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.

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.

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.

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.

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.

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.

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.

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.

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…

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.

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.

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

PubMed Central

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

2015-01-01

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

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

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.

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

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.

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 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.

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

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.

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.

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.

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.

STEP and STEPSPL: Computer programs for aerodynamic model structure determination and parameter estimation

NASA Technical Reports Server (NTRS)

Batterson, J. G.

1986-01-01

The successful parametric modeling of the aerodynamics for an airplane operating at high angles of attack or sideslip is performed in two phases. First the aerodynamic model structure must be determined and second the associated aerodynamic parameters (stability and control derivatives) must be estimated for that model. The purpose of this paper is to document two versions of a stepwise regression computer program which were developed for the determination of airplane aerodynamic model structure and to provide two examples of their use on computer generated data. References are provided for the application of the programs to real flight data. The two computer programs that are the subject of this report, STEP and STEPSPL, are written in FORTRAN IV (ANSI l966) compatible with a CDC FTN4 compiler. Both programs are adaptations of a standard forward stepwise regression algorithm. The purpose of the adaptation is to facilitate the selection of a adequate mathematical model of the aerodynamic force and moment coefficients of an airplane from flight test data. The major difference between STEP and STEPSPL is in the basis for the model. The basis for the model in STEP is the standard polynomial Taylor's series expansion of the aerodynamic function about some steady-state trim condition. Program STEPSPL utilizes a set of spline basis functions.

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

Some Considerations on the Partial Credit Model

ERIC Educational Resources Information Center

Verhelst, N. D.; Verstralen, H. H. F. M.

2008-01-01

The Partial Credit Model (PCM) is sometimes interpreted as a model for stepwise solution of polytomously scored items, where the item parameters are interpreted as difficulties of the steps. It is argued that this interpretation is not justified. A model for stepwise solution is discussed. It is shown that the PCM is suited to model sums of binary…

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

Using discrete choice modeling to generate resource selection functions for female polar bears in the Beaufort Sea

USGS Publications Warehouse

Durner, George M.; Amstrup, Steven C.; Nielson, Ryan M.; McDonald, Trent; Huzurbazar, Snehalata

2004-01-01

Polar bears (Ursus maritimus) depend on ice-covered seas to satisfy life history requirements. Modern threats to polar bears include oil spills in the marine environment and changes in ice composition resulting from climate change. Managers need practical models that explain the distribution of bears in order to assess the impacts of these threats. We explored the use of discrete choice models to describe habitat selection by female polar bears in the Beaufort Sea. Using stepwise procedures we generated resource selection models of habitat use. Sea ice characteristics and ocean depths at known polar bear locations were compared to the same features at randomly selected locations. Models generated for each of four seasons confirmed complexities of habitat use by polar bears and their response to numerous factors. Bears preferred shallow water areas where different ice types intersected. Variation among seasons was reflected mainly in differential selection of total ice concentration, ice stages, floe sizes, and their interactions. Distance to the nearest ice interface was a significant term in models for three seasons. Water depth was selected as a significant term in all seasons, possibly reflecting higher productivity in shallow water areas. Preliminary tests indicate seasonal models can predict polar bear distribution based on prior sea ice 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.

A stepwise model to predict monthly streamflow

NASA Astrophysics Data System (ADS)

Mahmood Al-Juboori, Anas; Guven, Aytac

2016-12-01

In this study, a stepwise model empowered with genetic programming is developed to predict the monthly flows of Hurman River in Turkey and Diyalah and Lesser Zab Rivers in Iraq. The model divides the monthly flow data to twelve intervals representing the number of months in a year. The flow of a month, t is considered as a function of the antecedent month's flow (t - 1) and it is predicted by multiplying the antecedent monthly flow by a constant value called K. The optimum value of K is obtained by a stepwise procedure which employs Gene Expression Programming (GEP) and Nonlinear Generalized Reduced Gradient Optimization (NGRGO) as alternative to traditional nonlinear regression technique. The degree of determination and root mean squared error are used to evaluate the performance of the proposed models. The results of the proposed model are compared with the conventional Markovian and Auto Regressive Integrated Moving Average (ARIMA) models based on observed monthly flow data. The comparison results based on five different statistic measures show that the proposed stepwise model performed better than Markovian model and ARIMA model. The R2 values of the proposed model range between 0.81 and 0.92 for the three rivers in this study.

Determination of Failure Point of Asphalt-Mixture Fatigue-Test Results Using the Flow Number Method

NASA Astrophysics Data System (ADS)

Wulan, C. E. P.; Setyawan, A.; Pramesti, F. P.

2018-03-01

The failure point of the results of fatigue tests of asphalt mixtures performed in controlled stress mode is difficult to determine. However, several methods from empirical studies are available to solve this problem. The objectives of this study are to determine the fatigue failure point of the results of indirect tensile fatigue tests using the Flow Number Method and to determine the best Flow Number model for the asphalt mixtures tested. In order to achieve these goals, firstly the best asphalt mixture of three was selected based on their Marshall properties. Next, the Indirect Tensile Fatigue Test was performed on the chosen asphalt mixture. The stress-controlled fatigue tests were conducted at a temperature of 20°C and frequency of 10 Hz, with the application of three loads: 500, 600, and 700 kPa. The last step was the application of the Flow Number methods, namely the Three-Stages Model, FNest Model, Francken Model, and Stepwise Method, to the results of the fatigue tests to determine the failure point of the specimen. The chosen asphalt mixture is EVA (Ethyl Vinyl Acetate) polymer -modified asphalt mixture with 6.5% OBC (Optimum Bitumen Content). Furthermore, the result of this study shows that the failure points of the EVA-modified asphalt mixture under loads of 500, 600, and 700 kPa are 6621, 4841, and 611 for the Three-Stages Model; 4271, 3266, and 537 for the FNest Model; 3401, 2431, and 421 for the Francken Model, and 6901, 6841, and 1291 for the Stepwise Method, respectively. These different results show that the bigger the loading, the smaller the number of cycles to failure. However, the best FN results are shown by the Three-Stages Model and the Stepwise Method, which exhibit extreme increases after the constant development of accumulated strain.

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.

A statistical model for Windstorm Variability over the British Isles based on Large-scale Atmospheric and Oceanic Mechanisms

NASA Astrophysics Data System (ADS)

Kirchner-Bossi, Nicolas; Befort, Daniel J.; Wild, Simon B.; Ulbrich, Uwe; Leckebusch, Gregor C.

2016-04-01

Time-clustered winter storms are responsible for a majority of the wind-induced losses in Europe. Over last years, different atmospheric and oceanic large-scale mechanisms as the North Atlantic Oscillation (NAO) or the Meridional Overturning Circulation (MOC) have been proven to drive some significant portion of the windstorm variability over Europe. In this work we systematically investigate the influence of different large-scale natural variability modes: more than 20 indices related to those mechanisms with proven or potential influence on the windstorm frequency variability over Europe - mostly SST- or pressure-based - are derived by means of ECMWF ERA-20C reanalysis during the last century (1902-2009), and compared to the windstorm variability for the European winter (DJF). Windstorms are defined and tracked as in Leckebusch et al. (2008). The derived indices are then employed to develop a statistical procedure including a stepwise Multiple Linear Regression (MLR) and an Artificial Neural Network (ANN), aiming to hindcast the inter-annual (DJF) regional windstorm frequency variability in a case study for the British Isles. This case study reveals 13 indices with a statistically significant coupling with seasonal windstorm counts. The Scandinavian Pattern (SCA) showed the strongest correlation (0.61), followed by the NAO (0.48) and the Polar/Eurasia Pattern (0.46). The obtained indices (standard-normalised) are selected as predictors for a windstorm variability hindcast model applied for the British Isles. First, a stepwise linear regression is performed, to identify which mechanisms can explain windstorm variability best. Finally, the indices retained by the stepwise regression are used to develop a multlayer perceptron-based ANN that hindcasted seasonal windstorm frequency and clustering. Eight indices (SCA, NAO, EA, PDO, W.NAtl.SST, AMO (unsmoothed), EA/WR and Trop.N.Atl SST) are retained by the stepwise regression. Among them, SCA showed the highest linear coefficient, followed by SST in western Atlantic, AMO and NAO. The explanatory regression model (considering all time steps) provided a Coefficient of Determination (R^2) of 0.75. A predictive version of the linear model applying a leave-one-out cross-validation (LOOCV) shows an R2 of 0.56 and a relative RMSE of 4.67 counts/season. An ANN-based nonlinear hindcast model for the seasonal windstorm frequency is developed with the aim to improve the stepwise hindcast ability and thus better predict a time-clustered season over the case study. A 7 node-hidden layer perceptron is set, and the LOOCV procedure reveals a R2 of 0.71. In comparison to the stepwise MLR the RMSE is reduced a 20%. This work shows that for the British Isles case study, most of the interannual variability can be explained by certain large-scale mechanisms, considering also nonlinear effects (ANN). This allows to discern a time-clustered season from a non-clustered one - a key issue for applications e.g., in the (re)insurance industry.

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

Impact of metal ionic characteristics on adsorption potential of Ficus carica leaves using QSPR modeling.

PubMed

Batool, Fozia; Iqbal, Shahid; Akbar, Jamshed

2018-04-03

The present study describes Quantitative Structure Property Relationship (QSPR) modeling to relate metal ions characteristics with adsorption potential of Ficus carica leaves for 13 selected metal ions (Ca +2 , Cr +3 , Co +2 , Cu +2 , Cd +2 , K +1 , Mg +2 , Mn +2 , Na +1 , Ni +2 , Pb +2 , Zn +2 , and Fe +2 ) to generate QSPR model. A set of 21 characteristic descriptors were selected and relationship of these metal characteristics with adsorptive behavior of metal ions was investigated. Stepwise Multiple Linear Regression (SMLR) analysis and Artificial Neural Network (ANN) were applied for descriptors selection and model generation. Langmuir and Freundlich isotherms were also applied on adsorption data to generate proper correlation for experimental findings. Model generated indicated covalent index as the most significant descriptor, which is responsible for more than 90% predictive adsorption (α = 0.05). Internal validation of model was performed by measuring [Formula: see text] (0.98). The results indicate that present model is a useful tool for prediction of adsorptive behavior of different metal ions based on their ionic characteristics.

QSRR modeling for diverse drugs using different feature selection methods coupled with linear and nonlinear regressions.

PubMed

Goodarzi, Mohammad; Jensen, Richard; Vander Heyden, Yvan

2012-12-01

A Quantitative Structure-Retention Relationship (QSRR) is proposed to estimate the chromatographic retention of 83 diverse drugs on a Unisphere poly butadiene (PBD) column, using isocratic elutions at pH 11.7. Previous work has generated QSRR models for them using Classification And Regression Trees (CART). In this work, Ant Colony Optimization is used as a feature selection method to find the best molecular descriptors from a large pool. In addition, several other selection methods have been applied, such as Genetic Algorithms, Stepwise Regression and the Relief method, not only to evaluate Ant Colony Optimization as a feature selection method but also to investigate its ability to find the important descriptors in QSRR. Multiple Linear Regression (MLR) and Support Vector Machines (SVMs) were applied as linear and nonlinear regression methods, respectively, giving excellent correlation between the experimental, i.e. extrapolated to a mobile phase consisting of pure water, and predicted logarithms of the retention factors of the drugs (logk(w)). The overall best model was the SVM one built using descriptors selected by ACO. Copyright © 2012 Elsevier B.V. All rights reserved.

Classification of lung cancer patients and controls by chromatography of modified nucleosides in serum

USGS Publications Warehouse

McEntire, John E.; Kuo, Kenneth C.; Smith, Mark E.; Stalling, David L.; Richens, Jack W.; Zumwalt, Robert W.; Gehrke, Charles W.; Papermaster, Ben W.

1989-01-01

A wide spectrum of modified nucleosides has been quantified by high-performance liquid chromatography in serum of 49 male lung cancer patients, 35 patients with other cancers, and 48 patients hospitalized for nonneoplastic diseases. Data for 29 modified nucleoside peaks were normalized to an internal standard and analyzed by discriminant analysis and stepwise discriminant analysis. A model based on peaks selected by a stepwise discriminant procedure correctly classified 79% of the cancer and 75% of the noncancer subjects. It also demonstrated 84% sensitivity and 79% specificity when comparing lung cancer to noncancer subjects, and 80% sensitivity and 55% specificity in comparing lung cancer to other cancers. The nucleoside peaks having the greatest influence on the models varied dependent on the subgroups compared, confirming the importance of quantifying a wide array of nucleosides. These data support and expand previous studies which reported the utility of measuring modified nucleoside levels in serum and show that precise measurement of an array of 29 modified nucleosides in serum by high-performance liquid chromatography with UV scanning with subsequent data modeling may provide a clinically useful approach to patient classification in diagnosis and subsequent therapeutic monitoring.

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.

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®

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.

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.

Experience, Reflect, Critique: The End of the "Learning Cycles" Era

ERIC Educational Resources Information Center

Seaman, Jayson

2008-01-01

According to prevailing models, experiential learning is by definition a stepwise process beginning with direct experience, followed by reflection, followed by learning. It has been argued, however, that stepwise models inadequately explain the holistic learning processes that are central to learning from experience, and that they lack scientific…

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.

Planning for robust reserve networks using uncertainty analysis

USGS Publications Warehouse

Moilanen, A.; Runge, M.C.; Elith, Jane; Tyre, A.; Carmel, Y.; Fegraus, E.; Wintle, B.A.; Burgman, M.; Ben-Haim, Y.

2006-01-01

Planning land-use for biodiversity conservation frequently involves computer-assisted reserve selection algorithms. Typically such algorithms operate on matrices of species presence?absence in sites, or on species-specific distributions of model predicted probabilities of occurrence in grid cells. There are practically always errors in input data?erroneous species presence?absence data, structural and parametric uncertainty in predictive habitat models, and lack of correspondence between temporal presence and long-run persistence. Despite these uncertainties, typical reserve selection methods proceed as if there is no uncertainty in the data or models. Having two conservation options of apparently equal biological value, one would prefer the option whose value is relatively insensitive to errors in planning inputs. In this work we show how uncertainty analysis for reserve planning can be implemented within a framework of information-gap decision theory, generating reserve designs that are robust to uncertainty. Consideration of uncertainty involves modifications to the typical objective functions used in reserve selection. Search for robust-optimal reserve structures can still be implemented via typical reserve selection optimization techniques, including stepwise heuristics, integer-programming and stochastic global search.

What to do When Scalar Invariance Fails: The Extended Alignment Method for Multi-Group Factor Analysis Comparison of Latent Means Across Many Groups.

PubMed

Marsh, Herbert W; Guo, Jiesi; Parker, Philip D; Nagengast, Benjamin; Asparouhov, Tihomir; Muthén, Bengt; Dicke, Theresa

2017-01-12

Scalar invariance is an unachievable ideal that in practice can only be approximated; often using potentially questionable approaches such as partial invariance based on a stepwise selection of parameter estimates with large modification indices. Study 1 demonstrates an extension of the power and flexibility of the alignment approach for comparing latent factor means in large-scale studies (30 OECD countries, 8 factors, 44 items, N = 249,840), for which scalar invariance is typically not supported in the traditional confirmatory factor analysis approach to measurement invariance (CFA-MI). Importantly, we introduce an alignment-within-CFA (AwC) approach, transforming alignment from a largely exploratory tool into a confirmatory tool, and enabling analyses that previously have not been possible with alignment (testing the invariance of uniquenesses and factor variances/covariances; multiple-group MIMIC models; contrasts on latent means) and structural equation models more generally. Specifically, it also allowed a comparison of gender differences in a 30-country MIMIC AwC (i.e., a SEM with gender as a covariate) and a 60-group AwC CFA (i.e., 30 countries × 2 genders) analysis. Study 2, a simulation study following up issues raised in Study 1, showed that latent means were more accurately estimated with alignment than with the scalar CFA-MI, and particularly with partial invariance scalar models based on the heavily criticized stepwise selection strategy. In summary, alignment augmented by AwC provides applied researchers from diverse disciplines considerable flexibility to address substantively important issues when the traditional CFA-MI scalar model does not fit the data. (PsycINFO Database Record (c) 2017 APA, all rights reserved).

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

Lateral-Directional Parameter Estimation on the X-48B Aircraft Using an Abstracted, Multi-Objective Effector Model

NASA Technical Reports Server (NTRS)

Ratnayake, Nalin A.; Waggoner, Erin R.; Taylor, Brian R.

2011-01-01

The problem of parameter estimation on hybrid-wing-body aircraft is complicated by the fact that many design candidates for such aircraft involve a large number of aerodynamic control effectors that act in coplanar motion. This adds to the complexity already present in the parameter estimation problem for any aircraft with a closed-loop control system. Decorrelation of flight and simulation data must be performed in order to ascertain individual surface derivatives with any sort of mathematical confidence. Non-standard control surface configurations, such as clamshell surfaces and drag-rudder modes, further complicate the modeling task. In this paper, time-decorrelation techniques are applied to a model structure selected through stepwise regression for simulated and flight-generated lateral-directional parameter estimation data. A virtual effector model that uses mathematical abstractions to describe the multi-axis effects of clamshell surfaces is developed and applied. Comparisons are made between time history reconstructions and observed data in order to assess the accuracy of the regression model. The Cram r-Rao lower bounds of the estimated parameters are used to assess the uncertainty of the regression model relative to alternative models. Stepwise regression was found to be a useful technique for lateral-directional model design for hybrid-wing-body aircraft, as suggested by available flight data. Based on the results of this study, linear regression parameter estimation methods using abstracted effectors are expected to perform well for hybrid-wing-body aircraft properly equipped for the task.

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

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.

Multiobjective Model of Time-of-Use and Stepwise Power Tariff for Residential Consumers in Regulated Power Markets

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

Zhou, Bin; Yang, Rui; Li, Canbing

Here, time-of-use (TOU) rates and stepwise power tariff (SPT) are important economic levers to motivate residents to shift their electricity usage in response to electricity price. In this paper, a new multiobjective optimal tariff-making model of time-of-use and stepwise power tariff (TOUSPT) is proposed, which combines the complementary characteristics of two power tariffs, for residential energy conservation and peak load shaving. In the proposed approach, the residential demand response with price elasticity in regulated power market is considered to determine the optimum peak-valley TOU tariffs for each stepwise electricity partition. Furthermore, a practical case study is implemented to test themore » effectiveness of the proposed TOUSPT, and the results demonstrate that TOUSPT can achieve efficient end-use energy saving and also shift load from peak to off-peak periods.« less

Multiobjective Model of Time-of-Use and Stepwise Power Tariff for Residential Consumers in Regulated Power Markets

DOE PAGES

Zhou, Bin; Yang, Rui; Li, Canbing; ...

2017-07-04

Here, time-of-use (TOU) rates and stepwise power tariff (SPT) are important economic levers to motivate residents to shift their electricity usage in response to electricity price. In this paper, a new multiobjective optimal tariff-making model of time-of-use and stepwise power tariff (TOUSPT) is proposed, which combines the complementary characteristics of two power tariffs, for residential energy conservation and peak load shaving. In the proposed approach, the residential demand response with price elasticity in regulated power market is considered to determine the optimum peak-valley TOU tariffs for each stepwise electricity partition. Furthermore, a practical case study is implemented to test themore » effectiveness of the proposed TOUSPT, and the results demonstrate that TOUSPT can achieve efficient end-use energy saving and also shift load from peak to off-peak periods.« less

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.

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.

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.

Procedure for the Selection and Validation of a Calibration Model I-Description and Application.

PubMed

Desharnais, Brigitte; Camirand-Lemyre, Félix; Mireault, Pascal; Skinner, Cameron D

2017-05-01

Calibration model selection is required for all quantitative methods in toxicology and more broadly in bioanalysis. This typically involves selecting the equation order (quadratic or linear) and weighting factor correctly modelizing the data. A mis-selection of the calibration model will generate lower quality control (QC) accuracy, with an error up to 154%. Unfortunately, simple tools to perform this selection and tests to validate the resulting model are lacking. We present a stepwise, analyst-independent scheme for selection and validation of calibration models. The success rate of this scheme is on average 40% higher than a traditional "fit and check the QCs accuracy" method of selecting the calibration model. Moreover, the process was completely automated through a script (available in Supplemental Data 3) running in RStudio (free, open-source software). The need for weighting was assessed through an F-test using the variances of the upper limit of quantification and lower limit of quantification replicate measurements. When weighting was required, the choice between 1/x and 1/x2 was determined by calculating which option generated the smallest spread of weighted normalized variances. Finally, model order was selected through a partial F-test. The chosen calibration model was validated through Cramer-von Mises or Kolmogorov-Smirnov normality testing of the standardized residuals. Performance of the different tests was assessed using 50 simulated data sets per possible calibration model (e.g., linear-no weight, quadratic-no weight, linear-1/x, etc.). This first of two papers describes the tests, procedures and outcomes of the developed procedure using real LC-MS-MS results for the quantification of cocaine and naltrexone. © The Author 2017. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

Multiple-Objective Stepwise Calibration Using Luca

USGS Publications Warehouse

Hay, Lauren E.; Umemoto, Makiko

2007-01-01

This report documents Luca (Let us calibrate), a multiple-objective, stepwise, automated procedure for hydrologic model calibration and the associated graphical user interface (GUI). Luca is a wizard-style user-friendly GUI that provides an easy systematic way of building and executing a calibration procedure. The calibration procedure uses the Shuffled Complex Evolution global search algorithm to calibrate any model compiled with the U.S. Geological Survey's Modular Modeling System. This process assures that intermediate and final states of the model are simulated consistently with measured values.

Infrared laser driven double proton transfer. An optimal control theory study

NASA Astrophysics Data System (ADS)

Abdel-Latif, Mahmoud K.; Kühn, Oliver

2010-02-01

Laser control of ultrafast double proton transfer is investigated for a two-dimensional model system describing stepwise and concerted transfer pathways. The pulse design has been done by employing optimal control theory in combination with the multiconfiguration time-dependent Hartree wave packet propagation. The obtained laser fields correspond to multiple pump-dump pulse sequences. Special emphasis is paid to the relative importance of stepwise and concerted transfer pathways for the driven wave packet and its dependence on the parameters of the model Hamiltonian as well as on the propagation time. While stepwise transfer is dominating in all cases considered, for high barrier systems concerted transfer proceeding via tunneling can make a contribution.

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

Improved model of the retardance in citric acid coated ferrofluids using stepwise regression

NASA Astrophysics Data System (ADS)

Lin, J. F.; Qiu, X. R.

2017-06-01

Citric acid (CA) coated Fe3O4 ferrofluids (FFs) have been conducted for biomedical application. The magneto-optical retardance of CA coated FFs was measured by a Stokes polarimeter. Optimization and multiple regression of retardance in FFs were executed by Taguchi method and Microsoft Excel previously, and the F value of regression model was large enough. However, the model executed by Excel was not systematic. Instead we adopted the stepwise regression to model the retardance of CA coated FFs. From the results of stepwise regression by MATLAB, the developed model had highly predictable ability owing to F of 2.55897e+7 and correlation coefficient of one. The average absolute error of predicted retardances to measured retardances was just 0.0044%. Using the genetic algorithm (GA) in MATLAB, the optimized parametric combination was determined as [4.709 0.12 39.998 70.006] corresponding to the pH of suspension, molar ratio of CA to Fe3O4, CA volume, and coating temperature. The maximum retardance was found as 31.712°, close to that obtained by evolutionary solver in Excel and a relative error of -0.013%. Above all, the stepwise regression method was successfully used to model the retardance of CA coated FFs, and the maximum global retardance was determined by the use of GA.

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.

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.

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.

Prediction of troponin-T degradation using color image texture features in 10d aged beef longissimus steaks.

PubMed

Sun, X; Chen, K J; Berg, E P; Newman, D J; Schwartz, C A; Keller, W L; Maddock Carlin, K R

2014-02-01

The objective was to use digital color image texture features to predict troponin-T degradation in beef. Image texture features, including 88 gray level co-occurrence texture features, 81 two-dimension fast Fourier transformation texture features, and 48 Gabor wavelet filter texture features, were extracted from color images of beef strip steaks (longissimus dorsi, n = 102) aged for 10d obtained using a digital camera and additional lighting. Steaks were designated degraded or not-degraded based on troponin-T degradation determined on d 3 and d 10 postmortem by immunoblotting. Statistical analysis (STEPWISE regression model) and artificial neural network (support vector machine model, SVM) methods were designed to classify protein degradation. The d 3 and d 10 STEPWISE models were 94% and 86% accurate, respectively, while the d 3 and d 10 SVM models were 63% and 71%, respectively, in predicting protein degradation in aged meat. STEPWISE and SVM models based on image texture features show potential to predict troponin-T degradation in meat. © 2013.

Shuffling cross-validation-bee algorithm as a new descriptor selection method for retention studies of pesticides in biopartitioning micellar chromatography.

PubMed

Zarei, Kobra; Atabati, Morteza; Ahmadi, Monire

2017-05-04

Bee algorithm (BA) is an optimization algorithm inspired by the natural foraging behaviour of honey bees to find the optimal solution which can be proposed to feature selection. In this paper, shuffling cross-validation-BA (CV-BA) was applied to select the best descriptors that could describe the retention factor (log k) in the biopartitioning micellar chromatography (BMC) of 79 heterogeneous pesticides. Six descriptors were obtained using BA and then the selected descriptors were applied for model development using multiple linear regression (MLR). The descriptor selection was also performed using stepwise, genetic algorithm and simulated annealing methods and MLR was applied to model development and then the results were compared with those obtained from shuffling CV-BA. The results showed that shuffling CV-BA can be applied as a powerful descriptor selection method. Support vector machine (SVM) was also applied for model development using six selected descriptors by BA. The obtained statistical results using SVM were better than those obtained using MLR, as the root mean square error (RMSE) and correlation coefficient (R) for whole data set (training and test), using shuffling CV-BA-MLR, were obtained as 0.1863 and 0.9426, respectively, while these amounts for the shuffling CV-BA-SVM method were obtained as 0.0704 and 0.9922, respectively.

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,…

Modelling of volunteer satisfaction and intention to remain in community service: A stepwise approach

NASA Astrophysics Data System (ADS)

Hasan, Hazlin; Wahid, Sharifah Norhuda Syed; Jais, Mohammad; Ridzuan, Arifi

2017-05-01

The purpose of this study is to obtain the most significant model of volunteer satisfaction and intention to remain in community service by using a stepwise approach. Currently, Malaysians, young and old are showing more interests in involving themselves in community service projects, either locally or internationally. This positive movement of serving the needy is somehow being halted by the lack of human and financial resources. Therefore, the trend today sees organizers of such projects depend heavily on voluntary supports as they enable project managers to add and to expand the quantity and diversity of services offered without exhausting the minimal budget available. Volunteers are considered a valuable commodity as the available pool of volunteers may be declining due to various reasons which include the volunteer satisfaction. In tandem with the existing situation, a selected sample of 215 diploma students from one of the public universities in Malaysia, who have been involved in at least one community service project, agreed that everybody should have a volunteering intention in helping others. The findings revealed that the most significant model obtained contains two factors that contributed towards intention to remain in community service; work assignment and organizational support, with work assignment becoming the most significant factor. Further research on the differences of intention to remain in community service between students' stream and gender would be conducted to contribute to the body of knowledge.

Delineating chalk sand distribution of Ekofisk formation using probabilistic neural network (PNN) and stepwise regression (SWR): Case study Danish North Sea field

NASA Astrophysics Data System (ADS)

Haris, A.; Nafian, M.; Riyanto, A.

2017-07-01

Danish North Sea Fields consist of several formations (Ekofisk, Tor, and Cromer Knoll) that was started from the age of Paleocene to Miocene. In this study, the integration of seismic and well log data set is carried out to determine the chalk sand distribution in the Danish North Sea field. The integration of seismic and well log data set is performed by using the seismic inversion analysis and seismic multi-attribute. The seismic inversion algorithm, which is used to derive acoustic impedance (AI), is model-based technique. The derived AI is then used as external attributes for the input of multi-attribute analysis. Moreover, the multi-attribute analysis is used to generate the linear and non-linear transformation of among well log properties. In the case of the linear model, selected transformation is conducted by weighting step-wise linear regression (SWR), while for the non-linear model is performed by using probabilistic neural networks (PNN). The estimated porosity, which is resulted by PNN shows better suited to the well log data compared with the results of SWR. This result can be understood since PNN perform non-linear regression so that the relationship between the attribute data and predicted log data can be optimized. The distribution of chalk sand has been successfully identified and characterized by porosity value ranging from 23% up to 30%.

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.

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.

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.

Generalized linear and generalized additive models in studies of species distributions: Setting the scene

USGS Publications Warehouse

Guisan, Antoine; Edwards, T.C.; Hastie, T.

2002-01-01

An important statistical development of the last 30 years has been the advance in regression analysis provided by generalized linear models (GLMs) and generalized additive models (GAMs). Here we introduce a series of papers prepared within the framework of an international workshop entitled: Advances in GLMs/GAMs modeling: from species distribution to environmental management, held in Riederalp, Switzerland, 6-11 August 2001. We first discuss some general uses of statistical models in ecology, as well as provide a short review of several key examples of the use of GLMs and GAMs in ecological modeling efforts. We next present an overview of GLMs and GAMs, and discuss some of their related statistics used for predictor selection, model diagnostics, and evaluation. Included is a discussion of several new approaches applicable to GLMs and GAMs, such as ridge regression, an alternative to stepwise selection of predictors, and methods for the identification of interactions by a combined use of regression trees and several other approaches. We close with an overview of the papers and how we feel they advance our understanding of their application to ecological modeling. ?? 2002 Elsevier Science B.V. All rights reserved.

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.

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.

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

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.

Predicting the diurnal blue-sky albedo of soils using their laboratory reflectance spectra and roughness indices

NASA Astrophysics Data System (ADS)

Cierniewski, Jerzy; Ceglarek, Jakub; Karnieli, Arnon; Królewicz, Sławomir; Kaźmierowski, Cezary; Zagajewski, Bogdan

2017-10-01

The objective of this study was to assess the relationship between the hyperspectral reflectance of soils and their albedo, measured under various roughness conditions. 108 soil surface measurements were conducted in Poland and Israel. Each surface was characterised by its diurnal albedo variation in the field as well as by its reflectance spectra obtained in the laboratory. The best fit to the model was achieved by post-processing manipulation of the spectra, namely second derivate transformation. Using a stepwise elimination process, four spectral wavelengths and the roughness index were selected for modelling. The resulting models allowed the albedo of a soil to be predicted for its different roughness states and any solar zenith angle, provided that hyperspectral reflectance data is available.

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.

Advances in variable selection methods I: Causal selection methods versus stepwise regression and principal component analysis on data of known and unknown functional relationships

EPA Science Inventory

Hydrological predictions at a watershed scale are commonly based on extrapolation and upscaling of hydrological behavior at plot and hillslope scales. Yet, dominant hydrological drivers at a hillslope may not be as dominant at the watershed scale because of the heterogeneity of w...

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

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.

Finite cohesion due to chain entanglement in polymer melts.

PubMed

Cheng, Shiwang; Lu, Yuyuan; Liu, Gengxin; Wang, Shi-Qing

2016-04-14

Three different types of experiments, quiescent stress relaxation, delayed rate-switching during stress relaxation, and elastic recovery after step strain, are carried out in this work to elucidate the existence of a finite cohesion barrier against free chain retraction in entangled polymers. Our experiments show that there is little hastened stress relaxation from step-wise shear up to γ = 0.7 and step-wise extension up to the stretching ratio λ = 1.5 at any time before or after the Rouse time. In contrast, a noticeable stress drop stemming from the built-in barrier-free chain retraction is predicted using the GLaMM model. In other words, the experiment reveals a threshold magnitude of step-wise deformation below which the stress relaxation follows identical dynamics whereas the GLaMM or Doi-Edwards model indicates a monotonic acceleration of the stress relaxation dynamics as a function of the magnitude of the step-wise deformation. Furthermore, a sudden application of startup extension during different stages of stress relaxation after a step-wise extension, i.e. the delayed rate-switching experiment, shows that the geometric condensation of entanglement strands in the cross-sectional area survives beyond the reptation time τd that is over 100 times the Rouse time τR. Our results point to the existence of a cohesion barrier that can prevent free chain retraction upon moderate deformation in well-entangled polymer melts.

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.

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.

Inclusion of Endogenous Hormone Levels in Risk Prediction Models of Postmenopausal Breast Cancer

PubMed Central

Tworoger, Shelley S.; Zhang, Xuehong; Eliassen, A. Heather; Qian, Jing; Colditz, Graham A.; Willett, Walter C.; Rosner, Bernard A.; Kraft, Peter; Hankinson, Susan E.

2014-01-01

Purpose Endogenous hormones are risk factors for postmenopausal breast cancer, and their measurement may improve our ability to identify high-risk women. Therefore, we evaluated whether inclusion of plasma estradiol, estrone, estrone sulfate, testosterone, dehydroepiandrosterone sulfate, prolactin, and sex hormone–binding globulin (SHBG) improved risk prediction for postmenopausal invasive breast cancer (n = 437 patient cases and n = 775 controls not using postmenopausal hormones) in the Nurses' Health Study. Methods We evaluated improvement in the area under the curve (AUC) for 5-year risk of invasive breast cancer by adding each hormone to the Gail and Rosner-Colditz risk scores. We used stepwise regression to identify the subset of hormones most associated with risk and assessed AUC improvement; we used 10-fold cross validation to assess model overfitting. Results Each hormone was associated with breast cancer risk (odds ratio doubling, 0.82 [SHBG] to 1.37 [estrone sulfate]). Individual hormones improved the AUC by 1.3 to 5.2 units relative to the Gail score and 0.3 to 2.9 for the Rosner-Colditz score. Estrone sulfate, testosterone, and prolactin were selected by stepwise regression and increased the AUC by 5.9 units (P = .003) for the Gail score and 3.4 (P = .04) for the Rosner-Colditz score. In cross validation, the average AUC change across the validation data sets was 6.0 (P = .002) and 3.0 units (P = .03), respectively. Similar results were observed for estrogen receptor–positive disease (selected hormones: estrone sulfate, testosterone, prolactin, and SHBG; change in AUC, 8.8 [P < .001] for Gail score and 5.8 [P = .004] for Rosner-Colditz score). Conclusion Our results support that endogenous hormones improve risk prediction for invasive breast cancer and could help identify women who may benefit from chemoprevention or more screening. PMID:25135988

In silico modelling of directed evolution: Implications for experimental design and stepwise evolution.

PubMed

Wedge, David C; Rowe, William; Kell, Douglas B; Knowles, Joshua

2009-03-07

We model the process of directed evolution (DE) in silico using genetic algorithms. Making use of the NK fitness landscape model, we analyse the effects of mutation rate, crossover and selection pressure on the performance of DE. A range of values of K, the epistatic interaction of the landscape, are considered, and high- and low-throughput modes of evolution are compared. Our findings suggest that for runs of or around ten generations' duration-as is typical in DE-there is little difference between the way in which DE needs to be configured in the high- and low-throughput regimes, nor across different degrees of landscape epistasis. In all cases, a high selection pressure (but not an extreme one) combined with a moderately high mutation rate works best, while crossover provides some benefit but only on the less rugged landscapes. These genetic algorithms were also compared with a "model-based approach" from the literature, which uses sequential fixing of the problem parameters based on fitting a linear model. Overall, we find that purely evolutionary techniques fare better than do model-based approaches across all but the smoothest landscapes.

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.

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.

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.

Relationship between COMLEX-USA scores and performance on the American Osteopathic Board of Emergency Medicine Part I certifying examination.

PubMed

Li, Feiming; Gimpel, John R; Arenson, Ethan; Song, Hao; Bates, Bruce P; Ludwin, Fredric

2014-04-01

Few studies have investigated how well scores from the Comprehensive Osteopathic Medical Licensing Examination-USA (COMLEX-USA) series predict resident outcomes, such as performance on board certification examinations. To determine how well COMLEX-USA predicts performance on the American Osteopathic Board of Emergency Medicine (AOBEM) Part I certification examination. The target study population was first-time examinees who took AOBEM Part I in 2011 and 2012 with matched performances on COMLEX-USA Level 1, Level 2-Cognitive Evaluation (CE), and Level 3. Pearson correlations were computed between AOBEM Part I first-attempt scores and COMLEX-USA performances to measure the association between these examinations. Stepwise linear regression analysis was conducted to predict AOBEM Part I scores by the 3 COMLEX-USA scores. An independent t test was conducted to compare mean COMLEX-USA performances between candidates who passed and who failed AOBEM Part I, and a stepwise logistic regression analysis was used to predict the log-odds of passing AOBEM Part I on the basis of COMLEX-USA scores. Scores from AOBEM Part I had the highest correlation with COMLEX-USA Level 3 scores (.57) and slightly lower correlation with COMLEX-USA Level 2-CE scores (.53). The lowest correlation was between AOBEM Part I and COMLEX-USA Level 1 scores (.47). According to the stepwise regression model, COMLEX-USA Level 1 and Level 2-CE scores, which residency programs often use as selection criteria, together explained 30% of variance in AOBEM Part I scores. Adding Level 3 scores explained 37% of variance. The independent t test indicated that the 397 examinees passing AOBEM Part I performed significantly better than the 54 examinees failing AOBEM Part I in all 3 COMLEX-USA levels (P<.001 for all 3 levels). The logistic regression model showed that COMLEX-USA Level 1 and Level 3 scores predicted the log-odds of passing AOBEM Part I (P=.03 and P<.001, respectively). The present study empirically supported the predictive and discriminant validities of the COMLEX-USA series in relation to the AOBEM Part I certification examination. Although residency programs may use COMLEX-USA Level 1 and Level 2-CE scores as partial criteria in selecting residents, Level 3 scores, though typically not available at the time of application, are actually the most statistically related to performances on AOBEM Part I.

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.

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.

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.

Endometrial cancer risk prediction including serum-based biomarkers: results from the EPIC cohort.

PubMed

Fortner, Renée T; Hüsing, Anika; Kühn, Tilman; Konar, Meric; Overvad, Kim; Tjønneland, Anne; Hansen, Louise; Boutron-Ruault, Marie-Christine; Severi, Gianluca; Fournier, Agnès; Boeing, Heiner; Trichopoulou, Antonia; Benetou, Vasiliki; Orfanos, Philippos; Masala, Giovanna; Agnoli, Claudia; Mattiello, Amalia; Tumino, Rosario; Sacerdote, Carlotta; Bueno-de-Mesquita, H B As; Peeters, Petra H M; Weiderpass, Elisabete; Gram, Inger T; Gavrilyuk, Oxana; Quirós, J Ramón; Maria Huerta, José; Ardanaz, Eva; Larrañaga, Nerea; Lujan-Barroso, Leila; Sánchez-Cantalejo, Emilio; Butt, Salma Tunå; Borgquist, Signe; Idahl, Annika; Lundin, Eva; Khaw, Kay-Tee; Allen, Naomi E; Rinaldi, Sabina; Dossus, Laure; Gunter, Marc; Merritt, Melissa A; Tzoulaki, Ioanna; Riboli, Elio; Kaaks, Rudolf

2017-03-15

Endometrial cancer risk prediction models including lifestyle, anthropometric and reproductive factors have limited discrimination. Adding biomarker data to these models may improve predictive capacity; to our knowledge, this has not been investigated for endometrial cancer. Using a nested case-control study within the European Prospective Investigation into Cancer and Nutrition (EPIC) cohort, we investigated the improvement in discrimination gained by adding serum biomarker concentrations to risk estimates derived from an existing risk prediction model based on epidemiologic factors. Serum concentrations of sex steroid hormones, metabolic markers, growth factors, adipokines and cytokines were evaluated in a step-wise backward selection process; biomarkers were retained at p < 0.157 indicating improvement in the Akaike information criterion (AIC). Improvement in discrimination was assessed using the C-statistic for all biomarkers alone, and change in C-statistic from addition of biomarkers to preexisting absolute risk estimates. We used internal validation with bootstrapping (1000-fold) to adjust for over-fitting. Adiponectin, estrone, interleukin-1 receptor antagonist, tumor necrosis factor-alpha and triglycerides were selected into the model. After accounting for over-fitting, discrimination was improved by 2.0 percentage points when all evaluated biomarkers were included and 1.7 percentage points in the model including the selected biomarkers. Models including etiologic markers on independent pathways and genetic markers may further improve discrimination. © 2016 UICC.

The use of sea ice habitat by female polar bears in the Beaufort Sea

USGS Publications Warehouse

Durner, George M.; Amstrup, Steven C.; Nielson, Ryan M.; McDonald, Trent

2003-01-01

Polar bears (Ursus maritimus) depend on ice-covered seas to satisfy life history requirements. Modern threats to polar bears include oil spills in the marine environment and changes in ice composition resulting from climate change. Managers need practical models that explain the distribution of bears in order to assess the impacts of these threats. We used stepwise procedures to create resource selection models of habitat use for radio-collared female polar bears in the Beaufort Sea. Sea ice characteristics and ocean depths at known polar bear locations were compared to the same features at randomly selected locations. Models generated for each of four seasons confirmed complexities of habitat use by polar bears and their response to numerous factors. Bears preferred shallow water areas where ice concentrations were > 80 % and different ice types intersected. Variation among seasons was reflected mainly in differential selection of ice stages, floe sizes, and their interactions. Water depth, total ice concentration and distance to the nearest interface between different ice types were significant terms in models for most seasons. Variation in ice stage and form also appeared in three models, and several interaction effects were identified. Habitat selection by polar bears is likely related to prey abundance and availability. Use of habitats in shallow water possibly reflects higher productivity in those areas. Habitat use in close proximity to ice edges is probably related to greater access of prey in those habitats.

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.

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.

A simple approach to quantitative analysis using three-dimensional spectra based on selected Zernike moments.

PubMed

Zhai, Hong Lin; Zhai, Yue Yuan; Li, Pei Zhen; Tian, Yue Li

2013-01-21

A very simple approach to quantitative analysis is proposed based on the technology of digital image processing using three-dimensional (3D) spectra obtained by high-performance liquid chromatography coupled with a diode array detector (HPLC-DAD). As the region-based shape features of a grayscale image, Zernike moments with inherently invariance property were employed to establish the linear quantitative models. This approach was applied to the quantitative analysis of three compounds in mixed samples using 3D HPLC-DAD spectra, and three linear models were obtained, respectively. The correlation coefficients (R(2)) for training and test sets were more than 0.999, and the statistical parameters and strict validation supported the reliability of established models. The analytical results suggest that the Zernike moment selected by stepwise regression can be used in the quantitative analysis of target compounds. Our study provides a new idea for quantitative analysis using 3D spectra, which can be extended to the analysis of other 3D spectra obtained by different methods or instruments.

Quantitative structure-activity relationship of the curcumin-related compounds using various regression methods

NASA Astrophysics Data System (ADS)

Khazaei, Ardeshir; Sarmasti, Negin; Seyf, Jaber Yousefi

2016-03-01

Quantitative structure activity relationship were used to study a series of curcumin-related compounds with inhibitory effect on prostate cancer PC-3 cells, pancreas cancer Panc-1 cells, and colon cancer HT-29 cells. Sphere exclusion method was used to split data set in two categories of train and test set. Multiple linear regression, principal component regression and partial least squares were used as the regression methods. In other hand, to investigate the effect of feature selection methods, stepwise, Genetic algorithm, and simulated annealing were used. In two cases (PC-3 cells and Panc-1 cells), the best models were generated by a combination of multiple linear regression and stepwise (PC-3 cells: r2 = 0.86, q2 = 0.82, pred_r2 = 0.93, and r2m (test) = 0.43, Panc-1 cells: r2 = 0.85, q2 = 0.80, pred_r2 = 0.71, and r2m (test) = 0.68). For the HT-29 cells, principal component regression with stepwise (r2 = 0.69, q2 = 0.62, pred_r2 = 0.54, and r2m (test) = 0.41) is the best method. The QSAR study reveals descriptors which have crucial role in the inhibitory property of curcumin-like compounds. 6ChainCount, T_C_C_1, and T_O_O_7 are the most important descriptors that have the greatest effect. With a specific end goal to design and optimization of novel efficient curcumin-related compounds it is useful to introduce heteroatoms such as nitrogen, oxygen, and sulfur atoms in the chemical structure (reduce the contribution of T_C_C_1 descriptor) and increase the contribution of 6ChainCount and T_O_O_7 descriptors. Models can be useful in the better design of some novel curcumin-related compounds that can be used in the treatment of prostate, pancreas, and colon cancers.

Computational Studies on the Synthesis of β-Lactams via [2+2] Thermal Cycloadditions

NASA Astrophysics Data System (ADS)

Arrieta, Ana; Lecea, Begoña; Cossío, Fernando P.

The main computational studies on the formation of β-lactams through [2+2] cycloadditions published during 1992-2008 are reported with special emphasis on the mechanistic and selectivity aspects of these reactions. Disconnection of the N1-C2 and C3-C4 bonds of the azetidin-2-one ring leads to the reaction between ketenes and imines. Computational and experimental results point to a stepwise mechanism for this reaction. The first step consists of a nucleophilic attack of the iminic nitrogen on the sp-hybridized carbon atom of the ketene. The zwitterionic intermediate thus formed yields the corresponding β-lactam by means of a four-electron conrotatoty electrocyclization. The steroecontrol and the periselectivity of the reaction support this two-step mechanism. The [2+2] cycloaddition between isocyanates and alkenes takes place via a concerted (but asynchronous) mechanism that can be interpreted in terms of a [π2s + (π2s + π2s)] interaction between both reactants. Both the regio and the stereochemistry observed are compatible with this computational model. However, the combination of solvent and substituent effects can result in a stepwise mechanism.

