Survival Data and Regression Models

NASA Astrophysics Data System (ADS)

Grégoire, G.

2014-12-01

We start this chapter by introducing some basic elements for the analysis of censored survival data. Then we focus on right censored data and develop two types of regression models. The first one concerns the so-called accelerated failure time models (AFT), which are parametric models where a function of a parameter depends linearly on the covariables. The second one is a semiparametric model, where the covariables enter in a multiplicative form in the expression of the hazard rate function. The main statistical tool for analysing these regression models is the maximum likelihood methodology and, in spite we recall some essential results about the ML theory, we refer to the chapter "Logistic Regression" for a more detailed presentation.

Meta-regression analysis of commensal and pathogenic Escherichia coli survival in soil and water.

PubMed

Franz, Eelco; Schijven, Jack; de Roda Husman, Ana Maria; Blaak, Hetty

2014-06-17

The extent to which pathogenic and commensal E. coli (respectively PEC and CEC) can survive, and which factors predominantly determine the rate of decline, are crucial issues from a public health point of view. The goal of this study was to provide a quantitative summary of the variability in E. coli survival in soil and water over a broad range of individual studies and to identify the most important sources of variability. To that end, a meta-regression analysis on available literature data was conducted. The considerable variation in reported decline rates indicated that the persistence of E. coli is not easily predictable. The meta-analysis demonstrated that for soil and water, the type of experiment (laboratory or field), the matrix subtype (type of water and soil), and temperature were the main factors included in the regression analysis. A higher average decline rate in soil of PEC compared with CEC was observed. The regression models explained at best 57% of the variation in decline rate in soil and 41% of the variation in decline rate in water. This indicates that additional factors, not included in the current meta-regression analysis, are of importance but rarely reported. More complete reporting of experimental conditions may allow future inference on the global effects of these variables on the decline rate of E. coli.

Rapid and simultaneous analysis of five alkaloids in four parts of Coptidis Rhizoma by near-infrared spectroscopy

NASA Astrophysics Data System (ADS)

Jintao, Xue; Yufei, Liu; Liming, Ye; Chunyan, Li; Quanwei, Yang; Weiying, Wang; Yun, Jing; Minxiang, Zhang; Peng, Li

2018-01-01

Near-Infrared Spectroscopy (NIRS) was first used to develop a method for rapid and simultaneous determination of 5 active alkaloids (berberine, coptisine, palmatine, epiberberine and jatrorrhizine) in 4 parts (rhizome, fibrous root, stem and leaf) of Coptidis Rhizoma. A total of 100 samples from 4 main places of origin were collected and studied. With HPLC analysis values as calibration reference, the quantitative analysis of 5 marker components was performed by two different modeling methods, partial least-squares (PLS) regression as linear regression and artificial neural networks (ANN) as non-linear regression. The results indicated that the 2 types of models established were robust, accurate and repeatable for five active alkaloids, and the ANN models was more suitable for the determination of berberine, coptisine and palmatine while the PLS model was more suitable for the analysis of epiberberine and jatrorrhizine. The performance of the optimal models was achieved as follows: the correlation coefficient (R) for berberine, coptisine, palmatine, epiberberine and jatrorrhizine was 0.9958, 0.9956, 0.9959, 0.9963 and 0.9923, respectively; the root mean square error of validation (RMSEP) was 0.5093, 0.0578, 0.0443, 0.0563 and 0.0090, respectively. Furthermore, for the comprehensive exploitation and utilization of plant resource of Coptidis Rhizoma, the established NIR models were used to analysis the content of 5 active alkaloids in 4 parts of Coptidis Rhizoma and 4 main origin of places. This work demonstrated that NIRS may be a promising method as routine screening for off-line fast analysis or on-line quality assessment of traditional Chinese medicine (TCM).

Cenesthopathy and Subjective Cognitive Complaints: An Exploratory Study in Schizophrenia.

PubMed

Jimeno, Natalia; Vargas, Martin L

2018-01-01

Cenesthopathy is mainly associated with schizophrenia; however, its neurobiological basis is nowadays unclear. The general objective was to explore clinical correlates of cenesthopathy and subjective cognitive complaints in schizophrenia. Participants (n = 30) meeting DSM-IV criteria for psychotic disorder were recruited from a psychiatry unit and assessed with: Association for Methodology and Documentation in Psychiatry (AMDP) system, Positive and Negative Syndrome Scale, Frankfurt Complaint Questionnaire (FCQ), and the Bonn Scale for the Assessment of Basic Symptoms (BSABS). For quantitative variables, means and Spearman correlation coefficients were calculated. Linear regression following backward method and principal component analysis with varimax rotation were used. 83.3% of subjects (73.3% male, mean age, 31.5 years) presented any type of cenesthopathy; all types of cenesthetic basic symptoms were found. Cenesthetic basic symptoms significantly correlated with the AMDP category "fear and anancasm," FCQ total score, and BSABS cognitive thought disturbances. In the regression analysis only 1 predictor, cognitive thought disturbances, entered the model. In the principal component analysis, a main component which accounted for 22.69% of the variance was found. Cenesthopathy, as assessed with the Bonn Scale (BSABS), is mainly associated with cog-nitive abnormalities including disturbances of thought initiative and mental intentionality, of receptive speech, and subjective retardation or pressure of thoughts. © 2018 S. Karger AG, Basel.

Remote-sensing data processing with the multivariate regression analysis method for iron mineral resource potential mapping: a case study in the Sarvian area, central Iran

NASA Astrophysics Data System (ADS)

Mansouri, Edris; Feizi, Faranak; Jafari Rad, Alireza; Arian, Mehran

2018-03-01

This paper uses multivariate regression to create a mathematical model for iron skarn exploration in the Sarvian area, central Iran, using multivariate regression for mineral prospectivity mapping (MPM). The main target of this paper is to apply multivariate regression analysis (as an MPM method) to map iron outcrops in the northeastern part of the study area in order to discover new iron deposits in other parts of the study area. Two types of multivariate regression models using two linear equations were employed to discover new mineral deposits. This method is one of the reliable methods for processing satellite images. ASTER satellite images (14 bands) were used as unique independent variables (UIVs), and iron outcrops were mapped as dependent variables for MPM. According to the results of the probability value (p value), coefficient of determination value (R2) and adjusted determination coefficient (Radj2), the second regression model (which consistent of multiple UIVs) fitted better than other models. The accuracy of the model was confirmed by iron outcrops map and geological observation. Based on field observation, iron mineralization occurs at the contact of limestone and intrusive rocks (skarn type).

Binding affinity toward human prion protein of some anti-prion compounds - Assessment based on QSAR modeling, molecular docking and non-parametric ranking.

PubMed

Kovačević, Strahinja; Karadžić, Milica; Podunavac-Kuzmanović, Sanja; Jevrić, Lidija

2018-01-01

The present study is based on the quantitative structure-activity relationship (QSAR) analysis of binding affinity toward human prion protein (huPrP C ) of quinacrine, pyridine dicarbonitrile, diphenylthiazole and diphenyloxazole analogs applying different linear and non-linear chemometric regression techniques, including univariate linear regression, multiple linear regression, partial least squares regression and artificial neural networks. The QSAR analysis distinguished molecular lipophilicity as an important factor that contributes to the binding affinity. Principal component analysis was used in order to reveal similarities or dissimilarities among the studied compounds. The analysis of in silico absorption, distribution, metabolism, excretion and toxicity (ADMET) parameters was conducted. The ranking of the studied analogs on the basis of their ADMET parameters was done applying the sum of ranking differences, as a relatively new chemometric method. The main aim of the study was to reveal the most important molecular features whose changes lead to the changes in the binding affinities of the studied compounds. Another point of view on the binding affinity of the most promising analogs was established by application of molecular docking analysis. The results of the molecular docking were proven to be in agreement with the experimental outcome. Copyright © 2017 Elsevier B.V. All rights reserved.

Methods for estimating the magnitude and frequency of peak streamflows for unregulated streams in Oklahoma

USGS Publications Warehouse

Lewis, Jason M.

2010-01-01

Peak-streamflow regression equations were determined for estimating flows with exceedance probabilities from 50 to 0.2 percent for the state of Oklahoma. These regression equations incorporate basin characteristics to estimate peak-streamflow magnitude and frequency throughout the state by use of a generalized least squares regression analysis. The most statistically significant independent variables required to estimate peak-streamflow magnitude and frequency for unregulated streams in Oklahoma are contributing drainage area, mean-annual precipitation, and main-channel slope. The regression equations are applicable for watershed basins with drainage areas less than 2,510 square miles that are not affected by regulation. The resulting regression equations had a standard model error ranging from 31 to 46 percent. Annual-maximum peak flows observed at 231 streamflow-gaging stations through water year 2008 were used for the regression analysis. Gage peak-streamflow estimates were used from previous work unless 2008 gaging-station data were available, in which new peak-streamflow estimates were calculated. The U.S. Geological Survey StreamStats web application was used to obtain the independent variables required for the peak-streamflow regression equations. Limitations on the use of the regression equations and the reliability of regression estimates for natural unregulated streams are described. Log-Pearson Type III analysis information, basin and climate characteristics, and the peak-streamflow frequency estimates for the 231 gaging stations in and near Oklahoma are listed. Methodologies are presented to estimate peak streamflows at ungaged sites by using estimates from gaging stations on unregulated streams. For ungaged sites on urban streams and streams regulated by small floodwater retarding structures, an adjustment of the statewide regression equations for natural unregulated streams can be used to estimate peak-streamflow magnitude and frequency.

Replica analysis of overfitting in regression models for time-to-event data

NASA Astrophysics Data System (ADS)

Coolen, A. C. C.; Barrett, J. E.; Paga, P.; Perez-Vicente, C. J.

2017-09-01

Overfitting, which happens when the number of parameters in a model is too large compared to the number of data points available for determining these parameters, is a serious and growing problem in survival analysis. While modern medicine presents us with data of unprecedented dimensionality, these data cannot yet be used effectively for clinical outcome prediction. Standard error measures in maximum likelihood regression, such as p-values and z-scores, are blind to overfitting, and even for Cox’s proportional hazards model (the main tool of medical statisticians), one finds in literature only rules of thumb on the number of samples required to avoid overfitting. In this paper we present a mathematical theory of overfitting in regression models for time-to-event data, which aims to increase our quantitative understanding of the problem and provide practical tools with which to correct regression outcomes for the impact of overfitting. It is based on the replica method, a statistical mechanical technique for the analysis of heterogeneous many-variable systems that has been used successfully for several decades in physics, biology, and computer science, but not yet in medical statistics. We develop the theory initially for arbitrary regression models for time-to-event data, and verify its predictions in detail for the popular Cox model.

Evaluation of the comprehensive palatability of Japanese sake paired with dishes by multiple regression analysis based on subdomains.

PubMed

Nakamura, Ryo; Nakano, Kumiko; Tamura, Hiroyasu; Mizunuma, Masaki; Fushiki, Tohru; Hirata, Dai

2017-08-01

Many factors contribute to palatability. In order to evaluate the palatability of Japanese alcohol sake paired with certain dishes by integrating multiple factors, here we applied an evaluation method previously reported for palatability of cheese by multiple regression analysis based on 3 subdomain factors (rewarding, cultural, and informational). We asked 94 Japanese participants/subjects to evaluate the palatability of sake (1st evaluation/E1 for the first cup, 2nd/E2 and 3rd/E3 for the palatability with aftertaste/afterglow of certain dishes) and to respond to a questionnaire related to 3 subdomains. In E1, 3 factors were extracted by a factor analysis, and the subsequent multiple regression analyses indicated that the palatability of sake was interpreted by mainly the rewarding. Further, the results of attribution-dissections in E1 indicated that 2 factors (rewarding and informational) contributed to the palatability. Finally, our results indicated that the palatability of sake was influenced by the dish eaten just before drinking.

Aroma profile and sensory characteristics of a sulfur dioxide-free mulberry (Morus nigra) wine subjected to non-thermal accelerating aging techniques.

PubMed

Tchabo, William; Ma, Yongkun; Kwaw, Emmanuel; Zhang, Haining; Xiao, Lulu; Tahir, Haroon Elrasheid

2017-10-01

The present study was undertaken to assess accelerating aging effects of high pressure, ultrasound and manosonication on the aromatic profile and sensorial attributes of aged mulberry wines (AMW). A total of 166 volatile compounds were found amongst the AMW. The outcomes of the investigation were presented by means of geometric mean (GM), cluster analysis (CA), principal component analysis (PCA), partial least squares regressions (PLSR) and principal component regression (PCR). GM highlighted 24 organoleptic attributes responsible for the sensorial profile of the AMW. Moreover, CA revealed that the volatile composition of the non-thermal accelerated aged wines differs from that of the conventional aged wines. Besides, PCA discriminated the AMW on the basis of their main sensorial characteristics. Furthermore, PLSR identified 75 aroma compounds which were mainly responsible for the olfactory notes of the AMW. Finally, the overall quality of the AMW was noted to be better predicted by PLSR than PCR. Copyright © 2017 Elsevier Ltd. All rights reserved.

Time Series Analysis and Forecasting of Wastewater Inflow into Bandar Tun Razak Sewage Treatment Plant in Selangor, Malaysia

NASA Astrophysics Data System (ADS)

Abunama, Taher; Othman, Faridah

2017-06-01

Analysing the fluctuations of wastewater inflow rates in sewage treatment plants (STPs) is essential to guarantee a sufficient treatment of wastewater before discharging it to the environment. The main objectives of this study are to statistically analyze and forecast the wastewater inflow rates into the Bandar Tun Razak STP in Kuala Lumpur, Malaysia. A time series analysis of three years’ weekly influent data (156weeks) has been conducted using the Auto-Regressive Integrated Moving Average (ARIMA) model. Various combinations of ARIMA orders (p, d, q) have been tried to select the most fitted model, which was utilized to forecast the wastewater inflow rates. The linear regression analysis was applied to testify the correlation between the observed and predicted influents. ARIMA (3, 1, 3) model was selected with the highest significance R-square and lowest normalized Bayesian Information Criterion (BIC) value, and accordingly the wastewater inflow rates were forecasted to additional 52weeks. The linear regression analysis between the observed and predicted values of the wastewater inflow rates showed a positive linear correlation with a coefficient of 0.831.

Prediction of coagulation and flocculation processes using ANN models and fuzzy regression.

PubMed

Zangooei, Hossein; Delnavaz, Mohammad; Asadollahfardi, Gholamreza

2016-09-01

Coagulation and flocculation are two main processes used to integrate colloidal particles into larger particles and are two main stages of primary water treatment. Coagulation and flocculation processes are only needed when colloidal particles are a significant part of the total suspended solid fraction. Our objective was to predict turbidity of water after the coagulation and flocculation process while other parameters such as types and concentrations of coagulants, pH, and influent turbidity of raw water were known. We used a multilayer perceptron (MLP), a radial basis function (RBF) of artificial neural networks (ANNs) and various kinds of fuzzy regression analysis to predict turbidity after the coagulation and flocculation processes. The coagulant used in the pilot plant, which was located in water treatment plant, was poly aluminum chloride. We used existing data, including the type and concentrations of coagulant, pH and influent turbidity, of the raw water because these types of data were available from the pilot plant for simulation and data was collected by the Tehran water authority. The results indicated that ANNs had more ability in simulating the coagulation and flocculation process and predicting turbidity removal with different experimental data than did the fuzzy regression analysis, and may have the ability to reduce the number of jar tests, which are time-consuming and expensive. The MLP neural network proved to be the best network compared to the RBF neural network and fuzzy regression analysis in this study. The MLP neural network can predict the effluent turbidity of the coagulation and the flocculation process with a coefficient of determination (R 2 ) of 0.96 and root mean square error of 0.0106.

Spatio-temporal water quality mapping from satellite images using geographically and temporally weighted regression

NASA Astrophysics Data System (ADS)

Chu, Hone-Jay; Kong, Shish-Jeng; Chang, Chih-Hua

2018-03-01

The turbidity (TB) of a water body varies with time and space. Water quality is traditionally estimated via linear regression based on satellite images. However, estimating and mapping water quality require a spatio-temporal nonstationary model, while TB mapping necessitates the use of geographically and temporally weighted regression (GTWR) and geographically weighted regression (GWR) models, both of which are more precise than linear regression. Given the temporal nonstationary models for mapping water quality, GTWR offers the best option for estimating regional water quality. Compared with GWR, GTWR provides highly reliable information for water quality mapping, boasts a relatively high goodness of fit, improves the explanation of variance from 44% to 87%, and shows a sufficient space-time explanatory power. The seasonal patterns of TB and the main spatial patterns of TB variability can be identified using the estimated TB maps from GTWR and by conducting an empirical orthogonal function (EOF) analysis.

An evaluation of treatment strategies for head and neck cancer in an African American population.

PubMed

Ignacio, D N; Griffin, J J; Daniel, M G; Serlemitsos-Day, M T; Lombardo, F A; Alleyne, T A

2013-07-01

This study evaluated treatment strategies for head and neck cancers in a predominantly African American population. Data were collected utilizing medical records and the tumour registry at the Howard University Hospital. Kaplan-Meier method was used for survival analysis and Cox proportional hazards regression analysis predicted the hazard of death. Analysis revealed that the main treatment strategy was radiation combined with platinum for all stages except stage I. Cetuximab was employed in only 1% of cases. Kaplan-Meier analysis revealed stage II patients had poorer outcome than stage IV while Cox proportional hazard regression analysis (p = 0.4662) showed that stage I had a significantly lower hazard of death than stage IV (HR = 0.314; p = 0.0272). Contributory factors included tobacco and alcohol but body mass index (BMI) was inversely related to hazard of death. There was no difference in survival using any treatment modality for African Americans.

[Phonological characteristics and rehabilitation training of abnormal velar in children with functional articulation disorders].

PubMed

Lina, Xu; Feng, Li; Yanyun, Zhang; Nan, Gao; Mingfang, Hu

2016-12-01

To explore the phonological characteristics and rehabilitation training of abnormal velar in patients with functional articulation disorders (FAD). Eighty-seven patients with FAD were observed of the phonological characteristics of velar. Seventy-two patients with abnormal velar accepted speech training. The correlation and simple linear regression analysis were carried out on abnormal velar articulation and age. The articulation disorder of /g/ mainly showed replacement by /d/, /b/ or omission. /k/ mainly showed replacement by /d/, /t/, /g/, /p/, /b/. /h/ mainly showed replacement by /g/, /f/, /p/, /b/ or omission. The common erroneous articulation forms of /g/, /k/, /h/ were fronting of tongue and replacement by bilabial consonants. When velar combined with vowels contained /a/ and /e/, the main error was fronting of tongue. When velar combined with vowels contained /u/, the errors trended to be replacement by bilabial consonants. After 3 to 10 times of speech training, the number of erroneous words decreased to (6.24±2.61) from (40.28±6.08) before the speech training was established, the difference was statistically significant (Z=-7.379, P=0.000). The number of erroneous words was negatively correlated with age (r=-0.691, P=0.000). The result of simple linear regression analysis showed that the determination coefficient was 0.472. The articulation disorder of velar mainly shows replacement, varies with the vowels. The targeted rehabilitation training hereby established is significantly effective. Age plays an important role in the outcome of velar.

Regression modeling of ground-water flow

USGS Publications Warehouse

Cooley, R.L.; Naff, R.L.

1985-01-01

Nonlinear multiple regression methods are developed to model and analyze groundwater flow systems. Complete descriptions of regression methodology as applied to groundwater flow models allow scientists and engineers engaged in flow modeling to apply the methods to a wide range of problems. Organization of the text proceeds from an introduction that discusses the general topic of groundwater flow modeling, to a review of basic statistics necessary to properly apply regression techniques, and then to the main topic: exposition and use of linear and nonlinear regression to model groundwater flow. Statistical procedures are given to analyze and use the regression models. A number of exercises and answers are included to exercise the student on nearly all the methods that are presented for modeling and statistical analysis. Three computer programs implement the more complex methods. These three are a general two-dimensional, steady-state regression model for flow in an anisotropic, heterogeneous porous medium, a program to calculate a measure of model nonlinearity with respect to the regression parameters, and a program to analyze model errors in computed dependent variables such as hydraulic head. (USGS)

CUSUM-Logistic Regression analysis for the rapid detection of errors in clinical laboratory test results.

PubMed

Sampson, Maureen L; Gounden, Verena; van Deventer, Hendrik E; Remaley, Alan T

2016-02-01

The main drawback of the periodic analysis of quality control (QC) material is that test performance is not monitored in time periods between QC analyses, potentially leading to the reporting of faulty test results. The objective of this study was to develop a patient based QC procedure for the more timely detection of test errors. Results from a Chem-14 panel measured on the Beckman LX20 analyzer were used to develop the model. Each test result was predicted from the other 13 members of the panel by multiple regression, which resulted in correlation coefficients between the predicted and measured result of >0.7 for 8 of the 14 tests. A logistic regression model, which utilized the measured test result, the predicted test result, the day of the week and time of day, was then developed for predicting test errors. The output of the logistic regression was tallied by a daily CUSUM approach and used to predict test errors, with a fixed specificity of 90%. The mean average run length (ARL) before error detection by CUSUM-Logistic Regression (CSLR) was 20 with a mean sensitivity of 97%, which was considerably shorter than the mean ARL of 53 (sensitivity 87.5%) for a simple prediction model that only used the measured result for error detection. A CUSUM-Logistic Regression analysis of patient laboratory data can be an effective approach for the rapid and sensitive detection of clinical laboratory errors. Published by Elsevier Inc.

Tensile properties of cooked meat sausages and their correlation with texture profile analysis (TPA) parameters and physico-chemical characteristics.

PubMed

Herrero, A M; de la Hoz, L; Ordóñez, J A; Herranz, B; Romero de Ávila, M D; Cambero, M I

2008-11-01

The possibilities of using breaking strength (BS) and energy to fracture (EF) for monitoring textural properties of some cooked meat sausages (chopped, mortadella and galantines) were studied. Texture profile analysis (TPA), folding test and physico-chemical measurements were also performed. Principal component analysis enabled these meat products to be grouped into three textural profiles which showed significant (p<0.05) differences mainly for BS, hardness, adhesiveness and cohesiveness. Multivariate analysis indicated that BS, EF and TPA parameters were correlated (p<0.05) for every individual meat product (chopped, mortadella and galantines) and all products together. On the basis of these results, TPA parameters could be used for constructing regression models to predict BS. The resulting regression model for all cooked meat products was BS=-0.160+6.600∗cohesiveness-1.255∗adhesiveness+0.048∗hardness-506.31∗springiness (R(2)=0.745, p<0.00005). Simple linear regression analysis showed significant coefficients of determination between BS (R(2)=0.586, p<0.0001) versus folding test grade (FG) and EF versus FG (R(2)=0.564, p<0.0001).

Methods for scalar-on-function regression.

PubMed

Reiss, Philip T; Goldsmith, Jeff; Shang, Han Lin; Ogden, R Todd

2017-08-01

Recent years have seen an explosion of activity in the field of functional data analysis (FDA), in which curves, spectra, images, etc. are considered as basic functional data units. A central problem in FDA is how to fit regression models with scalar responses and functional data points as predictors. We review some of the main approaches to this problem, categorizing the basic model types as linear, nonlinear and nonparametric. We discuss publicly available software packages, and illustrate some of the procedures by application to a functional magnetic resonance imaging dataset.

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

ERIC Educational Resources Information Center

Kock, Tan Hoe; Yoong, Folk Jee

2011-01-01

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

Comparing the index-flood and multiple-regression methods using L-moments

NASA Astrophysics Data System (ADS)

Malekinezhad, H.; Nachtnebel, H. P.; Klik, A.

In arid and semi-arid regions, the length of records is usually too short to ensure reliable quantile estimates. Comparing index-flood and multiple-regression analyses based on L-moments was the main objective of this study. Factor analysis was applied to determine main influencing variables on flood magnitude. Ward’s cluster and L-moments approaches were applied to several sites in the Namak-Lake basin in central Iran to delineate homogeneous regions based on site characteristics. Homogeneity test was done using L-moments-based measures. Several distributions were fitted to the regional flood data and index-flood and multiple-regression methods as two regional flood frequency methods were compared. The results of factor analysis showed that length of main waterway, compactness coefficient, mean annual precipitation, and mean annual temperature were the main variables affecting flood magnitude. The study area was divided into three regions based on the Ward’s method of clustering approach. The homogeneity test based on L-moments showed that all three regions were acceptably homogeneous. Five distributions were fitted to the annual peak flood data of three homogeneous regions. Using the L-moment ratios and the Z-statistic criteria, GEV distribution was identified as the most robust distribution among five candidate distributions for all the proposed sub-regions of the study area, and in general, it was concluded that the generalised extreme value distribution was the best-fit distribution for every three regions. The relative root mean square error (RRMSE) measure was applied for evaluating the performance of the index-flood and multiple-regression methods in comparison with the curve fitting (plotting position) method. In general, index-flood method gives more reliable estimations for various flood magnitudes of different recurrence intervals. Therefore, this method should be adopted as regional flood frequency method for the study area and the Namak-Lake basin in central Iran. To estimate floods of various return periods for gauged catchments in the study area, the mean annual peak flood of the catchments may be multiplied by corresponding values of the growth factors, and computed using the GEV distribution.

Value of Construction Company and its Dependence on Significant Variables

NASA Astrophysics Data System (ADS)

Vítková, E.; Hromádka, V.; Ondrušková, E.

2017-10-01

The paper deals with the value of the construction company assessment respecting usable approaches and determinable variables. The reasons of the value of the construction company assessment are different, but the most important reasons are the sale or the purchase of the company, the liquidation of the company, the fusion of the company with another subject or the others. According the reason of the value assessment it is possible to determine theoretically different approaches for valuation, mainly it concerns about the yield method of valuation and the proprietary method of valuation. Both approaches are dependant of detailed input variables, which quality will influence the final assessment of the company´s value. The main objective of the paper is to suggest, according to the analysis, possible ways of input variables, mainly in the form of expected cash-flows or the profit, determination. The paper is focused mainly on methods of time series analysis, regression analysis and mathematical simulation utilization. As the output, the results of the analysis on the case study will be demonstrated.

Role of social support in adolescent suicidal ideation and suicide attempts.

PubMed

Miller, Adam Bryant; Esposito-Smythers, Christianne; Leichtweis, Richard N

2015-03-01

The present study examined the relative contributions of perceptions of social support from parents, close friends, and school on current suicidal ideation (SI) and suicide attempt (SA) history in a clinical sample of adolescents. Participants were 143 adolescents (64% female; 81% white; range, 12-18 years; M = 15.38; standard deviation = 1.43) admitted to a partial hospitalization program. Data were collected with well-validated assessments and a structured clinical interview. Main and interactive effects of perceptions of social support on SI were tested with linear regression. Main and interactive effects of social support on the odds of SA were tested with logistic regression. Results from the linear regression analysis revealed that perceptions of lower school support independently predicted greater severity of SI, accounting for parent and close friend support. Further, the relationship between lower perceived school support and SI was the strongest among those who perceived lower versus higher parental support. Results from the logistic regression analysis revealed that perceptions of lower parental support independently predicted SA history, accounting for school and close friend support. Further, those who perceived lower support from school and close friends reported the greatest odds of an SA history. Results address a significant gap in the social support and suicide literature by demonstrating that perceptions of parent and school support are relatively more important than peer support in understanding suicidal thoughts and history of suicidal behavior. Results suggest that improving social support across these domains may be important in suicide prevention efforts. Copyright © 2015 Society for Adolescent Health and Medicine. Published by Elsevier Inc. All rights reserved.

Symplectic geometry spectrum regression for prediction of noisy time series

NASA Astrophysics Data System (ADS)

Xie, Hong-Bo; Dokos, Socrates; Sivakumar, Bellie; Mengersen, Kerrie

2016-05-01

We present the symplectic geometry spectrum regression (SGSR) technique as well as a regularized method based on SGSR for prediction of nonlinear time series. The main tool of analysis is the symplectic geometry spectrum analysis, which decomposes a time series into the sum of a small number of independent and interpretable components. The key to successful regularization is to damp higher order symplectic geometry spectrum components. The effectiveness of SGSR and its superiority over local approximation using ordinary least squares are demonstrated through prediction of two noisy synthetic chaotic time series (Lorenz and Rössler series), and then tested for prediction of three real-world data sets (Mississippi River flow data and electromyographic and mechanomyographic signal recorded from human body).

Analysis of a Rocket Based Combined Cycle Engine during Rocket Only Operation

NASA Technical Reports Server (NTRS)

Smith, T. D.; Steffen, C. J., Jr.; Yungster, S.; Keller, D. J.

1998-01-01

The all rocket mode of operation is a critical factor in the overall performance of a rocket based combined cycle (RBCC) vehicle. However, outside of performing experiments or a full three dimensional analysis, there are no first order parametric models to estimate performance. As a result, an axisymmetric RBCC engine was used to analytically determine specific impulse efficiency values based upon both full flow and gas generator configurations. Design of experiments methodology was used to construct a test matrix and statistical regression analysis was used to build parametric models. The main parameters investigated in this study were: rocket chamber pressure, rocket exit area ratio, percent of injected secondary flow, mixer-ejector inlet area, mixer-ejector area ratio, and mixer-ejector length-to-inject diameter ratio. A perfect gas computational fluid dynamics analysis was performed to obtain values of vacuum specific impulse. Statistical regression analysis was performed based on both full flow and gas generator engine cycles. Results were also found to be dependent upon the entire cycle assumptions. The statistical regression analysis determined that there were five significant linear effects, six interactions, and one second-order effect. Two parametric models were created to provide performance assessments of an RBCC engine in the all rocket mode of operation.

Performance of an Axisymmetric Rocket Based Combined Cycle Engine During Rocket Only Operation Using Linear Regression Analysis

NASA Technical Reports Server (NTRS)

Smith, Timothy D.; Steffen, Christopher J., Jr.; Yungster, Shaye; Keller, Dennis J.

1998-01-01

The all rocket mode of operation is shown to be a critical factor in the overall performance of a rocket based combined cycle (RBCC) vehicle. An axisymmetric RBCC engine was used to determine specific impulse efficiency values based upon both full flow and gas generator configurations. Design of experiments methodology was used to construct a test matrix and multiple linear regression analysis was used to build parametric models. The main parameters investigated in this study were: rocket chamber pressure, rocket exit area ratio, injected secondary flow, mixer-ejector inlet area, mixer-ejector area ratio, and mixer-ejector length-to-inlet diameter ratio. A perfect gas computational fluid dynamics analysis, using both the Spalart-Allmaras and k-omega turbulence models, was performed with the NPARC code to obtain values of vacuum specific impulse. Results from the multiple linear regression analysis showed that for both the full flow and gas generator configurations increasing mixer-ejector area ratio and rocket area ratio increase performance, while increasing mixer-ejector inlet area ratio and mixer-ejector length-to-diameter ratio decrease performance. Increasing injected secondary flow increased performance for the gas generator analysis, but was not statistically significant for the full flow analysis. Chamber pressure was found to be not statistically significant.

Relationship of Heath and Carter's Second Component to Lean Body Mass and Height in College Women

ERIC Educational Resources Information Center

Slaughter, M. H.; And Others

1977-01-01

The Heath and Carter approach to determining somatotypes is less accurate than is regression analysis, mainly because of the lack of association between skeletal widths and lean body mass as measured by body density and whole-body fat percentage, holding constant muscle circumference. (Author)

Assessing the risk of bovine fasciolosis using linear regression analysis for the state of Rio Grande do Sul, Brazil.

PubMed

Silva, Ana Elisa Pereira; Freitas, Corina da Costa; Dutra, Luciano Vieira; Molento, Marcelo Beltrão

2016-02-15

Fasciola hepatica is the causative agent of fasciolosis, a disease that triggers a chronic inflammatory process in the liver affecting mainly ruminants and other animals including humans. In Brazil, F. hepatica occurs in larger numbers in the most Southern state of Rio Grande do Sul. The objective of this study was to estimate areas at risk using an eight-year (2002-2010) time series of climatic and environmental variables that best relate to the disease using a linear regression method to municipalities in the state of Rio Grande do Sul. The positivity index of the disease, which is the rate of infected animal per slaughtered animal, was divided into three risk classes: low, medium and high. The accuracy of the known sample classification on the confusion matrix for the low, medium and high rates produced by the estimated model presented values between 39 and 88% depending of the year. The regression analysis showed the importance of the time-based data for the construction of the model, considering the two variables of the previous year of the event (positivity index and maximum temperature). The generated data is important for epidemiological and parasite control studies mainly because F. hepatica is an infection that can last from months to years. Copyright © 2015 Elsevier B.V. All rights reserved.

The effects of precipitation, river discharge, land use and coastal circulation on water quality in coastal Maine

PubMed Central

Tilburg, Charles E.; Jordan, Linda M.; Carlson, Amy E.; Zeeman, Stephan I.; Yund, Philip O.

2015-01-01

Faecal pollution in stormwater, wastewater and direct run-off can carry zoonotic pathogens to streams, rivers and the ocean, reduce water quality, and affect both recreational and commercial fishing areas of the coastal ocean. Typically, the closure of beaches and commercial fishing areas is governed by the testing for the presence of faecal bacteria, which requires an 18–24 h period for sample incubation. As water quality can change during this testing period, the need for accurate and timely predictions of coastal water quality has become acute. In this study, we: (i) examine the relationship between water quality, precipitation and river discharge at several locations within the Gulf of Maine, and (ii) use multiple linear regression models based on readily obtainable hydrometeorological measurements to predict water quality events at five coastal locations. Analysis of a 12 year dataset revealed that high river discharge and/or precipitation events can lead to reduced water quality; however, the use of only these two parameters to predict water quality can result in a number of errors. Analysis of a higher frequency, 2 year study using multiple linear regression models revealed that precipitation, salinity, river discharge, winds, seasonality and coastal circulation correlate with variations in water quality. Although there has been extensive development of regression models for freshwater, this is one of the first attempts to create a mechanistic model to predict water quality in coastal marine waters. Model performance is similar to that of efforts in other regions, which have incorporated models into water resource managers' decisions, indicating that the use of a mechanistic model in coastal Maine is feasible. PMID:26587258

Leaf phenological characters of main tree species in urban forest of Shenyang.

PubMed

Xu, Sheng; Xu, Wenduo; Chen, Wei; He, Xingyuan; Huang, Yanqing; Wen, Hua

2014-01-01

Plant leaves, as the main photosynthetic organs and the high energy converters among primary producers in terrestrial ecosystems, have attracted significant research attention. Leaf lifespan is an adaptive characteristic formed by plants to obtain the maximum carbon in the long-term adaption process. It determines important functional and structural characteristics exhibited in the environmental adaptation of plants. However, the leaf lifespan and leaf characteristics of urban forests were not studied up to now. By using statistic, linear regression methods and correlation analysis, leaf phenological characters of main tree species in urban forest of Shenyang were observed for five years to obtain the leafing phenology (including leafing start time, end time, and duration), defoliating phenology (including defoliation start time, end time, and duration), and the leaf lifespan of the main tree species. Moreover, the relationships between temperature and leafing phenology, defoliating phenology, and leaf lifespan were analyzed. The timing of leafing differed greatly among species. The early leafing species would have relatively early end of leafing; the longer it took to the end of leafing would have a later time of completed leafing. The timing of defoliation among different species varied significantly, the early defoliation species would have relatively longer duration of defoliation. If the mean temperature rise for 1°C in spring, the time of leafing would experience 5 days earlier in spring. If the mean temperature decline for 1°C, the time of defoliation would experience 3 days delay in autumn. There is significant correlation between leaf longevity and the time of leafing and defoliation. According to correlation analysis and regression analysis, there is significant correlation between temperature and leafing and defoliation phenology. Early leafing species would have a longer life span and consequently have advantage on carbon accumulation compared with later defoliation species.

Diesel engine exhaust and lung cancer risks - evaluation of the meta-analysis by Vermeulen et al. 2014.

PubMed

Morfeld, Peter; Spallek, Michael

2015-01-01

Vermeulen et al. 2014 published a meta-regression analysis of three relevant epidemiological US studies (Steenland et al. 1998, Garshick et al. 2012, Silverman et al. 2012) that estimated the association between occupational diesel engine exhaust (DEE) exposure and lung cancer mortality. The DEE exposure was measured as cumulative exposure to estimated respirable elemental carbon in μg/m(3)-years. Vermeulen et al. 2014 found a statistically significant dose-response association and described elevated lung cancer risks even at very low exposures. We performed an extended re-analysis using different modelling approaches (fixed and random effects regression analyses, Greenland/Longnecker method) and explored the impact of varying input data (modified coefficients of Garshick et al. 2012, results from Crump et al. 2015 replacing Silverman et al. 2012, modified analysis of Moehner et al. 2013). We reproduced the individual and main meta-analytical results of Vermeulen et al. 2014. However, our analysis demonstrated a heterogeneity of the baseline relative risk levels between the three studies. This heterogeneity was reduced after the coefficients of Garshick et al. 2012 were modified while the dose coefficient dropped by an order of magnitude for this study and was far from being significant (P = 0.6). A (non-significant) threshold estimate for the cumulative DEE exposure was found at 150 μg/m(3)-years when extending the meta-analyses of the three studies by hockey-stick regression modelling (including the modified coefficients for Garshick et al. 2012). The data used by Vermeulen and colleagues led to the highest relative risk estimate across all sensitivity analyses performed. The lowest relative risk estimate was found after exclusion of the explorative study by Steenland et al. 1998 in a meta-regression analysis of Garshick et al. 2012 (modified), Silverman et al. 2012 (modified according to Crump et al. 2015) and Möhner et al. 2013. The meta-coefficient was estimated to be about 10-20 % of the main effect estimate in Vermeulen et al. 2014 in this analysis. The findings of Vermeulen et al. 2014 should not be used without reservations in any risk assessments. This is particularly true for the low end of the exposure scale.

Cultural Beliefs, Partner Characteristics, Communication, and Sexual Risk Among Latino MSM

PubMed Central

Reisen, Carol A.; Poppen, Paul J.; Bianchi, Fernanda T.; Zea, Maria Cecilia

2013-01-01

This study examined factors associated with communication about condom use and unprotected anal intercourse (UAI) in a U.S. sample of immigrant Latino MSM (N = 356), with a focus on culturally based beliefs. Logistic regression analysis revealed that communication about condom use at participants' most recent encounter was associated with a lower likelihood of UAI during that encounter. UAI was more likely when the partner was a main partner and there was seroconcordance. A separate logistic regression indicated that communication about condom use was less likely when the most recent encounter involved a main partner, greater sexual desire, and intoxication due to substance use. Although cultural beliefs were not predictive of communication about condom use or UAI at the most recent encounter, they were related to the occurrence of UAI in the previous three months. There is a need for more research on the interplay of culture, safer sex communication, and sexual risk. PMID:20652629

Application of geographically-weighted regression analysis to assess risk factors for malaria hotspots in Keur Soce health and demographic surveillance site.

PubMed

Ndiath, Mansour M; Cisse, Badara; Ndiaye, Jean Louis; Gomis, Jules F; Bathiery, Ousmane; Dia, Anta Tal; Gaye, Oumar; Faye, Babacar

2015-11-18

In Senegal, considerable efforts have been made to reduce malaria morbidity and mortality during the last decade. This resulted in a marked decrease of malaria cases. With the decline of malaria cases, transmission has become sparse in most Senegalese health districts. This study investigated malaria hotspots in Keur Soce sites by using geographically-weighted regression. Because of the occurrence of hotspots, spatial modelling of malaria cases could have a considerable effect in disease surveillance. This study explored and analysed the spatial relationships between malaria occurrence and socio-economic and environmental factors in small communities in Keur Soce, Senegal, using 6 months passive surveillance. Geographically-weighted regression was used to explore the spatial variability of relationships between malaria incidence or persistence and the selected socio-economic, and human predictors. A model comparison of between ordinary least square and geographically-weighted regression was also explored. Vector dataset (spatial) of the study area by village levels and statistical data (non-spatial) on malaria confirmed cases, socio-economic status (bed net use), population data (size of the household) and environmental factors (temperature, rain fall) were used in this exploratory analysis. ArcMap 10.2 and Stata 11 were used to perform malaria hotspots analysis. From Jun to December, a total of 408 confirmed malaria cases were notified. The explanatory variables-household size, housing materials, sleeping rooms, sheep and distance to breeding site returned significant t values of -0.25, 2.3, 4.39, 1.25 and 2.36, respectively. The OLS global model revealed that it explained about 70 % (adjusted R(2) = 0.70) of the variation in malaria occurrence with AIC = 756.23. The geographically-weighted regression of malaria hotspots resulted in coefficient intercept ranging from 1.89 to 6.22 with a median of 3.5. Large positive values are distributed mainly in the southeast of the district where hotspots are more accurate while low values are mainly found in the centre and in the north. Geographically-weighted regression and OLS showed important risks factors of malaria hotspots in Keur Soce. The outputs of such models can be a useful tool to understand occurrence of malaria hotspots in Senegal. An understanding of geographical variation and determination of the core areas of the disease may provide an explanation regarding possible proximal and distal contributors to malaria elimination in Senegal.

Valid Statistical Analysis for Logistic Regression with Multiple Sources

NASA Astrophysics Data System (ADS)

Fienberg, Stephen E.; Nardi, Yuval; Slavković, Aleksandra B.

Considerable effort has gone into understanding issues of privacy protection of individual information in single databases, and various solutions have been proposed depending on the nature of the data, the ways in which the database will be used and the precise nature of the privacy protection being offered. Once data are merged across sources, however, the nature of the problem becomes far more complex and a number of privacy issues arise for the linked individual files that go well beyond those that are considered with regard to the data within individual sources. In the paper, we propose an approach that gives full statistical analysis on the combined database without actually combining it. We focus mainly on logistic regression, but the method and tools described may be applied essentially to other statistical models as well.

Analysis and improvement measures of flight delay in China

NASA Astrophysics Data System (ADS)

Zang, Yuhang

2017-03-01

Firstly, this paper establishes the principal component regression model to analyze the data quantitatively, based on principal component analysis to get the three principal component factors of flight delays. Then the least square method is used to analyze the factors and obtained the regression equation expression by substitution, and then found that the main reason for flight delays is airlines, followed by weather and traffic. Aiming at the above problems, this paper improves the controllable aspects of traffic flow control. For reasons of traffic flow control, an adaptive genetic queuing model is established for the runway terminal area. This paper, establish optimization method that fifteen planes landed simultaneously on the three runway based on Beijing capital international airport, comparing the results with the existing FCFS algorithm, the superiority of the model is proved.

Influence factors and forecast of carbon emission in China: structure adjustment for emission peak

NASA Astrophysics Data System (ADS)

Wang, B.; Cui, C. Q.; Li, Z. P.

2018-02-01

This paper introduced Principal Component Analysis and Multivariate Linear Regression Model to verify long-term balance relationships between Carbon Emissions and the impact factors. The integrated model of improved PCA and multivariate regression analysis model is attainable to figure out the pattern of carbon emission sources. Main empirical results indicate that among all selected variables, the role of energy consumption scale was largest. GDP and Population follow and also have significant impacts on carbon emission. Industrialization rate and fossil fuel proportion, which is the indicator of reflecting the economic structure and energy structure, have a higher importance than the factor of urbanization rate and the dweller consumption level of urban areas. In this way, some suggestions are put forward for government to achieve the peak of carbon emissions.

Detection of outliers in the response and explanatory variables of the simple circular regression model

NASA Astrophysics Data System (ADS)

Mahmood, Ehab A.; Rana, Sohel; Hussin, Abdul Ghapor; Midi, Habshah

2016-06-01

The circular regression model may contain one or more data points which appear to be peculiar or inconsistent with the main part of the model. This may be occur due to recording errors, sudden short events, sampling under abnormal conditions etc. The existence of these data points "outliers" in the data set cause lot of problems in the research results and the conclusions. Therefore, we should identify them before applying statistical analysis. In this article, we aim to propose a statistic to identify outliers in the both of the response and explanatory variables of the simple circular regression model. Our proposed statistic is robust circular distance RCDxy and it is justified by the three robust measurements such as proportion of detection outliers, masking and swamping rates.

Searching for the main anti-bacterial components in artificial Calculus bovis using UPLC and microcalorimetry coupled with multi-linear regression analysis.

PubMed

Zang, Qing-Ce; Wang, Jia-Bo; Kong, Wei-Jun; Jin, Cheng; Ma, Zhi-Jie; Chen, Jing; Gong, Qian-Feng; Xiao, Xiao-He

2011-12-01

The fingerprints of artificial Calculus bovis extracts from different solvents were established by ultra-performance liquid chromatography (UPLC) and the anti-bacterial activities of artificial C. bovis extracts on Staphylococcus aureus (S. aureus) growth were studied by microcalorimetry. The UPLC fingerprints were evaluated using hierarchical clustering analysis. Some quantitative parameters obtained from the thermogenic curves of S. aureus growth affected by artificial C. bovis extracts were analyzed using principal component analysis. The spectrum-effect relationships between UPLC fingerprints and anti-bacterial activities were investigated using multi-linear regression analysis. The results showed that peak 1 (taurocholate sodium), peak 3 (unknown compound), peak 4 (cholic acid), and peak 6 (chenodeoxycholic acid) are more significant than the other peaks with the standard parameter estimate 0.453, -0.166, 0.749, 0.025, respectively. So, compounds cholic acid, taurocholate sodium, and chenodeoxycholic acid might be the major anti-bacterial components in artificial C. bovis. Altogether, this work provides a general model of the combination of UPLC chromatography and anti-bacterial effect to study the spectrum-effect relationships of artificial C. bovis extracts, which can be used to discover the main anti-bacterial components in artificial C. bovis or other Chinese herbal medicines with anti-bacterial effects. Copyright © 2011 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

[Hyperspectral Estimation of Apple Tree Canopy LAI Based on SVM and RF Regression].

PubMed

Han, Zhao-ying; Zhu, Xi-cun; Fang, Xian-yi; Wang, Zhuo-yuan; Wang, Ling; Zhao, Geng-Xing; Jiang, Yuan-mao

2016-03-01

Leaf area index (LAI) is the dynamic index of crop population size. Hyperspectral technology can be used to estimate apple canopy LAI rapidly and nondestructively. It can be provide a reference for monitoring the tree growing and yield estimation. The Red Fuji apple trees of full bearing fruit are the researching objects. Ninety apple trees canopies spectral reflectance and LAI values were measured by the ASD Fieldspec3 spectrometer and LAI-2200 in thirty orchards in constant two years in Qixia research area of Shandong Province. The optimal vegetation indices were selected by the method of correlation analysis of the original spectral reflectance and vegetation indices. The models of predicting the LAI were built with the multivariate regression analysis method of support vector machine (SVM) and random forest (RF). The new vegetation indices, GNDVI527, ND-VI676, RVI682, FD-NVI656 and GRVI517 and the previous two main vegetation indices, NDVI670 and NDVI705, are in accordance with LAI. In the RF regression model, the calibration set decision coefficient C-R2 of 0.920 and validation set decision coefficient V-R2 of 0.889 are higher than the SVM regression model by 0.045 and 0.033 respectively. The root mean square error of calibration set C-RMSE of 0.249, the root mean square error validation set V-RMSE of 0.236 are lower than that of the SVM regression model by 0.054 and 0.058 respectively. Relative analysis of calibrating error C-RPD and relative analysis of validation set V-RPD reached 3.363 and 2.520, 0.598 and 0.262, respectively, which were higher than the SVM regression model. The measured and predicted the scatterplot trend line slope of the calibration set and validation set C-S and V-S are close to 1. The estimation result of RF regression model is better than that of the SVM. RF regression model can be used to estimate the LAI of red Fuji apple trees in full fruit period.

Association of nitrogen compounds in drinking water with incidence of esophageal squamous cell carcinoma in Shexian, China.

PubMed

Zhang, Nan; Yu, Cao; Wen, Denggui; Chen, Jun; Ling, Yiwei; Terajima, Kenshi; Akazawa, Kohei; Shan, Baoen; Wang, Shijie

2012-01-01

The incidence of esophageal squamous cell carcinoma (ESCC), which is the eighth most common malignancy worldwide, is highest in China. The purpose of this study was to investigate the association between nitrogen compounds in drinking water with the incidence of ESCC by geographical spatial analysis. The incidence of ESCC is high in Shexian county, China, and environmental factors, particularly nitrogen-contaminated drinking water, are the main suspected risk factors. This study focuses on three nitrogen compounds in drinking water, namely, nitrates, nitrites, and ammonia, all of which are derived mainly from domestic garbage and agricultural fertilizer. The study surveyed 48 villages in the Shexian area with a total population of 54,716 (661 adults with ESCC and 54,055 non-cancer subjects). Hot-spot analysis was used to identify spatial clusters with a high incidence of ESCC and a high concentration of nitrogen compounds. Logistic regression analysis was used to detect risk factors for ESCC incidence. Most areas with high concentrations of nitrate nitrogen in drinking water had a high incidence of ESCC. Correlation analysis revealed a significant positive relationship between nitrate concentration and ESCC (P = 0.01). Logistic regression analysis also confirmed that nitrate nitrogen has a significantly higher odds ratio. The results indicate that nitrate nitrogen is associated with ESCC incidence in Shexian county. In conclusion, high concentrations of nitrate nitrogen in drinking water may be a significant risk factor for the incidence of ESCC.

Wastewater-Based Epidemiology of Stimulant Drugs: Functional Data Analysis Compared to Traditional Statistical Methods.

PubMed

Salvatore, Stefania; Bramness, Jørgen Gustav; Reid, Malcolm J; Thomas, Kevin Victor; Harman, Christopher; Røislien, Jo

2015-01-01

Wastewater-based epidemiology (WBE) is a new methodology for estimating the drug load in a population. Simple summary statistics and specification tests have typically been used to analyze WBE data, comparing differences between weekday and weekend loads. Such standard statistical methods may, however, overlook important nuanced information in the data. In this study, we apply functional data analysis (FDA) to WBE data and compare the results to those obtained from more traditional summary measures. We analysed temporal WBE data from 42 European cities, using sewage samples collected daily for one week in March 2013. For each city, the main temporal features of two selected drugs were extracted using functional principal component (FPC) analysis, along with simpler measures such as the area under the curve (AUC). The individual cities' scores on each of the temporal FPCs were then used as outcome variables in multiple linear regression analysis with various city and country characteristics as predictors. The results were compared to those of functional analysis of variance (FANOVA). The three first FPCs explained more than 99% of the temporal variation. The first component (FPC1) represented the level of the drug load, while the second and third temporal components represented the level and the timing of a weekend peak. AUC was highly correlated with FPC1, but other temporal characteristic were not captured by the simple summary measures. FANOVA was less flexible than the FPCA-based regression, and even showed concordance results. Geographical location was the main predictor for the general level of the drug load. FDA of WBE data extracts more detailed information about drug load patterns during the week which are not identified by more traditional statistical methods. Results also suggest that regression based on FPC results is a valuable addition to FANOVA for estimating associations between temporal patterns and covariate information.

Growth and inactivation of Salmonella at low refrigerated storage temperatures and thermal inactivation on raw chicken meat and laboratory media: mixed effect meta-analysis.

PubMed

Smadi, Hanan; Sargeant, Jan M; Shannon, Harry S; Raina, Parminder

2012-12-01

Growth and inactivation regression equations were developed to describe the effects of temperature on Salmonella concentration on chicken meat for refrigerated temperatures (⩽10°C) and for thermal treatment temperatures (55-70°C). The main objectives were: (i) to compare Salmonella growth/inactivation in chicken meat versus laboratory media; (ii) to create regression equations to estimate Salmonella growth in chicken meat that can be used in quantitative risk assessment (QRA) modeling; and (iii) to create regression equations to estimate D-values needed to inactivate Salmonella in chicken meat. A systematic approach was used to identify the articles, critically appraise them, and pool outcomes across studies. Growth represented in density (Log10CFU/g) and D-values (min) as a function of temperature were modeled using hierarchical mixed effects regression models. The current meta-analysis analysis found a significant difference (P⩽0.05) between the two matrices - chicken meat and laboratory media - for both growth at refrigerated temperatures and inactivation by thermal treatment. Growth and inactivation were significantly influenced by temperature after controlling for other variables; however, no consistent pattern in growth was found. Validation of growth and inactivation equations against data not used in their development is needed. Copyright © 2012 Ministry of Health, Saudi Arabia. Published by Elsevier Ltd. All rights reserved.

Partnership-Level Analysis of African American Women's Risky Sexual Behavior in Main and Non-Main Partnerships.

PubMed

Broaddus, Michelle; Owczarzak, Jill; Pacella, Maria; Pinkerton, Steven; Wright, Cassandra

2016-12-01

The majority of research on risky sexual behavior in African American women has examined global associations between individual-level predictors and behavior. However, this method obscures the potentially significant impact of the specific relationship or relationship partner on risky sexual behavior. To address this gap, we conducted partnership-level analysis of risky sexual behavior among 718 African American women recruited from HIV counseling, testing, and referral sites in four states. Using mixed model regressions, we tested relationships between condomless vaginal intercourse with men and variables drawn from the Theory of Planned Behavior, Theory of Gender and Power, and previous research specifically on sexual risks among African American women. Significant associations with risky sexual behavior indicate the need for continued emphasis on condom attitudes, condom negotiation behaviors, and overcoming partner resistance to condoms within both main and non-main partnerships when implementing interventions designed to address HIV and sexually transmitted infection risks among African American women.

MicroCT angiography detects vascular formation and regression in skin wound healing

PubMed Central

Urao, Norifumi; Okonkwo, Uzoagu A.; Fang, Milie M.; Zhuang, Zhen W.; Koh, Timothy J.; DiPietro, Luisa A.

2016-01-01

Properly regulated angiogenesis and arteriogenesis are essential for effective wound healing. Tissue injury induces robust new vessel formation and subsequent vessel maturation, which involves vessel regression and remodeling. Although formation of functional vasculature is essential for healing, alterations in vascular structure over the time course of skin wound healing are not well understood. Here, using high-resolution ex vivo X-ray micro-computed tomography (microCT), we describe the vascular network during healing of skin excisional wounds with highly detailed three-dimensional (3D) reconstructed images and associated quantitative analysis. We found that relative vessel volume, surface area and branching number are significantly decreased in wounds from day 7 to day 14 and 21. Segmentation and skeletonization analysis of selected branches from high-resolution images as small as 2.5 μm voxel size show that branching orders are decreased in the wound vessels during healing. In histological analysis, we found that the contrast agent fills mainly arterioles, but not small capillaries nor large veins. In summary, high-resolution microCT revealed dynamic alterations of vessel structures during wound healing. This technique may be useful as a key tool in the study of the formation and regression of wound vessels. PMID:27009591

Studying Gene and Gene-Environment Effects of Uncommon and Common Variants on Continuous Traits: A Marker-Set Approach Using Gene-Trait Similarity Regression

PubMed Central

Tzeng, Jung-Ying; Zhang, Daowen; Pongpanich, Monnat; Smith, Chris; McCarthy, Mark I.; Sale, Michèle M.; Worrall, Bradford B.; Hsu, Fang-Chi; Thomas, Duncan C.; Sullivan, Patrick F.

2011-01-01

Genomic association analyses of complex traits demand statistical tools that are capable of detecting small effects of common and rare variants and modeling complex interaction effects and yet are computationally feasible. In this work, we introduce a similarity-based regression method for assessing the main genetic and interaction effects of a group of markers on quantitative traits. The method uses genetic similarity to aggregate information from multiple polymorphic sites and integrates adaptive weights that depend on allele frequencies to accomodate common and uncommon variants. Collapsing information at the similarity level instead of the genotype level avoids canceling signals that have the opposite etiological effects and is applicable to any class of genetic variants without the need for dichotomizing the allele types. To assess gene-trait associations, we regress trait similarities for pairs of unrelated individuals on their genetic similarities and assess association by using a score test whose limiting distribution is derived in this work. The proposed regression framework allows for covariates, has the capacity to model both main and interaction effects, can be applied to a mixture of different polymorphism types, and is computationally efficient. These features make it an ideal tool for evaluating associations between phenotype and marker sets defined by linkage disequilibrium (LD) blocks, genes, or pathways in whole-genome analysis. PMID:21835306

The Determinants of Academic Performance of under Graduate Students: In the Case of Arba Minch University Chamo Campus

ERIC Educational Resources Information Center

Yigermal, Moges Endalamaw

2017-01-01

The main objective of the paper is to investigate the determinant factors affecting the academic performance of regular undergraduate students of Arba Minch university (AMU) chamo campus students. To meet the objective, the Pearson product moment correlation statistical tool and econometrics data analysis (OLS regression) method were used with the…

On The Impact of Climate Change to Agricultural Productivity in East Java

NASA Astrophysics Data System (ADS)

Kuswanto, Heri; Salamah, Mutiah; Mumpuni Retnaningsih, Sri; Dwi Prastyo, Dedy

2018-03-01

Many researches showed that climate change has significant impact on agricultural sector, which threats the food security especially in developing countries. It has been observed also that the climate change increases the intensity of extreme events. This research investigated the impact climate to the agricultural productivity in East Java, as one of the main rice producers in Indonesia. Standard regression as well as panel regression models have been performed in order to find the best model which is able to describe the climate change impact. The analysis found that the fixed effect model of panel regression outperforms the others showing that climate change had negatively impacted the rice productivity in East Java. The effect in Malang and Pasuruan were almost the same, while the impact in Sumenep was the least one compared to other districts.

Prediction models for CO2 emission in Malaysia using best subsets regression and multi-linear regression

NASA Astrophysics Data System (ADS)

Tan, C. H.; Matjafri, M. Z.; Lim, H. S.

2015-10-01

This paper presents the prediction models which analyze and compute the CO2 emission in Malaysia. Each prediction model for CO2 emission will be analyzed based on three main groups which is transportation, electricity and heat production as well as residential buildings and commercial and public services. The prediction models were generated using data obtained from World Bank Open Data. Best subset method will be used to remove irrelevant data and followed by multi linear regression to produce the prediction models. From the results, high R-square (prediction) value was obtained and this implies that the models are reliable to predict the CO2 emission by using specific data. In addition, the CO2 emissions from these three groups are forecasted using trend analysis plots for observation purpose.

Magnitude and frequency of floods in Arkansas

USGS Publications Warehouse

Hodge, Scott A.; Tasker, Gary D.

1995-01-01

Methods are presented for estimating the magnitude and frequency of peak discharges of streams in Arkansas. Regression analyses were developed in which a stream's physical and flood characteristics were related. Four sets of regional regression equations were derived to predict peak discharges with selected recurrence intervals of 2, 5, 10, 25, 50, 100, and 500 years on streams draining less than 7,770 square kilometers. The regression analyses indicate that size of drainage area, main channel slope, mean basin elevation, and the basin shape factor were the most significant basin characteristics that affect magnitude and frequency of floods. The region of influence method is included in this report. This method is still being improved and is to be considered only as a second alternative to the standard method of producing regional regression equations. This method estimates unique regression equations for each recurrence interval for each ungaged site. The regression analyses indicate that size of drainage area, main channel slope, mean annual precipitation, mean basin elevation, and the basin shape factor were the most significant basin and climatic characteristics that affect magnitude and frequency of floods for this method. Certain recommendations on the use of this method are provided. A method is described for estimating the magnitude and frequency of peak discharges of streams for urban areas in Arkansas. The method is from a nationwide U.S. Geeological Survey flood frequency report which uses urban basin characteristics combined with rural discharges to estimate urban discharges. Annual peak discharges from 204 gaging stations, with drainage areas less than 7,770 square kilometers and at least 10 years of unregulated record, were used in the analysis. These data provide the basis for this analysis and are published in the Appendix of this report as supplemental data. Large rivers such as the Red, Arkansas, White, Black, St. Francis, Mississippi, and Ouachita Rivers have floodflow characteristics that differ from those of smaller tributary streams and were treated individually. Regional regression equations are not applicable to these large rivers. The magnitude and frequency of floods along these rivers are based on specific station data. This section is provided in the Appendix and has not been updated since the last Arkansas flood frequency report (1987b), but is included at the request of the cooperator.

A comparison between univariate probabilistic and multivariate (logistic regression) methods for landslide susceptibility analysis: the example of the Febbraro valley (Northern Alps, Italy)

NASA Astrophysics Data System (ADS)

Rossi, M.; Apuani, T.; Felletti, F.

2009-04-01

The aim of this paper is to compare the results of two statistical methods for landslide susceptibility analysis: 1) univariate probabilistic method based on landslide susceptibility index, 2) multivariate method (logistic regression). The study area is the Febbraro valley, located in the central Italian Alps, where different types of metamorphic rocks croup out. On the eastern part of the studied basin a quaternary cover represented by colluvial and secondarily, by glacial deposits, is dominant. In this study 110 earth flows, mainly located toward NE portion of the catchment, were analyzed. They involve only the colluvial deposits and their extension mainly ranges from 36 to 3173 m2. Both statistical methods require to establish a spatial database, in which each landslide is described by several parameters that can be assigned using a main scarp central point of landslide. The spatial database is constructed using a Geographical Information System (GIS). Each landslide is described by several parameters corresponding to the value of main scarp central point of the landslide. Based on bibliographic review a total of 15 predisposing factors were utilized. The width of the intervals, in which the maps of the predisposing factors have to be reclassified, has been defined assuming constant intervals to: elevation (100 m), slope (5 °), solar radiation (0.1 MJ/cm2/year), profile curvature (1.2 1/m), tangential curvature (2.2 1/m), drainage density (0.5), lineament density (0.00126). For the other parameters have been used the results of the probability-probability plots analysis and the statistical indexes of landslides site. In particular slope length (0 ÷ 2, 2 ÷ 5, 5 ÷ 10, 10 ÷ 20, 20 ÷ 35, 35 ÷ 260), accumulation flow (0 ÷ 1, 1 ÷ 2, 2 ÷ 5, 5 ÷ 12, 12 ÷ 60, 60 ÷27265), Topographic Wetness Index 0 ÷ 0.74, 0.74 ÷ 1.94, 1.94 ÷ 2.62, 2.62 ÷ 3.48, 3.48 ÷ 6,00, 6.00 ÷ 9.44), Stream Power Index (0 ÷ 0.64, 0.64 ÷ 1.28, 1.28 ÷ 1.81, 1.81 ÷ 4.20, 4.20 ÷ 9.40). Geological map and land use map were also used, considering geological and land use properties as categorical variables. Appling the univariate probabilistic method the Landslide Susceptibility Index (LSI) is defined as the sum of the ratio Ra/Rb calculated for each predisposing factor, where Ra is the ratio between number of pixel of class and the total number of pixel of the study area, and Rb is the ratio between number of landslides respect to the pixel number of the interval area. From the analysis of the Ra/Rb ratio the relationship between landslide occurrence and predisposing factors were defined. Then the equation of LSI was used in GIS to trace the landslide susceptibility maps. The multivariate method for landslide susceptibility analysis, based on logistic regression, was performed starting from the density maps of the predisposing factors, calculated with the intervals defined above using the equation Rb/Rbtot, where Rbtot is a sum of all Rb values. Using stepwise forward algorithms the logistic regression was performed in two successive steps: first a univariate logistic regression is used to choose the most significant predisposing factors, then the multivariate logistic regression can be performed. The univariate regression highlighted the importance of the following factors: elevation, accumulation flow, drainage density, lineament density, geology and land use. When the multivariate regression was applied the number of controlling factors was reduced neglecting the geological properties. The resulting final susceptibility equation is: P = 1 / (1 + exp-(6.46-22.34*elevation-5.33*accumulation flow-7.99* drainage density-4.47*lineament density-17.31*land use)) and using this equation the susceptibility maps were obtained. To easy compare the results of the two methodologies, the susceptibility maps were reclassified in five susceptibility intervals (very high, high, moderate, low and very low) using natural breaks. Then the maps were validated using two cumulative distribution curves, one related to the landslides (number of landslides in each susceptibility class) and one to the basin (number of pixel covering each class). Comparing the curves for each method, it results that the two approaches (univariate and multivariate) are appropriate, providing acceptable results. In both maps the distribution of high susceptibility condition is mainly localized on the left slope of the catchment in agreement with the field evidences. The comparison between the methods was obtained by subtraction of the two maps. This operation shows that about 40% of the basin is classified by the same class of susceptibility. In general the univariate probabilistic method tends to overestimate the areal extension of the high susceptibility class with respect to the maps obtained by the logistic regression method.

