Using Multiple Regression.

ERIC Educational Resources Information Center

Thayer, Jerome D.

The stepwise regression method of selecting predictors for computer assisted multiple regression analysis was compared with forward, backward, and best subsets regression, using 16 data sets. The results indicated the stepwise method was preferred because of its practical nature, when the models chosen by different selection methods were similar…

Stepwise and stagewise approaches for spatial cluster detection

PubMed Central

Xu, Jiale

2016-01-01

Spatial cluster detection is an important tool in many areas such as sociology, botany and public health. Previous work has mostly taken either hypothesis testing framework or Bayesian framework. In this paper, we propose a few approaches under a frequentist variable selection framework for spatial cluster detection. The forward stepwise methods search for multiple clusters by iteratively adding currently most likely cluster while adjusting for the effects of previously identified clusters. The stagewise methods also consist of a series of steps, but with tiny step size in each iteration. We study the features and performances of our proposed methods using simulations on idealized grids or real geographic area. From the simulations, we compare the performance of the proposed methods in terms of estimation accuracy and power of detections. These methods are applied to the the well-known New York leukemia data as well as Indiana poverty data. PMID:27246273

Stepwise and stagewise approaches for spatial cluster detection.

PubMed

Xu, Jiale; Gangnon, Ronald E

2016-05-01

Spatial cluster detection is an important tool in many areas such as sociology, botany and public health. Previous work has mostly taken either a hypothesis testing framework or a Bayesian framework. In this paper, we propose a few approaches under a frequentist variable selection framework for spatial cluster detection. The forward stepwise methods search for multiple clusters by iteratively adding currently most likely cluster while adjusting for the effects of previously identified clusters. The stagewise methods also consist of a series of steps, but with a tiny step size in each iteration. We study the features and performances of our proposed methods using simulations on idealized grids or real geographic areas. From the simulations, we compare the performance of the proposed methods in terms of estimation accuracy and power. These methods are applied to the the well-known New York leukemia data as well as Indiana poverty data. Copyright © 2016 Elsevier Ltd. All rights reserved.

Determination of variables in the prediction of strontium distribution coefficients for selected sediments

USGS Publications Warehouse

Pace, M.N.; Rosentreter, J.J.; Bartholomay, R.C.

2001-01-01

Idaho State University and the US Geological Survey, in cooperation with the US Department of Energy, conducted a study to determine and evaluate strontium distribution coefficients (Kds) of subsurface materials at the Idaho National Engineering and Environmental Laboratory (INEEL). The Kds were determined to aid in assessing the variability of strontium Kds and their effects on chemical transport of strontium-90 in the Snake River Plain aquifer system. Data from batch experiments done to determine strontium Kds of five sediment-infill samples and six standard reference material samples were analyzed by using multiple linear regression analysis and the stepwise variable-selection method in the statistical program, Statistical Product and Service Solutions, to derive an equation of variables that can be used to predict strontium Kds of sediment-infill samples. The sediment-infill samples were from basalt vesicles and fractures from a selected core at the INEEL; strontium Kds ranged from ???201 to 356 ml g-1. The standard material samples consisted of clay minerals and calcite. The statistical analyses of the batch-experiment results showed that the amount of strontium in the initial solution, the amount of manganese oxide in the sample material, and the amount of potassium in the initial solution are the most important variables in predicting strontium Kds of sediment-infill samples.

Determination of biodiesel content in biodiesel/diesel blends using NIR and visible spectroscopy with variable selection.

PubMed

Fernandes, David Douglas Sousa; Gomes, Adriano A; Costa, Gean Bezerra da; Silva, Gildo William B da; Véras, Germano

2011-12-15

This work is concerned of evaluate the use of visible and near-infrared (NIR) range, separately and combined, to determine the biodiesel content in biodiesel/diesel blends using Multiple Linear Regression (MLR) and variable selection by Successive Projections Algorithm (SPA). Full spectrum models employing Partial Least Squares (PLS) and variables selection by Stepwise (SW) regression coupled with Multiple Linear Regression (MLR) and PLS models also with variable selection by Jack-Knife (Jk) were compared the proposed methodology. Several preprocessing were evaluated, being chosen derivative Savitzky-Golay with second-order polynomial and 17-point window for NIR and visible-NIR range, with offset correction. A total of 100 blends with biodiesel content between 5 and 50% (v/v) prepared starting from ten sample of biodiesel. In the NIR and visible region the best model was the SPA-MLR using only two and eight wavelengths with RMSEP of 0.6439% (v/v) and 0.5741 respectively, while in the visible-NIR region the best model was the SW-MLR using five wavelengths and RMSEP of 0.9533% (v/v). Results indicate that both spectral ranges evaluated showed potential for developing a rapid and nondestructive method to quantify biodiesel in blends with mineral diesel. Finally, one can still mention that the improvement in terms of prediction error obtained with the procedure for variables selection was significant. Copyright © 2011 Elsevier B.V. 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.

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.

Method of selective reduction of polyhalosilanes with alkyltin hydrides

DOEpatents

Sharp, Kenneth G.; D'Errico, John J.

1989-01-01

The invention relates to the selective and stepwise reduction of polyhalosilanes by reacting at room temperature or below with alkyltin hydrides without the use of free radical intermediates. Alkyltin hydrides selectively and stepwise reduce the Si--Br, Si--Cl, or Si--I bonds while leaving intact any Si--F bonds. When two or more different halogens are present on the polyhalosilane, the halogen with the highest atomic weight is preferentially reduced.

MULGRES: a computer program for stepwise multiple regression analysis

Treesearch

A. Jeff Martin

1971-01-01

MULGRES is a computer program source deck that is designed for multiple regression analysis employing the technique of stepwise deletion in the search for most significant variables. The features of the program, along with inputs and outputs, are briefly described, with a note on machine compatibility.

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.

A comparison of logistic regression analysis and an artificial neural network using the BI-RADS lexicon for ultrasonography in conjunction with introbserver variability.

PubMed

Kim, Sun Mi; Han, Heon; Park, Jeong Mi; Choi, Yoon Jung; Yoon, Hoi Soo; Sohn, Jung Hee; Baek, Moon Hee; Kim, Yoon Nam; Chae, Young Moon; June, Jeon Jong; Lee, Jiwon; Jeon, Yong Hwan

2012-10-01

To determine which Breast Imaging Reporting and Data System (BI-RADS) descriptors for ultrasound are predictors for breast cancer using logistic regression (LR) analysis in conjunction with interobserver variability between breast radiologists, and to compare the performance of artificial neural network (ANN) and LR models in differentiation of benign and malignant breast masses. Five breast radiologists retrospectively reviewed 140 breast masses and described each lesion using BI-RADS lexicon and categorized final assessments. Interobserver agreements between the observers were measured by kappa statistics. The radiologists' responses for BI-RADS were pooled. The data were divided randomly into train (n = 70) and test sets (n = 70). Using train set, optimal independent variables were determined by using LR analysis with forward stepwise selection. The LR and ANN models were constructed with the optimal independent variables and the biopsy results as dependent variable. Performances of the models and radiologists were evaluated on the test set using receiver-operating characteristic (ROC) analysis. Among BI-RADS descriptors, margin and boundary were determined as the predictors according to stepwise LR showing moderate interobserver agreement. Area under the ROC curves (AUC) for both of LR and ANN were 0.87 (95% CI, 0.77-0.94). AUCs for the five radiologists ranged 0.79-0.91. There was no significant difference in AUC values among the LR, ANN, and radiologists (p > 0.05). Margin and boundary were found as statistically significant predictors with good interobserver agreement. Use of the LR and ANN showed similar performance to that of the radiologists for differentiation of benign and malignant breast masses.

Performance Variability as a Predictor of Response to Aphasia Treatment.

PubMed

Duncan, E Susan; Schmah, Tanya; Small, Steven L

2016-10-01

Performance variability in individuals with aphasia is typically regarded as a nuisance factor complicating assessment and treatment. We present the alternative hypothesis that intraindividual variability represents a fundamental characteristic of an individual's functioning and an important biomarker for therapeutic selection and prognosis. A total of 19 individuals with chronic aphasia participated in a 6-week trial of imitation-based speech therapy. We assessed improvement both on overall language functioning and repetition ability. Furthermore, we determined which pretreatment variables best predicted improvement on the repetition test. Significant gains were made on the Western Aphasia Battery-Revised (WAB) Aphasia Quotient, Cortical Quotient, and 2 subtests as well as on a separate repetition test. Using stepwise regression, we found that pretreatment intraindividual variability was the only predictor of improvement in performance on the repetition test, with greater pretreatment variability predicting greater improvement. Furthermore, the degree of reduction in this variability over the course of treatment was positively correlated with the degree of improvement. Intraindividual variability may be indicative of potential for improvement on a given task, with more uniform performance suggesting functioning at or near peak potential. © The Author(s) 2016.

Joint effect of unlinked genotypes: application to type 2 diabetes in the EPIC-Potsdam case-cohort study.

PubMed

Knüppel, Sven; Meidtner, Karina; Arregui, Maria; Holzhütter, Hermann-Georg; Boeing, Heiner

2015-07-01

Analyzing multiple single nucleotide polymorphisms (SNPs) is a promising approach to finding genetic effects beyond single-locus associations. We proposed the use of multilocus stepwise regression (MSR) to screen for allele combinations as a method to model joint effects, and compared the results with the often used genetic risk score (GRS), conventional stepwise selection, and the shrinkage method LASSO. In contrast to MSR, the GRS, conventional stepwise selection, and LASSO model each genotype by the risk allele doses. We reanalyzed 20 unlinked SNPs related to type 2 diabetes (T2D) in the EPIC-Potsdam case-cohort study (760 cases, 2193 noncases). No SNP-SNP interactions and no nonlinear effects were found. Two SNP combinations selected by MSR (Nagelkerke's R² = 0.050 and 0.048) included eight SNPs with mean allele combination frequency of 2%. GRS and stepwise selection selected nearly the same SNP combinations consisting of 12 and 13 SNPs (Nagelkerke's R² ranged from 0.020 to 0.029). LASSO showed similar results. The MSR method showed the best model fit measured by Nagelkerke's R² suggesting that further improvement may render this method a useful tool in genetic research. However, our comparison suggests that the GRS is a simple way to model genetic effects since it does not consider linkage, SNP-SNP interactions, and no non-linear effects. © 2015 John Wiley & Sons Ltd/University College London.

Probabilistic precipitation and temperature downscaling of the Twentieth Century Reanalysis over France

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

Caillouet, Laurie; Vidal, Jean -Philippe; Sauquet, Eric

This work proposes a daily high-resolution probabilistic reconstruction of precipitation and temperature fields in France over the 1871–2012 period built on the NOAA Twentieth Century global extended atmospheric reanalysis (20CR). The objective is to fill in the spatial and temporal data gaps in surface observations in order to improve our knowledge on the local-scale climate variability from the late nineteenth century onwards. The SANDHY (Stepwise ANalogue Downscaling method for HYdrology) statistical downscaling method, initially developed for quantitative precipitation forecast, is used here to bridge the scale gap between large-scale 20CR predictors and local-scale predictands from the Safran high-resolution near-surface reanalysis,more » available from 1958 onwards only. SANDHY provides a daily ensemble of 125 analogue dates over the 1871–2012 period for 608 climatically homogeneous zones paving France. Large precipitation biases in intermediary seasons are shown to occur in regions with high seasonal asymmetry like the Mediterranean. Moreover, winter and summer temperatures are respectively over- and under-estimated over the whole of France. Two analogue subselection methods are therefore developed with the aim of keeping the structure of the SANDHY method unchanged while reducing those seasonal biases. The calendar selection keeps the analogues closest to the target calendar day. The stepwise selection applies two new analogy steps based on similarity of the sea surface temperature (SST) and the large-scale 2 m temperature ( T). Comparisons to the Safran reanalysis over 1959–2007 and to homogenized series over the whole twentieth century show that biases in the interannual cycle of precipitation and temperature are reduced with both methods. The stepwise subselection moreover leads to a large improvement of interannual correlation and reduction of errors in seasonal temperature time series. When the calendar subselection is an easily applicable method suitable in a quantitative precipitation forecast context, the stepwise subselection method allows for potential season shifts and SST trends and is therefore better suited for climate reconstructions and climate change studies. Furthermore, the probabilistic downscaling of 20CR over the period 1871–2012 with the SANDHY probabilistic downscaling method combined with the stepwise subselection thus constitutes a perfect framework for assessing the recent observed meteorological events but also future events projected by climate change impact studies and putting them in a historical perspective.« less

Probabilistic precipitation and temperature downscaling of the Twentieth Century Reanalysis over France

DOE PAGES

Caillouet, Laurie; Vidal, Jean -Philippe; Sauquet, Eric; ...

2016-03-16

This work proposes a daily high-resolution probabilistic reconstruction of precipitation and temperature fields in France over the 1871–2012 period built on the NOAA Twentieth Century global extended atmospheric reanalysis (20CR). The objective is to fill in the spatial and temporal data gaps in surface observations in order to improve our knowledge on the local-scale climate variability from the late nineteenth century onwards. The SANDHY (Stepwise ANalogue Downscaling method for HYdrology) statistical downscaling method, initially developed for quantitative precipitation forecast, is used here to bridge the scale gap between large-scale 20CR predictors and local-scale predictands from the Safran high-resolution near-surface reanalysis,more » available from 1958 onwards only. SANDHY provides a daily ensemble of 125 analogue dates over the 1871–2012 period for 608 climatically homogeneous zones paving France. Large precipitation biases in intermediary seasons are shown to occur in regions with high seasonal asymmetry like the Mediterranean. Moreover, winter and summer temperatures are respectively over- and under-estimated over the whole of France. Two analogue subselection methods are therefore developed with the aim of keeping the structure of the SANDHY method unchanged while reducing those seasonal biases. The calendar selection keeps the analogues closest to the target calendar day. The stepwise selection applies two new analogy steps based on similarity of the sea surface temperature (SST) and the large-scale 2 m temperature ( T). Comparisons to the Safran reanalysis over 1959–2007 and to homogenized series over the whole twentieth century show that biases in the interannual cycle of precipitation and temperature are reduced with both methods. The stepwise subselection moreover leads to a large improvement of interannual correlation and reduction of errors in seasonal temperature time series. When the calendar subselection is an easily applicable method suitable in a quantitative precipitation forecast context, the stepwise subselection method allows for potential season shifts and SST trends and is therefore better suited for climate reconstructions and climate change studies. Furthermore, the probabilistic downscaling of 20CR over the period 1871–2012 with the SANDHY probabilistic downscaling method combined with the stepwise subselection thus constitutes a perfect framework for assessing the recent observed meteorological events but also future events projected by climate change impact studies and putting them in a historical perspective.« less

[Associated factors in newborns with intrauterine growth retardation].

PubMed

Thompson-Chagoyán, Oscar C; Vega-Franco, Leopoldo

2008-01-01

To identify the risk factors implicated in the intrauterine growth retardation (IUGR) of neonates born in a social security institution. Case controls design study in 376 neonates: 188 with IUGR (weight < 10 percentile) and 188 without IUGR. When they born, information about 30 variables of risk for IUGR were obtained from mothers. Risk analysis and logistical regression (stepwise) were used. Odds ratios were significant for 12 of the variables. The model obtains by stepwise regression included: weight gain at pregnancy, prenatal care attendance, toxemia, chocolate ingestion, father's weight, and the environmental house. Must of the variables included in the model are related to socioeconomic disadvantages related to the risk of RCIU in the population.

Quantifying Parkinson's disease finger-tapping severity by extracting and synthesizing finger motion properties.

PubMed

Sano, Yuko; Kandori, Akihiko; Shima, Keisuke; Yamaguchi, Yuki; Tsuji, Toshio; Noda, Masafumi; Higashikawa, Fumiko; Yokoe, Masaru; Sakoda, Saburo

2016-06-01

We propose a novel index of Parkinson's disease (PD) finger-tapping severity, called "PDFTsi," for quantifying the severity of symptoms related to the finger tapping of PD patients with high accuracy. To validate the efficacy of PDFTsi, the finger-tapping movements of normal controls and PD patients were measured by using magnetic sensors, and 21 characteristics were extracted from the finger-tapping waveforms. To distinguish motor deterioration due to PD from that due to aging, the aging effect on finger tapping was removed from these characteristics. Principal component analysis (PCA) was applied to the age-normalized characteristics, and principal components that represented the motion properties of finger tapping were calculated. Multiple linear regression (MLR) with stepwise variable selection was applied to the principal components, and PDFTsi was calculated. The calculated PDFTsi indicates that PDFTsi has a high estimation ability, namely a mean square error of 0.45. The estimation ability of PDFTsi is higher than that of the alternative method, MLR with stepwise regression selection without PCA, namely a mean square error of 1.30. This result suggests that PDFTsi can quantify PD finger-tapping severity accurately. Furthermore, the result of interpreting a model for calculating PDFTsi indicated that motion wideness and rhythm disorder are important for estimating PD finger-tapping severity.

Improving data analysis in herpetology: Using Akaike's information criterion (AIC) to assess the strength of biological hypotheses

USGS Publications Warehouse

Mazerolle, M.J.

2006-01-01

In ecology, researchers frequently use observational studies to explain a given pattern, such as the number of individuals in a habitat patch, with a large number of explanatory (i.e., independent) variables. To elucidate such relationships, ecologists have long relied on hypothesis testing to include or exclude variables in regression models, although the conclusions often depend on the approach used (e.g., forward, backward, stepwise selection). Though better tools have surfaced in the mid 1970's, they are still underutilized in certain fields, particularly in herpetology. This is the case of the Akaike information criterion (AIC) which is remarkably superior in model selection (i.e., variable selection) than hypothesis-based approaches. It is simple to compute and easy to understand, but more importantly, for a given data set, it provides a measure of the strength of evidence for each model that represents a plausible biological hypothesis relative to the entire set of models considered. Using this approach, one can then compute a weighted average of the estimate and standard error for any given variable of interest across all the models considered. This procedure, termed model-averaging or multimodel inference, yields precise and robust estimates. In this paper, I illustrate the use of the AIC in model selection and inference, as well as the interpretation of results analysed in this framework with two real herpetological data sets. The AIC and measures derived from it is should be routinely adopted by herpetologists. ?? Koninklijke Brill NV 2006.

Application of a single-objective, hybrid genetic algorithm approach to pharmacokinetic model building.

PubMed

Sherer, Eric A; Sale, Mark E; Pollock, Bruce G; Belani, Chandra P; Egorin, Merrill J; Ivy, Percy S; Lieberman, Jeffrey A; Manuck, Stephen B; Marder, Stephen R; Muldoon, Matthew F; Scher, Howard I; Solit, David B; Bies, Robert R

2012-08-01

A limitation in traditional stepwise population pharmacokinetic model building is the difficulty in handling interactions between model components. To address this issue, a method was previously introduced which couples NONMEM parameter estimation and model fitness evaluation to a single-objective, hybrid genetic algorithm for global optimization of the model structure. In this study, the generalizability of this approach for pharmacokinetic model building is evaluated by comparing (1) correct and spurious covariate relationships in a simulated dataset resulting from automated stepwise covariate modeling, Lasso methods, and single-objective hybrid genetic algorithm approaches to covariate identification and (2) information criteria values, model structures, convergence, and model parameter values resulting from manual stepwise versus single-objective, hybrid genetic algorithm approaches to model building for seven compounds. Both manual stepwise and single-objective, hybrid genetic algorithm approaches to model building were applied, blinded to the results of the other approach, for selection of the compartment structure as well as inclusion and model form of inter-individual and inter-occasion variability, residual error, and covariates from a common set of model options. For the simulated dataset, stepwise covariate modeling identified three of four true covariates and two spurious covariates; Lasso identified two of four true and 0 spurious covariates; and the single-objective, hybrid genetic algorithm identified three of four true covariates and one spurious covariate. For the clinical datasets, the Akaike information criterion was a median of 22.3 points lower (range of 470.5 point decrease to 0.1 point decrease) for the best single-objective hybrid genetic-algorithm candidate model versus the final manual stepwise model: the Akaike information criterion was lower by greater than 10 points for four compounds and differed by less than 10 points for three compounds. The root mean squared error and absolute mean prediction error of the best single-objective hybrid genetic algorithm candidates were a median of 0.2 points higher (range of 38.9 point decrease to 27.3 point increase) and 0.02 points lower (range of 0.98 point decrease to 0.74 point increase), respectively, than that of the final stepwise models. In addition, the best single-objective, hybrid genetic algorithm candidate models had successful convergence and covariance steps for each compound, used the same compartment structure as the manual stepwise approach for 6 of 7 (86 %) compounds, and identified 54 % (7 of 13) of covariates included by the manual stepwise approach and 16 covariate relationships not included by manual stepwise models. The model parameter values between the final manual stepwise and best single-objective, hybrid genetic algorithm models differed by a median of 26.7 % (q₁ = 4.9 % and q₃ = 57.1 %). Finally, the single-objective, hybrid genetic algorithm approach was able to identify models capable of estimating absorption rate parameters for four compounds that the manual stepwise approach did not identify. The single-objective, hybrid genetic algorithm represents a general pharmacokinetic model building methodology whose ability to rapidly search the feasible solution space leads to nearly equivalent or superior model fits to pharmacokinetic data.

Updated Value of Service Reliability Estimates for Electric Utility Customers in the United States

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

Sullivan, Michael; Schellenberg, Josh; Blundell, Marshall

2015-01-01

This report updates the 2009 meta-analysis that provides estimates of the value of service reliability for electricity customers in the United States (U.S.). The meta-dataset now includes 34 different datasets from surveys fielded by 10 different utility companies between 1989 and 2012. Because these studies used nearly identical interruption cost estimation or willingness-to-pay/accept methods, it was possible to integrate their results into a single meta-dataset describing the value of electric service reliability observed in all of them. Once the datasets from the various studies were combined, a two-part regression model was used to estimate customer damage functions that can bemore » generally applied to calculate customer interruption costs per event by season, time of day, day of week, and geographical regions within the U.S. for industrial, commercial, and residential customers. This report focuses on the backwards stepwise selection process that was used to develop the final revised model for all customer classes. Across customer classes, the revised customer interruption cost model has improved significantly because it incorporates more data and does not include the many extraneous variables that were in the original specification from the 2009 meta-analysis. The backwards stepwise selection process led to a more parsimonious model that only included key variables, while still achieving comparable out-of-sample predictive performance. In turn, users of interruption cost estimation tools such as the Interruption Cost Estimate (ICE) Calculator will have less customer characteristics information to provide and the associated inputs page will be far less cumbersome. The upcoming new version of the ICE Calculator is anticipated to be released in 2015.« less

Variable selection in near-infrared spectroscopy: benchmarking of feature selection methods on biodiesel data.

PubMed

Balabin, Roman M; Smirnov, Sergey V

2011-04-29

During the past several years, near-infrared (near-IR/NIR) spectroscopy has increasingly been adopted as an analytical tool in various fields from petroleum to biomedical sectors. The NIR spectrum (above 4000 cm(-1)) of a sample is typically measured by modern instruments at a few hundred of wavelengths. Recently, considerable effort has been directed towards developing procedures to identify variables (wavelengths) that contribute useful information. Variable selection (VS) or feature selection, also called frequency selection or wavelength selection, is a critical step in data analysis for vibrational spectroscopy (infrared, Raman, or NIRS). In this paper, we compare the performance of 16 different feature selection methods for the prediction of properties of biodiesel fuel, including density, viscosity, methanol content, and water concentration. The feature selection algorithms tested include stepwise multiple linear regression (MLR-step), interval partial least squares regression (iPLS), backward iPLS (BiPLS), forward iPLS (FiPLS), moving window partial least squares regression (MWPLS), (modified) changeable size moving window partial least squares (CSMWPLS/MCSMWPLSR), searching combination moving window partial least squares (SCMWPLS), successive projections algorithm (SPA), uninformative variable elimination (UVE, including UVE-SPA), simulated annealing (SA), back-propagation artificial neural networks (BP-ANN), Kohonen artificial neural network (K-ANN), and genetic algorithms (GAs, including GA-iPLS). Two linear techniques for calibration model building, namely multiple linear regression (MLR) and partial least squares regression/projection to latent structures (PLS/PLSR), are used for the evaluation of biofuel properties. A comparison with a non-linear calibration model, artificial neural networks (ANN-MLP), is also provided. Discussion of gasoline, ethanol-gasoline (bioethanol), and diesel fuel data is presented. The results of other spectroscopic techniques application, such as Raman, ultraviolet-visible (UV-vis), or nuclear magnetic resonance (NMR) spectroscopies, can be greatly improved by an appropriate feature selection choice. Copyright © 2011 Elsevier B.V. All rights reserved.

Nowcasting of Low-Visibility Procedure States with Ordered Logistic Regression at Vienna International Airport

NASA Astrophysics Data System (ADS)

Kneringer, Philipp; Dietz, Sebastian; Mayr, Georg J.; Zeileis, Achim

2017-04-01

Low-visibility conditions have a large impact on aviation safety and economic efficiency of airports and airlines. To support decision makers, we develop a statistical probabilistic nowcasting tool for the occurrence of capacity-reducing operations related to low visibility. The probabilities of four different low visibility classes are predicted with an ordered logistic regression model based on time series of meteorological point measurements. Potential predictor variables for the statistical models are visibility, humidity, temperature and wind measurements at several measurement sites. A stepwise variable selection method indicates that visibility and humidity measurements are the most important model inputs. The forecasts are tested with a 30 minute forecast interval up to two hours, which is a sufficient time span for tactical planning at Vienna Airport. The ordered logistic regression models outperform persistence and are competitive with human forecasters.

A multiple linear regression analysis of hot corrosion attack on a series of nickel base turbine alloys

NASA Technical Reports Server (NTRS)

Barrett, C. A.

1985-01-01

Multiple linear regression analysis was used to determine an equation for estimating hot corrosion attack for a series of Ni base cast turbine alloys. The U transform (i.e., 1/sin (% A/100) to the 1/2) was shown to give the best estimate of the dependent variable, y. A complete second degree equation is described for the centered" weight chemistries for the elements Cr, Al, Ti, Mo, W, Cb, Ta, and Co. In addition linear terms for the minor elements C, B, and Zr were added for a basic 47 term equation. The best reduced equation was determined by the stepwise selection method with essentially 13 terms. The Cr term was found to be the most important accounting for 60 percent of the explained variability hot corrosion attack.

Simultaneous use of geological, geophysical, and LANDSAT digital data in uranium exploration. [Libya

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

Missallati, A.; Prelat, A.E.; Lyon, R.J.P.

1979-08-01

The simultaneous use of geological, geophysical and Landsat data in uranium exploration in southern Libya is reported. The values of 43 geological, geophysical and digital data variables, including age and type of rock, geological contacts, aeroradio-metric and aeromagnetic values and brightness ratios, were used as input into a geomathematical model. Stepwise discriminant analysis was used to select grid cells most favorable for detailed mineral exploration and to evaluate the significance of each variable in discriminating between the anomalous (radioactive) and nonanomalous (nonradioactive) areas. It is found that the geological contact relationships, Landsat Bands 6 and Band 7/4 ratio values weremore » most useful in the discrimination. The procedure was found to be statistically and geologically reliable, and applicable to similar regions using only the most important geological and Landsat data.« less

Application of the modified chi-square ratio statistic in a stepwise procedure for cascade impactor equivalence testing.

PubMed

Weber, Benjamin; Lee, Sau L; Delvadia, Renishkumar; Lionberger, Robert; Li, Bing V; Tsong, Yi; Hochhaus, Guenther

2015-03-01

Equivalence testing of aerodynamic particle size distribution (APSD) through multi-stage cascade impactors (CIs) is important for establishing bioequivalence of orally inhaled drug products. Recent work demonstrated that the median of the modified chi-square ratio statistic (MmCSRS) is a promising metric for APSD equivalence testing of test (T) and reference (R) products as it can be applied to a reduced number of CI sites that are more relevant for lung deposition. This metric is also less sensitive to the increased variability often observed for low-deposition sites. A method to establish critical values for the MmCSRS is described here. This method considers the variability of the R product by employing a reference variance scaling approach that allows definition of critical values as a function of the observed variability of the R product. A stepwise CI equivalence test is proposed that integrates the MmCSRS as a method for comparing the relative shapes of CI profiles and incorporates statistical tests for assessing equivalence of single actuation content and impactor sized mass. This stepwise CI equivalence test was applied to 55 published CI profile scenarios, which were classified as equivalent or inequivalent by members of the Product Quality Research Institute working group (PQRI WG). The results of the stepwise CI equivalence test using a 25% difference in MmCSRS as an acceptance criterion provided the best matching with those of the PQRI WG as decisions of both methods agreed in 75% of the 55 CI profile scenarios.

Hyperspectral remote sensing of plant biochemistry using Bayesian model averaging with variable and band selection

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

Zhao, Kaiguang; Valle, Denis; Popescu, Sorin

2013-05-15

Model specification remains challenging in spectroscopy of plant biochemistry, as exemplified by the availability of various spectral indices or band combinations for estimating the same biochemical. This lack of consensus in model choice across applications argues for a paradigm shift in hyperspectral methods to address model uncertainty and misspecification. We demonstrated one such method using Bayesian model averaging (BMA), which performs variable/band selection and quantifies the relative merits of many candidate models to synthesize a weighted average model with improved predictive performances. The utility of BMA was examined using a portfolio of 27 foliage spectral–chemical datasets representing over 80 speciesmore » across the globe to estimate multiple biochemical properties, including nitrogen, hydrogen, carbon, cellulose, lignin, chlorophyll (a or b), carotenoid, polar and nonpolar extractives, leaf mass per area, and equivalent water thickness. We also compared BMA with partial least squares (PLS) and stepwise multiple regression (SMR). Results showed that all the biochemicals except carotenoid were accurately estimated from hyerspectral data with R2 values > 0.80.« less

A nomogram to predict the survival of stage IIIA-N2 non-small cell lung cancer after surgery.

PubMed

Mao, Qixing; Xia, Wenjie; Dong, Gaochao; Chen, Shuqi; Wang, Anpeng; Jin, Guangfu; Jiang, Feng; Xu, Lin

2018-04-01

Postoperative survival of patients with stage IIIA-N2 non-small cell lung cancer (NSCLC) is highly heterogeneous. Here, we aimed to identify variables associated with postoperative survival and develop a tool for survival prediction. A retrospective review was performed in the Surveillance, Epidemiology, and End Results database from January 2004 to December 2009. Significant variables were selected by use of the backward stepwise method. The nomogram was constructed with multivariable Cox regression. The model's performance was evaluated by concordance index and calibration curve. The model was validated via an independent cohort from the Jiangsu Cancer Hospital Lung Cancer Center. A total of 1809 patients with stage IIIA-N2 NSCLC who underwent surgery were included in the training cohort. Age, sex, grade, histology, tumor size, visceral pleural invasion, positive lymph nodes, lymph nodes examined, and surgery type (lobectomy vs pneumonectomy) were identified as significant prognostic variables using backward stepwise method. A nomogram was developed from the training cohort and validated using an independent Chinese cohort. The concordance index of the model was 0.673 (95% confidence interval, 0.654-0.692) in training cohort and 0.664 in validation cohort (95% confidence interval, 0.614-0.714). The calibration plot showed optimal consistency between nomogram predicted survival and observed survival. Survival analyses demonstrated significant differences between different subgroups stratified by prognostic scores. This nomogram provided the individual survival prediction for patients with stage IIIA-N2 NSCLC after surgery, which might benefit survival counseling for patients and clinicians, clinical trial design and follow-up, as well as postoperative strategy-making. Copyright © 2017 The American Association for Thoracic Surgery. Published by Elsevier Inc. All rights reserved.

Improving the Accuracy of Mapping Urban Vegetation Carbon Density by Combining Shadow Remove, Spectral Unmixing Analysis and Spatial Modeling

NASA Astrophysics Data System (ADS)

Qie, G.; Wang, G.; Wang, M.

2016-12-01

Mixed pixels and shadows due to buildings in urban areas impede accurate estimation and mapping of city vegetation carbon density. In most of previous studies, these factors are often ignored, which thus result in underestimation of city vegetation carbon density. In this study we presented an integrated methodology to improve the accuracy of mapping city vegetation carbon density. Firstly, we applied a linear shadow remove analysis (LSRA) on remotely sensed Landsat 8 images to reduce the shadow effects on carbon estimation. Secondly, we integrated a linear spectral unmixing analysis (LSUA) with a linear stepwise regression (LSR), a logistic model-based stepwise regression (LMSR) and k-Nearest Neighbors (kNN), and utilized and compared the integrated models on shadow-removed images to map vegetation carbon density. This methodology was examined in Shenzhen City of Southeast China. A data set from a total of 175 sample plots measured in 2013 and 2014 was used to train the models. The independent variables statistically significantly contributing to improving the fit of the models to the data and reducing the sum of squared errors were selected from a total of 608 variables derived from different image band combinations and transformations. The vegetation fraction from LSUA was then added into the models as an important independent variable. The estimates obtained were evaluated using a cross-validation method. Our results showed that higher accuracies were obtained from the integrated models compared with the ones using traditional methods which ignore the effects of mixed pixels and shadows. This study indicates that the integrated method has great potential on improving the accuracy of urban vegetation carbon density estimation. Key words: Urban vegetation carbon, shadow, spectral unmixing, spatial modeling, Landsat 8 images

Sex estimation from measurements of the first rib in a contemporary Polish population.

PubMed

Kubicka, Anna Maria; Piontek, Janusz

2016-01-01

The aim of this study was to evaluate the accuracy of sex assessment using measurements of the first rib from computed tomography (CT) to develop a discriminant formula. Four discriminant formulae were derived based on CT imaging of the right first rib of 85 female and 91 male Polish patients of known age and sex. In direct discriminant analysis, the first equation consisted of all first rib variables; the second included measurements of the rib body; the third comprised only two measurements of the sternal end of the first rib. The stepwise method selected the four best variables from all measurements. The discriminant function equation was then tested on a cross-validated group consisting of 23 females and 24 males. The direct discriminant analysis showed that sex assessment was possible in 81.5% of cases in the first group and in 91.5% in the cross-validated group when all variables for the first rib were included. The average accuracy for the original group for rib body and sternal end was 80.9 and 67.9%, respectively. The percentages of correctly assigned individuals for the functions based on the rib body and sternal end in the cross-validated group were 76.6 and 85.0%, respectively. Higher average accuracies were obtained for stepwise discriminant analysis: 83.1% for the original group and 91.2% for the cross-validated group. The exterior edge, anterior-posterior of the sternal end, and depth of the arc were the most reliable parameters. Our results suggest that the first rib is dimorphic and that the described method can be used for sex assessment.

Predicting the demand of physician workforce: an international model based on "crowd behaviors".

PubMed

Tsai, Tsuen-Chiuan; Eliasziw, Misha; Chen, Der-Fang

2012-03-26

Appropriateness of physician workforce greatly influences the quality of healthcare. When facing the crisis of physician shortages, the correction of manpower always takes an extended time period, and both the public and health personnel suffer. To calculate an appropriate number of Physician Density (PD) for a specific country, this study was designed to create a PD prediction model, based on health-related data from many countries. Twelve factors that could possibly impact physicians' demand were chosen, and data of these factors from 130 countries (by reviewing 195) were extracted. Multiple stepwise-linear regression was used to derive the PD prediction model, and a split-sample cross-validation procedure was performed to evaluate the generalizability of the results. Using data from 130 countries, with the consideration of the correlation between variables, and preventing multi-collinearity, seven out of the 12 predictor variables were selected for entry into the stepwise regression procedure. The final model was: PD = (5.014 - 0.128 × proportion under age 15 years + 0.034 × life expectancy)2, with R2 of 80.4%. Using the prediction equation, 70 countries had PDs with "negative discrepancy", while 58 had PDs with "positive discrepancy". This study provided a regression-based PD model to calculate a "norm" number of PD for a specific country. A large PD discrepancy in a country indicates the needs to examine physician's workloads and their well-being, the effectiveness/efficiency of medical care, the promotion of population health and the team resource management.

Biometric parameters in different stages of primary angle closure using low-coherence interferometry.

PubMed

Yazdani, Shahin; Akbarian, Shadi; Pakravan, Mohammad; Doozandeh, Azadeh; Afrouzifar, Mohsen

2015-03-01

To compare ocular biometric parameters using low-coherence interferometry among siblings affected with different degrees of primary angle closure (PAC). In this cross-sectional comparative study, a total of 170 eyes of 86 siblings from 47 families underwent low-coherence interferometry (LenStar 900; Haag-Streit, Koeniz, Switzerland) to determine central corneal thickness, anterior chamber depth (ACD), aqueous depth (AD), lens thickness (LT), vitreous depth, and axial length (AL). Regression coefficients were applied to show the trend of the measured variables in different stages of angle closure. To evaluate the discriminative power of the parameters, receiver operating characteristic curves were used. Best cutoff points were selected based on the Youden index. Sensitivity, specificity, positive and negative predicative values, positive and negative likelihood ratios, and diagnostic accuracy were determined for each variable. All biometric parameters changed significantly from normal eyes to PAC suspects, PAC, and PAC glaucoma; there was a significant stepwise decrease in central corneal thickness, ACD, AD, vitreous depth, and AL, and an increase in LT and LT/AL. Anterior chamber depth and AD had the best diagnostic power for detecting angle closure; best levels of sensitivity and specificity were obtained with cutoff values of 3.11 mm for ACD and 2.57 mm for AD. Biometric parameters measured by low-coherence interferometry demonstrated a significant and stepwise change among eyes affected with various degrees of angle closure. Although the current classification scheme for angle closure is based on anatomical features, it has excellent correlation with biometric parameters.

Multivariate class modeling techniques applied to multielement analysis for the verification of the geographical origin of chili pepper.

PubMed

Naccarato, Attilio; Furia, Emilia; Sindona, Giovanni; Tagarelli, Antonio

2016-09-01

Four class-modeling techniques (soft independent modeling of class analogy (SIMCA), unequal dispersed classes (UNEQ), potential functions (PF), and multivariate range modeling (MRM)) were applied to multielement distribution to build chemometric models able to authenticate chili pepper samples grown in Calabria respect to those grown outside of Calabria. The multivariate techniques were applied by considering both all the variables (32 elements, Al, As, Ba, Ca, Cd, Ce, Co, Cr, Cs, Cu, Dy, Fe, Ga, La, Li, Mg, Mn, Na, Nd, Ni, Pb, Pr, Rb, Sc, Se, Sr, Tl, Tm, V, Y, Yb, Zn) and variables selected by means of stepwise linear discriminant analysis (S-LDA). In the first case, satisfactory and comparable results in terms of CV efficiency are obtained with the use of SIMCA and MRM (82.3 and 83.2% respectively), whereas MRM performs better than SIMCA in terms of forced model efficiency (96.5%). The selection of variables by S-LDA permitted to build models characterized, in general, by a higher efficiency. MRM provided again the best results for CV efficiency (87.7% with an effective balance of sensitivity and specificity) as well as forced model efficiency (96.5%). Copyright © 2016 Elsevier Ltd. All rights reserved.

THE PEDIATRIC RHEUMATOLOGY INTERNATIONAL TRIALS ORGANIZATION PROVISIONAL CRITERIA FOR THE EVALUATION OF RESPONSE TO THERAPY IN JUVENILE DERMATOMYOSITIS

PubMed Central

Ruperto, Nicolino; Pistorio, Angela; Ravelli, Angelo; Rider, Lisa G.; Pilkington, Clarissa; Oliveira, Sheila; Wulffraat, Nico; Espada, Graciela; Garay, Stella; Cuttica, Ruben; Hofer, Michael; Quartier, Pierre; Melo-Gomes, Jose; Reed, Ann M.; Wierzbowska, Malgorzata; Feldman, Brian M.; Harjacek, Miroslav; Huppertz, Hans-Iko; Nielsen, Susan; Flato, Berit; Lahdenne, Pekka; Michels, Harmut; Murray, Kevin J.; Punaro, Lynn; Rennebohm, Robert; Russo, Ricardo; Balogh, Zsolt; Rooney, Madeleine; Pachman, Lauren M.; Wallace, Carol; Hashkes, Philip; Lovell, Daniel J.; Giannini, Edward H.; Martini, Alberto

2010-01-01

Objective To develop a provisional definition for the evaluation of response to therapy in juvenile dermatomyositis (JDM) based on the PRINTO JDM core set of variables. Methods Thirty-seven experienced pediatric rheumatologists from 27 countries, achieved consensus on 128 difficult patient profiles as clinically improved or not improved using a stepwise approach (patients rating, statistical analysis, definition selection). Using the physicians’ consensus ratings as the “gold-standard measure”, chi-square, sensitivity, specificity, false positive and negative rate, area under the ROC, and kappa agreement for candidate definitions of improvement were calculated. Definitions with kappa >0.8 were multiplied with the face validity score to select the top definitions. Results The top definition of improvement was: at least 20% improvement from baseline in 3/6 core set variables with no more than 1 of the remaining worsening by more than 30%, which cannot be muscle strength. The second highest scoring definition was at least 20% improvement from baseline in 3/6 core set variables with no more than 2 of the remaining worsening by more than 25%, which cannot be muscle strength which is definition P1 selected by the IMACS group. The third is similar to the second with the maximum amount of worsening set to 30%. This indicates convergent validity of the process. Conclusion we proposes a provisional data driven definition of improvement that reflects well the consensus rating of experienced clinicians, which incorporates clinically meaningful change in core set variables in a composite endpoint for the evaluation of global response to therapy in JDM. PMID:20583105

Evaluation of interpolation techniques for the creation of gridded daily precipitation (1 × 1 km2); Cyprus, 1980-2010

NASA Astrophysics Data System (ADS)

Camera, Corrado; Bruggeman, Adriana; Hadjinicolaou, Panos; Pashiardis, Stelios; Lange, Manfred A.

2014-01-01

High-resolution gridded daily data sets are essential for natural resource management and the analyses of climate changes and their effects. This study aims to evaluate the performance of 15 simple or complex interpolation techniques in reproducing daily precipitation at a resolution of 1 km2 over topographically complex areas. Methods are tested considering two different sets of observation densities and different rainfall amounts. We used rainfall data that were recorded at 74 and 145 observational stations, respectively, spread over the 5760 km2 of the Republic of Cyprus, in the Eastern Mediterranean. Regression analyses utilizing geographical copredictors and neighboring interpolation techniques were evaluated both in isolation and combined. Linear multiple regression (LMR) and geographically weighted regression methods (GWR) were tested. These included a step-wise selection of covariables, as well as inverse distance weighting (IDW), kriging, and 3D-thin plate splines (TPS). The relative rank of the different techniques changes with different station density and rainfall amounts. Our results indicate that TPS performs well for low station density and large-scale events and also when coupled with regression models. It performs poorly for high station density. The opposite is observed when using IDW. Simple IDW performs best for local events, while a combination of step-wise GWR and IDW proves to be the best method for large-scale events and high station density. This study indicates that the use of step-wise regression with a variable set of geographic parameters can improve the interpolation of large-scale events because it facilitates the representation of local climate dynamics.

Non-targeted 1H NMR fingerprinting and multivariate statistical analyses for the characterisation of the geographical origin of Italian sweet cherries.

PubMed

Longobardi, F; Ventrella, A; Bianco, A; Catucci, L; Cafagna, I; Gallo, V; Mastrorilli, P; Agostiano, A

2013-12-01

In this study, non-targeted (1)H NMR fingerprinting was used in combination with multivariate statistical techniques for the classification of Italian sweet cherries based on their different geographical origins (Emilia Romagna and Puglia). As classification techniques, Soft Independent Modelling of Class Analogy (SIMCA), Partial Least Squares Discriminant Analysis (PLS-DA), and Linear Discriminant Analysis (LDA) were carried out and the results were compared. For LDA, before performing a refined selection of the number/combination of variables, two different strategies for a preliminary reduction of the variable number were tested. The best average recognition and CV prediction abilities (both 100.0%) were obtained for all the LDA models, although PLS-DA also showed remarkable performances (94.6%). All the statistical models were validated by observing the prediction abilities with respect to an external set of cherry samples. The best result (94.9%) was obtained with LDA by performing a best subset selection procedure on a set of 30 principal components previously selected by a stepwise decorrelation. The metabolites that mostly contributed to the classification performances of such LDA model, were found to be malate, glucose, fructose, glutamine and succinate. Copyright © 2013 Elsevier Ltd. All rights reserved.

Relationships between vegetation and terrain variables in southeastern Arizona. Ph.D. Thesis

NASA Technical Reports Server (NTRS)

Mouat, D. A. (Principal Investigator)

1974-01-01

The author has identified the following significant results. Relationships were established between eight terrain variables and plant species and 31 vegetation types. Certain plant species are better than others for differentiating or discriminating groups of specified terrain variables. Certain terrain variables are better than others for differentiating or discriminating groups of vegetation types. Stepwise discriminant analysis was shown to be a useful tool in plant ecological studies.

Stochastic model search with binary outcomes for genome-wide association studies.

PubMed

Russu, Alberto; Malovini, Alberto; Puca, Annibale A; Bellazzi, Riccardo

2012-06-01

The spread of case-control genome-wide association studies (GWASs) has stimulated the development of new variable selection methods and predictive models. We introduce a novel Bayesian model search algorithm, Binary Outcome Stochastic Search (BOSS), which addresses the model selection problem when the number of predictors far exceeds the number of binary responses. Our method is based on a latent variable model that links the observed outcomes to the underlying genetic variables. A Markov Chain Monte Carlo approach is used for model search and to evaluate the posterior probability of each predictor. BOSS is compared with three established methods (stepwise regression, logistic lasso, and elastic net) in a simulated benchmark. Two real case studies are also investigated: a GWAS on the genetic bases of longevity, and the type 2 diabetes study from the Wellcome Trust Case Control Consortium. Simulations show that BOSS achieves higher precisions than the reference methods while preserving good recall rates. In both experimental studies, BOSS successfully detects genetic polymorphisms previously reported to be associated with the analyzed phenotypes. BOSS outperforms the other methods in terms of F-measure on simulated data. In the two real studies, BOSS successfully detects biologically relevant features, some of which are missed by univariate analysis and the three reference techniques. The proposed algorithm is an advance in the methodology for model selection with a large number of features. Our simulated and experimental results showed that BOSS proves effective in detecting relevant markers while providing a parsimonious model.

A stepwise-cluster microbial biomass inference model in food waste composting

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

Sun Wei; Huang, Guo H., E-mail: huangg@iseis.or; Chinese Research Academy of Environmental Science, North China Electric Power University, Beijing 100012-102206

2009-12-15

A stepwise-cluster microbial biomass inference (SMI) model was developed through introducing stepwise-cluster analysis (SCA) into composting process modeling to tackle the nonlinear relationships among state variables and microbial activities. The essence of SCA is to form a classification tree based on a series of cutting or mergence processes according to given statistical criteria. Eight runs of designed experiments in bench-scale reactors in a laboratory were constructed to demonstrate the feasibility of the proposed method. The results indicated that SMI could help establish a statistical relationship between state variables and composting microbial characteristics, where discrete and nonlinear complexities exist. Significance levelsmore » of cutting/merging were provided such that the accuracies of the developed forecasting trees were controllable. Through an attempted definition of input effects on the output in SMI, the effects of the state variables on thermophilic bacteria were ranged in a descending order as: Time (day) > moisture content (%) > ash content (%, dry) > Lower Temperature (deg. C) > pH > NH{sub 4}{sup +}-N (mg/Kg, dry) > Total N (%, dry) > Total C (%, dry); the effects on mesophilic bacteria were ordered as: Time > Upper Temperature (deg. C) > Total N > moisture content > NH{sub 4}{sup +}-N > Total C > pH. This study made the first attempt in applying SCA to mapping the nonlinear and discrete relationships in composting processes.« less

Vapor permeation-stepwise injection simultaneous determination of methanol and ethanol in biodiesel with voltammetric detection.

PubMed

Shishov, Andrey; Penkova, Anastasia; Zabrodin, Andrey; Nikolaev, Konstantin; Dmitrenko, Maria; Ermakov, Sergey; Bulatov, Andrey

2016-02-01

A novel vapor permeation-stepwise injection (VP-SWI) method for the determination of methanol and ethanol in biodiesel samples is discussed. In the current study, stepwise injection analysis was successfully combined with voltammetric detection and vapor permeation. This method is based on the separation of methanol and ethanol from a sample using a vapor permeation module (VPM) with a selective polymer membrane based on poly(phenylene isophtalamide) (PA) containing high amounts of a residual solvent. After the evaporation into the headspace of the VPM, methanol and ethanol were transported, by gas bubbling, through a PA membrane to a mixing chamber equipped with a voltammetric detector. Ethanol was selectively detected at +0.19 V, and both compounds were detected at +1.20 V. Current subtractions (using a correction factor) were used for the selective determination of methanol. A linear range between 0.05 and 0.5% (m/m) was established for each analyte. The limits of detection were estimated at 0.02% (m/m) for ethanol and methanol. The sample throughput was 5 samples h(-1). The method was successfully applied to the analysis of biodiesel samples. Copyright © 2015 Elsevier B.V. All rights reserved.

Identification of International Classification of Functioning, Disability and Health categories for patients with peripheral arterial disease.

PubMed

Vyskocil, Erich; Gruther, Wolfgang; Steiner, Irene; Schuhfried, Othmar

2014-07-01

Disease-specific categories of the International Classification of Functioning, Disability and Health have not yet been described for patients with chronic peripheral arterial obstructive disease (PAD). The authors examined the relationship between the categories of the Brief Core Sets for ischemic heart diseases with the Peripheral Artery Questionnaire and the ankle-brachial index to determine which International Classification of Functioning, Disability and Health categories are most relevant for patients with PAD. This is a retrospective cohort study including 77 patients with verified PAD. Statistical analyses of the relationship between International Classification of Functioning, Disability and Health categories as independent variables and the endpoints Peripheral Artery Questionnaire or ankle-brachial index were carried out by simple and stepwise linear regression models adjusting for age, sex, and leg (left vs. right). The stepwise linear regression model with the ankle-brachial index as dependent variable revealed a significant effect of the variables blood vessel functions and muscle endurance functions. Calculating a stepwise linear regression model with the Peripheral Artery Questionnaire as dependent variable, a significant effect of age, emotional functions, energy and drive functions, carrying out daily routine, as well as walking could be observed. This study identifies International Classification of Functioning, Disability and Health categories in the Brief Core Sets for ischemic heart diseases that show a significant effect on the ankle-brachial index and the Peripheral Artery Questionnaire score in patients with PAD. These categories provide fundamental information on functioning of patients with PAD and patient-centered outcomes for rehabilitation interventions.

Artificial nose, NIR and UV-visible spectroscopy for the characterisation of the PDO Chianti Classico olive oil.

PubMed

Forina, M; Oliveri, P; Bagnasco, L; Simonetti, R; Casolino, M C; Nizzi Grifi, F; Casale, M

2015-11-01

An authentication study of the Italian PDO (Protected Designation of Origin) olive oil Chianti Classico, based on artificial nose, near-infrared and UV-visible spectroscopy, with a set of samples representative of the whole Chianti Classico production area and a considerable number of samples from other Italian PDO regions was performed. The signals provided by the three analytical techniques were used both individually and jointly, after fusion of the respective variables, in order to build a model for the Chianti Classico PDO olive oil. Different signal pre-treatments were performed in order to investigate their importance and their effects in enhancing and extracting information from experimental data, correcting backgrounds or removing baseline variations. Stepwise-Linear Discriminant Analysis (STEP-LDA) was used as a feature selection technique and, afterward, Linear Discriminant Analysis (LDA) and the class-modelling technique Quadratic Discriminant Analysis-UNEQual dispersed classes (QDA-UNEQ) were applied to sub-sets of selected variables, in order to obtain efficient models capable of characterising the extra virgin olive oils produced in the Chianti Classico PDO area. Copyright © 2015 Elsevier B.V. All rights reserved.

Interpretation of tropospheric ozone variability in data with different vertical and temporal resolution

NASA Astrophysics Data System (ADS)

Petropavlovskikh, I. V.; Disterhoft, P.; Johnson, B. J.; Rieder, H. E.; Manney, G. L.; Daffer, W.

2012-12-01

This work attributes tropospheric ozone variability derived from the ground-based Dobson and Brewer Umkehr measurements and from ozone sonde data to local sources and transport. It assesses capability and limitations in both types of measurements that are often used to analyze long- and short-term variability in tropospheric ozone time series. We will address the natural and instrument-related contribution to the variability found in both Umkehr and sonde data. Validation of Umkehr methods is often done by intercomparisons against independent ozone measuring techniques such as ozone sounding. We will use ozone-sounding in its original and AK-smoothed vertical profiles for assessment of ozone inter-annual variability over Boulder, CO. We will discuss possible reasons for differences between different ozone measuring techniques and its effects on the derived ozone trends. Next to standard evaluation techniques we utilize a STL-decomposition method to address temporal variability and trends in the Boulder Umkehr data. Further, we apply a statistical modeling approach to the ozone data set to attribute ozone variability to individual driving forces associated with natural and anthropogenic causes. To this aim we follow earlier work applying a backward selection method (i.e., a stepwise elimination procedure out of a set of total 44 explanatory variables) to determine those explanatory variables which contribute most significantly to the observed variability. We will present also some results associated with completeness (sampling rate) of the existing data sets. We will also use MERRA (Modern-Era Retrospective analysis for Research and Applications) re-analysis results selected for Boulder location as a transfer function in understanding of the effects that the temporal sampling and vertical resolution bring into trend and ozone variability analysis. Analyzing intra-annual variability in ozone measurements over Boulder, CO, in relation to the upper tropospheric subtropical and polar jets, we will address the stratospheric and tropospheric intrusions in the middle latitude troposphere ozone field.

Plant community variability on a small area in southeastern Montana

Treesearch

James G. MacCracken; Daniel W. Uresk; Richard M. Hansen

1984-01-01

Plant communities are inherently variable due to a number of environmental and biological forces. Canopy cover and aboveground biomass were determined for understory vegetation in plant communities of a prairie grassland-forest ecotone in southeastern Montana. Vegetation units were described using polar ordination and stepwise discriminant analysis. Nine of a total of...

Variables Associated with Communicative Participation in People with Multiple Sclerosis: A Regression Analysis

ERIC Educational Resources Information Center

Baylor, Carolyn; Yorkston, Kathryn; Bamer, Alyssa; Britton, Deanna; Amtmann, Dagmar

2010-01-01

Purpose: To explore variables associated with self-reported communicative participation in a sample (n = 498) of community-dwelling adults with multiple sclerosis (MS). Method: A battery of questionnaires was administered online or on paper per participant preference. Data were analyzed using multiple linear backward stepwise regression. The…

[Discrimination of varieties of brake fluid using visual-near infrared spectra].

PubMed

Jiang, Lu-lu; Tan, Li-hong; Qiu, Zheng-jun; Lu, Jiang-feng; He, Yong

2008-06-01

A new method was developed to fast discriminate brands of brake fluid by means of visual-near infrared spectroscopy. Five different brands of brake fluid were analyzed using a handheld near infrared spectrograph, manufactured by ASD Company, and 60 samples were gotten from each brand of brake fluid. The samples data were pretreated using average smoothing and standard normal variable method, and then analyzed using principal component analysis (PCA). A 2-dimensional plot was drawn based on the first and the second principal components, and the plot indicated that the clustering characteristic of different brake fluid is distinct. The foregoing 6 principal components were taken as input variable, and the band of brake fluid as output variable to build the discriminate model by stepwise discriminant analysis method. Two hundred twenty five samples selected randomly were used to create the model, and the rest 75 samples to verify the model. The result showed that the distinguishing rate was 94.67%, indicating that the method proposed in this paper has good performance in classification and discrimination. It provides a new way to fast discriminate different brands of brake fluid.

Prediction of Academic Achievement in an NATA-Approved Graduate Athletic Training Education Program

PubMed Central

Keskula, Douglas R.; Sammarone, Paula G.; Perrin, David H.

1995-01-01

The Purpose of this investigation was to determine which information used in the applicant selection process would best predict the final grade point average of students in a National Athletic Trainers Association (NATA) graduate athletic training education program. The criterion variable used was the graduate grade-point average (GPAg) calculated at the completion of the program of study. The predictor variables included: 1) Graduate Record Examination-Quantitative (GRE-Q) scores; and 2) Graduate Record Examination-Verbal (GRE-V) scores, 3) preadmission grade point average (GPAp), 4) total athletic training hours (hours), and 5) curriculum or internship undergraduate athletic training education (program). Data from 55 graduate athletic training students during a 5-year period were evaluated. Stepwise multiple regression analysis indicated that GPAp was a significant predictor of GPAg, accounting for 34% of the variance. GRE-Q, GRE-V, hours, and program did not significantly contribute individually or in combination to the prediction of GPAg. The results of this investigation suggest that, of the variables examined, GPAp is the best predictor of academic success in an NATA-approved graduate athletic training education program. PMID:16558312

Changes in personality traits during treatment with sertraline or citalopram.

PubMed

Ekselius, L; Von Knorring, L

1999-05-01

Recent studies indicate that selective serotonin re-uptake inhibitors (SSRIs) reduce the symptoms accompanying personality disorders and modulate a normal personality. To examine the effect of two SSRIs, sertraline and citalopram, on personality traits in major depressed patients. Personality traits were evaluated at baseline and after six months using the Karolinska Scales of Personality (KSP). After treatment, significant changes in the direction of normalisation were seen in all scales. To determine whether the observed changes could be explained by improved depressive symptoms, multiple stepwise regressions with the separate KSP as dependent variables were performed. Improvements in depressive symptoms only accounted for 0-8.4% of the observed variance. In depressed patients treated with SSRIs significant effects are seen on personality traits measured by the KSP.

Financial Distress Prediction using Linear Discriminant Analysis and Support Vector Machine

NASA Astrophysics Data System (ADS)

Santoso, Noviyanti; Wibowo, Wahyu

2018-03-01

A financial difficulty is the early stages before the bankruptcy. Bankruptcies caused by the financial distress can be seen from the financial statements of the company. The ability to predict financial distress became an important research topic because it can provide early warning for the company. In addition, predicting financial distress is also beneficial for investors and creditors. This research will be made the prediction model of financial distress at industrial companies in Indonesia by comparing the performance of Linear Discriminant Analysis (LDA) and Support Vector Machine (SVM) combined with variable selection technique. The result of this research is prediction model based on hybrid Stepwise-SVM obtains better balance among fitting ability, generalization ability and model stability than the other models.

Boosted structured additive regression for Escherichia coli fed-batch fermentation modeling.

PubMed

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

2017-02-01

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

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.

The Paediatric Rheumatology International Trials Organisation provisional criteria for the evaluation of response to therapy in juvenile dermatomyositis.

PubMed

Ruperto, Nicolino; Pistorio, Angela; Ravelli, Angelo; Rider, Lisa G; Pilkington, Clarissa; Oliveira, Sheila; Wulffraat, Nico; Espada, Graciela; Garay, Stella; Cuttica, Ruben; Hofer, Michael; Quartier, Pierre; Melo-Gomes, Jose; Reed, Ann M; Wierzbowska, Malgorzata; Feldman, Brian M; Harjacek, Miroslav; Huppertz, Hans-Iko; Nielsen, Susan; Flato, Berit; Lahdenne, Pekka; Michels, Harmut; Murray, Kevin J; Punaro, Lynn; Rennebohm, Robert; Russo, Ricardo; Balogh, Zsolt; Rooney, Madeleine; Pachman, Lauren M; Wallace, Carol; Hashkes, Philip; Lovell, Daniel J; Giannini, Edward H; Gare, Boel Andersson; Martini, Alberto

2010-11-01

To develop a provisional definition for the evaluation of response to therapy in juvenile dermatomyositis (DM) based on the Paediatric Rheumatology International Trials Organisation juvenile DM core set of variables. Thirty-seven experienced pediatric rheumatologists from 27 countries achieved consensus on 128 difficult patient profiles as clinically improved or not improved using a stepwise approach (patient's rating, statistical analysis, definition selection). Using the physicians' consensus ratings as the "gold standard measure," chi-square, sensitivity, specificity, false-positive and-negative rates, area under the receiver operating characteristic curve, and kappa agreement for candidate definitions of improvement were calculated. Definitions with kappa values >0.8 were multiplied by the face validity score to select the top definitions. The top definition of improvement was at least 20% improvement from baseline in 3 of 6 core set variables with no more than 1 of the remaining worsening by more than 30%, which cannot be muscle strength. The second-highest scoring definition was at least 20% improvement from baseline in 3 of 6 core set variables with no more than 2 of the remaining worsening by more than 25%, which cannot be muscle strength (definition P1 selected by the International Myositis Assessment and Clinical Studies group). The third is similar to the second with the maximum amount of worsening set to 30%. This indicates convergent validity of the process. We propose a provisional data-driven definition of improvement that reflects well the consensus rating of experienced clinicians, which incorporates clinically meaningful change in core set variables in a composite end point for the evaluation of global response to therapy in juvenile DM. Copyright © 2010 by the American College of Rheumatology.

Cytopathologic differential diagnosis of low-grade urothelial carcinoma and reactive urothelial proliferation in bladder washings: a logistic regression analysis.

PubMed

Cakir, Ebru; Kucuk, Ulku; Pala, Emel Ebru; Sezer, Ozlem; Ekin, Rahmi Gokhan; Cakmak, Ozgur

2017-05-01

Conventional cytomorphologic assessment is the first step to establish an accurate diagnosis in urinary cytology. In cytologic preparations, the separation of low-grade urothelial carcinoma (LGUC) from reactive urothelial proliferation (RUP) can be exceedingly difficult. The bladder washing cytologies of 32 LGUC and 29 RUP were reviewed. The cytologic slides were examined for the presence or absence of the 28 cytologic features. The cytologic criteria showing statistical significance in LGUC were increased numbers of monotonous single (non-umbrella) cells, three-dimensional cellular papillary clusters without fibrovascular cores, irregular bordered clusters, atypical single cells, irregular nuclear overlap, cytoplasmic homogeneity, increased N/C ratio, pleomorphism, nuclear border irregularity, nuclear eccentricity, elongated nuclei, and hyperchromasia (p ˂ 0.05), and the cytologic criteria showing statistical significance in RUP were inflammatory background, mixture of small and large urothelial cells, loose monolayer aggregates, and vacuolated cytoplasm (p ˂ 0.05). When these variables were subjected to a stepwise logistic regression analysis, four features were selected to distinguish LGUC from RUP: increased numbers of monotonous single (non-umbrella) cells, increased nuclear cytoplasmic ratio, hyperchromasia, and presence of small and large urothelial cells (p = 0.0001). By this logistic model of the 32 cases with proven LGUC, the stepwise logistic regression analysis correctly predicted 31 (96.9%) patients with this diagnosis, and of the 29 patients with RUP, the logistic model correctly predicted 26 (89.7%) patients as having this disease. There are several cytologic features to separate LGUC from RUP. Stepwise logistic regression analysis is a valuable tool for determining the most useful cytologic criteria to distinguish these entities. © 2017 APMIS. Published by John Wiley & Sons Ltd.

Development of an affinity-matured humanized anti-epidermal growth factor receptor antibody for cancer immunotherapy.

PubMed

Nakanishi, Takeshi; Maru, Takamitsu; Tahara, Kazuhiro; Sanada, Hideaki; Umetsu, Mitsuo; Asano, Ryutaro; Kumagai, Izumi

2013-02-01

We showed previously that humanization of 528, a murine anti-epidermal growth factor receptor (EGFR) antibody, causes reduced affinity for its target. Here, to improve the affinity of the humanized antibody for use in cancer immunotherapy, we constructed phage display libraries focused on the complementarity-determining regions (CDRs) of the antibody and carried out affinity selection. Two-step selections using libraries constructed in a stepwise manner enabled a 32-fold affinity enhancement of humanized 528 (h528). Thermodynamic analysis of the interactions between the variable domain fragment of h528 (h528Fv) mutants and the soluble extracellular domain of EGFR indicated that the h528Fv mutants obtained from the first selection showed a large increase in negative enthalpy change due to binding, resulting in affinity enhancement. Furthermore, mutants from the second selection showed a decrease in entropy loss, which led to further affinity maturation. These results suggest that a single mutation in the heavy chain variable domain (i.e. Tyr(52) to Trp) enthalpically contributed for overcoming the energetic barrier to the antigen-antibody interaction, which was a major hurdle for the in vitro affinity maturation of h528. We reported previously that the humanized bispecific diabody hEx3 Db, which targets EGFR and CD3, shows strong anti-tumor activity. hEx3 Db mutants, in which the variable domains of h528 were replaced with those of the affinity-enhanced mutants, were prepared and characterized. In a growth inhibition assay of tumor cells, the hEx3 Db mutants showed stronger anti-tumor activity than that of hEx3 Db, suggesting that affinity enhancement of h528Fv enhances the anti-tumor activity of the bispecific diabody.

Random Survival Forest in practice: a method for modelling complex metabolomics data in time to event analysis.

PubMed

Dietrich, Stefan; Floegel, Anna; Troll, Martina; Kühn, Tilman; Rathmann, Wolfgang; Peters, Anette; Sookthai, Disorn; von Bergen, Martin; Kaaks, Rudolf; Adamski, Jerzy; Prehn, Cornelia; Boeing, Heiner; Schulze, Matthias B; Illig, Thomas; Pischon, Tobias; Knüppel, Sven; Wang-Sattler, Rui; Drogan, Dagmar

2016-10-01

The application of metabolomics in prospective cohort studies is statistically challenging. Given the importance of appropriate statistical methods for selection of disease-associated metabolites in highly correlated complex data, we combined random survival forest (RSF) with an automated backward elimination procedure that addresses such issues. Our RSF approach was illustrated with data from the European Prospective Investigation into Cancer and Nutrition (EPIC)-Potsdam study, with concentrations of 127 serum metabolites as exposure variables and time to development of type 2 diabetes mellitus (T2D) as outcome variable. Out of this data set, Cox regression with a stepwise selection method was recently published. Replication of methodical comparison (RSF and Cox regression) was conducted in two independent cohorts. Finally, the R-code for implementing the metabolite selection procedure into the RSF-syntax is provided. The application of the RSF approach in EPIC-Potsdam resulted in the identification of 16 incident T2D-associated metabolites which slightly improved prediction of T2D when used in addition to traditional T2D risk factors and also when used together with classical biomarkers. The identified metabolites partly agreed with previous findings using Cox regression, though RSF selected a higher number of highly correlated metabolites. The RSF method appeared to be a promising approach for identification of disease-associated variables in complex data with time to event as outcome. The demonstrated RSF approach provides comparable findings as the generally used Cox regression, but also addresses the problem of multicollinearity and is suitable for high-dimensional data. © The Author 2016; all rights reserved. Published by Oxford University Press on behalf of the International Epidemiological Association.

Prognostic grouping of metastatic prostate cancer using conventional pretreatment prognostic factors.

PubMed

Mikkola, Arto; Aro, Jussi; Rannikko, Sakari; Ruutu, Mirja

2009-01-01

To develop three prognostic groups for disease specific mortality based on the binary classified pretreatment variables age, haemoglobin concentration (Hb), erythrocyte sedimentation rate (ESR), alkaline phosphatase (ALP), prostate-specific antigen (PSA), plasma testosterone and estradiol level in hormonally treated patients with metastatic prostate cancer (PCa). The present study comprised 200 Finnprostate 6 study patients, but data on all variables were not known for every patient. The patients were divided into three prognostic risk groups (Rgs) using the prognostically best set of pretreatment variables. The best set was found by backward stepwise selection and the effect of every excluded variable on the binary classification cut-off points of the remaining variables was checked and corrected when needed. The best group of variables was ALP, PSA, ESR and age. All data were known in 142 patients. Patients were given one risk point each for ALP > 180 U/l (normal value 60-275 U/l), PSA > 35 microg/l, ESR > 80 mm/h and age < 60 years. Three risk groups were formed: Rg-a (0-1 risk points), Rg-b (2 risk points) and Rg-c (3-4 risk points). The risk of death from PCa increased statistically significantly with advancing prognostic group. Patients with metastatic PCa can be divided into three statistically significantly different prognostic risk groups for PCa-specific mortality by using the binary classified pretreatment variables ALP, PSA, ESR and age.

Linking the variability of atmospheric carbon monoxide to climate modes in the Southern Hemisphere

NASA Astrophysics Data System (ADS)

Buchholz, Rebecca; Monks, Sarah; Hammerling, Dorit; Worden, Helen; Deeter, Merritt; Emmons, Louisa; Edwards, David

2017-04-01

Biomass burning is a major driver of atmospheric carbon monoxide (CO) variability in the Southern Hemisphere. The magnitude of emissions, such as CO, from biomass burning is connected to climate through both the availability and dryness of fuel. We investigate the link between CO and climate using satellite measured CO and climate indices. Observations of total column CO from the satellite instrument MOPITT are used to build a record of interannual variability in CO since 2001. Four biomass burning regions in the Southern Hemisphere are explored. Data driven relationships are determined between CO and climate indices for the climate modes: El Niño Southern Oscillation (ENSO); the Indian Ocean Dipole (IOD); the Tropical Southern Atlantic (TSA); and the Southern Annular Mode (SAM). Stepwise forward and backward regression is used to select the best statistical model from combinations of lagged indices. We find evidence for the importance of first-order interaction terms of the climate modes when explaining CO variability. Implications of the model results are discussed for the Maritime Southeast Asia and Australasia regions. We also draw on the chemistry-climate model CAM-chem to explain the source contribution as well as the relative contributions of emissions and meteorology to CO variability.

Quantitative effects of composting state variables on C/N ratio through GA-aided multivariate analysis.

PubMed

Sun, Wei; Huang, Guo H; Zeng, Guangming; Qin, Xiaosheng; Yu, Hui

2011-03-01

It is widely known that variation of the C/N ratio is dependent on many state variables during composting processes. This study attempted to develop a genetic algorithm aided stepwise cluster analysis (GASCA) method to describe the nonlinear relationships between the selected state variables and the C/N ratio in food waste composting. The experimental data from six bench-scale composting reactors were used to demonstrate the applicability of GASCA. Within the GASCA framework, GA searched optimal sets of both specified state variables and SCA's internal parameters; SCA established statistical nonlinear relationships between state variables and the C/N ratio; to avoid unnecessary and time-consuming calculation, a proxy table was introduced to save around 70% computational efforts. The obtained GASCA cluster trees had smaller sizes and higher prediction accuracy than the conventional SCA trees. Based on the optimal GASCA tree, the effects of the GA-selected state variables on the C/N ratio were ranged in a descending order as: NH₄+-N concentration>Moisture content>Ash Content>Mean Temperature>Mesophilic bacteria biomass. Such a rank implied that the variation of ammonium nitrogen concentration, the associated temperature and the moisture conditions, the total loss of both organic matters and available mineral constituents, and the mesophilic bacteria activity, were critical factors affecting the C/N ratio during the investigated food waste composting. This first application of GASCA to composting modelling indicated that more direct search algorithms could be coupled with SCA or other multivariate analysis methods to analyze complicated relationships during composting and many other environmental processes. Copyright © 2010 Elsevier B.V. All rights reserved.

Relationship between balance performance in the elderly and some anthropometric variables.

PubMed

Fabunmi, A A; Gbiri, C A

2008-12-01

Ability to maintain either static or dynamic balance has been found to be influenced by many factors such as height and weight in the elderly. The relationship between other anthropometric variables and balance performance among elderly Nigerians has not been widely studied. The aim of this study was to investigate the relationship between these other anthropometric variables and balance performance among old individuals aged >60 years in Ibadan, Nigeria. The study used the ex-post facto design and involved two hundred and three apparently healthy (103 males and 100 females) elderly participants with ages between 60 years and 74 years, selected using multiple step-wise sampling techniques from churches, mosques and market place within Ibadan. They were without history of neurological problem, postural hypotension, orthopeadic conditions or injury to the back and/or upper and lower extremities within the past one year. Selected anthropometric variables were measured, Sharpened Romberg Test (SRT) and Functional Reach Test (FRT) was used to assess static balance and dynamic balance respectively. All data were summarized using range, mean and standard deviation. Pearson's product moment correlation coefficient was used to determine the relationship between the physical characteristics, anthropometric variables and performance on each of the two balance tests. The results showed that there were low but significant positive correlations between performance on FRT and each of height, weight, trunk length, foot length, shoulder girth and hip girth. (p<0.05). There was low significant and positive correlation between SRT with eyes closed and arm length, foot length and shoulder girth. (p<0.05) and there was low but significant positive correlation between SRT with eyes opened and shoulder girth and foot length (P<0.05). Anthropometric variables affect balance performances in apparently healthy elderly.

Using Long-Term Volunteer Records to Examine Dormouse (Muscardinusavellanarius) Nestbox Selection.

PubMed

Williams, Rachel L; Goodenough, Anne E; Hart, Adam G; Stafford, Richard

2013-01-01

Within ecology, there are unanswered questions about species-habitat interactions, which could potentially be resolved by a pragmatic analysis of a long-term volunteer-collected dataset. Here, we analysed 18 years of volunteer-collected data from a UK dormouse nestbox monitoring programme to determine the influence of habitat variables on nestbox choice by common dormice (Muscardinusavellanarius). We measured a range of habitat variables in a coppiced woodland in Gloucestershire, UK, and analysed these in relation to dormouse nestbox occupancy records (by dormice, other small mammals, and birds) collected by volunteers. While some characteristics of the woodland had changed over 18 years, simple transformation of the data and interpretation of the results indicated that the dataset was informative. Using stepwise regressions, multiple environmental and ecological factors were found to determine nestbox selection. Distance from the edge of the wood was the most influential (this did not change over 18 years), with boxes in the woodland interior being selected preferentially. There was a significant negative relationship with the presence of ferns (indicative of damp shady conditions). The presence of oak (a long-lived species), and the clumped structural complexity of the canopy were also important factors in the final model. There was no evidence of competition between dormice and birds or other mammals. The results provide greater understanding of artificial dormouse nest-site requirements and indicate that, in terms of habitat selection, long-term volunteer-collected datasets contribute usefully to understanding the requirements of species with an important conservation status.

Burnout Syndrome and associated factors among medical students: a cross-sectional study.

PubMed

Costa, Edméa Fontes de Oliva; Santos, Shirley Andrade; Santos, Ana Teresa Rodrigues de Abreu; Melo, Enaldo Vieira de; Andrade, Tarcísio Matos de

2012-01-01

To assess the prevalence and levels of burnout syndrome among medical students at the Universidade Federal de Sergipe-Brazil and to identify associated factors. A cross-sectional study was performed with randomly selected students in 2009. The Maslach Burnout Inventory/Student Survey (MBI-SS) and a structured questionnaire on socio-demographic characteristics, the educational process, and individual aspects were used. Statistical evaluation of multiple variables was performed through backward stepwise logistic regression analysis. The prevalence of burnout was 10.3% (n = 369). The prevalence was higher among those who did not have confidence in their clinical skills (Odds Ratio-OR = 6.47), those who felt uncomfortable with course activities (OR = 5.76), and those who did not see the coursework as a source of pleasure (OR = 4.68). There was a significant prevalence of burnout among the medical students studied. Three variables, in particular, were associated with burnout and were directly related to the medical education process. Preventive and intervention measures must be adopted, and longitudinal studies should be conducted.

Authentic leadership and its effect on employees' organizational citizenship behaviours.

PubMed

Edú Valsania, Sergio; Moriano León, Juan A; Molero Alonso, Fernando; Topa Cantisano, Gabriela

2012-11-01

The studies that have verified the positive association of authentic leadership with organizational citizenship behaviours (OCBs), have used global measures of both constructs. Therefore, the goal of this work is to analyze the effect of authentic leadership on employees' OCBs, specifically focusing on the relations of the four components of authentic leadership with the individual and organizational dimensions of the OCBs. The participants of this study were 220 Spanish employees (30.9% men and 69.1% women) who completed a questionnaire that included the variables of interest in this study: Authentic Leadership, OCB and Sociobiographical control variables. The results, obtained with stepwise multiple regression analysis, show that two components of authentic leadership-moral perspective and relational transparency-present significant relationships with OCB. Moreover, authentic leadership is a better predictor of employees' OCB when these behaviors are impersonal and directed towards the organization than when they are directed towards other people. These results have practical implications for human resources management in organizations, especially in selection processes and when training top executives.

Stepwise group sparse regression (SGSR): gene-set-based pharmacogenomic predictive models with stepwise selection of functional priors.

PubMed

Jang, In Sock; Dienstmann, Rodrigo; Margolin, Adam A; Guinney, Justin

2015-01-01

Complex mechanisms involving genomic aberrations in numerous proteins and pathways are believed to be a key cause of many diseases such as cancer. With recent advances in genomics, elucidating the molecular basis of cancer at a patient level is now feasible, and has led to personalized treatment strategies whereby a patient is treated according to his or her genomic profile. However, there is growing recognition that existing treatment modalities are overly simplistic, and do not fully account for the deep genomic complexity associated with sensitivity or resistance to cancer therapies. To overcome these limitations, large-scale pharmacogenomic screens of cancer cell lines--in conjunction with modern statistical learning approaches--have been used to explore the genetic underpinnings of drug response. While these analyses have demonstrated the ability to infer genetic predictors of compound sensitivity, to date most modeling approaches have been data-driven, i.e. they do not explicitly incorporate domain-specific knowledge (priors) in the process of learning a model. While a purely data-driven approach offers an unbiased perspective of the data--and may yield unexpected or novel insights--this strategy introduces challenges for both model interpretability and accuracy. In this study, we propose a novel prior-incorporated sparse regression model in which the choice of informative predictor sets is carried out by knowledge-driven priors (gene sets) in a stepwise fashion. Under regularization in a linear regression model, our algorithm is able to incorporate prior biological knowledge across the predictive variables thereby improving the interpretability of the final model with no loss--and often an improvement--in predictive performance. We evaluate the performance of our algorithm compared to well-known regularization methods such as LASSO, Ridge and Elastic net regression in the Cancer Cell Line Encyclopedia (CCLE) and Genomics of Drug Sensitivity in Cancer (Sanger) pharmacogenomics datasets, demonstrating that incorporation of the biological priors selected by our model confers improved predictability and interpretability, despite much fewer predictors, over existing state-of-the-art methods.