A systematic review finds methodological improvements necessary for prognostic models in determining traumatic brain injury outcomes.

PubMed

Mushkudiani, Nino A; Hukkelhoven, Chantal W P M; Hernández, Adrián V; Murray, Gordon D; Choi, Sung C; Maas, Andrew I R; Steyerberg, Ewout W

2008-04-01

To describe the modeling techniques used for early prediction of outcome in traumatic brain injury (TBI) and to identify aspects for potential improvements. We reviewed key methodological aspects of studies published between 1970 and 2005 that proposed a prognostic model for the Glasgow Outcome Scale of TBI based on admission data. We included 31 papers. Twenty-four were single-center studies, and 22 reported on fewer than 500 patients. The median of the number of initially considered predictors was eight, and on average five of these were selected for the prognostic model, generally including age, Glasgow Coma Score (or only motor score), and pupillary reactivity. The most common statistical technique was logistic regression with stepwise selection of predictors. Model performance was often quantified by accuracy rate rather than by more appropriate measures such as the area under the receiver-operating characteristic curve. Model validity was addressed in 15 studies, but mostly used a simple split-sample approach, and external validation was performed in only four studies. Although most models agree on the three most important predictors, many were developed on small sample sizes within single centers and hence lack generalizability. Modeling strategies have to be improved, and include external validation.

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.

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…

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.

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 mathematical model of breast cancer development, local treatment and recurrence.

PubMed

Enderling, Heiko; Chaplain, Mark A J; Anderson, Alexander R A; Vaidya, Jayant S

2007-05-21

Cancer development is a stepwise process through which normal somatic cells acquire mutations which enable them to escape their normal function in the tissue and become self-sufficient in survival. The number of mutations depends on the patient's age, genetic susceptibility and on the exposure of the patient to carcinogens throughout their life. It is believed that in every malignancy 4-6 crucial similar mutations have to occur on cancer-related genes. These genes are classified as oncogenes and tumour suppressor genes (TSGs) which gain or lose their function respectively, after they have received one mutative hit or both of their alleles have been knocked out. With the acquisition of each of the necessary mutations the transformed cell gains a selective advantage over normal cells, and the mutation will spread throughout the tissue via clonal expansion. We present a simplified model of this mutation and expansion process, in which we assume that the loss of two TSGs is sufficient to give rise to a cancer. Our mathematical model of the stepwise development of breast cancer verifies the idea that the normal mutation rate in genes is only sufficient to give rise to a tumour within a clinically observable time if a high number of breast stem cells and TSGs exist or genetic instability is involved as a driving force of the mutation pathway. Furthermore, our model shows that if a mutation occurred in stem cells pre-puberty, and formed a field of cells with this mutation through clonal formation of the breast, it is most likely that a tumour will arise from within this area. We then apply different treatment strategies, namely surgery and adjuvant external beam radiotherapy and targeted intraoperative radiotherapy (TARGIT) and use the model to identify different sources of local recurrence and analyse their prevention.

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.

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

PubMed

Sharp, T G

1984-02-01

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

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.

Population Pharmacokinetics of Intranasal Scopolamine

NASA Technical Reports Server (NTRS)

Wu, L.; Chow, D. S. L.; Putcha, L.

2013-01-01

Introduction: An intranasal gel dosage formulation of scopolamine (INSCOP) was developed for the treatment of Space Motion Sickness (SMS).The bioavailability and pharmacokinetics (PK) was evaluated using data collected in Phase II IND protocols. We reported earlier statistically significant gender differences in PK parameters of INSCOP at a dose level of 0.4 mg. To identify covariates that influence PK parameters of INSCOP, we examined population covariates of INSCOP PK model for 0.4 mg dose. Methods: Plasma scopolamine concentrations versus time data were collected from 20 normal healthy human subjects (11 male/9 female) after a 0.4 mg dose. Phoenix NLME was employed for PK analysis of these data using gender, body weight and age as covariates for model selection. Model selection was based on a likelihood ratio test on the difference of criteria (-2LL). Statistical significance for base model building and individual covariate analysis was set at P less than 0.05{delta(-2LL)=3.84}. Results: A one-compartment pharmacokinetic model with first-order elimination best described INSCOP concentration ]time profiles. Inclusion of gender, body weight and age as covariates individually significantly reduced -2LL by the cut-off value of 3.84(P less than 0.05) when tested against the base model. After the forward stepwise selection and backward elimination steps, gender was selected to add to the final model which had significant influence on absorption rate constant (ka) and the volume of distribution (V) of INSCOP. Conclusion: A population pharmacokinetic model for INSCOP has been identified and gender was a significant contributing covariate for the final model. The volume of distribution and Ka were significantly higher in males than in females which confirm gender-dependent pharmacokinetics of scopolamine after administration of a 0.4 mg dose.

Structure-based predictions of 13C-NMR chemical shifts for a series of 2-functionalized 5-(methylsulfonyl)-1-phenyl-1H-indoles derivatives using GA-based MLR method

NASA Astrophysics Data System (ADS)

Ghavami, Raouf; Sadeghi, Faridoon; Rasouli, Zolikha; Djannati, Farhad

2012-12-01

Experimental values for the 13C NMR chemical shifts (ppm, TMS = 0) at 300 K ranging from 96.28 ppm (C4' of indole derivative 17) to 159.93 ppm (C4' of indole derivative 23) relative to deuteride chloroform (CDCl3, 77.0 ppm) or dimethylsulfoxide (DMSO, 39.50 ppm) as internal reference in CDCl3 or DMSO-d6 solutions have been collected from literature for thirty 2-functionalized 5-(methylsulfonyl)-1-phenyl-1H-indole derivatives containing different substituted groups. An effective quantitative structure-property relationship (QSPR) models were built using hybrid method combining genetic algorithm (GA) based on stepwise selection multiple linear regression (SWS-MLR) as feature-selection tools and correlation models between each carbon atom of indole derivative and calculated descriptors. Each compound was depicted by molecular structural descriptors that encode constitutional, topological, geometrical, electrostatic, and quantum chemical features. The accuracy of all developed models were confirmed using different types of internal and external procedures and various statistical tests. Furthermore, the domain of applicability for each model which indicates the area of reliable predictions was defined.

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

USGS Publications Warehouse

Brown, C. Erwin

1993-01-01

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

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.

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.

Associations between state economic and health systems capacities and service use by children with special health care needs.

PubMed

Margolis, Lewis H; Mayer, Michelle; Clark, Kathryn A; Farel, Anita M

2011-08-01

To examine the relationship between measures of state economic, political, health services, and Title V capacity and individual level measures of the well-being of CSHCN. We selected five measures of Title V capacity from the Title V Information System and 13 state capacity measures from a variety of data sources, and eight indicators of intermediate health outcomes from the National Survey of Children with Special Health Care Needs. To assess the associations between Title V capacity and health services outcomes, we used stepwise regression to identify significant capacity measures while accounting for the survey design and clustering of observations by state. To assess the associations between economic, political and health systems capacity and health outcomes we fit weighted logistic regression models for each outcome, using a stepwise procedure to reduce the models. Using statistically significant capacity measures from the stepwise models, we fit reduced random effects logistic regression models to account for clustering of observations by state. Few measures of Title V and state capacity were associated with health services outcomes. For health systems measures, a higher percentage of uninsured children was associated with decreased odds of receipt of early intervention services, decreased odds of receipt of professional care coordination, and increased odds of delayed or missed care. Parents in states with higher per capita Medicaid expenditures on children were more likely to report receipt of special education services. Only two state capacity measures were associated explicitly with Title V: states with higher generalist physician to population ratios were associated with a greater likelihood of parent report of having heard of Title V and states with higher per capita gross state product were less likely to be associated with a report of using Title V services, conditional on having heard of Title V. The state level measure of family participation in Title V governance was negatively associated with receipt of care coordination and having used Title V services. The measures of state economic, political, health systems, and Title V capacity that we have analyzed are only weakly associated with the well-being of children with special health care needs. If Congress and other policymakers increase the expectations of the states in assuring that the needs of CSHCN and their families are addressed, it is essential to be cognizant of the capacities of the states to undertake that role.

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.

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

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

[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.

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.

Porous media fracturing dynamics: stepwise crack advancement and fluid pressure oscillations

NASA Astrophysics Data System (ADS)

Cao, Toan D.; Hussain, Fazle; Schrefler, Bernhard A.

2018-02-01

We present new results explaining why fracturing in saturated porous media is not smooth and continuous but is a distinct stepwise process concomitant with fluid pressure oscillations. All exact solutions and almost all numerical models yield smooth fracture advancement and fluid pressure evolution, while recent experimental results, mainly from the oil industry, observation from geophysics and a very few numerical results for the quasi-static case indeed reveal the stepwise phenomenon. We summarize first these new experiments and these few numerical solutions for the quasi-static case. Both mechanical loading and pressure driven fractures are considered because their behaviours differ in the direction of the pressure jumps. Then we explore stepwise crack tip advancement and pressure fluctuations in dynamic fracturing with a hydro-mechanical model of porous media based on the Hybrid Mixture Theory. Full dynamic analyses of examples dealing with both hydraulic fracturing and mechanical loading are presented. The stepwise fracture advancement is confirmed in the dynamic setting as well as in the pressure fluctuations, but there are substantial differences in the frequency contents of the pressure waves in the two loading cases. Comparison between the quasi-static and fully dynamic solutions reveals that the dynamic response gives much more information such as the type of pressure oscillations and related frequencies and should be applied whenever there is a doubt about inertia forces playing a role - the case in most fracturing events. In the absence of direct relevant dynamic tests on saturated media some experimental results on dynamic fracture in dry materials, a fast hydraulic fracturing test and observations from geophysics confirm qualitatively the obtained results such as the type of pressure oscillations and the substantial difference in the behaviour under the two loading cases.

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

Juvenile Offender Recidivism: An Examination of Risk Factors

ERIC Educational Resources Information Center

Calley, Nancy G.

2012-01-01

One hundred and seventy three male juvenile offenders were followed two years postrelease from a residential treatment facility to assess recidivism and factors related to recidivism. The overall recidivism rate was 23.9%. Logistic regression with stepwise and backward variable selection methods was used to examine the relationship between…

A stepwise under-prepared osteotomy technique improves primary stability in shallow-placed implants: a preliminary study for simultaneous vertical ridge augmentation.

PubMed

Ueno, Daisuke; Nakamura, Kei; Kojima, Kousuke; Toyoshima, Takeshi; Tanaka, Hideaki; Ueda, Kazuhiko; Koyano, Kiyoshi; Kodama, Toshiro

2018-04-01

Simultaneous vertical ridge augmentation (VRA) can reduce treatment procedures and surgery time, but the concomitant reduction in primary stability (PS) of a shallow-placed implant imparts risk to its prognosis. Although several studies have reported improvements in PS, there is little information from any simultaneous VRA model. This study aimed to evaluate whether tapered implants with stepwise under-prepared osteotomy could improve the PS of shallow-placed implants in an in vitro model of simultaneous VRA. Tapered implants (Straumann ® Bone Level Tapered implant; BLT) and hybrid implants (Straumann ® Bone Level implant; BL) were investigated in this study. A total of 80 osteotomies of different depths (4, 6, 8, 10 mm) were created in rigid polyurethane foam blocks, and each BLT and BL was inserted by either standard (BLT-S, BL-S) or a stepwise under-prepared (BLT-U, BL-U) osteotomy protocol. The PS was evaluated by measuring maximum insertion torque (IT), implant stability quotient (ISQ), and removal torque (RT). The significance level was set at P < 0.05. There were no significant differences in IT, ISQ or RT when comparing BLT-S and BL-S or BLT-U and BL-U at placement depths of 6 and 8 mm. When comparison was made between osteotomy protocols, IT was significantly greater in BLT-U than in BLT-S at all placement depths. A stepwise under-prepared osteotomy protocol improves initial stability of a tapered implant even in a shallow-placed implant model. BLT-U could be a useful protocol for simultaneous VRA.

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.

Non-Motor Correlates of Smoking Habits in de Novo Parkinson's Disease.

PubMed

Moccia, Marcello; Mollenhauer, Brit; Erro, Roberto; Picillo, Marina; Palladino, Raffaele; Barone, Paolo

2015-01-01

Parkinson's disease (PD) subjects are less likely to ever smoke and are more prone to quit smoking, as compared to controls. Therefore, smoking habits can be considered part of the non-motor phenotype, preceding the onset of motor PD by several years. To explore non-motor symptom (NMS) correlates of smoking habits in de novo PD. This cross-sectional study included 281 newly diagnosed, drug-naïve PD subjects, recruited in Naples (Italy) and in Kassel (Germany). All subjects completed the NMS Questionnaire (NMSQ), and were investigated for smoking status (never, current and former smokers) and intensity (pack-years). 140 PD subjects never smoked, 20 currently smoked, and 121 had quit smoking before PD diagnosis. NMSQ total score did not associate with smoking status, but with smoking intensity (p = 0.028; coefficient = 0.088). A multinomial logistic regression stepwise model presenting never smoking as reference, selected as NMSQ correlates of current smoking: sex difficulties (p = 0.002; OR = 5.254), daytime sleepiness (p = 0.046; OR = 0.085), insomnia (p = 0.025; OR = 0.135), and vivid dreams (p = 0.040; OR = 3.110); and of former smoking: swallowing (p = 0.013; OR = 0.311), nausea (p = 0.027; OR = 7.157), unexplained pains (p = 0.002; OR = 3.409), forgetfulness (p = 0.005; OR = 2.592), sex interest (p = 0.007; OR = 0.221), sex difficulties (p = 0.038; OR = 4.215), and daytime sleepiness (p = 0.05; OR = 0.372). An ordinal logistic regression stepwise model selected as NMSQ correlates of smoking intensity: nocturnal restlessness (p = 0.027; coefficient = 0.974), and leg swelling (p = 0.004; coefficient = 1.305). Certain NMSs are associated with different smoking status and intensity, suggesting a variety of adaptive mechanisms to cigarette smoking.

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.

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.

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.

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.

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.

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.

Multivariate analysis of early and late nest sites of Abert's Towhees

Treesearch

Deborah M. Finch

1985-01-01

Seasonal variation in nest site selection by the Abert's towhee (Pipilo aberti) was studied in honey mesquite (Prosopis glandulosa) habitat along the lower Colorado River from March to July, 1981. Stepwise discriminant function analysis identified nest vegetation type, nest direction, and nest height as the three most important variables that characterized the...

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.

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.

The use of machine learning for the identification of peripheral artery disease and future mortality risk.

PubMed

Ross, Elsie Gyang; Shah, Nigam H; Dalman, Ronald L; Nead, Kevin T; Cooke, John P; Leeper, Nicholas J

2016-11-01

A key aspect of the precision medicine effort is the development of informatics tools that can analyze and interpret "big data" sets in an automated and adaptive fashion while providing accurate and actionable clinical information. The aims of this study were to develop machine learning algorithms for the identification of disease and the prognostication of mortality risk and to determine whether such models perform better than classical statistical analyses. Focusing on peripheral artery disease (PAD), patient data were derived from a prospective, observational study of 1755 patients who presented for elective coronary angiography. We employed multiple supervised machine learning algorithms and used diverse clinical, demographic, imaging, and genomic information in a hypothesis-free manner to build models that could identify patients with PAD and predict future mortality. Comparison was made to standard stepwise linear regression models. Our machine-learned models outperformed stepwise logistic regression models both for the identification of patients with PAD (area under the curve, 0.87 vs 0.76, respectively; P = .03) and for the prediction of future mortality (area under the curve, 0.76 vs 0.65, respectively; P = .10). Both machine-learned models were markedly better calibrated than the stepwise logistic regression models, thus providing more accurate disease and mortality risk estimates. Machine learning approaches can produce more accurate disease classification and prediction models. These tools may prove clinically useful for the automated identification of patients with highly morbid diseases for which aggressive risk factor management can improve outcomes. Copyright © 2016 Society for Vascular Surgery. Published by Elsevier Inc. All rights reserved.

An inter-networking mechanism with stepwise synchronization for wireless sensor networks.

PubMed

Yamamoto, Hiroshi; Wakamiya, Naoki; Murata, Masayuki

2011-01-01

To realize the ambient information society, multiple wireless networks deployed in the region and devices carried by users are required to cooperate with each other. Since duty cycles and operational frequencies are different among networks, we need a mechanism to allow networks to efficiently exchange messages. For this purpose, we propose a novel inter-networking mechanism where two networks are synchronized with each other in a moderate manner, which we call stepwise synchronization. With our proposal, to bridge the gap between intrinsic operational frequencies, nodes near the border of networks adjust their operational frequencies in a stepwise fashion based on the pulse-coupled oscillator model as a fundamental theory of synchronization. Through simulation experiments, we show that the communication delay and the energy consumption of border nodes are reduced, which enables wireless sensor networks to communicate longer with each other.

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

Evidence for the Stepwise Stress Model: Gambusia holbrooki and Daphnia magna under acid mine drainage and acidified reference water stress.

PubMed

Gerhardt, Almut; Janssens de Bisthoven, Luc; Soares, Amadeu M V

2005-06-01

The Stepwise Stress Model (SSM) states that a cascade of regulative behavioral responses with different intrinsic sensitivities and threshold values offers increased behavioral plasticity and thus a wider range of tolerance for environmental changes or pollutant exposures. We tested the SSM with a widely introduced fish Gambusia holbrooki (Girard) (Pisces, Poeciliidae) and the standard laboratory test species Daphnia magna Straus (Crustacea, Daphniidae). The stress was simulated by short-term exposure to acid mine drainage (AMD) and to acidified reference water (ACID). Recording of behavioral responses with the multispecies freshwater biomonitor (MFB) generated continuous time-dependent dose-response data that were modeled in three-dimensional (3D) surface plots. Both the pH-dependent mortalities and the strong linear correlations between pH and aqueous metals confirmed the toxicity of the AMD and ACID gradients, respectively, for fish and Daphnia, the latter being more sensitive. AMD stress at pH < or = 5.5 amplified circadian rhythmicity in both species, while ACID stress did so only in G. holbrooki. A behavioral stepwise stress response was found in both species: D. magna decreased locomotion and ventilation (first step) (AMD, ACID), followed by increased ventilation (second step) (AMD). G. holbrooki decreased locomotion (first step) (AMD, ACID) and increased ventilation at intermediate pH levels (second step) (AMD). Both species, although from different taxonomic groups and feeding habits, followed the SSM, which might be expanded to a general concept for describing the behavioral responses of aquatic organims to pollution. Stepwise stress responses might be applied in online biomonitors to provide more sensitive and graduated alarm settings, hence optimizing the "early warning" detection of pollution waves.

Diffusion across the modified polyethylene separator GX in the heat-sterilizable AgO-Zn battery

NASA Technical Reports Server (NTRS)

Lutwack, R.

1973-01-01

Models of diffusion across an inert membrane have been studied using the computer program CINDA. The models were constructed to simulate various conditions obtained in the consideration of the diffusion of Ag (OH)2 ions in the AgO-Zn battery. The effects on concentrations across the membrane at the steady state and on the fluxout as a function of time were used to examine the consequences of stepwise reducing the number of sources of ions, of stepwise blocking the source and sink surfaces, of varying the magnitude of the diffusion coefficient for a uniform membrane, of varying the diffusion coefficient across the membrane, and of excluding volumes to diffusion.

[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.

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 framework for multi-criteria assessment of model enhancements

NASA Astrophysics Data System (ADS)

Francke, Till; Foerster, Saskia; Brosinsky, Arlena; Delgado, José; Güntner, Andreas; López-Tarazón, José A.; Bronstert, Axel

2016-04-01

Modellers are often faced with unsatisfactory model performance for a specific setup of a hydrological model. In these cases, the modeller may try to improve the setup by addressing selected causes for the model errors (i.e. data errors, structural errors). This leads to adding certain "model enhancements" (MEs), e.g. climate data based on more monitoring stations, improved calibration data, modifications in process formulations. However, deciding on which MEs to implement remains a matter of expert knowledge, guided by some sensitivity analysis at best. When multiple MEs have been implemented, a resulting improvement in model performance is not easily attributed, especially when considering different aspects of this improvement (e.g. better performance dynamics vs. reduced bias). In this study we present an approach for comparing the effect of multiple MEs in the face of multiple improvement aspects. A stepwise selection approach and structured plots help in addressing the multidimensionality of the problem. The approach is applied to a case study, which employs the meso-scale hydrosedimentological model WASA-SED for a sub-humid catchment. The results suggest that the effect of the MEs is quite diverse, with some MEs (e.g. augmented rainfall data) cause improvements for almost all aspects, while the effect of other MEs is restricted to few aspects or even deteriorate some. These specific results may not be generalizable. However, we suggest that based on studies like this, identifying the most promising MEs to implement may be facilitated.

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.

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.

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

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.

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

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…

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.

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...

Stepwise Elastic Behavior in a Model Elastomer

NASA Astrophysics Data System (ADS)

Bhawe, Dhananjay M.; Cohen, Claude; Escobedo, Fernando A.

2004-12-01

MonteCarlo simulations of an entanglement-free cross-linked polymer network of semiflexible chains reveal a peculiar stepwise elastic response. For increasing stress, step jumps in strain are observed that do not correlate with changes in the number of aligned chains. We show that this unusual behavior stems from the ability of the system to form multiple ordered chain domains that exclude the cross-linking species. This novel elastomer shows a toughening behavior similar to that observed in biological structural materials, such as muscle proteins and abalone shell adhesive.

[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.

Examining the development of attention and executive functions in children with a novel paradigm.

PubMed

Klimkeit, Ester I; Mattingley, Jason B; Sheppard, Dianne M; Farrow, Maree; Bradshaw, John L

2004-09-01

The development of attention and executive functions in normal children (7-12 years) was investigated using a novel selective reaching task, which involved reaching as rapidly as possible towards a target, while at times having to ignore a distractor. The information processing paradigm allowed the measurement of various distinct dimensions of behaviour within a single task. The largest improvements in vigilance, set-shifting, response inhibition, selective attention, and impulsive responding were observed to occur between the ages of 8 and 10, with a plateau in performance between 10 and 12 years of age. These findings, consistent with a step-wise model of development, coincide with the observed developmental spurt in frontal brain functions between 7 and 10 years of age, and indicate that attention and executive functions develop in parallel. This task appears to be a useful research tool in the assessment of attention and executive functions, within a single task. Thus it may have a role in determining which cognitive functions are most affected in different childhood disorders.

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.

An Inter-Networking Mechanism with Stepwise Synchronization for Wireless Sensor Networks

PubMed Central

Yamamoto, Hiroshi; Wakamiya, Naoki; Murata, Masayuki

2011-01-01

To realize the ambient information society, multiple wireless networks deployed in the region and devices carried by users are required to cooperate with each other. Since duty cycles and operational frequencies are different among networks, we need a mechanism to allow networks to efficiently exchange messages. For this purpose, we propose a novel inter-networking mechanism where two networks are synchronized with each other in a moderate manner, which we call stepwise synchronization. With our proposal, to bridge the gap between intrinsic operational frequencies, nodes near the border of networks adjust their operational frequencies in a stepwise fashion based on the pulse-coupled oscillator model as a fundamental theory of synchronization. Through simulation experiments, we show that the communication delay and the energy consumption of border nodes are reduced, which enables wireless sensor networks to communicate longer with each other. PMID:22164073

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...

Single and multiple in-season measurements as indicators of at-harvest cotton boll damage caused by verde plant bug (Hemiptera: Miridae).

PubMed

Brewer, Michael J; Armstrong, J Scott; Parker, Roy D

2013-06-01

The ability to monitor verde plant bug, Creontiades signatus Distant (Hemiptera: Miridae), and the progression of cotton, Gossypium hirsutum L., boll responses to feeding and associated cotton boll rot provided opportunity to assess if single in-season measurements had value in evaluating at-harvest damage to bolls and if multiple in-season measurements enhanced their combined use. One in-season verde plant bug density measurement, three in-season plant injury measurements, and two at-harvest damage measurements were taken in 15 cotton fields in South Texas, 2010. Linear regression selected two measurements as potentially useful indicators of at-harvest damage: verde plant bug density (adjusted r2 = 0.68; P = 0.0004) and internal boll injury of the carpel wall (adjusted r2 = 0.72; P = 0.004). Considering use of multiple measurements, a stepwise multiple regression of the four in-season measurements selected a univariate model (verde plant bug density) using a 0.15 selection criterion (adjusted r2 = 0.74; P = 0.0002) and a bivariate model (verde plant bug density-internal boll injury) using a 0.25 selection criterion (adjusted r2 = 0.76; P = 0.0007) as indicators of at-harvest damage. In a validation using cultivar and water regime treatments experiencing low verde plant bug pressure in 2011 and 2012, the bivariate model performed better than models using verde plant bug density or internal boll injury separately. Overall, verde plant bug damaging cotton bolls exemplified the benefits of using multiple in-season measurements in pest monitoring programs, under the challenging situation when at-harvest damage results from a sequence of plant responses initiated by in-season insect feeding.

Evaluation of a novel, hybrid model (Mumbai EUS II) for stepwise teaching and training in EUS-guided biliary drainage and rendezvous procedures.

PubMed

Dhir, Vinay; Itoi, Takao; Pausawasdi, Nonthalee; Khashab, Mouen A; Perez-Miranda, Manuel; Sun, Siyu; Park, Do Hyun; Iwashita, Takuji; Teoh, Anthony Y B; Maydeo, Amit P; Ho, Khek Yu

2017-11-01

 EUS-guided biliary drainage (EUS-BD) and rendezvous (EUS-RV) are acceptable rescue options for patients with failed endoscopic retrograde cholangiopancreatography (ERCP). However, there are limited training opportunities at most centers owing to low case volumes. The existing models do not replicate the difficulties encountered during EUS-BD. We aimed to develop and validate a model for stepwise learning of EUS-BD and EUS-RV, which replicates the actual EUS-BD procedures.  A hybrid model was created utilizing pig esophagus and stomach, with a synthetic duodenum and biliary system. The model was objectively assessed on a grade of 1 - 4 by two experts. Twenty-eight trainees were given initial training with didactic lectures and live procedures. This was followed by hands-on training in EUS-BD and EUS-RV on the hybrid model. Trainees were assessed for objective criteria of technical difficulties.  Both the experts graded the model as very good or above for all parameters. All trainees could complete the requisite steps of EUS-BD and EUS-RV in a mean time of 11 minutes (8 - 18 minutes). Thirty-six technical difficulties were noted during the training (wrong scope position, 13; incorrect duct puncture, 12; guidewire related problems, 11). Technical difficulties peaked for EUS-RV, followed by hepaticogastrostomy (HGS) and choledochoduodenostomy (CDS) (20, 9, and 7, P  = 0.001). At 10 days follow-up, nine of 28 trainees had successfully performed three EUS-RV and seven EUS-BD procedures independently.  The Mumbai EUS II hybrid model replicates situations encountered during EUS-RV and EUS-BD. Stepwise mentoring improves the chances of success in EUS-RV and EUS-BD procedures.

Stabilization of Two Radicals with One Metal: A Stepwise Coupling Model for Copper-Catalyzed Radical–Radical Cross-Coupling

PubMed Central

Qi, Xiaotian; Zhu, Lei; Bai, Ruopeng; Lan, Yu

2017-01-01

Transition metal-catalyzed radical–radical cross-coupling reactions provide innovative methods for C–C and C–heteroatom bond construction. A theoretical study was performed to reveal the mechanism and selectivity of the copper-catalyzed C–N radical–radical cross-coupling reaction. The concerted coupling pathway, in which a C–N bond is formed through the direct nucleophilic addition of a carbon radical to the nitrogen atom of the Cu(II)–N species, is demonstrated to be kinetically unfavorable. The stepwise coupling pathway, which involves the combination of a carbon radical with a Cu(II)–N species before C–N bond formation, is shown to be probable. Both the Mulliken atomic spin density distribution and frontier molecular orbital analysis on the Cu(II)–N intermediate show that the Cu site is more reactive than that of N; thus, the carbon radical preferentially react with the metal center. The chemoselectivity of the cross-coupling is also explained by the differences in electron compatibility of the carbon radical, the nitrogen radical and the Cu(II)–N intermediate. The higher activation free energy for N–N radical–radical homo-coupling is attributed to the mismatch of Cu(II)–N species with the nitrogen radical because the electrophilicity for both is strong. PMID:28272407

Stepwise microhydration of aromatic amide cations: water solvation networks revealed by the infrared spectra of acetanilide+-(H2O)n clusters (n ≤ 3).

PubMed

Klyne, Johanna; Schmies, Matthias; Miyazaki, Mitsuhiko; Fujii, Masaaki; Dopfer, Otto

2018-01-31

The structure and activity of peptides and proteins strongly rely on their charge state and the interaction with their hydration environment. Here, infrared photodissociation (IRPD) spectra of size-selected microhydrated clusters of cationic acetanilide (AA + , N-phenylacetamide), AA + -(H 2 O) n with n ≤ 3, are analysed by dispersion-corrected density functional theory calculations at the ωB97X-D/aug-cc-pVTZ level to determine the stepwise microhydration process of this aromatic peptide model. The IRPD spectra are recorded in the informative X-H stretch (ν OH , ν NH , ν CH , amide A, 2800-3800 cm -1 ) and fingerprint (amide I-II, 1000-1900 cm -1 ) ranges to probe the preferred hydration motifs and the cluster growth. In the most stable AA + -(H 2 O) n structures, the H 2 O ligands solvate the acidic NH proton of the amide by forming a hydrogen-bonded solvent network, which strongly benefits from cooperative effects arising from the excess positive charge. Comparison with neutral AA-H 2 O reveals the strong impact of ionization on the acidity of the NH proton and the topology of the interaction potential. Comparison with related hydrated formanilide clusters demonstrates the influence of methylation of the amide group (H → CH 3 ) on the shape of the intermolecular potential and the structure of the hydration shell.

Constructive neutral evolution: exploring evolutionary theory's curious disconnect.

PubMed

Stoltzfus, Arlin

2012-10-13

Constructive neutral evolution (CNE) suggests that neutral evolution may follow a stepwise path to extravagance. Whether or not CNE is common, the mere possibility raises provocative questions about causation: in classical neo-Darwinian thinking, selection is the sole source of creativity and direction, the only force that can cause trends or build complex features. However, much of contemporary evolutionary genetics departs from the conception of evolution underlying neo-Darwinism, resulting in a widening gap between what formal models allow, and what the prevailing view of the causes of evolution suggests. In particular, a mutationist conception of evolution as a 2-step origin-fixation process has been a source of theoretical innovation for 40 years, appearing not only in the Neutral Theory, but also in recent breakthroughs in modeling adaptation (the "mutational landscape" model), and in practical software for sequence analysis. In this conception, mutation is not a source of raw materials, but an agent that introduces novelty, while selection is not an agent that shapes features, but a stochastic sieve. This view, which now lays claim to important theoretical, experimental, and practical results, demands our attention. CNE provides a way to explore its most significant implications about the role of variation in evolution. Alex Kondrashov, Eugene Koonin and Johann Peter Gogarten reviewed this article.

Constructive neutral evolution: exploring evolutionary theory’s curious disconnect

PubMed Central

2012-01-01

Abstract Constructive neutral evolution (CNE) suggests that neutral evolution may follow a stepwise path to extravagance. Whether or not CNE is common, the mere possibility raises provocative questions about causation: in classical neo-Darwinian thinking, selection is the sole source of creativity and direction, the only force that can cause trends or build complex features. However, much of contemporary evolutionary genetics departs from the conception of evolution underlying neo-Darwinism, resulting in a widening gap between what formal models allow, and what the prevailing view of the causes of evolution suggests. In particular, a mutationist conception of evolution as a 2-step origin-fixation process has been a source of theoretical innovation for 40 years, appearing not only in the Neutral Theory, but also in recent breakthroughs in modeling adaptation (the “mutational landscape” model), and in practical software for sequence analysis. In this conception, mutation is not a source of raw materials, but an agent that introduces novelty, while selection is not an agent that shapes features, but a stochastic sieve. This view, which now lays claim to important theoretical, experimental, and practical results, demands our attention. CNE provides a way to explore its most significant implications about the role of variation in evolution. Reviewers Alex Kondrashov, Eugene Koonin and Johann Peter Gogarten reviewed this article. PMID:23062217

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...

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.

Quantitative analysis of the thermal requirements for stepwise physical dormancy-break in seeds of the winter annual Geranium carolinianum (Geraniaceae)

PubMed Central

Gama-Arachchige, N. S.; Baskin, J. M.; Geneve, R. L.; Baskin, C. C.

2013-01-01

Background and Aims Physical dormancy (PY)-break in some annual plant species is a two-step process controlled by two different temperature and/or moisture regimes. The thermal time model has been used to quantify PY-break in several species of Fabaceae, but not to describe stepwise PY-break. The primary aims of this study were to quantify the thermal requirement for sensitivity induction by developing a thermal time model and to propose a mechanism for stepwise PY-breaking in the winter annual Geranium carolinianum. Methods Seeds of G. carolinianum were stored under dry conditions at different constant and alternating temperatures to induce sensitivity (step I). Sensitivity induction was analysed based on the thermal time approach using the Gompertz function. The effect of temperature on step II was studied by incubating sensitive seeds at low temperatures. Scanning electron microscopy, penetrometer techniques, and different humidity levels and temperatures were used to explain the mechanism of stepwise PY-break. Key Results The base temperature (Tb) for sensitivity induction was 17·2 °C and constant for all seed fractions of the population. Thermal time for sensitivity induction during step I in the PY-breaking process agreed with the three-parameter Gompertz model. Step II (PY-break) did not agree with the thermal time concept. Q10 values for the rate of sensitivity induction and PY-break were between 2·0 and 3·5 and between 0·02 and 0·1, respectively. The force required to separate the water gap palisade layer from the sub-palisade layer was significantly reduced after sensitivity induction. Conclusions Step I and step II in PY-breaking of G. carolinianum are controlled by chemical and physical processes, respectively. This study indicates the feasibility of applying the developed thermal time model to predict or manipulate sensitivity induction in seeds with two-step PY-breaking processes. The model is the first and most detailed one yet developed for sensitivity induction in PY-break. PMID:23456728

Ethanol dehydration in HZSM-5 studied by density functional theory: evidence for a concerted process.

PubMed

Kim, Seonah; Robichaud, David J; Beckham, Gregg T; Paton, Robert S; Nimlos, Mark R

2015-04-16

Dehydration over acidic zeolites is an important reaction class for the upgrading of biomass pyrolysis vapors to hydrocarbon fuels or to precursors for myriad chemical products. Here, we examine the dehydration of ethanol at a Brønsted acid site, T12, found in HZSM-5 using density functional theory (DFT). The geometries of both cluster and mixed quantum mechanics/molecular mechanics (QM:MM) models are prepared from the ZSM-5 crystal structure. Comparisons between these models and different DFT methods are conducted to show similar results among the models and methods used. Inclusion of the full catalyst cavity through a QM:MM approach is found to be important, since activation barriers are computed on average as 7 kcal mol(-1) lower than those obtained with a smaller cluster model. Two different pathways, concerted and stepwise, have been considered when examining dehydration and deprotonation steps. The current study shows that a concerted dehydration process is possible with a lower (4-5 kcal mol(-1)) activation barrier while previous literature studies have focused on a stepwise mechanism. Overall, this work demonstrates that fairly high activation energies (∼50 kcal mol(-1)) are required for ethanol dehydration. A concerted mechanism is favored over a stepwise mechanism because charge separation in the transition state is minimized. QM:MM approaches appear to provide superior results to cluster calculations due to a more accurate representation of charges on framework oxygen atoms.

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.

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.

Preliminary experiments on quantification of skin condition

NASA Astrophysics Data System (ADS)

Kitajima, Kenzo; Iyatomi, Hitoshi

2014-03-01

In this study, we investigated a preliminary assessment method for skin conditions such as a moisturizing property and its fineness of the skin with an image analysis only. We captured a facial images from volunteer subjects aged between 30s and 60s by Pocket Micro (R) device (Scalar Co., Japan). This device has two image capturing modes; the normal mode and the non-reflection mode with the aid of the equipped polarization filter. We captured skin images from a total of 68 spots from subjects' face using both modes (i.e. total of 136 skin images). The moisture-retaining property of the skin and subjective evaluation score of the skin fineness in 5-point scale for each case were also obtained in advance as a gold standard (their mean and SD were 35.15 +/- 3.22 (μS) and 3.45 +/- 1.17, respectively). We extracted a total of 107 image features from each image and built linear regression models for estimating abovementioned criteria with a stepwise feature selection. The developed model for estimating the skin moisture achieved the MSE of 1.92 (μS) with 6 selected parameters, while the model for skin fineness achieved that of 0.51 scales with 7 parameters under the leave-one-out cross validation. We confirmed the developed models predicted the moisture-retaining property and fineness of the skin appropriately with only captured image.

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.

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.

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…

A threshold-based weather model for predicting stripe rust infection in winter wheat

USDA-ARS?s Scientific Manuscript database

Wheat stripe rust (WSR) (caused by Puccinia striiformis sp. tritici) is a major threat in most wheat growing regions worldwide, with potential to inflict regular yield losses when environmental conditions are favorable. We propose a threshold-based disease-forecasting model using a stepwise modeling...

A Comparative Investigation of the Combined Effects of Pre-Processing, Wavelength Selection, and Regression Methods on Near-Infrared Calibration Model Performance.

PubMed

Wan, Jian; Chen, Yi-Chieh; Morris, A Julian; Thennadil, Suresh N

2017-07-01

Near-infrared (NIR) spectroscopy is being widely used in various fields ranging from pharmaceutics to the food industry for analyzing chemical and physical properties of the substances concerned. Its advantages over other analytical techniques include available physical interpretation of spectral data, nondestructive nature and high speed of measurements, and little or no need for sample preparation. The successful application of NIR spectroscopy relies on three main aspects: pre-processing of spectral data to eliminate nonlinear variations due to temperature, light scattering effects and many others, selection of those wavelengths that contribute useful information, and identification of suitable calibration models using linear/nonlinear regression . Several methods have been developed for each of these three aspects and many comparative studies of different methods exist for an individual aspect or some combinations. However, there is still a lack of comparative studies for the interactions among these three aspects, which can shed light on what role each aspect plays in the calibration and how to combine various methods of each aspect together to obtain the best calibration model. This paper aims to provide such a comparative study based on four benchmark data sets using three typical pre-processing methods, namely, orthogonal signal correction (OSC), extended multiplicative signal correction (EMSC) and optical path-length estimation and correction (OPLEC); two existing wavelength selection methods, namely, stepwise forward selection (SFS) and genetic algorithm optimization combined with partial least squares regression for spectral data (GAPLSSP); four popular regression methods, namely, partial least squares (PLS), least absolute shrinkage and selection operator (LASSO), least squares support vector machine (LS-SVM), and Gaussian process regression (GPR). The comparative study indicates that, in general, pre-processing of spectral data can play a significant role in the calibration while wavelength selection plays a marginal role and the combination of certain pre-processing, wavelength selection, and nonlinear regression methods can achieve superior performance over traditional linear regression-based calibration.

Adaptive landscape and functional diversity of Neotropical cichlids: implications for the ecology and evolution of Cichlinae (Cichlidae; Cichliformes).

PubMed

Arbour, J H; López-Fernández, H

2014-11-01

Morphological, lineage and ecological diversity can vary substantially even among closely related lineages. Factors that influence morphological diversification, especially in functionally relevant traits, can help to explain the modern distribution of disparity across phylogenies and communities. Multivariate axes of feeding functional morphology from 75 species of Neotropical cichlid and a stepwise-AIC algorithm were used to estimate the adaptive landscape of functional morphospace in Cichlinae. Adaptive landscape complexity and convergence, as well as the functional diversity of Cichlinae, were compared with expectations under null evolutionary models. Neotropical cichlid feeding function varied primarily between traits associated with ram feeding vs. suction feeding/biting and secondarily with oral jaw muscle size and pharyngeal crushing capacity. The number of changes in selective regimes and the amount of convergence between lineages was higher than expected under a null model of evolution, but convergence was not higher than expected under a similarly complex adaptive landscape. Functional disparity was compatible with an adaptive landscape model, whereas the distribution of evolutionary change through morphospace corresponded with a process of evolution towards a single adaptive peak. The continentally distributed Neotropical cichlids have evolved relatively rapidly towards a number of adaptive peaks in functional trait space. Selection in Cichlinae functional morphospace is more complex than expected under null evolutionary models. The complexity of selective constraints in feeding morphology has likely been a significant contributor to the diversity of feeding ecology in this clade. © 2014 European Society For Evolutionary Biology. Journal of Evolutionary Biology © 2014 European Society For Evolutionary Biology.

A Proposed Method to Predict Preterm Birth Using Clinical Data, Standard Maternal Serum Screening, and Cholesterol

PubMed Central

ALLEMAN, Brandon W.; SMITH, Amanda R.; BYERS, Heather M.; BEDELL, Bruce; RYCKMAN, Kelli K.; MURRAY, Jeffrey C.; BOROWSKI, Kristi S.

2013-01-01

Objective To create a predictive model for preterm birth (PTB) from available clinical data and serum analytes. Study Design Serum analytes, routine pregnancy screening plus cholesterol and corresponding health information were linked to birth certificate data for a cohort of 2699 Iowa women with serum sampled in the first and second trimester. Stepwise logistic regression was used to select the best predictive model for PTB. Results Serum screening markers remained significant predictors of PTB even after controlling for maternal characteristics. The best predictive model included maternal characteristics, first trimester total cholesterol (TC), TC change between trimesters and second trimester alpha-fetoprotein and inhibin A. The model showed better discriminatory ability than PTB history alone and performed similarly in subgroups of women without past PTB. Conclusions Using clinical and serum screening data a potentially useful predictor of PTB was constructed. Validation and replication in other populations, and incorporation of other measures that identify PTB risk, like cervical length, can be a step towards identifying additional women who may benefit from new or currently available interventions. PMID:23500456

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

Predicting the aquatic toxicity mode of action using logistic regression and linear discriminant analysis.

PubMed

Ren, Y Y; Zhou, L C; Yang, L; Liu, P Y; Zhao, B W; Liu, H X

2016-09-01

The paper highlights the use of the logistic regression (LR) method in the construction of acceptable statistically significant, robust and predictive models for the classification of chemicals according to their aquatic toxic modes of action. Essentials accounting for a reliable model were all considered carefully. The model predictors were selected by stepwise forward discriminant analysis (LDA) from a combined pool of experimental data and chemical structure-based descriptors calculated by the CODESSA and DRAGON software packages. Model predictive ability was validated both internally and externally. The applicability domain was checked by the leverage approach to verify prediction reliability. The obtained models are simple and easy to interpret. In general, LR performs much better than LDA and seems to be more attractive for the prediction of the more toxic compounds, i.e. compounds that exhibit excess toxicity versus non-polar narcotic compounds and more reactive compounds versus less reactive compounds. In addition, model fit and regression diagnostics was done through the influence plot which reflects the hat-values, studentized residuals, and Cook's distance statistics of each sample. Overdispersion was also checked for the LR model. The relationships between the descriptors and the aquatic toxic behaviour of compounds are also discussed.

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%.

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

EPA MODELING TOOLS FOR CAPTURE ZONE DELINEATION

EPA Science Inventory

The EPA Office of Research and Development supports a step-wise modeling approach for design of wellhead protection areas for water supply wells. A web-based WellHEDSS (wellhead decision support system) is under development for determining when simple capture zones (e.g., centri...

An integrative top-down and bottom-up qualitative model construction framework for exploration of biochemical systems.

PubMed

Wu, Zujian; Pang, Wei; Coghill, George M

Computational modelling of biochemical systems based on top-down and bottom-up approaches has been well studied over the last decade. In this research, after illustrating how to generate atomic components by a set of given reactants and two user pre-defined component patterns, we propose an integrative top-down and bottom-up modelling approach for stepwise qualitative exploration of interactions among reactants in biochemical systems. Evolution strategy is applied to the top-down modelling approach to compose models, and simulated annealing is employed in the bottom-up modelling approach to explore potential interactions based on models constructed from the top-down modelling process. Both the top-down and bottom-up approaches support stepwise modular addition or subtraction for the model evolution. Experimental results indicate that our modelling approach is feasible to learn the relationships among biochemical reactants qualitatively. In addition, hidden reactants of the target biochemical system can be obtained by generating complex reactants in corresponding composed models. Moreover, qualitatively learned models with inferred reactants and alternative topologies can be used for further web-lab experimental investigations by biologists of interest, which may result in a better understanding of the system.

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 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.

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.

Binding-induced DNA walker for signal amplification in highly selective electrochemical detection of protein.