Early Warning Signals of Financial Crises with Multi-Scale Quantile Regressions of Log-Periodic Power Law Singularities.

PubMed

Zhang, Qun; Zhang, Qunzhi; Sornette, Didier

2016-01-01

We augment the existing literature using the Log-Periodic Power Law Singular (LPPLS) structures in the log-price dynamics to diagnose financial bubbles by providing three main innovations. First, we introduce the quantile regression to the LPPLS detection problem. This allows us to disentangle (at least partially) the genuine LPPLS signal and the a priori unknown complicated residuals. Second, we propose to combine the many quantile regressions with a multi-scale analysis, which aggregates and consolidates the obtained ensembles of scenarios. Third, we define and implement the so-called DS LPPLS Confidence™ and Trust™ indicators that enrich considerably the diagnostic of bubbles. Using a detailed study of the "S&P 500 1987" bubble and presenting analyses of 16 historical bubbles, we show that the quantile regression of LPPLS signals contributes useful early warning signals. The comparison between the constructed signals and the price development in these 16 historical bubbles demonstrates their significant predictive ability around the real critical time when the burst/rally occurs.

Chest cage angle difference and rotation of main thoracic curve are independent risk factors of postoperative shoulder imbalance in surgically treated patients with adolescent idiopathic scoliosis.

PubMed

Yagi, Mitsuru; Takemitsu, Masakazu; Machida, Masafumi

2013-09-01

Retrospective case series of surgically treated adolescent patients with scoliosis. To assess the prevalence and independent risk factors for postoperative shoulder imbalance in surgically treated adolescent patients with idiopathic scoliosis. Despite recent reports that have identified risk factors for postoperative shoulder imbalance, the relative risks remain unclear. A retrospective review of 85 consecutive patients treated with thoracic fusion with a minimum 2-year follow-up (mean, 3.1 yr) was conducted to investigate the patient radiographical measurements and demographics. Shoulder height difference (SHD) was measured as the graded height difference of the soft tissue shadows. A SHD more than 2 cm indicated an unbalanced shoulder. Patient demographics and radiographical data were studied to determine risk factors for postoperative SHD. The potential risk factors included age, sex, Risser sign, Cobb angle, flexibility, and apical vertebral rotation (AVR) of the main curve, upper-instrumented vertebra level, SHD, and clavicle chest cage angle difference (CCAD). Univariate and multivariate logistic regression analyses were performed to determine the independent risk factors for postoperative shoulder imbalance. Of the 85 patients, 21 patients presented postoperative shoulder imbalance. The univariate analysis indicated age, Risser sign, Cobb angle of the main curve, AVR of the main curve, and CCAD as risk factors, but the multivariate logistic regression analysis showed that only AVR of the main curve and CCAD were independent risk factors for postoperative shoulder imbalance (AVR, P = 0.04, odds ratio (OR): 3.54; CCAD, P = 0.01, OR: 5.10). Postoperative shoulder imbalance was observed in 25% of the surgically treated adolescent patients. The CCAD and AVR of the main thoracic curve were independent risk factors for postoperative shoulder imbalance in surgically treated patients with adolescent idiopathic scoliosis. The significant correlation between CCAD and postoperative shoulder imbalance seen in this study strongly suggests that the relationship of the shoulder girdle and chest cage has a role in maintaining shoulder balance.

Quantifying Main Trends in Lysozyme Nucleation: The Effects of Precipitant Concentration, Supersaturation and Impurities

NASA Technical Reports Server (NTRS)

Burke, Michael W.; Leardi, Riccardo; Judge, Russell A.; Pusey, Marc L.; Whitaker, Ann F. (Technical Monitor)

2001-01-01

Full factorial experimental design incorporating multi-linear regression analysis of the experimental data allows quick identification of main trends and effects using a limited number of experiments. In this study these techniques were employed to identify the effect of precipitant concentration, supersaturation, and the presence of an impurity, the physiological lysozyme dimer, on the nucleation rate and crystal dimensions of the tetragonal forin of chicken egg white lysozyme. Decreasing precipitant concentration, increasing supers aturation, and increasing impurity, were found to increase crystal numbers. The crystal axial ratio decreased with increasing precipitant concentration, independent of impurity.

Classification of sodium MRI data of cartilage using machine learning.

PubMed

Madelin, Guillaume; Poidevin, Frederick; Makrymallis, Antonios; Regatte, Ravinder R

2015-11-01

To assess the possible utility of machine learning for classifying subjects with and subjects without osteoarthritis using sodium magnetic resonance imaging data. Theory: Support vector machine, k-nearest neighbors, naïve Bayes, discriminant analysis, linear regression, logistic regression, neural networks, decision tree, and tree bagging were tested. Sodium magnetic resonance imaging with and without fluid suppression by inversion recovery was acquired on the knee cartilage of 19 controls and 28 osteoarthritis patients. Sodium concentrations were measured in regions of interests in the knee for both acquisitions. Mean (MEAN) and standard deviation (STD) of these concentrations were measured in each regions of interest, and the minimum, maximum, and mean of these two measurements were calculated over all regions of interests for each subject. The resulting 12 variables per subject were used as predictors for classification. Either Min [STD] alone, or in combination with Mean [MEAN] or Min [MEAN], all from fluid suppressed data, were the best predictors with an accuracy >74%, mainly with linear logistic regression and linear support vector machine. Other good classifiers include discriminant analysis, linear regression, and naïve Bayes. Machine learning is a promising technique for classifying osteoarthritis patients and controls from sodium magnetic resonance imaging data. © 2014 Wiley Periodicals, Inc.

Analysis of quality of life and influencing factors in 197 Chinese patients with port-wine stains

PubMed Central

Wang, Juan; Zhu, Yu-you; Wang, Zhong-ying; Yao, Xiu-hua; Zhang, Lan-fang; Lv, Hong; Zhang, Si-ping; Hu, Bai

2017-01-01

Abstract Port-wine stains (PWS) are congenital capillary malformations, usually occurring on the face, neck, and other exposed parts of the skin, that have serious psychological and social impact on the patient. Most researchers focus on the treatment of PWS, but the quality of life (QoL) of PWS patients is seldom researched. The objective of this study is to evaluate the QoL of patients with PWS on exposed parts and explore the factors influencing the QoL of PWS patients. The QoL of 197 cases with PWS on exposed parts were prospectively studied using the Dermatology Life Quality Index questionnaire (DLQI), and the factors influencing the patients’ QoL were analyzed by single-factor analysis and multiple-factor logistic regression analysis. The reliability and validity of the QoL of PWS patients were then assessed by DLQI. A total of 197 valid questionnaires were collected. The DLQI scores in PWS cases ranged from 2 to 16, with 2 to 5 in 52.29% (103/197), 6 to 10 in 42.13% (83/197), and 11 to 20 in 5.58% (11/197). The main score elements of the DLQI focused on symptoms and feelings, daily activities, and social entertainment. Single-factor analysis and multiple-factor logistic regression analysis showed that the main influencing factors were female sex, skin hypertrophy, and lesion area >30 cm2. The inter-item correlation averaged 47.46% and the Cronbach α was 0.740, indicating high internal consistency. Correlation of the 6 dimensions of the DLQI questionnaires with the total scores showed that the Spearman correlation coefficient r ranged from 0.550 to 0.782 (P < .001), with symptoms and feelings having a correlation coefficient of 0.782 and a high correlation with total scores. This study shows that PWS has mild to moderate influence on the QoL of most patients, mainly on daily activities, social entertainment, and feelings. PMID:29390578

Analysis of an experiment aimed at improving the reliability of transmission centre shafts.

PubMed

Davis, T P

1995-01-01

Smith (1991) presents a paper proposing the use of Weibull regression models to establish dependence of failure data (usually times) on covariates related to the design of the test specimens and test procedures. In his article Smith made the point that good experimental design was as important in reliability applications as elsewhere, and in view of the current interest in design inspired by Taguchi and others, we pay some attention in this article to that topic. A real case study from the Ford Motor Company is presented. Our main approach is to utilize suggestions in the literature for applying standard least squares techniques of experimental analysis even when there is likely to be nonnormal error, and censoring. This approach lacks theoretical justification, but its appeal is its simplicity and flexibility. For completeness we also include some analysis based on the proportional hazards model, and in an attempt to link back to Smith (1991), look at a Weibull regression model.

Analysis of a Split-Plot Experimental Design Applied to a Low-Speed Wind Tunnel Investigation

NASA Technical Reports Server (NTRS)

Erickson, Gary E.

2013-01-01

A procedure to analyze a split-plot experimental design featuring two input factors, two levels of randomization, and two error structures in a low-speed wind tunnel investigation of a small-scale model of a fighter airplane configuration is described in this report. Standard commercially-available statistical software was used to analyze the test results obtained in a randomization-restricted environment often encountered in wind tunnel testing. The input factors were differential horizontal stabilizer incidence and the angle of attack. The response variables were the aerodynamic coefficients of lift, drag, and pitching moment. Using split-plot terminology, the whole plot, or difficult-to-change, factor was the differential horizontal stabilizer incidence, and the subplot, or easy-to-change, factor was the angle of attack. The whole plot and subplot factors were both tested at three levels. Degrees of freedom for the whole plot error were provided by replication in the form of three blocks, or replicates, which were intended to simulate three consecutive days of wind tunnel facility operation. The analysis was conducted in three stages, which yielded the estimated mean squares, multiple regression function coefficients, and corresponding tests of significance for all individual terms at the whole plot and subplot levels for the three aerodynamic response variables. The estimated regression functions included main effects and two-factor interaction for the lift coefficient, main effects, two-factor interaction, and quadratic effects for the drag coefficient, and only main effects for the pitching moment coefficient.

Analysis of source regions and meteorological factors for the variability of spring PM10 concentrations in Seoul, Korea

NASA Astrophysics Data System (ADS)

Lee, Jangho; Kim, Kwang-Yul

2018-02-01

CSEOF analysis is applied for the springtime (March, April, May) daily PM10 concentrations measured at 23 Ministry of Environment stations in Seoul, Korea for the period of 2003-2012. Six meteorological variables at 12 pressure levels are also acquired from the ERA Interim reanalysis datasets. CSEOF analysis is conducted for each meteorological variable over East Asia. Regression analysis is conducted in CSEOF space between the PM10 concentrations and individual meteorological variables to identify associated atmospheric conditions for each CSEOF mode. By adding the regressed loading vectors with the mean meteorological fields, the daily atmospheric conditions are obtained for the first five CSEOF modes. Then, HYSPLIT model is run with the atmospheric conditions for each CSEOF mode in order to back trace the air parcels and dust reaching Seoul. The K-means clustering algorithm is applied to identify major source regions for each CSEOF mode of the PM10 concentrations in Seoul. Three main source regions identified based on the mean fields are: (1) northern Taklamakan Desert (NTD), (2) Gobi Desert and (GD), and (3) East China industrial area (ECI). The main source regions for the mean meteorological fields are consistent with those of previous study; 41% of the source locations are located in GD followed by ECI (37%) and NTD (21%). Back trajectory calculations based on CSEOF analysis of meteorological variables identify distinct source characteristics associated with each CSEOF mode and greatly facilitate the interpretation of the PM10 variability in Seoul in terms of transportation route and meteorological conditions including the source area.

Effect of removing the common mode errors on linear regression analysis of noise amplitudes in position time series of a regional GPS network & a case study of GPS stations in Southern California

NASA Astrophysics Data System (ADS)

Jiang, Weiping; Ma, Jun; Li, Zhao; Zhou, Xiaohui; Zhou, Boye

2018-05-01

The analysis of the correlations between the noise in different components of GPS stations has positive significance to those trying to obtain more accurate uncertainty of velocity with respect to station motion. Previous research into noise in GPS position time series focused mainly on single component evaluation, which affects the acquisition of precise station positions, the velocity field, and its uncertainty. In this study, before and after removing the common-mode error (CME), we performed one-dimensional linear regression analysis of the noise amplitude vectors in different components of 126 GPS stations with a combination of white noise, flicker noise, and random walking noise in Southern California. The results show that, on the one hand, there are above-moderate degrees of correlation between the white noise amplitude vectors in all components of the stations before and after removal of the CME, while the correlations between flicker noise amplitude vectors in horizontal and vertical components are enhanced from un-correlated to moderately correlated by removing the CME. On the other hand, the significance tests show that, all of the obtained linear regression equations, which represent a unique function of the noise amplitude in any two components, are of practical value after removing the CME. According to the noise amplitude estimates in two components and the linear regression equations, more accurate noise amplitudes can be acquired in the two components.

Analysis on the Climate Change Characteristics of Dianchi Lake Basin under the Background of Global Warming

NASA Astrophysics Data System (ADS)

Zhenyu, Yu; Luo, Yi; Yang, Kun; Qiongfei, Deng

2017-05-01

Based on the data published by the State Statistical Bureau and the weather station data, the annual mean temperature, wind speed, humidity, light duration and precipitation of Dianchi Lake in 1990 ~ 2014 were analysed. Combined with the population The results show that the climatic changes in Dianchi Lake basin are related to the climatic change in the past 25 years, and the correlation between these factors and the main climatic factors are analysed by linear regression, Mann-Kendall test, cumulative anomaly, R/S and Morlet wavelet analysis. Population, housing construction area growth and other aspects of the correlation trends and changes in the process, revealing the population expansion and housing construction area growth on the climate of the main factors of the cycle tendency of significant impact.

Leaf Phenological Characters of Main Tree Species in Urban Forest of Shenyang

PubMed Central

Xu, Sheng; Xu, Wenduo; Chen, Wei; He, Xingyuan; Huang, Yanqing; Wen, Hua

2014-01-01

Background Plant leaves, as the main photosynthetic organs and the high energy converters among primary producers in terrestrial ecosystems, have attracted significant research attention. Leaf lifespan is an adaptive characteristic formed by plants to obtain the maximum carbon in the long-term adaption process. It determines important functional and structural characteristics exhibited in the environmental adaptation of plants. However, the leaf lifespan and leaf characteristics of urban forests were not studied up to now. Methods By using statistic, linear regression methods and correlation analysis, leaf phenological characters of main tree species in urban forest of Shenyang were observed for five years to obtain the leafing phenology (including leafing start time, end time, and duration), defoliating phenology (including defoliation start time, end time, and duration), and the leaf lifespan of the main tree species. Moreover, the relationships between temperature and leafing phenology, defoliating phenology, and leaf lifespan were analyzed. Findings The timing of leafing differed greatly among species. The early leafing species would have relatively early end of leafing; the longer it took to the end of leafing would have a later time of completed leafing. The timing of defoliation among different species varied significantly, the early defoliation species would have relatively longer duration of defoliation. If the mean temperature rise for 1°C in spring, the time of leafing would experience 5 days earlier in spring. If the mean temperature decline for 1°C, the time of defoliation would experience 3 days delay in autumn. Interpretation There is significant correlation between leaf longevity and the time of leafing and defoliation. According to correlation analysis and regression analysis, there is significant correlation between temperature and leafing and defoliation phenology. Early leafing species would have a longer life span and consequently have advantage on carbon accumulation compared with later defoliation species. PMID:24963625

Quantification of brain lipids by FTIR spectroscopy and partial least squares regression

NASA Astrophysics Data System (ADS)

Dreissig, Isabell; Machill, Susanne; Salzer, Reiner; Krafft, Christoph

2009-01-01

Brain tissue is characterized by high lipid content. Its content decreases and the lipid composition changes during transformation from normal brain tissue to tumors. Therefore, the analysis of brain lipids might complement the existing diagnostic tools to determine the tumor type and tumor grade. Objective of this work is to extract lipids from gray matter and white matter of porcine brain tissue, record infrared (IR) spectra of these extracts and develop a quantification model for the main lipids based on partial least squares (PLS) regression. IR spectra of the pure lipids cholesterol, cholesterol ester, phosphatidic acid, phosphatidylcholine, phosphatidylethanolamine, phosphatidylserine, phosphatidylinositol, sphingomyelin, galactocerebroside and sulfatide were used as references. Two lipid mixtures were prepared for training and validation of the quantification model. The composition of lipid extracts that were predicted by the PLS regression of IR spectra was compared with lipid quantification by thin layer chromatography.

Impact of job characteristics on psychological health of Chinese single working women.

PubMed

Yeung, D Y; Tang, C S

2001-01-01

This study aims at investigating the impact of individual and contextual job characteristics of control, psychological and physical demand, and security on psychological distress of 193 Chinese single working women in Hong Kong. The mediating role of job satisfaction in the job characteristics-distress relation is also assessed. Multiple regression analysis results show that job satisfaction mediates the effects of job control and security in predicting psychological distress; whereas psychological job demand has an independent effect on mental distress after considering the effect of job satisfaction. This main effect model indicates that psychological distress is best predicted by small company size, high psychological job demand, and low job satisfaction. Results from a separate regression analysis fails to support the overall combined effect of job demand-control on psychological distress. However, a significant physical job demand-control interaction effect on mental distress is noted, which reduces slightly after controlling the effect of job satisfaction.

Cancer prevalence and education by cancer site: logistic regression analysis.

PubMed

Johnson, Stephanie; Corsten, Martin J; McDonald, James T; Gupta, Michael

2010-10-01

Previously, using the American National Health Interview Survey (NHIS) and a logistic regression analysis, we found that upper aerodigestive tract (UADT) cancer is correlated with low socioeconomic status (SES). The objective of this study was to determine if this correlation between low SES and cancer prevalence exists for other cancers. We again used the NHIS and employed education level as our main measure of SES. We controlled for potentially confounding factors, including smoking status and alcohol consumption. We found that only two cancer subsites shared the pattern of increased prevalence with low education level and decreased prevalence with high education level: UADT cancer and cervical cancer. UADT cancer and cervical cancer were the only two cancers identified that had a link between prevalence and lower education level. This raises the possibility that an associated risk factor for the two cancers is causing the relationship between lower education level and prevalence.

Unconditional analyses can increase efficiency in assessing gene-environment interaction of the case-combined-control design.

PubMed

Goldstein, Alisa M; Dondon, Marie-Gabrielle; Andrieu, Nadine

2006-08-01

A design combining both related and unrelated controls, named the case-combined-control design, was recently proposed to increase the power for detecting gene-environment (GxE) interaction. Under a conditional analytic approach, the case-combined-control design appeared to be more efficient and feasible than a classical case-control study for detecting interaction involving rare events. We now propose an unconditional analytic strategy to further increase the power for detecting gene-environment (GxE) interactions. This strategy allows the estimation of GxE interaction and exposure (E) main effects under certain assumptions (e.g. no correlation in E between siblings and the same exposure frequency in both control groups). Only the genetic (G) main effect cannot be estimated because it is biased. Using simulations, we show that unconditional logistic regression analysis is often more efficient than conditional analysis for detecting GxE interaction, particularly for a rare gene and strong effects. The unconditional analysis is also at least as efficient as the conditional analysis when the gene is common and the main and joint effects of E and G are small. Under the required assumptions, the unconditional analysis retains more information than does the conditional analysis for which only discordant case-control pairs are informative leading to more precise estimates of the odds ratios.

On the equivalence of case-crossover and time series methods in environmental epidemiology.

PubMed

Lu, Yun; Zeger, Scott L

2007-04-01

The case-crossover design was introduced in epidemiology 15 years ago as a method for studying the effects of a risk factor on a health event using only cases. The idea is to compare a case's exposure immediately prior to or during the case-defining event with that same person's exposure at otherwise similar "reference" times. An alternative approach to the analysis of daily exposure and case-only data is time series analysis. Here, log-linear regression models express the expected total number of events on each day as a function of the exposure level and potential confounding variables. In time series analyses of air pollution, smooth functions of time and weather are the main confounders. Time series and case-crossover methods are often viewed as competing methods. In this paper, we show that case-crossover using conditional logistic regression is a special case of time series analysis when there is a common exposure such as in air pollution studies. This equivalence provides computational convenience for case-crossover analyses and a better understanding of time series models. Time series log-linear regression accounts for overdispersion of the Poisson variance, while case-crossover analyses typically do not. This equivalence also permits model checking for case-crossover data using standard log-linear model diagnostics.

MicroCT angiography detects vascular formation and regression in skin wound healing.

PubMed

Urao, Norifumi; Okonkwo, Uzoagu A; Fang, Milie M; Zhuang, Zhen W; Koh, Timothy J; DiPietro, Luisa A

2016-07-01

Properly regulated angiogenesis and arteriogenesis are essential for effective wound healing. Tissue injury induces robust new vessel formation and subsequent vessel maturation, which involves vessel regression and remodeling. Although formation of functional vasculature is essential for healing, alterations in vascular structure over the time course of skin wound healing are not well understood. Here, using high-resolution ex vivo X-ray micro-computed tomography (microCT), we describe the vascular network during healing of skin excisional wounds with highly detailed three-dimensional (3D) reconstructed images and associated quantitative analysis. We found that relative vessel volume, surface area and branching number are significantly decreased in wounds from day 7 to days 14 and 21. Segmentation and skeletonization analysis of selected branches from high-resolution images as small as 2.5μm voxel size show that branching orders are decreased in the wound vessels during healing. In histological analysis, we found that the contrast agent fills mainly arterioles, but not small capillaries nor large veins. In summary, high-resolution microCT revealed dynamic alterations of vessel structures during wound healing. This technique may be useful as a key tool in the study of the formation and regression of wound vessels. Copyright © 2016 Elsevier Inc. All rights reserved.

Bankfull characteristics of Ohio streams and their relation to peak streamflows

USGS Publications Warehouse

Sherwood, James M.; Huitger, Carrie A.

2005-01-01

Regional curves, simple-regression equations, and multiple-regression equations were developed to estimate bankfull width, bankfull mean depth, bankfull cross-sectional area, and bankfull discharge of rural, unregulated streams in Ohio. The methods are based on geomorphic, basin, and flood-frequency data collected at 50 study sites on unregulated natural alluvial streams in Ohio, of which 40 sites are near streamflow-gaging stations. The regional curves and simple-regression equations relate the bankfull characteristics to drainage area. The multiple-regression equations relate the bankfull characteristics to drainage area, main-channel slope, main-channel elevation index, median bed-material particle size, bankfull cross-sectional area, and local-channel slope. Average standard errors of prediction for bankfull width equations range from 20.6 to 24.8 percent; for bankfull mean depth, 18.8 to 20.6 percent; for bankfull cross-sectional area, 25.4 to 30.6 percent; and for bankfull discharge, 27.0 to 78.7 percent. The simple-regression (drainage-area only) equations have the highest average standard errors of prediction. The multiple-regression equations in which the explanatory variables included drainage area, main-channel slope, main-channel elevation index, median bed-material particle size, bankfull cross-sectional area, and local-channel slope have the lowest average standard errors of prediction. Field surveys were done at each of the 50 study sites to collect the geomorphic data. Bankfull indicators were identified and evaluated, cross-section and longitudinal profiles were surveyed, and bed- and bank-material were sampled. Field data were analyzed to determine various geomorphic characteristics such as bankfull width, bankfull mean depth, bankfull cross-sectional area, bankfull discharge, streambed slope, and bed- and bank-material particle-size distribution. The various geomorphic characteristics were analyzed by means of a combination of graphical and statistical techniques. The logarithms of the annual peak discharges for the 40 gaged study sites were fit by a Pearson Type III frequency distribution to develop flood-peak discharges associated with recurrence intervals of 2, 5, 10, 25, 50, and 100 years. The peak-frequency data were related to geomorphic, basin, and climatic variables by multiple-regression analysis. Simple-regression equations were developed to estimate 2-, 5-, 10-, 25-, 50-, and 100-year flood-peak discharges of rural, unregulated streams in Ohio from bankfull channel cross-sectional area. The average standard errors of prediction are 31.6, 32.6, 35.9, 41.5, 46.2, and 51.2 percent, respectively. The study and methods developed are intended to improve understanding of the relations between geomorphic, basin, and flood characteristics of streams in Ohio and to aid in the design of hydraulic structures, such as culverts and bridges, where stability of the stream and structure is an important element of the design criteria. The study was done in cooperation with the Ohio Department of Transportation and the U.S. Department of Transportation, Federal Highway Administration.

Utility of an Abbreviated Dizziness Questionnaire to Differentiate between Causes of Vertigo and Guide Appropriate Referral: A Multicenter Prospective Blinded Study

PubMed Central

Roland, Lauren T.; Kallogjeri, Dorina; Sinks, Belinda C.; Rauch, Steven D.; Shepard, Neil T.; White, Judith A.; Goebel, Joel A.

2015-01-01

Objective Test performance of a focused dizziness questionnaire’s ability to discriminate between peripheral and non-peripheral causes of vertigo. Study Design Prospective multi-center Setting Four academic centers with experienced balance specialists Patients New dizzy patients Interventions A 32-question survey was given to participants. Balance specialists were blinded and a diagnosis was established for all participating patients within 6 months. Main outcomes Multinomial logistic regression was used to evaluate questionnaire performance in predicting final diagnosis and differentiating between peripheral and non-peripheral vertigo. Univariate and multivariable stepwise logistic regression were used to identify questions as significant predictors of the ultimate diagnosis. C-index was used to evaluate performance and discriminative power of the multivariable models. Results 437 patients participated in the study. Eight participants without confirmed diagnoses were excluded and 429 were included in the analysis. Multinomial regression revealed that the model had good overall predictive accuracy of 78.5% for the final diagnosis and 75.5% for differentiating between peripheral and non-peripheral vertigo. Univariate logistic regression identified significant predictors of three main categories of vertigo: peripheral, central and other. Predictors were entered into forward stepwise multivariable logistic regression. The discriminative power of the final models for peripheral, central and other causes were considered good as measured by c-indices of 0.75, 0.7 and 0.78, respectively. Conclusions This multicenter study demonstrates a focused dizziness questionnaire can accurately predict diagnosis for patients with chronic/relapsing dizziness referred to outpatient clinics. Additionally, this survey has significant capability to differentiate peripheral from non-peripheral causes of vertigo and may, in the future, serve as a screening tool for specialty referral. Clinical utility of this questionnaire to guide specialty referral is discussed. PMID:26485598

[A Review on the Use of Effect Size in Nursing Research].

PubMed

Kang, Hyuncheol; Yeon, Kyupil; Han, Sang Tae

2015-10-01

The purpose of this study was to introduce the main concepts of statistical testing and effect size and to provide researchers in nursing science with guidance on how to calculate the effect size for the statistical analysis methods mainly used in nursing. For t-test, analysis of variance, correlation analysis, regression analysis which are used frequently in nursing research, the generally accepted definitions of the effect size were explained. Some formulae for calculating the effect size are described with several examples in nursing research. Furthermore, the authors present the required minimum sample size for each example utilizing G*Power 3 software that is the most widely used program for calculating sample size. It is noted that statistical significance testing and effect size measurement serve different purposes, and the reliance on only one side may be misleading. Some practical guidelines are recommended for combining statistical significance testing and effect size measure in order to make more balanced decisions in quantitative analyses.

Regression analysis and transfer function in estimating the parameters of central pulse waves from brachial pulse wave.

PubMed

Chai Rui; Li Si-Man; Xu Li-Sheng; Yao Yang; Hao Li-Ling

2017-07-01

This study mainly analyzed the parameters such as ascending branch slope (A_slope), dicrotic notch height (Hn), diastolic area (Ad) and systolic area (As) diastolic blood pressure (DBP), systolic blood pressure (SBP), pulse pressure (PP), subendocardial viability ratio (SEVR), waveform parameter (k), stroke volume (SV), cardiac output (CO) and peripheral resistance (RS) of central pulse wave invasively and non-invasively measured. These parameters extracted from the central pulse wave invasively measured were compared with the parameters measured from the brachial pulse waves by a regression model and a transfer function model. The accuracy of the parameters which were estimated by the regression model and the transfer function model was compared too. Our findings showed that in addition to the k value, the above parameters of the central pulse wave and the brachial pulse wave invasively measured had positive correlation. Both the regression model parameters including A_slope, DBP, SEVR and the transfer function model parameters had good consistency with the parameters invasively measured, and they had the same effect of consistency. The regression equations of the three parameters were expressed by Y'=a+bx. The SBP, PP, SV, CO of central pulse wave could be calculated through the regression model, but their accuracies were worse than that of transfer function model.

Effect of micro-oxygenation on sensory characteristics and consumer preference of Cabernet Sauvignon wine.

PubMed

Parpinello, Giuseppina Paola; Plumejeau, François; Maury, Chantal; Versari, Andrea

2012-04-01

The main objective of this study was to improve the structure of a Cabernet Sauvignon red wine in a short period of time by micro-oxygenation (MOX) at high rates (25 and 50 mL L(-1) month(-1) ), the effects of which were evaluated based on sensory characteristics and consumer preference. Sensory data were analysed by principal component analysis, discriminant analysis and ordinal logistic regression (OLR). MOX led to significant differences in the colour, colour stability and phenolic compounds of wine. Sensory characteristics also changed through MOX treatment, and wine experts were able to discriminate between MOX-treated and untreated samples, with olfactory intensity, complexity, astringency and roundness being the main discriminating characteristics. Ordinal logistic regression enabled identification of the sensory characteristics that drove consumer preference. MOX at high rates improved the sensory characteristics of wine and may therefore be considered a valid technique for obtaining structured wines in a short period of time, i.e. within just a few months after the vintage. The results highlight the need for (i) careful selection of the MOX dosage rate and duration (the 25 mL L(-1) month(-1) dose for 6 days provided the best result) and (ii) continuous monitoring of the MOX treatment. Copyright © 2011 Society of Chemical Industry.

Multiple imputation for cure rate quantile regression with censored data.

PubMed

Wu, Yuanshan; Yin, Guosheng

2017-03-01

The main challenge in the context of cure rate analysis is that one never knows whether censored subjects are cured or uncured, or whether they are susceptible or insusceptible to the event of interest. Considering the susceptible indicator as missing data, we propose a multiple imputation approach to cure rate quantile regression for censored data with a survival fraction. We develop an iterative algorithm to estimate the conditionally uncured probability for each subject. By utilizing this estimated probability and Bernoulli sample imputation, we can classify each subject as cured or uncured, and then employ the locally weighted method to estimate the quantile regression coefficients with only the uncured subjects. Repeating the imputation procedure multiple times and taking an average over the resultant estimators, we obtain consistent estimators for the quantile regression coefficients. Our approach relaxes the usual global linearity assumption, so that we can apply quantile regression to any particular quantile of interest. We establish asymptotic properties for the proposed estimators, including both consistency and asymptotic normality. We conduct simulation studies to assess the finite-sample performance of the proposed multiple imputation method and apply it to a lung cancer study as an illustration. © 2016, The International Biometric Society.

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.

Methodologic considerations in the design and analysis of nested case-control studies: association between cytokines and postoperative delirium.

PubMed

Ngo, Long H; Inouye, Sharon K; Jones, Richard N; Travison, Thomas G; Libermann, Towia A; Dillon, Simon T; Kuchel, George A; Vasunilashorn, Sarinnapha M; Alsop, David C; Marcantonio, Edward R

2017-06-06

The nested case-control study (NCC) design within a prospective cohort study is used when outcome data are available for all subjects, but the exposure of interest has not been collected, and is difficult or prohibitively expensive to obtain for all subjects. A NCC analysis with good matching procedures yields estimates that are as efficient and unbiased as estimates from the full cohort study. We present methodological considerations in a matched NCC design and analysis, which include the choice of match algorithms, analysis methods to evaluate the association of exposures of interest with outcomes, and consideration of overmatching. Matched, NCC design within a longitudinal observational prospective cohort study in the setting of two academic hospitals. Study participants are patients aged over 70 years who underwent scheduled major non-cardiac surgery. The primary outcome was postoperative delirium from in-hospital interviews and medical record review. The main exposure was IL-6 concentration (pg/ml) from blood sampled at three time points before delirium occurred. We used nonparametric signed ranked test to test for the median of the paired differences. We used conditional logistic regression to model the risk of IL-6 on delirium incidence. Simulation was used to generate a sample of cohort data on which unconditional multivariable logistic regression was used, and the results were compared to those of the conditional logistic regression. Partial R-square was used to assess the level of overmatching. We found that the optimal match algorithm yielded more matched pairs than the greedy algorithm. The choice of analytic strategy-whether to consider measured cytokine levels as the predictor or outcome-- yielded inferences that have different clinical interpretations but similar levels of statistical significance. Estimation results from NCC design using conditional logistic regression, and from simulated cohort design using unconditional logistic regression, were similar. We found minimal evidence for overmatching. Using a matched NCC approach introduces methodological challenges into the study design and data analysis. Nonetheless, with careful selection of the match algorithm, match factors, and analysis methods, this design is cost effective and, for our study, yields estimates that are similar to those from a prospective cohort study design.

[Predicting the probability of development and progression of primary open angle glaucoma by regression modeling].

PubMed

Likhvantseva, V G; Sokolov, V A; Levanova, O N; Kovelenova, I V

2018-01-01

Prediction of the clinical course of primary open-angle glaucoma (POAG) is one of the main directions in solving the problem of vision loss prevention and stabilization of the pathological process. Simple statistical methods of correlation analysis show the extent of each risk factor's impact, but do not indicate the total impact of these factors in personalized combinations. The relationships between the risk factors is subject to correlation and regression analysis. The regression equation represents the dependence of the mathematical expectation of the resulting sign on the combination of factor signs. To develop a technique for predicting the probability of development and progression of primary open-angle glaucoma based on a personalized combination of risk factors by linear multivariate regression analysis. The study included 66 patients (23 female and 43 male; 132 eyes) with newly diagnosed primary open-angle glaucoma. The control group consisted of 14 patients (8 male and 6 female). Standard ophthalmic examination was supplemented with biochemical study of lacrimal fluid. Concentration of matrix metalloproteinase MMP-2 and MMP-9 in tear fluid in both eyes was determined using 'sandwich' enzyme-linked immunosorbent assay (ELISA) method. The study resulted in the development of regression equations and step-by-step multivariate logistic models that can help calculate the risk of development and progression of POAG. Those models are based on expert evaluation of clinical and instrumental indicators of hydrodynamic disturbances (coefficient of outflow ease - C, volume of intraocular fluid secretion - F, fluctuation of intraocular pressure), as well as personalized morphometric parameters of the retina (central retinal thickness in the macular area) and concentration of MMP-2 and MMP-9 in the tear film. The newly developed regression equations are highly informative and can be a reliable tool for studying of the influence vector and assessment of pathogenic potential of the independent risk factors in specific personalized combinations.

Sperm Retrieval in Patients with Klinefelter Syndrome: A Skewed Regression Model Analysis.

PubMed

Chehrazi, Mohammad; Rahimiforoushani, Abbas; Sabbaghian, Marjan; Nourijelyani, Keramat; Sadighi Gilani, Mohammad Ali; Hoseini, Mostafa; Vesali, Samira; Yaseri, Mehdi; Alizadeh, Ahad; Mohammad, Kazem; Samani, Reza Omani

2017-01-01

The most common chromosomal abnormality due to non-obstructive azoospermia (NOA) is Klinefelter syndrome (KS) which occurs in 1-1.72 out of 500-1000 male infants. The probability of retrieving sperm as the outcome could be asymmetrically different between patients with and without KS, therefore logistic regression analysis is not a well-qualified test for this type of data. This study has been designed to evaluate skewed regression model analysis for data collected from microsurgical testicular sperm extraction (micro-TESE) among azoospermic patients with and without non-mosaic KS syndrome. This cohort study compared the micro-TESE outcome between 134 men with classic KS and 537 men with NOA and normal karyotype who were referred to Royan Institute between 2009 and 2011. In addition to our main outcome, which was sperm retrieval, we also used logistic and skewed regression analyses to compare the following demographic and hormonal factors: age, level of follicle stimulating hormone (FSH), luteinizing hormone (LH), and testosterone between the two groups. A comparison of the micro-TESE between the KS and control groups showed a success rate of 28.4% (38/134) for the KS group and 22.2% (119/537) for the control group. In the KS group, a significantly difference (P<0.001) existed between testosterone levels for the successful sperm retrieval group (3.4 ± 0.48 mg/mL) compared to the unsuccessful sperm retrieval group (2.33 ± 0.23 mg/mL). The index for quasi Akaike information criterion (QAIC) had a goodness of fit of 74 for the skewed model which was lower than logistic regression (QAIC=85). According to the results, skewed regression is more efficient in estimating sperm retrieval success when the data from patients with KS are analyzed. This finding should be investigated by conducting additional studies with different data structures.

Features of natural and gonadotropin-releasing hormone antagonist-induced corpus luteum regression and effects of in vivo human chorionic gonadotropin.

PubMed

Del Canto, Felipe; Sierralta, Walter; Kohen, Paulina; Muñoz, Alex; Strauss, Jerome F; Devoto, Luigi

2007-11-01

The natural process of luteolysis and luteal regression is induced by withdrawal of gonadotropin support. The objectives of this study were: 1) to compare the functional changes and apoptotic features of natural human luteal regression and induced luteal regression; 2) to define the ultrastructural characteristics of the corpus luteum at the time of natural luteal regression and induced luteal regression; and 3) to examine the effect of human chorionic gonadotropin (hCG) on the steroidogenic response and apoptotic markers within the regressing corpus luteum. Twenty-three women with normal menstrual cycles undergoing tubal ligation donated corpus luteum at specific stages in the luteal phase. Some women received a GnRH antagonist prior to collection of corpus luteum, others received an injection of hCG with or without prior treatment with a GnRH antagonist. Main outcome measures were plasma hormone levels and analysis of excised luteal tissue for markers of apoptosis, histology, and ultrastructure. The progesterone and estradiol levels, corpus luteum DNA, and protein contents in induced luteal regression resembled those of natural luteal regression. hCG treatment raised progesterone and estradiol in both natural luteal regression and induced luteal regression. The increase in apoptosis detected in induced luteal regression by cytochrome c in the cytosol, activated caspase-3, and nuclear DNA fragmentation, was similar to that observed in natural luteal regression. The antiapoptotic protein Bcl-2 was significantly lower during natural luteal regression. The proapoptotic proteins Bax and Bak were at a constant level. Apoptotic and nonapoptotic death of luteal cells was observed in natural luteal regression and induced luteal regression at the ultrastructural level. hCG prevented apoptotic cell death, but not autophagy. The low number of apoptotic cells disclosed and the frequent autophagocytic suggest that multiple mechanisms are involved in cell death at luteal regression. hCG restores steroidogenic function and restrains the apoptotic process, but not autophagy.

Survey data on household electricity consumption and living status in Northwestern China.

PubMed

Niu, Shuwen; Jia, Yanqin; Ye, Liqiong; Dai, Runqi; Li, Na

2016-06-01

Based on 1128 survey questionnaires, main information on urban and rural household electricity consumption was obtained. Original data included household income, the price of electricity, all kinds of electrical appliances, purchase price of main appliances, household size, electricity consumption, as well as power, daily use time of electrical appliances in this data article. These data fully reflected behavior, preferences and living pattern of sample households in electricity use and provided the basis for analyzing the relationship between household electricity consumption and the quality of life ("Does electricity consumption improve residential living status in less developed regions? An empirical analysis using the quantile regression approach" [1]).

Flood-frequency prediction methods for unregulated streams of Tennessee, 2000

USGS Publications Warehouse

Law, George S.; Tasker, Gary D.

2003-01-01

Up-to-date flood-frequency prediction methods for unregulated, ungaged rivers and streams of Tennessee have been developed. Prediction methods include the regional-regression method and the newer region-of-influence method. The prediction methods were developed using stream-gage records from unregulated streams draining basins having from 1 percent to about 30 percent total impervious area. These methods, however, should not be used in heavily developed or storm-sewered basins with impervious areas greater than 10 percent. The methods can be used to estimate 2-, 5-, 10-, 25-, 50-, 100-, and 500-year recurrence-interval floods of most unregulated rural streams in Tennessee. A computer application was developed that automates the calculation of flood frequency for unregulated, ungaged rivers and streams of Tennessee. Regional-regression equations were derived by using both single-variable and multivariable regional-regression analysis. Contributing drainage area is the explanatory variable used in the single-variable equations. Contributing drainage area, main-channel slope, and a climate factor are the explanatory variables used in the multivariable equations. Deleted-residual standard error for the single-variable equations ranged from 32 to 65 percent. Deleted-residual standard error for the multivariable equations ranged from 31 to 63 percent. These equations are included in the computer application to allow easy comparison of results produced by the different methods. The region-of-influence method calculates multivariable regression equations for each ungaged site and recurrence interval using basin characteristics from 60 similar sites selected from the study area. Explanatory variables that may be used in regression equations computed by the region-of-influence method include contributing drainage area, main-channel slope, a climate factor, and a physiographic-region factor. Deleted-residual standard error for the region-of-influence method tended to be only slightly smaller than those for the regional-regression method and ranged from 27 to 62 percent.

The impact of young drivers' lifestyle on their road traffic accident risk in greater Athens area.

PubMed

Chliaoutakis, J E; Darviri, C; Demakakos, P T

1999-11-01

Young drivers (18-24) both in Greece and elsewhere appear to have high rates of road traffic accidents. Many factors contribute to the creation of these high road traffic accidents rates. It has been suggested that lifestyle is an important one. The main objective of this study is to find out and clarify the (potential) relationship between young drivers' lifestyle and the road traffic accident risk they face. Moreover, to examine if all the youngsters have the same elevated risk on the road or not. The sample consisted of 241 young Greek drivers of both sexes. The statistical analysis included factor analysis and logistic regression analysis. Through the principal component analysis a ten factor scale was created which included the basic lifestyle traits of young Greek drivers. The logistic regression analysis showed that the young drivers whose dominant lifestyle trait is alcohol consumption or drive without destination have high accident risk, while these whose dominant lifestyle trait is culture, face low accident risk. Furthermore, young drivers who are religious in one way or another seem to have low accident risk. Finally, some preliminary observations on how health promotion should be put into practice are discussed.

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

PubMed

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

2013-02-01

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

Early Warning Signals of Financial Crises with Multi-Scale Quantile Regressions of Log-Periodic Power Law Singularities

PubMed Central

Zhang, Qun; Zhang, Qunzhi; Sornette, Didier

2016-01-01

We augment the existing literature using the Log-Periodic Power Law Singular (LPPLS) structures in the log-price dynamics to diagnose financial bubbles by providing three main innovations. First, we introduce the quantile regression to the LPPLS detection problem. This allows us to disentangle (at least partially) the genuine LPPLS signal and the a priori unknown complicated residuals. Second, we propose to combine the many quantile regressions with a multi-scale analysis, which aggregates and consolidates the obtained ensembles of scenarios. Third, we define and implement the so-called DS LPPLS Confidence™ and Trust™ indicators that enrich considerably the diagnostic of bubbles. Using a detailed study of the “S&P 500 1987” bubble and presenting analyses of 16 historical bubbles, we show that the quantile regression of LPPLS signals contributes useful early warning signals. The comparison between the constructed signals and the price development in these 16 historical bubbles demonstrates their significant predictive ability around the real critical time when the burst/rally occurs. PMID:27806093

Review and Recommendations for Zero-inflated Count Regression Modeling of Dental Caries Indices in Epidemiological Studies

PubMed Central

Stamm, John W.; Long, D. Leann; Kincade, Megan E.

2012-01-01

Over the past five to ten years, zero-inflated count regression models have been increasingly applied to the analysis of dental caries indices (e.g., DMFT, dfms, etc). The main reason for that is linked to the broad decline in children’s caries experience, such that dmf and DMF indices more frequently generate low or even zero counts. This article specifically reviews the application of zero-inflated Poisson and zero-inflated negative binomial regression models to dental caries, with emphasis on the description of the models and the interpretation of fitted model results given the study goals. The review finds that interpretations provided in the published caries research are often imprecise or inadvertently misleading, particularly with respect to failing to discriminate between inference for the class of susceptible persons defined by such models and inference for the sampled population in terms of overall exposure effects. Recommendations are provided to enhance the use as well as the interpretation and reporting of results of count regression models when applied to epidemiological studies of dental caries. PMID:22710271

[Algorithms of artificial neural networks--practical application in medical science].

PubMed

Stefaniak, Bogusław; Cholewiński, Witold; Tarkowska, Anna

2005-12-01

Artificial Neural Networks (ANN) may be a tool alternative and complementary to typical statistical analysis. However, in spite of many computer applications of various ANN algorithms ready for use, artificial intelligence is relatively rarely applied to data processing. This paper presents practical aspects of scientific application of ANN in medicine using widely available algorithms. Several main steps of analysis with ANN were discussed starting from material selection and dividing it into groups, to the quality assessment of obtained results at the end. The most frequent, typical reasons for errors as well as the comparison of ANN method to the modeling by regression analysis were also described.

Determination of Uncertainties for the New SSME Model

NASA Technical Reports Server (NTRS)

Coleman, Hugh W.; Hawk, Clark W.

1996-01-01

This report discusses the uncertainty analysis performed in support of a new test analysis and performance prediction model for the Space Shuttle Main Engine. The new model utilizes uncertainty estimates for experimental data and for the analytical model to obtain the most plausible operating condition for the engine system. This report discusses the development of the data sets and uncertainty estimates to be used in the development of the new model. It also presents the application of uncertainty analysis to analytical models and the uncertainty analysis for the conservation of mass and energy balance relations is presented. A new methodology for the assessment of the uncertainty associated with linear regressions is presented.

A meta-analysis investigating factors underlying attrition rates in infant ERP studies.

PubMed

Stets, Manuela; Stahl, Daniel; Reid, Vincent M

2012-01-01

In this meta-analysis, we examined interrelationships between characteristics of infant event-related potential (ERP) studies and their attrition rates. One-hundred and forty-nine published studies provided information on 314 experimental groups of which 181 provided data on attrition. A random effects meta-analysis revealed a high average attrition rate of 49.2%. Additionally, we used meta-regression for 178 groups with attrition data to analyze which variables best explained attrition variance. Our main findings were that the nature of the stimuli-visual, auditory, or combined as well as if stimuli were animated-influenced exclusion rates from the final analysis and that infant age did not alter attrition rates.

Principal components and iterative regression analysis of geophysical series: Application to Sunspot number (1750 2004)

NASA Astrophysics Data System (ADS)

Nordemann, D. J. R.; Rigozo, N. R.; de Souza Echer, M. P.; Echer, E.

2008-11-01

We present here an implementation of a least squares iterative regression method applied to the sine functions embedded in the principal components extracted from geophysical time series. This method seems to represent a useful improvement for the non-stationary time series periodicity quantitative analysis. The principal components determination followed by the least squares iterative regression method was implemented in an algorithm written in the Scilab (2006) language. The main result of the method is to obtain the set of sine functions embedded in the series analyzed in decreasing order of significance, from the most important ones, likely to represent the physical processes involved in the generation of the series, to the less important ones that represent noise components. Taking into account the need of a deeper knowledge of the Sun's past history and its implication to global climate change, the method was applied to the Sunspot Number series (1750-2004). With the threshold and parameter values used here, the application of the method leads to a total of 441 explicit sine functions, among which 65 were considered as being significant and were used for a reconstruction that gave a normalized mean squared error of 0.146.

Forecasting Container Throughput at the Doraleh Port in Djibouti through Time Series Analysis

NASA Astrophysics Data System (ADS)

Mohamed Ismael, Hawa; Vandyck, George Kobina

The Doraleh Container Terminal (DCT) located in Djibouti has been noted as the most technologically advanced container terminal on the African continent. DCT's strategic location at the crossroads of the main shipping lanes connecting Asia, Africa and Europe put it in a unique position to provide important shipping services to vessels plying that route. This paper aims to forecast container throughput through the Doraleh Container Port in Djibouti by Time Series Analysis. A selection of univariate forecasting models has been used, namely Triple Exponential Smoothing Model, Grey Model and Linear Regression Model. By utilizing the above three models and their combination, the forecast of container throughput through the Doraleh port was realized. A comparison of the different forecasting results of the three models, in addition to the combination forecast is then undertaken, based on commonly used evaluation criteria Mean Absolute Deviation (MAD) and Mean Absolute Percentage Error (MAPE). The study found that the Linear Regression forecasting Model was the best prediction method for forecasting the container throughput, since its forecast error was the least. Based on the regression model, a ten (10) year forecast for container throughput at DCT has been made.

Quantification of endocrine disruptors and pesticides in water by gas chromatography-tandem mass spectrometry. Method validation using weighted linear regression schemes.

PubMed

Mansilha, C; Melo, A; Rebelo, H; Ferreira, I M P L V O; Pinho, O; Domingues, V; Pinho, C; Gameiro, P

2010-10-22

A multi-residue methodology based on a solid phase extraction followed by gas chromatography-tandem mass spectrometry was developed for trace analysis of 32 compounds in water matrices, including estrogens and several pesticides from different chemical families, some of them with endocrine disrupting properties. Matrix standard calibration solutions were prepared by adding known amounts of the analytes to a residue-free sample to compensate matrix-induced chromatographic response enhancement observed for certain pesticides. Validation was done mainly according to the International Conference on Harmonisation recommendations, as well as some European and American validation guidelines with specifications for pesticides analysis and/or GC-MS methodology. As the assumption of homoscedasticity was not met for analytical data, weighted least squares linear regression procedure was applied as a simple and effective way to counteract the greater influence of the greater concentrations on the fitted regression line, improving accuracy at the lower end of the calibration curve. The method was considered validated for 31 compounds after consistent evaluation of the key analytical parameters: specificity, linearity, limit of detection and quantification, range, precision, accuracy, extraction efficiency, stability and robustness. Copyright © 2010 Elsevier B.V. All rights reserved.

Mechanisms behind the estimation of photosynthesis traits from leaf reflectance observations

NASA Astrophysics Data System (ADS)

Dechant, Benjamin; Cuntz, Matthias; Doktor, Daniel; Vohland, Michael

2016-04-01

Many studies have investigated the reflectance-based estimation of leaf chlorophyll, water and dry matter contents of plants. Only few studies focused on photosynthesis traits, however. The maximum potential uptake of carbon dioxide under given environmental conditions is determined mainly by RuBisCO activity, limiting carboxylation, or the speed of photosynthetic electron transport. These two main limitations are represented by the maximum carboxylation capacity, V cmax,25, and the maximum electron transport rate, Jmax,25. These traits were estimated from leaf reflectance before but the mechanisms underlying the estimation remain rather speculative. The aim of this study was therefore to reveal the mechanisms behind reflectance-based estimation of V cmax,25 and Jmax,25. Leaf reflectance, photosynthetic response curves as well as nitrogen content per area, Narea, and leaf mass per area, LMA, were measured on 37 deciduous tree species. V cmax,25 and Jmax,25 were determined from the response curves. Partial Least Squares (PLS) regression models for the two photosynthesis traits V cmax,25 and Jmax,25 as well as Narea and LMA were studied using a cross-validation approach. Analyses of linear regression models based on Narea and other leaf traits estimated via PROSPECT inversion, PLS regression coefficients and model residuals were conducted in order to reveal the mechanisms behind the reflectance-based estimation. We found that V cmax,25 and Jmax,25 can be estimated from leaf reflectance with good to moderate accuracy for a large number of species and different light conditions. The dominant mechanism behind the estimations was the strong relationship between photosynthesis traits and leaf nitrogen content. This was concluded from very strong relationships between PLS regression coefficients, the model residuals as well as the prediction performance of Narea- based linear regression models compared to PLS regression models. While the PLS regression model for V cmax,25 was fully based on the correlation to Narea, the PLS regression model for Jmax,25 was not entirely based on it. Analyses of the contributions of different parts of the reflectance spectrum revealed that the information contributing to the Jmax,25 PLS regression model in addition to the main source of information, Narea, was mainly located in the visible part of the spectrum (500-900 nm). Estimated chlorophyll content could be excluded as potential source of this extra information. The PLS regression coefficients of the Jmax,25 model indicated possible contributions from chlorophyll fluorescence and cytochrome f content. In summary, we found that the main mechanism behind the estimation of V cmax,25 and Jmax,25 from leaf reflectance observations is the correlation to Narea but that there is additional information related to Jmax,25 mainly in the visible part of the spectrum.

Linear regression models and k-means clustering for statistical analysis of fNIRS data.

PubMed

Bonomini, Viola; Zucchelli, Lucia; Re, Rebecca; Ieva, Francesca; Spinelli, Lorenzo; Contini, Davide; Paganoni, Anna; Torricelli, Alessandro

2015-02-01

We propose a new algorithm, based on a linear regression model, to statistically estimate the hemodynamic activations in fNIRS data sets. The main concern guiding the algorithm development was the minimization of assumptions and approximations made on the data set for the application of statistical tests. Further, we propose a K-means method to cluster fNIRS data (i.e. channels) as activated or not activated. The methods were validated both on simulated and in vivo fNIRS data. A time domain (TD) fNIRS technique was preferred because of its high performances in discriminating cortical activation and superficial physiological changes. However, the proposed method is also applicable to continuous wave or frequency domain fNIRS data sets.

Linear regression models and k-means clustering for statistical analysis of fNIRS data

PubMed Central

Bonomini, Viola; Zucchelli, Lucia; Re, Rebecca; Ieva, Francesca; Spinelli, Lorenzo; Contini, Davide; Paganoni, Anna; Torricelli, Alessandro

2015-01-01

We propose a new algorithm, based on a linear regression model, to statistically estimate the hemodynamic activations in fNIRS data sets. The main concern guiding the algorithm development was the minimization of assumptions and approximations made on the data set for the application of statistical tests. Further, we propose a K-means method to cluster fNIRS data (i.e. channels) as activated or not activated. The methods were validated both on simulated and in vivo fNIRS data. A time domain (TD) fNIRS technique was preferred because of its high performances in discriminating cortical activation and superficial physiological changes. However, the proposed method is also applicable to continuous wave or frequency domain fNIRS data sets. PMID:25780751

A comparison of model-based imputation methods for handling missing predictor values in a linear regression model: A simulation study

NASA Astrophysics Data System (ADS)

Hasan, Haliza; Ahmad, Sanizah; Osman, Balkish Mohd; Sapri, Shamsiah; Othman, Nadirah

2017-08-01

In regression analysis, missing covariate data has been a common problem. Many researchers use ad hoc methods to overcome this problem due to the ease of implementation. However, these methods require assumptions about the data that rarely hold in practice. Model-based methods such as Maximum Likelihood (ML) using the expectation maximization (EM) algorithm and Multiple Imputation (MI) are more promising when dealing with difficulties caused by missing data. Then again, inappropriate methods of missing value imputation can lead to serious bias that severely affects the parameter estimates. The main objective of this study is to provide a better understanding regarding missing data concept that can assist the researcher to select the appropriate missing data imputation methods. A simulation study was performed to assess the effects of different missing data techniques on the performance of a regression model. The covariate data were generated using an underlying multivariate normal distribution and the dependent variable was generated as a combination of explanatory variables. Missing values in covariate were simulated using a mechanism called missing at random (MAR). Four levels of missingness (10%, 20%, 30% and 40%) were imposed. ML and MI techniques available within SAS software were investigated. A linear regression analysis was fitted and the model performance measures; MSE, and R-Squared were obtained. Results of the analysis showed that MI is superior in handling missing data with highest R-Squared and lowest MSE when percent of missingness is less than 30%. Both methods are unable to handle larger than 30% level of missingness.

Choice-Based Conjoint Analysis: Classification vs. Discrete Choice Models

NASA Astrophysics Data System (ADS)

Giesen, Joachim; Mueller, Klaus; Taneva, Bilyana; Zolliker, Peter

Conjoint analysis is a family of techniques that originated in psychology and later became popular in market research. The main objective of conjoint analysis is to measure an individual's or a population's preferences on a class of options that can be described by parameters and their levels. We consider preference data obtained in choice-based conjoint analysis studies, where one observes test persons' choices on small subsets of the options. There are many ways to analyze choice-based conjoint analysis data. Here we discuss the intuition behind a classification based approach, and compare this approach to one based on statistical assumptions (discrete choice models) and to a regression approach. Our comparison on real and synthetic data indicates that the classification approach outperforms the discrete choice models.

An Examination of the Demographic and Career Progression of Air Force Institute of Technology Cost Analysis Graduates.

DTIC Science & Technology

1997-09-01

program include the ACEIT software training and the combination of Department of Defense (DOD) application, regression, and statistics. The weaknesses...and Integrated Tools ( ACEIT ) software and training could not be praised enough. AFIT vs. Civilian Institutions. The GCA program provides a Department...very useful to the graduates and beneficial to their careers. The main strengths of the program include the ACEIT software training and the combination

The contribution of wind wave changes on diminishing ice period in Lake Pyhäjärvi during the last half-century.

PubMed

Wu, Tingfeng; Qin, Boqiang; Zhu, Guangwei; Huttula, Timo; Lindfors, Antti; Ventelä, Anne-Mari; Sheng, Yongwei; Ambrose, Richard F

2018-06-21

To address the contribution of long-term wind wave changes on diminishing ice period in Northern European lakes, an in situ observation of wind waves was conducted to calibrate a wind-wave numerical model for Lake Pyhäjärvi, which is the largest lake in southwest Finland. Using station-measured hydrometeorological data from 1963 to 2013 and model-simulated wind waves, correlation and regression analyses were conducted to assess the changing trend and main influences on ice period. Ice period in Lake Pyhäjärvi decreased significantly over 51 years (r = 0.47, P < 0.01). The analysis of main hydrometeorological factors to ice period showed that the significant air temperature rise is the main contributor for the diminishing of ice period in the lake. Besides air temperature, wind-induced waves can also weaken lake ice by increasing water mixing and lake ice breakage. The regression indicated that mean significant wave height in December and April was negatively related to ice period (r = - 0.48, P < 0.01). These results imply that long-term changes of wind waves related to climate change should be considered to fully understand the reduction of aquatic ice at high latitudes.

High Prevalence of Cardiovascular Disease in End-Stage Kidney Disease Patients Ongoing Hemodialysis in Peru: Why Should We Care About It?

PubMed

Bravo-Jaimes, Katia; Whittembury, Alvaro; Santivañez, Vilma

2015-01-01

Purpose. To determine clinical, biochemical, and pharmacological characteristics as well as cardiovascular disease prevalence and its associated factors among end-stage kidney disease patients receiving hemodialysis in the main hemodialysis center in Lima, Peru. Methods. This cross-sectional study included 103 patients. Clinical charts were reviewed and an echocardiogram was performed to determine prevalence of cardiovascular disease, defined as the presence of systolic/diastolic dysfunction, coronary heart disease, ventricular dysrhythmias, cerebrovascular disease, and/or peripheral vascular disease. Associations between cardiovascular disease and clinical, biochemical, and dialysis factors were sought using prevalence ratio. A robust Poisson regression model was used to quantify possible associations. Results. Cardiovascular disease prevalence was 81.6%, mainly due to diastolic dysfunction. It was significantly associated with age older than 50 years, metabolic syndrome, C-reactive protein levels, effective blood flow ≤ 300 mL/min, severe anemia, and absence of mild anemia. However, in the regression analysis only age older than 50 years, effective blood flow ≤ 300 mL/min, and absence of mild anemia were associated. Conclusions. Cardiovascular disease prevalence is high in patients receiving hemodialysis in the main center in Lima. Diastolic dysfunction, age, specific hemoglobin levels, and effective blood flow may play an important role.

Discrimination of orange beverage emulsions with different formulations using multivariate analysis.

PubMed

Mirhosseini, Hamed; Tan, Chin Ping

2010-06-01

The constituents in a food emulsion interact with each other, either physically or chemically, determining the overall physico-chemical and organoleptic properties of the final product. Thus, the main objective of present study was to investigate the effect of emulsion components on beverage emulsion properties. In most cases, the second-order polynomial regression models with no significant (P > 0.05) lack of fit and high adjusted coefficient of determination (adjusted R(2), 0.851-0.996) were significantly fitted to explain the beverage emulsion properties as function of main emulsion components. The main effect of gum arabic was found to be significant (P < 0.05) in all response regression models. Orange beverage emulsion containing 222.0 g kg(-1) gum arabic, 2.4 g kg(-1) xanthan gum and 152.7 g kg(-1) orange oil was predicted to provide the desirable emulsion properties. The present study suggests that the concentration of gum arabic should be considered as a primary critical factor for the formulation of orange beverage emulsion. This study also indicated that the interaction effect between xanthan gum and orange oil showed the most significant (P < 0.05) effect among all interaction effects influencing all the physicochemical properties except for density. Copyright (c) 2010 Society of Chemical Industry.

Multivariate random regression analysis for body weight and main morphological traits in genetically improved farmed tilapia (Oreochromis niloticus).

PubMed

He, Jie; Zhao, Yunfeng; Zhao, Jingli; Gao, Jin; Han, Dandan; Xu, Pao; Yang, Runqing

2017-11-02

Because of their high economic importance, growth traits in fish are under continuous improvement. For growth traits that are recorded at multiple time-points in life, the use of univariate and multivariate animal models is limited because of the variable and irregular timing of these measures. Thus, the univariate random regression model (RRM) was introduced for the genetic analysis of dynamic growth traits in fish breeding. We used a multivariate random regression model (MRRM) to analyze genetic changes in growth traits recorded at multiple time-point of genetically-improved farmed tilapia. Legendre polynomials of different orders were applied to characterize the influences of fixed and random effects on growth trajectories. The final MRRM was determined by optimizing the univariate RRM for the analyzed traits separately via penalizing adaptively the likelihood statistical criterion, which is superior to both the Akaike information criterion and the Bayesian information criterion. In the selected MRRM, the additive genetic effects were modeled by Legendre polynomials of three orders for body weight (BWE) and body length (BL) and of two orders for body depth (BD). By using the covariance functions of the MRRM, estimated heritabilities were between 0.086 and 0.628 for BWE, 0.155 and 0.556 for BL, and 0.056 and 0.607 for BD. Only heritabilities for BD measured from 60 to 140 days of age were consistently higher than those estimated by the univariate RRM. All genetic correlations between growth time-points exceeded 0.5 for either single or pairwise time-points. Moreover, correlations between early and late growth time-points were lower. Thus, for phenotypes that are measured repeatedly in aquaculture, an MRRM can enhance the efficiency of the comprehensive selection for BWE and the main morphological traits.

Drivers of wetland conversion: a global meta-analysis.

PubMed

van Asselen, Sanneke; Verburg, Peter H; Vermaat, Jan E; Janse, Jan H

2013-01-01

Meta-analysis of case studies has become an important tool for synthesizing case study findings in land change. Meta-analyses of deforestation, urbanization, desertification and change in shifting cultivation systems have been published. This present study adds to this literature, with an analysis of the proximate causes and underlying forces of wetland conversion at a global scale using two complementary approaches of systematic review. Firstly, a meta-analysis of 105 case-study papers describing wetland conversion was performed, showing that different combinations of multiple-factor proximate causes, and underlying forces, drive wetland conversion. Agricultural development has been the main proximate cause of wetland conversion, and economic growth and population density are the most frequently identified underlying forces. Secondly, to add a more quantitative component to the study, a logistic meta-regression analysis was performed to estimate the likelihood of wetland conversion worldwide, using globally-consistent biophysical and socioeconomic location factor maps. Significant factors explaining wetland conversion, in order of importance, are market influence, total wetland area (lower conversion probability), mean annual temperature and cropland or built-up area. The regression analyses results support the outcomes of the meta-analysis of the processes of conversion mentioned in the individual case studies. In other meta-analyses of land change, similar factors (e.g., agricultural development, population growth, market/economic factors) are also identified as important causes of various types of land change (e.g., deforestation, desertification). Meta-analysis helps to identify commonalities across the various local case studies and identify which variables may lead to individual cases to behave differently. The meta-regression provides maps indicating the likelihood of wetland conversion worldwide based on the location factors that have determined historic conversions.

Cement Leakage in Percutaneous Vertebral Augmentation for Osteoporotic Vertebral Compression Fractures: Analysis of Risk Factors.