Stochastic model search with binary outcomes for genome-wide association studies

PubMed Central

Malovini, Alberto; Puca, Annibale A; Bellazzi, Riccardo

2012-01-01

Objective The spread of case–control genome-wide association studies (GWASs) has stimulated the development of new variable selection methods and predictive models. We introduce a novel Bayesian model search algorithm, Binary Outcome Stochastic Search (BOSS), which addresses the model selection problem when the number of predictors far exceeds the number of binary responses. Materials and methods Our method is based on a latent variable model that links the observed outcomes to the underlying genetic variables. A Markov Chain Monte Carlo approach is used for model search and to evaluate the posterior probability of each predictor. Results BOSS is compared with three established methods (stepwise regression, logistic lasso, and elastic net) in a simulated benchmark. Two real case studies are also investigated: a GWAS on the genetic bases of longevity, and the type 2 diabetes study from the Wellcome Trust Case Control Consortium. Simulations show that BOSS achieves higher precisions than the reference methods while preserving good recall rates. In both experimental studies, BOSS successfully detects genetic polymorphisms previously reported to be associated with the analyzed phenotypes. Discussion BOSS outperforms the other methods in terms of F-measure on simulated data. In the two real studies, BOSS successfully detects biologically relevant features, some of which are missed by univariate analysis and the three reference techniques. Conclusion The proposed algorithm is an advance in the methodology for model selection with a large number of features. Our simulated and experimental results showed that BOSS proves effective in detecting relevant markers while providing a parsimonious model. PMID:22534080

A high surface area Zr(IV)-based metal–organic framework showing stepwise gas adsorption and selective dye uptake

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

Lv, Xiu-Liang; Tong, Minman; Huang, Hongliang

2015-03-15

Exploitation of new metal–organic framework (MOF) materials with high surface areas has been attracting great attention in related research communities due to their broad potential applications. In this work, a new Zr(IV)-based MOF, [Zr{sub 6}O{sub 4}(OH){sub 4}(eddb){sub 6}] (BUT-30, H{sub 2}eddb=4,4′-(ethyne-1,2-diyl)dibenzoic acid) has been solvothermally synthesized, characterized, and explored for gases and dyes adsorptions. Single-crystal X-ray diffraction analysis demonstrates a three-dimensional cubic framework structure of this MOF, in which each Zr{sub 6}O{sub 4}(OH){sub 4} building unit is linked by 12 linear eddb ligands. BUT-30 has been found stable up to 400 °C and has a Brunauer–Emmett–Teller (BET) surface area asmore » high as 3940.6 m{sup 2} g{sup −1} (based on the N{sub 2} adsorption at 77 K) and total pore volume of 1.55 cm{sup 3} g{sup −1}. It is more interesting that this MOF exhibits stepwise adsorption behaviors for Ar, N{sub 2}, and CO{sub 2} at low temperatures, and selective uptakes towards different ionic dyes. - Graphical abstract: A new Zr(IV)-based MOF with high surface area has been synthesized and structurally characterized, which shows stepwise gas adsorption at low temperature and selective dye uptake from solution. - Highlights: • A new Zr-based MOF was synthesized and structurally characterized. • This MOF shows a higher surface area compared with its analogous UiO-67 and 68. • This MOF shows a rare stepwise adsorption towards light gases at low temperature. • This MOF performs selective uptakes towards cationic dyes over anionic ones. • Using triple-bond spacer is confirmed feasible in enhancing MOF surface areas.« less

Proton radius from electron scattering data

NASA Astrophysics Data System (ADS)

Higinbotham, Douglas W.; Kabir, Al Amin; Lin, Vincent; Meekins, David; Norum, Blaine; Sawatzky, Brad

2016-05-01

Background: The proton charge radius extracted from recent muonic hydrogen Lamb shift measurements is significantly smaller than that extracted from atomic hydrogen and electron scattering measurements. The discrepancy has become known as the proton radius puzzle. Purpose: In an attempt to understand the discrepancy, we review high-precision electron scattering results from Mainz, Jefferson Lab, Saskatoon, and Stanford. Methods: We make use of stepwise regression techniques using the F test as well as the Akaike information criterion to systematically determine the predictive variables to use for a given set and range of electron scattering data as well as to provide multivariate error estimates. Results: Starting with the precision, low four-momentum transfer (Q2) data from Mainz (1980) and Saskatoon (1974), we find that a stepwise regression of the Maclaurin series using the F test as well as the Akaike information criterion justify using a linear extrapolation which yields a value for the proton radius that is consistent with the result obtained from muonic hydrogen measurements. Applying the same Maclaurin series and statistical criteria to the 2014 Rosenbluth results on GE from Mainz, we again find that the stepwise regression tends to favor a radius consistent with the muonic hydrogen radius but produces results that are extremely sensitive to the range of data included in the fit. Making use of the high-Q2 data on GE to select functions which extrapolate to high Q2, we find that a Padé (N =M =1 ) statistical model works remarkably well, as does a dipole function with a 0.84 fm radius, GE(Q2) =(1+Q2/0.66 GeV2) -2 . Conclusions: Rigorous applications of stepwise regression techniques and multivariate error estimates result in the extraction of a proton charge radius that is consistent with the muonic hydrogen result of 0.84 fm; either from linear extrapolation of the extremely-low-Q2 data or by use of the Padé approximant for extrapolation using a larger range of data. Thus, based on a purely statistical analysis of electron scattering data, we conclude that the electron scattering results and the muonic hydrogen results are consistent. It is the atomic hydrogen results that are the outliers.

Relationships between field-based measures of strength and power and golf club head speed.

PubMed

Read, Paul J; Lloyd, Rhodri S; De Ste Croix, Mark; Oliver, Jon L

2013-10-01

Increased golf club head speed (CHS) has been shown to result in greater driving distances and is also correlated with golf handicap. The purpose of this study was to investigate the relationships between field-based measures of strength and power and golf CHS with a secondary aim to determine the reliability of the selected tests. A correlation design was used to assess the following variables: anthropometrics, squat jump (SJ) height and squat jump peak power (SJPP), unilateral countermovement jump (CMJ) heights (right leg countermovement jump and left leg countermovement jump [LLCMJ]), bilateral CMJ heights, countermovement jump peak power (CMJPP), and medicine ball seated throw (MBST) and medicine ball rotational throw (MBRT). Fouty-eight male subjects participated in the study (age: 20.1 ± 3.2 years, height: 1.76 ± 0.07 m, mass: 72.8 ± 7.8 kg, handicap: 5.8 ± 2.2). Moderate significant correlations were reported between CHS and MBRT (r = 0.67; p < 0.01), MBST (r = 0.63; p < 0.01), CMJPP (r = 0.54; p < 0.01), and SJPP (r = 0.53; p < 0.01). Weak significant correlations (r = 0.3-0.5) were identified between CHS and the other remaining variables excluding LLCMJ. Stepwise multiple regression analysis identified that the MBST and SJ were the greatest predictors of CHS, explaining 49% of the variance. Additionally the intraclass correlation coefficients reported for tests of CHS and all performance variables were deemed acceptable (r = 0.7-0.9). The results of this study suggest that the strength and conditioning coach can accurately assess and monitor the physical abilities of golf athletes using the proposed battery of field tests. Additionally, movements that are more concentrically dominant in nature may display stronger relationships with CHS due to MBST and SJ displaying the highest explained variance after a stepwise linear regression.

Role of Anti-Inflammatory Cytokines on Muscle Mass and Performance Changes in Elderly Men and Women.

PubMed

Rossi, A P; Budui, S; Zoico, E; Caliari, C; Mazzali, G; Fantin, F; D'Urbano, M; Paganelli, R; Zamboni, M

2017-01-01

Investigate the presence of a correlation between systemic inflammatory profile of community-dwelling individuals and the loss of muscular mass and performance in old age over a 4.5y follow-up, focusing on the role of anti-inflammatory cytokines in muscular changes in elderly. Longitudinal clinical study. Subjects were randomly selected from lists of 11 general practitioners in the city of Verona, Italy. The study included 120 subjects, 92 women and 28 men aged 72.27±2.06 years and with BMI of 26.52±4.07 kg/m2 at baseline. Six minutes walking test (6MWT), appendicular and leg fat free mass (FFM) as measured with Dual Energy X-ray absorptiometry, were obtained at baseline and after 4.5 years (4.5y) of mean follow-up. Height, weight, body mass index (BMI), and circulating levels of TNFα, IL-4, IL-10, and IL-13 were evaluated at baseline. A significant reduction of appendicular FFM, leg FFM and 6MWT performance (all p<0.001) was observed after 4.5 y follow-up. In a stepwise regression model, considering appendicular FFM decline as dependent variable, lnIL-4, BMI, baseline appendicular FFM, lnTNFα and lnIL-13 were significant predictors of appendicular FFM decline explaining 30.8% of the variance. While building a stepwise multiple regression considering leg FFM as a dependent variable, lnIL-4, BMI and leg FFM were significant predictors of leg FFM decline and explained 27.4% of variance. When considering 6MWT decline as a dependent variable, baseline 6MWT, lnIL-13 and lnTNFα were significant predictors of 6MWT decline to explain 22.9% of variance. Our study suggest that higher serum levels of anti-inflammatory markers, and in particular IL-4 and IL-13, may play a protective role on FFM and performance maintenance in elderly subjects.

Efficient least angle regression for identification of linear-in-the-parameters models

PubMed Central

Beach, Thomas H.; Rezgui, Yacine

2017-01-01

Least angle regression, as a promising model selection method, differentiates itself from conventional stepwise and stagewise methods, in that it is neither too greedy nor too slow. It is closely related to L1 norm optimization, which has the advantage of low prediction variance through sacrificing part of model bias property in order to enhance model generalization capability. In this paper, we propose an efficient least angle regression algorithm for model selection for a large class of linear-in-the-parameters models with the purpose of accelerating the model selection process. The entire algorithm works completely in a recursive manner, where the correlations between model terms and residuals, the evolving directions and other pertinent variables are derived explicitly and updated successively at every subset selection step. The model coefficients are only computed when the algorithm finishes. The direct involvement of matrix inversions is thereby relieved. A detailed computational complexity analysis indicates that the proposed algorithm possesses significant computational efficiency, compared with the original approach where the well-known efficient Cholesky decomposition is involved in solving least angle regression. Three artificial and real-world examples are employed to demonstrate the effectiveness, efficiency and numerical stability of the proposed algorithm. PMID:28293140

Land-use regression with long-term satellite-based greenness index and culture-specific sources to model PM2.5 spatial-temporal variability.

PubMed

Wu, Chih-Da; Chen, Yu-Cheng; Pan, Wen-Chi; Zeng, Yu-Ting; Chen, Mu-Jean; Guo, Yue Leon; Lung, Shih-Chun Candice

2017-05-01

This study utilized a long-term satellite-based vegetation index, and considered culture-specific emission sources (temples and Chinese restaurants) with Land-use Regression (LUR) modelling to estimate the spatial-temporal variability of PM 2.5 using data from Taipei metropolis, which exhibits typical Asian city characteristics. Annual average PM 2.5 concentrations from 2006 to 2012 of 17 air quality monitoring stations established by Environmental Protection Administration of Taiwan were used for model development. PM 2.5 measurements from 2013 were used for external data verification. Monthly Normalized Difference Vegetation Index (NDVI) images coupled with buffer analysis were used to assess the spatial-temporal variations of greenness surrounding the monitoring sites. The distribution of temples and Chinese restaurants were included to represent the emission contributions from incense and joss money burning, and gas cooking, respectively. Spearman correlation coefficient and stepwise regression were used for LUR model development, and 10-fold cross-validation and external data verification were applied to verify the model reliability. The results showed a strongly negative correlation (r: -0.71 to -0.77) between NDVI and PM 2.5 while temples (r: 0.52 to 0.66) and Chinese restaurants (r: 0.31 to 0.44) were positively correlated to PM 2.5 concentrations. With the adjusted model R 2 of 0.89, a cross-validated adj-R 2 of 0.90, and external validated R 2 of 0.83, the high explanatory power of the resultant model was confirmed. Moreover, the averaged NDVI within a 1750 m circular buffer (p < 0.01), the number of Chinese restaurants within a 1750 m buffer (p < 0.01), and the number of temples within a 750 m buffer (p = 0.06) were selected as important predictors during the stepwise selection procedures. According to the partial R 2 , NDVI explained 66% of PM 2.5 variation and was the dominant variable in the developed model. We suggest future studies consider these three factors when establishing LUR models for estimating PM 2.5 in other Asian cities. Copyright © 2017 Elsevier Ltd. All rights reserved.

Factors influencing storm-generated suspended-sediment concentrations and loads in four basins of contrasting land use, humid-tropical Puerto Rico

Treesearch

A. C. Gellis; NO-VALUE

2013-01-01

The significant characteristics controlling the variability in storm-generated suspended-sediment loads and concentrations were analyzed for four basins of differing land use (forest, pasture, cropland, and urbanizing) in humid-tropical Puerto Rico. Statistical analysis involved stepwise regression on factor scores. The explanatory variables were attributes of flow,...

A method for the selection of a functional form for a thermodynamic equation of state using weighted linear least squares stepwise regression

NASA Technical Reports Server (NTRS)

Jacobsen, R. T.; Stewart, R. B.; Crain, R. W., Jr.; Rose, G. L.; Myers, A. F.

1976-01-01

A method was developed for establishing a rational choice of the terms to be included in an equation of state with a large number of adjustable coefficients. The methods presented were developed for use in the determination of an equation of state for oxygen and nitrogen. However, a general application of the methods is possible in studies involving the determination of an optimum polynomial equation for fitting a large number of data points. The data considered in the least squares problem are experimental thermodynamic pressure-density-temperature data. Attention is given to a description of stepwise multiple regression and the use of stepwise regression in the determination of an equation of state for oxygen and nitrogen.

Word Problems: A "Meme" for Our Times.

ERIC Educational Resources Information Center

Leamnson, Robert N.

1996-01-01

Discusses a novel approach to word problems that involves linear relationships between variables. Argues that working stepwise through intermediates is the way our minds actually work and therefore this should be used in solving word problems. (JRH)

A Hybrid Approach of Stepwise Regression, Logistic Regression, Support Vector Machine, and Decision Tree for Forecasting Fraudulent Financial Statements

PubMed Central

Goo, Yeong-Jia James; Shen, Zone-De

2014-01-01

As the fraudulent financial statement of an enterprise is increasingly serious with each passing day, establishing a valid forecasting fraudulent financial statement model of an enterprise has become an important question for academic research and financial practice. After screening the important variables using the stepwise regression, the study also matches the logistic regression, support vector machine, and decision tree to construct the classification models to make a comparison. The study adopts financial and nonfinancial variables to assist in establishment of the forecasting fraudulent financial statement model. Research objects are the companies to which the fraudulent and nonfraudulent financial statement happened between years 1998 to 2012. The findings are that financial and nonfinancial information are effectively used to distinguish the fraudulent financial statement, and decision tree C5.0 has the best classification effect 85.71%. PMID:25302338

A hybrid approach of stepwise regression, logistic regression, support vector machine, and decision tree for forecasting fraudulent financial statements.

PubMed

Chen, Suduan; Goo, Yeong-Jia James; Shen, Zone-De

2014-01-01

As the fraudulent financial statement of an enterprise is increasingly serious with each passing day, establishing a valid forecasting fraudulent financial statement model of an enterprise has become an important question for academic research and financial practice. After screening the important variables using the stepwise regression, the study also matches the logistic regression, support vector machine, and decision tree to construct the classification models to make a comparison. The study adopts financial and nonfinancial variables to assist in establishment of the forecasting fraudulent financial statement model. Research objects are the companies to which the fraudulent and nonfraudulent financial statement happened between years 1998 to 2012. The findings are that financial and nonfinancial information are effectively used to distinguish the fraudulent financial statement, and decision tree C5.0 has the best classification effect 85.71%.

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.

An Investigation into the Roles of Theory of Mind, Emotion Regulation, and Attachment Styles in Predicting the Traits of Borderline Personality Disorder

PubMed Central

Ghiasi, Hamed; Mohammadi, Abolalfazl; Zarrinfar, Pouria

2016-01-01

Objective: Borderline personality disorder is one of the most complex and prevalent personality disorders. Many variables have so far been studied in relation to this disorder. This study aimed to investigate the role of emotion regulation, attachment styles, and theory of mind in predicting the traits of borderline personality disorder. Method: In this study, 85 patients with borderline personality disorder were selected using convenience sampling method. To measure the desired variables, the questionnaires of Gross emotion regulation, Collins and Read attachment styles, and Baron Cohen's Reading Mind from Eyes Test were applied. The data were analyzed using multivariate stepwise regression technique. Results: Emotion regulation, attachment styles, and theory of mind predicted 41.2% of the variance criterion altogether; among which, the shares of emotion regulation, attachment styles and theory of mind to the distribution of the traits of borderline personality disorder were 27.5%, 9.8%, and 3.9%, respectively.‎‎ Conclusion: The results of the study revealed that emotion regulation, attachment styles, and theory of mind are important variables in predicting the traits of borderline personality disorder and that these variables can be well applied for both the treatment and identification of this disorder. PMID:28050180

An Investigation into the Roles of Theory of Mind, Emotion Regulation, and Attachment Styles in Predicting the Traits of Borderline Personality Disorder.

PubMed

Ghiasi, Hamed; Mohammadi, Abolalfazl; Zarrinfar, Pouria

2016-10-01

Objective: Borderline personality disorder is one of the most complex and prevalent personality disorders. Many variables have so far been studied in relation to this disorder. This study aimed to investigate the role of emotion regulation, attachment styles, and theory of mind in predicting the traits of borderline personality disorder. Method: In this study, 85 patients with borderline personality disorder were selected using convenience sampling method. To measure the desired variables, the questionnaires of Gross emotion regulation, Collins and Read attachment styles, and Baron Cohen's Reading Mind from Eyes Test were applied. The data were analyzed using multivariate stepwise regression technique. Results: Emotion regulation, attachment styles, and theory of mind predicted 41.2% of the variance criterion altogether; among which, the shares of emotion regulation, attachment styles and theory of mind to the distribution of the traits of borderline personality disorder were 27.5%, 9.8%, and 3.9%, respectively.‎‎ Conclusion : The results of the study revealed that emotion regulation, attachment styles, and theory of mind are important variables in predicting the traits of borderline personality disorder and that these variables can be well applied for both the treatment and identification of this disorder.

Family functioning mediates adaptation in caregivers of individuals with Rett syndrome.

PubMed

Lamb, Amanda E; Biesecker, Barbara B; Umstead, Kendall L; Muratori, Michelle; Biesecker, Leslie G; Erby, Lori H

2016-11-01

The objective of this study was to investigate factors related to family functioning and adaptation in caregivers of individuals with Rett syndrome (RS). A cross-sectional quantitative survey explored the relationships between demographics, parental self-efficacy, coping methods, family functioning and adaptation. A forward-backward, step-wise model selection procedure was used to evaluate variables associated with both family functioning and adaptation. Analyses also explored family functioning as a mediator of the relationship between other variables and adaptation. Bivariate analyses (N=400) revealed that greater parental self-efficacy, a greater proportion of problem-focused coping, and a lesser proportion of emotion-focused coping were associated with more effective family functioning. In addition, these key variables were significantly associated with greater adaptation, as was family functioning, while controlling for confounders. Finally, regression analyses suggest family functioning as a mediator of the relationships between three variables (parental self-efficacy, problem-focused coping, and emotion-focused coping) with adaptation. This study demonstrates the potentially predictive roles of expectations and coping methods and the mediator role of family functioning in adaptation among caregivers of individuals with RS, a chronic developmental disorder. A potential target for intervention is strengthening of caregiver competence in the parenting role to enhance caregiver adaptation. Published by Elsevier Ireland Ltd.

Variables contributing to an excellent customer service management profile within the regulated electric utility industry: A comparison of self-concept with customer satisfaction for customer service management

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

Johnson, L.E.

1991-01-01

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

Socioeconomic determinants of fertility: selected Mexican regions, 1976-1977.

PubMed

Pick, J B; Butler, E W; Pavgi, S

1988-01-01

Cumulative fertility is analyzed for 4 regions of Mexico, based on World Fertility Survey data of 1976-77; the state of Baja California, the Northwest region, the State of Jalisco, and the Northeast region. Based on stepwise regression methodology, the study compares results for 12 subsamples of married respondents, 3 age categories by 4 regions. The dependent variables are children ever born and children ever born in the last 5 years. Migration, urban, educational, and occupational variables are included as independent variables. Regression results reveal level of education is the major, and negative, influence on fertility. Other results include specific negative effects for prior occupation, size of place of residence, and childhood place of residence. Fertility effects appear different for migration origin and destination regions, but more similar for younger ages. Effects of migration on fertility are small. Mean fertility as measured by children ever born was 4.34 for the 1976-77 World Fertility Survey samples versus 3.69 for the Mexican census of 1980. Fertility varied somewhat by region with the highest and lowest values in Jalisco and the Northeast, respectively. Expected age-related changes in fertility were noted.

Alzheimer disease identification using amyloid imaging and reserve variables

PubMed Central

Roe, C.M.; Mintun, M.A.; Ghoshal, N.; Williams, M.M.; Grant, E.A.; Marcus, D.S.; Morris, J.C.

2010-01-01

Objective: Several factors may influence the relationship between Alzheimer disease (AD) lesions and the expression of dementia, including those related to brain and cognitive reserve. Other factors may confound the association between AD pathology and dementia. We tested whether factors thought to influence the association of AD pathology and dementia help to accurately identify dementia of the Alzheimer type (DAT) when considered together with amyloid imaging. Methods: Participants with normal cognition (n = 180) and with DAT (n = 25), aged 50 years or older, took part in clinical, neurologic, and psychometric assessments. PET with the Pittsburgh compound B (PiB) tracer was used to measure brain amyloid, yielding a mean cortical binding potential (MCBP) reflecting PiB uptake. Logistic regression was used to generate receiver operating characteristic curves, and the areas under those curves (AUC), to compare the predictive accuracy of using MCBP alone vs MCBP together with other variables selected using a stepwise selection procedure to identify participants with DAT vs normal cognition. Results: The AUC resulting from MCBP alone was 0.84 (95% confidence interval [CI] = 0.73–0.94; cross-validated AUC = 0.80, 95% CI = 0.68–0.92). The AUC for the predictive equation generated by a stepwise model including education, normalized whole brain volume, physical health rating, gender, and use of medications that may interfere with cognition was 0.94 (95% CI = 0.90–0.98; cross-validated AUC = 0.91, 95% CI = 0.85–0.96), an improvement (p = 0.025) over that yielded using MCBP alone. Conclusion: Results suggest that factors reported to influence associations between AD pathology and dementia can improve the predictive accuracy of amyloid imaging for the identification of symptomatic AD. GLOSSARY A β = amyloid-β; AD = Alzheimer disease; AUC = area under receiver operating characteristic curve; BP = binding potential; CDR = Clinical Dementia Rating; CI = confidence interval; DAT = dementia of the Alzheimer type; DV = distribution volume; MCBP = mean cortical binding potential; nWBV = normalized whole brain volume; OR = odds ratio; PiB = Pittsburgh compound B; ROC = receiver operating characteristic curve; ROI = region of interest. PMID:20603484

Variability in in vitro fertilization outcomes of prepubertal goat oocytes explained by basic semen analyses.

PubMed

Palomo, M J; Quintanilla, R; Izquierdo, M D; Mogas, T; Paramio, M T

2016-12-01

This work analyses the changes that caprine spermatozoa undergo during in vitro fertilization (IVF) of in vitro matured prepubertal goat oocytes and their relationship with IVF outcome, in order to obtain an effective model that allows prediction of in vitro fertility on the basis of semen assessment. The evolution of several sperm parameters (motility, viability and acrosomal integrity) during IVF and their relationship with three IVF outcome criteria (total penetration, normal penetration and cleavage rates) were studied in a total of 56 IVF replicates. Moderate correlation coefficients between some sperm parameters and IVF outcome were observed. In addition, stepwise multiple regression analyses were conducted that considered three grouping of sperm parameters as potential explanatory variables of the three IVF outcome criteria. The proportion of IVF outcome variation that can be explained by the fitted models ranged from 0.62 to 0.86, depending upon the trait analysed and the variables considered. Seven out of 32 sperm parameters were selected as partial covariates in at least one of the nine multiple regression models. Among these, progressive sperm motility assessed immediately after swim-up, the percentage of dead sperm with intact acrosome and the incidence of acrosome reaction both determined just before the gamete co-culture, and finally the proportion of viable spermatozoa at 17 h post-insemination were the most frequently selected sperm parameters. Nevertheless, the predictive ability of these models must be confirmed in a larger sample size experiment.

Impact of multicollinearity on small sample hydrologic regression models

NASA Astrophysics Data System (ADS)

Kroll, Charles N.; Song, Peter

2013-06-01

Often hydrologic regression models are developed with ordinary least squares (OLS) procedures. The use of OLS with highly correlated explanatory variables produces multicollinearity, which creates highly sensitive parameter estimators with inflated variances and improper model selection. It is not clear how to best address multicollinearity in hydrologic regression models. Here a Monte Carlo simulation is developed to compare four techniques to address multicollinearity: OLS, OLS with variance inflation factor screening (VIF), principal component regression (PCR), and partial least squares regression (PLS). The performance of these four techniques was observed for varying sample sizes, correlation coefficients between the explanatory variables, and model error variances consistent with hydrologic regional regression models. The negative effects of multicollinearity are magnified at smaller sample sizes, higher correlations between the variables, and larger model error variances (smaller R2). The Monte Carlo simulation indicates that if the true model is known, multicollinearity is present, and the estimation and statistical testing of regression parameters are of interest, then PCR or PLS should be employed. If the model is unknown, or if the interest is solely on model predictions, is it recommended that OLS be employed since using more complicated techniques did not produce any improvement in model performance. A leave-one-out cross-validation case study was also performed using low-streamflow data sets from the eastern United States. Results indicate that OLS with stepwise selection generally produces models across study regions with varying levels of multicollinearity that are as good as biased regression techniques such as PCR and PLS.

Diagnosis of Chronic Kidney Disease Based on Support Vector Machine by Feature Selection Methods.

PubMed

Polat, Huseyin; Danaei Mehr, Homay; Cetin, Aydin

2017-04-01

As Chronic Kidney Disease progresses slowly, early detection and effective treatment are the only cure to reduce the mortality rate. Machine learning techniques are gaining significance in medical diagnosis because of their classification ability with high accuracy rates. The accuracy of classification algorithms depend on the use of correct feature selection algorithms to reduce the dimension of datasets. In this study, Support Vector Machine classification algorithm was used to diagnose Chronic Kidney Disease. To diagnose the Chronic Kidney Disease, two essential types of feature selection methods namely, wrapper and filter approaches were chosen to reduce the dimension of Chronic Kidney Disease dataset. In wrapper approach, classifier subset evaluator with greedy stepwise search engine and wrapper subset evaluator with the Best First search engine were used. In filter approach, correlation feature selection subset evaluator with greedy stepwise search engine and filtered subset evaluator with the Best First search engine were used. The results showed that the Support Vector Machine classifier by using filtered subset evaluator with the Best First search engine feature selection method has higher accuracy rate (98.5%) in the diagnosis of Chronic Kidney Disease compared to other selected methods.

Relationship of nutrition and physical activity behaviors and fitness measures to academic performance for sixth graders in a midwest city school district.

PubMed

Edwards, Jane U; Mauch, Lois; Winkelman, Mark R

2011-02-01

To support curriculum and policy, a midwest city school district assessed the association of selected categories of nutrition and physical activity (NUTR/PA) behaviors, fitness measures, and body mass index (BMI) with academic performance (AP) for 800 sixth graders. Students completed an adapted Youth Risk Behavior Surveillance Survey (NUTR/PA behaviors), fitness assessments (mile run, curl-ups, push-ups, height, and weight) with results matched to standardized scores (Measures of Academic Progress [MAP]), meal price status, and gender. Differences in mean MAP scores (math and reading) were compared by selected categories of each variable utilizing 1-way analysis of variance. Associations were determined by stepwise multiple regression utilizing mean MAP scores (for math and for reading) as the dependent variable and NUTR/PA behaviors, fitness, and BMI categories as independent variables. Significance was set at α = 0.05. Higher MAP math scores were associated with NUTR (more milk and breakfast; less 100% fruit juice and sweetened beverages [SB]) and PA (increased vigorous PA and sports teams; reduced television), and fitness (higher mile run performance). Higher MAP reading scores were associated with NUTR (fewer SB) and PA (increased vigorous PA, reduced television). Regression analysis indicated about 11.1% of the variation in the mean MAP math scores and 6.7% of the mean MAP reading scores could be accounted for by selected NUTR/PA behaviors, fitness, meal price status, and gender. Many positive NUTR/PA behaviors and fitness measures were associated with higher MAP scores supporting the school district focus on healthy lifestyles. Additional factors, including meal price status and gender, contribute to AP. © 2011, Fargo Public School.

Clinical and cytological features predictive of malignancy in thyroid follicular neoplasms.

PubMed

Lubitz, Carrie C; Faquin, William C; Yang, Jingyun; Mekel, Michal; Gaz, Randall D; Parangi, Sareh; Randolph, Gregory W; Hodin, Richard A; Stephen, Antonia E

2010-01-01

The preoperative diagnosis of malignancy in nodules suspicious for a follicular neoplasm remains challenging. A number of clinical and cytological parameters have been previously studied; however, none have significantly impacted clinical practice. The aim of this study was to determine predictive characteristics of follicular neoplasms useful for clinical application. Four clinical (age, sex, nodule size, solitary nodule) and 17 cytological variables were retrospectively reviewed for 144 patients with a nodule suspicious for follicular neoplasm, diagnosed preoperatively by fine-needle aspiration (FNA), from a single institution over a 2-year period (January 2006 to December 2007). The FNAs were examined by a single, blinded pathologist and compared with final surgical pathology. Significance of clinical and cytological variables was determined by univariate analysis and backward stepwise logistic regression. Odds ratios (ORs) for malignancy, a receiver operating characteristic curve, and predicted probabilities of combined features were determined. There was an 11% incidence of malignancy (16/144). On univariate analysis, nodule size >OR=4.0 cm nears significance (p = 0.054) and 9 of 17 cytological features examined were significantly associated with malignancy. Three variables stay in the final model after performing backward stepwise selection in logistic regression: nodule size (OR = 0.25, p = 0.05), presence of a transgressing vessel (OR = 23, p < 0.0001), and nuclear grooves (OR = 4.3, p = 0.03). The predicted probability of malignancy was 88.4% with the presence of all three variables on preoperative FNA. When the two papillary carcinomas were excluded from the analysis, the presence of nuclear grooves was no longer significant, and anisokaryosis (OR = 12.74, p = 0.005) and presence of nucleolus (OR = 0.11, p = 0.04) were significantly associated with malignancy. Excluding the two papillary thyroid carcinomas, a nodule size >or=4 cm, with a transgressing vessel and anisokaryosis and lacking a nucleolus, has a predicted probability of malignancy of 96.5%. A combination of larger nodule size, transgressing vessels, and specific nuclear features are predictive of malignancy in patients with follicular neoplasms. These findings enhance our current limited predictive armamentarium and can be used to guide surgical decision making. Further study may result in the inclusion of these variables to the systematic evaluation of follicular neoplasms.

The influence of physical and cognitive factors on reactive agility performance in men basketball players.

PubMed

Scanlan, Aaron; Humphries, Brendan; Tucker, Patrick S; Dalbo, Vincent

2014-01-01

This study explored the influence of physical and cognitive measures on reactive agility performance in basketball players. Twelve men basketball players performed multiple sprint, Change of Direction Speed Test, and Reactive Agility Test trials. Pearson's correlation analyses were used to determine relationships between the predictor variables (stature, mass, body composition, 5-m, 10-m and 20-m sprint times, peak speed, closed-skill agility time, response time and decision-making time) and reactive agility time (response variable). Simple and stepwise regression analyses determined the individual influence of each predictor variable and the best predictor model for reactive agility time. Morphological (r = -0.45 to 0.19), sprint (r = -0.40 to 0.41) and change-of-direction speed measures (r = 0.43) had small to moderate correlations with reactive agility time. Response time (r = 0.76, P = 0.004) and decision-making time (r = 0.58, P = 0.049) had large to very large relationships with reactive agility time. Response time was identified as the sole predictor variable for reactive agility time in the stepwise model (R(2) = 0.58, P = 0.004). In conclusion, cognitive measures had the greatest influence on reactive agility performance in men basketball players. These findings suggest reaction and decision-making drills should be incorporated in basketball training programmes.

Predictors of competitive achievement among pubescent synchronized swimmers: an analysis of the solo-figure competition.

PubMed

Peric, M; Cavar, M; Zenic, N; Sekulic, D; Sajber, D

2014-02-01

This study examined the applicability of sport-specific fitness tests (SSTs), anthropometrics, and respiratory parameters in predicting competitive results among pubescent synchronized swimmers. A total of 25 synchronized swimmers (16-17 years; 166.2 ± 5.4 cm; and 58.4 ± 4.3 kg) volunteered for this study. The independent variables were body mass, body height, Body Mass Index (BMI), body fat percentage (BF%), lean body mass percentage, respiratory variables, and four SSTs (two specific power tests plus one aerobic- and one anaerobic-endurance test). The dependent variable was competitive achievement in the solo figure competition. The reliability analyses, Pearson's correlation coefficient and forward stepwise regression were calculated. The SSTs were reliable for testing fitness status among pubescent synchronized swimmers. The forward stepwise regression retained two SSTs, BF% and forced vital capacity (FVC, relative for age and stature) in a set of predictors of competitive achievement. Significant Beta coefficients are found for aerobic-endurance, SST and FVC. The sport-specific measure of aerobic endurance and FVC appropriately predicted competitive achievement with regard to the figures used in the competition when competitive results (the dependent variable) were obtained. Athletes and coaches should be aware of the probable negative influence of very low body fat levels on competitive achievement.

Self-Concept and Participation in School Activities Reanalyzed.

ERIC Educational Resources Information Center

Winne, Philip H.; Walsh, John

1980-01-01

Yarworth and Gauthier (EJ 189 606) examined whether self-concept variables enhanced predictions about students' participation in school activities, using unstructured stepwise regression techniques. A reanalysis of their data using hierarchial regression models tested their hypothesis more appropriately, and uncovered multicollinearity and…

Response kinetics of tethered bacteria to stepwise changes in nutrient concentration.

PubMed

Chernova, Anna A; Armitage, Judith P; Packer, Helen L; Maini, Philip K

2003-09-01

We examined the changes in swimming behaviour of the bacterium Rhodobacter sphaeroides in response to stepwise changes in a nutrient (propionate), following the pre-stimulus motion, the initial response and the adaptation to the sustained concentration of the chemical. This was carried out by tethering motile cells by their flagella to glass slides and following the rotational behaviour of their cell bodies in response to the nutrient change. Computerised motion analysis was used to analyse the behaviour. Distributions of run and stop times were obtained from rotation data for tethered cells. Exponential and Weibull fits for these distributions, and variability in individual responses are discussed. In terms of parameters derived from the run and stop time distributions, we compare the responses to stepwise changes in the nutrient concentration and the long-term behaviour of 84 cells under 12 propionate concentration levels from 1 nM to 25 mM. We discuss traditional assumptions for the random walk approximation to bacterial swimming and compare them with the observed R. sphaeroides motile behaviour.

Big Bang Tumor Growth and Clonal Evolution.

PubMed

Sun, Ruping; Hu, Zheng; Curtis, Christina

2018-05-01

The advent and application of next-generation sequencing (NGS) technologies to tumor genomes has reinvigorated efforts to understand clonal evolution. Although tumor progression has traditionally been viewed as a gradual stepwise process, recent studies suggest that evolutionary rates in tumors can be variable with periods of punctuated mutational bursts and relative stasis. For example, Big Bang dynamics have been reported, wherein after transformation, growth occurs in the absence of stringent selection, consistent with effectively neutral evolution. Although first noted in colorectal tumors, effective neutrality may be relatively common. Additionally, punctuated evolution resulting from mutational bursts and cataclysmic genomic alterations have been described. In this review, we contrast these findings with the conventional gradualist view of clonal evolution and describe potential clinical and therapeutic implications of different evolutionary modes and tempos. Copyright © 2018 Cold Spring Harbor Laboratory Press; all rights reserved.

Greater association of peak neuromuscular performance with cortical bone geometry, bone mass and bone strength than bone density: A study in 417 older women.

PubMed

Belavý, Daniel L; Armbrecht, Gabriele; Blenk, Tilo; Bock, Oliver; Börst, Hendrikje; Kocakaya, Emine; Luhn, Franziska; Rantalainen, Timo; Rawer, Rainer; Tomasius, Frederike; Willnecker, Johannes; Felsenberg, Dieter

2016-02-01

We evaluated which aspects of neuromuscular performance are associated with bone mass, density, strength and geometry. 417 women aged 60-94years were examined. Countermovement jump, sit-to-stand test, grip strength, forearm and calf muscle cross-sectional area, areal bone mineral content and density (aBMC and aBMD) at the hip and lumbar spine via dual X-ray absorptiometry, and measures of volumetric vBMC and vBMD, bone geometry and section modulus at 4% and 66% of radius length and 4%, 38% and 66% of tibia length via peripheral quantitative computed tomography were performed. The first principal component of the neuromuscular variables was calculated to generate a summary neuromuscular variable. Percentage of total variance in bone parameters explained by the neuromuscular parameters was calculated. Step-wise regression was also performed. At all pQCT bone sites (radius, ulna, tibia, fibula), a greater percentage of total variance in measures of bone mass, cortical geometry and/or bone strength was explained by peak neuromuscular performance than for vBMD. Sit-to-stand performance did not relate strongly to bone parameters. No obvious differential in the explanatory power of neuromuscular performance was seen for DXA aBMC versus aBMD. In step-wise regression, bone mass, cortical morphology, and/or strength remained significant in relation to the first principal component of the neuromuscular variables. In no case was vBMD positively related to neuromuscular performance in the final step-wise regression models. Peak neuromuscular performance has a stronger relationship with leg and forearm bone mass and cortical geometry as well as proximal forearm section modulus than with vBMD. Copyright © 2015 Elsevier Inc. All rights reserved.

Prevalence of consistent condom use with various types of sex partners and associated factors among money boys in Changsha, China.

PubMed

Wang, Lian-Hong; Yan, Jin; Yang, Guo-Li; Long, Shuo; Yu, Yong; Wu, Xi-Lin

2015-04-01

Money boys with inconsistent condom use (less than 100% of the time) are at high risk of infection by human immunodeficiency virus (HIV) or sexually transmitted infection (STI), but relatively little research has examined their risk behaviors. We investigated the prevalence of consistent condom use (100% of the time) and associated factors among money boys. A cross-sectional study using a structured questionnaire was conducted among money boys in Changsha, China, between July 2012 and January 2013. Independent variables included socio-demographic data, substance abuse history, work characteristics, and self-reported HIV and STI history. Dependent variables included the consistent condom use with different types of sex partners. Among the participants, 82.4% used condoms consistently with male clients, 80.2% with male sex partners, and 77.1% with female sex partners in the past 3 months. A multiple stepwise logistic regression model identified four statistically significant factors associated with lower likelihoods of consistent condom use with male clients: age group, substance abuse, lack of an "employment" arrangement, and having no HIV test within the prior 6 months. In a similar model, only one factor associated significantly with lower likelihoods of consistent condom use with male sex partners was identified in multiple stepwise logistic regression analyses: having no HIV test within the prior six months. As for female sex partners, two significant variables were statistically significant in the multiple stepwise logistic regression analysis: having no HIV test within the prior 6 months and having STI history. Interventions which are linked with more realistic and acceptable HIV prevention methods are greatly warranted and should increase risk awareness and the behavior of consistent condom use in both commercial and personal relationship. © 2015 International Society for Sexual Medicine.

Establishing an efficient way to utilize the drought resistance germplasm population in wheat.

PubMed

Wang, Jiancheng; Guan, Yajing; Wang, Yang; Zhu, Liwei; Wang, Qitian; Hu, Qijuan; Hu, Jin

2013-01-01

Drought resistance breeding provides a hopeful way to improve yield and quality of wheat in arid and semiarid regions. Constructing core collection is an efficient way to evaluate and utilize drought-resistant germplasm resources in wheat. In the present research, 1,683 wheat varieties were divided into five germplasm groups (high resistant, HR; resistant, R; moderate resistant, MR; susceptible, S; and high susceptible, HS). The least distance stepwise sampling (LDSS) method was adopted to select core accessions. Six commonly used genetic distances (Euclidean distance, Euclid; Standardized Euclidean distance, Seuclid; Mahalanobis distance, Mahal; Manhattan distance, Manhat; Cosine distance, Cosine; and Correlation distance, Correlation) were used to assess genetic distances among accessions. Unweighted pair-group average (UPGMA) method was used to perform hierarchical cluster analysis. Coincidence rate of range (CR) and variable rate of coefficient of variation (VR) were adopted to evaluate the representativeness of the core collection. A method for selecting the ideal constructing strategy was suggested in the present research. A wheat core collection for the drought resistance breeding programs was constructed by the strategy selected in the present research. The principal component analysis showed that the genetic diversity was well preserved in that core collection.

Burnout Syndrome and associated factors among medical students: a cross-sectional study

PubMed Central

de Oliva Costa, Edméa Fontes; Santos, Shirley Andrade; de Abreu Santos, Ana Teresa Rodrigues; de Melo, Enaldo Vieira; de Andrade, Tarcísio Matos

2012-01-01

OBJECTIVES: To assess the prevalence and levels of burnout syndrome among medical students at the Universidade Federal de Sergipe-Brazil and to identify associated factors. METHODS: A cross-sectional study was performed with randomly selected students in 2009. The Maslach Burnout Inventory/Student Survey (MBI-SS) and a structured questionnaire on socio-demographic characteristics, the educational process, and individual aspects were used. Statistical evaluation of multiple variables was performed through backward stepwise logistic regression analysis. RESULTS: The prevalence of burnout was 10.3% (n = 369). The prevalence was higher among those who did not have confidence in their clinical skills (Odds Ratio–OR = 6.47), those who felt uncomfortable with course activities (OR = 5.76), and those who did not see the coursework as a source of pleasure (OR = 4.68). CONCLUSION: There was a significant prevalence of burnout among the medical students studied. Three variables, in particular, were associated with burnout and were directly related to the medical education process. Preventive and intervention measures must be adopted, and longitudinal studies should be conducted. PMID:22760894

Application of the stepwise focusing method to optimize the cost-effectiveness of genome-wide association studies with limited research budgets for genotyping and phenotyping.

PubMed

Ohashi, J; Clark, A G

2005-05-01

The recent cataloguing of a large number of SNPs enables us to perform genome-wide association studies for detecting common genetic variants associated with disease. Such studies, however, generally have limited research budgets for genotyping and phenotyping. It is therefore necessary to optimize the study design by determining the most cost-effective numbers of SNPs and individuals to analyze. In this report we applied the stepwise focusing method, with two-stage design, developed by Satagopan et al. (2002) and Saito & Kamatani (2002), to optimize the cost-effectiveness of a genome-wide direct association study using a transmission/disequilibrium test (TDT). The stepwise focusing method consists of two steps: a large number of SNPs are examined in the first focusing step, and then all the SNPs showing a significant P-value are tested again using a larger set of individuals in the second focusing step. In the framework of optimization, the numbers of SNPs and families and the significance levels in the first and second steps were regarded as variables to be considered. Our results showed that the stepwise focusing method achieves a distinct gain of power compared to a conventional method with the same research budget.

Stepwise photochromism of bisnaphthopyrans exhibiting an excitation intensity-dependent color change.

PubMed

Inagaki, Yuki; Mutoh, Katsuya; Abe, Jiro

2018-06-07

Non-linear photoresponses against excitation light intensity are important for the development of attractive photofunctional materials exhibiting high spatial selective photoswitching that is not affected by weak background light. Biphotochromic systems composed of two fast photochromic units have the potential to show a stepwise two-photon absorption process in which the optical properties can be non-linearly controlled by changing the excitation light conditions. Herein, we designed and synthesized novel bisnaphthopyran derivatives containing fast photoswitchable naphthopyran units. The bisnaphthopyran derivatives show a stepwise two-photon-induced photochromic reaction upon UV light irradiation accompanied by a drastic color change due to a large change in the molecular structure between the one-photon product and the two-photon product. Consequently, the color of the bisnaphthopyran derivatives can be non-linearly controlled by changing the excitation intensity. This characteristic photochromic property of the biphotochromic system provides important insight into advanced photoresponsive materials.

The Schaake shuffle: A method for reconstructing space-time variability in forecasted precipitation and temperature fields

USGS Publications Warehouse

Clark, M.R.; Gangopadhyay, S.; Hay, L.; Rajagopalan, B.; Wilby, R.

2004-01-01

A number of statistical methods that are used to provide local-scale ensemble forecasts of precipitation and temperature do not contain realistic spatial covariability between neighboring stations or realistic temporal persistence for subsequent forecast lead times. To demonstrate this point, output from a global-scale numerical weather prediction model is used in a stepwise multiple linear regression approach to downscale precipitation and temperature to individual stations located in and around four study basins in the United States. Output from the forecast model is downscaled for lead times up to 14 days. Residuals in the regression equation are modeled stochastically to provide 100 ensemble forecasts. The precipitation and temperature ensembles from this approach have a poor representation of the spatial variability and temporal persistence. The spatial correlations for downscaled output are considerably lower than observed spatial correlations at short forecast lead times (e.g., less than 5 days) when there is high accuracy in the forecasts. At longer forecast lead times, the downscaled spatial correlations are close to zero. Similarly, the observed temporal persistence is only partly present at short forecast lead times. A method is presented for reordering the ensemble output in order to recover the space-time variability in precipitation and temperature fields. In this approach, the ensemble members for a given forecast day are ranked and matched with the rank of precipitation and temperature data from days randomly selected from similar dates in the historical record. The ensembles are then reordered to correspond to the original order of the selection of historical data. Using this approach, the observed intersite correlations, intervariable correlations, and the observed temporal persistence are almost entirely recovered. This reordering methodology also has applications for recovering the space-time variability in modeled streamflow. ?? 2004 American Meteorological Society.

A stepwise, multi-objective, multi-variable parameter optimization method for the APEX model

USDA-ARS?s Scientific Manuscript database

Proper parameterization enables hydrological models to make reliable estimates of non-point source pollution for effective control measures. The automatic calibration of hydrologic models requires significant computational power limiting its application. The study objective was to develop and eval...

Stepwise Bay Annulation of Indigo for the Synthesis of Desymmetrized Electron Acceptors and Donor–Acceptor Constructs

DOE PAGES

Kolaczkowski, Matthew A.; He, Bo; Liu, Yi

2016-10-10

In this work, a selective stepwise annulation of indigo has been demonstrated as a means of providing both monoannulated and differentially double-annulated indigo derivatives. Disparate substitution of the electron accepting bay-annulated indigo system allows for fine control over both the electronic properties as well as donor-acceptor structural architectures. Optical and electronic properties were characterized computationally as well as through UV-vis absorption spectroscopy and cyclic voltammetry. Finally, this straightforward method provides a modular approach for the design of indigo-based materials with tailored optoelectronic properties.

Proton radius from electron scattering data

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

Higinbotham, Douglas W.; Kabir, Al Amin; Lin, Vincent

Background: The proton charge radius extracted from recent muonic hydrogen Lamb shift measurements is significantly smaller than that extracted from atomic hydrogen and electron scattering measurements. The discrepancy has become known as the proton radius puzzle. Purpose: In an attempt to understand the discrepancy, we review high-precision electron scattering results from Mainz, Jefferson Lab, Saskatoon and Stanford. Methods: We make use of stepwise regression techniques using the F-test as well as the Akaike information criterion to systematically determine the predictive variables to use for a given set and range of electron scattering data as well as to provide multivariate errormore » estimates. Results: Starting with the precision, low four-momentum transfer (Q 2) data from Mainz (1980) and Saskatoon (1974), we find that a stepwise regression of the Maclaurin series using the F-test as well as the Akaike information criterion justify using a linear extrapolation which yields a value for the proton radius that is consistent with the result obtained from muonic hydrogen measurements. Applying the same Maclaurin series and statistical criteria to the 2014 Rosenbluth results on GE from Mainz, we again find that the stepwise regression tends to favor a radius consistent with the muonic hydrogen radius but produces results that are extremely sensitive to the range of data included in the fit. Making use of the high-Q 2 data on G E to select functions which extrapolate to high Q 2, we find that a Pad´e (N = M = 1) statistical model works remarkably well, as does a dipole function with a 0.84 fm radius, G E(Q 2) = (1 + Q 2/0.66 GeV 2) -2. Conclusions: Rigorous applications of stepwise regression techniques and multivariate error estimates result in the extraction of a proton charge radius that is consistent with the muonic hydrogen result of 0.84 fm; either from linear extrapolation of the extreme low-Q 2 data or by use of the Pad´e approximant for extrapolation using a larger range of data. Thus, based on a purely statistical analysis of electron scattering data, we conclude that the electron scattering result and the muonic hydrogen result are consistent. Lastly, it is the atomic hydrogen results that are the outliers.« less

Proton radius from electron scattering data

DOE PAGES

Higinbotham, Douglas W.; Kabir, Al Amin; Lin, Vincent; ...

2016-05-31

Background: The proton charge radius extracted from recent muonic hydrogen Lamb shift measurements is significantly smaller than that extracted from atomic hydrogen and electron scattering measurements. The discrepancy has become known as the proton radius puzzle. Purpose: In an attempt to understand the discrepancy, we review high-precision electron scattering results from Mainz, Jefferson Lab, Saskatoon and Stanford. Methods: We make use of stepwise regression techniques using the F-test as well as the Akaike information criterion to systematically determine the predictive variables to use for a given set and range of electron scattering data as well as to provide multivariate errormore » estimates. Results: Starting with the precision, low four-momentum transfer (Q 2) data from Mainz (1980) and Saskatoon (1974), we find that a stepwise regression of the Maclaurin series using the F-test as well as the Akaike information criterion justify using a linear extrapolation which yields a value for the proton radius that is consistent with the result obtained from muonic hydrogen measurements. Applying the same Maclaurin series and statistical criteria to the 2014 Rosenbluth results on GE from Mainz, we again find that the stepwise regression tends to favor a radius consistent with the muonic hydrogen radius but produces results that are extremely sensitive to the range of data included in the fit. Making use of the high-Q 2 data on G E to select functions which extrapolate to high Q 2, we find that a Pad´e (N = M = 1) statistical model works remarkably well, as does a dipole function with a 0.84 fm radius, G E(Q 2) = (1 + Q 2/0.66 GeV 2) -2. Conclusions: Rigorous applications of stepwise regression techniques and multivariate error estimates result in the extraction of a proton charge radius that is consistent with the muonic hydrogen result of 0.84 fm; either from linear extrapolation of the extreme low-Q 2 data or by use of the Pad´e approximant for extrapolation using a larger range of data. Thus, based on a purely statistical analysis of electron scattering data, we conclude that the electron scattering result and the muonic hydrogen result are consistent. Lastly, it is the atomic hydrogen results that are the outliers.« less

Population dynamics of pond zooplankton II Daphnia ambigua Scourfield

USGS Publications Warehouse

Angino, E.E.; Armitage, K.B.; Saxena, B.

1973-01-01

Calcium was the most important of 27 environmental components affecting density for a 50 week period. Simultaneous stepwise regression accounted for more variability in total number/1 and in the number of ovigerous females/1 than did any of the lag analyses; 1-week lag accounted for the greatest amount of variability in clutch size. Total number and clutch size were little affected by measures of food. ?? 1973 Dr. W. Junk b.v. Publishers.

Soil Cd, Cr, Cu, Ni, Pb and Zn sorption and retention models using SVM: Variable selection and competitive model.

PubMed

González Costa, J J; Reigosa, M J; Matías, J M; Covelo, E F

2017-09-01

The aim of this study was to model the sorption and retention of Cd, Cu, Ni, Pb and Zn in soils. To that extent, the sorption and retention of these metals were studied and the soil characterization was performed separately. Multiple stepwise regression was used to produce multivariate models with linear techniques and with support vector machines, all of which included 15 explanatory variables characterizing soils. When the R-squared values are represented, two different groups are noticed. Cr, Cu and Pb sorption and retention show a higher R-squared; the most explanatory variables being humified organic matter, Al oxides and, in some cases, cation-exchange capacity (CEC). The other group of metals (Cd, Ni and Zn) shows a lower R-squared, and clays are the most explanatory variables, including a percentage of vermiculite and slime. In some cases, quartz, plagioclase or hematite percentages also show some explanatory capacity. Support Vector Machine (SVM) regression shows that the different models are not as regular as in multiple regression in terms of number of variables, the regression for nickel adsorption being the one with the highest number of variables in its optimal model. On the other hand, there are cases where the most explanatory variables are the same for two metals, as it happens with Cd and Cr adsorption. A similar adsorption mechanism is thus postulated. These patterns of the introduction of variables in the model allow us to create explainability sequences. Those which are the most similar to the selectivity sequences obtained by Covelo (2005) are Mn oxides in multiple regression and change capacity in SVM. Among all the variables, the only one that is explanatory for all the metals after applying the maximum parsimony principle is the percentage of sand in the retention process. In the competitive model arising from the aforementioned sequences, the most intense competitiveness for the adsorption and retention of different metals appears between Cr and Cd, Cu and Zn in multiple regression; and between Cr and Cd in SVM regression. Copyright © 2017 Elsevier B.V. All rights reserved.

Sex determination based on a thoracic vertebra and ribs evaluation using clinical chest radiography.

PubMed

Tsubaki, Shun; Morishita, Junji; Usumoto, Yosuke; Sakaguchi, Kyoko; Matsunobu, Yusuke; Kawazoe, Yusuke; Okumura, Miki; Ikeda, Noriaki

2017-07-01

Our aim was to investigate whether sex can be determined from a combination of geometric features obtained from the 10th thoracic vertebra, 6th rib, and 7th rib. Six hundred chest radiographs (300 males and 300 females) were randomly selected to include patients of six age groups (20s, 30s, 40s, 50s, 60s, and 70s). Each group included 100 images (50 males and 50 females). A total of 14 features, including 7 lengths, 5 indices for the vertebra, and 2 types of widths for ribs, were utilized and analyzed for sex determination. Dominant features contributing to sex determination were selected by stepwise discriminant analysis after checking the variance inflation factors for multicollinearity. The accuracy of sex determination using a combination of the vertebra and ribs was evaluated from the selected features by the stepwise discriminant analysis. The accuracies in each age group were also evaluated in this study. The accuracy of sex determination based on a combination of features of the vertebra and ribs was 88.8% (533/600). This performance was superior to that of the vertebra or ribs only. Moreover, sex determination of subjects in their 20s demonstrated the highest accuracy (96.0%, 96/100). The features selected in the stepwise discriminant analysis included some features in both the vertebra and ribs. These results indicate the usefulness of combined information obtained from the vertebra and ribs for sex determination. We conclude that a combination of geometric characteristics obtained from the vertebra and ribs could be useful for determining sex. Copyright © 2017 Elsevier B.V. All rights reserved.

Identification of molecular markers associated with mite resistance in coconut (Cocos nucifera L.).

PubMed

Shalini, K V; Manjunatha, S; Lebrun, P; Berger, A; Baudouin, L; Pirany, N; Ranganath, R M; Prasad, D Theertha

2007-01-01

Coconut mite (Aceria guerreronis 'Keifer') has become a major threat to Indian coconut (Coçcos nucifera L.) cultivators and the processing industry. Chemical and biological control measures have proved to be costly, ineffective, and ecologically undesirable. Planting mite-resistant coconut cultivars is the most effective method of preventing yield loss and should form a major component of any integrated pest management stratagem. Coconut genotypes, and mite-resistant and -susceptible accessions were collected from different parts of South India. Thirty-two simple sequence repeat (SSR) and 7 RAPD primers were used for molecular analyses. In single-marker analysis, 9 SSR and 4 RAPD markers associated with mite resistance were identified. In stepwise multiple regression analysis of SSRs, a combination of 6 markers showed 100% association with mite infestation. Stepwise multiple regression analysis for RAPD data revealed that a combination of 3 markers accounted for 83.86% of mite resistance in the selected materials. Combined stepwise multiple regression analysis of RAPD and SSR data showed that a combination of 5 markers explained 100% of the association with mite resistance in coconut. Markers associated with mite resistance are important in coconut breeding programs and will facilitate the selection of mite-resistant plants at an early stage as well as mother plants for breeding programs.

In vitro selection and amplification protocols for isolation of aptameric sensors for small molecules

PubMed Central

Yang, Kyung-Ae; Pei, Renjun; Stojanovic, Milan N.

2016-01-01

We recently optimized a procedure that directly yields aptameric sensors for small molecules in so-called structure-switching format. The protocol has a high success rate, short time, and is sufficiently simple to be readily implemented in a non-specialist laboratory. We provide a stepwise guide to this selection protocol. PMID:27155227

DEVELOPMENT OF RESIDENTIAL WOOD COMSUMPTION ESTIMATION MODELS

EPA Science Inventory

The report gives data on the distribution and usage of firewood, obtained from a pool of household wood use surveys. ased on a series of regression models developed using the STEPWISE procedure in the SAS statistical package, two variables appear to be most predictive of wood use...

Evaluation of a stepwise, multi-objective, multi-variable parameter optimization method for the APEX model

USDA-ARS?s Scientific Manuscript database

Hydrologic models are essential tools for environmental assessment of agricultural non-point source pollution. The automatic calibration of hydrologic models, though efficient, demands significant computational power, which can limit its application. The study objective was to investigate a cost e...

The sounds of silence: language, cognition, and anxiety in selective mutism.

PubMed

Manassis, Katharina; Tannock, Rosemary; Garland, E Jane; Minde, Klaus; McInnes, Alison; Clark, Sandra

2007-09-01

To determine whether oral language, working memory, and social anxiety differentiate children with selective mutism (SM), children with anxiety disorders (ANX), and normal controls (NCs) and explore predictors of mutism severity. Children ages 6 to 10 years with SM (n = 44) were compared with children with ANX (n = 28) and NCs (n = 19) of similar age on standardized measures of language, nonverbal working memory, and social anxiety. Variables correlating with mutism severity were entered in stepwise regressions to determine predictors of mute behavior in SM. Children with SM scored significantly lower on standardized language measures than children with ANX and NCs and showed greater visual memory deficits and social anxiety relative to these two groups. Age and receptive grammar ability predicted less severe mutism, whereas social anxiety predicted more severe mutism. These factors accounted for 38% of the variance in mutism severity. Social anxiety and language deficits are evident in SM, may predict mutism severity, and should be evaluated in clinical assessment. Replication is indicated, as are further studies of cognition and of intervention in SM, using large, diverse samples.

Identification of eggs from different production systems based on hyperspectra and CS-SVM.

PubMed

Sun, J; Cong, S L; Mao, H P; Zhou, X; Wu, X H; Zhang, X D

2017-06-01

1. To identify the origin of table eggs more accurately, a method based on hyperspectral imaging technology was studied. 2. The hyperspectral data of 200 samples of intensive and extensive eggs were collected. Standard normalised variables combined with a Savitzky-Golay were used to eliminate noise, then stepwise regression (SWR) was used for feature selection. Grid search algorithm (GS), genetic search algorithm (GA), particle swarm optimisation algorithm (PSO) and cuckoo search algorithm (CS) were applied by support vector machine (SVM) methods to establish an SVM identification model with the optimal parameters. The full spectrum data and the data after feature selection were the input of the model, while egg category was the output. 3. The SWR-CS-SVM model performed better than the other models, including SWR-GS-SVM, SWR-GA-SVM, SWR-PSO-SVM and others based on full spectral data. The training and test classification accuracy of the SWR-CS-SVM model were respectively 99.3% and 96%. 4. SWR-CS-SVM proved effective for identifying egg varieties and could also be useful for the non-destructive identification of other types of egg.

Sparse Logistic Regression for Diagnosis of Liver Fibrosis in Rat by Using SCAD-Penalized Likelihood

PubMed Central

Yan, Fang-Rong; Lin, Jin-Guan; Liu, Yu

2011-01-01

The objective of the present study is to find out the quantitative relationship between progression of liver fibrosis and the levels of certain serum markers using mathematic model. We provide the sparse logistic regression by using smoothly clipped absolute deviation (SCAD) penalized function to diagnose the liver fibrosis in rats. Not only does it give a sparse solution with high accuracy, it also provides the users with the precise probabilities of classification with the class information. In the simulative case and the experiment case, the proposed method is comparable to the stepwise linear discriminant analysis (SLDA) and the sparse logistic regression with least absolute shrinkage and selection operator (LASSO) penalty, by using receiver operating characteristic (ROC) with bayesian bootstrap estimating area under the curve (AUC) diagnostic sensitivity for selected variable. Results show that the new approach provides a good correlation between the serum marker levels and the liver fibrosis induced by thioacetamide (TAA) in rats. Meanwhile, this approach might also be used in predicting the development of liver cirrhosis. PMID:21716672

Knowledge of and Attitudes to Influenza Vaccination among Community Pharmacists in Catalonia (Spain). 2013-2014 Season: A Cross Sectional Study.

PubMed

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

2017-07-11

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

4D-LQTA-QSAR and docking study on potent Gram-negative specific LpxC inhibitors: a comparison to CoMFA modeling.

PubMed

Ghasemi, Jahan B; Safavi-Sohi, Reihaneh; Barbosa, Euzébio G

2012-02-01

A quasi 4D-QSAR has been carried out on a series of potent Gram-negative LpxC inhibitors. This approach makes use of the molecular dynamics (MD) trajectories and topology information retrieved from the GROMACS package. This new methodology is based on the generation of a conformational ensemble profile, CEP, for each compound instead of only one conformation, followed by the calculation intermolecular interaction energies at each grid point considering probes and all aligned conformations resulting from MD simulations. These interaction energies are independent variables employed in a QSAR analysis. The comparison of the proposed methodology to comparative molecular field analysis (CoMFA) formalism was performed. This methodology explores jointly the main features of CoMFA and 4D-QSAR models. Step-wise multiple linear regression was used for the selection of the most informative variables. After variable selection, multiple linear regression (MLR) and partial least squares (PLS) methods used for building the regression models. Leave-N-out cross-validation (LNO), and Y-randomization were performed in order to confirm the robustness of the model in addition to analysis of the independent test set. Best models provided the following statistics: [Formula in text] (PLS) and [Formula in text] (MLR). Docking study was applied to investigate the major interactions in protein-ligand complex with CDOCKER algorithm. Visualization of the descriptors of the best model helps us to interpret the model from the chemical point of view, supporting the applicability of this new approach in rational drug design.

Drought prediction using co-active neuro-fuzzy inference system, validation, and uncertainty analysis (case study: Birjand, Iran)

NASA Astrophysics Data System (ADS)

Memarian, Hadi; Pourreza Bilondi, Mohsen; Rezaei, Majid

2016-08-01

This work aims to assess the capability of co-active neuro-fuzzy inference system (CANFIS) for drought forecasting of Birjand, Iran through the combination of global climatic signals with rainfall and lagged values of Standardized Precipitation Index (SPI) index. Using stepwise regression and correlation analyses, the signals NINO 1 + 2, NINO 3, Multivariate Enso Index, Tropical Southern Atlantic index, Atlantic Multi-decadal Oscillation index, and NINO 3.4 were recognized as the effective signals on the drought event in Birjand. Based on the results from stepwise regression analysis and regarding the processor limitations, eight models were extracted for further processing by CANFIS. The metrics P-factor and D-factor were utilized for uncertainty analysis, based on the sequential uncertainty fitting algorithm. Sensitivity analysis showed that for all models, NINO indices and rainfall variable had the largest impact on network performance. In model 4 (as the model with the lowest error during training and testing processes), NINO 1 + 2(t-5) with an average sensitivity of 0.7 showed the highest impact on network performance. Next, the variables rainfall, NINO 1 + 2(t), and NINO 3(t-6) with the average sensitivity of 0.59, 0.28, and 0.28, respectively, could have the highest effect on network performance. The findings based on network performance metrics indicated that the global indices with a time lag represented a better correlation with El Niño Southern Oscillation (ENSO). Uncertainty analysis of the model 4 demonstrated that 68 % of the observed data were bracketed by the 95PPU and D-Factor value (0.79) was also within a reasonable range. Therefore, the fourth model with a combination of the input variables NINO 1 + 2 (with 5 months of lag and without any lag), monthly rainfall, and NINO 3 (with 6 months of lag) and correlation coefficient of 0.903 (between observed and simulated SPI) was selected as the most accurate model for drought forecasting using CANFIS in the climatic region of Birjand.

Relationships between nutrient composition of flowers and fruit quality in orange trees grown in calcareous soil.

PubMed

Pestana, Maribela; Beja, Pedro; Correia, Pedro José; de Varennes, Amarilis; Faria, Eugénio Araújo

2005-06-01

To determine if flower nutrient composition can be used to predict fruit quality, a field experiment was conducted over three seasons (1996-1999) in a commercial orange orchard (Citrus sinensis (L.) Osbeck cv. 'Valencia Late', budded on Troyer citrange rootstock) established on a calcareous soil in southern Portugal. Flowers were collected from 20 trees during full bloom in April and their nutrient composition determined, and fruits were harvested the following March and their quality evaluated. Patterns of covariation in flower nutrient concentrations and in fruit quality variables were evaluated by principal component analysis. Regression models relating fruit quality variables to flower nutrient composition were developed by stepwise selection procedures. The predictive power of the regression models was evaluated with an independent data set. Nutrient composition of flowers at full bloom could be used to predict the fruit quality variables fresh fruit mass and maturation index in the following year. Magnesium, Ca and Zn concentrations measured in flowers were related to fruit fresh mass estimations and N, P, Mg and Fe concentrations were related to fruit maturation index. We also established reference values for the nutrient composition of flowers based on measurements made in trees that produced large (> 76 mm in diameter) fruit.

Body shape indices are predictors for estimating fat-free mass in male athletes

PubMed Central

Aoki, Toru; Komori, Daisuke; Oyamada, Kazuyuki; Murata, Kensuke; Fujita, Eiji; Akamine, Takuya; Urita, Yoshihisa; Yamamoto, Masayoshi

2018-01-01

It is unknown whether body size and body shape parameters can be predictors for estimating whole body fat-free mass (FFM) in male athletes. This study aimed to investigate whether body size and shape variables can be predictors for FFM in male athletes. Using a whole-body dual-energy X-ray absorptiometry scanner, whole body fat mass (FM) and FFM were determined in 132 male athletes and 14 sedentary males. The sample was divided into two groups: validation (N = 98) and cross-validation (N = 48) groups. Body height (BH), body mass (BM), and waist circumference at immediately above the iliac crest (W) were measured. BM-to-W and W-to-BH ratios were calculated as indices of body shapes. Stepwise multiple regression analysis revealed that BM/W and W/BH were selected as explainable variables for predicting FFM. The equation developed in the validation group was FFM (kg) = 0.883 × BM/W (kg/m) + 43.674 × W/BH (cm/cm)– 41.480 [R2 = 0.900, SEE (%SEE) = 2.3 kg (3.8%)], which was validated in the cross-validation group. Thus, the current results demonstrate that an equation using BM/W and W/BH as independent variables is applicable for predicting FFM in male athletes. PMID:29346452

Association between obesity-related biomarkers and cognitive and motor development in infants.

PubMed

Camargos, Ana Cristina R; Mendonça, Vanessa A; Oliveira, Katherine S C; de Andrade, Camila Alves; Leite, Hércules Ribeiro; da Fonseca, Sueli Ferreira; Vieira, Erica Leandro Marciano; Teixeira Júnior, Antônio Lúcio; Lacerda, Ana Cristina Rodrigues

2017-05-15

This study aimed to verify the association between obesity-related biomarkers and cognitive and motor development in infants between 6 and 24 months of age. A cross-sectional study was conducted with 50 infants and plasma levels of leptin, adiponectin, resistin, soluble tumor necrosis factor receptors 1 and 2 (sTNFR1 and sTNFR2), chemokines, brain-derived neurotrophic factor (BDNF), serum cortisol and redox status were measured. The Bayley-III test was utilized to evaluate cognitive and motor development, and multiple linear stepwise regression models were performed to verify the association between selected biomarkers and cognitive and motor development. A significant association was found among plasma leptin and sTNFR1 levels with cognitive composite scores, and these two independents variables together explained 37% of the variability of cognitive composite scores (p=0.001). Only plasma sTNFR1 levels were associated and explained 24% of the variability of motor composite scores (p=0.003). Plasma levels of sTNFR1 were associated with the increase in cognitive and motor development scores in infants between 6 and 24 months of age through a mechanism not directly related to excess body weight. Moreover, increase in plasma levels of leptin reduced the cognitive development in this age range. Copyright © 2017 Elsevier B.V. All rights reserved.

Geomorphic determinants of species composition of alpine tundra, Glacier National Park, U.S.A.

USGS Publications Warehouse

George P. Malanson,; Bengtson, Lindsey E.; Fagre, Daniel B.

2012-01-01

Because the distribution of alpine tundra is associated with spatially limited cold climates, global warming may threaten its local extent or existence. This notion has been challenged, however, based on observations of the diversity of alpine tundra in small areas primarily due to topographic variation. The importance of diversity in temperature or moisture conditions caused by topographic variation is an open question, and we extend this to geomorphology more generally. The extent to which geomorphic variation per se, based on relatively easily assessed indicators, can account for the variation in alpine tundra community composition is analyzed versus the inclusion of broad indicators of regional climate variation. Visual assessments of topography are quantified and reduced using principal components analysis (PCA). Observations of species cover are reduced using detrended correspondence analysis (DCA). A “best subsets” regression approach using the Akaike Information Criterion for selection of variables is compared to a simple stepwise regression with DCA scores as the dependent variable and scores on significant PCA axes plus more direct measures of topography as independent variables. Models with geographic coordinates (representing regional climate gradients) excluded explain almost as much variation in community composition as models with them included, although they are important contributors to the latter. The geomorphic variables in the model are those associated with local moisture differences such as snowbeds. The potential local variability of alpine tundra can be a buffer against climate change, but change in precipitation may be as important as change in temperature.

Climate mode links to atmospheric carbon monoxide over fire regions

NASA Astrophysics Data System (ADS)

Buchholz, R. R.; Hammerling, D.; Worden, H. M.; Monks, S. A.; Edwards, D. P.; Deeter, M. N.; Emmons, L. K.

2017-12-01

Fire is a strong contributor to variability in atmospheric carbon monoxide (CO), particularly for the Southern Hemisphere and tropics. The magnitude of emissions, such as CO, from biomass burning are related to climate through both the availability and dryness of fuel. We investigate this link between CO and climate using satellite measured CO and climate indices. Interannual variability in satellite-measured CO is determined for the time period covering 2001-2016. We use MOPITT total column retrievals and focus on biomass burning regions of the Southern Hemisphere and tropics. In each of the regions, data driven relationships are determined between CO and climate indices for the climate modes: El Niño Southern Oscillation (ENSO); the Indian Ocean Dipole (IOD); the Tropical Southern Atlantic (TSA); and the Antarctic Oscillation (AAO). Step-wise forward and backward regression combined with the Bayesian Information Criterion is used to select the best predictive model from combinations of lagged indices. We find evidence for the importance of first-order interaction terms of the climate modes when explaining CO variability. Generally, over 50% of the variability can be explained, with over 70% for the Maritime Southeast Asia and North Australasia regions. To help interpret variability, we draw on the chemistry-climate model CAM-chem, which provides information on source contributions and the relative influence of emissions and meteorology. Our results have implications for applications such as air quality forecasting and verifying climate-chemistry models.

Incorporating wind availability into land use regression modelling of air quality in mountainous high-density urban environment.

PubMed

Shi, Yuan; Lau, Kevin Ka-Lun; Ng, Edward

2017-08-01

Urban air quality serves as an important function of the quality of urban life. Land use regression (LUR) modelling of air quality is essential for conducting health impacts assessment but more challenging in mountainous high-density urban scenario due to the complexities of the urban environment. In this study, a total of 21 LUR models are developed for seven kinds of air pollutants (gaseous air pollutants CO, NO 2 , NO x , O 3 , SO 2 and particulate air pollutants PM 2.5 , PM 10 ) with reference to three different time periods (summertime, wintertime and annual average of 5-year long-term hourly monitoring data from local air quality monitoring network) in Hong Kong. Under the mountainous high-density urban scenario, we improved the traditional LUR modelling method by incorporating wind availability information into LUR modelling based on surface geomorphometrical analysis. As a result, 269 independent variables were examined to develop the LUR models by using the "ADDRESS" independent variable selection method and stepwise multiple linear regression (MLR). Cross validation has been performed for each resultant model. The results show that wind-related variables are included in most of the resultant models as statistically significant independent variables. Compared with the traditional method, a maximum increase of 20% was achieved in the prediction performance of annual averaged NO 2 concentration level by incorporating wind-related variables into LUR model development. Copyright © 2017 Elsevier Inc. All rights reserved.

Spatial Representation in Blind Children. 3: Effects of Individual Differences.

ERIC Educational Resources Information Center

Fletcher, Janet F.

1981-01-01

Data from a study of spatial representation in blind children were subjected to two stepwise regression analyses to determine the relationships between several subject related variables and responses to "map" (cognitive map) and "route" (sequential memory) questions about the position of furniture in a recently explored room. (Author/SBH)

Personality Factors and Instructional Methods

ERIC Educational Resources Information Center

Hunt, Dennis; Randhawa, Bikkar S.

The Children's Personality Questionnaire (CPQ) was administered to 23 academically handicapped children (mean IQ, 79) and 35 academically gifted students (mean IQ, 147). The CPQ measures 14 distinct personality factors; data on these variables were analyzed using a 2 x 2 (academic ability x sex) analysis of variance design. A stepwise discriminant…

Knowing When to Retire: The First Step towards Financial Planning in Malaysia

ERIC Educational Resources Information Center

Kock, Tan Hoe; Yoong, Folk Jee

2011-01-01

This article draws upon expected retirement age cohorts as a main determinant to financial planning preparation in Malaysia. The return rate was 55% from 600 questionnaires distributed. Five hypotheses were analyzed using hierarchical and stepwise regression analysis. The results revealed that expected retirement age cohort variables made…

Relationships between Lifestyle, Living Environments, and Incidence of Hypertension in Japan (in Men): Based on Participant’s Data from the Nationwide Medical Check-Up

PubMed Central

Oka, Mayumi; Yamamoto, Mio; Mure, Kanae; Takeshita, Tatsuya; Arita, Mikio

2016-01-01

This study aims to investigate factors that contribute to the differences in incidence of hypertension between different regions in Japan, by accounting for not only individual lifestyles, but also their living environments. The target participants of this survey were individuals who received medical treatment for hypertension, as well as hypertension patients who have not received any treatment. The objective variable for analysis was the incidence of hypertension as data aggregated per prefecture. We used data (in men) including obesity, salt intake, vegetable intake, habitual alcohol consumption, habitual smoking, and number of steps walked per day. The variables within living environment included number of rail stations, standard/light vehicle usage, and slope of habitable land. In addition, we analyzed data for the variables related to medical environment including, participation rate in medical check-ups and number of hospitals. We performed multiple stepwise regression analyses to elucidate the correlation of these variables by using hypertension incidence as the objective variable. Hypertension incidence showed a significant negative correlation with walking and medical check-ups, and a significant positive correlation with light-vehicle usage and slope. Between the number of steps and variables related to the living environment, number of rail stations showed a significant positive correlation, while, standard- and light-vehicle usage showed significant negative correlation. Moreover, with stepwise multiple regression analysis, walking showed the strongest effect. The differences in daily walking based on living environment were associated with the disparities in the hypertension incidence in Japan. PMID:27788198

Modeling the language learning strategies and English language proficiency of pre-university students in UMS: A case study

NASA Astrophysics Data System (ADS)

Kiram, J. J.; Sulaiman, J.; Swanto, S.; Din, W. A.

2015-10-01

This study aims to construct a mathematical model of the relationship between a student's Language Learning Strategy usage and English Language proficiency. Fifty-six pre-university students of University Malaysia Sabah participated in this study. A self-report questionnaire called the Strategy Inventory for Language Learning was administered to them to measure their language learning strategy preferences before they sat for the Malaysian University English Test (MUET), the results of which were utilised to measure their English language proficiency. We attempted the model assessment specific to Multiple Linear Regression Analysis subject to variable selection using Stepwise regression. We conducted various assessments to the model obtained, including the Global F-test, Root Mean Square Error and R-squared. The model obtained suggests that not all language learning strategies should be included in the model in an attempt to predict Language Proficiency.

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.

Perceived Social Support, Self-Esteem, and Internet Addiction Among Students of Al-Zahra University, Tehran, Iran.

PubMed

Naseri, Laila; Mohamadi, Jalal; Sayehmiri, Koroush; Azizpoor, Yosra

2015-09-01

Internet addiction is a global phenomenon that causes serious problems in mental health and social communication. Students form a vulnerable group, since they have free, easy, and daily access to the internet. The current study aimed to investigate perceived social support, self-esteem, and internet addiction among Al-Zahra University students. In the current descriptive research, the statistical sample consisted of 101 female students residing at AL-Zahra University dormitory, Tehran, Iran. Participants were randomly selected and their identities were classified. Then, they completed the Multidimensional Scale of Perceived Social Support, Rosenberg's Self-esteem Scale, and Yang Internet Addiction Test. After completion of the questionnaires, the data were analyzed using the correlation test and stepwise regression. The Pearson correlation coefficient indicated significant relationships between self-esteem and internet addiction (P < 0.05, r = -0.345), perceived social support (r = 0.224, P < 0.05), and the subscale of family (r = 0.311, P < 0.05). The findings also demonstrated a significant relationship between internet addiction and perceived social support (r = -0.332, P < 0.05), the subscale of family (P < 0.05, r = -0.402), and the other subscales (P < 0.05, r = -0.287). Results of the stepwise regression showed that the scale of internet addiction and the family subscale were predicative variables for self-esteem (r = 0.137, P < 0.01, F2, 96 = 77.7). Findings of the current study showed that persons with low self-esteem were more vulnerable to internet addiction.

Prediction of reported consumption of selected fat-containing foods.

PubMed

Tuorila, H; Pangborn, R M

1988-10-01

A total of 100 American females (mean age = 20.8 years) completed a questionnaire, in which their beliefs, evaluations, liking and consumption (frequency, consumption compared to others, intention to consume) of milk, cheese, ice cream, chocolate and "high-fat foods" were measured. For the design and analysis, the basic frame of reference was the Fishbein-Ajzen model of reasoned action, but the final analyses were carried out with stepwise multiple regression analysis. In addition to the components of the Fishbein-Ajzen model, beliefs and evaluations were used as independent variables. On the average, subjects reported liking all the products but not "high-fat foods", and thought that milk and cheese were "good for you" whereas the remaining items were "bad for you". Principal component analysis for beliefs revealed factors related to pleasantness/benefit aspects, to health and weight concern and to the "functionality" of the foods. In stepwise multiple regression analyses, liking was the predominant predictor of reported consumption for all the foods, but various belief factors, particularly those related to concern with weight, also significantly predicted consumption. Social factors played only a minor role. The multiple R's of the predictive functions varied from 0.49 to 0.74. The fact that all four foods studied elicited individual sets of beliefs and belief structures, and that none of them was rated similar to the generic "high-fat foods", emphasizes that consumers attach meaning to integrated food entities rather than to ingredients.