PubMed

Ji, Yuhang; Zhang, Lei; Zhu, Longyi; Lei, Jianping; Wu, Jie; Ju, Huangxian

2017-10-15

A binding-induced DNA walker-assisted signal amplification was developed for highly selective electrochemical detection of protein. Firstly, the track of DNA walker was constructed by self-assembly of the high density ferrocene (Fc)-labeled anchor DNA and aptamer 1 on the gold electrode surface. Sequentially, a long swing-arm chain containing aptamer 2 and walking strand DNA was introduced onto gold electrode through aptamers-target specific recognition, and thus initiated walker strand sequences to hybridize with anchor DNA. Then, the DNA walker was activated by the stepwise cleavage of the hybridized anchor DNA by nicking endonuclease to release multiple Fc molecules for signal amplification. Taking thrombin as the model target, the Fc-generated electrochemical signal decreased linearly with logarithm value of thrombin concentration ranging from 10pM to 100nM with a detection limit of 2.5pM under the optimal conditions. By integrating the specific recognition of aptamers to target with the enzymatic cleavage of nicking endonuclease, the aptasensor showed the high selectivity. The binding-induced DNA walker provides a promising strategy for signal amplification in electrochemical biosensor, and has the extensive applications in sensitive and selective detection of the various targets. Copyright © 2017 Elsevier B.V. All rights reserved.

Automated texture-based identification of ovarian cancer in confocal microendoscope images

NASA Astrophysics Data System (ADS)

Srivastava, Saurabh; Rodriguez, Jeffrey J.; Rouse, Andrew R.; Brewer, Molly A.; Gmitro, Arthur F.

2005-03-01

The fluorescence confocal microendoscope provides high-resolution, in-vivo imaging of cellular pathology during optical biopsy. There are indications that the examination of human ovaries with this instrument has diagnostic implications for the early detection of ovarian cancer. The purpose of this study was to develop a computer-aided system to facilitate the identification of ovarian cancer from digital images captured with the confocal microendoscope system. To achieve this goal, we modeled the cellular-level structure present in these images as texture and extracted features based on first-order statistics, spatial gray-level dependence matrices, and spatial-frequency content. Selection of the best features for classification was performed using traditional feature selection techniques including stepwise discriminant analysis, forward sequential search, a non-parametric method, principal component analysis, and a heuristic technique that combines the results of these methods. The best set of features selected was used for classification, and performance of various machine classifiers was compared by analyzing the areas under their receiver operating characteristic curves. The results show that it is possible to automatically identify patients with ovarian cancer based on texture features extracted from confocal microendoscope images and that the machine performance is superior to that of the human observer.

Risk factors for highly pathogenic avian influenza in commercial layer chicken farms in bangladesh during 2011.

PubMed

Osmani, M G; Thornton, R N; Dhand, N K; Hoque, M A; Milon, Sk M A; Kalam, M A; Hossain, M; Yamage, M

2014-12-01

A case-control study conducted during 2011 involved 90 randomly selected commercial layer farms infected with highly pathogenic avian influenza type A subtype H5N1 (HPAI) and 175 control farms randomly selected from within 5 km of infected farms. A questionnaire was designed to obtain information about potential risk factors for contracting HPAI and was administered to farm owners or managers. Logistic regression analyses were conducted to identify significant risk factors. A total of 20 of 43 risk factors for contracting HPAI were identified after univariable logistic regression analysis. A multivariable logistic regression model was derived by forward stepwise selection. Both unmatched and matched analyses were performed. The key risk factors identified were numbers of staff, frequency of veterinary visits, presence of village chickens roaming on the farm and staff trading birds. Aggregating these findings with those from other studies resulted in a list of 16 key risk factors identified in Bangladesh. Most of these related to biosecurity. It is considered feasible for Bangladesh to achieve a very low incidence of HPAI. Using the cumulative list of risk factors to enhance biosecurity pertaining to commercial farms would facilitate this objective. © 2013 Blackwell Verlag GmbH.

[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.

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.

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.

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.

Theory favors a stepwise mechanism of porphyrin degradation by a ferric hydroperoxide model of the active species of heme oxygenase.

PubMed

Kumar, Devesh; de Visser, Samuël P; Shaik, Sason

2005-06-08

The report uses density functional theory to address the mechanism of heme degradation by the enzyme heme oxygenase (HO) using a model ferric hydroperoxide complex. HO is known to trap heme molecules and degrade them to maintain iron homeostasis in the biosystem. The degradation is initiated by complexation of the heme, then formation of the iron-hydroperoxo species, which subsequently oxidizes the meso position of the porphyrin by hydroxylation, thereby enabling eventually the cleavage of the porphyrin ring. Kinetic isotope effect studies indicate that the mechanism is assisted by general acid catalysis, via a chain of water molecules, and that all the events occur in concert. However, previous theoretical treatments indicated that the concerted mechanism has a high barrier, much higher than an alternative mechanism that is initiated by O-O bond homolysis of iron-hydroperoxide. The present contribution studies the stepwise and concerted acid-catalyzed mechanisms using H(3)O(+)(H(2)O)(n)(), n = 0-2. The effect of the acid strength is tested using the H(4)N(+)(H(2)O)(2) cluster and a fully protonated ferric hydroperoxide. All the calculations show that a stepwise mechanism that involves proton relay and O-O homolysis, in the rate-determining step, has a much lower barrier (>10 kcal/mol) than the corresponding fully concerted mechanism. The best fit of the calculated solvent kinetic isotope effect, to the experimental data, is obtained for the H(3)O(+)(H(2)O)(2) cluster. The calculated alpha-deuterium secondary kinetic isotope effect is inverse (0.95-0.98), but much less so than the experimental value (0.7). Possible reasons for this quantitative difference are discussed. Some probes are suggested that may enable experiment to distinguish the stepwise from the concerted mechanism.

REML/BLUP and sequential path analysis in estimating genotypic values and interrelationships among simple maize grain yield-related traits.

PubMed

Olivoto, T; Nardino, M; Carvalho, I R; Follmann, D N; Ferrari, M; Szareski, V J; de Pelegrin, A J; de Souza, V Q

2017-03-22

Methodologies using restricted maximum likelihood/best linear unbiased prediction (REML/BLUP) in combination with sequential path analysis in maize are still limited in the literature. Therefore, the aims of this study were: i) to use REML/BLUP-based procedures in order to estimate variance components, genetic parameters, and genotypic values of simple maize hybrids, and ii) to fit stepwise regressions considering genotypic values to form a path diagram with multi-order predictors and minimum multicollinearity that explains the relationships of cause and effect among grain yield-related traits. Fifteen commercial simple maize hybrids were evaluated in multi-environment trials in a randomized complete block design with four replications. The environmental variance (78.80%) and genotype-vs-environment variance (20.83%) accounted for more than 99% of the phenotypic variance of grain yield, which difficult the direct selection of breeders for this trait. The sequential path analysis model allowed the selection of traits with high explanatory power and minimum multicollinearity, resulting in models with elevated fit (R 2 > 0.9 and ε < 0.3). The number of kernels per ear (NKE) and thousand-kernel weight (TKW) are the traits with the largest direct effects on grain yield (r = 0.66 and 0.73, respectively). The high accuracy of selection (0.86 and 0.89) associated with the high heritability of the average (0.732 and 0.794) for NKE and TKW, respectively, indicated good reliability and prospects of success in the indirect selection of hybrids with high-yield potential through these traits. The negative direct effect of NKE on TKW (r = -0.856), however, must be considered. The joint use of mixed models and sequential path analysis is effective in the evaluation of maize-breeding trials.

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.

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...

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.

Measuring Equity in Access to Pharmaceutical Services Using Concentration Curve; Model Development.

PubMed

Davari, Majid; Khorasani, Elahe; Bakhshizade, Zahra; Jafarian Jazi, Marzie; Ghaffari Darab, Mohsen; Maracy, Mohammad Reza

2015-01-01

This paper has two objectives. First, it establishes a model for scoring the access to pharmaceutical services. Second, it develops a model for measuring socioeconomic indicators independent of the time and place of study. These two measures are used for measuring equity in access to pharmaceutical services using concentration curve. We prepared an open-ended questionnaire and distributed it to academic experts to get their ideas to form access indicators and assign score to each indicator based on the pharmaceutical system. An extensive literature review was undertaken for the selection of indicators in order to determine the socioeconomic status (SES) of individuals. Experts' opinions were also considered for scoring these indicators. These indicators were weighted by the Stepwise Adoption of Weights and were used to develop a model for measuring SES independent of the time and place of study. Nine factors were introduced for assessing the access to pharmaceutical services, based on pharmaceutical systems in middle-income countries. Five indicators were selected for determining the SES of individuals. A model for income classification based on poverty line was established. Likewise, a model for scoring home status based on national minimum wage was introduced. In summary, five important findings emerged from this study. These findings may assist researchers in measuring equity in access to pharmaceutical services and also could help them to apply a model for determining SES independent of the time and place of study. These also could provide a good opportunity for researchers to compare the results of various studies in a reasonable way; particularly in middle-income countries.

Incorporating Single-nucleotide Polymorphisms Into the Lyman Model to Improve Prediction of Radiation Pneumonitis

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

Tucker, Susan L., E-mail: sltucker@mdanderson.org; Li Minghuan; Xu Ting

2013-01-01

Purpose: To determine whether single-nucleotide polymorphisms (SNPs) in genes associated with DNA repair, cell cycle, transforming growth factor-{beta}, tumor necrosis factor and receptor, folic acid metabolism, and angiogenesis can significantly improve the fit of the Lyman-Kutcher-Burman (LKB) normal-tissue complication probability (NTCP) model of radiation pneumonitis (RP) risk among patients with non-small cell lung cancer (NSCLC). Methods and Materials: Sixteen SNPs from 10 different genes (XRCC1, XRCC3, APEX1, MDM2, TGF{beta}, TNF{alpha}, TNFR, MTHFR, MTRR, and VEGF) were genotyped in 141 NSCLC patients treated with definitive radiation therapy, with or without chemotherapy. The LKB model was used to estimate the risk ofmore » severe (grade {>=}3) RP as a function of mean lung dose (MLD), with SNPs and patient smoking status incorporated into the model as dose-modifying factors. Multivariate analyses were performed by adding significant factors to the MLD model in a forward stepwise procedure, with significance assessed using the likelihood-ratio test. Bootstrap analyses were used to assess the reproducibility of results under variations in the data. Results: Five SNPs were selected for inclusion in the multivariate NTCP model based on MLD alone. SNPs associated with an increased risk of severe RP were in genes for TGF{beta}, VEGF, TNF{alpha}, XRCC1 and APEX1. With smoking status included in the multivariate model, the SNPs significantly associated with increased risk of RP were in genes for TGF{beta}, VEGF, and XRCC3. Bootstrap analyses selected a median of 4 SNPs per model fit, with the 6 genes listed above selected most often. Conclusions: This study provides evidence that SNPs can significantly improve the predictive ability of the Lyman MLD model. With a small number of SNPs, it was possible to distinguish cohorts with >50% risk vs <10% risk of RP when they were exposed to high MLDs.« less

Time Average Field and Secular Variations of Pleistocene to Recent Lava Flows From the Ruiz-Tolima Volcanic Complex (Colombia)

NASA Astrophysics Data System (ADS)

Mejia, V.; Sánchez-Duque, A.; Opdyke, N. D.; Huang, K.; Rosales, A.

2009-05-01

Thirty three Pleistocene to recent lava flows from the Ruiz-Tolima Volcanic Complex (Colombian Andes) have been sampled for time average field (TAF) and paleosecular variation studies. A total of 10 cores were drilled per flow (site) and stepwise AF demagnetization has been carried out. After principal component analysis and mean-site direction calculations, 29 sites (25 and 4 with normal and reverse polarity, respectively), with α95 < 5.5° were selected for further calculations. The overall mean direction among the sites (D = 1.8°, I = 6.3°, α95 = 5.6°) closely fits (at the 95% confidence level) the expected paleomagnetic direction (at the area of study) of a geomagnetic field composed primarily by a geocentric axial dipole with 5% axial quadrupole component (I = 5.72°), but also coincides with a simple GAD model. VGP scatter (13°) is similar to that expected from Model G (12.8°).

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.

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

[Regional scale remote sensing-based yield estimation of winter wheat by using MODIS-NDVI data: a case study of Jining City in Shandong Province].

PubMed

Ren, Jianqiang; Chen, Zhongxin; Tang, Huajun

2006-12-01

Taking Jining City of Shandong Province, one of the most important winter wheat production regions in Huanghuaihai Plain as an example, the winter wheat yield was estimated by using the 250 m MODIS-NDVI data smoothed by Savitzky-Golay filter. The NDVI values between 0. 20 and 0. 80 were selected, and the sum of NDVI value for each county was calculated to build its relation with winter wheat yield. By using stepwise regression method, the linear regression model between NDVI and winter wheat yield was established, with the precision validated by the ground survey data. The results showed that the relative error of predicted yield was between -3.6% and 3.9%, suggesting that the method was relatively accurate and feasible.

How are patients influenced when counseled for minimally invasive lumbar spine surgeries? A stepwise model assessing pivotal information for decision-making.

PubMed

Hey, Hwee Weng Dennis; Kumar, Nishant; Teo, Alex Quok An; Tan, Kimberly-Anne; Kumar, Naresh; Liu, Ka-Po Gabriel; Wong, Hee-Kit

2017-08-01

Although minimally invasive surgery (MIS)-transforaminal lumbar interbody fusion (TLIF) has many evidence-based short-term benefits over open TLIF, both procedures have similar long-term outcomes. Patients' preference for MIS over open TLIF may be confounded by a lack of understanding of what each approach entails. The study aimed to identify the various factors influencing patients' choice between MIS and open TLIF. This is a cross-sectional study conducted at a tertiary health-care institution. Patients, for whom TLIF procedures were indicated, were recruited over a 3-month period from specialist outpatient clinics. The outcome measure was patients' choice of surgical approach (MIS or open). All patients were subjected to a stepwise interviewing process and were asked to select between open and MIS approaches at each step. Further subgroup analysis stratifying subjects based on stages of decision-making was performed to identify key predictors of selection changes. No sources of funding were required for this study and there are no conflicts of interests. Fifty-four patients with a mean age of 55.8 years participated in the study. Thirteen (24.1%) consistently selected a single approach, whereas 31 (57.4%) changed their selection more than once during the interviewing process. Overall, 12 patients (22.2%) had a final decision different from their initial choice, and 15 patients (27.8%) were unable to decide. A large proportion of patients (65.0%) initially favored the open approach's midline incision. This proportion dropped to 16.7% (p<.001) upon mention of the term MIS. The proportion of patients favoring MIS dropped significantly following discussion on the pros and cons (p=.002) of each approach, as well as conversion or revision surgery (p=.017). Radiation and cosmesis were identified as the two most important factors influencing patients' final decisions. The longer midline incision of the open approach is cosmetically more appealing to patients than the paramedian stab wounds of MIS. The advantages of the MIS approach may not be as valued by patients as they are by surgeons. Given the equivalent long-term outcomes of both approaches, it is crucial that patients are adequately informed during preoperative counseling to achieve the best consensus decision. Copyright © 2017 Elsevier Inc. All rights reserved.

Stepwise and Pulse Transient Methods of Thermophysical Parameters Measurement

NASA Astrophysics Data System (ADS)

Malinarič, Svetozár; Dieška, Peter

2016-12-01

Stepwise transient and pulse transient methods are experimental techniques for measuring the thermal diffusivity and conductivity of solid materials. Theoretical models and experimental apparatus are presented, and the influence of the heat source capacity and the heat transfer coefficient is investigated using the experiment simulation. The specimens from low-density polyethylene (LDPE) and polymethylmethacrylate (PMMA) were measured by both methods. Coefficients of variation were better than 0.9 % for LDPE and 2.8 % for PMMA measurements. The time dependence of the temperature response to the input heat flux showed a small drop, which was caused by thermoelastic wave generated by thermal expansions of the heat source.

Effect of ionization on the oxidation kinetics of aluminum nanoparticles

NASA Astrophysics Data System (ADS)

Zheng, Yao-Ting; He, Min; Cheng, Guang-xu; Zhang, Zaoxiao; Xuan, Fu-Zhen; Wang, Zhengdong

2018-03-01

Molecular dynamics simulation (MD) of the observed stepwise oxidation of core-shell structured Al/Al2O3 nanoparticles is presented. Different from the metal ion hopping process in the Cabrera-Mott model, which is assumed to occur only at a certain distance from the oxide layer, the MD simulation shows that Al atoms jump over various interfacial gaps directly under the thermal driving force. The energy barrier for Al ionization is found to be increased along with the enlargement of interfacial gap. A mechanism of competition between thermal driving force and ionization potential barrier is proposed in the interpretation of stepwise oxidation behavior.

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.

[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.

Evaluation of a novel, hybrid model (Mumbai EUS II) for stepwise teaching and training in EUS-guided biliary drainage and rendezvous procedures

PubMed Central

Dhir, Vinay; Itoi, Takao; Pausawasdi, Nonthalee; Khashab, Mouen A.; Perez-Miranda, Manuel; Sun, Siyu; Park, Do Hyun; Iwashita, Takuji; Teoh, Anthony Y. B.; Maydeo, Amit P.; Ho, Khek Yu

2017-01-01

Background and aims  EUS-guided biliary drainage (EUS-BD) and rendezvous (EUS-RV) are acceptable rescue options for patients with failed endoscopic retrograde cholangiopancreatography (ERCP). However, there are limited training opportunities at most centers owing to low case volumes. The existing models do not replicate the difficulties encountered during EUS-BD. We aimed to develop and validate a model for stepwise learning of EUS-BD and EUS-RV, which replicates the actual EUS-BD procedures. Methods  A hybrid model was created utilizing pig esophagus and stomach, with a synthetic duodenum and biliary system. The model was objectively assessed on a grade of 1 – 4 by two experts. Twenty-eight trainees were given initial training with didactic lectures and live procedures. This was followed by hands-on training in EUS-BD and EUS-RV on the hybrid model. Trainees were assessed for objective criteria of technical difficulties. Results  Both the experts graded the model as very good or above for all parameters. All trainees could complete the requisite steps of EUS-BD and EUS-RV in a mean time of 11 minutes (8 – 18 minutes). Thirty-six technical difficulties were noted during the training (wrong scope position, 13; incorrect duct puncture, 12; guidewire related problems, 11). Technical difficulties peaked for EUS-RV, followed by hepaticogastrostomy (HGS) and choledochoduodenostomy (CDS) (20, 9, and 7, P  = 0.001). At 10 days follow-up, nine of 28 trainees had successfully performed three EUS-RV and seven EUS-BD procedures independently. Conclusions  The Mumbai EUS II hybrid model replicates situations encountered during EUS-RV and EUS-BD. Stepwise mentoring improves the chances of success in EUS-RV and EUS-BD procedures. PMID:29250585

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

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.

Prognostic stratification model for patients with stage I non-small cell lung cancer adenocarcinoma treated with surgical resection without adjuvant therapies using metabolic features measured on F-18 FDG PET and postoperative pathologic factors.

PubMed

Kang, Yeon-Koo; Song, Yoo Sung; Cho, Sukki; Jheon, Sanghoon; Lee, Won Woo; Kim, Kwhanmien; Kim, Sang Eun

2018-05-01

In the management of non-small cell lung cancer (NSCLC), the prognostic stratification of stage I tumors without indication of adjuvant therapy, remains to be elucidated in order to better select patients who can benefit from additional therapies. We aimed to stratify the prognosis of patients with stage I NSCLC adenocarcinoma using clinicopathologic factors and F-18 FDG PET. We retrospectively enrolled 128 patients with stage I NSCLC without any high-risk factors, who underwent curative surgical resection without adjuvant therapies. Preoperative clinical and postoperative pathologic factors were evaluated by medical record review. Standardized uptake value corrected with lean body mass (SUL max ) was measured on F-18 FDG PET. Among the factors, independent predictors for recurrence-free survival (RFS) were selected using univariate and stepwise multivariate survival analyses. A prognostic stratification model for RFS was designed using the selected factors. Tumors recurred in nineteen patients (14.8%). Among the investigated clinicopathologic and FDG PET factors, SUL max on PET and spread through air spaces (STAS) on pathologic review were determined to be independent prognostic factors for RFS. A prognostic model was designed using these two factors in the following manner: (1) Low-risk: SUL max  ≤ 1.9 and no STAS, (2) intermediate-risk: neither low-risk nor high-risk, (3) high-risk: SUL max> 1.9 and observed STAS. This model exhibited significant predictive power for RFS. We showed that FDG uptake and STAS are significant prognostic markers in stage I NSCLC adenocarcinoma treated with surgical resection without adjuvant therapies. Copyright © 2018 Elsevier B.V. All rights reserved.

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 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.

Can dispersion modeling of air pollution be improved by land-use regression? An example from Stockholm, Sweden

PubMed Central

Korek, Michal; Johansson, Christer; Svensson, Nina; Lind, Tomas; Beelen, Rob; Hoek, Gerard; Pershagen, Göran; Bellander, Tom

2017-01-01

Both dispersion modeling (DM) and land-use regression modeling (LUR) are often used for assessment of long-term air pollution exposure in epidemiological studies, but seldom in combination. We developed a hybrid DM–LUR model using 93 biweekly observations of NOx at 31 sites in greater Stockholm (Sweden). The DM was based on spatially resolved topographic, physiographic and emission data, and hourly meteorological data from a diagnostic wind model. Other data were from land use, meteorology and routine monitoring of NOx. We built a linear regression model for NOx, using a stepwise forward selection of covariates. The resulting model predicted observed NOx (R2=0.89) better than the DM without covariates (R2=0.68, P-interaction <0.001) and with minimal apparent bias. The model included (in descending order of importance) DM, traffic intensity on the nearest street, population (number of inhabitants) within 100 m radius, global radiation (direct sunlight plus diffuse or scattered light) and urban contribution to NOx levels (routine urban NOx, less routine rural NOx). Our results indicate that there is a potential for improving estimates of air pollutant concentrations based on DM, by incorporating further spatial characteristics of the immediate surroundings, possibly accounting for imperfections in the emission data. PMID:27485990

Can dispersion modeling of air pollution be improved by land-use regression? An example from Stockholm, Sweden.

PubMed

Korek, Michal; Johansson, Christer; Svensson, Nina; Lind, Tomas; Beelen, Rob; Hoek, Gerard; Pershagen, Göran; Bellander, Tom

2017-11-01

Both dispersion modeling (DM) and land-use regression modeling (LUR) are often used for assessment of long-term air pollution exposure in epidemiological studies, but seldom in combination. We developed a hybrid DM-LUR model using 93 biweekly observations of NO x at 31 sites in greater Stockholm (Sweden). The DM was based on spatially resolved topographic, physiographic and emission data, and hourly meteorological data from a diagnostic wind model. Other data were from land use, meteorology and routine monitoring of NO x . We built a linear regression model for NO x , using a stepwise forward selection of covariates. The resulting model predicted observed NO x (R 2 =0.89) better than the DM without covariates (R 2 =0.68, P-interaction <0.001) and with minimal apparent bias. The model included (in descending order of importance) DM, traffic intensity on the nearest street, population (number of inhabitants) within 100 m radius, global radiation (direct sunlight plus diffuse or scattered light) and urban contribution to NO x levels (routine urban NO x , less routine rural NO x ). Our results indicate that there is a potential for improving estimates of air pollutant concentrations based on DM, by incorporating further spatial characteristics of the immediate surroundings, possibly accounting for imperfections in the emission data.

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%.

Potential use of ionic species for identifying source land-uses of stormwater runoff.

PubMed

Lee, Dong Hoon; Kim, Jin Hwi; Mendoza, Joseph A; Lee, Chang-Hee; Kang, Joo-Hyon

2017-02-01

Identifying critical land-uses or source areas is important to prioritize resources for cost-effective stormwater management. This study investigated the use of information on ionic composition as a fingerprint to identify the source land-use of stormwater runoff. We used 12 ionic species in stormwater runoff monitored for a total of 20 storm events at five sites with different land-use compositions during the 2012-2014 wet seasons. A stepwise forward discriminant function analysis (DFA) with the jack-knifed cross validation approach was used to select ionic species that better discriminate the land-use of its source. Of the 12 ionic species, 9 species (K + , Mg 2+ , Na + , NH 4 + , Br - , Cl - , F - , NO 2 - , and SO 4 2- ) were selected for better performance of the DFA. The DFA successfully differentiated stormwater samples from urban, rural, and construction sites using concentrations of the ionic species (70%, 95%, and 91% of correct classification, respectively). Over 80% of the new data cases were correctly classified by the trained DFA model. When applied to data cases from a mixed land-use catchment and downstream, the DFA model showed the greater impact of urban areas and rural areas respectively in the earlier and later parts of a storm event.

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.

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.

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

A rapid learning and dynamic stepwise updating algorithm for flat neural networks and the application to time-series prediction.

PubMed

Chen, C P; Wan, J Z

1999-01-01

A fast learning algorithm is proposed to find an optimal weights of the flat neural networks (especially, the functional-link network). Although the flat networks are used for nonlinear function approximation, they can be formulated as linear systems. Thus, the weights of the networks can be solved easily using a linear least-square method. This formulation makes it easier to update the weights instantly for both a new added pattern and a new added enhancement node. A dynamic stepwise updating algorithm is proposed to update the weights of the system on-the-fly. The model is tested on several time-series data including an infrared laser data set, a chaotic time-series, a monthly flour price data set, and a nonlinear system identification problem. The simulation results are compared to existing models in which more complex architectures and more costly training are needed. The results indicate that the proposed model is very attractive to real-time processes.

An Integrated Modeling and Simulation Methodology for Intelligent Systems Design and Testing

DTIC Science & Technology

2002-08-01

simulation and actual execution. KEYWORDS: Model Continuity, Modeling, Simulation, Experimental Frame, Real Time Systems , Intelligent Systems...the methodology for a stand-alone real time system. Then it will scale up to distributed real time systems . For both systems, step-wise simulation...MODEL CONTINUITY Intelligent real time systems monitor, respond to, or control, an external environment. This environment is connected to the digital

Objective Quantification of Pre-and Postphonosurgery Vocal Fold Vibratory Characteristics Using High-Speed Videoendoscopy and a Harmonic Waveform Model

ERIC Educational Resources Information Center

Ikuma, Takeshi; Kunduk, Melda; McWhorter, Andrew J.

2014-01-01

Purpose: The model-based quantitative analysis of high-speed videoendoscopy (HSV) data at a low frame rate of 2,000 frames per second was assessed for its clinical adequacy. Stepwise regression was employed to evaluate the HSV parameters using harmonic models and their relationships to the Voice Handicap Index (VHI). Also, the model-based HSV…

Recovery of Volatile Fatty Acids from Fermented Wastewater by Adsorption

PubMed Central

2017-01-01

Separation of volatile fatty acids (VFAs) from fermented wastewater is challenging, due to low VFA concentrations in mineral-rich streams. As a result, separation capacity and selectivity with traditional solvents and adsorbents are both compromised. In this study, using a complex artificial model solution mimicking real fermented wastewaters, it is shown that a simple and robust adsorption-based separation technique can retain a remarkable capacity and selectivity for VFAs. Four types of polystyrene-divinylbenzene-based resins (primary, secondary, and tertiary amine-functionalized, and nonfunctionalized) were examined as the adsorbents. The presence of chloride, sulfate, and phosphate salts resulted in coadsorption of their acidic forms HCl, H2SO4, and H3PO4 on amine-functionalized adsorbents, and severely reduced the VFA capacity. With the nonfunctionalized adsorbent, almost no mineral acid coadsorption was observed. This together with a high total VFA capacity of up to 76 g/kg in equilibrium with the model solution containing a total VFA concentration of 1 wt % resulted in a very high selectivity for the VFAs. Nitrogen-stripping with various temperature profiles was applied to regenerate the adsorbent, and study the potential for fractionation of the VFAs during regeneration. Butyric acid (HBu) was obtained in mole fractions of up to 0.8 using a stepwise increase in the stripping temperature from 25 °C via 120 to 200 °C. During four successive adsorption–regeneration cycles, no reduction in the adsorption capacity was observed. PMID:28989827

The Equivalent Electrokinetic Circuit Model of Ion Concentration Polarization Layer: Electrical Double Layer, Extended Space Charge and Electro-convection

NASA Astrophysics Data System (ADS)

Cho, Inhee; Huh, Keon; Kwak, Rhokyun; Lee, Hyomin; Kim, Sung Jae

2016-11-01

The first direct chronopotentiometric measurement was provided to distinguish the potential difference through the extended space charge (ESC) layer which is formed with the electrical double layer (EDL) near a perm-selective membrane. From this experimental result, the linear relationship was obtained between the resistance of ESC and the applied current density. Furthermore, we observed the step-wise distributions of relaxation time at the limiting current regime, confirming the existence of ESC capacitance other than EDL's. In addition, we proposed the equivalent electrokinetic circuit model inside ion concentration polarization (ICP) layer under rigorous consideration of EDL, ESC and electro-convection (EC). In order to elucidate the voltage configuration in chronopotentiometric measurement, the EC component was considered as the "dependent voltage source" which is serially connected to the ESC layer. This model successfully described the charging behavior of the ESC layer with or without EC, where both cases determined each relaxation time, respectively. Finally, we quantitatively verified their values utilizing the Poisson-Nernst-Planck equations. Therefore, this unified circuit model would provide a key insight of ICP system and potential energy-efficient applications.

Optimization of elution salt concentration in stepwise elution of protein chromatography using linear gradient elution data. Reducing residual protein A by cation-exchange chromatography in monoclonal antibody purification.

PubMed

Ishihara, Takashi; Kadoya, Toshihiko; Endo, Naomi; Yamamoto, Shuichi

2006-05-05

Our simple method for optimization of the elution salt concentration in stepwise elution was applied to the actual protein separation system, which involves several difficulties such as detection of the target. As a model separation system, reducing residual protein A by cation-exchange chromatography in human monoclonal antibody (hMab) purification was chosen. We carried out linear gradient elution experiments and obtained the data for the peak salt concentration of hMab and residual protein A, respectively. An enzyme-linked immunosorbent assay was applied to the measurement of the residual protein A. From these data, we calculated the distribution coefficient of the hMab and the residual protein A as a function of salt concentration. The optimal salt concentration of stepwise elution to reduce the residual protein A from the hMab was determined based on the relationship between the distribution coefficient and the salt concentration. Using the optimized condition, we successfully performed the separation, resulting in high recovery of hMab and the elimination of residual protein A.

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.

[Introduction and advantage analysis of the stepwise method for the construction of vascular trees].

PubMed

Zhang, Yan; Xie, Haiwei; Zhu, Kai

2010-08-01

A new method for constructing the model of vascular trees was proposed in this paper. By use of this method, the arterial trees in good agreement with the actual structure could be grown. In this process, all vessels in the vascular tree were divided into two groups: the conveying vessels, and the delivering branches. And different branches could be built by different ways. Firstly, the distributing rules of conveying vessels were ascertained by use of measurement data, and then the conveying vessels were constructed in accordance to the statistical rule and optimization criterion. Lastly, delivering branches were modeled by constrained constructive optimization (CCO) on the conveying vessel-trees which had already been generated. In order to compare the CCO method and stepwise method proposed here, two 3D arterial trees of human tongue were grown with their vascular tree having a special structure. Based on the corrosion casts of real arterial tree of human tongue, the data about the two trees constructed by different methods were compared and analyzed, including the averaged segment diameters at respective levels, the distribution and the diameters of the branches of first level at respective directions. The results show that the vascular tree built by stepwise method is more similar to the true arterial of human tongue when compared against the tree built by CCO method.

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.

A Strategy for a Parametric Flood Insurance Using Proxies

NASA Astrophysics Data System (ADS)

Haraguchi, M.; Lall, U.

2017-12-01

Traditionally, the design of flood control infrastructure and flood plain zoning require the estimation of return periods, which have been calculated by river hydraulic models with rainfall-runoff models. However, this multi-step modeling process leads to significant uncertainty to assess inundation. In addition, land use change and changing climate alter the potential losses, as well as make the modeling results obsolete. For these reasons, there is a strong need to create parametric indexes for the financial risk transfer for large flood events, to enable rapid response and recovery. Hence, this study examines the possibility of developing a parametric flood index at the national or regional level in Asia, which can be quickly mobilized after catastrophic floods. Specifically, we compare a single trigger based on rainfall index with multiple triggers using rainfall and streamflow indices by conducting case studies in Bangladesh and Thailand. The proposed methodology is 1) selecting suitable indices of rainfall and streamflow (if available), 2) identifying trigger levels for specified return periods for losses using stepwise and logistic regressions, 3) measuring the performance of indices, and 4) deriving return periods of selected windows and trigger levels. Based on the methodology, actual trigger levels were identified for Bangladesh and Thailand. Models based on multiple triggers reduced basis risks, an inherent problem in an index insurance. The proposed parametric flood index can be applied to countries with similar geographic and meteorological characteristics, and serve as a promising method for ex-ante risk financing for developing countries. This work is intended to be a preliminary work supporting future work on pricing risk transfer mechanisms in ex-ante risk finance.

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.

Bayesian multimodel inference of soil microbial respiration models: Theory, application and future prospective

NASA Astrophysics Data System (ADS)

Elshall, A. S.; Ye, M.; Niu, G. Y.; Barron-Gafford, G.

2015-12-01

Models in biogeoscience involve uncertainties in observation data, model inputs, model structure, model processes and modeling scenarios. To accommodate for different sources of uncertainty, multimodal analysis such as model combination, model selection, model elimination or model discrimination are becoming more popular. To illustrate theoretical and practical challenges of multimodal analysis, we use an example about microbial soil respiration modeling. Global soil respiration releases more than ten times more carbon dioxide to the atmosphere than all anthropogenic emissions. Thus, improving our understanding of microbial soil respiration is essential for improving climate change models. This study focuses on a poorly understood phenomena, which is the soil microbial respiration pulses in response to episodic rainfall pulses (the "Birch effect"). We hypothesize that the "Birch effect" is generated by the following three mechanisms. To test our hypothesis, we developed and assessed five evolving microbial-enzyme models against field measurements from a semiarid Savannah that is characterized by pulsed precipitation. These five model evolve step-wise such that the first model includes none of these three mechanism, while the fifth model includes the three mechanisms. The basic component of Bayesian multimodal analysis is the estimation of marginal likelihood to rank the candidate models based on their overall likelihood with respect to observation data. The first part of the study focuses on using this Bayesian scheme to discriminate between these five candidate models. The second part discusses some theoretical and practical challenges, which are mainly the effect of likelihood function selection and the marginal likelihood estimation methods on both model ranking and Bayesian model averaging. The study shows that making valid inference from scientific data is not a trivial task, since we are not only uncertain about the candidate scientific models, but also about the statistical methods that are used to discriminate between these models.

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

Formation and nitrile hydrogenation performance of Ru nanoparticles on a K-doped Al2O3 surface.

PubMed

Muratsugu, Satoshi; Kityakarn, Sutasinee; Wang, Fei; Ishiguro, Nozomu; Kamachi, Takashi; Yoshizawa, Kazunari; Sekizawa, Oki; Uruga, Tomoya; Tada, Mizuki

2015-10-14

Decarbonylation-promoted Ru nanoparticle formation from Ru3(CO)12 on a basic K-doped Al2O3 surface was investigated by in situ FT-IR and in situ XAFS. Supported Ru3(CO)12 clusters on K-doped Al2O3 were converted stepwise to Ru nanoparticles, which catalyzed the selective hydrogenation of nitriles to the corresponding primary amines via initial decarbonylation, the nucleation of the Ru cluster core, and the growth of metallic Ru nanoparticles on the surface. As a result, small Ru nanoparticles, with an average diameter of less than 2 nm, were formed on the support and acted as efficient catalysts for nitrile hydrogenation at 343 K under hydrogen at atmospheric pressure. The structure and catalytic performance of Ru catalysts depended strongly on the type of oxide support, and the K-doped Al2O3 support acted as a good oxide for the selective nitrile hydrogenation without basic additives like ammonia. The activation of nitriles on the modelled Ru catalyst was also investigated by DFT calculations, and the adsorption structure of a nitrene-like intermediate, which was favourable for high primary amine selectivity, was the most stable structure on Ru compared with other intermediate structures.

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.

Discrimination of Active and Weakly Active Human BACE1 Inhibitors Using Self-Organizing Map and Support Vector Machine.

PubMed

Li, Hang; Wang, Maolin; Gong, Ya-Nan; Yan, Aixia

2016-01-01

β-secretase (BACE1) is an aspartyl protease, which is considered as a novel vital target in Alzheimer`s disease therapy. We collected a data set of 294 BACE1 inhibitors, and built six classification models to discriminate active and weakly active inhibitors using Kohonen's Self-Organizing Map (SOM) method and Support Vector Machine (SVM) method. Each molecular descriptor was calculated using the program ADRIANA.Code. We adopted two different methods: random method and Self-Organizing Map method, for training/test set split. The descriptors were selected by F-score and stepwise linear regression analysis. The best SVM model Model2C has a good prediction performance on test set with prediction accuracy, sensitivity (SE), specificity (SP) and Matthews correlation coefficient (MCC) of 89.02%, 90%, 88%, 0.78, respectively. Model 1A is the best SOM model, whose accuracy and MCC of the test set were 94.57% and 0.98, respectively. The lone pair electronegativity and polarizability related descriptors importantly contributed to bioactivity of BACE1 inhibitor. The Extended-Connectivity Finger-Prints_4 (ECFP_4) analysis found some vitally key substructural features, which could be helpful for further drug design research. The SOM and SVM models built in this study can be obtained from the authors by email or other contacts.

Modeling and Prediction of Solvent Effect on Human Skin Permeability using Support Vector Regression and Random Forest.

PubMed

Baba, Hiromi; Takahara, Jun-ichi; Yamashita, Fumiyoshi; Hashida, Mitsuru

2015-11-01

The solvent effect on skin permeability is important for assessing the effectiveness and toxicological risk of new dermatological formulations in pharmaceuticals and cosmetics development. The solvent effect occurs by diverse mechanisms, which could be elucidated by efficient and reliable prediction models. However, such prediction models have been hampered by the small variety of permeants and mixture components archived in databases and by low predictive performance. Here, we propose a solution to both problems. We first compiled a novel large database of 412 samples from 261 structurally diverse permeants and 31 solvents reported in the literature. The data were carefully screened to ensure their collection under consistent experimental conditions. To construct a high-performance predictive model, we then applied support vector regression (SVR) and random forest (RF) with greedy stepwise descriptor selection to our database. The models were internally and externally validated. The SVR achieved higher performance statistics than RF. The (externally validated) determination coefficient, root mean square error, and mean absolute error of SVR were 0.899, 0.351, and 0.268, respectively. Moreover, because all descriptors are fully computational, our method can predict as-yet unsynthesized compounds. Our high-performance prediction model offers an attractive alternative to permeability experiments for pharmaceutical and cosmetic candidate screening and optimizing skin-permeable topical formulations.

Assessment of questionnaire-based PCB exposure focused on food frequency in birth cohorts in Japan.

PubMed

Eguchi, Akifumi; Otake, Masae; Hanazato, Masamichi; Suzuki, Norimichi; Matsuno, Yoshiharu; Nakaoka, Hiroko; Todaka, Emiko; Mori, Chisato

2017-02-01

We investigated the relationship between food frequency questionnaire (FFQ) responses and serum polychlorinated biphenyl (PCB) levels of mothers and fathers recruited from the Chiba Regional Center, which is one of the 15 regional centers of the Japan Environment and Children's Study (mothers: n = 1477, fathers: n = 219). The expected PCB values were estimated from the participants' FFQ answers and medical records (age, body mass index and number of deliveries). Based on the stepwise forward selection results of Bayesian regression models, age and fish and egg consumption were positively associated with PCB concentrations and a number of deliveries were negatively associated with PCB concentrations in mothers, whereas only age was positively associated with PCB concentrations in fathers.These findings indicated that the estimation of daily dietary intake may be useful for the prediction of PCB concentration for mothers.

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.

Does Stepwise Voltage Ramping Protect the Kidney from Injury During Extracorporeal Shockwave Lithotripsy? Results of a Prospective Randomized Trial.

PubMed

Skuginna, Veronika; Nguyen, Daniel P; Seiler, Roland; Kiss, Bernhard; Thalmann, George N; Roth, Beat

2016-02-01

Renal damage is more frequent with new-generation lithotripters. However, animal studies suggest that voltage ramping minimizes the risk of complications following extracorporeal shock wave lithotripsy (SWL). In the clinical setting, the optimal voltage strategy remains unclear. To evaluate whether stepwise voltage ramping can protect the kidney from damage during SWL. A total of 418 patients with solitary or multiple unilateral kidney stones were randomized to receive SWL using a Modulith SLX-F2 lithotripter with either stepwise voltage ramping (n=213) or a fixed maximal voltage (n=205). SWL. The primary outcome was sonographic evidence of renal hematomas. Secondary outcomes included levels of urinary markers of renal damage, stone disintegration, stone-free rate, and rates of secondary interventions within 3 mo of SWL. Descriptive statistics were used to compare clinical outcomes between the two groups. A logistic regression model was generated to assess predictors of hematomas. Significantly fewer hematomas occurred in the ramping group(12/213, 5.6%) than in the fixed group (27/205, 13%; p=0.008). There was some evidence that the fixed group had higher urinary β2-microglobulin levels after SWL compared to the ramping group (p=0.06). Urinary microalbumin levels, stone disintegration, stone-free rate, and rates of secondary interventions did not significantly differ between the groups. The logistic regression model showed a significantly higher risk of renal hematomas in older patients (odds ratio [OR] 1.03, 95% confidence interval [CI] 1.00-1.05; p=0.04). Stepwise voltage ramping was associated with a lower risk of hematomas (OR 0.39, 95% CI 0.19-0.80; p=0.01). The study was limited by the use of ultrasound to detect hematomas. In this prospective randomized study, stepwise voltage ramping during SWL was associated with a lower risk of renal damage compared to a fixed maximal voltage without compromising treatment effectiveness. Lithotripsy is a noninvasive technique for urinary stone disintegration using ultrasonic energy. In this study, two voltage strategies are compared. The results show that a progressive increase in voltage during lithotripsy decreases the risk of renal hematomas while maintaining excellent outcomes. ISRCTN95762080. Copyright © 2015 European Association of Urology. Published by Elsevier B.V. All rights reserved.

Improving prediction of metal uptake by Chinese cabbage (Brassica pekinensis L.) based on a soil-plant stepwise analysis.

PubMed

Zhang, Sha; Song, Jing; Gao, Hui; Zhang, Qiang; Lv, Ming-Chao; Wang, Shuang; Liu, Gan; Pan, Yun-Yu; Christie, Peter; Sun, Wenjie

2016-11-01

It is crucial to develop predictive soil-plant transfer (SPT) models to derive the threshold values of toxic metals in contaminated arable soils. The present study was designed to examine the heavy metal uptake pattern and to improve the prediction of metal uptake by Chinese cabbage grown in agricultural soils with multiple contamination by Cd, Cu, Ni, Pb, and Zn. Pot experiments were performed with 25 historically contaminated soils to determine metal accumulation in different parts of Chinese cabbage. Different soil bioavailable metal fractions were determined using different extractants (0.43M HNO3, 0.01M CaCl2, 0.005M DTPA, and 0.01M LWMOAs), soil moisture samplers, and diffusive gradients in thin films (DGT), and the fractions were compared with shoot metal uptake using both direct and stepwise multiple regression analysis. The stepwise approach significantly improved the prediction of metal uptake by cabbage over the direct approach. Strongly pH dependent or nonlinear relationships were found for the adsorption of root surfaces and in root-shoot uptake processes. Metals were linearly translocated from the root surface to the root. Therefore, the nonlinearity of uptake pattern is an important explanation for the inadequacy of the direct approach in some cases. The stepwise approach offers an alternative and robust method to study the pattern of metal uptake by Chinese cabbage (Brassica pekinensis L.). Copyright © 2016. Published by Elsevier B.V.

Sexual Response Models: Toward a More Flexible Pattern of Women's Sexuality.

PubMed

Ferenidou, Fotini; Kirana, Paraskevi-Sofia; Fokas, Konstantinos; Hatzichristou, Dimitrios; Athanasiadis, Loukas

2016-09-01

Recent research suggests that none of the current theoretical models can sufficiently describe women's sexual response, because several factors and situations can influence this. To explore individual variations of a sexual model that describes women's sexual responses and to assess the association of endorsement of that model with sexual dysfunctions and reasons to engage in sexual activity. A sample of 157 randomly selected hospital employees completed self-administered questionnaires. Two models were developed: one merged the Master and Johnson model with the Kaplan model (linear) and the other was the Basson model (circular). Sexual function was evaluated by the Female Sexual Function Index and the Brief Sexual Symptom Checklist for Women. The Reasons for Having Sex Questionnaire was administered to investigate the reasons for which women have sex. Women reported that their current sexual experiences were at times consistent with the linear and circular models (66.9%), only the linear model (27%), only the circular model (5.4%), and neither model (0.7%). When the groups were reconfigured to the group that endorsed more than 5 of 10 sexual experiences, 64.3% of women endorsed the linear model, 20.4% chose the linear and circular models, 14.6% chose the circular model, and 0.7% selected neither. The Female Sexual Function Index, demographic factors, having sex for insecurity reasons, and sexual satisfaction correlated with the endorsement of a sexual response model. When these factors were entered in a stepwise logistic regression analysis, only the Female Sexual Function Index and having sex for insecurity reasons maintained a significant association with the sexual response model. The present study emphasizes the heterogeneity of female sexuality, with most of the sample reporting alternating between the linear and circular models. Sexual dysfunctions and having sex for insecurity reasons were associated with the Basson model. Copyright © 2016 International Society for Sexual Medicine. Published by Elsevier Inc. All rights reserved.