PubMed

Xie, Weixing; Jin, Daxiang; Ma, Hui; Ding, Jinyong; Xu, Jixi; Zhang, Shuncong; Liang, De

2016-05-01

The risk factors for cement leakage were retrospectively reviewed in 192 patients who underwent percutaneous vertebral augmentation (PVA). To discuss the factors related to the cement leakage in PVA procedure for the treatment of osteoporotic vertebral compression fractures. PVA is widely applied for the treatment of osteoporotic vertebral fractures. Cement leakage is a major complication of this procedure. The risk factors for cement leakage were controversial. A retrospective review of 192 patients who underwent PVA was conducted. The following data were recorded: age, sex, bone density, number of fractured vertebrae before surgery, number of treated vertebrae, severity of the treated vertebrae, operative approach, volume of injected bone cement, preoperative vertebral compression ratio, preoperative local kyphosis angle, intraosseous clefts, preoperative vertebral cortical bone defect, and ratio and type of cement leakage. To study the correlation between each factor and cement leakage ratio, bivariate regression analysis was employed to perform univariate analysis, whereas multivariate linear regression analysis was employed to perform multivariate analysis. The study included 192 patients (282 treated vertebrae), and cement leakage occurred in 100 vertebrae (35.46%). The vertebrae with preoperative cortical bone defects generally exhibited higher cement leakage ratio, and the leakage is typically type C. Vertebrae with intact cortical bones before the procedure tend to experience type S leakage. Univariate analysis showed that patient age, bone density, number of fractured vertebrae before surgery, and vertebral cortical bone were associated with cement leakage ratio (P<0.05). Multivariate analysis showed that the main factors influencing bone cement leakage are bone density and vertebral cortical bone defect, with standardized partial regression coefficients of -0.085 and 0.144, respectively. High bone density and vertebral cortical bone defect are independent risk factors associated with bone cement leakage.

Drivers of Wetland Conversion: a Global Meta-Analysis

PubMed Central

van Asselen, Sanneke; Verburg, Peter H.; Vermaat, Jan E.; Janse, Jan H.

2013-01-01

Meta-analysis of case studies has become an important tool for synthesizing case study findings in land change. Meta-analyses of deforestation, urbanization, desertification and change in shifting cultivation systems have been published. This present study adds to this literature, with an analysis of the proximate causes and underlying forces of wetland conversion at a global scale using two complementary approaches of systematic review. Firstly, a meta-analysis of 105 case-study papers describing wetland conversion was performed, showing that different combinations of multiple-factor proximate causes, and underlying forces, drive wetland conversion. Agricultural development has been the main proximate cause of wetland conversion, and economic growth and population density are the most frequently identified underlying forces. Secondly, to add a more quantitative component to the study, a logistic meta-regression analysis was performed to estimate the likelihood of wetland conversion worldwide, using globally-consistent biophysical and socioeconomic location factor maps. Significant factors explaining wetland conversion, in order of importance, are market influence, total wetland area (lower conversion probability), mean annual temperature and cropland or built-up area. The regression analyses results support the outcomes of the meta-analysis of the processes of conversion mentioned in the individual case studies. In other meta-analyses of land change, similar factors (e.g., agricultural development, population growth, market/economic factors) are also identified as important causes of various types of land change (e.g., deforestation, desertification). Meta-analysis helps to identify commonalities across the various local case studies and identify which variables may lead to individual cases to behave differently. The meta-regression provides maps indicating the likelihood of wetland conversion worldwide based on the location factors that have determined historic conversions. PMID:24282580

A model for national outcome audit in vascular surgery.

PubMed

Prytherch, D R; Ridler, B M; Beard, J D; Earnshaw, J J

2001-06-01

The aim was to model vascular surgical outcome in a national study using POSSUM scoring. One hundred and twenty-one British and Irish surgeons completed data questionnaires on patients undergoing arterial surgery under their care (mean 12 patients, range 1-49) in May/June 1998. A total of 1480 completed data records were available for logistic regression analysis using P-POSSUM methodology. Information collected included all POSSUM data items plus other factors thought to have a significant bearing on patient outcome: "extra items". The main outcome measures were death and major postoperative complications. The data were checked and inconsistent records were excluded. The remaining 1313 were divided into two sets for analysis. The first "training" set was used to obtain logistic regression models that were applied prospectively to the second "test" dataset. using POSSUM data items alone, it was possible to predict both mortality and morbidity after vascular reconstruction using P-POSSUM analysis. The addition of the "extra items" found significant in regression analysis did not significantly improve the accuracy of prediction. It was possible to predict both mortality and morbidity derived from the preoperative physiology components of the POSSUM data items alone. this study has shown that P-POSSUM methodology can be used to predict outcome after arterial surgery across a range of surgeons in different hospitals and could form the basis of a national outcome audit. It was also possible to obtain accurate models for both mortality and major morbidity from the POSSUM physiology scores alone. Copyright 2001 Harcourt Publishers Limited.

Healthcare Expenditures Associated with Depression Among Individuals with Osteoarthritis: Post-Regression Linear Decomposition Approach.

PubMed

Agarwal, Parul; Sambamoorthi, Usha

2015-12-01

Depression is common among individuals with osteoarthritis and leads to increased healthcare burden. The objective of this study was to examine excess total healthcare expenditures associated with depression among individuals with osteoarthritis in the US. Adults with self-reported osteoarthritis (n = 1881) were identified using data from the 2010 Medical Expenditure Panel Survey (MEPS). Among those with osteoarthritis, chi-square tests and ordinary least square regressions (OLS) were used to examine differences in healthcare expenditures between those with and without depression. Post-regression linear decomposition technique was used to estimate the relative contribution of different constructs of the Anderson's behavioral model, i.e., predisposing, enabling, need, personal healthcare practices, and external environment factors, to the excess expenditures associated with depression among individuals with osteoarthritis. All analysis accounted for the complex survey design of MEPS. Depression coexisted among 20.6 % of adults with osteoarthritis. The average total healthcare expenditures were $13,684 among adults with depression compared to $9284 among those without depression. Multivariable OLS regression revealed that adults with depression had 38.8 % higher healthcare expenditures (p < 0.001) compared to those without depression. Post-regression linear decomposition analysis indicated that 50 % of differences in expenditures among adults with and without depression can be explained by differences in need factors. Among individuals with coexisting osteoarthritis and depression, excess healthcare expenditures associated with depression were mainly due to comorbid anxiety, chronic conditions and poor health status. These expenditures may potentially be reduced by providing timely intervention for need factors or by providing care under a collaborative care model.

Soft sediment deformation structures in the Maastrichtian Ajali Formation Western Flank of Anambra Basin, Southern Nigeria

NASA Astrophysics Data System (ADS)

Olabode, Solomon Ojo

2014-01-01

Soft sediment deformation structures were recognized in the Maastrichtian shallow marine wave to tide influenced regressive sediments of Ajali Formation in the western flank of Anambra basin, southern Nigerian. The soft sediment deformation structures were in association with cross bedded sands, clay and silt and show different morphological types. Two main types recognised are plastic deformations represented by different types of recumbent folds and injection structure represented by clastic dykes. Other structures in association with the plastic deformation structures include distorted convolute lamination, subsidence lobes, pillars, cusps and sand balls. These structures are interpreted to have been formed by liquefaction and fluidization mechanisms. The driving forces inferred include gravitational instabilities and hydraulic processes. Facies analysis, detailed morphologic study of the soft sediment deformation structures and previous tectonic history of the basin indicate that the main trigger agent for deformation is earthquake shock. The soft sediment deformation structures recognised in the western part of Anambra basin provide a continuous record of the tectonic processes that acted on the regressive Ajali Formation during the Maastrichtian.

What is new about covered interest parity condition in the European Union? Evidence from fractal cross-correlation regressions

NASA Astrophysics Data System (ADS)

Ferreira, Paulo; Kristoufek, Ladislav

2017-11-01

We analyse the covered interest parity (CIP) using two novel regression frameworks based on cross-correlation analysis (detrended cross-correlation analysis and detrending moving-average cross-correlation analysis), which allow for studying the relationships at different scales and work well under non-stationarity and heavy tails. CIP is a measure of capital mobility commonly used to analyse financial integration, which remains an interesting feature of study in the context of the European Union. The importance of this features is related to the fact that the adoption of a common currency is associated with some benefits for countries, but also involves some risks such as the loss of economic instruments to face possible asymmetric shocks. While studying the Eurozone members could explain some problems in the common currency, studying the non-Euro countries is important to analyse if they are fit to take the possible benefits. Our results point to the CIP verification mainly in the Central European countries while in the remaining countries, the verification of the parity is only residual.

The perception of the relationship between environment and health according to data from Italian Behavioural Risk Factor Surveillance System (PASSI).

PubMed

Sampaolo, Letizia; Tommaso, Giulia; Gherardi, Bianca; Carrozzi, Giuliano; Freni Sterrantino, Anna; Ottone, Marta; Goldoni, Carlo Alberto; Bertozzi, Nicoletta; Scaringi, Meri; Bolognesi, Lara; Masocco, Maria; Salmaso, Stefania; Lauriola, Paolo

2017-01-01

"OBJECTIVES: to identify groups of people in relation to the perception of environmental risk and to assess the main characteristics using data collected in the environmental module of the surveillance network Italian Behavioral Risk Factor Surveillance System (PASSI). perceptive profiles were identified using a latent class analysis; later they were included as outcome in multinomial logistic regression models to assess the association between environmental risk perception and demographic, health, socio-economic and behavioural variables. the latent class analysis allowed to split the sample in "worried", "indifferent", and "positive" people. The multinomial logistic regression model showed that the "worried" profile typically includes people of Italian nationality, living in highly urbanized areas, with a high level of education, and with economic difficulties; they pay special attention to their own health and fitness, but they have a negative perception of their own psychophysical state. the application of advanced statistical analysis enable to appraise PASSI data in order to characterize the perception of environmental risk, making the planning of interventions related to risk communication possible. ".

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

PubMed

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

2012-11-14

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

Demand analysis of flood insurance by using logistic regression model and genetic algorithm

NASA Astrophysics Data System (ADS)

Sidi, P.; Mamat, M. B.; Sukono; Supian, S.; Putra, A. S.

2018-03-01

Citarum River floods in the area of South Bandung Indonesia, often resulting damage to some buildings belonging to the people living in the vicinity. One effort to alleviate the risk of building damage is to have flood insurance. The main obstacle is not all people in the Citarum basin decide to buy flood insurance. In this paper, we intend to analyse the decision to buy flood insurance. It is assumed that there are eight variables that influence the decision of purchasing flood assurance, include: income level, education level, house distance with river, building election with road, flood frequency experience, flood prediction, perception on insurance company, and perception towards government effort in handling flood. The analysis was done by using logistic regression model, and to estimate model parameters, it is done with genetic algorithm. The results of the analysis shows that eight variables analysed significantly influence the demand of flood insurance. These results are expected to be considered for insurance companies, to influence the decision of the community to be willing to buy flood insurance.

Acoustic Analysis of Nasal Vowels in Monguor Language

NASA Astrophysics Data System (ADS)

Zhang, Hanbin

2017-09-01

The purpose of the study is to analyze the spectrum characteristics and acoustic features for the nasal vowels [ɑ˜] and [ɔ˜] in Monguor language. On the base of acoustic parameter database of the Monguor speech, the study finds out that there are five main zero-pole pairs appearing for the nasal vowel [ɔ˜] and two zero-pole pairs appear for the nasal vowel [ɔ˜]. The results of regression analysis demonstrate that the duration of the nasal vowel [ɔ˜] or the nasal vowel [ɔ˜] can be predicted by its F1, F2 and F3 respectively.

Effect of heat stress on age at first calving of Japanese Black cows in Okinawa.

PubMed

Oikawa, Takuro

2017-03-01

Calving records from birth certificates of cows were analyzed to investigate the effect of heat stress on age at first calving (AFC) of Japanese Black cows. The data set covered 20 years (1990-2009) of calving records. Total number of records was 9279. Daily weather information from weather stations in the vicinity of the farms was used. Temperature-humidity index (THI) fitted to a linear model covered 30 days pre-insemination to 61 days post-insemination. Statistical analysis was conducted with procedures of SAS/STAT. Preliminary analysis showed that THI of the lowest temperature and humidity was most conducive to AFC. Covariance analysis, including main effect of sire, farm and year of insemination and covariates of THI on days showed that regression coefficients of THI on day -7, day -2 and day +31 were statistically significant. The estimated piecewise regression line showed different responses of AFC to THI on days: roof-shasped downward trend on day -7, hockey-stick shaped upward trend on day -2 and day +31. The difference among the estimated regression lines may be caused by direct and indirect factors on reproduction: indirect effect of reduced feed intake, failure of conception at previous insemination, direct effect of heat stress on oocyte and embryo development. © 2016 Japanese Society of Animal Science.

Real-Time Analysis of Isoprene in Breath by Using Ultraviolet-Absorption Spectroscopy with a Hollow Optical Fiber Gas Cell

PubMed Central

Iwata, Takuro; Katagiri, Takashi; Matsuura, Yuji

2016-01-01

A breath analysis system based on ultraviolet-absorption spectroscopy was developed by using a hollow optical fiber as a gas cell for real-time monitoring of isoprene in breath. The hollow optical fiber functions as an ultra-small-volume gas cell with a long path. The measurement sensitivity of the system was evaluated by using nitric-oxide gas as a gas sample. The evaluation result showed that the developed system, using a laser-driven, high-intensity light source and a 3-m-long, aluminum-coated hollow optical fiber, could successfully measure nitric-oxide gas with a 50 ppb concentration. An absorption spectrum of a breath sample in the wavelength region of around 200–300 nm was measured, and the measured spectrum revealed the main absorbing components in breath as water vapor, isoprene, and ozone converted from oxygen by radiation of ultraviolet light. The concentration of isoprene in breath was estimated by multiple linear regression. The regression analysis results showed that the proposed analysis system enables real-time monitoring of isoprene during the exhaling of breath. Accordingly, it is suitable for measuring the circadian variation of isoprene. PMID:27929387

A multivariate analysis of clinical and morphological prognostic factors in squamous cell carcinoma of the vulva.

PubMed

Smyczek-Gargya, B; Volz, B; Geppert, M; Dietl, J

1997-01-01

Clinical and histological data of 168 patients with squamous cell carcinoma of the vulva were analyzed with respect to survival. 151 patients underwent surgery, 12 patients were treated with primary radiation and in 5 patients no treatment was performed. Follow-up lasted from at least 2 up to 22 years' posttreatment. In univariate analysis, the following factors were highly significant: presurgery lymph node status, tumor infiltration beyond the vulva, tumor grading, histological inguinal lymph node status, pre- and postsurgery tumor stage, depth of invasion and tumor diameter. In the multivariate analysis (Cox regression), the most powerful factors were shown to be histological inguinal lymph node status, tumor diameter and tumor grading. The multivariate logistic regression analysis worked out as main prognostic factors for metastases of inguinal lymph nodes: presurgery inguinal lymph node status, tumor size, depth of invasion and tumor grading. Based on these results, tumor biology seems to be the decisive factor concerning recurrence and survival. Therefore, we suggest a more conservative treatment of vulvar carcinoma. Patients with confined carcinoma to the vulva, with a tumor diameter up to 3 cm and without clinical suspected lymph nodes, should be treated by wide excision/partial vulvectomy with ipsilateral lymphadenectomy.

Combining fibre optic Raman spectroscopy and tactile resonance measurement for tissue characterization

NASA Astrophysics Data System (ADS)

Candefjord, Stefan; Nyberg, Morgan; Jalkanen, Ville; Ramser, Kerstin; Lindahl, Olof A.

2010-12-01

Tissue characterization is fundamental for identification of pathological conditions. Raman spectroscopy (RS) and tactile resonance measurement (TRM) are two promising techniques that measure biochemical content and stiffness, respectively. They have potential to complement the golden standard--histological analysis. By combining RS and TRM, complementary information about tissue content can be obtained and specific drawbacks can be avoided. The aim of this study was to develop a multivariate approach to compare RS and TRM information. The approach was evaluated on measurements at the same points on porcine abdominal tissue. The measurement points were divided into five groups by multivariate analysis of the RS data. A regression analysis was performed and receiver operating characteristic (ROC) curves were used to compare the RS and TRM data. TRM identified one group efficiently (area under ROC curve 0.99). The RS data showed that the proportion of saturated fat was high in this group. The regression analysis showed that stiffness was mainly determined by the amount of fat and its composition. We concluded that RS provided additional, important information for tissue identification that was not provided by TRM alone. The results are promising for development of a method combining RS and TRM for intraoperative tissue characterization.

Real-Time Analysis of Isoprene in Breath by Using Ultraviolet-Absorption Spectroscopy with a Hollow Optical Fiber Gas Cell.

PubMed

Iwata, Takuro; Katagiri, Takashi; Matsuura, Yuji

2016-12-05

A breath analysis system based on ultraviolet-absorption spectroscopy was developed by using a hollow optical fiber as a gas cell for real-time monitoring of isoprene in breath. The hollow optical fiber functions as an ultra-small-volume gas cell with a long path. The measurement sensitivity of the system was evaluated by using nitric-oxide gas as a gas sample. The evaluation result showed that the developed system, using a laser-driven, high-intensity light source and a 3-m-long, aluminum-coated hollow optical fiber, could successfully measure nitric-oxide gas with a 50 ppb concentration. An absorption spectrum of a breath sample in the wavelength region of around 200-300 nm was measured, and the measured spectrum revealed the main absorbing components in breath as water vapor, isoprene, and ozone converted from oxygen by radiation of ultraviolet light. The concentration of isoprene in breath was estimated by multiple linear regression. The regression analysis results showed that the proposed analysis system enables real-time monitoring of isoprene during the exhaling of breath. Accordingly, it is suitable for measuring the circadian variation of isoprene.

Quantifying Main Trends in Lysozyme Nucleation: The Effect of Precipitant Concentration and Impurities

NASA Technical Reports Server (NTRS)

Burke, Michael W.; Judge, Russell A.; Pusey, Marc L.; Rose, M. Franklin (Technical Monitor)

2000-01-01

Full factorial experiment design incorporating multi-linear regression analysis of the experimental data allows the main trends and effects to be quickly identified while using only a limited number of experiments. These techniques were used to identify the effect of precipitant concentration and the presence of an impurity, the physiological lysozyme dimer, on the nucleation rate and crystal dimensions of the tetragonal form of chicken egg white lysozyme. Increasing precipitant concentration was found to decrease crystal numbers, the magnitude of this effect also depending on the supersaturation. The presence of the dimer generally increased nucleation. The crystal axial ratio decreased with increasing precipitant concentration independent of impurity.

PM10 modeling in the Oviedo urban area (Northern Spain) by using multivariate adaptive regression splines

NASA Astrophysics Data System (ADS)

Nieto, Paulino José García; Antón, Juan Carlos Álvarez; Vilán, José Antonio Vilán; García-Gonzalo, Esperanza

2014-10-01

The aim of this research work is to build a regression model of the particulate matter up to 10 micrometers in size (PM10) by using the multivariate adaptive regression splines (MARS) technique in the Oviedo urban area (Northern Spain) at local scale. This research work explores the use of a nonparametric regression algorithm known as multivariate adaptive regression splines (MARS) which has the ability to approximate the relationship between the inputs and outputs, and express the relationship mathematically. In this sense, hazardous air pollutants or toxic air contaminants refer to any substance that may cause or contribute to an increase in mortality or serious illness, or that may pose a present or potential hazard to human health. To accomplish the objective of this study, the experimental dataset of nitrogen oxides (NOx), carbon monoxide (CO), sulfur dioxide (SO2), ozone (O3) and dust (PM10) were collected over 3 years (2006-2008) and they are used to create a highly nonlinear model of the PM10 in the Oviedo urban nucleus (Northern Spain) based on the MARS technique. One main objective of this model is to obtain a preliminary estimate of the dependence between PM10 pollutant in the Oviedo urban area at local scale. A second aim is to determine the factors with the greatest bearing on air quality with a view to proposing health and lifestyle improvements. The United States National Ambient Air Quality Standards (NAAQS) establishes the limit values of the main pollutants in the atmosphere in order to ensure the health of healthy people. Firstly, this MARS regression model captures the main perception of statistical learning theory in order to obtain a good prediction of the dependence among the main pollutants in the Oviedo urban area. Secondly, the main advantages of MARS are its capacity to produce simple, easy-to-interpret models, its ability to estimate the contributions of the input variables, and its computational efficiency. Finally, on the basis of these numerical calculations, using the multivariate adaptive regression splines (MARS) technique, conclusions of this research work are exposed.

Polymorphisms within the FANCA gene associate with premature ovarian failure in Korean women.

PubMed

Pyun, Jung-A; Kim, Sunshin; Cha, Dong Hyun; Kwack, KyuBum

2014-05-01

This study investigated whether polymorphisms within the Fanconi anemia complementation group A (FANCA) gene contribute to the increased risk of premature ovarian failure (POF) in Korean women. Ninety-eight women with POF and 218 controls participated in this study. Genomic DNA from peripheral blood was isolated, and GoldenGate genotyping assay was used to identify single nucleotide polymorphisms (SNPs) within the FANCA gene. Two significant SNPs (rs1006547 and rs2239359; P < 0.05) were identified by logistic regression analysis, but results were insignificant after Bonferroni correction. Six SNPs formed a linkage disequilibrium block, and three main haplotypes were found. Two of three haplotypes (AAAGAA and GGGAGG) distributed highly in the POF group, whereas the remaining haplotype (GGAAGG) distributed highly in the control group by logistic regression analysis (highest odds ratio, 2.515; 95% CI, 1.515-4.175; P = 0.00036). Our observations suggest that genetic variations in the FANCA gene may increase the risk for POF in Korean women.

Increased copy number of the DLX4 homeobox gene in breast axillary lymph node metastasis

PubMed Central

Torresan, Clarissa; Oliveira, Márcia M.C.; Pereira, Silma R.F.; Ribeiro, Enilze M.S.F.; Marian, Catalin; Gusev, Yuriy; Lima, Rubens S.; Urban, Cicero A.; Berg, Patricia E.; Haddad, Bassem R.; Cavalli, Iglenir J.; Cavalli, Luciane R.

2017-01-01

DLX4 is a homeobox gene strongly implicated in breast tumor progression and invasion. Our main objective was to determine the DLX4 copy number status in sentinel lymph node (SLN) metastasis to assess its involvement in the initial stages of the axillary metastatic process. A total of 37 paired samples of SLN metastasis and primary breast tumors (PBT) were evaluated by fluorescence in situ hybridization, quantitative polymerase chain reaction and array comparative genomic hybridization assays. DLX4 increased copy number was observed in 21.6% of the PBT and 24.3% of the SLN metastasis; regression analysis demonstrated that the DLX4 alterations observed in the SLN metastasis were dependent on the ones in the PBT, indicating that they occur in the primary tumor cell populations and are maintained in the early axillary metastatic site. In addition, regression analysis demonstrated that DLX4 alterations (and other DLX and HOXB family members) occurred independently of the ones in the HER2/NEU gene, the main amplification driver on the 17q region. Additional studies evaluating DLX4 copy number in non-SLN axillary lymph nodes and/or distant breast cancer metastasis are necessary to determine if these alterations are carried on and maintained during more advanced stages of tumor progression and if could be used as a predictive marker for axillary involvement. PMID:24947980

Dispositional optimism, depression, disability and quality of life in Parkinson’s disease

PubMed Central

Gison, Annalisa; Dall’Armi, Valentina; Donati, Valentina; Rizza, Federica; Giaquinto, Salvatore

2014-01-01

Summary Very little research on dispositional optimism (DO) has been carried out in the field of Parkinson’s disease (PD). The present cross-sectional study, focusing on this personality trait, was performed with two main aims: i) to compare DO between patients with PD and a control group (CG); ii) to perform, in the PD group, a regression analysis including health-related variables, such as depression, anxiety, quality of life (QoL) and activities of daily living. Seventy PD participants and 70 healthy volunteers were enrolled in the study. The Mann-Whitney test was used to compare life orientation between the PD and CG groups. In the PD group, Pearson’s correlation analysis was used to investigate the relationship between the measures of DO and the other variables. Means of log-linear regression were also used. Mean ratios adjusted for sex, age, education, and severity of disease were estimated, with relative 95% confidence intervals and p-values. The main results were as follows: i) no significant difference in DO was found between the PD participants and the CG; ii) DO was positively associated with QoL and emotional distress and inversely correlated with the Unified Parkinson’s Disease Rating Scale; iii) DO was not correlated with disability. In conclusion, high DO predicts a satisfactory quality of life, low emotional distress and reduced disease severity in PD. PMID:25306121

An Airline-Based Multilevel Analysis of Airfare Elasticity for Passenger Demand

NASA Technical Reports Server (NTRS)

Castelli, Lorenzo; Ukovich, Walter; Pesenti, Raffaele

2003-01-01

Price elasticity of passenger demand for a specific airline is estimated. The main drivers affecting passenger demand for air transportation are identified. First, an Ordinary Least Squares regression analysis is performed. Then, a multilevel analysis-based methodology to investigate the pattern of variation of price elasticity of demand among the various routes of the airline under study is proposed. The experienced daily passenger demands on each fare-class are grouped for each considered route. 9 routes were studied for the months of February and May in years from 1999 to 2002, and two fare-classes were defined (business and economy). The analysis has revealed that the airfare elasticity of passenger demand significantly varies among the different routes of the airline.

Contributions to "k"-Means Clustering and Regression via Classification Algorithms

ERIC Educational Resources Information Center

Salman, Raied

2012-01-01

The dissertation deals with clustering algorithms and transforming regression problems into classification problems. The main contributions of the dissertation are twofold; first, to improve (speed up) the clustering algorithms and second, to develop a strict learning environment for solving regression problems as classification tasks by using…

Principal component regression analysis with SPSS.

PubMed

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

2003-06-01

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

Evaluation of drainage-area ratio method used to estimate streamflow for the Red River of the North Basin, North Dakota and Minnesota

USGS Publications Warehouse

Emerson, Douglas G.; Vecchia, Aldo V.; Dahl, Ann L.

2005-01-01

The drainage-area ratio method commonly is used to estimate streamflow for sites where no streamflow data were collected. To evaluate the validity of the drainage-area ratio method and to determine if an improved method could be developed to estimate streamflow, a multiple-regression technique was used to determine if drainage area, main channel slope, and precipitation were significant variables for estimating streamflow in the Red River of the North Basin. A separate regression analysis was performed for streamflow for each of three seasons-- winter, spring, and summer. Drainage area and summer precipitation were the most significant variables. However, the regression equations generally overestimated streamflows for North Dakota stations and underestimated streamflows for Minnesota stations. To correct the bias in the residuals for the two groups of stations, indicator variables were included to allow both the intercept and the coefficient for the logarithm of drainage area to depend on the group. Drainage area was the only significant variable in the revised regression equations. The exponents for the drainage-area ratio were 0.85 for the winter season, 0.91 for the spring season, and 1.02 for the summer season.

Community acquired bacterial meningitis in Cuba: a follow up of a decade

PubMed Central

2010-01-01

Background Community acquired Bacterial Meningitis (BM) remains a serious threat to global health. Cuban surveillance system for BM allowed to characterize the main epidemiological features of this group of diseases, as well as to assess the association of some variables with mortality. Results of the BM surveillance in Cuba are presented in this paper. Methods A follow up of BM cases reported to the Institute "Pedro Kourí" by the National Bacterial Meningitis Surveillance System from 1998 to 2007 was completed. Incidence and case-fatality rate (CFR) were calculated. Univariate analysis and logistic regression were used to elucidate associated factors to mortality comparing death versus survival. Relative Risk (RR) or odds ratio and its 95% confidence interval (CI 95%) were estimated, using either a Chi-squared Test or Fisher's Exact Test as appropriate. A Holt-Winters model was used to assess seasonality. Results 4 798 cases of BM (4.3 per 100 000 population) were reported, with a decreasing trend of the incidence. Highest incidence was observed in infants and elderly. Overall CFR reached 24.1% affecting mostly older adults. S. pneumoniae (23.6%), N. meningitidis(8.2%) and H. influenzaetype b (6.0%) were the main causative agents. Males predominate in the incidence. Highest incidence and CFR were mainly clustered in the centre of the island. The univariate analysis did not show association between delayed medical consultation (RR = 1.20; CI = 1.07-1.35) or delayed hospitalization (RR = 0.98; CI = 0.87-1.11) and the fatal outcome. Logistic regression model showed association of categories housewife, pensioned, imprisoned, unemployed, S. peumoniae and other bacteria with mortality. Seasonality during September, January and March was observed. Conclusions The results of the National Program for Control and Prevention of the Neurological Infectious Syndrome evidenced a reduction of the BM incidence, but not the CFR. Multivariate analysis identified an association of mortality with some societal groups as well as with S. peumoniae. PMID:20500858

Community acquired bacterial meningitis in Cuba: a follow up of a decade.

PubMed

Pérez, Antonio E; Dickinson, Félix O; Rodríguez, Misladys

2010-05-25

Community acquired Bacterial Meningitis (BM) remains a serious threat to global health. Cuban surveillance system for BM allowed to characterize the main epidemiological features of this group of diseases, as well as to assess the association of some variables with mortality. Results of the BM surveillance in Cuba are presented in this paper. A follow up of BM cases reported to the Institute "Pedro Kourí" by the National Bacterial Meningitis Surveillance System from 1998 to 2007 was completed. Incidence and case-fatality rate (CFR) were calculated. Univariate analysis and logistic regression were used to elucidate associated factors to mortality comparing death versus survival. Relative Risk (RR) or odds ratio and its 95% confidence interval (CI 95%) were estimated, using either a Chi-squared Test or Fisher's Exact Test as appropriate. A Holt-Winters model was used to assess seasonality. 4798 cases of BM (4.3 per 100,000 population) were reported, with a decreasing trend of the incidence. Highest incidence was observed in infants and elderly. Overall CFR reached 24.1% affecting mostly older adults. S. pneumoniae (23.6%), N. meningitidis (8.2%) and H. influenzae type b (6.0%) were the main causative agents. Males predominate in the incidence. Highest incidence and CFR were mainly clustered in the centre of the island. The univariate analysis did not show association between delayed medical consultation (RR = 1.20; CI = 1.07-1.35) or delayed hospitalization (RR = 0.98; CI = 0.87-1.11) and the fatal outcome. Logistic regression model showed association of categories housewife, pensioned, imprisoned, unemployed, S. pneumoniae and other bacteria with mortality. Seasonality during September, January and March was observed. The results of the National Program for Control and Prevention of the Neurological Infectious Syndrome evidenced a reduction of the BM incidence, but not the CFR. Multivariate analysis identified an association of mortality with some societal groups as well as with S. pneumoniae.

Regression Rates Following the Treatment of Aggressive Posterior Retinopathy of Prematurity with Bevacizumab Versus Laser: 8-Year Retrospective Analysis

PubMed Central

Nicoară, Simona D.; Ştefănuţ, Anne C.; Nascutzy, Constanta; Zaharie, Gabriela C.; Toader, Laura E.; Drugan, Tudor C.

2016-01-01

Background Retinopathy is a serious complication related to prematurity and a leading cause of childhood blindness. The aggressive posterior form of retinopathy of prematurity (APROP) has a worse anatomical and functional outcome following laser therapy, as compared with the classic form of the disease. The main outcome measures are the APROP regression rate, structural outcomes, and complications associated with intravitreal bevacizumab (IVB) versus laser photocoagulation in APROP. Material/Methods This is a retrospective case series that includes infants with APROP who received either IVB or laser photocoagulation and had a follow-up of at least 60 weeks (for the laser photocoagulation group) and 80 weeks (for the IVB group). In the first group, laser photocoagulation of the retina was carried out and in the second group, 1 bevacizumab injection was administered intravitreally. The following parameters were analyzed in each group: sex, gestational age, birth weight, postnatal age and postmenstrual age at treatment, APROP regression, sequelae, and complications. Statistical analysis was performed using Microsoft Excel and IBM SPSS (version 23.0). Results The laser photocoagulation group consisted of 6 premature infants (12 eyes) and the IVB group consisted of 17 premature infants (34 eyes). Within the laser photocoagulation group, the evolution was favorable in 9 eyes (75%) and unfavorable in 3 eyes (25%). Within the IVB group, APROP regressed in 29 eyes (85.29%) and failed to regress in 5 eyes (14.71%). These differences are statistically significant, as proved by the McNemar test (P<0.001). Conclusions The IVB group had a statistically significant better outcome compared with the laser photocoagulation group, in APROP in our series. PMID:27062023

Stature Estimation from Lower Limb Anthropometry using Linear Regression Analysis: A Study on the Malaysian Population.

PubMed

Abu Bakar, S N; Aspalilah, A; AbdelNasser, I; Nurliza, A; Hairuliza, M J; Swarhib, M; Das, S; Mohd Nor, F

2017-01-01

Stature is one of the characteristics that could be used to identify human, besides age, sex and racial affiliation. This is useful when the body found is either dismembered, mutilated or even decomposed, and helps in narrowing down the missing person's identity. The main aim of the present study was to construct regression functions for stature estimation by using lower limb bones in the Malaysian population. The sample comprised 87 adult individuals (81 males, 6 females) aged between 20 to 79 years. The parameters such as thigh length, lower leg length, leg length, foot length, foot height and foot breadth were measured. They were measured by a ruler and measuring tape. Statistical analysis involved independent t-test to analyse the difference between lower limbs in male and female. The Pearson's correlation test was used to analyse correlations between lower limb parameters and stature, and the linear regressions were used to form equations. The paired t-test was used to compare between actual stature and estimated stature by using the equations formed. Using independent t-test, there was a significant difference (p< 0.05) in the measurement between males and females with regard to leg length, thigh length, lower leg length, foot length and foot breadth. The thigh length, leg length and foot length were observed to have strong correlations with stature with p= 0.75, p= 0.81 and p= 0.69, respectively. Linear regressions were formulated for stature estimation. Paired t-test showed no significant difference between actual stature and estimated stature. It is concluded that regression functions can be used to estimate stature to identify skeletal remains in the Malaysia population.

Techniques for estimating flood-peak discharges of rural, unregulated streams in Ohio

USGS Publications Warehouse

Koltun, G.F.; Roberts, J.W.

1990-01-01

Multiple-regression equations are presented for estimating flood-peak discharges having recurrence intervals of 2, 5, 10, 25, 50, and 100 years at ungaged sites on rural, unregulated streams in Ohio. The average standard errors of prediction for the equations range from 33.4% to 41.4%. Peak discharge estimates determined by log-Pearson Type III analysis using data collected through the 1987 water year are reported for 275 streamflow-gaging stations. Ordinary least-squares multiple-regression techniques were used to divide the State into three regions and to identify a set of basin characteristics that help explain station-to- station variation in the log-Pearson estimates. Contributing drainage area, main-channel slope, and storage area were identified as suitable explanatory variables. Generalized least-square procedures, which include historical flow data and account for differences in the variance of flows at different gaging stations, spatial correlation among gaging station records, and variable lengths of station record were used to estimate the regression parameters. Weighted peak-discharge estimates computed as a function of the log-Pearson Type III and regression estimates are reported for each station. A method is provided to adjust regression estimates for ungaged sites by use of weighted and regression estimates for a gaged site located on the same stream. Limitations and shortcomings cited in an earlier report on the magnitude and frequency of floods in Ohio are addressed in this study. Geographic bias is no longer evident for the Maumee River basin of northwestern Ohio. No bias is found to be associated with the forested-area characteristic for the range used in the regression analysis (0.0 to 99.0%), nor is this characteristic significant in explaining peak discharges. Surface-mined area likewise is not significant in explaining peak discharges, and the regression equations are not biased when applied to basins having approximately 30% or less surface-mined area. Analyses of residuals indicate that the equations tend to overestimate flood-peak discharges for basins having approximately 30% or more surface-mined area. (USGS)

Regression Verification Using Impact Summaries

NASA Technical Reports Server (NTRS)

Backes, John; Person, Suzette J.; Rungta, Neha; Thachuk, Oksana

2013-01-01

Regression verification techniques are used to prove equivalence of syntactically similar programs. Checking equivalence of large programs, however, can be computationally expensive. Existing regression verification techniques rely on abstraction and decomposition techniques to reduce the computational effort of checking equivalence of the entire program. These techniques are sound but not complete. In this work, we propose a novel approach to improve scalability of regression verification by classifying the program behaviors generated during symbolic execution as either impacted or unimpacted. Our technique uses a combination of static analysis and symbolic execution to generate summaries of impacted program behaviors. The impact summaries are then checked for equivalence using an o-the-shelf decision procedure. We prove that our approach is both sound and complete for sequential programs, with respect to the depth bound of symbolic execution. Our evaluation on a set of sequential C artifacts shows that reducing the size of the summaries can help reduce the cost of software equivalence checking. Various reduction, abstraction, and compositional techniques have been developed to help scale software verification techniques to industrial-sized systems. Although such techniques have greatly increased the size and complexity of systems that can be checked, analysis of large software systems remains costly. Regression analysis techniques, e.g., regression testing [16], regression model checking [22], and regression verification [19], restrict the scope of the analysis by leveraging the differences between program versions. These techniques are based on the idea that if code is checked early in development, then subsequent versions can be checked against a prior (checked) version, leveraging the results of the previous analysis to reduce analysis cost of the current version. Regression verification addresses the problem of proving equivalence of closely related program versions [19]. These techniques compare two programs with a large degree of syntactic similarity to prove that portions of one program version are equivalent to the other. Regression verification can be used for guaranteeing backward compatibility, and for showing behavioral equivalence in programs with syntactic differences, e.g., when a program is refactored to improve its performance, maintainability, or readability. Existing regression verification techniques leverage similarities between program versions by using abstraction and decomposition techniques to improve scalability of the analysis [10, 12, 19]. The abstractions and decomposition in the these techniques, e.g., summaries of unchanged code [12] or semantically equivalent methods [19], compute an over-approximation of the program behaviors. The equivalence checking results of these techniques are sound but not complete-they may characterize programs as not functionally equivalent when, in fact, they are equivalent. In this work we describe a novel approach that leverages the impact of the differences between two programs for scaling regression verification. We partition program behaviors of each version into (a) behaviors impacted by the changes and (b) behaviors not impacted (unimpacted) by the changes. Only the impacted program behaviors are used during equivalence checking. We then prove that checking equivalence of the impacted program behaviors is equivalent to checking equivalence of all program behaviors for a given depth bound. In this work we use symbolic execution to generate the program behaviors and leverage control- and data-dependence information to facilitate the partitioning of program behaviors. The impacted program behaviors are termed as impact summaries. The dependence analyses that facilitate the generation of the impact summaries, we believe, could be used in conjunction with other abstraction and decomposition based approaches, [10, 12], as a complementary reduction technique. An evaluation of our regression verification technique shows that our approach is capable of leveraging similarities between program versions to reduce the size of the queries and the time required to check for logical equivalence. The main contributions of this work are: - A regression verification technique to generate impact summaries that can be checked for functional equivalence using an off-the-shelf decision procedure. - A proof that our approach is sound and complete with respect to the depth bound of symbolic execution. - An implementation of our technique using the LLVMcompiler infrastructure, the klee Symbolic Virtual Machine [4], and a variety of Satisfiability Modulo Theory (SMT) solvers, e.g., STP [7] and Z3 [6]. - An empirical evaluation on a set of C artifacts which shows that the use of impact summaries can reduce the cost of regression verification.

The Effect of Work Characteristics on Dermatologic Symptoms in Hairdressers

PubMed Central

2014-01-01

Objectives Hairdressers in Korea perform various tasks and are exposed to health risk factors such as chemical substances or prolonged duration of wet work. The objective of this study is to provide descriptive statistics on the demographics and work characteristics of hairdressers in Korea and to identify work-related risk factors for dermatologic symptoms in hairdressers. Methods 1,054 hairdressers were selected and analyzed for this study. Independent variables were exposure to chemical substances, the training status of the hairdressers, and the main tasks required of them, and the dependent variable was the incidence of dermatologic symptoms. The relationships between work characteristics and dermatologic symptoms were evaluated by estimating odds ratios using multiple logistic regression analysis. Results Among the 1,054 study subjects, 212 hairdressers (20.1%) complained of dermatologic symptoms, and the symptoms were more prevalent in younger, unmarried or highly educated hairdressers. The main tasks that comprise the majority of the wet work were strictly determined by training status, since 96.5% of staff hairdressers identified washing as their main task, while only 1.5% and 2.0% of master and designer hairdressers, respectively, identified this as their main task. Multiple logistic regressions was performed to estimate odds ratios. While exposure to hairdressing chemicals showed no significant effect on the odds ratio for the incidence of dermatologic symptoms, higher odds ratios of dermatologic symptoms were shown in staff hairdressers (2.70, 95% CI: 1.32 - 5.51) and in hairdressers who perform washing as their main task (2.03, 95% CI: 1.22 - 3.37), after adjusting for general and work characteristics. Conclusions This study showed that the training status and main tasks of hairdressers are closely related to each other and that the training status and main tasks of hairdressers are related to the incidence of dermatologic symptoms. This suggests that in the future, regulations on working conditions and health management guidelines for hairdressers should be established. PMID:25028609

Methodology for Estimation of Flood Magnitude and Frequency for New Jersey Streams

USGS Publications Warehouse

Watson, Kara M.; Schopp, Robert D.

2009-01-01

Methodologies were developed for estimating flood magnitudes at the 2-, 5-, 10-, 25-, 50-, 100-, and 500-year recurrence intervals for unregulated or slightly regulated streams in New Jersey. Regression equations that incorporate basin characteristics were developed to estimate flood magnitude and frequency for streams throughout the State by use of a generalized least squares regression analysis. Relations between flood-frequency estimates based on streamflow-gaging-station discharge and basin characteristics were determined by multiple regression analysis, and weighted by effective years of record. The State was divided into five hydrologically similar regions to refine the regression equations. The regression analysis indicated that flood discharge, as determined by the streamflow-gaging-station annual peak flows, is related to the drainage area, main channel slope, percentage of lake and wetland areas in the basin, population density, and the flood-frequency region, at the 95-percent confidence level. The standard errors of estimate for the various recurrence-interval floods ranged from 48.1 to 62.7 percent. Annual-maximum peak flows observed at streamflow-gaging stations through water year 2007 and basin characteristics determined using geographic information system techniques for 254 streamflow-gaging stations were used for the regression analysis. Drainage areas of the streamflow-gaging stations range from 0.18 to 779 mi2. Peak-flow data and basin characteristics for 191 streamflow-gaging stations located in New Jersey were used, along with peak-flow data for stations located in adjoining States, including 25 stations in Pennsylvania, 17 stations in New York, 16 stations in Delaware, and 5 stations in Maryland. Streamflow records for selected stations outside of New Jersey were included in the present study because hydrologic, physiographic, and geologic boundaries commonly extend beyond political boundaries. The StreamStats web application was developed cooperatively by the U.S. Geological Survey and the Environmental Systems Research Institute, Inc., and was designed for national implementation. This web application has been recently implemented for use in New Jersey. This program used in conjunction with a geographic information system provides the computation of values for selected basin characteristics, estimates of flood magnitudes and frequencies, and statistics for stream locations in New Jersey chosen by the user, whether the site is gaged or ungaged.

Regression Analysis by Example. 5th Edition

ERIC Educational Resources Information Center

Chatterjee, Samprit; Hadi, Ali S.

2012-01-01

Regression analysis is a conceptually simple method for investigating relationships among variables. Carrying out a successful application of regression analysis, however, requires a balance of theoretical results, empirical rules, and subjective judgment. "Regression Analysis by Example, Fifth Edition" has been expanded and thoroughly…

Random regression analysis for body weights and main morphological traits in genetically improved farmed tilapia (Oreochromis niloticus).

PubMed

He, Jie; Zhao, Yunfeng; Zhao, Jingli; Gao, Jin; Xu, Pao; Yang, Runqing

2018-02-01

To genetically analyse growth traits in genetically improved farmed tilapia (GIFT), the body weight (BWE) and main morphological traits, including body length (BL), body depth (BD), body width (BWI), head length (HL) and length of the caudal peduncle (CPL), were measured six times in growth duration on 1451 fish from 45 mixed families of full and half sibs. A random regression model (RRM) was used to model genetic changes of the growth traits with days of age and estimate the heritability for any growth point and genetic correlations between pairwise growth points. Using the covariance function based on optimal RRMs, the heritabilities were estimated to be from 0.102 to 0.662 for BWE, 0.157 to 0.591 for BL, 0.047 to 0.621 for BD, 0.018 to 0.577 for BWI, 0.075 to 0.597 for HL and 0.032 to 0.610 for CPL between 60 and 140 days of age. All genetic correlations exceeded 0.5 between pairwise growth points. Moreover, the traits at initial days of age showed less correlation with those at later days of age. With phenotypes observed repeatedly, the model choice showed that the optimal RRMs could more precisely predict breeding values at a specific growth time than repeatability models or multiple trait animal models, which enhanced the efficiency of selection for the BWE and main morphological traits.

The Role of Auxiliary Variables in Deterministic and Deterministic-Stochastic Spatial Models of Air Temperature in Poland

NASA Astrophysics Data System (ADS)

Szymanowski, Mariusz; Kryza, Maciej

2017-02-01

Our study examines the role of auxiliary variables in the process of spatial modelling and mapping of climatological elements, with air temperature in Poland used as an example. The multivariable algorithms are the most frequently applied for spatialization of air temperature, and their results in many studies are proved to be better in comparison to those obtained by various one-dimensional techniques. In most of the previous studies, two main strategies were used to perform multidimensional spatial interpolation of air temperature. First, it was accepted that all variables significantly correlated with air temperature should be incorporated into the model. Second, it was assumed that the more spatial variation of air temperature was deterministically explained, the better was the quality of spatial interpolation. The main goal of the paper was to examine both above-mentioned assumptions. The analysis was performed using data from 250 meteorological stations and for 69 air temperature cases aggregated on different levels: from daily means to 10-year annual mean. Two cases were considered for detailed analysis. The set of potential auxiliary variables covered 11 environmental predictors of air temperature. Another purpose of the study was to compare the results of interpolation given by various multivariable methods using the same set of explanatory variables. Two regression models: multiple linear (MLR) and geographically weighted (GWR) method, as well as their extensions to the regression-kriging form, MLRK and GWRK, respectively, were examined. Stepwise regression was used to select variables for the individual models and the cross-validation method was used to validate the results with a special attention paid to statistically significant improvement of the model using the mean absolute error (MAE) criterion. The main results of this study led to rejection of both assumptions considered. Usually, including more than two or three of the most significantly correlated auxiliary variables does not improve the quality of the spatial model. The effects of introduction of certain variables into the model were not climatologically justified and were seen on maps as unexpected and undesired artefacts. The results confirm, in accordance with previous studies, that in the case of air temperature distribution, the spatial process is non-stationary; thus, the local GWR model performs better than the global MLR if they are specified using the same set of auxiliary variables. If only GWR residuals are autocorrelated, the geographically weighted regression-kriging (GWRK) model seems to be optimal for air temperature spatial interpolation.

BrightStat.com: free statistics online.

PubMed

Stricker, Daniel

2008-10-01

Powerful software for statistical analysis is expensive. Here I present BrightStat, a statistical software running on the Internet which is free of charge. BrightStat's goals, its main capabilities and functionalities are outlined. Three different sample runs, a Friedman test, a chi-square test, and a step-wise multiple regression are presented. The results obtained by BrightStat are compared with results computed by SPSS, one of the global leader in providing statistical software, and VassarStats, a collection of scripts for data analysis running on the Internet. Elementary statistics is an inherent part of academic education and BrightStat is an alternative to commercial products.

Why do they leave? Factors associated with job termination among personal assistant workers in home care.

PubMed

Butler, Sandra S; Simpson, Nan; Brennan, Mark; Turner, Winston

2010-11-01

Recruiting and retaining an adequate number of personal support workers in home care is both challenging and essential to allowing elders to age in place. A mixed-method, longitudinal study examined turnover in a sample of 261 personal support workers in Maine; 70 workers (26.8%) left their employment in the first year of the study. Logistic regression analysis indicated that younger age and lack of health insurance were significant predictors of turnover. Analysis of telephone interviews revealed three overarching themes related to termination: job not worthwhile, personal reasons, and burnout. Implications of study findings for gerontological social workers are outlined.

Frontier-based techniques in measuring hospital efficiency in Iran: a systematic review and meta-regression analysis

PubMed Central

2013-01-01

Background In recent years, there has been growing interest in measuring the efficiency of hospitals in Iran and several studies have been conducted on the topic. The main objective of this paper was to review studies in the field of hospital efficiency and examine the estimated technical efficiency (TE) of Iranian hospitals. Methods Persian and English databases were searched for studies related to measuring hospital efficiency in Iran. Ordinary least squares (OLS) regression models were applied for statistical analysis. The PRISMA guidelines were followed in the search process. Results A total of 43 efficiency scores from 29 studies were retrieved and used to approach the research question. Data envelopment analysis was the principal frontier efficiency method in the estimation of efficiency scores. The pooled estimate of mean TE was 0.846 (±0.134). There was a considerable variation in the efficiency scores between the different studies performed in Iran. There were no differences in efficiency scores between data envelopment analysis (DEA) and stochastic frontier analysis (SFA) techniques. The reviewed studies are generally similar and suffer from similar methodological deficiencies, such as no adjustment for case mix and quality of care differences. The results of OLS regression revealed that studies that included more variables and more heterogeneous hospitals generally reported higher TE. Larger sample size was associated with reporting lower TE. Conclusions The features of frontier-based techniques had a profound impact on the efficiency scores among Iranian hospital studies. These studies suffer from major methodological deficiencies and were of sub-optimal quality, limiting their validity and reliability. It is suggested that improving data collection and processing in Iranian hospital databases may have a substantial impact on promoting the quality of research in this field. PMID:23945011

A cross-sectional study for predicting tail biting risk in pig farms using classification and regression tree analysis.

PubMed

Scollo, Annalisa; Gottardo, Flaviana; Contiero, Barbara; Edwards, Sandra A

2017-10-01

Tail biting in pigs has been an identified behavioural, welfare and economic problem for decades, and requires appropriate but sometimes difficult on-farm interventions. The aim of the paper is to introduce the Classification and Regression Tree (CRT) methodologies to develop a tool for prevention of acute tail biting lesions in pigs on-farm. A sample of 60 commercial farms rearing heavy pigs were involved; an on-farm visit and an interview with the farmer collected data on general management, herd health, disease prevention, climate control, feeding and production traits. Results suggest a value for the CRT analysis in managing the risk factors behind tail biting on a farm-specific level, showing 86.7% sensitivity for the Classification Tree and a correlation of 0.7 between observed and predicted prevalence of tail biting obtained with the Regression Tree. CRT analysis showed five main variables (stocking density, ammonia levels, number of pigs per stockman, type of floor and timeliness in feed supply) as critical predictors of acute tail biting lesions, which demonstrate different importance in different farms subgroups. The model might have reliable and practical applications for the support and implementation of tail biting prevention interventions, especially in case of subgroups of pigs with higher risk, helping farmers and veterinarians to assess the risk in their own farm and to manage their predisposing variables in order to reduce acute tail biting lesions. Copyright © 2017 Elsevier B.V. All rights reserved.

Solar cycle in current reanalyses: (non)linear attribution study

NASA Astrophysics Data System (ADS)

Kuchar, A.; Sacha, P.; Miksovsky, J.; Pisoft, P.

2014-12-01

This study focusses on the variability of temperature, ozone and circulation characteristics in the stratosphere and lower mesosphere with regard to the influence of the 11 year solar cycle. It is based on attribution analysis using multiple nonlinear techniques (Support Vector Regression, Neural Networks) besides the traditional linear approach. The analysis was applied to several current reanalysis datasets for the 1979-2013 period, including MERRA, ERA-Interim and JRA-55, with the aim to compare how this type of data resolves especially the double-peaked solar response in temperature and ozone variables and the consequent changes induced by these anomalies. Equatorial temperature signals in the lower and upper stratosphere were found to be sufficiently robust and in qualitative agreement with previous observational studies. The analysis also pointed to the solar signal in the ozone datasets (i.e. MERRA and ERA-Interim) not being consistent with the observed double-peaked ozone anomaly extracted from satellite measurements. Consequently the results obtained by linear regression were confirmed by the nonlinear approach through all datasets, suggesting that linear regression is a relevant tool to sufficiently resolve the solar signal in the middle atmosphere. Furthermore, the seasonal dependence of the solar response was also discussed, mainly as a source of dynamical causalities in the wave propagation characteristics in the zonal wind and the induced meridional circulation in the winter hemispheres. The hypothetical mechanism of a weaker Brewer Dobson circulation was reviewed together with discussion of polar vortex stability.

Techniques for estimating peak-streamflow frequency for unregulated streams and streams regulated by small floodwater retarding structures in Oklahoma

USGS Publications Warehouse

Tortorelli, Robert L.

1997-01-01

Statewide regression equations for Oklahoma were determined for estimating peak discharge and flood frequency for selected recurrence intervals from 2 to 500 years for ungaged sites on natural unregulated streams. The most significant independent variables required to estimate peak-streamflow frequency for natural unregulated streams in Oklahoma are contributing drainage area, main-channel slope, and mean-annual precipitation. The regression equations are applicable for watersheds with drainage areas less than 2,510 square miles that are not affected by regulation from manmade works. Limitations on the use of the regression relations and the reliability of regression estimates for natural unregulated streams are discussed. Log-Pearson Type III analysis information, basin and climatic characteristics, and the peak-stream-flow frequency estimates for 251 gaging stations in Oklahoma and adjacent states are listed. Techniques are presented to make a peak-streamflow frequency estimate for gaged sites on natural unregulated streams and to use this result to estimate a nearby ungaged site on the same stream. For ungaged sites on urban streams, an adjustment of the statewide regression equations for natural unregulated streams can be used to estimate peak-streamflow frequency. For ungaged sites on streams regulated by small floodwater retarding structures, an adjustment of the statewide regression equations for natural unregulated streams can be used to estimate peak-streamflow frequency. The statewide regression equations are adjusted by substituting the drainage area below the floodwater retarding structures, or drainage area that represents the percentage of the unregulated basin, in the contributing drainage area parameter to obtain peak-streamflow frequency estimates.

Evaluation of the psychometric properties of the main meal quality index when applied in the UK population.

PubMed

Gorgulho, B M; Pot, G K; Marchioni, D M

2017-05-01

The aim of this study was to evaluate the validity and reliability of the Main Meal Quality Index when applied on the UK population. The indicator was developed to assess meal quality in different populations, and is composed of 10 components: fruit, vegetables (excluding potatoes), ratio of animal protein to total protein, fiber, carbohydrate, total fat, saturated fat, processed meat, sugary beverages and desserts, and energy density, resulting in a score range of 0-100 points. The performance of the indicator was measured using strategies for assessing content validity, construct validity, discriminant validity and reliability, including principal component analysis, linear regression models and Cronbach's alpha. The indicator presented good reliability. The Main Meal Quality Index has been shown to be valid for use as an instrument to evaluate, monitor and compare the quality of meals consumed by adults in the United Kingdom.

[Photosynthetic characteristics of five arbor species in Shenyang urban area].

PubMed

Li, Hai-Me; He, Xing-Yuan; Wang, Kui-Ling; Chen, Wei

2007-08-01

By using LI-6400 infrared gas analyzer, this paper studied the diurnal and seasonal variations of the photosynthetic rate of main arbor species (Populus alba x P. berolinensis, Salix matsudana, Ulmus pumila, Robinia pseudoacacia and Prunus davidiana) in Shenyang urban area. The correlations between net photosynthetic rate and environmental factors (photosynthetic active radiation, temperature, and stomatal conductance) were assessed by multivariate regression analysis, and related equations were constructed. The results showed that for test arbor species, the diurnal variation of photosynthetic rate mainly presented a single peak curve, and the seasonal variation was in the order of summer > autumn > spring. The major factors affecting the photosynthetic rate were photosynthetic active radiation, stomatal conductance, and intercellular CO2 concentration.

[Women's opinion on abortion legalization in a middle size county in southern Brazil].

PubMed

César, J A; Gomes, G; Horta, B L; de Oliveira, A K; Saraiva, A K; Pardo, D O; Silva, L M; Rodghiero, C L; Gross, M R

1997-12-01

Induced abortion is the main cause of maternal death in Brazil. Question of its legalization has been the subject of frequent discussion. In order to assess the influence of the variables affecting the opinion of women of reproductive age, a population-based systematic sample in the county of Rio Grande (Southern Brazil) was examined. Of a total of 1,456 interviews 30% endorsed the legalization, whatever the circumstances; this percentage was directly associated with age, schooling, family income and previous induced abortion (p < 0.01). Adjusted analysis using logistic regression showed a significant effect of schooling and previous induced abortion on favourable opinion. Schooling and previous induced abortion were the main determinants of women's favorable opinions regarding abortion legalization.

Spatial assessment of air quality patterns in Malaysia using multivariate analysis

NASA Astrophysics Data System (ADS)

Dominick, Doreena; Juahir, Hafizan; Latif, Mohd Talib; Zain, Sharifuddin M.; Aris, Ahmad Zaharin

2012-12-01

This study aims to investigate possible sources of air pollutants and the spatial patterns within the eight selected Malaysian air monitoring stations based on a two-year database (2008-2009). The multivariate analysis was applied on the dataset. It incorporated Hierarchical Agglomerative Cluster Analysis (HACA) to access the spatial patterns, Principal Component Analysis (PCA) to determine the major sources of the air pollution and Multiple Linear Regression (MLR) to assess the percentage contribution of each air pollutant. The HACA results grouped the eight monitoring stations into three different clusters, based on the characteristics of the air pollutants and meteorological parameters. The PCA analysis showed that the major sources of air pollution were emissions from motor vehicles, aircraft, industries and areas of high population density. The MLR analysis demonstrated that the main pollutant contributing to variability in the Air Pollutant Index (API) at all stations was particulate matter with a diameter of less than 10 μm (PM10). Further MLR analysis showed that the main air pollutant influencing the high concentration of PM10 was carbon monoxide (CO). This was due to combustion processes, particularly originating from motor vehicles. Meteorological factors such as ambient temperature, wind speed and humidity were also noted to influence the concentration of PM10.

Deciphering the complex: methodological overview of statistical models to derive OMICS-based biomarkers.

PubMed

Chadeau-Hyam, Marc; Campanella, Gianluca; Jombart, Thibaut; Bottolo, Leonardo; Portengen, Lutzen; Vineis, Paolo; Liquet, Benoit; Vermeulen, Roel C H

2013-08-01

Recent technological advances in molecular biology have given rise to numerous large-scale datasets whose analysis imposes serious methodological challenges mainly relating to the size and complex structure of the data. Considerable experience in analyzing such data has been gained over the past decade, mainly in genetics, from the Genome-Wide Association Study era, and more recently in transcriptomics and metabolomics. Building upon the corresponding literature, we provide here a nontechnical overview of well-established methods used to analyze OMICS data within three main types of regression-based approaches: univariate models including multiple testing correction strategies, dimension reduction techniques, and variable selection models. Our methodological description focuses on methods for which ready-to-use implementations are available. We describe the main underlying assumptions, the main features, and advantages and limitations of each of the models. This descriptive summary constitutes a useful tool for driving methodological choices while analyzing OMICS data, especially in environmental epidemiology, where the emergence of the exposome concept clearly calls for unified methods to analyze marginally and jointly complex exposure and OMICS datasets. Copyright © 2013 Wiley Periodicals, Inc.

[Influence of sample surface roughness on mathematical model of NIR quantitative analysis of wood density].

PubMed

Huang, An-Min; Fei, Ben-Hua; Jiang, Ze-Hui; Hse, Chung-Yun

2007-09-01

Near infrared spectroscopy is widely used as a quantitative method, and the main multivariate techniques consist of regression methods used to build prediction models, however, the accuracy of analysis results will be affected by many factors. In the present paper, the influence of different sample roughness on the mathematical model of NIR quantitative analysis of wood density was studied. The result of experiments showed that if the roughness of predicted samples was consistent with that of calibrated samples, the result was good, otherwise the error would be much higher. The roughness-mixed model was more flexible and adaptable to different sample roughness. The prediction ability of the roughness-mixed model was much better than that of the single-roughness model.

[Character accentuations as a criterion for psychological risks in the professional activity of the builders of main gas pipelines in the conditions of arctic].

PubMed

Korneeva, Ia A; Simonova, N N

2015-01-01

The article is devoted to the study of character accentuations as a criterion for psychological risks in the professional activity of builders of main gas pipelines in the conditions of Arctic. to study the severity of character accentuations in rotation-employed builders of main gas pipelines, stipulated by their professional activities, as well as personal resources to overcome these destructions. The study involved 70 rotation-employed builders of trunk pipelines, working in the Tyumen Region (duration of the shift-in--52 days), aged from 23 to 59 (mean age 34,9 ± 8.1) years, with the experience of work from 0.5 years to 14 years (the average length of 4.42 ± 3.1). Methods of the study: questionnaires, psychological testing, participant observation. One-Sample t-test of Student, multiple regression analysis, incremental analysis. In the work there were revealed differences of expression of character accentuations in builders of trunk pipelines with experience in work on rotation less and more than five years. There was determined that builders of the main gas pipelines, working on the rotation in Arctic, with more pronounced accentuation ofthe character use mainly psychological defenses of compensation, substitution and denial, and have an average level of expression of flexibility as the regulatory process.

Motivation and Self-Management Behavior of the Individuals With Chronic Low Back Pain.

PubMed

Jung, Mi Jung; Jeong, Younhee

2016-01-01

Self-management behavior is an important component for successful pain management in individuals with chronic low back pain. Motivation has been considered as an effective way to change behavior. Because there are other physical, social, and psychological factors affecting individuals with pain, it is necessary to identify the main effect of motivation on self-management behavior without the influence of those factors. The purpose of this study was to investigate the effect of motivation on self-management in controlling pain, depression, and social support. We used a nonexperimental, cross-sectional, descriptive design with mediation analysis and included 120 participants' data in the final analysis. We also used hierarchical multiple regression to test the effect of motivation, and multiple regression analysis and Sobel test were used to examine the mediating effect. Motivation itself accounted for 23.4% of the variance in self-management, F(1, 118) = 35.003, p < .001. After controlling covariates, motivation was also a significant factor for self-management. In the mediation analysis, motivation completely mediated the relationship between education and self-management, z = 2.292, p = .021. Motivation is an important part of self-management, and self-management education is not effective without motivation. The results of our study suggest that nurses incorporate motivation in nursing intervention, rather than only giving information.

Psychological well-being and social support in chronic myeloid leukemia patients receiving lifelong targeted therapies.

PubMed

Efficace, Fabio; Breccia, Massimo; Cottone, Francesco; Okumura, Iris; Doro, Maribel; Riccardi, Francesca; Rosti, Gianantonio; Baccarani, Michele

2016-12-01

The main objective of this study was to investigate whether social support is independently associated with psychological well-being in chronic myeloid leukemia (CML) patients. Secondary objectives were to compare the psychological well-being profile of CML patients with that of their peers in general population and to examine possible age- and sex-related differences. Analysis was performed on 417 patients in treatment with lifelong molecularly targeted therapies. Mean age of patients analyzed was 56 years (range 19-87 years) and 247 (59 %) were male and 170 (41 %) were female. Social support was assessed with the Multidimensional Scale of Perceived Social Support and psychological well-being was evaluated with the short version of the Psychological General Well-Being Index. Descriptive statistics and multivariate logistic regression analyses were used. Multivariate logistic regression analysis revealed that a greater social support was independently associated with lower anxiety and depression, as well as with higher positive well-being, self-control, and vitality (p < 0.001). Female patients reported statistically significant worse outcomes in all dimensions of psychological well-being. Age- and sex-adjusted comparisons with population norms revealed that depression (ES = -0.42, p < 0.001) and self-control (ES = -0.48, p < 0.001) were the two main impaired psychological dimensions. This study indicates that social support is a critical factor associated with psychological well-being of CML patients treated with modern lifelong targeted therapies.

RISK FACTORS FOR CONTACT LENS INDUCED PAPILLARY CONJUNCTIVITIS ASSOCIATED WITH SILICONE HYDROGEL CONTACT LENS WEAR

PubMed Central

Tagliaferri, Angela; Love, Thomas E.; Szczotka-Flynn, Loretta

2014-01-01

BACKGROUND Contact lens induced papillary conjunctivitis (CLPC) continues to be a major cause of dropout during contact lens extended wear. This retrospective study explores risk factors for the development of CLPC during silicone hydrogel lens extended wear. METHODS Data from 205 subjects enrolled in the Longitudinal Analysis of Silicone Hydrogel Contact Lens (LASH) study wearing lotrafilcon A silicone hydrogel lenses for up to 30 days of continuous wear were used to determine risk factors for CLPC in this secondary analysis of the main cohort. The main covariates of interest included substantial lens-associated bacterial bioburden, and topographically determined lens base curve-to-cornea fitting relationships. Additional covariates of interest included history of prior adverse events, time of year, race, education level, gender and other subject demographics. Statistical analyses included univariate logistic regression to assess the impact of potential risk factors on the binary CLPC outcome, and Cox proportional hazards regression to describe the impact of those factors on time-to-CLPC diagnosis. RESULTS Across 12 months of follow-up, 52 subjects (25%) experienced CLPC. No associations were found between CLPC development and the presence of bacterial bioburden, lens-to-cornea fitting relationships, history of prior adverse events, gender or race. CLPC development followed the same seasonal trends as the local peaks in environmental allergans. CONCLUSIONS Lens fit and biodeposits, in the form of lens associated bacterial bioburden, were not associated with the development of CLPC during extended wear with lotrafilcon A silicone hydrogel lenses. PMID:24681609

Risk Factors for Complications in Acute Appendicitis among Paediatric Population.