Impacts of different climate change regimes and extreme climatic events on an alpine meadow community

PubMed Central

Alatalo, Juha M.; Jägerbrand, Annika K.; Molau, Ulf

2016-01-01

Climate variability is expected to increase in future but there exist very few experimental studies that apply different warming regimes on plant communities over several years. We studied an alpine meadow community under three warming regimes over three years. Treatments consisted of (a) a constant level of warming with open-top chambers (ca. 1.9 °C above ambient), (b) yearly stepwise increases in warming (increases of ca. 1.0, 1.9 and 3.5 °C), and (c) pulse warming, a single first-year pulse event of warming (increase of ca. 3.5 °C). Pulse warming and stepwise warming was hypothesised to cause distinct first-year and third-year effects, respectively. We found support for both hypotheses; however, the responses varied among measurement levels (whole community, canopy, bottom layer, and plant functional groups), treatments, and time. Our study revealed complex responses of the alpine plant community to the different experimentally imposed climate warming regimes. Plant cover, height and biomass frequently responded distinctly to the constant level of warming, the stepwise increase in warming and the extreme pulse-warming event. Notably, we found that stepwise warming had an accumulating effect on biomass, the responses to the different warming regimes varied among functional groups, and the short-term perturbations had negative effect on species richness and diversity PMID:26888225

The Effect of College Selection Factors on Persistence: An Examination of Black and Latino Males in the Community College

ERIC Educational Resources Information Center

Wood, J. Luke; Harris, Frank, III

2015-01-01

The purpose of this study was to understand the relationship (if any) between college selection factors and persistence for Black and Latino males in the community college. Using data derived from the Educational Longitudinal Study, backwards stepwise logistic regression models were developed for both groups. Findings are contextualized in light…

Computational technique for stepwise quantitative assessment of equation correctness

NASA Astrophysics Data System (ADS)

Othman, Nuru'l Izzah; Bakar, Zainab Abu

2017-04-01

Many of the computer-aided mathematics assessment systems that are available today possess the capability to implement stepwise correctness checking of a working scheme for solving equations. The computational technique for assessing the correctness of each response in the scheme mainly involves checking the mathematical equivalence and providing qualitative feedback. This paper presents a technique, known as the Stepwise Correctness Checking and Scoring (SCCS) technique that checks the correctness of each equation in terms of structural equivalence and provides quantitative feedback. The technique, which is based on the Multiset framework, adapts certain techniques from textual information retrieval involving tokenization, document modelling and similarity evaluation. The performance of the SCCS technique was tested using worked solutions on solving linear algebraic equations in one variable. 350 working schemes comprising of 1385 responses were collected using a marking engine prototype, which has been developed based on the technique. The results show that both the automated analytical scores and the automated overall scores generated by the marking engine exhibit high percent agreement, high correlation and high degree of agreement with manual scores with small average absolute and mixed errors.

Suicidal Ideation and Schizophrenia: Contribution of Appraisal, Stigmatization, and Cognition.

PubMed

Stip, Emmanuel; Caron, Jean; Tousignant, Michel; Lecomte, Yves

2017-10-01

To predict suicidal ideation in people with schizophrenia, certain studies have measured its relationship with the variables of defeat and entrapment. The relationships are positive, but their interactions remain undefined. To further their understanding, this research sought to measure the relationship between suicidal ideation with the variables of loss, entrapment, and humiliation. The convenience sample included 30 patients with schizophrenia spectrum disorders. The study was prospective (3 measurement times) during a 6-month period. Results were analyzed by stepwise multiple regression. The contribution of the 3 variables to the variance of suicidal ideation was not significant at any of the 3 times (T1: 16.2%, P = 0.056; T2: 19.9%, P = 0.117; T3: 11.2%, P = 0.109). Further analyses measured the relationship between the variables of stigmatization, perceived cognitive dysfunction, symptoms, depression, self-esteem, reason to live, spirituality, social provision, and suicidal ideation. Stepwise multiple regression demonstrated that the contribution of the variables of stigmatization and perceived cognitive dysfunction to the variance of suicidal ideation was significant at all 3 times (T1: 41.7.5%, P = 0.000; T2: 35.2%, P = 0.001; T3: 21.5%, P = 0.012). Yet, over time, the individual contribution of the variables changed: T1, stigmatization (β = 0.518; P = 0.002); T2, stigmatization (β = 0.394; P = 0.025) and perceived cognitive dysfunction (β = 0.349; P = 0.046). Then, at T3, only perceived cognitive dysfunction contributed significantly to suicidal ideation (β = 0.438; P = 0.016). The results highlight the importance of the contribution of the variables of perceived cognitive dysfunction and stigmatization in the onset of suicidal ideation in people with schizophrenia spectrum disorders.

Suicidal Ideation and Schizophrenia: Contribution of Appraisal, Stigmatization, and Cognition

PubMed Central

Stip, Emmanuel; Caron, Jean; Tousignant, Michel

2017-01-01

Objective: To predict suicidal ideation in people with schizophrenia, certain studies have measured its relationship with the variables of defeat and entrapment. The relationships are positive, but their interactions remain undefined. To further their understanding, this research sought to measure the relationship between suicidal ideation with the variables of loss, entrapment, and humiliation. Method: The convenience sample included 30 patients with schizophrenia spectrum disorders. The study was prospective (3 measurement times) during a 6-month period. Results were analyzed by stepwise multiple regression. Results: The contribution of the 3 variables to the variance of suicidal ideation was not significant at any of the 3 times (T1: 16.2%, P = 0.056; T2: 19.9%, P = 0.117; T3: 11.2%, P = 0.109). Further analyses measured the relationship between the variables of stigmatization, perceived cognitive dysfunction, symptoms, depression, self-esteem, reason to live, spirituality, social provision, and suicidal ideation. Stepwise multiple regression demonstrated that the contribution of the variables of stigmatization and perceived cognitive dysfunction to the variance of suicidal ideation was significant at all 3 times (T1: 41.7.5%, P = 0.000; T2: 35.2%, P = 0.001; T3: 21.5%, P = 0.012). Yet, over time, the individual contribution of the variables changed: T1, stigmatization (β = 0.518; P = 0.002); T2, stigmatization (β = 0.394; P = 0.025) and perceived cognitive dysfunction (β = 0.349; P = 0.046). Then, at T3, only perceived cognitive dysfunction contributed significantly to suicidal ideation (β = 0.438; P = 0.016). Conclusion: The results highlight the importance of the contribution of the variables of perceived cognitive dysfunction and stigmatization in the onset of suicidal ideation in people with schizophrenia spectrum disorders. PMID:28673099

What makes patients with fibromyalgia feel better? Correlations between Patient Global Impression of Improvement and changes in clinical symptoms and function: a pooled analysis of 4 randomized placebo-controlled trials of duloxetine.

PubMed

Hudson, James I; Arnold, Lesley M; Bradley, Laurence A; Choy, Ernest H S; Mease, Philip J; Wang, Fujun; Ahl, Jonna; Wohlreich, Madelaine M

2009-11-01

To investigate the relationship between changes in clinical rating scale items and endpoint Patient Global Impression of Improvement (PGI-I). Data were pooled from 4 randomized, double-blind, placebo-controlled studies of duloxetine in patients with fibromyalgia (FM). Variables included in the analyses were those that assessed symptoms in FM domains of pain, fatigue, sleep, cognitive difficulties, emotional well-being, physical function, and impact on daily living. The association of endpoint PGI-I with changes from baseline in individual variables was assessed using Pearson product-moment correlations (r). Stepwise linear regression was used to identify those variables for which changes from baseline were statistically significant independent predictors of the endpoint PGI-I ratings. Changes in pain variables and interference of symptoms with the ability to work were highly correlated (r >or= 0.5 or r

Cognitive functioning and insight in schizophrenia and in schizoaffective disorder.

PubMed

Birindelli, Nadia; Montemagni, Cristiana; Crivelli, Barbara; Bava, Irene; Mancini, Irene; Rocca, Paola

2014-01-01

The aim of this study was to investigate cognitive functioning and insight of illness in two groups of patients during their stable phases, one with schizophrenia and one with schizoaffective disorder. We recruited 104 consecutive outpatients, 64 with schizophrenia, 40 with schizoaffective disorder, in the period between July 2010 and July 2011. They all fulfilled formal Diagnostic and Statistical Manual of Mental disorders (DSM-IV-TR) diagnostic criteria for schizophrenia and schizoaffective disorder. Psychiatric assessment included the Clinical Global Impression Scale-Severity (CGI-S), the Positive and Negative Sindrome Scale (PANSS), the Calgary Depression Scale for Schizophrenia (CDSS) and the Global Assessment of Functioning (GAF). Insight of illness was evaluated using SUMD. Neuropsychological assessment included Winsconsin Card Sorting Test (WCST), California Verbal Learning Test (CVLT), Stroop Test and Trail Making Test (TMT). Differences between the groups were tested using Chi-square test for categorical variables and one-way analysis of variance (ANOVA) for continuous variables. All variables significantly different between the two groups of subjects were subsequently analysed using a logistic regression with a backward stepwise procedure using diagnosis (schizophrenia/schizoaffective disorder) as dependent variable. After backward selection of variables, four variables predicted a schizoaffective disorder diagnosis: marital status, a higher number of admission, better attentive functions and awareness of specific signs or symptoms of disease. The prediction model accounted for 55% of the variance of schizoaffective disorder diagnosis. With replication, our findings would allow higher diagnostic accuracy and have an impact on clinical decision making, in light of an amelioration of vocational functioning.

Causal correlation of foliar biochemical concentrations with AVIRIS spectra using forced entry linear regression

NASA Technical Reports Server (NTRS)

Dawson, Terence P.; Curran, Paul J.; Kupiec, John A.

1995-01-01

A major goal of airborne imaging spectrometry is to estimate the biochemical composition of vegetation canopies from reflectance spectra. Remotely-sensed estimates of foliar biochemical concentrations of forests would provide valuable indicators of ecosystem function at regional and eventually global scales. Empirical research has shown a relationship exists between the amount of radiation reflected from absorption features and the concentration of given biochemicals in leaves and canopies (Matson et al., 1994, Johnson et al., 1994). A technique commonly used to determine which wavelengths have the strongest correlation with the biochemical of interest is unguided (stepwise) multiple regression. Wavelengths are entered into a multivariate regression equation, in their order of importance, each contributing to the reduction of the variance in the measured biochemical concentration. A significant problem with the use of stepwise regression for determining the correlation between biochemical concentration and spectra is that of 'overfitting' as there are significantly more wavebands than biochemical measurements. This could result in the selection of wavebands which may be more accurately attributable to noise or canopy effects. In addition, there is a real problem of collinearity in that the individual biochemical concentrations may covary. A strong correlation between the reflectance at a given wavelength and the concentration of a biochemical of interest, therefore, may be due to the effect of another biochemical which is closely related. Furthermore, it is not always possible to account for potentially suitable waveband omissions in the stepwise selection procedure. This concern about the suitability of stepwise regression has been identified and acknowledged in a number of recent studies (Wessman et al., 1988, Curran, 1989, Curran et al., 1992, Peterson and Hubbard, 1992, Martine and Aber, 1994, Kupiec, 1994). These studies have pointed to the lack of a physical link between wavelengths chosen by stepwise regression and the biochemical of interest, and this in turn has cast doubts on the use of imaging spectrometry for the estimation of foliar biochemical concentrations at sites distant from the training sites. To investigate this problem, an analysis was conducted on the variation in canopy biochemical concentrations and reflectance spectra using forced entry linear regression.

Association between Satisfaction with State of Health and Meals, Physical Condition and Food Diversity, Health Behavior, and Perceptions of Shopping Difficulty among Older People Living Alone in Japan.

PubMed

Ishikawa, M; Yokoyama, T; Takemi, Y; Fukuda, Y; Nakaya, T; Kusama, K; Yoshiike, N; Nozue, M; Yoshiba, K; Murayama, N

2017-01-01

This study aimed to examine perceptions of shopping difficulty, and the relationships with satisfaction with state of health and meals, physical condition, food diversity and health behavior in older people living alone in Japan. A cross-sectional, multilevel survey was designed. The questionnaire was distributed by mail and self-completed by participants. The sample was drawn from seven towns and cities across Japan. A geographic information system was used to select the sample of older people living alone, by proximity to a supermarket. In total, 2,346 older people (827 men and 1,519 women) completed the questionnaire. The dependent variable was whether shopping was easy or difficult. A logistic regression analysis was performed, adjusting for age, socioeconomic status and proximity of residence to a supermarket using stepwise variable analyses. The response rate was 67.8%. Overall, 14.5% of men and 21.7% of women considered shopping difficult. The stepwise logistic analysis showed that the factors most strongly related to shopping difficulty were a subjective feeling of poor health (men: OR = 3.01, women: OR = 2.16) and lack of satisfaction with meals (men: OR = 2.82, women: OR = 3.69). Other related physical condition and dietary factors were requiring nursing care (men: OR = 3.69, women: OR = 1.54), a high level of frailty, measured using the frailty index score (women: OR = 0.36) and low food diversity score (men: OR = 1.84, women: OR = 1.36). The study found that older people's assessment of their shopping difficulty was related to satisfaction aspects, including a subjective feeling of poor health, and lack of satisfaction with meals, as well as physical condition. These have a greater influence on shopping difficulty than income in both sexes, and proximity to a supermarket in women.

COPD: A stepwise or a hit hard approach?

PubMed

Ferreira, A J; Reis, A; Marçal, N; Pinto, P; Bárbara, C

2016-01-01

Current guidelines differ slightly on the recommendations for treatment of Chronic Obstructive Pulmonary Disease (COPD) patients, and although there are some undisputed recommendations, there is still debate regarding the management of COPD. One of the hindrances to deciding which therapeutic approach to choose is late diagnosis or misdiagnosis of COPD. After a proper diagnosis is achieved and severity assessed, the choice between a stepwise or "hit hard" approach has to be made. For GOLD A patients the stepwise approach is recommended, whilst for B, C and D patients this remains debatable. Moreover, in patients for whom inhaled corticosteroids (ICS) are recommended, a step-up or "hit hard" approach with triple therapy will depend on the patient's characteristics and, for patients who are being over-treated with ICS, ICS withdrawal should be performed, in order to optimize therapy and reduce excessive medications. This paper discusses and proposes stepwise, "hit hard", step-up and ICS withdrawal therapeutic approaches for COPD patients based on their GOLD group. We conclude that all approaches have benefits, and only a careful patient selection will determine which approach is better, and which patients will benefit the most from each approach. Copyright © 2016 Sociedade Portuguesa de Pneumologia. Published by Elsevier España, S.L.U. All rights reserved.

Transverse Motion of a Particle with an Oscillating Charge and Variable Mass in a Magnetic Field

NASA Astrophysics Data System (ADS)

Alisultanov, Z. Z.; Ragimkhanov, G. B.

2018-03-01

The problem of motion of a particle with an oscillating electric charge and variable mass in an uniform magnetic field has been solved. Three laws of mass variation have been considered: linear growth, oscillations, and stepwise growth. Analytical expressions for the particle velocity at different time dependences of the particle mass are obtained. It is established that simultaneous consideration of changes in the mass and charge leads to a significant change in the particle trajectory.

Classification of Kiwifruit Grades Based on Fruit Shape Using a Single Camera

PubMed Central

Fu, Longsheng; Sun, Shipeng; Li, Rui; Wang, Shaojin

2016-01-01

This study aims to demonstrate the feasibility for classifying kiwifruit into shape grades by adding a single camera to current Chinese sorting lines equipped with weight sensors. Image processing methods are employed to calculate fruit length, maximum diameter of the equatorial section, and projected area. A stepwise multiple linear regression method is applied to select significant variables for predicting minimum diameter of the equatorial section and volume and to establish corresponding estimation models. Results show that length, maximum diameter of the equatorial section and weight are selected to predict the minimum diameter of the equatorial section, with the coefficient of determination of only 0.82 when compared to manual measurements. Weight and length are then selected to estimate the volume, which is in good agreement with the measured one with the coefficient of determination of 0.98. Fruit classification based on the estimated minimum diameter of the equatorial section achieves a low success rate of 84.6%, which is significantly improved using a linear combination of the length/maximum diameter of the equatorial section and projected area/length ratios, reaching 98.3%. Thus, it is possible for Chinese kiwifruit sorting lines to reach international standards of grading kiwifruit on fruit shape classification by adding a single camera. PMID:27376292

Discriminant Analysis as a Tool for Admission Selection to Special Academic Programs. AIR 1986 Annual Forum Paper.

ERIC Educational Resources Information Center

Kissel, Mary Ann

The use of stepwise discriminant analysis as a means to select entering students who would benefit from a special program for the disadvantaged was studied. In fall 1984, 278 full-time black students were admitted as first-time students to a large urban university. Of the total, 200 entered a special program for the disadvantaged and 78 entered…

Cognitive Strategies and Physical Activity in Older Adults: A Discriminant Analysis

PubMed Central

Ferrand, Claude; Audiffren, Michel

2018-01-01

Background Although a number of studies have examined sociodemographic, psychosocial, and environmental determinants of the level of physical activity (PA) for older people, little attention has been paid to the predictive power of cognitive strategies for independently living older adults. However, cognitive strategies have recently been considered to be critical in the management of day-to-day living. Methods Data were collected from 243 men and women aged 55 years and older living in France using face-to-face interviews between 2011 and 2013. Results A stepwise discriminant analysis selected five predictor variables (age, perceived health status, barriers' self-efficacy, internal memory, and attentional control strategies) of the level of PA. The function showed that the rate of correct prediction was 73% for the level of PA. The calculated discriminant function based on the five predictor variables is useful for detecting individuals at high risk of lapses once engaged in regular PA. Conclusions This study highlighted the need to consider cognitive functions as a determinant of the level of PA and, more specifically, those cognitive functions related to executive functions (internal memory and attentional control), to facilitate the maintenance of regular PA. These results are discussed in relation to successful aging. PMID:29850247

Cognitive Strategies and Physical Activity in Older Adults: A Discriminant Analysis.

PubMed

André, Nathalie; Ferrand, Claude; Albinet, Cédric; Audiffren, Michel

2018-01-01

Although a number of studies have examined sociodemographic, psychosocial, and environmental determinants of the level of physical activity (PA) for older people, little attention has been paid to the predictive power of cognitive strategies for independently living older adults. However, cognitive strategies have recently been considered to be critical in the management of day-to-day living. Data were collected from 243 men and women aged 55 years and older living in France using face-to-face interviews between 2011 and 2013. A stepwise discriminant analysis selected five predictor variables (age, perceived health status, barriers' self-efficacy, internal memory, and attentional control strategies) of the level of PA. The function showed that the rate of correct prediction was 73% for the level of PA. The calculated discriminant function based on the five predictor variables is useful for detecting individuals at high risk of lapses once engaged in regular PA. This study highlighted the need to consider cognitive functions as a determinant of the level of PA and, more specifically, those cognitive functions related to executive functions (internal memory and attentional control), to facilitate the maintenance of regular PA. These results are discussed in relation to successful aging.

Identification of environmental covariates of West Nile virus vector mosquito population abundance.

PubMed

Trawinski, Patricia R; Mackay, D Scott

2010-06-01

The rapid spread of West Nile virus (WNv) in North America is a major public health concern. Culex pipiens-restuans is the principle mosquito vector of WNv in the northeastern United States while Aedes vexans is an important bridge vector of the virus in this region. Vector mosquito abundance is directly dependent on physical environmental factors that provide mosquito habitats. The objective of this research is to determine landscape elements that explain the population abundance and distribution of WNv vector mosquitoes using stepwise linear regression. We developed a novel approach for examining a large set of landscape variables based on a land use and land cover classification by selecting variables in stages to minimize multicollinearity. We also investigated the distance at which landscape elements influence abundance of vector populations using buffer distances of 200, 400, and 1000 m. Results show landscape effects have a significant impact on Cx. pipiens-estuans population distribution while the effects of landscape features are less important for prediction of Ae. vexans population distributions. Cx. pipiens-restuans population abundance is positively correlated with human population density, housing unit density, and urban land use and land cover classes and negatively correlated with age of dwellings and amount of forested land.

Cation Selectivity in Biological Cation Channels Using Experimental Structural Information and Statistical Mechanical Simulation.

PubMed

Finnerty, Justin John; Peyser, Alexander; Carloni, Paolo

2015-01-01

Cation selective channels constitute the gate for ion currents through the cell membrane. Here we present an improved statistical mechanical model based on atomistic structural information, cation hydration state and without tuned parameters that reproduces the selectivity of biological Na+ and Ca2+ ion channels. The importance of the inclusion of step-wise cation hydration in these results confirms the essential role partial dehydration plays in the bacterial Na+ channels. The model, proven reliable against experimental data, could be straightforwardly used for designing Na+ and Ca2+ selective nanopores.

Social Conditions and High Levels of Dental Caries in Five-year-old Children in Brazil.

PubMed

Dantas Cabral de Melo, Márcia M; de Souza, Wayner Vieira; Tavares, Maria Cristina; de Lima, Maria Luiza Carvalho; Jamelli, Silvia; Couto, Geraldo Bosco Lindoso

2015-01-01

To identify factors associated with dental caries experience in at least four primary teeth in five-year-old Brazilian children. This was a case-control study, part of a prior investigation of the prevalence of dental caries in the primary dentition of five-year-old children conducted in 2006 in public health services in Recife, Brazil. Study patients had a decayed, missing, and filled teeth [dmft] score ≥ 4 and controls had a dmft score ≤ 3. The cutoff point was based on the dmft scores mean value of the study population. Categories of independent variables were sociodemographic, family structure, oral health behavior, and use of oral health services. Crude odds ratios and 95% CI were calculated. Variables associated with dmft greater than or equal to four at a significance level of P≤.20 in univariate analyses were included in multivariate logistic regression models using a backward stepwise variable selection method and permanence criterion in the final model of P≤.10. The study included 479 children (171 study patients and 308 controls). After controlling for confounding variables, factors associated with a dmft score ≥ 4 were children living in households with at least six people, residence in a poor area, caregiver's low educational level, consumption of sweets between meals, and the reason for and location of oral health care seeking. Most factors associated with high levels of dental caries in five-year-old children were related to the social conditions in which they lived.

Variation of facial features among three African populations: Body height match analyses.

PubMed

Taura, M G; Adamu, L H; Gudaji, A

2017-01-01

Body height is one of the variables that show a correlation with facial craniometry. Here we seek to discriminate the three populations (Nigerians, Ugandans and Kenyans) using facial craniometry based on different categories of body height of adult males. A total of 513 individuals comprising 234 Nigerians, 169 Ugandans and 110 Kenyans with mean age of 25.27, s=5.13 (18-40 years) participated. Paired and unpaired facial features were measured using direct craniometry. Multivariate and stepwise discriminate function analyses were used for differentiation of the three populations. The result showed significant overall facial differences among the three populations in all the body height categories. Skull height, total facial height, outer canthal distance, exophthalmometry, right ear width and nasal length were significantly different among the three different populations irrespective of body height categories. Other variables were sensitive to body height. Stepwise discriminant function analyses included maximum of six variables for better discrimination between the three populations. The single best discriminator of the groups was total facial height, however, for body height >1.70m the single best discriminator was nasal length. Most of the variables were better used with function 1, hence, better discrimination than function 2. In conclusion, adult body height in addition to other factors such as age, sex, and ethnicity should be considered in making decision on facial craniometry. However, not all the facial linear dimensions were sensitive to body height. Copyright © 2016 Elsevier GmbH. All rights reserved.

Mission requirements for a manned earth observatory. Volume 1, task 1: Experiment selection, definition, and documentation

NASA Technical Reports Server (NTRS)

1973-01-01

Information related to proposed earth observation experiments for shuttle sortie missions (SSM) in the 1980's is presented. The step-wise progression of study activities and the development of the rationale that led to the identification, selection, and description of earth observation experiments for SSM are listed. The selected experiments are described, defined, and documented by individual disciplines. These disciplines include: oceanography; meteorology; agriculture, forestry, and rangeland; geology; hydrology; and environmental impact.

D-Cycloserine Augmentation of Cognitive Behavioral Group Therapy of Social Anxiety Disorder: Prognostic and Prescriptive Variables

PubMed Central

Smits, Jasper A. J.; Hofmann, Stefan G.; Rosenfield, David; DeBoer, Lindsey B.; Costa, Paul T.; Simon, Naomi M.; O'Cleirigh, Conall; Meuret, Alicia E.; Marques, Luana; Otto, Michael W.; Pollack, Mark H.

2014-01-01

Objective The aim of the current study was to identify individual characteristics that (1) predict symptom improvement with group cognitive behavioral therapy (CBT) for social anxiety disorder (SAD; i.e., prognostic variables) or (2) moderate the effects of d-cycloserine vs. placebo augmentation of CBT for SAD (i.e., prescriptive variables). Method Adults with SAD (N=169) provided Liebowitz Social Anxiety Scale (LSAS) scores in a trial evaluating DCS augmentation of group CBT. Rate of symptom improvement during therapy and posttreatment symptom severity were evaluated using multilevel modeling. As predictors of these two parameters, we selected the range of variables assessed at baseline (demographic characteristics, clinical characteristics, personality traits). Using step-wise analyses, we first identified prognostic and prescriptive variables within each of these domains and then entered these significant predictors simultaneously in one final model. Results African American ethnicity and cohabitation status were associated with greater overall rates of improvement during therapy and lower posttreatment severity. Higher initial severity was associated with a greater improvement during therapy, but also higher posttreatment severity (the greater improvement was not enough to overcome the initial higher severity). D-cycloserine augmentation was evident only among individuals low in conscientiousness and high in agreeableness. Conclusions African American ethnicity, cohabitation status, and initial severity are prognostic of favorable CBT outcomes in SAD. D-cycloserine augmentation appears particularly useful for patients low in conscientiousness and high in agreeableness. These findings can guide clinicians in making decisions about treatment strategies and can help direct research on the mechanisms of these treatments. PMID:23937345

Statistical and dynamical assessment of land-ocean-atmosphere interactions across North Africa

NASA Astrophysics Data System (ADS)

Yu, Yan

North Africa is highly vulnerable to hydrologic variability and extremes, including impacts of climate change. The current understanding of oceanic versus terrestrial drivers of North African droughts and pluvials is largely model-based, with vast disagreement among models in terms of the simulated oceanic impacts and vegetation feedbacks. Regarding oceanic impacts, the relative importance of the tropical Pacific, tropical Indian, and tropical Atlantic Oceans in regulating the North African rainfall variability, as well as the underlying mechanism, remains debated among different modeling studies. Classic theory of land-atmosphere interactions across the Sahel ecotone, largely based on climate modeling experiments, has promoted positive vegetation-rainfall feedbacks associated with a dominant surface albedo mechanism. However, neither the proposed positive vegetation-rainfall feedback with its underlying albedo mechanism, nor its relative importance compared with oceanic drivers, has been convincingly demonstrated up to now using observational data. Here, the multivariate Generalized Equilibrium Feedback Assessment (GEFA) is applied in order to identify the observed oceanic and terrestrial drivers of North African climate and quantify their impacts. The reliability of the statistical GEFA method is first evaluated against dynamical experiments within the Community Earth System Model (CESM). In order to reduce the sampling error caused by short data records, the traditional GEFA approach is refined through stepwise GEFA, in which unimportant forcings are dropped through stepwise selection. In order to evaluate GEFA's reliability in capturing oceanic impacts, the atmospheric response to a sea-surface temperature (SST) forcing across the tropical Pacific, tropical Indian, and tropical Atlantic Ocean is estimated independently through ensembles of dynamical experiments and compared with GEFA-based assessments. Furthermore, GEFA's performance in capturing terrestrial impacts is evaluated through ensembles of fully coupled CESM dynamical experiments, with modified leaf area index (LAI) and soil moisture across the Sahel or West African Monsoon (WAM) region. The atmospheric responses to oceanic and terrestrial forcings are generally consistent between the dynamical experiments and statistical GEFA, confirming GEFA's capability of isolating the individual impacts of oceanic and terrestrial forcings on North African climate. Furthermore, with the incorporation of stepwise selection, GEFA can now provide reliable estimates of the oceanic and terrestrial impacts on the North African climate with the typical length of observational datasets, thereby enhancing the method's applicability. After the successful validation of GEFA, the key observed oceanic and terrestrial drivers of North African climate are identified through the application of GEFA to gridded observations, remote sensing products, and reanalyses. According to GEFA, oceanic drivers dominate over terrestrial drivers in terms of their observed impacts on North African climate in most seasons. Terrestrial impacts are comparable to, or more important than, oceanic impacts on rainfall during the post-monsoon across the Sahel and WAM region, and after the short rain across the Horn of Africa (HOA). The key ocean basins that regulate North African rainfall are typically located in the tropics. While the observed impacts of SST variability across the tropical Pacific and tropical Atlantic Oceans on the Sahel rainfall are largely consistent with previous model-based findings, minimal impacts from tropical Indian Ocean variability on Sahel rainfall are identified in observations, in contrast to previous modeling studies. The current observational analysis verifies model-hypothesized positive vegetation-rainfall feedback across the Sahel and HOA, which is confined to the post-monsoon and post-short rains season, respectively. However, the observed positive vegetation feedback to rainfall in the semi-arid Sahel and HOA is largely due to moisture recycling, rather than the classic albedo mechanism. Future projections of Sahel rainfall remain highly uncertain in terms of both sign and magnitude within phases three and five of the Coupled Model Intercomparison Project (CMIP3 and CMIP5). The GEFA-based observational analyses will provide a benchmark for evaluating climate models, which will facilitate effective process-based model weighting for more reliable projections of regional climate, as well as model development.

Oak decline risk rating for the southeastern United States

Treesearch

S. Oak; F. Tainter; J. Williams; D. Starkey

1996-01-01

Oak decline risk rating models were developed for upland hardwood forests in the southeastern United States using data gathered during regional oak decline surveys. Stepwise discriminant analyses were used to relate 12 stand and site variables with major oak decline incidence for each of three subregions plus one incorporating all subregions. The best model for the...

Simple models for estimating local removals of timber in the northeast

Treesearch

David N. Larsen; David A. Gansner

1975-01-01

Provides a practical method of estimating subregional removals of timber and demonstrates its application to a typical problem. Stepwise multiple regression analysis is used to develop equations for estimating removals of softwood, hardwood, and all timber from selected characteristics of socioeconomic structure.

Prediction of Carcass Composition Using Carcass Grading Traits in Hanwoo Steers.

PubMed

Lee, Jooyoung; Won, Seunggun; Lee, Jeongkoo; Kim, Jongbok

2016-09-01

The prediction of carcass composition in Hanwoo steers is very important for value-based marketing, and the improvement of prediction accuracy and precision can be achieved through the analyses of independent variables using a prediction equation with a sufficient dataset. The present study was conducted to develop a prediction equation for Hanwoo carcass composition for which data was collected from 7,907 Hanwoo steers raised at a private farm in Gangwon Province, South Korea, and slaughtered in the period between January 2009 and September 2014. Carcass traits such as carcass weight (CWT), back fat thickness (BFT), eye-muscle area (EMA), and marbling score (MAR) were used as independent variables for the development of a prediction equation for carcass composition, such as retail cut weight and percentage (RC, and %RC, respectively), trimmed fat weight and percentage (FAT, and %FAT, respectively), and separated bone weight and percentage (BONE, and %BONE), and its feasibility for practical use was evaluated using the estimated retail yield percentage (ELP) currently used in Korea. The equations were functions of all the variables, and the significance was estimated via stepwise regression analyses. Further, the model equations were verified by means of the residual standard deviation and the coefficient of determination (R(2)) between the predicted and observed values. As the results of stepwise analyses, CWT was the most important single variable in the equation for RC and FAT, and BFT was the most important variable for the equation of %RC and %FAT. The precision and accuracy of three variable equation consisting CWT, BFT, and EMA were very similar to those of four variable equation that included all for independent variables (CWT, BFT, EMA, and MAR) in RC and FAT, while the three variable equations provided a more accurate prediction for %RC. Consequently, the three-variable equation might be more appropriate for practical use than the four-variable equation based on its easy and cost-effective measurement. However, a relatively high average difference for the ELP in absolute value implies a revision of the official equation may be required, although the current official equation for predicting RC with three variables is still valid.

A Bayesian method for assessing multiscalespecies-habitat relationships

USGS Publications Warehouse

Stuber, Erica F.; Gruber, Lutz F.; Fontaine, Joseph J.

2017-01-01

ContextScientists face several theoretical and methodological challenges in appropriately describing fundamental wildlife-habitat relationships in models. The spatial scales of habitat relationships are often unknown, and are expected to follow a multi-scale hierarchy. Typical frequentist or information theoretic approaches often suffer under collinearity in multi-scale studies, fail to converge when models are complex or represent an intractable computational burden when candidate model sets are large.ObjectivesOur objective was to implement an automated, Bayesian method for inference on the spatial scales of habitat variables that best predict animal abundance.MethodsWe introduce Bayesian latent indicator scale selection (BLISS), a Bayesian method to select spatial scales of predictors using latent scale indicator variables that are estimated with reversible-jump Markov chain Monte Carlo sampling. BLISS does not suffer from collinearity, and substantially reduces computation time of studies. We present a simulation study to validate our method and apply our method to a case-study of land cover predictors for ring-necked pheasant (Phasianus colchicus) abundance in Nebraska, USA.ResultsOur method returns accurate descriptions of the explanatory power of multiple spatial scales, and unbiased and precise parameter estimates under commonly encountered data limitations including spatial scale autocorrelation, effect size, and sample size. BLISS outperforms commonly used model selection methods including stepwise and AIC, and reduces runtime by 90%.ConclusionsGiven the pervasiveness of scale-dependency in ecology, and the implications of mismatches between the scales of analyses and ecological processes, identifying the spatial scales over which species are integrating habitat information is an important step in understanding species-habitat relationships. BLISS is a widely applicable method for identifying important spatial scales, propagating scale uncertainty, and testing hypotheses of scaling relationships.

Spirometry results (FEV1 and FVC) in young Bantu men from Tanzania vs environmental and family characteristics.

PubMed

Rębacz-Maron, Ewa; Parafiniuk, Mirosław

2014-01-01

The aim of this paper was to examine the extent to which socioeconomic factors, anthropological data and somatic indices influenced the results of spirometric measurements (FEV1 and FVC) in Tanzanian youth. The population studied were young black Bantu men aged 12.8-24.0 years. Analysis was performed for the whole data set (n = 255), as well as separately for two age groups: under 17.5 years (n = 168) and 17.5 + (n = 87). A backward stepwise multiple regression analysis was performed for FEV1 and FVC as dependent variables on socioeconomic and anthropometric data. Multiple regression analysis for the whole group revealed that the socioeconomic and anthropometric data under analysis accounted for 38% of the variation in FEV1. In addition the analysis demonstrated that 34% of the variation in FVC could be accounted for by the variables used in the regression. A significant impact in explaining the variability of FVC was exhibited by the thorax mobility, financial situation of the participants and Pignet-Verwaecka Index. Analysis of the data indicates the significant role of selected socio-economic factors on the development of the biological specimens investigated. There were no perceptible pathologies, and the results can be treated as a credible interpretation of the influence exerted by the environment in which the teenagers under study grew up.

Estimating annoyance to calculated wind turbine shadow flicker is improved when variables associated with wind turbine noise exposure are considered.

PubMed

Voicescu, Sonia A; Michaud, David S; Feder, Katya; Marro, Leonora; Than, John; Guay, Mireille; Denning, Allison; Bower, Tara; van den Berg, Frits; Broner, Norm; Lavigne, Eric

2016-03-01

The Community Noise and Health Study conducted by Health Canada included randomly selected participants aged 18-79 yrs (606 males, 632 females, response rate 78.9%), living between 0.25 and 11.22 km from operational wind turbines. Annoyance to wind turbine noise (WTN) and other features, including shadow flicker (SF) was assessed. The current analysis reports on the degree to which estimating high annoyance to wind turbine shadow flicker (HAWTSF) was improved when variables known to be related to WTN exposure were also considered. As SF exposure increased [calculated as maximum minutes per day (SFm)], HAWTSF increased from 3.8% at 0 ≤ SFm < 10 to 21.1% at SFm ≥ 30, p < 0.0001. For each unit increase in SFm the odds ratio was 2.02 [95% confidence interval: (1.68,2.43)]. Stepwise regression models for HAWTSF had a predictive strength of up to 53% with 10% attributed to SFm. Variables associated with HAWTSF included, but were not limited to, annoyance to other wind turbine-related features, concern for physical safety, and noise sensitivity. Reported dizziness was also retained in the final model at p = 0.0581. Study findings add to the growing science base in this area and may be helpful in identifying factors associated with community reactions to SF exposure from wind turbines.

Co-evolution of MHC class I and variable NK cell receptors in placental mammals.

PubMed

Guethlein, Lisbeth A; Norman, Paul J; Hilton, Hugo G; Parham, Peter

2015-09-01

Shaping natural killer (NK) cell functions in human immunity and reproduction are diverse killer cell immunoglobulin-like receptors (KIRs) that recognize polymorphic MHC class I determinants. A survey of placental mammals suggests that KIRs serve as variable NK cell receptors only in certain primates and artiodactyls. Divergence of the functional and variable KIRs in primates and artiodactyls predates placental reproduction. Among artiodactyls, cattle but not pigs have diverse KIRs. Catarrhine (humans, apes, and Old World monkeys) and platyrrhine (New World monkeys) primates, but not prosimians, have diverse KIRs. Platyrrhine and catarrhine systems of KIR and MHC class I are highly diverged, but within the catarrhines, a stepwise co-evolution of MHC class I and KIR is discerned. In Old World monkeys, diversification focuses on MHC-A and MHC-B and their cognate lineage II KIR. With evolution of C1-bearing MHC-C from MHC-B, as informed by orangutan, the focus changes to MHC-C and its cognate lineage III KIR. Evolution of C2 from C1 and fixation of MHC-C drove further elaboration of MHC-C-specific KIR, as exemplified by chimpanzee. In humans, the evolutionary trajectory changes again. Emerging from reorganization of the KIR locus and selective attenuation of KIR avidity for MHC class I are the functionally distinctive KIR A and KIR B haplotypes. © 2015 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.

Influences of MAA on the porous morphology of P(St-MAA) latex particles produced by batch soap-free emulsion polymerization followed by stepwise alkali/acid post-treatment.

PubMed

Deng, Wei; Li, Ronglong; Zhang, Mengjun; Gong, Lixiang; Kan, Chengyou

2010-09-01

Soap-free P(St-MAA) latex particles with variable styrene (St)/methacrylic acid (MAA) ratio were synthesized by batch emulsion copolymerization at 70 degrees C for 7h, and the particles with porous structure were obtained after stepwise alkali/acid post-treatment. The effects of MAA amount on the particle morphologies after the alkali and the stepwise alkali/acid post-treatments were investigated by transmission electron microscopy (TEM) and scanning electron microscopy (SEM). Results indicated that the alkali-treated latex particles showed anomalous structure with rough surface, and no hollow was clearly identified inside them. When these alkali-treated particles were further treated with acid solution, the particle surface became much smoother, and porous morphology appeared. It was found that when the MAA amount was less than or equal to 4mol%, no obvious morphological variation was observed; while the latex particles showed clearly porous structure as the MAA amount increased to 6mol%; with the further increase of MAA amount to 8mol%, the pore size decreased distinctly. Copyright 2010 Elsevier Inc. All rights reserved.

The relationship between personality traits and sexual self-esteem and its components

PubMed Central

Firoozi, Mahbobe; Azmoude, Elham; Asgharipoor, Negar

2016-01-01

Background: Women's sexual self-esteem is one of the most important factors that affect women's sexual satisfaction and their sexual anxiety. Various aspects of sexual life are blended with the entire personality. Determining the relationship between personality traits and self-concept aspects such as sexual self-esteem leads to better understanding of sexual behavior in people with different personality traits and helps in identifying the psychological variables affecting their sexual performance. The aim this study was to determine the relationship between personality traits and sexual self-esteem. Materials and Methods: This correlation study was performed on 127 married women who referred to selected health care centers of Mashhad in 2014–2015. Data collection tools included NEO personality inventory dimensions and Zeanah and Schwarz sexual self-esteem questionnaire. Data were analyzed through Pearson correlation coefficient test and stepwise regression model. Results: The results of Pearson correlation test showed a significant relationship between neuroticism personality dimension (r = −0.414), extroversion (r = 0.363), agreeableness (r = 0.420), and conscientiousness (r = 0.364) with sexual self-esteem (P < 0.05). The relationship between openness with sexual self-esteem was not significant (P > 0.05). In addition, based on the results of the stepwise regression model, three dimensions of agreeableness, neuroticism, and extraversion could predict 27% of the women's sexual self-esteem variance. Conclusions: The results showed a correlation between women's personality characteristics and their sexual self-esteem. Paying attention to personality characteristics may be important to identify at-risk group or the women having low sexual self-esteem in premarital and family counseling. PMID:27186198

Obstetric antecedents to body-cooling treatment of the newborn infant.

PubMed

Nelson, David B; Lucke, Ashley M; McIntire, Donald D; Sánchez, Pablo J; Leveno, Kenneth J; Chalak, Lina F

2014-08-01

Obstetric antecedents were analyzed in births in which the infant received whole-body cooling for neonatal encephalopathy. This retrospective cohort study included all live-born singleton infants delivered at or beyond 36 weeks' gestation from October 2005 through December 2011. Infants who had received whole-body cooling identified by review of a prospective neonatal registry were compared with a control group comprising the remaining obstetric population delivered at greater than 36 weeks but not cooled. Univariable analysis was followed up by a staged, stepwise selection of variables with the intent to rank significant risk factors for cooling. A total of 86,371 women delivered during the study period and 98 infants received whole-body cooling (1.1 per 1000 live births). Of these 98 infants, 80 newborns (88%) had moderate encephalopathy and 10 (12%) had severe encephalopathy prior to cooling. Maternal age of 15 years or younger, low parity, maternal body habitus (body mass index of ≥40 kg/m(2)), diabetes, preeclampsia, induction, epidural analgesia, chorioamnionitis, length of labor, and mode of delivery were associated with significantly increased risk of infant cooling during a univariable analysis. Catastrophic events to include umbilical cord prolapse (odds ratio [OR], 14; 95% confidence interval [CI], 3-72), placental abruption (OR, 17; 95% CI, 7-44), uterine rupture (OR, 130; 95% CI, 11-1477) were the strongest factors associated with infant cooling after staged-stepwise logistic analysis. A variety of intrapartum characteristics were associated with infant cooling for neonatal encephalopathy, with the most powerful antecedents being umbilical cord prolapse, placental abruption, and uterine rupture. Copyright © 2014 Mosby, Inc. All rights reserved.

Obstetric Antecedents to Body Cooling Treatment of the Newborn Infant

PubMed Central

Nelson, David B.; Lucke, Ashley M.; McIntire, Donald D.; Sánchez, Pablo J.; Leveno, Kenneth J.; Chalak, Lina F.

2014-01-01

Objective Obstetric antecedents were analyzed in births where the infant received whole-body cooling for neonatal encephalopathy. Methods This retrospective cohort study included all live-born singleton infants delivered at or beyond 36 weeks gestation from October 2005 through December 2011. Infants who had received whole-body cooling identified by review of a prospective neonatal registry were compared to a control group comprising the remaining obstetric population delivered at greater than 36 weeks but not cooled. Univariable analysis was followed by a staged, stepwise selection of variables with the intent to rank significant risk factors for cooling. Results A total of 86,371 women delivered during the study period and 98 infants received whole-body cooling (1.1/1,000 livebirths). Of these 98 infants, 80 (88%) newborns had moderate encephalopathy and 10 (12%) had severe encephalopathy prior to cooling. Maternal age less than or equal to 15 years, low parity, maternal body habitus (BMI ≥ 40 kg/m2), diabetes, preeclampsia, induction, epidural analgesia, chorioamnionitis, length of labor, and mode of delivery were associated with significantly increased risk of infant cooling during univariable analysis. Catastrophic events to include umbilical cord prolapse (OR 14; 95%CI, 3–72), placental abruption (OR 17; 95%CI, 7–44), uterine rupture (OR 130; 95%CI, 11–1477) were the strongest factors associated with infant cooling after staged-stepwise logistic analysis. Conclusion A variety of intrapartum characteristics were associated with infant cooling for neonatal encephalopathy with the most powerful antecedents being umbilical cord prolapse, placental abruption, and uterine rupture. PMID:24530976

The relationship between personality traits and sexual self-esteem and its components.

PubMed

Firoozi, Mahbobe; Azmoude, Elham; Asgharipoor, Negar

2016-01-01

Women's sexual self-esteem is one of the most important factors that affect women's sexual satisfaction and their sexual anxiety. Various aspects of sexual life are blended with the entire personality. Determining the relationship between personality traits and self-concept aspects such as sexual self-esteem leads to better understanding of sexual behavior in people with different personality traits and helps in identifying the psychological variables affecting their sexual performance. The aim this study was to determine the relationship between personality traits and sexual self-esteem. This correlation study was performed on 127 married women who referred to selected health care centers of Mashhad in 2014-2015. Data collection tools included NEO personality inventory dimensions and Zeanah and Schwarz sexual self-esteem questionnaire. Data were analyzed through Pearson correlation coefficient test and stepwise regression model. The results of Pearson correlation test showed a significant relationship between neuroticism personality dimension (r = -0.414), extroversion (r = 0.363), agreeableness (r = 0.420), and conscientiousness (r = 0.364) with sexual self-esteem (P < 0.05). The relationship between openness with sexual self-esteem was not significant (P > 0.05). In addition, based on the results of the stepwise regression model, three dimensions of agreeableness, neuroticism, and extraversion could predict 27% of the women's sexual self-esteem variance. The results showed a correlation between women's personality characteristics and their sexual self-esteem. Paying attention to personality characteristics may be important to identify at-risk group or the women having low sexual self-esteem in premarital and family counseling.

Lithium might be associated with better decision-making performance in euthymic bipolar patients.

PubMed

Adida, Marc; Jollant, Fabrice; Clark, Luke; Guillaume, Sebastien; Goodwin, Guy M; Azorin, Jean-Michel; Courtet, Philippe

2015-06-01

Bipolar disorder is associated with impaired decision-making. Little is known about how treatment, especially lithium, influences decision-making abilities in bipolar patients when euthymic. We aimed at testing for an association between lithium medication and decision-making performance in remitted bipolar patients. Decision-making was measured using the Iowa Gambling Task in 3 groups of subjects: 34 and 56 euthymic outpatients with bipolar disorder, treated with lithium (monotherapy and lithium combined with anticonvulsant or antipsychotic) and without lithium (anticonvulsant, antipsychotic and combination treatment), respectively, and 152 matched healthy controls. Performance was compared between the 3 groups. In the 90 euthymic patients, the relationship between different sociodemographic and clinical variables and decision-making was assessed by stepwise multivariate regression analysis. Euthymic patients with lithium (p=0.007) and healthy controls (p=0.001) selected significantly more cards from the safe decks than euthymic patients without lithium, with no significant difference between euthymic patients with lithium and healthy controls (p=0.9). In the 90 euthymic patients, the stepwise linear multivariate regression revealed that decision-making was significantly predicted (p<0.001) by lithium dose, level of education and no family history of bipolar disorder (all p≤0.01). Because medication was not randomized, it was not possible to discriminate the effect of different medications. Lithium medication might be associated with better decision-making in remitted bipolar patients. A randomized trial is required to test for the hypothesis that lithium, but not other mood stabilizers, may specifically improve decision-making abilities in bipolar disorder. Copyright © 2015 Elsevier B.V. and ECNP. All rights reserved.

Environmental influences on alcohol consumption practices of alcoholic beverage servers.

PubMed

Nusbaumer, Michael R; Reiling, Denise M

2002-11-01

Public drinking establishments have long been associated with heavy drinking among both their patrons and servers. Whether these environments represent locations where heavy drinking is learned (learning hypothesis) or simply places where already-heavy drinkers gather in a supportive environment (selection hypothesis) remains an important question. A sample of licensed alcoholic beverage servers in the state of Indiana, USA, was surveyed to better understand the drinking behaviors of servers within the alcohol service industry. Responses (N = 938) to a mailed questionnaire were analyzed to assess the relative influence of environmental and demographic factors on the drinking behavior of servers. Stepwise regression revealed "drinking on the job" as the most influential environmental factor on heavy drinking behaviors, followed by age and gender as influential demographic factors. Support was found for the selection hypothesis, but not for the learning hypothesis. Policy implications are discussed. factors on the drinking behavior of servers. Stepwise regression revealed "drinking on the job" as the most influential environmental factor on heavy drinking behaviors, followed by age and gender as influential demographic factors. Support was found for the selection hypothesis, but not for the learning hypothesis. Policy implications are discussed.

Essential Indicators Identifying Chronic Inorganic Mercury Intoxication: Pooled Analysis across Multiple Cross-Sectional Studies.

PubMed

Doering, Stefan; Bose-O'Reilly, Stephan; Berger, Ursula

2016-01-01

The continuous exposure to inorganic mercury vapour in artisanal small-scale gold mining (ASGM) areas leads to chronic health problems. It is therefore essential to have a quick, but reliable risk assessing tool to diagnose chronic inorganic mercury intoxication. This study re-evaluates the state-of-the-art toolkit to diagnose chronic inorganic mercury intoxication by analysing data from multiple pooled cross-sectional studies. The primary research question aims to reduce the currently used set of indicators without affecting essentially the capability to diagnose chronic inorganic mercury intoxication. In addition, a sensitivity analysis is performed on established biomonitoring exposure limits for mercury in blood, hair, urine and urine adjusted by creatinine, where the biomonitoring exposure limits are compared to thresholds most associated with chronic inorganic mercury intoxication in artisanal small-scale gold mining. Health data from miners and community members in Indonesia, Tanzania and Zimbabwe were obtained as part of the Global Mercury Project and pooled into one dataset together with their biomarkers mercury in urine, blood and hair. The individual prognostic impact of the indicators on the diagnosis of mercury intoxication is quantified using logistic regression models. The selection is performed by a stepwise forward/backward selection. Different models are compared based on the Bayesian information criterion (BIC) and Cohen`s kappa is used to evaluate the level of agreement between the diagnosis of mercury intoxication based on the currently used set of indicators and the result based on our reduced set of indicators. The sensitivity analysis of biomarker exposure limits of mercury is based on a sequence of chi square tests. The variable selection in logistic regression reduced the number of medical indicators from thirteen to ten in addition to the biomarkers. The estimated level of agreement using ten of thirteen medical indicators and all four biomarkers to diagnose chronic inorganic mercury intoxication yields a Cohen`s Kappa of 0.87. While in an additional stepwise selection the biomarker blood was not selected, the level of agreement based on ten medical indicators and only the three biomarkers urine, urine/creatinine and hair reduced Cohen`s Kappa to 0.46. The optimal cut-point for the biomarkers blood, hair, urine and urine/creatinine were estimated at 11. 6 μg/l, 3.84 μg/g, 24.4 μg/l and 4.26 μg/g, respectively. The results show that a reduction down to only ten indicators still allows a reliable diagnosis of chronic inorganic mercury intoxication. This reduction of indicators will simplify health assessments in artisanal small-scale gold mining areas.

Dealing with office emergencies. Stepwise approach for family physicians.

PubMed Central

Sempowski, Ian P.; Brison, Robert J.

2002-01-01

OBJECTIVE: To develop a simple stepwise approach to initial management of emergencies in family physicians' offices; to review how to prepare health care teams and equipment; and to illustrate a general approach to three of the most common office emergencies. QUALITY OF EVIDENCE: MEDLINE was searched from January 1980 to December 2001. Articles were selected based on their clinical relevance, quality of evidence, and date of publication. We reviewed American family medicine, pediatric, dental, and dermatologic articles, but found that the area has not been well studied from a Canadian family medicine perspective. Consensus statements by specialty professional groups were used to identify accepted emergency medical treatments. MAIN MESSAGE: Family medicine offices are frequently poorly equipped and inadequately prepared to deal with emergencies. Straightforward emergency response plans can be designed and tailored to an office's risk profile. A systematic team approach and effective use of skills, support staff, and equipment is important. The general approach can be modified for specific patients or conditions. CONCLUSION: Family physicians can plan ahead and use a team approach to develop a simple stepwise response to emergency situations in the office. PMID:12371305

One-Step and Stepwise Magnification of a BOBBED LETHAL Chromosome in DROSOPHILA MELANOGASTER

PubMed Central

Endow, Sharyn A.; Komma, Donald J.

1986-01-01

Bobbed lethal (bbl) chromosomes carry too few ribosomal genes for homozygous flies to be viable. Reversion of bbl chromosomes to bb or nearly bb + occurs under magnifying conditions at a low frequency in a single generation. These reversions occur too rapidly to be accounted for by single unequal sister chromatid exchanges and seem unlikely to be due to multiple sister strand exchanges within a given cell lineage. Analysis of several one-step revertants indicates that they are X-Y recombinant chromosomes which probably arise from X-Y recombination at bb. The addition of ribosomal genes from the Y chromosome to the bbl chromosome explains the more rapid reversion of the bbl chromosome than is permitted by single events of unequal sister chromatid exchange. Analysis of stepwise bbl magnified chromosomes, which were selected over a period of 4–9 magnifying generations, shows ribosomal gene patterns that are closely similar to each other. Similarity in rDNA pattern among stepwise magnified products of the same parental chromosome is consistent with reversion by a mechanism of unequal sister strand exchange. PMID:3095184

Disorganized Symptoms Predicted Worse Functioning Outcome in Schizophrenia Patients with Established Illness.

PubMed

Ortiz, Bruno Bertolucci; Gadelha, Ary; Higuchi, Cinthia Hiroko; Noto, Cristiano; Medeiros, Daiane; Pitta, José Cássio do Nascimento; de Araújo Filho, Gerardo Maria; Hallak, Jaime Eduardo Cecílio; Bressan, Rodrigo Affonseca

Most patients with schizophrenia will have subsequent relapses of the disorder, with continuous impairments in functioning. However, evidence is lacking on how symptoms influence functioning at different phases of the disease. This study aims to investigate the relationship between symptom dimensions and functioning at different phases: acute exacerbation, nonremission and remission. Patients with schizophrenia were grouped into acutely ill (n=89), not remitted (n=89), and remitted (n=69). Three exploratory stepwise linear regression analyses were performed for each phase of schizophrenia, in which the five PANSS factors and demographic variables were entered as the independent variables and the total Global Assessment of Functioning Scale (GAF) score was entered as the dependent variable. An additional exploratory stepwise logistic regression analysis was performed to predict subsequent remission at discharge in the inpatient population. The Disorganized factor was the most significant predictor for acutely ill patients (p<0.001), while the Hostility factor was the most significant for not-remitted patients and the Negative factor was the most significant for remitted patients (p=0.001 and p<0.001, respectively). In the logistic regression, the Disorganized factor score presented a significant negative association with remission (p=0.007). Higher disorganization symptoms showed the greatest impact in functioning at acute phase, and prevented patients from achieving remission, suggesting it may be a marker of symptom severity and worse outcome in schizophrenia.

Cation Selectivity in Biological Cation Channels Using Experimental Structural Information and Statistical Mechanical Simulation

PubMed Central

Finnerty, Justin John

2015-01-01

Cation selective channels constitute the gate for ion currents through the cell membrane. Here we present an improved statistical mechanical model based on atomistic structural information, cation hydration state and without tuned parameters that reproduces the selectivity of biological Na+ and Ca2+ ion channels. The importance of the inclusion of step-wise cation hydration in these results confirms the essential role partial dehydration plays in the bacterial Na+ channels. The model, proven reliable against experimental data, could be straightforwardly used for designing Na+ and Ca2+ selective nanopores. PMID:26460827

Predictors of Global Self-Worth and Academic Performance among Regular Education, Learning Disabled, and Continuation High School Students.

ERIC Educational Resources Information Center

Wiest, Dudley J.; Wong, Eugene H.; Kreil, Dennis A.

1998-01-01

The ability of measures of perceived competence, control, and autonomy support to predict self-worth and academic performance was studied across groups of high school students. Stepwise regression analyses indicate these variables in model predict self-worth and grade point average. In addition, levels of school status and depression predict…

Whole genome sequencing revealed host adaptation-focused genomic plasticity of pathogenic Leptospira

PubMed Central

Xu, Yinghua; Zhu, Yongzhang; Wang, Yuezhu; Chang, Yung-Fu; Zhang, Ying; Jiang, Xiugao; Zhuang, Xuran; Zhu, Yongqiang; Zhang, Jinlong; Zeng, Lingbing; Yang, Minjun; Li, Shijun; Wang, Shengyue; Ye, Qiang; Xin, Xiaofang; Zhao, Guoping; Zheng, Huajun; Guo, Xiaokui; Wang, Junzhi

2016-01-01

Leptospirosis, caused by pathogenic Leptospira spp., has recently been recognized as an emerging infectious disease worldwide. Despite its severity and global importance, knowledge about the molecular pathogenesis and virulence evolution of Leptospira spp. remains limited. Here we sequenced and analyzed 102 isolates representing global sources. A high genomic variability were observed among different Leptospira species, which was attributed to massive gene gain and loss events allowing for adaptation to specific niche conditions and changing host environments. Horizontal gene transfer and gene duplication allowed the stepwise acquisition of virulence factors in pathogenic Leptospira evolved from a recent common ancestor. More importantly, the abundant expansion of specific virulence-related protein families, such as metalloproteases-associated paralogs, were exclusively identified in pathogenic species, reflecting the importance of these protein families in the pathogenesis of leptospirosis. Our observations also indicated that positive selection played a crucial role on this bacteria adaptation to hosts. These novel findings may lead to greater understanding of the global diversity and virulence evolution of Leptospira spp. PMID:26833181

[Quantitative structure-gas chromatographic retention relationship of polycyclic aromatic sulfur heterocycles using molecular electronegativity-distance vector].

PubMed

Li, Zhenghua; Cheng, Fansheng; Xia, Zhining

2011-01-01

The chemical structures of 114 polycyclic aromatic sulfur heterocycles (PASHs) have been studied by molecular electronegativity-distance vector (MEDV). The linear relationships between gas chromatographic retention index and the MEDV have been established by a multiple linear regression (MLR) model. The results of variable selection by stepwise multiple regression (SMR) and the powerful predictive abilities of the optimization model appraised by leave-one-out cross-validation showed that the optimization model with the correlation coefficient (R) of 0.994 7 and the cross-validated correlation coefficient (Rcv) of 0.994 0 possessed the best statistical quality. Furthermore, when the 114 PASHs compounds were divided into calibration and test sets in the ratio of 2:1, the statistical analysis showed our models possesses almost equal statistical quality, the very similar regression coefficients and the good robustness. The quantitative structure-retention relationship (QSRR) model established may provide a convenient and powerful method for predicting the gas chromatographic retention of PASHs.

Changes in macrominerals, trace elements and pigments content during lettuce (Lactuca sativa L.) growth: influence of soil composition.

PubMed

Pinto, Edgar; Almeida, Agostinho A; Aguiar, Ana A R M; Ferreira, Isabel M P L V O

2014-01-01

Changes in macrominerals, trace elements and photosynthetic pigments were monitored at 5 stages of lettuce growth. Plants were grown in three experimental agriculture greenhouse fields (A1, A2 and A3). Soil composition was also monitored to understand its influence on lettuce composition. In general, the content of macrominerals, trace elements, chlorophylls and carotenoids decreased during lettuce growth and consequently, high nutritional value was observed at younger stages. A2 lettuces showed an increase of Fe, Al, Cr, V and Pb due to the different soil physicochemical parameters. Multiple linear regression analysis with stepwise variable selection, indicated that soil characteristics, namely, pH(CaCl2) for Fe and Cr, silt and fine-sand for Al and V, OM for Al and Pb, coarse-sand and CEC for Cr, had a key role determining element bioavailability and plant mineral content. Thus, lettuce nutritional value was strongly dependent of growth stage and soil characteristics. Copyright © 2013 Elsevier Ltd. All rights reserved.

Wavenumber selection based analysis in Raman spectroscopy improves skin cancer diagnostic specificity at high sensitivity levels (Conference Presentation)

NASA Astrophysics Data System (ADS)

Zhao, Jianhua; Zeng, Haishan; Kalia, Sunil; Lui, Harvey

2017-02-01

Background: Raman spectroscopy is a non-invasive optical technique which can measure molecular vibrational modes within tissue. A large-scale clinical study (n = 518) has demonstrated that real-time Raman spectroscopy could distinguish malignant from benign skin lesions with good diagnostic accuracy; this was validated by a follow-up independent study (n = 127). Objective: Most of the previous diagnostic algorithms have typically been based on analyzing the full band of the Raman spectra, either in the fingerprint or high wavenumber regions. Our objective in this presentation is to explore wavenumber selection based analysis in Raman spectroscopy for skin cancer diagnosis. Methods: A wavenumber selection algorithm was implemented using variably-sized wavenumber windows, which were determined by the correlation coefficient between wavenumbers. Wavenumber windows were chosen based on accumulated frequency from leave-one-out cross-validated stepwise regression or least and shrinkage selection operator (LASSO). The diagnostic algorithms were then generated from the selected wavenumber windows using multivariate statistical analyses, including principal component and general discriminant analysis (PC-GDA) and partial least squares (PLS). A total cohort of 645 confirmed lesions from 573 patients encompassing skin cancers, precancers and benign skin lesions were included. Lesion measurements were divided into training cohort (n = 518) and testing cohort (n = 127) according to the measurement time. Result: The area under the receiver operating characteristic curve (ROC) improved from 0.861-0.891 to 0.891-0.911 and the diagnostic specificity for sensitivity levels of 0.99-0.90 increased respectively from 0.17-0.65 to 0.20-0.75 by selecting specific wavenumber windows for analysis. Conclusion: Wavenumber selection based analysis in Raman spectroscopy improves skin cancer diagnostic specificity at high sensitivity levels.

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

Penna, M.L.; Duchiade, M.P.

The authors report the results of an investigation into the possible association between air pollution and infant mortality from pneumonia in the Rio de Janeiro Metropolitan Area. This investigation employed multiple linear regression analysis (stepwise method) for infant mortality from pneumonia in 1980, including the study population's areas of residence, incomes, and pollution exposure as independent variables. With the income variable included in the regression, a statistically significant association was observed between the average annual level of particulates and infant mortality from pneumonia. While this finding should be accepted with caution, it does suggest a biological association between these variables.more » The authors' conclusion is that air quality indicators should be included in studies of acute respiratory infections in developing countries.« less

Communicative participation restrictions in multiple sclerosis: associated variables and correlation with social functioning.

PubMed

Yorkston, Kathryn M; Baylor, Carolyn; Amtmann, Dagmar

2014-01-01

Individuals with multiple sclerosis (MS) are at risk for communication problems that may restrict their ability to take participation in important life roles such as maintenance of relationships, work, or household management. The aim of this project is to examine selected demographic and symptom-related variables that may contribute to participation restrictions. This examination is intended to aid clinicians in predicting who might be at risk for such restrictions and what variables may be targeted in interventions. Community-dwelling adults with MS (n=216) completed a survey either online or using paper forms. The survey included the 46-item version of the Communicative Participation Item Bank, demographics (age, sex, living situation, employment status, education, and time since onset of diagnosis of MS), and self-reported symptom-related variables (physical activity, emotional problems, fatigue, pain, speech severity, and cognitive/communication skills). In order to identify predictors of restrictions in communicative participation, these variables were entered into a backwards stepwise multiple linear regression analysis. Five variables (cognitive/communication skills, speech severity, speech usage, physical activity, and education) were statistically significant predictors of communication participation. In order to examine the relationship of communicative participation and social role variables, bivariate Spearman correlations were conducted. Results suggest only a fair to moderate relationship between communicative participation and measures of social roles. Communicative participation is a complex construct associated with a number of self-reported variables. Clinicians should be alert to risk factors for reduced communicative participation including reduced cognitive and speech skills, lower levels of speech usage, limitations in physical activities and higher levels of education. The reader will be able to: (a) describe the factors that may restrict participation in individuals with multiple sclerosis; (b) list measures of social functioning that may be pertinent in adults with multiple sclerosis; (c) discuss factors that can be used to predict communicative participation in multiple sclerosis. Copyright © 2014 Elsevier Inc. All rights reserved.

Estimating severity of sideways fall using a generic multi linear regression model based on kinematic input variables.

PubMed

van der Zijden, A M; Groen, B E; Tanck, E; Nienhuis, B; Verdonschot, N; Weerdesteyn, V

2017-03-21

Many research groups have studied fall impact mechanics to understand how fall severity can be reduced to prevent hip fractures. Yet, direct impact force measurements with force plates are restricted to a very limited repertoire of experimental falls. The purpose of this study was to develop a generic model for estimating hip impact forces (i.e. fall severity) in in vivo sideways falls without the use of force plates. Twelve experienced judokas performed sideways Martial Arts (MA) and Block ('natural') falls on a force plate, both with and without a mat on top. Data were analyzed to determine the hip impact force and to derive 11 selected (subject-specific and kinematic) variables. Falls from kneeling height were used to perform a stepwise regression procedure to assess the effects of these input variables and build the model. The final model includes four input variables, involving one subject-specific measure and three kinematic variables: maximum upper body deceleration, body mass, shoulder angle at the instant of 'maximum impact' and maximum hip deceleration. The results showed that estimated and measured hip impact forces were linearly related (explained variances ranging from 46 to 63%). Hip impact forces of MA falls onto the mat from a standing position (3650±916N) estimated by the final model were comparable with measured values (3698±689N), even though these data were not used for training the model. In conclusion, a generic linear regression model was developed that enables the assessment of fall severity through kinematic measures of sideways falls, without using force plates. Copyright © 2017 Elsevier Ltd. All rights reserved.

Rapid initiation of fetal therapy services with a system of learner-centred training under proctorship: the National University Hospital (Singapore) experience

PubMed Central

Gosavi, Arundhati; Vijayakumar, Pradip D; Ng, Bryan SW; Loh, May-Han; Tan, Lay Geok; Johana, Nuryanti; Tan, Yi Wan; Sandikin, Dedy; Su, Lin Lin; Wataganara, Tuangsit; Biswas, Arijit; Choolani, Mahesh A; Mattar, Citra NZ

2017-01-01

INTRODUCTION Management of complicated monochorionic twins and certain intrauterine structural anomalies is a pressing challenge in communities that still lack advanced fetal therapy. We describe our efforts to rapidly initiate selective feticide using radiofrequency ablation (RFA) and selective fetoscopic laser photocoagulation (SFLP) for twin-to-twin transfusion syndrome (TTTS), and present the latter as a potential model for aspiring fetal therapy units. METHODS Five pregnancies with fetal complications were identified for RFA. Three pregnancies with Stage II TTTS were selected for SFLP. While RFA techniques utilising ultrasonography skills were quickly mastered, SFLP required stepwise technical learning with an overseas-based proctor, who provided real-time hands-off supervision. RESULTS All co-twins were live-born following selective feticide; one singleton pregnancy was lost. Fetoscopy techniques were learned in a stepwise manner and procedures were performed by a novice team of surgeons under proctorship. Dichorionisation was completed in only one patient. Five of six twins were live-born near term. One pregnancy developed twin anaemia-polycythaemia sequence, while another was complicated by co-twin demise. DISCUSSION Proctor-supervised directed learning facilitated the rapid provision of basic fetal therapy services by our unit. While traditional apprenticeship is important for building individual expertise, this system is complementary and may benefit other small units committed to providing these services. PMID:27439783

Predictive equations for the estimation of body size in seals and sea lions (Carnivora: Pinnipedia)

PubMed Central

Churchill, Morgan; Clementz, Mark T; Kohno, Naoki

2014-01-01

Body size plays an important role in pinniped ecology and life history. However, body size data is often absent for historical, archaeological, and fossil specimens. To estimate the body size of pinnipeds (seals, sea lions, and walruses) for today and the past, we used 14 commonly preserved cranial measurements to develop sets of single variable and multivariate predictive equations for pinniped body mass and total length. Principal components analysis (PCA) was used to test whether separate family specific regressions were more appropriate than single predictive equations for Pinnipedia. The influence of phylogeny was tested with phylogenetic independent contrasts (PIC). The accuracy of these regressions was then assessed using a combination of coefficient of determination, percent prediction error, and standard error of estimation. Three different methods of multivariate analysis were examined: bidirectional stepwise model selection using Akaike information criteria; all-subsets model selection using Bayesian information criteria (BIC); and partial least squares regression. The PCA showed clear discrimination between Otariidae (fur seals and sea lions) and Phocidae (earless seals) for the 14 measurements, indicating the need for family-specific regression equations. The PIC analysis found that phylogeny had a minor influence on relationship between morphological variables and body size. The regressions for total length were more accurate than those for body mass, and equations specific to Otariidae were more accurate than those for Phocidae. Of the three multivariate methods, the all-subsets approach required the fewest number of variables to estimate body size accurately. We then used the single variable predictive equations and the all-subsets approach to estimate the body size of two recently extinct pinniped taxa, the Caribbean monk seal (Monachus tropicalis) and the Japanese sea lion (Zalophus japonicus). Body size estimates using single variable regressions generally under or over-estimated body size; however, the all-subset regression produced body size estimates that were close to historically recorded body length for these two species. This indicates that the all-subset regression equations developed in this study can estimate body size accurately. PMID:24916814

Using Seasonal Forecasts for medium-term Electricity Demand Forecasting on Italy

NASA Astrophysics Data System (ADS)

De Felice, M.; Alessandri, A.; Ruti, P.

2012-12-01

Electricity demand forecast is an essential tool for energy management and operation scheduling for electric utilities. In power engineering, medium-term forecasting is defined as the prediction up to 12 months ahead, and commonly is performed considering weather climatology and not actual forecasts. This work aims to analyze the predictability of electricity demand on seasonal time scale, considering seasonal samples, i.e. average on three months. Electricity demand data has been provided by Italian Transmission System Operator for eight different geographical areas, in Fig. 1 for each area is shown the average yearly demand anomaly for each season. This work uses data for each summer during 1990-2010 and all the datasets have been pre-processed to remove trends and reduce the influence of calendar and economic effects. The choice of focusing this research on the summer period is due to the critical peaks of demand that power grid is subject during hot days. Weather data have been included considering observations provided by ECMWF ERA-INTERIM reanalyses. Primitive variables (2-metres temperature, pressure, etc) and derived variables (cooling and heating degree days) have been averaged for summer months. A particular attention has been given to the influence of persistence of positive temperature anomaly and a derived variable which count the number of consecutive days of extreme-days has been used. Electricity demand forecast has been performed using linear and nonlinear regression methods and stepwise model selection procedures have been used to perform a variable selection with respect to performance measures. Significance tests on multiple linear regression showed the importance of cooling degree days during summer in the North-East and South of Italy with an increase of statistical significance after 2003, a result consistent with the diffusion of air condition and ventilation equipment in the last decade. Finally, using seasonal climate forecasts we evaluate the performances of electricity demand forecast performed with predicted variables on Italian regions with encouraging results on the South of Italy. This work gives an initial assessment on the predictability of electricity demand on seasonal time scale, evaluating the relevance of climate information provided by seasonal forecasts for electricity management during high-demand periods.;

What Factors Influence Community Oral and Maxillofacial Surgeons' Choice to Use Capnography in the Office-Based Ambulatory Anesthesia Setting?

PubMed

Matin, Mehdi B; Gonzalez, Martin L; Dodson, Thomas B

2015-08-01

The American Association of Oral and Maxillofacial Surgeons Board of Trustees mandated monitoring using capnography during moderate sedation (MS) and deep sedation or general anesthesia (DS/GA) delivered in the office setting effective January 1, 2014. The purpose of this study was to estimate the frequency of capnography use and to identify variables associated with a clinician's choice to use capnography before the mandate. To address the research purpose, the authors designed a prospective cohort study and enrolled 2 samples: 1) American private practicing oral and maxillofacial surgeons (OMSs) and 2) all eligible patients for whom these OMSs delivered MS or DS/GA. The predictor variables were categorized as surgeon or patient demographics, anesthesia risk factors, procedure-related variables, and anesthetic medications. The outcome variable was capnography use during MS or DS/GA. Descriptive, bivariate, and forward stepwise multiple logistic regression statistics were computed to evaluate the association between the predictor variables and capnography use, with statistical significance set at a P value less than or equal to .05. The surgeon sample was composed of 95 OMSs and 13.7% reported using capnography. The patient sample included 3,495 patients with a mean age of 30.6 years (standard deviation, 17.8 yr), 43.5% were men, and 5.6% were monitored using capnography. Based on bivariate analyses, 17 variables were associated with capnography use. Forward stepwise regression modeling identified 9 variables statistically associated with capnography use. These variables were patient's age, Mallampati airway score, alcohol consumption, board certification, sevoflurane use, number of monitoring methods, electrocardiogram use, precordial stethoscope use, and number of personnel in operating suite. Although this study might be of historical interest at this time, the results offer insight into OMSs' practice patterns before the mandatory requirement to use capnography. As more OMSs comply with the capnography mandate, their practice patterns involving variables found to statistically correlate with capnography use might become more similar to those of early adopters of this technology. Copyright © 2015 American Association of Oral and Maxillofacial Surgeons. Published by Elsevier Inc. All rights reserved.

[Remote sensing estimation of urban forest carbon stocks based on QuickBird images].

PubMed

Xu, Li-Hua; Zhang, Jie-Cun; Huang, Bo; Wang, Huan-Huan; Yue, Wen-Ze

2014-10-01

Urban forest is one of the positive factors that increase urban carbon sequestration, which makes great contribution to the global carbon cycle. Based on the high spatial resolution imagery of QuickBird in the study area within the ring road in Yiwu, Zhejiang, the forests in the area were divided into four types, i. e., park-forest, shelter-forest, company-forest and others. With the carbon stock from sample plot as dependent variable, at the significance level of 0.01, the stepwise linear regression method was used to select independent variables from 50 factors such as band grayscale values, vegetation index, texture information and so on. Finally, the remote sensing based forest carbon stock estimation models for the four types of forest were established. The estimation accuracies for all the models were around 70%, with the total carbon reserve of each forest type in the area being estimated as 3623. 80, 5245.78, 5284.84, 5343.65 t, respectively. From the carbon density map, it was found that the carbon reserves were mainly in the range of 25-35 t · hm(-2). In the future, urban forest planners could further improve the ability of forest carbon sequestration through afforestation and interplanting of trees and low shrubs.

[Study on the classification of dominant pathogens related to febrile respiratory syndrome, based on the method of Bayes discriminant analysis].

PubMed

Li, X C; Li, J S; Meng, L; Bai, Y N; Yu, D S; Liu, X N; Liu, X F; Jiang, X J; Ren, X W; Yang, X T; Shen, X P; Zhang, J W

2017-08-10

Objective: To understand the dominant pathogens of febrile respiratory syndrome (FRS) patients in Gansu province and to establish the Bayes discriminant function in order to identify the patients infected with the dominant pathogens. Methods: FRS patients were collected in various sentinel hospitals of Gansu province from 2009 to 2015 and the dominant pathogens were determined by describing the composition of pathogenic profile. Significant clinical variables were selected by stepwise discriminant analysis to establish the Bayes discriminant function. Results: In the detection of pathogens for FRS, both influenza virus and rhinovirus showed higher positive rates than those caused by other viruses (13.79%, 8.63%), that accounting for 54.38%, 13.73% of total viral positive patients. Most frequently detected bacteria would include Streptococcus pneumoniae , and haemophilus influenza (44.41%, 18.07%) that accounting for 66.21% and 24.55% among the bacterial positive patients. The original-validated rate of discriminant function, established by 11 clinical variables, was 73.1%, with the cross-validated rate as 70.6%. Conclusion: Influenza virus, Rhinovirus, Streptococcus pneumoniae and Haemophilus influenzae were the dominant pathogens of FRS in Gansu province. Results from the Bayes discriminant analysis showed both higher accuracy in the classification of dominant pathogens, and applicative value for FRS.

A Predictive Score for Bronchopleural Fistula Established Using the French Database Epithor.

PubMed

Pforr, Arnaud; Pagès, Pierre-Benoit; Baste, Jean-Marc; Thomas, Pascal; Falcoz, Pierre-Emmanuel; Lepimpec Barthes, Francoise; Dahan, Marcel; Bernard, Alain

2016-01-01

Bronchopleural fistula (BPF) remains a rare but fatal complication of thoracic surgery. The aim of this study was to develop and validate a predictive model of BPF after pulmonary resection and to identify patients at high risk for BPF. From January 2005 to December 2012, 34,000 patients underwent major pulmonary resection (lobectomy, bilobectomy, or pneumonectomy) and were entered into the French National database Epithor. The primary outcome was the occurrence of postoperative BPF at 30 days. The logistic regression model was built using a backward stepwise variable selection. Bronchopleural fistula occurred in 318 patients (0.94%); its prevalence was 0.5% for lobectomy (n = 139), 2.2% for bilobectomy (n = 39), and 3% for pneumonectomy (n = 140). The mortality rate was 25.9% for lobectomy (n = 36), 16.7% for bilobectomy (n = 6), and 20% for pneumonectomy (n = 28). In the final model, nine variables were selected: sex, body mass index, dyspnea score, number of comorbidities per patient, bilobectomy, pneumonectomy, emergency surgery, sleeve resection, and the side of the resection. In the development data set, the C-index was 0.8 (95% confidence interval: 0.78 to 0.82). This model was well calibrated because the Hosmer-Lemeshow test was not significant (χ(2) = 10.5, p = 0.23). We then calculated the logistic regression coefficient to build the predictive score for BPF. This strong model could be easily used by surgeons to identify patient at high risk for BPF. This score needs to be confirmed prospectively in an independent cohort. Copyright © 2016 The Society of Thoracic Surgeons. Published by Elsevier Inc. All rights reserved.

Combined Effects of Soil Biotic and Abiotic Factors, Influenced by Sewage Sludge Incorporation, on the Incidence of Corn Stalk Rot

PubMed Central

Fortes, Nara Lúcia Perondi; Navas-Cortés, Juan A; Silva, Carlos Alberto; Bettiol, Wagner

2016-01-01

The objectives of this study were to evaluate the combined effects of soil biotic and abiotic factors on the incidence of Fusarium corn stalk rot, during four annual incorporations of two types of sewage sludge into soil in a 5-years field assay under tropical conditions and to predict the effects of these variables on the disease. For each type of sewage sludge, the following treatments were included: control with mineral fertilization recommended for corn; control without fertilization; sewage sludge based on the nitrogen concentration that provided the same amount of nitrogen as in the mineral fertilizer treatment; and sewage sludge that provided two, four and eight times the nitrogen concentration recommended for corn. Increasing dosages of both types of sewage sludge incorporated into soil resulted in increased corn stalk rot incidence, being negatively correlated with corn yield. A global analysis highlighted the effect of the year of the experiment, followed by the sewage sludge dosages. The type of sewage sludge did not affect the disease incidence. A multiple logistic model using a stepwise procedure was fitted based on the selection of a model that included the three explanatory parameters for disease incidence: electrical conductivity, magnesium and Fusarium population. In the selected model, the probability of higher disease incidence increased with an increase of these three explanatory parameters. When the explanatory parameters were compared, electrical conductivity presented a dominant effect and was the main variable to predict the probability distribution curves of Fusarium corn stalk rot, after sewage sludge application into the soil. PMID:27176597

Development of a prediction model for residual disease in newly diagnosed advanced ovarian cancer.