Concurrent Validity of Hill's Educational Cognitive Style Model as a Prototype for Successful Academic Programs Among Lower-Class Students.

ERIC Educational Resources Information Center

London, David T.

Data from the stepwise multiple regression of four educational cognitive style predictor sets on each of six academic competence criteria were used to define the concurrent validity of Hill's educational cognitive style model. The purpose was to determine how appropriate it may be to use this model as a prototype for successful academic programs…

Estimating extent of mortality associated with the Douglas-fir beetle in the Central and Northern Rockies

Treesearch

Jose F. Negron; Willis C. Schaupp; Kenneth E. Gibson; John Anhold; Dawn Hansen; Ralph Thier; Phil Mocettini

1999-01-01

Data collected from Douglas-fir stands infected by the Douglas-fir beetle in Wyoming, Montana, Idaho, and Utah, were used to develop models to estimate amount of mortality in terms of basal area killed. Models were built using stepwise linear regression and regression tree approaches. Linear regression models using initial Douglas-fir basal area were built for all...

NRVS and EPR Spectroscopy of 57Fe-enriched [FeFe] Hydrogenase Indicate Stepwise Assembly of the H-cluster†

PubMed Central

Kuchenreuther, Jon M.; Guo, Yisong; Wang, Hongxin; Myers, William K.; George, Simon J.; Boyke, Christine A.; Yoda, Yoshitaka; Alp, E. Ercan; Zhao, Jiyong; Britt, R. David; Swartz, James R.; Cramer, Stephen P.

2013-01-01

The [FeFe] hydrogenase from Clostridium pasteurianum (CpI) harbors four Fe–S clusters that facilitate electron transfer to the H-cluster, a ligand-coordinated six-iron prosthetic group that catalyzes the redox interconversion of protons and H2. Here, we have used 57Fe nuclear resonance vibrational spectroscopy (NRVS) to study the iron centers in CpI, and we compare our data to that for a [4Fe–4S] ferredoxin as well as a model complex resembling the [2Fe]H catalytic domain of the H-cluster. In order to enrich the hydrogenase with 57Fe nuclei, we used cell-free methods to post-translationally mature the enzyme. Specifically, inactive CpI apoprotein with 56Fe-labeled Fe–S clusters was activated in vitro using 57Fe-enriched maturation proteins. This approach enabled us to selectively label the [2Fe]H subcluster with 57Fe, which NRVS confirms by detecting 57Fe–CO and 57Fe–CN normal modes from the H-cluster nonprotein ligands. The NRVS and iron quantification results also suggest that the hydrogenase contains a second 57Fe–S cluster. EPR spectroscopy indicates that this 57Fe-enriched metal center is not the [4Fe– 4S]H subcluster of the H-cluster. This finding demonstrates that the CpI hydrogenase retained an 56Fe-enriched [4Fe–4S]H cluster during in vitro maturation, providing unambiguous evidence for stepwise assembly of the H-cluster. In addition, this work represents the first NRVS characterization of [FeFe] hydrogenases. PMID:23249091

A Population Biology Perspective on the Stepwise Infection Process of the Bacterial Pathogen Pasteuria ramosa in Daphnia.

PubMed

Ebert, Dieter; Duneau, David; Hall, Matthew D; Luijckx, Pepijn; Andras, Jason P; Du Pasquier, Louis; Ben-Ami, Frida

2016-01-01

The infection process of many diseases can be divided into series of steps, each one required to successfully complete the parasite's life and transmission cycle. This approach often reveals that the complex phenomenon of infection is composed of a series of more simple mechanisms. Here we demonstrate that a population biology approach, which takes into consideration the natural genetic and environmental variation at each step, can greatly aid our understanding of the evolutionary processes shaping disease traits. We focus in this review on the biology of the bacterial parasite Pasteuria ramosa and its aquatic crustacean host Daphnia, a model system for the evolutionary ecology of infectious disease. Our analysis reveals tremendous differences in the degree to which the environment, host genetics, parasite genetics and their interactions contribute to the expression of disease traits at each of seven different steps. This allows us to predict which steps may respond most readily to selection and which steps are evolutionarily constrained by an absence of variation. We show that the ability of Pasteuria to attach to the host's cuticle (attachment step) stands out as being strongly influenced by the interaction of host and parasite genotypes, but not by environmental factors, making it the prime candidate for coevolutionary interactions. Furthermore, the stepwise approach helps us understanding the evolution of resistance, virulence and host ranges. The population biological approach introduced here is a versatile tool that can be easily transferred to other systems of infectious disease. Copyright © 2016 Elsevier Ltd. All rights reserved.

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...

QSPR study of polychlorinated diphenyl ethers by molecular electronegativity distance vector (MEDV-4).

PubMed

Sun, Lili; Zhou, Liping; Yu, Yu; Lan, Yukun; Li, Zhiliang

2007-01-01

Polychlorinated diphenyl ethers (PCDEs) have received more and more concerns as a group of ubiquitous potential persistent organic pollutants (POPs). By using molecular electronegativity distance vector (MEDV-4), multiple linear regression (MLR) models are developed for sub-cooled liquid vapor pressures (P(L)), n-octanol/water partition coefficients (K(OW)) and sub-cooled liquid water solubilities (S(W,L)) of 209 PCDEs and diphenyl ether. The correlation coefficients (R) and the leave-one-out cross-validation (LOO) correlation coefficients (R(CV)) of all the 6-descriptor models for logP(L), logK(OW) and logS(W,L) are more than 0.98. By using stepwise multiple regression (SMR), the descriptors are selected and the resulting models are 5-descriptor model for logP(L), 4-descriptor model for logK(OW), and 6-descriptor model for logS(W,L), respectively. All these models exhibit excellent estimate capabilities for internal sample set and good predictive capabilities for external samples set. The consistency between observed and estimated/predicted values for logP(L) is the best (R=0.996, R(CV)=0.996), followed by logK(OW) (R=0.992, R(CV)=0.992) and logS(W,L) (R=0.983, R(CV)=0.980). By using MEDV-4 descriptors, the QSPR models can be used for prediction and the model predictions can hence extend the current database of experimental values.

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.

Rooted tRNAomes and evolution of the genetic code

PubMed Central

Pak, Daewoo; Du, Nan; Kim, Yunsoo; Sun, Yanni

2018-01-01

ABSTRACT We advocate for a tRNA- rather than an mRNA-centric model for evolution of the genetic code. The mechanism for evolution of cloverleaf tRNA provides a root sequence for radiation of tRNAs and suggests a simplified understanding of code evolution. To analyze code sectoring, rooted tRNAomes were compared for several archaeal and one bacterial species. Rooting of tRNAome trees reveals conserved structures, indicating how the code was shaped during evolution and suggesting a model for evolution of a LUCA tRNAome tree. We propose the polyglycine hypothesis that the initial product of the genetic code may have been short chain polyglycine to stabilize protocells. In order to describe how anticodons were allotted in evolution, the sectoring-degeneracy hypothesis is proposed. Based on sectoring, a simple stepwise model is developed, in which the code sectors from a 1→4→8→∼16 letter code. At initial stages of code evolution, we posit strong positive selection for wobble base ambiguity, supporting convergence to 4-codon sectors and ∼16 letters. In a later stage, ∼5–6 letters, including stops, were added through innovating at the anticodon wobble position. In archaea and bacteria, tRNA wobble adenine is negatively selected, shrinking the maximum size of the primordial genetic code to 48 anticodons. Because 64 codons are recognized in mRNA, tRNA-mRNA coevolution requires tRNA wobble position ambiguity leading to degeneracy of the code. PMID:29372672

Prediction of Maternal Cytomegalovirus Serostatus in Early Pregnancy: A Retrospective Analysis in Western Europe

PubMed Central

Kuessel, Lorenz; Husslein, Heinrich; Marschalek, Julian; Brunner, Julia; Ristl, Robin; Popow-Kraupp, Theresia; Kiss, Herbert

2015-01-01

Background Cytomegalovirus (CMV) is the most prevalent congenital viral infection and thus places an enormous disease burden on newborn infants. Seroprevalence of maternal antibodies to CMV due to CMV exposure prior to pregnancy is currently the most important protective factor against congenital CMV disease. The aim of this study was to identify potential predictors, and to develop and evaluate a risk-predicting model for the maternal CMV serostatus in early pregnancy. Methods Maternal and paternal background information, as well as maternal CMV serostatus in early pregnancy from 882 pregnant women were analyzed. Women were divided into two groups based on their CMV serostatus, and were compared using univariate analysis. To predict serostatus based on epidemiological baseline characteristics, a multiple logistic regression model was calculated using stepwise model selection. Sensitivity and specificity were analyzed using ROC curves. A nomogram based on the model was developed. Results 646 women were CMV seropositive (73.2%), and 236 were seronegative (26.8%). The groups differed significantly with respect to maternal age (p = 0.006), gravidity (p<0.001), parity (p<0.001), use of assisted reproduction techniques (p = 0.018), maternal and paternal migration background (p<0.001), and maternal and paternal education level (p<0.001). ROC evaluation of the selected prediction model revealed an area under the curve of 0.83 (95%CI: 0.8–0.86), yielding sensitivity and specificity values of 0.69 and 0.86, respectively. Conclusion We identified predictors of maternal CMV serostatus in early pregnancy and developed a risk-predicting model based on baseline epidemiological characteristics. Our findings provide easy accessible information that can influence the counseling of pregnant woman in terms of their CMV-associated risk. PMID:26693714

RRegrs: an R package for computer-aided model selection with multiple regression models.

PubMed

Tsiliki, Georgia; Munteanu, Cristian R; Seoane, Jose A; Fernandez-Lozano, Carlos; Sarimveis, Haralambos; Willighagen, Egon L

2015-01-01

Predictive regression models can be created with many different modelling approaches. Choices need to be made for data set splitting, cross-validation methods, specific regression parameters and best model criteria, as they all affect the accuracy and efficiency of the produced predictive models, and therefore, raising model reproducibility and comparison issues. Cheminformatics and bioinformatics are extensively using predictive modelling and exhibit a need for standardization of these methodologies in order to assist model selection and speed up the process of predictive model development. A tool accessible to all users, irrespectively of their statistical knowledge, would be valuable if it tests several simple and complex regression models and validation schemes, produce unified reports, and offer the option to be integrated into more extensive studies. Additionally, such methodology should be implemented as a free programming package, in order to be continuously adapted and redistributed by others. We propose an integrated framework for creating multiple regression models, called RRegrs. The tool offers the option of ten simple and complex regression methods combined with repeated 10-fold and leave-one-out cross-validation. Methods include Multiple Linear regression, Generalized Linear Model with Stepwise Feature Selection, Partial Least Squares regression, Lasso regression, and Support Vector Machines Recursive Feature Elimination. The new framework is an automated fully validated procedure which produces standardized reports to quickly oversee the impact of choices in modelling algorithms and assess the model and cross-validation results. The methodology was implemented as an open source R package, available at https://www.github.com/enanomapper/RRegrs, by reusing and extending on the caret package. The universality of the new methodology is demonstrated using five standard data sets from different scientific fields. Its efficiency in cheminformatics and QSAR modelling is shown with three use cases: proteomics data for surface-modified gold nanoparticles, nano-metal oxides descriptor data, and molecular descriptors for acute aquatic toxicity data. The results show that for all data sets RRegrs reports models with equal or better performance for both training and test sets than those reported in the original publications. Its good performance as well as its adaptability in terms of parameter optimization could make RRegrs a popular framework to assist the initial exploration of predictive models, and with that, the design of more comprehensive in silico screening applications.Graphical abstractRRegrs is a computer-aided model selection framework for R multiple regression models; this is a fully validated procedure with application to QSAR modelling.

Stepwise kinetic equilibrium models of quantitative polymerase chain reaction.

PubMed

Cobbs, Gary

2012-08-16

Numerous models for use in interpreting quantitative PCR (qPCR) data are present in recent literature. The most commonly used models assume the amplification in qPCR is exponential and fit an exponential model with a constant rate of increase to a select part of the curve. Kinetic theory may be used to model the annealing phase and does not assume constant efficiency of amplification. Mechanistic models describing the annealing phase with kinetic theory offer the most potential for accurate interpretation of qPCR data. Even so, they have not been thoroughly investigated and are rarely used for interpretation of qPCR data. New results for kinetic modeling of qPCR are presented. Two models are presented in which the efficiency of amplification is based on equilibrium solutions for the annealing phase of the qPCR process. Model 1 assumes annealing of complementary targets strands and annealing of target and primers are both reversible reactions and reach a dynamic equilibrium. Model 2 assumes all annealing reactions are nonreversible and equilibrium is static. Both models include the effect of primer concentration during the annealing phase. Analytic formulae are given for the equilibrium values of all single and double stranded molecules at the end of the annealing step. The equilibrium values are then used in a stepwise method to describe the whole qPCR process. Rate constants of kinetic models are the same for solutions that are identical except for possibly having different initial target concentrations. Analysis of qPCR curves from such solutions are thus analyzed by simultaneous non-linear curve fitting with the same rate constant values applying to all curves and each curve having a unique value for initial target concentration. The models were fit to two data sets for which the true initial target concentrations are known. Both models give better fit to observed qPCR data than other kinetic models present in the literature. They also give better estimates of initial target concentration. Model 1 was found to be slightly more robust than model 2 giving better estimates of initial target concentration when estimation of parameters was done for qPCR curves with very different initial target concentration. Both models may be used to estimate the initial absolute concentration of target sequence when a standard curve is not available. It is argued that the kinetic approach to modeling and interpreting quantitative PCR data has the potential to give more precise estimates of the true initial target concentrations than other methods currently used for analysis of qPCR data. The two models presented here give a unified model of the qPCR process in that they explain the shape of the qPCR curve for a wide variety of initial target concentrations.

Length of stay of stroke rehabilitation inpatients: prediction through the functional independence measure.

PubMed

Franchignoni, F; Tesio, L; Martino, M T; Benevolo, E; Castagna, M

1998-01-01

A model for prediction of length of stay (LOS, in days) of stroke rehabilitation inpatients was developed, based on patients' age (years) and function at admission (scored on the Functional Independence Measure, FIMSM). One hundred and twenty-nine cases, consecutively admitted to three free-standing rehabilitation centres in Italy, were analyzed. A multiple linear regression using forward stepwise selection procedure was adopted. Median admission and discharge scores were: 57 and 75 for the total FIM score, 29 and 48 for the 13-item motor FIM subscore, 29 and 30 for the 5-item cognitive FIM subscore (potential range: 18-126, 13-91, 5-35, respectively). Median LOS was 44 days (interquartile range 30-62). The logLOS predictive model included three FIM items ("toilet transfer", TTr; "social interaction"; "expression") and patient's age (R2 = 0.48). TTr alone explained 31.3% of the variance of logLOS. These results are consistent with previous American studies, showing that FIM scores at admission are strong predictors of patients' LOS, with the transfer items having the greatest predictive power.

A multidisciplinary selection model for youth soccer: the Ghent Youth Soccer Project

PubMed Central

Vaeyens, R; Malina, R M; Janssens, M; Van Renterghem, B; Bourgois, J; Vrijens, J; Philippaerts, R M

2006-01-01

Objectives To determine the relationships between physical and performance characteristics and level of skill in youth soccer players aged 12–16 years. Methods Anthropometry, maturity status, functional and sport‐specific parameters were assessed in elite, sub‐elite, and non‐elite youth players in four age groups: U13 (n = 117), U14 (n = 136), U15 (n = 138) and U16 (n = 99). Results Multivariate analyses of covariance by age group with maturity status as the covariate showed that elite players scored better than the non‐elite players on strength, flexibility, speed, aerobic endurance, anaerobic capacity and several technical skills (p<0.05). Stepwise discriminant analyses showed that running speed and technical skills were the most important characteristics in U13 and U14 players, while cardiorespiratory endurance was more important in U15 and U16 players. The results suggest that discriminating characteristics change with competitive age levels. Conclusions Characteristics that discriminate youth soccer players vary by age group. Talent identification models should thus be dynamic and provide opportunities for changing parameters in a long‐term developmental context. PMID:16980535

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.

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…

Selective visual attention in object detection processes

NASA Astrophysics Data System (ADS)

Paletta, Lucas; Goyal, Anurag; Greindl, Christian

2003-03-01

Object detection is an enabling technology that plays a key role in many application areas, such as content based media retrieval. Attentive cognitive vision systems are here proposed where the focus of attention is directed towards the most relevant target. The most promising information is interpreted in a sequential process that dynamically makes use of knowledge and that enables spatial reasoning on the local object information. The presented work proposes an innovative application of attention mechanisms for object detection which is most general in its understanding of information and action selection. The attentive detection system uses a cascade of increasingly complex classifiers for the stepwise identification of regions of interest (ROIs) and recursively refined object hypotheses. While the most coarse classifiers are used to determine first approximations on a region of interest in the input image, more complex classifiers are used for more refined ROIs to give more confident estimates. Objects are modelled by local appearance based representations and in terms of posterior distributions of the object samples in eigenspace. The discrimination function to discern between objects is modeled by a radial basis functions (RBF) network that has been compared with alternative networks and been proved consistent and superior to other artifical neural networks for appearance based object recognition. The experiments were led for the automatic detection of brand objects in Formula One broadcasts within the European Commission's cognitive vision project DETECT.

Solvent effects on the excited-state double proton transfer mechanism in the 7-azaindole dimer: a TDDFT study with the polarizable continuum model.

PubMed

Yu, Xue-Fang; Yamazaki, Shohei; Taketsugu, Tetsuya

2017-08-30

Solvent effects on the excited-state double proton transfer (ESDPT) mechanism in the 7-azaindole (7AI) dimer were investigated using the time-dependent density functional theory (TDDFT) method. Excited-state potential energy profiles along the reaction paths in a locally excited (LE) state and a charge transfer (CT) state were calculated using the polarizable continuum model (PCM) to include the solvent effect. A series of non-polar and polar solvents with different dielectric constants were used to examine the polarity effect on the ESDPT mechanism. The present results suggest that in a non-polar solvent and a polar solvent with a small dielectric constant, ESDPT follows a concerted mechanism, similar to the case in the gas phase. In a polar solvent with a relatively large dielectric constant, however, ESDPT is likely to follow a stepwise mechanism via a stable zwitterionic intermediate in the LE state on the adiabatic potential energy surface, although inclusion of zero-point vibrational energy (ZPE) corrections again suggests the concerted mechanism. In the meantime, the stepwise reaction path involving the CT state with neutral intermediates is also examined, and is found to be less competitive than the concerted or stepwise path in the LE state in both non-polar and polar solvents. The present study provides a new insight into the experimental controversy of the ESDPT mechanism of the 7AI dimer in a solution.

Cerebral Activations Related to Ballistic, Stepwise Interrupted and Gradually Modulated Movements in Parkinson Patients

PubMed Central

Toxopeus, Carolien M.; Maurits, Natasha M.; Valsan, Gopal; Conway, Bernard A.; Leenders, Klaus L.; de Jong, Bauke M.

2012-01-01

Patients with Parkinson’s disease (PD) experience impaired initiation and inhibition of movements such as difficulty to start/stop walking. At single-joint level this is accompanied by reduced inhibition of antagonist muscle activity. While normal basal ganglia (BG) contributions to motor control include selecting appropriate muscles by inhibiting others, it is unclear how PD-related changes in BG function cause impaired movement initiation and inhibition at single-joint level. To further elucidate these changes we studied 4 right-hand movement tasks with fMRI, by dissociating activations related to abrupt movement initiation, inhibition and gradual movement modulation. Initiation and inhibition were inferred from ballistic and stepwise interrupted movement, respectively, while smooth wrist circumduction enabled the assessment of gradually modulated movement. Task-related activations were compared between PD patients (N = 12) and healthy subjects (N = 18). In healthy subjects, movement initiation was characterized by antero-ventral striatum, substantia nigra (SN) and premotor activations while inhibition was dominated by subthalamic nucleus (STN) and pallidal activations, in line with the known role of these areas in simple movement. Gradual movement mainly involved antero-dorsal putamen and pallidum. Compared to healthy subjects, patients showed reduced striatal/SN and increased pallidal activation for initiation, whereas for inhibition STN activation was reduced and striatal-thalamo-cortical activation increased. For gradual movement patients showed reduced pallidal and increased thalamo-cortical activation. We conclude that PD-related changes during movement initiation fit the (rather static) model of alterations in direct and indirect BG pathways. Reduced STN activation and regional cortical increased activation in PD during inhibition and gradual movement modulation are better explained by a dynamic model that also takes into account enhanced responsiveness to external stimuli in this disease and the effects of hyper-fluctuating cortical inputs to the striatum and STN in particular. PMID:22911738

Advancing alternatives analysis: The role of predictive toxicology in selecting safer chemical products and processes.

PubMed

Malloy, Timothy; Zaunbrecher, Virginia; Beryt, Elizabeth; Judson, Richard; Tice, Raymond; Allard, Patrick; Blake, Ann; Cote, Ila; Godwin, Hilary; Heine, Lauren; Kerzic, Patrick; Kostal, Jakub; Marchant, Gary; McPartland, Jennifer; Moran, Kelly; Nel, Andre; Ogunseitan, Oladele; Rossi, Mark; Thayer, Kristina; Tickner, Joel; Whittaker, Margaret; Zarker, Ken

2017-09-01

Alternatives analysis (AA) is a method used in regulation and product design to identify, assess, and evaluate the safety and viability of potential substitutes for hazardous chemicals. It requires toxicological data for the existing chemical and potential alternatives. Predictive toxicology uses in silico and in vitro approaches, computational models, and other tools to expedite toxicological data generation in a more cost-effective manner than traditional approaches. The present article briefly reviews the challenges associated with using predictive toxicology in regulatory AA, then presents 4 recommendations for its advancement. It recommends using case studies to advance the integration of predictive toxicology into AA, adopting a stepwise process to employing predictive toxicology in AA beginning with prioritization of chemicals of concern, leveraging existing resources to advance the integration of predictive toxicology into the practice of AA, and supporting transdisciplinary efforts. The further incorporation of predictive toxicology into AA would advance the ability of companies and regulators to select alternatives to harmful ingredients, and potentially increase the use of predictive toxicology in regulation more broadly. Integr Environ Assess Manag 2017;13:915-925. © 2017 SETAC. © 2017 SETAC.

A TiO₂ nanoparticle system for sacrificial solar H₂ production prepared by rational combination of a hydrogenase with a ruthenium photosensitizer.

PubMed

Reisner, Erwin; Armstrong, Fraser A

2011-01-01

A hybrid system comprising a hydrogenase and a photosensitizer co-attached to a nanoparticle serves as a rational model for fast dihydrogen (H(2)) production using visible light. This chapter describes a stepwise procedure for preparing TiO(2) nanoparticles functionalized with a hydrogenase from Desulfomicrobium baculatum (Db [NiFeSe]-H) and a tris(bipyridyl)ruthenium photosensitizer (RuP). Upon irradiation with visible light, these particles produce H(2) from neutral water at room temperature in the presence of a sacrificial electron donor - a test-system for the cathodic half reaction of water splitting. In particular, we describe how a hydrogenase and a photosensitizer with desired properties, including strong adsorption on TiO(2), can be selected by electrochemical methods. The catalyst Db [NiFeSe]-H is selected for its high H(2) production activity even when H(2) and traces of O(2) are present. Adsorption of Db [NiFeSe]-H and RuP on TiO(2) electrodes results in high electrochemical and photocatalytic activities that translate into nanoparticles exhibiting efficient light harvesting, charge separation, and sacrificial H(2) generation.

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

The unsuitability of implantable Doppler probes for the early detection of renal vascular complications – a porcine model for prevention of renal transplant loss

PubMed Central

Jespersen, Bente; Møldrup, Ulla; Keller, Anna K.

2017-01-01

Background Vascular occlusion is a rare, but serious complication after kidney transplantation often resulting in graft loss. We therefore aimed to develop an experimental porcine model for stepwise reduction of the renal venous blood flow and to compare an implantable Doppler probe and microdialysis for fast detection of vascular occlusion. Methods In 20 pigs, implantable Doppler probes were placed on the renal artery and vein and a microdialysis catheter was placed in the renal cortex. An arterial flowprobe served as gold standard. Following two-hour baseline measurements, the pigs were randomised to stepwise venous occlusion, complete venous occlusion, complete arterial occlusion or controls. Results All parameters were stable through baseline measurements. Glutamate and lactate measured by microdialysis increased significantly (p = 0.02 and p = 0.03 respectively) 30 minutes after a 2/3 (66%) reduction in renal blood flow. The implantable Doppler probe was not able to detect flow changes until there was total venous occlusion. Microdialysis detected changes in local metabolism after both arterial and venous occlusion; the implantable Doppler probe could only detect vascular occlusions on the vessel it was placed. Conclusions We developed a new model for stepwise renal venous blood flow occlusion. Furthermore, the first comparison of the implantable Doppler probe and microdialysis for detection of renal vascular occlusions was made. The implantable Doppler probe could only detect flow changes after a complete occlusion, whereas microdialysis detected changes earlier, and could detect both arterial and venous occlusion. Based on these results, the implantable Doppler probe for early detection of vascular occlusions cannot be recommended. PMID:28542429

A step-wise approach for analysis of the mouse embryonic heart using 17.6 Tesla MRI

PubMed Central

Gabbay-Benziv, Rinat; Reece, E. Albert; Wang, Fang; Bar-Shir, Amnon; Harman, Chris; Turan, Ozhan M.; Yang, Peixin; Turan, Sifa

2018-01-01

Background The mouse embryo is ideal for studying human cardiac development. However, laboratory discoveries do not easily translate into clinical findings partially because of histological diagnostic techniques that induce artifacts and lack standardization. Aim To present a step-wise approach using 17.6 T MRI, for evaluation of mice embryonic heart and accurate identification of congenital heart defects. Subjects 17.5-embryonic days embryos from low-risk (non-diabetic) and high-risk (diabetic) model dams. Study design Embryos were imaged using 17.6 Tesla MRI. Three-dimensional volumes were analyzed using ImageJ software. Outcome measures Embryonic hearts were evaluated utilizing anatomic landmarks to locate the four-chamber view, the left- and right-outflow tracts, and the arrangement of the great arteries. Inter- and intra-observer agreement were calculated using kappa scores by comparing two researchers’ evaluations independently analyzing all hearts, blinded to the model, on three different, timed occasions. Each evaluated 16 imaging volumes of 16 embryos: 4 embryos from normal dams, and 12 embryos from diabetic dams. Results Inter-observer agreement and reproducibility were 0.779 (95% CI 0.653–0.905) and 0.763 (95% CI 0.605–0.921), respectively. Embryonic hearts were structurally normal in 4/4 and 7/12 embryos from normal and diabetic dams, respectively. Five embryos from diabetic dams had defects: ventricular septal defects (n = 2), transposition of great arteries (n = 2) and Tetralogy of Fallot (n = 1). Both researchers identified all cardiac lesions. Conclusion A step-wise approach for analysis of MRI-derived 3D imaging provides reproducible detailed cardiac evaluation of normal and abnormal mice embryonic hearts. This approach can accurately reveal cardiac structure and, thus, increases the yield of animal model in congenital heart defect research. PMID:27569369

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.

Stepwise assembly of multiple Lin28 proteins on the terminal loop of let-7 miRNA precursors

PubMed Central

Desjardins, Alexandre; Bouvette, Jonathan; Legault, Pascale

2014-01-01

Lin28 inhibits the biogenesis of let-7 miRNAs through direct interactions with let-7 precursors. Previous studies have described seemingly inconsistent Lin28 binding sites on pre-let-7 RNAs. Here, we reconcile these data by examining the binding mechanism of Lin28 to the terminal loop of pre-let-7g (TL-let-7g) using biochemical and biophysical methods. First, we investigate Lin28 binding to TL-let-7g variants and short RNA fragments and identify three independent binding sites for Lin28 on TL-let-7g. We then determine that Lin28 assembles in a stepwise manner on TL-let-7g to form a stable 1:3 complex. We show that the cold-shock domain (CSD) of Lin28 is responsible for remodelling the terminal loop of TL-let-7g, whereas the NCp7-like domain facilitates the initial binding of Lin28 to TL-let-7g. This stable binding of multiple Lin28 molecules to the terminal loop of pre-let-7g extends to other precursors of the let-7 family, but not to other pre-miRNAs tested. We propose a model for stepwise assembly of the 1:1, 1:2 and 1:3 pre-let-7g/Lin28 complexes. Stepwise multimerization of Lin28 on pre-let-7 is required for maximum inhibition of Dicer cleavage for a least one member of the let-7 family and may be important for orchestrating the activity of the several factors that regulate let-7 biogenesis. PMID:24452802

Inverse Modeling of Water-Rock-CO2 Batch Experiments: Potential Impacts on Groundwater Resources at Carbon Sequestration Sites.

PubMed

Yang, Changbing; Dai, Zhenxue; Romanak, Katherine D; Hovorka, Susan D; Treviño, Ramón H

2014-01-01

This study developed a multicomponent geochemical model to interpret responses of water chemistry to introduction of CO2 into six water-rock batches with sedimentary samples collected from representative potable aquifers in the Gulf Coast area. The model simulated CO2 dissolution in groundwater, aqueous complexation, mineral reactions (dissolution/precipitation), and surface complexation on clay mineral surfaces. An inverse method was used to estimate mineral surface area, the key parameter for describing kinetic mineral reactions. Modeling results suggested that reductions in groundwater pH were more significant in the carbonate-poor aquifers than in the carbonate-rich aquifers, resulting in potential groundwater acidification. Modeled concentrations of major ions showed overall increasing trends, depending on mineralogy of the sediments, especially carbonate content. The geochemical model confirmed that mobilization of trace metals was caused likely by mineral dissolution and surface complexation on clay mineral surfaces. Although dissolved inorganic carbon and pH may be used as indicative parameters in potable aquifers, selection of geochemical parameters for CO2 leakage detection is site-specific and a stepwise procedure may be followed. A combined study of the geochemical models with the laboratory batch experiments improves our understanding of the mechanisms that dominate responses of water chemistry to CO2 leakage and also provides a frame of reference for designing monitoring strategy in potable aquifers.

Analysis of a database to predict the result of allergy testing in vivo in patients with chronic nasal symptoms.

PubMed

Lacagnina, Valerio; Leto-Barone, Maria S; La Piana, Simona; Seidita, Aurelio; Pingitore, Giuseppe; Di Lorenzo, Gabriele

2014-01-01

This article uses the logistic regression model for diagnostic decision making in patients with chronic nasal symptoms. We studied the ability of the logistic regression model, obtained by the evaluation of a database, to detect patients with positive allergy skin-prick test (SPT) and patients with negative SPT. The model developed was validated using the data set obtained from another medical institution. The analysis was performed using a database obtained from a questionnaire administered to the patients with nasal symptoms containing personal data, clinical data, and results of allergy testing (SPT). All variables found to be significantly different between patients with positive and negative SPT (p < 0.05) were selected for the logistic regression models and were analyzed with backward stepwise logistic regression, evaluated with area under the curve of the receiver operating characteristic curve. A second set of patients from another institution was used to prove the model. The accuracy of the model in identifying, over the second set, both patients whose SPT will be positive and negative was high. The model detected 96% of patients with nasal symptoms and positive SPT and classified 94% of those with negative SPT. This study is preliminary to the creation of a software that could help the primary care doctors in a diagnostic decision making process (need of allergy testing) in patients complaining of chronic nasal symptoms.

Development of a prediction model for child maltreatment recurrence in Japan: A historical cohort study using data from a Child Guidance Center.

PubMed

Horikawa, Hiroyuki; Suguimoto, S Pilar; Musumari, Patou Masika; Techasrivichien, Teeranee; Ono-Kihara, Masako; Kihara, Masahiro

2016-09-01

To develop a prediction model for the first recurrence of child maltreatment within the first year after the initial report, we carried out a historical cohort study using administrative data from 716 incident cases of child maltreatment (physical abuse, psychological abuse, or neglect) not receiving support services, reported between April 1, 1996 through March 31, 2011 to Shiga Central Child Guidance Center, Japan. In total, 23 items related to characteristics of the child, the maltreatment, the offender, household, and other related factors were selected as predictive variables and analyzed by multivariate logistic regression model for association with first recurrence of maltreatment. According to the stepwise selection procedure six factors were identified that include 9-13year age of child (AOR=3.43/95%CI=1.52-7.72), <40year age of the offender (AOR=1.65/95%CI=1.09-2.51), offender's history of maltreatment during childhood (AOR=2.56/95%CI=1.31-4.99), household financial instability or poverty (AOR=1.64/95%CI=1.10-2.45), absence of someone in the community who could watch over the child (AOR=1.68/95%CI=1.16-2.44), and the organization as the referral source (AOR=2.21/95%CI=1.24-3.93). Using these six predictors, we generated a linear prediction model with a sensitivity and specificity of 45.2% and 82.4%, respectively. The model may be useful to assess the risk of further maltreatment and help the child and family welfare administrations to develop preventive strategies for recurrence. Copyright © 2016 The Author(s). Published by Elsevier Ltd.. All rights reserved.

Context-Sensitive Ethics in School Psychology

ERIC Educational Resources Information Center

Lasser, Jon; Klose, Laurie McGarry; Robillard, Rachel

2013-01-01

Ethical codes and licensing rules provide foundational guidance for practicing school psychologists, but these sources fall short in their capacity to facilitate effective decision-making. When faced with ethical dilemmas, school psychologists can turn to decision-making models, but step-wise decision trees frequently lack the situation…

Better Care Teams: A Stepwise Skill Reinforcement Model.

PubMed

Christopher, Beth-Anne; Grantner, Mary; Coke, Lola A; Wideman, Marilyn; Kwakwa, Francis

2016-06-01

The Building Healthy Urban Communities initiative presents a path for organizations partnering to improve patient outcomes with continuing education (CE) as a key component. Components of the CE initiative included traditional CE delivery formats with an essential element of adaptability and new methods, with rigorous evaluation over time that included evaluation prior to the course, immediately following the CE session, 6 to 8 weeks after the CE session, and then subsequent monthly "testlets." Outcome measures were designed to allow for ongoing adaptation of content, reinforcement of key learning objectives, and use of innovative concordant testing and retrieval practice techniques. The results after 1 year of programming suggest the stepwise skill reinforcement model is effective for learning and is an efficient use of financial and human resources. More important, its design is one that could be adopted at low cost by organizations willing to work in close partnership. J Contin Educ Nurs. 2016;47(6):283-288. Copyright 2016, SLACK Incorporated.

Predicting pork loin intramuscular fat using computer vision system.

PubMed

Liu, J-H; Sun, X; Young, J M; Bachmeier, L A; Newman, D J

2018-09-01

The objective of this study was to investigate the ability of computer vision system to predict pork intramuscular fat percentage (IMF%). Center-cut loin samples (n = 85) were trimmed of subcutaneous fat and connective tissue. Images were acquired and pixels were segregated to estimate image IMF% and 18 image color features for each image. Subjective IMF% was determined by a trained grader. Ether extract IMF% was calculated using ether extract method. Image color features and image IMF% were used as predictors for stepwise regression and support vector machine models. Results showed that subjective IMF% had a correlation of 0.81 with ether extract IMF% while the image IMF% had a 0.66 correlation with ether extract IMF%. Accuracy rates for regression models were 0.63 for stepwise and 0.75 for support vector machine. Although subjective IMF% has shown to have better prediction, results from computer vision system demonstrates the potential of being used as a tool in predicting pork IMF% in the future. Copyright © 2018 Elsevier Ltd. All rights reserved.

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

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.

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.

Regional danger assessment of Debris flow and its engineering mitigation practice in Sichuan-Tibet highway

NASA Astrophysics Data System (ADS)

Su, Pengcheng; Sun, Zhengchao; li, Yong

2017-04-01

Luding-Kangding highway cross the eastern edge of Qinghai-Tibet Plateau where belong to the most deep canyon area of plateau and mountains in western Sichuan with high mountain and steep slope. This area belongs to the intersection among Xianshuihe, Longmenshan and Anninghe fault zones which are best known in Sichuan province. In the region, seismic intensity is with high frequency and strength, new tectonic movement is strong, rock is cracked, there are much loose solid materials. Debris flow disaster is well developed under the multiple effects of the earthquake, strong rainfall and human activity which poses a great threat to the local people's life and property security. So this paper chooses Kangding and LuDing as the study area to do the debris flow hazard assessment through the in-depth analysis of development characteristics and formation mechanism of debris flow. Which can provide important evidence for local disaster assessment and early warning forecast. It also has the important scientific significance and practical value to safeguard the people's life and property safety and the security implementation of the national major project. In this article, occurrence mechanism of debris flow disasters in the study area is explored, factor of evaluation with high impact to debris flow hazards is identified, the database of initial evaluation factors is made by the evaluation unit of basin. The factors with high impact to hazards occurrence are selected by using the stepwise regression method of logistic regression model, at the same time the factors with low impact are eliminated, then the hazard evaluation factor system of debris flow is determined in the study area. Then every factors of evaluation factor system are quantified, and the weights of all evaluation factors are determined by using the analysis of stepwise regression. The debris flows hazard assessment and regionalization of all the whole study area are achieved eventually after establishing the hazard assessment model. In this paper, regional debris flows hazard assessment method with strong universality and reliable evaluation result is presented. The whole study area is divided into 1674 units by automatically extracting and artificial identification, and then 11 factors are selected as the initial assessment factors of debris flow hazard assessment in the study area. The factors of the evaluation index system are quantified using the method of standardized watershed unit amount ratio. The relationship between debris flow occurrence and each evaluation factor is simulated using logistic regression model. The weights of evaluation factors are determined, and the model of debris flows hazard assessment is established in the study area. Danger assessment result of debris flow was applied in line optimization and engineering disaster reduction of Sichuan-Tibet highway (section of Luding-Kangding).

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

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.

Optimizing separate phase light hydrocarbon recovery from contaminated unconfined aquifers

NASA Astrophysics Data System (ADS)

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

A modeling approach is presented that optimizes separate phase recovery of light non-aqueous phase liquids (LNAPL) for a single dual-extraction well in a homogeneous, isotropic unconfined aquifer. A simulation/regression/optimization (S/R/O) model is developed to predict, analyze, and optimize the oil recovery process. The approach combines detailed simulation, nonlinear regression, and optimization. The S/R/O model utilizes nonlinear regression equations describing system response to time-varying water pumping and oil skimming. Regression equations are developed for residual oil volume and free oil volume. The S/R/O model determines optimized time-varying (stepwise) pumping rates which minimize residual oil volume and maximize free oil recovery while causing free oil volume to decrease a specified amount. This S/R/O modeling approach implicitly immobilizes the free product plume by reversing the water table gradient while achieving containment. Application to a simple representative problem illustrates the S/R/O model utility for problem analysis and remediation design. When compared with the best steady pumping strategies, the optimal stepwise pumping strategy improves free oil recovery by 11.5% and reduces the amount of residual oil left in the system due to pumping by 15%. The S/R/O model approach offers promise for enhancing the design of free phase LNAPL recovery systems and to help in making cost-effective operation and management decisions for hydrogeologists, engineers, and regulators.

Dechlorination of chlorinated phenols by subnanoscale Pd 0 /Fe 0 intercalated in smectite: pathway, reactivity, and selectivity.

PubMed

Jia, Hanzhong; Wang, Chuanyi

2015-12-30

Smectite clay was employed as templated matrix to prepare subnanoscale Pd(0)/Fe(0) particles, and their components as well as intercalated architectures were well characterized by X-ray energy dispersive spectroscopy (X-EDS) and X-ray diffraction (XRD). Furthermore, as-prepared Pd(0)/Fe(0) subnanoscale nanoparticles were evaluated for their dechlorination effect using chlorinated phenols as model molecules. As a result, pentachlorophenol (PCP) is selectively transformed to phenol in a stepwise dechlorination pathway within 6h, and the dechlorination rate constants show linearly relationship with contents of Pd as its loadings <0.065%. Comparing with PCP, other chlorinated phenols display similar degradation pattern but within much shorter time frame. The dechlorination rate of chlorinated phenols increases with decreasing in number of -Cl attached to aromatic ring, which can be predicted by the total charge of the aromatic ring, exhibiting an inversely linear relationship with the dechlorination rates. While the selectivity of dechlorination depends on the charges associated with the individual aromatic carbon. Chloro-functional groups at the ortho-position are easier to be dechlorinated than that at meta- and para- positions yielding primarily 3,4,5-TCP as intermediate from PCP, further to phenol. The effective dechlorination warrants their potential utilizations in development of in-situ remediation technologies for organic pollutants in contaminated water. Copyright © 2015 Elsevier B.V. All rights reserved.

Influence of probe pressure on diffuse reflectance spectra of human skin measured in vivo

NASA Astrophysics Data System (ADS)

Popov, Alexey P.; Bykov, Alexander V.; Meglinski, Igor V.

2017-11-01

Mechanical pressure superficially applied on the human skin surface by a fiber-optic probe influences the spatial distribution of blood within the cutaneous tissues. Upon gradual load of weight on the probe, a stepwise increase in the skin reflectance spectra is observed. The decrease in the load follows the similar inverse staircase-like tendency. The observed stepwise reflectance spectra changes are due to, respectively, sequential extrusion of blood from the topical cutaneous vascular beds and their filling afterward. The obtained results are confirmed by Monte Carlo modeling. This implies that pressure-induced influence during the human skin diffuse reflectance spectra measurements in vivo should be taken into consideration, in particular, in the rapidly developing area of wearable gadgets for real-time monitoring of various human body parameters.

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.

A new model for estimating total body water from bioelectrical resistance

NASA Technical Reports Server (NTRS)

Siconolfi, S. F.; Kear, K. T.

1992-01-01

Estimation of total body water (T) from bioelectrical resistance (R) is commonly done by stepwise regression models with height squared over R, H(exp 2)/R, age, sex, and weight (W). Polynomials of H(exp 2)/R have not been included in these models. We examined the validity of a model with third order polynomials and W. Methods: T was measured with oxygen-18 labled water in 27 subjects. R at 50 kHz was obtained from electrodes placed on the hand and foot while subjects were in the supine position. A stepwise regression equation was developed with 13 subjects (age 31.5 plus or minus 6.2 years, T 38.2 plus or minus 6.6 L, W 65.2 plus or minus 12.0 kg). Correlations, standard error of estimates and mean differences were computed between T and estimated T's from the new (N) model and other models. Evaluations were completed with the remaining 14 subjects (age 32.4 plus or minus 6.3 years, T 40.3 plus or minus 8 L, W 70.2 plus or minus 12.3 kg) and two of its subgroups (high and low) Results: A regression equation was developed from the model. The only significant mean difference was between T and one of the earlier models. Conclusion: Third order polynomials in regression models may increase the accuracy of estimating total body water. Evaluating the model with a larger population is needed.

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

Ethnic differences in colon cancer care in the Netherlands: a nationwide registry-based study.

PubMed

Lamkaddem, M; Elferink, M A G; Seeleman, M C; Dekker, E; Punt, C J A; Visser, O; Essink-Bot, M L

2017-05-04

Ethnic differences in colon cancer (CC) care were shown in the United States, but results are not directly applicable to European countries due to fundamental healthcare system differences. This is the first study addressing ethnic differences in treatment and survival for CC in the Netherlands. Data of 101,882 patients diagnosed with CC in 1996-2011 were selected from the Netherlands Cancer Registry and linked to databases from Statistics Netherlands. Ethnic differences in lymph node (LN) evaluation, anastomotic leakage and adjuvant chemotherapy were analysed using stepwise logistic regression models. Stepwise Cox regression was used to examine the influence of ethnic differences in adjuvant chemotherapy on 5-year all-cause and colorectal cancer-specific survival. Adequate LN evaluation was significantly more likely for patients from 'other Western' countries than for the Dutch (OR 1.09; 95% CI 1.01-1.16). 'Other Western' patients had a significantly higher risk of anastomotic leakage after resection (OR 1.24; 95% CI 1.05-1.47). Patients of Moroccan origin were significantly less likely to receive adjuvant chemotherapy (OR 0.27; 95% CI 0.13-0.59). Ethnic differences were not fully explained by differences in socioeconomic and hospital-related characteristics. The higher 5-year all-cause mortality of Moroccan patients (HR 1.64; 95% CI 1.03-2.61) was statistically explained by differences in adjuvant chemotherapy receipt. These results suggest the presence of ethnic inequalities in CC care in the Netherlands. We recommend further analysis of the role of comorbidity, communication in patient-provider interaction and patients' health literacy when looking at ethnic differences in treatment for CC.

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.

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.

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.

Modelling in-stream temperature and dissolved oxygen at sub-daily time steps: an application to the River Kennet, UK.