PubMed

Poudel, R; Bhandari, T R

2017-01-01

Appendicitis is one of the most common causes of acute abdomen in children. Patients who are diagnosed early and undergo an appendectomy before perforation have a good outcome. However, it is difficult to diagnose in young children because its clinical manifestations may be atypical. The aim of this study was to determine the risk factors for complications in acute appendicitis in paediatric population. We performed a cross sectional study on children (age ≤18 years) who underwent appendectomy for suspected appendicitis from January 2014 to December 2015. Medical records of patients who met inclusion criteria were reviewed. Preoperative, operative and post-operative data were analyzed. The main outcome measure was intraoperative confirmation of gangrenous or perforated appendicitis. Multivariate logistic regression analysis was performed, and the main predictors of interest were patient's age, duration of pain and total leucocyte count. Total 73 paediatric patients (46 males) with mean age 13±3.8 were studied. In multivariate logistic regression analysis, patients having pain duration more than 72 hours and patients with leucocyte count >15000/mm3 were more likely to have complicated appendicitis [(OR:14.6), (95% CI= 2.40 - 89.77), (P= 0.004)] and [(OR=16.38), (95% CI = 1.836-146), (P = 0.012)] respectively. However, the age of the patient is not independently associated with complicated appendicitis. Increase in total leucocyte count and duration of the presentation can be a good marker of complicated appendicitis.

Relationship between betel quid chewing and radiographic alveolar bone loss among Taiwanese aboriginals: a retrospective study.

PubMed

Hsiao, Chun-Nan; Ting, Chun-Chan; Shieh, Tien-Yu; Ko, Edward Chengchuan

2014-11-04

Betel quid chewing is associated with the periodontal status; however, results of epidemiological studies are inconsistent. To the best of our knowledge, no study has reported radiographic alveolar bone loss (RABL) associated with betel quid chewing. This survey was conducted in an aboriginal community in Taiwan because almost all betel quid chewers were city-dwelling cigarette smokers. In total, 114 subjects, aged 30-60 years, were included. Full-mouth intraoral RABL was retrospectively measured and adjusted for age, gender, and plaque index (PI). Multiple regression analysis was used to assess the relationship between RABL and potential risk factors. Age-, gender-, and PI-adjusted mean RABL was significantly higher in chewers with or without cigarette smoking than in controls. Multiple regression analysis showed that the RABL for consumption of 100,000 pieces betel quid for the chewer group was 0.40 mm. Full-mouth plotted curves for adjusted mean RABL in the maxilla were similar between the chewer and control groups, suggesting that chemical effects were not the main factors affecting the association between betel quid chewing and the periodontal status. Betel quid chewing significantly increases RABL. The main contributory factors are age and oral hygiene; however, the major mechanism underlying this process may not be a chemical mechanism. Regular dental visits, maintenance of good oral hygiene, and reduction in the consumption of betel quid, additives, and cigarettes are highly recommended to improve the periodontal status.

A comprehensive evaluation of various sensitivity analysis methods: A case study with a hydrological model

DOE PAGES

Gan, Yanjun; Duan, Qingyun; Gong, Wei; ...

2014-01-01

Sensitivity analysis (SA) is a commonly used approach for identifying important parameters that dominate model behaviors. We use a newly developed software package, a Problem Solving environment for Uncertainty Analysis and Design Exploration (PSUADE), to evaluate the effectiveness and efficiency of ten widely used SA methods, including seven qualitative and three quantitative ones. All SA methods are tested using a variety of sampling techniques to screen out the most sensitive (i.e., important) parameters from the insensitive ones. The Sacramento Soil Moisture Accounting (SAC-SMA) model, which has thirteen tunable parameters, is used for illustration. The South Branch Potomac River basin nearmore » Springfield, West Virginia in the U.S. is chosen as the study area. The key findings from this study are: (1) For qualitative SA methods, Correlation Analysis (CA), Regression Analysis (RA), and Gaussian Process (GP) screening methods are shown to be not effective in this example. Morris One-At-a-Time (MOAT) screening is the most efficient, needing only 280 samples to identify the most important parameters, but it is the least robust method. Multivariate Adaptive Regression Splines (MARS), Delta Test (DT) and Sum-Of-Trees (SOT) screening methods need about 400–600 samples for the same purpose. Monte Carlo (MC), Orthogonal Array (OA) and Orthogonal Array based Latin Hypercube (OALH) are appropriate sampling techniques for them; (2) For quantitative SA methods, at least 2777 samples are needed for Fourier Amplitude Sensitivity Test (FAST) to identity parameter main effect. McKay method needs about 360 samples to evaluate the main effect, more than 1000 samples to assess the two-way interaction effect. OALH and LPτ (LPTAU) sampling techniques are more appropriate for McKay method. For the Sobol' method, the minimum samples needed are 1050 to compute the first-order and total sensitivity indices correctly. These comparisons show that qualitative SA methods are more efficient but less accurate and robust than quantitative ones.« less

Patient experience of general practice and use of emergency hospital services in England: regression analysis of national cross-sectional time series data.

PubMed

Cowling, Thomas E; Majeed, Azeem; Harris, Matthew J

2018-01-22

The UK Government has introduced several national policies to improve access to primary care. We examined associations between patient experience of general practice and rates of visits to accident and emergency (A&E) departments and emergency hospital admissions in England. The study included 8124 general practices between 2011-2012 and 2013-2014. Outcome measures were annual rates of A&E visits and emergency admissions by general practice population, according to administrative hospital records. Explanatory variables included three patient experience measures from the General Practice Patient Survey: practice-level means of experience of making an appointment, satisfaction with opening hours and overall experience (on 0-100 scales). The main analysis used random-effects Poisson regression for cross-sectional time series. Five sensitivity analyses examined changes in model specification. Mean practice-level rates of A&E visits and emergency admissions increased from 2011-2012 to 2013-2014 (310.3-324.4 and 98.8-102.9 per 1000 patients). Each patient experience measure decreased; for example, mean satisfaction with opening hours was 79.4 in 2011-2012 and 76.6 in 2013-2014. In the adjusted regression analysis, an SD increase in experience of making appointments (equal to 9 points) predicted decreases of 1.8% (95% CI -2.4% to -1.2%) in A&E visit rates and 1.4% (95% CI -1.9% to -0.9%) in admission rates. This equalled 301 174 fewer A&E visits and 74 610 fewer admissions nationally per year. Satisfaction with opening hours and overall experience were not consistently associated with either outcome measure across the main and sensitivity analyses. Associations between patient experience of general practice and use of emergency hospital services were small or inconsistent. In England, realistic short-term improvements in patient experience of general practice may only have modest effects on A&E visits and emergency admissions. © Article author(s) (or their employer(s) unless otherwise stated in the text of the article) 2018. All rights reserved. No commercial use is permitted unless otherwise expressly granted.

[Relationships of wetland landscape fragmentation with climate change in middle reaches of Heihe River, China].

PubMed

Jiang, Peng-Hui; Zhao, Rui-Feng; Zhao, Hai-Li; Lu, Li-Peng; Xie, Zuo-Lun

2013-06-01

Based on the 1975-2010 multi-temporal remotely sensed TM and ETM images and meteorological data, in combining with wavelet analysis, trend surface simulation, and interpolation method, this paper analyzed the meteorological elements' spatial distribution and change characteristics in the middle reaches of Heihe River, and elucidated the process of wetland landscape fragmentation in the study area by using the landscape indices patch density (PD), mean patch size (MPS), and patch shape fragment index (FS). The relationships between the wetland landscape fragmentation and climate change were also approached through correlation analysis and multiple stepwise regression analysis. In 1975-2010, the overall distribution patterns of precipitation and temperature in the study area were low precipitation in high temperature regions and high precipitation in low temperature regions, and the main characteristics of climate change were the conversion from dry to wet and from cold to warm. In the whole study period, the wetland landscape fragmentation was mainly manifested in the decrease of MPS, with a decrement of 48.95 hm2, and the increase of PD, with an increment of 0.006 ind x hm(-2).

P300 Amplitude in Alzheimer's Disease: A Meta-Analysis and Meta-Regression.

PubMed

Hedges, Dawson; Janis, Rebecca; Mickelson, Stephen; Keith, Cierra; Bennett, David; Brown, Bruce L

2016-01-01

Alzheimer's disease accounts for 60% of all dementia. Numerous biomarkers have been developed that can help in making an early diagnosis. The P300 is an event-related potential that may be abnormal in Alzheimer's disease. Given the possible association between P300 amplitude and Alzheimer's disease and the need for biomarkers in early Alzheimer's disease, the main purpose of this meta-analysis and meta-regression was to characterize P300 amplitude in probable Alzheimer's disease compared to healthy controls. Using online search engines, we identified peer-reviewed articles containing amplitude measures for the P300 in response to a visual or auditory oddball stimulus in subjects with Alzheimer's disease and in a healthy control group and pooled effect sizes for differences in P300 amplitude between Alzheimer's disease and control groups to obtain summary effect sizes. We also used meta-regression to determine whether age, sex, educational attainment, or dementia severity affected the association between P300 amplitude and Alzheimer's disease. Twenty articles containing a total of 646 subjects met inclusion and exclusion criteria. The overall effect size from all electrode locations was 1.079 (95% confidence interval=0.745-1.412, P<.001). The pooled effect sizes for the Cz, Fz, and Pz locations were 1.226 (P<.001), 0.724 (P=.0007), and 1.430 (P<.001), respectively. Meta-regression showed an association between amplitude and educational attainment, but no association between amplitude and age, sex, and dementia severity. In conclusion, P300 amplitude is smaller in subjects with Alzheimer's disease than in healthy controls. © EEG and Clinical Neuroscience Society (ECNS) 2014.

Spatial quantile regression using INLA with applications to childhood overweight in Malawi.

PubMed

Mtambo, Owen P L; Masangwi, Salule J; Kazembe, Lawrence N M

2015-04-01

Analyses of childhood overweight have mainly used mean regression. However, using quantile regression is more appropriate as it provides flexibility to analyse the determinants of overweight corresponding to quantiles of interest. The main objective of this study was to fit a Bayesian additive quantile regression model with structured spatial effects for childhood overweight in Malawi using the 2010 Malawi DHS data. Inference was fully Bayesian using R-INLA package. The significant determinants of childhood overweight ranged from socio-demographic factors such as type of residence to child and maternal factors such as child age and maternal BMI. We observed significant positive structured spatial effects on childhood overweight in some districts of Malawi. We recommended that the childhood malnutrition policy makers should consider timely interventions based on risk factors as identified in this paper including spatial targets of interventions. Copyright © 2015 Elsevier Ltd. All rights reserved.

Peak flow regression equations For small, ungaged streams in Maine: Comparing map-based to field-based variables

USGS Publications Warehouse

Lombard, Pamela J.; Hodgkins, Glenn A.

2015-01-01

Regression equations to estimate peak streamflows with 1- to 500-year recurrence intervals (annual exceedance probabilities from 99 to 0.2 percent, respectively) were developed for small, ungaged streams in Maine. Equations presented here are the best available equations for estimating peak flows at ungaged basins in Maine with drainage areas from 0.3 to 12 square miles (mi2). Previously developed equations continue to be the best available equations for estimating peak flows for basin areas greater than 12 mi2. New equations presented here are based on streamflow records at 40 U.S. Geological Survey streamgages with a minimum of 10 years of recorded peak flows between 1963 and 2012. Ordinary least-squares regression techniques were used to determine the best explanatory variables for the regression equations. Traditional map-based explanatory variables were compared to variables requiring field measurements. Two field-based variables—culvert rust lines and bankfull channel widths—either were not commonly found or did not explain enough of the variability in the peak flows to warrant inclusion in the equations. The best explanatory variables were drainage area and percent basin wetlands; values for these variables were determined with a geographic information system. Generalized least-squares regression was used with these two variables to determine the equation coefficients and estimates of accuracy for the final equations.

A statistical method for predicting seizure onset zones from human single-neuron recordings

NASA Astrophysics Data System (ADS)

Valdez, André B.; Hickman, Erin N.; Treiman, David M.; Smith, Kris A.; Steinmetz, Peter N.

2013-02-01

Objective. Clinicians often use depth-electrode recordings to localize human epileptogenic foci. To advance the diagnostic value of these recordings, we applied logistic regression models to single-neuron recordings from depth-electrode microwires to predict seizure onset zones (SOZs). Approach. We collected data from 17 epilepsy patients at the Barrow Neurological Institute and developed logistic regression models to calculate the odds of observing SOZs in the hippocampus, amygdala and ventromedial prefrontal cortex, based on statistics such as the burst interspike interval (ISI). Main results. Analysis of these models showed that, for a single-unit increase in burst ISI ratio, the left hippocampus was approximately 12 times more likely to contain a SOZ; and the right amygdala, 14.5 times more likely. Our models were most accurate for the hippocampus bilaterally (at 85% average sensitivity), and performance was comparable with current diagnostics such as electroencephalography. Significance. Logistic regression models can be combined with single-neuron recording to predict likely SOZs in epilepsy patients being evaluated for resective surgery, providing an automated source of clinically useful information.

Perceived Organizational Support for Enhancing Welfare at Work: A Regression Tree Model

PubMed Central

Giorgi, Gabriele; Dubin, David; Perez, Javier Fiz

2016-01-01

When trying to examine outcomes such as welfare and well-being, research tends to focus on main effects and take into account limited numbers of variables at a time. There are a number of techniques that may help address this problem. For example, many statistical packages available in R provide easy-to-use methods of modeling complicated analysis such as classification and tree regression (i.e., recursive partitioning). The present research illustrates the value of recursive partitioning in the prediction of perceived organizational support in a sample of more than 6000 Italian bankers. Utilizing the tree function party package in R, we estimated a regression tree model predicting perceived organizational support from a multitude of job characteristics including job demand, lack of job control, lack of supervisor support, training, etc. The resulting model appears particularly helpful in pointing out several interactions in the prediction of perceived organizational support. In particular, training is the dominant factor. Another dimension that seems to influence organizational support is reporting (perceived communication about safety and stress concerns). Results are discussed from a theoretical and methodological point of view. PMID:28082924

Predictors of effects of lifestyle intervention on diabetes mellitus type 2 patients.

PubMed

Jacobsen, Ramune; Vadstrup, Eva; Røder, Michael; Frølich, Anne

2012-01-01

The main aim of the study was to identify predictors of the effects of lifestyle intervention on diabetes mellitus type 2 patients by means of multivariate analysis. Data from a previously published randomised clinical trial, which compared the effects of a rehabilitation programme including standardised education and physical training sessions in the municipality's health care centre with the same duration of individual counseling in the diabetes outpatient clinic, were used. Data from 143 diabetes patients were analysed. The merged lifestyle intervention resulted in statistically significant improvements in patients' systolic blood pressure, waist circumference, exercise capacity, glycaemic control, and some aspects of general health-related quality of life. The linear multivariate regression models explained 45% to 80% of the variance in these improvements. The baseline outcomes in accordance to the logic of the regression to the mean phenomenon were the only statistically significant and robust predictors in all regression models. These results are important from a clinical point of view as they highlight the more urgent need for and better outcomes following lifestyle intervention for those patients who have worse general and disease-specific health.

The use of segmented regression in analysing interrupted time series studies: an example in pre-hospital ambulance care.

PubMed

Taljaard, Monica; McKenzie, Joanne E; Ramsay, Craig R; Grimshaw, Jeremy M

2014-06-19

An interrupted time series design is a powerful quasi-experimental approach for evaluating effects of interventions introduced at a specific point in time. To utilize the strength of this design, a modification to standard regression analysis, such as segmented regression, is required. In segmented regression analysis, the change in intercept and/or slope from pre- to post-intervention is estimated and used to test causal hypotheses about the intervention. We illustrate segmented regression using data from a previously published study that evaluated the effectiveness of a collaborative intervention to improve quality in pre-hospital ambulance care for acute myocardial infarction (AMI) and stroke. In the original analysis, a standard regression model was used with time as a continuous variable. We contrast the results from this standard regression analysis with those from segmented regression analysis. We discuss the limitations of the former and advantages of the latter, as well as the challenges of using segmented regression in analysing complex quality improvement interventions. Based on the estimated change in intercept and slope from pre- to post-intervention using segmented regression, we found insufficient evidence of a statistically significant effect on quality of care for stroke, although potential clinically important effects for AMI cannot be ruled out. Segmented regression analysis is the recommended approach for analysing data from an interrupted time series study. Several modifications to the basic segmented regression analysis approach are available to deal with challenges arising in the evaluation of complex quality improvement interventions.

Standardized Regression Coefficients as Indices of Effect Sizes in Meta-Analysis

ERIC Educational Resources Information Center

Kim, Rae Seon

2011-01-01

When conducting a meta-analysis, it is common to find many collected studies that report regression analyses, because multiple regression analysis is widely used in many fields. Meta-analysis uses effect sizes drawn from individual studies as a means of synthesizing a collection of results. However, indices of effect size from regression analyses…

Partitioning sources of variation in vertebrate species richness

USGS Publications Warehouse

Boone, R.B.; Krohn, W.B.

2000-01-01

Aim: To explore biogeographic patterns of terrestrial vertebrates in Maine, USA using techniques that would describe local and spatial correlations with the environment. Location: Maine, USA. Methods: We delineated the ranges within Maine (86,156 km2) of 275 species using literature and expert review. Ranges were combined into species richness maps, and compared to geomorphology, climate, and woody plant distributions. Methods were adapted that compared richness of all vertebrate classes to each environmental correlate, rather than assessing a single explanatory theory. We partitioned variation in species richness into components using tree and multiple linear regression. Methods were used that allowed for useful comparisons between tree and linear regression results. For both methods we partitioned variation into broad-scale (spatially autocorrelated) and fine-scale (spatially uncorrelated) explained and unexplained components. By partitioning variance, and using both tree and linear regression in analyses, we explored the degree of variation in species richness for each vertebrate group that Could be explained by the relative contribution of each environmental variable. Results: In tree regression, climate variation explained richness better (92% of mean deviance explained for all species) than woody plant variation (87%) and geomorphology (86%). Reptiles were highly correlated with environmental variation (93%), followed by mammals, amphibians, and birds (each with 84-82% deviance explained). In multiple linear regression, climate was most closely associated with total vertebrate richness (78%), followed by woody plants (67%) and geomorphology (56%). Again, reptiles were closely correlated with the environment (95%), followed by mammals (73%), amphibians (63%) and birds (57%). Main conclusions: Comparing variation explained using tree and multiple linear regression quantified the importance of nonlinear relationships and local interactions between species richness and environmental variation, identifying the importance of linear relationships between reptiles and the environment, and nonlinear relationships between birds and woody plants, for example. Conservation planners should capture climatic variation in broad-scale designs; temperatures may shift during climate change, but the underlying correlations between the environment and species richness will presumably remain.

A cross-sectional study for estimation of associations between education level and osteoporosis in a Chinese men sample.

PubMed

Yu, Cai-Xia; Zhang, Xiu-Zhen; Zhang, Keqin; Tang, Zihui

2015-12-09

The main aim of this study was to evaluate the association between education level and osteoporosis (OP) in general Chinese Men. We conducted a large-scale, community-based, cross-sectional study to investigate the association by using self-report questionnaire to assess education levels. The data of 1092 men were available for analysis in this study. Multiple regression models controlling for confounding factors to include education level were performed to explore the relationship between education level and OP. Positive correlations between education level and T-score of quantitative bone ultrasound (QUS-T score) were reported (β = 0.108, P value < 0.001). Multiple regression analysis indicated that the education level was independently and significantly associated with OP (P < 0.1 for all models). The men with lower education level had a higher prevalence of OP. The education level was independently and significantly associated with OP. The prevalence of OP was more frequent in Chinese men with lower education level. ClinicalTrials.gov Identifier: NCT02451397 ; date of registration: 05/28/2015).

The role of depression and anxiety in impulsive and obsessive-compulsive behaviors among anorexic and bulimic patients.

PubMed

Finzi-Dottan, Ricky; Zubery, Eynat

2009-01-01

Eating disorders are believed to range across a spectrum of varying degrees of obsessive-compulsive and impulsive behavior. Sixty anorexic (mean age = 19.8; sd = 5.9) and 109 bulimic (mean age = 26.9; sd = 11.3) female patients completed self-report questionnaires assessing obsessive-compulsiveness, impulsivity, depression and anxiety, as well as two eating disorder scales. Results yielded significantly higher levels of impulsivity and negative body image in the bulimic compared to the anorexic group. Regression analysis predicting impulsivity showed that bulimia and negative body image were the main contributors. Regression analysis for predicting obsessive-compulsive behavior suggested that depression and anxiety obscure the link between anorexia and obsessive-compulsive behavior, and a high BMI intensifies the association between anxiety and obsessive-compulsive behavior. The high rates of both impulsivity and obsessive-compulsiveness found in both groups, and their association with the severity of the eating disorder, may suggest that impulsivity and obsessive-compulsiveness are not mutually exclusive and can both be found among anorexic and bulimic patients.

Frequency and clinical predictors of coronary artery disease in chronic renal failure renal transplant candidates.

PubMed

de Albuquerque Seixas, Emerson; Carmello, Beatriz Leone; Kojima, Christiane Akemi; Contti, Mariana Moraes; Modeli de Andrade, Luiz Gustavo; Maiello, José Roberto; Almeida, Fernando Antonio; Martin, Luis Cuadrado

2015-05-01

Cardiovascular diseases are major causes of mortality in chronic renal failure patients before and after renal transplantation. Among them, coronary disease presents a particular risk; however, risk predictors have been used to diagnose coronary heart disease. This study evaluated the frequency and importance of clinical predictors of coronary artery disease in chronic renal failure patients undergoing dialysis who were renal transplant candidates, and assessed a previously developed scoring system. Coronary angiographies conducted between March 2008 and April 2013 from 99 candidates for renal transplantation from two transplant centers in São Paulo state were analyzed for associations between significant coronary artery diseases (≥70% stenosis in one or more epicardial coronary arteries or ≥50% in the left main coronary artery) and clinical parameters. Univariate logistic regression analysis identified diabetes, angina, and/or previous infarction, clinical peripheral arterial disease and dyslipidemia as predictors of coronary artery disease. Multiple logistic regression analysis identified only diabetes and angina and/or previous infarction as independent predictors. The results corroborate previous studies demonstrating the importance of these factors when selecting patients for coronary angiography in clinical pretransplant evaluation.

A study on Turkish adolescent's Internet use: possible predictors of Internet addiction.

PubMed

Ak, Serife; Koruklu, Nermin; Yılmaz, Yusuf

2013-03-01

The purpose of this study is to investigate the internet use of Turkish adolescents, with a (particular) focus on the risk of Internet addiction. A web-based questionnaire was completed by a total of 4,311 adolescents attending public high schools in grades 9-12, in a small-sized city in western Turkey. Ages ranged from 15 to 19 years, 54 percent were female and 46 percent male. The questionnaire included items on sociodemographic information, Internet usage, and a Turkish version of the Young's Internet Addiction Test. The data were analyzed in SPPS 15.0 program using the t test, the Mann-Whitney U test, correlation and hierarchic regression analysis. The findings show that, regardless of gender, Facebook ranked highest in the classification of students' purpose of Internet use; it was also found that females mainly used the Internet for communication, whereas males were more interested in playing online games and reading newspapers and magazines. The results of hierarchic regression analysis indicated that the significant predictors of the internet addiction were the presence of Internet access at home, gender, and family income levels.

The Effect of Aging and Severity of Sleep Apnea on Heart Rate Variability Indices in Obstructive Sleep Apnea Syndrome

PubMed Central

Song, Man-Kyu; Ha, Jee Hyun; Ryu, Seung-Ho; Yu, Jaehak

2012-01-01

Objective This study aims to analyze how much heart rate variability (HRV) indices discriminatively respond to age and severity of sleep apnea in the obstructive sleep apnea syndrome (OSAS). Methods 176 male OSAS patients were classified into four groups according to their age and apnea-hypopnea index (AHI). The HRV indices were compared via analysis of covariance (ANCOVA). In particular, the partial correlation method was performed to identify the most statistically significant HRV indices in the time and frequency domains. Stepwise multiple linear regressions were further executed to examine the effects of age, AHI, body mass index (BMI), systolic blood pressure (SBP), diastolic blood pressure (DBP), and sleep parameters on the significant HRV indices. Results The partial correlation analysis yielded the NN50 count (defined as the number of adjacent R-wave to R-wave intervals differing by more than 50 ms) and low frequency/high frequency (LF/HF) ratio to be two most statistically significant HRV indices in both time and frequency domains. The two indices showed significant differences between the groups. The NN50 count was affected by age (p<0.001) and DBP (p=0.039), while the LF/HF ratio was affected by AHI (p<0.001), the amount of Stage 2 sleep (p=0.005), and age (p=0.021) in the order named in the regression analysis. Conclusion The NN50 count more sensitively responded to age than to AHI, suggesting that the index is mainly associated with an age-related parasympathetic system. On the contrary, the LF/HF ratio responded to AHI more sensitively than to age, suggesting that it is mainly associated with a sympathetic tone likely reflecting the severity of sleep apnea. PMID:22396687

Long-term prognosis of patients with acute myocardial infarction due to unprotected left main coronary artery disease: a single-centre experience over 14 years.

PubMed

Xu, Li; Sun, Hao; Wang, Le-Feng; Yang, Xin-Chun; Li, Kui-Bao; Zhang, Da-Peng; Wang, Hong-Shi; Li, Wei-Ming

2016-07-01

Acute myocardial infarction (AMI) due to unprotected left main coronary artery (ULMCA) disease is clinically catastrophic although it has a low incidence. Studies on the long-term prognosis of these patients are rare. From January 1999 to September 2013, 55 patients whose infarct-related artery was the ULMCA were enrolled. Clinical, angiographic and interventional data was collected. Short-term and long-term clinical follow-up results as well as prognostic determinants during hospitalisation and follow-up were analysed. Cardiogenic shock (CS) occurred in 30 (54.5%) patients. During hospitalisation, 22 (40.0%) patients died. Multivariate logistic regression analysis showed that CS (odds ratio [OR] 5.86; p = 0.03), collateral circulation of Grade 2 or 3 (OR 0.14; p = 0.02) and final flow of thrombolysis in myocardial infarction (TIMI) Grade 3 (OR 0.05; p = 0.03) correlated with death during hospitalisation. 33 patients survived to discharge; another seven patients died during the follow-up period of 44.6 ± 31.3 (median 60, range 0.67-117.00) months. The overall mortality rate was 52.7% (n = 29). Kaplan-Meier analysis showed that the total cumulative survival rate was 30.7%. Cox multivariate regression analysis showed that CS during hospitalisation was the only predictor of overall mortality (hazard ratio 4.07, 95% confidence interval 1.40-11.83; p = 0.01). AMI caused by ULMCA lesions is complicated by high incidence of CS and mortality. CS, poor collateral blood flow and failure to restore final flow of TIMI Grade 3 correlated with death during hospitalisation. CS is the only predictor of long-term overall mortality. Copyright: © Singapore Medical Association.

Demand-supply dynamics in tourism systems: A spatio-temporal GIS analysis. The Alberta ski industry case study

NASA Astrophysics Data System (ADS)

Bertazzon, Stefania

The present research focuses on the interaction of supply and demand of down-hill ski tourism in the province of Alberta. The main hypothesis is that the demand for skiing depends on the socio-economic and demographic characteristics of the population living in the province and outside it. A second, consequent hypothesis is that the development of ski resorts (supply) is a response to the demand for skiing. From the latter derives the hypothesis of a dynamic interaction between supply (ski resorts) and demand (skiers). Such interaction occurs in space, within a range determined by physical distance and the means available to overcome it. The above hypotheses implicitly define interactions that take place in space and evolve over time. The hypotheses are tested by temporal, spatial, and spatio-temporal regression models, using the best available data and the latest commercially available software. The main purpose of this research is to explore analytical techniques to model spatial, temporal, and spatio-temporal dynamics in the context of regional science. The completion of the present research has produced more significant contributions than was originally expected. Many of the unexpected contributions resulted from theoretical and applied needs arising from the application of spatial regression models. Spatial regression models are a new and largely under-applied technique. The models are fairly complex and a considerable amount of preparatory work is needed, prior to their specification and estimation. Most of this work is specific to the field of application. The originality of the solutions devised is increased by the lack of applications in the field of tourism. The scarcity of applications in other fields adds to their value for other applications. The estimation of spatio-temporal models has been only partially attained in the present research. This apparent limitation is due to the novelty and complexity of the analytical methods applied. This opens new directions for further work in the field of spatial analysis, in conjunction with the development of specific software.

Modeling of boldine alkaloid adsorption onto pure and propyl-sulfonic acid-modified mesoporous silicas. A comparative study.

PubMed

Geszke-Moritz, Małgorzata; Moritz, Michał

2016-12-01

The present study deals with the adsorption of boldine onto pure and propyl-sulfonic acid-functionalized SBA-15, SBA-16 and mesocellular foam (MCF) materials. Siliceous adsorbents were characterized by nitrogen sorption analysis, transmission electron microscopy (TEM), scanning electron microscopy (SEM), Fourier-transform infrared (FT-IR) spectroscopy and thermogravimetric analysis. The equilibrium adsorption data were analyzed using the Langmuir, Freundlich, Redlich-Peterson, and Temkin isotherms. Moreover, the Dubinin-Radushkevich and Dubinin-Astakhov isotherm models based on the Polanyi adsorption potential were employed. The latter was calculated using two alternative formulas including solubility-normalized (S-model) and empirical C-model. In order to find the best-fit isotherm, both linear regression and nonlinear fitting analysis were carried out. The Dubinin-Astakhov (S-model) isotherm revealed the best fit to the experimental points for adsorption of boldine onto pure mesoporous materials using both linear and nonlinear fitting analysis. Meanwhile, the process of boldine sorption onto modified silicas was described the best by the Langmuir and Temkin isotherms using linear regression and nonlinear fitting analysis, respectively. The values of adsorption energy (below 8kJ/mol) indicate the physical nature of boldine adsorption onto unmodified silicas whereas the ionic interactions seem to be the main force of alkaloid adsorption onto functionalized sorbents (energy of adsorption above 8kJ/mol). Copyright © 2016 Elsevier B.V. All rights reserved.

Coupling Analysis of Heat Island Effects, Vegetation Coverage and Urban Flood in Wuhan

NASA Astrophysics Data System (ADS)

Liu, Y.; Liu, Q.; Fan, W.; Wang, G.

2018-04-01

In this paper, satellite image, remote sensing technique and geographic information system technique are main technical bases. Spectral and other factors comprehensive analysis and visual interpretation are main methods. We use GF-1 and Landsat8 remote sensing satellite image of Wuhan as data source, and from which we extract vegetation distribution, urban heat island relative intensity distribution map and urban flood submergence range. Based on the extracted information, through spatial analysis and regression analysis, we find correlations among heat island effect, vegetation coverage and urban flood. The results show that there is a high degree of overlap between of urban heat island and urban flood. The area of urban heat island has buildings with little vegetation cover, which may be one of the reasons for the local heavy rainstorms. Furthermore, the urban heat island has a negative correlation with vegetation coverage, and the heat island effect can be alleviated by the vegetation to a certain extent. So it is easy to understand that the new industrial zones and commercial areas which under constructions distribute in the city, these land surfaces becoming bare or have low vegetation coverage, can form new heat islands easily.

Hepatitis B virus mutation may play a role in hepatocellular carcinoma recurrence: A systematic review and meta-regression analysis.

PubMed

Zhou, Hua-ying; Luo, Yue; Chen, Wen-dong; Gong, Guo-zhong

2015-06-01

A number of studies have confirmed that antiviral therapy with nucleotide analogs (NAs) can improve the prognosis of hepatitis B virus (HBV)-related hepatocellular carcinoma (HCC) after curative therapy. However, what factors affected the prognosis of HBV-HCC after removal of the primary tumor and inhibition of HBV replication? A meta-regression analysis was conducted to explore the prognostic factor for this subgroup of patients. MEDLINE, EMBASE, Web of Science, and Cochrane library were searched from January 1995 to February 2014 for clinical trials evaluating the effect of NAs on the prognosis of HBV-HCC after curative therapy. Data were extracted for host, viral, and intervention information. Single-arm meta-analysis was performed to assess overall survival (OS) rates and HCC recurrence. Meta-regression analysis was carried out to explore risk factors for 1-year OS rate and HCC recurrence for HBV-HCC patients after curative therapy and antiviral therapy. Fourteen observational studies with 1284 patients met the inclusion criteria. Influential factors for prognosis of HCC were mainly baseline HBeAg positivity, cirrhotic stage, advanced Tumor-Node-Metastasis (TNM) stage, macrovascular invasion, and antiviral agent type. The 1-year OS rate decreased by more than four times (coefficient -4.45, P<0.001) and the 1-year HCC recurrence increased by more than one time (coefficient 1.20, P=0.003) when lamivudine was chosen for HCC after curative therapy, relative to entecavir for HCC. HBV mutation may play a role in HCC recurrence. Entecavir or tenofovir, a high genetic barrier to resistance, should be recommended for HBV-HCC patients. © 2015 The Authors. Journal of Gastroenterology and Hepatology published by Journal of Gastroenterology and Hepatology Foundation and Wiley Publishing Asia Pty Ltd.

[Investigation and analysis of factors that affect the health of children in the plastic recycling and regeneration processing region].

PubMed

Wang, Juanli; Li, Liping; Lu, Yaogui

2014-09-01

To investigate the main influential factors for the health of children in the plastic waste recovery and recycling area. A cross-sectional survey was performed among children aged 9∼17 years from three natural villages engaged in plastic waste recovery and recycling and four control villages engaged in planting. The health status of children was investigated by random household survey using a face-to-face questionnaire, and the main influential factors were analyzed accordingly. The incidence rates of respiratory symptoms (cough and expectoration, nasal congestion, and sore throat) (78.4%, 69/88) and digestive diseases (gastrointestinal disease and liver disease) (14.8%, 13/88) in the waste processing area were significantly higher than those in the control area (64.0%, 71/111; 6.3%, 7/111) (P < 0.05). Multivariate logistic regression analysis indicated that skin diseases are related to whether plastic can be smelt around the residential area.

[Study on depressive disorder and related factors in surgical inpatients].

PubMed

Ge, Hong-min; Liu, Lan-fen; Han, Jian-bo

2008-03-01

To investigate the prevalence and possible influencing factors of depressive disorder in surgical inpatients. Two hundred and sixty-six surgical inpatients meeting the inclusion criteria were first screened with the self rating depression scale (SDS), and then the subjects screened positive and 20% of those screened negative were evaluated with Structured Clinical Interview for DSM-IV Axis I Disorders (SCID) as a gold standard for diagnosis of depressive disorder. Possible influencing factors were also analyzed by experienced psychiatrists. The standard score of SDS in the surgical inpatients were significantly higher than those in the Chinese norm, and the incidence of depressive disorder in the surgical inpatients was 37.2%. Unvaried analysis showed that depressive disorder were associated with gender, education, economic condition, variety of diseases, hospitalization duration, and treatment methods. Logistic regression analysis revealed that gender, economic condition, treatment methods and previous history were the main influencing factors. The incidence of depressive disorder in the surgical inpatients is high, and it is mainly influenced by gender, economic condition, treatment methods and previous history.

Using Dominance Analysis to Determine Predictor Importance in Logistic Regression

ERIC Educational Resources Information Center

Azen, Razia; Traxel, Nicole

2009-01-01

This article proposes an extension of dominance analysis that allows researchers to determine the relative importance of predictors in logistic regression models. Criteria for choosing logistic regression R[superscript 2] analogues were determined and measures were selected that can be used to perform dominance analysis in logistic regression. A…

Thoracic Idiopathic Scoliosis Severity Is Highly Correlated with 3D Measures of Thoracic Kyphosis.

PubMed

Sullivan, T Barrett; Reighard, Fredrick G; Osborn, Emily J; Parvaresh, Kevin C; Newton, Peter O

2017-06-07

Loss of thoracic kyphosis has been associated with thoracic idiopathic scoliosis. Modern 3-dimensional (3D) imaging systems allow more accurate characterization of the scoliotic deformity than traditional radiographs. In this study, we utilized 3D calculations to characterize the association between increasing scoliosis severity and changes in the sagittal and axial planes. Patients evaluated in a scoliosis clinic and determined to have either a normal spine or idiopathic scoliosis were included in the analysis. All underwent upright, biplanar radiography with 3D reconstructions. Two-dimensional (2D) measurements of the magnitude of the thoracic major curve and the thoracic kyphosis were recorded. Image processing and MATLAB analysis were utilized to produce a 3D calculation of thoracic kyphosis and apical vertebral axial rotation. Regression analysis was performed to determine the correlation of 2D kyphosis, 3D kyphosis, and apical axial rotation with the magnitude of the thoracic major curve. The 442 patients for whom 2D and 3D data were collected had a main thoracic curve magnitude ranging from 1° to 118°. Linear regression analysis of the 2D and 3D T5-T12 kyphosis versus main thoracic curve magnitude yielded significant models (p < 0.05). The 2D model had a minimally negative slope (-0.07), a small R value (0.02), and a poor correlation coefficient (-0.14). In contrast, the 3D model had a strongly negative slope (-0.54), a high R value (0.56), and a strong correlation coefficient (-0.75). Curve magnitude also had a strong correlation with loss of 3D T1-T12 kyphosis and increasing apical axial rotation. Segmentally calculated 3D thoracic kyphosis had a strongly negative correlation with the magnitude of the main thoracic curve. With near uniformity, 3D thoracic kyphosis progressively decreased as scoliosis magnitude increased, at a rate of more than half the increase in the main thoracic curve magnitude. Analysis confirmed a surprisingly strong correlation between scoliosis severity and loss of 3D kyphosis that was absent in the 2D analysis. A similarly strong correlation between curve magnitude and apical axial rotation was evident. These findings lend further credence to the concept that scoliosis progresses in the coronal, sagittal, and axial planes simultaneously. The findings of this study suggest that 3D assessment is critical for adequate characterization of the multiplanar deformity of idiopathic scoliosis and deformity in the sagittal plane is linked to deformity in the coronal plane. Increasing severity of coronal plane curvature is associated with a progressive loss of thoracic kyphosis that should be anticipated so that the appropriate intraoperative techniques for correction of idiopathic scoliosis can be applied in all 3 planes.

Development of a multi-variate calibration approach for quantitative analysis of oxidation resistant Mo-Si-B coatings using laser ablation inductively coupled plasma mass spectrometry

NASA Astrophysics Data System (ADS)

Cakara, Anja; Bonta, Maximilian; Riedl, Helmut; Mayrhofer, Paul H.; Limbeck, Andreas

2016-06-01

Nowadays, for the production of oxidation protection coatings in ultrahigh temperature environments, alloys of Mo-Si-B are employed. The properties of the material, mainly the oxidation resistance, are strongly influenced by the Si to B ratio; thus reliable analytical methods are needed to assure exact determination of the material composition for the respective applications. For analysis of such coatings, laser ablation inductively coupled mass spectrometry (LA-ICP-MS) has been reported as a versatile method with no specific requirements on the nature of the sample. However, matrix effects represent the main limitation of laser-based solid sampling techniques and usually the use of matrix-matched standards for quantitative analysis is required. In this work, LA-ICP-MS analysis of samples with known composition and varying Mo, Si and B content was carried out. Between known analyte concentrations and derived LA-ICP-MS signal intensities no linear correlation could be found. In order to allow quantitative analysis independent of matrix effects, a multiple linear regression model was developed. Besides the three target analytes also the signals of possible argides (40Ar36Ar and 98Mo40Ar) as well as detected impurities of the Mo-Si-B coatings (108Pd) were considered. Applicability of the model to unknown samples was confirmed using external validation. Relative deviations from the values determined using conventional liquid analysis after sample digestion between 5 and 10% for the main components Mo and Si were observed.

Motivations and self-presentation strategies on Korean-based "Cyworld" weblog format personal homepages.

PubMed

Jung, Taejin; Youn, Hyunsook; McClung, Steven

2007-02-01

The main purposes of this study are to find out individuals' motives and interpersonal self-presentation strategies on constructing Korean weblog format personal homepage (e.g., "Cyworld mini-homepage"). The study also attempts to find predictor motives that lead to the activities of posting and maintaining a homepage and compare the self-presentation strategies used on the Web with those commonly used in interpersonal situations. By using a principal component factor analysis, four salient self-presentation strategy factors and five interpretable mini-homepage hosting motive factors were identified. Accompanying multiple regression analysis shows that entertainment and personal income factors are major predictors in explaining homepage maintenance expenditures and frequencies of updating.

Selenium uptake and assessment of the biochemical changes in Arthrospira (Spirulina) platensis biomass during the synthesis of selenium nanoparticles.

PubMed

Zinicovscaia, I; Chiriac, T; Cepoi, L; Rudi, L; Culicov, O; Frontasyeva, M; Rudic, V

2017-01-01

The process of selenium uptake by biomass of the cyanobacterium Arthrospira (Spirulina) platensis was investigated by neutron activation analysis at different selenium concentrations in solution and at different contact times. Experimental data showed good fit with the Freundlich adsorption isotherm model, with a regression coefficient value of 0.99. In terms of absorption dependence on time, the maximal selenium content was adsorbed in the first 5 min of interaction without significant further changes. It was also found that A. platensis biomass forms spherical selenium nanoparticles. Biochemical analysis was used to assess the changes in the main components of spirulina biomass (proteins, lipids, carbohydrates, and phycobilin) during nanoparticle formation.

Study of blasting seismic effects of underground powerhouse of pumped storage project in granite condition

NASA Astrophysics Data System (ADS)

Wan, Sheng; Li, Hui

2018-03-01

Though the test of blasting vibration, the blasting seismic wave propagation laws in southern granite pumped storage power project are studied. Attenuation coefficient of seismic wave and factors coefficient are acquired by the method of least squares regression analysis according to Sadaovsky empirical formula, and the empirical formula of seismic wave is obtained. This paper mainly discusses on the test of blasting vibration and the procedure of calculation. Our practice might as well serve as a reference for similar projects to come.

Spacecraft platform cost estimating relationships

NASA Technical Reports Server (NTRS)

Gruhl, W. M.

1972-01-01

The three main cost areas of unmanned satellite development are discussed. The areas are identified as: (1) the spacecraft platform (SCP), (2) the payload or experiments, and (3) the postlaunch ground equipment and operations. The SCP normally accounts for over half of the total project cost and accurate estimates of SCP costs are required early in project planning as a basis for determining total project budget requirements. The development of single formula SCP cost estimating relationships (CER) from readily available data by statistical linear regression analysis is described. The advantages of single formula CER are presented.

Modular organization and hospital performance.

PubMed

Kuntz, Ludwig; Vera, Antonio

2007-02-01

The concept of modularization represents a modern form of organization, which contains the vertical disaggregation of the firm and the use of market mechanisms within hierarchies. The objective of this paper is to examine whether the use of modular structures has a positive effect on hospital performance. The empirical section makes use of multiple regression analyses and leads to the main result that modularization does not have a positive effect on hospital performance. However, the analysis also finds out positive efficiency effects of two central ideas of modularization, namely process orientation and internal market mechanisms.

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.

Development of surrogate models for the prediction of the flow around an aircraft propeller

NASA Astrophysics Data System (ADS)

Salpigidou, Christina; Misirlis, Dimitris; Vlahostergios, Zinon; Yakinthos, Kyros

2018-05-01

In the present work, the derivation of two surrogate models (SMs) for modelling the flow around a propeller for small aircrafts is presented. Both methodologies use derived functions based on computations with the detailed propeller geometry. The computations were performed using k-ω shear stress transport for modelling turbulence. In the SMs, the modelling of the propeller was performed in a computational domain of disk-like geometry, where source terms were introduced in the momentum equations. In the first SM, the source terms were polynomial functions of swirl and thrust, mainly related to the propeller radius. In the second SM, regression analysis was used to correlate the source terms with the velocity distribution through the propeller. The proposed SMs achieved faster convergence, in relation to the detail model, by providing also results closer to the available operational data. The regression-based model was the most accurate and required less computational time for convergence.

Binary logistic regression modelling: Measuring the probability of relapse cases among drug addict

NASA Astrophysics Data System (ADS)

Ismail, Mohd Tahir; Alias, Siti Nor Shadila

2014-07-01

For many years Malaysia faced the drug addiction issues. The most serious case is relapse phenomenon among treated drug addict (drug addict who have under gone the rehabilitation programme at Narcotic Addiction Rehabilitation Centre, PUSPEN). Thus, the main objective of this study is to find the most significant factor that contributes to relapse to happen. The binary logistic regression analysis was employed to model the relationship between independent variables (predictors) and dependent variable. The dependent variable is the status of the drug addict either relapse, (Yes coded as 1) or not, (No coded as 0). Meanwhile the predictors involved are age, age at first taking drug, family history, education level, family crisis, community support and self motivation. The total of the sample is 200 which the data are provided by AADK (National Antidrug Agency). The finding of the study revealed that age and self motivation are statistically significant towards the relapse cases..

Association Between Dietary Intake and Function in Amyotrophic Lateral Sclerosis

PubMed Central

Nieves, Jeri W.; Gennings, Chris; Factor-Litvak, Pam; Hupf, Jonathan; Singleton, Jessica; Sharf, Valerie; Oskarsson, Björn; Fernandes Filho, J. Americo M.; Sorenson, Eric J.; D’Amico, Emanuele; Goetz, Ray; Mitsumoto, Hiroshi

2017-01-01

IMPORTANCE There is growing interest in the role of nutrition in the pathogenesis and progression of amyotrophic lateral sclerosis (ALS). OBJECTIVE To evaluate the associations between nutrients, individually and in groups, and ALS function and respiratory function at diagnosis. DESIGN, SETTING, AND PARTICIPANTS A cross-sectional baseline analysis of the Amyotrophic Lateral Sclerosis Multicenter Cohort Study of Oxidative Stress study was conducted from March 14, 2008, to February 27, 2013, at 16 ALS clinics throughout the United States among 302 patients with ALS symptom duration of 18 months or less. EXPOSURES Nutrient intake, measured using a modified Block Food Frequency Questionnaire (FFQ). MAIN OUTCOMES AND MEASURES Amyotrophic lateral sclerosis function, measured using the ALS Functional Rating Scale–Revised (ALSFRS-R), and respiratory function, measured using percentage of predicted forced vital capacity (FVC). RESULTS Baseline data were available on 302 patients with ALS (median age, 63.2 years [interquartile range, 55.5–68.0 years]; 178 men and 124 women). Regression analysis of nutrients found that higher intakes of antioxidants and carotenes from vegetables were associated with higher ALSFRS-R scores or percentage FVC. Empirically weighted indices using the weighted quantile sum regression method of “good” micronutrients and “good” food groups were positively associated with ALSFRS-R scores (β [SE], 2.7 [0.69] and 2.9 [0.9], respectively) and percentage FVC (β [SE], 12.1 [2.8] and 11.5 [3.4], respectively) (all P < .001). Positive and significant associations with ALSFRS-R scores (β [SE], 1.5 [0.61]; P = .02) and percentage FVC (β [SE], 5.2 [2.2]; P = .02) for selected vitamins were found in exploratory analyses. CONCLUSIONS AND RELEVANCE Antioxidants, carotenes, fruits, and vegetables were associated with higher ALS function at baseline by regression of nutrient indices and weighted quantile sum regression analysis. We also demonstrated the usefulness of the weighted quantile sum regression method in the evaluation of diet. Those responsible for nutritional care of the patient with ALS should consider promoting fruit and vegetable intake since they are high in antioxidants and carotenes. PMID:27775751

Visual Acuity Is Correlated with the Area of the Foveal Avascular Zone in Diabetic Retinopathy and Retinal Vein Occlusion.

PubMed

Balaratnasingam, Chandrakumar; Inoue, Maiko; Ahn, Seungjun; McCann, Jesse; Dhrami-Gavazi, Elona; Yannuzzi, Lawrence A; Freund, K Bailey

2016-11-01

To determine if the area of the foveal avascular zone (FAZ) is correlated with visual acuity (VA) in diabetic retinopathy (DR) and retinal vein occlusion (RVO). Cross-sectional study. Ninety-five eyes of 66 subjects with DR (65 eyes), branch retinal vein occlusion (19 eyes), and central retinal vein occlusion (11 eyes). Structural optical coherence tomography (OCT; Spectralis, Heidelberg Engineering) and OCT angiography (OCTA; Avanti, Optovue RTVue XR) data from a single visit were analyzed. FAZ area, point thickness of central fovea, central 1-mm subfield thickness, the occurrence of intraretinal cysts, ellipsoid zone disruption, and disorganization of retinal inner layers (DRIL) length were measured. VA was also recorded. Correlations between FAZ area and VA were explored using regression models. Main outcome measure was VA. Mean age was 62.9±13.2 years. There was no difference in demographic and OCT-derived anatomic measurements between branch retinal vein occlusion and central retinal vein occlusion groups (all P ≥ 0.058); therefore, data from the 2 groups were pooled together to a single RVO group for further statistical comparisons. Univariate and multiple regression analysis showed that the area of the FAZ was significantly correlated with VA in DR and RVO (all P ≤ 0.003). The relationship between FAZ area and VA varied with age (P = 0.026) such that for a constant FAZ area, an increase in patient age was associated with poorer vision (rise in logarithm of the minimum angle of resolution visual acuity). Disruption of the ellipsoid zone was significantly correlated with VA in univariate and multiple regression analysis (both P < 0.001). Occurrence of intraretinal cysts, DRIL length, and lens status were significantly correlated with VA in the univariate regression analysis (P ≤ 0.018) but not the multiple regression analysis (P ≥ 0.210). Remaining variables evaluated in this study were not predictive of VA (all P ≥ 0.225). The area of the FAZ is significantly correlated with VA in DR and RVO and this relationship is modulated by patient age. Further study about FAZ area and VA correlations during the natural course of retinal vascular diseases and following treatment is warranted. Copyright © 2016 American Academy of Ophthalmology. Published by Elsevier Inc. All rights reserved.

Technique for estimating the 2- to 500-year flood discharges on unregulated streams in rural Missouri

USGS Publications Warehouse

Alexander, Terry W.; Wilson, Gary L.

1995-01-01

A generalized least-squares regression technique was used to relate the 2- to 500-year flood discharges from 278 selected streamflow-gaging stations to statistically significant basin characteristics. The regression relations (estimating equations) were defined for three hydrologic regions (I, II, and III) in rural Missouri. Ordinary least-squares regression analyses indicate that drainage area (Regions I, II, and III) and main-channel slope (Regions I and II) are the only basin characteristics needed for computing the 2- to 500-year design-flood discharges at gaged or ungaged stream locations. The resulting generalized least-squares regression equations provide a technique for estimating the 2-, 5-, 10-, 25-, 50-, 100-, and 500-year flood discharges on unregulated streams in rural Missouri. The regression equations for Regions I and II were developed from stream-flow-gaging stations with drainage areas ranging from 0.13 to 11,500 square miles and 0.13 to 14,000 square miles, and main-channel slopes ranging from 1.35 to 150 feet per mile and 1.20 to 279 feet per mile. The regression equations for Region III were developed from streamflow-gaging stations with drainage areas ranging from 0.48 to 1,040 square miles. Standard errors of estimate for the generalized least-squares regression equations in Regions I, II, and m ranged from 30 to 49 percent.

Vegetation physiology controls continental water cycle responses to climate change

NASA Astrophysics Data System (ADS)

Lemordant, L. A.; Swann, A. L. S.; Cook, B.; Scheff, J.; Gentine, P.

2017-12-01

Abstract per se:Predicting how climate change will affect the hydrologic cycle is of utmost importance for ecological systems and for human life and activities. A typical perspective is that global warming will cause an intensification of the mean state, the so-called "dry gets drier, wet gets wetter" paradigm. While this result is robust over the oceans, recent works suggest it may be less appropriate for terrestrial regions. Using Earth System Models (ESMs) with decoupled surface (vegetation physiology, PHYS) and atmospheric (radiative, ATMO) CO2 responses, we show that the CO2 physiological response dominates the change in the continental hydrologic cycle compared to radiative and precipitation changes due to increased atmospheric CO2, counter to previous assumptions. Using multiple linear regression analysis, we estimate the individual contribution of each of the three main drivers, precipitation, radiation and physiological CO2 forcing (see attached figure). Our analysis reveals that physiological effects dominate changes for 3 key indicators of dryness and/or vegetation stress (namely LAI, P-ET and EF) over the largest fraction of the globe, except for soil moisture which exhibits a more complex response. This highlights the key role of vegetation in controlling future terrestrial hydrologic response.Legend of the Figure attached:Decomposition along the three main drivers of LAI (a), P-ET (b), EF (c) in the control run. Green quantifies the effect of the vegetation physiology based on the run PHYS; red and blue quantify the contribution of, respectively, net radiation and precipitation, based on multiple linear regression in ATMO. Pie charts show for each variable the fraction (labelled in %) of land under the main influence (more than 50% of the changes is attributed to this driver) of one the three main drivers (green for grid points dominated by vegetation physiology, red for grid points dominated by net radiation, and blue for grid points dominated by the precipitation), and under no single driver influence (grey). Based on an article in review at Nature Climate Change as of Aug, 2nd 2017

Remarkable experiences of the nuclear tests in residents near the Semipalatinsk nuclear test site: analysis based on the questionnaire surveys.

PubMed

Kawano, Noriyuki; Ohtaki, Megu

2006-02-01

The main objective of this paper is to identify salient experiences of those who were exposed to radiation by the nuclear tests at the Semipalatinsk Nuclear Tests Site (SNTS). In 2002, our research team of the Research Institute for Radiation Biology and Medicine, Hiroshima University, started to conduct some field research by means of a questionnaire survey. Through this, we expected to examine the health condition of the residents near the SNTS, identify their experiences from the nuclear tests, and understand the exposure path. This attempt at clarifying the reality of radiation exposure at Semipalatinsk through the use of a survey research method is the first of its kind. Among the responses to our survey, the present paper focuses mainly upon responses to the questions concerning the experiences of the nuclear tests. It deals mainly with direct experiences of nuclear tests of the residents characteristic to Semipalatinsk, including some new experiences hitherto unnoticed. The present paper touches upon their concrete direct experiences of flash, bomb blast, heat, rain and dust. We also discuss distinct experiences in Semipalatinsk such as evacuation, through the additional use of their testimonies. The data have been compared with the results obtained in a similar survey made in Hiroshima and Nagasaki. For the data analysis, a statistical method called logistic multiple linear regression analysis has been used.

Analysis of temporal and spatial trends of hydro-climatic variables in the Wei River Basin.

PubMed

Zhao, Jing; Huang, Qiang; Chang, Jianxia; Liu, Dengfeng; Huang, Shengzhi; Shi, Xiaoyu

2015-05-01

The Wei River is the largest tributary of the Yellow River in China. The relationship between runoff and precipitation in the Wei River Basin has been changed due to the changing climate and increasingly intensified human activities. In this paper, we determine abrupt changes in hydro-climatic variables and identify the main driving factors for the changes in the Wei River Basin. The nature of the changes is analysed based on data collected at twenty-one weather stations and five hydrological stations in the period of 1960-2010. The sequential Mann-Kendall test analysis is used to capture temporal trends and abrupt changes in the five sub-catchments of the Wei River Basin. A non-parametric trend test at the basin scale for annual data shows a decreasing trend of precipitation and runoff over the past fifty-one years. The temperature exhibits an increase trend in the entire period. The potential evaporation was calculated based on the Penman-Monteith equation, presenting an increasing trend of evaporation since 1990. The stations with a significant decreasing trend in annual runoff mainly are located in the west of the Wei River primarily interfered by human activities. Regression analysis indicates that human activity was possibly the main cause of the decline of runoff after 1970. Copyright © 2015. Published by Elsevier Inc.

Applied Multiple Linear Regression: A General Research Strategy

ERIC Educational Resources Information Center

Smith, Brandon B.

1969-01-01

Illustrates some of the basic concepts and procedures for using regression analysis in experimental design, analysis of variance, analysis of covariance, and curvilinear regression. Applications to evaluation of instruction and vocational education programs are illustrated. (GR)

Interaction Models for Functional Regression.

PubMed

Usset, Joseph; Staicu, Ana-Maria; Maity, Arnab

2016-02-01

A functional regression model with a scalar response and multiple functional predictors is proposed that accommodates two-way interactions in addition to their main effects. The proposed estimation procedure models the main effects using penalized regression splines, and the interaction effect by a tensor product basis. Extensions to generalized linear models and data observed on sparse grids or with measurement error are presented. A hypothesis testing procedure for the functional interaction effect is described. The proposed method can be easily implemented through existing software. Numerical studies show that fitting an additive model in the presence of interaction leads to both poor estimation performance and lost prediction power, while fitting an interaction model where there is in fact no interaction leads to negligible losses. The methodology is illustrated on the AneuRisk65 study data.

To Control False Positives in Gene-Gene Interaction Analysis: Two Novel Conditional Entropy-Based Approaches

PubMed Central

Lin, Meihua; Li, Haoli; Zhao, Xiaolei; Qin, Jiheng

2013-01-01

Genome-wide analysis of gene-gene interactions has been recognized as a powerful avenue to identify the missing genetic components that can not be detected by using current single-point association analysis. Recently, several model-free methods (e.g. the commonly used information based metrics and several logistic regression-based metrics) were developed for detecting non-linear dependence between genetic loci, but they are potentially at the risk of inflated false positive error, in particular when the main effects at one or both loci are salient. In this study, we proposed two conditional entropy-based metrics to challenge this limitation. Extensive simulations demonstrated that the two proposed metrics, provided the disease is rare, could maintain consistently correct false positive rate. In the scenarios for a common disease, our proposed metrics achieved better or comparable control of false positive error, compared to four previously proposed model-free metrics. In terms of power, our methods outperformed several competing metrics in a range of common disease models. Furthermore, in real data analyses, both metrics succeeded in detecting interactions and were competitive with the originally reported results or the logistic regression approaches. In conclusion, the proposed conditional entropy-based metrics are promising as alternatives to current model-based approaches for detecting genuine epistatic effects. PMID:24339984

Coffee agroforestry for sustainability of Upper Sekampung Watershed management

NASA Astrophysics Data System (ADS)

Fitriani; Arifin, Bustanul; Zakaria, Wan Abbas; Hanung Ismono, R.

2018-03-01

The main objective of watershed management is to ensure the optimal hydrological and natural resource use for ecological, social and economic importance. One important adaptive management step in dealing with the risk of damage to forest ecosystems is the practice of agroforestry coffee. This study aimed to (1) assess the farmer's response to ecological service responsibility and (2) analyze the Sekampung watersheds management by providing environmental services. The research location was Air Naningan sub-district, Tanggamus, Lampung Province, Indonesia. The research was conducted from July until November 2016. Stratification random sampling based on the pattern of ownership of land rights is used to determine the respondents. Data were analyzed using descriptive statistics and logistic regression analysis. Based on the analysis, it was concluded that coffee farmers' participation in the practice of coffee agroforestry in the form of 38% shade plants and multiple cropping (62%). The logistic regression analysis indicated that the variables of experience and status of land ownership, and incentive-size plans were able to explain variations in the willingness of coffee growers to follow the scheme of providing environmental services. The existence of farmer with partnership and CBFM scheme on different land tenure on upper Sekampung has a strategic position to minimize the deforestation and recovery watersheds destruction.

A Skew-t space-varying regression model for the spectral analysis of resting state brain activity.

PubMed

Ismail, Salimah; Sun, Wenqi; Nathoo, Farouk S; Babul, Arif; Moiseev, Alexader; Beg, Mirza Faisal; Virji-Babul, Naznin

2013-08-01

It is known that in many neurological disorders such as Down syndrome, main brain rhythms shift their frequencies slightly, and characterizing the spatial distribution of these shifts is of interest. This article reports on the development of a Skew-t mixed model for the spatial analysis of resting state brain activity in healthy controls and individuals with Down syndrome. Time series of oscillatory brain activity are recorded using magnetoencephalography, and spectral summaries are examined at multiple sensor locations across the scalp. We focus on the mean frequency of the power spectral density, and use space-varying regression to examine associations with age, gender and Down syndrome across several scalp regions. Spatial smoothing priors are incorporated based on a multivariate Markov random field, and the markedly non-Gaussian nature of the spectral response variable is accommodated by the use of a Skew-t distribution. A range of models representing different assumptions on the association structure and response distribution are examined, and we conduct model selection using the deviance information criterion. (1) Our analysis suggests region-specific differences between healthy controls and individuals with Down syndrome, particularly in the left and right temporal regions, and produces smoothed maps indicating the scalp topography of the estimated differences.

Can the provision of a home help service for the elderly population reduce the incidence of fall-related injuries? A quasi-experimental study of the community-level effects on hospital admissions in Swedish municipalities.

PubMed

Bonander, Carl; Gustavsson, Johanna; Nilson, Finn

2016-12-01

Fall-related injuries are a global public health problem, especially in elderly populations. The effect of an intervention aimed at reducing the risk of falls in the homes of community-dwelling elderly persons was evaluated. The intervention mainly involves the performance of complicated tasks and hazards assessment by a trained assessor, and has been adopted gradually over the last decade by 191 of 290 Swedish municipalities. A quasi-experimental design was used where intention-to-treat effect estimates were derived using panel regression analysis and a regression discontinuity (RD) design. The outcome measure was the incidence of fall-related hospitalisations in the treatment population, the age of which varied by municipality (≥65 years, ≥67 years, ≥70 years or ≥75 years). We found no statistically significant reductions in injury incidence in the panel regression (IRR 1.01 (95% CI 0.98 to 1.05)) or RD (IRR 1.00 (95% CI 0.97 to 1.03)) analyses. The results are robust to several different model specifications, including segmented panel regression analysis with linear trend change and community fixed effects parameters. It is unclear whether the absence of an effect is due to a low efficacy of the services provided, or a result of low adherence. Additional studies of the effects on other quality-of-life measures are recommended before conclusions are drawn regarding the cost-effectiveness of the provision of home help service programmes. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://www.bmj.com/company/products-services/rights-and-licensing/.

Relational event models for longitudinal network data with an application to interhospital patient transfers.

PubMed

Vu, Duy; Lomi, Alessandro; Mascia, Daniele; Pallotti, Francesca

2017-06-30

The main objective of this paper is to introduce and illustrate relational event models, a new class of statistical models for the analysis of time-stamped data with complex temporal and relational dependencies. We outline the main differences between recently proposed relational event models and more conventional network models based on the graph-theoretic formalism typically adopted in empirical studies of social networks. Our main contribution involves the definition and implementation of a marked point process extension of currently available models. According to this approach, the sequence of events of interest is decomposed into two components: (a) event time and (b) event destination. This decomposition transforms the problem of selection of event destination in relational event models into a conditional multinomial logistic regression problem. The main advantages of this formulation are the possibility of controlling for the effect of event-specific data and a significant reduction in the estimation time of currently available relational event models. We demonstrate the empirical value of the model in an analysis of interhospital patient transfers within a regional community of health care organizations. We conclude with a discussion of how the models we presented help to overcome some the limitations of statistical models for networks that are currently available. Copyright © 2017 John Wiley & Sons, Ltd. Copyright © 2017 John Wiley & Sons, Ltd.

Multivariate research in areas of phosphorus cast-iron brake shoes manufacturing using the statistical analysis and the multiple regression equations

NASA Astrophysics Data System (ADS)

Kiss, I.; Cioată, V. G.; Alexa, V.; Raţiu, S. A.