PubMed

Janco, Jo Marie Tran; Glaser, Gretchen; Kim, Bohyun; McGree, Michaela E; Weaver, Amy L; Cliby, William A; Dowdy, Sean C; Bakkum-Gamez, Jamie N

2015-07-01

To construct a tool, using computed tomography (CT) imaging and preoperative clinical variables, to estimate successful primary cytoreduction for advanced epithelial ovarian cancer (EOC). Women who underwent primary cytoreductive surgery for stage IIIC/IV EOC at Mayo Clinic between 1/2/2003 and 12/30/2011 and had preoperative CT images of the abdomen and pelvis within 90days prior to their surgery available for review were included. CT images were reviewed for large-volume ascites, diffuse peritoneal thickening (DPT), omental cake, lymphadenopathy (LP), and spleen or liver involvement. Preoperative factors included age, body mass index (BMI), Eastern Cooperative Oncology Group performance status (ECOG PS), American Society of Anesthesiologists (ASA) score, albumin, CA-125, and thrombocytosis. Two prediction models were developed to estimate the probability of (i) complete and (ii) suboptimal cytoreduction (residual disease (RD) >1cm) using multivariable logistic analysis with backward and stepwise variable selection methods. Internal validation was assessed using bootstrap resampling to derive an optimism-corrected estimate of the c-index. 279 patients met inclusion criteria: 143 had complete cytoreduction, 26 had suboptimal cytoreduction (RD>1cm), and 110 had measurable RD ≤1cm. On multivariable analysis, age, absence of ascites, omental cake, and DPT on CT imaging independently predicted complete cytoreduction (c-index=0.748). Conversely, predictors of suboptimal cytoreduction were ECOG PS, DPT, and LP on preoperative CT imaging (c-index=0.685). The generated models serve as preoperative evaluation tools that may improve counseling and selection for primary surgery, but need to be externally validated. Copyright © 2015 Elsevier Inc. All rights reserved.

Adoption of innovations by specialised nurses: personal, work and organisational characteristics.

PubMed

van der Weide, Marian; Smits, Jeroen

2004-04-01

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

Anthropometric Survey of US Army Personnel (1988): Correlation Coefficients and Regression Equations. Part 5. Stepwise and Standard Multiple Regression Tables

DTIC Science & Technology

1990-05-01

0.759 0.744 0.768 0.753 106 (THUMBBR) THUMB BREADTH -0.652 -0.673 -0.539 -0.663 217 (LIPLGTHH) LIP LENGTH HEADBOARD 0.017 0.019 0.020 51 (FTBRHOR) FOOT...DEPENDENT VARIABLE: (106) THUMB BREADTH (THUBBR) MODEL INDEPENDENT VARIABLE 1 2 3 4 5 INTERCEPT 6.621 5.016 6.267 5.697 4.528 59 (HANDCIRC) HAND...95 (SLLSPEL) SLEEVE LENGTH: SPINE-ELBOW -0.020 -0.019 -C.018 9 (BLFTCIRC) BALL OF FOOT CIRCUMFERENCE -0.032 -0.039 106 (THUMBBR) THUMB BREADTH 0.228

Shark Attack: high affinity binding proteins derived from shark vNAR domains by stepwise in vitro affinity maturation.

PubMed

Zielonka, Stefan; Weber, Niklas; Becker, Stefan; Doerner, Achim; Christmann, Andreas; Christmann, Christine; Uth, Christina; Fritz, Janine; Schäfer, Elena; Steinmann, Björn; Empting, Martin; Ockelmann, Pia; Lierz, Michael; Kolmar, Harald

2014-12-10

A novel method for stepwise in vitro affinity maturation of antigen-specific shark vNAR domains is described that exclusively relies on semi-synthetic repertoires derived from non-immunized sharks. Target-specific molecules were selected from a CDR3-randomized bamboo shark (Chiloscyllium plagiosum) vNAR library using yeast surface display as platform technology. Various antigen-binding vNAR domains were easily isolated by screening against several therapeutically relevant antigens, including the epithelial cell adhesion molecule (EpCAM), the Ephrin type-A receptor 2 (EphA2), and the human serine protease HTRA1. Affinity maturation was demonstrated for EpCAM and HTRA1 by diversifying CDR1 of target-enriched populations which allowed for the rapid selection of nanomolar binders. EpCAM-specific vNAR molecules were produced as soluble proteins and more extensively characterized via thermal shift assays and biolayer interferometry. Essentially, we demonstrate that high-affinity binders can be generated in vitro without largely compromising the desirable high thermostability of the vNAR scaffold. Copyright © 2014 Elsevier B.V. All rights reserved.

Use of GLM approach to assess the responses of tropical trees to urban air pollution in relation to leaf functional traits and tree characteristics.

PubMed

Mukherjee, Arideep; Agrawal, Madhoolika

2018-05-15

Responses of urban vegetation to air pollution stress in relation to their tolerance and sensitivity have been extensively studied, however, studies related to air pollution responses based on different leaf functional traits and tree characteristics are limited. In this paper, we have tried to assess combined and individual effects of major air pollutants PM 10 (particulate matter ≤ 10 µm), TSP (total suspended particulate matter), SO 2 (sulphur dioxide), NO 2 (nitrogen dioxide) and O 3 (ozone) on thirteen tropical tree species in relation to fifteen leaf functional traits and different tree characteristics. Stepwise linear regression a general linear modelling approach was used to quantify the pollution response of trees against air pollutants. The study was performed for six successive seasons for two years in three distinct urban areas (traffic, industrial and residential) of Varanasi city in India. At all the study sites, concentrations of air pollutants, specifically PM (particulate matter) and NO 2 were above the specified standards. Distinct variations were recorded in all the fifteen leaf functional traits with pollution load. Caesalpinia sappan was identified as most tolerant species followed by Psidium guajava, Dalbergia sissoo and Albizia lebbeck. Stepwise regression analysis identified maximum response of Eucalyptus citriodora and P. guajava to air pollutants explaining overall 59% and 58% variability's in leaf functional traits, respectively. Among leaf functional traits, maximum effect of air pollutants was observed on non-enzymatic antioxidants followed by photosynthetic pigments and leaf water status. Among the pollutants, PM was identified as the major stress factor followed by O 3 explaining 47% and 33% variability's in leaf functional traits. Tolerance and pollution response were regulated by different tree characteristics such as height, canopy size, leaf from, texture and nature of tree. Outcomes of this study will help in urban forest development by selection of specific pollutant tolerant tree species and leaf traits, which is suitable as air pollution mitigation measure. Copyright © 2018 Elsevier Inc. All rights reserved.

A Web-based nomogram predicting para-aortic nodal metastasis in incompletely staged patients with endometrial cancer: a Korean Multicenter Study.

PubMed

Kang, Sokbom; Lee, Jong-Min; Lee, Jae-Kwan; Kim, Jae-Weon; Cho, Chi-Heum; Kim, Seok-Mo; Park, Sang-Yoon; Park, Chan-Yong; Kim, Ki-Tae

2014-03-01

The purpose of this study is to develop a Web-based nomogram for predicting the individualized risk of para-aortic nodal metastasis in incompletely staged patients with endometrial cancer. From 8 institutions, the medical records of 397 patients who underwent pelvic and para-aortic lymphadenectomy as a surgical staging procedure were retrospectively reviewed. A multivariate logistic regression model was created and internally validated by rigorous bootstrap resampling methods. Finally, the model was transformed into a user-friendly Web-based nomogram (http://http://www.kgog.org/nomogram/empa001.html). The rate of para-aortic nodal metastasis was 14.4% (57/397 patients). Using a stepwise variable selection, 4 variables including deep myometrial invasion, non-endometrioid subtype, lymphovascular space invasion, and log-transformed CA-125 levels were finally adopted. After 1000 repetitions of bootstrapping, all of these 4 variables retained a significant association with para-aortic nodal metastasis in the multivariate analysis-deep myometrial invasion (P = 0.001), non-endometrioid histologic subtype (P = 0.034), lymphovascular space invasion (P = 0.003), and log-transformed serum CA-125 levels (P = 0.004). The model showed good discrimination (C statistics = 0.87; 95% confidence interval, 0.82-0.92) and accurate calibration (Hosmer-Lemeshow P = 0.74). This nomogram showed good performance in predicting para-aortic metastasis in patients with endometrial cancer. The tool may be useful in determining the extent of lymphadenectomy after incomplete surgery.

Predictors of change in life skills in schizophrenia after cognitive remediation.

PubMed

Kurtz, Matthew M; Seltzer, James C; Fujimoto, Marco; Shagan, Dana S; Wexler, Bruce E

2009-02-01

Few studies have investigated predictors of response to cognitive remediation interventions in patients with schizophrenia. Predictor studies to date have selected treatment outcome measures that were either part of the remediation intervention itself or closely linked to the intervention with few studies investigating factors that predict generalization to measures of everyday life-skills as an index of treatment-related improvement. In the current study we investigated the relationship between four measures of neurocognitive function, crystallized verbal ability, auditory sustained attention and working memory, verbal learning and memory, and problem-solving, two measures of symptoms, total positive and negative symptoms, and the process variables of treatment intensity and duration, to change on a performance-based measure of everyday life-skills after a year of computer-assisted cognitive remediation offered as part of intensive outpatient rehabilitation treatment. Thirty-six patients with schizophrenia or schizoaffective disorder were studied. Results of a linear regression model revealed that auditory attention and working memory predicted a significant amount of the variance in change in performance-based measures of everyday life skills after cognitive remediation, even when variance for all other neurocognitive variables in the model was controlled. Stepwise regression revealed that auditory attention and working memory predicted change in everyday life-skills across the trial even when baseline life-skill scores, symptoms and treatment process variables were controlled. These findings emphasize the importance of sustained auditory attention and working memory for benefiting from extended programs of cognitive remediation.

Forecasting obesity prevalence in Korean adults for the years 2020 and 2030 by the analysis of contributing factors.

PubMed

Baik, Inkyung

2018-06-01

There are few studies that forecast the future prevalence of obesity based on the predicted prevalence model including contributing factors. The present study aimed to identify factors associated with obesity and construct forecasting models including significant contributing factors to estimate the 2020 and 2030 prevalence of obesity and abdominal obesity. Panel data from the Korea National Health and Nutrition Examination Survey and national statistics from the Korean Statistical Information Service were used for the analysis. The study subjects were 17,685 male and 24,899 female adults aged 19 years or older. The outcome variables were the prevalence of obesity (body mass index ≥ 25 kg/m 2 ) and abdominal obesity (waist circumference ≥ 90 cm for men and ≥ 85 cm for women). Stepwise logistic regression analysis was used to select significant variables from potential exposures. The survey year, age, marital status, job status, income status, smoking, alcohol consumption, sleep duration, psychological factors, dietary intake, and fertility rate were found to contribute to the prevalence of obesity and abdominal obesity. Based on the forecasting models including these variables, the 2020 and 2030 estimates for obesity prevalence were 47% and 62% for men and 32% and 37% for women, respectively. The present study suggested an increased prevalence of obesity and abdominal obesity in 2020 and 2030. Lifestyle factors were found to be significantly associated with the increasing trend in obesity prevalence and, therefore, they may require modification to prevent the rising trend.

Lesion mapping of social problem solving

PubMed Central

Colom, Roberto; Paul, Erick J.; Chau, Aileen; Solomon, Jeffrey; Grafman, Jordan H.

2014-01-01

Accumulating neuroscience evidence indicates that human intelligence is supported by a distributed network of frontal and parietal regions that enable complex, goal-directed behaviour. However, the contributions of this network to social aspects of intellectual function remain to be well characterized. Here, we report a human lesion study (n = 144) that investigates the neural bases of social problem solving (measured by the Everyday Problem Solving Inventory) and examine the degree to which individual differences in performance are predicted by a broad spectrum of psychological variables, including psychometric intelligence (measured by the Wechsler Adult Intelligence Scale), emotional intelligence (measured by the Mayer, Salovey, Caruso Emotional Intelligence Test), and personality traits (measured by the Neuroticism-Extraversion-Openness Personality Inventory). Scores for each variable were obtained, followed by voxel-based lesion–symptom mapping. Stepwise regression analyses revealed that working memory, processing speed, and emotional intelligence predict individual differences in everyday problem solving. A targeted analysis of specific everyday problem solving domains (involving friends, home management, consumerism, work, information management, and family) revealed psychological variables that selectively contribute to each. Lesion mapping results indicated that social problem solving, psychometric intelligence, and emotional intelligence are supported by a shared network of frontal, temporal, and parietal regions, including white matter association tracts that bind these areas into a coordinated system. The results support an integrative framework for understanding social intelligence and make specific recommendations for the application of the Everyday Problem Solving Inventory to the study of social problem solving in health and disease. PMID:25070511

Caregiver financial distress, depressive symptoms and limited social capital as barriers to children's dental care in a mid-western county in the United States.

PubMed

Kruger, J S; Kodjebacheva, G D; Kunkel, L; Smith, K D; Kruger, D J

2015-12-01

To identify barriers to children's access to dental care. A cross-sectional health survey. All residential census tracts in Genesee County, Michigan, USA. 498 adults who reported having children in their households, extracted from 2,932 randomly selected adult participants in the 2009 and 2011 surveys. Stepwise logistic regression was used to predict two dependent variables: children's lack of any visits to dentists' offices and unmet dental care needs (defined as needing dental care but not receiving it due to cost) in the previous year as reported by the adults. Independent variables included gender, age, education, race/ethnicity, financial planning, financial distress, fear of crime, stress, depressive symptoms, experiences of discrimination, and neighbourhood social capital. Of the 498 adults, 29.9% reported that they had children who had not visited a dentist in the past 12 months and 13% reported that they had household children with unmet dental care needs in the past year. Adults who reported higher depressive symptoms, lower neighbourhood social capital, greater financial distress, and who were younger were more likely to have household children who did not visit a dentist in the past year. Financial distress was the only significant predictor when controlling for other variables to predict unmet dental care needs. Factors beyond financial distress affect children's dental care; these include parental depressive symptoms and lower neighbourhood social capital. Interventions promoting parental mental health and social integration may increase dental care among children.

2D-QSAR study of fullerene nanostructure derivatives as potent HIV-1 protease inhibitors

NASA Astrophysics Data System (ADS)

Barzegar, Abolfazl; Jafari Mousavi, Somaye; Hamidi, Hossein; Sadeghi, Mehdi

2017-09-01

The protease of human immunodeficiency virus1 (HIV-PR) is an essential enzyme for antiviral treatments. Carbon nanostructures of fullerene derivatives, have nanoscale dimension with a diameter comparable to the diameter of the active site of HIV-PR which would in turn inhibit HIV. In this research, two dimensional quantitative structure-activity relationships (2D-QSAR) of fullerene derivatives against HIV-PR activity were employed as a powerful tool for elucidation the relationships between structure and experimental observations. QSAR study of 49 fullerene derivatives was performed by employing stepwise-MLR, GAPLS-MLR, and PCA-MLR models for variable (descriptor) selection and model construction. QSAR models were obtained with higher ability to predict the activity of the fullerene derivatives against HIV-PR by a correlation coefficient (R2training) of 0.942, 0.89, and 0.87 as well as R2test values of 0.791, 0.67and 0.674 for stepwise-MLR, GAPLS-MLR, and PCA -MLR models, respectively. Leave-one-out cross-validated correlation coefficient (R2CV) and Y-randomization methods confirmed the models robustness. The descriptors indicated that the HIV-PR inhibition depends on the van der Waals volumes, polarizability, bond order between two atoms and electronegativities of fullerenes derivatives. 2D-QSAR simulation without needing receptor's active site geometry, resulted in useful descriptors mainly denoting ;C60 backbone-functional groups; and ;C60 functional groups; properties. Both properties in fullerene refer to the ligand fitness and improvement van der Waals interactions with HIV-PR active site. Therefore, the QSAR models can be used in the search for novel HIV-PR inhibitors based on fullerene derivatives.

Correlation of P-wave dispersion with insulin sensitivity in obese adolescents.

PubMed

Sert, Ahmet; Aslan, Eyup; Buyukınan, Muammer; Pirgon, Ozgur

2017-03-01

P-wave dispersion is a new and simple electrocardiographic marker that has been reported to be associated with inhomogeneous and discontinuous propagation of sinus impulses. In the present study, we evaluated P-wave dispersion in obese adolescents and investigated the relationship between P-wave dispersion, cardiovascular risk factors, and echocardiographic parameters. We carried out a case-control study comparing 150 obese adolescents and 50 healthy controls. Maximum and minimum P-wave durations were measured using a 12-lead surface electrocardiogram, and P-wave dispersion was calculated as the difference between these two measures. Echocardiographic examination was also performed for each subject. Multivariate linear regression analysis with stepwise variable selection was used to evaluate parameters associated with increased P-wave dispersion in obese subjects. Maximum P-wave duration and P-wave dispersion were significantly higher in obese adolescents than control subjects (143±19 ms versus 117±20 ms and 49±15 ms versus 29±9 ms, p<0.0001 for both). P-wave dispersion was positively correlated with body mass index, waist and hip circumferences, systolic and diastolic blood pressures, total cholesterol, serum levels of low-density lipoprotein cholesterol, triglycerides, glucose, and insulin, homoeostasis model assessment for insulin resistance score, left ventricular mass, and left atrial dimension. P-wave dispersion was negatively correlated with high-density lipoprotein cholesterol levels. By multiple stepwise regression analysis, left atrial dimension (β: 0.252, p=0.008) and homoeostasis model assessment for insulin resistance (β: 0.205; p=0.009) were independently associated with increased P-wave dispersion in obese adolescents. Insulin resistance is a significant, independent predictor of P-wave dispersion in obese adolescents.

The left hand second to fourth digit ratio (2D:4D) is not related to any physical fitness component in adolescent girls.

PubMed

Peeters, Maarten W; Van Aken, Katrijn; Claessens, Albrecht L

2013-01-01

The second to fourth-digit-ratio (2D:4D), a putative marker of prenatal androgen action and a sexually dimorphic trait, has been suggested to be related with fitness and sports performance, although results are not univocal. Most studies however focus on a single aspect of physical fitness or one sports discipline. In this study the 2D:4D ratio of 178 adolescent girls (age 13.5-18 y) was measured on X-rays of the left hand. The relation between 2D:4D digit ratio and multiple aspects of physical fitness (balance, speed of limb movement, flexibility, explosive strength, static strength, trunk strength, functional strength, running speed/agility, and endurance) was studied by correlation analyses and stepwise multiple regression. For comparison the relation between these physical fitness components and a selected number of objectively measured anthropometric traits (stature, mass, BMI, somatotype components and the Bayer & Bailey androgyny index) are presented alongside the results of 2D:4D digit ratio. Left hand 2D:4D digit ratio (0.925±0.019) was not significantly correlated with any of the physical fitness components nor any of the anthropometric variables included in the present study. 2D:4D did not enter the multiple stepwise regression for any of the physical fitness components in which other anthropometric traits explained between 9.2% (flexibility) and 33.9% (static strength) of variance. Unlike other anthropometric traits the 2D:4D digit ratio does not seem to be related to any physical fitness component in adolescent girls and therefore most likely should not be considered in talent detection programs for sporting ability in girls.

Patients' sense of security during palliative care-what are the influencing factors?

PubMed

Milberg, Anna; Friedrichsen, Maria; Jakobsson, Maria; Nilsson, Eva-Carin; Niskala, Birgitta; Olsson, Maria; Wåhlberg, Rakel; Krevers, Barbro

2014-07-01

Having a sense of security is vitally important to patients who have a limited life expectancy. We sought to identify the factors associated with patients' sense of security during the palliative care period. We recruited 174 adult patients (65% of those eligible) from six palliative home care units. The relationship between the patients' sense of security during palliative care and individual factors was evaluated in a stepwise procedure using the generalized linear model (ordinal multinomial distribution and logit link). Respondents' ratings of their sense of security ranged from 1 (never) to 6 (always), with a mean value of 4.6 (SD 1.19). Patients with lower feelings of security experienced higher stress; more worry about personal finances; lower feelings of self-efficacy; a lower sense of security with the palliative care provided (lower ratings on subscales of care interaction); mastery; prevailed own identity; higher symptom intensity (especially depression, anxiety, and lack of well-being); lower health-related quality of life; lower attachment anxiety and avoidance; less support from family, relatives, and friends; lower comfort for those closest to them; and more often had gynecological cancer. Six variables (mastery, nervousness and stress, gynecological cancer, self-efficacy, worrying about personal finances, and avoidance) were selected in building the stepwise model. These findings stress the importance of palliative care services in supporting dying patients' sense of security through symptom management with a wide scope and through supporting the patients' sense of mastery, identity, and perception of a secure care interaction and also through attention to the family members' situation. Copyright © 2014 U.S. Cancer Pain Relief Committee. Published by Elsevier Inc. All rights reserved.

Max-AUC Feature Selection in Computer-Aided Detection of Polyps in CT Colonography

PubMed Central

Xu, Jian-Wu; Suzuki, Kenji

2014-01-01

We propose a feature selection method based on a sequential forward floating selection (SFFS) procedure to improve the performance of a classifier in computerized detection of polyps in CT colonography (CTC). The feature selection method is coupled with a nonlinear support vector machine (SVM) classifier. Unlike the conventional linear method based on Wilks' lambda, the proposed method selected the most relevant features that would maximize the area under the receiver operating characteristic curve (AUC), which directly maximizes classification performance, evaluated based on AUC value, in the computer-aided detection (CADe) scheme. We presented two variants of the proposed method with different stopping criteria used in the SFFS procedure. The first variant searched all feature combinations allowed in the SFFS procedure and selected the subsets that maximize the AUC values. The second variant performed a statistical test at each step during the SFFS procedure, and it was terminated if the increase in the AUC value was not statistically significant. The advantage of the second variant is its lower computational cost. To test the performance of the proposed method, we compared it against the popular stepwise feature selection method based on Wilks' lambda for a colonic-polyp database (25 polyps and 2624 nonpolyps). We extracted 75 morphologic, gray-level-based, and texture features from the segmented lesion candidate regions. The two variants of the proposed feature selection method chose 29 and 7 features, respectively. Two SVM classifiers trained with these selected features yielded a 96% by-polyp sensitivity at false-positive (FP) rates of 4.1 and 6.5 per patient, respectively. Experiments showed a significant improvement in the performance of the classifier with the proposed feature selection method over that with the popular stepwise feature selection based on Wilks' lambda that yielded 18.0 FPs per patient at the same sensitivity level. PMID:24608058

Max-AUC feature selection in computer-aided detection of polyps in CT colonography.

PubMed

Xu, Jian-Wu; Suzuki, Kenji

2014-03-01

We propose a feature selection method based on a sequential forward floating selection (SFFS) procedure to improve the performance of a classifier in computerized detection of polyps in CT colonography (CTC). The feature selection method is coupled with a nonlinear support vector machine (SVM) classifier. Unlike the conventional linear method based on Wilks' lambda, the proposed method selected the most relevant features that would maximize the area under the receiver operating characteristic curve (AUC), which directly maximizes classification performance, evaluated based on AUC value, in the computer-aided detection (CADe) scheme. We presented two variants of the proposed method with different stopping criteria used in the SFFS procedure. The first variant searched all feature combinations allowed in the SFFS procedure and selected the subsets that maximize the AUC values. The second variant performed a statistical test at each step during the SFFS procedure, and it was terminated if the increase in the AUC value was not statistically significant. The advantage of the second variant is its lower computational cost. To test the performance of the proposed method, we compared it against the popular stepwise feature selection method based on Wilks' lambda for a colonic-polyp database (25 polyps and 2624 nonpolyps). We extracted 75 morphologic, gray-level-based, and texture features from the segmented lesion candidate regions. The two variants of the proposed feature selection method chose 29 and 7 features, respectively. Two SVM classifiers trained with these selected features yielded a 96% by-polyp sensitivity at false-positive (FP) rates of 4.1 and 6.5 per patient, respectively. Experiments showed a significant improvement in the performance of the classifier with the proposed feature selection method over that with the popular stepwise feature selection based on Wilks' lambda that yielded 18.0 FPs per patient at the same sensitivity level.

A novel simple QSAR model for the prediction of anti-HIV activity using multiple linear regression analysis.

PubMed

Afantitis, Antreas; Melagraki, Georgia; Sarimveis, Haralambos; Koutentis, Panayiotis A; Markopoulos, John; Igglessi-Markopoulou, Olga

2006-08-01

A quantitative-structure activity relationship was obtained by applying Multiple Linear Regression Analysis to a series of 80 1-[2-hydroxyethoxy-methyl]-6-(phenylthio) thymine (HEPT) derivatives with significant anti-HIV activity. For the selection of the best among 37 different descriptors, the Elimination Selection Stepwise Regression Method (ES-SWR) was utilized. The resulting QSAR model (R (2) (CV) = 0.8160; S (PRESS) = 0.5680) proved to be very accurate both in training and predictive stages.

Selective laser ionisation of radionuclide 63Ni

NASA Astrophysics Data System (ADS)

Tsvetkov, G. O.; D'yachkov, A. B.; Gorkunov, A. A.; Labozin, A. V.; Mironov, S. M.; Firsov, V. A.; Panchenko, V. Ya.

2017-02-01

We report a search for a scheme of selective laser stepwise ionisation of radionuclide 63Ni by radiation of a dye laser pumped by a copper vapour laser. A three-stage scheme is found with ionisation through an autoionising state (AIS): 3d 84s2 3F4(E = 0) → 3d 94p 1Fo3(31030.99 cm-1) → 3d 94d 2[7/2]4(49322.56 cm-1) → AIS(67707.61 cm-1) which, by employing saturated radiation intensities provides the ionisation selectivity of above 1200 for 63Ni.

Forest tree species discrimination in western Himalaya using EO-1 Hyperion

NASA Astrophysics Data System (ADS)

George, Rajee; Padalia, Hitendra; Kushwaha, S. P. S.

2014-05-01

The information acquired in the narrow bands of hyperspectral remote sensing data has potential to capture plant species spectral variability, thereby improving forest tree species mapping. This study assessed the utility of spaceborne EO-1 Hyperion data in discrimination and classification of broadleaved evergreen and conifer forest tree species in western Himalaya. The pre-processing of 242 bands of Hyperion data resulted into 160 noise-free and vertical stripe corrected reflectance bands. Of these, 29 bands were selected through step-wise exclusion of bands (Wilk's Lambda). Spectral Angle Mapper (SAM) and Support Vector Machine (SVM) algorithms were applied to the selected bands to assess their effectiveness in classification. SVM was also applied to broadband data (Landsat TM) to compare the variation in classification accuracy. All commonly occurring six gregarious tree species, viz., white oak, brown oak, chir pine, blue pine, cedar and fir in western Himalaya could be effectively discriminated. SVM produced a better species classification (overall accuracy 82.27%, kappa statistic 0.79) than SAM (overall accuracy 74.68%, kappa statistic 0.70). It was noticed that classification accuracy achieved with Hyperion bands was significantly higher than Landsat TM bands (overall accuracy 69.62%, kappa statistic 0.65). Study demonstrated the potential utility of narrow spectral bands of Hyperion data in discriminating tree species in a hilly terrain.

Emergence of context-dependent variability across a basal ganglia network.

PubMed

Woolley, Sarah C; Rajan, Raghav; Joshua, Mati; Doupe, Allison J

2014-04-02

Context dependence is a key feature of cortical-basal ganglia circuit activity, and in songbirds the cortical outflow of a basal ganglia circuit specialized for song, LMAN, shows striking increases in trial-by-trial variability and bursting when birds sing alone rather than to females. To reveal where this variability and its social regulation emerge, we recorded stepwise from corticostriatal (HVC) neurons and their target spiny and pallidal neurons in Area X. We find that corticostriatal and spiny neurons both show precise singing-related firing across both social settings. Pallidal neurons, in contrast, exhibit markedly increased trial-by-trial variation when birds sing alone, created by highly variable pauses in firing. This variability persists even when recurrent inputs from LMAN are ablated. These data indicate that variability and its context sensitivity emerge within the basal ganglia network, suggest a network mechanism for this emergence, and highlight variability generation and regulation as basal ganglia functions. Copyright © 2014 Elsevier Inc. All rights reserved.

Emergence of context-dependent variability across a basal ganglia network

PubMed Central

Woolley, Sarah C.; Rajan, Raghav; Joshua, Mati; Doupe, Allison J.

2014-01-01

Summary Context-dependence is a key feature of cortical-basal ganglia circuit activity, and in songbirds, the cortical outflow of a basal ganglia circuit specialized for song, LMAN, shows striking increases in trial-by-trial variability and bursting when birds sing alone rather than to females. To reveal where this variability and its social regulation emerge, we recorded stepwise from cortico-striatal (HVC) neurons and their target spiny and pallidal neurons in Area X. We find that cortico-striatal and spiny neurons both show precise singing-related firing across both social settings. Pallidal neurons, in contrast, exhibit markedly increased trial-by-trial variation when birds sing alone, created by highly variable pauses in firing. This variability persists even when recurrent inputs from LMAN are ablated. These data indicate that variability and its context-sensitivity emerge within the basal ganglia network, suggest a network mechanism for this emergence, and highlight variability generation and regulation as basal ganglia functions. PMID:24698276

A Statistical Discrimination Experiment for Eurasian Events Using a Twenty-Seven-Station Network

DTIC Science & Technology

1980-07-08

to test the effectiveness of a multivariate method of analysis for distinguishing earthquakes from explosions. The data base for the experiment...to test the effectiveness of a multivariate method of analysis for distinguishing earthquakes from explosions. The data base for the experiment...the weight assigned to each variable whenever a new one is added. Jennrich, R. I. (1977). Stepwise discriminant analysis , in Statistical Methods for

Information Scent Determines Attention Allocation and Link Selection among Multiple Information Patches on a Webpage

ERIC Educational Resources Information Center

Blackmon, Marilyn Hughes

2012-01-01

This paper draws from cognitive psychology and cognitive neuroscience to develop a preliminary similarity-choice theory of how people allocate attention among information patches on webpages while completing search tasks in complex informational websites. Study 1 applied stepwise multiple regression to a large dataset and showed that success rate…

The Relationship of Selected Supply- and Demand-Side Factors to Forms of Perceived Discrimination among Adults with Multiple Sclerosis

ERIC Educational Resources Information Center

Roessler, Richard T.; Neath, Jeanne; McMahon, Brian T.; Rumrill, Phillip D.

2007-01-01

Single-predictor and stepwise multinomial logistic regression analyses and an external validation were completed on 3,082 allegations of employment discrimination by adults with multiple sclerosis. Women filed two thirds of the allegations, and individuals between 31 and 50 made the vast majority of discrimination charges (73%). Allegations…

Tracing the role of human civilization in the globalization of plant pathogens

Treesearch

Alberto Santini; Andrew Liebhold; Duccio Migliorini; Steve Woodward

2018-01-01

Co-evolution between plants and parasites, including herbivores and pathogens, has arguably generated much of Earth’s biological diversity. Within an ecosystem, coevolution of plants and pathogens is a stepwise reciprocal evolutionary interaction: epidemics result in intense selection pressures on both host and pathogen populations, ultimately allowing long-term...

A stepwise recovery of metals from hybrid cathodes of spent Li-ion batteries with leaching-flotation-precipitation process

NASA Astrophysics Data System (ADS)

Huang, Yanfang; Han, Guihong; Liu, Jiongtian; Chai, Wencui; Wang, Wenjuan; Yang, Shuzhen; Su, Shengpeng

2016-09-01

The recovering of valuable metals in spent lithium-ion battery cathodes brings about economic and environmental benefits. A stepwise leaching-flotation-precipitation process is adopted to separate and recover Li/Fe/Mn from the mixed types of cathode materials (hybrid wastes of LiFePO4 and LiMn2O4). The optimal operating conditions for the stepwise recovery process are determined and analyzed by factorial design, thermodynamics calculation, XRD and SEM characterization in this study. First, Li/Fe/Mn ions are released from the cathode using HCl assisted with H2O2 in the acid leaching step. The leachability of metals follows the series Li > Fe > Mn in the acidic environment. Then Fe3+ ions are selectively floated and recovered as FeCl3 from the leachate in the flotation step. Finally, Mn2+/Mn3+ and Li+ ions are sequentially precipitated and separated as MnO2/Mn2O3 and Li3PO4 using saturated KMnO4 solution and hot saturated Na3PO4 solution, respectively. Under the optimized and advisable conditions, the total recovery of Li, Fe and Mn is respectively 80.93 ± 0.16%, 85.40 ± 0.12% and 81.02 ± 0.08%. The purity for lithium, ferrum and manganese compounds is respectively 99.32 ± 0.07%, 97.91 ± 0.05% and 98.73 ± 0.05%. This stepwise process could provide an alternative way for the effective separation and recovery of metal values from spent Li-ion battery cathodes in industry.

Essential Indicators Identifying Chronic Inorganic Mercury Intoxication: Pooled Analysis across Multiple Cross-Sectional Studies

PubMed Central

Doering, Stefan

2016-01-01

Background The continuous exposure to inorganic mercury vapour in artisanal small-scale gold mining (ASGM) areas leads to chronic health problems. It is therefore essential to have a quick, but reliable risk assessing tool to diagnose chronic inorganic mercury intoxication. This study re-evaluates the state-of-the-art toolkit to diagnose chronic inorganic mercury intoxication by analysing data from multiple pooled cross-sectional studies. The primary research question aims to reduce the currently used set of indicators without affecting essentially the capability to diagnose chronic inorganic mercury intoxication. In addition, a sensitivity analysis is performed on established biomonitoring exposure limits for mercury in blood, hair, urine and urine adjusted by creatinine, where the biomonitoring exposure limits are compared to thresholds most associated with chronic inorganic mercury intoxication in artisanal small-scale gold mining. Methods Health data from miners and community members in Indonesia, Tanzania and Zimbabwe were obtained as part of the Global Mercury Project and pooled into one dataset together with their biomarkers mercury in urine, blood and hair. The individual prognostic impact of the indicators on the diagnosis of mercury intoxication is quantified using logistic regression models. The selection is performed by a stepwise forward/backward selection. Different models are compared based on the Bayesian information criterion (BIC) and Cohen`s kappa is used to evaluate the level of agreement between the diagnosis of mercury intoxication based on the currently used set of indicators and the result based on our reduced set of indicators. The sensitivity analysis of biomarker exposure limits of mercury is based on a sequence of chi square tests. Results The variable selection in logistic regression reduced the number of medical indicators from thirteen to ten in addition to the biomarkers. The estimated level of agreement using ten of thirteen medical indicators and all four biomarkers to diagnose chronic inorganic mercury intoxication yields a Cohen`s Kappa of 0.87. While in an additional stepwise selection the biomarker blood was not selected, the level of agreement based on ten medical indicators and only the three biomarkers urine, urine/creatinine and hair reduced Cohen`s Kappa to 0.46. The optimal cut-point for the biomarkers blood, hair, urine and urine/creatinine were estimated at 11. 6 μg/l, 3.84 μg/g, 24.4 μg/l and 4.26 μg/g, respectively. Conclusion The results show that a reduction down to only ten indicators still allows a reliable diagnosis of chronic inorganic mercury intoxication. This reduction of indicators will simplify health assessments in artisanal small-scale gold mining areas. PMID:27575533

Monthly Rainfall Erosivity Assessment for Switzerland

NASA Astrophysics Data System (ADS)

Schmidt, Simon; Meusburger, Katrin; Alewell, Christine

2016-04-01

Water erosion is crucially controlled by rainfall erosivity, which is quantified out of the kinetic energy of raindrop impact and associated surface runoff. Rainfall erosivity is often expressed as the R-factor in soil erosion risk models like the Universal Soil Loss Equation (USLE) and its revised version (RUSLE). Just like precipitation, the rainfall erosivity of Switzerland has a characteristic seasonal dynamic throughout the year. This inter-annual variability is to be assessed by a monthly and seasonal modelling approach. We used a network of 86 precipitation gauging stations with a 10-minute temporal resolution to calculate long-term average monthly R-factors. Stepwise regression and Monte Carlo Cross Validation (MCCV) was used to select spatial covariates to explain the spatial pattern of R-factor for each month across Switzerland. The regionalized monthly R-factor is mapped by its individual regression equation and the ordinary kriging interpolation of its residuals (Regression-Kriging). As covariates, a variety of precipitation indicator data has been included like snow height, a combination of hourly gauging measurements and radar observations (CombiPrecip), mean monthly alpine precipitation (EURO4M-APGD) and monthly precipitation sums (Rhires). Topographic parameters were also significant explanatory variables for single months. The comparison of all 12 monthly rainfall erosivity maps showed seasonality with highest rainfall erosivity in summer (June, July, and August) and lowest rainfall erosivity in winter months. Besides the inter-annual temporal regime, a seasonal spatial variability was detectable. Spatial maps of monthly rainfall erosivity are presented for the first time for Switzerland. The assessment of the spatial and temporal dynamic behaviour of the R-factor is valuable for the identification of more susceptible seasons and regions as well as for the application of selective erosion control measures. A combination with monthly vegetation cover (C-factor) maps would enable the assessment of seasonal dynamics of erosion processes in Switzerland.

Impacts of land use and population density on seasonal surface water quality using a modified geographically weighted regression.

PubMed

Chen, Qiang; Mei, Kun; Dahlgren, Randy A; Wang, Ting; Gong, Jian; Zhang, Minghua

2016-12-01

As an important regulator of pollutants in overland flow and interflow, land use has become an essential research component for determining the relationships between surface water quality and pollution sources. This study investigated the use of ordinary least squares (OLS) and geographically weighted regression (GWR) models to identify the impact of land use and population density on surface water quality in the Wen-Rui Tang River watershed of eastern China. A manual variable excluding-selecting method was explored to resolve multicollinearity issues. Standard regression coefficient analysis coupled with cluster analysis was introduced to determine which variable had the greatest influence on water quality. Results showed that: (1) Impact of land use on water quality varied with spatial and seasonal scales. Both positive and negative effects for certain land-use indicators were found in different subcatchments. (2) Urban land was the dominant factor influencing N, P and chemical oxygen demand (COD) in highly urbanized regions, but the relationship was weak as the pollutants were mainly from point sources. Agricultural land was the primary factor influencing N and P in suburban and rural areas; the relationship was strong as the pollutants were mainly from agricultural surface runoff. Subcatchments located in suburban areas were identified with urban land as the primary influencing factor during the wet season while agricultural land was identified as a more prevalent influencing factor during the dry season. (3) Adjusted R 2 values in OLS models using the manual variable excluding-selecting method averaged 14.3% higher than using stepwise multiple linear regressions. However, the corresponding GWR models had adjusted R 2 ~59.2% higher than the optimal OLS models, confirming that GWR models demonstrated better prediction accuracy. Based on our findings, water resource protection policies should consider site-specific land-use conditions within each watershed to optimize mitigation strategies for contrasting land-use characteristics and seasonal variations. Copyright © 2016 Elsevier B.V. All rights reserved.

Discovery of serum biomarkers predicting development of a subsequent depressive episode in social anxiety disorder.

PubMed

Gottschalk, M G; Cooper, J D; Chan, M K; Bot, M; Penninx, B W J H; Bahn, S

2015-08-01

Although social anxiety disorder (SAD) is strongly associated with the subsequent development of a depressive disorder (major depressive disorder or dysthymia), no underlying biological risk factors are known. We aimed to identify biomarkers which predict depressive episodes in SAD patients over a 2-year follow-up period. One hundred sixty-five multiplexed immunoassay analytes were investigated in blood serum of 143 SAD patients without co-morbid depressive disorders, recruited within the Netherlands Study of Depression and Anxiety (NESDA). Predictive performance of identified biomarkers, clinical variables and self-report inventories was assessed using receiver operating characteristics curves (ROC) and represented by the area under the ROC curve (AUC). Stepwise logistic regression resulted in the selection of four serum analytes (AXL receptor tyrosine kinase, vascular cell adhesion molecule 1, vitronectin, collagen IV) and four additional variables (Inventory of Depressive Symptomatology, Beck Anxiety Inventory somatic subscale, depressive disorder lifetime diagnosis, BMI) as optimal set of patient parameters. When combined, an AUC of 0.86 was achieved for the identification of SAD individuals who later developed a depressive disorder. Throughout our analyses, biomarkers yielded superior discriminative performance compared to clinical variables and self-report inventories alone. We report the discovery of a serum marker panel with good predictive performance to identify SAD individuals prone to develop subsequent depressive episodes in a naturalistic cohort design. Furthermore, we emphasise the importance to combine biological markers, clinical variables and self-report inventories for disease course predictions in psychiatry. Following replication in independent cohorts, validated biomarkers could help to identify SAD patients at risk of developing a depressive disorder, thus facilitating early intervention. Copyright © 2015 Elsevier Inc. All rights reserved.

Statistical summary of selected physical, chemical, and toxicity characteristics and estimates of annual constituent loads in urban stormwater, Maricopa County, Arizona

USGS Publications Warehouse

Fossum, Kenneth D.; O'Day, Christie M.; Wilson, Barbara J.; Monical, Jim E.

2001-01-01

Stormwater and streamflow in Maricopa County were monitored to (1) describe the physical, chemical, and toxicity characteristics of stormwater from areas having different land uses, (2) describe the physical, chemical, and toxicity characteristics of streamflow from areas that receive urban stormwater, and (3) estimate constituent loads in stormwater. Urban stormwater and streamflow had similar ranges in most constituent concentrations. The mean concentration of dissolved solids in urban stormwater was lower than in streamflow from the Salt River and Indian Bend Wash. Urban stormwater, however, had a greater chemical oxygen demand and higher concentrations of most nutrients. Mean seasonal loads and mean annual loads of 11 constituents and volumes of runoff were estimated for municipalities in the metropolitan Phoenix area, Arizona, by adjusting regional regression equations of loads. This adjustment procedure uses the original regional regression equation and additional explanatory variables that were not included in the original equation. The adjusted equations had standard errors that ranged from 161 to 196 percent. The large standard errors of the prediction result from the large variability of the constituent concentration data used in the regression analysis. Adjustment procedures produced unsatisfactory results for nine of the regressions?suspended solids, dissolved solids, total phosphorus, dissolved phosphorus, total recoverable cadmium, total recoverable copper, total recoverable lead, total recoverable zinc, and storm runoff. These equations had no consistent direction of bias and no other additional explanatory variables correlated with the observed loads. A stepwise-multiple regression or a three-variable regression (total storm rainfall, drainage area, and impervious area) and local data were used to develop local regression equations for these nine constituents. These equations had standard errors from 15 to 183 percent.

Predictors of upper trapezius pain with myofascial trigger points in food service workers: The STROBE study.

PubMed

Hwang, Ui-Jae; Kwon, Oh-Yun; Yi, Chung-Hwi; Jeon, Hye-Seon; Weon, Jong-Hyuck; Ha, Sung-Min

2017-06-01

Shoulder pain occurs commonly in food service workers (FSWs) who repetitively perform motions of the upper limbs. Myofascial trigger points (MTrPs) on the upper trapezius (UT) are among the most common musculoskeletal shoulder pain syndromes. This study determined the psychological, posture, mobility, and strength factors associated with pain severity in FSWs with UT pain due to MTrPs.In this cross-sectional study, we measured 17 variables in 163 FSWs with UT pain due to MTrPs: a visual analog scale (VAS) pain score, age, sex, Borg rating of perceived exertion (BRPE) scale, beck depression inventory, forward head posture angle, rounded shoulder angle (RSA), shoulder slope angle, scapular downward rotation ratio, cervical lateral-bending side difference angle, cervical rotation side difference angle, glenohumeral internal rotation angle, shoulder horizontal adduction angle, serratus anterior (SA) strength, lower trapezius (LT) strength, bicep strength, and glenohumeral external rotator strength, in 163 FSWs with UT pain due to MTrPs.The model for factors influencing UT pain with MTrPs included SA strength, age, BRPE, LT strength, and RSA as predictor variables that accounted for 68.7% of the variance in VAS (P < .001) in multiple regression models with a stepwise selection procedure. The following were independent variables influencing the VAS in the order of standardized coefficients: SA strength (β = -0.380), age (β = 0.287), BRPE (β = 0.239), LT strength (β = -0.195), and RSA (β = 0.125).SA strength, age, BRPE, LT strength, and RSA variables should be considered when evaluating and intervening in UT pain with MTrPs in FSWs.

Predictors of upper trapezius pain with myofascial trigger points in food service workers

PubMed Central

Hwang, Ui-Jae; Kwon, Oh-Yun; Yi, Chung-Hwi; Jeon, Hye-Seon; Weon, Jong-Hyuck; Ha, Sung-Min

2017-01-01

Abstract Shoulder pain occurs commonly in food service workers (FSWs) who repetitively perform motions of the upper limbs. Myofascial trigger points (MTrPs) on the upper trapezius (UT) are among the most common musculoskeletal shoulder pain syndromes. This study determined the psychological, posture, mobility, and strength factors associated with pain severity in FSWs with UT pain due to MTrPs. In this cross-sectional study, we measured 17 variables in 163 FSWs with UT pain due to MTrPs: a visual analog scale (VAS) pain score, age, sex, Borg rating of perceived exertion (BRPE) scale, beck depression inventory, forward head posture angle, rounded shoulder angle (RSA), shoulder slope angle, scapular downward rotation ratio, cervical lateral-bending side difference angle, cervical rotation side difference angle, glenohumeral internal rotation angle, shoulder horizontal adduction angle, serratus anterior (SA) strength, lower trapezius (LT) strength, bicep strength, and glenohumeral external rotator strength, in 163 FSWs with UT pain due to MTrPs. The model for factors influencing UT pain with MTrPs included SA strength, age, BRPE, LT strength, and RSA as predictor variables that accounted for 68.7% of the variance in VAS (P < .001) in multiple regression models with a stepwise selection procedure. The following were independent variables influencing the VAS in the order of standardized coefficients: SA strength (β = −0.380), age (β = 0.287), BRPE (β = 0.239), LT strength (β = −0.195), and RSA (β = 0.125). SA strength, age, BRPE, LT strength, and RSA variables should be considered when evaluating and intervening in UT pain with MTrPs in FSWs. PMID:28658117

Minimizing the Standard Deviation of Spatially Averaged Surface Cross-Sectional Data from the Dual-Frequency Precipitation Radar

NASA Technical Reports Server (NTRS)

Meneghini, Robert; Kim, Hyokyung

2016-01-01

For an airborne or spaceborne radar, the precipitation-induced path attenuation can be estimated from the measurements of the normalized surface cross section, sigma 0, in the presence and absence of precipitation. In one implementation, the mean rain-free estimate and its variability are found from a lookup table (LUT) derived from previously measured data. For the dual-frequency precipitation radar aboard the global precipitation measurement satellite, the nominal table consists of the statistics of the rain-free 0 over a 0.5 deg x 0.5 deg latitude-longitude grid using a three-month set of input data. However, a problem with the LUT is an insufficient number of samples in many cells. An alternative table is constructed by a stepwise procedure that begins with the statistics over a 0.25 deg x 0.25 deg grid. If the number of samples at a cell is too few, the area is expanded, cell by cell, choosing at each step that cell that minimizes the variance of the data. The question arises, however, as to whether the selected region corresponds to the smallest variance. To address this question, a second type of variable-averaging grid is constructed using all possible spatial configurations and computing the variance of the data within each region. Comparisons of the standard deviations for the fixed and variable-averaged grids are given as a function of incidence angle and surface type using a three-month set of data. The advantage of variable spatial averaging is that the average standard deviation can be reduced relative to the fixed grid while satisfying the minimum sample requirement.

[Population of Lytechinus variegatus (Echinoidea: Toxopneustidae) and structural characteristics of seagrass of Thalassia testudinum in Mochima Bay, Venezuela)].

PubMed

Noriega, Nicida; Cróquer, Aldo; Pauls, Sheila M

2002-03-01

To compare the general features of Thalassia testudinum seagrass at Mochima Bay with sea urchin (Lxtechinus variegatus) abundance and distribution, three T. testudinum seagrass beds were selected, from the mouth (strong wave exposure) to the inner bay (calm waters). Each site was surveyed by using 5 line transects (20 m long) parallel to the coast and 1 m2 quadrats. In situ measurements of T. testudinum cover, shoot and leaf density were taken. Estimation of dry biomass for each seagrass fraction (leaves, rhizomes and roots) and leaf length were obtained from 25 vegetation samples extracted per site using cores (15 cm diameter). A multivariate analysis of variance (Manova) and a less significative difference test (LSD) were performed to examine differences between sites and within sites at different depths. A stepwise multiple regression analysis was done, dependent variable was sea urchin density; independent variables: vegetation values at each site. The only seagrass species found in the three sites was T. testudinum, and cover was 56-100%, leaf density 100-1000 leaf/m2, lengths 6-18.8 cm and shoot density 20-475 shoots/m2. The highest sea urchin densities were found at Isla Redonda and Ensenada Toporo (1-3.6 ind/m2), the lowest at Playa Colorada (0.6-0.8 ind/m2). Significant differences in seagrass features between sites were obtained (Manova p < 0.001), but not between depths (Manova p < 0.320). The regression coefficient between sea urchin density and seagrass parameters was statistically significant (r2 = 0.154, p < 0.007), however, total biomass was the only variable with a significant effect on sea urchin distribution (beta = 0.308, p < 0.032). The other variables did not explain satisfactorily L. variegatus abundance and distribution.

Single neuropsychological test scores associated with rate of cognitive decline in early Alzheimer disease.

PubMed

Parikh, Mili; Hynan, Linda S; Weiner, Myron F; Lacritz, Laura; Ringe, Wendy; Cullum, C Munro

2014-01-01

Alzheimer disease (AD) characteristically begins with episodic memory impairment followed by other cognitive deficits; however, the course of illness varies, with substantial differences in the rate of cognitive decline. For research and clinical purposes it would be useful to distinguish between persons who will progress slowly from persons who will progress at an average or faster rate. Our objective was to use neurocognitive performance features and disease-specific and health information to determine a predictive model for the rate of cognitive decline in participants with mild AD. We reviewed the records of a series of 96 consecutive participants with mild AD from 1995 to 2011 who had been administered selected neurocognitive tests and clinical measures. Based on Clinical Dementia Rating (CDR) of functional and cognitive decline over 2 years, participants were classified as Faster (n = 45) or Slower (n = 51) Progressors. Stepwise logistic regression analyses using neurocognitive performance features, disease-specific, health, and demographic variables were performed. Neuropsychological scores that distinguished Faster from Slower Progressors included Trail Making Test - A, Digit Symbol, and California Verbal Learning Test (CVLT) Total Learned and Primacy Recall. No disease-specific, health, or demographic variable predicted rate of progression; however, history of heart disease showed a trend. Among the neuropsychological variables, Trail Making Test - A best distinguished Faster from Slower Progressors, with an overall accuracy of 68%. In an omnibus model including neuropsychological, disease-specific, health, and demographic variables, only Trail Making Test - A distinguished between groups. Several neuropsychological performance features were associated with the rate of cognitive decline in mild AD, with baseline Trail Making Test - A performance best separating those who declined at an average or faster rate from those who showed slower progression.

Application of logistic regression for landslide susceptibility zoning of Cekmece Area, Istanbul, Turkey

NASA Astrophysics Data System (ADS)

Duman, T. Y.; Can, T.; Gokceoglu, C.; Nefeslioglu, H. A.; Sonmez, H.

2006-11-01

As a result of industrialization, throughout the world, cities have been growing rapidly for the last century. One typical example of these growing cities is Istanbul, the population of which is over 10 million. Due to rapid urbanization, new areas suitable for settlement and engineering structures are necessary. The Cekmece area located west of the Istanbul metropolitan area is studied, because the landslide activity is extensive in this area. The purpose of this study is to develop a model that can be used to characterize landslide susceptibility in map form using logistic regression analysis of an extensive landslide database. A database of landslide activity was constructed using both aerial-photography and field studies. About 19.2% of the selected study area is covered by deep-seated landslides. The landslides that occur in the area are primarily located in sandstones with interbedded permeable and impermeable layers such as claystone, siltstone and mudstone. About 31.95% of the total landslide area is located at this unit. To apply logistic regression analyses, a data matrix including 37 variables was constructed. The variables used in the forwards stepwise analyses are different measures of slope, aspect, elevation, stream power index (SPI), plan curvature, profile curvature, geology, geomorphology and relative permeability of lithological units. A total of 25 variables were identified as exerting strong influence on landslide occurrence, and included by the logistic regression equation. Wald statistics values indicate that lithology, SPI and slope are more important than the other parameters in the equation. Beta coefficients of the 25 variables included the logistic regression equation provide a model for landslide susceptibility in the Cekmece area. This model is used to generate a landslide susceptibility map that correctly classified 83.8% of the landslide-prone areas.

Total energy expenditure in adults with cerebral palsy as assessed by doubly labeled water.

PubMed

Johnson, R K; Hildreth, H G; Contompasis, S H; Goran, M I

1997-09-01

To characterize total energy expenditure (TEE) in free-living adults with cerebral palsy (CP) using the doubly labeled water technique, and to determine those physiologic variables and characteristics of CP that were markers of TEE in adults with CP. TEE was measured using the doubly labeled water technique in 30 free-living adults with CP (12 women, 18 men). To determine the best markers of TEE, the following factors were examined: CP status, resting metabolic rate (RMR), anthropometric characteristics and body composition by means of dual-energy x-ray absorptiometry (DXA) and skinfold thickness measurements, energy cost of leisure-time activities, and oral-motor impairment. Means +/- standard deviations, t tests, Pearson product-moment correlation coefficients, Spearman rank correlation coefficients, chi 2, stepwise multiple-correlation regression analysis, and analysis of covariance were used to examine the relationships among variables of interest. TEE was highly variable in the sample (mean = 2,455 +/- 622 kcal/day for men and 1,986 +/- 363 kcal/day for women). Stepwise regression analysis showed that TEE was best predicted in the sample by RMR, percentage body fat determined by DXA, ambulation status, and sex (multiple R = .68, P = .003). When practical, easily measured variables were used, TEE was best predicted by height, ambulation status, percentage body fat by skinfold thickness measurements, and sex (multiple R = .61, P. = 018). The contribution of energy expended in physical activity to TEE was significantly higher in the ambulatory subjects than the nonambulatory subjects (25% vs 16%, respectively; P = .009). The high degree of variability in TEE, largely attributable to high interindividual variation in energy expended in physical activity, makes it difficult to provide general guidelines for energy requirements for adults with CP. Because ambulation status was an important predictor of TEE, it must be accounted for in estimating energy requirements in this population.

Why Wait? Early Determinants of School Dropout in Preventive Pediatric Primary Care.

PubMed

Theunissen, Marie-José; Bosma, Hans; Verdonk, Petra; Feron, Frans

2015-01-01

To answer the question of what bio-psychosocial determinants in infancy, early and middle childhood, and adolescence predict school drop-out in young adulthood, we approached the complex process towards school dropout as a multidimensional, life-course phenomenon. The aim is to find signs of heightened risks of school dropout as early as possible which will eventually help public health workers in reducing these risks. In a case-control design, we used data from both the Preventive Pediatric Primary Care (PPPC) files (that contain information from birth onwards) and additional questionnaires filled out by 529 youngsters, aged 18-23 years, and living in the South-east of the Netherlands. We first conducted univariate logistic regression analyses with school-dropout as the dependent variable. Backward and forward stepwise analyses with the significant variables were done with variables pertaining to the 0 to 4 year period. Remaining significant variables were forced into the next model and subsequently variables pertaining to respectively the 4 to 8, 8 to 12 and 12 to 16 year period were introduced in a stepwise analysis. All analyses were cross-validated in an exploratory and confirmatory random half of the sample. One parent families and families with a non-Western background less often attended the health examinations of the PPPC and such less attendance was related to school dropout. The birth of a sibling (OR 0.63, 95% CI 0.43-0.93) in infancy and self-efficacy (OR 0.53, 95% CI 0.38-0.74) in adolescence decreased the odds of school dropout; externalizing behavior (OR 2.81, 95% CI 1.53-5.14) in middle childhood and (sickness) absence (OR 5.62, 95% CI 2.18-14.52) in adolescence increased the risks. To prevent school dropout, PPPC professionals should not wait until imminent dropout, but should identify and tackle risk factors as early as possible and actively approach youngsters who withdraw from public health care.

Development and validation of a continuous measure of patient condition using the Electronic Medical Record.

PubMed

Rothman, Michael J; Rothman, Steven I; Beals, Joseph

2013-10-01

Patient condition is a key element in communication between clinicians. However, there is no generally accepted definition of patient condition that is independent of diagnosis and that spans acuity levels. We report the development and validation of a continuous measure of general patient condition that is independent of diagnosis, and that can be used for medical-surgical as well as critical care patients. A survey of Electronic Medical Record data identified common, frequently collected non-static candidate variables as the basis for a general, continuously updated patient condition score. We used a new methodology to estimate in-hospital risk associated with each of these variables. A risk function for each candidate input was computed by comparing the final pre-discharge measurements with 1-year post-discharge mortality. Step-wise logistic regression of the variables against 1-year mortality was used to determine the importance of each variable. The final set of selected variables consisted of 26 clinical measurements from four categories: nursing assessments, vital signs, laboratory results and cardiac rhythms. We then constructed a heuristic model quantifying patient condition (overall risk) by summing the single-variable risks. The model's validity was assessed against outcomes from 170,000 medical-surgical and critical care patients, using data from three US hospitals. Outcome validation across hospitals yields an area under the receiver operating characteristic curve(AUC) of ≥0.92 when separating hospice/deceased from all other discharge categories, an AUC of ≥0.93 when predicting 24-h mortality and an AUC of 0.62 when predicting 30-day readmissions. Correspondence with outcomes reflective of patient condition across the acuity spectrum indicates utility in both medical-surgical units and critical care units. The model output, which we call the Rothman Index, may provide clinicians with a longitudinal view of patient condition to help address known challenges in caregiver communication, continuity of care, and earlier detection of acuity trends. Copyright © 2013 The Authors. Published by Elsevier Inc. All rights reserved.

The relationship of document and quantitative literacy with learning styles and selected personal variables for aerospace technology students at Indiana State University

NASA Astrophysics Data System (ADS)

Martin, Royce Ann

The purpose of this study was to determine the extent that student scores on a researcher-constructed quantitative and document literacy test, the Aviation Documents Delineator (ADD), were associated with (a) learning styles (imaginative, analytic, common sense, dynamic, and undetermined), as identified by the Learning Type Measure, (b) program curriculum (aerospace administration, professional pilot, both aerospace administration and professional pilot, other, or undeclared), (c) overall cumulative grade point average at Indiana State University, and (d) year in school (freshman, sophomore, junior, or senior). The Aviation Documents Delineator (ADD) was a three-part, 35 question survey that required students to interpret graphs, tables, and maps. Tasks assessed in the ADD included (a) locating, interpreting, and describing specific data displayed in the document, (b) determining data for a specified point on the table through interpolation, (c) comparing data for a string of variables representing one aspect of aircraft performance to another string of variables representing a different aspect of aircraft performance, (d) interpreting the documents to make decisions regarding emergency situations, and (e) performing single and/or sequential mathematical operations on a specified set of data. The Learning Type Measure (LTM) was a 15 item self-report survey developed by Bernice McCarthy (1995) to profile an individual's processing and perception tendencies in order to reveal different individual approaches to learning. The sample used in this study included 143 students enrolled in Aerospace Technology Department courses at Indiana State University in the fall of 1996. The ADD and the LTM were administered to each subject. Data collected in this investigation were analyzed using a stepwise multiple regression analysis technique. Results of the study revealed that the variables, year in school and GPA, were significant predictors of the criterion variables, document, quantitative, and total literacy, when utilizing the ADD. The variables learning style and program of study were found not to be significant predictors of literacy scores on the ADD instrument.

Stepwise strategies to successfully recruit diabetes patients in a large research study in Mexican population.

PubMed

Wacher, Niels H; Reyes-Sánchez, Mario; Vargas-Sánchez, Héctor Raúl; Gamiochipi-Cano, Mireya; Rascón-Pacheco, Ramón Alberto; Gómez-Díaz, Rita A; Doubova, Svetlana V; Valladares-Salgado, Adán; Sánchez-Becerra, Martha Catalina; Méndez-Padrón, Araceli; Valdez-González, Leticia A; Mondragón-González, Rafael; Cruz, Miguel; Salinas-Martinez, Ana María; Garza-Sagástegui, María Guadalupe; Hernández-Rubí, Jaime; González-Hermosillo, Arturo; Borja-Aburto, Víctor H

2017-06-01

Describe stepwise strategies (electronic chart review, patient preselection, call-center, personnel dedicated to recruitment) for the successful recruitment of >5000 type 2 diabetes patients in four months. Twenty-five family medicine clinics from Mexico City and the State of Mexico participated: 13 usual care, 6 specialized diabetes care and 6 chronic disease care. Appointments were scheduled from 11/3/2015 to 3/31/2016. Phone calls were generated automatically from an electronic database. A telephone questionnaire verified inclusion criteria, and scheduled an appointment, with a daily report of appointments, patient attendance, acceptance rate, and questionnaire completeness. Another recruitment log reviewed samples collected. Absolute number (percentage) of patients are reported. Means and standard deviations were estimated for continuous variables, χ 2 test and independent "t" tests were used. OR and 95% CI were estimated. 14,358 appointments were scheduled, 9146 (63.7%) attended their appointment: 5710 (62.4%) fulfilled inclusion criteria and 5244 agreed to participate (91.8% acceptance). Those accepting participation were more likely women, younger and with longer disease duration (p<0.05). The cost of the call-center service was $3,010,000.00 Mexican pesos (∼$31.70 USD per recruited patient). Stepwise strategies recruit a high number of patients in a short time. Call centers offer a low cost per patient. Copyright © 2017 Primary Care Diabetes Europe. Published by Elsevier Ltd. All rights reserved.

Flood Risk Vulnerability Assessment: What are the Main Factors? Hierarchization of The Main Factors at a Regional Scale

NASA Astrophysics Data System (ADS)

Jouannic, G.; Kolli, Z.; Legendre, T.; Marchetti, M.; Gastaud, P.; Gargani, J.; Lermet, R.; Augeard, C.; Felts, D.; Arki, F.

2015-12-01

Recent studies have shown that the national flood risk exposure is high in France, with one fourth of the total population and a third of jobs located in risk areas. In this context, a global vulnerability assessment methodology is currently being developed in France to bring adequate tools for local territories to manage flood risk. This study addresses the question of the quantification, the qualification and the choice of these vulnerability indicators for a given territory. This work aims to propose a classification of nearly 40 of these indicators in terms of their relative impacts on the risk level estimated on two territories: Chalon-sur-Saône (Saône river) Garonne estuary (Garonne and Dordogne rivers, and Atlantic ocean) Through these cases study, 3 different spatial scales have been compared: the Prés-Saint-Jean district inside Chalon (0.6 km²), the city of Ambès (28.8 km²) and Chalon with its suburbs (72.2 km²). A principal component analysis (PCA) was applied and indicated a threshold in terms of urban impacts between the different flood scenarios. On Chalon, the PCA discriminates 2 groups of flood and highlighted a threshold between T20 and T50. A partial least-square regression (PLS) was computed to make predictions on vulnerability indicators values modelled on new flood scenarios. Their results were is useful to identify the most relevant vulnerability indicators as a function of their flood exposure. These statistical analysis aims to highlight the relationship between a variable of exposure level (hydrologic impact: water levels and flow velocity) with spatialized vulnerability indicators in a 100 m grid (e.g., population, job, etc.). Finally, to get a hierarchy of variables depending on their impact on the risk level, an ANOVA was computed. The selection of variables was performed with a stepwise selection to assess contributions of each dependant variable on the F-statistic as they are added to or removed from the model.

Quantitative structure activity relationship studies of sulfamide derivatives as carbonic anhydrase inhibitor: as antiglaucoma agents.

PubMed

Kumar, Surendra; Singh, Vineet; Tiwari, Meena

2007-07-01

Selective inhibition of ciliary process enzyme i.e. Carbonic Anhydrase-II is an excellent approach in reducing elevated intraocular pressure, thus treating glaucoma. Due to characteristic physicochemical properties of sulphonamide (Inhibition of Carbonic Anhydrase), they are clinically effective against glaucoma. But the non-specificity of sulphonamide derivatives to isozyme, leads to a range of side effects. Presently, the absence of comparative studies related to the binding of the sulphonamides as inhibitors to CA isozymes limits their use. In this paper we have represented "Three Dimensional Quantitative Structure Activity Relationship" study to characterize structural features of Sulfamide derivative [RR'NSO(2)NH(2)] as inhibitors, that are required for selective binding of carbonic anhydrase isozymes (CAI and CAII). In the analysis, stepwise multiple linear regression was performed using physiochemical parameters as independent variable and CA-I and CA-II inhibitory activity as dependent variable, respectively. The best multiparametric QSAR model obtained for CA-I inhibitory activity shows good statistical significance (r= 0.9714) and predictability (Q(2)=0.8921), involving the Electronic descriptors viz. Highest Occupied Molecular Orbital, Lowest Unoccupied Molecular Orbital and Steric descriptors viz. Principal moment of Inertia at X axis. Similarly, CA-II inhibitory activity also shows good statistical significance (r=0.9644) and predictability (Q(2)=0.8699) involving aforementioned descriptors. The predictive power of the model was successfully tested externally using a set of six compounds as test set for CA-I inhibitory activity and a set of seven compounds in case of CA-II inhibitory activity with good predictive squared correlation coefficient, r(2)(pred)=0.6016 and 0.7662, respectively. Overview of analysis favours substituents with high electronegativity and less bulk at R and R' positions of the parent nucleus, provides a basis to design new Sulfamide derivatives possessing potent and selective carbonic anhydrase-II inhibitory activity.

Surgical Risk Preoperative Assessment System (SURPAS): II. Parsimonious Risk Models for Postoperative Adverse Outcomes Addressing Need for Laboratory Variables and Surgeon Specialty-specific Models.

PubMed

Meguid, Robert A; Bronsert, Michael R; Juarez-Colunga, Elizabeth; Hammermeister, Karl E; Henderson, William G

2016-07-01

To develop parsimonious prediction models for postoperative mortality, overall morbidity, and 6 complication clusters applicable to a broad range of surgical operations in adult patients. Quantitative risk assessment tools are not routinely used for preoperative patient assessment, shared decision making, informed consent, and preoperative patient optimization, likely due in part to the burden of data collection and the complexity of incorporation into routine surgical practice. Multivariable forward selection stepwise logistic regression analyses were used to develop predictive models for 30-day mortality, overall morbidity, and 6 postoperative complication clusters, using 40 preoperative variables from 2,275,240 surgical cases in the American College of Surgeons National Surgical Quality Improvement Program data set, 2005 to 2012. For the mortality and overall morbidity outcomes, prediction models were compared with and without preoperative laboratory variables, and generic models (based on all of the data from 9 surgical specialties) were compared with specialty-specific models. In each model, the cumulative c-index was used to examine the contribution of each added predictor variable. C-indexes, Hosmer-Lemeshow analyses, and Brier scores were used to compare discrimination and calibration between models. For the mortality and overall morbidity outcomes, the prediction models without the preoperative laboratory variables performed as well as the models with the laboratory variables, and the generic models performed as well as the specialty-specific models. The c-indexes were 0.938 for mortality, 0.810 for overall morbidity, and for the 6 complication clusters ranged from 0.757 for infectious to 0.897 for pulmonary complications. Across the 8 prediction models, the first 7 to 11 variables entered accounted for at least 99% of the c-index of the full model (using up to 28 nonlaboratory predictor variables). Our results suggest that it will be possible to develop parsimonious models to predict 8 important postoperative outcomes for a broad surgical population, without the need for surgeon specialty-specific models or inclusion of laboratory variables.

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.

Severe chronic heart failure in patients considered for heart transplantation in Poland.

PubMed

Korewicki, Jerzy; Leszek, Przemysław; Zieliński, Tomasz; Rywik, Tomasz; Piotrowski, Walerian; Kurjata, Paweł; Kozar-Kamińska, Katarzyna; Kodziszewska, Katarzyna

2012-01-01

Based on the results of clinical trials, the prognosis for patients with severe heart failure (HF) has improved over the last 20 years. However, clinical trials do not reflect 'real life' due to patient selection. Thus, the aim of the POLKARD-HF registry was the analysis of survival of patients with refractory HF referred for orthotopic heart transplantation (OHT). Between 1 November 2003 and 31 October 2007, 983 patients with severe HF, referred for OHT in Poland, were included into the registry. All patients underwent routine clinical and hemodynamic evaluation, with NT-proBNP and hsCRP assessment. Death or an emergency OHT were assumed as the endpoints. The average observation period was 601 days. Kaplan-Meier curves with log-rank and univariate together with multifactor Cox regression model the stepwise variable selection method were used to determine the predictive value of analyzed variables. Among the 983 patients, the probability of surviving for one year was approximately 80%, for two years 70%, and for three years 67%. Etiology of the HF did not significantly influence the prognosis. The patients in NYHA class IV had a three-fold higher risk of death or emergency OHT. The univariate/multifactor Cox regression analysis revealed that NYHA IV class (HR 2.578, p < 0.0001), HFSS score (HR 2.572, p < 0.0001) and NT-proBNP plasma level (HR 1.600, p = 0.0200), proved to influence survival without death or emergency OHT. Despite optimal treatment, the prognosis for patients with refractory HF is still not good. NYHA class IV, NT-proBNP and HFSS score can help define the highest risk group. The results are consistent with the prognosis of patients enrolled into the randomized trials.

Evidence Linking Hypoglycemic Events to an Increased Risk of Acute Cardiovascular Events in Patients With Type 2 Diabetes

PubMed Central

Johnston, Stephen S.; Conner, Christopher; Aagren, Mark; Smith, David M.; Bouchard, Jonathan; Brett, Jason

2011-01-01

OBJECTIVE This retrospective study examined the association between ICD-9-CM–coded outpatient hypoglycemic events (HEs) and acute cardiovascular events (ACVEs), i.e., acute myocardial infarction, coronary artery bypass grafting, revascularization, percutaneous coronary intervention, and incident unstable angina, in patients with type 2 diabetes. RESEARCH DESIGN AND METHODS Data were derived from healthcare claims for individuals with employer-sponsored primary or Medicare supplemental insurance. A baseline period (30 September 2006 to 30 September 2007) was used to identify eligible patients and collect information on their clinical and demographic characteristics. An evaluation period (1 October 2007 to 30 September 2008) was used to identify HEs and ACVEs. Patients aged ≥18 years with type 2 diabetes were selected for analysis by a modified Healthcare Effectiveness Data and Information Set algorithm. Data were analyzed with multiple logistic regression and backward stepwise selection (maximum P = 0.01) with adjustment for important confounding variables, including age, sex, geography, insurance type, comorbidity scores, cardiovascular risk factors, diabetes complications, total baseline medical expenditures, and prior ACVEs. RESULTS Of the 860,845 patients in the analysis set, 27,065 (3.1%) had ICD-9-CM–coded HEs during the evaluation period. The main model retained 17 significant independent variables. Patients with HEs had 79% higher regression-adjusted odds (HE odds ratio [OR] 1.79; 95% CI 1.69–1.89) of ACVEs than patients without HEs; results in patients aged ≥65 years were similar to those for the entire population (HE OR 1.78, 95% CI 1.65–1.92). CONCLUSIONS ICD-9-CM–coded HEs were independently associated with an increased risk of ACVEs. Further studies of the relationship between hypoglycemia and the risk of ACVEs are warranted. PMID:21421802

Influence of the mechanical properties of resilient denture liners on the retention of overdenture attachments.

PubMed

Kubo, Keitaro; Koike, Takashi; Ueda, Takayuki; Sakurai, Kaoru

2018-03-15

Information is lacking about the selection criteria for silicone resilient denture liners applied as a matrix material for attachments on overdentures. The purpose of this in vitro study was to investigate the mechanical properties of silicone resilient denture liners and their influence on the initial retention force of overdenture attachments and the reduction in retention force over time. Nine types of silicone resilient denture liner were injected and fixed to the matrix section of an experimental denture base. They were then fitted to an epoxy resin model that simulated the residual ridge with a patrix ball attachment (n=10). The retention force of the denture was measured with a digital force gauge, and the maximum force of traction (N) was regarded as the initial retention force. The retention force reduction (N) after repeated insertion and removal (n=5) was calculated by subtracting the retention force after 3348 cycles (3-year simulated insertion and removal) from the initial retention force. The intaglio of the matrix was observed with a scanning electron microscope (SEM) before and after the 3348 cycles. Four mechanical properties (hardness, strain-in-compression, tensile strength, and arithmetic mean roughness) of the resilient denture liners were measured. One-way ANOVA of the initial retention force of each lining material was performed, followed by the Scheffe test (α=.05). Pearson correlation analysis was used (α=.05) to analyze correlations of the initial retention force with the retention force reduction after insertion and removal and the mechanical properties of each material. Multiple regression analysis with the stepwise method extracted the initial retention force and the retention force reduction as dependent variables, and the resilient denture liner mechanical properties as explanatory variables (α=.05). The initial retention force of the resilient denture liners was 1.3 to 5.4 N. Multiple comparisons showed significant differences in some groups (P<.05). The retention force reduction of the resilient denture liners was 0.2 to 1.9 N. Multiple regression analysis with the stepwise method extracted hardness and strain-in-compression as explanatory variables for the initial retention force and the retention force reduction. Within the limitations of this in vitro study, we found that hardness influenced the initial retention force of the overdenture, and that strain-in-compression influenced the retention force reduction in the 3-year simulation. Copyright © 2017 Editorial Council for the Journal of Prosthetic Dentistry. Published by Elsevier Inc. All rights reserved.

Stepwise multiple regression method of greenhouse gas emission modeling in the energy sector in Poland.

PubMed

Kolasa-Wiecek, Alicja

2015-04-01

The energy sector in Poland is the source of 81% of greenhouse gas (GHG) emissions. Poland, among other European Union countries, occupies a leading position with regard to coal consumption. Polish energy sector actively participates in efforts to reduce GHG emissions to the atmosphere, through a gradual decrease of the share of coal in the fuel mix and development of renewable energy sources. All evidence which completes the knowledge about issues related to GHG emissions is a valuable source of information. The article presents the results of modeling of GHG emissions which are generated by the energy sector in Poland. For a better understanding of the quantitative relationship between total consumption of primary energy and greenhouse gas emission, multiple stepwise regression model was applied. The modeling results of CO2 emissions demonstrate a high relationship (0.97) with the hard coal consumption variable. Adjustment coefficient of the model to actual data is high and equal to 95%. The backward step regression model, in the case of CH4 emission, indicated the presence of hard coal (0.66), peat and fuel wood (0.34), solid waste fuels, as well as other sources (-0.64) as the most important variables. The adjusted coefficient is suitable and equals R2=0.90. For N2O emission modeling the obtained coefficient of determination is low and equal to 43%. A significant variable influencing the amount of N2O emission is the peat and wood fuel consumption. Copyright © 2015. Published by Elsevier B.V.

A warming tropical central Pacific dries the lower stratosphere

NASA Astrophysics Data System (ADS)

Ding, Qinghua; Fu, Qiang

2018-04-01

The amount of water vapor in the tropical lower stratosphere (TLS), which has an important influence on the radiative energy budget of the climate system, is modulated by the temperature variability of the tropical tropopause layer (TTL). The TTL temperature variability is caused by a complex combination of the stratospheric quasi-biennial oscillation (QBO), tropospheric convective processes in the tropics, and the Brewer-Dobson circulation (BDC) driven by mid-latitude and subtropical atmospheric waves. In 2000, the TLS water vapor amount exhibited a stepwise transition to a dry phase, apparently caused by a change in the BDC. In this study, we present observational and modeling evidence that the epochal change of water vapor between the periods of 1992-2000 and 2001-2005 was also partly caused by a concurrent sea surface temperature (SST) warming in the tropical central Pacific. This SST warming cools the TTL above by enhancing the equatorial wave-induced upward motion near the tropopause, which consequently reduces the amount of water vapor entering the stratosphere. The QBO affects the TLS water vapor primarily on inter-annual timescales, whereas a classical El Niño southern oscillation (ENSO) event has small effect on tropical mean TLS water vapor because its responses are longitudinally out of phase. This study suggests that the tropical central Pacific SST is another driver of TLS water vapor variability on inter-decadal timescales and the tropical SST changes could contribute to about 30% of the step-wise drop of the lower stratospheric water vapor from 1992-2000 to 2001-2005.

Illustration of Step-Wise Latent Class Modeling With Covariates and Taxometric Analysis in Research Probing Children's Mental Models in Learning Sciences

PubMed Central

Stamovlasis, Dimitrios; Papageorgiou, George; Tsitsipis, Georgios; Tsikalas, Themistoklis; Vaiopoulou, Julie

2018-01-01

This paper illustrates two psychometric methods, latent class analysis (LCA) and taxometric analysis (TA) using empirical data from research probing children's mental representation in science learning. LCA is used to obtain a typology based on observed variables and to further investigate how the encountered classes might be related to external variables, where the effectiveness of classification process and the unbiased estimations of parameters become the main concern. In the step-wise LCA, the class membership is assigned and subsequently its relationship with covariates is established. This leading-edge modeling approach suffers from severe downward-biased estimations. The illustration of LCA is focused on alternative bias correction approaches and demonstrates the effect of modal and proportional class-membership assignment along with BCH and ML correction procedures. The illustration of LCA is presented with three covariates, which are psychometric variables operationalizing formal reasoning, divergent thinking and field dependence-independence, respectively. Moreover, taxometric analysis, a method designed to detect the type of the latent structural model, categorical or dimensional, is introduced, along with the relevant basic concepts and tools. TA was applied complementarily in the same data sets to answer the fundamental hypothesis about children's naïve knowledge on the matters under study and it comprises an additional asset in building theory which is fundamental for educational practices. Taxometric analysis provided results that were ambiguous as far as the type of the latent structure. This finding initiates further discussion and sets a problematization within this framework rethinking fundamental assumptions and epistemological issues. PMID:29713300

Strategy for design NIR calibration sets based on process spectrum and model space: An innovative approach for process analytical technology.

PubMed

Cárdenas, V; Cordobés, M; Blanco, M; Alcalà, M

2015-10-10

The pharmaceutical industry is under stringent regulations on quality control of their products because is critical for both, productive process and consumer safety. According to the framework of "process analytical technology" (PAT), a complete understanding of the process and a stepwise monitoring of manufacturing are required. Near infrared spectroscopy (NIRS) combined with chemometrics have lately performed efficient, useful and robust for pharmaceutical analysis. One crucial step in developing effective NIRS-based methodologies is selecting an appropriate calibration set to construct models affording accurate predictions. In this work, we developed calibration models for a pharmaceutical formulation during its three manufacturing stages: blending, compaction and coating. A novel methodology is proposed for selecting the calibration set -"process spectrum"-, into which physical changes in the samples at each stage are algebraically incorporated. Also, we established a "model space" defined by Hotelling's T(2) and Q-residuals statistics for outlier identification - inside/outside the defined space - in order to select objectively the factors to be used in calibration set construction. The results obtained confirm the efficacy of the proposed methodology for stepwise pharmaceutical quality control, and the relevance of the study as a guideline for the implementation of this easy and fast methodology in the pharma industry. Copyright © 2015 Elsevier B.V. All rights reserved.

Students' Achievement, Skill and Confidence in Using Stepwise Problem-Solving Strategies

ERIC Educational Resources Information Center

Gok, Tolga

2014-01-01

The main purpose of this study was to examine the effects of Problem-Solving Strategy Steps (PSSS) on students' achievement, skill, and confidence. The study was conducted in a two-year college classroom with 70 students from two different groups enrolled in a physics course. One of them was randomly selected as an experimental group (EG) and the…

Supramolecular self-assembly of heterobimetallic complexes: a new N,P-based, selective heteroditopic ligand.