PubMed

Williams, Richard J; Boorman, David B

2012-04-15

The River Kennet in southern England shows a clear diurnal signal in both water temperature and dissolved oxygen concentrations through the summer months. The water quality model QUESTOR was applied in a stepwise manner (adding modelled processes or additional data) to simulate the flow, water temperature and dissolved oxygen concentrations along a 14 km reach. The aim of the stepwise model building was to find the simplest process-based model which simulated the observed behaviour accurately. The upstream boundary used was a diurnal signal of hourly measurements of water temperature and dissolved oxygen. In the initial simulations, the amplitude of the signal quickly reduced to zero as it was routed through the model; a behaviour not seen in the observed data. In order to keep the correct timing and amplitude of water temperature a heating term had to be introduced into the model. For dissolved oxygen, primary production from macrophytes was introduced to better simulate the oxygen pattern. Following the modifications an excellent simulation of both water temperature and dissolved oxygen was possible at an hourly resolution. It is interesting to note that it was not necessary to include nutrient limitation to the primary production model. The resulting model is not sufficiently proven to support river management but suggests that the approach has some validity and merits further development. Crown Copyright © 2012. Published by Elsevier B.V. All rights reserved.

Project Physics Handbook 2, Motion in the Heavens.

ERIC Educational Resources Information Center

Harvard Univ., Cambridge, MA. Harvard Project Physics.

Nine experiments and 17 activities are presented in this handbook. The experiments are related to the earth's size and orbit, Piton height, telescope operations, Mars and Mercury orbits, stepwise approximation, and models of comet orbits. Further naked-eye observations in astronomy are designed in connection with the sun, moon, and planet…

Increasing Success Rates in Developmental Math: The Complementary Role of Individual and Institutional Characteristics

ERIC Educational Resources Information Center

Fong, Kristen E.; Melguizo, Tatiana; Prather, George

2015-01-01

This study tracks students' progression through developmental math sequences and defines progression as both attempting and passing each level of the sequence. A model of successful progression in developmental education was built utilizing individual-, institutional-, and developmental math-level factors. Employing step-wise logistic regression…

An Exploring Model of Intelligence and Personality in Different Culture

ERIC Educational Resources Information Center

Wu, Yufeng; Qian, Guoying

2005-01-01

Middle school subjects of 13-21 years (from 4 nationalities) were used for studying the relationship between progressive cognition and personality characteristics by Raven's Standard Progressive Matrices and Eysenk's Personality Questionnaire. The results showed: (1) the correlation and stepwise regression were completely identical: P score was…

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.

Risk factors of suicide attempt among people with suicidal ideation in South Korea: a cross-sectional study.

PubMed

Choi, Soo Beom; Lee, Wanhyung; Yoon, Jin-Ha; Won, Jong-Uk; Kim, Deok Won

2017-06-15

Suicide is a serious public health concern worldwide, and the fourth leading cause of death in Korea. Few studies have focused on risk factors for suicide attempt among people with suicidal ideation. The aim of the present study was to investigate the risk factors and develop prediction models for suicide attempt among people with suicidal ideation in the Korean population. This study included 1567 men and 3726 women aged 20 years and older who had suicidal ideation from the Korea National Health and Nutrition Examination Survey from 2007 to 2012. Among them, 106 men and 188 women attempted suicide. Multivariate logistic regression analysis with backward stepwise elimination was performed to find risk factors for suicide attempt. Sub-group analysis, dividing participants into under 50 and at least 50 years old was also performed. Among people with suicidal ideation, age, education, cancer, and depressive disorder were selected as risk factors for suicide attempt in men. Age, education, national basic livelihood security, daily activity limitation, depressive disorder, stress, smoking, and regular exercise were selected in women. Area under curves of our prediction models in men and women were 0.728 and 0.716, respectively. It is important to pay attention to populations with suicidal ideation and the risk factors mentioned above. Prediction models using the determined risk factors could be useful to detect high-risk groups early for suicide attempt among people with suicidal ideation. It is necessary to develop specific action plans for these high-risk groups to prevent suicide.

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.

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.

Stepwise calibration procedure for regional coupled hydrological-hydrogeological models

NASA Astrophysics Data System (ADS)

Labarthe, Baptiste; Abasq, Lena; de Fouquet, Chantal; Flipo, Nicolas

2014-05-01

Stream-aquifer interaction is a complex process depending on regional and local processes. Indeed, the groundwater component of hydrosystem and large scale heterogeneities control the regional flows towards the alluvial plains and the rivers. In second instance, the local distribution of the stream bed permeabilities controls the dynamics of stream-aquifer water fluxes within the alluvial plain, and therefore the near-river piezometric head distribution. In order to better understand the water circulation and pollutant transport in watersheds, the integration of these multi-dimensional processes in modelling platform has to be performed. Thus, the nested interfaces concept in continental hydrosystem modelling (where regional fluxes, simulated by large scale models, are imposed at local stream-aquifer interfaces) has been presented in Flipo et al (2014). This concept has been implemented in EauDyssée modelling platform for a large alluvial plain model (900km2) part of a 11000km2 multi-layer aquifer system, located in the Seine basin (France). The hydrosystem modelling platform is composed of four spatially distributed modules (Surface, Sub-surface, River and Groundwater), corresponding to four components of the terrestrial water cycle. Considering the large number of parameters to be inferred simultaneously, the calibration process of coupled models is highly computationally demanding and therefore hardly applicable to a real case study of 10000km2. In order to improve the efficiency of the calibration process, a stepwise calibration procedure is proposed. The stepwise methodology involves determining optimal parameters of all components of the coupled model, to provide a near optimum prior information for the global calibration. It starts with the surface component parameters calibration. The surface parameters are optimised based on the comparison between simulated and observed discharges (or filtered discharges) at various locations. Once the surface parameters have been determined, the groundwater component is calibrated. The calibration procedure is performed under steady state hypothesis (to minimize the procedure time length) using recharge rates given by the surface component calibration and imposed fluxes boundary conditions given by the regional model. The calibration is performed using pilot point where the prior variogram is calculated from observed transmissivities values. This procedure uses PEST (http//:www.pesthomepage.org/Home.php) as the inverse modelling tool and EauDyssée as the direct model. During the stepwise calibration process, each modules, even if they are actually dependant from each other, are run and calibrated independently, therefore contributions between each module have to be determined. For the surface module, groundwater and runoff contributions have been determined by hydrograph separation. Among the automated base-flow separation methods, the one-parameter Chapman filter (Chapman et al 1999) has been chosen. This filter is a decomposition of the actual base-flow between the previous base-flow and the discharge gradient weighted by functions of the recession coefficient. For the groundwater module, the recharge has been determined from surface and sub-surface module. References : Flipo, N., A. Mourhi, B. Labarthe, and S. Biancamaria (2014). Continental hydrosystem modelling : the concept of nested stream-aquifer interfaces. Hydrol. Earth Syst. Sci. Discuss. 11, 451-500. Chapman,TG. (1999). A comparison of algorithms for stream flow recession and base-flow separation. hydrological Processes 13, 701-714.

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.

Predicting Low Accrual in the National Cancer Institute’s Cooperative Group Clinical Trials

PubMed Central

Bennette, Caroline S.; Ramsey, Scott D.; McDermott, Cara L.; Carlson, Josh J.; Basu, Anirban; Veenstra, David L.

2016-01-01

Background: The extent to which trial-level factors differentially influence accrual to trials has not been comprehensively studied. Our objective was to evaluate the empirical relationship and predictive properties of putative risk factors for low accrual in the National Cancer Institute’s (NCI’s) Cooperative Group Program, now the National Clinical Trials Network (NCTN). Methods: Data from 787 phase II/III adult NCTN-sponsored trials launched between 2000 and 2011 were used to develop a logistic regression model to predict low accrual, defined as trials that closed with or were accruing at less than 50% of target; 46 trials opened between 2012 and 2013 were used for prospective validation. Candidate predictors were identified from a literature review and expert interviews; final predictors were selected using stepwise regression. Model performance was evaluated by calibration and discrimination via the area under the curve (AUC). All statistical tests were two-sided. Results: Eighteen percent (n = 145) of NCTN-sponsored trials closed with low accrual or were accruing at less than 50% of target three years or more after initiation. A multivariable model of twelve trial-level risk factors had good calibration and discrimination for predicting trials with low accrual (AUC in trials launched 2000–2011 = 0.739, 95% confidence interval [CI] = 0.696 to 0.783]; 2012–2013: AUC = 0.732, 95% CI = 0.547 to 0.917). Results were robust to different definitions of low accrual and predictor selection strategies. Conclusions: We identified multiple characteristics of NCTN-sponsored trials associated with low accrual, several of which have not been previously empirically described, and developed a prediction model that can provide a useful estimate of accrual risk based on these factors. Future work should assess the role of such prediction tools in trial design and prioritization decisions. PMID:26714555

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.

Investigating the potential of Bacillus subtilis alpha-amylase as a pressure-temperature-time indicator for high hydrostatic pressure pasteurization processes.

PubMed

Grauwet, Tara; Van der Plancken, Iesel; Vervoort, Liesbeth; Hendrickx, Marc E; Van Loey, Ann

2009-01-01

The potential of Bacillus subtilis alpha-amylase (BSA) as a pressure-temperature-time indicator (pTTI) for high pressure pasteurization processing (400-600 MPa; T(i) 10-40 degrees C; 1-15 min) was investigated. A stepwise approach was followed for the development of an enzyme-based, extrinsic, isolated pTTI. First, based on literature data on the pressure stability, BSA was selected as a candidate indicator. Next to the accuracy and ease of the measurement of the indicator's response (residual activity) to the pressure treatment, the storage and handling stability of BSA at atmospheric pressure was verified. Second, the stability of BSA at a constant temperature (T) and time in function of pressure (p) was investigated. Solvent engineering was used to shift the inactivation window of BSA in the processing range of interest. Third, the enzyme (1 g/L BSA-MES 0.05 M pH 5.0) was kinetically calibrated under isobaric-isothermal conditions. Time dependent changes in activity could be modeled best by a first-order model. Except for low pressures and high temperatures, a synergistic effect between pressure and temperature could be observed. Based on the model selected to describe the combined p,T-dependency of the inactivation rate constant, an elliptically shaped isorate contour plot could be constructed, illustrating the processing range where BSA can be used to demonstrate temperature gradients. Fourth, the validity of the kinetic model was tested successfully under dynamic conditions similar to those used in food industry. Finally, the indicator was found suitable to demonstrate nonuniformity in two-sectional planes of a vertical, single vessel system. (c) 2009 American Institute of Chemical Engineers. Biotechnol. Prog., 2009.

Feature extraction and selection from volatile compounds for analytical classification of Chinese red wines from different varieties.

PubMed

Zhang, Jian; Li, Li; Gao, Nianfa; Wang, Depei; Gao, Qiang; Jiang, Shengping

2010-03-10

This work was undertaken to evaluate whether it is possible to determine the variety of a Chinese wine on the basis of its volatile compounds, and to investigate if discrimination models could be developed with the experimental wines that could be used for the commercial ones. A headspace solid-phase microextraction gas chromatographic (HS-SPME-GC) procedure was used to determine the volatile compounds and a blind analysis based on Ac/Ais (peak area of volatile compound/peak area of internal standard) was carried out for statistical purposes. One way analysis of variance (ANOVA), principal component analysis (PCA) and stepwise linear discriminant analysis (SLDA) were used to process data and to develop discriminant models. Only 11 peaks enabled to differentiate and classify the experimental wines. SLDA allowed 100% recognition ability for three grape varieties, 100% prediction ability for Cabernet Sauvignon and Cabernet Gernischt wines, but only 92.31% for Merlot wines. A more valid and robust way was to use the PCA scores to do the discriminant analysis. When we performed SLDA this way, 100% recognition ability and 100% prediction ability were obtained. At last, 11 peaks which selected by SLDA from raw analysis set had been identified. When we demonstrated the models using commercial wines, the models showed 100% recognition ability for the wines collected directly from winery and without ageing, but only 65% for the others. Therefore, the varietal factor was currently discredited as a differentiating parameter for commercial wines in China. Nevertheless, this method could be applied as a screening tool and as a complement to other methods for grape base liquors which do not need ageing and blending procedures. 2010 Elsevier B.V. All rights reserved.

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.

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.

Learning templates for artistic portrait lighting analysis.

PubMed

Chen, Xiaowu; Jin, Xin; Wu, Hongyu; Zhao, Qinping

2015-02-01

Lighting is a key factor in creating impressive artistic portraits. In this paper, we propose to analyze portrait lighting by learning templates of lighting styles. Inspired by the experience of artists, we first define several novel features that describe the local contrasts in various face regions. The most informative features are then selected with a stepwise feature pursuit algorithm to derive the templates of various lighting styles. After that, the matching scores that measure the similarity between a testing portrait and those templates are calculated for lighting style classification. Furthermore, we train a regression model by the subjective scores and the feature responses of a template to predict the score of a portrait lighting quality. Based on the templates, a novel face illumination descriptor is defined to measure the difference between two portrait lightings. Experimental results show that the learned templates can well describe the lighting styles, whereas the proposed approach can assess the lighting quality of artistic portraits as human being does.

Development of a Bayesian model to estimate health care outcomes in the severely wounded

PubMed Central

Stojadinovic, Alexander; Eberhardt, John; Brown, Trevor S; Hawksworth, Jason S; Gage, Frederick; Tadaki, Douglas K; Forsberg, Jonathan A; Davis, Thomas A; Potter, Benjamin K; Dunne, James R; Elster, E A

2010-01-01

Background: Graphical probabilistic models have the ability to provide insights as to how clinical factors are conditionally related. These models can be used to help us understand factors influencing health care outcomes and resource utilization, and to estimate morbidity and clinical outcomes in trauma patient populations. Study design: Thirty-two combat casualties with severe extremity injuries enrolled in a prospective observational study were analyzed using step-wise machine-learned Bayesian belief network (BBN) and step-wise logistic regression (LR). Models were evaluated using 10-fold cross-validation to calculate area-under-the-curve (AUC) from receiver operating characteristics (ROC) curves. Results: Our BBN showed important associations between various factors in our data set that could not be developed using standard regression methods. Cross-validated ROC curve analysis showed that our BBN model was a robust representation of our data domain and that LR models trained on these findings were also robust: hospital-acquired infection (AUC: LR, 0.81; BBN, 0.79), intensive care unit length of stay (AUC: LR, 0.97; BBN, 0.81), and wound healing (AUC: LR, 0.91; BBN, 0.72) showed strong AUC. Conclusions: A BBN model can effectively represent clinical outcomes and biomarkers in patients hospitalized after severe wounding, and is confirmed by 10-fold cross-validation and further confirmed through logistic regression modeling. The method warrants further development and independent validation in other, more diverse patient populations. PMID:21197361

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.

A Statistical Model-Based Decision Support System for Managing Summer Stream Temperatures with Quantified Confidence Analysis

NASA Astrophysics Data System (ADS)

Neumann, D. W.; Zagona, E. A.; Rajagopalan, B.

2005-12-01

Warm summer stream temperatures due to low flows and high air temperatures are a critical water quality problem in many western U.S. river basins because they impact threatened fish species' habitat. Releases from storage reservoirs and river diversions are typically driven by human demands such as irrigation, municipal and industrial uses and hydropower production. Historically, fish needs have not been formally incorporated in the operating procedures, which do not supply adequate flows for fish in the warmest, driest periods. One way to address this problem is for local and federal organizations to purchase water rights to be used to increase flows, hence decrease temperatures. A statistical model-predictive technique for efficient and effective use of a limited supply of fish water has been developed and incorporated in a Decision Support System (DSS) that can be used in an operations mode to effectively use water acquired to mitigate warm stream temperatures. The DSS is a rule-based system that uses the empirical, statistical predictive model to predict maximum daily stream temperatures based on flows that meet the non-fish operating criteria, and to compute reservoir releases of allocated fish water when predicted temperatures exceed fish habitat temperature targets with a user specified confidence of the temperature predictions. The empirical model is developed using a step-wise linear regression procedure to select significant predictors, and includes the computation of a prediction confidence interval to quantify the uncertainty of the prediction. The DSS also includes a strategy for managing a limited amount of water throughout the season based on degree-days in which temperatures are allowed to exceed the preferred targets for a limited number of days that can be tolerated by the fish. The DSS is demonstrated by an example application to the Truckee River near Reno, Nevada using historical flows from 1988 through 1994. In this case, the statistical model predicts maximum daily Truckee River stream temperatures in June, July, and August using predicted maximum daily air temperature and modeled average daily flow. The empirical relationship was created using a step-wise linear regression selection process using 1993 and 1994 data. The adjusted R2 value for this relationship is 0.91. The model is validated using historic data and demonstrated in a predictive mode with a prediction confidence interval to quantify the uncertainty. Results indicate that the DSS could substantially reduce the number of target temperature violations, i.e., stream temperatures exceeding the target temperature levels detrimental to fish habitat. The results show that large volumes of water are necessary to meet a temperature target with a high degree of certainty and violations may still occur if all of the stored water is depleted. A lower degree of certainty requires less water but there is a higher probability that the temperature targets will be exceeded. Addition of the rules that consider degree-days resulted in a reduction of the number of temperature violations without increasing the amount of water used. This work is described in detail in publications referenced in the URL below.

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.

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.

Predictions of BuChE inhibitors using support vector machine and naive Bayesian classification techniques in drug discovery.

PubMed

Fang, Jiansong; Yang, Ranyao; Gao, Li; Zhou, Dan; Yang, Shengqian; Liu, Ai-Lin; Du, Guan-hua

2013-11-25

Butyrylcholinesterase (BuChE, EC 3.1.1.8) is an important pharmacological target for Alzheimer's disease (AD) treatment. However, the currently available BuChE inhibitor screening assays are expensive, labor-intensive, and compound-dependent. It is necessary to develop robust in silico methods to predict the activities of BuChE inhibitors for the lead identification. In this investigation, support vector machine (SVM) models and naive Bayesian models were built to discriminate BuChE inhibitors (BuChEIs) from the noninhibitors. Each molecule was initially represented in 1870 structural descriptors (1235 from ADRIANA.Code, 334 from MOE, and 301 from Discovery studio). Correlation analysis and stepwise variable selection method were applied to figure out activity-related descriptors for prediction models. Additionally, structural fingerprint descriptors were added to improve the predictive ability of models, which were measured by cross-validation, a test set validation with 1001 compounds and an external test set validation with 317 diverse chemicals. The best two models gave Matthews correlation coefficient of 0.9551 and 0.9550 for the test set and 0.9132 and 0.9221 for the external test set. To demonstrate the practical applicability of the models in virtual screening, we screened an in-house data set with 3601 compounds, and 30 compounds were selected for further bioactivity assay. The assay results showed that 10 out of 30 compounds exerted significant BuChE inhibitory activities with IC50 values ranging from 0.32 to 22.22 μM, at which three new scaffolds as BuChE inhibitors were identified for the first time. To our best knowledge, this is the first report on BuChE inhibitors using machine learning approaches. The models generated from SVM and naive Bayesian approaches successfully predicted BuChE inhibitors. The study proved the feasibility of a new method for predicting bioactivities of ligands and discovering novel lead compounds.

Analysis of flight data from a High-Incidence Research Model by system identification methods

NASA Technical Reports Server (NTRS)

Batterson, James G.; Klein, Vladislav

1989-01-01

Data partitioning and modified stepwise regression were applied to recorded flight data from a Royal Aerospace Establishment high incidence research model. An aerodynamic model structure and corresponding stability and control derivatives were determined for angles of attack between 18 and 30 deg. Several nonlinearities in angles of attack and sideslip as well as a unique roll-dominated set of lateral modes were found. All flight estimated values were compared to available wind tunnel measurements.

Branching Patterns and Stepped Leaders in an Electric-Circuit Model for Creeping Discharge

NASA Astrophysics Data System (ADS)

Hidetsugu Sakaguchi,; Sahim M. Kourkouss,

2010-06-01

We construct a two-dimensional electric circuit model for creeping discharge. Two types of discharge, surface corona and surface leader, are modeled by a two-step function of conductance. Branched patterns of surface leaders surrounded by the surface corona appear in numerical simulation. The fractal dimension of branched discharge patterns is calculated by changing voltage and capacitance. We find that surface leaders often grow stepwise in time, as is observed in lightning leaders of thunder.

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.

A new stepwise carbon cycle data assimilation system using multiple data streams to constrain the simulated land surface carbon cycle

DOE PAGES

Peylin, Philippe; Bacour, Cédric; MacBean, Natasha; ...

2016-09-20

Here, large uncertainties in land surface models (LSMs) simulations still arise from inaccurate forcing, poor description of land surface heterogeneity (soil and vegetation properties), incorrect model parameter values and incomplete representation of biogeochemical processes. The recent increase in the number and type of carbon cycle-related observations, including both in situ and remote sensing measurements, has opened a new road to optimize model parameters via robust statistical model–data integration techniques, in order to reduce the uncertainties of simulated carbon fluxes and stocks. In this study we present a carbon cycle data assimilation system that assimilates three major data streams, namely themore » Moderate Resolution Imaging Spectroradiometer (MODIS)-Normalized Difference Vegetation Index (NDVI) observations of vegetation activity, net ecosystem exchange (NEE) and latent heat (LE) flux measurements at more than 70 sites (FLUXNET), as well as atmospheric CO 2 concentrations at 53 surface stations, in order to optimize the main parameters (around 180 parameters in total) of the Organizing Carbon and Hydrology in Dynamics Ecosystems (ORCHIDEE) LSM (version 1.9.5 used for the Coupled Model Intercomparison Project Phase 5 (CMIP5) simulations). The system relies on a stepwise approach that assimilates each data stream in turn, propagating the information gained on the parameters from one step to the next. Overall, the ORCHIDEE model is able to achieve a consistent fit to all three data streams, which suggests that current LSMs have reached the level of development to assimilate these observations. The assimilation of MODIS-NDVI (step 1) reduced the growing season length in ORCHIDEE for temperate and boreal ecosystems, thus decreasing the global mean annual gross primary production (GPP). Using FLUXNET data (step 2) led to large improvements in the seasonal cycle of the NEE and LE fluxes for all ecosystems (i.e., increased amplitude for temperate ecosystems). The assimilation of atmospheric CO 2, using the general circulation model (GCM) of the Laboratoire de Météorologie Dynamique (LMDz; step 3), provides an overall constraint (i.e., constraint on large-scale net CO 2 fluxes), resulting in an improvement of the fit to the observed atmospheric CO 2 growth rate. Thus, the optimized model predicts a land C (carbon) sink of around 2.2 PgC yr -1 (for the 2000–2009 period), which is more compatible with current estimates from the Global Carbon Project (GCP) than the prior value. The consistency of the stepwise approach is evaluated with back-compatibility checks. The final optimized model (after step 3) does not significantly degrade the fit to MODIS-NDVI and FLUXNET data that were assimilated in the first two steps, suggesting that a stepwise approach can be used instead of the more “challenging” implementation of a simultaneous optimization in which all data streams are assimilated together. Most parameters, including the scalar of the initial soil carbon pool size, changed during the optimization with a large error reduction. This work opens new perspectives for better predictions of the land carbon budgets.« less

A new stepwise carbon cycle data assimilation system using multiple data streams to constrain the simulated land surface carbon cycle

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

Peylin, Philippe; Bacour, Cédric; MacBean, Natasha

Here, large uncertainties in land surface models (LSMs) simulations still arise from inaccurate forcing, poor description of land surface heterogeneity (soil and vegetation properties), incorrect model parameter values and incomplete representation of biogeochemical processes. The recent increase in the number and type of carbon cycle-related observations, including both in situ and remote sensing measurements, has opened a new road to optimize model parameters via robust statistical model–data integration techniques, in order to reduce the uncertainties of simulated carbon fluxes and stocks. In this study we present a carbon cycle data assimilation system that assimilates three major data streams, namely themore » Moderate Resolution Imaging Spectroradiometer (MODIS)-Normalized Difference Vegetation Index (NDVI) observations of vegetation activity, net ecosystem exchange (NEE) and latent heat (LE) flux measurements at more than 70 sites (FLUXNET), as well as atmospheric CO 2 concentrations at 53 surface stations, in order to optimize the main parameters (around 180 parameters in total) of the Organizing Carbon and Hydrology in Dynamics Ecosystems (ORCHIDEE) LSM (version 1.9.5 used for the Coupled Model Intercomparison Project Phase 5 (CMIP5) simulations). The system relies on a stepwise approach that assimilates each data stream in turn, propagating the information gained on the parameters from one step to the next. Overall, the ORCHIDEE model is able to achieve a consistent fit to all three data streams, which suggests that current LSMs have reached the level of development to assimilate these observations. The assimilation of MODIS-NDVI (step 1) reduced the growing season length in ORCHIDEE for temperate and boreal ecosystems, thus decreasing the global mean annual gross primary production (GPP). Using FLUXNET data (step 2) led to large improvements in the seasonal cycle of the NEE and LE fluxes for all ecosystems (i.e., increased amplitude for temperate ecosystems). The assimilation of atmospheric CO 2, using the general circulation model (GCM) of the Laboratoire de Météorologie Dynamique (LMDz; step 3), provides an overall constraint (i.e., constraint on large-scale net CO 2 fluxes), resulting in an improvement of the fit to the observed atmospheric CO 2 growth rate. Thus, the optimized model predicts a land C (carbon) sink of around 2.2 PgC yr -1 (for the 2000–2009 period), which is more compatible with current estimates from the Global Carbon Project (GCP) than the prior value. The consistency of the stepwise approach is evaluated with back-compatibility checks. The final optimized model (after step 3) does not significantly degrade the fit to MODIS-NDVI and FLUXNET data that were assimilated in the first two steps, suggesting that a stepwise approach can be used instead of the more “challenging” implementation of a simultaneous optimization in which all data streams are assimilated together. Most parameters, including the scalar of the initial soil carbon pool size, changed during the optimization with a large error reduction. This work opens new perspectives for better predictions of the land carbon budgets.« less

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.

Factors affecting cadmium absorbed by pistachio kernel in calcareous soils, southeast of Iran.

PubMed

Shirani, H; Hosseinifard, S J; Hashemipour, H

2018-03-01

Cadmium (Cd) which does not have a biological role is one of the most toxic heavy metals for organisms. This metal enters environment through industrial processes and fertilizers. The main objective of this study was to determine the relationships between absorbed Cd by pistachio kernel and some of soil physical and chemical characteristics using modeling by stepwise regression and Artificial Neural Network (ANN), in calcareous soils in Rafsanjan region, southeast of Iran. For these purposes, 220 pistachio orchards were selected, and soil samples were taken from two depths of 0-40 and 40-80cm. Besides, fruit and leaf samples from branches with and without fruit were taken in each sampling point. The results showed that affecting factors on absorbed Cd by pistachio kernel which were obtained by regression method (pH and clay percent) were not interpretable, and considering unsuitable vales of determinant coefficient (R 2 ) and Root Mean Squares Error (RMSE), the model did not have sufficient validity. However, ANN modeling was highly accurate and reliable. Based on its results, soil available P and Zn and soil salinity were the most important factors affecting the concentration of Cd in pistachio kernel in pistachio growing areas of Rafsanjan. Copyright © 2017 Elsevier B.V. All rights reserved.

An Educational Model for Disruption of Bacteria for Protein Studies.

ERIC Educational Resources Information Center

Bhaduri, Saumya; Demchick, Paul H.

1984-01-01

A simple, rapid, and safe method has been developed for disrupting bacterial cells for protein studies. The method involved stepwise treatment of cells with acetone and with sodium dodecyl sulfate solution to allow extraction of cellular proteins for analysis by polyacrylamide gel electrophoresis. Applications for instructional purposes are noted.…

Identifying Pedophiles "Eligible" for Community Notification under Megan's Law: A Multivariate Model for Actuarially Anchored Decisions.

ERIC Educational Resources Information Center

Pallone, Nathaniel J.; Hennessy, James J.; Voelbel, Gerald T.

1998-01-01

A scientifically sound methodology for identifying offenders about whose presence the community should be notified is demonstrated. A stepwise multiple regression was calculated among incarcerated pedophiles (N=52) including both psychological and legal data; a precision-weighted equation produced 90.4% "true positives." This methodology can be…

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.

Trajectories of cognitive function in dementia-free subjects: Radiation Effects Research Foundation Adult Health Study.

PubMed

Yamada, Michiko; Landes, Reid D; Mimori, Yasuyo; Nagano, Yoshito; Sasaki, Hideo

2015-04-15

To investigate associations between age, sex, education, and birth cohort and global cognitive decline among a population that would most likely not progress to dementia. A total of 1538 dementia-free subjects aged 60 to 80years in 1992 were followed up through 2011 without dementia occurrence. We assessed cognitive function using the Cognitive Ability Screening Instrument (CASI). Using stepwise-like model selection procedure, we built mixed-effects models for initial cognition and longitudinal cognition. Initial CASI scores for younger age and more years of formal education were higher than those for older and less education. Sex did not show a significant effect. In the longitudinal analysis, cognitive decline became more rapid with increasing age. Sex and education did not modify the degree of deterioration with age. CASI scores were higher for younger cohorts and men due to differences in education levels. Among dementia-free subjects, age is an important predictor of cognitive function level and cognitive decline. Education level affects cognitive function level, but did not affect cognitive decline. The results have implications not only for elucidation of the aging process, but also for reference in dementia screening. Copyright © 2015 Elsevier B.V. All rights reserved.

Better diagnostic accuracy of neuropathy in obesity: A new challenge for neurologists.

PubMed

Callaghan, Brian C; Xia, Rong; Reynolds, Evan; Banerjee, Mousumi; Burant, Charles; Rothberg, Amy; Pop-Busui, Rodica; Villegas-Umana, Emily; Feldman, Eva L

2018-03-01

To determine the comparative diagnostic characteristics of neuropathy measures in an obese population. We recruited obese participants from the University of Michigan's Weight Management Program. Receiver operative characteristic analysis determined the area under the curve (AUC) of neuropathy measures for distal symmetric polyneuropathy (DSP), small fiber neuropathy (SFN), and cardiovascular autonomic neuropathy (CAN). The best test combinations were determined using stepwise and Score subset selection models. We enrolled 120 obese participants. For DSP, seven of 42 neuropathy measures (Utah Early Neuropathy Score (UENS, N = 62), Michigan Neuropathy Screening Instrument (MNSI) reduced combined index, MNSI examination, nerve fiber density (NFD) leg, tibial F response, MNSI questionnaire, peroneal distal motor latency) had AUCs ≥ 0.75. Three of 19 small fiber nerve measures for SFN (UENS, NFD leg, Sudoscan feet (N = 70)) and zero of 16 CAN measures had AUCs ≥ 0.75. Combinations of tests performed better than individual tests with AUCs of 0.82 for DSP (two parameters) and 0.84 for SFN (three parameters). Many neuropathy measures demonstrate good test performance for DSP in obese participants. Select few small fiber nerve measures performed well for SFN, and none for CAN. Specific combinations of tests should be used for research studies to maximize diagnostic performance in obese cohorts. Published by Elsevier B.V.

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.

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.

Models to predict emissions of health-damaging pollutants and global warming contributions of residential fuel/stove combinations in China.

PubMed

Edwards, Rufus D; Smith, Kirk R; Zhang, Junfeng; Ma, Yuqing

2003-01-01

Residential energy use in developing countries has traditionally been associated with combustion devices of poor energy efficiency, which have been shown to produce substantial health-damaging pollution, contributing significantly to the global burden of disease, and greenhouse gas (GHG) emissions. Precision of these estimates in China has been hampered by limited data on stove use and fuel consumption in residences. In addition limited information is available on variability of emissions of pollutants from different stove/fuel combinations in typical use, as measurement of emission factors requires measurement of multiple chemical species in complex burn cycle tests. Such measurements are too costly and time consuming for application in conjunction with national surveys. Emissions of most of the major health-damaging pollutants (HDP) and many of the gases that contribute to GHG emissions from cooking stoves are the result of the significant portion of fuel carbon that is diverted to products of incomplete combustion (PIC) as a result of poor combustion efficiencies. The approximately linear increase in emissions of PIC with decreasing combustion efficiencies allows development of linear models to predict emissions of GHG and HDP intrinsically linked to CO2 and PIC production, and ultimately allows the prediction of global warming contributions from residential stove emissions. A comprehensive emissions database of three burn cycles of 23 typical fuel/stove combinations tested in a simulated village house in China has been used to develop models to predict emissions of HDP and global warming commitment (GWC) from cooking stoves in China, that rely on simple survey information on stove and fuel use that may be incorporated into national surveys. Stepwise regression models predicted 66% of the variance in global warming commitment (CO2, CO, CH4, NOx, TNMHC) per 1 MJ delivered energy due to emissions from these stoves if survey information on fuel type was available. Subsequently if stove type is known, stepwise regression models predicted 73% of the variance. Integrated assessment of policies to change stove or fuel type requires that implications for environmental impacts, energy efficiency, global warming and human exposures to HDP emissions can be evaluated. Frequently, this involves measurement of TSP or CO as the major HDPs. Incorporation of this information into models to predict GWC predicted 79% and 78% of the variance respectively. Clearly, however, the complexity of making multiple measurements in conjunction with a national survey would be both expensive and time consuming. Thus, models to predict HDP using simple survey information, and with measurement of either CO/CO2 or TSP/CO2 to predict emission factors for the other HDP have been derived. Stepwise regression models predicted 65% of the variance in emissions of total suspended particulate as grams of carbon (TSPC) per 1 MJ delivered if survey information on fuel and stove type was available and 74% if the CO/CO2 ratio was measured. Similarly stepwise regression models predicted 76% of the variance in COC emissions per MJ delivered with survey information on stove and fuel type and 85% if the TSPC/CO2 ratio was measured. Ultimately, with international agreements on emissions trading frameworks, similar models based on extensive databases of the fate of fuel carbon during combustion from representative household stoves would provide a mechanism for computing greenhouse credits in the residential sector as part of clean development mechanism frameworks and monitoring compliance to control regimes.

An Integration of Ground-Penetrating Radar, Remote Sensing, and Discharge Records of the Modern Kicking Horse River, BC

NASA Astrophysics Data System (ADS)

Cyples, N.; Ielpi, A.; Dirszowsky, R.

2017-12-01

The Kicking Horse River is a gravel-bed stream originating from glacial meltwater supplied by the Wapta Icefields in south-eastern British Columbia. An alluvial tract extends for 7 km through Field, BC, where the trunk channel undergoes diurnal and seasonal fluctuations in flow as a result of varying glacial-meltwater supply and runoff recharge. Prior studies erected the Kicking Horse River as a reference for proximal braided systems, and documented bar formation and sediment distribution patterns from ground observations. However, a consistent model of planform evolution and related stratigraphic signature is lacking. Specific objectives of this study are to examine the morphodynamic evolution and stratigraphic signature of channel-bar complexes using high-resolution satellite imagery, sedimentologic and discharge observations, and ground-penetrating radar (GPR). Remote sensing highlights rates of lateral channel migration of as much as 270 meters over eight years ( 34 meters/year), and demonstrates how flood stages are associated with stepwise episodes of channel braiding and anabranching. GPR analysis aided in the identification of five distinct radar facies, including: discontinuous, inclined, planar, trough-shaped, and mounded reflectors, which were respectively related to specific architectural elements and fluvial processes responsible for bar evolution. Across-stream GPR transects demonstrated higher heterogeneity in facies distribution, while downstream-oriented transects yielded a more monotonous distribution in radar facies. Notably, large-scale inclined reflectors related to step-wise bar accretion are depicted only in downstream-oriented transects, while discontinuous reflectors related to bedform stacking appear to be dominant in along-stream transects. Integration of sedimentological data with remote sensing, gauging records, and GPR analysis allows for high-resolution modelling of stepwise changes in alluvial morphology. Conceptual models stemming from such analyses can be employed to understand the depositional history and stratigraphic signature of proximal and coarse-grained fluvial systems.

Tibiofemoral contact forces during walking, running and sidestepping.

PubMed

Saxby, David J; Modenese, Luca; Bryant, Adam L; Gerus, Pauline; Killen, Bryce; Fortin, Karine; Wrigley, Tim V; Bennell, Kim L; Cicuttini, Flavia M; Lloyd, David G

2016-09-01

We explored the tibiofemoral contact forces and the relative contributions of muscles and external loads to those contact forces during various gait tasks. Second, we assessed the relationships between external gait measures and contact forces. A calibrated electromyography-driven neuromusculoskeletal model estimated the tibiofemoral contact forces during walking (1.44±0.22ms(-1)), running (4.38±0.42ms(-1)) and sidestepping (3.58±0.50ms(-1)) in healthy adults (n=60, 27.3±5.4years, 1.75±0.11m, and 69.8±14.0kg). Contact forces increased from walking (∼1-2.8 BW) to running (∼3-8 BW), sidestepping had largest maximum total (8.47±1.57 BW) and lateral contact forces (4.3±1.05 BW), while running had largest maximum medial contact forces (5.1±0.95 BW). Relative muscle contributions increased across gait tasks (up to 80-90% of medial contact forces), and peaked during running for lateral contact forces (∼90%). Knee adduction moment (KAM) had weak relationships with tibiofemoral contact forces (all R(2)<0.36) and the relationships were gait task-specific. Step-wise regression of multiple external gait measures strengthened relationships (0.20

Who wants to enroll in health care PhD programs? An analysis of a nationwide graduate destination survey.

PubMed

Pan, Ying-Ju; Lee, Lung-Sheng

2012-06-01

PhD training is important for national human resource development in the era of the "knowledge economy". However, it is not clear what factors are associated with the decision of a master's degree graduate to pursue a PhD degree in health care, including medicine, public health, and nursing. It is postulated that the intention to pursue a PhD degree in health care is associated with a graduate's attributes, academic publication, socioeconomic status, and extent of financial support. A cross-sectional investigation was conducted to analyze data from the 2007 nationwide graduate destination survey in Taiwan. Logistic regression with a forward stepwise model selection strategy was applied to identify those significant factors related to the intention of master's degree graduates to pursue a PhD degree in health care. The predictive validity of the selected model was evaluated using the receiver operating characteristics curve analysis. Of the 1668 master's degree graduates who responded to the survey, only 240 (14.4%) indicated a desire to pursue a PhD degree. Seven factors are identified to be independently associated with the intention to pursue a PhD degree in health care, including female gender [odds ratio (OR)=0.18, 95% confidence interval: 0.13-0.26], more than 2 years in graduate school (OR=0.46), working during graduate school (OR=0.47), submission of conference and journal articles (OR=1.61 and 1.48, respectively), tuition source, and parents' educational level. The predictive validity of the selected model was 0.77. These findings provide an overview of potential PhD students in the field of health care. Based on this assessment, effective strategies need to be developed to attract and retain qualified candidates, as well as other types of PhD students who are in demand in this field. Copyright © 2012. Published by Elsevier B.V.

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.

Stepwise inference of likely dynamic flux distributions from metabolic time series data.

PubMed

Faraji, Mojdeh; Voit, Eberhard O

2017-07-15

Most metabolic pathways contain more reactions than metabolites and therefore have a wide stoichiometric matrix that corresponds to infinitely many possible flux distributions that are perfectly compatible with the dynamics of the metabolites in a given dataset. This under-determinedness poses a challenge for the quantitative characterization of flux distributions from time series data and thus for the design of adequate, predictive models. Here we propose a method that reduces the degrees of freedom in a stepwise manner and leads to a dynamic flux distribution that is, in a statistical sense, likely to be close to the true distribution. We applied the proposed method to the lignin biosynthesis pathway in switchgrass. The system consists of 16 metabolites and 23 enzymatic reactions. It has seven degrees of freedom and therefore admits a large space of dynamic flux distributions that all fit a set of metabolic time series data equally well. The proposed method reduces this space in a systematic and biologically reasonable manner and converges to a likely dynamic flux distribution in just a few iterations. The estimated solution and the true flux distribution, which is known in this case, show excellent agreement and thereby lend support to the method. The computational model was implemented in MATLAB (version R2014a, The MathWorks, Natick, MA). The source code is available at https://github.gatech.edu/VoitLab/Stepwise-Inference-of-Likely-Dynamic-Flux-Distributions and www.bst.bme.gatech.edu/research.php . mojdeh@gatech.edu or eberhard.voit@bme.gatech.edu. Supplementary data are available at Bioinformatics online. © The Author (2017). Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com

A model for predicting sulcus-to-sulcus diameter in posterior chamber phakic intraocular lens candidates: correlation between ocular biometric parameters.

PubMed

Ghoreishi, Mohammad; Abdi-Shahshahani, Mehdi; Peyman, Alireza; Pourazizi, Mohsen

2018-02-21

The aim of this study was to determine the correlation between ocular biometric parameters and sulcus-to-sulcus (STS) diameter. This was a cross-sectional study of preoperative ocular biometry data of patients who were candidates for phakic intraocular lens (IOL) surgery. Subjects underwent ocular biometry analysis, including refraction error evaluation using an autorefractor and Orbscan topography for white-to-white (WTW) corneal diameter and measurement. Pentacam was used to perform WTW corneal diameter and measurements of minimum and maximum keratometry (K). Measurements of STS and angle-to-angle (ATA) were obtained using a 50-MHz B-mode ultrasound device. Anterior optical coherence tomography was performed for anterior chamber depth measurement. Pearson's correlation test and stepwise linear regression analysis were used to find a model to predict STS. Fifty-eight eyes of 58 patients were enrolled. Mean age ± standard deviation of sample was 28.95 ± 6.04 years. The Pearson's correlation coefficient between STS with WTW, ATA, mean K was 0.383, 0.492, and - 0.353, respectively, which was statistically significant (all P < 0.001). Using stepwise linear regression analysis, there is a statistically significant association between STS with WTW (P = 0.011) and mean K (P = 0.025). The standardized coefficient was 0.323 and - 0.284 for WTW and mean K, respectively. The stepwise linear regression analysis equation was: (STS = 9.549 + 0.518 WTW - 0.083 mean K). Based on our result, given the correlation of STS with WTW and mean K and potential of direct and essay measurement of WTW and mean K, it seems that current IOL sizing protocols could be estimating with WTW and mean K.

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.

Development, validation and operating room-transfer of a six-step laparoscopic training program for the vesicourethral anastomosis.

PubMed

Klein, Jan; Teber, Dogu; Frede, Tom; Stock, Christian; Hruza, Marcel; Gözen, Ali; Seemann, Othmar; Schulze, Michael; Rassweiler, Jens

2013-03-01

Development and full validation of a laparoscopic training program for stepwise learning of a reproducible application of a standardized laparoscopic anastomosis technique and integration into the clinical course. The training of vesicourethral anastomosis (VUA) was divided into six simple standardized steps. To fix the objective criteria, four experienced surgeons performed the stepwise training protocol. Thirty-eight participants with no previous laparoscopic experience were investigated in their training performance. The times needed to manage each training step and the total training time were recorded. The integration into the clinical course was investigated. The training results and the corresponding steps during laparoscopic radical prostatectomy (LRP) were analyzed. Data analysis of corresponding operating room (OR) sections of 793 LRP was performed. Based on the validity, criteria were determined. In the laboratory section, a significant reduction of OR time for every step was seen in all participants. Coordination: 62%; longitudinal incision: 52%; inverted U-shape incision: 43%; plexus: 47%. Anastomosis catheter model: 38%. VUA: 38%. The laboratory section required a total time of 29 hours (minimum: 16 hours; maximum: 42 hours). All participants had shorter execution times in the laboratory than under real conditions. The best match was found within the VUA model. To perform an anastomosis under real conditions, 25% more time was needed. By using the training protocol, the performance of the VUA is comparable to that of an surgeon with experience of about 50 laparoscopic VUA. Data analysis proved content, construct, and prognostic validity. The use of stepwise training approaches enables a surgeon to learn and reproduce complex reconstructive surgical tasks: eg, the VUA in a safe environment. The validity of the designed system is given at all levels and should be used as a standard in the clinical surgical training in laparoscopic reconstructive urology.

Insulin degludec/insulin aspart once daily in Type 2 diabetes: a comparison of simple or stepwise titration algorithms (BOOST® : SIMPLE USE).

PubMed

Park, S W; Bebakar, W M W; Hernandez, P G; Macura, S; Hersløv, M L; de la Rosa, R

2017-02-01

To compare the efficacy and safety of two titration algorithms for insulin degludec/insulin aspart (IDegAsp) administered once daily with metformin in participants with insulin-naïve Type 2 diabetes mellitus. This open-label, parallel-group, 26-week, multicentre, treat-to-target trial, randomly allocated participants (1:1) to two titration arms. The Simple algorithm titrated IDegAsp twice weekly based on a single pre-breakfast self-monitored plasma glucose (SMPG) measurement. The Stepwise algorithm titrated IDegAsp once weekly based on the lowest of three consecutive pre-breakfast SMPG measurements. In both groups, IDegAsp once daily was titrated to pre-breakfast plasma glucose values of 4.0-5.0 mmol/l. Primary endpoint was change from baseline in HbA 1c (%) after 26 weeks. Change in HbA 1c at Week 26 was IDegAsp Simple -14.6 mmol/mol (-1.3%) (to 52.4 mmol/mol; 6.9%) and IDegAsp Stepwise -11.9 mmol/mol (-1.1%) (to 54.7 mmol/mol; 7.2%). The estimated between-group treatment difference was -1.97 mmol/mol [95% confidence interval (CI) -4.1, 0.2] (-0.2%, 95% CI -0.4, 0.02), confirming the non-inferiority of IDegAsp Simple to IDegAsp Stepwise (non-inferiority limit of ≤ 0.4%). Mean reduction in fasting plasma glucose and 8-point SMPG profiles were similar between groups. Rates of confirmed hypoglycaemia were lower for IDegAsp Stepwise [2.1 per patient years of exposure (PYE)] vs. IDegAsp Simple (3.3 PYE) (estimated rate ratio IDegAsp Simple /IDegAsp Stepwise 1.8; 95% CI 1.1, 2.9). Nocturnal hypoglycaemia rates were similar between groups. No severe hypoglycaemic events were reported. In participants with insulin-naïve Type 2 diabetes mellitus, the IDegAsp Simple titration algorithm improved HbA 1c levels as effectively as a Stepwise titration algorithm. Hypoglycaemia rates were lower in the Stepwise arm. © 2016 The Authors. Diabetic Medicine published by John Wiley & Sons Ltd on behalf of Diabetes UK.