2017-05-01

The braking system is one of the most important and complex subsystems of railway vehicles, especially when it comes for safety. Therefore, installing efficient safe brakes on the modern railway vehicles is essential. Nowadays is devoted attention to solving problems connected with using high performance brake materials and its impact on thermal and mechanical loading of railway wheels. The main factor that influences the selection of a friction material for railway applications is the performance criterion, due to the interaction between the brake block and the wheel produce complex thermos-mechanical phenomena. In this work, the investigated subjects are the cast-iron brake shoes, which are still widely used on freight wagons. Therefore, the cast-iron brake shoes - with lamellar graphite and with a high content of phosphorus (0.8-1.1%) - need a special investigation. In order to establish the optimal condition for the cast-iron brake shoes we proposed a mathematical modelling study by using the statistical analysis and multiple regression equations. Multivariate research is important in areas of cast-iron brake shoes manufacturing, because many variables interact with each other simultaneously. Multivariate visualization comes to the fore when researchers have difficulties in comprehending many dimensions at one time. Technological data (hardness and chemical composition) obtained from cast-iron brake shoes were used for this purpose. In order to settle the multiple correlation between the hardness of the cast-iron brake shoes, and the chemical compositions elements several model of regression equation types has been proposed. Because a three-dimensional surface with variables on three axes is a common way to illustrate multivariate data, in which the maximum and minimum values are easily highlighted, we plotted graphical representation of the regression equations in order to explain interaction of the variables and locate the optimal level of each variable for maximal response. For the calculation of the regression coefficients, dispersion and correlation coefficients, the software Matlab was used.

Quotation accuracy in medical journal articles-a systematic review and meta-analysis.

PubMed

Jergas, Hannah; Baethge, Christopher

2015-01-01

Background. Quotations and references are an indispensable element of scientific communication. They should support what authors claim or provide important background information for readers. Studies indicate, however, that quotations not serving their purpose-quotation errors-may be prevalent. Methods. We carried out a systematic review, meta-analysis and meta-regression of quotation errors, taking account of differences between studies in error ascertainment. Results. Out of 559 studies screened we included 28 in the main analysis, and estimated major, minor and total quotation error rates of 11,9%, 95% CI [8.4, 16.6] 11.5% [8.3, 15.7], and 25.4% [19.5, 32.4]. While heterogeneity was substantial, even the lowest estimate of total quotation errors was considerable (6.7%). Indirect references accounted for less than one sixth of all quotation problems. The findings remained robust in a number of sensitivity and subgroup analyses (including risk of bias analysis) and in meta-regression. There was no indication of publication bias. Conclusions. Readers of medical journal articles should be aware of the fact that quotation errors are common. Measures against quotation errors include spot checks by editors and reviewers, correct placement of citations in the text, and declarations by authors that they have checked cited material. Future research should elucidate if and to what degree quotation errors are detrimental to scientific progress.

Quotation accuracy in medical journal articles—a systematic review and meta-analysis

PubMed Central

Jergas, Hannah

2015-01-01

Background. Quotations and references are an indispensable element of scientific communication. They should support what authors claim or provide important background information for readers. Studies indicate, however, that quotations not serving their purpose—quotation errors—may be prevalent. Methods. We carried out a systematic review, meta-analysis and meta-regression of quotation errors, taking account of differences between studies in error ascertainment. Results. Out of 559 studies screened we included 28 in the main analysis, and estimated major, minor and total quotation error rates of 11,9%, 95% CI [8.4, 16.6] 11.5% [8.3, 15.7], and 25.4% [19.5, 32.4]. While heterogeneity was substantial, even the lowest estimate of total quotation errors was considerable (6.7%). Indirect references accounted for less than one sixth of all quotation problems. The findings remained robust in a number of sensitivity and subgroup analyses (including risk of bias analysis) and in meta-regression. There was no indication of publication bias. Conclusions. Readers of medical journal articles should be aware of the fact that quotation errors are common. Measures against quotation errors include spot checks by editors and reviewers, correct placement of citations in the text, and declarations by authors that they have checked cited material. Future research should elucidate if and to what degree quotation errors are detrimental to scientific progress. PMID:26528420

[Effect of occupational stress on mental health].

PubMed

Yu, Shan-fa; Zhang, Rui; Ma, Liang-qing; Gu, Gui-zhen; Yang, Yan; Li, Kui-rong

2003-02-01

To study the effect of job psychological demands and job control on mental health and their interaction. 93 male freight train dispatchers were evaluated by using revised Job Demand-Control Scale and 7 strain scales. Stepwise regression analysis, Univariate ANOVA, Kruskal-Wallis H and Modian methods were used in statistic analysis. Kruskal-Wallis H and Modian methods analysis revealed the difference in mental health scores among groups of decision latitude (mean rank 55.57, 47.95, 48.42, 33.50, P < 0.05), the differences in scores of mental health (37.45, 40.01, 58.35), job satisfaction (53.18, 46.91, 32.43), daily life strains (33.00, 44.96, 56.12) and depression (36.45, 42.25, 53.61) among groups of job time demands (P < 0.05) were all statistically significant. ANOVA showed that job time demands and decision latitude had interaction effects on physical complains (R(2) = 0.24), state-anxiety (R(2) = 0.26), and daytime fatigue (R(2) = 0.28) (P < 0.05). Regression analysis revealed a significant job time demands and job decision latitude interaction effect as well as significant main effects of the some independent variables on different job strains (R(2) > 0.05). Job time demands and job decision latitude have direct and interactive effects on psychosomatic health, the more time demands, the more psychological strains, the effect of job time demands is greater than that of job decision latitude.

Temporal framing and the hidden-zero effect: rate-dependent outcomes on delay discounting.

PubMed

Naudé, Gideon P; Kaplan, Brent A; Reed, Derek D; Henley, Amy J; DiGennaro Reed, Florence D

2018-05-01

Recent research suggests that presenting time intervals as units (e.g., days) or as specific dates, can modulate the degree to which humans discount delayed outcomes. Another framing effect involves explicitly stating that choosing a smaller-sooner reward is mutually exclusive to receiving a larger-later reward, thus presenting choices as an extended sequence. In Experiment 1, participants (N = 201) recruited from Amazon Mechanical Turk completed the Monetary Choice Questionnaire in a 2 (delay framing) by 2 (zero framing) design. Regression suggested a main effect of delay, but not zero, framing after accounting for other demographic variables and manipulations. We observed a rate-dependent effect for the date-framing group, such that those with initially steep discounting exhibited greater sensitivity to the manipulation than those with initially shallow discounting. Subsequent analyses suggest these effects cannot be explained by regression to the mean. Experiment 2 addressed the possibility that the null effect of zero framing was due to within-subject exposure to the hidden- and explicit-zero conditions. A new Amazon Mechanical Turk sample completed the Monetary Choice Questionnaire in either hidden- or explicit-zero formats. Analyses revealed a main effect of reward magnitude, but not zero framing, suggesting potential limitations to the generality of the hidden-zero effect. © 2018 Society for the Experimental Analysis of Behavior.

Information integration in health care organizations: The case of a European health system.

PubMed

Calciolari, Stefano; Buccoliero, Luca

2010-01-01

Information system integration is an important dimension of a company's information system maturity and plays a relevant role in meeting information needs and accountability targets. However, no generalizable evidence exists about whether and how the main integrating technologies influence information system integration in health care organizations. This study examined how integrating technologies are adopted in public health care organizations and chief information officers' (CIOs) perceptions about their influence on information system integration. We used primary data on integrating technologies' adoption and CIOs' perception regarding information system integration in public health care organizations. Analysis of variance (ANOVA) and multinomial logistic regression were used to examine the relationship between CIOs' perception about information system integration and the adopted technologies. Data from 90 health care organizations were available for analyses. Integrating technologies are relatively diffused in public health care organizations, and CIOs seem to shape information system toward integrated architectures. There is a significant positive (although modest, .3) correlation between the number of integrating technologies adopted and the CIO's satisfaction with them. However, regression analysis suggests that organizations covering a broader spectrum of these technologies are less likely to have their CIO reporting main problems concerning integration in the administrative area of the information system compared with the clinical area and where the two areas overlap. Integrating technologies are associated with less perceived problems in the information system administrative area rather than in other areas. Because CIOs play the role of information resource allocators, by influencing information system toward integrated architecture, health care organization leaders should foster cooperation between CIOs and medical staff to enhance information system integration.

Irritable bowel syndrome is concentrated in people with higher educations in Iran: an inequality analysis.

PubMed

Mansouri, Asieh; Rarani, Mostafa Amini; Fallahi, Mosayeb; Alvandi, Iman

2017-01-01

Like any other health-related disorder, irritable bowel syndrome (IBS) has a differential distribution with respect to socioeconomic factors. This study aimed to estimate and decompose educational inequalities in the prevalence of IBS. Sampling was performed using a multi-stage random cluster sampling approach. The data of 1,850 residents of Kish Island aged 15 years or older were included, and the determinants of IBS were identified using a generalized estimating equation regression model. The concentration index of educational inequality in cases of IBS was estimated and decomposed as the specific inequality index. The prevalence of IBS in this study was 21.57% (95% confidence interval [CI], 19.69 to 23.44%). The concentration index of IBS was 0.20 (95% CI, 0.14 to 0.26). A multivariable regression model revealed that age, sex, level of education, marital status, anxiety, and poor general health were significant determinants of IBS. In the decomposition analysis, level of education (89.91%), age (-11.99%), and marital status (9.11%) were the three main contributors to IBS inequality. Anxiety and poor general health were the next two contributors to IBS inequality, and were responsible for more than 12% of the total observed inequality. The main contributors of IBS inequality were education level, age, and marital status. Given the high percentage of anxious individuals among highly educated, young, single, and divorced people, we can conclude that all contributors to IBS inequality may be partially influenced by psychological factors. Therefore, programs that promote the development of mental health to alleviate the abovementioned inequality in this population are highly warranted.

External Tank Liquid Hydrogen (LH2) Prepress Regression Analysis Independent Review Technical Consultation Report

NASA Technical Reports Server (NTRS)

Parsons, Vickie s.

2009-01-01

The request to conduct an independent review of regression models, developed for determining the expected Launch Commit Criteria (LCC) External Tank (ET)-04 cycle count for the Space Shuttle ET tanking process, was submitted to the NASA Engineering and Safety Center NESC on September 20, 2005. The NESC team performed an independent review of regression models documented in Prepress Regression Analysis, Tom Clark and Angela Krenn, 10/27/05. This consultation consisted of a peer review by statistical experts of the proposed regression models provided in the Prepress Regression Analysis. This document is the consultation's final report.

Using the Ridge Regression Procedures to Estimate the Multiple Linear Regression Coefficients

NASA Astrophysics Data System (ADS)

Gorgees, HazimMansoor; Mahdi, FatimahAssim

2018-05-01

This article concerns with comparing the performance of different types of ordinary ridge regression estimators that have been already proposed to estimate the regression parameters when the near exact linear relationships among the explanatory variables is presented. For this situations we employ the data obtained from tagi gas filling company during the period (2008-2010). The main result we reached is that the method based on the condition number performs better than other methods since it has smaller mean square error (MSE) than the other stated methods.

[Quality of life and its influential factors of children and adolescents with congenital cleft lip and palate].

PubMed

Zhou, Meng; Liu, Zong-xiang; Wang, Peng-lai; Liu, Chao

2016-02-01

To investigate the quality of life of children undergoing clef lip or and palate repair as well as the influential factors of the quality of life, and provide theoretical foundation for future studies such as psychological interventions. Totally 164 children and young adolescent patients with cleft lip and palate undergoing maxillofacial surgery and orthodontic treatment in Xuzhou Stomatology Hospital were selected as experimental group, and 102 normal children and young adolescents were selected as control group. Both groups were investigated by general information questionnaire and child and adolescents' quality of life scale (CAQOL). The results were analyzed and the influential factors on quality of life were evaluated by multivariate regression analysis with SPSS 19.0 software package. The overall CAQOL scores and most of the subscale scores (teacher-student relationship, peer relationships, parent-child relationship, self-awareness, physical discomfort, negative emotions, attitude about homework, access to transportation from home, extra curricular activities, self-esteem) in the experimental group were significantly lower compared with the control group (P<0.05). Single factor analysis of the quality of life showed that there was no significant difference between gender distribution; on the contrary, residential areas, parents' level of education, the main caregivers, family income and types of the disease had significant difference (P<0.05). Multiple linear regression equation showed that mother's education level of patients, cleft lip and palate category, family income, the main caregivers and residential areas were the important influential factors on children' quality of life. Among them, the type of disease was the most important influential factor (beta=0.260), followed by mother's education level (beta=0.215). The quality of life of children with cleft lip/palate is poor. Patients' scores of CAQOL are closely related with mothers' education level, type of cleft lip/palate, family income, the main caregivers and residential areas.

Neutrophil/lymphocyte ratio and platelet/lymphocyte ratio in mood disorders: A meta-analysis.

PubMed

Mazza, Mario Gennaro; Lucchi, Sara; Tringali, Agnese Grazia Maria; Rossetti, Aurora; Botti, Eugenia Rossana; Clerici, Massimo

2018-06-08

The immune and inflammatory system is involved in the etiology of mood disorders. Neutrophil/lymphocyte ratio (NLR), platelet/lymphocyte ratio (PLR) and monocyte/lymphocyte ratio (MLR) are inexpensive and reproducible biomarkers of inflammation. This is the first meta-analysis exploring the role of NLR and PLR in mood disorder. We identified 11 studies according to our inclusion criteria from the main Electronic Databases. Meta-analyses were carried out generating pooled standardized mean differences (SMDs) between index and healthy controls (HC). Heterogeneity was estimated. Relevant sensitivity and meta-regression analyses were conducted. Subjects with bipolar disorder (BD) had higher NLR and PLR as compared with HC (respectively SMD = 0.672; p < 0.001; I 2  = 82.4% and SMD = 0.425; p = 0.048; I 2  = 86.53%). Heterogeneity-based sensitivity analyses confirmed these findings. Subgroup analysis evidenced an influence of bipolar phase on the overall estimate whit studies including subjects in manic and any bipolar phase showing a significantly higher NLR and PLR as compared with HC whereas the effect was not significant among studies including only euthymic bipolar subjects. Meta-regression showed that age and sex influenced the relationship between BD and NLR but not the relationship between BD and PLR. Meta-analysis was not carried out for MLR because our search identified only one study when comparing BD to HC, and only one study when comparing MDD to HC. Subjects with major depressive disorder (MDD) had higher NLR as compared with HC (SMD = 0.670; p = 0.028; I 2  = 89.931%). Heterogeneity-based sensitivity analyses and meta-regression confirmed these findings. Our meta-analysis supports the hypothesis that an inflammatory activation occurs in mood disorders and NLR and PLR may be useful to detect this activation. More researches including comparison of NLR, PLR and MLR between different bipolar phases and between BD and MDD are needed. Copyright © 2018 Elsevier Inc. All rights reserved.

Estimating peak discharges, flood volumes, and hydrograph shapes of small ungaged urban streams in Ohio

USGS Publications Warehouse

Sherwood, J.M.

1986-01-01

Methods are presented for estimating peak discharges, flood volumes and hydrograph shapes of small (less than 5 sq mi) urban streams in Ohio. Examples of how to use the various regression equations and estimating techniques also are presented. Multiple-regression equations were developed for estimating peak discharges having recurrence intervals of 2, 5, 10, 25, 50, and 100 years. The significant independent variables affecting peak discharge are drainage area, main-channel slope, average basin-elevation index, and basin-development factor. Standard errors of regression and prediction for the peak discharge equations range from +/-37% to +/-41%. An equation also was developed to estimate the flood volume of a given peak discharge. Peak discharge, drainage area, main-channel slope, and basin-development factor were found to be the significant independent variables affecting flood volumes for given peak discharges. The standard error of regression for the volume equation is +/-52%. A technique is described for estimating the shape of a runoff hydrograph by applying a specific peak discharge and the estimated lagtime to a dimensionless hydrograph. An equation for estimating the lagtime of a basin was developed. Two variables--main-channel length divided by the square root of the main-channel slope and basin-development factor--have a significant effect on basin lagtime. The standard error of regression for the lagtime equation is +/-48%. The data base for the study was established by collecting rainfall-runoff data at 30 basins distributed throughout several metropolitan areas of Ohio. Five to eight years of data were collected at a 5-min record interval. The USGS rainfall-runoff model A634 was calibrated for each site. The calibrated models were used in conjunction with long-term rainfall records to generate a long-term streamflow record for each site. Each annual peak-discharge record was fitted to a Log-Pearson Type III frequency curve. Multiple-regression techniques were then used to analyze the peak discharge data as a function of the basin characteristics of the 30 sites. (Author 's abstract)

Ultraviolet spectroscopic breath analysis using hollow-optical fiber as gas cell

NASA Astrophysics Data System (ADS)

Iwata, T.; Katagiri, T.; Matsuura, Y.

2017-02-01

For breath analysis on ultraviolet absorption spectroscopy, an analysis system using a hollow optical fiber as gas cell is developed. The hollow optical fiber functions as a long path and extremely small volume gas cell. Firstly, the measurement sensitivity of the system is evaluated by using NO gas as a gas sample. The result shows that NO gas with 50 ppb concentration is measured by using a system with a laser-driven, high intensity light source and a 3-meter long, aluminum-coated hollow optical fiber. Then an absorption spectrum of breath sample is measured in the wavelength region of around 200-300 nm and from the spectrum, it is found that the main absorbing components in breath were H2O, isoprene, and O3 converted from O2 by radiation of ultraviolet light. Then the concentration of isoprene in breath is estimated by using multiple linear regression analysis.

Analysis of perceived risk among construction workers: a cross-cultural study and reflection on the Hofstede model.

PubMed

Martinez-Fiestas, Myriam; Rodríguez-Garzón, Ignacio; Delgado-Padial, Antonio; Lucas-Ruiz, Valeriano

2017-09-01

This article presents a cross-cultural study on perceived risk in the construction industry. Worker samples from three different countries were studied: Spain, Peru and Nicaragua. The main goal was to explain how construction workers perceive their occupational hazard and to analyze how this is related to their national culture. The model used to measure perceived risk was the psychometric paradigm. The results show three very similar profiles, indicating that risk perception is independent of nationality. A cultural analysis was conducted using the Hofstede model. The results of this analysis and the relation to perceived risk showed that risk perception in construction is independent of national culture. Finally, a multiple lineal regression analysis was conducted to determine what qualitative attributes could predict the global quantitative size of risk perception. All of the findings have important implications regarding the management of safety in the workplace.

Association between developmental defects of enamel and dental caries: A systematic review and meta-analysis.

PubMed

Vargas-Ferreira, F; Salas, M M S; Nascimento, G G; Tarquinio, S B C; Faggion, C M; Peres, M A; Thomson, W M; Demarco, F F

2015-06-01

Dental caries is the main problem oral health and it is not well established in the literature if the enamel defects are a risk factor for its development. Studies have reported a potential association between developmental defects enamel (DDE) and dental caries occurrence. We investigated the association between DDE and caries in permanent dentition of children and teenagers. A systematic review was carried out using four databases (Pubmed, Web of Science, Embase, and Science Direct), which were searched from their earliest records until December 31, 2014. Population-based studies assessing differences in dental caries experience according to the presence of enamel defects (and their types) were included. PRISMA guidelines for reporting systematic reviews were followed. Meta-analysis was performed to assess the pooled effect, and meta-regression was carried out to identify heterogeneity sources. From the 2558 initially identified papers, nine studies fulfilled all inclusion criteria after checking the titles, abstracts, references, and complete reading. Seven of them were included in the meta-analysis with random model. A positive association between enamel defects and dental caries was identified; meta-analysis showed that individuals with DDE had higher pooled odds of having dental caries experience [OR 2.21 (95% CI 1.3; 3.54)]. Meta-regression analysis demonstrated that adjustment for sociodemographic factors, countries' socioeconomic status, and bias (quality of studies) explained the high heterogeneity observed. A higher chance of dental caries should be expected among individuals with enamel defects. Copyright © 2015 Elsevier Ltd. All rights reserved.

Physiologic noise regression, motion regression, and TOAST dynamic field correction in complex-valued fMRI time series.

PubMed

Hahn, Andrew D; Rowe, Daniel B

2012-02-01

As more evidence is presented suggesting that the phase, as well as the magnitude, of functional MRI (fMRI) time series may contain important information and that there are theoretical drawbacks to modeling functional response in the magnitude alone, removing noise in the phase is becoming more important. Previous studies have shown that retrospective correction of noise from physiologic sources can remove significant phase variance and that dynamic main magnetic field correction and regression of estimated motion parameters also remove significant phase fluctuations. In this work, we investigate the performance of physiologic noise regression in a framework along with correction for dynamic main field fluctuations and motion regression. Our findings suggest that including physiologic regressors provides some benefit in terms of reduction in phase noise power, but it is small compared to the benefit of dynamic field corrections and use of estimated motion parameters as nuisance regressors. Additionally, we show that the use of all three techniques reduces phase variance substantially, removes undesirable spatial phase correlations and improves detection of the functional response in magnitude and phase. Copyright © 2011 Elsevier Inc. All rights reserved.

Use of AMMI and linear regression models to analyze genotype-environment interaction in durum wheat.

PubMed

Nachit, M M; Nachit, G; Ketata, H; Gauch, H G; Zobel, R W

1992-03-01

The joint durum wheat (Triticum turgidum L var 'durum') breeding program of the International Maize and Wheat Improvement Center (CIMMYT) and the International Center for Agricultural Research in the Dry Areas (ICARDA) for the Mediterranean region employs extensive multilocation testing. Multilocation testing produces significant genotype-environment (GE) interaction that reduces the accuracy for estimating yield and selecting appropriate germ plasm. The sum of squares (SS) of GE interaction was partitioned by linear regression techniques into joint, genotypic, and environmental regressions, and by Additive Main effects and the Multiplicative Interactions (AMMI) model into five significant Interaction Principal Component Axes (IPCA). The AMMI model was more effective in partitioning the interaction SS than the linear regression technique. The SS contained in the AMMI model was 6 times higher than the SS for all three regressions. Postdictive assessment recommended the use of the first five IPCA axes, while predictive assessment AMMI1 (main effects plus IPCA1). After elimination of random variation, AMMI1 estimates for genotypic yields within sites were more precise than unadjusted means. This increased precision was equivalent to increasing the number of replications by a factor of 3.7.

Maintenance Operations in Mission Oriented Protective Posture Level IV (MOPPIV)

DTIC Science & Technology

1987-10-01

Repair FADAC Printed Circuit Board ............. 6 3. Data Analysis Techniques ............................. 6 a. Multiple Linear Regression... ANALYSIS /DISCUSSION ............................... 12 1. Exa-ple of Regression Analysis ..................... 12 S2. Regression results for all tasks...6 * TABLE 9. Task Grouping for Analysis ........................ 7 "TABXLE 10. Remove/Replace H60A3 Power Pack................. 8 TABLE

From Attitudes to Actions: Predictors of Lion Killing by Maasai Warriors.

PubMed

Hazzah, Leela; Bath, Alistair; Dolrenry, Stephanie; Dickman, Amy; Frank, Laurence

2017-01-01

Despite legal protection, deliberate killing by local people is one of the major threats to the conservation of lions and other large carnivores in Africa. Addressing this problem poses particular challenges, mainly because it is difficult to uncover illicit behavior. This article examined two groups of Maasai warriors: individuals who have killed African lions (Panthera leo) and those who have not. We conducted interviews to explore the relationship between attitudes, intentions and known lion killing behavior. Factor analysis and logistic regression revealed that lion killing was mainly determined by: (a) general attitudes toward lions, (b) engagement in traditional customs, (c) lion killing intentions to defend property, and (d) socio-cultural killing intentions. Our results indicated that general attitudes toward lions were the strongest predictor of lion killing behavior. Influencing attitudes to encourage pro-conservation behavior may help reduce killing.

Estimating residential price elasticity of demand for water: A contingent valuation approach

NASA Astrophysics Data System (ADS)

Thomas, John F.; Syme, Geoffrey J.

1988-11-01

Residential households in Perth, Western Australia have access to privately extracted groundwater as well as a public mains water supply, which has been charged through a two-part block tariff. A contingent valuation approach is developed to estimate price elasticity of demand for public supply. Results are compared with those of a multivariate time series analysis. Validation tests for the contingent approach are proposed, based on a comparison of predicted behaviors following hypothesised price changes with relevant independent data. Properly conducted, the contingent approach appears to be reliable, applicable where the available data do not favor regression analysis, and a fruitful source of information about social, technical, and behavioral responses to change in the price of water.

Blood lead levels and risk factors in pregnant women from Durango, Mexico.

PubMed

La-Llave-León, Osmel; Estrada-Martínez, Sergio; Manuel Salas-Pacheco, José; Peña-Elósegui, Rocío; Duarte-Sustaita, Jaime; Candelas Rangel, Jorge-Luís; García Vargas, Gonzalo

2011-01-01

In this cross-sectional study the authors determined blood lead levels (BLLs) and some risk factors for lead exposure in pregnant women. Two hundred ninety-nine pregnant women receiving medical attention by the Secretary of Health, State of Durango, Mexico, participated in this study between 2007 and 2008. BLLs were evaluated with graphite furnace atomic absorption spectrometry. The authors used Student t test, 1-way analysis of variance (ANOVA), and linear regression as statistical treatments. BLLs ranged from 0.36 to 23.6 μg/dL (mean = 2.79 μg/dL, standard deviation = 2.14). Multivariate analysis showed that the main predictors of BLLs were working in a place where lead is used, using lead glazed pottery, and eating soil.

Determinants of efficiency in reducing child mortality in developing countries. The role of inequality and government effectiveness.

PubMed

Ortega, Bienvenido; Sanjuán, Jesús; Casquero, Antonio

2017-12-01

The main aim of this article was to analyze the relationship of income inequality and government effectiveness with differences in efficiency in the use of health inputs to improve the under-five survival rate (U5SR) in developing countries. Robust Data Envelopment Analysis (DEA) and regression analysis were conducted using data for 47 developing countries for the periods 2000-2004, 2005-2009, and 2010-2012. The estimations show that countries with a more equal income distribution and better government effectiveness (i.e. a more competent bureaucracy and good quality public service delivery) may need fewer health inputs to achieve a specific level of the U5SR than other countries with higher inequality and worse government effectiveness.

Water quality and non-point sources of risk: the Jiulong River Watershed, P. R. of China.

PubMed

Zhang, Jingjing; Zhang, Luoping; Ricci, Paolo F

2012-01-01

Retrospective water quality assessment plays an essential role in identifying trends and causal associations between exposures and risks, thus it can be a guide for water resources management. We have developed empirical relationships between several time-varying social and economic factors of economic development, water quality variables such as nitrate-nitrogen, COD(Mn), BOD(5), and DO, in the Jiulong River Watershed and its main tributary, the West River. Our analyses used alternative statistical methods to reduce the dimensionality of the analysis first and then strengthen the study's causal associations. The statistical methods included: factor analysis (FA), trend analysis, Monte Carlo/bootstrap simulations, robust regressions and a coupled equations model, integrated into a framework that allows an investigation and resolution of the issues that may affect the estimated results. After resolving these, we found that the concentrations of nitrogen compounds increased over time in the West River region, and that fertilizer used in agricultural fruit crops was the main risk with regard to nitrogen pollution. The relationships we developed can identify hazards and explain the impact of sources of different types of pollution, such as urbanization, and agriculture.

Creep-Rupture Data Analysis - Engineering Application of Regression Techniques. Ph.D. Thesis - North Carolina State Univ.

NASA Technical Reports Server (NTRS)

Rummler, D. R.

1976-01-01

The results are presented of investigations to apply regression techniques to the development of methodology for creep-rupture data analysis. Regression analysis techniques are applied to the explicit description of the creep behavior of materials for space shuttle thermal protection systems. A regression analysis technique is compared with five parametric methods for analyzing three simulated and twenty real data sets, and a computer program for the evaluation of creep-rupture data is presented.

Toxocara infection in the United States: the relevance of poverty, geography and demography as risk factors, and implications for estimating county prevalence.

PubMed

Congdon, Peter; Lloyd, Patsy

2011-02-01

To estimate Toxocara infection rates by age, gender and ethnicity for US counties using data from the National Health and Nutrition Examination Survey (NHANES). After initial analysis to account for missing data, a binary regression model is applied to obtain relative risks of Toxocara infection for 20,396 survey subjects. The regression incorporates interplay between demographic attributes (age, ethnicity and gender), family poverty and geographic context (region, metropolitan status). Prevalence estimates for counties are then made, distinguishing between subpopulations in poverty and not in poverty. Even after allowing for elevated infection risk associated with poverty, seropositivity is elevated among Black non-Hispanics and other ethnic groups. There are also distinct effects of region. When regression results are translated into county prevalence estimates, the main influences on variation in county rates are percentages of non-Hispanic Blacks and county poverty. For targeting prevention it is important to assess implications of national survey data for small area prevalence. Using data from NHANES, the study confirms that both individual level risk factors and geographic contextual factors affect chances of Toxocara infection.

Resting-state functional magnetic resonance imaging: the impact of regression analysis.

PubMed

Yeh, Chia-Jung; Tseng, Yu-Sheng; Lin, Yi-Ru; Tsai, Shang-Yueh; Huang, Teng-Yi

2015-01-01

To investigate the impact of regression methods on resting-state functional magnetic resonance imaging (rsfMRI). During rsfMRI preprocessing, regression analysis is considered effective for reducing the interference of physiological noise on the signal time course. However, it is unclear whether the regression method benefits rsfMRI analysis. Twenty volunteers (10 men and 10 women; aged 23.4 ± 1.5 years) participated in the experiments. We used node analysis and functional connectivity mapping to assess the brain default mode network by using five combinations of regression methods. The results show that regressing the global mean plays a major role in the preprocessing steps. When a global regression method is applied, the values of functional connectivity are significantly lower (P ≤ .01) than those calculated without a global regression. This step increases inter-subject variation and produces anticorrelated brain areas. rsfMRI data processed using regression should be interpreted carefully. The significance of the anticorrelated brain areas produced by global signal removal is unclear. Copyright © 2014 by the American Society of Neuroimaging.

Testing Gene-Gene Interactions in the Case-Parents Design

PubMed Central

Yu, Zhaoxia

2011-01-01

The case-parents design has been widely used to detect genetic associations as it can prevent spurious association that could occur in population-based designs. When examining the effect of an individual genetic locus on a disease, logistic regressions developed by conditioning on parental genotypes provide complete protection from spurious association caused by population stratification. However, when testing gene-gene interactions, it is unknown whether conditional logistic regressions are still robust. Here we evaluate the robustness and efficiency of several gene-gene interaction tests that are derived from conditional logistic regressions. We found that in the presence of SNP genotype correlation due to population stratification or linkage disequilibrium, tests with incorrectly specified main-genetic-effect models can lead to inflated type I error rates. We also found that a test with fully flexible main genetic effects always maintains correct test size and its robustness can be achieved with negligible sacrifice of its power. When testing gene-gene interactions is the focus, the test allowing fully flexible main effects is recommended to be used. PMID:21778736

On self-propagating methodological flaws in performance normalization for strength and power sports.

PubMed

Arandjelović, Ognjen

2013-06-01

Performance in strength and power sports is greatly affected by a variety of anthropometric factors. The goal of performance normalization is to factor out the effects of confounding factors and compute a canonical (normalized) performance measure from the observed absolute performance. Performance normalization is applied in the ranking of elite athletes, as well as in the early stages of youth talent selection. Consequently, it is crucial that the process is principled and fair. The corpus of previous work on this topic, which is significant, is uniform in the methodology adopted. Performance normalization is universally reduced to a regression task: the collected performance data are used to fit a regression function that is then used to scale future performances. The present article demonstrates that this approach is fundamentally flawed. It inherently creates a bias that unfairly penalizes athletes with certain allometric characteristics, and, by virtue of its adoption in the ranking and selection of elite athletes, propagates and strengthens this bias over time. The main flaws are shown to originate in the criteria for selecting the data used for regression, as well as in the manner in which the regression model is applied in normalization. This analysis brings into light the aforesaid methodological flaws and motivates further work on the development of principled methods, the foundations of which are also laid out in this work.

Analysis of occlusal variables, dental attrition, and age for distinguishing healthy controls from female patients with intracapsular temporomandibular disorders.

PubMed

Seligman, D A; Pullinger, A G

2000-01-01

Confusion about the relationship of occlusion to temporomandibular disorders (TMD) persists. This study attempted to identify occlusal and attrition factors plus age that would characterize asymptomatic normal female subjects. A total of 124 female patients with intracapsular TMD were compared with 47 asymptomatic female controls for associations to 9 occlusal factors, 3 attrition severity measures, and age using classification tree, multiple stepwise logistic regression, and univariate analyses. Models were tested for accuracy (sensitivity and specificity) and total contribution to the variance. The classification tree model had 4 terminal nodes that used only anterior attrition and age. "Normals" were mainly characterized by low attrition levels, whereas patients had higher attrition and tended to be younger. The tree model was only moderately useful (sensitivity 63%, specificity 94%) in predicting normals. The logistic regression model incorporated unilateral posterior crossbite and mediotrusive attrition severity in addition to the 2 factors in the tree, but was slightly less accurate than the tree (sensitivity 51%, specificity 90%). When only occlusal factors were considered in the analysis, normals were additionally characterized by a lack of anterior open bite, smaller overjet, and smaller RCP-ICP slides. The log likelihood accounted for was similar for both the tree (pseudo R(2) = 29.38%; mean deviance = 0.95) and the multiple logistic regression (Cox Snell R(2) = 30.3%, mean deviance = 0.84) models. The occlusal and attrition factors studied were only moderately useful in differentiating normals from TMD patients.

Inferring microhabitat preferences of Lilium catesbaei (Liliaceae).

PubMed

Sommers, Kristen Penney; Elswick, Michael; Herrick, Gabriel I; Fox, Gordon A

2011-05-01

Microhabitat studies use varied statistical methods, some treating site occupancy as a dependent and others as an independent variable. Using the rare Lilium catesbaei as an example, we show why approaches to testing hypotheses of differences between occupied and unoccupied sites can lead to erroneous conclusions about habitat preferences. Predictive approaches like logistic regression can better lead to understanding of habitat requirements. Using 32 lily locations and 30 random locations >2 m from a lily (complete data: 31 lily and 28 random spots), we measured physical conditions--photosynthetically active radiation (PAR), canopy cover, litter depth, distance to and height of nearest shrub, and soil moisture--and number and identity of neighboring plants. Twelve lilies were used to estimate a photosynthetic assimilation curve. Analyses used logistic regression, discriminant function analysis (DFA), (multivariate) analysis of variance, and resampled Wilcoxon tests. Logistic regression and DFA found identical predictors of presence (PAR, canopy cover, distance to shrub, litter), but hypothesis tests pointed to a different set (PAR, litter, canopy cover, height of nearest shrub). Lilies are mainly in high-PAR spots, often close to light saturation. By contrast, PAR in random spots was often near the lily light compensation point. Lilies were near Serenoa repens less than at random; otherwise, neighbor identity had no significant effect. Predictive methods are more useful in this context than the hypothesis tests. Light availability plays a big role in lily presence, which may help to explain increases in flowering and emergence after fire and roller-chopping.

An investigation on fatality of drivers in vehicle-fixed object accidents on expressways in China: Using multinomial logistic regression model.

PubMed

Peng, Yong; Peng, Shuangling; Wang, Xinghua; Tan, Shiyang

2018-06-01

This study aims to identify the effects of characteristics of vehicle, roadway, driver, and environment on fatality of drivers in vehicle-fixed object accidents on expressways in Changsha-Zhuzhou-Xiangtan district of Hunan province in China by developing multinomial logistic regression models. For this purpose, 121 vehicle-fixed object accidents from 2011-2017 are included in the modeling process. First, descriptive statistical analysis is made to understand the main characteristics of the vehicle-fixed object crashes. Then, 19 explanatory variables are selected, and correlation analysis of each two variables is conducted to choose the variables to be concluded. Finally, five multinomial logistic regression models including different independent variables are compared, and the model with best fitting and prediction capability is chosen as the final model. The results showed that the turning direction in avoiding fixed objects raised the possibility that drivers would die. About 64% of drivers died in the accident were found being ejected out of the car, of which 50% did not use a seatbelt before the fatal accidents. Drivers are likely to die when they encounter bad weather on the expressway. Drivers with less than 10 years of driving experience are more likely to die in these accidents. Fatigue or distracted driving is also a significant factor in fatality of drivers. Findings from this research provide an insight into reducing fatality of drivers in vehicle-fixed object accidents.

Standards for Standardized Logistic Regression Coefficients

ERIC Educational Resources Information Center

Menard, Scott

2011-01-01

Standardized coefficients in logistic regression analysis have the same utility as standardized coefficients in linear regression analysis. Although there has been no consensus on the best way to construct standardized logistic regression coefficients, there is now sufficient evidence to suggest a single best approach to the construction of a…

Linear regression analysis: part 14 of a series on evaluation of scientific publications.

PubMed

Schneider, Astrid; Hommel, Gerhard; Blettner, Maria

2010-11-01

Regression analysis is an important statistical method for the analysis of medical data. It enables the identification and characterization of relationships among multiple factors. It also enables the identification of prognostically relevant risk factors and the calculation of risk scores for individual prognostication. This article is based on selected textbooks of statistics, a selective review of the literature, and our own experience. After a brief introduction of the uni- and multivariable regression models, illustrative examples are given to explain what the important considerations are before a regression analysis is performed, and how the results should be interpreted. The reader should then be able to judge whether the method has been used correctly and interpret the results appropriately. The performance and interpretation of linear regression analysis are subject to a variety of pitfalls, which are discussed here in detail. The reader is made aware of common errors of interpretation through practical examples. Both the opportunities for applying linear regression analysis and its limitations are presented.

An improved multiple linear regression and data analysis computer program package

NASA Technical Reports Server (NTRS)

Sidik, S. M.

1972-01-01

NEWRAP, an improved version of a previous multiple linear regression program called RAPIER, CREDUC, and CRSPLT, allows for a complete regression analysis including cross plots of the independent and dependent variables, correlation coefficients, regression coefficients, analysis of variance tables, t-statistics and their probability levels, rejection of independent variables, plots of residuals against the independent and dependent variables, and a canonical reduction of quadratic response functions useful in optimum seeking experimentation. A major improvement over RAPIER is that all regression calculations are done in double precision arithmetic.

A Meta-analysis on Resting State High-frequency Heart Rate Variability in Bulimia Nervosa.

PubMed

Peschel, Stephanie K V; Feeling, Nicole R; Vögele, Claus; Kaess, Michael; Thayer, Julian F; Koenig, Julian

2016-09-01

Autonomic nervous system function is altered in eating disorders. We aimed to quantify differences in resting state vagal activity, indexed by high-frequency heart rate variability comparing patients with bulimia nervosa (BN) and healthy controls. A systematic search of the literature to identify studies eligible for inclusion and meta-analytical methods were applied. Meta-regression was used to identify potential covariates. Eight studies reporting measures of resting high-frequency heart rate variability in individuals with BN (n = 137) and controls (n = 190) were included. Random-effects meta-analysis revealed a sizeable main effect (Z = 2.22, p = .03; Hedge's g = 0.52, 95% CI [0.06;0.98]) indicating higher resting state vagal activity in individuals with BN. Meta-regression showed that body mass index and medication intake are significant covariates. Findings suggest higher vagal activity in BN at rest, particularly in unmedicated samples with lower body mass index. Potential mechanisms underlying these findings and implications for routine clinical care are discussed. Copyright © 2016 John Wiley & Sons, Ltd and Eating Disorders Association. Copyright © 2016 John Wiley & Sons, Ltd and Eating Disorders Association.

Standing on the shoulders of apes: Analyzing the form and function of the hominoid scapula using geometric morphometrics and finite element analysis.

PubMed

Püschel, Thomas A; Sellers, William I

2016-02-01

The aim was to analyze the relationship between scapular form and function in hominoids by using geometric morphometrics (GM) and finite element analysis (FEA). FEA was used to analyze the biomechanical performance of different hominoid scapulae by simulating static postural scenarios. GM was used to quantify scapular shape differences and the relationship between form and function was analyzed by applying both multivariate-multiple regressions and phylogenetic generalized least-squares regressions (PGLS). Although it has been suggested that primate scapular morphology is mainly a product of function rather than phylogeny, our results showed that shape has a significant phylogenetic signal. There was a significant relationship between scapular shape and its biomechanical performance; hence at least part of the scapular shape variation is due to non-phylogenetic factors, probably related to functional demands. This study has shown that a combined approach using GM and FEA was able to cast some light regarding the functional and phylogenetic contributions in hominoid scapular morphology, thus contributing to a better insight of the association between scapular form and function. © 2015 Wiley Periodicals, Inc.

Liver volume measurement: reason of the difference between in vivo CT-volumetry and intraoperative ex vivo determination and how to cope it.

PubMed

Niehues, Stefan M; Unger, J K; Malinowski, M; Neymeyer, J; Hamm, B; Stockmann, M

2010-08-20

Volumetric assessment of the liver regularly yields discrepant results between pre- and intraoperatively determined volumes. Nevertheless, the main factor responsible for this discrepancy remains still unclear. The aim of this study was to systematically determine the difference between in vivo CT-volumetry and ex vivo volumetry in a pig animal model. Eleven pigs were studied. Liver density assessment, CT-volumetry and water displacement volumetry was performed after surgical removal of the complete liver. Known possible errors of volume determination like resection or segmentation borders were eliminated in this model. Regression analysis was performed and differences between CT-volumetry and water displacement determined. Median liver density was 1.07g/ml. Regression analysis showed a high correlation of r(2) = 0.985 between CT-volumetry and water displacement. CT-volumetry was found to be 13% higher than water displacement volumetry (p<0.0001). In this study the only relevant factor leading to the difference between in vivo CT-volumetry and ex vivo water displacement volumetry seems to be blood perfusion of the liver. The systematic difference of 13 percent has to be taken in account when dealing with those measures.

Gender differences in clinical status at time of coronary revascularisation in Spain

PubMed Central

Aguilar, M; Lazaro, P; Fitch, K; Luengo, S

2002-01-01

Design: Retrospective study of clinical records. Two stage stratified cluster sampling was used to select a nationally representative sample of patients receiving a coronary revascularisation procedure in 1997. Setting: All of Spain. Main outcome measures: Odds ratios (OR) in men and women for different clinical and diagnostic variables related with coronary disease. A logistic regression model was developed to estimate the association between coronary symptoms and gender. Results: In the univariate analysis the prevalence of the following risk factors for coronary heart disease was higher in women than in men: obesity (OR=1.8), hypertension (OR=2.9) and diabetes (OR=2.1). High surgical risk was also more prevalent among women (OR=2.6). In the logistic regression analysis women's risk of being symptomatic at the time of revascularisation was more than double that of men (OR=2.4). Conclusions: Women have more severe coronary symptoms at the time of coronary revascularisation than do men. These results suggest that women receive revascularisation at a more advanced stage of coronary disease. Further research is needed to clarify what social, cultural or biological factors may be implicated in the gender differences observed. PMID:12080167

Analyzing the association between fish consumption and osteoporosis in a sample of Chinese men.

PubMed

Li, Xia; Lei, Tao; Tang, Zihui; Dong, Jingcheng

2017-04-19

The main purpose of this study was to estimate the associations between frequency of fish food consumption and osteoporosis (OP) in general Chinese men. We conducted a large-scale, community-based, cross-sectional study to investigate the associations by using self-report questionnaire to access frequency of fish food intake. A total of 1092 men were available for data analysis in this study. Multiple regression models controlling for confounding factors to include frequency of fish food consumption variable were performed to investigate the relationships for OP. Positive correlations between frequency of fish food consumption and T score were reported (β = 0.084, P value = 0.025). Multiple regression analysis indicated that the frequency of fish food consumption was significantly associated with OP (P < 0.05 for model 1 and model 2). The men with high frequency of fish food consumption had a lower prevalence of OP. The findings indicated that frequency of fish food consumption was independently and significantly associated with OP. The prevalence of OP was less frequent in Chinese men preferring fish food habits. ClinicalTrials.gov Identifier: NCT02451397 retrospectively registered 28 May 2015.

Scientific Productivity on Research in Ethical Issues over the Past Half Century: A JoinPoint Regression Analysis.

PubMed

Long, Nguyen Phuoc; Huy, Nguyen Tien; Trang, Nguyen Thi Huyen; Luan, Nguyen Thien; Anh, Nguyen Hoang; Nghi, Tran Diem; Hieu, Mai Van; Hirayama, Kenji; Karbwang, Juntra

2014-09-01

Ethics is one of the main pillars in the development of science. We performed a JoinPoint regression analysis to analyze the trends of ethical issue research over the past half century. The question is whether ethical issues are neglected despite their importance in modern research. PubMed electronic library was used to retrieve publications of all fields and ethical issues. JoinPoint regression analysis was used to identify the significant time trends of publications of all fields and ethical issues, as well as the proportion of publications on ethical issues to all fields over the past half century. Annual percent changes (APC) were computed with their 95% confidence intervals, and a p-value < 0.05 was considered statistically significant. We found that publications of ethical issues increased during the period of 1965-1996 but slightly fell in recent years (from 1996 to 2013). When comparing the absolute number of ethics related articles (APEI) to all publications of all fields (APAF) on PubMed, the results showed that the proportion of APEI to APAF statistically increased during the periods of 1965-1974, 1974-1986, and 1986-1993, with APCs of 11.0, 2.1, and 8.8, respectively. However, the trend has gradually dropped since 1993 and shown a marked decrease from 2002 to 2013 with an annual percent change of -7.4%. Scientific productivity in ethical issues research on over the past half century rapidly increased during the first 30-year period but has recently been in decline. Since ethics is an important aspect of scientific research, we suggest that greater attention is needed in order to emphasize the role of ethics in modern research.

Prevalence of difficult venous access and associated risk factors in highly complex hospitalised patients.

PubMed

Armenteros-Yeguas, Victoria; Gárate-Echenique, Lucía; Tomás-López, Maria Aranzazu; Cristóbal-Domínguez, Estíbaliz; Moreno-de Gusmão, Breno; Miranda-Serrano, Erika; Moraza-Dulanto, Maria Inmaculada

2017-12-01

To estimate the prevalence of difficult venous access in complex patients with multimorbidity and to identify associated risk factors. In highly complex patients, factors like ageing, the need for frequent use of irritant medication and multiple venous catheterisations to complete treatment could contribute to exhaustion of venous access. A cross-sectional study was conducted. 'Highly complex' patients (n = 135) were recruited from March 2013-November 2013. The main study variable was the prevalence of difficult venous access, assessed using one of the following criteria: (1) a history of difficulties obtaining venous access based on more than two attempts to insert an intravenous line and (2) no visible or palpable veins. Other factors potentially associated with the risk of difficult access were also measured (age, gender and chronic illnesses). Univariate analysis was performed for each potential risk factor. Factors with p < 0·2 were then included in multivariable logistic regression analysis. Odds ratios were also calculated. The prevalence of difficult venous access was 59·3%. The univariate logistic regression analysis indicated that gender, a history of vascular access complications and osteoarticular disease were significantly associated with difficult venous access. The multivariable logistic regression showed that only gender was an independent risk factor and the odds ratios was 2·85. The prevalence of difficult venous access is high in this population. Gender (female) is the only independent risk factor associated with this. Previous history of several attempts at catheter insertion is an important criterion in the assessment of difficult venous access. The prevalence of difficult venous access in complex patients is 59·3%. Significant risk factors include being female and a history of complications related to vascular access. © 2017 John Wiley & Sons Ltd.

Brain networks of temporal preparation: A multiple regression analysis of neuropsychological data.

PubMed

Triviño, Mónica; Correa, Ángel; Lupiáñez, Juan; Funes, María Jesús; Catena, Andrés; He, Xun; Humphreys, Glyn W

2016-11-15

There are only a few studies on the brain networks involved in the ability to prepare in time, and most of them followed a correlational rather than a neuropsychological approach. The present neuropsychological study performed multiple regression analysis to address the relationship between both grey and white matter (measured by magnetic resonance imaging in patients with brain lesion) and different effects in temporal preparation (Temporal orienting, Foreperiod and Sequential effects). Two versions of a temporal preparation task were administered to a group of 23 patients with acquired brain injury. In one task, the cue presented (a red versus green square) to inform participants about the time of appearance (early versus late) of a target stimulus was blocked, while in the other task the cue was manipulated on a trial-by-trial basis. The duration of the cue-target time intervals (400 versus 1400ms) was always manipulated within blocks in both tasks. Regression analysis were conducted between either the grey matter lesion size or the white matter tracts disconnection and the three temporal preparation effects separately. The main finding was that each temporal preparation effect was predicted by a different network of structures, depending on cue expectancy. Specifically, the Temporal orienting effect was related to both prefrontal and temporal brain areas. The Foreperiod effect was related to right and left prefrontal structures. Sequential effects were predicted by both parietal cortex and left subcortical structures. These findings show a clear dissociation of brain circuits involved in the different ways to prepare in time, showing for the first time the involvement of temporal areas in the Temporal orienting effect, as well as the parietal cortex in the Sequential effects. Copyright © 2016 Elsevier Inc. All rights reserved.

Determinants of single family residential water use across scales in four western US cities.

PubMed

Chang, Heejun; Bonnette, Matthew Ryan; Stoker, Philip; Crow-Miller, Britt; Wentz, Elizabeth

2017-10-15

A growing body of literature examines urban water sustainability with increasing evidence that locally-based physical and social spatial interactions contribute to water use. These studies however are based on single-city analysis and often fail to consider whether these interactions occur more generally. We examine a multi-city comparison using a common set of spatially-explicit water, socioeconomic, and biophysical data. We investigate the relative importance of variables for explaining the variations of single family residential (SFR) water uses at Census Block Group (CBG) and Census Tract (CT) scales in four representative western US cities - Austin, Phoenix, Portland, and Salt Lake City, - which cover a wide range of climate and development density. We used both ordinary least squares regression and spatial error regression models to identify the influence of spatial dependence on water use patterns. Our results show that older downtown areas show lower water use than newer suburban areas in all four cities. Tax assessed value and building age are the main determinants of SFR water use across the four cities regardless of the scale. Impervious surface area becomes an important variable for summer water use in all cities, and it is important in all seasons for arid environments such as Phoenix. CT level analysis shows better model predictability than CBG analysis. In all cities, seasons, and spatial scales, spatial error regression models better explain the variations of SFR water use. Such a spatially-varying relationship of urban water consumption provides additional evidence for the need to integrate urban land use planning and municipal water planning. Copyright © 2017 Elsevier B.V. All rights reserved.

[A SAS marco program for batch processing of univariate Cox regression analysis for great database].

PubMed

Yang, Rendong; Xiong, Jie; Peng, Yangqin; Peng, Xiaoning; Zeng, Xiaomin

2015-02-01

To realize batch processing of univariate Cox regression analysis for great database by SAS marco program. We wrote a SAS macro program, which can filter, integrate, and export P values to Excel by SAS9.2. The program was used for screening survival correlated RNA molecules of ovarian cancer. A SAS marco program could finish the batch processing of univariate Cox regression analysis, the selection and export of the results. The SAS macro program has potential applications in reducing the workload of statistical analysis and providing a basis for batch processing of univariate Cox regression analysis.

Empirical research on the correlation between economic development and environmental pollution in natural resource abundant regions: the case of China Shaanxi province

NASA Astrophysics Data System (ADS)

Luo, Bo; Zhang, Jinsuo

2018-02-01

This paper investigates the relationship between economic development and environmental pollution in natural resource abundant regions via testing the Environmental Kuznets Curve (EKC) hypothesis by regression analysis, based on the statistical data of per capita GDP growth and environmental pollution indicators in Shaanxi Province from 1989 to 2015. The results show that the per capita GDP and environmental pollution in Shaanxi Province do not always accord with the “inverted U” Environmental Kuznets Curve, which mainly show “N” shapes; only SO2 show the “Inverted U” shapes.

Exact Analysis of Squared Cross-Validity Coefficient in Predictive Regression Models

ERIC Educational Resources Information Center

Shieh, Gwowen

2009-01-01

In regression analysis, the notion of population validity is of theoretical interest for describing the usefulness of the underlying regression model, whereas the presumably more important concept of population cross-validity represents the predictive effectiveness for the regression equation in future research. It appears that the inference…

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

USDA-ARS?s Scientific Manuscript database

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

Estimating basin lagtime and hydrograph-timing indexes used to characterize stormflows for runoff-quality analysis

USGS Publications Warehouse

Granato, Gregory E.

2012-01-01

A nationwide study to better define triangular-hydrograph statistics for use with runoff-quality and flood-flow studies was done by the U.S. Geological Survey (USGS) in cooperation with the Federal Highway Administration. Although the triangular hydrograph is a simple linear approximation, the cumulative distribution of stormflow with a triangular hydrograph is a curvilinear S-curve that closely approximates the cumulative distribution of stormflows from measured data. The temporal distribution of flow within a runoff event can be estimated using the basin lagtime, (which is the time from the centroid of rainfall excess to the centroid of the corresponding runoff hydrograph) and the hydrograph recession ratio (which is the ratio of the duration of the falling limb to the rising limb of the hydrograph). This report documents results of the study, methods used to estimate the variables, and electronic files that facilitate calculation of variables. Ten viable multiple-linear regression equations were developed to estimate basin lagtimes from readily determined drainage basin properties using data published in 37 stormflow studies. Regression equations using the basin lag factor (BLF, which is a variable calculated as the main-channel length, in miles, divided by the square root of the main-channel slope in feet per mile) and two variables describing development in the drainage basin were selected as the best candidates, because each equation explains about 70 percent of the variability in the data. The variables describing development are the USGS basin development factor (BDF, which is a function of the amount of channel modifications, storm sewers, and curb-and-gutter streets in a basin) and the total impervious area variable (IMPERV) in the basin. Two datasets were used to develop regression equations. The primary dataset included data from 493 sites that have values for the BLF, BDF, and IMPERV variables. This dataset was used to develop the best-fit regression equation using the BLF and BDF variables. The secondary dataset included data from 896 sites that have values for the BLF and IMPERV variables. This dataset was used to develop the best-fit regression equation using the BLF and IMPERV variables. Analysis of hydrograph recession ratios and basin characteristics for 41 sites indicated that recession ratios are random variables. Thus, recession ratios cannot be estimated quantitatively using multiple linear regression equations developed using the data available for these sites. The minimums of recession ratios for different streamgages are well characterized by a value of one. The most probable values and maximum values of recession ratios for different streamgages are, however, more variable than the minimums. The most probable values of recession ratios for the 41 streamgages analyzed ranged from 1.0 to 3.52 and had a median of 1.85. The maximum values ranged from 2.66 to 11.3 and had a median of 4.36.

Controls on Albian-Cenomanian carbonate platform sedimentation in middle eastern region: Kesalon event, a middle Cretaceous sea level change in Israel and its correlation with global sea level changes

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

Braun, M.; Hirsch, F.

1987-05-01

After Neocomian regional denudation, Aptian Telemim (= Blanche) carbonates onlapped the Arabian subplate, followed by Yavne-Tammun regression and Albian transgression. Near the Levant coast, the Albian-early Coniacian Judea carbonate platform interfingers with the Talme Yaffe basin to the west. To the south and east, Judea-type carbonates gradually onlap the mainly continental Kurnub (Nubia type) clastics of the peri-Arabian belt. Detailed analysis of the cyclic sedimentation within the 700-m thick Judea Limestone reveals a regressive trend near the top of the Albian Yagur Formation in Galilee, the Hevyon Formation in the Negev, and the ledge of the Kesalon formation in centralmore » Israel Judean Hills, which represents the end of the Early Cretaceous sedimentary cycle. The early Cenomanian marly chalk of the En Yorqeam Formation starts the Cenomanian cycle, followed by bedded and massive dolomite and ammonoid-bearing limestone. Platform sedimentation before this Kesalon event is dominated by bank facies with some rudistid bioherms of presumable Albian age. After the Kesalon event, Cenomanian and Turonian platforms have fast-changing paleogeography on basinal chalks, shales, bioherms and backreef lagoons. Facies boundaries, running mainly east-west to southwest-northeast up to the Early Cretaceous, became close to north-south in the Late Cretaceous. Albian-Cenomanian regressive-transgressive cycles in Israel match fairly well with global sea level changes, in particular the Kesalon event, which corresponds to the Ka-Kb sea level change of Vail et al. Late Turonian-early Senonian thrusting of the peri-Arabian alpine belt and folding in the Syrian arc heavily affect the unraveling of global sea level changes on the Arabian subplate.« less

Regression analysis utilizing subjective evaluation of emotional experience in PET studies on emotions.

PubMed

Aalto, Sargo; Wallius, Esa; Näätänen, Petri; Hiltunen, Jaana; Metsähonkala, Liisa; Sipilä, Hannu; Karlsson, Hasse

2005-09-01

A methodological study on subject-specific regression analysis (SSRA) exploring the correlation between the neural response and the subjective evaluation of emotional experience in eleven healthy females is presented. The target emotions, i.e., amusement and sadness, were induced using validated film clips, regional cerebral blood flow (rCBF) was measured using positron emission tomography (PET), and the subjective intensity of the emotional experience during the PET scanning was measured using a category ratio (CR-10) scale. Reliability analysis of the rating data indicated that the subjects rated the intensity of their emotional experience fairly consistently on the CR-10 scale (Cronbach alphas 0.70-0.97). A two-phase random-effects analysis was performed to ensure the generalizability and inter-study comparability of the SSRA results. Random-effects SSRAs using Statistical non-Parametric Mapping 99 (SnPM99) showed that rCBF correlated with the self-rated intensity of the emotional experience mainly in the brain regions that were identified in the random-effects subtraction analyses using the same imaging data. Our results give preliminary evidence of a linear association between the neural responses related to amusement and sadness and the self-evaluated intensity of the emotional experience in several regions involved in the emotional response. SSRA utilizing subjective evaluation of emotional experience turned out a feasible and promising method of analysis. It allows versatile exploration of the neurobiology of emotions and the neural correlates of actual and individual emotional experience. Thus, SSRA might be able to catch the idiosyncratic aspects of the emotional response better than traditional subtraction analysis.

Development of a User Interface for a Regression Analysis Software Tool

NASA Technical Reports Server (NTRS)

Ulbrich, Norbert Manfred; Volden, Thomas R.

2010-01-01

An easy-to -use user interface was implemented in a highly automated regression analysis tool. The user interface was developed from the start to run on computers that use the Windows, Macintosh, Linux, or UNIX operating system. Many user interface features were specifically designed such that a novice or inexperienced user can apply the regression analysis tool with confidence. Therefore, the user interface s design minimizes interactive input from the user. In addition, reasonable default combinations are assigned to those analysis settings that influence the outcome of the regression analysis. These default combinations will lead to a successful regression analysis result for most experimental data sets. The user interface comes in two versions. The text user interface version is used for the ongoing development of the regression analysis tool. The official release of the regression analysis tool, on the other hand, has a graphical user interface that is more efficient to use. This graphical user interface displays all input file names, output file names, and analysis settings for a specific software application mode on a single screen which makes it easier to generate reliable analysis results and to perform input parameter studies. An object-oriented approach was used for the development of the graphical user interface. This choice keeps future software maintenance costs to a reasonable limit. Examples of both the text user interface and graphical user interface are discussed in order to illustrate the user interface s overall design approach.

Regression Analysis and the Sociological Imagination

ERIC Educational Resources Information Center

De Maio, Fernando

2014-01-01

Regression analysis is an important aspect of most introductory statistics courses in sociology but is often presented in contexts divorced from the central concerns that bring students into the discipline. Consequently, we present five lesson ideas that emerge from a regression analysis of income inequality and mortality in the USA and Canada.

[Use of multiple regression models in observational studies (1970-2013) and requirements of the STROBE guidelines in Spanish scientific journals].

PubMed

Real, J; Cleries, R; Forné, C; Roso-Llorach, A; Martínez-Sánchez, J M

In medicine and biomedical research, statistical techniques like logistic, linear, Cox and Poisson regression are widely known. The main objective is to describe the evolution of multivariate techniques used in observational studies indexed in PubMed (1970-2013), and to check the requirements of the STROBE guidelines in the author guidelines in Spanish journals indexed in PubMed. A targeted PubMed search was performed to identify papers that used logistic linear Cox and Poisson models. Furthermore, a review was also made of the author guidelines of journals published in Spain and indexed in PubMed and Web of Science. Only 6.1% of the indexed manuscripts included a term related to multivariate analysis, increasing from 0.14% in 1980 to 12.3% in 2013. In 2013, 6.7, 2.5, 3.5, and 0.31% of the manuscripts contained terms related to logistic, linear, Cox and Poisson regression, respectively. On the other hand, 12.8% of journals author guidelines explicitly recommend to follow the STROBE guidelines, and 35.9% recommend the CONSORT guideline. A low percentage of Spanish scientific journals indexed in PubMed include the STROBE statement requirement in the author guidelines. Multivariate regression models in published observational studies such as logistic regression, linear, Cox and Poisson are increasingly used both at international level, as well as in journals published in Spanish. Copyright © 2015 Sociedad Española de Médicos de Atención Primaria (SEMERGEN). Publicado por Elsevier España, S.L.U. All rights reserved.

Characterization and classification of Japanese consumer perceptions for beef tenderness using descriptive texture characteristics assessed by a trained sensory panel.

PubMed

Sasaki, Keisuke; Motoyama, Michiyo; Narita, Takumi; Hagi, Tatsuro; Ojima, Koichi; Oe, Mika; Nakajima, Ikuyo; Kitsunai, Katsuhiro; Saito, Yosuke; Hatori, Hikari; Muroya, Susumu; Nomura, Masaru; Miyaguchi, Yuji; Chikuni, Koichi

2014-02-01

Meat tenderness is an important characteristic in terms of consumer preference and satisfaction. However, each consumer may have his/her own criteria to judge meat tenderness, because consumers are neither selected nor trained like an expert sensory panel. This study aimed to characterize consumer tenderness using descriptive texture profiles such as chewiness and hardness assessed by a trained panel. Longissimus muscles cooked at four different end-point temperatures were subjected to a trained sensory panel (n=18) and consumer (n=107) tenderness tests. Multiple regression analysis showed that consumer tenderness was characterized as 'low-chewiness and low hardness texture.' Subsequently, consumers were divided into two groups by cluster analysis according to tenderness perceptions in each participant, and the two groups were characterized as 'tenderness is mainly low-chewiness' and 'tenderness is mainly low-hardness' for tenderness perception, respectively. These results demonstrate objective characteristics and variability of consumer meat tenderness, and provide new information regarding the evaluation and management of meat tenderness for meat manufacturers. © 2013.

A qualitative and quantitative HPTLC densitometry method for the analysis of cannabinoids in Cannabis sativa L.

PubMed

Fischedick, Justin T; Glas, Ronald; Hazekamp, Arno; Verpoorte, Rob

2009-01-01

Cannabis and cannabinoid based medicines are currently under serious investigation for legitimate development as medicinal agents, necessitating new low-cost, high-throughput analytical methods for quality control. The goal of this study was to develop and validate, according to ICH guidelines, a simple rapid HPTLC method for the quantification of Delta(9)-tetrahydrocannabinol (Delta(9)-THC) and qualitative analysis of other main neutral cannabinoids found in cannabis. The method was developed and validated with the use of pure cannabinoid reference standards and two medicinal cannabis cultivars. Accuracy was determined by comparing results obtained from the HTPLC method with those obtained from a validated HPLC method. Delta(9)-THC gives linear calibration curves in the range of 50-500 ng at 206 nm with a linear regression of y = 11.858x + 125.99 and r(2) = 0.9968. Results have shown that the HPTLC method is reproducible and accurate for the quantification of Delta(9)-THC in cannabis. The method is also useful for the qualitative screening of the main neutral cannabinoids found in cannabis cultivars.