PubMed

Hutchinson, Daniel John; Clauss, Reike; Sárosi, Menyhárt-Botond; Hey-Hawkins, Evamarie

2018-01-23

Pyrimidine-hydrazone and phosphole architectures have been combined to create a new heteroditopic ligand capable of forming heterobimetallic Zn II /Pd II , Pb II /Pd II and Cu II /Pd II complexes in high yielding stepwise or one pot reactions. The catalytic activity of these complexes in Heck coupling and Miyaura borylation reactions was investigated.

Factors Associated with Level of Living in Washington County, Mississippi. Technical Bulletin No. 1501.

ERIC Educational Resources Information Center

McCoy, John L.

Step-wise multiple regression and typological analysis were used to analyze the extent to which selected factors influence vertical mobility and achieved level of living. A sample of 418 male household heads who were 18 to 45 years old in Washington County, Mississippi were interviewed during 1971. A prescreening using census and local housing…

Reporting of occupational injury and illness in the semiconductor manufacturing industry.

PubMed

McCurdy, S A; Schenker, M B; Samuels, S J

1991-01-01

In the United States, occupational illness and injury cases meeting specific reporting criteria are recorded on company Occupational Safety and Health Administration (OSHA) 200 logs; case description data are submitted to participating state agencies for coding and entry in the national Supplementary Data System (SDS). We evaluated completeness of reporting (the percentage of reportable cases that were recorded in the company OSHA 200 log) in the semiconductor manufacturing industry by reviewing company health clinic records for 1984 of 10 manufacturing sites of member companies of a national semiconductor manufacturing industry trade association. Of 416 randomly selected work-related cases, 101 met OSHA reporting criteria. Reporting completeness was 60 percent and was lowest for occupational illnesses (44 percent). Case-description data from 150 reported cases were submitted twice to state coding personnel to evaluate coding reliability. Reliability was high (kappa 0.82-0.93) for "nature," "affected body part," "source," and "type" variables. Coding for the SDS appears reliable; reporting completeness may be improved by use of a stepwise approach by company personnel responsible for reporting decisions.

Effectiveness of Collision-Involved Motorcycle Helmets in Thailand

PubMed Central

Wobrock, Jesse; Smith, Terry; Kasantikul, Vira; Whiting, William

2003-01-01

The purpose of this study was to analyze variables present in selected motorcycle crashes involving helmeted riders to find the best injury predictors. The helmets used in this study were collected from motorcycle crashes in Thailand. Pertinent data were collected, a conventional helmet impact drop test apparatus was used to quantify the head impact forces, and stepwise multiple regression analyses were performed. The results indicate that the geometry of the object impacting the head and GSI were the best predictors for MAIS (R2=.875) while geometry of the object, liner thickness and impact energy were the best predictors for ISS (R2=.911). Analysis of motor vehicle crashes in the United States in the year 2001 reveals that motorcyclist fatalities increased 7.2%, from 2,862 fatalities in 2000 to 3,067 in 2001 [NHTSA 2002]. In 2001, 59,000 motorcyclists were injured, which represents an increase of 2.0% from 2000. These statistics are indicative of the risk that motorcycle riders face in the traffic environment and warrant the need for further research focusing on injury potential in motorcycle crashes. PMID:12941212

Diversity of soil yeasts isolated from South Victoria Land, Antarctica

USGS Publications Warehouse

Connell, L.; Redman, R.; Craig, S.; Scorzetti, G.; Iszard, M.; Rodriguez, R.

2008-01-01

Unicellular fungi, commonly referred to as yeasts, were found to be components of the culturable soil fungal population in Taylor Valley, Mt. Discovery, Wright Valley, and two mountain peaks of South Victoria Land, Antarctica. Samples were taken from sites spanning a diversity of soil habitats that were not directly associated with vertebrate activity. A large proportion of yeasts isolated in this study were basidiomycetous species (89%), of which 43% may represent undescribed species, demonstrating that culturable yeasts remain incompletely described in these polar desert soils. Cryptococcus species represented the most often isolated genus (33%) followed by Leucosporidium (22%). Principle component analysis and multiple linear regression using stepwise selection was used to model the relation between abiotic variables (principle component 1 and principle component 2 scores) and yeast biodiversity (the number of species present at a given site). These analyses identified soil pH and electrical conductivity as significant predictors of yeast biodiversity. Species-specific PCR primers were designed to rapidly discriminate among the Dioszegia and Leucosporidium species collected in this study. ?? 2008 Springer Science+Business Media, LLC.

Multiple regression analysis of anthropometric measurements influencing the cephalic index of male Japanese university students.

PubMed

Hossain, Md Golam; Saw, Aik; Alam, Rashidul; Ohtsuki, Fumio; Kamarul, Tunku

2013-09-01

Cephalic index (CI), the ratio of head breadth to head length, is widely used to categorise human populations. The aim of this study was to access the impact of anthropometric measurements on the CI of male Japanese university students. This study included 1,215 male university students from Tokyo and Kyoto, selected using convenient sampling. Multiple regression analysis was used to determine the effect of anthropometric measurements on CI. The variance inflation factor (VIF) showed no evidence of a multicollinearity problem among independent variables. The coefficients of the regression line demonstrated a significant positive relationship between CI and minimum frontal breadth (p < 0.01), bizygomatic breadth (p < 0.01) and head height (p < 0.05), and a negative relationship between CI and morphological facial height (p < 0.01) and head circumference (p < 0.01). Moreover, the coefficient and odds ratio of logistic regression analysis showed a greater likelihood for minimum frontal breadth (p < 0.01) and bizygomatic breadth (p < 0.01) to predict round-headedness, and morphological facial height (p < 0.05) and head circumference (p < 0.01) to predict long-headedness. Stepwise regression analysis revealed bizygomatic breadth, head circumference, minimum frontal breadth, head height and morphological facial height to be the best predictor craniofacial measurements with respect to CI. The results suggest that most of the variables considered in this study appear to influence the CI of adult male Japanese students.

Application of Multivariate Modeling for Radiation Injury Assessment: A Proof of Concept

PubMed Central

Bolduc, David L.; Villa, Vilmar; Sandgren, David J.; Ledney, G. David; Blakely, William F.; Bünger, Rolf

2014-01-01

Multivariate radiation injury estimation algorithms were formulated for estimating severe hematopoietic acute radiation syndrome (H-ARS) injury (i.e., response category three or RC3) in a rhesus monkey total-body irradiation (TBI) model. Classical CBC and serum chemistry blood parameters were examined prior to irradiation (d 0) and on d 7, 10, 14, 21, and 25 after irradiation involving 24 nonhuman primates (NHP) (Macaca mulatta) given 6.5-Gy 60Co Υ-rays (0.4 Gy min−1) TBI. A correlation matrix was formulated with the RC3 severity level designated as the “dependent variable” and independent variables down selected based on their radioresponsiveness and relatively low multicollinearity using stepwise-linear regression analyses. Final candidate independent variables included CBC counts (absolute number of neutrophils, lymphocytes, and platelets) in formulating the “CBC” RC3 estimation algorithm. Additionally, the formulation of a diagnostic CBC and serum chemistry “CBC-SCHEM” RC3 algorithm expanded upon the CBC algorithm model with the addition of hematocrit and the serum enzyme levels of aspartate aminotransferase, creatine kinase, and lactate dehydrogenase. Both algorithms estimated RC3 with over 90% predictive power. Only the CBC-SCHEM RC3 algorithm, however, met the critical three assumptions of linear least squares demonstrating slightly greater precision for radiation injury estimation, but with significantly decreased prediction error indicating increased statistical robustness. PMID:25165485

The development and evaluation of accident predictive models

NASA Astrophysics Data System (ADS)

Maleck, T. L.

1980-12-01

A mathematical model that will predict the incremental change in the dependent variables (accident types) resulting from changes in the independent variables is developed. The end product is a tool for estimating the expected number and type of accidents for a given highway segment. The data segments (accidents) are separated in exclusive groups via a branching process and variance is further reduced using stepwise multiple regression. The standard error of the estimate is calculated for each model. The dependent variables are the frequency, density, and rate of 18 types of accidents among the independent variables are: district, county, highway geometry, land use, type of zone, speed limit, signal code, type of intersection, number of intersection legs, number of turn lanes, left-turn control, all-red interval, average daily traffic, and outlier code. Models for nonintersectional accidents did not fit nor validate as well as models for intersectional accidents.

Risk factors for eating disorders in Greek- and Anglo-Australian adolescent girls.

PubMed

Mildred, H; Paxton, S J; Wertheim, E H

1995-01-01

Past research indicates ethnicity may be related to eating disorder and related risk factors. The present study examines risk factors for eating disorders in 50 Anglo- and 50 Greek-Australian girls (mean age = 13.5 years). The variables assessed included bulimic tendencies, body dissatisfaction, use of extreme weight loss behaviors (EWLBs), self-esteem, depression and family cohesion and adaptability. Cultural eating patterns were also explored. A stepwise discriminant function analysis to examine whether the two groups could be discriminated on these variables was significant and correctly classified 73.9% of the sample, the chief discriminating variables being Pressure to Eat, EWLBs, and Family Adaptability. Univariate analyses indicated differences between the groups on Pressure to Eat, Family Adaptability, and Mother's Shape. Although the groups were discriminable, a number of variables generally associated with eating disorder did not contribute to the function. These data are discussed in terms of cultural assimilation.

A combined model of sensory and cognitive representations underlying tonal expectations in music: from audio signals to behavior.

PubMed

Collins, Tom; Tillmann, Barbara; Barrett, Frederick S; Delbé, Charles; Janata, Petr

2014-01-01

Listeners' expectations for melodies and harmonies in tonal music are perhaps the most studied aspect of music cognition. Long debated has been whether faster response times (RTs) to more strongly primed events (in a music theoretic sense) are driven by sensory or cognitive mechanisms, such as repetition of sensory information or activation of cognitive schemata that reflect learned tonal knowledge, respectively. We analyzed over 300 stimuli from 7 priming experiments comprising a broad range of musical material, using a model that transforms raw audio signals through a series of plausible physiological and psychological representations spanning a sensory-cognitive continuum. We show that RTs are modeled, in part, by information in periodicity pitch distributions, chroma vectors, and activations of tonal space--a representation on a toroidal surface of the major/minor key relationships in Western tonal music. We show that in tonal space, melodies are grouped by their tonal rather than timbral properties, whereas the reverse is true for the periodicity pitch representation. While tonal space variables explained more of the variation in RTs than did periodicity pitch variables, suggesting a greater contribution of cognitive influences to tonal expectation, a stepwise selection model contained variables from both representations and successfully explained the pattern of RTs across stimulus categories in 4 of the 7 experiments. The addition of closure--a cognitive representation of a specific syntactic relationship--succeeded in explaining results from all 7 experiments. We conclude that multiple representational stages along a sensory-cognitive continuum combine to shape tonal expectations in music. (PsycINFO Database Record (c) 2014 APA, all rights reserved).

Factors affecting medication-order processing time.

PubMed

Beaman, M A; Kotzan, J A

1982-11-01

The factors affecting medication-order processing time at one hospital were studied. The order processing time was determined by directly observing the time to process randomly selected new drug orders on all three work shifts during two one-week periods. An order could list more than one drug for an individual patient. The observer recorded the nature, location, and cost of the drugs ordered, as well as the time to process the order. The time and type of interruptions also were noted. The time to process a drug order was classified as six dependent variables: (1) total time, (2) work time, (3) check time, (4) waiting time I--time from arrival on the dumbwaiter until work was initiated, (5) waiting time II--time between completion of the work and initiation of checking, and (6) waiting time III--time after the check was completed until the order left on the dumbwaiter. The significant predictors of each of the six dependent variables were determined using stepwise multiple regression. The total time to process a prescription order was 58.33 +/- 48.72 minutes; the urgency status of the order was the only significant determinant of total time. Urgency status also significantly predicted the three waiting-time variables. Interruptions and the number of drugs on the order were significant determinants of work time and check time. Each telephone interruption increased the work time by 1.72 minutes. While the results of this study cannot be generalized to other institutions, pharmacy managers can use the method of determining factors that affect medication-order processing time to identify problem areas in their institutions.

Relationships among body weight, joint moments generated during functional activities, and hip bone mass in older adults

PubMed Central

Wang, Man-Ying; Flanagan, Sean P.; Song, Joo-Eun; Greendale, Gail A.; Salem, George J.

2012-01-01

Objective To investigate the relationships among hip joint moments produced during functional activities and hip bone mass in sedentary older adults. Methods Eight male and eight female older adults (70–85 yr) performed functional activities including walking, chair sit–stand–sit, and stair stepping at a self-selected pace while instrumented for biomechanical analysis. Bone mass at proximal femur, femoral neck, and greater trochanter were measured by dual-energy X-ray absorptiometry. Three-dimensional hip moments were obtained using a six-camera motion analysis system, force platforms, and inverse dynamics techniques. Pearson’s correlation coefficients were employed to assess the relationships among hip bone mass, height, weight, age, and joint moments. Stepwise regression analyses were performed to determine the factors that significantly predicted bone mass using all significant variables identified in the correlation analysis. Findings Hip bone mass was not significantly correlated with moments during activities in men. Conversely, in women bone mass at all sites were significantly correlated with weight, moments generated with stepping, and moments generated with walking (p < 0.05 to p < 0.001). Regression analysis results further indicated that the overall moments during stepping independently predicted up to 93% of the variability in bone mass at femoral neck and proximal femur; whereas weight independently predicted up to 92% of the variability in bone mass at greater trochanter. Interpretation Submaximal loading events produced during functional activities were highly correlated with hip bone mass in sedentary older women, but not men. The findings may ultimately be used to modify exercise prescription for the preservation of bone mass. PMID:16631283

Ranking the Potential Yield of Salinity and Selenium from Subbasins in the Lower Gunnison River Basin Using Seasonal, Multi-parameter Regression Models

NASA Astrophysics Data System (ADS)

Linard, J.; Leib, K.; Colorado Water Science Center

2010-12-01

Elevated levels of salinity and dissolved selenium can detrimentally effect the quality of water where anthropogenic and natural uses are concerned. In areas, such as the lower Gunnison Basin of western Colorado, salinity and selenium are such a concern that control projects are implemented to limit their mobilization. To prioritize the locations in which control projects are implemented, multi-parameter regression models were developed to identify subbasins in the lower Gunnison River Basin that were most likely to have elevated salinity and dissolved selenium levels. The drainage area is about 5,900 mi2 and is underlain by Cretaceous marine shale, which is the most common source of salinity and dissolved selenium. To characterize the complex hydrologic and chemical processes governing constituent mobilization, geospatial variables representing 70 different environmental characteristics were correlated to mean seasonal (irrigation and nonirrigation seasons) salinity and selenium yields estimated at 154 sampling sites. The variables generally represented characteristics of the physical basin, precipitation, soil, geology, land use, and irrigation water delivery systems. Irrigation and nonirrigation seasons were selected due to documented effects of irrigation on constituent mobilization. Following a stepwise approach, combinations of the geospatial variables were used to develop four multi-parameter regression models. These models predicted salinity and selenium yield, within a 95 percent confidence range, at individual points in the Lower Gunnison Basin for irrigation and non-irrigation seasons. The corresponding subbasins were ranked according to their potential to yield salinity and selenium and rankings were used to prioritize areas that would most benefit from control projects.

Relationships between presenteeism and work-related musculoskeletal disorders among physical therapists in the Republic of Korea.

PubMed

Bae, Young-Hyeon

2017-12-14

This study investigated the relationship between presenteeism and work-related musculoskeletal disorders (WMSDs) among physical therapists (PTs) in the Republic of Korea. Questionnaires were given to 600 PTs in the Republic of Korea. General and occupational characteristics and the prevalence of presenteeism and absenteeism were self-reported on the questionnaire. Stepwise regression analyses were used to evaluate the effects of presenteeism and other variables on general and occupational characteristics. Of the 490 PTs who responded, 399 (81.4%) reported at least one WMSD. There was a low incidence rate of absenteeism, but work impairment scores indicate there was a high incidence of presenteeism. In the stepwise regression analyses, the incidence of WMSDs was highest in cases of presenteeism. The results of this study demonstrate that there is a high incidence rate of WMSDs in Republic of Korean PTs, that WMSDs are related to presenteeism and that PTs demonstrate high presenteeism and low absenteeism.

Step-wise loss of antidepressant effectiveness with repeated antidepressant trials in bipolar II depression.

PubMed

Amsterdam, Jay D; Lorenzo-Luaces, Lorenzo; DeRubeis, Robert J

2016-11-01

This study examined the relationship between the number of prior antidepressant treatment trials and step-wise increase in pharmacodynamic tolerance (or progressive loss of effectiveness) in subjects with bipolar II depression. Subjects ≥18 years old with bipolar II depression (n=129) were randomized to double-blind venlafaxine or lithium carbonate monotherapy for 12 weeks. Responders (n=59) received continuation monotherapy for six additional months. After controlling for baseline covariates of prior medications, there was a 25% reduction in the likelihood of response to treatment with each increase in the number of prior antidepressant trials (odds ratio [OR]=0.75, unstandardized coefficient [B]=-0.29, standard error (SE)=0.12; χ 2 =5.70, P<.02], as well as a 32% reduction in the likelihood of remission with each prior antidepressant trial (OR=0.68, B=-0.39, SE=0.13; χ 2 =9.71, P=.002). This step-wise increase in pharmacodynamic tolerance occurred in both treatment conditions. Prior selective serotonin reuptake inhibitor (SSRI) therapy was specifically associated with a step-wise increase in tolerance, whereas other prior antidepressants or mood stabilizers were not associated with pharmacodynamic tolerance. Neither the number of prior antidepressants, nor the number of prior SSRIs, or mood stabilizers, were associated with an increase in relapse during continuation therapy. The odds of responding or remitting during venlafaxine or lithium monotherapy were reduced by 25% and 32%, respectively, with each increase in the number of prior antidepressant treatment trials. There was no relationship between prior antidepressant exposure and depressive relapse during continuation therapy of bipolar II disorder. © 2016 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.

Stepwise cycloaddition reaction of N-phenacylbenzothiazolium bromides and nitroalkenes for tetrahydro-, dihydro- and benzo[d]pyrrolo[2,1-b]thiazoles

NASA Astrophysics Data System (ADS)

Jin, Gong; Sun, Jing; Yang, Ren-Yin; Yan, Chao-Guo

2017-04-01

The triethylamine promoted stepwise 1,3-dipolar cycloaddition reaction of N-phenacylbenzothiazolium bromides with nitroalkenes in ethanol resulted in a mixture of two isomeric tetrahydrobenzo[d]pyrrolo[2,1-b]thiazoles with cis/trans/cis- and all-trans-configurations. More importantly, the corresponding dihydrobenzo[d]pyrrolo[2,1-b]thiazoles can be selectively prepared in refluxing ethanol and the benzo[d]pyrrolo[2,1-b]thiazoles can be obtained in satisfactory yields by sequential dehydrogenation with DDQ as oxidizer. On the other hand, the similar cycloaddition reaction of N-phenacylbenzothiazolium bromides with 1-methy-1-nitroalkenes in refluxing ethanol afforded benzo[d]pyrrolo[2,1-b]thiazoles with splitting out of nitro group. The stereochemistry of the spiro compounds was clearly elucidated on the basis of NMR spectra and sixteen single crystal structures.

Controlled, Stepwise Reduction and Band Gap Manipulation of Graphene Oxide.

PubMed

Mathkar, Akshay; Tozier, Dylan; Cox, Paris; Ong, Peijie; Galande, Charudatta; Balakrishnan, Kaushik; Leela Mohana Reddy, Arava; Ajayan, Pulickel M

2012-04-19

Graphene oxide (GO) has drawn tremendous interest as a tunable precursor in numerous areas, due to its readily manipulable surface. However, its inhomogeneous and nonstoichiometric structure makes achieving chemical control a major challenge. Here, we present a room-temperature based, controlled method for the stepwise reduction of GO, with evidence of sequential removal of each organic moiety. By analyzing signature infrared absorption frequencies, we identify the carbonyl group as the first to be reduced, while the tertiary alcohol takes the longest to be completely removed from the GO surface. Controlled reduction allows for progressive tuning of the optical gap from 3.5 eV down to 1 eV, while XPS spectra show a concurrent increase in the C/O ratio. This study is the first step toward selectively enhancing the chemical homogeneity of GO, thus providing greater control over its structure, and elucidating the order of removal of functional groups and hydrazine-vapor reduction.

Guest-Induced Switchable Breathing Behavior in a Flexible Metal-Organic Framework with Pronounced Negative Gas Pressure.

PubMed

Shi, Yi-Xiang; Li, Wu-Xiang; Zhang, Wen-Hua; Lang, Jian-Ping

2018-06-29

Flexible metal-organic frameworks (MOFs) have attracted great interest for their dynamically structural transformability in response to external stimuli. Herein, we report a switchable "breathing" or "gate-opening" behavior associated with the phase transformation between a narrow pore (np) and a large pore (lp) in a flexible pillared-layered MOF, denoted as MOF-1 as, which is also confirmed by SCXRD and PXRD. The desolvated phase (MOF-1 des) features a unique stepwise adsorption isotherm for N 2 coupled with a pronounced negative gas adsorption pressure. For comparison, however, no appreciable CO 2 adsorption and gate-opening phenomenon with stepwise sorption can be observed. Furthermore, the polar micropore walls decorated with thiophene groups in MOF-1 des reveals the selective sorption of toluene over benzene and p-xylene associated with self-structural adjustment in spite of the markedly similar physicochemical properties of these vapor molecules.

High drug loading self-microemulsifying/micelle formulation: design by high-throughput formulation screening system and in vivo evaluation.

PubMed

Sakai, Kenichi; Obata, Kouki; Yoshikawa, Mayumi; Takano, Ryusuke; Shibata, Masaki; Maeda, Hiroyuki; Mizutani, Akihiko; Terada, Katsuhide

2012-10-01

To design a high drug loading formulation of self-microemulsifying/micelle system. A poorly-soluble model drug (CH5137291), 8 hydrophilic surfactants (HS), 10 lipophilic surfactants (LS), 5 oils, and PEG400 were used. A high loading formulation was designed by a following stepwise approach using a high-throughput formulation screening (HTFS) system: (1) an oil/solvent was selected by solubility of the drug; (2) a suitable HS for highly loading was selected by the screenings of emulsion/micelle size and phase stability in binary systems (HS, oil/solvent) with increasing loading levels; (3) a LS that formed a broad SMEDDS/micelle area on a phase diagram containing the HS and oil/solvent was selected by the same screenings; (4) an optimized formulation was selected by evaluating the loading capacity of the crystalline drug. Aqueous solubility behavior and oral absorption (Beagle dog) of the optimized formulation were compared with conventional formulations (jet-milled, PEG400). As an optimized formulation, d-α-tocopheryl polyoxyethylene 1000 succinic ester: PEG400 = 8:2 was selected, and achieved the target loading level (200 mg/mL). The formulation formed fine emulsion/micelle (49.1 nm), and generated and maintained a supersaturated state at a higher level compared with the conventional formulations. In the oral absorption test, the area under the plasma concentration-time curve of the optimized formulation was 16.5-fold higher than that of the jet-milled formulation. The high loading formulation designed by the stepwise approach using the HTFS system improved the oral absorption of the poorly-soluble model drug.

Inflammatory bowel diseases phenotype, C. difficile and NOD2 genotype are associated with shifts in human ileum associated microbial composition.

PubMed

Li, Ellen; Hamm, Christina M; Gulati, Ajay S; Sartor, R Balfour; Chen, Hongyan; Wu, Xiao; Zhang, Tianyi; Rohlf, F James; Zhu, Wei; Gu, Chi; Robertson, Charles E; Pace, Norman R; Boedeker, Edgar C; Harpaz, Noam; Yuan, Jeffrey; Weinstock, George M; Sodergren, Erica; Frank, Daniel N

2012-01-01

We tested the hypothesis that Crohn's disease (CD)-related genetic polymorphisms involved in host innate immunity are associated with shifts in human ileum-associated microbial composition in a cross-sectional analysis of human ileal samples. Sanger sequencing of the bacterial 16S ribosomal RNA (rRNA) gene and 454 sequencing of 16S rRNA gene hypervariable regions (V1-V3 and V3-V5), were conducted on macroscopically disease-unaffected ileal biopsies collected from 52 ileal CD, 58 ulcerative colitis and 60 control patients without inflammatory bowel diseases (IBD) undergoing initial surgical resection. These subjects also were genotyped for the three major NOD2 risk alleles (Leu1007fs, R708W, G908R) and the ATG16L1 risk allele (T300A). The samples were linked to clinical metadata, including body mass index, smoking status and Clostridia difficile infection. The sequences were classified into seven phyla/subphyla categories using the Naïve Bayesian Classifier of the Ribosome Database Project. Centered log ratio transformation of six predominant categories was included as the dependent variable in the permutation based MANCOVA for the overall composition with stepwise variable selection. Polymerase chain reaction (PCR) assays were conducted to measure the relative frequencies of the Clostridium coccoides - Eubacterium rectales group and the Faecalibacterium prausnitzii spp. Empiric logit transformations of the relative frequencies of these two microbial groups were included in permutation-based ANCOVA. Regardless of sequencing method, IBD phenotype, Clostridia difficile and NOD2 genotype were selected as associated (FDR ≤ 0.05) with shifts in overall microbial composition. IBD phenotype and NOD2 genotype were also selected as associated with shifts in the relative frequency of the C. coccoides--E. rectales group. IBD phenotype, smoking and IBD medications were selected as associated with shifts in the relative frequency of F. prausnitzii spp. These results indicate that the effects of genetic and environmental factors on IBD are mediated at least in part by the enteric microbiota.

Inflammatory Bowel Diseases Phenotype, C. difficile and NOD2 Genotype Are Associated with Shifts in Human Ileum Associated Microbial Composition

PubMed Central

Li, Ellen; Hamm, Christina M.; Gulati, Ajay S.; Sartor, R. Balfour; Chen, Hongyan; Wu, Xiao; Zhang, Tianyi; Rohlf, F. James; Zhu, Wei; Gu, Chi; Robertson, Charles E.; Pace, Norman R.; Boedeker, Edgar C.; Harpaz, Noam; Yuan, Jeffrey; Weinstock, George M.; Sodergren, Erica; Frank, Daniel N.

2012-01-01

We tested the hypothesis that Crohn’s disease (CD)-related genetic polymorphisms involved in host innate immunity are associated with shifts in human ileum–associated microbial composition in a cross-sectional analysis of human ileal samples. Sanger sequencing of the bacterial 16S ribosomal RNA (rRNA) gene and 454 sequencing of 16S rRNA gene hypervariable regions (V1–V3 and V3–V5), were conducted on macroscopically disease-unaffected ileal biopsies collected from 52 ileal CD, 58 ulcerative colitis and 60 control patients without inflammatory bowel diseases (IBD) undergoing initial surgical resection. These subjects also were genotyped for the three major NOD2 risk alleles (Leu1007fs, R708W, G908R) and the ATG16L1 risk allele (T300A). The samples were linked to clinical metadata, including body mass index, smoking status and Clostridia difficile infection. The sequences were classified into seven phyla/subphyla categories using the Naïve Bayesian Classifier of the Ribosome Database Project. Centered log ratio transformation of six predominant categories was included as the dependent variable in the permutation based MANCOVA for the overall composition with stepwise variable selection. Polymerase chain reaction (PCR) assays were conducted to measure the relative frequencies of the Clostridium coccoides – Eubacterium rectales group and the Faecalibacterium prausnitzii spp. Empiric logit transformations of the relative frequencies of these two microbial groups were included in permutation-based ANCOVA. Regardless of sequencing method, IBD phenotype, Clostridia difficile and NOD2 genotype were selected as associated (FDR ≤0.05) with shifts in overall microbial composition. IBD phenotype and NOD2 genotype were also selected as associated with shifts in the relative frequency of the C. coccoides – E. rectales group. IBD phenotype, smoking and IBD medications were selected as associated with shifts in the relative frequency of F. prausnitzii spp. These results indicate that the effects of genetic and environmental factors on IBD are mediated at least in part by the enteric microbiota. PMID:22719818

[Fast optimization of stepwise gradient conditions for ternary mobile phase in reversed-phase high performance liquid chromatography].

PubMed

Shan, Yi-chu; Zhang, Yu-kui; Zhao, Rui-huan

2002-07-01

In high performance liquid chromatography, it is necessary to apply multi-composition gradient elution for the separation of complex samples such as environmental and biological samples. Multivariate stepwise gradient elution is one of the most efficient elution modes, because it combines the high selectivity of multi-composition mobile phase and shorter analysis time of gradient elution. In practical separations, the separation selectivity of samples can be effectively adjusted by using ternary mobile phase. For the optimization of these parameters, the retention equation of samples must be obtained at first. Traditionally, several isocratic experiments are used to get the retention equation of solute. However, it is time consuming especially for the separation of complex samples with a wide range of polarity. A new method for the fast optimization of ternary stepwise gradient elution was proposed based on the migration rule of solute in column. First, the coefficients of retention equation of solute are obtained by running several linear gradient experiments, then the optimal separation conditions are searched according to the hierarchical chromatography response function which acts as the optimization criterion. For each kind of organic modifier, two initial linear gradient experiments are used to obtain the primary coefficients of retention equation of each solute. For ternary mobile phase, only four linear gradient runs are needed to get the coefficients of retention equation. Then the retention times of solutes under arbitrary mobile phase composition can be predicted. The initial optimal mobile phase composition is obtained by resolution mapping for all of the solutes. A hierarchical chromatography response function is used to evaluate the separation efficiencies and search the optimal elution conditions. In subsequent optimization, the migrating distance of solute in the column is considered to decide the mobile phase composition and sustaining time of the latter steps until all the solutes are eluted out. Thus the first stepwise gradient elution conditions are predicted. If the resolution of samples under the predicted optimal separation conditions is satisfactory, the optimization procedure is stopped; otherwise, the coefficients of retention equation are adjusted according to the experimental results under the previously predicted elution conditions. Then the new stepwise gradient elution conditions are predicted repeatedly until satisfactory resolution is obtained. Normally, the satisfactory separation conditions can be found only after six experiments by using the proposed method. In comparison with the traditional optimization method, the time needed to finish the optimization procedure can be greatly reduced. The method has been validated by its application to the separation of several samples such as amino acid derivatives, aromatic amines, in which satisfactory separations were obtained with predicted resolution.

Applicant Characteristics Associated With Selection for Ranking at Independent Surgery Residency Programs.

PubMed

Dort, Jonathan M; Trickey, Amber W; Kallies, Kara J; Joshi, Amit R T; Sidwell, Richard A; Jarman, Benjamin T

2015-01-01

This study evaluated characteristics of applicants selected for interview and ranked by independent general surgery residency programs and assessed independent program application volumes, interview selection, rank list formation, and match success. Demographic and academic information was analyzed for 2014-2015 applicants. Applicant characteristics were compared by ranking status using univariate and multivariable statistical techniques. Characteristics independently associated with whether or not an applicant was ranked were identified using multivariable logistic regression modeling with backward stepwise variable selection and cluster-correlated robust variance estimates to account for correlations among individuals who applied to multiple programs. The Electronic Residency Application Service was used to obtain applicant data and program match outcomes at 33 independent surgery programs. All applicants selected to interview at 33 participating independent general surgery residency programs were included in the study. Applicants were 60% male with median age of 26 years. Birthplace was well distributed. Most applicants (73%) had ≥1 academic publication. Median United States Medical Licensing Exams (USMLE) Step 1 score was 228 (interquartile range: 218-240), and median USMLE Step 2 clinical knowledge score was 241 (interquartile range: 231-250). Residency programs in some regions more often ranked applicants who attended medical school within the same region. On multivariable analysis, significant predictors of ranking by an independent residency program were: USMLE scores, medical school region, and birth region. Independent programs received an average of 764 applications (range: 307-1704). On average, 12% interviews, and 81% of interviewed applicants were ranked. Most programs (84%) matched at least 1 applicant ranked in their top 10. Participating independent programs attract a large volume of applicants and have high standards in the selection process. This information can be used by surgery residency applicants to gauge their candidacy at independent programs. Independent programs offer a select number of interviews, rank most applicants that they interview, and successfully match competitive applicants. Copyright © 2015 Association of Program Directors in Surgery. Published by Elsevier Inc. All rights reserved.

The relationship between context, structure, and processes with outcomes of 6 regional diabetes networks in Europe.

PubMed

Mahdavi, Mahdi; Vissers, Jan; Elkhuizen, Sylvia; van Dijk, Mattees; Vanhala, Antero; Karampli, Eleftheria; Faubel, Raquel; Forte, Paul; Coroian, Elena; van de Klundert, Joris

2018-01-01

While health service provisioning for the chronic condition Type 2 Diabetes (T2D) often involves a network of organisations and professionals, most evidence on the relationships between the structures and processes of service provisioning and the outcomes considers single organisations or solo practitioners. Extending Donabedian's Structure-Process-Outcome (SPO) model, we investigate how differences in quality of life, effective coverage of diabetes, and service satisfaction are associated with differences in the structures, processes, and context of T2D services in six regions in Finland, Germany, Greece, Netherlands, Spain, and UK. Data collection consisted of: a) systematic modelling of provider network's structures and processes, and b) a cross-sectional survey of patient reported outcomes and other information. The survey resulted in data from 1459 T2D patients, during 2011-2012. Stepwise linear regression models were used to identify how independent cumulative proportion of variance in quality of life and service satisfaction are related to differences in context, structure and process. The selected context, structure and process variables are based on Donabedian's SPO model, a service quality research instrument (SERVQUAL), and previous organization and professional level evidence. Additional analysis deepens the possible bidirectional relation between outcomes and processes. The regression models explain 44% of variance in service satisfaction, mostly by structure and process variables (such as human resource use and the SERVQUAL dimensions). The models explained 23% of variance in quality of life between the networks, much of which is related to contextual variables. Our results suggest that effectiveness of A1c control is negatively correlated with process variables such as total hours of care provided per year and cost of services per year. While the selected structure and process variables explain much of the variance in service satisfaction, this is less the case for quality of life. Moreover, it appears that the effect of the clinical outcome A1c control on processes is stronger than the other way around, as poorer control seems to relate to more service use, and higher cost. The standardized operational models used in this research prove to form a basis for expanding the network level evidence base for effective T2D service provisioning.

The relationship between context, structure, and processes with outcomes of 6 regional diabetes networks in Europe

PubMed Central

Elkhuizen, Sylvia; van Dijk, Mattees; Vanhala, Antero; Karampli, Eleftheria; Faubel, Raquel; Forte, Paul; Coroian, Elena

2018-01-01

Background While health service provisioning for the chronic condition Type 2 Diabetes (T2D) often involves a network of organisations and professionals, most evidence on the relationships between the structures and processes of service provisioning and the outcomes considers single organisations or solo practitioners. Extending Donabedian’s Structure-Process-Outcome (SPO) model, we investigate how differences in quality of life, effective coverage of diabetes, and service satisfaction are associated with differences in the structures, processes, and context of T2D services in six regions in Finland, Germany, Greece, Netherlands, Spain, and UK. Methods Data collection consisted of: a) systematic modelling of provider network’s structures and processes, and b) a cross-sectional survey of patient reported outcomes and other information. The survey resulted in data from 1459 T2D patients, during 2011–2012. Stepwise linear regression models were used to identify how independent cumulative proportion of variance in quality of life and service satisfaction are related to differences in context, structure and process. The selected context, structure and process variables are based on Donabedian’s SPO model, a service quality research instrument (SERVQUAL), and previous organization and professional level evidence. Additional analysis deepens the possible bidirectional relation between outcomes and processes. Results The regression models explain 44% of variance in service satisfaction, mostly by structure and process variables (such as human resource use and the SERVQUAL dimensions). The models explained 23% of variance in quality of life between the networks, much of which is related to contextual variables. Our results suggest that effectiveness of A1c control is negatively correlated with process variables such as total hours of care provided per year and cost of services per year. Conclusions While the selected structure and process variables explain much of the variance in service satisfaction, this is less the case for quality of life. Moreover, it appears that the effect of the clinical outcome A1c control on processes is stronger than the other way around, as poorer control seems to relate to more service use, and higher cost. The standardized operational models used in this research prove to form a basis for expanding the network level evidence base for effective T2D service provisioning. PMID:29447220

Estimating Rangeland Forage Production Using Remote Sensing Data from a Small Unmanned Aerial System (sUAS)

NASA Astrophysics Data System (ADS)

Liu, H.; Jin, Y.; Devine, S.; Dahlgren, R. A.; Covello, S.; Larsen, R.; O'Geen, A. T.

2017-12-01

California rangelands cover 23 million hectares and support a $3.4 billion annual cattle industry. Rangeland forage production varies appreciably from year-to-year and across short distances on the landscape. Spatially explicit and near real-time information on forage production at a high resolution is critical for effective rangeland management, especially during an era of climatic extremes. We here integrated a multispectral MicaSense RedEdge camera with a 3DR solo quad-copter and acquired time-series images during the 2017 growing season over a topographically complex 10-hectare rangeland in San Luis Obispo County, CA. Soil moisture and temperature sensors were installed at 16 landscape positions, and vegetation clippings were collected at 36 plots to quantify forage dry biomass. We built four centimeter-level models for forage production mapping using time series of sUAS images and ground measurements of forage biomass and soil temperature and moisture. The biophysical model based on Monteith's eco-physiological plant growth theory estimated forage production reasonably well with a coefficient of determination (R2) of 0.86 and a root-mean-square error (RMSE) of 424 kg/ha when the soil parameters were included, and a R2 of 0.79 and a RMSE of 510 kg/ha when only remote sensing and topographical variables were included. We built two empirical models of forage production using a stepwise variable selection technique, one with soil variables. Results showed that cumulative absorbed photosynthetically active radiation (APAR) and elevation were the most important variables in both models, explaining more than 40% of the spatio-temporal variance in forage production. Soil moisture accounted for an additional 29% of the variance. Illumination condition was selected as a proxy for soil moisture in the model without soil variables, and accounted for 18% of the variance. We applied the remote sensing-based models to map daily forage production at 30-cm resolution for the whole study area during the 2017 growing season. The forage maps captured similar seasonal and spatial patterns of forage production as ground measured dry biomass. This study demonstrated a near real-time monitoring tool for ranchers to estimate forage production with sUAS technology and improved watershed-scale rangeland management.

Stepwise pumping approach to improve free phase light hydrocarbon recovery from unconfined aquifers

NASA Astrophysics Data System (ADS)

Cooper, Grant S.; Peralta, Richard C.; Kaluarachchi, Jagath J.

1995-04-01

A stepwise, time-varying pumping approach is developed to improve free phase oil recovery of light non-aqueous phase liquids (LNAPL) from a homogeneous, unconfined aquifer. Stepwise pumping is used to contain the floating oil plume and obtain efficient free oil recovery. The graphical plots. The approach uses ARMOS ©, an areal two-dimensional multiphase flow, finite-element simulation model. Systematic simulations of free oil area changes to pumping rates are analyzed. Pumping rates are determined that achieve LNAPL plume containment at different times (i.e. 90, 180 and 360 days) for a planning period of 360 days. These pumping rates are used in reverse order as a stepwise (monotonically increasing) pumping strategy. This stepwise pumping strategy is analyzed further by performing additional simulations at different pumping rates for the last pumping period. The final stepwise pumping strategy is varied by factors of -25% and +30% to evaluate sensitivity in the free oil recovery process. Stepwise pumping is compared to steady pumping rates to determine the best free oil recovery strategy. Stepwise pumping is shown to improve oil recovery by increasing recoveredoil volume (11%) and decreasing residual oil (15%) when compared with traditional steady pumping strategies. The best stepwise pumping strategy recovers more free oil by reducing the amount of residual oil left in the system due to pumping drawdown. This stepwise pumping pproach can be used to enhance free oil recovery and provide for cost-effective design and management of LNAPL cleanup.

Evaluation of RPE-Select: A Web-Based Respiratory Protective Equipment Selector Tool.

PubMed

Vaughan, Nick; Rajan-Sithamparanadarajah, Bob; Atkinson, Robert

2016-08-01

This article describes the evaluation of an open-access web-based respiratory protective equipment selector tool (RPE-Select, accessible at http://www.healthyworkinglives.com/rpe-selector). This tool is based on the principles of the COSHH-Essentials (C-E) control banding (CB) tool, which was developed for the exposure risk management of hazardous chemicals in the workplace by small and medium sized enterprises (SMEs) and general practice H&S professionals. RPE-Select can be used for identifying adequate and suitable RPE for dusts, fibres, mist (solvent, water, and oil based), sprays, volatile solids, fumes, gases, vapours, and actual or potential oxygen deficiency. It can be applied for substances and products with safety data sheets as well as for a large number of commonly encountered process-generated substances (PGS), such as poultry house dusts or welding fume. Potential international usability has been built-in by using the Hazard Statements developed for the Globally Harmonised System (GHS) and providing recommended RPE in picture form as well as with a written specification. Illustration helps to compensate for the variabilities in assigned protection factors across the world. RPE-Select uses easily understandable descriptions/explanations and an interactive stepwise flow for providing input/answers at each step. The output of the selection process is a report summarising the user input data and a selection of RPE, including types of filters where applicable, from which the user can select the appropriate one for each wearer. In addition, each report includes 'Dos' and 'Don'ts' for the recommended RPE. RPE-Select outcomes, based on up to 20 hypothetical use scenarios, were evaluated in comparison with other available RPE selection processes and tools, and by 32 independent users with a broad range of familiarities with industrial use scenarios in general and respiratory protection in particular. For scenarios involving substances having safety data sheets, 87% of RPE-Select outcomes resulted in a 'safe' RPE selection, while 98% 'safe' outcomes were achieved for scenarios involving process-generated substances. Reasons for the outliers were examined. User comments and opinions on the mechanics and usability of RPE-Select are also presented. © Crown copyright 2016.

Evaluation of RPE-Select: A Web-Based Respiratory Protective Equipment Selector Tool

PubMed Central

Vaughan, Nick; Rajan-Sithamparanadarajah, Bob; Atkinson, Robert

2016-01-01

This article describes the evaluation of an open-access web-based respiratory protective equipment selector tool (RPE-Select, accessible at http://www.healthyworkinglives.com/rpe-selector). This tool is based on the principles of the COSHH-Essentials (C-E) control banding (CB) tool, which was developed for the exposure risk management of hazardous chemicals in the workplace by small and medium sized enterprises (SMEs) and general practice H&S professionals. RPE-Select can be used for identifying adequate and suitable RPE for dusts, fibres, mist (solvent, water, and oil based), sprays, volatile solids, fumes, gases, vapours, and actual or potential oxygen deficiency. It can be applied for substances and products with safety data sheets as well as for a large number of commonly encountered process-generated substances (PGS), such as poultry house dusts or welding fume. Potential international usability has been built-in by using the Hazard Statements developed for the Globally Harmonised System (GHS) and providing recommended RPE in picture form as well as with a written specification. Illustration helps to compensate for the variabilities in assigned protection factors across the world. RPE-Select uses easily understandable descriptions/explanations and an interactive stepwise flow for providing input/answers at each step. The output of the selection process is a report summarising the user input data and a selection of RPE, including types of filters where applicable, from which the user can select the appropriate one for each wearer. In addition, each report includes ‘Dos’ and ‘Don’ts’ for the recommended RPE. RPE-Select outcomes, based on up to 20 hypothetical use scenarios, were evaluated in comparison with other available RPE selection processes and tools, and by 32 independent users with a broad range of familiarities with industrial use scenarios in general and respiratory protection in particular. For scenarios involving substances having safety data sheets, 87% of RPE-Select outcomes resulted in a ‘safe’ RPE selection, while 98% ‘safe’ outcomes were achieved for scenarios involving process-generated substances. Reasons for the outliers were examined. User comments and opinions on the mechanics and usability of RPE-Select are also presented. PMID:27286763

Stepwise magnetic-geochemical approach for efficient assessment of heavy metal polluted sites

NASA Astrophysics Data System (ADS)

Appel, E.; Rösler, W.; Ojha, G.

2012-04-01

Previous studies have shown that magnetometry can outline the distribution of fly ash deposition in the surroundings of coal-burning power plants and steel industries. Especially the easy-to-measure magnetic susceptibility (MS) is capable to act as a proxy for heavy metal (HM) pollution caused by such kind of point source pollution. Here we present a demonstration project around the coal-burning power plant complex "Schwarze Pumpe" in eastern Germany. Before reunification of West and East Germany huge amounts of HM pollutants were emitted from the "Schwarze Pumpe" into the environment by both fly ash emission and dumped clinker. The project has been conducted as part of the TASK Centre of Competence which aims at bringing new innovative techniques closer to the market. Our project combines in situ and laboratory MS measurements and HM analyses in order to demonstrate the efficiency of a stepwise approach for site assessment of HM pollution around point sources of fly-ash emission and deposition into soil. The following scenario is played through: We assume that the "true" spatial distribution of HM pollution (given by the pollution load index PLI comprising Fe, Zn, Pb, and Cu) is represented by our entire set of 85 measured samples (XRF analyses) from forest sites around the "Schwarze Pumpe". Surface MS data (collected with a Bartington MS2D) and in situ vertical MS sections (logged by an SM400 instrument) are used to determine a qualitative overview of potentially higher and lower polluted areas. A suite of spatial HM distribution maps obtained by random selections of 30 out of the 85 analysed sites is compared to the HM map obtained from a targeted 30-sites-selection based on pre-information from the MS results. The PLI distribution map obtained from the targeted 30-sites-selection shows all essential details of the "true" pollution map, while the different random 30-sites-selections miss important features. This comparison shows that, for the same cost investment, a stepwise combined magnetic-geochemical site assessment leads to a clearly more significant characterization of soil pollution than by a common approach with exclusively random sampling for geochemical analysis, or alternatively to an equal quality result for lower costs.

Quantitative evaluation of light scattering intensities of the crystalline lens for radiation related minimal change in interventional radiologists: a cross-sectional pilot study.

PubMed

Abe, Toshi; Furui, Shigeru; Sasaki, Hiroshi; Sakamoto, Yasuo; Suzuki, Shigeru; Ishitake, Tatsuya; Terasaki, Kinuyo; Kohtake, Hiroshi; Norbash, Alexander M; Behrman, Richard H; Hayabuchi, Naofumi

2013-03-01

To evaluate low-dose X-ray radiation effects on the eye by measuring the amount of light scattering in specific regions of the lens, we compared exposed subjects (interventional radiologists) with unexposed subjects (employees of medical service companies), as a pilot study. According to numerous exclusionary rules, subjects with confounding variables contributing to cataract formation were excluded. Left eye examinations were performed on 68 exposed subjects and 171 unexposed subjects. The eye examinations consisted of an initial screening examination, followed by Scheimpflug imaging of the lens using an anterior eye segment analysis system. The subjects were assessed for the quantity of light scattering intensities found in each of the six layers of the lens. Multiple stepwise regression analyses were performed with the stepwise regression for six variables: age, radiation exposure, smoking, drinking, wearing glasses and workplace. In addition, an age-matched comparison between exposed and unexposed subjects was performed. Minimal increased light scattering intensity in the posterior subcapsular region showed statistical significance. Our results indicate that occupational radiation exposure in interventional radiologists may affect the posterior subcapsular region of the lens. Since by its very nature this retrospective study had many limitations, further well-designed studies concerning minimal radiation-related lens changes should be carried out in a low-dose exposure group.

[Identification of Dendrobium varieties by infrared spectroscopy].

PubMed

Liu, Fei; Wang, Yuan-Zhong; Yang, Chun-Yan; Jin, Hang

2014-11-01

The difference of Dendrobium varieties were analyzed by Fourier transform infrared (FTIR) spectroscopy. The infrared spectra of 206 stems from 30 Dendrobium varieties were obtained, and showed that polysaccharides, especially fiber, were the main components in Dendrobium plants. FTIR combined with Wilks' Lambda stepwise discriminative analysis was used to identify Dendrobium varieties. The effects of spectral range and number of training samples on the discrimination results were also analysed. Two hundred eighty seven variables in the spectral range of 1 800-1 250 cm(-1) were studied, and showed that the return discrimination is 100% correct when the training samples number of each species was 2, 3, 4, 5, and 6, respectively, whereas for the remaining samples the correct rates of identification were equal to 79.4%, 91.3%, 93.0%, 98.2%, and 100%, respectively. The same discriminative analyses on five different training samples in the spectral range of 1 800-1 500, 1 500-1 250, 1 250-600, 1 250-950 and 950-650 cm(-1) were compared, which showed that the variables in the range of 1 800-1 250, 1 800-1 500 and 950-600 cm(-1) were more suitable for variety identification, and one can obtain the satisfactory result for discriminative analysis when the training sample is more than 3. Our results indicate that FTIR combined with stepwise discriminative analysis is an effective way to distinguish different Dendrobium varieties.

Spatial prediction of Soil Organic Carbon contents in croplands, grasslands and forests using environmental covariates and Generalized Additive Models (Southern Belgium)

NASA Astrophysics Data System (ADS)

Chartin, Caroline; Stevens, Antoine; van Wesemael, Bas

2015-04-01

Providing spatially continuous Soil Organic Carbon data (SOC) is needed to support decisions regarding soil management, and inform the political debate with quantified estimates of the status and change of the soil resource. Digital Soil Mapping techniques are based on relations existing between a soil parameter (measured at different locations in space at a defined period) and relevant covariates (spatially continuous data) that are factors controlling soil formation and explaining the spatial variability of the target variable. This study aimed at apply DSM techniques to recent SOC content measurements (2005-2013) in three different landuses, i.e. cropland, grassland, and forest, in the Walloon region (Southern Belgium). For this purpose, SOC databases of two regional Soil Monitoring Networks (CARBOSOL for croplands and grasslands, and IPRFW for forests) were first harmonized, totalising about 1,220 observations. Median values of SOC content for croplands, grasslands, and forests, are respectively of 12.8, 29.0, and 43.1 g C kg-1. Then, a set of spatial layers were prepared with a resolution of 40 meters and with the same grid topology, containing environmental covariates such as, landuses, Digital Elevation Model and its derivatives, soil texture, C factor, carbon inputs by manure, and climate. Here, in addition to the three classical texture classes (clays, silt, and sand), we tested the use of clays + fine silt content (particles < 20 µm and related to stable carbon fraction) as soil covariate explaining SOC variations. For each of the three land uses (cropland, grassland and forest), a Generalized Additive Model (GAM) was calibrated on two thirds of respective dataset. The remaining samples were assigned to a test set to assess model performance. A backward stepwise procedure was followed to select the relevant environmental covariates using their approximate p-values (the level of significance was set at p < 0.05). Standard errors were estimated for each of the three models. The backward stepwise procedure selected coordinates, elevation and clays + fine silt content as environment covariates to model SOC variation in cropland soils; latitude, precipitation, and clays + fine silt content (< 20 µm) for grassland soils; and latitude, elevation, topographic position index and clays + fine silt content (< 20 µm) for forest soils. The validation of the models gave a R² of 0.62 for croplands, 0.38 for grasslands, and 0.35 for forests. These results will be developed and discussed based on implications of natural against anthropogenic drivers on SOC distribution for these three landuses. To finish, a map combining detailed information of SOC content for agricultural soils and forests was for the first time computed for the Walloon region.

Association between masseter muscle activity levels recorded during sleep and signs and symptoms of temporomandibular disorders in healthy young adults.

PubMed

Baba, Kazuyoshi; Haketa, Tadasu; Sasaki, Yoshiyuki; Ohyama, Takashi; Clark, Glenn T

2005-01-01

To examine whether any signs and symptoms of temporomandibular disorders were significantly associated with masseter muscle activity levels during sleep. One hundred three healthy adult subjects (age range, 22 to 32 years) participated in the study. They were asked to fill out questionnaires, undergo a calibrated clinical examination of their jaws and teeth, and perform 6 consecutive nightly masseter electromyographic (EMG) recordings with a portable EMG recording system in their home. The EMG data were considered dependent variables, while the questionnaire and examination data were considered independent variables. Multiple stepwise linear regression analysis was utilized to assess possible associations between these variables. Both gender and joint sound scores were significantly related to the duration of EMG activity. None of the other independent variables were found to be related to any of the muscle activity variables. The results suggest that both gender and clicking are significantly related to duration of masseter EMG activity during sleep.

Self-regulated learning and achievement by middle-school children.

PubMed

Sink, C A; Barnett, J E; Hixon, J E

1991-12-01

The relationship of self-regulated learning to the achievement test scores of 62 Grade 6 students was studied. Generally, the metacognitive and affective variables correlated significantly with teachers' grades and standardized test scores in mathematics, reading, and science. Planning and self-assessment significantly predicted the six measures of achievement. Step-wise multiple regression analyses using the metacognitive and affective variables largely indicate that students' and teachers' perceptions of scholastic ability and planning appear to be the most salient factors in predicting academic performance. The locus of control dimension had no utility in predicting classroom grades and performance on standardized measures of achievement. The implications of the findings for teaching and learning are discussed.

Models of subjective response to in-flight motion data

NASA Technical Reports Server (NTRS)

Rudrapatna, A. N.; Jacobson, I. D.

1973-01-01

Mathematical relationships between subjective comfort and environmental variables in an air transportation system are investigated. As a first step in model building, only the motion variables are incorporated and sensitivities are obtained using stepwise multiple regression analysis. The data for these models have been collected from commercial passenger flights. Two models are considered. In the first, subjective comfort is assumed to depend on rms values of the six-degrees-of-freedom accelerations. The second assumes a Rustenburg type human response function in obtaining frequency weighted rms accelerations, which are used in a linear model. The form of the human response function is examined and the results yield a human response weighting function for different degrees of freedom.

Determinants of violence against health workers in Portugal.

PubMed

Craveiro, Isabel; Fronteira, Inês; Candeias, Anabela

2007-01-01

The focus of this article is on the determinants of workplace violence against health workers identified in two cross-sectional analytical studies. The prevalence of victims of the several types and of any-type of workplace violence was estimated in each study, as well as the relative frequency of the associated characteristics. Each dependent variable was also analyzed, in relation to the dichotomized independent variables using a stepwise logistic regression strategy. The Ministry of Health has adopted strategies, which include guidelines on what to do to prevent and correct violence against health workers and a workplace violence observatory. Workplace violence has societal, organizational and individual determinants that can be prevented and monitored.

Characterization of complex systems using the design of experiments approach: transient protein expression in tobacco as a case study.

PubMed

Buyel, Johannes Felix; Fischer, Rainer

2014-01-31

Plants provide multiple benefits for the production of biopharmaceuticals including low costs, scalability, and safety. Transient expression offers the additional advantage of short development and production times, but expression levels can vary significantly between batches thus giving rise to regulatory concerns in the context of good manufacturing practice. We used a design of experiments (DoE) approach to determine the impact of major factors such as regulatory elements in the expression construct, plant growth and development parameters, and the incubation conditions during expression, on the variability of expression between batches. We tested plants expressing a model anti-HIV monoclonal antibody (2G12) and a fluorescent marker protein (DsRed). We discuss the rationale for selecting certain properties of the model and identify its potential limitations. The general approach can easily be transferred to other problems because the principles of the model are broadly applicable: knowledge-based parameter selection, complexity reduction by splitting the initial problem into smaller modules, software-guided setup of optimal experiment combinations and step-wise design augmentation. Therefore, the methodology is not only useful for characterizing protein expression in plants but also for the investigation of other complex systems lacking a mechanistic description. The predictive equations describing the interconnectivity between parameters can be used to establish mechanistic models for other complex systems.

Forecasting peak asthma admissions in London: an application of quantile regression models.

PubMed

Soyiri, Ireneous N; Reidpath, Daniel D; Sarran, Christophe

2013-07-01

Asthma is a chronic condition of great public health concern globally. The associated morbidity, mortality and healthcare utilisation place an enormous burden on healthcare infrastructure and services. This study demonstrates a multistage quantile regression approach to predicting excess demand for health care services in the form of asthma daily admissions in London, using retrospective data from the Hospital Episode Statistics, weather and air quality. Trivariate quantile regression models (QRM) of asthma daily admissions were fitted to a 14-day range of lags of environmental factors, accounting for seasonality in a hold-in sample of the data. Representative lags were pooled to form multivariate predictive models, selected through a systematic backward stepwise reduction approach. Models were cross-validated using a hold-out sample of the data, and their respective root mean square error measures, sensitivity, specificity and predictive values compared. Two of the predictive models were able to detect extreme number of daily asthma admissions at sensitivity levels of 76 % and 62 %, as well as specificities of 66 % and 76 %. Their positive predictive values were slightly higher for the hold-out sample (29 % and 28 %) than for the hold-in model development sample (16 % and 18 %). QRMs can be used in multistage to select suitable variables to forecast extreme asthma events. The associations between asthma and environmental factors, including temperature, ozone and carbon monoxide can be exploited in predicting future events using QRMs.

Forecasting peak asthma admissions in London: an application of quantile regression models

NASA Astrophysics Data System (ADS)

Soyiri, Ireneous N.; Reidpath, Daniel D.; Sarran, Christophe

2013-07-01

Asthma is a chronic condition of great public health concern globally. The associated morbidity, mortality and healthcare utilisation place an enormous burden on healthcare infrastructure and services. This study demonstrates a multistage quantile regression approach to predicting excess demand for health care services in the form of asthma daily admissions in London, using retrospective data from the Hospital Episode Statistics, weather and air quality. Trivariate quantile regression models (QRM) of asthma daily admissions were fitted to a 14-day range of lags of environmental factors, accounting for seasonality in a hold-in sample of the data. Representative lags were pooled to form multivariate predictive models, selected through a systematic backward stepwise reduction approach. Models were cross-validated using a hold-out sample of the data, and their respective root mean square error measures, sensitivity, specificity and predictive values compared. Two of the predictive models were able to detect extreme number of daily asthma admissions at sensitivity levels of 76 % and 62 %, as well as specificities of 66 % and 76 %. Their positive predictive values were slightly higher for the hold-out sample (29 % and 28 %) than for the hold-in model development sample (16 % and 18 %). QRMs can be used in multistage to select suitable variables to forecast extreme asthma events. The associations between asthma and environmental factors, including temperature, ozone and carbon monoxide can be exploited in predicting future events using QRMs.

Geometric Image Biomarker Changes of the Parotid Gland Are Associated With Late Xerostomia.

PubMed

van Dijk, Lisanne V; Brouwer, Charlotte L; van der Laan, Hans Paul; Burgerhof, Johannes G M; Langendijk, Johannes A; Steenbakkers, Roel J H M; Sijtsema, Nanna M

2017-12-01

To identify a surrogate marker for late xerostomia 12 months after radiation therapy (Xer 12m ), according to information obtained shortly after treatment. Differences in parotid gland (PG) were quantified in image biomarkers (ΔIBMs) before and 6 weeks after radiation therapy in 107 patients. By performing stepwise forward selection, ΔIBMs that were associated with Xer 12m were selected. Subsequently other variables, such as PG dose and acute xerostomia scores, were added to improve the prediction performance. All models were internally validated. Prediction of Xer 12m based on PG surface reduction (ΔPG-surface) was good (area under the receiver operating characteristic curve, 0.82). Parotid gland dose was related to ΔPG-surface (P<.001, R 2  = 0.27). The addition of acute xerostomia scores to the ΔPG-surface improved the prediction of Xer 12m significantly, and vice versa. The final model including ΔPG-surface and acute xerostomia had outstanding performance in predicting Xer 12m early after radiation therapy (area under the receiver operating characteristic curve, 0.90). Parotid gland surface reduction was associated with late xerostomia. The early posttreatment model with ΔPG-surface and acute xerostomia scores can be considered as a surrogate marker for late xerostomia. Copyright © 2017 The Author(s). Published by Elsevier Inc. All rights reserved.

High resolution reconstruction of monthly precipitation of Iberian Peninsula using circulation weather types

NASA Astrophysics Data System (ADS)

Cortesi, N.; Trigo, R.; Gonzalez-Hidalgo, J. C.; Ramos, A. M.

2012-06-01

Precipitation over the Iberian Peninsula (IP) is highly variable and shows large spatial contrasts between wet mountainous regions, to the north, and dry regions in the inland plains and southern areas. In this work, a high-density monthly precipitation dataset for the IP was coupled with a set of 26 atmospheric circulation weather types (Trigo and DaCamara, 2000) to reconstruct Iberian monthly precipitation from October to May with a very high resolution of 3030 precipitation series (overall mean density one station each 200 km2). A stepwise linear regression model with forward selection was used to develop monthly reconstructed precipitation series calibrated and validated over 1948-2003 period. Validation was conducted by means of a leave-one-out cross-validation over the calibration period. The results show a good model performance for selected months, with a mean coefficient of variation (CV) around 0.6 for validation period, being particularly robust over the western and central sectors of IP, while the predicted values in the Mediterranean and northern coastal areas are less acute. We show for three long stations (Lisbon, Madrid and Valencia) the comparison between model and original data as an example to how these models can be used in order to obtain monthly precipitation fields since the 1850s over most of IP for this very high density network.

In the eye of the beholder: the effect of rater variability and different rating scales on QTL mapping.

PubMed

Poland, Jesse A; Nelson, Rebecca J

2011-02-01

The agronomic importance of developing durably resistant cultivars has led to substantial research in the field of quantitative disease resistance (QDR) and, in particular, mapping quantitative trait loci (QTL) for disease resistance. The assessment of QDR is typically conducted by visual estimation of disease severity, which raises concern over the accuracy and precision of visual estimates. Although previous studies have examined the factors affecting the accuracy and precision of visual disease assessment in relation to the true value of disease severity, the impact of this variability on the identification of disease resistance QTL has not been assessed. In this study, the effects of rater variability and rating scales on mapping QTL for northern leaf blight resistance in maize were evaluated in a recombinant inbred line population grown under field conditions. The population of 191 lines was evaluated by 22 different raters using a direct percentage estimate, a 0-to-9 ordinal rating scale, or both. It was found that more experienced raters had higher precision and that using a direct percentage estimation of diseased leaf area produced higher precision than using an ordinal scale. QTL mapping was then conducted using the disease estimates from each rater using stepwise general linear model selection (GLM) and inclusive composite interval mapping (ICIM). For GLM, the same QTL were largely found across raters, though some QTL were only identified by a subset of raters. The magnitudes of estimated allele effects at identified QTL varied drastically, sometimes by as much as threefold. ICIM produced highly consistent results across raters and for the different rating scales in identifying the location of QTL. We conclude that, despite variability between raters, the identification of QTL was largely consistent among raters, particularly when using ICIM. However, care should be taken in estimating QTL allele effects, because this was highly variable and rater dependent.

Identification of relationships between climate indices and long-term precipitation in South Korea using ensemble empirical mode decomposition

NASA Astrophysics Data System (ADS)

Kim, Taereem; Shin, Ju-Young; Kim, Sunghun; Heo, Jun-Haeng

2018-02-01

Climate indices characterize climate systems and may identify important indicators for long-term precipitation, which are driven by climate interactions in atmosphere-ocean circulation. In this study, we investigated the climate indices that are effective indicators of long-term precipitation in South Korea, and examined their relationships based on statistical methods. Monthly total precipitation was collected from a total of 60 meteorological stations, and they were decomposed by ensemble empirical mode decomposition (EEMD) to identify the inherent oscillating patterns or cycles. Cross-correlation analysis and stepwise variable selection were employed to select the significant climate indices at each station. The climate indices that affect the monthly precipitation in South Korea were identified based on the selection frequencies of the selected indices at all stations. The NINO12 indices with four- and ten-month lags and AMO index with no lag were identified as indicators of monthly precipitation in South Korea. Moreover, they indicate meaningful physical information (e.g. periodic oscillations and long-term trend) inherent in the monthly precipitation. The NINO12 indices with four- and ten- month lags was a strong indicator representing periodic oscillations in monthly precipitation. In addition, the long-term trend of the monthly precipitation could be explained by the AMO index. A multiple linear regression model was constructed to investigate the influences of the identified climate indices on the prediction of monthly precipitation. Three identified climate indices successfully explained the monthly precipitation in the winter dry season. Compared to the monthly precipitation in coastal areas, the monthly precipitation in inland areas showed stronger correlation to the identified climate indices.

Modeling of sorption processes on solid-phase ion-exchangers

NASA Astrophysics Data System (ADS)

Dorofeeva, Ludmila; Kuan, Nguyen Anh

2018-03-01

Research of alkaline elements separation on solid-phase ion-exchangers is carried out to define the selectivity coefficients and height of an equivalent theoretical stage for both continuous and stepwise filling of column by ionite. On inorganic selective sorbents the increase in isotope enrichment factor up to 0.0127 is received. Also, parametrical models that are adequately describing dependence of the pressure difference and the magnitude expansion in the ion-exchange layer from the flow rate and temperature have been obtained. The concentration rate value under the optimum realization conditions of process and depending on type of a selective material changes in a range 1.021÷1.092. Calculated results show agreement with experimental data.

Statistical learning and selective inference.

PubMed

Taylor, Jonathan; Tibshirani, Robert J

2015-06-23

We describe the problem of "selective inference." This addresses the following challenge: Having mined a set of data to find potential associations, how do we properly assess the strength of these associations? The fact that we have "cherry-picked"--searched for the strongest associations--means that we must set a higher bar for declaring significant the associations that we see. This challenge becomes more important in the era of big data and complex statistical modeling. The cherry tree (dataset) can be very large and the tools for cherry picking (statistical learning methods) are now very sophisticated. We describe some recent new developments in selective inference and illustrate their use in forward stepwise regression, the lasso, and principal components analysis.

Selective cleavage of the C(α)-C(β) linkage in lignin model compounds via Baeyer-Villiger oxidation.

PubMed

Patil, Nikhil D; Yao, Soledad G; Meier, Mark S; Mobley, Justin K; Crocker, Mark

2015-03-21

Lignin is an amorphous aromatic polymer derived from plants and is a potential source of fuels and bulk chemicals. Herein, we present a survey of reagents for selective stepwise oxidation of lignin model compounds. Specifically, we have targeted the oxidative cleavage of Cα-Cβ bonds as a means to depolymerize lignin and obtain useful aromatic compounds. In this work, we prepared several lignin model compounds that possess structures, characteristic reactivity, and linkages closely related to the parent lignin polymer. We observed that selective oxidation of benzylic hydroxyl groups, followed by Baeyer-Villiger oxidation of the resulting ketones, successfully cleaves the Cα-Cβ linkage in these model compounds.

Subjective Well-Being Is Associated with Food Behavior and Demographic Factors in Chronically Ill Older Japanese People Living Alone.

PubMed

Ishikawa, M; Yokoyama, T; Hayashi, F; Takemi, Y; Nakaya, T; Fukuda, Y; Kusama, K; Nozue, M; Yoshiike, N; Murayama, N

2018-01-01

This study aimed to examine the relationships among subjective well-being, food and health behaviors, socioeconomic factors, and geography in chronically ill older Japanese adults living alone. The design was a cross-sectional, multilevel survey. A questionnaire was distributed by post and self-completed by participants. The sample was drawn from seven towns and cities across Japan. A geographic information system was used to select a representative sample of older people living alone based on their proximity to a supermarket. Study recruitment was conducted with municipal assistance. To assess subjective well-being and food and health behaviors of respondents with disease, a logistic regression analysis was performed using stepwise variable analyses, adjusted for respondent age, socioeconomic status, and proximity to a supermarket. The dependent variable was good or poor subjective well-being. In total, 2,165 older people (744 men, 1,421 women) completed the questionnaire (63.5% response rate). Data from 737 men and 1,414 women were used in this study. Among people with a chronic disease, individuals with good subjective well-being had significantly higher rates than those with poor subjective well-being for satisfaction with meal quality and chewing ability, food diversity, food intake frequency, perception of shopping ease, having someone to help with food shopping, eating home-produced vegetables, preparing breakfast themselves, eating with other people, and high alcohol consumption. A stepwise logistic analysis showed that the factors strongly related to poor subjective well-being were shopping difficulty (men: odds ratio [OR] = 3.19, 95% confidence interval [CI], 1.94-5.23; P < 0.0001; women: OR = 2.20, 95% CI, 1.54-3.14; P < 0.0001), not having someone to help with food shopping (women: OR = 1.41, 95% CI, 1.01-1.97; P = 0.043), not preparing breakfast (women: OR = 2.36, 95% CI, 1.40-3.98; P = 0.001), and eating together less often (women: OR = 1.99, 95% CI, 1.32-3.00; P = 0.002). Subjective well-being of people with chronic diseases is associated with food intake and food behavior. The factors that affect poor subjective well-being in chronically ill older Japanese people living alone include food accessibility and social communication.

Caries risk assessment in schoolchildren - a form based on Cariogram® software

PubMed Central

CABRAL, Renata Nunes; HILGERT, Leandro Augusto; FABER, Jorge; LEAL, Soraya Coelho

2014-01-01

Identifying caries risk factors is an important measure which contributes to best understanding of the cariogenic profile of the patient. The Cariogram® software provides this analysis, and protocols simplifying the method were suggested. Objectives The aim of this study was to determine whether a newly developed Caries Risk Assessment (CRA) form based on the Cariogram® software could classify schoolchildren according to their caries risk and to evaluate relationships between caries risk and the variables in the form. Material and Methods 150 schoolchildren aged 5 to 7 years old were included in this survey. Caries prevalence was obtained according to International Caries Detection and Assessment System (ICDAS) II. Information for filling in the form based on Cariogram® was collected clinically and from questionnaires sent to parents. Linear regression and a forward stepwise multiple regression model were applied to correlate the variables included in the form with the caries risk. Results Caries prevalence, in primary dentition, including enamel and dentine carious lesions was 98.6%, and 77.3% when only dentine lesions were considered. Eighty-six percent of the children were classified as at moderate caries risk. The forward stepwise multiple regression model result was significant (R2=0.904; p<0.00001), showing that the most significant factors influencing caries risk were caries experience, oral hygiene, frequency of food consumption, sugar consumption and fluoride sources. Conclusion The use of the form based on the Cariogram® software enabled classification of the schoolchildren at low, moderate and high caries risk. Caries experience, oral hygiene, frequency of food consumption, sugar consumption and fluoride sources are the variables that were shown to be highly correlated with caries risk. PMID:25466473

The relation of digital vascular function to cardiovascular risk factors in African-Americans using digital tonometry: the Jackson Heart Study.

PubMed

McClendon, Eric E; Musani, Solomon K; Samdarshi, Tandaw E; Khaire, Sushant; Stokes, Donny; Hamburg, Naomi M; Sheffy, Koby; Mitchell, Gary F; Taylor, Herman R; Benjamin, Emelia J; Fox, Ervin R

2017-06-01

Digital vascular tone and function, as measured by peripheral arterial tonometry (PAT), are associated with cardiovascular risk and events in non-Hispanic whites. There are limited data on relations between PAT and cardiovascular risk in African-Americans. PAT was performed on a subset of Jackson Heart Study participants using a fingertip tonometry device. Resting digital vascular tone was assessed as baseline pulse amplitude. Hyperemic vascular response to 5 minutes of ischemia was expressed as the PAT ratio (hyperemic/baseline amplitude ratio). Peripheral augmentation index (AI), a measure of relative wave reflection, also was estimated. The association of baseline pulse amplitude (PA), PAT ratio, and AI to risk factors was assessed using stepwise multivariable models. The study sample consisted of 837 participants from the Jackson Heart Study (mean age, 54 ± 11 years; 61% women). In stepwise multivariable regression models, baseline pulse amplitude was related to male sex, body mass index, and diastolic blood pressure (BP), accounting for 16% of the total variability of the baseline pulse amplitude. Age, male sex, systolic BP, diastolic BP, antihypertensive medication, and prevalent cardiovascular disease contributed to 11% of the total variability of the PAT ratio. Risk factors (primarily age, sex, and heart rate) explained 47% of the total variability of the AI. We confirmed in our cohort of African-Americans, a significant relation between digital vascular tone and function measured by PAT and multiple traditional cardiovascular risk factors. Further studies are warranted to investigate the utility of these measurements in predicting clinical outcomes in African-Americans. Copyright © 2017 American Society of Hypertension. Published by Elsevier Inc. All rights reserved.

Discriminating between Graduates and Failure in the USAF Medical Laboratory Specialist School: An Explorative Approach.

DTIC Science & Technology

1981-12-01

occurred on the Introversion Scale of the NMPI. 20 A review of the use of psychological tests on MT’s was accomplished by Driver and Feeley [1974...programs, Gondek [1981] has recommended that the best pro- cedure for variable inclusion when using a stepwise procedure is to use the threshold default...values supplied by the package, since no simple rules exist for determining entry or removal thresholds for partial F’s, tolerance statistics, or any of

Parsimony and goodness-of-fit in multi-dimensional NMR inversion

NASA Astrophysics Data System (ADS)

Babak, Petro; Kryuchkov, Sergey; Kantzas, Apostolos

2017-01-01

Multi-dimensional nuclear magnetic resonance (NMR) experiments are often used for study of molecular structure and dynamics of matter in core analysis and reservoir evaluation. Industrial applications of multi-dimensional NMR involve a high-dimensional measurement dataset with complicated correlation structure and require rapid and stable inversion algorithms from the time domain to the relaxation rate and/or diffusion domains. In practice, applying existing inverse algorithms with a large number of parameter values leads to an infinite number of solutions with a reasonable fit to the NMR data. The interpretation of such variability of multiple solutions and selection of the most appropriate solution could be a very complex problem. In most cases the characteristics of materials have sparse signatures, and investigators would like to distinguish the most significant relaxation and diffusion values of the materials. To produce an easy to interpret and unique NMR distribution with the finite number of the principal parameter values, we introduce a new method for NMR inversion. The method is constructed based on the trade-off between the conventional goodness-of-fit approach to multivariate data and the principle of parsimony guaranteeing inversion with the least number of parameter values. We suggest performing the inversion of NMR data using the forward stepwise regression selection algorithm. To account for the trade-off between goodness-of-fit and parsimony, the objective function is selected based on Akaike Information Criterion (AIC). The performance of the developed multi-dimensional NMR inversion method and its comparison with conventional methods are illustrated using real data for samples with bitumen, water and clay.

A public hedonic analysis of environmental attributes in an open space preservation program

NASA Astrophysics Data System (ADS)

Nordman, Erik E.

The Town of Brookhaven, on Long Island, NY, has implemented an open space preservation program to protect natural areas, and the ecosystem services they provide, from suburban growth. I used a public hedonic model of Brookhaven's open space purchases to estimate implicit prices for various environmental attributes, locational variables and spatial metrics. I also measured the correlation between cost per acre and non-monetary environmental benefit scores and tested whether including cost data, as opposed to non-monetary environmental benefit score alone, would change the prioritization ranks of acquired properties. The mean acquisition cost per acre was 82,501. I identified the key on-site environmental and locational variables using stepwise regression for four functional forms. The log-log specification performed best ( R2adj= 0.727). I performed a second stepwise regression (log-log form) which included spatial metrics, calculated from a high-resolution land cover classification, in addition to the environmental and locational variables. This markedly improved the model's performance ( R2adj=0.866). Statistically significant variables included the property size, location in the Pine Barrens Compatible Growth Area, location in a FEMA flood zone, adjacency to public land, and several other environmental dummy variables. The single significant spatial metric, the fractal dimension of the tree cover class, had the largest elasticity of any variable. Of the dummy variables, location within the Compatible Growth Area had the largest implicit price (298,792 per acre). The priority rank for the two methods, non-monetary environmental benefit score alone and the ratio of non-monetary environmental benefit score to acquisition cost were significantly positively correlated. This suggests that, despite the lack of cost data in their ranking method, Brookhaven does not suffer from efficiency losses. The economics literature encourages using both environmental benefits and acquisition costs to ensure cost-effective conservation programs. I recommend that Brookhaven consider acquisition costs in addition to environmental benefits to avert potential efficiency losses in future open space purchases. This dissertation shows that the addition of spatial metrics can enhance the performance of hedonic models. It also provides a baseline valuation for the environmental attributes of Brookhaven' open spaces and shows that location is critical when dealing with open space preservation programs.

Effect of Adding McKenzie Syndrome, Centralization, Directional Preference, and Psychosocial Classification Variables to a Risk-Adjusted Model Predicting Functional Status Outcomes for Patients With Lumbar Impairments.

PubMed

Werneke, Mark W; Edmond, Susan; Deutscher, Daniel; Ward, Jason; Grigsby, David; Young, Michelle; McGill, Troy; McClenahan, Brian; Weinberg, Jon; Davidow, Amy L

2016-09-01

Study Design Retrospective cohort. Background Patient-classification subgroupings may be important prognostic factors explaining outcomes. Objectives To determine effects of adding classification variables (McKenzie syndrome and pain patterns, including centralization and directional preference; Symptom Checklist Back Pain Prediction Model [SCL BPPM]; and the Fear-Avoidance Beliefs Questionnaire subscales of work and physical activity) to a baseline risk-adjusted model predicting functional status (FS) outcomes. Methods Consecutive patients completed a battery of questionnaires that gathered information on 11 risk-adjustment variables. Physical therapists trained in Mechanical Diagnosis and Therapy methods classified each patient by McKenzie syndromes and pain pattern. Functional status was assessed at discharge by patient-reported outcomes. Only patients with complete data were included. Risk of selection bias was assessed. Prediction of discharge FS was assessed using linear stepwise regression models, allowing 13 variables to enter the model. Significant variables were retained in subsequent models. Model power (R(2)) and beta coefficients for model variables were estimated. Results Two thousand sixty-six patients with lumbar impairments were evaluated. Of those, 994 (48%), 10 (<1%), and 601 (29%) were excluded due to incomplete psychosocial data, McKenzie classification data, and missing FS at discharge, respectively. The final sample for analyses was 723 (35%). Overall R(2) for the baseline prediction FS model was 0.40. Adding classification variables to the baseline model did not result in significant increases in R(2). McKenzie syndrome or pain pattern explained 2.8% and 3.0% of the variance, respectively. When pain pattern and SCL BPPM were added simultaneously, overall model R(2) increased to 0.44. Although none of these increases in R(2) were significant, some classification variables were stronger predictors compared with some other variables included in the baseline model. Conclusion The small added prognostic capabilities identified when combining McKenzie or pain-pattern classifications with the SCL BPPM classification did not significantly improve prediction of FS outcomes in this study. Additional research is warranted to investigate the importance of classification variables compared with those used in the baseline model to maximize predictive power. Level of Evidence Prognosis, level 4. J Orthop Sports Phys Ther 2016;46(9):726-741. Epub 31 Jul 2016. doi:10.2519/jospt.2016.6266.

Stepwise evolution of resistance to toxic cardenolides via genetic substitutions in the Na+/K+ -ATPase of milkweed butterflies (lepidoptera: Danaini).