Transforming paper-based assessment forms to a digital format: Exemplified by the Housing Enabler prototype app.

PubMed

Svarre, Tanja; Lunn, Tine Bieber Kirkegaard; Helle, Tina

2017-11-01

The aim of this paper is to provide the reader with an overall impression of the stepwise user-centred design approach including the specific methods used and lessons learned when transforming paper-based assessment forms into a prototype app, taking the Housing Enabler as an example. Four design iterations were performed, building on a domain study, workshops, expert evaluation and controlled and realistic usability tests. The user-centred design process involved purposefully selected participants with different Housing Enabler knowledge and housing adaptation experience. The design iterations resulted in the development of a Housing Enabler prototype app. The prototype app has several features and options that are new compared with the original paper-based Housing Enabler assessment form. These new features include a user friendly overview of the assessment form; easy navigation by swiping back and forth between items; onsite data analysis; and ranking of the accessibility score, photo documentation and a data export facility. Based on the presented stepwise approach, a high-fidelity Housing Enabler prototype app was successfully developed. The development process has emphasized the importance of combining design participants' knowledge and experiences, and has shown that methods should seem relevant to participants to increase their engagement.

Specificity in endoplasmic reticulum-stress signaling in yeast entails a step-wise engagement of HAC1 mRNA to clusters of the stress sensor Ire1.

PubMed

van Anken, Eelco; Pincus, David; Coyle, Scott; Aragón, Tomás; Osman, Christof; Lari, Federica; Gómez Puerta, Silvia; Korennykh, Alexei V; Walter, Peter

2014-12-30

Insufficient protein-folding capacity in the endoplasmic reticulum (ER) induces the unfolded protein response (UPR). In the ER lumen, accumulation of unfolded proteins activates the transmembrane ER-stress sensor Ire1 and drives its oligomerization. In the cytosol, Ire1 recruits HAC1 mRNA, mediating its non-conventional splicing. The spliced mRNA is translated into Hac1, the key transcription activator of UPR target genes that mitigate ER-stress. In this study, we report that oligomeric assembly of the ER-lumenal domain is sufficient to drive Ire1 clustering. Clustering facilitates Ire1's cytosolic oligomeric assembly and HAC1 mRNA docking onto a positively charged motif in Ire1's cytosolic linker domain that tethers the kinase/RNase to the transmembrane domain. By the use of a synthetic bypass, we demonstrate that mRNA docking per se is a pre-requisite for initiating Ire1's RNase activity and, hence, splicing. We posit that such step-wise engagement between Ire1 and its mRNA substrate contributes to selectivity and efficiency in UPR signaling.

Enhancement of In Situ Enzymatic Saccharification of Corn Stover by a Stepwise Sodium Hydroxide and Organic Acid Pretreatment.

PubMed

Qing, Qing; Guo, Qi; Zhou, Linlin; He, Yucai; Wang, Liqun; Zhang, Yue

2017-01-01

A stepwise pretreatment method that combines sodium hydroxide and organic acid pretreatments was proposed and investigated to maximize the recovery of main constituents of lignocellulose. The sodium hydroxide pretreatment was firstly optimized by a designed orthogonal experiment with the optimum pretreatment conditions determined as 1 wt% NaOH at 70 °C for 1 h, and 60.42 % of lignin was successfully removed during this stage. In the second stage, 0.5 % acetic acid was selected to pretreat the first-stage solid residue at 80 °C for 40 min in order to decompose hemicelluloses to soluble oligomers or monomers. Then, the whole slurry was subjected to in situ enzymatic saccharification by cellullase with a supplementation of xylanase to further degrade the xylooligosaccharides generated during the acetic acid pretreatment. The maximum reducing sugar and glucose yields achieved were 20.74 and 12.03 g/L, respectively. Furthermore, rapid ethanol fermentation and a yield of 80.3 % also testified this pretreatment method, and the in situ saccharification did not bring any negative impact on ethanol fermentation and has a broad application prospect.

Retro-regression--another important multivariate regression improvement.

PubMed

Randić, M

2001-01-01

We review the serious problem associated with instabilities of the coefficients of regression equations, referred to as the MRA (multivariate regression analysis) "nightmare of the first kind". This is manifested when in a stepwise regression a descriptor is included or excluded from a regression. The consequence is an unpredictable change of the coefficients of the descriptors that remain in the regression equation. We follow with consideration of an even more serious problem, referred to as the MRA "nightmare of the second kind", arising when optimal descriptors are selected from a large pool of descriptors. This process typically causes at different steps of the stepwise regression a replacement of several previously used descriptors by new ones. We describe a procedure that resolves these difficulties. The approach is illustrated on boiling points of nonanes which are considered (1) by using an ordered connectivity basis; (2) by using an ordering resulting from application of greedy algorithm; and (3) by using an ordering derived from an exhaustive search for optimal descriptors. A novel variant of multiple regression analysis, called retro-regression (RR), is outlined showing how it resolves the ambiguities associated with both "nightmares" of the first and the second kind of MRA.

Preparative isolation of alkaloids from Corydalis bungeana Turcz. by high-speed counter-current chromatography using stepwise elution

PubMed Central

Niu, Lili; Xie, Zhensheng; Cai, Tanxi; Wu, Peng; Xue, Peng; Chen, Xiulan; Wu, Zhiyong; Ito, Yoichiro; Li, Famei; Yang, Fuquan

2011-01-01

High-speed counter-current chromatography (HSCCC) was successfully applied for the preparative separation and purification of alkaloids from Corydalis bungeana Turcz. (Kudiding in Chinese) for the first time. After the measurement of partition coefficient of seven target alkaloids in the nine two-phase solvent systems composed of CHCl3–MeOH–(0.1 M; 0.2 M; 0.3 M) HCl (4:1.5:2; 4:2:2; 4:3:2, v/v), CHCl3–MeOH–0.2 M HCl (4:2:2, v/v) and CHCl3–MeOH–0.3 M HCl (4:3:2, v/v) were finally selected for the HSCCC separation using the first upper phase as the stationary phase and the stepwise elution of the two lower mobile phases. Consequently, sanguinarine (10 mg), corynoline (25 mg), protopine (20 mg), corynoloxine (18 mg), and 12-hydroxycorynoline (8 mg) were obtained from 200 mg of crude alkaloid extracts with purities of 94–99% as determined by HPLC. Their chemical structures were characterized on the basis of 1H-NMR, 13C-NMR, and LC-ESI-Q-TOF-MS/MS analyses. PMID:21387560

Evaluation of 41 Candidate Gene Variants for Obesity in the EPIC-Potsdam Cohort by Multi-Locus Stepwise Regression

PubMed Central

Knüppel, Sven; Rohde, Klaus; Meidtner, Karina; Drogan, Dagmar; Holzhütter, Hermann-Georg; Boeing, Heiner; Fisher, Eva

2013-01-01

Objective Obesity has become a leading preventable cause of morbidity and mortality in many parts of the world. It is thought to originate from multiple genetic and environmental determinants. The aim of the current study was to introduce haplotype-based multi-locus stepwise regression (MSR) as a method to investigate combinations of unlinked single nucleotide polymorphisms (SNPs) for obesity phenotypes. Methods In 2,122 healthy randomly selected men and women of the EPIC-Potsdam cohort, the association between 41 SNPs from 18 obesity-candidate genes and either body mass index (BMI, mean = 25.9 kg/m2, SD = 4.1) or waist circumference (WC, mean = 85.2 cm, SD = 12.6) was assessed. Single SNP analyses were done by using linear regression adjusted for age, sex, and other covariates. Subsequently, MSR was applied to search for the ‘best’ SNP combinations. Combinations were selected according to specific AICc and p-value criteria. Model uncertainty was accounted for by a permutation test. Results The strongest single SNP effects on BMI were found for TBC1D1 rs637797 (β = −0.33, SE = 0.13), FTO rs9939609 (β = 0.28, SE = 0.13), MC4R rs17700144 (β = 0.41, SE = 0.15), and MC4R rs10871777 (β = 0.34, SE = 0.14). All these SNPs showed similar effects on waist circumference. The two ‘best’ six-SNP combinations for BMI (global p-value = 3.45⋅10–6 and 6.82⋅10–6) showed effects ranging from −1.70 (SE = 0.34) to 0.74 kg/m2 (SE = 0.21) per allele combination. We selected two six-SNP combinations on waist circumference (global p-value = 7.80⋅10–6 and 9.76⋅10–6) with an allele combination effect of −2.96 cm (SE = 0.76) at maximum. Additional adjustment for BMI revealed 15 three-SNP combinations (global p-values ranged from 3.09⋅10–4 to 1.02⋅10–2). However, after carrying out the permutation test all SNP combinations lost significance indicating that the statistical associations might have occurred by chance. Conclusion MSR provides a tool to search for risk-related SNP combinations of common traits or diseases. However, the search process does not always find meaningful SNP combinations in a dataset. PMID:23874820

Systematic feature selection improves accuracy of methylation-based forensic age estimation in Han Chinese males.

PubMed

Feng, Lei; Peng, Fuduan; Li, Shanfei; Jiang, Li; Sun, Hui; Ji, Anquan; Zeng, Changqing; Li, Caixia; Liu, Fan

2018-03-23

Estimating individual age from biomarkers may provide key information facilitating forensic investigations. Recent progress has shown DNA methylation at age-associated CpG sites as the most informative biomarkers for estimating the individual age of an unknown donor. Optimal feature selection plays a critical role in determining the performance of the final prediction model. In this study we investigate methylation levels at 153 age-associated CpG sites from 21 previously reported genomic regions using the EpiTYPER system for their predictive power on individual age in 390 Han Chinese males ranging from 15 to 75 years of age. We conducted a systematic feature selection using a stepwise backward multiple linear regression analysis as well as an exhaustive searching algorithm. Both approaches identified the same subset of 9 CpG sites, which in linear combination provided the optimal model fitting with mean absolute deviation (MAD) of 2.89 years of age and explainable variance (R 2 ) of 0.92. The final model was validated in two independent Han Chinese male samples (validation set 1, N = 65, MAD = 2.49, R 2  = 0.95, and validation set 2, N = 62, MAD = 3.36, R 2  = 0.89). Other competing models such as support vector machine and artificial neural network did not outperform the linear model to any noticeable degree. The validation set 1 was additionally analyzed using Pyrosequencing technology for cross-platform validation and was termed as validation set 3. Directly applying our model, in which the methylation levels were detected by the EpiTYPER system, to the data from pyrosequencing technology showed, however, less accurate results in terms of MAD (validation set 3, N = 65 Han Chinese males, MAD = 4.20, R 2  = 0.93), suggesting the presence of a batch effect between different data generation platforms. This batch effect could be partially overcome by a z-score transformation (MAD = 2.76, R 2  = 0.93). Overall, our systematic feature selection identified 9 CpG sites as the optimal subset for forensic age estimation and the prediction model consisting of these 9 markers demonstrated high potential in forensic practice. An age estimator implementing our prediction model allowing missing markers is freely available at http://liufan.big.ac.cn/AgePrediction. Copyright © 2018 Elsevier B.V. All rights reserved.

QSAR modeling of flotation collectors using principal components extracted from topological indices.

PubMed

Natarajan, R; Nirdosh, Inderjit; Basak, Subhash C; Mills, Denise R

2002-01-01

Several topological indices were calculated for substituted-cupferrons that were tested as collectors for the froth flotation of uranium. The principal component analysis (PCA) was used for data reduction. Seven principal components (PC) were found to account for 98.6% of the variance among the computed indices. The principal components thus extracted were used in stepwise regression analyses to construct regression models for the prediction of separation efficiencies (Es) of the collectors. A two-parameter model with a correlation coefficient of 0.889 and a three-parameter model with a correlation coefficient of 0.913 were formed. PCs were found to be better than partition coefficient to form regression equations, and inclusion of an electronic parameter such as Hammett sigma or quantum mechanically derived electronic charges on the chelating atoms did not improve the correlation coefficient significantly. The method was extended to model the separation efficiencies of mercaptobenzothiazoles (MBT) and aminothiophenols (ATP) used in the flotation of lead and zinc ores, respectively. Five principal components were found to explain 99% of the data variability in each series. A three-parameter equation with correlation coefficient of 0.985 and a two-parameter equation with correlation coefficient of 0.926 were obtained for MBT and ATP, respectively. The amenability of separation efficiencies of chelating collectors to QSAR modeling using PCs based on topological indices might lead to the selection of collectors for synthesis and testing from a virtual database.

Physiological control of a rotary blood pump with selectable therapeutic options: control of pulsatility gradient.

PubMed

Arndt, Andreas; Nüsser, Peter; Graichen, Kurt; Müller, Johannes; Lampe, Bernhard

2008-10-01

A control strategy for rotary blood pumps meeting different user-selectable control objectives is proposed: maximum support with the highest feasible flow rate versus medium support with maximum ventricular washout and controlled opening of the aortic valve (AoV). A pulsatility index (PI) is calculated from the pressure difference, which is deduced from the axial thrust measured by the magnetic bearing of the pump. The gradient of PI with respect to pump speed (GPI) is estimated via online system identification. The outer loop of a cascaded controller regulates GPI to a reference value satisfying the selected control objective. The inner loop controls the PI to a reference value set by the outer loop. Adverse pumping states such as suction and regurgitation can be detected on the basis of the GPI estimates and corrected by the controller. A lumped-parameter computer model of the assisted circulation was used to simulate variations of ventricular contractility, pulmonary venous pressure, and aortic pressure. The performance of the outer control loop was demonstrated by transitions between the two control modes. Fast reaction of the inner loop was tested by stepwise reduction of venous return. For maximum support, a low PI was maintained without inducing ventricular collapse. For maximum washout, the pump worked at a high PI in the transition region between the opening and the permanently closed AoV. The cascaded control of GPI and PI is able to meet different control objectives and is worth testing in vitro and in vivo.

Effect of Stepwise Pressure Change on Porosity Evolution during Directional Solidification in Small Cylindrical Channels

NASA Technical Reports Server (NTRS)

Grugel, R.N.; Lee, C.P.; Cox, M.C.; Blandford, B.T.; Anilkumar, A.V.

2008-01-01

Controlled directional solidification experiments were performed in capillary channels, using nitrogen-saturated succinonitrile, to examine the effect of an in-situ stepwise processing pressure increase on an isolated pore evolution. Two experiments were performed using different processing pressure input profiles. The results indicate that a processing pressure increase has a transient effect on pore growth geometry characterized by an initial phase of decreasing pore diameter, followed by a recovery phase of increasing pore diameter. The experimental results also show that processing pressure can be used as a control parameter to either increase or terminate porosity formation. A theoretical model is introduced which indicates that the pore formation process is limited by the diffusion of solute-gas through the melt, and that the observed response toa pressure increase is attributed to the re-equilibration of solute concentration in the melt associated with the increased melt pressure.

Determining absolute protein numbers by quantitative fluorescence microscopy.

PubMed

Verdaasdonk, Jolien Suzanne; Lawrimore, Josh; Bloom, Kerry

2014-01-01

Biological questions are increasingly being addressed using a wide range of quantitative analytical tools to examine protein complex composition. Knowledge of the absolute number of proteins present provides insights into organization, function, and maintenance and is used in mathematical modeling of complex cellular dynamics. In this chapter, we outline and describe three microscopy-based methods for determining absolute protein numbers--fluorescence correlation spectroscopy, stepwise photobleaching, and ratiometric comparison of fluorescence intensity to known standards. In addition, we discuss the various fluorescently labeled proteins that have been used as standards for both stepwise photobleaching and ratiometric comparison analysis. A detailed procedure for determining absolute protein number by ratiometric comparison is outlined in the second half of this chapter. Counting proteins by quantitative microscopy is a relatively simple yet very powerful analytical tool that will increase our understanding of protein complex composition. © 2014 Elsevier Inc. All rights reserved.

A density functional theory study on peptide bond cleavage at aspartic residues: direct vs cyclic intermediate hydrolysis.

PubMed

Sang-aroon, Wichien; Amornkitbamrung, Vittaya; Ruangpornvisuti, Vithaya

2013-12-01

In this work, peptide bond cleavages at carboxy- and amino-sides of the aspartic residue in a peptide model via direct (concerted and step-wise) and cyclic intermediate hydrolysis reaction pathways were explored computationally. The energetics, thermodynamic properties, rate constants, and equilibrium constants of all hydrolysis reactions, as well as their energy profiles were computed at the B3LYP/6-311++G(d,p) level of theory. The result indicated that peptide bond cleavage of the Asp residue occurred most preferentially via the cyclic intermediate hydrolysis pathway. In all reaction pathways, cleavage of the peptide bond at the amino-side occurred less preferentially than at the carboxy-side. The overall reaction rate constants of peptide bond cleavage of the Asp residue at the carboxy-side for the assisted system were, in increasing order: concerted < step-wise < cyclic intermediate.

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.

Heterosexual Risk Behaviors Among Urban Young Adolescents

ERIC Educational Resources Information Center

O'Donnell, Lydia; Stueve, Ann; Wilson-Simmons, Renee; Dash, Kim; Agronick, Gail; JeanBaptiste, Varzi

2006-01-01

Urban 6th graders (n = 294) participate in a survey assessing early heterosexual risk behaviors as part of the Reach for Health Middle Childhood Study. About half the boys (47%) and 20% of girls report having a girlfriend or boyfriend; 42% of boys and 10% of girls report kissing and hugging for a long time. Stepwise regressions model the…

Teaching Simulation and Computer-Aided Separation Optimization in Liquid Chromatography by Means of Illustrative Microsoft Excel Spreadsheets

ERIC Educational Resources Information Center

Fasoula, S.; Nikitas, P.; Pappa-Louisi, A.

2017-01-01

A series of Microsoft Excel spreadsheets were developed to simulate the process of separation optimization under isocratic and simple gradient conditions. The optimization procedure is performed in a stepwise fashion using simple macros for an automatic application of this approach. The proposed optimization approach involves modeling of the peak…

Multivariate Profiles of Selected versus Non-Selected Elite Youth Brazilian Soccer Players

PubMed Central

Alves, Isabella S.; Padilha, Maickel B.; Casanova, Filipe; Puggina, Enrico F.; Maia, José

2017-01-01

Abstract This study determined whether a multivariate profile more effectively discriminated selected than non-selected elite youth Brazilian soccer players. This examination was carried out on 66 youth soccer players (selected, n = 28, mean age 16.3 ± 0.1; non-selected, n = 38, mean age 16.7 ± 0.4) using objective instruments. Multivariate profiles were assessed through anthropometric characteristics, biological maturation, tactical-technical skills, and motor performance. The Student’s t-test identified that selected players exhibited significantly higher values for height (t = 2.331, p = 0.02), lean body mass (t = 2.441, p = 0.01), and maturity offset (t = 4.559, p < 0.001), as well as performed better in declarative tactical knowledge (t = 10.484, p < 0.001), shooting (t = 2.188, p = 0.03), dribbling (t = 5.914, p < 0.001), speed – 30 m (t = 8.304, p < 0.001), countermovement jump (t = 2.718, p = 0.008), and peak power tests (t = 2.454, p = 0.01). Forward stepwise discriminant function analysis showed that declarative tactical knowledge, running speed –30 m, maturity offset, dribbling, height, and peak power correctly classified 97% of the selected players. These findings may have implications for a highly efficient selection process with objective measures of youth players in soccer clubs. PMID:29339991

Assessing the environmental hazard of individual and combined pharmaceuticals: acute and chronic toxicity of fluoxetine and propranolol in the crustacean Daphnia magna.

PubMed

Varano, Valentina; Fabbri, Elena; Pasteris, Andrea

2017-08-01

Pharmaceuticals are widespread emerging contaminants and, like all pollutants, are present in combination with others in the ecosystems. The aim of the present work was to evaluate the toxic response of the crustacean Daphnia magna exposed to individual and combined pharmaceuticals. Fluoxetine, a selective serotonin re-uptake inhibitor widely prescribed as antidepressant, and propranolol, a non-selective β-adrenergic receptor-blocking agent used to treat hypertension, were tested. Several experimental trials of an acute immobilization test and a chronic reproduction test were performed. Single chemicals were first tested separately. Toxicity of binary mixtures was then assessed using a fixed ratio experimental design. Five concentrations and 5 percentages of each substance in the mixture (0, 25, 50, 75, and 100%) were tested. The MIXTOX model was applied to analyze the experimental results. This tool is a stepwise statistical procedure that evaluates if and how observed data deviate from a reference model, either concentration addition (CA) or independent action (IA), and provides significance testing for synergism, antagonism, or more complex interactions. Acute EC50 values ranged from 6.4 to 7.8 mg/L for propranolol and from 6.4 to 9.1 mg/L for fluoxetine. Chronic EC50 values ranged from 0.59 to 1.00 mg/L for propranolol and from 0.23 to 0.24 mg/L for fluoxetine. Results showed a significant antagonism between chemicals in both the acute and the chronic mixture tests when CA was adopted as the reference model, while absence of interactive effects when IA was used.

Effects of stepwise dry/wet-aging and freezing on meat quality of beef loins.

PubMed

Kim, Yuan H Brad; Meyers, Brandon; Kim, Hyun-Wook; Liceaga, Andrea M; Lemenager, Ronald P

2017-01-01

The objective of this study was to evaluate the effects of stepwise dry/wet-aging and freezing method on quality attributes of beef loins. Paired loins (M. Longissimus lumborum) from eight carcasses were assigned to either stepwise dry/wet-aging (carcass dry-aging for 10days then further wet-aging for 7days in vacuum bags) or carcass dry-aging only for 17days. Then, each loin was divided into three sections for freezing (never-frozen, blast or cryogenic freezing). Stepwise dry/wet-aged loin had lower purge/drip loss and shear force than conventionally dry-aged loin (P<0.05), but similar color and sensory characteristics (P>0.05). The cryogenic freezing resulted in a significant decrease in shear force values and a significant improvement in water-holding capacity (WHC). These findings indicate that the stepwise dry/wet-aging coupled with cryogenic freezing could provide beneficial impacts to the local meat industry by providing equivalent quality attributes as conventional dry-aging and improving WHC of frozen/thawed meat, while reducing the time needed for dry-aging. Copyright © 2016 Elsevier Ltd. All rights reserved.

Hysteresis Analysis Based on the Ferroelectric Effect in Hybrid Perovskite Solar Cells.

PubMed

Wei, Jing; Zhao, Yicheng; Li, Heng; Li, Guobao; Pan, Jinlong; Xu, Dongsheng; Zhao, Qing; Yu, Dapeng

2014-11-06

The power conversion efficiency (PCE) of CH3NH3PbX3 (X = I, Br, Cl) perovskite solar cells has been developed rapidly from 6.5 to 18% within 3 years. However, the anomalous hysteresis found in I-V measurements can cause an inaccurate estimation of the efficiency. We attribute the phenomena to the ferroelectric effect and build a model based on the ferroelectric diode to explain it. The ferroelectric effect of CH3NH3PbI3-xClx is strongly suggested by characterization methods and the E-P (electrical field-polarization) loop. The hysteresis in I-V curves is found to greatly depend on the scan range as well as the velocity, which is well explained by the ferroelectric diode model. We also find that the current signals show exponential decay in ∼10 s under prolonged stepwise measurements, and the anomalous hysteresis disappears using these stabilized current values. The experimental results accord well with the model based on ferroelectric properties and prove that prolonged stepwise measurement is an effective way to evaluate the real efficiency of perovskite solar cells. Most importantly, this work provides a meaningful perspective that the ferroelectric effect (if it really exists) should be paid special attention in the optimization of perovskite solar cells.

Modeling and optimization of Look-Locker spin labeling for measuring perfusion and transit time changes in activation studies taking into account arterial blood volume.

PubMed

Francis, S T; Bowtell, R; Gowland, P A

2008-02-01

This work describes a new compartmental model with step-wise temporal analysis for a Look-Locker (LL)-flow-sensitive alternating inversion-recovery (FAIR) sequence, which combines the FAIR arterial spin labeling (ASL) scheme with a LL echo planar imaging (EPI) measurement, using a multireadout EPI sequence for simultaneous perfusion and T*(2) measurements. The new model highlights the importance of accounting for the transit time of blood through the arteriolar compartment, delta, in the quantification of perfusion. The signal expected is calculated in a step-wise manner to avoid discontinuities between different compartments. The optimal LL-FAIR pulse sequence timings for the measurement of perfusion with high signal-to-noise ratio (SNR), and high temporal resolution at 1.5, 3, and 7T are presented. LL-FAIR is shown to provide better SNR per unit time compared to standard FAIR. The sequence has been used experimentally for simultaneous monitoring of perfusion, transit time, and T*(2) changes in response to a visual stimulus in four subjects. It was found that perfusion increased by 83 +/- 4% on brain activation from a resting state value of 94 +/- 13 ml/100 g/min, while T*(2) increased by 3.5 +/- 0.5%. (c) 2008 Wiley-Liss, Inc.

A New Combined Stepwise-Based High-Order Decoupled Direct and Reduced-Form Method To Improve Uncertainty Analysis in PM2.5 Simulations.

PubMed

Huang, Zhijiong; Hu, Yongtao; Zheng, Junyu; Yuan, Zibing; Russell, Armistead G; Ou, Jiamin; Zhong, Zhuangmin

2017-04-04

The traditional reduced-form model (RFM) based on the high-order decoupled direct method (HDDM), is an efficient uncertainty analysis approach for air quality models, but it has large biases in uncertainty propagation due to the limitation of the HDDM in predicting nonlinear responses to large perturbations of model inputs. To overcome the limitation, a new stepwise-based RFM method that combines several sets of local sensitive coefficients under different conditions is proposed. Evaluations reveal that the new RFM improves the prediction of nonlinear responses. The new method is applied to quantify uncertainties in simulated PM 2.5 concentrations in the Pearl River Delta (PRD) region of China as a case study. Results show that the average uncertainty range of hourly PM 2.5 concentrations is -28% to 57%, which can cover approximately 70% of the observed PM 2.5 concentrations, while the traditional RFM underestimates the upper bound of the uncertainty range by 1-6%. Using a variance-based method, the PM 2.5 boundary conditions and primary PM 2.5 emissions are found to be the two major uncertainty sources in PM 2.5 simulations. The new RFM better quantifies the uncertainty range in model simulations and can be applied to improve applications that rely on uncertainty information.

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.

Modelling of the AGS using Zgoubi - Status

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

Meot F.; Ahrens, L.; Dutheil, Y.

2012-05-20

This paper summarizes the progress achieved so far, and discusses various outcomes, regarding the development of a model of the Alternating Gradient Synchrotron at the RHIC collider. The model, based on stepwise ray-tracing methods, includes beam and polarization dynamics. This is an on-going work, and a follow-on of code developments and particle and spin dynamics simulations that have been subject to earlier publications at IPAC and PAC [1, 2, 3]. A companion paper [4] gives additional informations, regarding the use of the measured magnetic field maps of the AGS main magnets.

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.

Topographic Controls on Soil Carbon Distribution in Iowa Croplands, USA

NASA Astrophysics Data System (ADS)

McCarty, Greg; Li, Xia

2017-04-01

Topography is a key factor affecting soil organic carbon (SOC) redistribution (erosion or deposition) because it influences several hydrological indices including soil moisture dynamics, runoff velocity and acceleration, and flow divergence and convergence. In this study, we examined the relationship between 15 topographic metrics derived from Light Detection and Ranging (Lidar) data and SOC redistribution in agricultural fields. We adopted the fallout 137Cesium (137Cs) technique to estimate SOC redistribution rates across 560 sampling plots in Iowa. Then, using stepwise ordinarily least square regression (SOLSR) and stepwise principle component analysis (SPCA), topography-based SOC models were developed to simulate spatial patterns of SOC content and redistribution. Results suggested that erosion and deposition of topsoil SOC were regulated by topography with SOC gain in lowland areas and SOC loss in sloping areas. Topographic wetness index (TWI) and slope were the most influential variables controlling SOC content and redistribution. The topography-based models exhibited good performances in simulating SOC content and redistribution across two crop sites with intensive samplings. SPCA models had slightly lower coefficients of determination and Nash-Sutcliffe efficiency values compared to SOLSR models at the field scale. However, significantly SPCA outperformed SOLAR in predicting SOC redistribution patterns at the watershed scale. Results of this study suggest that the topography-based SPCA model was more robust for scaling up models to the watershed scale because SPCA models may better represent the landscapes and are less subject to over fitting. This work suggests an improved method to sample and characterize landscapes for better prediction of soil property distribution.

Predicting Speech Intelligibility Decline in Amyotrophic Lateral Sclerosis Based on the Deterioration of Individual Speech Subsystems

PubMed Central

Yunusova, Yana; Wang, Jun; Zinman, Lorne; Pattee, Gary L.; Berry, James D.; Perry, Bridget; Green, Jordan R.

2016-01-01

Purpose To determine the mechanisms of speech intelligibility impairment due to neurologic impairments, intelligibility decline was modeled as a function of co-occurring changes in the articulatory, resonatory, phonatory, and respiratory subsystems. Method Sixty-six individuals diagnosed with amyotrophic lateral sclerosis (ALS) were studied longitudinally. The disease-related changes in articulatory, resonatory, phonatory, and respiratory subsystems were quantified using multiple instrumental measures, which were subjected to a principal component analysis and mixed effects models to derive a set of speech subsystem predictors. A stepwise approach was used to select the best set of subsystem predictors to model the overall decline in intelligibility. Results Intelligibility was modeled as a function of five predictors that corresponded to velocities of lip and jaw movements (articulatory), number of syllable repetitions in the alternating motion rate task (articulatory), nasal airflow (resonatory), maximum fundamental frequency (phonatory), and speech pauses (respiratory). The model accounted for 95.6% of the variance in intelligibility, among which the articulatory predictors showed the most substantial independent contribution (57.7%). Conclusion Articulatory impairments characterized by reduced velocities of lip and jaw movements and resonatory impairments characterized by increased nasal airflow served as the subsystem predictors of the longitudinal decline of speech intelligibility in ALS. Declines in maximum performance tasks such as the alternating motion rate preceded declines in intelligibility, thus serving as early predictors of bulbar dysfunction. Following the rapid decline in speech intelligibility, a precipitous decline in maximum performance tasks subsequently occurred. PMID:27148967

Three-dimensional reconstruction of teeth and jaws based on segmentation of CT images using watershed transformation.

PubMed

Naumovich, S S; Naumovich, S A; Goncharenko, V G

2015-01-01

The objective of the present study was the development and clinical testing of a three-dimensional (3D) reconstruction method of teeth and a bone tissue of the jaw on the basis of CT images of the maxillofacial region. 3D reconstruction was performed using the specially designed original software based on watershed transformation. Computed tomograms in digital imaging and communications in medicine format obtained on multispiral CT and CBCT scanners were used for creation of 3D models of teeth and the jaws. The processing algorithm is realized in the stepwise threshold image segmentation with the placement of markers in the mode of a multiplanar projection in areas relating to the teeth and a bone tissue. The developed software initially creates coarse 3D models of the entire dentition and the jaw. Then, certain procedures specify the model of the jaw and cut the dentition into separate teeth. The proper selection of the segmentation threshold is very important for CBCT images having a low contrast and high noise level. The developed semi-automatic algorithm of multispiral and cone beam computed tomogram processing allows 3D models of teeth to be created separating them from a bone tissue of the jaws. The software is easy to install in a dentist's workplace, has an intuitive interface and takes little time in processing. The obtained 3D models can be used for solving a wide range of scientific and clinical tasks.

Predicting Speech Intelligibility Decline in Amyotrophic Lateral Sclerosis Based on the Deterioration of Individual Speech Subsystems.

PubMed

Rong, Panying; Yunusova, Yana; Wang, Jun; Zinman, Lorne; Pattee, Gary L; Berry, James D; Perry, Bridget; Green, Jordan R

2016-01-01

To determine the mechanisms of speech intelligibility impairment due to neurologic impairments, intelligibility decline was modeled as a function of co-occurring changes in the articulatory, resonatory, phonatory, and respiratory subsystems. Sixty-six individuals diagnosed with amyotrophic lateral sclerosis (ALS) were studied longitudinally. The disease-related changes in articulatory, resonatory, phonatory, and respiratory subsystems were quantified using multiple instrumental measures, which were subjected to a principal component analysis and mixed effects models to derive a set of speech subsystem predictors. A stepwise approach was used to select the best set of subsystem predictors to model the overall decline in intelligibility. Intelligibility was modeled as a function of five predictors that corresponded to velocities of lip and jaw movements (articulatory), number of syllable repetitions in the alternating motion rate task (articulatory), nasal airflow (resonatory), maximum fundamental frequency (phonatory), and speech pauses (respiratory). The model accounted for 95.6% of the variance in intelligibility, among which the articulatory predictors showed the most substantial independent contribution (57.7%). Articulatory impairments characterized by reduced velocities of lip and jaw movements and resonatory impairments characterized by increased nasal airflow served as the subsystem predictors of the longitudinal decline of speech intelligibility in ALS. Declines in maximum performance tasks such as the alternating motion rate preceded declines in intelligibility, thus serving as early predictors of bulbar dysfunction. Following the rapid decline in speech intelligibility, a precipitous decline in maximum performance tasks subsequently occurred.

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.

Identification method of laser gyro error model under changing physical field

NASA Astrophysics Data System (ADS)

Wang, Qingqing; Niu, Zhenzhong

2018-04-01

In this paper, the influence mechanism of temperature, temperature changing rate and temperature gradient on the inertial devices is studied. The two-order model of zero bias and the three-order model of the calibration factor of lster gyro under temperature variation are deduced. The calibration scheme of temperature error is designed, and the experiment is carried out. Two methods of stepwise regression analysis and BP neural network are used to identify the parameters of the temperature error model, and the effectiveness of the two methods is proved by the temperature error compensation.

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

Impact of chemical oxidation on indigenous bacteria and mobilization of nutrients and subsequent bioremediation of crude oil-contaminated soil.

PubMed

Xu, Jinlan; Deng, Xin; Cui, Yiwei; Kong, Fanxing

2016-12-15

Fenton pre-oxidation provides nutrients to promote bioremediation. However, the effects of the indigenous bacteria that remain following Fenton oxidation on nutrient mobilization and subsequent bioremediation remain unclear. Experiments were performed with inoculation with native bacteria and foreign bacteria or without inoculation after four regimens of stepwise pre-oxidations. The effects of the indigenous bacteria remaining after stepwise oxidation on nutrient mobilization and subsequent bioremediation over 80 days were investigated. After stepwise Fenton pre-oxidation at a low H 2 O 2 concentration (225×4), the remaining indigenous bacterial populations reached their peak (4.8±0.17×10 6 CFU/g), the nutrients were mobilized rapidly, and the subsequent bioremediation of crude oil was improved (biodegradation efficiency of 35%). However, after stepwise Fenton pre-oxidation at a high H 2 O 2 concentration (450×4), only 3.6±0.16×10 3 CFU/g of indigenous bacteria remained, and the indigenous bacteria that degrade C 15 -C 30 alkanes were inhibited. The nutrient mobilization was then highly limited, and only 19% of total petroleum hydrocarbon was degraded. Furthermore, the recovery period after the low H 2 O 2 concentration stepwise Fenton pre-oxidation (225×4) was less than 20 days, which was 20-30 days shorter than with the other pre-oxidation treatments. Therefore, stepwise Fenton pre-oxidation at a low H 2 O 2 concentration protects indigenous bacterial populations and improves the nutrient mobilization and subsequent bioremediation. Copyright © 2016 Elsevier B.V. All rights reserved.

Shreddability of pizza Mozzarella cheese predicted using physicochemical properties.

PubMed

Banville, V; Morin, P; Pouliot, Y; Britten, M

2014-07-01

This study used rheological techniques such as uniaxial compression, wire cutting, and dynamic oscillatory shear to probe the physical properties of pizza Mozzarella cheeses. Predictive models were built using compositional and textural descriptors to predict cheese shreddability. Experimental cheeses were made using milk with (0.25% wt/wt) or without denatured whey protein and renneted at pH 6.5 or 6.4. The cheeses were aged for 8, 22, or 36 d and then tested at 4, 13, or 22°C for textural attributes using 11 descriptors. Adding denatured whey protein and reducing the milk renneting pH strongly affected cheese mechanical properties, but these effects were usually dependent on testing temperature. Cheeses were generally weaker as they aged. None of the compositional or rheological descriptors taken alone could predict the shredding behavior of the cheeses. Using the stepwise method, an objective selection of a few (<4) relevant descriptors made it possible to predict the production of fines (R(2)=0.82), the percentage of long shreds (R(2)=0.67), and to a lesser degree, the adhesion of cheese to the shredding blade (R(2)=0.45). The principal component analysis markedly contrasted the adhesion of cheese to the shredding blade with other shredding properties such as the production of fines or long shreds. The predictive models and principal component analysis can help manufacturers select relevant descriptors for the development of cheese with optimal mechanical behavior under shredding conditions. Copyright © 2014 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.

PubMed

Junne, Florian; Rieger, Monika; Michaelis, Martina; Nikendei, Christoph; Gündel, Harald; Zipfel, Stephan; Rothermund, Eva

2017-04-01

Psycho-mental stressors and increased perceived stress in workplace settings may determine the onset and course of stress-related mental and psychosomatic disorders. For the description of psycho-mental stressors three distinct models have widely been used in the analyses of the matter: the Demand-Control-Model by Karasek and Theorell, the Effort-Reward-Imbalance Model by Siegrist, and the Model of Organisational Justice.The interactional or social dimension in work-place settings can be seen as a cross-sectional dimension to the above mentioned models. Here, social conflicts and mobbing, as specific forms of interactional problems, are of importance.Besides measures of primary prevention which can be derived from applying the above mentioned models, attention is paid increasingly to secondary and tertiary preventive measures in work-place settings. Concepts such as the psychosomatic consultation-hour within the context of workplace showed to be effective measures for the early detection of people at risk or early stages of e. g. stress-related psychosomatic disorders.Furthermore, step-wise reintegration of members of the work-force play an important role within the effort to retain the ability to work and the workplace of individuals who suffered from stress-related mental disorders, as it has to be stressed that working and social interactions at the workplace may well be a resource that enhances and stipulates psycho-mental well-being and mental health.This CME-Article describes the above mentioned models and discusses selected perspectives of preventive measures to avoid stress-related mental disorders in members of the work-force. © Georg Thieme Verlag KG Stuttgart · New York.

Modelling road accidents: An approach using structural time series

NASA Astrophysics Data System (ADS)

Junus, Noor Wahida Md; Ismail, Mohd Tahir

2014-09-01

In this paper, the trend of road accidents in Malaysia for the years 2001 until 2012 was modelled using a structural time series approach. The structural time series model was identified using a stepwise method, and the residuals for each model were tested. The best-fitted model was chosen based on the smallest Akaike Information Criterion (AIC) and prediction error variance. In order to check the quality of the model, a data validation procedure was performed by predicting the monthly number of road accidents for the year 2012. Results indicate that the best specification of the structural time series model to represent road accidents is the local level with a seasonal model.

Transmissible cancers in an evolutionary context.

PubMed

Ujvari, Beata; Papenfuss, Anthony T; Belov, Katherine

2016-07-01

Cancer is an evolutionary and ecological process in which complex interactions between tumour cells and their environment share many similarities with organismal evolution. Tumour cells with highest adaptive potential have a selective advantage over less fit cells. Naturally occurring transmissible cancers provide an ideal model system for investigating the evolutionary arms race between cancer cells and their surrounding micro-environment and macro-environment. However, the evolutionary landscapes in which contagious cancers reside have not been subjected to comprehensive investigation. Here, we provide a multifocal analysis of transmissible tumour progression and discuss the selection forces that shape it. We demonstrate that transmissible cancers adapt to both their micro-environment and macro-environment, and evolutionary theories applied to organisms are also relevant to these unique diseases. The three naturally occurring transmissible cancers, canine transmissible venereal tumour (CTVT) and Tasmanian devil facial tumour disease (DFTD) and the recently discovered clam leukaemia, exhibit different evolutionary phases: (i) CTVT, the oldest naturally occurring cell line is remarkably stable; (ii) DFTD exhibits the signs of stepwise cancer evolution; and (iii) clam leukaemia shows genetic instability. While all three contagious cancers carry the signature of ongoing and fairly recent adaptations to selective forces, CTVT appears to have reached an evolutionary stalemate with its host, while DFTD and the clam leukaemia appear to be still at a more dynamic phase of their evolution. Parallel investigation of contagious cancer genomes and transcriptomes and of their micro-environment and macro-environment could shed light on the selective forces shaping tumour development at different time points: during the progressive phase and at the endpoint. A greater understanding of transmissible cancers from an evolutionary ecology perspective will provide novel avenues for the prevention and treatment of both contagious and non-communicable cancers. © 2016 The Authors. BioEssays published by WILEY Periodicals, Inc.

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.

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.

Role of community health workers in type 2 diabetes mellitus self-management: A scoping review.

PubMed

Egbujie, Bonaventure Amandi; Delobelle, Peter Arthur; Levitt, Naomi; Puoane, Thandi; Sanders, David; van Wyk, Brian

2018-01-01

Globally the number of people with Type 2 diabetes mellitus (T2DM) has risen significantly over the last few decades. Aligned to this is a growing use of community health workers (CHWs) to deliver T2DM self-management support with good clinical outcomes especially in High Income Countries (HIC). Evidence and lessons from these interventions can be useful for Low- and Middle-Income countries (LMICs) such as South Africa that are experiencing a marked increase in T2DM prevalence. This study aimed to examine how CHW have been utilized to support T2DM self-management globally, their preparation for and supervision to perform their functions. The review was guided by a stepwise approach outlined in the framework for scoping reviews developed by Arksey and O'Malley. Peer reviewed scientific and grey literature was searched using a string of keywords, selecting English full-text articles published between 2000 and 2015. Articles were selected using inclusion criteria, charted and content analyzed. 1008 studies were identified of which 54 full text articles were selected. Most (53) of the selected studies were in HIC and targeted mostly minority populations in low resource settings. CHWs were mostly deployed to provide education, support, and advocacy. Structured curriculum based education was the most frequently reported service provided by CHWs to support T2DM self-management. Support services included informational, emotional, appraisal and instrumental support. Models of CHW care included facility linked nurse-led CHW coordination, facility-linked CHW led coordination and standalone CHW interventions without facility interaction. CHWs play several roles in T2DM self-management, including structured education, ongoing support and health system advocacy. Preparing and coordinating CHWs for these roles is crucial and needs further research and strengthening.

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…

Illuminating Tradespace Decisions Using Efficient Experimental Space-Filling Designs for the Engineered Resilient System Architecture

DTIC Science & Technology

2015-06-30

7. Building Statistical Metamodels using Simulation Experimental Designs ............................................... 34 7.1. Statistical Design...system design drivers across several different domain models, our methodology uses statistical metamodeling to approximate the simulations’ behavior. A...output. We build metamodels using a number of statistical methods that include stepwise regression, boosted trees, neural nets, and bootstrap forest

Illuminating Tradespace Decisions Using Efficient Experimental Space-Filling Designs for the Engineered Resilient System Architecture

DTIC Science & Technology

2015-06-01

7. Building Statistical Metamodels using Simulation Experimental Designs ............................................... 34 7.1. Statistical Design...system design drivers across several different domain models, our methodology uses statistical metamodeling to approximate the simulations’ behavior. A...output. We build metamodels using a number of statistical methods that include stepwise regression, boosted trees, neural nets, and bootstrap forest

Analysis of oscillatory motion of a light airplane at high values of lift coefficient

NASA Technical Reports Server (NTRS)

Batterson, J. G.

1983-01-01

A modified stepwise regression is applied to flight data from a light research air-plane operating at high angles at attack. The well-known phenomenon referred to as buckling or porpoising is analyzed and modeled using both power series and spline expansions of the aerodynamic force and moment coefficients associated with the longitudinal equations of motion.

Moloney leukemia virus immortalizes B lymphocytes in vitro.

PubMed Central

Runnels, J; Serunian, L; Thursby, M; Rosenberg, N

1991-01-01

An in vitro culture system in which Moloney murine leukemia virus induces immortalization of mature B lymphocytes has been developed. The cell lines derived in this way are nontumorigenic, and virus production is not required to sustain them. This system provides a new in vitro model with which to study the stepwise process of transformation by retroviruses lacking oncogenes. Images PMID:1895405

Cushing's syndrome: Stepwise approach to diagnosis

PubMed Central

Lila, Anurag R.; Sarathi, Vijaya; Jagtap, Varsha S.; Bandgar, Tushar; Menon, Padmavathy; Shah, Nalini S.