A Quantum Chemical and Statistical Study of Phenolic Schiff Bases with Antioxidant Activity against DPPH Free Radical

PubMed Central

Anouar, El Hassane

2014-01-01

Phenolic Schiff bases are known as powerful antioxidants. To select the electronic, 2D and 3D descriptors responsible for the free radical scavenging ability of a series of 30 phenolic Schiff bases, a set of molecular descriptors were calculated by using B3P86 (Becke’s three parameter hybrid functional with Perdew 86 correlation functional) combined with 6-31 + G(d,p) basis set (i.e., at the B3P86/6-31 + G(d,p) level of theory). The chemometric methods, simple and multiple linear regressions (SLR and MLR), principal component analysis (PCA) and hierarchical cluster analysis (HCA) were employed to reduce the dimensionality and to investigate the relationship between the calculated descriptors and the antioxidant activity. The results showed that the antioxidant activity mainly depends on the first and second bond dissociation enthalpies of phenolic hydroxyl groups, the dipole moment and the hydrophobicity descriptors. The antioxidant activity is inversely proportional to the main descriptors. The selected descriptors discriminate the Schiff bases into active and inactive antioxidants. PMID:26784873

Cytotoxicity of trichothecenes and fusarochromanone produced by Fusarium equiseti strains isolated from Norwegian cereals.

PubMed

Morrison, Ellen; Rundberget, Thomas; Kosiak, Barbara; Aastveit, Are H; Bernhoft, Aksel

2002-01-01

The cytotoxicity and secondary metabolites of 28 Norwegian strains of Fusarium equiseti have been characterized. Trichothecenes and fusarochromanone (FUCH) in rice culture extracts of the strains were analysed by gas chromatography-mass spectrometry (GC-MS) and high performance liquid chromatography (HPLC). The following metabolites were found in all isolates: FUCH, nivalenol (NIV), scirpentriol (SCIRP), 4-acetylnivalenol (4-ac-NIV, also called fusarenon-X), 15-acetyl-nivalenol (15-ac-NIV), and diacetoxyscirpenol (DAS). 4,15-diacetyl-nivalenol (diacetyl-NIV) was found in 5 isolates. Porcine kidney epithelial cells (PK15. American Type Culture Collection) were exposed to rice culture extracts to study cytotoxicity. Descriptive statistics and factor analysis of the identified secondary metabolites show that their main metabolites were FUCH, NIV, SCIRP, DAS and 15-ac-NIV, consecutively. The individual trichothecenes were highly intercorrelated, whereas the production of acetylated NIV and DAS was slightly less. Stepwise multiple regression analysis of cytotoxicity and metabolite profiles of rice culture extracts ascribed the toxicity mainly to a combination of FUCH and 15-ac-NIV, though SCIRP or DAS are agents in the combined toxicity as well.

Comparison of Peak-Flow Estimation Methods for Small Drainage Basins in Maine

USGS Publications Warehouse

Hodgkins, Glenn A.; Hebson, Charles; Lombard, Pamela J.; Mann, Alexander

2007-01-01

Understanding the accuracy of commonly used methods for estimating peak streamflows is important because the designs of bridges, culverts, and other river structures are based on these flows. Different methods for estimating peak streamflows were analyzed for small drainage basins in Maine. For the smallest basins, with drainage areas of 0.2 to 1.0 square mile, nine peak streamflows from actual rainfall events at four crest-stage gaging stations were modeled by the Rational Method and the Natural Resource Conservation Service TR-20 method and compared to observed peak flows. The Rational Method had a root mean square error (RMSE) of -69.7 to 230 percent (which means that approximately two thirds of the modeled flows were within -69.7 to 230 percent of the observed flows). The TR-20 method had an RMSE of -98.0 to 5,010 percent. Both the Rational Method and TR-20 underestimated the observed flows in most cases. For small basins, with drainage areas of 1.0 to 10 square miles, modeled peak flows were compared to observed statistical peak flows with return periods of 2, 50, and 100 years for 17 streams in Maine and adjoining parts of New Hampshire. Peak flows were modeled by the Rational Method, the Natural Resources Conservation Service TR-20 method, U.S. Geological Survey regression equations, and the Probabilistic Rational Method. The regression equations were the most accurate method of computing peak flows in Maine for streams with drainage areas of 1.0 to 10 square miles with an RMSE of -34.3 to 52.2 percent for 50-year peak flows. The Probabilistic Rational Method was the next most accurate method (-38.5 to 62.6 percent). The Rational Method (-56.1 to 128 percent) and particularly the TR-20 method (-76.4 to 323 percent) had much larger errors. Both the TR-20 and regression methods had similar numbers of underpredictions and overpredictions. The Rational Method overpredicted most peak flows and the Probabilistic Rational Method tended to overpredict peak flows from the smaller (less than 5 square miles) drainage basins and underpredict peak flows from larger drainage basins. The results of this study are consistent with the most comprehensive analysis of observed and modeled peak streamflows in the United States, which analyzed statistical peak flows from 70 drainage basins in the Midwest and the Northwest.

Multivariate Regression Analysis and Slaughter Livestock,

DTIC Science & Technology

AGRICULTURE, *ECONOMICS), (*MEAT, PRODUCTION), MULTIVARIATE ANALYSIS, REGRESSION ANALYSIS , ANIMALS, WEIGHT, COSTS, PREDICTIONS, STABILITY, MATHEMATICAL MODELS, STORAGE, BEEF, PORK, FOOD, STATISTICAL DATA, ACCURACY

Space Shuttle Main Engine performance analysis

NASA Technical Reports Server (NTRS)

Santi, L. Michael

1993-01-01

For a number of years, NASA has relied primarily upon periodically updated versions of Rocketdyne's power balance model (PBM) to provide space shuttle main engine (SSME) steady-state performance prediction. A recent computational study indicated that PBM predictions do not satisfy fundamental energy conservation principles. More recently, SSME test results provided by the Technology Test Bed (TTB) program have indicated significant discrepancies between PBM flow and temperature predictions and TTB observations. Results of these investigations have diminished confidence in the predictions provided by PBM, and motivated the development of new computational tools for supporting SSME performance analysis. A multivariate least squares regression algorithm was developed and implemented during this effort in order to efficiently characterize TTB data. This procedure, called the 'gains model,' was used to approximate the variation of SSME performance parameters such as flow rate, pressure, temperature, speed, and assorted hardware characteristics in terms of six assumed independent influences. These six influences were engine power level, mixture ratio, fuel inlet pressure and temperature, and oxidizer inlet pressure and temperature. A BFGS optimization algorithm provided the base procedure for determining regression coefficients for both linear and full quadratic approximations of parameter variation. Statistical information relative to data deviation from regression derived relations was also computed. A new strategy for integrating test data with theoretical performance prediction was also investigated. The current integration procedure employed by PBM treats test data as pristine and adjusts hardware characteristics in a heuristic manner to achieve engine balance. Within PBM, this integration procedure is called 'data reduction.' By contrast, the new data integration procedure, termed 'reconciliation,' uses mathematical optimization techniques, and requires both measurement and balance uncertainty estimates. The reconciler attempts to select operational parameters that minimize the difference between theoretical prediction and observation. Selected values are further constrained to fall within measurement uncertainty limits and to satisfy fundamental physical relations (mass conservation, energy conservation, pressure drop relations, etc.) within uncertainty estimates for all SSME subsystems. The parameter selection problem described above is a traditional nonlinear programming problem. The reconciler employs a mixed penalty method to determine optimum values of SSME operating parameters associated with this problem formulation.

Solid Rocket Booster Large Main and Drogue Parachute Reliability Analysis

NASA Technical Reports Server (NTRS)

Clifford, Courtenay B.; Hengel, John E.

2009-01-01

The parachutes on the Space Transportation System (STS) Solid Rocket Booster (SRB) are the means for decelerating the SRB and allowing it to impact the water at a nominal vertical velocity of 75 feet per second. Each SRB has one pilot, one drogue, and three main parachutes. About four minutes after SRB separation, the SRB nose cap is jettisoned, deploying the pilot parachute. The pilot chute then deploys the drogue parachute. The drogue chute provides initial deceleration and proper SRB orientation prior to frustum separation. At frustum separation, the drogue pulls the frustum from the SRB and allows the main parachutes that are mounted in the frustum to unpack and inflate. These chutes are retrieved, inspected, cleaned, repaired as needed, and returned to the flight inventory and reused. Over the course of the Shuttle Program, several improvements have been introduced to the SRB main parachutes. A major change was the replacement of the small (115 ft. diameter) main parachutes with the larger (136 ft. diameter) main parachutes. Other modifications were made to the main parachutes, main parachute support structure, and SRB frustum to eliminate failure mechanisms, improve damage tolerance, and improve deployment and inflation characteristics. This reliability analysis is limited to the examination of the SRB Large Main Parachute (LMP) and drogue parachute failure history to assess the reliability of these chutes. From the inventory analysis, 68 Large Main Parachutes were used in 651 deployments, and 7 chute failures occurred in the 651 deployments. Logistic regression was used to analyze the LMP failure history, and it showed that reliability growth has occurred over the period of use resulting in a current chute reliability of R = .9983. This result was then used to determine the reliability of the 3 LMPs on the SRB, when all must function. There are 29 drogue parachutes that were used in 244 deployments, and no in-flight failures have occurred. Since there are no observed drogue chute failures, Jeffreys Prior was used to calculate a reliability of R =.998. Based on these results, it is concluded that the LMP and drogue parachutes on the Shuttle SRB are suited to their mission and changes made over their life have improved the reliability of the parachute.

Regression Analysis: Legal Applications in Institutional Research

ERIC Educational Resources Information Center

Frizell, Julie A.; Shippen, Benjamin S., Jr.; Luna, Andrew L.

2008-01-01

This article reviews multiple regression analysis, describes how its results should be interpreted, and instructs institutional researchers on how to conduct such analyses using an example focused on faculty pay equity between men and women. The use of multiple regression analysis will be presented as a method with which to compare salaries of…

RAWS II: A MULTIPLE REGRESSION ANALYSIS PROGRAM,

DTIC Science & Technology

This memorandum gives instructions for the use and operation of a revised version of RAWS, a multiple regression analysis program. The program...of preprocessed data, the directed retention of variable, listing of the matrix of the normal equations and its inverse, and the bypassing of the regression analysis to provide the input variable statistics only. (Author)

A dynamic factor model of the evaluation of the financial crisis in Turkey.

PubMed

Sezgin, F; Kinay, B

2010-01-01

Factor analysis has been widely used in economics and finance in situations where a relatively large number of variables are believed to be driven by few common causes of variation. Dynamic factor analysis (DFA) which is a combination of factor and time series analysis, involves autocorrelation matrices calculated from multivariate time series. Dynamic factor models were traditionally used to construct economic indicators, macroeconomic analysis, business cycles and forecasting. In recent years, dynamic factor models have become more popular in empirical macroeconomics. They have more advantages than other methods in various respects. Factor models can for instance cope with many variables without running into scarce degrees of freedom problems often faced in regression-based analysis. In this study, a model which determines the effect of the global crisis on Turkey is proposed. The main aim of the paper is to analyze how several macroeconomic quantities show an alteration before the evolution of the crisis and to decide if a crisis can be forecasted or not.

[Comparison of application of Cochran-Armitage trend test and linear regression analysis for rate trend analysis in epidemiology study].

PubMed

Wang, D Z; Wang, C; Shen, C F; Zhang, Y; Zhang, H; Song, G D; Xue, X D; Xu, Z L; Zhang, S; Jiang, G H

2017-05-10

We described the time trend of acute myocardial infarction (AMI) from 1999 to 2013 in Tianjin incidence rate with Cochran-Armitage trend (CAT) test and linear regression analysis, and the results were compared. Based on actual population, CAT test had much stronger statistical power than linear regression analysis for both overall incidence trend and age specific incidence trend (Cochran-Armitage trend P value

Emission of hydrogen sulfide (H2S) at a waterfall in a sewer: study of main factors affecting H2S emission and modeling approaches.

PubMed

Jung, Daniel; Hatrait, Laetitia; Gouello, Julien; Ponthieux, Arnaud; Parez, Vincent; Renner, Christophe

2017-11-01

Hydrogen sulfide (H 2 S) represents one of the main odorant gases emitted from sewer networks. A mathematical model can be a fast and low-cost tool for estimating its emission. This study investigates two approaches to modeling H 2 S gas transfer at a waterfall in a discharge manhole. The first approach is based on an adaptation of oxygen models for H 2 S emission at a waterfall and the second consists of a new model. An experimental set-up and a statistical data analysis allowed the main factors affecting H 2 S emission to be studied. A new model of the emission kinetics was developed using linear regression and taking into account H 2 S liquid concentration, waterfall height and fluid velocity at the outlet pipe of a rising main. Its prediction interval was estimated by the residual standard deviation (15.6%) up to a rate of 2.3 g H 2 S·h -1 . Finally, data coming from four sampling campaigns on sewer networks were used to perform simulations and compare predictions of all developed models.

A primer for biomedical scientists on how to execute model II linear regression analysis.

PubMed

Ludbrook, John

2012-04-01

1. There are two very different ways of executing linear regression analysis. One is Model I, when the x-values are fixed by the experimenter. The other is Model II, in which the x-values are free to vary and are subject to error. 2. I have received numerous complaints from biomedical scientists that they have great difficulty in executing Model II linear regression analysis. This may explain the results of a Google Scholar search, which showed that the authors of articles in journals of physiology, pharmacology and biochemistry rarely use Model II regression analysis. 3. I repeat my previous arguments in favour of using least products linear regression analysis for Model II regressions. I review three methods for executing ordinary least products (OLP) and weighted least products (WLP) regression analysis: (i) scientific calculator and/or computer spreadsheet; (ii) specific purpose computer programs; and (iii) general purpose computer programs. 4. Using a scientific calculator and/or computer spreadsheet, it is easy to obtain correct values for OLP slope and intercept, but the corresponding 95% confidence intervals (CI) are inaccurate. 5. Using specific purpose computer programs, the freeware computer program smatr gives the correct OLP regression coefficients and obtains 95% CI by bootstrapping. In addition, smatr can be used to compare the slopes of OLP lines. 6. When using general purpose computer programs, I recommend the commercial programs systat and Statistica for those who regularly undertake linear regression analysis and I give step-by-step instructions in the Supplementary Information as to how to use loss functions. © 2011 The Author. Clinical and Experimental Pharmacology and Physiology. © 2011 Blackwell Publishing Asia Pty Ltd.

Water quality parameter measurement using spectral signatures

NASA Technical Reports Server (NTRS)

White, P. E.

1973-01-01

Regression analysis is applied to the problem of measuring water quality parameters from remote sensing spectral signature data. The equations necessary to perform regression analysis are presented and methods of testing the strength and reliability of a regression are described. An efficient algorithm for selecting an optimal subset of the independent variables available for a regression is also presented.

A systematic evaluation of normalization methods in quantitative label-free proteomics.

PubMed

Välikangas, Tommi; Suomi, Tomi; Elo, Laura L

2018-01-01

To date, mass spectrometry (MS) data remain inherently biased as a result of reasons ranging from sample handling to differences caused by the instrumentation. Normalization is the process that aims to account for the bias and make samples more comparable. The selection of a proper normalization method is a pivotal task for the reliability of the downstream analysis and results. Many normalization methods commonly used in proteomics have been adapted from the DNA microarray techniques. Previous studies comparing normalization methods in proteomics have focused mainly on intragroup variation. In this study, several popular and widely used normalization methods representing different strategies in normalization are evaluated using three spike-in and one experimental mouse label-free proteomic data sets. The normalization methods are evaluated in terms of their ability to reduce variation between technical replicates, their effect on differential expression analysis and their effect on the estimation of logarithmic fold changes. Additionally, we examined whether normalizing the whole data globally or in segments for the differential expression analysis has an effect on the performance of the normalization methods. We found that variance stabilization normalization (Vsn) reduced variation the most between technical replicates in all examined data sets. Vsn also performed consistently well in the differential expression analysis. Linear regression normalization and local regression normalization performed also systematically well. Finally, we discuss the choice of a normalization method and some qualities of a suitable normalization method in the light of the results of our evaluation. © The Author 2016. Published by Oxford University Press.

Simultaneous Determination of Gallic Acid, Ellagic Acid, and Eugenol in Syzygium aromaticum and Verification of Chemical Antagonistic Effect by the Combination with Curcuma aromatica Using Regression Analysis

PubMed Central

Seo, Chang-Seob; Kim, Seong-Sil; Ha, Hyekyung

2013-01-01

This study was designed to perform simultaneous determination of three reference compounds in Syzygium aromaticum (SA), gallic acid, ellagic acid, and eugenol, and to investigate the chemical antagonistic effect when combining Curcuma aromatica (CA) with SA, based on chromatographic analysis. The values of LODs and LOQs were 0.01–0.11 μg/mL and 0.03–0.36 μg/mL, respectively. The intraday and interday precisions were <3.0 of RSD values, and the recovery was in the range of 92.19–103.24%, with RSD values <3.0%. Repeatability and stability were 0.38–0.73% and 0.49–2.24%, respectively. Compared with the content of reference and relative peaks in SA and SA combined with CA (SAC), the amounts of gallic acid and eugenol were increased, while that of ellagic acid was decreased in SAC (compared with SA), and most of peak areas in SA were reduced in SAC. Regression analysis of the relative peak areas between SA and SAC showed r 2 values >0.87, indicating a linear relationship between SA and SAC. These results demonstrate that the components contained in CA could affect the extraction of components of SA mainly in a decreasing manner. The antagonistic effect of CA on SA was verified by chemical analysis. PMID:23878761

Low-level lead exposure and the IQ of children. A meta-analysis of modern studies

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

Needleman, H.L.; Gatsonis, C.A.

1990-02-02

We identified 24 modern studies of childhood exposures to lead in relation to IQ. From this population, 12 that employed multiple regression analysis with IQ as the dependent variable and lead as the main effect and that controlled for nonlead covariates were selected for a quantitative, integrated review or meta-analysis. The studies were grouped according to type of tissue analyzed for lead. There were 7 blood and 5 tooth lead studies. Within each group, we obtained joint P values by two different methods and average effect sizes as measured by the partial correlation coefficients. We also investigated the sensitivity ofmore » the results to any single study. The sample sizes ranged from 75 to 724. The sign of the regression coefficient for lead was negative in 11 of 12 studies. The negative partial r's for lead ranged from -.27 to -.003. The power to find an effect was limited, below 0.6 in 7 of 12 studies. The joint P values for the blood lead studies were less than .0001 for both methods of analysis (95% confidence interval for group partial r, -.15 {plus minus} .05), while for the tooth lead studies they were .0005 and .004, respectively (95% confidence interval for group partial r, -.08 {plus minus} .05). The hypothesis that lead impairs children's IQ at low dose is strongly supported by this quantitative review. The effect is robust to the impact of any single study.« less

Numerical and experimental investigation of the effect of geometry on combustion characteristics of solid-fuel ramjet

NASA Astrophysics Data System (ADS)

Gong, Lunkun; Chen, Xiong; Musa, Omer; Yang, Haitao; Zhou, Changsheng

2017-12-01

Numerical and experimental investigation on the solid-fuel ramjet was carried out to study the effect of geometry on combustion characteristics. The two-dimensional axisymmetric program developed in the present study adopted finite rate chemistry and second-order moment turbulence-chemistry models, together with k-ω shear stress transport (SST) turbulence model. Experimental data were obtained by burning cylindrical polyethylene using a connected pipe facility. The simulation results show that a fuel-rich zone near the solid fuel surface and an air-rich zone in the core exist in the chamber, and the chemical reactions occur mainly in the interface of this two regions; The physical reasons for the effect of geometry on regression rate is the variation of turbulent viscosity due to the geometry change. Port-to-inlet diameter ratio is the main parameter influencing the turbulent viscosity, and a linear relationship between port-to-inlet diameter and regression rate were obtained. The air mass flow rate and air-fuel ratio are the main influencing factors on ramjet performances. Based on the simulation results, the correlations between geometry and air-fuel ratio were obtained, and the effect of geometry on ramjet performances was analyzed according to the correlation. Three-dimensional regression rate contour obtained experimentally indicates that the regression rate which shows axisymmetric distribution due to the symmetry structure increases sharply, followed by slow decrease in axial direction. The radiation heat transfer in recirculation zone cannot be ignored. Compared with the experimental results, the deviations of calculated average regression rate and characteristic velocity are about 5%. Concerning the effect of geometry on air-fuel ratio, the deviations between experimental and theoretical results are less than 10%.

Risk Factors for Proximal Junctional Kyphosis Associated With Dual-rod Growing-rod Surgery for Early-onset Scoliosis.

PubMed

Watanabe, Kota; Uno, Koki; Suzuki, Teppei; Kawakami, Noriaki; Tsuji, Taichi; Yanagida, Haruhisa; Ito, Manabu; Hirano, Toru; Yamazaki, Ken; Minami, Shohei; Taneichi, Hiroshi; Imagama, Shiro; Takeshita, Katsushi; Yamamoto, Takuya; Matsumoto, Morio

2016-10-01

A retrospective, multicenter study. To identify risk factors for proximal junctional kyphosis (PJK) when treating early-onset scoliosis (EOS) with dual-rod growing-rod (GR) procedure. The risk factors for PJK associated with GR treatment for EOS have not been adequately studied. We evaluated clinical and radiographic results from 88 patients with EOS who underwent dual-rod GR surgery in 12 spine centers in Japan. The mean age at the time of the initial surgery was 6.5±2.2 years (range, 1.5-9.8 y), and the mean follow-up period was 3.9±2.6 years (range, 2.0-12.0 y). Risk factors for PJK were analyzed by binomial multiple logistic regression analysis. The potential factors analyzed were sex, etiology, age, the number of rod-lengthening procedures, coronal and sagittal parameters on radiographs, the type of foundation (pedicle screws or hooks), the uppermost level of the proximal foundation, and the lowermost level of the distal foundation. PJK developed in 23 patients (26%); in 19 of these, the proximal foundation became dislodged following PJK. Binomial multiple logistic regression analysis identified the following significant independent risk factors for PJK: a lower instrumented vertebra at or cranial to L3 [odds ratio (OR), 3.32], a proximal thoracic scoliosis of ≥40 degrees (OR, 2.95), and a main thoracic kyphosis of ≥60 degrees (OR, 5.08). The significant independent risk factors for PJK during dual-rod GR treatment for EOS were a lower instrumented vertebra at or cranial to L3, a proximal thoracic scoliosis of ≥40 degrees, and a main thoracic kyphosis of ≥60 degrees.

Quality of life in multiple sclerosis (MS) and role of fatigue, depression, anxiety, and stress: A bicenter study from north of Iran.

PubMed

Salehpoor, Ghasem; Rezaei, Sajjad; Hosseininezhad, Mozaffar

2014-11-01

Although studies have demonstrated significant negative relationships between quality of life (QOL), fatigue, and the most common psychological symptoms (depression, anxiety, stress), the main ambiguity of previous studies on QOL is in the relative importance of these predictors. Also, there is lack of adequate knowledge about the actual contribution of each of them in the prediction of QOL dimensions. Thus, the main objective of this study is to assess the role of fatigue, depression, anxiety, and stress in relation to QOL of multiple sclerosis (MS) patients. One hundred and sixty-two MS patients completed the questionnaire on demographic variables, and then they were evaluated by the Persian versions of Short-Form Health Survey Questionnaire (SF-36), Fatigue Survey Scale (FSS), and Depression, Anxiety, Stress Scale-21 (DASS-21). Data were analyzed by Pearson correlation coefficient and hierarchical regression. Correlation analysis showed a significant relationship between QOL elements in SF-36 (physical component summary and mental component summary) and depression, fatigue, stress, and anxiety (P < 0.01). Hierarchical regression analysis indicated that among the predictor variables in the final step, fatigue, depression, and anxiety were identified as the physical component summary predictor variables. Anxiety was found to be the most powerful predictor variable amongst all (β = -0.46, P < 0.001). Furthermore, results have shown depression as the only significant mental component summary predictor variable (β = -0.39, P < 0.001). This study has highlighted the role of anxiety, fatigue, and depression in physical dimensions and the role of depression in psychological dimensions of the lives of MS patients. In addition, the findings of this study indirectly suggest that psychological interventions for reducing fatigue, depression, and anxiety can lead to improved QOL of MS patients.

Antimicrobial resistance: the major contribution of poor governance and corruption to this growing problem.

PubMed

Collignon, Peter; Athukorala, Prema-Chandra; Senanayake, Sanjaya; Khan, Fahad

2015-01-01

To determine how important governmental, social, and economic factors are in driving antibiotic resistance compared to the factors usually considered the main driving factors-antibiotic usage and levels of economic development. A retrospective multivariate analysis of the variation of antibiotic resistance in Europe in terms of human antibiotic usage, private health care expenditure, tertiary education, the level of economic advancement (per capita GDP), and quality of governance (corruption). The model was estimated using a panel data set involving 7 common human bloodstream isolates and covering 28 European countries for the period 1998-2010. Only 28% of the total variation in antibiotic resistance among countries is attributable to variation in antibiotic usage. If time effects are included the explanatory power increases to 33%. However when the control of corruption indicator is included as an additional variable, 63% of the total variation in antibiotic resistance is now explained by the regression. The complete multivariate regression only accomplishes an additional 7% in terms of goodness of fit, indicating that corruption is the main socioeconomic factor that explains antibiotic resistance. The income level of a country appeared to have no effect on resistance rates in the multivariate analysis. The estimated impact of corruption was statistically significant (p< 0.01). The coefficient indicates that an improvement of one unit in the corruption indicator is associated with a reduction in antibiotic resistance by approximately 0.7 units. The estimated coefficient of private health expenditure showed that one unit reduction is associated with a 0.2 unit decrease in antibiotic resistance. These findings support the hypothesis that poor governance and corruption contributes to levels of antibiotic resistance and correlate better than antibiotic usage volumes with resistance rates. We conclude that addressing corruption and improving governance will lead to a reduction in antibiotic resistance.

Case-control studies in cancer patients as a surveillance system of occupational exposure in the European Community. European Community Working Party.

PubMed Central

Rona, R J; Taub, N A; Rasmussen, S

1993-01-01

STUDY OBJECTIVE--The main aim was to detect known relationships between lung and blood cancers and various occupational exposures (using job titles as proxies) using a case-control design. The suitability of this system for routine surveillance could then be assessed. DESIGN--A case-control study was carried out in 1989. SETTING--Hospitals in eight European Community countries. SUBJECTS--Men aged 25 to 75 years with incident and prevalent cancer of the lung (190 cases), haematopoietic system (210 cases), or gastrointestinal tract (245 controls) were studied. MEASUREMENTS AND MAIN RESULTS--The crude estimate of the overall odds ratio exposure (OR) for relevant occupational exposure of lung cancer relative to gastrointestinal cancer was 1.20 (95% confidence interval (CI) 0.82, 1.77). In a logistic regression analysis adjusting for country, age at diagnosis, smoking, and alcohol consumption, the overall OR was not greatly changed. A significant interaction of occupational exposure and age at diagnosis showed that lung cancer patients diagnosed at a younger age had a higher OR than patients diagnosed at an older age. Thus, the overall, insignificant result may have been due to a low reliability of occupational history in older age or to a selective mechanism related to age. The overall OR for occupational exposure of cancer of the blood relative to gastrointestinal cancer was 0.88 (95% CI 0.60, 1.31). The logistic regression analysis did not alter these results. CONCLUSION--A surveillance based on a case-control design using job titles would not be sensitive enough to detect possible occupational risks. PMID:8228771

The costs of heparin-induced thrombocytopenia: a patient-based cost of illness analysis.

PubMed

Wilke, T; Tesch, S; Scholz, A; Kohlmann, T; Greinacher, A

2009-05-01

SUMMARY BACKGROUND AND OBJECTIVES: Due to the complexity of heparin-induced thrombocytopenia (HIT), currently available cost analyses are rough estimates. The objectives of this study were quantification of costs involved in HIT and identification of main cost drivers based on a patient-oriented approach. Patients diagnosed with HIT (1995-2004, University-hospital Greifswald, Germany) based on a positive functional assay (HIPA test) were retrieved from the laboratory records and scored (4T-score) by two medical experts using the patient file. For cost of illness analysis, predefined HIT-relevant cost parameters (medication costs, prolonged in-hospital stay, diagnostic and therapeutic interventions, laboratory tests, blood transfusions) were retrieved from the patient files. The data were analysed by linear regression estimates with the log of costs and a gamma regression model. Mean length of stay data of non-HIT patients were obtained from the German Federal Statistical Office, adjusted for patient characteristics, comorbidities and year of treatment. Hospital costs were provided by the controlling department. One hundred and thirty HIT cases with a 4T-score >or=4 and a positive HIPA test were analyzed. Mean additional costs of a HIT case were 9008 euro. The main cost drivers were prolonged in-hospital stay (70.3%) and costs of alternative anticoagulants (19.7%). HIT was more costly in surgical patients compared with medical patients and in patients with thrombosis. Early start of alternative anticoagulation did not increase HIT costs despite the high medication costs indicating prevention of costly complications. An HIT cost calculator is provided, allowing online calculation of HIT costs based on local cost structures and different currencies.

Irritable bowel syndrome is concentrated in people with higher educations in Iran: an inequality analysis

PubMed Central

2017-01-01

OBJECTIVES Like any other health-related disorder, irritable bowel syndrome (IBS) has a differential distribution with respect to socioeconomic factors. This study aimed to estimate and decompose educational inequalities in the prevalence of IBS. METHODS Sampling was performed using a multi-stage random cluster sampling approach. The data of 1,850 residents of Kish Island aged 15 years or older were included, and the determinants of IBS were identified using a generalized estimating equation regression model. The concentration index of educational inequality in cases of IBS was estimated and decomposed as the specific inequality index. RESULTS The prevalence of IBS in this study was 21.57% (95% confidence interval [CI], 19.69 to 23.44%). The concentration index of IBS was 0.20 (95% CI, 0.14 to 0.26). A multivariable regression model revealed that age, sex, level of education, marital status, anxiety, and poor general health were significant determinants of IBS. In the decomposition analysis, level of education (89.91%), age (−11.99%), and marital status (9.11%) were the three main contributors to IBS inequality. Anxiety and poor general health were the next two contributors to IBS inequality, and were responsible for more than 12% of the total observed inequality. CONCLUSIONS The main contributors of IBS inequality were education level, age, and marital status. Given the high percentage of anxious individuals among highly educated, young, single, and divorced people, we can conclude that all contributors to IBS inequality may be partially influenced by psychological factors. Therefore, programs that promote the development of mental health to alleviate the abovementioned inequality in this population are highly warranted. PMID:28171714

Analysis of the low-flow characteristics of streams in Louisiana

USGS Publications Warehouse

Lee, Fred N.

1985-01-01

The U.S. Geological Survey, in cooperation with the Louisiana Department of Transportation and Development, Office of Public Works, used geologic maps, soils maps, precipitation data, and low-flow data to define four hydrographic regions in Louisiana having distinct low-flow characteristics. Equations were derived, using regression analyses, to estimate the 7Q2, 7Q10, and 7Q20 flow rates for basically unaltered stream basins smaller than 525 square miles. Independent variables in the equations include drainage area (square miles), mean annual precipitation index (inches), and main channel slope (feet per mile). Average standard errors of regression ranged from +44 to +61 percent. Graphs are given for estimating the 7Q2, 7Q10, and 7Q20 for stream basins for which the drainage area of the most downstream data-collection site is larger than 525 square miles. Detailed examples are given in this report for the use of the equations and graphs.

Polycyclic aromatic hydrocarbons in ambient air, surface soil and wheat grain near a large steel-smelting manufacturer in northern China.

PubMed

Liu, Weijian; Wang, Yilong; Chen, Yuanchen; Tao, Shu; Liu, Wenxin

2017-07-01

The total concentrations and component profiles of polycyclic aromatic hydrocarbons (PAHs) in ambient air, surface soil and wheat grain collected from wheat fields near a large steel-smelting manufacturer in Northern China were determined. Based on the specific isomeric ratios of paired species in ambient air, principle component analysis and multivariate linear regression, the main emission source of local PAHs was identified as a mixture of industrial and domestic coal combustion, biomass burning and traffic exhaust. The total organic carbon (TOC) fraction was considerably correlated with the total and individual PAH concentrations in surface soil. The total concentrations of PAHs in wheat grain were relatively low, with dominant low molecular weight constituents, and the compositional profile was more similar to that in ambient air than in topsoil. Combined with more significant results from partial correlation and linear regression models, the contribution from air PAHs to grain PAHs may be greater than that from soil PAHs. Copyright © 2016. Published by Elsevier B.V.

[Simulation of three-dimensional green biomass of urban forests in Shenyang City and the factors affecting the biomass].

PubMed

Liu, Chang-Fu; He, Xing-Yuan; Chen, Wei; Zhao, Gui-Ling; Xue, Wen-Duo

2008-06-01

Based on the fractal theory of forest growth, stepwise regression was employed to pursue a convenient and efficient method of measuring the three-dimensional green biomass (TGB) of urban forests in small area. A total of thirteen simulation equations of TGB of urban forests in Shenyang City were derived, with the factors affecting the TGB analyzed. The results showed that the coefficients of determination (R2) of the 13 simulation equations ranged from 0.612 to 0.842. No evident pattern was shown in residual analysis, and the precisions were all higher than 87% (alpha = 0.05) and 83% (alpha = 0.01). The most convenient simulation equation was ln Y = 7.468 + 0.926 lnx1, where Y was the simulated TGB and x1 was basal area at breast height per hectare (SDB). The correlations between the standard regression coefficients of the simulation equations and 16 tree characteristics suggested that SDB was the main factor affecting the TGB of urban forests in Shenyang.

Weight loss efficacy of a novel mobile Diabetes Prevention Program delivery platform with human coaching

PubMed Central

Michaelides, Andreas; Raby, Christine; Wood, Meghan; Farr, Kit

2016-01-01

Objective To evaluate the weight loss efficacy of a novel mobile platform delivering the Diabetes Prevention Program. Research Design and Methods 43 overweight or obese adult participants with a diagnosis of prediabetes signed-up to receive a 24-week virtual Diabetes Prevention Program with human coaching, through a mobile platform. Weight loss and engagement were the main outcomes, evaluated by repeated measures analysis of variance, backward regression, and mediation regression. Results Weight loss at 16 and 24 weeks was significant, with 56% of starters and 64% of completers losing over 5% body weight. Mean weight loss at 24 weeks was 6.58% in starters and 7.5% in completers. Participants were highly engaged, with 84% of the sample completing 9 lessons or more. In-app actions related to self-monitoring significantly predicted weight loss. Conclusions Our findings support the effectiveness of a uniquely mobile prediabetes intervention, producing weight loss comparable to studies with high engagement, with potential for scalable population health management. PMID:27651911

Techniques for estimating magnitude and frequency of floods in Minnesota

USGS Publications Warehouse

Guetzkow, Lowell C.

1977-01-01

 Estimating relations have been developed to provide engineers and designers with improved techniques for defining flow-frequency characteristics to satisfy hydraulic planning and design requirements. The magnitude and frequency of floods up to the 100-year recurrence interval can be determined for most streams in Minnesota by methods presented. By multiple regression analysis, equations have been developed for estimating flood-frequency relations at ungaged sites on natural flow streams. Eight distinct hydrologic regions are delineated within the State with boundaries defined generally by river basin divides. Regression equations are provided for each region which relate selected frequency floods to significant basin parameters. For main-stem streams, graphs are presented showing floods for selected recurrence intervals plotted against contributing drainage area. Flow-frequency estimates for intervening sites along the Minnesota River, Mississippi River, and the Red River of the North can be derived from these graphs. Flood-frequency characteristics are tabulated for 201 paging stations having 10 or more years of record.

[Spatial differentiation and impact factors of Yutian Oasis's soil surface salt based on GWR model].

PubMed

Yuan, Yu Yun; Wahap, Halik; Guan, Jing Yun; Lu, Long Hui; Zhang, Qin Qin

2016-10-01

In this paper, topsoil salinity data gathered from 24 sampling sites in the Yutian Oasis were used, nine different kinds of environmental variables closely related to soil salinity were selec-ted as influencing factors, then, the spatial distribution characteristics of topsoil salinity and spatial heterogeneity of influencing factors were analyzed by combining the spatial autocorrelation with traditional regression analysis and geographically weighted regression model. Results showed that the topsoil salinity in Yutian Oasis was not of random distribution but had strong spatial dependence, and the spatial autocorrelation index for topsoil salinity was 0.479. Groundwater salinity, groundwater depth, elevation and temperature were the main factors influencing topsoil salt accumulation in arid land oases and they were spatially heterogeneous. The nine selected environmental variables except soil pH had significant influences on topsoil salinity with spatial disparity. GWR model was superior to the OLS model on interpretation and estimation of spatial non-stationary data, also had a remarkable advantage in visualization of modeling parameters.

Application of glas laser altimetry to detect elevation changes in East Antarctica

NASA Astrophysics Data System (ADS)

Scaioni, M.; Tong, X.; Li, R.

2013-10-01

In this paper the use of ICESat/GLAS laser altimeter for estimating multi-temporal elevation changes on polar ice sheets is afforded. Due to non-overlapping laser spots during repeat passes, interpolation methods are required to make comparisons. After reviewing the main methods described in the literature (crossover point analysis, cross-track DEM projection, space-temporal regressions), the last one has been chosen for its capability of providing more elevation change rate measurements. The standard implementation of the space-temporal linear regression technique has been revisited and improved to better cope with outliers and to check the estimability of model's parameters. GLAS data over the PANDA route in East Antarctica have been used for testing. Obtained results have been quite meaningful from a physical point of view, confirming the trend reported by the literature of a constant snow accumulation in the area during the two past decades, unlike the most part of the continent that has been losing mass.

Modeling the prediction of business intelligence system effectiveness.

PubMed

Weng, Sung-Shun; Yang, Ming-Hsien; Koo, Tian-Lih; Hsiao, Pei-I

2016-01-01

Although business intelligence (BI) technologies are continually evolving, the capability to apply BI technologies has become an indispensable resource for enterprises running in today's complex, uncertain and dynamic business environment. This study performed pioneering work by constructing models and rules for the prediction of business intelligence system effectiveness (BISE) in relation to the implementation of BI solutions. For enterprises, effectively managing critical attributes that determine BISE to develop prediction models with a set of rules for self-evaluation of the effectiveness of BI solutions is necessary to improve BI implementation and ensure its success. The main study findings identified the critical prediction indicators of BISE that are important to forecasting BI performance and highlighted five classification and prediction rules of BISE derived from decision tree structures, as well as a refined regression prediction model with four critical prediction indicators constructed by logistic regression analysis that can enable enterprises to improve BISE while effectively managing BI solution implementation and catering to academics to whom theory is important.

Modeling Information Content Via Dirichlet-Multinomial Regression Analysis.

PubMed

Ferrari, Alberto

2017-01-01

Shannon entropy is being increasingly used in biomedical research as an index of complexity and information content in sequences of symbols, e.g. languages, amino acid sequences, DNA methylation patterns and animal vocalizations. Yet, distributional properties of information entropy as a random variable have seldom been the object of study, leading to researchers mainly using linear models or simulation-based analytical approach to assess differences in information content, when entropy is measured repeatedly in different experimental conditions. Here a method to perform inference on entropy in such conditions is proposed. Building on results coming from studies in the field of Bayesian entropy estimation, a symmetric Dirichlet-multinomial regression model, able to deal efficiently with the issue of mean entropy estimation, is formulated. Through a simulation study the model is shown to outperform linear modeling in a vast range of scenarios and to have promising statistical properties. As a practical example, the method is applied to a data set coming from a real experiment on animal communication.

From Attitudes to Actions: Predictors of Lion Killing by Maasai Warriors

PubMed Central

Dickman, Amy; Frank, Laurence

2017-01-01

Despite legal protection, deliberate killing by local people is one of the major threats to the conservation of lions and other large carnivores in Africa. Addressing this problem poses particular challenges, mainly because it is difficult to uncover illicit behavior. This article examined two groups of Maasai warriors: individuals who have killed African lions (Panthera leo) and those who have not. We conducted interviews to explore the relationship between attitudes, intentions and known lion killing behavior. Factor analysis and logistic regression revealed that lion killing was mainly determined by: (a) general attitudes toward lions, (b) engagement in traditional customs, (c) lion killing intentions to defend property, and (d) socio-cultural killing intentions. Our results indicated that general attitudes toward lions were the strongest predictor of lion killing behavior. Influencing attitudes to encourage pro-conservation behavior may help reduce killing. PMID:28135338

[Anemia status and correlation factors in rural regions of Hebei province].

PubMed

Wang, Yue-jin; Li, Jian-guo; Xu, Wei-ling; Wang, Xiao-bo; Liu, Yan-li; Jiang, Hong

2008-05-01

To investigate anemia status and correlation infection factors in rural regions of Hebei province and to find out evidence for preventing and controlling anemia. A random-sampling survey was conducted among 3367 houses in Hebei rural areas. The investigation involved economic levels, ages, education levels and occupations of 11,627 questionnaire. The hemoprotein and serum iron were measured. Unconditional logistic regression was performed. The anemia prevalence rate was shown up to 8.4% in rural regions of Hebei province, and in men and women was 5.5% and 11.0%, respectively;mainly in infant (< 2 years old, 27.2%) child bearing age women, the anemia prevalence rate was 11.0%-16.0%. The analysis showed that the main risk factors of anemia were sex and serum iron. The anemia prevalence is highest in infant and child bearing age women;supplying of iron should be an important measure for preventing and controlling anemia.

Coupling GIS spatial analysis and Ensemble Niche Modelling to investigate climate change-related threats to the Sicilian pond turtle Emys trinacris, an endangered species from the Mediterranean.

PubMed

Iannella, Mattia; Cerasoli, Francesco; D'Alessandro, Paola; Console, Giulia; Biondi, Maurizio

2018-01-01

The pond turtle Emys trinacris is an endangered endemic species of Sicily showing a fragmented distribution throughout the main island. In this study, we applied "Ensemble Niche Modelling", combining more classical statistical techniques as Generalized Linear Models and Multivariate Adaptive Regression Splines with machine-learning approaches as Boosted Regression Trees and Maxent, to model the potential distribution of the species under current and future climatic conditions. Moreover, a "gap analysis" performed on both the species' presence sites and the predictions from the Ensemble Models is proposed to integrate outputs from these models, in order to assess the conservation status of this threatened species in the context of biodiversity management. For this aim, four "Representative Concentration Pathways", corresponding to different greenhouse gases emissions trajectories were considered to project the obtained models to both 2050 and 2070. Areas lost, gained or remaining stable for the target species in the projected models were calculated. E. trinacris ' potential distribution resulted to be significantly dependent upon precipitation-linked variables, mainly precipitation of wettest and coldest quarter. Future negative effects for the conservation of this species, because of more unstable precipitation patterns and extreme meteorological events, emerged from our analyses. Further, the sites currently inhabited by E. trinacris are, for more than a half, out of the Protected Areas network, highlighting an inadequate management of the species by the authorities responsible for its protection. Our results, therefore, suggest that in the next future the Sicilian pond turtle will need the utmost attention by the scientific community to avoid the imminent risk of extinction. Finally, the gap analysis performed in GIS environment resulted to be a very informative post-modeling technique, potentially applicable to the management of species at risk and to Protected Areas' planning in many contexts.

Land Reclamation in Brazilian Amazônia: A chronosequence study of floristic development in the national forest of Jamiri-RO mined areas

NASA Astrophysics Data System (ADS)

Fengler, Felipe; Ribeiro, Admilson; Longo, Regina; Merides, Marcela; Soares, Herlon; Melo, Wanderley

2017-04-01

Although reclamation techniques for forest ecosystems recovery have been developed over the past decades, there is still a great difficulty in the establishment on environment assessment, especially when compared to the non-disturbed ecosystems. This work evaluated the results and limitations on cassiterite-mined areas in reclamation, at Brazilian Amazônia. Floristic variables from 29 plots located on 15-year-old native species reforestation sites and two plots from preserved open/closed canopy forests were analyzed in a chronosequece way (2010-2015). Regeneration density, species richness, average girth, and average height were evaluated every year, by means of cluster analysis (Euclidian distance, Ward method) and submitted to multiscale bootstrap resampling (a=5%). It was conduced the regression analysis for each identified group in 2015 in order to verify differences between the chronosequece development. The results showed the existence of two main groups in 2010, one witch all mined plots were allocated and other with open/closed canopy plots. After 2011 some mined areas became allocated in the open/closed canopy plots group. From 2013 and on open/closed canopy plots appeared shuffled in the formed groups, indicating the reclamation sites conditions became similar to natural areas. Finally, in 2015 three main groups were formed. The regression analysis showed that group three had a higher trend of development for regeneration density, with higher angular coefficient and higher values. For species richness all the groups had a similar trend, with values lower than open/closed canopy forest. In average girth higher trends were observed in group one and all values were near to open canopy forest in 2015. Average height showed better trends and higher values in group two. It was concluded that all mined sites had a forest recovery process. However, different responses to reclamation process were observed due to the differences in the degraded soils characteristics. Keywords: Recovery, Restoration, Forest, Chronosequece, Cassiterite.

Burnout of Physicians Working in Primary Health Care Centers under Ministry of Health Jeddah, Saudi Arabia.

PubMed

Bawakid, Khalid; Abdulrashid, Ola; Mandoura, Najlaa; Shah, Hassan Bin Usman; Ibrahim, Adel; Akkad, Noura Mohammad; Mufti, Fauad

2017-11-25

Introduction The levels of physicians' job satisfaction and burnout directly affect their professionalism, punctuality, absenteeism, and ultimately, patients' care. Despite its crucial importance, little is known about professional burnout of the physicians in Saudi Arabia. The objectives of this research are two-fold: (1) To assess the prevalence of burnout in physicians working in primary health care centers under Ministry of Health; and (2) to find the modifiable factors which can decrease the burnout ratio. Methodology Through a cross-sectional study design, a representative sample of the physicians working in primary health care centers (PHCCs) Jeddah (n=246) was randomly selected. The overall burnout level was assessed using the validated abbreviated Maslach burnout inventory (aMBI) questionnaire. It measures the overall burnout prevalence based on three main domains i.e., emotional exhaustion, depersonalization, and personal accomplishment. Independent sample T-test, analysis of variance (ANOVA), and multivariate regression analysis were performed using Statistical Package for the Social Sciences (SPSS Version 22, IBM, Armonk, NY). Results Overall, moderate to high burnout was prevalent in 25.2% of the physicians. Emotional exhaustion was noted in 69.5%. Multivariate regression analysis showed that patient pressure/violence (p <0.001), unorganized patients flow to clinics (p=0.021), more paperwork (p<0.001), and less co-operative colleague doctors (p=0.045) were the significant predictors for high emotional exhaustion. A positive correlation was noted between the number of patients per day and burnout. The patient's pressure/violence was the only significant independent predictor of overall burnout. Conclusion Emotional exhaustion is the most prominent feature of overall burnout in the physicians of primary health care centers. The main reasons include patient's pressure/violence, unorganized patient flow, less cooperative colleague doctors, fewer support services at the PHCCs, more paperwork, and less cooperative colleagues. Addressing these issues could lead to a decrease in physician's burnout.

Energy and speleogenesis: Key determinants of terrestrial species richness in caves.

PubMed

Jiménez-Valverde, Alberto; Sendra, Alberto; Garay, Policarp; Reboleira, Ana Sofia P S

2017-12-01

The aim of this study was to unravel the relative role played by speleogenesis (i.e., the process in which a cave is formed), landscape-scale variables, and geophysical factors in the determination of species richness in caves. Biological inventories from 21 caves located in the southeastern Iberian Peninsula along with partial least square (PLS) regression analysis were used to assess the relative importance of the different explanatory variables. The caves were grouped according to the similarity in their species composition; the effect that spatial distance could have on similarity was also studied using correlation between matrices. The energy and speleogenesis of caves accounted for 44.3% of the variation in species richness. The trophic level of each cave was the most significant factor in PLS regression analysis, and epigenic caves (i.e., those formed by the action of percolating water) had significantly more species than hypogenic ones (i.e., those formed by the action of upward flows in confined aquifers). Dissimilarity among the caves was very high (multiple-site β sim  = 0.92). Two main groups of caves were revealed through the cluster analysis, one formed by the western caves and the other by the eastern ones. The significant-but low-correlation found between faunistic dissimilarity and geographical distance ( r  =   .16) disappeared once the caves were split into the two groups. The extreme beta-diversity suggests a very low connection among the caves and/or a very low dispersal capacity of the species. In the region under study, two main factors are intimately related to the richness of terrestrial subterranean species in caves: the amount of organic material (trophic level) and the formation process (genesis). This is the first time that the history of a cave genesis has been quantitatively considered to assess its importance in explaining richness patterns in comparison with other factors more widely recognized.

[Analysis on the incidence and relevant risk factors of campus violence among college students].

PubMed

Wang, Pei-Xi; Wang, Mian-Zhen; Lan, Ya-Jia; Pang, Qing-Juan; Wang, Zhi-Ming; Shao, Li-Ye; Lu, Bo

2005-12-01

To study the incidence and risk factors of campus violence and to provide evidence for preventing campus violence among college students. 5300 college students in two universities in a province of China were selected to participate in the study and were interviewed with questionnaires. Logistic regression was used for data analysis. (1) In total, 3910 useable questionnaires were gathered to show a response rate of 73.77%. 17.98% of the college students reported they had ever experienced campus violence in the last one year. 29.60% of the male students experienced campus violence so as 7.27% of the female students. The incidence of violence among male students was significantly higher than those of female students (chi2 = 329.89, P = 0.000). (2) Among male students who were victims of campus violence, the incidence rates of threat or blackmail, emotional abuse, physical assault, verbal sexual harassment, sexual assault were 18.03%, 13.97%, 10.77%, 0.85%, 0.48% respectively. Among female students who were victims of campus violence, the incidence rates were 3.64%, 5.84%, 1.38%, 1.33%, 1.13% respectively. The main source of the violence was from their schoolmates. (3) 10.40% of the male students reported they were perpetrators of campus violence in the last year, while 1.47% of the female students reported so. Schoolmates were the main subjects of their aggressive behaviors. (4) Among the college students who were victims of campus violence, logistic regression analysis revealed that smoking, drinking alcohol, frequently getting computer online were important risk factors. The OR values were 1.48, 2.96, 1.66 respectively. Among college students who were perpetrators of campus violence, the OR values were 2.92, 1.88, 2.09 respectively. Campus violence among college students was serious, suggesting that intervention measures should be taken.

Adherence to Antitumor Necrosis Factor Use Recommendations in Spondyloarthritis: Measurement and Effect in the DESIR Cohort.

PubMed

Harvard, Stephanie; Guh, Daphne; Bansback, Nick; Richette, Pascal; Saraux, Alain; Fautrel, Bruno; Anis, Aslam H

2017-10-01

To evaluate a classification system to define adherence to axial spondyloarthritis (axSpA) anti-tumor necrosis factor (anti-TNF) use recommendations and examine the effect of adherence on outcomes in the DESIR cohort (Devenir des Spondylarthropathies Indifférenciées Récentes). Using alternate definitions of adherence, patients were classified as adherent "timely" anti-TNF users, nonadherent "late" anti-TNF users, adherent nonusers ("no anti-TNF need"), non-adherent nonusers ("unmet anti-TNF need"). Multivariate models were fitted to examine the effect of adherence on quality-adjusted life-years (QALY), total costs, and nonbiologic costs 1 year following an index date. Generalized linear regression models assuming a γ-distribution with log link were used for costs outcomes and linear regression models for QALY outcomes. Using the main definition of adherence, there were no significant differences between late anti-TNF users and timely anti-TNF users in total costs (RR 0.86, 95% CI 0.54-1.36, p = 0.516) or nonbiologic costs (RR 0.72, 95% CI 0.44-1.18, p = 0.187). However, in the sensitivity analysis, late anti-TNF users had significantly increased nonbiologic costs compared with timely users (RR 1.58, 95% CI 1.06-2.36, p = 0.026). In the main analysis, there were no significant differences in QALY between timely anti-TNF users and late anti-TNF users, or between timely users and patients with unmet anti-TNF need. In the sensitivity analysis, patients with unmet anti-TNF need had significantly lower QALY than timely anti-TNF users (-0.04, 95% CI -0.07 to -0.01, p = 0.016). The effect of adherence to anti-TNF recommendations on outcomes was sensitive to the definition of adherence used, highlighting the need to validate methods to measure adherence.

Using runoff slope-break to determine dominate factors of runoff decline in Hutuo River Basin, North China.

PubMed

Tian, Fei; Yang, Yonghui; Han, Shumin

2009-01-01

Water resources in North China have declined sharply in recent years. Low runoff (especially in the mountain areas) has been identified as the main factor. Hutuo River Basin (HRB), a typical up-stream basin in North China with two subcatchments (Ye and Hutuo River Catchments), was investigated in this study. Mann-Kendall test was used to determine the general trend of precipitation and runoff for 1960-1999. Then Sequential Mann-Kendall test was used to establish runoff slope-break from which the beginning point of sharp decline in runoff was determined. Finally, regression analysis was done to illustrate runoff decline via comparison of precipitation-runoff correlation for the period prior to and after sharp runoff decline. This was further verified by analysis of rainy season peak runoff flows. The results are as follows: (1) annual runoff decline in the basin is significant while that of precipitation is insignificant at alpha=0.05 confidence level; (2) sharp decline in runoff in Ye River Catchment (YRC) occurred in 1968 while that in Hutuo River Catchment (HRC) occurred in 1978; (3) based on the regression analysis, human activity has the highest impact on runoff decline in the basin. As runoff slope-breaks in both Catchments strongly coincided with increase in agricultural activity, agricultural water use is considered the dominate factor of runoff decline in the study area.

Multiplication factor versus regression analysis in stature estimation from hand and foot dimensions.

PubMed

Krishan, Kewal; Kanchan, Tanuj; Sharma, Abhilasha

2012-05-01

Estimation of stature is an important parameter in identification of human remains in forensic examinations. The present study is aimed to compare the reliability and accuracy of stature estimation and to demonstrate the variability in estimated stature and actual stature using multiplication factor and regression analysis methods. The study is based on a sample of 246 subjects (123 males and 123 females) from North India aged between 17 and 20 years. Four anthropometric measurements; hand length, hand breadth, foot length and foot breadth taken on the left side in each subject were included in the study. Stature was measured using standard anthropometric techniques. Multiplication factors were calculated and linear regression models were derived for estimation of stature from hand and foot dimensions. Derived multiplication factors and regression formula were applied to the hand and foot measurements in the study sample. The estimated stature from the multiplication factors and regression analysis was compared with the actual stature to find the error in estimated stature. The results indicate that the range of error in estimation of stature from regression analysis method is less than that of multiplication factor method thus, confirming that the regression analysis method is better than multiplication factor analysis in stature estimation. Copyright © 2012 Elsevier Ltd and Faculty of Forensic and Legal Medicine. All rights reserved.

Handling nonnormality and variance heterogeneity for quantitative sublethal toxicity tests.

PubMed

Ritz, Christian; Van der Vliet, Leana

2009-09-01

The advantages of using regression-based techniques to derive endpoints from environmental toxicity data are clear, and slowly, this superior analytical technique is gaining acceptance. As use of regression-based analysis becomes more widespread, some of the associated nuances and potential problems come into sharper focus. Looking at data sets that cover a broad spectrum of standard test species, we noticed that some model fits to data failed to meet two key assumptions-variance homogeneity and normality-that are necessary for correct statistical analysis via regression-based techniques. Failure to meet these assumptions often is caused by reduced variance at the concentrations showing severe adverse effects. Although commonly used with linear regression analysis, transformation of the response variable only is not appropriate when fitting data using nonlinear regression techniques. Through analysis of sample data sets, including Lemna minor, Eisenia andrei (terrestrial earthworm), and algae, we show that both the so-called Box-Cox transformation and use of the Poisson distribution can help to correct variance heterogeneity and nonnormality and so allow nonlinear regression analysis to be implemented. Both the Box-Cox transformation and the Poisson distribution can be readily implemented into existing protocols for statistical analysis. By correcting for nonnormality and variance heterogeneity, these two statistical tools can be used to encourage the transition to regression-based analysis and the depreciation of less-desirable and less-flexible analytical techniques, such as linear interpolation.

Intercellular adhesion molecule, plasma adiponectin and albuminuria in type 2 diabetic patients.

PubMed

Lenghel, Alina Ramona; Kacso, Ina Maria; Bondor, Cosmina Ioana; Rusu, Crina; Rahaian, Rodica; Gherman Caprioara, Mirela

2012-01-01

Our study addressed the influence of early inflammatory stages of diabetic kidney disease: leukocyte adhesion and monocyte activation (as assessed by intercellular leukocyte adhesion molecule-ICAM-1 and monocyte chemoatractant protein-MCP-1) on the degree of albuminuria. Plasma levels of adiponectin, a possible anti-inflammatory counteracting mechanism, were also studied in correlation to the above-mentioned cytokines. 79 consecutive type 2 diabetic outpatients and 46 controls were included. Routine laboratory analysis, urinary albumin to creatinine ratio (uACR), plasma adiponectin, plasma ICAM-1 and urinary MPC-1 were assessed. In multiple regression ICAM-1 (p=0.004) and adiponectin (p=0.04) were the main determinants of uACR. Plasma adiponectin positively correlated to ICAM-1 (p=0.03, r=0.24). In albuminuric patients (uACR ≥30 mg/g) plasma adiponectin was significantly higher compared to normoalbuminuric ones (uACR <30 mg/g). In albuminuric patients the main determinants of uACR were plasma ICAM-1 and adiponectin. In multiple regression ICAM-1 is the only one that retains statistical significance (p=0.02). Urinary MCP-1 did not correlate to uACR. In our type 2 diabetic patients, plasma levels of ICAM-1 and adiponectin are predictive for albuminuria. Urinary MCP-1 does not correlated to uACR. Plasma adiponectin positively correlates to adhesion molecule ICAM-1 in our cohort. Copyright © 2011 Elsevier Ireland Ltd. All rights reserved.

Intrafirm planning and mathematical modeling of owner's equity in industrial enterprises

NASA Astrophysics Data System (ADS)

Ponomareva, S. V.; Zheleznova, I. V.

2018-05-01

The article aims to review the different approaches to intrafirm planning of owner's equity in industrial enterprises. Since charter capital, additional capital and reserve capital do not change in the process of enterprise activity, the main interest lies on the field of share repurchases from shareholders and retained earnings within the owner's equity of the enterprise. In order to study the effect of share repurchases on the activities of the enterprise, let us use such mathematical methods as event study and econometric modeling. This article describes the step-by-step algorithm of carrying out event study and justifies the choice of Logit model in econometric analysis. The article represents basic results of conducted regression analysis on the effect of share repurchases on the key financial indicators in industrial enterprises.

[Study on influence of floods on bacillary dysentery incidence in Liaoning province, 2004 -2010].

PubMed

Xu, X; Liu, Z D; Han, D B; Xu, Y Q; Jiang, B F

2016-05-01

To understand the influence of floods on bacillary dysentery in Liaoning province. The monthly surveillance data of bacillary dysentery, floods, meteorological and demographic data in Liaoning from 2004 to 2010 were collected. Panel Poisson regression analysis was conducted to evaluate the influence of floods on the incidence of bacillary dysentery in Liaoning. The mean monthly morbidity of bacillary dysentery was 2.17 per 100 000 during the study period, the bacillary dysentery cases mainly occurred in during July-September. Spearman correlation analysis showed that no lagged effect was detected in the influence of floods on the incidence of bacillary dysentery. After adjusting the influence of meteorological factors, panel data analysis showed that the influence of floods on the incidence of bacillary dysentery existed and the incidence rate ratio was 1.439 4(95%CI: 1.408 1-1.471 4). Floods could significantly increase the risk of bacillary dysentery for population in Liaoning.

Mediation analysis: a retrospective snapshot of practice and more recent directions.

PubMed

Gelfand, Lois A; Mensinger, Janell L; Tenhave, Thomas

2009-04-01

R. Baron and D. A. Kenny's (1986) paper introducing mediation analysis has been cited over 9,000 times, but concerns have been expressed about how this method is used. The authors review past and recent methodological literature and make recommendations for how to address 3 main issues: association, temporal order, and the no omitted variables assumption. The authors briefly visit the topics of reliability and the confirmatory-exploratory distinction. In addition, to provide a sense of the extent to which the earlier literature had been absorbed into practice, the authors examined a sample of 50 articles from 2002 citing R. Baron and D. A. Kenny and containing at least 1 mediation analysis via ordinary least squares regression. A substantial proportion of these articles included problematic reporting; as of 2002, there appeared to be room for improvement in conducting such mediation analyses. Future literature reviews will demonstrate the extent to which the situation has improved.

Using Robust Standard Errors to Combine Multiple Regression Estimates with Meta-Analysis

ERIC Educational Resources Information Center

Williams, Ryan T.

2012-01-01

Combining multiple regression estimates with meta-analysis has continued to be a difficult task. A variety of methods have been proposed and used to combine multiple regression slope estimates with meta-analysis, however, most of these methods have serious methodological and practical limitations. The purpose of this study was to explore the use…

A Quality Assessment Tool for Non-Specialist Users of Regression Analysis

ERIC Educational Resources Information Center

Argyrous, George

2015-01-01

This paper illustrates the use of a quality assessment tool for regression analysis. It is designed for non-specialist "consumers" of evidence, such as policy makers. The tool provides a series of questions such consumers of evidence can ask to interrogate regression analysis, and is illustrated with reference to a recent study published…

A retrospective analysis to identify the factors affecting infection in patients undergoing chemotherapy.

PubMed

Park, Ji Hyun; Kim, Hyeon-Young; Lee, Hanna; Yun, Eun Kyoung

2015-12-01

This study compares the performance of the logistic regression and decision tree analysis methods for assessing the risk factors for infection in cancer patients undergoing chemotherapy. The subjects were 732 cancer patients who were receiving chemotherapy at K university hospital in Seoul, Korea. The data were collected between March 2011 and February 2013 and were processed for descriptive analysis, logistic regression and decision tree analysis using the IBM SPSS Statistics 19 and Modeler 15.1 programs. The most common risk factors for infection in cancer patients receiving chemotherapy were identified as alkylating agents, vinca alkaloid and underlying diabetes mellitus. The logistic regression explained 66.7% of the variation in the data in terms of sensitivity and 88.9% in terms of specificity. The decision tree analysis accounted for 55.0% of the variation in the data in terms of sensitivity and 89.0% in terms of specificity. As for the overall classification accuracy, the logistic regression explained 88.0% and the decision tree analysis explained 87.2%. The logistic regression analysis showed a higher degree of sensitivity and classification accuracy. Therefore, logistic regression analysis is concluded to be the more effective and useful method for establishing an infection prediction model for patients undergoing chemotherapy. Copyright © 2015 Elsevier Ltd. All rights reserved.

Visual grading characteristics and ordinal regression analysis during optimisation of CT head examinations.

PubMed

Zarb, Francis; McEntee, Mark F; Rainford, Louise

2015-06-01

To evaluate visual grading characteristics (VGC) and ordinal regression analysis during head CT optimisation as a potential alternative to visual grading assessment (VGA), traditionally employed to score anatomical visualisation. Patient images (n = 66) were obtained using current and optimised imaging protocols from two CT suites: a 16-slice scanner at the national Maltese centre for trauma and a 64-slice scanner in a private centre. Local resident radiologists (n = 6) performed VGA followed by VGC and ordinal regression analysis. VGC alone indicated that optimised protocols had similar image quality as current protocols. Ordinal logistic regression analysis provided an in-depth evaluation, criterion by criterion allowing the selective implementation of the protocols. The local radiology review panel supported the implementation of optimised protocols for brain CT examinations (including trauma) in one centre, achieving radiation dose reductions ranging from 24 % to 36 %. In the second centre a 29 % reduction in radiation dose was achieved for follow-up cases. The combined use of VGC and ordinal logistic regression analysis led to clinical decisions being taken on the implementation of the optimised protocols. This improved method of image quality analysis provided the evidence to support imaging protocol optimisation, resulting in significant radiation dose savings. • There is need for scientifically based image quality evaluation during CT optimisation. • VGC and ordinal regression analysis in combination led to better informed clinical decisions. • VGC and ordinal regression analysis led to dose reductions without compromising diagnostic efficacy.

Predicting the occurrence of wildfires with binary structured additive regression models.