PubMed

Petschenka, Georg; Fandrich, Steffi; Sander, Nils; Wagschal, Vera; Boppré, Michael; Dobler, Susanne

2013-09-01

Despite the monarch butterfly (Danaus plexippus) being famous for its adaptations to the defensive traits of its milkweed host plants, little is known about the macroevolution of these traits. Unlike most other animal species, monarchs are largely insensitive to cardenolides, because their target site, the sodium pump (Na(+)/K(+) -ATPase), has evolved amino acid substitutions that reduce cardenolide binding (so-called target site insensitivity, TSI). Because many, but not all, species of milkweed butterflies (Danaini) are associated with cardenolide-containing host plants, we analyzed 16 species, representing all phylogenetic lineages of milkweed butterflies, for the occurrence of TSI by sequence analyses of the Na(+)/K(+) -ATPase gene and by enzymatic assays with extracted Na(+)/K(+) -ATPase. Here we report that sensitivity to cardenolides was reduced in a stepwise manner during the macroevolution of milkweed butterflies. Strikingly, not all Danaini typically consuming cardenolides showed TSI, but rather TSI was more strongly associated with sequestration of toxic cardenolides. Thus, the interplay between bottom-up selection by plant compounds and top-down selection by natural enemies can explain the evolutionary sequence of adaptations to these toxins. © 2013 The Author(s). Evolution © 2013 The Society for the Study of Evolution.

Relation between trinucleotide GAA repeat length and sensory neuropathy in Friedreich's ataxia.

PubMed

Santoro, L; De Michele, G; Perretti, A; Crisci, C; Cocozza, S; Cavalcanti, F; Ragno, M; Monticelli, A; Filla, A; Caruso, G

1999-01-01

To verify if GAA expansion size in Friedreich's ataxia could account for the severity of sensory neuropathy. Retrospective study of 56 patients with Friedreich's ataxia selected according to homozygosity for GAA expansion and availability of electrophysiological findings. Orthodromic sensory conduction velocity in the median nerve was available in all patients and that of the tibial nerve in 46 of them. Data of sural nerve biopsy and of a morphometric analysis were available in 12 of the selected patients. The sensory action potential amplitude at the wrist (wSAP) and at the medial malleolus (m mal SAP) and the percentage of myelinated fibres with diameter larger than 7, 9, and 11 microm in the sural nerve were correlated with disease duration and GAA expansion size on the shorter (GAA1) and larger (GAA2) expanded allele in each pair. Pearson's correlation test and stepwise multiple regression were used for statistical analysis. A significant inverse correlation between GAA1 size and wSAP, m mal SAP, and percentage of myelinated fibres was found. Stepwise multiple regression showed that GAA1 size significantly affects electrophysiological and morphometric data, whereas duration of disease has no effect. The data suggest that the severity of the sensory neuropathy is probably genetically determined and that it is not progressive.

Predicting Retention Times of Naturally Occurring Phenolic Compounds in Reversed-Phase Liquid Chromatography: A Quantitative Structure-Retention Relationship (QSRR) Approach

PubMed Central

Akbar, Jamshed; Iqbal, Shahid; Batool, Fozia; Karim, Abdul; Chan, Kim Wei

2012-01-01

Quantitative structure-retention relationships (QSRRs) have successfully been developed for naturally occurring phenolic compounds in a reversed-phase liquid chromatographic (RPLC) system. A total of 1519 descriptors were calculated from the optimized structures of the molecules using MOPAC2009 and DRAGON softwares. The data set of 39 molecules was divided into training and external validation sets. For feature selection and mapping we used step-wise multiple linear regression (SMLR), unsupervised forward selection followed by step-wise multiple linear regression (UFS-SMLR) and artificial neural networks (ANN). Stable and robust models with significant predictive abilities in terms of validation statistics were obtained with negation of any chance correlation. ANN models were found better than remaining two approaches. HNar, IDM, Mp, GATS2v, DISP and 3D-MoRSE (signals 22, 28 and 32) descriptors based on van der Waals volume, electronegativity, mass and polarizability, at atomic level, were found to have significant effects on the retention times. The possible implications of these descriptors in RPLC have been discussed. All the models are proven to be quite able to predict the retention times of phenolic compounds and have shown remarkable validation, robustness, stability and predictive performance. PMID:23203132

Significance of the cosmogenic argon correction in deciphering the 40Ar/39Ar ages of the Nakhlite (Martian) meteorites

NASA Astrophysics Data System (ADS)

Cohen, B. E.; Cassata, W.; Mark, D. F.; Tomkinson, T.; Lee, M. R.; Smith, C. L.

2015-12-01

All meteorites contain variable amounts of cosmogenic 38Ar and 36Ar produced during extraterrestrial exposure, and in order to calculate reliable 40Ar/39Ar ages this cosmogenic Ar must be removed from the total Ar budget. The amount of cosmogenic Ar has usually been calculated from the step-wise 38Ar/36Ar, minimum 36Ar/37Ar, or average 38Arcosmogenic/37Ar from the irradiated meteorite fragment. However, if Cl is present in the meteorite, then these values will be disturbed by Ar produced during laboratory neutron irradiation of Cl. Chlorine is likely to be a particular issue for the Nakhlite group of Martian meteorites, which can contain over 1000 ppm Cl [1]. An alternative method for the cosmogenic Ar correction uses the meteorite's exposure age as calculated from an un-irradiated fragment and step-wise production rates based on the measured Ca/K [2]. This calculation is independent of the Cl concentration. We applied this correction method to seven Nakhlites, analyzed in duplicate or triplicate. Selected samples were analyzed at both Lawrence Livermore National Laboratory and SUERC to ensure inter-laboratory reproducibility. We find that the cosmogenic argon correction of [2] has a significant influence on the ages calculated for individual steps, particularly for those at lower temperatures (i.e., differences of several tens of million years for some steps). The lower-temperature steps are more influenced by the alternate cosmogenic correction method of [2], as these analyses yielded higher concentrations of Cl-derived 38Ar. As a result, the Nakhlite data corrected using [2] yields step-heating spectra that are flat or nearly so across >70% of the release spectra (in contrast to downward-stepping spectra often reported for Nakhlite samples), allowing for the calculation of precise emplacement ages for these meteorites. [1] Cartwright J. A. et al. (2013) GCA, 105, 255-293. [2] Cassata W. S., and Borg L. E. (2015) 46th LPSC, Abstract #2742.

Beef customer satisfaction: factors affecting consumer evaluations of clod steaks.

PubMed

Goodson, K J; Morgan, W W; Reagan, J O; Gwartney, B L; Courington, S M; Wise, J W; Savell, J W

2002-02-01

An in-home beef study evaluated consumer ratings of clod steaks (n = 1,264) as influenced by USDA quality grade (Top Choice, Low Choice, High Select, and Low Select), city (Chicago and Philadelphia), consumer segment (Beef Loyals, who are heavy consumers of beef; Budget Rotators, who are cost-driven and split meat consumption between beef and chicken; and Variety Rotators, who have higher incomes and education and split their meat consumption among beef, poultry, and other foods), degree of doneness, and cooking method. Consumers evaluated each steak for Overall Like, Tenderness, Juiciness, Flavor Like, and Flavor Amount using 10-point scales. Grilling was the predominant cooking method used, and steaks were cooked to medium-well and greater degrees of doneness. Interactions existed involving the consumer-controlled factors of degree of doneness and(or) cooking method for all consumer-evaluated traits for the clod steak (P < 0.05). USDA grade did not affect any consumer evaluation traits or Warner-Bratzler shear force values (P > 0.05). One significant main effect, segment (P = 0.006), and one significant interaction, cooking method x city (P = 0.0407), existed for Overall Like ratings. Consumers in the Beef Loyals segment rated clod steaks higher in Overall Like than the other segments. Consumers in Chicago tended to give more uniform Overall Like ratings to clod steaks cooked by various methods; however, consumers in Philadelphia gave among the highest ratings to clod steaks that were fried and among the lowest to those that were grilled. Additionally, although clod steaks that were fried were given generally high ratings by consumers in Philadelphia, consumers in Chicago rated clod steaks cooked in this manner significantly lower than those in Philadelphia. Conversely, consumers in Chicago rated clod steaks that were grilled significantly higher than consumers in Philadelphia. Correlation and stepwise regression analyses indicated that Flavor Like was driving customer satisfaction of the clod steak. Flavor Like was the sensory trait most highly correlated to Overall Like, followed by Tenderness, Flavor Amount, and Juiciness. Flavor Like was the first variable to enter into the stepwise regression equation for predicting Overall Like, followed by Tenderness and Flavor Amount. For the clod steak, it is likely that preparation techniques that improve flavor without reducing tenderness positively affect customer satisfaction.

Why Wait? Early Determinants of School Dropout in Preventive Pediatric Primary Care

PubMed Central

Theunissen, Marie-José; Bosma, Hans; Verdonk, Petra; Feron, Frans

2015-01-01

Background To answer the question of what bio-psychosocial determinants in infancy, early and middle childhood, and adolescence predict school drop-out in young adulthood, we approached the complex process towards school dropout as a multidimensional, life-course phenomenon. The aim is to find signs of heightened risks of school dropout as early as possible which will eventually help public health workers in reducing these risks. Methods In a case-control design, we used data from both the Preventive Pediatric Primary Care (PPPC) files (that contain information from birth onwards) and additional questionnaires filled out by 529 youngsters, aged 18–23 years, and living in the South-east of the Netherlands. We first conducted univariate logistic regression analyses with school-dropout as the dependent variable. Backward and forward stepwise analyses with the significant variables were done with variables pertaining to the 0 to 4 year period. Remaining significant variables were forced into the next model and subsequently variables pertaining to respectively the 4 to 8, 8 to 12 and 12 to 16 year period were introduced in a stepwise analysis. All analyses were cross-validated in an exploratory and confirmatory random half of the sample. Results One parent families and families with a non-Western background less often attended the health examinations of the PPPC and such less attendance was related to school dropout. The birth of a sibling (OR 0.63, 95% CI 0.43–0.93) in infancy and self-efficacy (OR 0.53, 95% CI 0.38–0.74) in adolescence decreased the odds of school dropout; externalizing behavior (OR 2.81, 95% CI 1.53–5.14) in middle childhood and (sickness) absence (OR 5.62, 95% CI 2.18–14.52) in adolescence increased the risks. Conclusion To prevent school dropout, PPPC professionals should not wait until imminent dropout, but should identify and tackle risk factors as early as possible and actively approach youngsters who withdraw from public health care. PMID:26555443

Step-wise pulling protocols for non-equilibrium dynamics

NASA Astrophysics Data System (ADS)

Ngo, Van Anh

The fundamental laws of thermodynamics and statistical mechanics, and the deeper understandings of quantum mechanics have been rebuilt in recent years. It is partly because of the increasing power of computing resources nowadays, that allow shedding direct insights into the connections among the thermodynamics laws, statistical nature of our world, and the concepts of quantum mechanics, which have not yet been understood. But mostly, the most important reason, also the ultimate goal, is to understand the mechanisms, statistics and dynamics of biological systems, whose prevailing non-equilibrium processes violate the fundamental laws of thermodynamics, deviate from statistical mechanics, and finally complicate quantum effects. I believe that investigations of the fundamental laws of non-equilibrium dynamics will be a frontier research for at least several more decades. One of the fundamental laws was first discovered in 1997 by Jarzynski, so-called Jarzynski's Equality. Since then, different proofs, alternative descriptions of Jarzynski's Equality, and its further developments and applications have been quickly accumulated. My understandings, developments and applications of an alternative theory on Jarzynski's Equality form the bulk of this dissertation. The core of my theory is based on stepwise pulling protocols, which provide deeper insight into how fluctuations of reaction coordinates contribute to free-energy changes along a reaction pathway. We find that the most optimal pathways, having the largest contribution to free-energy changes, follow the principle of detailed balance. This is a glimpse of why the principle of detailed balance appears so powerful for sampling the most probable statistics of events. In a further development on Jarzynski's Equality, I have been trying to use it in the formalism of diagonal entropy to propose a way to extract useful thermodynamic quantities such temperature, work and free-energy profiles from far-from-equilibrium ensembles, which can be used to characterize non-equilibrium dynamics. Furthermore, we have applied the stepwise pulling protocols and Jarzynski's Equality to investigate the ion selectivity of potassium channels via molecular dynamics simulations. The mechanism of the potassium ion selectivity has remained poorly understood for over fifty years, although a Nobel Prize was awarded to the discovery of the molecular structure of a potassium-selective channel in 2003. In one year of performing simulations, we were able to reproduce the major results of ion selectivity accumulated in fifty years. We have been even boldly going further to propose a new model for ion selectivity based on the structural rearrangement of the selectivity filter of potassium-selective KcsA channels. This structural rearrangement has never been shown to play such a pivotal role in selecting and conducting potassium ions, but effectively rejecting sodium ions. Using the stepwise pulling protocols, we are also able to estimate conductance for ion channels, which remains elusive by using other methods. In the light of ion channels, we have also investigated how a synthetic channel of telemeric G-quadruplex conducts different types of ions. These two studies on ion selectivity not only constitute an interesting part of this dissertation, but also will enable us to further explore a new set of ion-selectivity principles. Beside the focus of my dissertation, I used million-atom molecular dynamics simulations to investigate the mechanical properties of body-centered-cubic (BCCS) and face-centered-cubic (FCCS) supercrystals of DNA-functionalized gold nanoparticles. These properties are valuable for examining whether these supercrystals can be used in gene delivery and gene therapy. The formation of such ordered supercrystals is useful to protect DNAs or RNAs from being attacked and destroyed by enzymes in cells. I also performed all-atom molecular dynamics simulations to study a pure oleic acid (OA) membrane in water that results into a triple-layer structure. The simulations show that the trans-membrane movement of water and OAs is cooperative and correlated, and agrees with experimentally measured absorption rates. The simulation results support the idea that OA flip-flop is more favorable than transport by means of functional proteins. This study might provide further insight into how primitive cell membranes work, and how the interplay and correlation between water and fatty acids may occur.

Norms and nurse management of conflicts: keys to understanding nurse-physician collaboration.

PubMed

Keenan, G M; Cooke, R; Hillis, S L

1998-02-01

In this cross-sectional study, registered nurses from 36 emergency rooms completed an abridged version of the Organizational Culture Inventory (Cooke & Lafferty, 1989) and responded to nine hypothetical conflict vignettes. Stepwise regressions were performed with nurse conflict style intentions as dependent variables and 10 independent variable (three sets of norms, five measures of conflict styles expected to be used by the physician, gender, and education). Nurses' expectations for physicians to collaborate and strong constructive and aggressive norms were found to explain a moderate amount of variance (32%) in nurses' intentions to collaborate in conflicts conducive to nurse-physician collaboration. The findings of this study provide support for the proposed theoretical framework and can be used to design interventions that promote nurse-physician collaboration.

Model for predicting the injury severity score.

PubMed

Hagiwara, Shuichi; Oshima, Kiyohiro; Murata, Masato; Kaneko, Minoru; Aoki, Makoto; Kanbe, Masahiko; Nakamura, Takuro; Ohyama, Yoshio; Tamura, Jun'ichi

2015-07-01

To determine the formula that predicts the injury severity score from parameters that are obtained in the emergency department at arrival. We reviewed the medical records of trauma patients who were transferred to the emergency department of Gunma University Hospital between January 2010 and December 2010. The injury severity score, age, mean blood pressure, heart rate, Glasgow coma scale, hemoglobin, hematocrit, red blood cell count, platelet count, fibrinogen, international normalized ratio of prothrombin time, activated partial thromboplastin time, and fibrin degradation products, were examined in those patients on arrival. To determine the formula that predicts the injury severity score, multiple linear regression analysis was carried out. The injury severity score was set as the dependent variable, and the other parameters were set as candidate objective variables. IBM spss Statistics 20 was used for the statistical analysis. Statistical significance was set at P  < 0.05. To select objective variables, the stepwise method was used. A total of 122 patients were included in this study. The formula for predicting the injury severity score (ISS) was as follows: ISS = 13.252-0.078(mean blood pressure) + 0.12(fibrin degradation products). The P -value of this formula from analysis of variance was <0.001, and the multiple correlation coefficient (R) was 0.739 (R 2  = 0.546). The multiple correlation coefficient adjusted for the degrees of freedom was 0.538. The Durbin-Watson ratio was 2.200. A formula for predicting the injury severity score in trauma patients was developed with ordinary parameters such as fibrin degradation products and mean blood pressure. This formula is useful because we can predict the injury severity score easily in the emergency department.

Probability-based nitrate contamination map of groundwater in Kinmen.

PubMed

Liu, Chen-Wuing; Wang, Yeuh-Bin; Jang, Cheng-Shin

2013-12-01

Groundwater supplies over 50% of drinking water in Kinmen. Approximately 16.8% of groundwater samples in Kinmen exceed the drinking water quality standard (DWQS) of NO3 (-)-N (10 mg/L). The residents drinking high nitrate-polluted groundwater pose a potential risk to health. To formulate effective water quality management plan and assure a safe drinking water in Kinmen, the detailed spatial distribution of nitrate-N in groundwater is a prerequisite. The aim of this study is to develop an efficient scheme for evaluating spatial distribution of nitrate-N in residential well water using logistic regression (LR) model. A probability-based nitrate-N contamination map in Kinmen is constructed. The LR model predicted the binary occurrence probability of groundwater nitrate-N concentrations exceeding DWQS by simple measurement variables as independent variables, including sampling season, soil type, water table depth, pH, EC, DO, and Eh. The analyzed results reveal that three statistically significant explanatory variables, soil type, pH, and EC, are selected for the forward stepwise LR analysis. The total ratio of correct classification reaches 92.7%. The highest probability of nitrate-N contamination map presents in the central zone, indicating that groundwater in the central zone should not be used for drinking purposes. Furthermore, a handy EC-pH-probability curve of nitrate-N exceeding the threshold of DWQS was developed. This curve can be used for preliminary screening of nitrate-N contamination in Kinmen groundwater. This study recommended that the local agency should implement the best management practice strategies to control nonpoint nitrogen sources and carry out a systematic monitoring of groundwater quality in residential wells of the high nitrate-N contamination zones.

IDENTIFICATION OF AN {sup 84}Sr-DEPLETED CARRIER IN PRIMITIVE METEORITES AND IMPLICATIONS FOR THERMAL PROCESSING IN THE SOLAR PROTOPLANETARY DISK

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

Paton, Chad; Schiller, Martin; Bizzarro, Martin, E-mail: chadpaton@gmail.com, E-mail: schiller@snm.ku.dk, E-mail: bizzarro@snm.ku.dk

2013-02-01

The existence of correlated nucleosynthetic heterogeneities in solar system reservoirs is now well demonstrated for numerous nuclides. However, it has proven difficult to discriminate between the two disparate processes that can explain such correlated variability: incomplete mixing of presolar material or secondary processing of a well-mixed disk. Using stepwise acid-leaching of the Ivuna CI-chondrite, we show that unlike other nuclides such as {sup 54}Cr and {sup 50}Ti, Sr-isotope variability is the result of a carrier depleted in {sup 84}Sr. The carrier is most likely presolar SiC, which is known to have both high Sr-concentrations relative to solar abundances and extremelymore » depleted {sup 84}Sr compositions. Thus, variability in {sup 84}Sr in meteorites and their components can be attributed to varying contributions from presolar SiC. The observed {sup 84}Sr excesses in calcium-aluminum refractory inclusions (CAIs) suggest their formation from an SiC-free gaseous reservoir, whereas the {sup 84}Sr depletions present in differentiated meteorites require their formation from material with an increased concentration of SiC relative to CI chondrites. The presence of a positive correlation between {sup 84}Sr and {sup 54}Cr, despite being hosted in carriers of negative and positive anomalies, respectively, is not compatible with incomplete mixing of presolar material but instead suggests that the solar system's nucleosynthetic heterogeneity reflects selective thermal processing of dust. Based on vaporization experiments of SiC under nebular conditions, the lack of SiC material in the CAI-forming gas inferred from our data requires that the duration of thermal processing of dust resulting in the vaporization of CAI precursors was extremely short-lived, possibly lasting only hours to days.« less

Lesion mapping of social problem solving.

PubMed

Barbey, Aron K; Colom, Roberto; Paul, Erick J; Chau, Aileen; Solomon, Jeffrey; Grafman, Jordan H

2014-10-01

Accumulating neuroscience evidence indicates that human intelligence is supported by a distributed network of frontal and parietal regions that enable complex, goal-directed behaviour. However, the contributions of this network to social aspects of intellectual function remain to be well characterized. Here, we report a human lesion study (n = 144) that investigates the neural bases of social problem solving (measured by the Everyday Problem Solving Inventory) and examine the degree to which individual differences in performance are predicted by a broad spectrum of psychological variables, including psychometric intelligence (measured by the Wechsler Adult Intelligence Scale), emotional intelligence (measured by the Mayer, Salovey, Caruso Emotional Intelligence Test), and personality traits (measured by the Neuroticism-Extraversion-Openness Personality Inventory). Scores for each variable were obtained, followed by voxel-based lesion-symptom mapping. Stepwise regression analyses revealed that working memory, processing speed, and emotional intelligence predict individual differences in everyday problem solving. A targeted analysis of specific everyday problem solving domains (involving friends, home management, consumerism, work, information management, and family) revealed psychological variables that selectively contribute to each. Lesion mapping results indicated that social problem solving, psychometric intelligence, and emotional intelligence are supported by a shared network of frontal, temporal, and parietal regions, including white matter association tracts that bind these areas into a coordinated system. The results support an integrative framework for understanding social intelligence and make specific recommendations for the application of the Everyday Problem Solving Inventory to the study of social problem solving in health and disease. © The Author (2014). Published by Oxford University Press on behalf of the Guarantors of Brain. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

Short-term quality of life after subthalamic stimulation depends on non-motor symptoms in Parkinson's disease.

PubMed

Dafsari, Haidar Salimi; Weiß, Luisa; Silverdale, Monty; Rizos, Alexandra; Reddy, Prashanth; Ashkan, Keyoumars; Evans, Julian; Reker, Paul; Petry-Schmelzer, Jan Niklas; Samuel, Michael; Visser-Vandewalle, Veerle; Antonini, Angelo; Martinez-Martin, Pablo; Ray-Chaudhuri, K; Timmermann, Lars

2018-02-24

Subthalamic nucleus (STN) deep brain stimulation (DBS) improves quality of life (QoL), motor, and non-motor symptoms (NMS) in advanced Parkinson's disease (PD). However, considerable inter-individual variability has been observed for QoL outcome. We hypothesized that demographic and preoperative NMS characteristics can predict postoperative QoL outcome. In this ongoing, prospective, multicenter study (Cologne, Manchester, London) including 88 patients, we collected the following scales preoperatively and on follow-up 6 months postoperatively: PDQuestionnaire-8 (PDQ-8), NMSScale (NMSS), NMSQuestionnaire (NMSQ), Scales for Outcomes in PD (SCOPA)-motor examination, -complications, and -activities of daily living, levodopa equivalent daily dose. We dichotomized patients into "QoL responders"/"non-responders" and screened for factors associated with QoL improvement with (1) Spearman-correlations between baseline test scores and QoL improvement, (2) step-wise linear regressions with baseline test scores as independent and QoL improvement as dependent variables, (3) logistic regressions using aforementioned "responders/non-responders" as dependent variable. All outcomes improved significantly on follow-up. However, approximately 44% of patients were categorized as "QoL non-responders". Spearman-correlations, linear and logistic regression analyses were significant for NMSS and NMSQ but not for SCOPA-motor examination. Post-hoc, we identified specific NMS (flat moods, difficulties experiencing pleasure, pain, bladder voiding) as significant contributors to QoL outcome. Our results provide evidence that QoL improvement after STN-DBS depends on preoperative NMS characteristics. These findings are important in the advising and selection of individuals for DBS therapy. Future studies investigating motor and non-motor PD clusters may enable stratifying QoL outcomes and help predict patients' individual prospects of benefiting from DBS. Copyright © 2018. Published by Elsevier Inc.

A novel and simple test of gait adaptability predicts gold standard measures of functional mobility in stroke survivors.

PubMed

Hollands, K L; Pelton, T A; van der Veen, S; Alharbi, S; Hollands, M A

2016-01-01

Although there is evidence that stroke survivors have reduced gait adaptability, the underlying mechanisms and the relationship to functional recovery are largely unknown. We explored the relationships between walking adaptability and clinical measures of balance, motor recovery and functional ability in stroke survivors. Stroke survivors (n=42) stepped to targets, on a 6m walkway, placed to elicit step lengthening, shortening and narrowing on paretic and non-paretic sides. The number of targets missed during six walks and target stepping speed was recorded. Fugl-Meyer (FM), Berg Balance Scale (BBS), self-selected walking speed (SWWS) and single support (SS) and step length (SL) symmetry (using GaitRite when not walking to targets) were also assessed. Stepwise multiple-linear regression was used to model the relationships between: total targets missed, number missed with paretic and non-paretic legs, target stepping speed, and each clinical measure. Regression revealed a significant model for each outcome variable that included only one independent variable. Targets missed by the paretic limb, was a significant predictor of FM (F(1,40)=6.54, p=0.014,). Speed of target stepping was a significant predictor of each of BBS (F(1,40)=26.36, p<0.0001), SSWS (F(1,40)=37.00, p<0.0001). No variables were significant predictors of SL or SS asymmetry. Speed of target stepping was significantly predictive of BBS and SSWS and paretic targets missed predicted FM, suggesting that fast target stepping requires good balance and accurate stepping demands good paretic leg function. The relationships between these parameters indicate gait adaptability is a clinically meaningful target for measurement and treatment of functionally adaptive walking ability in stroke survivors. Copyright © 2015 Elsevier B.V. All rights reserved.

Applications of modern statistical methods to analysis of data in physical science

NASA Astrophysics Data System (ADS)

Wicker, James Eric

Modern methods of statistical and computational analysis offer solutions to dilemmas confronting researchers in physical science. Although the ideas behind modern statistical and computational analysis methods were originally introduced in the 1970's, most scientists still rely on methods written during the early era of computing. These researchers, who analyze increasingly voluminous and multivariate data sets, need modern analysis methods to extract the best results from their studies. The first section of this work showcases applications of modern linear regression. Since the 1960's, many researchers in spectroscopy have used classical stepwise regression techniques to derive molecular constants. However, problems with thresholds of entry and exit for model variables plagues this analysis method. Other criticisms of this kind of stepwise procedure include its inefficient searching method, the order in which variables enter or leave the model and problems with overfitting data. We implement an information scoring technique that overcomes the assumptions inherent in the stepwise regression process to calculate molecular model parameters. We believe that this kind of information based model evaluation can be applied to more general analysis situations in physical science. The second section proposes new methods of multivariate cluster analysis. The K-means algorithm and the EM algorithm, introduced in the 1960's and 1970's respectively, formed the basis of multivariate cluster analysis methodology for many years. However, several shortcomings of these methods include strong dependence on initial seed values and inaccurate results when the data seriously depart from hypersphericity. We propose new cluster analysis methods based on genetic algorithms that overcomes the strong dependence on initial seed values. In addition, we propose a generalization of the Genetic K-means algorithm which can accurately identify clusters with complex hyperellipsoidal covariance structures. We then use this new algorithm in a genetic algorithm based Expectation-Maximization process that can accurately calculate parameters describing complex clusters in a mixture model routine. Using the accuracy of this GEM algorithm, we assign information scores to cluster calculations in order to best identify the number of mixture components in a multivariate data set. We will showcase how these algorithms can be used to process multivariate data from astronomical observations.

Factors associated with mouth breathing in children with -developmental -disabilities.

PubMed

de Castilho, Lia Silva; Abreu, Mauro Henrique Nogueira Guimarães; de Oliveira, Renata Batista; Souza E Silva, Maria Elisa; Resende, Vera Lúcia Silva

2016-01-01

To investigate the prevalence and factors associated with mouth breathing among patients with developmental disabilities of a dental service. We analyzed 408 dental records. Mouth breathing was reported by the patients' parents and from direct observation. Other variables were as -follows: history of asthma, bronchitis, palate shape, pacifier use, thumb -sucking, nail biting, use of medications, gastroesophageal reflux, bruxism, gender, age, and diagnosis of the patient. Statistical analysis included descriptive analysis with ratio calculation and multiple logistic regression. Variables with p < 0.25 were included in the model to estimate the adjusted OR (95% CI), calculated by the forward stepwise method. Variables with p ​​< 0.05 were kept in the model. Being male (p = 0.016) and use of centrally acting drugs (p = 0.001) were the variables that remained in the model. Among patients with -developmental disabilities, boys and psychotropic drug users had a greater chance of being mouth breathers. © 2016 Special Care Dentistry Association and Wiley Periodicals, Inc.

The QSAR study of flavonoid-metal complexes scavenging rad OH free radical

NASA Astrophysics Data System (ADS)

Wang, Bo-chu; Qian, Jun-zhen; Fan, Ying; Tan, Jun

2014-10-01

Flavonoid-metal complexes have antioxidant activities. However, quantitative structure-activity relationships (QSAR) of flavonoid-metal complexes and their antioxidant activities has still not been tackled. On the basis of 21 structures of flavonoid-metal complexes and their antioxidant activities for scavenging rad OH free radical, we optimised their structures using Gaussian 03 software package and we subsequently calculated and chose 18 quantum chemistry descriptors such as dipole, charge and energy. Then we chose several quantum chemistry descriptors that are very important to the IC50 of flavonoid-metal complexes for scavenging rad OH free radical through method of stepwise linear regression, Meanwhile we obtained 4 new variables through the principal component analysis. Finally, we built the QSAR models based on those important quantum chemistry descriptors and the 4 new variables as the independent variables and the IC50 as the dependent variable using an Artificial Neural Network (ANN), and we validated the two models using experimental data. These results show that the two models in this paper are reliable and predictable.

Multicentennial record of Labrador Sea primary productivity and sea-ice variability archived in coralline algal barium

PubMed Central

Chan, P.; Halfar, J.; Adey, W.; Hetzinger, S.; Zack, T.; Moore, G.W.K.; Wortmann, U. G.; Williams, B.; Hou, A.

2017-01-01

Accelerated warming and melting of Arctic sea-ice has been associated with significant increases in phytoplankton productivity in recent years. Here, utilizing a multiproxy approach, we reconstruct an annually resolved record of Labrador Sea productivity related to sea-ice variability in Labrador, Canada that extends well into the Little Ice Age (LIA; 1646 AD). Barium-to-calcium ratios (Ba/Ca) and carbon isotopes (δ13C) measured in long-lived coralline algae demonstrate significant correlations to both observational and proxy records of sea-ice variability, and show persistent patterns of co-variability broadly consistent with the timing and phasing of the Atlantic Multidecadal Oscillation (AMO). Results indicate reduced productivity in the Subarctic Northwest Atlantic associated with AMO cool phases during the LIA, followed by a step-wise increase from 1910 to present levels—unprecedented in the last 363 years. Increasing phytoplankton productivity is expected to fundamentally alter marine ecosystems as warming and freshening is projected to intensify over the coming century. PMID:28569839

Exploration of an oculometer-based model of pilot workload

NASA Technical Reports Server (NTRS)

Krebs, M. J.; Wingert, J. W.; Cunningham, T.

1977-01-01

Potential relationships between eye behavior and pilot workload are discussed. A Honeywell Mark IIA oculometer was used to obtain the eye data in a fixed base transport aircraft simulation facility. The data were analyzed to determine those parameters of eye behavior which were related to changes in level of task difficulty of the simulated manual approach and landing on instruments. A number of trends and relationships between eye variables and pilot ratings were found. A preliminary equation was written based on the results of a stepwise linear regression. High variability in time spent on various instruments was related to differences in scanning strategy among pilots. A more detailed analysis of individual runs by individual pilots was performed to investigate the source of this variability more closely. Results indicated a high degree of intra-pilot variability in instrument scanning. No consistent workload related trends were found. Pupil diameter which had demonstrated a strong relationship to task difficulty was extensively re-exmained.

Modelling dengue fever risk in the State of Yucatan, Mexico using regional-scale satellite-derived sea surface temperature.

PubMed

Laureano-Rosario, Abdiel E; Garcia-Rejon, Julian E; Gomez-Carro, Salvador; Farfan-Ale, Jose A; Muller-Karger, Frank E

2017-08-01

Accurately predicting vector-borne diseases, such as dengue fever, is essential for communities worldwide. Changes in environmental parameters such as precipitation, air temperature, and humidity are known to influence dengue fever dynamics. Furthermore, previous studies have shown how oceanographic variables, such as El Niño Southern Oscillation (ENSO)-related sea surface temperature from the Pacific Ocean, influences dengue fever in the Americas. However, literature is lacking on the use of regional-scale satellite-derived sea surface temperature (SST) to assess its relationship with dengue fever in coastal areas. Data on confirmed dengue cases, demographics, precipitation, and air temperature were collected. Incidence of weekly dengue cases was examined. Stepwise multiple regression analyses (AIC model selection) were used to assess which environmental variables best explained increased dengue incidence rates. SST, minimum air temperature, precipitation, and humidity substantially explained 42% of the observed variation (r 2 =0.42). Infectious diseases are characterized by the influence of past cases on current cases and results show that previous dengue cases alone explained 89% of the variation. Ordinary least-squares analyses showed a positive trend of 0.20±0.03°C in SST from 2006 to 2015. An important element of this study is to help develop strategic recommendations for public health officials in Mexico by providing a simple early warning capability for dengue incidence. Copyright © 2017 Elsevier B.V. All rights reserved.

An overview of techniques for linking high-dimensional molecular data to time-to-event endpoints by risk prediction models.

PubMed

Binder, Harald; Porzelius, Christine; Schumacher, Martin

2011-03-01

Analysis of molecular data promises identification of biomarkers for improving prognostic models, thus potentially enabling better patient management. For identifying such biomarkers, risk prediction models can be employed that link high-dimensional molecular covariate data to a clinical endpoint. In low-dimensional settings, a multitude of statistical techniques already exists for building such models, e.g. allowing for variable selection or for quantifying the added value of a new biomarker. We provide an overview of techniques for regularized estimation that transfer this toward high-dimensional settings, with a focus on models for time-to-event endpoints. Techniques for incorporating specific covariate structure are discussed, as well as techniques for dealing with more complex endpoints. Employing gene expression data from patients with diffuse large B-cell lymphoma, some typical modeling issues from low-dimensional settings are illustrated in a high-dimensional application. First, the performance of classical stepwise regression is compared to stage-wise regression, as implemented by a component-wise likelihood-based boosting approach. A second issues arises, when artificially transforming the response into a binary variable. The effects of the resulting loss of efficiency and potential bias in a high-dimensional setting are illustrated, and a link to competing risks models is provided. Finally, we discuss conditions for adequately quantifying the added value of high-dimensional gene expression measurements, both at the stage of model fitting and when performing evaluation. Copyright © 2011 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

From descriptive to predictive distribution models: a working example with Iberian amphibians and reptiles.

PubMed

Arntzen, J W

2006-05-04

Aim of the study was to identify the conditions under which spatial-environmental models can be used for the improved understanding of species distributions, under the explicit criterion of model predictive performance. I constructed distribution models for 17 amphibian and 21 reptile species in Portugal from atlas data and 13 selected ecological variables with stepwise logistic regression and a geographic information system. Models constructed for Portugal were extrapolated over Spain and tested against range maps and atlas data. Descriptive model precision ranged from 'fair' to 'very good' for 12 species showing a range border inside Portugal ('edge species', kappa (k) 0.35-0.89, average 0.57) and was at best 'moderate' for 26 species with a countrywide Portuguese distribution ('non-edge species', k = 0.03-0.54, average 0.29). The accuracy of the prediction for Spain was significantly related to the precision of the descriptive model for the group of edge species and not for the countrywide species. In the latter group data were consistently better captured with the single variable search-effort than by the panel of environmental data. Atlas data in presence-absence format are often inadequate to model the distribution of species if the considered area does not include part of the range border. Conversely, distribution models for edge-species, especially those displaying high precision, may help in the correct identification of parameters underlying the species range and assist with the informed choice of conservation measures.

Tumor gene expression and prognosis in breast cancer patients with 10 or more positive lymph nodes.

PubMed

Cobleigh, Melody A; Tabesh, Bita; Bitterman, Pincas; Baker, Joffre; Cronin, Maureen; Liu, Mei-Lan; Borchik, Russell; Mosquera, Juan-Miguel; Walker, Michael G; Shak, Steven

2005-12-15

This study, along with two others, was done to develop the 21-gene Recurrence Score assay (Oncotype DX) that was validated in a subsequent independent study and is used to aid decision making about chemotherapy in estrogen receptor (ER)-positive, node-negative breast cancer patients. Patients with >or=10 nodes diagnosed from 1979 to 1999 were identified. RNA was extracted from paraffin blocks, and expression of 203 candidate genes was quantified using reverse transcription-PCR (RT-PCR). Seventy-eight patients were studied. As of August 2002, 77% of patients had distant recurrence or breast cancer death. Univariate Cox analysis of clinical and immunohistochemistry variables indicated that HER2/immunohistochemistry, number of involved nodes, progesterone receptor (PR)/immunohistochemistry (% cells), and ER/immunohistochemistry (% cells) were significantly associated with distant recurrence-free survival (DRFS). Univariate Cox analysis identified 22 genes associated with DRFS. Higher expression correlated with shorter DRFS for the HER2 adaptor GRB7 and the macrophage marker CD68. Higher expression correlated with longer DRFS for tumor protein p53-binding protein 2 (TP53BP2) and the ER axis genes PR and Bcl2. Multivariate methods, including stepwise variable selection and bootstrap resampling of the Cox proportional hazards regression model, identified several genes, including TP53BP2 and Bcl2, as significant predictors of DRFS. Tumor gene expression profiles of archival tissues, some more than 20 years old, provide significant information about risk of distant recurrence even among patients with 10 or more nodes.

The cumulative effects of life event, personal and social resources on subjective well-being of elderly widowers.

PubMed

Balaswamy, S; Richardson, V E

2001-01-01

A multidimensional Life Stress Model was used to test the independent contributions of background characteristics, personal resources, life event, and environmental influences on 200 widowers' levels of well-being, measured by the Affect Balance Scale. Stepwise regression analyses revealed that environmental resources were unrelated to negative affect which is influenced more by the life event and personal resource variables. The environmental resource variables, particularly interactions with friends and neighbors, mostly influenced positive affect. The explanatory model for well-being included multiple variables and explained 33 percent of the variance. Although background characteristics had the greatest impact, absence of hospitalization, higher mastery, higher self-esteem, contacts with friends, and interaction with neighbors enhanced well-being. The results support previous speculations on the importance of positive exchanges for positive affect. African-American widowers showed higher levels of well-being than Caucasian widowers did. The results advance knowledge about differences among elderly men.

Exploring scientific creativity of eleventh-grade students in Taiwan

NASA Astrophysics Data System (ADS)

Liang, Jia-Chi

2002-04-01

Although most researchers focus on scientists' creativity, students' scientific creativity should be considered, especially for high school and college students. It is generally assumed that most professional creators in science emerge from amateur creators. Therefore, the purpose of this study is to investigate the relationship between students' scientific creativity and selected variables including creativity, problem finding, formulating hypotheses, science achievement, the nature of science, and attitudes toward science for finding significant predictors of eleventh grade students' scientific creativity. A total of 130 male eleventh-grade students in three biology classes participated in this study. The main instruments included the Test of Divergent Thinking (TDT) for creativity measurement, the Creativity Rating Scale (CRS) and the Creative Activities and Accomplishments Check Lists (CAACL ) for measurement of scientific creativity, the Nature of Scientific Knowledge Scale (NSKS) for measurement of the nature of science, and the Science Attitude Inventory II (SAI II) for measurement of attitudes toward science. In addition, two instruments on measuring students' abilities of problem finding and abilities of formulating hypotheses were developed by the researcher in this study. Data analysis involved descriptive statistics, Pearson product-moment correlations, and stepwise multiple regressions. The major findings suggested the following: (1) students' scientific creativity significantly correlated with some of selected variables such as attitudes toward science, problem finding, formulating hypotheses, the nature of science, resistance to closure, originality, and elaboration; (2) four significant predictors including attitudes toward science, problem finding, resistance to closure, and originality accounted for 48% of the variance of students' scientific creativity; (3) there were big differences between students with a higher and a lower degree of scientific creativity on the variables of family support, career images, and readings about science; and (4) many students were confused about the creative and moral levels on NSKS and the concept of "almighty of science" and purposes of science on SAI II. The results of this study may provide a more holistic and integrative interpretation of students' scientific creativity and propose better ways of evaluating students' scientific creativity. In addition, the research results may encourage teachers to view scientific creativity as an ability that can be enhanced through various means in classroom science teaching.

The World Health Organization STEPwise Approach to Noncommunicable Disease Risk-Factor Surveillance: Methods, Challenges, and Opportunities

PubMed Central

Guthold, Regina; Cowan, Melanie; Savin, Stefan; Bhatti, Lubna; Armstrong, Timothy; Bonita, Ruth

2016-01-01

Objectives. We sought to outline the framework and methods used by the World Health Organization (WHO) STEPwise approach to noncommunicable disease (NCD) surveillance (STEPS), describe the development and current status, and discuss strengths, limitations, and future directions of STEPS surveillance. Methods. STEPS is a WHO-developed, standardized but flexible framework for countries to monitor the main NCD risk factors through questionnaire assessment and physical and biochemical measurements. It is coordinated by national authorities of the implementing country. The STEPS surveys are generally household-based and interviewer-administered, with scientifically selected samples of around 5000 participants. Results. To date, 122 countries across all 6 WHO regions have completed data collection for STEPS or STEPS-aligned surveys. Conclusions. STEPS data are being used to inform NCD policies and track risk-factor trends. Future priorities include strengthening these linkages from data to action on NCDs at the country level, and continuing to develop STEPS’ capacities to enable a regular and continuous cycle of risk-factor surveillance worldwide. PMID:26696288

The World Health Organization STEPwise Approach to Noncommunicable Disease Risk-Factor Surveillance: Methods, Challenges, and Opportunities.

PubMed

Riley, Leanne; Guthold, Regina; Cowan, Melanie; Savin, Stefan; Bhatti, Lubna; Armstrong, Timothy; Bonita, Ruth

2016-01-01

We sought to outline the framework and methods used by the World Health Organization (WHO) STEPwise approach to noncommunicable disease (NCD) surveillance (STEPS), describe the development and current status, and discuss strengths, limitations, and future directions of STEPS surveillance. STEPS is a WHO-developed, standardized but flexible framework for countries to monitor the main NCD risk factors through questionnaire assessment and physical and biochemical measurements. It is coordinated by national authorities of the implementing country. The STEPS surveys are generally household-based and interviewer-administered, with scientifically selected samples of around 5000 participants. To date, 122 countries across all 6 WHO regions have completed data collection for STEPS or STEPS-aligned surveys. STEPS data are being used to inform NCD policies and track risk-factor trends. Future priorities include strengthening these linkages from data to action on NCDs at the country level, and continuing to develop STEPS' capacities to enable a regular and continuous cycle of risk-factor surveillance worldwide.

Risk maps of Lassa fever in West Africa.

PubMed

Fichet-Calvet, Elisabeth; Rogers, David John

2009-01-01

Lassa fever is caused by a viral haemorrhagic arenavirus that affects two to three million people in West Africa, causing a mortality of between 5,000 and 10,000 each year. The natural reservoir of Lassa virus is the multi-mammate rat Mastomys natalensis, which lives in houses and surrounding fields. With the aim of gaining more information to control this disease, we here carry out a spatial analysis of Lassa fever data from human cases and infected rodent hosts covering the period 1965-2007. Information on contemporary environmental conditions (temperature, rainfall, vegetation) was derived from NASA Terra MODIS satellite sensor data and other sources and for elevation from the GTOPO30 surface for the region from Senegal to the Congo. All multi-temporal data were analysed using temporal Fourier techniques to generate images of means, amplitudes and phases which were used as the predictor variables in the models. In addition, meteorological rainfall data collected between 1951 and 1989 were used to generate a synoptic rainfall surface for the same region. Three different analyses (models) are presented, one superimposing Lassa fever outbreaks on the mean rainfall surface (Model 1) and the other two using non-linear discriminant analytical techniques. Model 2 selected variables in a step-wise inclusive fashion, and Model 3 used an information-theoretic approach in which many different random combinations of 10 variables were fitted to the Lassa fever data. Three combinations of absenceratiopresence clusters were used in each of Models 2 and 3, the 2 absenceratio1 presence cluster combination giving what appeared to be the best result. Model 1 showed that the recorded outbreaks of Lassa fever in human populations occurred in zones receiving between 1,500 and 3,000 mm rainfall annually. Rainfall, and to a much lesser extent temperature variables, were most strongly selected in both Models 2 and 3, and neither vegetation nor altitude seemed particularly important. Both Models 2 and 3 produced mean kappa values in excess of 0.91 (Model 2) or 0.86 (Model 3), making them 'Excellent'. The Lassa fever areas predicted by the models cover approximately 80% of each of Sierra Leone and Liberia, 50% of Guinea, 40% of Nigeria, 30% of each of Côte d'Ivoire, Togo and Benin, and 10% of Ghana.

Effect of fiber source on cell wall digestibility and rate of passage in rabbits.

PubMed

García, J; Carabaño, R; de Blas, J C

1999-04-01

The influence of fiber source on fiber digestion and mean retention time was investigated. Six fibrous feedstuffs with wide differences in chemical composition and particle size were selected: paprika meal, olive leaves, alfalfa hay, soybean hulls, sodium hydroxide-treated barley straw, and sunflower hulls. Six diets were formulated to contain one of these ingredients as the sole source of fiber. To avoid nutrient imbalances, fiber sources were supplemented with different proportions of a concentrate free of fiber based on soy protein isolate, wheat flour, lard, and a vitamin and mineral mix to obtain diets containing at least 18.5% CP and 5% starch. Fecal apparent digestibility of nonstarch polysaccharides (NSPd) and its monomers, NDF, NDF-ADL, and ADF-ADL, were determined using four New Zealand White x California growing rabbits per diet. Total, ileorectal, and cecal mean retention times (tMRT, i-rMRT, and cMRT, respectively) were determined for diets based on paprika meal, olive leaves, soybean hulls, and sunflower hulls in 16 does (four per diet) fitted with T-cannulas at the terminal ileum. In both trials, DMI was negatively correlated with the proportion of fine particles (FP: < .315 mm) and positively correlated with the proportion of large particles (LP: > 1.25 mm) (P < .01). Stepwise regression analysis showed that FP was the dietary characteristic best related to digestibilities of NSP, uronic acids, glucose and NDF, tMRT, and cMRT (P < .001), showing a positive correlation with these variables. In all these cases, this procedure selected the proportion of large particles as a second variable in the model. Degree of lignification of NDF, considering lignin as the difference between ADL and acid detergent cutin, was only included as the third variable for the model of NDF digestibility. Digestibility of NSP was positively correlated with those of NDF, NDF-ADL, and ADF-ADL (r = .82, .87 and .85, respectively, P < .001); the latter was also highly correlated with the digestibility of the glucose included in the NSP fraction (r = .86; P < .001). Cecal mean retention time accounted for 63% of average tMRT, for most of the variability in tMRT (r = .99; P < .001), and was positively related to NSPd (r = .89; P < .001). From these results, we conclude that particle size is a major factor affecting fiber digestion efficiency, rate of passage, and feed intake in rabbits.

Evaluation and simplification of the occupational slip, trip and fall risk-assessment test

PubMed Central

NAKAMURA, Takehiro; OYAMA, Ichiro; FUJINO, Yoshihisa; KUBO, Tatsuhiko; KADOWAKI, Koji; KUNIMOTO, Masamizu; ODOI, Haruka; TABATA, Hidetoshi; MATSUDA, Shinya

2016-01-01

Objective: The purpose of this investigation is to evaluate the efficacy of the occupational slip, trip and fall (STF) risk assessment test developed by the Japan Industrial Safety and Health Association (JISHA). We further intended to simplify the test to improve efficiency. Methods: A previous cohort study was performed using 540 employees aged ≥50 years who took the JISHA’s STF risk assessment test. We conducted multivariate analysis using these previous results as baseline values and answers to questionnaire items or score on physical fitness tests as variables. The screening efficiency of each model was evaluated based on the obtained receiver operating characteristic (ROC) curve. Results: The area under the ROC obtained in multivariate analysis was 0.79 when using all items. Six of the 25 questionnaire items were selected for stepwise analysis, giving an area under the ROC curve of 0.77. Conclusion: Based on the results of follow-up performed one year after the initial examination, we successfully determined the usefulness of the STF risk assessment test. Administering a questionnaire alone is sufficient for screening subjects at risk of STF during the subsequent one-year period. PMID:27021057

Pharmacy Student Attitudes and Willingness to Engage in Care with People Living with HIV/AIDS

PubMed Central

Furtek, Kari J.; Malladi, Ruthvik; Ng, Eric; Zhou, Maria

2016-01-01

Objective. To describe the extent to which pharmacy students hold negative attitudes toward people living with HIV/AIDS (PLWHA) and to determine whether background variables, student knowledge, and professional attitudes may affect willingness to care for PLWHA. Methods. An online survey tool was developed and administered to 150 pharmacy students in their third professional year. Descriptive and stepwise multivariate regressions were performed. Results. While descriptive results showed a majority of respondents had favorable professional attitudes towards caring for PLWHA, most pharmacy students expressed discomfort with specific attitudes about being in close physical contact and receiving selected services from PLWHA. Multivariate results revealed that: (1) being a minority predicted greater knowledge; (2) having received prior HIV instruction and greater HIV knowledge predicted more positive professional attitudes caring for PLWHA; (3) being more socially liberal, having more positive professional attitudes caring for PLWHA, and having greater empathy towards PLWHA predicted student willingness to provide services. Conclusion. Future educational interventions specifically targeted toward socially conservative whites may impact greater student willingness to care for PLWHA. Additional research should also explore the generalizability of the present findings and modeling to pharmacy students in other regions of the country. PMID:27170816

Risk factors for postoperative liver failure after hepatectomy for hepatocellular carcinoma.

PubMed

Maeda, Yoshitaka; Nishida, Minekatsu; Takao, Takashi; Mori, Naohide; Tamesa, Takao; Tangoku, Akira; Oka, Masaaki

2004-01-01

Selection of patients for hepatectomy for hepatocellular carcinoma conventionally has been based upon Child-Pugh grading. However, postoperative liver failure after hepatectomy is a major cause of hospital mortality. A new predictor of postoperative liver failure is required. The objective of this study was to identify risk factors for postoperative liver failure after hepatectomy. Perioperative risk factors for liver failure after hepatectomy were analyzed in 112 patients with hepatocellular carcinoma Eight of these patients died of liver failure. Stepwise multivariate logistic regression was performed to investigate significant independent factors among 17 variables, including the serum alkaline phosphatase ratio (ALPR) on the first day after hepatectomy. ALPR was calculated as the postoperative ALP level divided by the ALP level before surgery. Significant risk factors of postoperative liver failure were ALPR on postoperative day 1 (ALPR1), sex, operative blood loss, and operative procedure. As an indicator of liver failure, the diagnostic accuracy of the ALPR1 was 93.7% when the ALPR was less than 0.4 on the first postoperative day. The ALPR and the serum total bilirubin concentration after hepatectomy were uncorrelated. ALPR1 is a useful predictor of liver failure after hepatectomy.

Characterization of French and Spanish dry-cured hams: influence of the volatiles from the muscles and the subcutaneous fat quantified by SPME-GC.

PubMed

Sánchez-Peña, Carolina M; Luna, Guadalupe; García-González, Diego L; Aparicio, Ramón

2005-04-01

The influence of the volatile compounds on the characterization of Spanish and French dry-cured hams was studied. Thirty volatiles were quantified in each one of four locations (biceps femoris, semimembranosus and semitendinosus muscles and subcutaneous fat) of 29 dry-cured hams by solid-phase microextraction gas-chromatography (SPME-GC). The Brown-Forsythe univariate test allowed determination of the volatiles that individually could characterize (p<0.05) the samples by their geographical origin (France, Spain) and breed type (Iberian, white). Stepwise linear discriminant procedure, under very strict conditions (F-to-Enter for a F-distribution>0.95), then selected the most remarkable volatile compounds. Four compounds from the subcutaneous fat (methyl benzene and octanol) and the semitendinosus muscle (2-butanone and 2-octanone) allowed 100% correct classifications by geographic origin. On the other hand, only two compounds from the subcutaneous fat (octanol) and the biceps femoris muscle (3-methyl 1-butanol) correctly classified all the samples by the breed type. The ability of these variables to classify the samples was checked by the unsupervised procedure of principal components.

Designing of a fluoride selective receptor through molecular orbital engineering

NASA Astrophysics Data System (ADS)

Mishra, Rakesh K.; Kumar, Virendra; Diwan, Uzra; Upadhyay, K. K.; Roy Chowdhury, P. K.

2012-11-01

The stepwise substitution of appropriate groups over the 3-[(2,4-dinitro-phenyl)-hydrazono]-butyric acid ethyl ester (R3) lead receptor R1 which showed selectivity towards fluoride in DMSO. The UV-vis and 1H NMR titration studies revealed the details of the binding between receptor R1 and fluoride. The receptor R1 also recognized fluoride in a toothpaste solution to as low as 50 ppm. The theoretical simulations of recognition event at Density Functional Theory (DFT) level using B3LYP/6-31G** basis set and polarizable continuum model (PCM) approach lead a semi-quantitative match with the experimental results.

Natural Resources Inventory and Land Evaluation in Switzerland

NASA Technical Reports Server (NTRS)

Haefner, H. (Principal Investigator)

1975-01-01

The author has identified the following significant results. A system was developed to operationally map and measure the areal extent of various land use categories for updating existing and producing new and actual thematic maps showing the latest state of rural and urban landscapes and its changes. The processing system includes: (1) preprocessing steps for radiometric and geometric corrections; (2) classification of the data by a multivariate procedure, using a stepwise linear discriminant analysis based on carefully selected training cells; and (3) output in form of color maps by printing black and white theme overlays of a selected scale with photomation system and its coloring and combination into a color composite.

Study of the thermal properties of selected PCMs for latent heat storage in buildings

NASA Astrophysics Data System (ADS)

Valentova, Katerina; Pechackova, Katerina; Prikryl, Radek; Ostry, Milan; Zmeskal, Oldrich

2017-07-01

The paper is focused on measurements of thermal properties of selected phase change materials (PCMs) which can be used for latent heat storage in building structures. The thermal properties were measured by the transient step-wise method and analyzed by the thermal spectroscopy. The results of three different materials (RT18HC, RT28HC, and RT35HC) and their thermal properties in solid, liquid, and phase change region were determined. They were correlated with the differential scanning calorimetry (DSC) measurement. The results will be used to determine the optimum ratio of components for the construction of drywall and plasters containing listed ingredients, respectively.

Prediction of Ba, Mn and Zn for tropical soils using iron oxides and magnetic susceptibility

NASA Astrophysics Data System (ADS)

Marques Júnior, José; Arantes Camargo, Livia; Reynaldo Ferracciú Alleoni, Luís; Tadeu Pereira, Gener; De Bortoli Teixeira, Daniel; Santos Rabelo de Souza Bahia, Angelica

2017-04-01

Agricultural activity is an important source of potentially toxic elements (PTEs) in soil worldwide but particularly in heavily farmed areas. Spatial distribution characterization of PTE contents in farming areas is crucial to assess further environmental impacts caused by soil contamination. Designing prediction models become quite useful to characterize the spatial variability of continuous variables, as it allows prediction of soil attributes that might be difficult to attain in a large number of samples through conventional methods. This study aimed to evaluate, in three geomorphic surfaces of Oxisols, the capacity for predicting PTEs (Ba, Mn, Zn) and their spatial variability using iron oxides and magnetic susceptibility (MS). Soil samples were collected from three geomorphic surfaces and analyzed for chemical, physical, mineralogical properties, as well as magnetic susceptibility (MS). PTE prediction models were calibrated by multiple linear regression (MLR). MLR calibration accuracy was evaluated using the coefficient of determination (R2). PTE spatial distribution maps were built using the values calculated by the calibrated models that reached the best accuracy by means of geostatistics. The high correlations between the attributes clay, MS, hematite (Hm), iron oxides extracted by sodium dithionite-citrate-bicarbonate (Fed), and iron oxides extracted using acid ammonium oxalate (Feo) with the elements Ba, Mn, and Zn enabled them to be selected as predictors for PTEs. Stepwise multiple linear regression showed that MS and Fed were the best PTE predictors individually, as they promoted no significant increase in R2 when two or more attributes were considered together. The MS-calibrated models for Ba, Mn, and Zn prediction exhibited R2 values of 0.88, 0.66, and 0.55, respectively. These are promising results since MS is a fast, cheap, and non-destructive tool, allowing the prediction of a large number of samples, which in turn enables detailed mapping of large areas. MS predicted values enabled the characterization and the understanding of spatial variability of the studied PTEs.

Impact of power index, hydroureteronephrosis, stone size, and composition on the efficacy of in situ boosted ESWL for primary proximal ureteral calculi.

PubMed

Singh, I; Gupta, N P; Hemal, A K; Dogra, P N; Ansari, M S; Seth, A; Aron, M

2001-07-01

The efficacy, safety, feasibility, and outcome of in situ treatment applied to select proximal ureteral calculi was assessed and analyzed with a view to avoiding auxiliary interventions and providing high clearance rates in the shortest possible time. We studied the impact of several clinically important variables, including power index, degree of hydroureteronephrosis (HDUN), stone size, and composition on the efficacy of sequential in situ boosted extracorporeal shock wave lithotripsy (ESWL) in a select group. The power index requirement for the in situ boosted protocol and the impact of the stone size/composition, degree of HDUN, and clearance rates were also analyzed. An in situ (no instrumentation) boosted protocol was applied to 130 primary unimpacted proximal ureteral calculi with no prior intervention. A typical session with the Siemens Lithostar Plus comprised 3000 shock waves, in installments of 500, deployed at a power setting of 1 to 4 kV with a gradual stepwise escalation. Sequential boosted additional sessions of ESWL were administered on days 2, 7, and 14, tailored to the degree of fragmentation, clearance status, and amount of residual stone bulk. Several parameters (shock waves, kilovolts used, fluoroscopy time, number of sessions, stone size, composition, fragmentation, clearance, and HDUN) were recorded and the results analyzed statistically. The results were excellent in 83.8%, with a mean duration to complete clearance of 11.3 days. In situ ESWL failed in 7.69%, and the auxiliary intervention rate was 10.7%. Pre-ESWL HDUN was present in 78.3%, the mean power index was 184.6/session/case, and the average stone burden was 8.9 mm(2). Calcium oxalate monohydrate was the most common stone (56%). Renal colic was the most common side effect observed. The power index, fragmentation at the first session, and stone size were found to be the most favorable significant variables affecting stone clearance. The degree of HDUN, number of sessions, and stone composition did not significantly impact the clearance rates. In situ boosted ESWL should be the first-line therapeutic modality in select unimpacted primary proximal ureteral stones.

Feature selection and recognition from nonspecific volatile profiles for discrimination of apple juices according to variety and geographical origin.

PubMed

Guo, Jing; Yue, Tianli; Yuan, Yahong

2012-10-01

Apple juice is a complex mixture of volatile and nonvolatile components. To develop discrimination models on the basis of the volatile composition for an efficient classification of apple juices according to apple variety and geographical origin, chromatography volatile profiles of 50 apple juice samples belonging to 6 varieties and from 5 counties of Shaanxi (China) were obtained by headspace solid-phase microextraction coupled with gas chromatography. The volatile profiles were processed as continuous and nonspecific signals through multivariate analysis techniques. Different preprocessing methods were applied to raw chromatographic data. The blind chemometric analysis of the preprocessed chromatographic profiles was carried out. Stepwise linear discriminant analysis (SLDA) revealed satisfactory discriminations of apple juices according to variety and geographical origin, provided respectively 100% and 89.8% success rate in terms of prediction ability. Finally, the discriminant volatile compounds selected by SLDA were identified by gas chromatography-mass spectrometry. The proposed strategy was able to verify the variety and geographical origin of apple juices involving only a reduced number of discriminate retention times selected by the stepwise procedure. This result encourages the similar procedures to be considered in quality control of apple juices. This work presented a method for an efficient discrimination of apple juices according to apple variety and geographical origin using HS-SPME-GC-MS together with chemometric tools. Discrimination models developed could help to achieve greater control over the quality of the juice and to detect possible adulteration of the product. © 2012 Institute of Food Technologists®

A Multimetric Benthic Macroinvertebrate Index for the Assessment of Stream Biotic Integrity in Korea

PubMed Central

Jun, Yung-Chul; Won, Doo-Hee; Lee, Soo-Hyung; Kong, Dong-Soo; Hwang, Soon-Jin

2012-01-01

At a time when anthropogenic activities are increasingly disturbing the overall ecological integrity of freshwater ecosystems, monitoring of biological communities is central to assessing the health and function of streams. This study aimed to use a large nation-wide database to develop a multimetric index (the Korean Benthic macroinvertebrate Index of Biological Integrity—KB-IBI) applicable to the biological assessment of Korean streams. Reference and impaired conditions were determined based on watershed, chemical and physical criteria. Eight of an initial 34 candidate metrics were selected using a stepwise procedure that evaluated metric variability, redundancy, sensitivity and responsiveness to environmental gradients. The selected metrics were number of taxa, percent Ephemeroptera-Plecoptera-Trichoptera (EPT) individuals, percent of a dominant taxon, percent taxa abundance without Chironomidae, Shannon’s diversity index, percent gatherer individuals, ratio of filterers and scrapers, and the Korean saprobic index. Our multimetric index successfully distinguished reference from impaired conditions. A scoring system was established for each core metric using its quartile range and response to anthropogenic disturbances. The multimetric index was classified by aggregating the individual metric ..scores and the value range was quadrisected to provide a narrative criterion (Poor, Fair, Good and Excellent) to describe the biological integrity of the streams in the study. A validation procedure showed that the index is an effective method for evaluating stream conditions, and thus is appropriate for use in future studies measuring the long-term status of streams, and the effectiveness of restoration methods. PMID:23202765

A continuous damage model based on stepwise-stress creep rupture tests

NASA Technical Reports Server (NTRS)

Robinson, D. N.

1985-01-01

A creep damage accumulation model is presented that makes use of the Kachanov damage rate concept with a provision accounting for damage that results from a variable stress history. This is accomplished through the introduction of an additional term in the Kachanov rate equation that is linear in the stress rate. Specification of the material functions and parameters in the model requires two types of constituting a data base: (1) standard constant-stress creep rupture tests, and (2) a sequence of two-step creep rupture tests.

Exploration of charity toward busking (street performance) as a function of religion.

PubMed

Lemay, John O; Bates, Larry W

2013-04-01

To examine conceptions of religion and charity in a new venue--busking (street performance)--103 undergraduate students at a regional university in the southeastern U.S. completed a battery of surveys regarding religion, and attitudes and behaviors toward busking. For those 85 participants who had previously encountered a busker, stepwise regression was used to predict increased frequency of giving to buskers. The best predictive model of giving to buskers consisted of three variables including less experienced irritation toward buskers, prior experience with giving to the homeless, and lower religious fundamentalism.

Sediment fingerprinting experiments to test the sensitivity of multivariate mixing models

NASA Astrophysics Data System (ADS)

Gaspar, Leticia; Blake, Will; Smith, Hugh; Navas, Ana

2014-05-01

Sediment fingerprinting techniques provide insight into the dynamics of sediment transfer processes and support for catchment management decisions. As questions being asked of fingerprinting datasets become increasingly complex, validation of model output and sensitivity tests are increasingly important. This study adopts an experimental approach to explore the validity and sensitivity of mixing model outputs for materials with contrasting geochemical and particle size composition. The experiments reported here focused on (i) the sensitivity of model output to different fingerprint selection procedures and (ii) the influence of source material particle size distributions on model output. Five soils with significantly different geochemistry, soil organic matter and particle size distributions were selected as experimental source materials. A total of twelve sediment mixtures were prepared in the laboratory by combining different quantified proportions of the < 63 µm fraction of the five source soils i.e. assuming no fluvial sorting of the mixture. The geochemistry of all source and mixture samples (5 source soils and 12 mixed soils) were analysed using X-ray fluorescence (XRF). Tracer properties were selected from 18 elements for which mass concentrations were found to be significantly different between sources. Sets of fingerprint properties that discriminate target sources were selected using a range of different independent statistical approaches (e.g. Kruskal-Wallis test, Discriminant Function Analysis (DFA), Principal Component Analysis (PCA), or correlation matrix). Summary results for the use of the mixing model with the different sets of fingerprint properties for the twelve mixed soils were reasonably consistent with the initial mixing percentages initially known. Given the experimental nature of the work and dry mixing of materials, geochemical conservative behavior was assumed for all elements, even for those that might be disregarded in aquatic systems (e.g. P). In general, the best fits between actual and modeled proportions were found using a set of nine tracer properties (Sr, Rb, Fe, Ti, Ca, Al, P, Si, K, Si) that were derived using DFA coupled with a multivariate stepwise algorithm, with errors between real and estimated value that did not exceed 6.7 % and values of GOF above 94.5 %. The second set of experiments aimed to explore the sensitivity of model output to variability in the particle size of source materials assuming that a degree of fluvial sorting of the resulting mixture took place. Most particle size correction procedures assume grain size affects are consistent across sources and tracer properties which is not always the case. Consequently, the < 40 µm fraction of selected soil mixtures was analysed to simulate the effect of selective fluvial transport of finer particles and the results were compared to those for source materials. Preliminary findings from this experiment demonstrate the sensitivity of the numerical mixing model outputs to different particle size distributions of source material and the variable impact of fluvial sorting on end member signatures used in mixing models. The results suggest that particle size correction procedures require careful scrutiny in the context of variable source characteristics.

Step-wise refolding of recombinant proteins.

PubMed

Tsumoto, Kouhei; Arakawa, Tsutomu; Chen, Linda

2010-04-01

Protein refolding is still on trial-and-error basis. Here we describe step-wise dialysis refolding, in which denaturant concentration is altered in step-wise fashion. This technology controls the folding pathway by adjusting the concentrations of the denaturant and other solvent additives to induce sequential folding or disulfide formation.

The relationship between environmental exposure to cadmium and lead and blood selenium concentration in randomly selected population of children inhabiting industrial regions of Silesian Voivodship (Poland).

PubMed

Gać, P; Pawlas, N; Poręba, R; Poręba, M; Pawlas, K

2014-06-01

This study aimed at determining the relationship between environmental exposure to lead (Pb) and cadmium (Cd) and blood selenium (Se) concentration in randomly selected population of children inhabiting the industrial regions of Silesian Voivodship, Poland. The study was conducted on a group of consecutive randomly selected 349 children aged below 15 years and inhabiting the industrial regions in Upper Silesia. The examined variables included whole blood Cd concentration (Cd-B), whole blood Pb concentration (Pb-B) and whole blood Se concentration (Se-B). The concentration of Cd-B, Pb-B and Se-B in the studied group of children amounted to 0.26 ± 0.14, 37.62 ± 25.30 and 78.31 ± 12.82 μg/L, respectively. In the entire examined group a statistically significant negative linear relationship was noted between Pb-B and Se-B (r = -0.12, p < 0.05). Also, a statistically insignificant negative correlation was detected between Cd-B and Se-B (r = -0.02, p > 0.05) and a statistically insignificant positive correlation between Pb-B and Cd-B (r = 0.08, p > 0.05). A multivariate backward stepwise regression analysis demonstrated that in the studied group of children higher Pb-B and a more advanced age-represented independent risk factors for a decreased Se-B. Environmental exposure to Pb may represent an independent risk factor for Se deficit in blood of the studied population of children. In children, the lowered Se-B may create one of the mechanisms in which Pb unfavourably affects human body. © The Author(s) 2014.

Blindness, low vision, and other handicaps as risk factors attached to institutional residence.

PubMed

Brézin, A P; Lafuma, A; Fagnani, F; Mesbah, M; Berdeaux, G

2004-10-01

To estimate the risk of living in an institution and being visually impaired. Two national surveys were pooled: (1) 2075 institutions (for children or adults with handicaps, old people, and psychiatric centres) were selected randomly, in 18 predefined strata, from the French health ministry files. From these institutions, 15 403 subjects were selected randomly and handicap was documented by interview in 14 603 (94.9%) of them; (2) level of handicap was documented in a randomised, stratified sample of 356 208 citizens living in the community; from this sample, 21 760 subjects were further selected at random and 16 945 people were interviewed. Data on handicaps (visual, auditory, speech, brain, visceral, motor, and other) and activities of daily living (ADL) were extracted. The odds ratio (OR) of living in an institution was estimated, using stepwise logistic regressions with age, geographical area, handicaps, and ADL as co-variables. Subjects in institutions, compared to those living at home, were, respectively, more often female (64.3% v 52.4%) and older (68.7 v 38.0 years); they more often had handicaps (ORs: speech, 6.59; brain, 10.17; motor, 8.86; visceral, 3.49; auditory, 2.66; other, 1.53); and were less often able to perform their ADL (46.2% v 97.1%) without assistance. Below 80 years, blind people were more often in institutions (ORs 0.239 to 0.306); whereas in older people the association was reversed (OR: 3.277). Low vision was always significantly associated with institutional residence (ORs from 0.262 to 0.752). Visual handicap was associated with institutional residence. The link persisted after adjustment for known confounding factors.

Traditional Admissions Variables as Predictors of Minority Students' Performance in Medical School — A Cause for Concern

PubMed Central

Johnson, Henry C.; Rosevear, G. Craig

1977-01-01

This study explored the relationship between traditional admissions criteria, performance in the first semester of medical school, and performance on the National Board of Medical Examiners' (NBME) Examination, Part 1 for minority medical students, non-minority medical students, and the two groups combined. Correlational analysis and step-wise multiple regression procedures were used as the analysis techniques. A different pattern of admissions variables related to National Board Part 1 performance for the two groups. The General Information section of the Medical College Admission Test (MCAT) contributed the most variance for the minority student group. MCAT-Science contributed the most variance for the non-minority student group. MCATs accounted for a substantial portion of the variance on the National Board examination. PMID:904005

Spectrotemporal Modulation Sensitivity as a Predictor of Speech Intelligibility for Hearing-Impaired Listeners

PubMed Central

Bernstein, Joshua G.W.; Mehraei, Golbarg; Shamma, Shihab; Gallun, Frederick J.; Theodoroff, Sarah M.; Leek, Marjorie R.

2014-01-01

Background A model that can accurately predict speech intelligibility for a given hearing-impaired (HI) listener would be an important tool for hearing-aid fitting or hearing-aid algorithm development. Existing speech-intelligibility models do not incorporate variability in suprathreshold deficits that are not well predicted by classical audiometric measures. One possible approach to the incorporation of such deficits is to base intelligibility predictions on sensitivity to simultaneously spectrally and temporally modulated signals. Purpose The likelihood of success of this approach was evaluated by comparing estimates of spectrotemporal modulation (STM) sensitivity to speech intelligibility and to psychoacoustic estimates of frequency selectivity and temporal fine-structure (TFS) sensitivity across a group of HI listeners. Research Design The minimum modulation depth required to detect STM applied to an 86 dB SPL four-octave noise carrier was measured for combinations of temporal modulation rate (4, 12, or 32 Hz) and spectral modulation density (0.5, 1, 2, or 4 cycles/octave). STM sensitivity estimates for individual HI listeners were compared to estimates of frequency selectivity (measured using the notched-noise method at 500, 1000measured using the notched-noise method at 500, 2000, and 4000 Hz), TFS processing ability (2 Hz frequency-modulation detection thresholds for 500, 10002 Hz frequency-modulation detection thresholds for 500, 2000, and 4000 Hz carriers) and sentence intelligibility in noise (at a 0 dB signal-to-noise ratio) that were measured for the same listeners in a separate study. Study Sample Eight normal-hearing (NH) listeners and 12 listeners with a diagnosis of bilateral sensorineural hearing loss participated. Data Collection and Analysis STM sensitivity was compared between NH and HI listener groups using a repeated-measures analysis of variance. A stepwise regression analysis compared STM sensitivity for individual HI listeners to audiometric thresholds, age, and measures of frequency selectivity and TFS processing ability. A second stepwise regression analysis compared speech intelligibility to STM sensitivity and the audiogram-based Speech Intelligibility Index. Results STM detection thresholds were elevated for the HI listeners, but only for low rates and high densities. STM sensitivity for individual HI listeners was well predicted by a combination of estimates of frequency selectivity at 4000 Hz and TFS sensitivity at 500 Hz but was unrelated to audiometric thresholds. STM sensitivity accounted for an additional 40% of the variance in speech intelligibility beyond the 40% accounted for by the audibility-based Speech Intelligibility Index. Conclusions Impaired STM sensitivity likely results from a combination of a reduced ability to resolve spectral peaks and a reduced ability to use TFS information to follow spectral-peak movements. Combining STM sensitivity estimates with audiometric threshold measures for individual HI listeners provided a more accurate prediction of speech intelligibility than audiometric measures alone. These results suggest a significant likelihood of success for an STM-based model of speech intelligibility for HI listeners. PMID:23636210

Site-selective oxidation, amination and epimerization reactions of complex polyols enabled by transfer hydrogenation

NASA Astrophysics Data System (ADS)

Hill, Christopher K.; Hartwig, John F.

2017-12-01

Polyoxygenated hydrocarbons that bear one or more hydroxyl groups comprise a large set of natural and synthetic compounds, often with potent biological activity. In synthetic chemistry, alcohols are important precursors to carbonyl groups, which then can be converted into a wide range of oxygen- or nitrogen-based functionality. Therefore, the selective conversion of a single hydroxyl group in natural products into a ketone would enable the selective introduction of unnatural functionality. However, the methods known to convert a simple alcohol, or even an alcohol in a molecule that contains multiple protected functional groups, are not suitable for selective reactions of complex polyol structures. We present a new ruthenium catalyst with a unique efficacy for the selective oxidation of a single hydroxyl group among many in unprotected polyol natural products. This oxidation enables the introduction of nitrogen-based functional groups into such structures that lack nitrogen atoms and enables a selective alcohol epimerization by stepwise or reversible oxidation and reduction.

Diels–Alder Reactions of Allene with Benzene and Butadiene: Concerted, Stepwise, and Ambimodal Transition States

PubMed Central

2015-01-01

Multiconfigurational complete active space methods (CASSCF and CASPT2) have been used to investigate the (4 + 2) cycloadditions of allene with butadiene and with benzene. Both concerted and stepwise radical pathways were examined to determine the mechanism of the Diels–Alder reactions with an allene dienophile. Reaction with butadiene occurs via a single ambimodal transition state that can lead to either the concerted or stepwise trajectories along the potential energy surface, while reaction with benzene involves two separate transition states and favors the concerted mechanism relative to the stepwise mechanism via a diradical intermediate. PMID:25216056

Shaped Ceria Nanocrystals Catalyze Efficient and Selective Para-Hydrogen-Enhanced Polarization.

PubMed

Zhao, Evan W; Zheng, Haibin; Zhou, Ronghui; Hagelin-Weaver, Helena E; Bowers, Clifford R

2015-11-23

Intense para-hydrogen-enhanced NMR signals are observed in the hydrogenation of propene and propyne over ceria nanocubes, nano-octahedra, and nanorods. The well-defined ceria shapes, synthesized by a hydrothermal method, expose different crystalline facets with various oxygen vacancy densities, which are known to play a role in hydrogenation and oxidation catalysis. While the catalytic activity of the hydrogenation of propene over ceria is strongly facet-dependent, the pairwise selectivity is low (2.4% at 375 °C), which is consistent with stepwise H atom transfer, and it is the same for all three nanocrystal shapes. Selective semi-hydrogenation of propyne over ceria nanocubes yields hyperpolarized propene with a similar pairwise selectivity of (2.7% at 300 °C), indicating product formation predominantly by a non-pairwise addition. Ceria is also shown to be an efficient pairwise replacement catalyst for propene. © 2015 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Assessment of craniometric traits in South Indian dry skulls for sex determination.

PubMed

Ramamoorthy, Balakrishnan; Pai, Mangala M; Prabhu, Latha V; Muralimanju, B V; Rai, Rajalakshmi

2016-01-01

The skeleton plays an important role in sex determination in forensic anthropology. The skull bone is considered as the second best after the pelvic bone in sex determination due to its better retention of morphological features. Different populations have varying skeletal characteristics, making population specific analysis for sex determination essential. Hence the objective of this investigation is to obtain the accuracy of sex determination using cranial parameters of adult skulls to the highest percentage in South Indian population and to provide a baseline data for sex determination in South India. Seventy adult preserved human skulls were taken and based on the morphological traits were classified into 43 male skulls and 27 female skulls. A total of 26 craniometric parameters were studied. The data were analyzed by using the SPSS discriminant function. The analysis of stepwise, multivariate, and univariate discriminant function gave an accuracy of 77.1%, 85.7%, and 72.9% respectively. Multivariate direct discriminant function analysis classified skull bones into male and female with highest levels of accuracy. Using stepwise discriminant function analysis, the most dimorphic variable to determine sex of the skull, was biauricular breadth followed by weight. Subjecting the best dimorphic variables to univariate discriminant analysis, high levels of accuracy of sexual dimorphism was obtained. Percentage classification of high accuracies were obtained in this study indicating high level of sexual dimorphism in the crania, setting specific discriminant equations for the gender determination in South Indian people. Copyright © 2015 Elsevier Ltd and Faculty of Forensic and Legal Medicine. All rights reserved.

Spatial and temporal variability of reference evapotranspiration and influenced meteorological factors in the Jialing River Basin, China

NASA Astrophysics Data System (ADS)

Herath, Imali Kaushalya; Ye, Xuchun; Wang, Jianli; Bouraima, Abdel-Kabirou

2018-02-01

Reference evapotranspiration (ETr) is one of the important parameters in the hydrological cycle. The spatio-temporal variation of ETr and other meteorological parameters that influence ETr were investigated in the Jialing River Basin (JRB), China. The ETr was estimated using the CROPWAT 8.0 computer model based on the Penman-Montieth equation for the period 1964-2014. Mean temperature (MT), relative humidity (RH), sunshine duration (SD), and wind speed (WS) were the main input parameters of CROPWAT while 12 meteorological stations were evaluated. Linear regression and Mann-Kendall methods were applied to study the spatio-temporal trends while the inverse distance weighted (IDW) method was used to identify the spatial distribution of ETr. Stepwise regression and partial correlation methods were used to identify the meteorological variables that most significantly influenced the changes in ETr. The highest annual ETr was found in the northern part of the basin, whereas the lowest rate was recorded in the western part. In the autumn, the highest ETr was recorded in the southeast part of JRB. The annual ETr reflected neither significant increasing nor decreasing trends. Except for the summer, ETr is slightly increasing in other seasons. The MT significantly increased whereas SD and RH were significantly decreased during the 50-year period. Partial correlation and stepwise regression methods found that the impact of meteorological parameters on ETr varies on an annual and seasonal basis while SD, MT, and RH contributed to the changes of annual and seasonal ETr in the JRB.

Power Relative to Body Mass Best Predicts Change in Core Temperature During Exercise-Heat Stress.