2011-01-01

The projected prevalence of Cushing's syndrome (CS) inclusive of subclinical cases in the adult population ranges from 0.2–2% and it may no longer be considered as an orphan disease (2–3 cases/million/year). The recognition of CS by physicians is important for early diagnosis and treatment. Late-night salivary cortisol, dexamethasone suppressiontesti, or 24-h urine free cortisol are good screening tests. Positively screened cases need stepwise evaluation by an endocrinologist. This paper discusses the importance of screening for CS and suggests a stepwise diagnostic approach to a case of suspected hypercortisolism. PMID:22145134

Stepwise pH-responsive nanoparticles for enhanced cellular uptake and on-demand intracellular release of doxorubicin.

PubMed

Chen, Wei-Liang; Li, Fang; Tang, Yan; Yang, Shu-di; Li, Ji-Zhao; Yuan, Zhi-Qiang; Liu, Yang; Zhou, Xiao-Feng; Liu, Chun; Zhang, Xue-Nong

2017-01-01

Physicochemical properties, including particle size, zeta potential, and drug release behavior, affect targeting efficiency, cellular uptake, and antitumor effect of nanocarriers in a formulated drug-delivery system. In this study, a novel stepwise pH-responsive nanodrug delivery system was developed to efficiently deliver and significantly promote the therapeutic effect of doxorubicin (DOX). The system comprised dimethylmaleic acid-chitosan-urocanic acid and elicited stepwise responses to extracellular and intracellular pH. The nanoparticles (NPs), which possessed negative surface charge under physiological conditions and an appropriate nanosize, exhibited advantageous stability during blood circulation and enhanced accumulation in tumor sites via enhanced permeability and retention effect. The tumor cellular uptake of DOX-loaded NPs was significantly promoted by the first-step pH response, wherein surface charge reversion of NPs from negative to positive was triggered by the slightly acidic tumor extracellular environment. After internalization into tumor cells, the second-step pH response in endo/lysosome acidic environment elicited the on-demand intracellular release of DOX from NPs, thereby increasing cytotoxicity against tumor cells. Furthermore, stepwise pH-responsive NPs showed enhanced antiproliferation effect and reduced systemic side effect in vivo. Hence, the stepwise pH-responsive NPs provide a promising strategy for efficient delivery of antitumor agents.

Stepwise pH-responsive nanoparticles for enhanced cellular uptake and on-demand intracellular release of doxorubicin

PubMed Central

Chen, Wei-liang; Li, Fang; Tang, Yan; Yang, Shu-di; Li, Ji-zhao; Yuan, Zhi-qiang; Liu, Yang; Zhou, Xiao-feng; Liu, Chun; Zhang, Xue-nong

2017-01-01

Physicochemical properties, including particle size, zeta potential, and drug release behavior, affect targeting efficiency, cellular uptake, and antitumor effect of nanocarriers in a formulated drug-delivery system. In this study, a novel stepwise pH-responsive nanodrug delivery system was developed to efficiently deliver and significantly promote the therapeutic effect of doxorubicin (DOX). The system comprised dimethylmaleic acid-chitosan-urocanic acid and elicited stepwise responses to extracellular and intracellular pH. The nanoparticles (NPs), which possessed negative surface charge under physiological conditions and an appropriate nanosize, exhibited advantageous stability during blood circulation and enhanced accumulation in tumor sites via enhanced permeability and retention effect. The tumor cellular uptake of DOX-loaded NPs was significantly promoted by the first-step pH response, wherein surface charge reversion of NPs from negative to positive was triggered by the slightly acidic tumor extracellular environment. After internalization into tumor cells, the second-step pH response in endo/lysosome acidic environment elicited the on-demand intracellular release of DOX from NPs, thereby increasing cytotoxicity against tumor cells. Furthermore, stepwise pH-responsive NPs showed enhanced antiproliferation effect and reduced systemic side effect in vivo. Hence, the stepwise pH-responsive NPs provide a promising strategy for efficient delivery of antitumor agents. PMID:28652730

Inverse modelling for real-time estimation of radiological consequences in the early stage of an accidental radioactivity release.

PubMed

Pecha, Petr; Šmídl, Václav

2016-11-01

A stepwise sequential assimilation algorithm is proposed based on an optimisation approach for recursive parameter estimation and tracking of radioactive plume propagation in the early stage of a radiation accident. Predictions of the radiological situation in each time step of the plume propagation are driven by an existing short-term meteorological forecast and the assimilation procedure manipulates the model parameters to match the observations incoming concurrently from the terrain. Mathematically, the task is a typical ill-posed inverse problem of estimating the parameters of the release. The proposed method is designated as a stepwise re-estimation of the source term release dynamics and an improvement of several input model parameters. It results in a more precise determination of the adversely affected areas in the terrain. The nonlinear least-squares regression methodology is applied for estimation of the unknowns. The fast and adequately accurate segmented Gaussian plume model (SGPM) is used in the first stage of direct (forward) modelling. The subsequent inverse procedure infers (re-estimates) the values of important model parameters from the actual observations. Accuracy and sensitivity of the proposed method for real-time forecasting of the accident propagation is studied. First, a twin experiment generating noiseless simulated "artificial" observations is studied to verify the minimisation algorithm. Second, the impact of the measurement noise on the re-estimated source release rate is examined. In addition, the presented method can be used as a proposal for more advanced statistical techniques using, e.g., importance sampling. Copyright © 2016 Elsevier Ltd. All rights reserved.

Mass balance evaluation of polybrominated diphenyl ethers in landfill leachate and potential for transfer from e-waste.

PubMed

Danon-Schaffer, Monica N; Mahecha-Botero, Andrés; Grace, John R; Ikonomou, Michael

2013-09-01

Previous research on brominated flame retardants (BFRs), including polybrominated diphenyl ethers (PBDEs) has largely focussed on their concentrations in the environment and their adverse effects on human health. This paper explores their transfer from waste streams to water and soil. A comprehensive mass balance model is developed to track polybrominated diphenyl ethers (PBDEs), originating from e-waste and non-e-waste solids leaching from a landfill. Stepwise debromination is assumed to occur in three sub-systems (e-waste, aqueous leachate phase, and non-e-waste solids). Analysis of landfill samples and laboratory results from a solid-liquid contacting chamber are used to estimate model parameters to simulate an urban landfill system, for past and future scenarios. Sensitivity tests to key model parameters were conducted. Lower BDEs require more time to disappear than high-molecular weight PBDEs, since debromination takes place in a stepwise manner, according to the simplified reaction scheme. Interphase mass transfer causes the decay pattern to be similar in all three sub-systems. The aqueous phase is predicted to be the first sub-system to eliminate PBDEs if their input to the landfill were to be stopped. The non-e-waste solids would be next, followed by the e-waste sub-system. The model shows that mass transfer is not rate-limiting, but the evolution over time depends on the kinetic degradation parameters. Experimental scatter makes model testing difficult. Nevertheless, the model provides qualitative understanding of the influence of key variables. Copyright © 2013 Elsevier B.V. All rights reserved.

Interactive computer simulations of knee-replacement surgery.

PubMed

Gunther, Stephen B; Soto, Gabriel E; Colman, William W

2002-07-01

Current surgical training programs in the United States are based on an apprenticeship model. This model is outdated because it does not provide conceptual scaffolding, promote collaborative learning, or offer constructive reinforcement. Our objective was to create a more useful approach by preparing students and residents for operative cases using interactive computer simulations of surgery. Total-knee-replacement surgery (TKR) is an ideal procedure to model on the computer because there is a systematic protocol for the procedure. Also, this protocol is difficult to learn by the apprenticeship model because of the multiple instruments that must be used in a specific order. We designed an interactive computer tutorial to teach medical students and residents how to perform knee-replacement surgery. We also aimed to reinforce the specific protocol of the operative procedure. Our final goal was to provide immediate, constructive feedback. We created a computer tutorial by generating three-dimensional wire-frame models of the surgical instruments. Next, we applied a surface to the wire-frame models using three-dimensional modeling. Finally, the three-dimensional models were animated to simulate the motions of an actual TKR. The tutorial is a step-by-step tutorial that teaches and tests the correct sequence of steps in a TKR. The student or resident must select the correct instruments in the correct order. The learner is encouraged to learn the stepwise surgical protocol through repetitive use of the computer simulation. Constructive feedback is acquired through a grading system, which rates the student's or resident's ability to perform the task in the correct order. The grading system also accounts for the time required to perform the simulated procedure. We evaluated the efficacy of this teaching technique by testing medical students who learned by the computer simulation and those who learned by reading the surgical protocol manual. Both groups then performed TKR on manufactured bone models using real instruments. Their technique was graded with the standard protocol. The students who learned on the computer simulation performed the task in a shorter time and with fewer errors than the control group. They were also more engaged in the learning process. Surgical training programs generally lack a consistent approach to preoperative education related to surgical procedures. This interactive computer tutorial has allowed us to make a quantum leap in medical student and resident teaching in our orthopedic department because the students actually participate in the entire process. Our technique provides a linear, sequential method of skill acquisition and direct feedback, which is ideally suited for learning stepwise surgical protocols. Since our initial evaluation has shown the efficacy of this program, we have implemented this teaching tool into our orthopedic curriculum. Our plans for future work with this simulator include modeling procedures involving other anatomic areas of interest, such as the hip and shoulder.

Manganese-catalysed benzylic C(sp3)-H amination for late-stage functionalization

NASA Astrophysics Data System (ADS)

Clark, Joseph R.; Feng, Kaibo; Sookezian, Anasheh; White, M. Christina

2018-06-01

Reactions that directly install nitrogen into C-H bonds of complex molecules are significant because of their potential to change the chemical and biological properties of a given compound. Although selective intramolecular C-H amination reactions are known, achieving high levels of reactivity while maintaining excellent site selectivity and functional-group tolerance remains a challenge for intermolecular C-H amination. Here, we report a manganese perchlorophthalocyanine catalyst [MnIII(ClPc)] for intermolecular benzylic C-H amination of bioactive molecules and natural products that proceeds with unprecedented levels of reactivity and site selectivity. In the presence of a Brønsted or Lewis acid, the [MnIII(ClPc)]-catalysed C-H amination demonstrates unique tolerance for tertiary amine, pyridine and benzimidazole functionalities. Mechanistic studies suggest that C-H amination likely proceeds through an electrophilic metallonitrene intermediate via a stepwise pathway where C-H cleavage is the rate-determining step of the reaction. Collectively, these mechanistic features contrast with previous base-metal-catalysed C-H aminations and provide new opportunities for tunable selectivities.

Manganese-catalysed benzylic C(sp3)-H amination for late-stage functionalization.

PubMed

Clark, Joseph R; Feng, Kaibo; Sookezian, Anasheh; White, M Christina

2018-06-01

Reactions that directly install nitrogen into C-H bonds of complex molecules are significant because of their potential to change the chemical and biological properties of a given compound. Although selective intramolecular C-H amination reactions are known, achieving high levels of reactivity while maintaining excellent site selectivity and functional-group tolerance remains a challenge for intermolecular C-H amination. Here, we report a manganese perchlorophthalocyanine catalyst [MnIII(ClPc)] for intermolecular benzylic C-H amination of bioactive molecules and natural products that proceeds with unprecedented levels of reactivity and site selectivity. In the presence of a Brønsted or Lewis acid, the [MnIII(ClPc)]-catalysed C-H amination demonstrates unique tolerance for tertiary amine, pyridine and benzimidazole functionalities. Mechanistic studies suggest that C-H amination likely proceeds through an electrophilic metallonitrene intermediate via a stepwise pathway where C-H cleavage is the rate-determining step of the reaction. Collectively, these mechanistic features contrast with previous base-metal-catalysed C-H aminations and provide new opportunities for tunable selectivities.

Probability Models Based on Soil Properties for Predicting Presence-Absence of Pythium in Soybean Roots.

PubMed

Zitnick-Anderson, Kimberly K; Norland, Jack E; Del Río Mendoza, Luis E; Fortuna, Ann-Marie; Nelson, Berlin D

2017-10-01

Associations between soil properties and Pythium groups on soybean roots were investigated in 83 commercial soybean fields in North Dakota. A data set containing 2877 isolates of Pythium which included 26 known spp. and 1 unknown spp. and 13 soil properties from each field were analyzed. A Pearson correlation analysis was performed with all soil properties to observe any significant correlation between properties. Hierarchical clustering, indicator spp., and multi-response permutation procedures were used to identify groups of Pythium. Logistic regression analysis using stepwise selection was employed to calculate probability models for presence of groups based on soil properties. Three major Pythium groups were identified and three soil properties were associated with these groups. Group 1, characterized by P. ultimum, was associated with zinc levels; as zinc increased, the probability of group 1 being present increased (α = 0.05). Pythium group 2, characterized by Pythium kashmirense and an unknown Pythium sp., was associated with cation exchange capacity (CEC) (α < 0.05); as CEC increased, these spp. increased. Group 3, characterized by Pythium heterothallicum and Pythium irregulare, were associated with CEC and calcium carbonate exchange (CCE); as CCE increased and CEC decreased, these spp. increased (α = 0.05). The regression models may have value in predicting pathogenic Pythium spp. in soybean fields in North Dakota and adjacent states.

A Model For Change: An Approach for Forecasting Well-Being ...

EPA Pesticide Factsheets

Every community decision incorporates a "forecasting" strategy (whether formal or implicit) to help visualize expected results and evaluate the potential “feelings” that people living in that community may have about those results. With more communities seeking to make decisions based on sustainable alternatives, forecasting efforts that examine potential impacts of decisions on overall community well-being may prove to be valuable for not only gaging future benefits and trade-offs, but also for recognizing a community’s affective response to the outcomes of those decisions. This paper describes a forecasting approach based on concepts introduced in the development of the U.S. Environmental Protection Agency’s (US EPA) Human Well-Being Index (HWBI) (Smith, et. al. 2014; Summers et al. 2014). The approach examines the relationships among selected economic, environmental and social services that can be directly impacted by community decisions and eight domains of human well-being. Using models developed from constructed- or fixed-effect step-wise and multiple regressions and eleven years of data (2000-2010), these relationship functions may be used to characterize likely direct impacts of decisions on future well-being as well as the possible intended and unintended secondary and tertiary effects relative to any main decision effects. This paper describes an approach to using HWBI in decision making models to characterize likely impacts of decisions on fut

Ion permeability of artificial membranes evaluated by diffusion potential and electrical resistance measurements.

PubMed

Shlyonsky, Vadim

2013-12-01

In the present article, a novel model of artificial membranes that provides efficient assistance in teaching the origins of diffusion potentials is proposed. These membranes are made of polycarbonate filters fixed to 12-mm plastic rings and then saturated with a mixture of creosol and n-decane. The electrical resistance and potential difference across these membranes can be easily measured using a low-cost volt-ohm meter and home-made Ag/AgCl electrodes. The advantage of the model is the lack of ionic selectivity of the membrane, which can be modified by the introduction of different ionophores to the organic liquid mixture. A membrane treated with the mixture containing valinomycin generates voltages from -53 to -25 mV in the presence of a 10-fold KCl gradient (in to out) and from -79 to -53 mV in the presence of a bi-ionic KCl/NaCl gradient (in to out). This latter bi-ionic gradient potential reverses to a value from +9 to +20 mV when monensin is present in the organic liquid mixture. Thus, the model can be build stepwise, i.e., all factors leading to the development of diffusion potentials can be introduced sequentially, helping students to understand the quantitative relationships of ionic gradients and differential membrane permeability in the generation of cell electrical signals.

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

A conceptual framework for the collection of food products in a Total Diet Study.

PubMed

Turrini, Aida; Lombardi-Boccia, Ginevra; Aureli, Federica; Cubadda, Francesco; D'Addezio, Laura; D'Amato, Marilena; D'Evoli, Laura; Darnerud, PerOla; Devlin, Niamh; Dias, Maria Graça; Jurković, Marina; Kelleher, Cecily; Le Donne, Cinzia; López Esteban, Maite; Lucarini, Massimo; Martinez Burgos, Maria Alba; Martínez-Victoria, Emilio; McNulty, Breige; Mistura, Lorenza; Nugent, Anne; Oktay Basegmez, Hatice Imge; Oliveira, Luisa; Ozer, Hayrettin; Perelló, Gemma; Pite, Marina; Presser, Karl; Sokolić, Darja; Vasco, Elsa; Volatier, Jean-Luc

2018-02-01

A total diet study (TDS) provides representative and realistic data for assessing the dietary intake of chemicals, such as contaminants and residues, and nutrients, at a population level. Reproducing the diet through collection of customarily consumed foods and their preparation as habitually eaten is crucial to ensure representativeness, i.e., all relevant foods are included and all potential dietary sources of the substances investigated are captured. Having this in mind, a conceptual framework for building a relevant food-shopping list was developed as a research task in the European Union's 7th Framework Program project, 'Total Diet Study Exposure' (TDS-Exposure), aimed at standardising methods for food sampling, analyses, exposure assessment calculations and modelling, priority foods, and selection of chemical contaminants. A stepwise approach following the knowledge translation (KT) model for concept analysis is proposed to set up a general protocol for the collection of food products in a TDS in terms of steps (characterisation of the food list, development of the food-shopping list, food products collection) and pillars (background documentation, procedures, and tools). A simple model for structuring the information in a way to support the implementation of the process, by presenting relevant datasets, forms to store inherent information, and folders to record the results is also proposed. Reproducibility of the process and possibility to exploit the gathered information are two main features of such a system for future applications.

Causal Methods for Observational Research: A Primer.

PubMed

Almasi-Hashiani, Amir; Nedjat, Saharnaz; Mansournia, Mohammad Ali

2018-04-01

The goal of many observational studies is to estimate the causal effect of an exposure on an outcome after adjustment for confounders, but there are still some serious errors in adjusting confounders in clinical journals. Standard regression modeling (e.g., ordinary logistic regression) fails to estimate the average effect of exposure in total population in the presence of interaction between exposure and covariates, and also cannot adjust for time-varying confounding appropriately. Moreover, stepwise algorithms of the selection of confounders based on P values may miss important confounders and lead to bias in effect estimates. Causal methods overcome these limitations. We illustrate three causal methods including inverse-probability-of-treatment-weighting (IPTW) and parametric g-formula, with an emphasis on a clever combination of these 2 methods: targeted maximum likelihood estimation (TMLE) which enjoys a double-robust property against bias. © 2018 The Author(s). This is an open-access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Food Reformulation, Responsive Regulation, and “Regulatory Scaffolding”: Strengthening Performance of Salt Reduction Programs in Australia and the United Kingdom

PubMed Central

Magnusson, Roger; Reeve, Belinda

2015-01-01

Strategies to reduce excess salt consumption play an important role in preventing cardiovascular disease, which is the largest contributor to global mortality from non-communicable diseases. In many countries, voluntary food reformulation programs seek to reduce salt levels across selected product categories, guided by aspirational targets to be achieved progressively over time. This paper evaluates the industry-led salt reduction programs that operate in the United Kingdom and Australia. Drawing on theoretical concepts from the field of regulatory studies, we propose a step-wise or “responsive” approach that introduces regulatory “scaffolds” to progressively increase levels of government oversight and control in response to industry inaction or under-performance. Our model makes full use of the food industry’s willingness to reduce salt levels in products to meet reformulation targets, but recognizes that governments remain accountable for addressing major diet-related health risks. Creative regulatory strategies can assist governments to fulfill their public health obligations, including in circumstances where there are political barriers to direct, statutory regulation of the food industry. PMID:26133973

The relationship between organizational policies and practices and work limitations among hospital patient care workers.

PubMed

Sparer, Emily H; Boden, Leslie I; Sorensen, Glorian; Dennerlein, Jack T; Stoddard, Anne; Wagner, Gregory R; Nagler, Eve M; Hashimoto, Dean M; Hopcia, Karen; Sabbath, Erika L

2018-05-29

We examined relationships between organizational policies and practices (OPPs) (safety practices, ergonomic practices, and people-oriented culture) and work limitations in a sample of hospital workers. We used the 6-item Work Limitations Questionnaire (WLQ) to assess workers' perceptions of health-related work limitations. Self-reported OPPs and the WLQ were collected from workers in Boston, Massachusetts (n = 1277). We conducted random-intercept multi-level logistic regression models for each OPP using stepwise selection of covariates. As the unit-average ergonomic practice score increased by one, the odds of a worker reporting work limitations decreased by approximately 39% (P-value = 0.018), adjusted for job title, age, and body mass index. A similar relationship existed for people-oriented culture (P-value = 0.038). The association between safety practices and work limitations was similar, but not statistically significant. This study demonstrated the importance of workplace OPPs. OPPs that promote positive and supportive environments and that foster improvements in ergonomics may help reduce work limitations. © 2018 Wiley Periodicals, Inc.

Theoretical Investigations of Transition Metal Surface Energies under Lattice Strain and CO Environment

DOE PAGES

Tang, Michael T.; Ulissi, Zachary W.; Chan, Karen

2018-05-30

Here, an understanding of the relative stability of surface facets is crucial to develop predictive models of catalyst activity and to fabricate catalysts with a controlled morphology. In this work, we present a systematic density functional theory study of the effect of lattice strain and CO environment on the surface formation energies of Cu, Pt, and Ni. First, we show that both compressive and tensile lattice strains favor the formation of stepped versus low-index terraces such as (111) and (100). Then, we investigate the effect of the CO environment using configurations of CO at various coverages, determined using a greedy,more » systematic approach, inspired by forward stepwise feature selection. We find that the CO environment favors stepped facets on Ni, Cu, and Pt. These trends are illustrated with the corresponding equilibrium Wulff shapes at various strains and CO pressures. In general, the surface energies of the studied transition metals are highly sensitive to strain and CO coverage, which should be considered when rationalizing trends in the catalytic activity.« less

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.

Theoretical Investigations of Transition Metal Surface Energies under Lattice Strain and CO Environment

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

Tang, Michael T.; Ulissi, Zachary W.; Chan, Karen

Here, an understanding of the relative stability of surface facets is crucial to develop predictive models of catalyst activity and to fabricate catalysts with a controlled morphology. In this work, we present a systematic density functional theory study of the effect of lattice strain and CO environment on the surface formation energies of Cu, Pt, and Ni. First, we show that both compressive and tensile lattice strains favor the formation of stepped versus low-index terraces such as (111) and (100). Then, we investigate the effect of the CO environment using configurations of CO at various coverages, determined using a greedy,more » systematic approach, inspired by forward stepwise feature selection. We find that the CO environment favors stepped facets on Ni, Cu, and Pt. These trends are illustrated with the corresponding equilibrium Wulff shapes at various strains and CO pressures. In general, the surface energies of the studied transition metals are highly sensitive to strain and CO coverage, which should be considered when rationalizing trends in the catalytic activity.« less

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.

One-day-ahead streamflow forecasting via super-ensembles of several neural network architectures based on the Multi-Level Diversity Model

NASA Astrophysics Data System (ADS)

Brochero, Darwin; Hajji, Islem; Pina, Jasson; Plana, Queralt; Sylvain, Jean-Daniel; Vergeynst, Jenna; Anctil, Francois

2015-04-01

Theories about generalization error with ensembles are mainly based on the diversity concept, which promotes resorting to many members of different properties to support mutually agreeable decisions. Kuncheva (2004) proposed the Multi Level Diversity Model (MLDM) to promote diversity in model ensembles, combining different data subsets, input subsets, models, parameters, and including a combiner level in order to optimize the final ensemble. This work tests the hypothesis about the minimisation of the generalization error with ensembles of Neural Network (NN) structures. We used the MLDM to evaluate two different scenarios: (i) ensembles from a same NN architecture, and (ii) a super-ensemble built by a combination of sub-ensembles of many NN architectures. The time series used correspond to the 12 basins of the MOdel Parameter Estimation eXperiment (MOPEX) project that were used by Duan et al. (2006) and Vos (2013) as benchmark. Six architectures are evaluated: FeedForward NN (FFNN) trained with the Levenberg Marquardt algorithm (Hagan et al., 1996), FFNN trained with SCE (Duan et al., 1993), Recurrent NN trained with a complex method (Weins et al., 2008), Dynamic NARX NN (Leontaritis and Billings, 1985), Echo State Network (ESN), and leak integrator neuron (L-ESN) (Lukosevicius and Jaeger, 2009). Each architecture performs separately an Input Variable Selection (IVS) according to a forward stepwise selection (Anctil et al., 2009) using mean square error as objective function. Post-processing by Predictor Stepwise Selection (PSS) of the super-ensemble has been done following the method proposed by Brochero et al. (2011). IVS results showed that the lagged stream flow, lagged precipitation, and Standardized Precipitation Index (SPI) (McKee et al., 1993) were the most relevant variables. They were respectively selected as one of the firsts three selected variables in 66, 45, and 28 of the 72 scenarios. A relationship between aridity index (Arora, 2002) and NN performance showed that wet basins are more easily modelled than dry basins. Nash-Sutcliffe (NS) Efficiency criterion was used to evaluate the performance of the models. Test results showed that in 9 of the 12 basins, the mean sub-ensembles performance was better than the one presented by Vos (2013). Furthermore, in 55 of 72 cases (6 NN structures x 12 basins) the mean sub-ensemble performance was better than the best individual performance, and in 10 basins the performance of the mean super-ensemble was better than the best individual super-ensemble member. As well, it was identified that members of ESN and L-ESN sub-ensembles have very similar and good performance values. Regarding the mean super-ensemble performance, we obtained an average gain in performance of 17%, and found that PSS preserves sub-ensemble members from different NN structures, indicating the pertinence of diversity in the super-ensemble. Moreover, it was demonstrated that around 100 predictors from the different structures are enough to optimize the super-ensemble. Although sub-ensembles of FFNN-SCE showed unstable performances, FFNN-SCE members were picked-up several times in the final predictor selection. References Anctil, F., M. Filion, and J. Tournebize (2009). "A neural network experiment on the simulation of daily nitrate-nitrogen and suspended sediment fluxes from a small agricultural catchment". In: Ecol. Model. 220.6, pp. 879-887. Arora, V. K. (2002). "The use of the aridity index to assess climate change effect on annual runoff". In: J. Hydrol. 265.164, pp. 164 -177 . Brochero, D., F. Anctil, and C. Gagn'e (2011). "Simplifying a hydrological ensemble prediction system with a backward greedy selection of members Part 1: Optimization criteria". In: Hydrol. Earth Syst. Sci. 15.11, pp. 3307-3325. Duan, Q., J. Schaake, V. Andr'eassian, S. Franks, G. Goteti, H. Gupta, Y. Gusev, F. Habets, A. Hall, L. Hay, T. Hogue, M. Huang, G. Leavesley, X. Liang, O. Nasonova, J. Noilhan, L. Oudin, S. Sorooshian, T. Wagener, and E. Wood (2006). "Model Parameter Estimation Experiment (MOPEX): An overview of science strategy and major results from the second and third workshops". In: J. Hydrol. 320.12, pp. 3-17. Duan, Q., V. Gupta, and S. Sorooshian (1993). "Shuffled complex evolution approach for effective and efficient global minimization". In: J. Optimiz. Theory App. 76.3, pp. 501-521. Hagan, M. T., H. B. Demuth, and M. Beale (1996). Neural network design . 1st ed. PWS Publishing Co., p. 730. Kuncheva, L. I. (2004). Combining Pattern Classifiers: Methods and Algorithms . Wiley-Interscience, p. 350. Leontaritis, I. and S. Billings (1985). "Input-output parametric models for non-linear systems Part I: deterministic non-linear systems". In: International Journal of Control 41.2, pp. 303-328. Lukosevicius, M. and H. Jaeger (2009). "Reservoir computing approaches to recurrent neural network training". In: Computer Science Review 3.3, pp. 127-149. McKee, T., N. Doesken, and J. Kleist (1993). The Relationship of Drought Frequency and Duration to Time Scales . In: Eighth Conference on Applied Climatology. Vos, N. J. de (2013). "Echo state networks as an alternative to traditional artificial neural networks in rainfall-runoff modelling". In: Hydrol. Earth Syst. Sci. 17.1, pp. 253-267. Weins, T., R. Burton, G. Schoenau, and D. Bitner (2008). Recursive Generalized Neural Networks (RGNN) for the Modeling of a Load Sensing Pump. In: ASME Joint Conference on Fluid Power, Transmission and Control.

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.

Short-column anion-exchange chromatography for soil and peat humic substances profiling by step-wise gradient of high pH aqueous sodium ethylenediaminetetraacetate.

PubMed

Hutta, Milan; Ráczová, Janka; Góra, Róbert; Pessl, Juraj

2015-08-21

Novel anion-exchange liquid chromatographic method with step gradient of aqueous EDTA(4-) based mobile phase elution has been developed to profile available Slovak soil humic substances and alkaline extracts of various soils. The method utilize short glass column (30mm×3mm) filled in with hydrolytically stable particles (60μm diameter) Separon HEMA-BIO 1000 having (diethylamino)ethyl functional groups. Step gradient was programmed by mixing mobile phase composed of aqueous solution of sodium EDTA (pH 12.0; 5mmolL(-1)) and mobile phase constituted of aqueous solution of sodium EDTA (pH 12.0, 500mmolL(-1)). The FLD of HSs was set to excitation wavelength 480nm and emission wavelength 530nm (λem). Separation mechanism was studied by use of selected aromatic acids related to humic acids with the aid of UV spectrophotometric detection at 280nm. The proposed method benefits from high ionic strength (I=5molL(-1)) of the end mobile phase buffer and provides high recovery of humic acids (98%). Accurate and reproducible profiling of studied humic substances, alkaline extracts of various types of soils enables straightforward characterization and differentiation of HSs in arable and forest soils. Selected model aromatic acids were used for separation mechanism elucidation. Copyright © 2015 Elsevier B.V. All rights reserved.

Urology technical and non-technical skills development: the emerging role of simulation.

PubMed

Rashid, Prem; Gianduzzo, Troy R J

2016-04-01

To review the emerging role of technical and non-technical simulation in urological education and training. A review was conducted to examine the current role of simulation in urology training. A PUBMED search of the terms 'urology training', 'urology simulation' and 'urology education' revealed 11,504 titles. Three hundred and fifty-seven abstracts were identified as English language, peer reviewed papers pertaining to the role of simulation in urology and related topics. Key papers were used to explore themes. Some cross-referenced papers were also included. There is an ongoing need to ensure that training time is efficiently utilised while ensuring that optimal technical and non-technical skills are achieved. Changing working conditions and the need to minimise patient harm by inadvertent errors must be taken into account. Simulation models for specific technical aspects have been the mainstay of graduated step-wise low and high fidelity training. Whole scenario environments as well as non-technical aspects can be slowly incorporated into the curriculum. Doing so should also help define what have been challenging competencies to teach and evaluate. Dedicated time, resources and trainer up-skilling are important. Concurrent studies are needed to help evaluate the effectiveness of introducing step-wise simulation for technical and non-technical competencies. Simulation based learning remains the best avenue of progressing surgical education. Technical and non-technical simulation could be used in the selection process. There are good economic, logistic and safety reasons to pursue the process of ongoing development of simulation co-curricula. While the role of simulation is assured, its progress will depend on a structured program that takes advantage of what can be delivered via this medium. Overall, simulation can be developed further for urological training programs to encompass technical and non-technical skill development at all stages, including recertification. © 2015 The Authors BJU International © 2015 BJU International Published by John Wiley & Sons Ltd.

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.

Postoperative Delirium in Severely Burned Patients Undergoing Early Escharotomy: Incidence, Risk Factors, and Outcomes.

PubMed

Guo, Zhenggang; Liu, Jiabin; Li, Jia; Wang, Xiaoyan; Guo, Hui; Ma, Panpan; Su, Xiaojun; Li, Ping

The aim of this study is to investigate the incidence, related risk factors, and outcomes of postoperative delirium (POD) in severely burned patients undergoing early escharotomy. This study included 385 severely burned patients (injured <1 week; TBSA, 31-50% or 11-20%; American Society of Anesthesiologists physical status, II-IV) aged 18 to 65 years, who underwent early escharotomy between October 2014 and December 2015, and were selected by cluster sampling. The authors excluded patients with preoperative delirium or diagnosed dementia, depression, or cognitive dysfunction. Preoperative, perioperative, intraoperative, and postoperative information, such as demographic characteristics, vital signs, and health history were collected. The Confusion Assessment Method was used once daily for 5 days after surgery to identify POD. Stepwise binary logistic regression analysis was used to identify the risk factors for POD, t-tests, and χ tests were performed to compare the outcomes of patients with and without the condition. Fifty-six (14.55%) of the patients in the sample were diagnosed with POD. Stepwise binary logistic regression showed that the significant risk factors for POD in severely burned patients undergoing early escharotomy were advanced age (>50 years old), a history of alcohol consumption (>3/week), high American Society of Anesthesiologists classification (III or IV), time between injury and surgery (>2 days), number of previous escharotomies (>2), combined intravenous and inhalation anesthesia, no bispectral index applied, long duration surgery (>180 min), and intraoperative hypotension (mean arterial pressure < 55 mm Hg). On the basis of the different odds ratios, the authors established a weighted model. When the score of a patient's weighted odds ratios is more than 6, the incidence of POD increased significantly (P < .05). When the score of a patient's weighted odds ratios is more than 6, the incidence of POD increased significantly (P < .05). Further, POD was associated with more postoperative complications, including hepatic and renal function impairment and hypernatremia, as well as prolonged hospitalization, increased medical costs, and higher mortality.

Relating calls to US poison centers for potential exposures to medications to Centers for Disease Control and Prevention reporting of influenza-like illness.

PubMed

Beauchamp, Gillian A; McKeown, Nathanael J; Rodriguez, Sergio; Spyker, Daniel A

2016-03-01

The Centers for Disease Control (CDC) monitors influenza like illness (ILI) and the National Poison Data System (NPDS) warehouses call data uploaded by US poison centers regarding reported exposures to medication. We examined the relationship between calls to poison centers regarding reported exposures to medications commonly used to treat ILI and weekly reports of ILI. The CDC reports ILI, by age group, for each of 10 Health and Human Services (HHS) regions. We examined NPDS summary data from calls reported to poison centers regarding reported exposures to acetaminophen, cough/cold medications, and promethazine, for the same weeks, age groups, and HHS regions for influenza seasons 2000-2013. ILI and NPDS exposures were examined using graphical plots, descriptive statistics, stepwise regression analysis, and Geographic Information Systems (GIS). About 5,101,841 influenza-like illness cases were reported to the CDC, and 2,122,940 calls regarding reported exposures to medications commonly used to treat ILI, were reported by poison centers to the NPDS over the 13 flu seasons. Analysis of stepwise models of the linear untransformed data involving 24 NPDS data groups and for 60 ILI measures, over the 13 influenza seasons, demonstrated that reported exposures to medications used to treat ILI correlated with reported cases of ILI with a median R(2 )=( )0.489 (min R(2 )=( )0.248, max R(2 )=( )0.717), with mean ± SD of R(2 )=( )0.494 ± 0.121. Median number of parameters used (degrees of freedom - 1) was 7. NPDS data regarding poison center calls for selected ILI medication exposures were highly correlated with CDC ILI data. Since NPDS data are available in real time, it provides complimentary ILI monitoring. This approach may provide public health value in predicting other illnesses which are not currently as thoroughly monitored.

Recent trends of groundwater temperatures in Austria

NASA Astrophysics Data System (ADS)

Benz, Susanne A.; Bayer, Peter; Winkler, Gerfried; Blum, Philipp

2018-06-01

Climate change is one of if not the most pressing challenge modern society faces. Increasing temperatures are observed all over the planet and the impact of climate change on the hydrogeological cycle has long been shown. However, so far we have insufficient knowledge on the influence of atmospheric warming on shallow groundwater temperatures. While some studies analyse the implication climate change has for selected wells, large-scale studies are so far lacking. Here we focus on the combined impact of climate change in the atmosphere and local hydrogeological conditions on groundwater temperatures in 227 wells in Austria, which have in part been observed since 1964. A linear analysis finds a temperature change of +0.7 ± 0.8 K in the years from 1994 to 2013. In the same timeframe surface air temperatures in Austria increased by 0.5 ± 0.3 K, displaying a much smaller variety. However, most of the extreme changes in groundwater temperatures can be linked to local hydrogeological conditions. Correlation between groundwater temperatures and nearby surface air temperatures was additionally analysed. They vary greatly, with correlation coefficients of -0.3 in central Linz to 0.8 outside of Graz. In contrast, the correlation of nationwide groundwater temperatures and surface air temperatures is high, with a correlation coefficient of 0.83. All of these findings indicate that while atmospheric climate change can be observed in nationwide groundwater temperatures, individual wells are often primarily dominated by local hydrogeological conditions. In addition to the linear temperature trend, a step-wise model was also applied that identifies climate regime shifts, which were observed globally in the late 70s, 80s, and 90s. Hinting again at the influence of local conditions, at most 22 % of all wells show these climate regime shifts. However, we were able to identify an additional shift in 2007, which was observed by 37 % of all wells. Overall, the step-wise representation provides a slightly more accurate picture of observed temperatures than the linear trend.

High early cardiovascular mortality following liver transplantation

PubMed Central

VanWagner, Lisa B.; Lapin, Brittany; Levitsky, Josh; Wilkins, John T.; Abecassis, Michael M.; Skaro, Anton I.; Lloyd-Jones, Donald M.

2014-01-01

Cardiovascular disease (CVD) contributes to excess long-term mortality after liver transplantation (LT), however little is known about early post-operative CVD mortality in the current era. In addition, there is no model to predict early post-operative CVD mortality across centers. We analyzed adult recipients of primary LT in the Organ Procurement and Transplantation Network (OPTN) database between February 2002 and December 2012 to assess prevalence and predictors of early (30-day) CVD mortality, defined as death from arrhythmia, heart failure, myocardial infarction, cardiac arrest, thromboembolism, and/or stroke. We performed logistic regression with stepwise selection to develop a predictive model of early CVD mortality. Sex and center volume were forced into the final model, which was validated using bootstrapping techniques. Among 54,697 LT recipients, there were 1576 (2.9%) deaths within 30 days. CVD death was the leading cause of 30-day mortality (42.1%), followed by infection (27.9%) and graft failure (12.2%). In multivariate analysis, 9 (6 recipient, 2 donor, 1 operative) significant covariates were identified: age, pre-operative hospitalization, ICU and ventilator status, calculated MELD score, portal vein thrombosis, national organ sharing, donor BMI and cold ischemia time. The model showed moderate discrimination (c-statistic 0.66, 95% CI: 0.63–0.68). We provide the first multicenter prognostic model for the prediction of early post-LT CVD death, the most common cause of early post-LT mortality in the current transplant era. However, evaluation of additional CVD-related variables not collected by the OPTN are needed in order to improve model accuracy and potential clinical utility. PMID:25044256

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.

Radiological score for hemorrhage in the patients with portal hypertension.

PubMed

Ge, Wei; Wang, Yi; Cao, Ya-Juan; Xie, Min; Ding, Yi-Tao; Zhang, Ming; Yu, De-Cai

2015-01-01

To analyze the risk factors from radiological indices for hemorrhage in the patients with portal hypertension and weight risk factors. We retrospectively analyzed all cases of portal hypertension with hepatitis B from June 2008 to June 2014 in Nanjing Drum Tower hospital. Patients with hepatocellular carcinoma, portal vein thrombosis, or portal hypertension with other causes, such as autoimmune hepatitis, pancreatitis, or hematological diseases were excluded. Ninety-eight patients were recruited and divided into hemorrhage and non-hemorrhage groups. There were no statistical differences in clinical indexes such as age, prothrombin time, serum albumin, serum creatinine, serum sodium, hemameba, and blood platelet count. However, the differences were statistically significant in total bilirubin, hemoglobin, and liver function with the p values of 0.023, 0.000, and 0.039 respectively. For radiological indices, hemorrhage was correlated with diameter of inferior mesenteric vein (P=0.0528), posterior gastric vein (P=0.0283), and esophageal varices scores (P=0.0221). Logistic procedure was used to construct the model with stepwise selection and finally inferior mesenteric vein, posterior gastric vein, esophageal varices, and short gastric vein were enrolled into the model. These veins were scored according to the diameters and the rates of hemorrhage were increased with the score. We then validated the model with 26 patents from July 2014 to December 2014. The AUC value was 0.8849 in ROC curves for this radiological model. A risk model was constructed including inferior mesenteric vein, esophageal varices, posterior gastric vein, and short gastric vein. This radiological scoring model may be a valuable indicator for hemorrhage of portal hypertension.

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.

The systematic development of ROsafe: an intervention to promote STI testing among vocational school students.

PubMed

Wolfers, Mireille; de Zwart, Onno; Kok, Gerjo

2012-05-01

This article describes the development of ROsafe, an intervention to promote sexually transmitted infection (STI) testing at vocational schools in the Netherlands. Using the planning model of intervention mapping (IM), an educational intervention was designed that consisted of two lessons, an Internet site, and sexual health services at the school sites. IM is a stepwise approach for theory- and evidence-based development and implementation of interventions. It includes six steps: needs assessment, specification of the objectives in matrices, selection of theoretical methods and practical strategies, program design, implementation planning, and evaluation. The processes and outcomes that are performed during Steps 1 to 4 of IM are presented, that is, literature review and qualitative and quantitative research in needs assessment, leading to the definition of the desired behavioral outcomes and objectives. The matrix of change objectives for STI-testing behavior is presented, and then the development of theory into program is described, using examples from the program. Finally, the planning for implementation and evaluation is discussed. The educational intervention used methods that were derived from the social cognitive theory, the elaboration likelihood model, the persuasive communication matrix, and theories about risk communication. Strategies included short movies, discussion, knowledge quiz, and an interactive behavioral self-test through the Internet.

Estimation of abbreviated mycophenolic acid area under the concentration-time curve during early posttransplant period by limited sampling strategy.

PubMed

Mohammadpour, A-H; Nazemian, F; Abtahi, B; Naghibi, M; Gholami, K; Rezaee, S; Nazari, M-R A; Rajabi, O

2008-12-01

Area under the concentration curve (AUC) of mycophenolic acid (MPA) could help to optimize therapeutic drug monitoring during the early post-renal transplant period. The aim of this study was to develop a limited sampling strategy to estimate an abbreviated MPA AUC within the first month after renal transplantation. In this study we selected 19 patients in the early posttransplant period with normal renal graft function (glomerular filtration rate > 70 mL/min). Plasma MPA concentrations were measured using reverse-phase high-performance liquid chromatography. MPA AUC(0-12h) was calculated using the linear trapezoidal rule. Multiple stepwise regression analysis was used to determine the minimal and convenient time points of MPA levels that could be used to derive model equations best fitted to MPA AUC(0-12h). The regression equation for AUC estimation that gave the best performance was AUC = 14.46 C(10) + 15.547 (r(2) = .882). The validation of the method was performed using the jackknife method. Mean prediction error of this model was not different from zero (P > .05) and had a high root mean square prediction error (8.06). In conclusion, this limited sampling strategy provided an effective approach for therapeutic drug monitoring during the early posttransplant period.

[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.

Are we ready for Taenia solium cysticercosis elimination in sub-Saharan Africa?

PubMed

Johansen, Maria Vang; Trevisan, Chiara; Gabriël, Sarah; Magnussen, Pascal; Braae, Uffe Christian

2017-01-01

The World Health Organization announced in November 2014 at the fourth international meeting on 'the control of neglected zoonotic diseases - from advocacy to action', that intervention tools for eliminating Taenia solium taeniosis/cysticercosis (TSTC) are in place. The aim of this work was to elucidate theoretical outcomes of various control options suggested for TSTC elimination in sub-Saharan Africa (SSA) over a 4-year period. Our current knowledge regarding T. solium epidemiology and control primarily builds on studies from Latin America. A simple transmission model - built on data from Latin America - has been used to predict the effect of various interventions such as mass treatment of humans, vaccination and treatment of pigs, and health education of communities, potentially leading to change in bad practices and reducing transmission risks. Based on simulations of the transmission model, even a 4-year integrated One Health approach fails to eliminate TSTC from a small community and in all simulations, the prevalence of human taeniosis and porcine cysticercosis start to rise as soon as the programmes end. Our current knowledge regarding transmission and burden of TSTC in SSA is scarce and while claiming to be tool ready, the selection of diagnostic and surveillance tools, as well as the algorithms and stepwise approaches for control and elimination of TSTC remain major challenges.

Genetically fluorescent melanoma bone and organ metastasis models.

PubMed

Yang, M; Jiang, P; An, Z; Baranov, E; Li, L; Hasegawa, S; Al-Tuwaijri, M; Chishima, T; Shimada, H; Moossa, A R; Hoffman, R M

1999-11-01

We report here the establishment and metastatic properties of bright, highly stable, green fluorescent protein (GFP) expression transductants of the B16 mouse malignant melanoma cell line and the LOX human melanoma line. The highly fluorescent malignant melanoma cell lines allowed the visualization of skeletal and multiorgan metastases after i.v. injection of B16 cells in C57BL/6 mice and intradermal injection of LOX cells in nude mice. The melanoma cell lines were transduced with the pLEIN expression retroviral vector containing the GFP and neomycin resistance genes. Stable B16F0 and LOX clones expressing high levels of GFP were selected stepwise in vitro in levels of G418 of up to 800 microg/ml. Extensive bone and bone marrow metastases of B16F0 were visualized by GFP expression when the animals were sacrificed 3 weeks after cell implantation. Metastases for both cell lines were visualized in many organs, including the brain, lung, pleural membrane, liver, kidney, adrenal gland, lymph nodes, skeleton, muscle, and skin by GFP fluorescence. This is the first observation of experimental skeletal metastases of melanoma, which was made possible by GFP expression. These models should facilitate future studies of the mechanism and therapy of bone and multiorgan metastasis of melanoma.