PubMed

Ríos-Pena, Laura; Kneib, Thomas; Cadarso-Suárez, Carmen; Marey-Pérez, Manuel

2017-02-01

Wildfires are one of the main environmental problems facing societies today, and in the case of Galicia (north-west Spain), they are the main cause of forest destruction. This paper used binary structured additive regression (STAR) for modelling the occurrence of wildfires in Galicia. Binary STAR models are a recent contribution to the classical logistic regression and binary generalized additive models. Their main advantage lies in their flexibility for modelling non-linear effects, while simultaneously incorporating spatial and temporal variables directly, thereby making it possible to reveal possible relationships among the variables considered. The results showed that the occurrence of wildfires depends on many covariates which display variable behaviour across space and time, and which largely determine the likelihood of ignition of a fire. The joint possibility of working on spatial scales with a resolution of 1 × 1 km cells and mapping predictions in a colour range makes STAR models a useful tool for plotting and predicting wildfire occurrence. Lastly, it will facilitate the development of fire behaviour models, which can be invaluable when it comes to drawing up fire-prevention and firefighting plans. Copyright © 2016 Elsevier Ltd. All rights reserved.

Main-channel slopes of selected streams in Iowa for estimation of flood-frequency discharges.

DOT National Transportation Integrated Search

2003-01-01

This report describes a statewide study : conducted to develop main-channel slope (MCS) : curves for 138 selected streams in Iowa with : drainage areas greater than 100 square miles. : MCS values determined from the curves can be : used in regression...

REGRESSION ANALYSIS OF SEA-SURFACE-TEMPERATURE PATTERNS FOR THE NORTH PACIFIC OCEAN.

DTIC Science & Technology

SEA WATER, *SURFACE TEMPERATURE, *OCEANOGRAPHIC DATA, PACIFIC OCEAN, REGRESSION ANALYSIS , STATISTICAL ANALYSIS, UNDERWATER EQUIPMENT, DETECTION, UNDERWATER COMMUNICATIONS, DISTRIBUTION, THERMAL PROPERTIES, COMPUTERS.

Oral cavity cancer trends over the past 25 years in Hong Kong: a multidirectional statistical analysis.

PubMed

Ushida, Keisuke; McGrath, Colman P; Lo, Edward C M; Zwahlen, Roger A

2015-07-24

Even though oral cavity cancer (OCC; ICD 10 codes C01, C02, C03, C04, C05, and C06) ranks eleventh among the world's most common cancers, accounting for approximately 2 % of all cancers, a trend analysis of OCC in Hong Kong is lacking. Hong Kong has experienced rapid economic growth with socio-cultural and environmental change after the Second World War. This together with the collected data in the cancer registry provides interesting ground for an epidemiological study on the influence of socio-cultural and environmental factors on OCC etiology. A multidirectional statistical analysis of the OCC trends over the past 25 years was performed using the databases of the Hong Kong Cancer Registry. The age, period, and cohort (APC) modeling was applied to determine age, period, and cohort effects on OCC development. Joinpoint regression analysis was used to find secular trend changes of both age-standardized and age-specific incidence rates. The APC model detected that OCC development in men was mainly dominated by the age effect, whereas in women an increasing linear period effect together with an age effect became evident. The joinpoint regression analysis showed a general downward trend of age-standardized incidence rates of OCC for men during the entire investigated period, whereas women demonstrated a significant upward trend from 2001 onwards. The results suggest that OCC incidence in Hong Kong appears to be associated with cumulative risk behaviors of the population, despite considerable socio-cultural and environmental changes after the Second World War.

Dual oxidase 1: A predictive tool for the prognosis of hepatocellular carcinoma patients.

PubMed

Chen, Shengsen; Ling, Qingxia; Yu, Kangkang; Huang, Chong; Li, Ning; Zheng, Jianming; Bao, Suxia; Cheng, Qi; Zhu, Mengqi; Chen, Mingquan

2016-06-01

Dual oxidase 1 (DUOX1), which is the main source of reactive oxygen species (ROS) production in the airway, can be silenced in human lung cancer and hepatocellular carcinomas. However, the prognostic value of DUOX1 expression in hepatocellular carcinoma patients is still unclear. We investigated the prognostic value of DUOX1 expression in liver cancer patients. DUOX1 mRNA expression was determined in tumor tissues and non-tumor tissues by real‑time PCR. For evaluation of the prognostic value of DUOX1 expression, Kaplan-Meier method and Cox's proportional hazards model (univariate analysis and multivariate analysis) were employed. A simple risk score was devised by using significant variables obtained from the Cox's regression analysis to further predict the HCC patient prognosis. We observed a reduced DUOX1 mRNA level in the cancer tissues in comparison to the non‑cancer tissues. More importantly, Kaplan-Meier analysis showed that patients with high DUOX1 expression had longer disease-free survival and overall survival compared with those with low expression of DUOX1. Cox's regression analysis indicated that DUOX1 expression, age, and intrahepatic metastasis may be significant prognostic factors for disease-free survival and overall survival. Finally, we found that patients with total scores of >2 and >1 were more likely to relapse and succumb to the disease than patients whose total scores were ≤2 and ≤1. In conclusion, DUOX1 expression in liver tumors is a potential prognostic tool for patients. The risk scoring system is useful for predicting the survival of liver cancer patients after tumor resection.

Estimating Causal Effects of Education Interventions Using a Two-Rating Regression Discontinuity Design: Lessons from a Simulation Study

ERIC Educational Resources Information Center

Porter, Kristin E.; Reardon, Sean F.; Unlu, Fatih; Bloom, Howard S.; Robinson-Cimpian, Joseph P.

2014-01-01

A valuable extension of the single-rating regression discontinuity design (RDD) is a multiple-rating RDD (MRRDD). To date, four main methods have been used to estimate average treatment effects at the multiple treatment frontiers of an MRRDD: the "surface" method, the "frontier" method, the "binding-score" method, and…

Going Backward to Go Forward: The Critical Role of Regressive Movement in Cognitive Development

ERIC Educational Resources Information Center

Feldman, David Henry; Benjamin, Ann C.

2004-01-01

There is by this point no doubt that backward, regressive, negative or degenerative movements occur in cognitive development. The question is "why?" The challenges of the phenomenon have been and continue to be mainly two: identify the range and variety of systematic backward movements that appear in development; and, provide better and better…

The process and utility of classification and regression tree methodology in nursing research

PubMed Central

Kuhn, Lisa; Page, Karen; Ward, John; Worrall-Carter, Linda

2014-01-01

Aim This paper presents a discussion of classification and regression tree analysis and its utility in nursing research. Background Classification and regression tree analysis is an exploratory research method used to illustrate associations between variables not suited to traditional regression analysis. Complex interactions are demonstrated between covariates and variables of interest in inverted tree diagrams. Design Discussion paper. Data sources English language literature was sourced from eBooks, Medline Complete and CINAHL Plus databases, Google and Google Scholar, hard copy research texts and retrieved reference lists for terms including classification and regression tree* and derivatives and recursive partitioning from 1984–2013. Discussion Classification and regression tree analysis is an important method used to identify previously unknown patterns amongst data. Whilst there are several reasons to embrace this method as a means of exploratory quantitative research, issues regarding quality of data as well as the usefulness and validity of the findings should be considered. Implications for Nursing Research Classification and regression tree analysis is a valuable tool to guide nurses to reduce gaps in the application of evidence to practice. With the ever-expanding availability of data, it is important that nurses understand the utility and limitations of the research method. Conclusion Classification and regression tree analysis is an easily interpreted method for modelling interactions between health-related variables that would otherwise remain obscured. Knowledge is presented graphically, providing insightful understanding of complex and hierarchical relationships in an accessible and useful way to nursing and other health professions. PMID:24237048

The process and utility of classification and regression tree methodology in nursing research.

PubMed

Kuhn, Lisa; Page, Karen; Ward, John; Worrall-Carter, Linda

2014-06-01

This paper presents a discussion of classification and regression tree analysis and its utility in nursing research. Classification and regression tree analysis is an exploratory research method used to illustrate associations between variables not suited to traditional regression analysis. Complex interactions are demonstrated between covariates and variables of interest in inverted tree diagrams. Discussion paper. English language literature was sourced from eBooks, Medline Complete and CINAHL Plus databases, Google and Google Scholar, hard copy research texts and retrieved reference lists for terms including classification and regression tree* and derivatives and recursive partitioning from 1984-2013. Classification and regression tree analysis is an important method used to identify previously unknown patterns amongst data. Whilst there are several reasons to embrace this method as a means of exploratory quantitative research, issues regarding quality of data as well as the usefulness and validity of the findings should be considered. Classification and regression tree analysis is a valuable tool to guide nurses to reduce gaps in the application of evidence to practice. With the ever-expanding availability of data, it is important that nurses understand the utility and limitations of the research method. Classification and regression tree analysis is an easily interpreted method for modelling interactions between health-related variables that would otherwise remain obscured. Knowledge is presented graphically, providing insightful understanding of complex and hierarchical relationships in an accessible and useful way to nursing and other health professions. © 2013 The Authors. Journal of Advanced Nursing Published by John Wiley & Sons Ltd.

Advantages of the net benefit regression framework for economic evaluations of interventions in the workplace: a case study of the cost-effectiveness of a collaborative mental health care program for people receiving short-term disability benefits for psychiatric disorders.

PubMed

Hoch, Jeffrey S; Dewa, Carolyn S

2014-04-01

Economic evaluations commonly accompany trials of new treatments or interventions; however, regression methods and their corresponding advantages for the analysis of cost-effectiveness data are not well known. To illustrate regression-based economic evaluation, we present a case study investigating the cost-effectiveness of a collaborative mental health care program for people receiving short-term disability benefits for psychiatric disorders. We implement net benefit regression to illustrate its strengths and limitations. Net benefit regression offers a simple option for cost-effectiveness analyses of person-level data. By placing economic evaluation in a regression framework, regression-based techniques can facilitate the analysis and provide simple solutions to commonly encountered challenges. Economic evaluations of person-level data (eg, from a clinical trial) should use net benefit regression to facilitate analysis and enhance results.

CADDIS Volume 4. Data Analysis: Basic Analyses

EPA Pesticide Factsheets

Use of statistical tests to determine if an observation is outside the normal range of expected values. Details of CART, regression analysis, use of quantile regression analysis, CART in causal analysis, simplifying or pruning resulting trees.

The application of neural network model to the simulation nitrous oxide emission in the hydro-fluctuation belt of Three Gorges Reservoir

NASA Astrophysics Data System (ADS)

Song, Lanlan

2017-04-01

Nitrous oxide is much more potent greenhouse gas than carbon dioxide. However, the estimation of N2O flux is usually clouded with uncertainty, mainly due to high spatial and temporal variations. This hampers the development of general mechanistic models for N2O emission as well, as most previously developed models were empirical or exhibited low predictability with numerous assumptions. In this study, we tested General Regression Neural Networks (GRNN) as an alternative to classic empirical models for simulating N2O emission in riparian zones of Reservoirs. GRNN and nonlinear regression (NLR) were applied to estimate the N2O flux of 1-year observations in riparian zones of Three Gorge Reservoir. NLR resulted in lower prediction power and higher residuals compared to GRNN. Although nonlinear regression model estimated similar average values of N2O, it could not capture the fluctuation patterns accurately. In contrast, GRNN model achieved a fairly high predictability, with an R2 of 0.59 for model validation, 0.77 for model calibration (training), and a low root mean square error (RMSE), indicating a high capacity to simulate the dynamics of N2O flux. According to a sensitivity analysis of the GRNN, nonlinear relationships between input variables and N2O flux were well explained. Our results suggest that the GRNN developed in this study has a greater performance in simulating variations in N2O flux than nonlinear regressions.

Comparison of the Relationship between Women' Empowerment and Fertility between Single-child and Multi-child Families

PubMed Central

Saberi, Tahereh; Ehsanpour, Soheila; Mahaki, Behzad; Kohan, Shahnaz

2018-01-01

Background: The reduction in fertility and increase in the number of single-child families in Iran will result in an increased risk of population aging. One of the factors affecting fertility is women's empowerment. This study aimed to evaluate the relationship between women's empowerment and fertility in single-child and multi-child families. Materials and Methods: This case-control study was conducted among 350 women (120 who had only 1 child as case group and 230 who had 2 or more children as control group) of 15–49 years of age in Isfahan, Iran, in 2016. For data collection, a 2-part questionnaire was designed. Data were analyzed using independent t-test, Chi-square test, and logistic regression analysis. Results: The difference between average scores of women's empowerment in the case group 54.08 (9.88) and control group 51.47 (8.57) was significant (p = 0.002). Simple logistic regression analysis showed that under diploma education, compared to postgraduate education, (OR = 0.21, p = 0.001) and being a housewife, compared to being employed, (OR = 0.45, p = 0.004) decreased the odds of having only 1 child. Multiple logistic regression results showed that the relationship between women's empowerment and fertility was not significant (p = 0.265). Conclusions: Although women in single-child families were more empowered, this was not the main reason for their preference to have only 1 child. In fact, educated and employed women postpone marriage and childbearing and limit fertility to only 1 child despite their desire. PMID:29628961

Sparse modeling of spatial environmental variables associated with asthma

PubMed Central

Chang, Timothy S.; Gangnon, Ronald E.; Page, C. David; Buckingham, William R.; Tandias, Aman; Cowan, Kelly J.; Tomasallo, Carrie D.; Arndt, Brian G.; Hanrahan, Lawrence P.; Guilbert, Theresa W.

2014-01-01

Geographically distributed environmental factors influence the burden of diseases such as asthma. Our objective was to identify sparse environmental variables associated with asthma diagnosis gathered from a large electronic health record (EHR) dataset while controlling for spatial variation. An EHR dataset from the University of Wisconsin’s Family Medicine, Internal Medicine and Pediatrics Departments was obtained for 199,220 patients aged 5–50 years over a three-year period. Each patient’s home address was geocoded to one of 3,456 geographic census block groups. Over one thousand block group variables were obtained from a commercial database. We developed a Sparse Spatial Environmental Analysis (SASEA). Using this method, the environmental variables were first dimensionally reduced with sparse principal component analysis. Logistic thin plate regression spline modeling was then used to identify block group variables associated with asthma from sparse principal components. The addresses of patients from the EHR dataset were distributed throughout the majority of Wisconsin’s geography. Logistic thin plate regression spline modeling captured spatial variation of asthma. Four sparse principal components identified via model selection consisted of food at home, dog ownership, household size, and disposable income variables. In rural areas, dog ownership and renter occupied housing units from significant sparse principal components were associated with asthma. Our main contribution is the incorporation of sparsity in spatial modeling. SASEA sequentially added sparse principal components to Logistic thin plate regression spline modeling. This method allowed association of geographically distributed environmental factors with asthma using EHR and environmental datasets. SASEA can be applied to other diseases with environmental risk factors. PMID:25533437

Sparse modeling of spatial environmental variables associated with asthma.

PubMed

Chang, Timothy S; Gangnon, Ronald E; David Page, C; Buckingham, William R; Tandias, Aman; Cowan, Kelly J; Tomasallo, Carrie D; Arndt, Brian G; Hanrahan, Lawrence P; Guilbert, Theresa W

2015-02-01

Geographically distributed environmental factors influence the burden of diseases such as asthma. Our objective was to identify sparse environmental variables associated with asthma diagnosis gathered from a large electronic health record (EHR) dataset while controlling for spatial variation. An EHR dataset from the University of Wisconsin's Family Medicine, Internal Medicine and Pediatrics Departments was obtained for 199,220 patients aged 5-50years over a three-year period. Each patient's home address was geocoded to one of 3456 geographic census block groups. Over one thousand block group variables were obtained from a commercial database. We developed a Sparse Spatial Environmental Analysis (SASEA). Using this method, the environmental variables were first dimensionally reduced with sparse principal component analysis. Logistic thin plate regression spline modeling was then used to identify block group variables associated with asthma from sparse principal components. The addresses of patients from the EHR dataset were distributed throughout the majority of Wisconsin's geography. Logistic thin plate regression spline modeling captured spatial variation of asthma. Four sparse principal components identified via model selection consisted of food at home, dog ownership, household size, and disposable income variables. In rural areas, dog ownership and renter occupied housing units from significant sparse principal components were associated with asthma. Our main contribution is the incorporation of sparsity in spatial modeling. SASEA sequentially added sparse principal components to Logistic thin plate regression spline modeling. This method allowed association of geographically distributed environmental factors with asthma using EHR and environmental datasets. SASEA can be applied to other diseases with environmental risk factors. Copyright © 2014 Elsevier Inc. All rights reserved.

Sickness absence and psychosocial job quality: an analysis from a longitudinal survey of working Australians, 2005-2012.

PubMed

Milner, Allison; Butterworth, Peter; Bentley, Rebecca; Kavanagh, Anne M; LaMontagne, Anthony D

2015-05-15

Sickness absence is associated with adverse health, organizational, and societal outcomes. Using data from a longitudinal cohort study of working Australians (the Household, Income and Labour Dynamics in Australia (HILDA) Survey), we examined the relationship between changes in individuals' overall psychosocial job quality and variation in sickness absence. The outcome variables were paid sickness absence (yes/no) and number of days of paid sickness absence in the past year (2005-2012). The main exposure variable was psychosocial job quality, measured using a psychosocial job quality index (levels of job control, demands and complexity, insecurity, and perceptions of unfair pay). Analysis was conducted using longitudinal fixed-effects logistic regression models and negative binomial regression models. There was a dose-response relationship between the number of psychosocial job stressors reported by an individual and the odds of paid sickness absence (1 adversity: odds ratio (OR) = 1.26, 95% confidence interval (CI): 1.09, 1.45 (P = 0.002); 2 adversities: OR = 1.28, 95% CI: 1.09, 1.51 (P = 0.002); ≥3 adversities: OR = 1.58, 95% CI: 1.29, 1.94 (P < 0.001)). The negative binomial regression models also indicated that respondents reported a greater number of days of sickness absence in response to worsening psychosocial job quality. These results suggest that workplace interventions aiming to improve the quality of work could help reduce sickness absence. © The Author 2015. Published by Oxford University Press on behalf of the Johns Hopkins Bloomberg School of Public Health. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

Mangrove succession enriches the sediment microbial community in South China

PubMed Central

Chen, Quan; Zhao, Qian; Li, Jing; Jian, Shuguang; Ren, Hai

2016-01-01

Sediment microorganisms help create and maintain mangrove ecosystems. Although the changes in vegetation during mangrove forest succession have been well studied, the changes in the sediment microbial community during mangrove succession are poorly understood. To investigate the changes in the sediment microbial community during succession of mangroves at Zhanjiang, South China, we used phospholipid fatty acid (PLFA) analysis and the following chronosequence from primary to climax community: unvegetated shoal; Avicennia marina community; Aegiceras corniculatum community; and Bruguiera gymnorrhiza + Rhizophora stylosa community. The PLFA concentrations of all sediment microbial groups (total microorganisms, fungi, gram-positive bacteria, gram-negative bacteria, and actinomycetes) increased significantly with each stage of mangrove succession. Microbial PLFA concentrations in the sediment were significantly lower in the wet season than in the dry season. Regression and ordination analyses indicated that the changes in the microbial community with mangrove succession were mainly associated with properties of the aboveground vegetation (mainly plant height) and the sediment (mainly sediment organic matter and total nitrogen). The changes in the sediment microbial community can probably be explained by increases in nutrients and microhabitat heterogeneity during mangrove succession. PMID:27265262

Mangrove succession enriches the sediment microbial community in South China.

PubMed

Chen, Quan; Zhao, Qian; Li, Jing; Jian, Shuguang; Ren, Hai

2016-06-06

Sediment microorganisms help create and maintain mangrove ecosystems. Although the changes in vegetation during mangrove forest succession have been well studied, the changes in the sediment microbial community during mangrove succession are poorly understood. To investigate the changes in the sediment microbial community during succession of mangroves at Zhanjiang, South China, we used phospholipid fatty acid (PLFA) analysis and the following chronosequence from primary to climax community: unvegetated shoal; Avicennia marina community; Aegiceras corniculatum community; and Bruguiera gymnorrhiza + Rhizophora stylosa community. The PLFA concentrations of all sediment microbial groups (total microorganisms, fungi, gram-positive bacteria, gram-negative bacteria, and actinomycetes) increased significantly with each stage of mangrove succession. Microbial PLFA concentrations in the sediment were significantly lower in the wet season than in the dry season. Regression and ordination analyses indicated that the changes in the microbial community with mangrove succession were mainly associated with properties of the aboveground vegetation (mainly plant height) and the sediment (mainly sediment organic matter and total nitrogen). The changes in the sediment microbial community can probably be explained by increases in nutrients and microhabitat heterogeneity during mangrove succession.

Metabolic profiling of body fluids and multivariate data analysis.

PubMed

Trezzi, Jean-Pierre; Jäger, Christian; Galozzi, Sara; Barkovits, Katalin; Marcus, Katrin; Mollenhauer, Brit; Hiller, Karsten

2017-01-01

Metabolome analyses of body fluids are challenging due pre-analytical variations, such as pre-processing delay and temperature, and constant dynamical changes of biochemical processes within the samples. Therefore, proper sample handling starting from the time of collection up to the analysis is crucial to obtain high quality samples and reproducible results. A metabolomics analysis is divided into 4 main steps: 1) Sample collection, 2) Metabolite extraction, 3) Data acquisition and 4) Data analysis. Here, we describe a protocol for gas chromatography coupled to mass spectrometry (GC-MS) based metabolic analysis for biological matrices, especially body fluids. This protocol can be applied on blood serum/plasma, saliva and cerebrospinal fluid (CSF) samples of humans and other vertebrates. It covers sample collection, sample pre-processing, metabolite extraction, GC-MS measurement and guidelines for the subsequent data analysis. Advantages of this protocol include: •Robust and reproducible metabolomics results, taking into account pre-analytical variations that may occur during the sampling process•Small sample volume required•Rapid and cost-effective processing of biological samples•Logistic regression based determination of biomarker signatures for in-depth data analysis.

Population heterogeneity in the salience of multiple risk factors for adolescent delinquency.

PubMed

Lanza, Stephanie T; Cooper, Brittany R; Bray, Bethany C

2014-03-01

To present mixture regression analysis as an alternative to more standard regression analysis for predicting adolescent delinquency. We demonstrate how mixture regression analysis allows for the identification of population subgroups defined by the salience of multiple risk factors. We identified population subgroups (i.e., latent classes) of individuals based on their coefficients in a regression model predicting adolescent delinquency from eight previously established risk indices drawn from the community, school, family, peer, and individual levels. The study included N = 37,763 10th-grade adolescents who participated in the Communities That Care Youth Survey. Standard, zero-inflated, and mixture Poisson and negative binomial regression models were considered. Standard and mixture negative binomial regression models were selected as optimal. The five-class regression model was interpreted based on the class-specific regression coefficients, indicating that risk factors had varying salience across classes of adolescents. Standard regression showed that all risk factors were significantly associated with delinquency. Mixture regression provided more nuanced information, suggesting a unique set of risk factors that were salient for different subgroups of adolescents. Implications for the design of subgroup-specific interventions are discussed. Copyright © 2014 Society for Adolescent Health and Medicine. Published by Elsevier Inc. All rights reserved.

Optimization of bio-ethanol autothermal reforming and carbon monoxide removal processes

NASA Astrophysics Data System (ADS)

Markova, D.; Bazbauers, G.; Valters, K.; Alhucema Arias, R.; Weuffen, C.; Rochlitz, L.

Experimental investigation of bio-ethanol autothermal reforming (ATR) and water-gas shift (WGS) processes for hydrogen production and regression analysis of the data is performed in the study. The main goal was to obtain regression relations between the most critical dependent variables such as hydrogen, carbon monoxide and methane content in the reformate gas and independent factors such as air-to-fuel ratio (λ), steam-to-carbon ratio (S/C), inlet temperature of reactants into reforming process (T ATRin), pressure (p) and temperature (T ATR) in the ATR reactor from the experimental data. Purpose of the regression models is to provide optimum values of the process factors that give the maximum amount of hydrogen. The experimental ATR system consisted of an evaporator, an ATR reactor and a one-stage WGS reactor. Empirical relations between hydrogen, carbon monoxide, methane content and the controlling parameters downstream of the ATR reactor are shown in the work. The optimization results show that within the considered range of the process factors the maximum hydrogen concentration of 42 dry vol. % and yield of 3.8 mol mol -1 of ethanol downstream of the ATR reactor can be achieved at S/C = 2.5, λ = 0.20-0.23, p = 0.4 bar, T ATRin = 230 °C, T ATR = 640 °C.

Logistic Regression Likelihood Ratio Test Analysis for Detecting Signals of Adverse Events in Post-market Safety Surveillance.

PubMed

Nam, Kijoeng; Henderson, Nicholas C; Rohan, Patricia; Woo, Emily Jane; Russek-Cohen, Estelle

2017-01-01

The Vaccine Adverse Event Reporting System (VAERS) and other product surveillance systems compile reports of product-associated adverse events (AEs), and these reports may include a wide range of information including age, gender, and concomitant vaccines. Controlling for possible confounding variables such as these is an important task when utilizing surveillance systems to monitor post-market product safety. A common method for handling possible confounders is to compare observed product-AE combinations with adjusted baseline frequencies where the adjustments are made by stratifying on observable characteristics. Though approaches such as these have proven to be useful, in this article we propose a more flexible logistic regression approach which allows for covariates of all types rather than relying solely on stratification. Indeed, a main advantage of our approach is that the general regression framework provides flexibility to incorporate additional information such as demographic factors and concomitant vaccines. As part of our covariate-adjusted method, we outline a procedure for signal detection that accounts for multiple comparisons and controls the overall Type 1 error rate. To demonstrate the effectiveness of our approach, we illustrate our method with an example involving febrile convulsion, and we further evaluate its performance in a series of simulation studies.

The Seismic Tool-Kit (STK): an open source software for seismology and signal processing.

NASA Astrophysics Data System (ADS)

Reymond, Dominique

2016-04-01

We present an open source software project (GNU public license), named STK: Seismic ToolKit, that is dedicated mainly for seismology and signal processing. The STK project that started in 2007, is hosted by SourceForge.net, and count more than 19 500 downloads at the date of writing. The STK project is composed of two main branches: First, a graphical interface dedicated to signal processing (in the SAC format (SAC_ASCII and SAC_BIN): where the signal can be plotted, zoomed, filtered, integrated, derivated, ... etc. (a large variety of IFR and FIR filter is proposed). The estimation of spectral density of the signal are performed via the Fourier transform, with visualization of the Power Spectral Density (PSD) in linear or log scale, and also the evolutive time-frequency representation (or sonagram). The 3-components signals can be also processed for estimating their polarization properties, either for a given window, or either for evolutive windows along the time. This polarization analysis is useful for extracting the polarized noises, differentiating P waves, Rayleigh waves, Love waves, ... etc. Secondly, a panel of Utilities-Program are proposed for working in a terminal mode, with basic programs for computing azimuth and distance in spherical geometry, inter/auto-correlation, spectral density, time-frequency for an entire directory of signals, focal planes, and main components axis, radiation pattern of P waves, Polarization analysis of different waves (including noize), under/over-sampling the signals, cubic-spline smoothing, and linear/non linear regression analysis of data set. A MINimum library of Linear AlGebra (MIN-LINAG) is also provided for computing the main matrix process like: QR/QL decomposition, Cholesky solve of linear system, finding eigen value/eigen vectors, QR-solve/Eigen-solve of linear equations systems ... etc. STK is developed in C/C++, mainly under Linux OS, and it has been also partially implemented under MS-Windows. Usefull links: http://sourceforge.net/projects/seismic-toolkit/ http://sourceforge.net/p/seismic-toolkit/wiki/browse_pages/

Causes and predictors of mortality in hospitalized lupus patient in Sarawak General Hospital, Malaysia.

PubMed

Teh, C L; Ling, G R

2013-01-01

Systemic lupus erythematosus (SLE) is a serious autoimmune disease that can be life threatening and fatal if left untreated. Causes and prognostic indicators of death in SLE have been well studied in developed countries but lacking in developing countries. We aimed to investigate the causes of mortality in hospitalized patients with SLE and determine the prognostic indicators of mortality during hospitalization in our center. All SLE patients who were admitted to Sarawak General Hospital from January 1, 2006 to December 31, 2010, were followed up in a prospective study using a standard protocol. Demographic data, clinical features, disease activities and damage indices were collected. Logistic regression and Cox regression analysis were used to determine the prognostic indicators of mortality in our patients. There were a total of 251 patients in our study, with the female to male ratio 10 to 1. Our study patients were of multiethnic origins. They had a mean age of 30.5 ± 12.2 years and a mean duration of illness of 36.5 ± 51.6 months. The main involvements were hematologic (73.3%), renal (70.9%) and mucocutaneous (67.3%). There were 26 deaths (10.4%), with the main causes being: infection and flare (50%), infection alone (19%), flare alone (19%) and others (12%). Independent predictors of mortality in our cohort of SLE patients were the presence of both infection and flare of disease (hazard ratio (HR) 5.56) and high damage indices at the time of admission (HR 1.91). Infection and flare were the main causes of death in hospitalized Asian patients with SLE. The presence of infection with flare and high damage indices at the time of admission were independent prognostic indicators of mortality.

Altered Esthetics in Primary Central Incisors: The Child's Perception.

PubMed

Soares, Fernanda Cunha; Cardoso, Mariane; Bolan, Michele

2015-01-01

This study's purpose was to determine preschool-age children's social perceptions and self-perceptions regarding altered dental esthetics. A cross-sectional study was carried out involving 431 four- to five-year-olds. The participants were shown four photographs of children with incisors exhibiting discoloration, crown fracture, missing tooth, or normal teeth. The children were asked four questions for analysis of social perceptions and two additional questions for analysis of self-perceptions. Binary logistic regression was used for the statistical analysis. Children had negative social perceptions, as a significant association was found between their negative feelings and the altered dental esthetics in children pictured in the photographs. The affected anterior incisor was indicated as the main reason for this feeling (odds ratio equals 4.68, 95 percent confidence interval [CI] equals 2.39 to 9.15). When analyzing self-perceptions, a significant association was found between negative feelings and the child's own altered dental esthetics. Children with altered esthetics felt 1.92-fold sadder than those without altered esthetics (95 percent CI equals 1.22 to 3.02). Again, the affected teeth were indicated as the main reason for this feeling (prevalence ratio equals 1.22) in comparison to reasons cited. Four- to five-year-olds have negative social perceptions and self-perceptions regarding altered dental esthetics.

[Responses of Picea likiangensis radial growth to climate change in the Small Zhongdian area of Yunnan Province, Southwest China].

PubMed

Zhao, Zhi-Jiang; Tan, Liu-Yi; Kang, Dong-Wei; Liu, Qi-Jing; Li, Jun-Qing

2012-03-01

Picea likiangensis (Franch. ) Pritz. primary forest is one of the dominant forest types in the Small Zhongdian area in Shangri-La County of Yunnan Province. In this paper, the responses of P. likiangensis tree-ring width to climate change were analyzed by dendrochronological methods, and the dendrochronology was built by using relatively conservative detrending negative exponential curves or linear regression. Correlation analysis and response function analysis were applied to explore the relationships between the residual chronology series (RES) and climatic factors at different time scales, and pointer year analysis was used to explain the reasons of producing narrow and wide rings. In the study area, the radial growth of P. likiangensis and the increasing air temperature from 1990 to 2008 had definite 'abruption'. The temperature and precipitation in previous year growth season were the main factors limiting the present year radial growth, and especially, the temperature in previous July played a negative feedback role in the radial growth, while the sufficient precipitation in previous July promoted the radial growth. The differences in the temperature variation and precipitation variation in previous year were the main reasons for the formation of narrow and wide rings. P. likiangensis radial growth was not sensitive to the variation of PDSI.

A Note on the Relationship between the Number of Indicators and Their Reliability in Detecting Regression Coefficients in Latent Regression Analysis

ERIC Educational Resources Information Center

Dolan, Conor V.; Wicherts, Jelte M.; Molenaar, Peter C. M.

2004-01-01

We consider the question of how variation in the number and reliability of indicators affects the power to reject the hypothesis that the regression coefficients are zero in latent linear regression analysis. We show that power remains constant as long as the coefficient of determination remains unchanged. Any increase in the number of indicators…

Hospital compliance with a state unfunded mandate: the case of California's Earthquake Safety Law.

PubMed

McCue, Michael J; Thompson, Jon M

2012-01-01

Abstract In recent years, community hospitals have experienced heightened regulation with many unfunded mandates. The authors assessed the market, organizational, operational, and financial characteristics of general acute care hospitals in California that have a main acute care hospital building that is noncompliant with state requirements and at risk of major structural collapse from earthquakes. Using California hospital data from 2007 to 2009, and employing logistic regression analysis, the authors found that hospitals having buildings that are at the highest risk of collapse are located in larger population markets, possess smaller market share, have a higher percentage of Medicaid patients, and have less liquidity.

Access to Care and Satisfaction Among Health Center Patients With Chronic Conditions.

PubMed

Shi, Leiyu; Lee, De-Chih; Haile, Geraldine Pierre; Liang, Hailun; Chung, Michelle; Sripipatana, Alek

This study examined access to care and satisfaction among health center patients with chronic conditions. Data for this study were obtained from the 2009 Health Center Patient Survey. Dependent variables of interest included 5 measures of access to and satisfaction with care, whereas the main independent variable was number of chronic conditions. Results of bivariate analysis and multiple logistic regressions showed that patients with chronic conditions had significantly higher odds of reporting access barriers than those without chronic conditions. Our results suggested that additional efforts and resources are necessary to address the needs of health center patients with chronic conditions.

Chi-squared Automatic Interaction Detection Decision Tree Analysis of Risk Factors for Infant Anemia in Beijing, China

PubMed Central

Ye, Fang; Chen, Zhi-Hua; Chen, Jie; Liu, Fang; Zhang, Yong; Fan, Qin-Ying; Wang, Lin

2016-01-01

Background: In the past decades, studies on infant anemia have mainly focused on rural areas of China. With the increasing heterogeneity of population in recent years, available information on infant anemia is inconclusive in large cities of China, especially with comparison between native residents and floating population. This population-based cross-sectional study was implemented to determine the anemic status of infants as well as the risk factors in a representative downtown area of Beijing. Methods: As useful methods to build a predictive model, Chi-squared automatic interaction detection (CHAID) decision tree analysis and logistic regression analysis were introduced to explore risk factors of infant anemia. A total of 1091 infants aged 6–12 months together with their parents/caregivers living at Heping Avenue Subdistrict of Beijing were surveyed from January 1, 2013 to December 31, 2014. Results: The prevalence of anemia was 12.60% with a range of 3.47%–40.00% in different subgroup characteristics. The CHAID decision tree model has demonstrated multilevel interaction among risk factors through stepwise pathways to detect anemia. Besides the three predictors identified by logistic regression model including maternal anemia during pregnancy, exclusive breastfeeding in the first 6 months, and floating population, CHAID decision tree analysis also identified the fourth risk factor, the maternal educational level, with higher overall classification accuracy and larger area below the receiver operating characteristic curve. Conclusions: The infant anemic status in metropolis is complex and should be carefully considered by the basic health care practitioners. CHAID decision tree analysis has demonstrated a better performance in hierarchical analysis of population with great heterogeneity. Risk factors identified by this study might be meaningful in the early detection and prompt treatment of infant anemia in large cities. PMID:27174328

Chi-squared Automatic Interaction Detection Decision Tree Analysis of Risk Factors for Infant Anemia in Beijing, China.

PubMed

Ye, Fang; Chen, Zhi-Hua; Chen, Jie; Liu, Fang; Zhang, Yong; Fan, Qin-Ying; Wang, Lin

2016-05-20

In the past decades, studies on infant anemia have mainly focused on rural areas of China. With the increasing heterogeneity of population in recent years, available information on infant anemia is inconclusive in large cities of China, especially with comparison between native residents and floating population. This population-based cross-sectional study was implemented to determine the anemic status of infants as well as the risk factors in a representative downtown area of Beijing. As useful methods to build a predictive model, Chi-squared automatic interaction detection (CHAID) decision tree analysis and logistic regression analysis were introduced to explore risk factors of infant anemia. A total of 1091 infants aged 6-12 months together with their parents/caregivers living at Heping Avenue Subdistrict of Beijing were surveyed from January 1, 2013 to December 31, 2014. The prevalence of anemia was 12.60% with a range of 3.47%-40.00% in different subgroup characteristics. The CHAID decision tree model has demonstrated multilevel interaction among risk factors through stepwise pathways to detect anemia. Besides the three predictors identified by logistic regression model including maternal anemia during pregnancy, exclusive breastfeeding in the first 6 months, and floating population, CHAID decision tree analysis also identified the fourth risk factor, the maternal educational level, with higher overall classification accuracy and larger area below the receiver operating characteristic curve. The infant anemic status in metropolis is complex and should be carefully considered by the basic health care practitioners. CHAID decision tree analysis has demonstrated a better performance in hierarchical analysis of population with great heterogeneity. Risk factors identified by this study might be meaningful in the early detection and prompt treatment of infant anemia in large cities.

Utility of an Abbreviated Dizziness Questionnaire to Differentiate Between Causes of Vertigo and Guide Appropriate Referral: A Multicenter Prospective Blinded Study.

PubMed

Roland, Lauren T; Kallogjeri, Dorina; Sinks, Belinda C; Rauch, Steven D; Shepard, Neil T; White, Judith A; Goebel, Joel A

2015-12-01

Test performance of a focused dizziness questionnaire's ability to discriminate between peripheral and nonperipheral causes of vertigo. Prospective multicenter. Four academic centers with experienced balance specialists. New dizzy patients. A 32-question survey was given to participants. Balance specialists were blinded and a diagnosis was established for all participating patients within 6 months. Multinomial logistic regression was used to evaluate questionnaire performance in predicting final diagnosis and differentiating between peripheral and nonperipheral vertigo. Univariate and multivariable stepwise logistic regression were used to identify questions as significant predictors of the ultimate diagnosis. C-index was used to evaluate performance and discriminative power of the multivariable models. In total, 437 patients participated in the study. Eight participants without confirmed diagnoses were excluded and 429 were included in the analysis. Multinomial regression revealed that the model had good overall predictive accuracy of 78.5% for the final diagnosis and 75.5% for differentiating between peripheral and nonperipheral vertigo. Univariate logistic regression identified significant predictors of three main categories of vertigo: peripheral, central, and other. Predictors were entered into forward stepwise multivariable logistic regression. The discriminative power of the final models for peripheral, central, and other causes was considered good as measured by c-indices of 0.75, 0.7, and 0.78, respectively. This multicenter study demonstrates a focused dizziness questionnaire can accurately predict diagnosis for patients with chronic/relapsing dizziness referred to outpatient clinics. Additionally, this survey has significant capability to differentiate peripheral from nonperipheral causes of vertigo and may, in the future, serve as a screening tool for specialty referral. Clinical utility of this questionnaire to guide specialty referral is discussed.

Trochanteric entry femoral nails yield better femoral version and lower revision rates-A large cohort multivariate regression analysis.

PubMed

Yoon, Richard S; Gage, Mark J; Galos, David K; Donegan, Derek J; Liporace, Frank A

2017-06-01

Intramedullary nailing (IMN) has become the standard of care for the treatment of most femoral shaft fractures. Different IMN options include trochanteric and piriformis entry as well as retrograde nails, which may result in varying degrees of femoral rotation. The objective of this study was to analyze postoperative femoral version between three types of nails and to delineate any significant differences in femoral version (DFV) and revision rates. Over a 10-year period, 417 patients underwent IMN of a diaphyseal femur fracture (AO/OTA 32A-C). Of these patients, 316 met inclusion criteria and obtained postoperative computed tomography (CT) scanograms to calculate femoral version and were thus included in the study. In this study, our main outcome measure was the difference in femoral version (DFV) between the uninjured limb and the injured limb. The effect of the following variables on DFV and revision rates were determined via univariate, multivariate, and ordinal regression analyses: gender, age, BMI, ethnicity, mechanism of injury, operative side, open fracture, and table type/position. Statistical significance was set at p<0.05. A total of 316 patients were included. Piriformis entry nails made up the majority (n=141), followed by retrograde (n=108), then trochanteric entry nails (n=67). Univariate regression analysis revealed that a lower BMI was significantly associated with a lower DFV (p=0.006). Controlling for possible covariables, multivariate analysis yielded a significantly lower DFV for trochanteric entry nails than piriformis or retrograde nails (7.9±6.10 vs. 9.5±7.4 vs. 9.4±7.8°, p<0.05). Using revision as an endpoint, trochanteric entry nails also had a significantly lower revision rate, even when controlling for all other variables (p<0.05). Comparative, objective comparisons between DFV between different nails based on entry point revealed that trochanteric nails had a significantly lower DFV and a lower revision rate, even after regression analysis. However, this is not to state that the other nail types exhibited abnormal DFV. Translation to the clinical impact of a few degrees of DFV is also unknown. Future studies to more in-depth study the intricacies of femoral version may lead to improved technology in addition to potentially improved clinical outcomes. Copyright © 2017 Elsevier Ltd. All rights reserved.

Moderation analysis using a two-level regression model.

PubMed

Yuan, Ke-Hai; Cheng, Ying; Maxwell, Scott

2014-10-01

Moderation analysis is widely used in social and behavioral research. The most commonly used model for moderation analysis is moderated multiple regression (MMR) in which the explanatory variables of the regression model include product terms, and the model is typically estimated by least squares (LS). This paper argues for a two-level regression model in which the regression coefficients of a criterion variable on predictors are further regressed on moderator variables. An algorithm for estimating the parameters of the two-level model by normal-distribution-based maximum likelihood (NML) is developed. Formulas for the standard errors (SEs) of the parameter estimates are provided and studied. Results indicate that, when heteroscedasticity exists, NML with the two-level model gives more efficient and more accurate parameter estimates than the LS analysis of the MMR model. When error variances are homoscedastic, NML with the two-level model leads to essentially the same results as LS with the MMR model. Most importantly, the two-level regression model permits estimating the percentage of variance of each regression coefficient that is due to moderator variables. When applied to data from General Social Surveys 1991, NML with the two-level model identified a significant moderation effect of race on the regression of job prestige on years of education while LS with the MMR model did not. An R package is also developed and documented to facilitate the application of the two-level model.

Estimating total forest biomass in Maine, 1995

Treesearch

Eric H. Wharton; Douglas M. Griffith; Douglas M. Griffith

1998-01-01

Presents methods for synthesizing information from existing biomass literature for estimating biomass over extensive forest areas with specific applications to Maine. Tables of appropriate regression equations and the tree and shrub species to which these equations can be applied are presented as well as biomass estimates at the county and state level.

Game theory based models to analyze water conflicts in the Middle Route of the South-to-North Water Transfer Project in China.

PubMed

Wei, Shouke; Yang, Hong; Abbaspour, Karim; Mousavi, Jamshid; Gnauck, Albrecht

2010-04-01

This study applied game theory based models to analyze and solve water conflicts concerning water allocation and nitrogen reduction in the Middle Route of the South-to-North Water Transfer Project in China. The game simulation comprised two levels, including one main game with five players and four sub-games with each containing three sub-players. We used statistical and econometric regression methods to formulate payoff functions of the players, economic valuation methods (EVMs) to transform non-monetary value into economic one, cost-benefit Analysis (CBA) to compare the game outcomes, and scenario analysis to investigate the future uncertainties. The validity of game simulation was evaluated by comparing predictions with observations. The main results proved that cooperation would make the players collectively better off, though some player would face losses. However, players were not willing to cooperate, which would result in a prisoners' dilemma. Scenarios simulation results displayed that players in water scare area could not solve its severe water deficit problem without cooperation with other players even under an optimistic scenario, while the uncertainty of cooperation would come from the main polluters. The results suggest a need to design a mechanism to reduce the risk of losses of those players by a side payment, which provides them with economic incentives to cooperate. Copyright (c) 2010 Elsevier Ltd. All rights reserved.

Estimating Causal Effects of Education Interventions Using a Two-Rating Regression Discontinuity Design: Lessons from a Simulation Study and an Application

ERIC Educational Resources Information Center

Porter, Kristin E.; Reardon, Sean F.; Unlu, Fatih; Bloom, Howard S.; Cimpian, Joseph R.

2017-01-01

A valuable extension of the single-rating regression discontinuity design (RDD) is a multiple-rating RDD (MRRDD). To date, four main methods have been used to estimate average treatment effects at the multiple treatment frontiers of an MRRDD: the "surface" method, the "frontier" method, the "binding-score" method, and…

Regression Analyses on the Butterfly Ballot Effect: A Statistical Perspective of the US 2000 Election

ERIC Educational Resources Information Center

Wu, Dane W.

2002-01-01

The year 2000 US presidential election between Al Gore and George Bush has been the most intriguing and controversial one in American history. The state of Florida was the trigger for the controversy, mainly, due to the use of the misleading "butterfly ballot". Using prediction (or confidence) intervals for least squares regression lines…

Kepler AutoRegressive Planet Search: Motivation & Methodology

NASA Astrophysics Data System (ADS)

Caceres, Gabriel; Feigelson, Eric; Jogesh Babu, G.; Bahamonde, Natalia; Bertin, Karine; Christen, Alejandra; Curé, Michel; Meza, Cristian

2015-08-01

The Kepler AutoRegressive Planet Search (KARPS) project uses statistical methodology associated with autoregressive (AR) processes to model Kepler lightcurves in order to improve exoplanet transit detection in systems with high stellar variability. We also introduce a planet-search algorithm to detect transits in time-series residuals after application of the AR models. One of the main obstacles in detecting faint planetary transits is the intrinsic stellar variability of the host star. The variability displayed by many stars may have autoregressive properties, wherein later flux values are correlated with previous ones in some manner. Auto-Regressive Moving-Average (ARMA) models, Generalized Auto-Regressive Conditional Heteroskedasticity (GARCH), and related models are flexible, phenomenological methods used with great success to model stochastic temporal behaviors in many fields of study, particularly econometrics. Powerful statistical methods are implemented in the public statistical software environment R and its many packages. Modeling involves maximum likelihood fitting, model selection, and residual analysis. These techniques provide a useful framework to model stellar variability and are used in KARPS with the objective of reducing stellar noise to enhance opportunities to find as-yet-undiscovered planets. Our analysis procedure consisting of three steps: pre-processing of the data to remove discontinuities, gaps and outliers; ARMA-type model selection and fitting; and transit signal search of the residuals using a new Transit Comb Filter (TCF) that replaces traditional box-finding algorithms. We apply the procedures to simulated Kepler-like time series with known stellar and planetary signals to evaluate the effectiveness of the KARPS procedures. The ARMA-type modeling is effective at reducing stellar noise, but also reduces and transforms the transit signal into ingress/egress spikes. A periodogram based on the TCF is constructed to concentrate the signal of these periodic spikes. When a periodic transit is found, the model is displayed on a standard period-folded averaged light curve. We also illustrate the efficient coding in R.

Building a new predictor for multiple linear regression technique-based corrective maintenance turnaround time.

PubMed

Cruz, Antonio M; Barr, Cameron; Puñales-Pozo, Elsa

2008-01-01

This research's main goals were to build a predictor for a turnaround time (TAT) indicator for estimating its values and use a numerical clustering technique for finding possible causes of undesirable TAT values. The following stages were used: domain understanding, data characterisation and sample reduction and insight characterisation. Building the TAT indicator multiple linear regression predictor and clustering techniques were used for improving corrective maintenance task efficiency in a clinical engineering department (CED). The indicator being studied was turnaround time (TAT). Multiple linear regression was used for building a predictive TAT value model. The variables contributing to such model were clinical engineering department response time (CE(rt), 0.415 positive coefficient), stock service response time (Stock(rt), 0.734 positive coefficient), priority level (0.21 positive coefficient) and service time (0.06 positive coefficient). The regression process showed heavy reliance on Stock(rt), CE(rt) and priority, in that order. Clustering techniques revealed the main causes of high TAT values. This examination has provided a means for analysing current technical service quality and effectiveness. In doing so, it has demonstrated a process for identifying areas and methods of improvement and a model against which to analyse these methods' effectiveness.

Multiple Correlation versus Multiple Regression.

ERIC Educational Resources Information Center

Huberty, Carl J.

2003-01-01

Describes differences between multiple correlation analysis (MCA) and multiple regression analysis (MRA), showing how these approaches involve different research questions and study designs, different inferential approaches, different analysis strategies, and different reported information. (SLD)

Spatial variation of natural radiation and childhood leukaemia incidence in Great Britain.

PubMed

Richardson, S; Monfort, C; Green, M; Draper, G; Muirhead, C

This paper describes an analysis of the geographical variation of childhood leukaemia incidence in Great Britain over a 15 year period in relation to natural radiation (gamma and radon). Data at the level of the 459 district level local authorities in England, Wales and regional districts in Scotland are analysed in two complementary ways: first, by Poisson regressions with the inclusion of environmental covariates and a smooth spatial structure; secondly, by a hierarchical Bayesian model in which extra-Poisson variability is modelled explicitly in terms of spatial and non-spatial components. From this analysis, we deduce a strong indication that a main part of the variability is accounted for by a local neighbourhood 'clustering' structure. This structure is furthermore relatively stable over the 15 year period for the lymphocytic leukaemias which make up the majority of observed cases. We found no evidence of a positive association of childhood leukaemia incidence with outdoor or indoor gamma radiation levels. There is no consistent evidence of any association with radon levels. Indeed, in the Poisson regressions, a significant positive association was only observed for one 5-year period, a result which is not compatible with a stable environmental effect. Moreover, this positive association became clearly non-significant when over-dispersion relative to the Poisson distribution was taken into account.

[Burnout syndrome and suicide risk among primary care nurses].

PubMed

Tomás-Sábado, Joaquín; Maynegre-Santaulària, Montserrat; Pérez-Bartolomé, Meritxell; Alsina-Rodríguez, Marta; Quinta-Barbero, Roser; Granell-Navas, Sergi

2010-01-01

To observe the prevalence of the burnout syndrome and the relationship with suicide risk, self-esteem, anxiety and depression, in a sample of primary care nurses. Observational, cross-sectional and correlational study. The sample consisted of 146 nursing professionals, 131 women and 15 men, with an average age of 44.02 years (SD=10.89). Participants responded to a questionnaire which included the Spanish forms of the Maslach burnout inventory (MBI), the Plutchik Suicide Risk Scale (SR), the Kuwait University Anxiety Scale (KUAS), the Self-Rating Depression Scale (SDS) and the Rosenberg Self-esteem Scale (RSES). In the inferential statistical analysis, Pearson's r coefficients and multiple linear regression were calculated. Significant correlations between suicidal risk and anxiety, depression, self-esteem, emotional exhaustion and personal performance, were obtained. In the multiple regression analysis, depression was the main predictor of suicidal risk, followed by anxiety and emotional exhaustion. The scores obtained in burnout and suicidal risk were, in general, lower than those observed in other studies, emphasising the high level observed in personal performance, which reflects reasonable professional satisfaction. The results show the important role of working atmosphere and early recognition of mental disorders in burnout and suicidal risk prevention. Copyright (c) 2009 Elsevier España, S.L. All rights reserved.

Stream profile analysis using a step backwater model for selected reaches in the Chippewa Creek basin in Medina, Wayne, and Summit Counties, Ohio

USGS Publications Warehouse

Straub, David E.; Ebner, Andrew D.

2011-01-01

The USGS, in cooperation with the Chippewa Subdistrict of the Muskingum Watershed Conservancy District, performed hydrologic and hydraulic analyses for selected reaches of three streams in Medina, Wayne, Stark, and Summit Counties in northeast Ohio: Chippewa Creek, Little Chippewa Creek, and River Styx. This study was done to facilitate assessment of various alternatives for mitigating flood hazards in the Chippewa Creek basin. StreamStats regional regression equations were used to estimate instantaneous peak discharges approximately corresponding to bankfull flows. Explanatory variables used in the regression equations were drainage area, main-channel slope, and storage area. Hydraulic models were developed to determine water-surface profiles along the three stream reaches studied for the bankfull discharges established in the hydrologic analyses. The HEC-RAS step-backwater hydraulic analysis model was used to determine water-surface profiles for the three streams. Starting water-surface elevations for all streams were established using normal depth computations in the HEC-RAS models. Cross-sectional elevation data, hydraulic-structure geometries, and roughness coefficients were collected in the field and (along with peak-discharge estimates) used as input for the models. Reach-averaged reductions in water-surface elevations ranged from 0.11 to 1.29 feet over the four roughness coefficient reduction scenarios.

Impact of preoperative antithrombotic therapy on blood management after implantation of primary total knee arthroplasty

PubMed Central

Leitner, Lukas; Musser, Ewald; Kastner, Norbert; Friesenbichler, Jörg; Hirzberger, Daniela; Radl, Roman; Leithner, Andreas; Sadoghi, Patrick

2016-01-01

Red blood cell concentrates (RCC) substitution after total knee arthroplasty (TKA) is correlated with multifold of complications and an independent predictor for higher postoperative mortality. TKA is mainly performed in elderly patients with pre-existing polymorbidity, often requiring permanent preoperative antithrombotic therapy (PAT). The aim of this retrospective analysis was to investigate the impact of demand for PAT on inpatient blood management in patients undergoing TKA. In this study 200 patients were retrospectively evaluated after TKA for differences between PAT and non-PAT regarding demographic parameters, preoperative ASA score > 2, duration of operation, pre-, and intraoperative hemoglobin level, and postoperative parameters including amount of wound drainage, RCC requirement, and inpatient time. In a multivariate logistic regression analysis the independent influences of PAT, demographic parameters, ASA score > 2, and duration of the operation on RCC demand following TKA were analyzed. Patients with PAT were significantly older, more often had an ASA > 2 at surgery, needed a higher number of RCCs units and more frequently and had lower perioperative hemoglobin levels. Multivariate logistic regression revealed PAT was an independent predictor for RCC requirement. PAT patients are more likely to require RCC following TKA and should be accurately monitored with respect to postoperative blood loss. PMID:27488941

Impact of preoperative antithrombotic therapy on blood management after implantation of primary total knee arthroplasty.

PubMed

Leitner, Lukas; Musser, Ewald; Kastner, Norbert; Friesenbichler, Jörg; Hirzberger, Daniela; Radl, Roman; Leithner, Andreas; Sadoghi, Patrick

2016-08-04

Red blood cell concentrates (RCC) substitution after total knee arthroplasty (TKA) is correlated with multifold of complications and an independent predictor for higher postoperative mortality. TKA is mainly performed in elderly patients with pre-existing polymorbidity, often requiring permanent preoperative antithrombotic therapy (PAT). The aim of this retrospective analysis was to investigate the impact of demand for PAT on inpatient blood management in patients undergoing TKA. In this study 200 patients were retrospectively evaluated after TKA for differences between PAT and non-PAT regarding demographic parameters, preoperative ASA score > 2, duration of operation, pre-, and intraoperative hemoglobin level, and postoperative parameters including amount of wound drainage, RCC requirement, and inpatient time. In a multivariate logistic regression analysis the independent influences of PAT, demographic parameters, ASA score > 2, and duration of the operation on RCC demand following TKA were analyzed. Patients with PAT were significantly older, more often had an ASA > 2 at surgery, needed a higher number of RCCs units and more frequently and had lower perioperative hemoglobin levels. Multivariate logistic regression revealed PAT was an independent predictor for RCC requirement. PAT patients are more likely to require RCC following TKA and should be accurately monitored with respect to postoperative blood loss.

A Linguistic Inquiry and Word Count Analysis of the Adult Attachment Interview in Two Large Corpora.

PubMed

Waters, Theodore E A; Steele, Ryan D; Roisman, Glenn I; Haydon, Katherine C; Booth-LaForce, Cathryn

2016-01-01

An emerging literature suggests that variation in Adult Attachment Interview (AAI; George, Kaplan, & Main, 1985) states of mind about childhood experiences with primary caregivers is reflected in specific linguistic features captured by the Linguistic Inquiry Word Count automated text analysis program (LIWC; Pennebaker, Booth, & Francis, 2007). The current report addressed limitations of prior studies in this literature by using two large AAI corpora ( N s = 826 and 857) and a broader range of linguistic variables, as well as examining associations of LIWC-derived AAI dimensions with key developmental antecedents. First, regression analyses revealed that dismissing states of mind were associated with transcripts that were more truncated and deemphasized discussion of the attachment relationship whereas preoccupied states of mind were associated with longer, more conflicted, and angry narratives. Second, in aggregate, LIWC variables accounted for over a third of the variation in AAI dismissing and preoccupied states of mind, with regression weights cross-validating across samples. Third, LIWC-derived dismissing and preoccupied state of mind dimensions were associated with direct observations of maternal and paternal sensitivity as well as infant attachment security in childhood, replicating the pattern of results reported in Haydon, Roisman, Owen, Booth-LaForce, and Cox (2014) using coder-derived dismissing and preoccupation scores in the same sample.

Motor Nerve Conduction Velocity In Postmenopausal Women with Peripheral Neuropathy.

PubMed

Singh, Akanksha; Asif, Naiyer; Singh, Paras Nath; Hossain, Mohd Mobarak

2016-12-01

The post-menopausal phase is characterized by a decline in the serum oestrogen and progesterone levels. This phase is also associated with higher incidence of peripheral neuropathy. To explore the relationship between the peripheral motor nerve status and serum oestrogen and progesterone levels through assessment of Motor Nerve Conduction Velocity (MNCV) in post-menopausal women with peripheral neuropathy. This cross-sectional study was conducted at Jawaharlal Nehru Medical College during 2011-2013. The study included 30 post-menopausal women with peripheral neuropathy (age: 51.4±7.9) and 30 post-menopausal women without peripheral neuropathy (control) (age: 52.5±4.9). They were compared for MNCV in median, ulnar and common peroneal nerves and serum levels of oestrogen and progesterone estimated through enzyme immunoassays. To study the relationship between hormone levels and MNCV, a stepwise linear regression analysis was done. The post-menopausal women with peripheral neuropathy had significantly lower MNCV and serum oestrogen and progesterone levels as compared to control subjects. Stepwise linear regression analysis showed oestrogen with main effect on MNCV. The findings of the present study suggest that while the post-menopausal age group is at a greater risk of peripheral neuropathy, it is the decline in the serum estrogen levels which is critical in the development of peripheral neuropathy.

Community-Based Management of Child Malnutrition in Zambia: HIV/AIDS Infection and Other Risk Factors on Child Survival.

PubMed

Moramarco, Stefania; Amerio, Giulia; Ciarlantini, Clarice; Chipoma, Jean Kasengele; Simpungwe, Matilda Kakungu; Nielsen-Saines, Karin; Palombi, Leonardo; Buonomo, Ersilia

2016-07-01

(1) BACKGROUND: Supplementary feeding programs (SFPs) are effective in the community-based treatment of moderate acute malnutrition (MAM) and prevention of severe acute malnutrition (SAM); (2) METHODS: A retrospective study was conducted on a sample of 1266 Zambian malnourished children assisted from 2012 to 2014 in the Rainbow Project SFPs. Nutritional status was evaluated according to WHO/Unicef methodology. We performed univariate and multivariate Cox proportional risk regression to identify the main predictors of mortality. In addition, a time-to event analysis was performed to identify predictors of failure and time to cure events; (3) RESULTS: The analysis included 858 malnourished children (19 months ± 9.4; 49.9% males). Program outcomes met international standards with a better performance for MAM compared to SAM. Cox regression identified SAM (3.8; 2.1-6.8), HIV infection (3.1; 1.7-5.5), and WAZ <-3 (3.1; 1.6-5.7) as predictors of death. Time to event showed 80% of children recovered by SAM/MAM at 24 weeks. (4) CONCLUSIONS: Preventing deterioration of malnutrition, coupled to early detection of HIV/AIDS with adequate antiretroviral treatment, and extending the duration of feeding supplementation, could be crucial elements for ensuring full recovery and improve child survival in malnourished Zambian children.

Vietnamese validation of the short version of Internet Addiction Test.

PubMed

Tran, Bach Xuan; Mai, Hue Thi; Nguyen, Long Hoang; Nguyen, Cuong Tat; Latkin, Carl A; Zhang, Melvyn W B; Ho, Roger C M

2017-12-01

The main goal of the present study was to examine the psychometric properties of a Vietnamese version of the short-version of Internet Addiction Test (s-IAT) and to assess the relationship between s-IAT scores and demographics, health related qualify of life and perceived stress scores in young Vietnamese. The Vietnamese version of s-IAT was administered to a sample of 589 participants. Exploratory factor and reliability analyses were performed. Regression analysis was used to identify the associated factors. The two-factor model of Vietnamese version of s-IAT demonstrated good psychometric properties. The internal consistency of Factor 1 (loss of control/time management) was high (Cronbach's alpha = 0.82) and Factor 2 (craving/social problems) was satisfactory (Cronbach's alpha = 0.75). Findings indicated that 20.9% youths were addicted to the Internet. Regression analysis revealed significant associations between Internet addiction and having problems in self-care, lower quality of life and high perceived stress scores. The Vietnamese version of s-IAT is a valid and reliable instrument to assess IA in Vietnamese population. Due to the high prevalence of IA among Vietnamese youths, IA should be paid attention in future intervention programs. s-IAT can be a useful screening tool for IA to promptly inform and treat the IA among Vietnamese youths.

A multivariate geostatistical methodology to delineate areas of potential interest for future sedimentary gold exploration.

PubMed

Goovaerts, P; Albuquerque, Teresa; Antunes, Margarida

2016-11-01

This paper describes a multivariate geostatistical methodology to delineate areas of potential interest for future sedimentary gold exploration, with an application to an abandoned sedimentary gold mining region in Portugal. The main challenge was the existence of only a dozen gold measurements confined to the grounds of the old gold mines, which precluded the application of traditional interpolation techniques, such as cokriging. The analysis could, however, capitalize on 376 stream sediment samples that were analyzed for twenty two elements. Gold (Au) was first predicted at all 376 locations using linear regression (R 2 =0.798) and four metals (Fe, As, Sn and W), which are known to be mostly associated with the local gold's paragenesis. One hundred realizations of the spatial distribution of gold content were generated using sequential indicator simulation and a soft indicator coding of regression estimates, to supplement the hard indicator coding of gold measurements. Each simulated map then underwent a local cluster analysis to identify significant aggregates of low or high values. The one hundred classified maps were processed to derive the most likely classification of each simulated node and the associated probability of occurrence. Examining the distribution of the hot-spots and cold-spots reveals a clear enrichment in Au along the Erges River downstream from the old sedimentary mineralization.

[Mother-child relationship and associated factors: Hierarchical analysis of the population base in a Brazilian state capital - BRISA Study].

PubMed

Cavalcante, Milady Cutrim Vieira; Lamy, Fernando; França, Ana Karina Teixeira da Cunha; Lamy, Zeni Carvalho

2017-05-01

Several factors can interfere in the mother-child relationship. Studies about different maternal characteristics and this relationship are scarce; they mainly evaluate women with psychopathology and use simultaneous regression models with adjustment for multiple confounders. This study aimed to assess factors associated with losses in the mother-child relationship through a cohort of 3,215 mothers of children between 15 and 36 months of age. Losses in the mother-child relationship, assessed by the Postpartum Bonding Questionnaire, was the outcome variable and the explanatory variables were demographic, socioeconomic, reproductive health and mental health of mothers as well as the conditions of the birth of children. It used multivariate regression analysis with a hierarchical approach in which the hierarchical blocks were structured according to the influence on the mother-child relationship. The prevalence of losses in the mother-child relationship was high (12.6%) and associated risk factors to lower maternal education (RR = 1.64), having unplanned pregnancy (RR = 1.42), consumption of alcoholic beverages during pregnancy (RR = 1.42) and maternal stress symptoms (RR = 1.88) and depression (RR = 2.00). Education and elements related to mental health were risks for damage in the mother-child relationship.

Demographic and clinical features related to perceived discrimination in schizophrenia.

PubMed

Fresán, Ana; Robles-García, Rebeca; Madrigal, Eduardo; Tovilla-Zarate, Carlos-Alfonso; Martínez-López, Nicolás; Arango de Montis, Iván

2018-04-01

Perceived discrimination contributes to the development of internalized stigma among those with schizophrenia. Evidence on demographic and clinical factors related to the perception of discrimination among this population is both contradictory and scarce in low- and middle-income countries. Accordingly, the main purpose of this study is to determine the demographic and clinical factors predicting the perception of discrimination among Mexican patients with schizophrenia. Two hundred and seventeen adults with paranoid schizophrenia completed an interview on their demographic status and clinical characteristics. Symptom severity was assessed using the Positive and Negative Syndrome Scale; and perceived discrimination using 13 items from the King's Internalized Stigma Scale. Bivariate linear associations were determined to identify the variables of interest to be included in a linear regression analysis. Years of education, age of illness onset and length of hospitalization were associated with discrimination. However, only age of illness onset and length of hospitalization emerged as predictors of perceived discrimination in the final regression analysis, with longer length of hospitalization being the independent variable with the greatest contribution. Fortunately, this is a modifiable factor regarding the perception of discrimination and self-stigma. Strategies for achieving this as part of community-based mental health care are also discussed. Copyright © 2017 Elsevier B.V. All rights reserved.