PubMed

Gibson, Oliver R; Willmott, Ashley G B; James, Carl A; Hayes, Mark; Maxwell, Neil S

2017-02-01

Gibson, OR, Willmott, AGB, James, CA, Hayes, M, and Maxwell, NS. Power relative to body mass best predicts change in core temperature during exercise-heat stress. J Strength Cond Res 31(2): 403-414, 2017-Controlling internal temperature is crucial when prescribing exercise-heat stress, particularly during interventions designed to induce thermoregulatory adaptations. This study aimed to determine the relationship between the rate of rectal temperature (Trec) increase, and various methods for prescribing exercise-heat stress, to identify the most efficient method of prescribing isothermic heat acclimation (HA) training. Thirty-five men cycled in hot conditions (40° C, 39% R.H.) for 29 ± 2 minutes. Subjects exercised at 60 ± 9% V[Combining Dot Above]O2peak, with methods for prescribing exercise retrospectively observed for each participant. Pearson product moment correlations were calculated for each prescriptive variable against the rate of change in Trec (° C·h), with stepwise multiple regressions performed on statistically significant variables (p ≤ 0.05). Linear regression identified the predicted intensity required to increase Trec by 1.0-2.0° C between 20- and 45-minute periods and the duration taken to increase Trec by 1.5° C in response to incremental intensities to guide prescription. Significant (p ≤ 0.05) relationships with the rate of change in Trec were observed for prescriptions based on relative power (W·kg; r = 0.764), power (%Powermax; r = 0.679), rating of perceived exertion (RPE) (r = 0.577), V[Combining Dot Above]O2 (%V[Combining Dot Above]O2peak; r = 0.562), heart rate (HR) (%HRmax; r = 0.534), and thermal sensation (r = 0.311). Stepwise multiple regressions observed relative power and RPE as variables to improve the model (r = 0.791), with no improvement after inclusion of any anthropometric variable. Prescription of exercise under heat stress using power (W·kg or %Powermax) has the strongest relationship with the rate of change in Trec with no additional requirement to correct for body composition within a normal range. Practitioners should therefore prescribe exercise intensity using relative power during isothermic HA training to increase Trec efficiently and maximize adaptation.

Monitoring spatial variations in soil organic carbon using remote sensing and geographic information systems

NASA Astrophysics Data System (ADS)

Jaber, Salahuddin M.

Soil organic carbon (SOC) sequestration is a component of larger strategies to control the accumulation of greenhouse gases that may be causing global warming. To implement this approach, it is necessary to improve the methods of measuring SOC content. Among these methods are indirect remote sensing and geographic information systems (GIS) techniques that are required to provide non-intrusive, low cost, and spatially continuous information that cover large areas on a repetitive basis. The main goal of this study is to evaluate the effects of using Hyperion hyperspectral data on improving the existing remote sensing and GIS-based methodologies for rapidly, efficiently, and accurately measuring SOC content on farmland. The study area is Big Creek Watershed (BCW) in Southern Illinois. The methodology consists of compiling a GIS database (consisting of remote sensing and soil variables) for 303 composite soil samples collected from representative pixels along the Hyperion coverage area of the watershed. Stepwise procedures were used to calibrate and validate linear multiple regression models where SOC was regarded as the response and the other remote sensing and soil variables as the predictors. Two models were selected. The first was the best all variables model and the second was the best only raster variables model. Map algebra was implemented to extrapolate the best only raster variables model and produce a SOC map for the BGW. This study concluded that Hyperion data marginally improved the predictability of the existing SOC statistical models based on multispectral satellite remote sensing sensors with correlation coefficient of 0.37 and root mean square error of 3.19 metric tons/hectare to a 15-cm depth. The total SOC pool of the study area is about 225,232 metric tons to 15-cm depth. The nonforested wetlands contained the highest SOC density (34.3 metric tons/hectare/15cm) with total SOC content of about 2,003.5 metric tons to 15-cm depth, where croplands had the lowest SOC density (21.6 metric tons/hectare/15cm) with total SOC content of about 44,571.2 metric tons to 15-cm depth.

Stepwise Analysis of Differential Item Functioning Based on Multiple-Group Partial Credit Model.

ERIC Educational Resources Information Center

Muraki, Eiji

1999-01-01

Extended an Item Response Theory (IRT) method for detection of differential item functioning to the partial credit model and applied the method to simulated data using a stepwise procedure. Then applied the stepwise DIF analysis based on the multiple-group partial credit model to writing trend data from the National Assessment of Educational…

Fetal alcohol effects in alcoholic veteran patients.

PubMed

Tishler, P V; Henschel, C E; Ngo, T A; Walters, E E; Worobec, T G

1998-11-01

Fetal alcohol syndrome is often associated with severe physical and neuropsychiatric maldevelopment. On the other hand, some offspring of women who drank during pregnancy appear to be affected in minimal ways and function relatively well within society. We questioned whether this effect of prenatal alcohol in the adult is generally minimal. To bear on this, we determined whether we could distinguish alcohol-exposed from nonexposed individuals in a population of male veterans, selected because of both their accepted level of function within society (e.g., honorable discharge from the military) and their admission to an alcohol treatment unit (thus, a greater likelihood of parental alcoholism, because of its familial aggregation). Consecutively admitted alcoholics (cases; n = 77) with likely maternal alcohol ingestion during their pregnancy or the first 10 years of life were matched with alcoholics with no maternal alcohol exposure during these periods (controls; n = 161). Each subject completed questionnaires regarding personal birthweight, alcohol, drug, educational and work histories, and family (including parental) alcohol and drug histories. We measured height, weight, and head circumference; checked for facial and hand anomalies; and took a frontal facial photograph, from which measurements of features were made. Data were analyzed by univariate statistics and stepwise logistic regression. No case had bona fide fetal alcohol syndrome. With univariate statistical analyses, the cases differed from the controls in 10 variables, including duration of drinking, width of alae nasae, being hyperactive or having a short attention span, and being small at birth. By stepwise logistic regression, the variables marital status, small size at birth, duration of drinking, and the presence of a smooth philtrum were marginally (the first two) or definitely (the last two) significant predictors of case status. Analysis of only the 37 cases in whom maternal prenatal drinking was the most likely yielded a marginal association for small size at birth (odds ratio = 3.1, p = 0.08) and a significant association for the presence of a smooth philtrum (odds ratio = 11.9, p = 0.005). Predictability was poor in all regression models. Based on the presence of this single physical finding (smooth philtrum), we estimate that the prevalence of manifestations of fetal alcohol exposure (fetal alcohol effects) is 6 to 13% in adult male veteran children (not necessarily nonveteran offspring) of women who drank alcohol during pregnancy. Thus, in our study of adult veterans, most individuals who were born to women who drank during pregnancy could not be differentiated from normal individuals, and those who were affected were distinguished by a single, nonspecific physical finding.

Entanglement and co-tunneling of two equivalent protons in hydrogen bond pairs

NASA Astrophysics Data System (ADS)

Smedarchina, Zorka; Siebrand, Willem; Fernández-Ramos, Antonio

2018-03-01

A theoretical study is reported of a system of two identical symmetric hydrogen bonds, weakly coupled such that the two mobile protons can move either separately (stepwise) or together (concerted). It is modeled by two equivalent quartic potentials interacting through dipolar and quadrupolar coupling terms. The tunneling Hamiltonian has two imaginary modes (reaction coordinates) and a potential with a single maximum that may turn into a saddle-point of second order and two sets of (inequivalent) minima. Diagonalization is achieved via a modified Jacobi-Davidson algorithm. From this Hamiltonian the mechanism of proton transfer is derived. To find out whether the two protons move stepwise or concerted, a new tool is introduced, based on the distribution of the probability flux in the dividing plane of the transfer mode. While stepwise transfer dominates for very weak coupling, it is found that concerted transfer (co-tunneling) always occurs, even when the coupling vanishes since the symmetry of the Hamiltonian imposes permanent entanglement on the motions of the two protons. We quantify this entanglement and show that, for a wide range of parameters of interest, the lowest pair of states of the Hamiltonian represents a perfect example of highly entangled quantum states in continuous variables. The method is applied to the molecule porphycene for which the observed tunneling splitting is calculated in satisfactory agreement with experiment, and the mechanism of double-proton tunneling is found to be predominantly concerted. We show that, under normal conditions, when they are in the ground state, the two porphycene protons are highly entangled, which may have interesting applications. The treatment also identifies the conditions under which such a system can be handled by conventional one-instanton techniques.

Aerobic Fitness Does Not Contribute to Prediction of Orthostatic Intolerance

NASA Technical Reports Server (NTRS)

Convertino, Victor A.; Sather, Tom M.; Goldwater, Danielle J.; Alford, William R.

1986-01-01

Several investigations have suggested that orthostatic tolerance may be inversely related to aerobic fitness (VO (sub 2max)). To test this hypothesis, 18 males (age 29 to 51 yr) underwent both treadmill VO(sub 2max) determination and graded lower body negative pressures (LBNP) exposure to tolerance. VO(2max) was measured during the last minute of a Bruce treadmill protocol. LBNP was terminated based on pre-syncopal symptoms and LBNP tolerance (peak LBNP) was expressed as the cumulative product of LBNP and time (torr-min). Changes in heart rate, stroke volume cardiac output, blood pressure and impedance rheographic indices of mid-thigh-leg initial accumulation were measured at rest and during the final minute of LBNP. For all 18 subjects, mean (plus or minus SE) fluid accumulation index and leg venous compliance index at peak LBNP were 139 plus or minus 3.9 plus or minus 0.4 ml-torr-min(exp -2) x 10(exp 3), respectively. Pearson product-moment correlations and step-wise linear regression were used to investigate relationships with peak LBNP. Variables associated with endurance training, such as VO(sub 2max) and percent body fat were not found to correlate significantly (P is less than 0.05) with peak LBNP and did not add sufficiently to the prediction of peak LBNP to be included in the step-wise regression model. The step-wise regression model included only fluid accumulation index leg venous compliance index, and blood volume and resulted in a squared multiple correlation coefficient of 0.978. These data do not support the hypothesis that orthostatic tolerance as measured by LBNP is lower in individuals with high aerobic fitness.

Sex discrimination potential of buccolingual and mesiodistal tooth dimensions.

PubMed

Acharya, Ashith B; Mainali, Sneedha

2008-07-01

Tooth crown dimensions are reasonably accurate predictors of sex and are useful adjuncts in sex assessment. This study explores the utility of buccolingual (BL) and mesiodistal (MD) measurements in sex differentiation when used independently. BL and MD measurements of 28 teeth (third molars excluded) were obtained from a group of 53 Nepalese subjects (22 women and 31 men) aged 19-28 years. Stepwise discriminant analyses were undertaken separately for both types of tooth crown variables and their accuracy in sex classification compared with one another. MD dimensions had recognizably greater accuracy (77.4-83%) in sex identification than BL measurements (62.3-64.2%)--results that are consistent with previous reports. However, the accuracy of MD variables is not high enough to warrant their exclusive use in odontometric sex assessment--higher accuracy levels have been obtained when both types of dimensions were used concurrently, implying that BL variables contribute to sex assessment to some extent. Hence, it is inferred that optimal results in dental sex assessment are obtained when both MD and BL variables are used together.

Episiotomy increases perineal laceration length in primiparous women.

PubMed

Nager, C W; Helliwell, J P

2001-08-01

The aim of this study was to determine the clinical factors that contribute to posterior perineal laceration length. A prospective observational study was performed in 80 consenting, mostly primiparous women with term pregnancies. Posterior perineal lacerations were measured immediately after delivery. Numerous maternal, fetal, and operator variables were evaluated against laceration length and degree of tear. Univariate and multivariate regression analyses were performed to evaluate laceration length and parametric clinical variables. Nonparametric clinical variables were evaluated against laceration length by the Mann-Whitney U test. A multivariate stepwise linear regression equation revealed that episiotomy adds nearly 3 cm to perineal lacerations. Tear length was highly associated with the degree of tear (R = 0.86, R(2) = 0.73) and the risk of recognized anal sphincter disruption. None of 35 patients without an episiotomy had a recognized anal sphincter disruption, but 6 of 27 patients with an episiotomy did (P <.001). Body mass index was the only maternal or fetal variable that showed even a slight correlation with laceration length (R = 0.30, P =.04). Episiotomy is the overriding determinant of perineal laceration length and recognized anal sphincter disruption.

Correlation between mobility assessed by the Modified Rivermead Mobility Index and physical function in stroke patients

PubMed Central

Park, Gi-Tae; Kim, Mihyun

2016-01-01

[Purpose] The purpose of this study was to investigate the relationship between mobility assessed by the Modified Rivermead Mobility Index and variables associated with physical function in stroke patients. [Subjects and Methods] One hundred stroke patients (35 males and 65 females; age 58.60 ± 13.91 years) participated in this study. Modified Rivermead Mobility Index, muscle strength (manual muscle test), muscle tone (Modified Ashworth Scale), range of motion of lower extremity, sensory function (light touch and proprioception tests), and coordination (heel to shin and lower-extremity motor coordination tests) were assessed. [Results] The Modified Rivermead Mobility Index was correlated with all the physical function variables assessed, except the degree of knee extension. In addition, stepwise linear regression analysis revealed that coordination (heel to shin test) was the explanatory variable closely associated with mobility in stroke patients. [Conclusion] The Modified Rivermead Mobility Index score was significantly correlated with all the physical function variables. Coordination (heel to shin test) was closely related to mobility function. These results may be useful in developing rehabilitation programs for stroke patients. PMID:27630440

Motivation for change as a predictor of treatment response for dysthymia.

PubMed

Frías Ibáñez, Álvaro; González Vallespí, Laura; Palma Sevillano, Carol; Farriols Hernando, Núria

2016-05-01

Dysthymia constitutes a chronic, mild affective disorder characterized by heterogeneous treatment effects. Several predictors of clinical response and attendance have been postulated, although research on the role of the psychological variables involved in this mental disorder is still scarce. Fifty-four adult patients, who met criteria for dysthymia completed an ongoing naturalistic treatment based on the brief interpersonal psychotherapy (IPT-B), which was delivered bimonthly over 16 months. As potential predictor variables, the therapeutic alliance, coping strategies, perceived self-efficacy, and motivation for change were measured at baseline. Outcome variables were response to treatment (Clinical Global Impression and Beck’s Depression Inventory) and treatment attendance. Stepwise multiple linear regression analyses revealed that higher motivation for change predicted better response to treatment. Moreover, higher motivation for change also predicted treatment attendance. Therapeutic alliance was not a predictor variable of neither clinical response nor treatment attendance. These preliminary findings support the adjunctive use of motivational interviewing (MI) techniques in the treatment of dysthymia. Further research with larger sample size and follow-up assessment is warranted.

Modeling and forecasting US presidential election using learning algorithms

NASA Astrophysics Data System (ADS)

Zolghadr, Mohammad; Niaki, Seyed Armin Akhavan; Niaki, S. T. A.

2017-09-01

The primary objective of this research is to obtain an accurate forecasting model for the US presidential election. To identify a reliable model, artificial neural networks (ANN) and support vector regression (SVR) models are compared based on some specified performance measures. Moreover, six independent variables such as GDP, unemployment rate, the president's approval rate, and others are considered in a stepwise regression to identify significant variables. The president's approval rate is identified as the most significant variable, based on which eight other variables are identified and considered in the model development. Preprocessing methods are applied to prepare the data for the learning algorithms. The proposed procedure significantly increases the accuracy of the model by 50%. The learning algorithms (ANN and SVR) proved to be superior to linear regression based on each method's calculated performance measures. The SVR model is identified as the most accurate model among the other models as this model successfully predicted the outcome of the election in the last three elections (2004, 2008, and 2012). The proposed approach significantly increases the accuracy of the forecast.

What are the important surgical factors affecting the wound healing after primary total knee arthroplasty?

PubMed

Harato, Kengo; Tanikawa, Hidenori; Morishige, Yutaro; Kaneda, Kazuya; Niki, Yasuo

2016-01-13

Wound condition after primary total knee arthroplasty (TKA) is an important issue to avoid any postoperative adverse events. Our purpose was to investigate and to clarify the important surgical factors affecting wound score after TKA. A total of 139 knees in 128 patients (mean 73 years) without severe comorbidity were enrolled in the present study. All primary unilateral or bilateral TKAs were done using the same skin incision line, measured resection technique, and wound closure technique using unidirectional barbed suture. In terms of the wound healing, Hollander Wound Evaluation Score (HWES) was assessed on postoperative day 14. We performed multiple regression analysis using stepwise method to identify the factors affecting HWES. Variables considered in the analysis were age, sex, body mass index (kg/m(2)), HbA1C (%), femorotibial angle (degrees) on plain radiographs, intraoperative patella eversion during the cutting phase of the femur and the tibia in knee flexion, intraoperative anterior translation of the tibia, patella resurfacing, surgical time (min), tourniquet time (min), length of skin incision (cm), postoperative drainage (ml), patellar height on postoperative lateral radiographs, and HWES. HWES was treated as a dependent variable, and others were as independent variables. The average HWES was 5.0 ± 0.8 point. According to stepwise forward regression test, patella eversion during the cutting phase of the femur and the tibia in knee flexion and anterior translation of the tibia were entered in this model, while other factors were not entered. Standardized partial regression coefficient was as follows: 0.57 in anterior translation of the tibia and 0.38 in patella eversion. Fortunately, in the present study using the unidirectional barbed suture, major wound healing problem did not occur. As to the surgical technique, intraoperative patella eversion and anterior translation of the tibia should be avoided for quality cosmesis in primary TKA.

Time-series Oxygen-18 Precipitation Isoscapes for Canada and the Northern United States

NASA Astrophysics Data System (ADS)

Delavau, Carly J.; Chun, Kwok P.; Stadnyk, Tricia A.; Birks, S. Jean; Welker, Jeffrey M.

2014-05-01

The present and past hydrological cycle from the watershed to regional scale can be greatly enhanced using water isotopes (δ18O and δ2H), displayed today as isoscapes. The development of water isoscapes has both hydrological and ecological applications, such as ground water recharge and food web ecology, and can provide critical information when observations are not available due to spatial and temporal gaps in sampling and data networks. This study focuses on the creation of δ18O precipitation (δ18Oppt) isoscapes at a monthly temporal frequency across Canada and the northern United States (US) utilizing CNIP (Canadian Network for Isotopes in Precipitation) and USNIP (United States Network for Isotopes in Precipitation) measurements. Multiple linear stepwise regressions of CNIP and USNIP observations alongside NARR (North American Regional Reanalysis) climatological variables, teleconnection indices, and geographic indicators are utilized to create empirical models that predict the δ18O of monthly precipitation across Canada and the northern US. Pooling information from nearby locations within a region can be useful due to the similarity of processes and mechanisms controlling the variability of δ18O. We expect similarity in the controls on isotopic composition to strengthen the correlation between δ18Oppt and predictor variables, resulting in model simulation improvements. For this reason, three different regionalization approaches are used to separate the study domain into 'isotope zones' to explore the effect of regionalization on model performance. This methodology results in 15 empirical models, five within each regionalization. A split sample calibration and validation approach is employed for model development, and parameter selection is based on demonstrated improvement of the Akaike Information Criteria (AIC). Simulation results indicate the empirical models are generally able to capture the overall monthly variability in δ18Oppt. For the three regionalizations, average adjusted-R2 and RMSE (weighted to number of observations within each isotope zone) range from 0.70 - 0.72 and 2.76 - 2.91, respectively, indicating that on average the different spatial groupings perform comparably. Validation weighted R2and RMSE show a larger spread between models and poorer performance, ranging from 0.45 - 0.59 and 3.28 - 3.39, respectively. Additional evaluation of simulated δ18Oppt at each station and inter/intra-annually is conducted to evaluate model performance over various space and time scales. Stepwise regression derived parameterizations indicate the significance of precipitable water content and latitude as predictor variables for all regionalizations. Long-term (1981-2010) annual average δ18Oppt isoscapes are produced for Canada and the northern US, highlighting the differences between regionalization approaches. 95% confidence interval maps are generated to provide an estimate of the uncertainty associated with long-term δ18Oppt simulations. This is the first ever time-series empirical modelling of δ18Oppt for Canada utilizing CNIP data, as well as the first modelling collaboration between the CNIP and USNIP networks. This study is the initial step towards empirically derived time-series δ18Oppt for use in iso-hydrological modelling studies. Methods and results from this research are equally applicable to ecology and forensics as the simulated δ18Oppt isoscapes provide the primary oxygen source for many plants and foodwebs at refined temporal and spatial scales across Canada and the northern US.

Brucine diol-copper-catalyzed asymmetric synthesis of endo-pyrrolidines: the mechanistic dichotomy of imino esters.

PubMed

Li, Jian-Yuan; Kim, Hun Young; Oh, Kyungsoo

2015-03-06

Enantio- and diastereodivergent approaches to pyrrolidines are described by using catalyst- and substrate-controlled reaction pathways. A concerted endo-selective [3 + 2]-cycloaddition pathway is developed for the reaction of methyl imino ester, whereas endo-pyrrolidines with an opposite absolute stereochemical outcome are prepared by using the stepwise reaction pathway of tert-butyl imino ester. The development of catalyst- and substrate-controlled stereodivergent approaches highlights the inherent substrate-catalyst interactions in the [3 + 2]-cycloaddition reactions of metalated azomethine ylides.

A simple randomisation procedure for validating discriminant analysis: a methodological note.

PubMed

Wastell, D G

1987-04-01

Because the goal of discriminant analysis (DA) is to optimise classification, it designedly exaggerates between-group differences. This bias complicates validation of DA. Jack-knifing has been used for validation but is inappropriate when stepwise selection (SWDA) is employed. A simple randomisation test is presented which is shown to give correct decisions for SWDA. The general superiority of randomisation tests over orthodox significance tests is discussed. Current work on non-parametric methods of estimating the error rates of prediction rules is briefly reviewed.

The ground state of the Frenkel-Kontorova model

NASA Astrophysics Data System (ADS)

Babushkin, A. Yu.; Abkaryan, A. K.; Dobronets, B. S.; Krasikov, V. S.; Filonov, A. N.

2016-09-01

The continual approximation of the ground state of the discrete Frenkel-Kontorova model is tested using a symmetric algorithm of numerical simulation. A "kaleidoscope effect" is found, which means that the curves representing the dependences of the relative extension of an N-atom chain vary periodically with increasing N. Stairs of structural transitions for N ≫ 1 are analyzed by the channel selection method with the approximation N = ∞. Images of commensurable and incommensurable structures are constructed. The commensurable-incommensurable phase transitions are stepwise.

Use of a pretest strategy for physical therapist assistant programs to predict success rate on the national physical therapy exam.

PubMed

Sloas, Stacey B; Keith, Becky; Whitehead, Malcolm T

2013-01-01

This study investigated a pretest strategy that identified physical therapist assistant (PTA) students who were at risk of failure on the National Physical Therapy Examination (NPTE). Program assessment data from five cohorts of PTA students (2005-2009) were used to develop a stepwise multiple regression formula that predicted first-time NPTE licensure scores. Data used included the Nelson-Denny Reading Test, grades from eight core courses, grade point average upon admission to the program, and scores from three mock NPTE exams given during the program. Pearson correlation coefficients were calculated between each of the 15 variables and NPTE scores. Stepwise multiple regression analysis was performed using data collected at the ends of the first, second, and third (final) semesters of the program. Data from the class of 2010 were then used to validate the formula. The end-of-program formula accounted for the greatest variance (57%) in predicted scores. Those students scoring below a predicted scaled score of 620 were identified to be at risk of failure of the licensure exam. These students were counseled, and a remedial plan was developed based on regression predictions prior to them sitting for the licensure exam.

Impact of “Sick” and “Recovery” Roles on Brain Injury Rehabilitation Outcomes

PubMed Central

Barclay, David A.

2012-01-01

This study utilizes a multivariate, correlational, expost facto research design to examine Parsons' “sick role” as a dynamic, time-sensitive process of “sick role” and “recovery role” and the impact of this process on goal attainment (H1) and psychosocial distress (H2) of adult survivors of acquired brain injury. Measures used include the Brief Symptom Inventory-18, a Goal Attainment Scale, and an original instrument to measure sick role process. 60 survivors of ABI enrolled in community reentry rehabilitation participated. Stepwise regression analyses did not fully support the multivariate hypotheses. Two models emerged from the stepwise analyses. Goal attainment, gender, and postrehab responsibilities accounted for 40% of the shared variance of psychosocial distress. Anxiety and depression accounted for 22% of the shared variance of goal attainment with anxiety contributing to the majority of the explained variance. Bivariate analysis found sick role variables, anxiety, somatization, depression, gender, and goal attainment as significant. The study has implications for ABI rehabilitation in placing greater emphasis on sick role processes, anxiety, gender, and goal attainment in guiding program planning and future research with survivors of ABI. PMID:23119164

Satisfaction among early and mid-career dentists in a metropolitan dental hospital in China

PubMed Central

Cui, Xiaoxi; Dunning, David G; An, Na

2017-01-01

A growing body of research has examined career satisfaction among dentists using a standardized instrument, dentist satisfaction survey (DSS). This project examined career satisfaction of early to mid-career dentists in China, a population whose career satisfaction, heretofore, has not been studied. This is an especially critical time to examine career satisfaction because of health care reform measures being implemented in China. A culturally sensitive Chinese-language version of the DSS (CDSS) was developed and electronically administered to 367 early and mid-career dentists in a tertiary dental hospital in Beijing, China. One hundred and seventy respondents completed the survey. The average total career score was 123, with a range of 82–157. Data analysis showed some significant differences in total career score and several subscales based on gender, working hours per week, and years in practice. A stepwise regression model revealed that two variables predicted total career score: working hours per week and gender. Stepwise regression also demonstrated that four subscales significantly predicted the overall professional satisfaction subscale score: respect, delivery of care, income and patient relations. Implications of these results are discussed in light of the health care delivery system and dentist career paths in China. PMID:29355243

Satisfaction among early and mid-career dentists in a metropolitan dental hospital in China.

PubMed

Cui, Xiaoxi; Dunning, David G; An, Na

2017-01-01

A growing body of research has examined career satisfaction among dentists using a standardized instrument, dentist satisfaction survey (DSS). This project examined career satisfaction of early to mid-career dentists in China, a population whose career satisfaction, heretofore, has not been studied. This is an especially critical time to examine career satisfaction because of health care reform measures being implemented in China. A culturally sensitive Chinese-language version of the DSS (CDSS) was developed and electronically administered to 367 early and mid-career dentists in a tertiary dental hospital in Beijing, China. One hundred and seventy respondents completed the survey. The average total career score was 123, with a range of 82-157. Data analysis showed some significant differences in total career score and several subscales based on gender, working hours per week, and years in practice. A stepwise regression model revealed that two variables predicted total career score: working hours per week and gender. Stepwise regression also demonstrated that four subscales significantly predicted the overall professional satisfaction subscale score: respect, delivery of care, income and patient relations. Implications of these results are discussed in light of the health care delivery system and dentist career paths in China.

Lungworm Infections in German Dairy Cattle Herds — Seroprevalence and GIS-Supported Risk Factor Analysis

PubMed Central

Schunn, Anne-Marie; Conraths, Franz J.; Staubach, Christoph; Fröhlich, Andreas; Forbes, Andrew; Strube, Christina

2013-01-01

In November 2008, a total of 19,910 bulk tank milk (BTM) samples were obtained from dairy farms from all over Germany, corresponding to about 20% of all German dairy herds, and analysed for antibodies against the bovine lungworm Dictyocaulus viviparus by use of the recombinant MSP-ELISA. A total number of 3,397 (17.1%; n = 19,910) BTM samples tested seropositive. The prevalences in individual German federal states varied between 0.0% and 31.2% positive herds. A geospatial map was drawn to show the distribution of seropositive and seronegative herds per postal code area. ELISA results were further analysed for associations with land-use and climate data. Bivariate statistical analysis was used to identify potential spatial risk factors for dictyocaulosis. Statistically significant positive associations were found between lungworm seropositive herds and the proportion of water bodies and grassed area per postal code area. Variables that showed a statistically significant association with a positive BTM test were included in a logistic regression model, which was further refined by controlled stepwise selection of variables. The low Pseudo R2 values (0.08 for the full model and 0.06 for the final model) and further evaluation of the model by ROC analysis indicate that additional, unrecorded factors (e.g. management factors) or random effects may substantially contribute to lungworm infections in dairy cows. Veterinarians should include lungworms in the differential diagnosis of respiratory disease in dairy cattle, particularly those at pasture. Monitoring of herds through BTM screening for antibodies can help farmers and veterinarians plan and implement appropriate control measures. PMID:24040243

Dietary relationships among shrumsteppe passerine birds: competition or opportunism in a variable environment

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

Rotenberry, J.T.

1980-03-01

The suggestion that in less stable environments resource limitation and subsequent interspecific competition may be relatively unimportant in determining bird community structure is explored by examining the dietary relationships within a guild of three ground-foraging passerine birds (Horned Lark, Sage Sparrow, and Western Meadowlark) in the shrubsteppe of southeastern Washington, USA, an area of severe, arid, unstable climate. General dietary analyses indicated a strong temporal component to the organization of bird diets: different species collected at the same time ate much the same things while the same species collected at different times ate different things. This pattern is reinforced bymore » cluster analysis and stepwise discriminant analysis. Similarities in diet extended to other components as well. Dietary diversities tended to be the same for contemporaneous collections of birds, as did averge prey sizes, although the latter evidenced a few statistically significant exceptions. Theoretically predicted relationships between diet and trophic structure morphology emerged only at the most general level, and even then were not always observed. In general, differences in body size or bill length were insufficient to account for variations in prey sizes, although meadowlarks did on occasion take significantly larger items than the other, smaller species. Average prey size was significantly correlated with the proportion of seeds in the diet and varied seasonally as seed consumption varied. Several aspects of this study indicate that shrubsteppe passerines are largely opportunistic in their foraging and diet selection, and that the apparent absence of fine tuning to their competitive milieu is most likely a function of the variable environment in which they coexist.« less

Prediction of polycystic ovarian syndrome based on ultrasound findings and clinical parameters.

PubMed

Moschos, Elysia; Twickler, Diane M

2015-03-01

To determine the accuracy of sonographic-diagnosed polycystic ovaries and clinical parameters in predicting polycystic ovarian syndrome. Medical records and ultrasounds of 151 women with sonographically diagnosed polycystic ovaries were reviewed. Sonographic criteria for polycystic ovaries were based on 2003 Rotterdam European Society of Human Reproduction and Embryology/American Society for Reproductive Medicine guidelines: at least one ovary with 12 or more follicles measuring 2-9 mm and/or increased ovarian volume >10 cm(3) . Clinical variables of age, gravidity, ethnicity, body mass index, and sonographic indication were collected. One hundred thirty-five patients had final outcomes (presence/absence of polycystic ovarian syndrome). Polycystic ovarian syndrome was diagnosed if a patient had at least one other of the following two criteria: oligo/chronic anovulation and/or clinical/biochemical hyperandrogenism. A logistic regression model was constructed using stepwise selection to identify variables significantly associated with polycystic ovarian syndrome (p < .05). The validity of the model was assessed using receiver operating characteristics and Hosmer-Lemeshow χ(2) analyses. One hundred twenty-eight patients met official sonographic criteria for polycystic ovaries and 115 (89.8%) had polycystic ovarian syndrome (p = .009). Lower gravidity, abnormal bleeding, and body mass index >33 were significant in predicting polycystic ovarian syndrome (receiver operating characteristics curve, c = 0.86). Pain decreased the likelihood of polycystic ovarian syndrome. Polycystic ovaries on ultrasound were sensitive in predicting polycystic ovarian syndrome. Ultrasound, combined with clinical parameters, can be used to generate a predictive index for polycystic ovarian syndrome. © 2014 Wiley Periodicals, Inc.

Temporal Associations between Weather and Headache: Analysis by Empirical Mode Decomposition

PubMed Central

Yang, Albert C.; Fuh, Jong-Ling; Huang, Norden E.; Shia, Ben-Chang; Peng, Chung-Kang; Wang, Shuu-Jiun

2011-01-01

Background Patients frequently report that weather changes trigger headache or worsen existing headache symptoms. Recently, the method of empirical mode decomposition (EMD) has been used to delineate temporal relationships in certain diseases, and we applied this technique to identify intrinsic weather components associated with headache incidence data derived from a large-scale epidemiological survey of headache in the Greater Taipei area. Methodology/Principal Findings The study sample consisted of 52 randomly selected headache patients. The weather time-series parameters were detrended by the EMD method into a set of embedded oscillatory components, i.e. intrinsic mode functions (IMFs). Multiple linear regression models with forward stepwise methods were used to analyze the temporal associations between weather and headaches. We found no associations between the raw time series of weather variables and headache incidence. For decomposed intrinsic weather IMFs, temperature, sunshine duration, humidity, pressure, and maximal wind speed were associated with headache incidence during the cold period, whereas only maximal wind speed was associated during the warm period. In analyses examining all significant weather variables, IMFs derived from temperature and sunshine duration data accounted for up to 33.3% of the variance in headache incidence during the cold period. The association of headache incidence and weather IMFs in the cold period coincided with the cold fronts. Conclusions/Significance Using EMD analysis, we found a significant association between headache and intrinsic weather components, which was not detected by direct comparisons of raw weather data. Contributing weather parameters may vary in different geographic regions and different seasons. PMID:21297940

Quantitative structure-retention relationships of polycyclic aromatic hydrocarbons gas-chromatographic retention indices.

PubMed

Drosos, Juan Carlos; Viola-Rhenals, Maricela; Vivas-Reyes, Ricardo

2010-06-25

Polycyclic aromatic compounds (PAHs) are of concern in environmental chemistry and toxicology. In the present work, a QSRR study was performed for 209 previously reported PAHs using quantum mechanics and other sources descriptors estimated by different approaches. The B3LYP/6-31G* level of theory was used for geometrical optimization and quantum mechanics related variables. A good linear relationship between gas-chromatographic retention index and electronic or topologic descriptors was found by stepwise linear regression analysis. The molecular polarizability (alpha) and the second order molecular connectivity Kier and Hall index ((2)chi) showed evidence of significant correlation with retention index by means of important squared coefficient of determination, (R(2)), values (R(2)=0.950 and 0.962, respectively). A one variable QSRR model is presented for each descriptor and both models demonstrates a significant predictive capacity established using the leave-many-out LMO (excluding 25% of rows) cross validation method's q(2) cross-validation coefficients q(2)(CV-LMO25%), (obtained q(2)(CV-LMO25%) 0.947 and 0.960, respectively). Furthermore, the physicochemical interpretation of selected descriptors allowed detailed explanation of the source of the observed statistical correlation. The model analysis suggests that only one descriptor is sufficient to establish a consistent retention index-structure relationship. Moderate or non-significant improve was observed for quantitative results or statistical validation parameters when introducing more terms in predictive equation. The one parameter QSRR proposed model offers a consistent scheme to predict chromatographic properties of PAHs compounds. Copyright 2010 Elsevier B.V. All rights reserved.

A novel fibrosis index comprising a non-cholesterol sterol accurately predicts HCV-related liver cirrhosis.

PubMed

Ydreborg, Magdalena; Lisovskaja, Vera; Lagging, Martin; Brehm Christensen, Peer; Langeland, Nina; Buhl, Mads Rauning; Pedersen, Court; Mørch, Kristine; Wejstål, Rune; Norkrans, Gunnar; Lindh, Magnus; Färkkilä, Martti; Westin, Johan

2014-01-01

Diagnosis of liver cirrhosis is essential in the management of chronic hepatitis C virus (HCV) infection. Liver biopsy is invasive and thus entails a risk of complications as well as a potential risk of sampling error. Therefore, non-invasive diagnostic tools are preferential. The aim of the present study was to create a model for accurate prediction of liver cirrhosis based on patient characteristics and biomarkers of liver fibrosis, including a panel of non-cholesterol sterols reflecting cholesterol synthesis and absorption and secretion. We evaluated variables with potential predictive significance for liver fibrosis in 278 patients originally included in a multicenter phase III treatment trial for chronic HCV infection. A stepwise multivariate logistic model selection was performed with liver cirrhosis, defined as Ishak fibrosis stage 5-6, as the outcome variable. A new index, referred to as Nordic Liver Index (NoLI) in the paper, was based on the model: Log-odds (predicting cirrhosis) = -12.17+ (age × 0.11) + (BMI (kg/m(2)) × 0.23) + (D7-lathosterol (μg/100 mg cholesterol)×(-0.013)) + (Platelet count (x10(9)/L) × (-0.018)) + (Prothrombin-INR × 3.69). The area under the ROC curve (AUROC) for prediction of cirrhosis was 0.91 (95% CI 0.86-0.96). The index was validated in a separate cohort of 83 patients and the AUROC for this cohort was similar (0.90; 95% CI: 0.82-0.98). In conclusion, the new index may complement other methods in diagnosing cirrhosis in patients with chronic HCV infection.

Desmoid-type fibromatosis: a front-line conservative approach to select patients for surgical treatment.

PubMed

Fiore, Marco; Rimareix, Françoise; Mariani, Luigi; Domont, Julien; Collini, Paola; Le Péchoux, Cecile; Casali, Paolo G; Le Cesne, Axel; Gronchi, Alessandro; Bonvalot, Sylvie

2009-09-01

Surgery is still the standard treatment for desmoid-type fibromatosis (DF). Recently, the Institut Gustave Roussy (IGR), Villejuif, France, reported a series of patients treated with a front-line conservative approach (no surgery and no radiotherapy). The disease remained stable in more than half of patients. This study was designed to evaluate this approach on the natural history of the disease in a larger series of patients. A total of 142 patients presenting to the IGR or Istituto Nazionale Tumori (INT), Milan, Italy, were initially treated using a front-line deliberately conservative policy. Their progression-free survival (PFS) was observed and a multivariate analysis was performed for major clinical variables. Seventy-four patients presented with primary tumor, 68 with recurrence. Eighty-three patients received a "wait & see" policy (W&S), whereas 59 were initially offered medical therapy (MT), mainly hormonal therapy and chemotherapy. A family history of sporadic colorectal cancer was present in 8% of patients. The 5-year PFS was 49.9% for the W&S group and 58.6% for the medically treated patients (P = 0.3196). Similar results emerged for primary and recurrent DF. Multivariate analysis identified no clinical variables as independent predictors of PFS. In the event of progression, all patients were subsequently managed safely. A conservative policy could be a safe approach to primary and recurrent DF, which could avoid unnecessary morbidity from surgery and/or radiation therapy. Half of patients had medium-term stable disease after W&S or MT. A multidisciplinary, stepwise approach should be prospectively tested in DF.

Strategic responses to intimate partner violence against women in Spain: a national study in primary care.

PubMed

Montero, Isabel; Ruiz-Pérez, Isabel; Escribà-Agüir, Vicenta; Vives-Cases, Carmen; Plazaola-Castaño, Juncal; Talavera, Marta; Martín-Baena, David; Peiró, Rosana

2012-04-01

Research on women''s responses to intimate partner violence (IPV) has largely been limited to women who have been exposed to severe physical violence with scarce generalisation. This study aimed to analyse how Spanish abused women from different backgrounds and with different IPV characteristics respond to violence. Women experiencing IPV before the previous year (1469) were selected from a large cross-sectional national survey of adult women recruited during 2006-7 among female patients seeking medical care for whatever reason in primary healthcare services. The outcome variables were women's responses to IPV and the predictor variables were personal and social resources profiles and characteristics of the abuse (type, duration and women's age at onset). Stepwise logistic regression models were fitted. 87.5% of abused women took some kind of action to overcome IPV. Significant differences on personal and social profile and type and duration of the abuse were detected between the three strategic responses: distancing, in process and inhibition. The probability of a woman responding with a distancing strategy (seeking outside help or leaving temporarily) is almost three times greater if she is employed, was young when the abuse began, had experienced physical and psychological abuse and when the abuse was under 5 years. The results of this study show that personal and social resources and the specific circumstances of the abuse should be taken into account to understand women's responses to IPV. Well-validated interventions targeted at abused women's needs and the circumstances of IPV remain a priority.

Quantitative structure-activation barrier relationship modeling for Diels-Alder ligations utilizing quantum chemical structural descriptors.

PubMed

Nandi, Sisir; Monesi, Alessandro; Drgan, Viktor; Merzel, Franci; Novič, Marjana

2013-10-30

In the present study, we show the correlation of quantum chemical structural descriptors with the activation barriers of the Diels-Alder ligations. A set of 72 non-catalysed Diels-Alder reactions were subjected to quantitative structure-activation barrier relationship (QSABR) under the framework of theoretical quantum chemical descriptors calculated solely from the structures of diene and dienophile reactants. Experimental activation barrier data were obtained from literature. Descriptors were computed using Hartree-Fock theory using 6-31G(d) basis set as implemented in Gaussian 09 software. Variable selection and model development were carried out by stepwise multiple linear regression methodology. Predictive performance of the quantitative structure-activation barrier relationship (QSABR) model was assessed by training and test set concept and by calculating leave-one-out cross-validated Q2 and predictive R2 values. The QSABR model can explain and predict 86.5% and 80% of the variances, respectively, in the activation energy barrier training data. Alternatively, a neural network model based on back propagation of errors was developed to assess the nonlinearity of the sought correlations between theoretical descriptors and experimental reaction barriers. A reasonable predictability for the activation barrier of the test set reactions was obtained, which enabled an exploration and interpretation of the significant variables responsible for Diels-Alder interaction between dienes and dienophiles. Thus, studies in the direction of QSABR modelling that provide efficient and fast prediction of activation barriers of the Diels-Alder reactions turn out to be a meaningful alternative to transition state theory based computation.

The association between insured male expatriates' knowledge of health insurance benefits and lack of access to health care in Saudi Arabia.

PubMed

Alkhamis, Abdulwahab A

2018-03-15

Insufficient knowledge of health insurance benefits could be associated with lack of access to health care, particularly for minority populations. This study aims to assess the association between expatriates' knowledge of health insurance benefits and lack of access to health care. A cross-sectional study design was conducted from March 2015 to February 2016 among 3398 insured male expatriates in Riyadh, Saudi Arabia. The dependent variable was binary and expresses access or lack of access to health care. Independent variables included perceived and validated knowledge of health insurance benefits and other variables. Data were summarized by computing frequencies and percentage of all quantities of variables. To evaluate variations in knowledge, personal and job characteristics with lack of access to health care, the Chi square test was used. Odds ratio (OR) and 95% confidence interval (CI) were recorded for each independent variable. Multiple logistic regression and stepwise logistic regression were performed and adjusted ORs were extracted. Descriptive analysis showed that 15% of participants lacked access to health care. The majority of these were unskilled laborers, usually with no education (17.5%), who had been working for less than 3 years (28.1%) in Saudi Arabia. A total of 23.3% worked for companies with less than 50 employees and 16.5% earned less than 4500 Saudi Riyals monthly ($1200). Many (20.3%) were young (< 30 years old) or older (17.9% ≥ 56 years old) and had no formal education (24.7%). Nearly half had fair or poor health status (49.5%), were uncomfortable conversing in Arabic (29.7%) or English (16.7%) and lacked previous knowledge of health insurance (18%). For perceived knowledge of health insurance, 55.2% scored 1 or 0 from total of 3. For validated knowledge, 16.9% scored 1 or 0 from total score of 4. Multiple logistic regression analysis showed that only perceived knowledge of health insurance had significant associations with lack of access to health care ((OR) = 0.393, (CI) = 0.335-0.461), but the result was insignificant for validated knowledge. Stepwise logistic regression gave similar findings. Our results confirmed that low perceived knowledge of health insurance in expatriates was associated with less access to health care.

Development of a food frequency questionnaire for Sri Lankan adults

PubMed Central

2012-01-01

Background Food Frequency Questionnaires (FFQs) are commonly used in epidemiologic studies to assess long-term nutritional exposure. Because of wide variations in dietary habits in different countries, a FFQ must be developed to suit the specific population. Sri Lanka is undergoing nutritional transition and diet-related chronic diseases are emerging as an important health problem. Currently, no FFQ has been developed for Sri Lankan adults. In this study, we developed a FFQ to assess the regular dietary intake of Sri Lankan adults. Methods A nationally representative sample of 600 adults was selected by a multi-stage random cluster sampling technique and dietary intake was assessed by random 24-h dietary recall. Nutrient analysis of the FFQ required the selection of foods, development of recipes and application of these to cooked foods to develop a nutrient database. We constructed a comprehensive food list with the units of measurement. A stepwise regression method was used to identify foods contributing to a cumulative 90% of variance to total energy and macronutrients. In addition, a series of photographs were included. Results We obtained dietary data from 482 participants and 312 different food items were recorded. Nutritionists grouped similar food items which resulted in a total of 178 items. After performing step-wise multiple regression, 93 foods explained 90% of the variance for total energy intake, carbohydrates, protein, total fat and dietary fibre. Finally, 90 food items and 12 photographs were selected. Conclusion We developed a FFQ and the related nutrient composition database for Sri Lankan adults. Culturally specific dietary tools are central to capturing the role of diet in risk for chronic disease in Sri Lanka. The next step will involve the verification of FFQ reproducibility and validity. PMID:22937734

Is there a step-wise migration in Nigeria? A case study of the migrational histories of migrants in Lagos.

PubMed

Afolayan, A A

1985-09-01

"The paper sets out to test whether or not the movement pattern of people in Nigeria is step-wise. It examines the spatial order in the country and the movement pattern of people. It then analyzes the survey data and tests for the validity of step-wise migration in the country. The findings show that step-wise migration cannot adequately describe all the patterns observed." The presence of large-scale circulatory migration between rural and urban areas is noted. Ways to decrease the pressure on Lagos by developing intermediate urban areas are considered. excerpt

Estimation of Sensory Pork Loin Tenderness Using Warner-Bratzler Shear Force and Texture Profile Analysis Measurements

PubMed Central

Choe, Jee-Hwan; Choi, Mi-Hee; Rhee, Min-Suk; Kim, Byoung-Chul

2016-01-01

This study investigated the degree to which instrumental measurements explain the variation in pork loin tenderness as assessed by the sensory evaluation of trained panelists. Warner-Bratzler shear force (WBS) had a significant relationship with the sensory tenderness variables, such as softness, initial tenderness, chewiness, and rate of breakdown. In a regression analysis, WBS could account variations in these sensory variables, though only to a limited proportion of variation. On the other hand, three parameters from texture profile analysis (TPA)—hardness, gumminess, and chewiness—were significantly correlated with all sensory evaluation variables. In particular, from the result of stepwise regression analysis, TPA hardness alone explained over 15% of variation in all sensory evaluation variables, with the exception of perceptible residue. Based on these results, TPA analysis was found to be better than WBS measurement, with the TPA parameter hardness likely to prove particularly useful, in terms of predicting pork loin tenderness as rated by trained panelists. However, sensory evaluation should be conducted to investigate practical pork tenderness perceived by consumer, because both instrumental measurements could explain only a small portion (less than 20%) of the variability in sensory evaluation. PMID:26954174

Estimation of Sensory Pork Loin Tenderness Using Warner-Bratzler Shear Force and Texture Profile Analysis Measurements.

PubMed

Choe, Jee-Hwan; Choi, Mi-Hee; Rhee, Min-Suk; Kim, Byoung-Chul

2016-07-01

This study investigated the degree to which instrumental measurements explain the variation in pork loin tenderness as assessed by the sensory evaluation of trained panelists. Warner-Bratzler shear force (WBS) had a significant relationship with the sensory tenderness variables, such as softness, initial tenderness, chewiness, and rate of breakdown. In a regression analysis, WBS could account variations in these sensory variables, though only to a limited proportion of variation. On the other hand, three parameters from texture profile analysis (TPA)-hardness, gumminess, and chewiness-were significantly correlated with all sensory evaluation variables. In particular, from the result of stepwise regression analysis, TPA hardness alone explained over 15% of variation in all sensory evaluation variables, with the exception of perceptible residue. Based on these results, TPA analysis was found to be better than WBS measurement, with the TPA parameter hardness likely to prove particularly useful, in terms of predicting pork loin tenderness as rated by trained panelists. However, sensory evaluation should be conducted to investigate practical pork tenderness perceived by consumer, because both instrumental measurements could explain only a small portion (less than 20%) of the variability in sensory evaluation.

An open-source software package for multivariate modeling and clustering: applications to air quality management.

PubMed

Wang, Xiuquan; Huang, Guohe; Zhao, Shan; Guo, Junhong

2015-09-01

This paper presents an open-source software package, rSCA, which is developed based upon a stepwise cluster analysis method and serves as a statistical tool for modeling the relationships between multiple dependent and independent variables. The rSCA package is efficient in dealing with both continuous and discrete variables, as well as nonlinear relationships between the variables. It divides the sample sets of dependent variables into different subsets (or subclusters) through a series of cutting and merging operations based upon the theory of multivariate analysis of variance (MANOVA). The modeling results are given by a cluster tree, which includes both intermediate and leaf subclusters as well as the flow paths from the root of the tree to each leaf subcluster specified by a series of cutting and merging actions. The rSCA package is a handy and easy-to-use tool and is freely available at http://cran.r-project.org/package=rSCA . By applying the developed package to air quality management in an urban environment, we demonstrate its effectiveness in dealing with the complicated relationships among multiple variables in real-world problems.

Environmental strategies: A case study of systematic evaluation

NASA Astrophysics Data System (ADS)

Sherman, Douglas J.; Garès, Paul A.

1982-09-01

A major problem facing environmental managers is the necessity to effectively evaluate management alternatives. Traditional environmental assessments have emphasized the use of economic analyses. These approaches are often deficient due to difficulty in assigning dollar values to environmental systems and to social amenities. A more flexible decisionmaking model has been developed to analyze management options for coping with beach erosion problems at the Sandy Hook Unit of Gateway National Recreation Area in New Jersey. This model is comprised of decision-making variables which are formulated from a combination of environmental and management criteria, and it has an accept-reject format in which the management options are analyzed in terms of the variables. Through logical ordering of the insertion of the variables into the model, stepwise elimination of alternatives is possible. A hierarchy of variables is determined through estimating work required to complete an assessment of the alternatives for each variable. The assessment requiring the least work is performed first so that the more difficult evaluation will be limited to fewer alternatives. The application of this approach is illustrated with a case study in which beach protection alternatives were evaluated for the United States National Park Service.

Decreasing spatial variability in precipitation extremes in southwestern China and the local/large-scale influencing factors

NASA Astrophysics Data System (ADS)

Liu, Meixian; Xu, Xianli; Sun, Alex

2015-07-01

Climate extremes can cause devastating damage to human society and ecosystems. Recent studies have drawn many conclusions about trends in climate extremes, but few have focused on quantitative analysis of their spatial variability and underlying mechanisms. By using the techniques of overlapping moving windows, the Mann-Kendall trend test, correlation, and stepwise regression, this study examined the spatial-temporal variation of precipitation extremes and investigated the potential key factors influencing this variation in southwestern (SW) China, a globally important biodiversity hot spot and climate-sensitive region. Results showed that the changing trends of precipitation extremes were not spatially uniform, but the spatial variability of these precipitation extremes decreased from 1959 to 2012. Further analysis found that atmospheric circulations rather than local factors (land cover, topographic conditions, etc.) were the main cause of such precipitation extremes. This study suggests that droughts or floods may become more homogenously widespread throughout SW China. Hence, region-wide assessments and coordination are needed to help mitigate the economic and ecological impacts.

Adolescent suicidal ideation.

PubMed

Field, T; Diego, M; Sanders, C E

2001-01-01

Adolescent suicidal ideation and its relationship to other variables was tapped by a self-report questionnaire administered to 88 high school seniors. Eighteen percent responded positively to the statement "sometimes I feel suicidal." Those who reported suicidal ideation were found to differ from those who did not on a number of variables, including family relationships (quality of relationship with mother, intimacy with parents, and closeness to siblings), family history of depression (maternal depression), peer relations (quality of peer relationships, popularity, and number of friends), emotional well-being (happiness, anger, and depression), drug use (cigarettes, marijuana, and cocaine), and grade point average. Stepwise regression indicated that happiness explained 46% of the variance in suicidal ideation, and number of friends, anger, and marijuana use explained an additional 20%, for a total of 66% of the variance. While 34% of the variance remained unexplained, it is suggested that the questions used to measure these four variables be included in global screenings to identify adolescents at risk for suicidal ideation.

The effect of structural empowerment and organizational commitment on Chinese nurses' job satisfaction.

PubMed

Yang, Jinhua; Liu, Yanhui; Chen, Yan; Pan, Xiaoyan

2014-08-01

The purposes of this study were (1) to examine the level of structural empowerment, organizational commitment and job satisfaction in Chinese nurses; and (2) to investigate the relationships among the three variables. A high turnover rate was identified in Chinese staff nurses, and it was highly correlated with lower job satisfaction. Structural empowerment and organizational commitment have been positively related to job satisfaction in western countries. A cross-sectional survey design was employed. Data analysis included descriptive statistics and multiple step-wise regression to test the hypothesized model. Moderate levels of the three variables were found in this study. Both empowerment and commitment were found to be significantly associated with job satisfaction (r=0.722, r=0.693, p<0.01, respectively). The variables of work objectives, resources, support and informal power, normative and ideal commitment were significant predictors of job satisfaction. Support for an expanded model of Kanter's structural empowerment was achieved in this study. Copyright © 2014 Elsevier Inc. All rights reserved.

Blind prediction of noncanonical RNA structure at atomic accuracy.

PubMed

Watkins, Andrew M; Geniesse, Caleb; Kladwang, Wipapat; Zakrevsky, Paul; Jaeger, Luc; Das, Rhiju

2018-05-01

Prediction of RNA structure from nucleotide sequence remains an unsolved grand challenge of biochemistry and requires distinct concepts from protein structure prediction. Despite extensive algorithmic development in recent years, modeling of noncanonical base pairs of new RNA structural motifs has not been achieved in blind challenges. We report a stepwise Monte Carlo (SWM) method with a unique add-and-delete move set that enables predictions of noncanonical base pairs of complex RNA structures. A benchmark of 82 diverse motifs establishes the method's general ability to recover noncanonical pairs ab initio, including multistrand motifs that have been refractory to prior approaches. In a blind challenge, SWM models predicted nucleotide-resolution chemical mapping and compensatory mutagenesis experiments for three in vitro selected tetraloop/receptors with previously unsolved structures (C7.2, C7.10, and R1). As a final test, SWM blindly and correctly predicted all noncanonical pairs of a Zika virus double pseudoknot during a recent community-wide RNA-Puzzle. Stepwise structure formation, as encoded in the SWM method, enables modeling of noncanonical RNA structure in a variety of previously intractable problems.

A stepwise approach to the evaluation and treatment of subclinical hyperthyroidism.

PubMed

Mai, Vinh Q; Burch, Henry B

2012-01-01

To review a stepwise approach to the evaluation and treatment of subclinical hyperthyroidism. English-language articles regarding clinical management of subclinical hyperthyroidism published between 2007 and 2012 were reviewed. Subclinical hyperthyroidism is encountered on a daily basis in clinical practice. When evaluating patients with a suppressed serum thyrotropin value, it is important to exclude other potential etiologies such as overt triiodothyronine toxicosis, drug effect, nonthyroidal illness, and central hypothyroidism. In younger patients with mild thyrotropin suppression, it is acceptable to perform testing again in 3 to 6 months to assess for persistence before performing further diagnostic testing. In older patients or patients with thyrotropin values less than 0.1 mIU/L, diagnostic testing should proceed without delay. Persistence of thyrotropin suppression is more typical of nodular thyroid autonomy, whereas thyroiditis and mild Graves disease frequently resolve spontaneously. The clinical consequences of subclinical hyperthyroidism, such as atrial dysrhythmia, accelerated bone loss, increased fracture rate, and higher rates of cardiovascular mortality, are dependent on age and severity. The decision to treat subclinical hyperthyroidism is directly tied to an assessment of the potential for clinical consequences in untreated disease. Definitive therapy is generally selected for patients with nodular autonomous function, whereas antithyroid drug therapy is more appropriate for mild, persistent Graves disease. The presented stepwise approach to the care of patients presenting with an isolated suppression of serum thyrotropin focuses on the differential diagnosis, a prediction of the likelihood of persistence, an assessment of potential risks posed to the patient, and, finally, a personalized choice of therapy.

Embracing change: practical and theoretical considerations for successful implementation of technology assisting upper limb training in stroke

PubMed Central

2012-01-01

Background Rehabilitation technology for upper limb training of stroke patients may play an important role as therapy tool in future, in order to meet the increasing therapy demand. Currently, implementation of this technology in the clinic remains low. This study aimed at identifying criteria and conditions that people, involved in development of such technology, should take into account to achieve a (more) successful implementation of the technology in the clinic. Methods A literature search was performed in PubMed and IEEE databases, and semi-structured interviews with therapists in stroke rehabilitation were held, to identify criteria and conditions technology should meet to facilitate (implementation of) technology-assisted arm-hand skills training in rehabilitation therapy of stroke patients. In addition, an implementation strategy frequently applied in general health care was used to compose a stepwise guidance to facilitate successful implementation of this technology in therapy of stroke patients. Implementation-related criteria mentioned by therapists during the interviews were integrated in this guidance. Results Results indicate that, related to therapy content, technology should facilitate repetition of task-related movements, tailored to the patient and patient’s goals, in a meaningful context. Variability and increasing levels of difficulty in exercises should be on offer. Regarding hardware and software design of technology, the system should facilitate quick familiarisation and be easily adjustable to individual patients during therapy by therapists (and assistants). The system should facilitate adaptation to individual patients’ needs and their progression over time, should be adjustable as to various task-related variables, should be able to provide instructions and feedback, and should be able to document patient’s progression. The implementation process of technology in the clinic is provided as a stepwise guidance that consists of five phases therapists have to go through. The guidance includes criteria and conditions that motivate therapists, and make it possible for them, to actually use technology in their daily clinical practice. Conclusions The reported requirements are important as guidance for people involved in the development of rehabilitation technology for arm-hand therapy of stroke patients. The stepwise guide provides a tool for facilitating successful implementation of technology in clinical practice, thus meeting future therapy demand. PMID:22856548

Stationary-phase optimized selectivity liquid chromatography: development of a linear gradient prediction algorithm.

PubMed

De Beer, Maarten; Lynen, Fréderic; Chen, Kai; Ferguson, Paul; Hanna-Brown, Melissa; Sandra, Pat

2010-03-01

Stationary-phase optimized selectivity liquid chromatography (SOS-LC) is a tool in reversed-phase LC (RP-LC) to optimize the selectivity for a given separation by combining stationary phases in a multisegment column. The presently (commercially) available SOS-LC optimization procedure and algorithm are only applicable to isocratic analyses. Step gradient SOS-LC has been developed, but this is still not very elegant for the analysis of complex mixtures composed of components covering a broad hydrophobicity range. A linear gradient prediction algorithm has been developed allowing one to apply SOS-LC as a generic RP-LC optimization method. The algorithm allows operation in isocratic, stepwise, and linear gradient run modes. The features of SOS-LC in the linear gradient mode are demonstrated by means of a mixture of 13 steroids, whereby baseline separation is predicted and experimentally demonstrated.

Marker-Based Hierarchical Segmentation and Classification Approach for Hyperspectral Imagery

NASA Technical Reports Server (NTRS)

Tarabalka, Yuliya; Tilton, James C.; Benediktsson, Jon Atli; Chanussot, Jocelyn

2011-01-01

The Hierarchical SEGmentation (HSEG) algorithm, which is a combination of hierarchical step-wise optimization and spectral clustering, has given good performances for hyperspectral image analysis. This technique produces at its output a hierarchical set of image segmentations. The automated selection of a single segmentation level is often necessary. We propose and investigate the use of automatically selected markers for this purpose. In this paper, a novel Marker-based HSEG (M-HSEG) method for spectral-spatial classification of hyperspectral images is proposed. First, pixelwise classification is performed and the most reliably classified pixels are selected as markers, with the corresponding class labels. Then, a novel constrained marker-based HSEG algorithm is applied, resulting in a spectral-spatial classification map. The experimental results show that the proposed approach yields accurate segmentation and classification maps, and thus is attractive for hyperspectral image analysis.

Dimerization of A-[alpha]-[SiNb3W9O40]7- by pH-controlled formation of individual Nb−µ-O−Nb linkages

Treesearch

Gyu-Shik Kim; Huadong Zeng; Wade A. Neiwert; Jennifer J. Cowan; Donald VanDerveer; Craig L. Hill; Ira A. Weinstock

2003-01-01

The reversible, stepwise formation of individual Nb−µ-O−Nb linkages during acid condensation of 2 equiv of A-[alpha]-[SiNb3W9O40]7- (1) to the tri-µ-oxo-bridged structure A-[alpha]-[Si2Nb6W18O77]8- (4) is demonstrated by a combination of X-ray crystallography and variable-pD solution 183W and 29Si NMR spectroscopy. Addition of DCl to a pD 8.4...

Children's attitudes toward violence on television.

PubMed

Hough, K J; Erwin, P G

1997-07-01

Children's attitudes toward television violence were studied. A 47-item questionnaire collecting attitudinal and personal information was administered to 316 children aged 11 to 16 years. Cluster analysis was used to split the participants into two groups based on their attitudes toward television violence. A stepwise discriminant function analysis was performed to determine which personal characteristics would predict group membership. The only significant predictor of attitudes toward violence on television was the amount of television watched on school days (p < .05), but we also found that the impact of other predictor variables may have been mediated by this factor.

The Role of Auxiliary Variables in Deterministic and Deterministic-Stochastic Spatial Models of Air Temperature in Poland

NASA Astrophysics Data System (ADS)

Szymanowski, Mariusz; Kryza, Maciej

2017-02-01

Our study examines the role of auxiliary variables in the process of spatial modelling and mapping of climatological elements, with air temperature in Poland used as an example. The multivariable algorithms are the most frequently applied for spatialization of air temperature, and their results in many studies are proved to be better in comparison to those obtained by various one-dimensional techniques. In most of the previous studies, two main strategies were used to perform multidimensional spatial interpolation of air temperature. First, it was accepted that all variables significantly correlated with air temperature should be incorporated into the model. Second, it was assumed that the more spatial variation of air temperature was deterministically explained, the better was the quality of spatial interpolation. The main goal of the paper was to examine both above-mentioned assumptions. The analysis was performed using data from 250 meteorological stations and for 69 air temperature cases aggregated on different levels: from daily means to 10-year annual mean. Two cases were considered for detailed analysis. The set of potential auxiliary variables covered 11 environmental predictors of air temperature. Another purpose of the study was to compare the results of interpolation given by various multivariable methods using the same set of explanatory variables. Two regression models: multiple linear (MLR) and geographically weighted (GWR) method, as well as their extensions to the regression-kriging form, MLRK and GWRK, respectively, were examined. Stepwise regression was used to select variables for the individual models and the cross-validation method was used to validate the results with a special attention paid to statistically significant improvement of the model using the mean absolute error (MAE) criterion. The main results of this study led to rejection of both assumptions considered. Usually, including more than two or three of the most significantly correlated auxiliary variables does not improve the quality of the spatial model. The effects of introduction of certain variables into the model were not climatologically justified and were seen on maps as unexpected and undesired artefacts. The results confirm, in accordance with previous studies, that in the case of air temperature distribution, the spatial process is non-stationary; thus, the local GWR model performs better than the global MLR if they are specified using the same set of auxiliary variables. If only GWR residuals are autocorrelated, the geographically weighted regression-kriging (GWRK) model seems to be optimal for air temperature spatial interpolation.

A Java-based tool for the design of classification microarrays.

PubMed

Meng, Da; Broschat, Shira L; Call, Douglas R

2008-08-04

Classification microarrays are used for purposes such as identifying strains of bacteria and determining genetic relationships to understand the epidemiology of an infectious disease. For these cases, mixed microarrays, which are composed of DNA from more than one organism, are more effective than conventional microarrays composed of DNA from a single organism. Selection of probes is a key factor in designing successful mixed microarrays because redundant sequences are inefficient and limited representation of diversity can restrict application of the microarray. We have developed a Java-based software tool, called PLASMID, for use in selecting the minimum set of probe sequences needed to classify different groups of plasmids or bacteria. The software program was successfully applied to several different sets of data. The utility of PLASMID was illustrated using existing mixed-plasmid microarray data as well as data from a virtual mixed-genome microarray constructed from different strains of Streptococcus. Moreover, use of data from expression microarray experiments demonstrated the generality of PLASMID. In this paper we describe a new software tool for selecting a set of probes for a classification microarray. While the tool was developed for the design of mixed microarrays-and mixed-plasmid microarrays in particular-it can also be used to design expression arrays. The user can choose from several clustering methods (including hierarchical, non-hierarchical, and a model-based genetic algorithm), several probe ranking methods, and several different display methods. A novel approach is used for probe redundancy reduction, and probe selection is accomplished via stepwise discriminant analysis. Data can be entered in different formats (including Excel and comma-delimited text), and dendrogram, heat map, and scatter plot images can be saved in several different formats (including jpeg and tiff). Weights generated using stepwise discriminant analysis can be stored for analysis of subsequent experimental data. Additionally, PLASMID can be used to construct virtual microarrays with genomes from public databases, which can then be used to identify an optimal set of probes.

Intra- and inter-individual variation of BIS-index and Entropy during controlled sedation with midazolam/remifentanil and dexmedetomidine/remifentanil in healthy volunteers: an interventional study.

PubMed

Haenggi, Matthias; Ypparila-Wolters, Heidi; Hauser, Kathrin; Caviezel, Claudio; Takala, Jukka; Korhonen, Ilkka; Jakob, Stephan M

2009-01-01

We studied intra-individual and inter-individual variability of two online sedation monitors, BIS and Entropy, in volunteers under sedation. Ten healthy volunteers were sedated in a stepwise manner with doses of either midazolam and remifentanil or dexmedetomidine and remifentanil. One week later the procedure was repeated with the remaining drug combination. The doses were adjusted to achieve three different sedation levels (Ramsay Scores 2, 3 and 4) and controlled by a computer-driven drug-delivery system to maintain stable plasma concentrations of the drugs. At each level of sedation, BIS and Entropy (response entropy and state entropy) values were recorded for 20 minutes. Baseline recordings were obtained before the sedative medications were administered. Both inter-individual and intra-individual variability increased as the sedation level deepened. Entropy values showed greater variability than BIS(R) values, and the variability was greater during dexmedetomidine/remifentanil sedation than during midazolam/remifentanil sedation. The large intra-individual and inter-individual variability of BIS and Entropy values in sedated volunteers makes the determination of sedation levels by processed electroencephalogram (EEG) variables impossible. Reports in the literature which draw conclusions based on processed EEG variables obtained from sedated intensive care unit (ICU) patients may be inaccurate due to this variability. clinicaltrials.gov Nr. NCT00641563.

Experimental design for evaluating WWTP data by linear mass balances.

PubMed

Le, Quan H; Verheijen, Peter J T; van Loosdrecht, Mark C M; Volcke, Eveline I P

2018-05-15

A stepwise experimental design procedure to obtain reliable data from wastewater treatment plants (WWTPs) was developed. The proposed procedure aims at determining sets of additional measurements (besides available ones) that guarantee the identifiability of key process variables, which means that their value can be calculated from other, measured variables, based on available constraints in the form of linear mass balances. Among all solutions, i.e. all possible sets of additional measurements allowing the identifiability of all key process variables, the optimal solutions were found taking into account two objectives, namely the accuracy of the identified key variables and the cost of additional measurements. The results of this multi-objective optimization problem were represented in a Pareto-optimal front. The presented procedure was applied to a full-scale WWTP. Detailed analysis of the relation between measurements allowed the determination of groups of overlapping mass balances. Adding measured variables could only serve in identifying key variables that appear in the same group of mass balances. Besides, the application of the experimental design procedure to these individual groups significantly reduced the computational effort in evaluating available measurements and planning additional monitoring campaigns. The proposed procedure is straightforward and can be applied to other WWTPs with or without prior data collection. Copyright © 2018 Elsevier Ltd. All rights reserved.

Selection and outcome of the potential live liver donor.

PubMed

Pamecha, Viniyendra; Mahansaria, Shyam Sunder; Bharathy, Kishore G S; Kumar, Senthil; Sasturkar, Shridhar Vasantrao; Sinha, Piyush Kumar; Sarin, Shiv Kumar

2016-07-01

A thorough donor evaluation in the living donation process is mandatory to ensure a safe outcome in an otherwise healthy individual. The aim of the current study was to evaluate the reasons for not proceeding to donation and the outcome of live liver donors. A prospective study of potential donors who underwent evaluation and proceeded to surgery from 1 April 2012 to 31 January 2015 was conducted. The process of donor selection, its outcome and peri-operative complications were recorded. A total of 460 donors were evaluated in a stepwise manner for 367 potential recipients. Of the 321 (69.7 %) donors not proceeding to donation, the reasons were donor-related in 63.6 % and recipient-related in the rest. Common donor-related reasons were: donor reluctance (23.5 %), negative liver attenuation index (16.2 %), anatomic variations (10.3 %), inadequate remnant liver volume (9.8 %), unacceptable liver biopsy (8.8 %), and inadequate graft volume (5.4 %). A majority of donors (82.8 %) were turned down early in the (steps 1 and 2) evaluation process. Recipient death was the most common recipient-related reason [n = 51 (43.6 %)] for not proceeding to donation. There was no donor mortality. The overall complication rate was 19.8 % and major complication rate (grade 3 or higher) was 4.4 %. A stringent stepwise donor evaluation process leads to early recognition of unsuitable donors and a low complication rate.

Facies-dependent variations in sediment physical properties on the Mississippi River Delta Front, USA: evidence for depositional and post-depositional processes

NASA Astrophysics Data System (ADS)

Smith, J. E., IV; Bentley, S. J.; Courtois, A. J.; Obelcz, J.; Chaytor, J. D.; Maloney, J. M.; Georgiou, I. Y.; Xu, K.; Miner, M. D.

2017-12-01

Recent studies on Mississippi River Delta have documented sub-aerial land loss, driven in part by declining sediment load over the past century. Impacts of changing sediment load on the subaqueous delta are less well known. The subaqueous Mississippi River Delta Front is known to be shaped by extensive submarine mudflows operating at a range of temporal and spatial scales, however impacts of changing sediment delivery on mudflow deposits have not been investigated. To better understand seabed morphology and stratigraphy as impacted by plume sedimentation and mudflows, an integrated geological/geophysical study was undertaken in delta front regions offshore the three main passes of the Mississippi River Delta. This study focuses on stratigraphy and physical properties of 30 piston cores (5-9 m length) collected in June 2017. Coring locations were selected in gully, lobe and prodelta settings based on multibeam bathymetry and seismic profiles collected in mid-May 2017. Cores were analyzed for density, magnetic susceptibility, P-wave speed, and resistivity using a Geotek multi sensor core logger; here, we focus on density data. Core density profiles generally vary systematically across facies. Density profiles of gully cores are nearly invariant with some downward stepwise increases delineating units meters thick, and abundant gaps likely caused by gas expansion. Lobe cores generally have subtle downward increases in density, some stepwise density increases, and fewer gaps. Prodelta cores show more pronounced downward density increases, decimeter-scale peaks and valleys in density profiles, but stepwise increases are less evident. We hypothesize that density profiles in gully and lobe settings (uniform profiles except for stepwise increases) reflect remolding by mudflows, whereas density variations in prodelta settings instead reflect grain size variations (decimeter-scale) and more advanced consolidation (overall downward density increase) consistent with slower sediment deposition. These hypotheses will be evaluated by a more detailed study of seismic stratigraphy and core properties, including geochronology, grain size distribution and X-radiographic imaging, to further relate important sedimentary processes with resulting deposits.

Mapping Robinia pseudoacacia forest health in the Yellow River delta by using high-resolution IKONOS imagery and object-based image analysis

NASA Astrophysics Data System (ADS)

Wang, Hong; Lu, Kaiyu; Pu, Ruiliang

2016-10-01

The Robinia pseudoacacia forest in the Yellow River delta of China has been planted since the 1970s, and a large area of dieback of the forest has occurred since the 1990s. To assess the condition of the R. pseudoacacia forest in three forest areas (i.e., Gudao, Machang, and Abandoned Yellow River) in the delta, we combined an estimation of scale parameters tool and geometry/topology assessment criteria to determine the optimal scale parameters, selected optimal predictive variables determined by stepwise discriminant analysis, and compared object-based image analysis (OBIA) and pixel-based approaches using IKONOS data. The experimental results showed that the optimal segmentation scale is 5 for both the Gudao and Machang forest areas, and 12 for the Abandoned Yellow River forest area. The results produced by the OBIA method were much better than those created by the pixel-based method. The overall accuracy of the OBIA method was 93.7% (versus 85.4% by the pixel-based) for Gudao, 89.0% (versus 72.7%) for Abandoned Yellow River, and 91.7% (versus 84.4%) for Machang. Our analysis results demonstrated that the OBIA method was an effective tool for rapidly mapping and assessing the health levels of forest.

Association between ICP pulse waveform morphology and ICP B waves.

PubMed

Kasprowicz, Magdalena; Bergsneider, Marvin; Czosnyka, Marek; Hu, Xiao

2012-01-01

The study aimed to investigate changes in the shape of ICP pulses associated with different patterns of the ICP slow waves (0.5-2.0 cycles/min) during ICP overnight monitoring in hydrocephalus. Four patterns of ICP slow waves were characterized in 44 overnight ICP recordings (no waves - NW, slow symmetrical waves - SW, slow asymmetrical waves - AS, slow waves with plateau phase - PW). The morphological clustering and analysis of ICP pulse (MOCAIP) algorithm was utilized to calculate a set of metrics describing ICP pulse morphology based on the location of three sub-peaks in an ICP pulse: systolic peak (P(1)), tidal peak (P(2)) and dicrotic peak (P(3)). Step-wise discriminant analysis was applied to select the most characteristic morphological features to distinguish between different ICP slow waves. Based on relative changes in variability of amplitudes of P(2) and P(3) we were able to distinguish between the combined groups NW + SW and AS + PW (p < 0.000001). The AS pattern can be differentiated from PW based on respective changes in the mean curvature of P(2) and P(3) (p < 0.000001); however, none of the MOCAIP feature separates between NW and SW. The investigation of ICP pulse morphology associated with different ICP B waves may provide additional information for analysing recordings of overnight ICP.

Peripheral muscle composition and health status in patients with COPD.

PubMed

Montes de Oca, María; Torres, Sonia H; Gonzalez, Yudith; Romero, Elizabeth; Hernández, Noelina; Mata, Abdón; Tálamo, Carlos

2006-10-01

The present study evaluated the relationship between health status (HS) and peripheral muscle histochemical characteristics in chronic obstructive pulmonary disease (COPD), and identified selected independent respiratory and extrapulmonary variables that predicted the HS of these patients. Cross-sectional study. Outpatient respiratory clinic of a university hospital. We studied 29 patients (63+/-10 yrs) with a forced expiratory volume in 1s (FEV1) of 39+/-12%. All patients underwent vastus lateralis muscle biopsies for histochemical analysis. They also had spirometry, arterial blood gas analysis, body mass index (BMI), dyspnea determined with the MMRC scale and responded to the St. George's Respiratory Questionnaire (SGRQ) for HS assessment. SGRQ total score correlated with fiber type distribution. A stepwise multiple regression identified three independent predictors of SGRQ total score: type I fiber proportion, BMI, and FEV1; r = 0.78 and r2 = 0.61. These results indicate that impaired HS in COPD is related to the peripheral muscle changes characterized by less type I fibers proportion. The findings argue in favor of an important contribution of the systemic consequences on the HS in COPD independently from the airflow limitation severity, and help to explain the observation of the poor correlation between the degree of airflow limitation and SGRQ total score.

Salivary Testosterone Levels Under Psychological Stress and Its Relationship with Rumination and Five Personality Traits in Medical Students

PubMed Central

Afrisham, Reza; Sadegh-Nejadi, Sahar; SoliemaniFar, Omid; Kooti, Wesam; Ashtary-Larky, Damoon; Alamiri, Fatima; Najjar-Asl, Sedigheh; Khaneh-Keshi, Ali

2016-01-01

Objective The purpose of this study was to evaluate the salivary testosterone levels under psychological stress and its relationship with rumination and five personality traits in medical students. Methods A total of 58 medical students, who wanted to participate in the final exam, were selected by simple random sampling. Two months before the exam, in the basal conditions, the NEO Inventory short form, and the Emotional Control Questionnaire (ECQ) were completed. Saliva samples were taken from students in both the basal conditions and under exam stress. Salivary testosterone was measured by ELISA. Data was analyzed using multivariate analysis of variance with repeated measures, paired samples t-test, Pearson correlation and stepwise regression analysis. Results Salivary testosterone level of men showed a significant increase under exam stress (p<0.05). However, a non-significant although substantial reduction observed in women. A significant correlation was found between extroversion (r=-0.33) and openness to experience (r=0.30) with salivary testosterone (p<0.05). Extraversion, aggression control and emotional inhibition predicted 28% of variance of salivary testosterone under stress. Conclusion Salivary testosterone reactivity to stress can be determined by sexual differences, personality traits, and emotional control variables which may decrease or increase stress effects on biological responses, especially the salivary testosterone. PMID:27909455

Predicting Sasang Constitution Using Body-Shape Information

PubMed Central

Jang, Eunsu; Do, Jun-Hyeong; Jin, HeeJeong; Park, KiHyun; Ku, Boncho; Lee, Siwoo; Kim, Jong Yeol

2012-01-01

Objectives. Body measurement plays a pivotal role not only in the diagnosis of disease but also in the classification of typology. Sasang constitutional medicine, which is one of the forms of Traditional Korean Medicine, is considered to be strongly associated with body shape. We attempted to determine whether a Sasang constitutional analytic tool based on body shape information (SCAT-B) could predict Sasang constitution (SC). Methods. After surveying 23 Oriental medical clinics, 2,677 subjects were recruited and body shape information was collected. The SCAT-Bs for males and females were developed using multinomial logistic regression. Stepwise forward-variable selection was applied using the score statistic and Wald's test. Results. The predictive rates of the SCAT-B for Tae-eumin (TE), Soeumin (SE), and Soyangin (SY) types in males and females were 80.2%, 56.9%, and 37.7% (males) and 69.3%, 38.9%, and 50.0% (females) in the training set and were 74%, 70.1%, and 35% (males), and 67.4%, 66.3%, and 53.7% (females) in the test set, respectively. Higher constitutional probability scores showed a trend for association with higher predictability. Conclusions. This study shows that the Sasang constitutional analytic tool, which is based on body shape information, may be relatively highly predictive of TE type but may be less predictive when used for SY type. PMID:22792124

Personal Well-being and Stress Symptoms in Wives of Iranian Martyrs, Prisoners of wars and Disabled Veterans

PubMed Central

Sharif, Nasim

2010-01-01

Objective This study was conducted to compare the personal well-being among the wives of Iranian veterans living in the city of Qom. Method A sample of 300 was randomly selected from a database containing the addresses of veteran's families at Iran's Veterans Foundation in Qom (Bonyad-e-Shahid va Omoore Isargaran). The veterans' wives were divided into three groups: wives of martyrs (killed veterans), wives of prisoners of war, and wives of disabled veterans. The Persian translation of Personal Well-being Index and Stress Symptoms Checklist (SSC) were administered for data collection. Four women chose not to respond to Personal Well-being Index. Data were then analyzed using linear multivariate regression (stepwise method), analysis of variance, and by computing the correlation between variables. Results Results showed a negative correlation between well-being and stress symptoms. However, each group demonstrated different levels of stress symptoms. Furthermore, multivariate linear regression in the 3 groups showed that overall satisfaction of life and personal well-being (total score and its domains) could be predicted by different symptoms. Conclusion Each group experienced different challenges and thus different stress symptoms. Therefore, although they all need help, each group needs to be helped in a different way. PMID:22952487

Environmental Factors Correlated with Culturable Enterococci Concentrations in Tropical Recreational Waters: A Case Study in Escambron Beach, San Juan, Puerto Rico.