H. Pylori as a predictor of marginal ulceration: A nationwide analysis.

PubMed

Schulman, Allison R; Abougergi, Marwan S; Thompson, Christopher C

2017-03-01

Helicobacter pylori has been implicated as a risk factor for development of marginal ulceration following gastric bypass, although studies have been small and yielded conflicting results. This study sought to determine the relationship between H. pylori infection and development of marginal ulceration following bariatric surgery in a nationwide analysis. This was a retrospective cohort study using the 2012 Nationwide Inpatient Sample (NIS) database. Discharges with ICD-9-CM code indicating marginal ulceration and a secondary ICD-9-CM code for bariatric surgery were included. Primary outcome was incidence of marginal ulceration. A stepwise forward selection model was used to build the multivariate logistic regression model based on known risk factors. A P value of 0.05 was considered significant. There were 253,765 patients who met inclusion criteria. Prevalence of marginal ulceration was 3.90%. Of those patients found to have marginal ulceration, 31.20% of patients were H. pylori-positive. Final multivariate regression analysis revealed that H. pylori was the strongest independent predictor of marginal ulceration. H. pylori is an independent predictor of marginal ulceration using a large national database. Preoperative testing for and eradication of H. pylori prior to bariatric surgery may be an important preventive measure to reduce the incidence of ulcer development. © 2017 The Obesity Society.

Sulfide response analysis for sulfide control using a pS electrode in sulfate reducing bioreactors.

PubMed

Villa-Gomez, D K; Cassidy, J; Keesman, K J; Sampaio, R; Lens, P N L

2014-03-01

Step changes in the organic loading rate (OLR) through variations in the influent chemical oxygen demand (CODin) concentration or in the hydraulic retention time (HRT) at constant COD/SO4(2-) ratio (0.67) were applied to create sulfide responses for the design of a sulfide control in sulfate reducing bioreactors. The sulfide was measured using a sulfide ion selective electrode (pS) and the values obtained were used to calculate proportional-integral-derivative (PID) controller parameters. The experiments were performed in an inverse fluidized bed bioreactor with automated operation using the LabVIEW software version 2009(®). A rapid response and high sulfide increment was obtained through a stepwise increase in the CODin concentration, while a stepwise decrease to the HRT exhibited a slower response with smaller sulfide increment. Irrespective of the way the OLR was decreased, the pS response showed a time-varying behavior due to sulfide accumulation (HRT change) or utilization of substrate sources that were not accounted for (CODin change). The pS electrode response, however, showed to be informative for applications in sulfate reducing bioreactors. Nevertheless, the recorded pS values need to be corrected for pH variations and high sulfide concentrations (>200 mg/L). Copyright © 2013 Elsevier Ltd. All rights reserved.

Enzymatically enhanced collisions on ultramicroelectrodes for specific and rapid detection of individual viruses

PubMed Central

Dick, Jeffrey E.; Hilterbrand, Adam T.; Strawsine, Lauren M.; Upton, Jason W.; Bard, Allen J.

2016-01-01

We report the specific collision of a single murine cytomegalovirus (MCMV) on a platinum ultramicroelectrode (UME, radius of 1 μm). Antibody directed against the viral surface protein glycoprotein B functionalized with glucose oxidase (GOx) allowed for specific detection of the virus in solution and a biological sample (urine). The oxidation of ferrocene methanol to ferrocenium methanol was carried out at the electrode surface, and the ferrocenium methanol acted as the cosubstrate to GOx to catalyze the oxidation of glucose to gluconolactone. In the presence of glucose, the incident collision of a GOx-covered virus onto the UME while ferrocene methanol was being oxidized produced stepwise increases in current as observed by amperometry. These current increases were observed due to the feedback loop of ferrocene methanol to the surface of the electrode after GOx reduces ferrocenium methanol back to ferrocene. Negative controls (i) without glucose, (ii) with an irrelevant virus (murine gammaherpesvirus 68), and (iii) without either virus do not display these current increases. Stepwise current decreases were observed for the prior two negative controls and no discrete events were observed for the latter. We further apply this method to the detection of MCMV in urine of infected mice. The method provides for a selective, rapid, and sensitive detection technique based on electrochemical collisions. PMID:27217569

Risk and preventive factors for heat illness in radiation decontamination workers after the Fukushima Daiichi Nuclear Power Plant accident.

PubMed

Kakamu, Takeyasu; Hidaka, Tomoo; Hayakawa, Takehito; Kumagai, Tomohiro; Jinnouchi, Takanobu; Tsuji, Masayoshi; Nakano, Shinichi; Koyama, Kikuo; Fukushima, Tetsuhito

2015-01-01

The aim of this study was to reveal factors related to heat illness in radiation decontamination workers and determine effective preventive measures. A self-administered questionnaire was sent to 1,505 radiation decontamination workers. The questionnaire included age, sex, duration of decontamination work, previous occupation, education provided by employers regarding heat illness, preventive action against heat illness, and subjective symptoms of heat illness during work. We included 528 men, who replied and answered all questions, in the statistical analysis. Subjective symptoms of heat illness were categorized as "no symptoms", "Grade I" and "Grade II" according to severity. A multiple linear regression model was used to determine the factors associated with the severity of heat illness. The mean age of the subjects was 47.6 years old (standard deviation: 13.4). Of the 528 workers, 316 (59.8%) experienced heat illness symptoms (213 at Grade I and 103 at Grade II). The results of the stepwise selection revealed that age, outdoor manual labor, adequate sleep, use of a cool vest, and salt intake were selected as preventive factors, whereas living in a company dormitory or temporary housing, wearing light clothing, and consuming breakfast were selected as risk factors for heat illness. Both working conditions and living environment are associated with heat illness in radiation decontamination workers. Type of housing and sleep are also strongly related to heat illness during work. Employers should consider not only the working conditions of the employee but also the employee's daily living conditions, in order to prevent heat illness.

Utilization of the NSQIP-Pediatric Database in Development and Validation of a New Predictive Model of Pediatric Postoperative Wound Complications.

PubMed

Maizlin, Ilan I; Redden, David T; Beierle, Elizabeth A; Chen, Mike K; Russell, Robert T

2017-04-01

Surgical wound classification, introduced in 1964, stratifies the risk of surgical site infection (SSI) based on a clinical estimate of the inoculum of bacteria encountered during the procedure. Recent literature has questioned the accuracy of predicting SSI risk based on wound classification. We hypothesized that a more specific model founded on specific patient and perioperative factors would more accurately predict the risk of SSI. Using all observations from the 2012 to 2014 pediatric National Surgical Quality Improvement Program-Pediatric (NSQIP-P) Participant Use File, patients were randomized into model creation and model validation datasets. Potential perioperative predictive factors were assessed with univariate analysis for each of 4 outcomes: wound dehiscence, superficial wound infection, deep wound infection, and organ space infection. A multiple logistic regression model with a step-wise backwards elimination was performed. A receiver operating characteristic curve with c-statistic was generated to assess the model discrimination for each outcome. A total of 183,233 patients were included. All perioperative NSQIP factors were evaluated for clinical pertinence. Of the original 43 perioperative predictive factors selected, 6 to 9 predictors for each outcome were significantly associated with postoperative SSI. The predictive accuracy level of our model compared favorably with the traditional wound classification in each outcome of interest. The proposed model from NSQIP-P demonstrated a significantly improved predictive ability for postoperative SSIs than the current wound classification system. This model will allow providers to more effectively counsel families and patients of these risks, and more accurately reflect true risks for individual surgical patients to hospitals and payers. Copyright © 2017 American College of Surgeons. Published by Elsevier Inc. All rights reserved.

Integrated healthy workplace model: An experience from North Indian industry

PubMed Central

Thakur, Jarnail Singh; Bains, Puneet; Kar, Sitanshu Sekhar; Wadhwa, Sanjay; Moirangthem, Prabha; Kumar, Rajesh; Wadwalker, Sanjay; Sharma, Yashpal

2012-01-01

Background: Keeping in view of rapid industrialization and growing Indian economy, there has been a substantial increase in the workforce in India. Currently there is no organized workplace model for promoting health of industrial workers in India. Objective: To develop and implement a healthy workplace model in three industrial settings of North India. Materials and Methods: An operations research was conducted for 12 months in purposively selected three industries of Chandigarh. In phase I, a multi-stakeholder workshop was conducted to finalize the components and tools for the healthy workplace model. NCD risk factors were assessed in 947 employees in these three industries. In phase II, the healthy workplace model was implemented on pilot basis for a period of 12 months in these three industries to finalize the model. Findings: Healthy workplace committee with involvement of representatives of management, labor union and research organization was formed in three industries. Various tools like comprehensive and rapid healthy workplace assessment forms, NCD work-lite format for risk factors surveillance and monitoring and evaluation format were developed. The prevalence of tobacco use, ever alcoholics was found to be 17.8% and 47%, respectively. Around one-third (28%) of employees complained of back pain in the past 12 months. Healthy workplace model with focus on three key components (physical environment, psychosocial work environment, and promoting healthy habits) was developed, implemented on pilot basis, and finalized based on experience in participating industries. A stepwise approach for model with a core, expanded, and optional components were also suggested. An accreditation system is also required for promoting healthy workplace program. Conclusion: Integrated healthy workplace model is feasible, could be implemented in industrial setting in northern India and needs to be pilot tested in other parts of the country. PMID:23776318

Patterns of Alloy Deformation by Pulsed Pressure

NASA Astrophysics Data System (ADS)

Chebotnyagin, L. M.; Potapov, V. V.; Lopatin, V. V.

2015-06-01

Patterns of alloy deformation for optimization of a welding regime are studied by the method of modeling and deformation profiles providing high deformation quality are determined. A model of stepwise kinetics of the alloy deformation by pulsed pressure from the expanding plasma channel inside of a deformable cylinder is suggested. The model is based on the analogy between the acoustic and electromagnetic wave processes in long lines. The shock wave pattern of alloy deformation in the presence of multiple reflections of pulsed pressure waves in the gap plasma channel - cylinder wall and the influence of unloading waves from free surfaces are confirmed.

New stereoselective intramolecular

PubMed

Alajarin; Vidal; Tovar; Ramirez De Arellano MC; Cossio; Arrieta; Lecea

2000-11-03

Efficient 1,4-asymmetric induction has been achieved in the highly stereocontrolled intramolecular [2 + 2] cycloadditions between ketenimines and imines, leading to 1,2-dihydroazeto[2, 1-b]quinazolines. The chiral methine carbon adjacent to the iminic nitrogen controls the exclusive formation of the cycloadducts with relative trans configuration at C2 and C8. The stepwise mechanistic model, based on theoretical calculations, fully supports the stereochemical outcome of these cycloadditions.

A Step-Wise Approach to Elicit Triangular Distributions

NASA Technical Reports Server (NTRS)

Greenberg, Marc W.

2013-01-01

Adapt/combine known methods to demonstrate an expert judgment elicitation process that: 1.Models expert's inputs as a triangular distribution, 2.Incorporates techniques to account for expert bias and 3.Is structured in a way to help justify expert's inputs. This paper will show one way of "extracting" expert opinion for estimating purposes. Nevertheless, as with most subjective methods, there are many ways to do this.

A statistical model of expansion in a colony of black-tailed prairie dogs

Treesearch

R. P. Cincotta; Daniel W. Uresk; R. M. Hansen

1988-01-01

To predict prairie dog establishment in areas adjacent to a colony we sample: (1) VISIBILITY through the vegetation using a target, (2) POPULATION DENSITY at the cology edge, (3) DISTANCE from the edge to the potential site of settlement, and (4) % FORB COVER. Step-wise regression analysis indicated that establishment of prairie dogs in adjacent prairie was most likely...

Are the argon metastables important in high power impulse magnetron sputtering discharges?

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

Gudmundsson, J. T., E-mail: tumi@hi.is; Science Institute, University of Iceland, Dunhaga 3, IS-107 Reykjavik; Lundin, D.

2015-11-15

We use an ionization region model to explore the ionization processes in the high power impulse magnetron sputtering (HiPIMS) discharge in argon with a titanium target. In conventional dc magnetron sputtering (dcMS), stepwise ionization can be an important route for ionization of the argon gas. However, in the HiPIMS discharge stepwise ionization is found to be negligible during the breakdown phase of the HiPIMS pulse and becomes significant (but never dominating) only later in the pulse. For the sputtered species, Penning ionization can be a significant ionization mechanism in the dcMS discharges, while in the HiPIMS discharge Penning ionization ismore » always negligible as compared to electron impact ionization. The main reasons for these differences are a higher plasma density in the HiPIMS discharge, and a higher electron temperature. Furthermore, we explore the ionization fraction and the ionized flux fraction of the sputtered vapor and compare with recent experimental work.« less

Particle Engulfment and Pushing by Solidifying Interfaces. Pt. 2; Micro-Gravity Experiments and Theoretical Analysis

NASA Technical Reports Server (NTRS)

Stefanescu, Doru M.; Juretzko, Frank R.; Dhindaw, Brij K.; Catalina, Adrian; Sen, Subhayu; Curreri, Peter A.

1998-01-01

Results of the directional solidification experiments on Particle Engulfment and Pushing by Solidifying Interfaces (PEP) conducted on the space shuttle Columbia during the Life and Microgravity Science Mission are reported. Two pure aluminum (99.999%) 9 mm cylindrical rods, loaded with about 2 vol.% 500 micrometers diameter zirconia particles were melted and resolidified in the microgravity (microg) environment of the shuttle. One sample was processed at step-wise increased solidification velocity, while the other at step-wise decreased velocity. It was found that a pushing-to-engulfment transition (PET) occurred in the velocity range of 0.5 to 1 micrometers. This is smaller than the ground PET velocity of 1.9 to 2.4 micrometers. This demonstrates that natural convection increases the critical velocity. A previously proposed analytical model for PEP was further developed. A major effort to identify and produce data for the surface energy of various interfaces required for calculation was undertaken. The predicted critical velocity for PET was of 0.775 micrometers/s.

Work Done by Titin Protein Folding Assists Muscle Contraction.

PubMed

Rivas-Pardo, Jaime Andrés; Eckels, Edward C; Popa, Ionel; Kosuri, Pallav; Linke, Wolfgang A; Fernández, Julio M

2016-02-16

Current theories of muscle contraction propose that the power stroke of a myosin motor is the sole source of mechanical energy driving the sliding filaments of a contracting muscle. These models exclude titin, the largest protein in the human body, which determines the passive elasticity of muscles. Here, we show that stepwise unfolding/folding of titin immunoglobulin (Ig) domains occurs in the elastic I band region of intact myofibrils at physiological sarcomere lengths and forces of 6-8 pN. We use single-molecule techniques to demonstrate that unfolded titin Ig domains undergo a spontaneous stepwise folding contraction at forces below 10 pN, delivering up to 105 zJ of additional contractile energy, which is larger than the mechanical energy delivered by the power stroke of a myosin motor. Thus, it appears inescapable that folding of titin Ig domains is an important, but as yet unrecognized, contributor to the force generated by a contracting muscle. Copyright © 2016 The Authors. Published by Elsevier Inc. All rights reserved.

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.

Outpost in Jovian system - a stepwise long-term undertaking

NASA Astrophysics Data System (ADS)

Yasaka, Tetsuo

2003-11-01

Space has been attracting human attention since the dawn of our history, and clues thus given have triggered scientific and cultural evolutions. Now the space is in our hands. Near earth space has been developed, providing benefits to daily life. Moon and Mars will become the stage of human activity in a few decades. What will be the next logical step? The next step should be an undertaking that promises substantial influence to human history, both in knowledge and productive activities. Looking into the future directions of technology development combined with their outcome, Kyushu University selected a stepwise long-term undertaking toward establishment of an outpost in the Jovian system. Jupiter is our closest gas planet, which is a replica of the Sun. Its true understanding is essential to our knowledge of the universe. Its satellites abounds versatility providing not only the crucial knowledge of science but energy and materials vital to space activities. Jovian outpost consists of the central station on or around Callisto, controlling several laboratories on other Galilean satellites and dispatching probes to the main planet including Jovian-Crafts to cruise within its atmosphere and Deep Probes to explore the depth of the hydrogen ocean. Utilization of materials especially water on Europa will enable energy management of the stations and probes, and will further provide sound base toward exploration of the outskirts of the solar system and beyond. This understanding needs a long term endeavor that should be handed over many generations. This is a technology development program but education is an essential part of the process. The task is based on a series of short (5 year) targets. Each target provides stepwise solution to the objective, yet provides substantial outputs to the society and industries in a timely manner. The paper describes the overall program and details of the first 5 year targets.

Outpost in Jovian system—a stepwise long-term undertaking

NASA Astrophysics Data System (ADS)

Yasaka, Tetsuo

2006-10-01

Space has been attracting human attention since the dawn of our history, and clues thus given have triggered scientific and cultural evolutions. Now the space is in our hands. Near earth space has been developed, providing benefits to daily life. Moon and Mars will become the stage of human activity in a few decades. What will be the next logical step? The next step should be an undertaking that promises substantial influence to human history, both in knowledge and productive activities. Looking into the future directions of technology development combined with their outcome, Kyushu University selected a stepwise long-term undertaking toward establishment of an outpost in the Jovian system. Jupiter is our closest gas planet, which is a replica of the Sun. Its true understanding is essential to our knowledge of the universe. Its satellites abounds versatility providing not only the crucial knowledge of science but energy and materials vital to space activities. Jovian outpost consists of the central station on or around Callisto, controlling several laboratories on other Galilean satellites and dispatching probes to the main planet including Jovian-Crafts to cruise within its atmosphere and Deep Probes to explore the depth of the hydrogen ocean. Utilization of materials especially water on Europa will enable energy management of the stations and probes, and will further provide sound base toward exploration of the outskirts of the solar system and beyond. This understanding needs a long-term endeavor that should be handed over many generations. This is a technology development program but education is an essential part of the process. The task is based on a series of short (5 year) targets. Each target provides stepwise solution to the objective, yet provides substantial outputs to the society and industries in a timely manner. The paper describes the overall program and details of the first 5 year targets.

Using a Paradigm Shift to Teach Neurobiology and the Nature of Science-a C.R.E.A.T.E.-based Approach.

PubMed

Hoskins, Sally G

2008-01-01

Decades ago, classic experiments established the phenomenon of "neural induction" (Spemann and Mangold, 1924; Holtfreter, 1933). It appeared clear that amphibian ectoderm was pre-programmed to form epidermis, and that the neural phenotype was induced by a chemical signal from mesoderm. The "ectoderm makes skin, unless induced to make nervous system" model appeared in many textbooks. This interpretation, however, was not simply incorrect but 180 degrees out of alignment with the actual situation. As subsequently demonstrated, the default state of amphibian ectoderm is neuronal, and the expression of the epidermal phenotype requires cell signaling (Hemmati-Brivanlou and Melton, 1992; 1994; 1997). In this activity, students are presented with key experiments in a stepwise fashion. At several points, they work in groups to devise models that explain particular experimental results. The stepwise presentation of results mirrors the history of discoveries in this experimental system. Eventually, faced with seemingly contradictory data, students must revise their models substantially and in doing so, experience the paradigm shift. The lesson also examines the history of this paradigm shift. Data inconsistent with the "epidermal default" model were published years before the "neural default" model was proposed, but the significance of the surprising new data was underemphasized by the scientists who made the discovery. Discussing this situation provides insight into how science works and highlights the possibility that working scientists may become entrenched in prevailing paradigms. Such "nature of science" discussions emphasize research as a human activity, and help to dispel student misconceptions about science and scientists.

Enteric disease episodes and the risk of acquiring a future sexually transmitted infection: a prediction model in Montreal residents.

PubMed

Caron, Melissa; Allard, Robert; Bédard, Lucie; Latreille, Jérôme; Buckeridge, David L

2016-11-01

The sexual transmission of enteric diseases poses an important public health challenge. We aimed to build a prediction model capable of identifying individuals with a reported enteric disease who could be at risk of acquiring future sexually transmitted infections (STIs). Passive surveillance data on Montreal residents with at least 1 enteric disease report was used to construct the prediction model. Cases were defined as all subjects with at least 1 STI report following their initial enteric disease episode. A final logistic regression prediction model was chosen using forward stepwise selection. The prediction model with the greatest validity included age, sex, residential location, number of STI episodes experienced prior to the first enteric disease episode, type of enteric disease acquired, and an interaction term between age and male sex. This model had an area under the curve of 0.77 and had acceptable calibration. A coordinated public health response to the sexual transmission of enteric diseases requires that a distinction be made between cases of enteric diseases transmitted through sexual activity from those transmitted through contaminated food or water. A prediction model can aid public health officials in identifying individuals who may have a higher risk of sexually acquiring a reportable disease. Once identified, these individuals could receive specialized intervention to prevent future infection. The information produced from a prediction model capable of identifying higher risk individuals can be used to guide efforts in investigating and controlling reported cases of enteric diseases and STIs. © The Author 2016. Published by Oxford University Press on behalf of the American Medical Informatics Association. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

Population Pharmacokinetic Modeling of Diltiazem in Chinese Renal Transplant Recipients.

PubMed

Guan, Xiao-Feng; Li, Dai-Yang; Yin, Wen-Jun; Ding, Jun-Jie; Zhou, Ling-Yun; Wang, Jiang-Lin; Ma, Rong-Rong; Zuo, Xiao-Cong

2018-02-01

Diltiazem is a benzothiazepine calcium blocker and widely used in renal transplant patients since it improves the level of tacrolimus or cyclosporine A concentration. Several population pharmacokinetic (PopPK) models had been established for cyclosporine A and tacrolimus but no specific PopPK model was established for diltiazem. The aim of the study is to develop a PopPK model for diltiazem in renal transplant recipients and provide relevant pharmacokinetic parameters of diltiazem for further pharmacokinetic interaction study. Patients received tacrolimus as primary immunosuppressant agent after renal transplant and started administration of diltiazem 90 mg twice daily on 5th day. The concentration of diltiazem at 0, 0.5, 1, 2, 8, and 12 h was measured by high-performance liquid chromatography-tandem mass spectrometry (HPLC-MS/MS). Genotyping for CYP3A4*1G, CYP3A5*3, and MDR1 3435 was conducted by polymerase chain reaction-restriction fragment length polymorphism (PCR-RFLP). 25 covariates were considered in the stepwise covariate model (SCM) building procedure. One-compartment structural pharmacokinetic model with first-order absorption and elimination was used to describe the pharmacokinetic characteristics of diltiazem. Total bilirubin (TBIL) influenced apparent volume of distribution (V/F) of diltiazem in the forward selection. The absorption rate constant (K a ), V/F, and apparent oral clearance (CL/F) of the final population pharmacokinetic (PopPK) model of diltiazem were 1.96/h, 3550 L, and 92.4 L/h, respectively. A PopPK model of diltiazem is established in Chinese renal transplant recipients and it will provide relevant pharmacokinetic parameters of diltiazem for further pharmacokinetic interaction study.

Computer-aided classification of breast microcalcification clusters: merging of features from image processing and radiologists

NASA Astrophysics Data System (ADS)

Lo, Joseph Y.; Gavrielides, Marios A.; Markey, Mia K.; Jesneck, Jonathan L.

2003-05-01

We developed an ensemble classifier for the task of computer-aided diagnosis of breast microcalcification clusters,which are very challenging to characterize for radiologists and computer models alike. The purpose of this study is to help radiologists identify whether suspicious calcification clusters are benign vs. malignant, such that they may potentially recommend fewer unnecessary biopsies for actually benign lesions. The data consists of mammographic features extracted by automated image processing algorithms as well as manually interpreted by radiologists according to a standardized lexicon. We used 292 cases from a publicly available mammography database. From each cases, we extracted 22 image processing features pertaining to lesion morphology, 5 radiologist features also pertaining to morphology, and the patient age. Linear discriminant analysis (LDA) models were designed using each of the three data types. Each local model performed poorly; the best was one based upon image processing features which yielded ROC area index AZ of 0.59 +/- 0.03 and partial AZ above 90% sensitivity of 0.08 +/- 0.03. We then developed ensemble models using different combinations of those data types, and these models all improved performance compared to the local models. The final ensemble model was based upon 5 features selected by stepwise LDA from all 28 available features. This ensemble performed with AZ of 0.69 +/- 0.03 and partial AZ of 0.21 +/- 0.04, which was statistically significantly better than the model based on the image processing features alone (p<0.001 and p=0.01 for full and partial AZ respectively). This demonstrated the value of the radiologist-extracted features as a source of information for this task. It also suggested there is potential for improved performance using this ensemble classifier approach to combine different sources of currently available data.

[Discriminant analysis to predict the clinical diagnosis of primary immunodeficiencies: a preliminary report].

PubMed

Murata, Chiharu; Ramírez, Ana Belén; Ramírez, Guadalupe; Cruz, Alonso; Morales, José Luis; Lugo-Reyes, Saul Oswaldo

2015-01-01

The features in a clinical history from a patient with suspected primary immunodeficiency (PID) direct the differential diagnosis through pattern recognition. PIDs are a heterogeneous group of more than 250 congenital diseases with increased susceptibility to infection, inflammation, autoimmunity, allergy and malignancy. Linear discriminant analysis (LDA) is a multivariate supervised classification method to sort objects of study into groups by finding linear combinations of a number of variables. To identify the features that best explain membership of pediatric PID patients to a group of defect or disease. An analytic cross-sectional study was done with a pre-existing database with clinical and laboratory records from 168 patients with PID, followed at the National Institute of Pediatrics during 1991-2012, it was used to build linear discriminant models that would explain membership of each patient to the different group defects and to the most prevalent PIDs in our registry. After a preliminary run only 30 features were included (4 demographic, 10 clinical, 10 laboratory, 6 germs), with which the training models were developed through a stepwise regression algorithm. We compared the automatic feature selection with a selection made by a human expert, and then assessed the diagnostic usefulness of the resulting models (sensitivity, specificity, prediction accuracy and kappa coefficient), with 95% confidence intervals. The models incorporated 6 to 14 features to explain membership of PID patients to the five most abundant defect groups (combined, antibody, well-defined, dysregulation and phagocytosis), and to the four most prevalent PID diseases (X-linked agammaglobulinemia, chronic granulomatous disease, common variable immunodeficiency and ataxiatelangiectasia). In practically all cases of feature selection the machine outperformed the human expert. Diagnosis prediction using the equations created had a global accuracy of 83 to 94%, with sensitivity of 60 to 100%, specificity of 83 to 95% and kappa coefficient of 0.37 to 0.76. In general, the selection of features has clinical plausibility, and the practical advantage of utilizing only clinical attributes, infecting germs and routine lab results (blood cell counts and serum immunoglobulins). The performance of the model as a diagnostic tool was acceptable. The study's main limitations are a limited sample size and a lack of cross validation. This is only the first step in the construction of a machine learning system, with a wider approach that includes a larger database and different methodologies, to assist the clinical diagnosis of primary immunodeficiencies.

SEPARATION PROCESS FOR THORIUM SALTS

DOEpatents

Bridger, G.L.; Whatley, M.E.; Shaw, K.G.

1957-12-01

A process is described for the separation of uranium, thorium, and rare earths extracted from monazite by digesting with sulfuric acid. By carefully increasing the pH of the solution, stepwise, over the range 0.8 to 5.5, a series of selective precipitations will be achieved, with the thorium values coming out at lower pH, the rare earths at intermediate pH and the uranium last. Some mixed precipitates will be obtained, and these may be treated by dissolving in HNO/sub 3/ and contacting with dibutyl phosphate, whereby thorium or uranium are taken up by the organic phase while the rare earths preferentially remain in the aqueous solution.

Neuropsychology 3.0: Evidence-Based Science and Practice

PubMed Central

Bilder, Robert M.

2011-01-01

Neuropsychology is poised for transformations of its concepts and methods, leveraging advances in neuroimaging, the human genome project, psychometric theory, and information technologies. It is argued that a paradigm shift towards evidence-based science and practice can be enabled by innovations, including: (1) formal definition of neuropsychological concepts and tasks in cognitive ontologies; (2) creation of collaborative neuropsychological knowledgebases; and (3) design of web-based assessment methods that permit free development, large-sample implementation, and dynamic refinement of neuropsychological tests and the constructs these aim to assess. This article considers these opportunities, highlights selected obstacles, and offers suggestions for stepwise progress towards these goals. PMID:21092355

Polar cloud and surface classification using AVHRR imagery - An intercomparison of methods

NASA Technical Reports Server (NTRS)

Welch, R. M.; Sengupta, S. K.; Goroch, A. K.; Rabindra, P.; Rangaraj, N.; Navar, M. S.

1992-01-01

Six Advanced Very High-Resolution Radiometer local area coverage (AVHRR LAC) arctic scenes are classified into ten classes. Three different classifiers are examined: (1) the traditional stepwise discriminant analysis (SDA) method; (2) the feed-forward back-propagation (FFBP) neural network; and (3) the probabilistic neural network (PNN). More than 200 spectral and textural measures are computed. These are reduced to 20 features using sequential forward selection. Theoretical accuracy of the classifiers is determined using the bootstrap approach. Overall accuracy is 85.6 percent, 87.6 percent, and 87.0 percent for the SDA, FFBP, and PNN classifiers, respectively, with standard deviations of approximately 1 percent.

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)

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.

Checkpoints to Progression: Qualitative Analysis of the Personal and Contextual Factors That Influence Selection of Upper Extremity Reconstruction Among Patients With Tetraplegia.

PubMed

Harris, Chelsea A; Muller, John-Michael; Shauver, Melissa J; Chung, Kevin C

2017-07-01

Patients with tetraplegia consistently rank better use of the upper extremity as their top functional priority. Multiple case series have demonstrated that upper extremity reconstruction (UER) is well-tolerated and can produce substantial functional improvements for appropriate candidates; however, UER remains critically underutilized. The mechanisms that drive differences in provider practice and referral patterns have been studied, but comprehensive examination of the patient factors that influence UER decisions has not been performed for American patients. Nineteen patients with C4-8 cervical spinal injuries were selected using purposive sampling: 9 patients had undergone UER, 10 had not undergone UER. Semistructured interviews were conducted and transcripts evaluated using grounded theory methodology. Our study yielded a conceptual model that describes the characteristics common to all patients who undergo UER. Patients who selected reconstruction proceeded stepwise through a shared sequence of steps: (1) functional dissatisfaction, (2) awareness of UER, and (3) acceptance of surgery. Patients' ability to meet these criteria was determined by 3 checkpoints: how well they coped, their access to information, and the acceptability of surgery. Extremely positive or negative coping prevented patients from moving from the Coping to the Information Checkpoint; thus, they remained unaware of UER and did not undergo surgery. A lack of knowledge regarding reconstruction was the strongest barrier to surgery among our participants. We built a conceptual model that outlines how patients' personal and contextual factors drive their progression to UER. Moving from functional dissatisfaction to understanding that they were candidates for UER was a substantial barrier for participants, particularly those with very high and very low coping skills. To improve utilization for all patients, interventions are needed to increase UER awareness. Standardizing introduction to UER during the rehabilitation process or improving e-content may represent key awareness access points. Copyright © 2017 American Society for Surgery of the Hand. Published by Elsevier Inc. All rights reserved.

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.

Risk-adjusted hospital outcomes for children's surgery.

PubMed

Saito, Jacqueline M; Chen, Li Ern; Hall, Bruce L; Kraemer, Kari; Barnhart, Douglas C; Byrd, Claudia; Cohen, Mark E; Fei, Chunyuan; Heiss, Kurt F; Huffman, Kristopher; Ko, Clifford Y; Latus, Melissa; Meara, John G; Oldham, Keith T; Raval, Mehul V; Richards, Karen E; Shah, Rahul K; Sutton, Laura C; Vinocur, Charles D; Moss, R Lawrence

2013-09-01

BACKGROUND The American College of Surgeons National Surgical Quality Improvement Program-Pediatric was initiated in 2008 to drive quality improvement in children's surgery. Low mortality and morbidity in previous analyses limited differentiation of hospital performance. Participating institutions included children's units within general hospitals and free-standing children's hospitals. Cases selected by Current Procedural Terminology codes encompassed procedures within pediatric general, otolaryngologic, orthopedic, urologic, plastic, neurologic, thoracic, and gynecologic surgery. Trained personnel abstracted demographic, surgical profile, preoperative, intraoperative, and postoperative variables. Incorporating procedure-specific risk, hierarchical models for 30-day mortality and morbidities were developed with significant predictors identified by stepwise logistic regression. Reliability was estimated to assess the balance of information versus error within models. In 2011, 46 281 patients from 43 hospitals were accrued; 1467 codes were aggregated into 226 groupings. Overall mortality was 0.3%, composite morbidity 5.8%, and surgical site infection (SSI) 1.8%. Hierarchical models revealed outlier hospitals with above or below expected performance for composite morbidity in the entire cohort, pediatric abdominal subgroup, and spine subgroup; SSI in the entire cohort and pediatric abdominal subgroup; and urinary tract infection in the entire cohort. Based on reliability estimates, mortality discriminates performance poorly due to very low event rate; however, reliable model construction for composite morbidity and SSI that differentiate institutions is feasible. The National Surgical Quality Improvement Program-Pediatric expansion has yielded risk-adjusted models to differentiate hospital performance in composite and specific morbidities. However, mortality has low utility as a children's surgery performance indicator. Programmatic improvements have resulted in actionable data.

Outcome Trajectories in Extremely Preterm Infants

PubMed Central

Carlo, Waldemar A.; Tyson, Jon E.; Langer, John C.; Walsh, Michele C.; Parikh, Nehal A.; Das, Abhik; Van Meurs, Krisa P.; Shankaran, Seetha; Stoll, Barbara J.; Higgins, Rosemary D.

2012-01-01

OBJECTIVE: Methods are required to predict prognosis with changes in clinical course. Death or neurodevelopmental impairment in extremely premature neonates can be predicted at birth/admission to the ICU by considering gender, antenatal steroids, multiple birth, birth weight, and gestational age. Predictions may be improved by using additional information available later during the clinical course. Our objective was to develop serial predictions of outcome by using prognostic factors available over the course of NICU hospitalization. METHODS: Data on infants with birth weight ≤1.0 kg admitted to 18 large academic tertiary NICUs during 1998–2005 were used to develop multivariable regression models following stepwise variable selection. Models were developed by using all survivors at specific times during hospitalization (in delivery room [n = 8713], 7-day [n = 6996], 28-day [n = 6241], and 36-week postmenstrual age [n = 5118]) to predict death or death/neurodevelopmental impairment at 18 to 22 months. RESULTS: Prediction of death or neurodevelopmental impairment in extremely premature infants is improved by using information available later during the clinical course. The importance of birth weight declines, whereas the importance of respiratory illness severity increases with advancing postnatal age. The c-statistic in validation models ranged from 0.74 to 0.80 with misclassification rates ranging from 0.28 to 0.30. CONCLUSIONS: Dynamic models of the changing probability of individual outcome can improve outcome predictions in preterm infants. Various current and future scenarios can be modeled by input of different clinical possibilities to develop individual “outcome trajectories” and evaluate impact of possible morbidities on outcome. PMID:22689874

Selective anaerobic oxidation of methane enables direct synthesis of methanol.

PubMed

Sushkevich, Vitaly L; Palagin, Dennis; Ranocchiari, Marco; van Bokhoven, Jeroen A

2017-05-05

Direct functionalization of methane in natural gas remains a key challenge. We present a direct stepwise method for converting methane into methanol with high selectivity (~97%) over a copper-containing zeolite, based on partial oxidation with water. The activation in helium at 673 kelvin (K), followed by consecutive catalyst exposures to 7 bars of methane and then water at 473 K, consistently produced 0.204 mole of CH 3 OH per mole of copper in zeolite. Isotopic labeling confirmed water as the source of oxygen to regenerate the zeolite active centers and renders methanol desorption energetically favorable. On the basis of in situ x-ray absorption spectroscopy, infrared spectroscopy, and density functional theory calculations, we propose a mechanism involving methane oxidation at Cu II oxide active centers, followed by Cu I reoxidation by water with concurrent formation of hydrogen. Copyright © 2017, American Association for the Advancement of Science.

Dispersion-free radial transmission lines

DOEpatents

Caporaso, George J [Livermore, CA; Nelson, Scott D [Patterson, CA

2011-04-12

A dispersion-free radial transmission line ("DFRTL") preferably for linear accelerators, having two plane conductors each with a central hole, and an electromagnetically permeable material ("EPM") between the two conductors and surrounding a channel connecting the two holes. At least one of the material parameters of relative magnetic permeability, relative dielectric permittivity, and axial width of the EPM is varied as a function of radius, so that the characteristic impedance of the DFRTL is held substantially constant, and pulse transmission therethrough is substantially dispersion-free. Preferably, the EPM is divided into concentric radial sections, with the varied material parameters held constant in each respective section but stepwise varied between sections as a step function of the radius. The radial widths of the concentric sections are selected so that pulse traversal time across each section is the same, and the varied material parameters of the concentric sections are selected to minimize traversal error.

The hydrodeoxygenation of bioderived furans into alkanes.

PubMed

Sutton, Andrew D; Waldie, Fraser D; Wu, Ruilian; Schlaf, Marcel; Silks, Louis A Pete; Gordon, John C

2013-05-01

The conversion of biomass into fuels and chemical feedstocks is one part of a drive to reduce the world's dependence on crude oil. For transportation fuels in particular, wholesale replacement of a fuel is logistically problematic, not least because of the infrastructure that is already in place. Here, we describe the catalytic defunctionalization of a series of biomass-derived molecules to provide linear alkanes suitable for use as transportation fuels. These biomass-derived molecules contain a variety of functional groups, including olefins, furan rings and carbonyl groups. We describe the removal of these in either a stepwise process or a one-pot process using common reagents and catalysts under mild reaction conditions to provide n-alkanes in good yields and with high selectivities. Our general synthetic approach is applicable to a range of precursors with different carbon content (chain length). This allows the selective generation of linear alkanes with carbon chain lengths between eight and sixteen carbons.

The hydrodeoxygenation of bioderived furans into alkanes

NASA Astrophysics Data System (ADS)

Sutton, Andrew D.; Waldie, Fraser D.; Wu, Ruilian; Schlaf, Marcel; ‘Pete' Silks, Louis A.; Gordon, John C.

2013-05-01

The conversion of biomass into fuels and chemical feedstocks is one part of a drive to reduce the world's dependence on crude oil. For transportation fuels in particular, wholesale replacement of a fuel is logistically problematic, not least because of the infrastructure that is already in place. Here, we describe the catalytic defunctionalization of a series of biomass-derived molecules to provide linear alkanes suitable for use as transportation fuels. These biomass-derived molecules contain a variety of functional groups, including olefins, furan rings and carbonyl groups. We describe the removal of these in either a stepwise process or a one-pot process using common reagents and catalysts under mild reaction conditions to provide n-alkanes in good yields and with high selectivities. Our general synthetic approach is applicable to a range of precursors with different carbon content (chain length). This allows the selective generation of linear alkanes with carbon chain lengths between eight and sixteen carbons.

Changing selective pressure during antigenic changes in human influenza H3.

PubMed

Blackburne, Benjamin P; Hay, Alan J; Goldstein, Richard A

2008-05-02

The rapid evolution of influenza viruses presents difficulties in maintaining the optimal efficiency of vaccines. Amino acid substitutions result in antigenic drift, a process whereby antisera raised in response to one virus have reduced effectiveness against future viruses. Interestingly, while amino acid substitutions occur at a relatively constant rate, the antigenic properties of H3 move in a discontinuous, step-wise manner. It is not clear why this punctuated evolution occurs, whether this represents simply the fact that some substitutions affect these properties more than others, or if this is indicative of a changing relationship between the virus and the host. In addition, the role of changing glycosylation of the haemagglutinin in these shifts in antigenic properties is unknown. We analysed the antigenic drift of HA1 from human influenza H3 using a model of sequence change that allows for variation in selective pressure at different locations in the sequence, as well as at different parts of the phylogenetic tree. We detect significant changes in selective pressure that occur preferentially during major changes in antigenic properties. Despite the large increase in glycosylation during the past 40 years, changes in glycosylation did not correlate either with changes in antigenic properties or with significantly more rapid changes in selective pressure. The locations that undergo changes in selective pressure are largely in places undergoing adaptive evolution, in antigenic locations, and in locations or near locations undergoing substitutions that characterise the change in antigenicity of the virus. Our results suggest that the relationship of the virus to the host changes with time, with the shifts in antigenic properties representing changes in this relationship. This suggests that the virus and host immune system are evolving different methods to counter each other. While we are able to characterise the rapid increase in glycosylation of the haemagglutinin during time in human influenza H3, an increase not present in influenza in birds, this increase seems unrelated to the observed changes in antigenic properties.

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.

Population dynamics coded in DNA: genetic traces of the expansion of modern humans

NASA Astrophysics Data System (ADS)

Kimmel, Marek

1999-12-01

It has been proposed that modern humans evolved from a small ancestral population, which appeared several hundred thousand years ago in Africa. Descendants of the founder group migrated to Europe and then to Asia, not mixing with the pre-existing local populations but replacing them. Two demographic elements are present in this “out of Africa” hypothesis: numerical growth of the modern humans and their migration into Eurasia. Did these processes leave an imprint in our DNA? To address this question, we use the classical Fisher-Wright-Moran model of population genetics, assuming variable population size and two models of mutation: the infinite-sites model and the stepwise-mutation model. We use the coalescence theory, which amounts to tracing the common ancestors of contemporary genes. We obtain mathematical formulae expressing the distribution of alleles given the time changes of population size . In the framework of the infinite-sites model, simulations indicate that the pattern of past population size change leaves its signature on the pattern of DNA polymorphism. Application of the theory to the published mitochondrial DNA sequences indicates that the current mitochondrial DNA sequence variation is not inconsistent with the logistic growth of the modern human population. In the framework of the stepwise-mutation model, we demonstrate that population bottleneck followed by growth in size causes an imbalance between allele-size variance and heterozygosity. We analyze a set of data on tetranucleotide repeats which reveals the existence of this imbalance. The pattern of imbalance is consistent with the bottleneck being most ancient in Africans, most recent in Asians and intermediate in Europeans. These findings are consistent with the “out of Africa” hypothesis, although by no means do they constitute its proof.

Response time of mitochondrial oxygen consumption following stepwise changes in cardiac energy demand.

PubMed

van Beek, J H; Westerhof, N

1990-01-01

We determined the speed with which mitochondrial oxygen consumption and therefore the mitochondrial ATP-synthesis adapted to changes in metabolic demand in the rabbit heart. This was done by measuring the oxygen uptake of the whole heart during a stepwise change in heart rate and correcting for the time taken by diffusion and by convective transport in the blood vessels. Data for the correction for transport time were obtained from the response of venous oxygen concentration to a stepwise change of arterial oxygen concentration. The time constant of the response of mitochondrial oxygen consumption to a step change in heart rate was found to be 4-8 s.

Application of stepwise multiple regression techniques to inversion of Nimbus 'IRIS' observations.

NASA Technical Reports Server (NTRS)

Ohring, G.

1972-01-01

Exploratory studies with Nimbus-3 infrared interferometer-spectrometer (IRIS) data indicate that, in addition to temperature, such meteorological parameters as geopotential heights of pressure surfaces, tropopause pressure, and tropopause temperature can be inferred from the observed spectra with the use of simple regression equations. The technique of screening the IRIS spectral data by means of stepwise regression to obtain the best radiation predictors of meteorological parameters is validated. The simplicity of application of the technique and the simplicity of the derived linear regression equations - which contain only a few terms - suggest usefulness for this approach. Based upon the results obtained, suggestions are made for further development and exploitation of the stepwise regression analysis technique.

Impacts of future climate change on river discharge based on hydrological inference: A case study of the Grand River Watershed in Ontario, Canada.

PubMed

Li, Zhong; Huang, Guohe; Wang, Xiuquan; Han, Jingcheng; Fan, Yurui

2016-04-01

Over the recent years, climate change impacts have been increasingly studied at the watershed scale. However, the impact assessment is strongly dependent upon the performance of the climatic and hydrological models. This study developed a two-step method to assess climate change impacts on water resources based on the Providing Regional Climates for Impacts Studies (PRECIS) modeling system and a Hydrological Inference Model (HIM). PRECIS runs provided future temperature and precipitation projections for the watershed under the Intergovernmental Panel on Climate Change SRES A2 and B2 emission scenarios. The HIM based on stepwise cluster analysis is developed to imitate the complex nonlinear relationships between climate input variables and targeted hydrological variables. Its robust mathematical structure and flexibility in predictor selection makes it a desirable tool for fully utilizing various climate modeling outputs. Although PRECIS and HIM cannot fully cover the uncertainties in hydro-climate modeling, they could provide efficient decision support for investigating the impacts of climate change on water resources. The proposed method is applied to the Grand River Watershed in Ontario, Canada. The model performance is demonstrated with comparison to observation data from the watershed during the period 1972-2006. Future river discharge intervals that accommodate uncertainties in hydro-climatic modeling are presented and future river discharge variations are analyzed. The results indicate that even though the total annual precipitation would not change significantly in the future, the inter-annual distribution is very likely to be altered. The water availability is expected to increase in Winter while it is very likely to decrease in Summer over the Grand River Watershed, and adaptation strategies would be necessary. Copyright © 2016 Elsevier B.V. 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.