PubMed

Laureano-Rosario, Abdiel E; Symonds, Erin M; Rueda-Roa, Digna; Otis, Daniel; Muller-Karger, Frank E

2017-12-19

Enterococci concentration variability at Escambron Beach, San Juan, Puerto Rico, was examined in the context of environmental conditions observed during 2005-2015. Satellite-derived sea surface temperature (SST), turbidity, direct normal irradiance, and dew point were combined with local precipitation, winds, and mean sea level (MSL) observations in a stepwise multiple regression analyses (Akaike Information Criteria model selection). Precipitation, MSL, irradiance, SST, and turbidity explained 20% of the variation in observed enterococci concentrations based upon these analyses. Changes in these parameters preceded increases in enterococci concentrations by 24 h up to 11 days, particularly during positive anomalies of turbidity, SST, and 480-960 mm of accumulated (4 days) precipitation, which relates to bacterial ecology. Weaker, yet still significant, increases in enterococci concentrations were also observed during positive dew point anomalies. Enterococci concentrations decreased with elevated irradiance and MSL anomalies. Unsafe enterococci concentrations per US EPA recreational water quality guidelines occurred when 4-day cumulative precipitation ranged 481-960 mm; irradiance < 667 W·m -2 ; daily average turbidity anomaly >0.005 sr -1 ; SST anomaly >0.8 °C; and 3-day average MSL anomaly <-18.8 cm. This case study shows that satellite-derived environmental data can be used to inform future water quality studies and protect human health.

GSTARI model of BPR assets in West Java, Central Java, and East Java

NASA Astrophysics Data System (ADS)

Susanti, Susi; Sulistijowati Handajani, Sri; Indriati, Diari

2018-05-01

Bank Perkreditan Rakyat (BPR) is a financial institution in Indonesia dealing with Micro, Small, and Medium Enterprises (MSMEs). Though limited to MSMEs, the development of the BPR industry continues to increase. West Java, Central Java, and East Java have high BPR asset development are suspected to be interconnected because of their economic activities as a neighboring provincies. BPR assets are nonstationary time series data that follow the uptrend pattern. Therefore, the suitable model with the data is generalized space time autoregressive integrated (GSTARI) which considers the spatial and time interrelationships. GSTARI model used spatial order 1 and the autoregressive order is obtained of optimal lag which has the smallest value of Akaike information criterion corrected. The correlation test results showed that the location used in this study had a close relationship. Based on the results of model identification, the best model obtained is GSTAR(31)-I(1). The parameter estimation used the ordinary least squares with the selection of significant variables used the stepwise method and the normalization cross correlation weighting. The residual model fulfilled the assumption of white noise and normal multivariate, so the model was appropriate. The average RMSE and MAPE values of the model were 498.75 and 2.48%.

Climatic, ecological, and socioeconomic factors associated with West Nile virus incidence in Atlanta, Georgia, U.S.A.

PubMed

Lockaby, Graeme; Noori, Navideh; Morse, Wayde; Zipperer, Wayne; Kalin, Latif; Governo, Robin; Sawant, Rajesh; Ricker, Matthew

2016-12-01

The integrated effects of the many risk factors associated with West Nile virus (WNV) incidence are complex and not well understood. We studied an array of risk factors in and around Atlanta, GA, that have been shown to be linked with WNV in other locations. This array was comprehensive and included climate and meteorological metrics, vegetation characteristics, land use / land cover analyses, and socioeconomic factors. Data on mosquito abundance and WNV mosquito infection rates were obtained for 58 sites and covered 2009-2011, a period following the combined storm water - sewer overflow remediation in that city. Risk factors were compared to mosquito abundance and the WNV vector index (VI) using regression analyses individually and in combination. Lagged climate variables, including soil moisture and temperature, were significantly correlated (positively) with vector index as were forest patch size and percent pine composition of patches (both negatively). Socioeconomic factors that were most highly correlated (positively) with the VI included the proportion of low income households and homes built before 1960 and housing density. The model selected through stepwise regression that related risk factors to the VI included (in the order of decreasing influence) proportion of houses built before 1960, percent of pine in patches, and proportion of low income households. © 2016 The Society for Vector Ecology.

Variable Resistance to Plasminogen Activator Initiated Fibrinolysis for Intermediate-Risk Pulmonary Embolism.

PubMed

Stubblefield, William B; Alves, Nathan J; Rondina, Matthew T; Kline, Jeffrey A

2016-01-01

We examine the clinical significance and biomarkers of tissue plasminogen activator (tPA)-catalyzed clot lysis time (CLT) in patients with intermediate-risk pulmonary embolism (PE). Platelet-poor, citrated plasma was obtained from patients with PE. Healthy age- and sex-matched patients served as disease-negative controls. Fibrinogen, α2-antiplasmin, plasminogen, thrombin activatable fibrinolysis inhibitor (TAFI), plasminogen activator Inhibitor 1 (PAI-1), thrombin time and D-dimer were quantified. Clotting was induced using CaCl2, tissue factor, and phospholipid. Lysis was induced using 60 ng/mL tPA. Time to 50% clot lysis (CLT) was assessed by both thromboelastography (TEG) and turbidimetry (A405). Compared with disease-negative controls, patients with PE exhibited significantly longer mean CLT on TEG (+2,580 seconds, 95% CI 1,380 to 3,720 sec). Patients with PE and a short CLT who were treated with tenecteplase had increased risk of bleeding, whereas those with long CLT had significantly worse exercise tolerance and psychometric testing for quality of life at 3 months. A multivariate stepwise removal regression model selected PAI-1 and TAFI as predictive biomarkers of CLT. The CLT from TEG predicted increased risk of bleeding and clinical failure with tenecteplase treatment for intermediate-risk PE. Plasmatic PAI-1 and TAFI were independent predictors of CLT.

The effect of thermal history on crystalline structure and mechanical properties of β-nucleated isotactic polypropylene

NASA Astrophysics Data System (ADS)

Tian, Yefei; Zhou, Jian; Feng, Jiachun

2018-04-01

The effect of thermal history on β-nucleated iPP was systematically investigated by comparing the variance of crystalline microstructures and mechanical property of stepwise crystallized sample and annealed sample, which experienced different thermal history. The mechanical property tests exhibit that that the toughness of stepwise crystallized sample and annealed sample were both decreased compared to control sample, while the tensile strength of the stepwise crystallized sample increased slightly. Structure investigation showed that the α-relaxation peak, which is related to the assignment of chains in rigid amorphous phase, moved to the high temperature region for stepwise crystallized sample, while it moved to the low temperature region for annealed sample. The results indicated the weakening in rigid amorphous fraction (RAF) and the increase in lamellar thickness of β-iPP after stepwise crystallization treatment. For annealed sample, the RAF strengthened and lamellar thickness decreased slightly after thermal treatment. A mechanism of crystalline microstructures evolution of restricted area between the main lamellar under different treatments was proposed.

Improving meat quality of organic pork through post mortem handling of carcasses: an innovative approach.

PubMed

Therkildsen, Margrethe; Kristensen, Lars; Kyed, Sybille; Oksbjerg, Niels

2012-06-01

This study was conducted to examine the best combination of post mortem chilling, suspension and ageing in order to optimize tenderness of organic pork at slaughter, which may be tougher than conventionally produced pork, because of lower daily gain. Combinations of stepwise chilling with a holding period of 6h at 10°C or traditional blast tunnel chilling, suspension in the pelvic bone or Achilles Tendon and ageing 2 or 4 days post mortem were tested. Stepwise chilling and ageing improved tenderness of the loin, and the effects were additive, whereas pelvic suspension was less effective in texture improvements, and non-additive to stepwise chilling. Stepwise chilling improved tenderness to a similar degree as can be obtained within 2-4 days of extended ageing, however, the minimum temperature during the holding period seems to be crucial in order to obtain a positive effect of stepwise chilling, and it should be above 7.5°C. Copyright © 2011 Elsevier Ltd. All rights reserved.

Determinants of between-country differences in ambulatory antibiotic use and antibiotic resistance in Europe: a longitudinal observational study.

PubMed

Blommaert, A; Marais, C; Hens, N; Coenen, S; Muller, A; Goossens, H; Beutels, P

2014-02-01

To identify key determinants explaining country-year variations in antibiotic use and resistance. Ambulatory antibiotic use data [in defined daily doses per 1000 inhabitants per day (DIDs)] for 19 European countries from 1999 to 2007 were collected, along with 181 variables describing countries in terms of their agriculture, culture, demography, disease burden, education, healthcare organization and socioeconomics. After assessing data availability, overlap and relevance, multiple imputation generalized estimating equations were applied with a stepwise selection procedure to select significant determinants of global antibiotic use (expressed in DIDs), relative use of subgroups (amoxicillin and co-amoxiclav) and resistance of Escherichia coli and Streptococcus pneumoniae. Relative humidity, healthcare expenditure proportional to gross domestic product, feelings of distrust, proportion of population aged >65 years and availability of treatment guidelines were associated with higher total antibiotic use expressed in DIDs. Restrictions on marketing activities towards prescribers, population density, number of antibiotics, educational attainment and degree of atheism were associated with a lower number of total DIDs used. Relative prescribing of amoxicillin and co-amoxiclav was mainly determined by healthcare system choices [e.g. general practitioner (GP) registration and restricted marketing]. Specific antibiotic use was found to be a significant determinant of resistance for some but not all drug/organism combinations. Incentives to stimulate GP gatekeeping were associated with lower levels of resistance, and life expectancy at age 65+ and atheism were associated with more resistance. Myriad factors influence antibiotic use and resistance at the country level and an important part of these can be modified by policy choices.

Stepwise introduction of laparoscopic liver surgery: validation of guideline recommendations.

PubMed

van der Poel, Marcel J; Huisman, Floor; Busch, Olivier R; Abu Hilal, Mohammad; van Gulik, Thomas M; Tanis, Pieter J; Besselink, Marc G

2017-10-01

Uncontrolled introduction of laparoscopic liver surgery (LLS) could compromise postoperative outcomes. A stepwise introduction of LLS combined with structured training is advised. This study aimed to evaluate the impact of such a stepwise introduction. A retrospective, single-center case series assessing short term outcomes of all consecutive LLS in the period November 2006-January 2017. The technique was implemented in a stepwise fashion. To evaluate the impact of this stepwise approach combined with structured training, outcomes of LLS before and after a laparoscopic HPB fellowship were compared. A total of 135 laparoscopic resections were performed. Overall conversion rate was 4% (n = 5), clinically relevant complication rate 13% (n = 18) and mortality 0.7% (n = 1). A significant increase in patients with major LLS, multiple liver resections, previous abdominal surgery, malignancies and lesions located in posterior segments was observed after the fellowship as well as a decrease in the use of hand-assistance. Increasing complexity in the post fellowship period was reflected by an increase in operating times, but without comprising other surgical outcomes. A stepwise introduction of LLS combined with structured training reduced the clinical impact of the learning curve, thereby confirming guideline recommendations. Copyright © 2017 International Hepato-Pancreato-Biliary Association Inc. Published by Elsevier Ltd. All rights reserved.

Preoperative galactose elimination capacity predicts complications and survival after hepatic resection.

PubMed

Redaelli, Claudio A; Dufour, Jean-François; Wagner, Markus; Schilling, Martin; Hüsler, Jürg; Krähenbühl, Lukas; Büchler, Markus W; Reichen, Jürg

2002-01-01

To analyze a single center's 6-year experience with 258 consecutive patients undergoing major hepatic resection for primary or secondary malignancy of the liver, and to examine the predictive value of preoperative liver function assessment. Despite the substantial improvements in diagnostic and surgical techniques that have made liver surgery a safer procedure, careful patient selection remains mandatory to achieve good results in patients with hepatic tumors. In this prospective study, 258 patients undergoing hepatic resection were enrolled: 111 for metastases, 78 for hepatocellular carcinoma (HCC), 21 for cholangiocellular carcinoma, and 48 for other primary hepatic tumors. One hundred fifty-eight patients underwent segment-oriented liver resection, including hemihepatectomies, and 100 had subsegmental resections. Thirty-two clinical and biochemical parameters were analyzed, including liver function assessment by the galactose elimination capacity (GEC) test, a measure of hepatic functional reserve, to predict postoperative (60-day) rates of death and complications and long-term survival. All variables were determined within 5 days before surgery. Data were subjected to univariate and multivariate analysis for two patient subgroups (HCC and non-HCC). The cutoffs for GEC in both groups were predefined. Long-term survival (>60 days) was subjected to Kaplan-Meier analysis and the Cox proportional hazard model. In the entire group of 258 patients, a GEC less than 6 mg/min/kg was the only preoperative biochemical parameter that predicted postoperative complications and death by univariate and stepwise regression analysis. A GEC of more than 6 mg/min/kg was also significantly associated with longer survival. This predictive value could also be shown in the subgroup of 180 patients with tumors other than HCC. In the subgroup of 78 patients with HCC, a GEC less than 4 mg/min/kg predicted postoperative complications and death by univariate and stepwise regression analysis. Further, a GEC of more than 4 mg/min/kg was also associated with longer survival. This prospective study establishes the preoperative determination of the hepatic reserve by GEC as a strong independent and valuable predictor for short- and long-term outcome in patients with primary and secondary hepatic tumors undergoing resection.

Preoperative Galactose Elimination Capacity Predicts Complications and Survival After Hepatic Resection

PubMed Central

Redaelli, Claudio A.; Dufour, Jean-François; Wagner, Markus; Schilling, Martin; Hüsler, Jürg; Krähenbühl, Lukas; Büchler, Markus W.; Reichen, Jürg

2002-01-01

Objective To analyze a single center’s 6-year experience with 258 consecutive patients undergoing major hepatic resection for primary or secondary malignancy of the liver, and to examine the predictive value of preoperative liver function assessment. Summary Background Data Despite the substantial improvements in diagnostic and surgical techniques that have made liver surgery a safer procedure, careful patient selection remains mandatory to achieve good results in patients with hepatic tumors. Methods In this prospective study, 258 patients undergoing hepatic resection were enrolled: 111 for metastases, 78 for hepatocellular carcinoma (HCC), 21 for cholangiocellular carcinoma, and 48 for other primary hepatic tumors. One hundred fifty-eight patients underwent segment-oriented liver resection, including hemihepatectomies, and 100 had subsegmental resections. Thirty-two clinical and biochemical parameters were analyzed, including liver function assessment by the galactose elimination capacity (GEC) test, a measure of hepatic functional reserve, to predict postoperative (60-day) rates of death and complications and long-term survival. All variables were determined within 5 days before surgery. Data were subjected to univariate and multivariate analysis for two patient subgroups (HCC and non-HCC). The cutoffs for GEC in both groups were predefined. Long-term survival (>60 days) was subjected to Kaplan-Meier analysis and the Cox proportional hazard model. Results In the entire group of 258 patients, a GEC less than 6 mg/min/kg was the only preoperative biochemical parameter that predicted postoperative complications and death by univariate and stepwise regression analysis. A GEC of more than 6 mg/min/kg was also significantly associated with longer survival. This predictive value could also be shown in the subgroup of 180 patients with tumors other than HCC. In the subgroup of 78 patients with HCC, a GEC less than 4 mg/min/kg predicted postoperative complications and death by univariate and stepwise regression analysis. Further, a GEC of more than 4 mg/min/kg was also associated with longer survival. Conclusions This prospective study establishes the preoperative determination of the hepatic reserve by GEC as a strong independent and valuable predictor for short- and long-term outcome in patients with primary and secondary hepatic tumors undergoing resection. PMID:11753045

Prevalence and Predisposing Factors for Depressive Status in Chinese Patients with Obstructive Sleep Apnoea: A Large-Sample Survey.

PubMed

Dai, Yaozhang; Li, Xuewu; Zhang, Xin; Wang, Sihua; Sang, Jianzhong; Tian, Xiufen; Cao, Hua

2016-01-01

Recently, there are few studies reporting on depressive status and obstructive sleep apnoea (OSA) in China. A large-sample survey was to be performed to explore the prevalence of depressive status and related factors in Chinese patients with OSA. From among a randomly-selected group of OSA patients, 1,327 met inclusion criteria. After screening with the Symptom Checklist 90 (SCL-90) and Self-Rating Depression Scale (SDS), patients were assigned to OSA without depressive status (control group, n = 698) and OSA with depressive status (n = 629) groups. Using chi-squared testing, the correlation analyses between the depressive status and OSA patient demographic and clinical variables were tested. Then depression-related risk factors in OSA patients were analysed using stepwise linear regression analysis. The effects of family and social factors on depressive status in OSA patients were investigated using Mann-Whitney U (one of nonparametric test). The prevalence of depressive status was 47.4% in OSA patients. Depressive status was significantly associated with female gender, single status, Family Burden Scale of Disease (FBS), Family APGAR Index (APGAR), apnoea-hypopnea index (AHI), and Perceived Social Support Scale (PSSS). Stepwise linear regression analysis further indicated that single status, hypoxemia, APGAR, AHI, PSSS, AHI, and FBS were all risk factors for depressive status in OSA patients. The total of the FBS score and three of its sub-factors scores (family daily activities, family relationships and mental health of family members) were higher, and the total of the APGAR score and two of its sub-factors scores (adaptability and affection) were lower in OSA with depressive status compared with the control group. Besides, the total score for the PSSS and scores for its two sub-factors (family support and social support) were all lower in OSA patients with depressive status than those of the control group. Depressive status has high comorbid rate in Chinese OSA patients and is significantly associated with single status, apnoea-hypopnea index, hypoxemia, family and social supports.

Prevalence and Predisposing Factors for Depressive Status in Chinese Patients with Obstructive Sleep Apnoea: A Large-Sample Survey

PubMed Central

Dai, Yaozhang; Li, Xuewu; Zhang, Xin; Wang, Sihua; Sang, Jianzhong; Tian, Xiufen; Cao, Hua

2016-01-01

Background and Objective Recently, there are few studies reporting on depressive status and obstructive sleep apnoea (OSA) in China. A large-sample survey was to be performed to explore the prevalence of depressive status and related factors in Chinese patients with OSA. Methods From among a randomly-selected group of OSA patients, 1,327 met inclusion criteria. After screening with the Symptom Checklist 90 (SCL-90) and Self-Rating Depression Scale (SDS), patients were assigned to OSA without depressive status (control group, n = 698) and OSA with depressive status (n = 629) groups. Using chi-squared testing, the correlation analyses between the depressive status and OSA patient demographic and clinical variables were tested. Then depression-related risk factors in OSA patients were analysed using stepwise linear regression analysis. The effects of family and social factors on depressive status in OSA patients were investigated using Mann-Whitney U (one of nonparametric test). Results The prevalence of depressive status was 47.4% in OSA patients. Depressive status was significantly associated with female gender, single status, Family Burden Scale of Disease (FBS), Family APGAR Index (APGAR), apnoea-hypopnea index (AHI), and Perceived Social Support Scale (PSSS). Stepwise linear regression analysis further indicated that single status, hypoxemia, APGAR, AHI, PSSS, AHI, and FBS were all risk factors for depressive status in OSA patients. The total of the FBS score and three of its sub-factors scores (family daily activities, family relationships and mental health of family members) were higher, and the total of the APGAR score and two of its sub-factors scores (adaptability and affection) were lower in OSA with depressive status compared with the control group. Besides, the total score for the PSSS and scores for its two sub-factors (family support and social support) were all lower in OSA patients with depressive status than those of the control group. Conclusions Depressive status has high comorbid rate in Chinese OSA patients and is significantly associated with single status, apnoea-hypopnea index, hypoxemia, family and social supports. PMID:26934192

Habitat selection and seasonal movements of young bearded seals (Erignathus barbatus) in the Bering Sea

PubMed Central

2018-01-01

The first year of life is typically the most critical to a pinniped’s survival, especially for Arctic phocids which are weaned at only a few weeks of age and left to locate and capture prey on their own. Their seasonal movements and habitat selection are therefore important factors in their survival. During a cooperative effort between scientists and subsistence hunters in October 2004, 2005, and 2006, 13 female and 13 male young (i.e., age <2) bearded seals (Erignathus barbatus) were tagged with satellite-linked dive recorders (SDRs) in Kotzebue Sound, Alaska. Shortly after being released, most seals moved south with the advancing sea-ice through the Bering Strait and into the Bering Sea where they spent the winter and early spring. The SDRs of 17 (8 female and 9 male) seals provided frequent high-quality positions in the Bering Sea; their data were used in our analysis. To investigate habitat selection, we simulated 20 tracks per seal by randomly selecting from the pooled distributions of the absolute bearings and swim speeds of the tagged seals. For each point in the observed and simulated tracks, we obtained the depth, sea-ice concentration, and the distances to sea-ice, open water, the shelf break and coastline. Using logistic regression with a stepwise model selection procedure, we compared the simulated tracks to those of the tagged seals and obtained a model for describing habitat selection. The regression coefficients indicated that the bearded seals in our study selected locations near the ice edge. In contrast, aerial surveys of the bearded seal population, predominantly composed of adults, indicated higher abundances in areas farther north and in heavier pack ice. We hypothesize that this discrepancy is the result of behavioral differences related to age. Ice concentration was also shown to be a statistically significant variable in our model. All else being equal, areas of higher ice concentration are selected for up to about 80%. The effects of sex and bathymetry were not statistically significant. The close association of young bearded seals to the ice edge in the Bering Sea is important given the likely effects of climate warming on the extent of sea-ice and subsequent changes in ice edge habitat. PMID:29489846

Selective CO2 Sequestration with Monolithic Bimodal Micro/Macroporous Carbon Aerogels Derived from Stepwise Pyrolytic Decomposition of Polyamide-Polyimide-Polyurea Random Copolymers.

PubMed

Saeed, Adnan M; Rewatkar, Parwani M; Majedi Far, Hojat; Taghvaee, Tahereh; Donthula, Suraj; Mandal, Chandana; Sotiriou-Leventis, Chariklia; Leventis, Nicholas

2017-04-19

Polymeric aerogels (PA-xx) were synthesized via room-temperature reaction of an aromatic triisocyanate (tris(4-isocyanatophenyl) methane) with pyromellitic acid. Using solid-state CPMAS 13 C and 15 N NMR, it was found that the skeletal framework of PA-xx was a statistical copolymer of polyamide, polyurea, polyimide, and of the primary condensation product of the two reactants, a carbamic-anhydride adduct. Stepwise pyrolytic decomposition of those components yielded carbon aerogels with both open and closed microporosity. The open micropore surface area increased from <15 m 2 g -1 in PA-xx to 340 m 2 g -1 in the carbons. Next, reactive etching at 1,000 °C with CO 2 opened access to the closed pores and the micropore area increased by almost 4× to 1150 m 2 g -1 (out of 1750 m 2 g -1 of a total BET surface area). At 0 °C, etched carbon aerogels demonstrated a good balance of adsorption capacity for CO 2 (up to 4.9 mmol g -1 ), and selectivity toward other gases (via Henry's law). The selectivity for CO 2 versus H 2 (up to 928:1) is suitable for precombustion fuel purification. Relevant to postcombustion CO 2 capture and sequestration (CCS), the selectivity for CO 2 versus N 2 was in the 17:1 to 31:1 range. In addition to typical factors involved in gas sorption (kinetic diameters, quadrupole moments and polarizabilities of the adsorbates), it is also suggested that CO 2 is preferentially engaged by surface pyridinic and pyridonic N on carbon (identified with XPS) in an energy-neutral surface reaction. Relatively high uptake of CH 4 (2.16 mmol g -1 at 0 °C/1 bar) was attributed to its low polarizability, and that finding paves the way for further studies on adsorption of higher (i.e., more polarizable) hydrocarbons. Overall, high CO 2 selectivities, in combination with attractive CO 2 adsorption capacities, low monomer cost, and the innate physicochemical stability of carbon render the materials of this study reasonable candidates for further practical consideration.

[Correlates of mental health in Japanese caseworkers employed at social welfare offices].

PubMed

Akama, Yumi; Morikagi, Yuko; Ohtake, Mariko; Suzuki, Ikuko; Kanoya, Yuka; Hosoya, Takiko; Kobayashi, Atsuko

2014-01-01

The aim of this study was to evaluate the correlation between the mental health status of caseworkers at welfare offices and factors affecting mental health (i.e., working conditions, participation in training courses and workshops, lifestyle habits, presence of illness, social support, and self-efficacy). The welfare offices in Japan (1,230 locations) were arranged in descending order according to their establishment and region. Systematic sampling was then conducted to select 20% (n =246) of the welfare institutions for this study. A total of 1,230 caseworkers on welfare (five from each institution) were administered anonymous self-completed questionnaires. The questionnaires involved the General Health Questionnaire (GHQ)-28 Japanese version and questions regarding basic attributes, working conditions, social support (i.e., family, friends, superiors, and colleagues), participation in training courses and workshops, presence of illness, lifestyle habits, mental health, and self-efficacy. Subjects were divided into the following 2 groups: low-score group (GHQ-28 score ≤5) and high-score group (GHQ-28 score ≥6). Data were analyzed using a t-test, χ(2) test, and Fisher's exact test. The GHQ-28 high- and low-score groups were considered gender-specific dependent variables due to the sex differences observed in the univariate analysis. Significant variables in the univariate analysis were considered independent variables in the multiple logistic regression analysis (forward stepwise selection). Five hundred and six people (410 male and 96 female) provided valid responses. Most respondents had poor mental health (66%, high-score group; 34%, low-score group). Both men and women who worked ≥10 hours/day had significantly poorer mental health than individuals who worked ≤9 hours/day. Individuals with low self-efficacy had significantly poorer mental health compared to people with high self-efficacy. Men who were able to maintain moderate hours of sleep and received support from colleagues, friends, and family had good mental health. Among women, mental health deteriorated with age. Furthermore, women who devoted most work time to home visit had good mental health. The mental health of caseworkers at welfare offices can be improved by reducing overtime work hours, ensuring sufficient hours of sleep for each worker, and fostering supportive communication and self-efficacy in the workplace. Furthermore, sex differences should be considered when assessing the mental health of workers at welfare offices in Japan.

Why Surinamese migrants in the Netherlands continue to use medicinal herbs from their home country.

PubMed

van Andel, Tinde; Westers, Paul

2010-02-17

When people migrate, they tend to bring along their medicinal plants. In order to improve migrant health, we need information on their traditional health beliefs and practices. This paper investigates medicinal plant use among Surinamese migrants in the Netherlands. Data from 210 semi-structured interviews among 1st and 2nd generation Surinamese migrants were analysed to determine which medicinal plants were used, for what purposes, which demographic, socio-economic or psycho-social factors play a role in the choice for traditional medicine and to clarify people's personal motives to use herbs. Variables associated with medicinal plant use were identified by using the Pearson gamma2 test and the two-sample t-test. After selecting significant variables by means of bivariate analyses, multinomial logistic regression with stepwise forward selection was used to assess whether medicinal plant use could be explained by a combination of these variables. More than 75% of the respondents used herbal medicine, and 66% did so in the past year. Herbs were more frequently employed for health promotion (39%) than for disease prevention or cure (both 27%). Almost half of the respondents who had been ill the last year had used herbal medicine. More than 140 herb species were mentioned during the interviews. Plant use was often related to certain culture-bound health beliefs. Spiritual baths were the most popular traditional practice, followed by genital steam baths, bitter tonics, and the consumption of bitter vegetables. Afro-Surinamers more frequently used herbal medicine than Hindustani. The WINTI belief strongly influenced plant use, as well as the occurrence of an illness in the past year, and frequent visits to Suriname. Age, gender, income and education had no significant effect on the use of traditional medicine. Surinamers stated that they used medicinal herbs because they grew up with them; herbs were more effective and had fewer side effects than conventional therapies. As long as certain culture-bound beliefs and health concepts remain prevalent among Surinamese migrants, and ties with their home country remain strong, they will continue using medicinal herbs from their country of origin. More research is needed on the health effects of frequently used medicinal plants by migrants in the Netherlands. Copyright 2009 Elsevier Ireland Ltd. All rights reserved.

Sex determination from the mandibular ramus flexure of Koreans by discrimination function analysis using three-dimensional mandible models.

PubMed

Lin, Chenghe; Jiao, Benzheng; Liu, Shanshan; Guan, Feng; Chung, Nak-Eun; Han, Seung-Ho; Lee, U-Young

2014-03-01

It has been known that mandible ramus flexure is an important morphologic trait for sex determination. However, it will be unavailable when mandible is incomplete or fragmented. Therefore, the anthropometric analysis on incomplete or fragmented mandible becomes more important. The aim of this study is to investigate the sex-discriminant potential of mandible ramus flexure on the Korean three-dimensional (3D) mandible models with anthropometric analysis. The sample consists of 240 three dimensional mandibular models obtained from Korean population (M:F; 120:120, mean age 46.2 y), collected by The Catholic Institute for Applied Anatomy, The Catholic University of Korea. Anthropometric information about 11 metric was taken with Mimics, anthropometry libraries toolkit. These parameters were subjected to different discriminant function analyses using SPSS 17.0. Univariate analyses showed that the resubstitution accuracies for sex determination range from 50.4 to 77.1%. Mandibular flexure upper border (MFUB), maximum ramus vertical height (MRVH), and upper ramus vertical height (URVH) expressed the greatest dimorphism, 72.1 to 77.1%. Bivariate analyses indicated that the combination of MFUB and MRVH hold even higher resubstitution accuracy of 81.7%. Furthermore, the direct and stepwise discriminant analyses with the variables on the upper ramus above flexure could predict sex in 83.3 and 85.0%, respectively. When all variables of mandibular ramus flexure were input in stepwise discriminant analysis, the resubstitution accuracy arrived as high as 88.8%. Therefore, we concluded that the upper ramus above flexure hold the larger potentials than the mandibular ramus flexure itself to predict sexes, and that the equations in bivariate and multivariate analysis from our study will be helpful for sex determination on Korean population in forensic science and law. Copyright © 2013 Elsevier Ireland Ltd. All rights reserved.

Factorial experimental design for the culture of human embryonic stem cells as aggregates in stirred suspension bioreactors reveals the potential for interaction effects between bioprocess parameters.

PubMed

Hunt, Megan M; Meng, Guoliang; Rancourt, Derrick E; Gates, Ian D; Kallos, Michael S

2014-01-01

Traditional optimization of culture parameters for the large-scale culture of human embryonic stem cells (ESCs) as aggregates is carried out in a stepwise manner whereby the effect of varying each culture parameter is investigated individually. However, as evidenced by the wide range of published protocols and culture performance indicators (growth rates, pluripotency marker expression, etc.), there is a lack of systematic investigation into the true effect of varying culture parameters especially with respect to potential interactions between culture variables. Here we describe the design and execution of a two-parameter, three-level (3(2)) factorial experiment resulting in nine conditions that were run in duplicate 125-mL stirred suspension bioreactors. The two parameters investigated here were inoculation density and agitation rate, which are easily controlled, but currently, poorly characterized. Cell readouts analyzed included fold expansion, maximum density, and exponential growth rate. Our results reveal that the choice of best case culture parameters was dependent on which cell property was chosen as the primary output variable. Subsequent statistical analyses via two-way analysis of variance indicated significant interaction effects between inoculation density and agitation rate specifically in the case of exponential growth rates. Results indicate that stepwise optimization has the potential to miss out on the true optimal case. In addition, choosing an optimum condition for a culture output of interest from the factorial design yielded similar results when repeated with the same cell line indicating reproducibility. We finally validated that human ESCs remain pluripotent in suspension culture as aggregates under our optimal conditions and maintain their differentiation capabilities as well as a stable karyotype and strong expression levels of specific human ESC markers over several passages in suspension bioreactors.

Evaluation of job satisfaction and working atmosphere of dental nurses in Germany.

PubMed

Goetz, Katja; Hasse, Philipp; Campbell, Stephen M; Berger, Sarah; Dörfer, Christof E; Hahn, Karolin; Szecsenyi, Joachim

2016-02-01

The purpose of the study was to assess the level of job satisfaction of dental nurses in ambulatory care and to explore the impact of aspects of working atmosphere on and their association with job satisfaction. This cross-sectional study was based on a job satisfaction survey. Data were collected from 612 dental nurses working in 106 dental care practices. Job satisfaction was measured with the 10-item Warr-Cook-Wall job satisfaction scale. Working atmosphere was measured with five items. Linear regression analyses were performed in which each item of the job satisfaction scale was handled as dependent variables. A stepwise linear regression analysis was performed with overall job satisfaction and the five items of working atmosphere, job satisfaction, and individual characteristics. The response rate was 88.3%. Dental nurses were satisfied with 'colleagues' and least satisfied with 'income.' Different aspects of job satisfaction were mostly associated with the following working atmosphere issues: 'responsibilities within the practice team are clear,' 'suggestions for improvement are taken seriously,' 'working atmosphere in the practice team is good,' and 'made easier to admit own mistakes.' Within the stepwise linear regression analysis, the aspect 'physical working condition' (β = 0.304) showed the highest association with overall job satisfaction. The total explained variance of the 14 associated variables was 0.722 with overall job satisfaction. Working atmosphere within this discrete sample of dental care practice seemed to be an important influence on reported working condition and job satisfaction for dental nurses. Because of the high association of job satisfaction with physical working condition, the importance of paying more attention to an ergonomic working position for dental nurses to ensure optimal quality of care is highlighted. © 2015 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.

Factors affecting Korean nursing student empowerment in clinical practice.

PubMed

Ahn, Yang-Heui; Choi, Jihea

2015-12-01

Understanding the phenomenon of nursing student empowerment in clinical practice is important. Investigating the cognition of empowerment and identifying predictors are necessary to enhance nursing student empowerment in clinical practice. To identify empowerment predictors for Korean nursing students in clinical practice based on studies by Bradbury-Jones et al. and Spreitzer. A cross-sectional design was used for this study. This study was performed in three nursing colleges in Korea, all of which had similar baccalaureate nursing curricula. Three hundred seven junior or senior nursing students completed a survey designed to measure factors that were hypothesized to influence nursing student empowerment in clinical practice. Data were collected from November to December 2011. Study variables included self-esteem, clinical decision making, being valued as a learner, satisfaction regarding practice with a team member, perception on professor/instructor/clinical preceptor attitude, and total number of clinical practice fields. Data were analyzed using stepwise multiple regression analyses. All of the hypothesized study variables were significantly correlated to nursing student empowerment. Stepwise multiple regression analysis revealed that clinical decision making in nursing (t=7.59, p<0.001), being valued as a learner (t=6.24, p<0.001), self-esteem (t=3.62, p<0.001), and total number of clinical practice fields (t=2.06, p=0.040). The explanatory power of these predictors was 35% (F=40.71, p<0.001). Enhancing nursing student empowerment in clinical practice will be possible by using educational strategies to improve nursing student clinical decision making. Simultaneously, attitudes of nurse educators are also important to ensure that nursing students are treated as valued learners and to increase student self-esteem in clinical practice. Finally, diverse clinical practice field environments should be considered to enhance experience. Copyright © 2015 Elsevier Ltd. All rights reserved.

Overgeneral autobiographical memory in patients with chronic pain.

PubMed

Liu, Xianhua; Liu, Yanling; Li, Li; Hu, Yiqiu; Wu, Siwei; Yao, Shuqiao

2014-03-01

Overgenerality and delay of the retrieval of autobiographical memory (AM) are well documented in a range of clinical conditions, particularly in patients with emotional disorder. The present study extended the investigation to chronic pain, attempting to identify whether the retrieval of AM in patients with chronic pain tends to be overgeneral or delayed. With an observational cross-sectional design, we evaluated the AM both in patients with chronic pain and healthy controls by Autobiographical Memory Test. Pain conditions were assessed using the pain diagnostic protocol, the short-form McGill Pain Questionnaire (SF-MPQ), and the Pain Self-Efficacy Questionnaire (PSEQ). Emotion was assessed using the Beck Depression Inventory-II (BDI-II) and the Beck Anxiety Inventory. Subjects included 176 outpatients with chronic pain lasting for at least 6 months and 170 healthy controls. 1) Compared with the healthy group, the chronic pain group had more overgeneral memories (OGMs) (F = 29.061, P < 0.01) and longer latency (F = 13.602, P < 0.01). 2) In the chronic pain group, the stepwise multiple regression models for variables predicting OGM were significant (P < 0.01). Specifically, the variance in OGM scores could be predicted by the BDI score (9.7%), pain chronicity (4.3%), PSEQ score (7.1%), and Affective Index (of SF-MPQ) score (2.7%). 3) In the chronic pain group, the stepwise multiple regression models for variables predicting latency were significant (P < 0.05). Specifically, the variance in latency could be predicted by age (3.1%), pain chronicity (2.7%), pain duration (4.3%), and PSEQ score (2.0%). The retrieval of AM in patients with chronic pain tends to be overgeneral and delayed, and the retrieval style of AM may be contributed to negative emotions and chronic pain conditions. Wiley Periodicals, Inc.

Melanin fluorescence spectra by step-wise three photon excitation

NASA Astrophysics Data System (ADS)

Lai, Zhenhua; Kerimo, Josef; DiMarzio, Charles A.

2012-03-01

Melanin is the characteristic chromophore of human skin with various potential biological functions. Kerimo discovered enhanced melanin fluorescence by stepwise three-photon excitation in 2011. In this article, step-wise three-photon excited fluorescence (STPEF) spectrum between 450 nm -700 nm of melanin is reported. The melanin STPEF spectrum exhibited an exponential increase with wavelength. However, there was a probability of about 33% that another kind of step-wise multi-photon excited fluorescence (SMPEF) that peaks at 525 nm, shown by previous research, could also be generated using the same process. Using an excitation source at 920 nm as opposed to 830 nm increased the potential for generating SMPEF peaks at 525 nm. The SMPEF spectrum peaks at 525 nm photo-bleached faster than STPEF spectrum.

[Satellite remote sensing retrieval of canopy nitrogen nutritional status of apple trees at blossom stage].

PubMed

Wang, Ling; Zhao, Geng-Xing; Zhu, Xi-Cun; Wang, Rui-Yan; Chang, Chun-Yan

2013-10-01

Taking Qixia City of Shandong, China as the study area, and based on the Landsat-5 TM and ALOS AVNIR-2 images, the canopy retrieval reflectance of apple trees at blossom stage was acquired. In combining with the measured reflectance of sample trees, the nitrogen-sensitive spectral indices were constructed and selected. By using the sensitive spectral indices as the independent variables, the nitrogen retrieval models were established, and the model with the best accuracy was used for spatial retrieve. The correlations between the spectral indices and the nitrogen nutritional status were in the order of canopy > leaf > flower. The sensitive indices were mainly composed of green, red, and near infrared bands. The accuracy of the retrieval models was in the order of support vector regression > multi-variable stepwise regression > one-variable regression. The retrieval results based on different images were similar, and showed that the leaf nitrogen content was mainly of grades 3-4 (27-33 g x kg(-1)), and the canopy nitrogen nutrient indices were mainly of grades 2-4 (TM: 38-47 g x kg(-1); ALOS: 32-41 g x kg(-1)). The spatial distribution of the retrieval nitrogen nutritional status based on different images also showed the similar trend, i. e., the nitrogen nutritional status was higher in the north and south than that in the middle part of the study area, and the areas with the high grades of leaf nitrogen and canopy nitrogen were mainly located in Sujiadian Town and Songshan subdistrict in the northwest, Zangjiazhuang Town and Tingkou Town in the northeast, and Shewopo Town in the south, which were consistent with the distribution of the key towns for apple production in Qixia City. This study provided a feasible method for the acquisition of nitrogen nutritional status of apple trees on macroscopic scale, and also, provided reference for other similar remote sensing retrievals.

Verifying and Postprocesing the Ensemble Spread-Error Relationship

NASA Astrophysics Data System (ADS)

Hopson, Tom; Knievel, Jason; Liu, Yubao; Roux, Gregory; Wu, Wanli

2013-04-01

With the increased utilization of ensemble forecasts in weather and hydrologic applications, there is a need to verify their benefit over less expensive deterministic forecasts. One such potential benefit of ensemble systems is their capacity to forecast their own forecast error through the ensemble spread-error relationship. The paper begins by revisiting the limitations of the Pearson correlation alone in assessing this relationship. Next, we introduce two new metrics to consider in assessing the utility an ensemble's varying dispersion. We argue there are two aspects of an ensemble's dispersion that should be assessed. First, and perhaps more fundamentally: is there enough variability in the ensembles dispersion to justify the maintenance of an expensive ensemble prediction system (EPS), irrespective of whether the EPS is well-calibrated or not? To diagnose this, the factor that controls the theoretical upper limit of the spread-error correlation can be useful. Secondly, does the variable dispersion of an ensemble relate to variable expectation of forecast error? Representing the spread-error correlation in relation to its theoretical limit can provide a simple diagnostic of this attribute. A context for these concepts is provided by assessing two operational ensembles: 30-member Western US temperature forecasts for the U.S. Army Test and Evaluation Command and 51-member Brahmaputra River flow forecasts of the Climate Forecast and Applications Project for Bangladesh. Both of these systems utilize a postprocessing technique based on quantile regression (QR) under a step-wise forward selection framework leading to ensemble forecasts with both good reliability and sharpness. In addition, the methodology utilizes the ensemble's ability to self-diagnose forecast instability to produce calibrated forecasts with informative skill-spread relationships. We will describe both ensemble systems briefly, review the steps used to calibrate the ensemble forecast, and present verification statistics using error-spread metrics, along with figures from operational ensemble forecasts before and after calibration.

Special Judo Fitness Test Level and Anthropometric Profile of Elite Spanish Judo Athletes.

PubMed

Casals, Cristina; Huertas, Jesús R; Franchini, Emerson; Sterkowicz-Przybycień, Katarzyna; Sterkowicz, Stanislaw; Gutiérrez-García, Carlos; Escobar-Molina, Raquel

2017-05-01

Casals, C, Huertas, JR, Franchini, E, Sterkowicz-Przybycień, K, Sterkowicz, S, Gutiérrez-García, C, and Escobar-Molina, R. Special judo fitness test level and anthropometric profile of elite spanish judo athletes. J Strength Cond Res 31(5): 1229-1235, 2017-The aim of this study was to determine the anthropometric variables that best predict Special Judo Fitness Test (SJFT) performance. In addition, anthropometric profiles of elite Spanish judo athletes were compared by sex and age category (seniors and juniors). In this cross-sectional study, a total of 51 (29 females) athletes from the Spanish National Judo Team were evaluated during a competitive period. All athletes performed the SJFT and underwent an anthropometric assessment through skinfold thickness measurements. Mann-Whitney comparisons by sex and age category showed that males had significantly higher muscle mass and lower fat mass than females (p < 0.001), whereas juniors and seniors exhibited few differences in body composition. Linear regression analyses (stepwise method) were performed to explore the relationships between anthropometric characteristics and SJFT variables. Model 1 included sex, age category, and body mass as predictors. Body mass and sex significantly predicted the SJFT index (R = 0.27, p < 0.001); thus, both criteria should be considered before interpreting the test. The predictors of model 2 were quick-assessment variables, including skinfolds, breadths, girths, and height. This regression model showed that the biceps skinfold significantly predicted the SJFT index in elite athletes (R = 0.31, p < 0.001). Model 3 included body compositions and somatotypes as predictors. Higher muscle and bone masses and lower ectomorphy were associated with better SJFT performance (R = 0.44, p < 0.001). Hence, training programs should attempt to increase the muscle mass percentage and reduce the upper arm fat, whereas the bone percentage could be considered in the selection of talented athletes in conjunction with other factors.

Multivariate analyses of Erzgebirge granite and rhyolite composition: Implications for classification of granites and their genetic relations

USGS Publications Warehouse

Forster, H.-J.; Davis, J.C.; Tischendorf, G.; Seltmann, R.

1999-01-01

High-precision major, minor and trace element analyses for 44 elements have been made of 329 Late Variscan granitic and rhyolitic rocks from the Erzgebirge metallogenic province of Germany. The intrusive histories of some of these granites are not completely understood and exposures of rock are not adequate to resolve relationships between what apparently are different plutons. Therefore, it is necessary to turn to chemical analyses to decipher the evolution of the plutons and their relationships. A new classification of Erzgebirge plutons into five major groups of granites, based on petrologic interpretations of geochemical and mineralogical relationships (low-F biotite granites; low-F two-mica granites; high-F, high-P2O5 Li-mica granites; high-F, low-P2O5 Li-mica granites; high-F, low-P2O5 biotite granites) was tested by multivariate techniques. Canonical analyses of major elements, minor elements, trace elements and ratio variables all distinguish the groups with differing amounts of success. Univariate ANOVA's, in combination with forward-stepwise and backward-elimination canonical analyses, were used to select ten variables which were most effective in distinguishing groups. In a biplot, groups form distinct clusters roughly arranged along a quadratic path. Within groups, individual plutons tend to be arranged in patterns possibly reflecting granitic evolution. Canonical functions were used to classify samples of rhyolites of unknown association into the five groups. Another canonical analysis was based on ten elements traditionally used in petrology and which were important in the new classification of granites. Their biplot pattern is similar to that from statistically chosen variables but less effective at distinguishing the five groups of granites. This study shows that multivariate statistical techniques can provide significant insight into problems of granitic petrogenesis and may be superior to conventional procedures for petrological interpretation.

An assessment of landscape characteristics affecting estuarine nitrogen loading in an urban watershed.

PubMed

Yang, Xiaojun

2012-02-01

Exploring the quantitative association between landscape characteristics and the ecological conditions of receiving waters has recently become an emerging area for eco-environmental research. While the landscape-water relationship research has largely targeted on inland aquatic systems, there has been an increasing need to develop methods and techniques that can better work with coastal and estuarine ecosystems. In this paper, we present a geospatial approach to examine the quantitative relationship between landscape characteristics and estuarine nitrogen loading in an urban watershed. The case study site is in the Pensacola estuarine drainage area, home of the city of Pensacola, Florida, USA, where vigorous urban sprawling has prompted growing concerns on the estuarine ecological health. Central to this research is a remote sensor image that has been used to extract land use/cover information and derive landscape metrics. Several significant landscape metrics are selected and spatially linked with the nitrogen loading data for the Pensacola bay area. Landscape metrics and nitrogen loading are summarized by equal overland flow-length rings, and their association is examined by using multivariate statistical analysis. And a stepwise model-building protocol is used for regression designs to help identify significant variables that can explain much of the variance in the nitrogen loading dataset. It is found that using landscape composition or spatial configuration alone can explain most of the nitrogen loading variability. Of all the regression models using metrics derived from a single land use/cover class as the independent variables, the one from the low density urban gives the highest adjusted R-square score, suggesting the impact of the watershed-wide urban sprawl upon this sensitive estuarine ecosystem. Measures towards the reduction of non-point source pollution from urban development are necessary in the area to protect the Pensacola bay ecosystem and its ecosystem services. Copyright © 2011 Elsevier Ltd. All rights reserved.

Early Holocene humidity patterns in the Iberian Peninsula reconstructed from lake, pollen and speleothem records

NASA Astrophysics Data System (ADS)

Morellón, Mario; Aranbarri, Josu; Moreno, Ana; González-Sampériz, Penélope; Valero-Garcés, Blas L.

2018-02-01

Comparison of selected, well-dated, lacustrine, speleothem and terrestrial pollen records spanning the Holocene onset and the Early Holocene (ca. 11.7-8 cal kyrs BP) in the Iberian Peninsula shows large hydrological fluctuations and landscape changes with a complex regional pattern in timing and intensity. Marine pollen records from Alboran, the Mediterranean and off shore Atlantic sites show a step-wise increase in moisture and forest during this transition. However, available continental records point to two main patterns of spatial and temporal hydrological variability: i) Atlantic-influenced sites located at the northwestern areas (Enol, Sanabria, Lucenza, PRD-4), characterized by a gradual increase in humidity from the end of the Younger Dryas to the Mid Holocene, similarly to most North Atlantic records; and ii) continental and Mediterranean-influenced sites (Laguna Grande, Villarquemado, Fuentillejo, Padul, Estanya, Banyoles, Salines), with prolonged arid conditions of variable temporal extension after the Younger Dryas, followed by an abrupt increase in moisture at 10-9 cal kyrs BP. Different local climate conditions influenced by topography or the variable sensitivity (gradual versus threshold values) of the proxies analyzed in each case are evaluated. Vegetation composition (conifers versus mesothermophilous taxa) and resilience would explain a subdued response of vegetation in central continental areas while in Mediterranean sites, insufficient summer moisture availability could not maintain high lake levels and promote mesophyte forest, in contrast to Atlantic-influenced areas. Comparison with available climate models, Greenland ice cores, North Atlantic marine sequences and continental records from Central and Northern Europe and the whole Mediterranean region underlines the distinctive character of the hydrological changes occurred in inner Iberia throughout the Early Holocene. The persistent arid conditions might be explained by the intensification of the summer drought due to the high seasonality contrast at these latitudes caused by the orbital-induced summer insolation maximum. New records, particularly from western and southernmost Iberia, and palaeoclimate models with higher spatial resolution would help to constrain these hypotheses.

Predictors of "occult" intra-abdominal injuries in blunt trauma patients.

PubMed

Parreira, José Gustavo; Malpaga, Juliano Mangini Dias; Olliari, Camilla Bilac; Perlingeiro, Jacqueline A G; Soldá, Silvia C; Assef, José Cesar

2015-01-01

to assess predictors of intra-abdominal injuries in blunt trauma patients admitted without abdominal pain or abnormalities on the abdomen physical examination. We conducted a retrospective analysis of trauma registry data, including adult blunt trauma patients admitted from 2008 to 2010 who sustained no abdominal pain or abnormalities on physical examination of the abdomen at admission and were submitted to computed tomography of the abdomen and/or exploratory laparotomy. Patients were assigned into: Group 1 (with intra-abdominal injuries) or Group 2 (without intra-abdominal injuries). Variables were compared between groups to identify those significantly associated with the presence of intra-abdominal injuries, adopting p<0.05 as significant. Subsequently, the variables with p<0.20 on bivariate analysis were selected to create a logistic regression model using the forward stepwise method. A total of 268 cases met the inclusion criteria. Patients in Group I were characterized as having significantly (p<0.05) lower mean AIS score for the head segment (1.0 ± 1.4 vs. 1.8 ± 1.9), as well as higher mean AIS thorax score (1.6 ± 1.7 vs. 0.9 ± 1.5) and ISS (25.7 ± 14.5 vs. 17,1 ± 13,1). The rate of abdominal injuries was significantly higher in run-over pedestrians (37.3%) and in motorcyclists (36.0%) (p<0.001). The resultant logistic regression model provided 73.5% accuracy for identifying abdominal injuries. The variables included were: motorcyclist accident as trauma mechanism (p<0.001 - OR 5.51; 95%CI 2.40-12.64), presence of rib fractures (p<0.003 - OR 3.00; 95%CI 1.47-6.14), run-over pedestrian as trauma mechanism (p=0.008 - OR 2.85; 95%CI 1.13-6.22) and abnormal neurological physical exam at admission (p=0.015 - OR 0.44; 95%CI 0.22-0.85). Intra-abdominal injuries were predominantly associated with trauma mechanism and presence of chest injuries.

Predictors of contemporary coronary artery bypass grafting outcomes.

PubMed

Weisel, Richard D; Nussmeier, Nancy; Newman, Mark F; Pearl, Ronald G; Wechsler, Andrew S; Ambrosio, Giuseppe; Pitt, Bertram; Clare, Robert M; Pieper, Karen S; Mongero, Linda; Reece, Tammy L; Yau, Terrence M; Fremes, Stephen; Menasché, Philippe; Lira, Armando; Harrington, Robert A; Ferguson, T Bruce

2014-12-01

The study objective was to identify the predictors of outcomes in a contemporary cohort of patients from the Reduction in cardiovascular Events by acaDesine in patients undergoing CABG (RED-CABG) trial. Despite the increasing risk profile of patients who undergo coronary artery bypass grafting, morbidity and mortality have remained low, and identification of the current predictors of adverse outcomes may permit new treatments to further improve outcomes. The RED-CABG trial was a multicenter, randomized, double-blind, placebo-controlled study that determined that acadesine did not reduce adverse events in moderately high-risk patients undergoing nonemergency coronary artery bypass grafting. The primary efficacy end point was a composite of all-cause death, nonfatal stroke, or the need for mechanical support for severe left ventricular dysfunction through postoperative day 28. Logistic regression modeling with stepwise variable selection identified which prespecified baseline characteristics were associated with the primary outcome. A second logistic model included intraoperative variables as potential covariates. The 4 independent preoperative risk factors predictive of the composite end point were (1) a history of heart failure (odds ratio, 2.9); (2) increasing age (odds ratio, 1.033 per decade); (3) a history of peripheral vascular disease (odds ratio, 1.6); and (4) receiving aspirin before coronary artery bypass grafting (odds ratio, 0.5), which was protective. The duration of the cardiopulmonary bypass (odds ratio, 1.8) was the only intraoperative variable that contributed to adverse outcomes. Patients who had heart failure and preserved systolic function had a similar high risk of adverse outcomes as those with low ejection fractions, and new approaches may mitigate this risk. Recognition of patients with excessive atherosclerotic burden may permit perioperative interventions to improve their outcomes. The contemporary risks of coronary artery bypass grafting have changed, and their identification may permit new methods to improve outcomes. Copyright © 2014 The American Association for Thoracic Surgery. Published by Elsevier Inc. All rights reserved.

Risk-adjusted capitation funding models for chronic disease in Australia: alternatives to casemix funding.

PubMed

Antioch, K M; Walsh, M K

2002-01-01

Under Australian casemix funding arrangements that use Diagnosis-Related Groups (DRGs) the average price is policy based, not benchmarked. Cost weights are too low for State-wide chronic disease services. Risk-adjusted Capitation Funding Models (RACFM) are feasible alternatives. A RACFM was developed for public patients with cystic fibrosis treated by an Australian Health Maintenance Organization (AHMO). Adverse selection is of limited concern since patients pay solidarity contributions via Medicare levy with no premium contributions to the AHMO. Sponsors paying premium subsidies are the State of Victoria and the Federal Government. Cost per patient is the dependent variable in the multiple regression. Data on DRG 173 (cystic fibrosis) patients were assessed for heteroskedasticity, multicollinearity, structural stability and functional form. Stepwise linear regression excluded non-significant variables. Significant variables were 'emergency' (1276.9), 'outlier' (6377.1), 'complexity' (3043.5), 'procedures' (317.4) and the constant (4492.7) (R(2)=0.21, SE=3598.3, F=14.39, Prob<0.0001. Regression coefficients represent the additional per patient costs summed to the base payment (constant). The model explained 21% of the variance in cost per patient. The payment rate is adjusted by a best practice annual admission rate per patient. The model is a blended RACFM for in-patient, out-patient, Hospital In The Home, Fee-For-Service Federal payments for drugs and medical services; lump sum lung transplant payments and risk sharing through cost (loss) outlier payments. State and Federally funded home and palliative services are 'carved out'. The model, which has national application via Coordinated Care Trials and by Australian States for RACFMs may be instructive for Germany, which plans to use Australian DRGs for casemix funding. The capitation alternative for chronic disease can improve equity, allocative efficiency and distributional justice. The use of Diagnostic Cost Groups (DCGs) is a promising alternative classification system for capitation arrangements.

Attitudes towards informed consent, confidentiality, and substitute treatment decisions in southern African medical students: a case study from Zimbabwe.

PubMed

Hipshman, L

1999-08-01

This study explored the attitudes of biomedical science students (medical students) in a non-Western setting towards three medical ethics concepts that are based on fundamental Western culture ethical principles. A dichotomous (agree/disagree) response questionnaire was constructed using Western ethnocentric culture (WEC) based perspectives of informed consent, confidentiality, and substitute decision-making. Hypothesized WEC-Biased responses were assigned to the questionnaire's questions or propositions. A number of useful responses (169) were obtained from a large, cross-sectional, convenience sample of the MBChB students at the University of Zimbabwe Medical School. Statistical analysis described the differences in response patterns between the student's responses compared to the hypothesized WEC-Biased response. The effect of the nine independent variables on selected dependent variables (responses to certain questionnaire questions) was analyzed by stepwise logistic regression. Students concurred with the hypothesized WEC-Biased responses for two-thirds of the questionnaire items. This agreement included support for the role of legal advocacy in the substitute decision-making process. The students disagreed with the hypothesized WEC-Biased responses in several important medical ethics aspects. Most notably, the students indicated that persons with mental dysfunctions, as a class, were properly considered incompetent to make treatment decisions. None of the studied independent variables was often associated with students' responses, but training year was more frequently implicated than either ethnicity or gender. In order to develop internationally and culturally relevant medical ethics standards, non-Western perspectives ought to be acknowledged and incorporated. Two main areas for further efforts include: curriculum development in ethics reasoning and related clinical (medico-legal) decision-making processes that would be relevant to medical students from various cultures, and; the testing of models that could increase legal system input in the clinical process in societies with limited jurisprudence resources.

Anthropometric Variables Accurately Predict Dual Energy X-Ray Absorptiometric-Derived Body Composition and Can Be Used to Screen for Diabetes

PubMed Central

Yavari, Reza; McEntee, Erin; McEntee, Michael; Brines, Michael

2011-01-01

The current world-wide epidemic of obesity has stimulated interest in developing simple screening methods to identify individuals with undiagnosed diabetes mellitus type 2 (DM2) or metabolic syndrome (MS). Prior work utilizing body composition obtained by sophisticated technology has shown that the ratio of abdominal fat to total fat is a good predictor for DM2 or MS. The goals of this study were to determine how well simple anthropometric variables predict the fat mass distribution as determined by dual energy x-ray absorptometry (DXA), and whether these are useful to screen for DM2 or MS within a population. To accomplish this, the body composition of 341 females spanning a wide range of body mass indices and with a 23% prevalence of DM2 and MS was determined using DXA. Stepwise linear regression models incorporating age, weight, height, waistline, and hipline predicted DXA body composition (i.e., fat mass, trunk fat, fat free mass, and total mass) with good accuracy. Using body composition as independent variables, nominal logistic regression was then performed to estimate the probability of DM2. The results show good discrimination with the receiver operating characteristic (ROC) having an area under the curve (AUC) of 0.78. The anthropometrically-derived body composition equations derived from the full DXA study group were then applied to a group of 1153 female patients selected from a general endocrinology practice. Similar to the smaller study group, the ROC from logistical regression using body composition had an AUC of 0.81 for the detection of DM2. These results are superior to screening based on questionnaires and compare favorably with published data derived from invasive testing, e.g., hemoglobin A1c. This anthropometric approach offers promise for the development of simple, inexpensive, non-invasive screening to identify individuals with metabolic dysfunction within large populations. PMID:21915276

Laboratory quality improvement in Tanzania.

PubMed

Andiric, Linda R; Massambu, Charles G

2015-04-01

The article describes the implementation and improvement in the first groups of medical laboratories in Tanzania selected to participate in the training program on Strengthening Laboratory Management Toward Accreditation (SLMTA). As in many other African nations, the selected improvement plan consisted of formalized hands-on training (SLMTA) that teaches the tasks and skills of laboratory management and provides the tools for implementation of best laboratory practice. Implementation of the improvements learned during training was verified before and after SLMTA with the World Health Organization African Region Stepwise Laboratory Improvement Process Towards Accreditation checklist. During a 4-year period, the selected laboratories described in this article demonstrated improvement with a range of 2% to 203% (cohort I) and 12% to 243% (cohort II) over baseline scores. The article describes the progress made in Tanzania's first cohorts, the obstacles encountered, and the lessons learned during the pilot and subsequent implementations. Copyright© by the American Society for Clinical Pathology.

Assessment of predictive models for chlorophyll-a concentration of a tropical lake

PubMed Central

2011-01-01

Background This study assesses four predictive ecological models; Fuzzy Logic (FL), Recurrent Artificial Neural Network (RANN), Hybrid Evolutionary Algorithm (HEA) and multiple linear regressions (MLR) to forecast chlorophyll- a concentration using limnological data from 2001 through 2004 of unstratified shallow, oligotrophic to mesotrophic tropical Putrajaya Lake (Malaysia). Performances of the models are assessed using Root Mean Square Error (RMSE), correlation coefficient (r), and Area under the Receiving Operating Characteristic (ROC) curve (AUC). Chlorophyll-a have been used to estimate algal biomass in aquatic ecosystem as it is common in most algae. Algal biomass indicates of the trophic status of a water body. Chlorophyll- a therefore, is an effective indicator for monitoring eutrophication which is a common problem of lakes and reservoirs all over the world. Assessments of these predictive models are necessary towards developing a reliable algorithm to estimate chlorophyll- a concentration for eutrophication management of tropical lakes. Results Same data set was used for models development and the data was divided into two sets; training and testing to avoid biasness in results. FL and RANN models were developed using parameters selected through sensitivity analysis. The selected variables were water temperature, pH, dissolved oxygen, ammonia nitrogen, nitrate nitrogen and Secchi depth. Dissolved oxygen, selected through stepwise procedure, was used to develop the MLR model. HEA model used parameters selected using genetic algorithm (GA). The selected parameters were pH, Secchi depth, dissolved oxygen and nitrate nitrogen. RMSE, r, and AUC values for MLR model were (4.60, 0.5, and 0.76), FL model were (4.49, 0.6, and 0.84), RANN model were (4.28, 0.7, and 0.79) and HEA model were (4.27, 0.7, and 0.82) respectively. Performance inconsistencies between four models in terms of performance criteria in this study resulted from the methodology used in measuring the performance. RMSE is based on the level of error of prediction whereas AUC is based on binary classification task. Conclusions Overall, HEA produced the best performance in terms of RMSE, r, and AUC values. This was followed by FL, RANN, and MLR. PMID:22372859

Race/ethnicity differences in the prevalence of noise-induced hearing loss in a group of metal fabricating workers.

PubMed

Ishii, E K; Talbott, E O

1998-08-01

The National Institute of Occupational Safety and Health rates noise-induced hearing loss as one of the top 10 work-related problems, involving at least 11 million workers. This retrospective study examines the differences between pure-tone hearing loss and race/ethnicity in 216 white and 70 non-white male metal fabricating workers. Significant variables upon univariate analysis found to be associated with race/ethnicity were mean years of employment and proportion of time worked without hearing protection. Among whites, the permanent threshold average for 1, 2, 3 and 5 kHz was 25.99 dB, compared with 17.71 dB in non-whites (P < 0.01). Backwards stepwise regression indicated that race/ethnicity, after being adjusted for years of employment, was the major-effect variable. The results of this study suggest that occupational noise exposure alone does not alone account for the racial hearing differences.

MMI: Multimodel inference or models with management implications?

USGS Publications Warehouse

Fieberg, J.; Johnson, Douglas H.

2015-01-01

We consider a variety of regression modeling strategies for analyzing observational data associated with typical wildlife studies, including all subsets and stepwise regression, a single full model, and Akaike's Information Criterion (AIC)-based multimodel inference. Although there are advantages and disadvantages to each approach, we suggest that there is no unique best way to analyze data. Further, we argue that, although multimodel inference can be useful in natural resource management, the importance of considering causality and accurately estimating effect sizes is greater than simply considering a variety of models. Determining causation is far more valuable than simply indicating how the response variable and explanatory variables covaried within a data set, especially when the data set did not arise from a controlled experiment. Understanding the causal mechanism will provide much better predictions beyond the range of data observed. Published 2015. This article is a U.S. Government work and is in the public domain in the USA.

Cultural predictors of caregiving burden of Chinese-Canadian family caregivers.

PubMed

Lai, Daniel W L

2007-01-01

The growth of research knowledge on culturally diverse family caregivers for the aging population lags behind the increase of culturally diverse populations in Canada. This study examines the effects of culture, as manifested through cultural variables, on the caregiving burden of family caregivers in a Chinese-Canadian community. A random sample of 339 Chinese-Canadian caregivers for elderly relatives completed a telephone survey. Results of hierarchical stepwise multiple regression analysis reported the predicting effects of culture-related variables on caregiving burden. The findings indicated that being an immigrant, having a Western or non-Western religion as compared to having no religion, and having a lower level of filial piety, predicted a higher level of caregiving burden. Chinese tradition does not exempt the caregivers from being burdened. Policies and practices should address the needs of family caregivers according to the intra-cultural variations identified in this study.

Non-fluent speech following stroke is caused by impaired efference copy.

PubMed

Feenaughty, Lynda; Basilakos, Alexandra; Bonilha, Leonardo; den Ouden, Dirk-Bart; Rorden, Chris; Stark, Brielle; Fridriksson, Julius

2017-09-01

Efference copy is a cognitive mechanism argued to be critical for initiating and monitoring speech: however, the extent to which breakdown of efference copy mechanisms impact speech production is unclear. This study examined the best mechanistic predictors of non-fluent speech among 88 stroke survivors. Objective speech fluency measures were subjected to a principal component analysis (PCA). The primary PCA factor was then entered into a multiple stepwise linear regression analysis as the dependent variable, with a set of independent mechanistic variables. Participants' ability to mimic audio-visual speech ("speech entrainment response") was the best independent predictor of non-fluent speech. We suggest that this "speech entrainment" factor reflects integrity of internal monitoring (i.e., efference copy) of speech production, which affects speech initiation and maintenance. Results support models of normal speech production and suggest that therapy focused on speech initiation and maintenance may improve speech fluency for individuals with chronic non-fluent aphasia post stroke.

Semi-local machine-learned kinetic energy density functional with third-order gradients of electron density

NASA Astrophysics Data System (ADS)

Seino, Junji; Kageyama, Ryo; Fujinami, Mikito; Ikabata, Yasuhiro; Nakai, Hiromi

2018-06-01

A semi-local kinetic energy density functional (KEDF) was constructed based on machine learning (ML). The present scheme adopts electron densities and their gradients up to third-order as the explanatory variables for ML and the Kohn-Sham (KS) kinetic energy density as the response variable in atoms and molecules. Numerical assessments of the present scheme were performed in atomic and molecular systems, including first- and second-period elements. The results of 37 conventional KEDFs with explicit formulae were also compared with those of the ML KEDF with an implicit formula. The inclusion of the higher order gradients reduces the deviation of the total kinetic energies from the KS calculations in a stepwise manner. Furthermore, our scheme with the third-order gradient resulted in the closest kinetic energies to the KS calculations out of the presented functionals.

Modeling of electrohydrodynamic drying process using response surface methodology

PubMed Central

Dalvand, Mohammad Jafar; Mohtasebi, Seyed Saeid; Rafiee, Shahin

2014-01-01

Energy consumption index is one of the most important criteria for judging about new, and emerging drying technologies. One of such novel and promising alternative of drying process is called electrohydrodynamic (EHD) drying. In this work, a solar energy was used to maintain required energy of EHD drying process. Moreover, response surface methodology (RSM) was used to build a predictive model in order to investigate the combined effects of independent variables such as applied voltage, field strength, number of discharge electrode (needle), and air velocity on moisture ratio, energy efficiency, and energy consumption as responses of EHD drying process. Three-levels and four-factor Box–Behnken design was employed to evaluate the effects of independent variables on system responses. A stepwise approach was followed to build up a model that can map the entire response surface. The interior relationships between parameters were well defined by RSM. PMID:24936289

Maintenance cost study of rotary wing aircraft, phase 2

NASA Technical Reports Server (NTRS)

1979-01-01

The Navy's maintenance and materials management data base was used in a study to determine the feasibility of predicting unscheduled maintenance costs for the dynamic systems of military rotary wing aircraft. The major operational and design variables were identified and the direct maintenance man hours per flight hour were obtained by step-wise multiple regression analysis. Five nonmilitary helicopter users were contacted to supply data on which variables were important factors in civil applications. These uses included offshore oil exploration and support, police and fire department rescue and enforcement, logging and heavy equipment movement, and U.S. Army military operations. The equations developed were highly effective in predicting unscheduled direct maintenance man hours per flying hours for military aircraft, but less effective for commercial or public service helicopters, probably because of the longer mission durations and the much higher utilization of civil users.

[Correlation between the width of lingual vein and the changes of hemodynamics of portal system in patients with primary liver cancer].

PubMed

Yue, Xiao-Qiang; Gao, Jing-Dong; Zhai, Xiao-Feng; Liu, Qing; Jiang, Dong; Ling, Chang-Quan

2006-09-01

To explore the correlation between the width of lingual varix and changes of hemodynamics of portal system in patients with primary liver cancer so as to supply the data for the forecast of portal hypertension by observing lingual varix. The diameter of lingual vein (Dlv) was measured by vernier caliper as dependent variable, and the diameters and indexes of hemodynamics of portal vessels were measured by Doppler as independent variables, then a multipe stepwise analysis was performed. The diameters of portal vein (Dpv) and splenic vein (Dsv) entered the formula Dlv (mm) = 0.185 + 0.311 Dsv (mm) + 0.236 Dpv (mm) when the entry and removal values were alpha(in)=0.10 and alpha(out)=0.15, respectively. The width of lingual vein is closely correlated with the diameters of portal vein and splenic vein in patients with primary liver cancer.

Selective catalytic two-step process for ethylene glycol from carbon monoxide

PubMed Central

Dong, Kaiwu; Elangovan, Saravanakumar; Sang, Rui; Spannenberg, Anke; Jackstell, Ralf; Junge, Kathrin; Li, Yuehui; Beller, Matthias

2016-01-01

Upgrading C1 chemicals (for example, CO, CO/H2, MeOH and CO2) with C–C bond formation is essential for the synthesis of bulk chemicals. In general, these industrially important processes (for example, Fischer Tropsch) proceed at drastic reaction conditions (>250 °C; high pressure) and suffer from low selectivity, which makes high capital investment necessary and requires additional purifications. Here, a different strategy for the preparation of ethylene glycol (EG) via initial oxidative coupling and subsequent reduction is presented. Separating coupling and reduction steps allows for a completely selective formation of EG (99%) from CO. This two-step catalytic procedure makes use of a Pd-catalysed oxycarbonylation of amines to oxamides at room temperature (RT) and subsequent Ru- or Fe-catalysed hydrogenation to EG. Notably, in the first step the required amines can be efficiently reused. The presented stepwise oxamide-mediated coupling provides the basis for a new strategy for selective upgrading of C1 chemicals. PMID:27377550

Stepwise hydrolysis to improve carbon releasing efficiency from sludge.

PubMed

Liu, Hongbo; Wang, Yuanyuan; Wang, Ling; Yu, Tiantian; Fu, Bo; Liu, He

2017-08-01

Based on thermal alkaline hydrolysis (TAH), a novel strategy of stepwise hydrolysis was developed to improve carbon releasing efficiency from waste activated sludge (WAS). By stepwise increasing hydrolysis intensity, conventional sludge hydrolysis (the control) was divided into four stages for separately recovering sludge carbon sources with different bonding strengths, namely stage 1 (60 °C, pH 6.0-8.0), stage 2 (80 °C, pH 6.0-8.0), stage 3 (80 °C, pH 10.0) and stage 4 (90 °C, pH 12.0). Results indicate stepwise hydrolysis could enhance the amount of released soluble chemical oxygen demand (SCOD) for almost 2 times, from 7200 to 14,693 mg/L, and the released carbon presented better biodegradability, with BOD/COD of 0.47 and volatile fatty acids (VFAs) yield of 0.37 g VFAs/g SCOD via anaerobic fermentation. Moreover, stepwise hydrolysis also improved the dewaterability of hydrolyzed sludge, capillary suction time (CST) reducing from 2500 to 1600 s. Economic assessment indicates stepwise hydrolysis shows less alkali demand and lower thermal energy consumption than those of the control. Furthermore, results of this study help support the concepts of improving carbon recovery in wastewater by manipulating WAS composition and the idea of classifiably recovering the nutrients in WAS. Copyright © 2017 Elsevier Ltd. All rights reserved.

Discrimination of Geographical Origin of Asian Garlic Using Isotopic and Chemical Datasets under Stepwise Principal Component Analysis.

PubMed

Liu, Tsang-Sen; Lin, Jhen-Nan; Peng, Tsung-Ren

2018-01-16

Isotopic compositions of δ 2 H, δ 18 O, δ 13 C, and δ 15 N and concentrations of 22 trace elements from garlic samples were analyzed and processed with stepwise principal component analysis (PCA) to discriminate garlic's country of origin among Asian regions including South Korea, Vietnam, Taiwan, and China. Results indicate that there is no single trace-element concentration or isotopic composition that can accomplish the study's purpose and the stepwise PCA approach proposed does allow for discrimination between countries on a regional basis. Sequentially, Step-1 PCA distinguishes garlic's country of origin among Taiwanese, South Korean, and Vietnamese samples; Step-2 PCA discriminates Chinese garlic from South Korean garlic; and Step-3 and Step-4 PCA, Chinese garlic from Vietnamese garlic. In model tests, countries of origin of all audit samples were correctly discriminated by stepwise PCA. Consequently, this study demonstrates that stepwise PCA as applied is a simple and effective approach to discriminating country of origin among Asian garlics. © 2018 American Academy of Forensic Sciences.

Development of land-use regression models for exposure assessment to ultrafine particles in Rome, Italy

NASA Astrophysics Data System (ADS)

Cattani, Giorgio; Gaeta, Alessandra; di Menno di Bucchianico, Alessandro; de Santis, Antonella; Gaddi, Raffaela; Cusano, Mariacarmela; Ancona, Carla; Badaloni, Chiara; Forastiere, Francesco; Gariazzo, Claudio; Sozzi, Roberto; Inglessis, Marco; Silibello, Camillo; Salvatori, Elisabetta; Manes, Fausto; Cesaroni, Giulia; The Viias Study Group

2017-05-01

The health effects of long-term exposure to ultrafine particles (UFPs) are poorly understood. Data on spatial contrasts in ambient ultrafine particles (UFPs) concentrations are needed with fine resolution. This study aimed to assess the spatial variability of total particle number concentrations (PNC, a proxy for UFPs) in the city of Rome, Italy, using land use regression (LUR) models, and the correspondent exposure of population here living. PNC were measured using condensation particle counters at the building facade of 28 homes throughout the city. Three 7-day monitoring periods were carried out during cold, warm and intermediate seasons. Geographic Information System predictor variables, with buffers of varying size, were evaluated to model spatial variations of PNC. A stepwise forward selection procedure was used to develop a "base" linear regression model according to the European Study of Cohorts for Air Pollution Effects project methodology. Other variables were then included in more enhanced models and their capability of improving model performance was evaluated. Four LUR models were developed. Local variation in UFPs in the study area can be largely explained by the ratio of traffic intensity and distance to the nearest major road. The best model (adjusted R2 = 0.71; root mean square error = ±1,572 particles/cm³, leave one out cross validated R2 = 0.68) was achieved by regressing building and street configuration variables against residual from the "base" model, which added 3% more to the total variance explained. Urban green and population density in a 5,000 m buffer around each home were also relevant predictors. The spatial contrast in ambient PNC across the large conurbation of Rome, was successfully assessed. The average exposure of subjects living in the study area was 16,006 particles/cm³ (SD 2165 particles/cm³, range: 11,075-28,632 particles/cm³). A total of 203,886 subjects (16%) lives in Rome within 50 m from a high traffic road and they experience the highest exposure levels (18,229 particles/cm³). The results will be used to estimate the long-term health effects of ultrafine particle exposure of participants in Rome.

Predictors of Dropout by Female Obese Patients Treated with a Group Cognitive Behavioral Therapy to Promote Weight Loss.

PubMed

Sawamoto, Ryoko; Nozaki, Takehiro; Furukawa, Tomokazu; Tanahashi, Tokusei; Morita, Chihiro; Hata, Tomokazu; Komaki, Gen; Sudo, Nobuyuki

2016-01-01

To investigate predictors of dropout from a group cognitive behavioral therapy (CBT) intervention for overweight or obese women. 119 overweight and obese Japanese women aged 25-65 years who attended an outpatient weight loss intervention were followed throughout the 7-month weight loss phase. Somatic characteristics, socioeconomic status, obesity-related diseases, diet and exercise habits, and psychological variables (depression, anxiety, self-esteem, alexithymia, parenting style, perfectionism, and eating attitude) were assessed at baseline. Significant variables, extracted by univariate statistical analysis, were then used as independent variables in a stepwise multiple logistic regression analysis with dropout as the dependent variable. 90 participants completed the weight loss phase, giving a dropout rate of 24.4%. The multiple logistic regression analysis demonstrated that compared to completers the dropouts had significantly stronger body shape concern, tended to not have jobs, perceived their mothers to be less caring, and were more disorganized in temperament. Of all these factors, the best predictor of dropout was shape concern. Shape concern, job condition, parenting care, and organization predicted dropout from the group CBT weight loss intervention for overweight or obese Japanese women. © 2016 S. Karger GmbH, Freiburg.

Cognitive and Personality Components Underlying Spoken Idiom Comprehension in Context. An Exploratory Study.

PubMed

Cacciari, Cristina; Corrardini, Paola; Ferlazzo, Fabio

2018-01-01

In this exploratory study, we investigated whether and to what extent individual differences in cognitive and personality variables are associated with spoken idiom comprehension in context. Language unimpaired participants were enrolled in a cross-modal lexical decision study in which semantically ambiguous Italian idioms (i.e., strings with both a literal and an idiomatic interpretation as, for instance, break the ice ), predictable or unpredictable before the string offset, were embedded in idiom-biasing contexts. To explore the contributions of different cognitive and personality components, participants also completed a series of tests respectively assessing general speed, inhibitory control, short-term and working memory, cognitive flexibility, crystallized and fluid intelligence, and personality. Stepwise regression analyses revealed that online idiom comprehension was associated with the participants' working memory, inhibitory control and crystallized verbal intelligence, an association modulated by idiom type. Also personality-related variables (State Anxiety and Openness to Experience) were associated with idiom comprehension, although in marginally significant ways. These results contribute to the renewed interest on how individual variability modulates language comprehension, and for the first time document contributions of individual variability on lexicalized, high frequency multi-word expressions as idioms adding new knowledge to the existing evidence on metaphor and sarcasm.

Predictors of Dropout by Female Obese Patients Treated with a Group Cognitive Behavioral Therapy to Promote Weight Loss

PubMed Central

Sawamoto, Ryoko; Nozaki, Takehiro; Furukawa, Tomokazu; Tanahashi, Tokusei; Morita, Chihiro; Hata, Tomokazu; Komaki, Gen; Sudo, Nobuyuki

2016-01-01

Objective To investigate predictors of dropout from a group cognitive behavioral therapy (CBT) intervention for overweight or obese women. Methods 119 overweight and obese Japanese women aged 25-65 years who attended an outpatient weight loss intervention were followed throughout the 7-month weight loss phase. Somatic characteristics, socioeconomic status, obesity-related diseases, diet and exercise habits, and psychological variables (depression, anxiety, self-esteem, alexithymia, parenting style, perfectionism, and eating attitude) were assessed at baseline. Significant variables, extracted by univariate statistical analysis, were then used as independent variables in a stepwise multiple logistic regression analysis with dropout as the dependent variable. Results 90 participants completed the weight loss phase, giving a dropout rate of 24.4%. The multiple logistic regression analysis demonstrated that compared to completers the dropouts had significantly stronger body shape concern, tended to not have jobs, perceived their mothers to be less caring, and were more disorganized in temperament. Of all these factors, the best predictor of dropout was shape concern. Conclusion Shape concern, job condition, parenting care, and organization predicted dropout from the group CBT weight loss intervention for overweight or obese Japanese women. PMID:26745715