MBS Measurement Tool for Swallow Impairment—MBSImp: Establishing a Standard

PubMed Central

Martin-Harris, Bonnie; Brodsky, Martin B.; Michel, Yvonne; Castell, Donald O.; Schleicher, Melanie; Sandidge, John; Maxwell, Rebekah; Blair, Julie

2014-01-01

The aim of this study was to test reliability, content, construct, and external validity of a new modified barium swallowing study (MBSS) tool (MBSImp) that is used to quantify swallowing impairment. Multiple regression, confirmatory factor, and correlation analyses were used to analyze 300 in- and outpatients with heterogeneous medical and surgical diagnoses who were sequentially referred for MBS exams at a university medical center and private tertiary care community hospital. Main outcome measures were the MBSImp and index scores of aspiration, health status, and quality of life. Inter- and intrarater concordance were 80% or greater for blinded scoring of MBSSs. Regression analysis revealed contributions of eight of nine swallow types to impressions of overall swallowing impairment (p ≤ 0.05). Factor analysis revealed 13 significant components (loadings ≥ 0.5) that formed two impairment groupings (oral and pharyngeal). Significant correlations were found between Oral and Pharyngeal Impairment scores and Penetration-Aspiration Scale scores, and indexes of intake status, nutrition, health status, and quality of life. The MBSImp demonstrated clinical practicality, favorable inter- and intrarater reliability following standardized training, content, and external validity. This study reflects potential for establishment of a new standard for quantification and comparison of oropharyngeal swallowing impairment across patient diagnoses as measured on MBSS. PMID:18855050

Case study on prediction of remaining methane potential of landfilled municipal solid waste by statistical analysis of waste composition data.

PubMed

Sel, İlker; Çakmakcı, Mehmet; Özkaya, Bestamin; Suphi Altan, H

2016-10-01

Main objective of this study was to develop a statistical model for easier and faster Biochemical Methane Potential (BMP) prediction of landfilled municipal solid waste by analyzing waste composition of excavated samples from 12 sampling points and three waste depths representing different landfilling ages of closed and active sections of a sanitary landfill site located in İstanbul, Turkey. Results of Principal Component Analysis (PCA) were used as a decision support tool to evaluation and describe the waste composition variables. Four principal component were extracted describing 76% of data set variance. The most effective components were determined as PCB, PO, T, D, W, FM, moisture and BMP for the data set. Multiple Linear Regression (MLR) models were built by original compositional data and transformed data to determine differences. It was observed that even residual plots were better for transformed data the R(2) and Adjusted R(2) values were not improved significantly. The best preliminary BMP prediction models consisted of D, W, T and FM waste fractions for both versions of regressions. Adjusted R(2) values of the raw and transformed models were determined as 0.69 and 0.57, respectively. Copyright © 2016 Elsevier Ltd. All rights reserved.

Evaluating Alcoholics Anonymous's effect on drinking in Project MATCH using cross-lagged regression panel analysis.

PubMed

Magura, Stephen; Cleland, Charles M; Tonigan, J Scott

2013-05-01

The objective of the study is to determine whether Alcoholics Anonymous (AA) participation leads to reduced drinking and problems related to drinking within Project MATCH (Matching Alcoholism Treatments to Client Heterogeneity), an existing national alcoholism treatment data set. The method used is structural equation modeling of panel data with cross-lagged partial regression coefficients. The main advantage of this technique for the analysis of AA outcomes is that potential reciprocal causation between AA participation and drinking behavior can be explicitly modeled through the specification of finite causal lags. For the outpatient subsample (n = 952), the results strongly support the hypothesis that AA attendance leads to increases in alcohol abstinence and reduces drinking/ problems, whereas a causal effect in the reverse direction is unsupported. For the aftercare subsample (n = 774), the results are not as clear but also suggest that AA attendance leads to better outcomes. Although randomized controlled trials are the surest means of establishing causal relations between interventions and outcomes, such trials are rare in AA research for practical reasons. The current study successfully exploited the multiple data waves in Project MATCH to examine evidence of causality between AA participation and drinking outcomes. The study obtained unique statistical results supporting the effectiveness of AA primarily in the context of primary outpatient treatment for alcoholism.

Functional Relationships and Regression Analysis.

ERIC Educational Resources Information Center

Preece, Peter F. W.

1978-01-01

Using a degenerate multivariate normal model for the distribution of organismic variables, the form of least-squares regression analysis required to estimate a linear functional relationship between variables is derived. It is suggested that the two conventional regression lines may be considered to describe functional, not merely statistical,…

Isolating and Examining Sources of Suppression and Multicollinearity in Multiple Linear Regression

ERIC Educational Resources Information Center

Beckstead, Jason W.

2012-01-01

The presence of suppression (and multicollinearity) in multiple regression analysis complicates interpretation of predictor-criterion relationships. The mathematical conditions that produce suppression in regression analysis have received considerable attention in the methodological literature but until now nothing in the way of an analytic…

General Nature of Multicollinearity in Multiple Regression Analysis.

ERIC Educational Resources Information Center

Liu, Richard

1981-01-01

Discusses multiple regression, a very popular statistical technique in the field of education. One of the basic assumptions in regression analysis requires that independent variables in the equation should not be highly correlated. The problem of multicollinearity and some of the solutions to it are discussed. (Author)

Partial Least Squares Based Gene Expression Analysis in EBV- Positive and EBV-Negative Posttransplant Lymphoproliferative Disorders.

PubMed

Wu, Sa; Zhang, Xin; Li, Zhi-Ming; Shi, Yan-Xia; Huang, Jia-Jia; Xia, Yi; Yang, Hang; Jiang, Wen-Qi

2013-01-01

Post-transplant lymphoproliferative disorder (PTLD) is a common complication of therapeutic immunosuppression after organ transplantation. Gene expression profile facilitates the identification of biological difference between Epstein-Barr virus (EBV) positive and negative PTLDs. Previous studies mainly implemented variance/regression analysis without considering unaccounted array specific factors. The aim of this study is to investigate the gene expression difference between EBV positive and negative PTLDs through partial least squares (PLS) based analysis. With a microarray data set from the Gene Expression Omnibus database, we performed PLS based analysis. We acquired 1188 differentially expressed genes. Pathway and Gene Ontology enrichment analysis identified significantly over-representation of dysregulated genes in immune response and cancer related biological processes. Network analysis identified three hub genes with degrees higher than 15, including CREBBP, ATXN1, and PML. Proteins encoded by CREBBP and PML have been reported to be interact with EBV before. Our findings shed light on expression distinction of EBV positive and negative PTLDs with the hope to offer theoretical support for future therapeutic study.

Determinants of orphan drugs prices in France: a regression analysis.

PubMed

Korchagina, Daria; Millier, Aurelie; Vataire, Anne-Lise; Aballea, Samuel; Falissard, Bruno; Toumi, Mondher

2017-04-21

The introduction of the orphan drug legislation led to the increase in the number of available orphan drugs, but the access to them is often limited due to the high price. Social preferences regarding funding orphan drugs as well as the criteria taken into consideration while setting the price remain unclear. The study aimed at identifying the determinant of orphan drug prices in France using a regression analysis. All drugs with a valid orphan designation at the moment of launch for which the price was available in France were included in the analysis. The selection of covariates was based on a literature review and included drug characteristics (Anatomical Therapeutic Chemical (ATC) class, treatment line, age of target population), diseases characteristics (severity, prevalence, availability of alternative therapeutic options), health technology assessment (HTA) details (actual benefit (AB) and improvement in actual benefit (IAB) scores, delay between the HTA and commercialisation), and study characteristics (type of study, comparator, type of endpoint). The main data sources were European public assessment reports, HTA reports, summaries of opinion on orphan designation of the European Medicines Agency, and the French insurance database of drugs and tariffs. A generalized regression model was developed to test the association between the annual treatment cost and selected covariates. A total of 68 drugs were included. The mean annual treatment cost was €96,518. In the univariate analysis, the ATC class (p = 0.01), availability of alternative treatment options (p = 0.02) and the prevalence (p = 0.02) showed a significant correlation with the annual cost. The multivariate analysis demonstrated significant association between the annual cost and availability of alternative treatment options, ATC class, IAB score, type of comparator in the pivotal clinical trial, as well as commercialisation date and delay between the HTA and commercialisation. The orphan drug pricing is a multivariate phenomenon. The complex association between drug prices and the studied attributes and shows that payers integrate multiple variables in decision making when setting orphan drug prices. The interpretation of the study results is limited by the small sample size and the complex data structure.

Applying Regression Analysis to Problems in Institutional Research.

ERIC Educational Resources Information Center

Bohannon, Tom R.

1988-01-01

Regression analysis is one of the most frequently used statistical techniques in institutional research. Principles of least squares, model building, residual analysis, influence statistics, and multi-collinearity are described and illustrated. (Author/MSE)

Multicollinearity in Regression Analyses Conducted in Epidemiologic Studies

PubMed Central

Vatcheva, Kristina P.; Lee, MinJae; McCormick, Joseph B.; Rahbar, Mohammad H.

2016-01-01

The adverse impact of ignoring multicollinearity on findings and data interpretation in regression analysis is very well documented in the statistical literature. The failure to identify and report multicollinearity could result in misleading interpretations of the results. A review of epidemiological literature in PubMed from January 2004 to December 2013, illustrated the need for a greater attention to identifying and minimizing the effect of multicollinearity in analysis of data from epidemiologic studies. We used simulated datasets and real life data from the Cameron County Hispanic Cohort to demonstrate the adverse effects of multicollinearity in the regression analysis and encourage researchers to consider the diagnostic for multicollinearity as one of the steps in regression analysis. PMID:27274911

Multicollinearity in Regression Analyses Conducted in Epidemiologic Studies.

PubMed

Vatcheva, Kristina P; Lee, MinJae; McCormick, Joseph B; Rahbar, Mohammad H

2016-04-01

The adverse impact of ignoring multicollinearity on findings and data interpretation in regression analysis is very well documented in the statistical literature. The failure to identify and report multicollinearity could result in misleading interpretations of the results. A review of epidemiological literature in PubMed from January 2004 to December 2013, illustrated the need for a greater attention to identifying and minimizing the effect of multicollinearity in analysis of data from epidemiologic studies. We used simulated datasets and real life data from the Cameron County Hispanic Cohort to demonstrate the adverse effects of multicollinearity in the regression analysis and encourage researchers to consider the diagnostic for multicollinearity as one of the steps in regression analysis.

Stepwise versus Hierarchical Regression: Pros and Cons

ERIC Educational Resources Information Center

Lewis, Mitzi

2007-01-01

Multiple regression is commonly used in social and behavioral data analysis. In multiple regression contexts, researchers are very often interested in determining the "best" predictors in the analysis. This focus may stem from a need to identify those predictors that are supportive of theory. Alternatively, the researcher may simply be interested…

Interpreting Bivariate Regression Coefficients: Going beyond the Average

ERIC Educational Resources Information Center

Halcoussis, Dennis; Phillips, G. Michael

2010-01-01

Statistics, econometrics, investment analysis, and data analysis classes often review the calculation of several types of averages, including the arithmetic mean, geometric mean, harmonic mean, and various weighted averages. This note shows how each of these can be computed using a basic regression framework. By recognizing when a regression model…

Regression Commonality Analysis: A Technique for Quantitative Theory Building

ERIC Educational Resources Information Center

Nimon, Kim; Reio, Thomas G., Jr.

2011-01-01

When it comes to multiple linear regression analysis (MLR), it is common for social and behavioral science researchers to rely predominately on beta weights when evaluating how predictors contribute to a regression model. Presenting an underutilized statistical technique, this article describes how organizational researchers can use commonality…

Precision Efficacy Analysis for Regression.

ERIC Educational Resources Information Center

Brooks, Gordon P.

When multiple linear regression is used to develop a prediction model, sample size must be large enough to ensure stable coefficients. If the derivation sample size is inadequate, the model may not predict well for future subjects. The precision efficacy analysis for regression (PEAR) method uses a cross- validity approach to select sample sizes…

Estimation of 1RM for knee extension based on the maximal isometric muscle strength and body composition.

PubMed

Kanada, Yoshikiyo; Sakurai, Hiroaki; Sugiura, Yoshito; Arai, Tomoaki; Koyama, Soichiro; Tanabe, Shigeo

2017-11-01

[Purpose] To create a regression formula in order to estimate 1RM for knee extensors, based on the maximal isometric muscle strength measured using a hand-held dynamometer and data regarding the body composition. [Subjects and Methods] Measurement was performed in 21 healthy males in their twenties to thirties. Single regression analysis was performed, with measurement values representing 1RM and the maximal isometric muscle strength as dependent and independent variables, respectively. Furthermore, multiple regression analysis was performed, with data regarding the body composition incorporated as another independent variable, in addition to the maximal isometric muscle strength. [Results] Through single regression analysis with the maximal isometric muscle strength as an independent variable, the following regression formula was created: 1RM (kg)=0.714 + 0.783 × maximal isometric muscle strength (kgf). On multiple regression analysis, only the total muscle mass was extracted. [Conclusion] A highly accurate regression formula to estimate 1RM was created based on both the maximal isometric muscle strength and body composition. Using a hand-held dynamometer and body composition analyzer, it was possible to measure these items in a short time, and obtain clinically useful results.

Regression Model Optimization for the Analysis of Experimental Data

NASA Technical Reports Server (NTRS)

Ulbrich, N.

2009-01-01

A candidate math model search algorithm was developed at Ames Research Center that determines a recommended math model for the multivariate regression analysis of experimental data. The search algorithm is applicable to classical regression analysis problems as well as wind tunnel strain gage balance calibration analysis applications. The algorithm compares the predictive capability of different regression models using the standard deviation of the PRESS residuals of the responses as a search metric. This search metric is minimized during the search. Singular value decomposition is used during the search to reject math models that lead to a singular solution of the regression analysis problem. Two threshold dependent constraints are also applied. The first constraint rejects math models with insignificant terms. The second constraint rejects math models with near-linear dependencies between terms. The math term hierarchy rule may also be applied as an optional constraint during or after the candidate math model search. The final term selection of the recommended math model depends on the regressor and response values of the data set, the user s function class combination choice, the user s constraint selections, and the result of the search metric minimization. A frequently used regression analysis example from the literature is used to illustrate the application of the search algorithm to experimental data.

Trend Estimation and Regression Analysis in Climatological Time Series: An Application of Structural Time Series Models and the Kalman Filter.

NASA Astrophysics Data System (ADS)

Visser, H.; Molenaar, J.

1995-05-01

The detection of trends in climatological data has become central to the discussion on climate change due to the enhanced greenhouse effect. To prove detection, a method is needed (i) to make inferences on significant rises or declines in trends, (ii) to take into account natural variability in climate series, and (iii) to compare output from GCMs with the trends in observed climate data. To meet these requirements, flexible mathematical tools are needed. A structural time series model is proposed with which a stochastic trend, a deterministic trend, and regression coefficients can be estimated simultaneously. The stochastic trend component is described using the class of ARIMA models. The regression component is assumed to be linear. However, the regression coefficients corresponding with the explanatory variables may be time dependent to validate this assumption. The mathematical technique used to estimate this trend-regression model is the Kaiman filter. The main features of the filter are discussed.Examples of trend estimation are given using annual mean temperatures at a single station in the Netherlands (1706-1990) and annual mean temperatures at Northern Hemisphere land stations (1851-1990). The inclusion of explanatory variables is shown by regressing the latter temperature series on four variables: Southern Oscillation index (SOI), volcanic dust index (VDI), sunspot numbers (SSN), and a simulated temperature signal, induced by increasing greenhouse gases (GHG). In all analyses, the influence of SSN on global temperatures is found to be negligible. The correlations between temperatures and SOI and VDI appear to be negative. For SOI, this correlation is significant, but for VDI it is not, probably because of a lack of volcanic eruptions during the sample period. The relation between temperatures and GHG is positive, which is in agreement with the hypothesis of a warming climate because of increasing levels of greenhouse gases. The prediction performance of the model is rather poor, and possible explanations are discussed.

Analysis of factors controlling soil phosphorus loss with surface runoff in Huihe National Nature Reserve by principal component and path analysis methods.

PubMed

He, Jing; Su, Derong; Lv, Shihai; Diao, Zhaoyan; Bu, He; Wo, Qiang

2018-01-01

Phosphorus (P) loss with surface runoff accounts for the P input to and acceleration of eutrophication of the freshwater. Many studies have focused on factors affecting P loss with surface runoff from soils, but rarely on the relationship among these factors. In the present study, rainfall simulation on P loss with surface runoff was conducted in Huihe National Nature Reserve, in Hulunbeier grassland, China, and the relationships between P loss with surface runoff, soil properties, and rainfall conditions were examined. Principal component analysis and path analysis were used to analyze the direct and indirect effects on P loss with surface runoff. The results showed that P loss with surface runoff was closely correlated with soil electrical conductivity, soil pH, soil Olsen P, soil total nitrogen (TN), soil total phosphorus (TP), and soil organic carbon (SOC). The main driving factors which influenced P loss with surface runoff were soil TN, soil pH, soil Olsen P, and soil water content. Path analysis and determination coefficient analysis indicated that the standard multiple regression equation for P loss with surface runoff and each main factor was Y = 7.429 - 0.439 soil TN - 6.834 soil pH + 1.721 soil Olsen-P + 0.183 soil water content (r = 0.487, p < 0.01, n = 180). Soil TN, soil pH, soil Olsen P, and soil water content and the interactions between them were the main factors affecting P loss with surface runoff. The effect of physical and chemical properties of undisturbed soils on P loss with surface runoff was discussed, and the soil water content and soil Olsen P were strongly positive influences on the P loss with surface runoff.

[Application of negative binomial regression and modified Poisson regression in the research of risk factors for injury frequency].

PubMed

Cao, Qingqing; Wu, Zhenqiang; Sun, Ying; Wang, Tiezhu; Han, Tengwei; Gu, Chaomei; Sun, Yehuan

2011-11-01

To Eexplore the application of negative binomial regression and modified Poisson regression analysis in analyzing the influential factors for injury frequency and the risk factors leading to the increase of injury frequency. 2917 primary and secondary school students were selected from Hefei by cluster random sampling method and surveyed by questionnaire. The data on the count event-based injuries used to fitted modified Poisson regression and negative binomial regression model. The risk factors incurring the increase of unintentional injury frequency for juvenile students was explored, so as to probe the efficiency of these two models in studying the influential factors for injury frequency. The Poisson model existed over-dispersion (P < 0.0001) based on testing by the Lagrangemultiplier. Therefore, the over-dispersion dispersed data using a modified Poisson regression and negative binomial regression model, was fitted better. respectively. Both showed that male gender, younger age, father working outside of the hometown, the level of the guardian being above junior high school and smoking might be the results of higher injury frequencies. On a tendency of clustered frequency data on injury event, both the modified Poisson regression analysis and negative binomial regression analysis can be used. However, based on our data, the modified Poisson regression fitted better and this model could give a more accurate interpretation of relevant factors affecting the frequency of injury.

Logistic regression analysis of conventional ultrasonography, strain elastosonography, and contrast-enhanced ultrasound characteristics for the differentiation of benign and malignant thyroid nodules

PubMed Central

Deng, Yingyuan; Wang, Tianfu; Chen, Siping; Liu, Weixiang

2017-01-01

The aim of the study is to screen the significant sonographic features by logistic regression analysis and fit a model to diagnose thyroid nodules. A total of 525 pathological thyroid nodules were retrospectively analyzed. All the nodules underwent conventional ultrasonography (US), strain elastosonography (SE), and contrast -enhanced ultrasound (CEUS). Those nodules’ 12 suspicious sonographic features were used to assess thyroid nodules. The significant features of diagnosing thyroid nodules were picked out by logistic regression analysis. All variables that were statistically related to diagnosis of thyroid nodules, at a level of p < 0.05 were embodied in a logistic regression analysis model. The significant features in the logistic regression model of diagnosing thyroid nodules were calcification, suspected cervical lymph node metastasis, hypoenhancement pattern, margin, shape, vascularity, posterior acoustic, echogenicity, and elastography score. According to the results of logistic regression analysis, the formula that could predict whether or not thyroid nodules are malignant was established. The area under the receiver operating curve (ROC) was 0.930 and the sensitivity, specificity, accuracy, positive predictive value, and negative predictive value were 83.77%, 89.56%, 87.05%, 86.04%, and 87.79% respectively. PMID:29228030

Logistic regression analysis of conventional ultrasonography, strain elastosonography, and contrast-enhanced ultrasound characteristics for the differentiation of benign and malignant thyroid nodules.

PubMed

Pang, Tiantian; Huang, Leidan; Deng, Yingyuan; Wang, Tianfu; Chen, Siping; Gong, Xuehao; Liu, Weixiang

2017-01-01

The aim of the study is to screen the significant sonographic features by logistic regression analysis and fit a model to diagnose thyroid nodules. A total of 525 pathological thyroid nodules were retrospectively analyzed. All the nodules underwent conventional ultrasonography (US), strain elastosonography (SE), and contrast -enhanced ultrasound (CEUS). Those nodules' 12 suspicious sonographic features were used to assess thyroid nodules. The significant features of diagnosing thyroid nodules were picked out by logistic regression analysis. All variables that were statistically related to diagnosis of thyroid nodules, at a level of p < 0.05 were embodied in a logistic regression analysis model. The significant features in the logistic regression model of diagnosing thyroid nodules were calcification, suspected cervical lymph node metastasis, hypoenhancement pattern, margin, shape, vascularity, posterior acoustic, echogenicity, and elastography score. According to the results of logistic regression analysis, the formula that could predict whether or not thyroid nodules are malignant was established. The area under the receiver operating curve (ROC) was 0.930 and the sensitivity, specificity, accuracy, positive predictive value, and negative predictive value were 83.77%, 89.56%, 87.05%, 86.04%, and 87.79% respectively.

Granulomatous inflammation of pulmonary squamous cell carcinoma: a rare phenomenon.

PubMed

Tajima, Shogo; Koda, Kenji

2015-01-01

Some neoplasms are associated with granulomatous inflammation. Granuloma formation in tumor tissue is caused by the cytokines derived from either the main tumor or other cells surrounding the tumor. In other instances, granulomatous inflammation is observed in the lymph nodes draining a tumor. This has been recognized as a sarcoid-like reaction. Herein, we report of a 75-year-old man with pulmonary squamous cell carcinoma (SCC), where granulomatous inflammation was observed extensively at the primary site. The carcinoma seemed to partly regress. In the regressing area, tumor cell debris was surrounded by granuloma. In contrast, no granuloma was identified in the dissected regional lymph nodes. To the best of our knowledge, such a case of SCC had not been described thus far. More case studies are required to determine whether tumor-related granuloma is the main cause of regression or whether it is just a secondary phenomenon caused by the attack and destruction of the tumor by lymphocytes.

[Analysis on nitrogen and phosphorus loading of non-point sources in Shiqiao river watershed based on L-THIA model].

PubMed

Li, Kai; Zeng, Fan-Tang; Fang, Huai-Yang; Lin, Shu

2013-11-01

Based on the Long-term Hydrological Impact Assessment (L-THIA) model, the effect of land use and rainfall change on nitrogen and phosphorus loading of non-point sources in Shiqiao river watershed was analyzed. The parameters in L-THIA model were revised according to the data recorded in the scene of runoff plots, which were set up in the watershed. The results showed that the distribution of areas with high pollution load was mainly concentrated in agricultural land and urban land. Agricultural land was the biggest contributor to nitrogen and phosphorus load. From 1995 to 2010, the load of major pollutants, namely TN and TP, showed an obviously increasing trend with increase rates of 17.91% and 25.30%, respectively. With the urbanization in the watershed, urban land increased rapidly and its area proportion reached 43.94%. The contribution of urban land to nitrogen and phosphorus load was over 40% in 2010. This was the main reason why pollution load still increased obviously while the agricultural land decreased greatly in the past 15 years. The rainfall occurred in the watershed was mainly concentrated in the flood season, so the nitrogen and phosphorus load of the flood season was far higher than that of the non-flood season and the proportion accounting for the whole year was over 85%. Pearson regression analysis between pollution load and the frequency of different patterns of rainfall demonstrated that rainfall exceeding 20 mm in a day was the main rainfall type causing non-point source pollution.

Design, analysis and presentation of factorial randomised controlled trials

PubMed Central

Montgomery, Alan A; Peters, Tim J; Little, Paul

2003-01-01

Background The evaluation of more than one intervention in the same randomised controlled trial can be achieved using a parallel group design. However this requires increased sample size and can be inefficient, especially if there is also interest in considering combinations of the interventions. An alternative may be a factorial trial, where for two interventions participants are allocated to receive neither intervention, one or the other, or both. Factorial trials require special considerations, however, particularly at the design and analysis stages. Discussion Using a 2 × 2 factorial trial as an example, we present a number of issues that should be considered when planning a factorial trial. The main design issue is that of sample size. Factorial trials are most often powered to detect the main effects of interventions, since adequate power to detect plausible interactions requires greatly increased sample sizes. The main analytical issues relate to the investigation of main effects and the interaction between the interventions in appropriate regression models. Presentation of results should reflect the analytical strategy with an emphasis on the principal research questions. We also give an example of how baseline and follow-up data should be presented. Lastly, we discuss the implications of the design, analytical and presentational issues covered. Summary Difficulties in interpreting the results of factorial trials if an influential interaction is observed is the cost of the potential for efficient, simultaneous consideration of two or more interventions. Factorial trials can in principle be designed to have adequate power to detect realistic interactions, and in any case they are the only design that allows such effects to be investigated. PMID:14633287

Apoptosis-mediated seasonal testicular regression in the Japanese Jungle Crow (Corvus macrorhynchos).

PubMed

Islam, M Nazrul; Tsukahara, N; Sugita, S

2012-06-01

The present study investigated effects of apoptosis observed during seasonal testicular regression in Japanese Jungle Crows. The study was conducted during January to June 2008, 2009. Testes from adults captured during non-breeding (January), prebreeding (February to mid-March), main-breeding (late March to early May), transition (mid-May to late May), and post-breeding (June) seasons were analyzed. Apoptosis was assessed by in situ terminal deoxynucleotidyl transferase-mediated dUTP nick end-labeling (TUNEL) assay. Paired-testis volume increased 95-fold from the non-breeding to the main-breeding season (P < 0.05), and subsequently decreased 26-fold from the main breeding to the post-breeding season (P < 0.05). Testicular activity was evaluated from the total germ cell count and sperm index, which increased 42- and 5-fold, respectively, in the main-breeding season, and subsequently decreased 33- and 5-fold in the post-breeding season. In testes, TUNEL-positive germ cells were at low levels in the non-breeding season, absent in the prebreeding and the main-breeding seasons, and highest in mid-May (P < 0.05). In contrast, TUNEL-positive Sertoli cells occurred only in late-April. In addition, TUNEL-positive fibroblast-like cells were observed in the outer zone of the tunica albuginea in the post-breeding season. Collectively, these data suggested that the seasonal rise in the testicular competence occurred slowly in Japanese Jungle Crows; however, testis function was terminated rapidly after the breeding season. Furthermore, we concluded, similar to other avian species, Sertoli cell apoptosis followed by massive germ cell death was responsible for rapid testicular regression in Jungle Crows. Copyright © 2012 Elsevier Inc. All rights reserved.

Influence of Different Factors on Relative Air Humidity in Zaragoza, Spain

NASA Astrophysics Data System (ADS)

Cuadrat, José M.

2015-03-01

In this study, the spatial patterns of relative air humidity and its relation to urban, geographical and meteorological factors in the city of Zaragoza (Spain) is discussed. We created a relative humidity database by means of 32 urban transects. Data were taken on different days and with different weather types. This data set was used to map the mean spatial distribution of urban dry island (UDI). Using stepwise multiple regression analysis and Landsat ETM+ images the relationships between mean UDI and the main geographic-urban factors: topography, land cover and surface reflectivity, have been analyzed. Different spatial patterns of UDI were determined using Principal Component Analysis (Varimax rotation). The three components extracted accounted for 91% of the total variance. PC1 accounted for the most general patterns (similar to mean UDI); PC2 showed a shift of dry areas to the SE and PC3 a shift to NW. Using data on wind direction in Zaragoza, we have found that the displacement of dry areas to the SE (PC 2) was greater during NW winds while the shift to the NW (PC 3) was produced mainly by SE winds.

Chemical characteristics combined with bioactivity for comprehensive evaluation of Panax ginseng C.A. Meyer in different ages and seasons based on HPLC-DAD and chemometric methods.

PubMed

Shan, Si-Ming; Luo, Jian-Guang; Huang, Fang; Kong, Ling-Yi

2014-02-01

Panax ginseng C.A. Meyer has been known as a valuable traditional Chinese medicines for thousands years of history. Ginsenosides, the main active constituents, exhibit prominent immunoregulation effect. The present study first describes a holistic method based on chemical characteristic and lymphocyte proliferative capacity to evaluate systematically the quality of P. ginseng in thirty samples from different seasons during 2-6 years. The HPLC fingerprints were evaluated using principle component analysis (PCA) and hierarchical clustering analysis (HCA). The spectrum-efficacy model between HPLC fingerprints and T-lymphocyte proliferative activities was investigated by principal component regression (PCR) and partial least squares (PLS). The results indicated that the growth of the ginsenosides could be grouped into three periods and from August of the fifth year, P. ginseng appeared significant lymphocyte proliferative capacity. Close correlation existed between the spectrum-efficacy relationship and ginsenosides Rb1, Ro, Rc, Rb2 and Re were the main contributive components to the lymphocyte proliferative capacity. This comprehensive strategy, providing reliable and adequate scientific evidence, could be applied to other TCMs to ameliorate their quality control. Copyright © 2013 Elsevier B.V. All rights reserved.

ADA Title I allegations and the Mining, Quarrying, and Oil/Gas Extraction industry.

PubMed

Van Wieren, Todd A; Rhoades, Laura; McMahon, Brian T

2017-01-01

The majority of research about employment discrimination in the U.S. Mining, Quarrying, and Oil/Gas (MQOGE) industries has concentrated on gender and race, while little attention has focused on disability. To explore allegations of Americans with Disabilities Act (ADA) Title I discrimination made to the Equal Employment Opportunity Commission (EEOC) by individuals with disabilities against MQOGE employers. Key data available to this study included demographic characteristics of charging parties, size of employers, types of allegations, and case outcomes. Using descriptive analysis, allegation profiles were developed for MQOGE's three main sectors (i.e., Oil/Gas Extraction, Mining except Oil/Gas, and Support Activities). These three profiles where then comparatively analyzed. Lastly, regression analysis explored whether some of the available data could partially predict MQOGE case outcomes. The predominant characteristics of MQOGE allegations were found to be quite similar to the allegation profile of U.S. private-sector industry as a whole, and fairly representative of MQOGE's workforce demographics. Significant differences between MQOGE's three main sector profiles were noted on some important characteristics. Lastly, it was found that MQOGE case outcomes could be partially predicted via some of the available variables. The study's limitations were presented and recommendations were offered for further research.

The influence of training characteristics on the effect of aerobic exercise training in patients with chronic heart failure: A meta-regression analysis.

PubMed

Vromen, T; Kraal, J J; Kuiper, J; Spee, R F; Peek, N; Kemps, H M

2016-04-01

Although aerobic exercise training has shown to be an effective treatment for chronic heart failure patients, there has been a debate about the design of training programs and which training characteristics are the strongest determinants of improvement in exercise capacity. Therefore, we performed a meta-regression analysis to determine a ranking of the individual effect of the training characteristics on the improvement in exercise capacity of an aerobic exercise training program in chronic heart failure patients. We focused on four training characteristics; session frequency, session duration, training intensity and program length, and their product; total energy expenditure. A systematic literature search was performed for randomized controlled trials comparing continuous aerobic exercise training with usual care. Seventeen unique articles were included in our analysis. Total energy expenditure appeared the only training characteristic with a significant effect on improvement in exercise capacity. However, the results were strongly dominated by one trial (HF-action trial), accounting for 90% of the total patient population and showing controversial results compared to other studies. A repeated analysis excluding the HF-action trial confirmed that the increase in exercise capacity is primarily determined by total energy expenditure, followed by session frequency, session duration and session intensity. These results suggest that the design of a training program requires high total energy expenditure as a main goal. Increases in training frequency and session duration appear to yield the largest improvement in exercise capacity. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

A study on satisfaction with publicly financed health services in China.

PubMed

Zhai, Shaoguo; Wang, Pei; Wang, Anli; Dong, Quanfang; Cai, Jiaoli; Coyte, Peter C

2017-08-28

With implementation of Chinese universal healthcare, the performance of urban and rural residents' healthcare and the degree of satisfaction with publicly financed health services have become a hot issue in assessing health reforms in China. An evaluation model of health services in community and evaluation indexes of health-system performance have been put forward in related researches. This study examines variation in satisfaction with publicly financed health services among urban and rural residents in five Chinese cities and assesses their determinants. The data are derived from a survey of 1198 urban and rural residents from five nationally representative regions concerning their perceptions of satisfaction with China's publicly financed health services. The respondents assessed their degree of satisfaction with publicly financed health services on a 5-point Likert scale. It is a kind of questionaire scale that features the answers for 1-5 points labeled very unsatisfied, unsatisfied, neither unsatisfied nor satisfied, satisfied and very satisfied linking to each factor or variable, where a score of 1 reflects the lowest degree of satisfaction and a score of 5 represents the highest degree. The logistic regression methods are used to identify the variables into its determining components. The overall satisfaction degree representing satisfaction of all factors (variables) is 3.02, which is at the middle level of a 1-5 Likert scale, inferring respondents' neutral attitude to publicly financed health services. According to the correlation test, the factors with characteristic root greater than 0.5 are chosen to take the factor analysis and 12 extracted factors can explain 77.97% of original 18 variables' total variance. Regression analysis based on the survey data finds that health records, vaccinations, pediatric care, elder care, and mental health management are the main factors accounting for degree of satisfaction with publicly financed health services for both urban and rural residents. What can be done to increase the degree of satisfaction with health services needs to be considered based on our findings. Regression analysis based on the survey data finds that health records, vaccinations, pediatric care, elder care, and mental health management are the main factors accounting for degree of satisfaction with publicly financed health services for both urban and rural residents. Therefore, with improvements in health records, timely vaccination, elder care for women or elder, pediatric care and major psychosis management, degree of satisfaction with publicly financed health services are likely to grow.

White blood cells levels and PCOS: direct and indirect relationship with obesity and insulin resistance, but not with hyperandogenemia.

PubMed

Papalou, Olga; Livadas, Sarantis; Karachalios, Athanasios; Tolia, Nikoleta; Kokkoris, Panayiotis; Tripolitakis, Konstantinos; Diamanti-Kandarakis, Evanthia

2015-01-01

To study white blood cells count (WBC) in women suffering from PCOS and compare these results with age and BMI-matched healthy women. The specific aim of this study was to assess the possible correlations of WBC with the major components of PCOS, obesity, insulin resistance and hyperandrogenism. Anthropometrical, metabolic and hormonal data were analyzed from 203 women with PCOS (NIH criteria) and 76 age-matched controls. In the total population studied (N=279), WBC was significantly higher (P=0.003) in the PCOS group compared with age-matched healthy women and was positively correlated with BMI (r=0.461, p<0.001), total testosterone (r= 0.210, p<0.001), insulin (r=0.271, p<0.001), triglycerides (r=0.285, p<0.001), HOMA score (r=0.206, p=0.001), FAI (r=0.329, p<0.001) and negatively correlated with SHBG (r=-0.300, p<0.001) and HDL (r=-0.222, p<0.001). Due to the fact that WHR was only available in the group of PCOS women, the role of central adiposity is assessed only in this group. Multiple regression analysis in the PCOS group, including WHR, revealed BMI, SHBG and TGL as the main predicting factors of WBC. Multinomial logistic regression analysis was also conducted and overweight/obesity was the sole independent risk factor for elevated WBC (higher tertile) (OR:0.907 CI:0.85-0.96, p=0.002). After dividing the sample based on BMI in the lean subgroups, WBC did not differ significantly between PCOS and controls, while multiple regression analysis indicated SHBG as the main predicting factor of WBC. Finally, we picked out the group of overweight/obese (BMI ≥25 kg/m2) women with PCOS and conducted another classification based on HOMA score (HOMA-IR≤2: insulin-sensitive women, HOMA-IR>2: insulin-resistant women) in the group of overweight and obese women with PCOS separately. In overweight women with PCOS, WBC, although higher in the group of insulin-resistant, did not differ significantly between the two groups, while in the subcategory of overweight women WBC was significantly (p=0.02) higher in the group of insulin-resistant women (HOMA-IR >2). Chronic low-grade inflammation and increased white cell count do occur in PCOS. Obesity and insulin resistance are the two leading parameters that act accumulatively in the development of leucocytosis, whereas hyperandrogenism does not seem to affect it.

Patterns of medicinal plant use: an examination of the Ecuadorian Shuar medicinal flora using contingency table and binomial analyses.

PubMed

Bennett, Bradley C; Husby, Chad E

2008-03-28

Botanical pharmacopoeias are non-random subsets of floras, with some taxonomic groups over- or under-represented. Moerman [Moerman, D.E., 1979. Symbols and selectivity: a statistical analysis of Native American medical ethnobotany, Journal of Ethnopharmacology 1, 111-119] introduced linear regression/residual analysis to examine these patterns. However, regression, the commonly-employed analysis, suffers from several statistical flaws. We use contingency table and binomial analyses to examine patterns of Shuar medicinal plant use (from Amazonian Ecuador). We first analyzed the Shuar data using Moerman's approach, modified to better meet requirements of linear regression analysis. Second, we assessed the exact randomization contingency table test for goodness of fit. Third, we developed a binomial model to test for non-random selection of plants in individual families. Modified regression models (which accommodated assumptions of linear regression) reduced R(2) to from 0.59 to 0.38, but did not eliminate all problems associated with regression analyses. Contingency table analyses revealed that the entire flora departs from the null model of equal proportions of medicinal plants in all families. In the binomial analysis, only 10 angiosperm families (of 115) differed significantly from the null model. These 10 families are largely responsible for patterns seen at higher taxonomic levels. Contingency table and binomial analyses offer an easy and statistically valid alternative to the regression approach.

Modeling the milling tool wear by using an evolutionary SVM-based model from milling runs experimental data

NASA Astrophysics Data System (ADS)

Nieto, Paulino José García; García-Gonzalo, Esperanza; Vilán, José Antonio Vilán; Robleda, Abraham Segade

2015-12-01

The main aim of this research work is to build a new practical hybrid regression model to predict the milling tool wear in a regular cut as well as entry cut and exit cut of a milling tool. The model was based on Particle Swarm Optimization (PSO) in combination with support vector machines (SVMs). This optimization mechanism involved kernel parameter setting in the SVM training procedure, which significantly influences the regression accuracy. Bearing this in mind, a PSO-SVM-based model, which is based on the statistical learning theory, was successfully used here to predict the milling tool flank wear (output variable) as a function of the following input variables: the time duration of experiment, depth of cut, feed, type of material, etc. To accomplish the objective of this study, the experimental dataset represents experiments from runs on a milling machine under various operating conditions. In this way, data sampled by three different types of sensors (acoustic emission sensor, vibration sensor and current sensor) were acquired at several positions. A second aim is to determine the factors with the greatest bearing on the milling tool flank wear with a view to proposing milling machine's improvements. Firstly, this hybrid PSO-SVM-based regression model captures the main perception of statistical learning theory in order to obtain a good prediction of the dependence among the flank wear (output variable) and input variables (time, depth of cut, feed, etc.). Indeed, regression with optimal hyperparameters was performed and a determination coefficient of 0.95 was obtained. The agreement of this model with experimental data confirmed its good performance. Secondly, the main advantages of this PSO-SVM-based model are its capacity to produce a simple, easy-to-interpret model, its ability to estimate the contributions of the input variables, and its computational efficiency. Finally, the main conclusions of this study are exposed.

The Precision Efficacy Analysis for Regression Sample Size Method.

ERIC Educational Resources Information Center

Brooks, Gordon P.; Barcikowski, Robert S.

The general purpose of this study was to examine the efficiency of the Precision Efficacy Analysis for Regression (PEAR) method for choosing appropriate sample sizes in regression studies used for precision. The PEAR method, which is based on the algebraic manipulation of an accepted cross-validity formula, essentially uses an effect size to…

Factors supporting dentist leaders' retention in leadership.

PubMed

Tuononen, T; Lammintakanen, J; Suominen, A L

2017-12-01

The aim was to study factors associated with staying in a dentist leadership position. We used an electronic questionnaire to gather data from 156 current or former Finnish dentist leaders in 2014. Principal component analysis categorized statements regarding time usage and opportunities in managerial work into five main components. Associations between these main component scores and the tendency to stay as a leader were analyzed with logistic regression. Out of the five main components, two were significantly associated with staying as a leader: 'career intentions', which represented intent to continue or to leave the leadership position; and 'work time control opportunities', which represented how leaders could control their own work time. Other factors that supported staying were leadership education, more work time available for leadership work, and lower age. The main component 'work pressure' decreased, although not significantly, the odds of continuing; it included lack of leadership work time, and pressure from superiors or subordinates. Leaders have important roles in health care, ensuring everyday operations as well as developing their organizations to meet future challenges. Knowledge of these supporting factors will enable dentist leaders and their organizations to improve working conditions in order to recruit and retain motivated and competent persons. In addition, well-designed education is important to inspire and encourage future leaders. Copyright© 2017 Dennis Barber Ltd.

Effect of Contact Damage on the Strength of Ceramic Materials.

DTIC Science & Technology

1982-10-01

variables that are important to erosion, and a multivariate , linear regression analysis is used to fit the data to the dimensional analysis. The...of Equations 7 and 8 by a multivariable regression analysis (room tem- perature data) Exponent Regression Standard error Computed coefficient of...1980) 593. WEAVER, Proc. Brit. Ceram. Soc. 22 (1973) 125. 39. P. W. BRIDGMAN, "Dimensional Analaysis ", (Yale 18. R. W. RICE, S. W. FREIMAN and P. F

Correlation analysis between work-related musculoskeletal disorders and the nursing practice environment, quality of life, and social support in the nursing professionals

PubMed Central

Yan, Ping; Yang, Yi; Zhang, Li; Li, Fuye; Huang, Amei; Wang, Yanan; Dai, Yali; Yao, Hua

2018-01-01

Abstract We aim to analyze the correlated influential factors between work-related musculoskeletal disorders (WMSDs) and nursing practice environment and quality of life and social support. From January 2015 to October 2015, cluster sampling was performed on the nurses from 12 hospitals in the 6 areas in Xinjiang. The questionnaires including the modified Nordic Musculoskeletal Questionnaire, Practice Environment Scale (PES), the Mos 36-item Short Form Health Survey, and Social Support Rating Scale were used to investigate. Multivariate logistic regression analysis was used to explore the influential factors of WMSDs. The total prevalence of WMSDs was 79.52% in the nurses ever since the working occupation, which was mainly involved waist (64.83%), neck (61.83%), and shoulder (52.36%). Multivariate logistic regression analysis indicated age (≥26 years), working in the Department of Surgery, Department of Critical Care, Outpatient Department, and Department of Anesthesia, working duration of >40 hours per week were the risk factors of WMSDs in the nurses. The physiological function (PF), body pain, total healthy condition, adequate working force and financial support, and social support were the protective factors of WMSDs. The prevalence of WMSDs in the nurses in Xinjiang Autonomous Region was high. PF, bodily pain, total healthy condition, having adequate staff and support resources to provide quality patient care, and social support were the protective factors of WMSDs in the nurses. PMID:29489648

Anxiety, Affect, Self-Esteem, and Stress: Mediation and Moderation Effects on Depression

PubMed Central

Nima, Ali Al; Rosenberg, Patricia; Archer, Trevor; Garcia, Danilo

2013-01-01

Background Mediation analysis investigates whether a variable (i.e., mediator) changes in regard to an independent variable, in turn, affecting a dependent variable. Moderation analysis, on the other hand, investigates whether the statistical interaction between independent variables predict a dependent variable. Although this difference between these two types of analysis is explicit in current literature, there is still confusion with regard to the mediating and moderating effects of different variables on depression. The purpose of this study was to assess the mediating and moderating effects of anxiety, stress, positive affect, and negative affect on depression. Methods Two hundred and two university students (males  = 93, females  = 113) completed questionnaires assessing anxiety, stress, self-esteem, positive and negative affect, and depression. Mediation and moderation analyses were conducted using techniques based on standard multiple regression and hierarchical regression analyses. Main Findings The results indicated that (i) anxiety partially mediated the effects of both stress and self-esteem upon depression, (ii) that stress partially mediated the effects of anxiety and positive affect upon depression, (iii) that stress completely mediated the effects of self-esteem on depression, and (iv) that there was a significant interaction between stress and negative affect, and between positive affect and negative affect upon depression. Conclusion The study highlights different research questions that can be investigated depending on whether researchers decide to use the same variables as mediators and/or moderators. PMID:24039896

Correlation analysis between work-related musculoskeletal disorders and the nursing practice environment, quality of life, and social support in the nursing professionals.

PubMed

Yan, Ping; Yang, Yi; Zhang, Li; Li, Fuye; Huang, Amei; Wang, Yanan; Dai, Yali; Yao, Hua

2018-03-01

We aim to analyze the correlated influential factors between work-related musculoskeletal disorders (WMSDs) and nursing practice environment and quality of life and social support.From January 2015 to October 2015, cluster sampling was performed on the nurses from 12 hospitals in the 6 areas in Xinjiang. The questionnaires including the modified Nordic Musculoskeletal Questionnaire, Practice Environment Scale (PES), the Mos 36-item Short Form Health Survey, and Social Support Rating Scale were used to investigate. Multivariate logistic regression analysis was used to explore the influential factors of WMSDs.The total prevalence of WMSDs was 79.52% in the nurses ever since the working occupation, which was mainly involved waist (64.83%), neck (61.83%), and shoulder (52.36%). Multivariate logistic regression analysis indicated age (≥26 years), working in the Department of Surgery, Department of Critical Care, Outpatient Department, and Department of Anesthesia, working duration of >40 hours per week were the risk factors of WMSDs in the nurses. The physiological function (PF), body pain, total healthy condition, adequate working force and financial support, and social support were the protective factors of WMSDs.The prevalence of WMSDs in the nurses in Xinjiang Autonomous Region was high. PF, bodily pain, total healthy condition, having adequate staff and support resources to provide quality patient care, and social support were the protective factors of WMSDs in the nurses.

Factors associated with trait anger level of juvenile offenders in Hubei province: A binary logistic regression analysis.

PubMed

Tang, Li-Na; Ye, Xiao-Zhou; Yan, Qiu-Ge; Chang, Hong-Juan; Ma, Yu-Qiao; Liu, De-Bin; Li, Zhi-Gen; Yu, Yi-Zhen

2017-02-01

The risk factors of high trait anger of juvenile offenders were explored through questionnaire study in a youth correctional facility of Hubei province, China. A total of 1090 juvenile offenders in Hubei province were investigated by self-compiled social-demographic questionnaire, Childhood Trauma Questionnaire (CTQ), and State-Trait Anger Expression Inventory-II (STAXI-II). The risk factors were analyzed by chi-square tests, correlation analysis, and binary logistic regression analysis with SPSS 19.0. A total of 1082 copies of valid questionnaires were collected. High trait anger group (n=316) was defined as those who scored in the upper 27th percentile of STAXI-II trait anger scale (TAS), and the rest were defined as low trait anger group (n=766). The risk factors associated with high level of trait anger included: childhood emotional abuse, childhood sexual abuse, step family, frequent drug abuse, and frequent internet using (P<0.05 or P<0.01). Birth sequence, number of sibling, ranking in the family, identity of the main care-taker, the education level of care-taker, educational style of care-taker, family income, relationship between parents, social atmosphere of local area, frequent drinking, and frequent smoking did not predict to high level of trait anger (P>0.05). It was suggested that traumatic experience in childhood and unhealthy life style may significantly increase the level of trait anger in adulthood. The risk factors of high trait anger and their effects should be taken into consideration seriously.

Common pitfalls in statistical analysis: Linear regression analysis

PubMed Central

Aggarwal, Rakesh; Ranganathan, Priya

2017-01-01

In a previous article in this series, we explained correlation analysis which describes the strength of relationship between two continuous variables. In this article, we deal with linear regression analysis which predicts the value of one continuous variable from another. We also discuss the assumptions and pitfalls associated with this analysis. PMID:28447022

Altitude Above Sea Level and Body Mass Index as Determinants of Oxygen Saturation in Children: The SON@ Study.

PubMed

Gochicoa-Rangel, Laura; Pérez-Padilla, José Rogelio; Rodríguez-Moreno, Luis; Montero-Matamoros, Arturo; Ojeda-Luna, Nancy; Martínez-Carbajal, Gema; Hernández-Raygoza, Roberto; Ruiz-Pedraza, Dolores; Fernández-Plata, María Rosario; Torre-Bouscoulet, Luis

2015-01-01

Altitude above sea level and body mass index are well-recognized determinants of oxygen saturation in adult populations; however, the contribution of these factors to oxygen saturation in children is less clear. To explore the contribution of altitude above sea level and body mass index to oxygen saturation in children. A multi-center, cross-sectional study conducted in nine cities in Mexico. Parents signed informed consent forms and completed a health status questionnaire. Height, weight, and pulse oximetry were recorded. We studied 2,200 subjects (52% girls) aged 8.7 ± 3.0 years. Mean body mass index, z-body mass index, and oxygen saturation were 18.1 ± 3.6 kg·m-2, 0.58 ± 1.3, and 95.5 ± 2.4%, respectively. By multiple regression analysis, altitude proved to be the main predictor of oxygen saturation, with non-significant contributions of age, gender, and body mass index. According to quantile regression, the median estimate of oxygen saturation was 98.7 minus 1.7% per km of altitude above sea level, and the oxygen saturation fifth percentile 97.4 minus 2.7% per km of altitude. Altitude was the main determinant of oxygen saturation, which on average decreased 1.7% per km of elevation from a percentage of 98.7 at sea level. In contrast with adults, this study in children found no association between oxygen saturation and obesity or age.

MULTIVARIATE ANALYSIS OF DRINKING BEHAVIOUR IN A RURAL POPULATION

PubMed Central

Mathrubootham, N.; Bashyam, V.S.P.; Shahjahan

1997-01-01

This study was carried out to find out the drinking pattern in a rural population, using multivariate techniques. 386 current users identified in a community were assessed with regard to their drinking behaviours using a structured interview. For purposes of the study the questions were condensed into 46 meaningful variables. In bivariate analysis, 14 variables including dependent variables such as dependence, MAST & CAGE (measuring alcoholic status), Q.F. Index and troubled drinking were found to be significant. Taking these variables and other multivariate techniques too such as ANOVA, correlation, regression analysis and factor analysis were done using both SPSS PC + and HCL magnum mainframe computer with FOCUS package and UNIX systems. Results revealed that number of factors such as drinking style, duration of drinking, pattern of abuse, Q.F. Index and various problems influenced drinking and some of them set up a vicious circle. Factor analysis revealed mainly 3 factors, abuse, dependence and social drinking factors. Dependence could be divided into low/moderate dependence. The implications and practical applications of these tests are also discussed. PMID:21584077

Green Building Implementation at Schools in North Sulawesi, Indonesia

NASA Astrophysics Data System (ADS)

Harimu, D. A. J.; Tumanduk, M. S. S. S.

2018-02-01

This research aims at investigating the green building implementation at schools in North Sulawesi, Indonesia; and to analysis the relationship between implementation of green building concept at school with students’ green behaviour. This research is Survey Research with quantitative descriptive method. The analysis unit is taken purposively, that is school that had been implemented the green building concept, Manado’s 3rd Public Vocational High School, Lokon High School at Tomohon, Manado Independent School at North Minahasa, and Tondano’s 3rd Public Vocational High School. Data collecting is acquired by observation and questionnaire. The Assessment Criteria of green building on Analysis Unit, is taken from Greenship Existing Building ver 1. There are 4 main points that being assessed, which are Energy Conservation and Efficiency; Water Conservation; Indoor Health and Comfort; Waste Managerial. The Analysis technique used in this research is the simple regression analysis. The result of the research shows that there is a significant relation between green building implementation at school and students’ green behavior. The result is accordance with the Gesalts Psychologist theories, that architecture can change the user’s behaviour.

Exploratory Network Meta Regression Analysis of Stroke Prevention in Atrial Fibrillation Fails to Identify Any Interactions with Treatment Effect.

PubMed

Batson, Sarah; Sutton, Alex; Abrams, Keith

2016-01-01

Patients with atrial fibrillation are at a greater risk of stroke and therefore the main goal for treatment of patients with atrial fibrillation is to prevent stroke from occurring. There are a number of different stroke prevention treatments available to include warfarin and novel oral anticoagulants. Previous network meta-analyses of novel oral anticoagulants for stroke prevention in atrial fibrillation acknowledge the limitation of heterogeneity across the included trials but have not explored the impact of potentially important treatment modifying covariates. To explore potentially important treatment modifying covariates using network meta-regression analyses for stroke prevention in atrial fibrillation. We performed a network meta-analysis for the outcome of ischaemic stroke and conducted an exploratory regression analysis considering potentially important treatment modifying covariates. These covariates included the proportion of patients with a previous stroke, proportion of males, mean age, the duration of study follow-up and the patients underlying risk of ischaemic stroke. None of the covariates explored impacted relative treatment effects relative to placebo. Notably, the exploration of 'study follow-up' as a covariate supported the assumption that difference in trial durations is unimportant in this indication despite the variation across trials in the network. This study is limited by the quantity of data available. Further investigation is warranted, and, as justifying further trials may be difficult, it would be desirable to obtain individual patient level data (IPD) to facilitate an effort to relate treatment effects to IPD covariates in order to investigate heterogeneity. Observational data could also be examined to establish if there are potential trends elsewhere. The approach and methods presented have potentially wide applications within any indication as to highlight the potential benefit of extending decision problems to include additional comparators outside of those of primary interest to allow for the exploration of heterogeneity.

Selenium in irrigated agricultural areas of the western United States

USGS Publications Warehouse

Nolan, B.T.; Clark, M.L.

1997-01-01

A logistic regression model was developed to predict the likelihood that Se exceeds the USEPA chronic criterion for aquatic life (5 ??g/L) in irrigated agricultural areas of the western USA. Preliminary analysis of explanatory variables used in the model indicated that surface-water Se concentration increased with increasing dissolved solids (DS) concentration and with the presence of Upper Cretaceous, mainly marine sediment. The presence or absence of Cretaceous sediment was the major variable affecting Se concentration in surface-water samples from the National Irrigation Water Quality Program. Median Se concentration was 14 ??g/L in samples from areas underlain by Cretaceous sediments and < 1 ??g/L in samples from areas underlain by non-Cretaceous sediments. Wilcoxon rank sum tests indicated that elevated Se concentrations in samples from areas with Cretaceous sediments, irrigated areas, and from closed lakes and ponds were statistically significant. Spearman correlations indicated that Se was positively correlated with a binary geology variable (0.64) and DS (0.45). Logistic regression models indicated that the concentration of Se in surface water was almost certain to exceed the Environmental Protection Agency aquatic-life chronic criterion of 5 ??g/L when DS was greater than 3000 mg/L in areas with Cretaceous sediments. The 'best' logistic regression model correctly predicted Se exceedances and nonexceedances 84.4% of the time, and model sensitivity was 80.7%. A regional map of Cretaceous sediment showed the location of potential problem areas. The map and logistic regression model are tools that can be used to determine the potential for Se contamination of irrigated agricultural areas in the western USA.

Quality of life in breast cancer patients--a quantile regression analysis.

PubMed

Pourhoseingholi, Mohamad Amin; Safaee, Azadeh; Moghimi-Dehkordi, Bijan; Zeighami, Bahram; Faghihzadeh, Soghrat; Tabatabaee, Hamid Reza; Pourhoseingholi, Asma

2008-01-01

Quality of life study has an important role in health care especially in chronic diseases, in clinical judgment and in medical resources supplying. Statistical tools like linear regression are widely used to assess the predictors of quality of life. But when the response is not normal the results are misleading. The aim of this study is to determine the predictors of quality of life in breast cancer patients, using quantile regression model and compare to linear regression. A cross-sectional study conducted on 119 breast cancer patients that admitted and treated in chemotherapy ward of Namazi hospital in Shiraz. We used QLQ-C30 questionnaire to assessment quality of life in these patients. A quantile regression was employed to assess the assocciated factors and the results were compared to linear regression. All analysis carried out using SAS. The mean score for the global health status for breast cancer patients was 64.92+/-11.42. Linear regression showed that only grade of tumor, occupational status, menopausal status, financial difficulties and dyspnea were statistically significant. In spite of linear regression, financial difficulties were not significant in quantile regression analysis and dyspnea was only significant for first quartile. Also emotion functioning and duration of disease statistically predicted the QOL score in the third quartile. The results have demonstrated that using quantile regression leads to better interpretation and richer inference about predictors of the breast cancer patient quality of life.

Factors associated with secondhand smoke exposure in different settings: Results from the German Health Update (GEDA) 2012.

PubMed

Fischer, Florian; Kraemer, Alexander

2016-04-14

The ubiquity of secondhand smoke (SHS) exposure at home or in private establishments, workplaces and public areas poses several challenges for the reduction of SHS exposure. This study aimed to describe the prevalence of SHS exposure in Germany and key factors associated with exposure. Results were also differentiated by place of exposure. A secondary data analysis based on the public use file of the German Health Update 2012 was conducted (n = 13,933). Only non-smokers were included in the analysis. In a multivariable logistic regression model the factors associated with SHS exposure were calculated. In addition, a further set of multivariable logistic regressions were calculated for factors associated with the place of SHS exposure (workplace, at home, bars/discotheques, restaurants, at the house of a friend). More than a quarter of non-smoking study participants were exposed to SHS. The main area of exposure was the workplace (40.9 %). The multivariable logistic regression indicated young age as the most important factor associated with SHS exposure. The odds for SHS exposure was higher in men than in women. The likelihood of SHS exposure decreased with higher education. SHS exposure and the associated factors varied between different places of exposure. Despite several actions to protect non-smokers which were implemented in Germany during the past years, SHS exposure still remains a relevant risk factor at a population level. According to the results of this study, particularly the workplace and other public places such as bars and discotheques have to be taken into account for the development of strategies to reduce SHS exposure.

Spatial and temporal variation of rainfall trends of Sri Lanka

NASA Astrophysics Data System (ADS)

Wickramagamage, P.

2016-08-01

This study was based on daily rainfall data of 48 stations distributed over the entire island covering a 30-year period from 1981 to 2010. Data analysis was done to identify the spatial pattern of rainfall trends. The methods employed in data analysis are linear regression and interpolation by Universal Kriging and Radial Basis function. The slope of linear regression curves of 48 stations was used in interpolation. The regression coefficients show spatially and seasonally variable positive and negative trends of annual and seasonal rainfall. About half of the mean annual pentad series show negative trends, while the rest shows positive trends. By contrast, the rainfall trends of the Southwest Monsoon (SWM) season are predominantly negative throughout the country. The first phase of the Northeast Monsoon (NEM1) displays downward trends everywhere, with the exception of the Southeastern coastal area. The strongest negative trends were found in the Northeast and in the Central Highlands. The second phase (NEM2) is mostly positive, except in the Northeast. The Inter-Monsoon (IM) periods have predominantly upward trends almost everywhere, but still the trends in some parts of the Highlands and Northeast are negative. The long-term data at Watawala Nuwara Eliya and Sandringham show a consistent decline in the rainfall over the last 100 years, particularly during the SWM. There seems to be a faster decline in the rainfall in the last 3 decades. These trends are consistent with the observations in India. It is generally accepted that there has been changes in the circulation pattern. Weakening of the SWM circulation parameters caused by global warming appears to be the main causes of recent changes. Effect of the Asian Brown Cloud may also play a role in these changes.

Relation between increased numbers of safe playing areas and decreased vehicle related child mortality rates in Japan from 1970 to 1985: a trend analysis

PubMed Central

Nakahara, S.; Nakamura, Y.; Ichikawa, M.; Wakai, S.

2004-01-01

Objectives: To examine vehicle related mortality trends of children in Japan; and to investigate how environmental modifications such as the installation of public parks and pavements are associated with these trends. Design: Poisson regression was used for trend analysis, and multiple regression modelling was used to investigate the associations between trends in environmental modifications and trends in motor vehicle related child mortality rates. Setting: Mortality data of Japan from 1970 to 1994, defined as E-code 810–23 from 1970 to 1978 and E810–25 from 1979 to 1994, were obtained from vital statistics. Multiple regression modelling was confined to the 1970–1985 data. Data concerning public parks and other facilities were obtained from the Ministry of Land, Infrastructure, and Transport. Subjects: Children aged 0–14 years old were examined in this study and divided into two groups: 0–4 and 5–14 years. Main results: An increased number of public parks was associated with decreased vehicle related mortality rates among children aged 0–4 years, but not among children aged 5–14. In contrast, there was no association between trends in pavements and mortality rates. Conclusions: An increased number of public parks might reduce vehicle related preschooler deaths, in particular those involving pedestrians. Safe play areas in residential areas might reduce the risk of vehicle related child death by lessening the journey both to and from such areas as well as reducing the number of children playing on the street. However, such measures might not be effective in reducing the vehicle related mortalities of school age children who have an expanded range of activities and walk longer distances. PMID:15547055

[Bibliometrics and visualization analysis of land use regression models in ambient air pollution research].

PubMed

Zhang, Y J; Zhou, D H; Bai, Z P; Xue, F X

2018-02-10

Objective: To quantitatively analyze the current status and development trends regarding the land use regression (LUR) models on ambient air pollution studies. Methods: Relevant literature from the PubMed database before June 30, 2017 was analyzed, using the Bibliographic Items Co-occurrence Matrix Builder (BICOMB 2.0). Keywords co-occurrence networks, cluster mapping and timeline mapping were generated, using the CiteSpace 5.1.R5 software. Relevant literature identified in three Chinese databases was also reviewed. Results: Four hundred sixty four relevant papers were retrieved from the PubMed database. The number of papers published showed an annual increase, in line with the growing trend of the index. Most papers were published in the journal of Environmental Health Perspectives . Results from the Co-word cluster analysis identified five clusters: cluster#0 consisted of birth cohort studies related to the health effects of prenatal exposure to air pollution; cluster#1 referred to land use regression modeling and exposure assessment; cluster#2 was related to the epidemiology on traffic exposure; cluster#3 dealt with the exposure to ultrafine particles and related health effects; cluster#4 described the exposure to black carbon and related health effects. Data from Timeline mapping indicated that cluster#0 and#1 were the main research areas while cluster#3 and#4 were the up-coming hot areas of research. Ninety four relevant papers were retrieved from the Chinese databases with most of them related to studies on modeling. Conclusion: In order to better assess the health-related risks of ambient air pollution, and to best inform preventative public health intervention policies, application of LUR models to environmental epidemiology studies in China should be encouraged.

Prediction model of critical weight loss in cancer patients during particle therapy.

PubMed

Zhang, Zhihong; Zhu, Yu; Zhang, Lijuan; Wang, Ziying; Wan, Hongwei

2018-01-01

The objective of this study is to investigate the predictors of critical weight loss in cancer patients receiving particle therapy, and build a prediction model based on its predictive factors. Patients receiving particle therapy were enroled between June 2015 and June 2016. Body weight was measured at the start and end of particle therapy. Association between critical weight loss (defined as >5%) during particle therapy and patients' demographic, clinical characteristic, pre-therapeutic nutrition risk screening (NRS 2002) and BMI were evaluated by logistic regression and decision tree analysis. Finally, 375 cancer patients receiving particle therapy were included. Mean weight loss was 0.55 kg, and 11.5% of patients experienced critical weight loss during particle therapy. The main predictors of critical weight loss during particle therapy were head and neck tumour location, total radiation dose ≥70 Gy on the primary tumour, and without post-surgery, as indicated by both logistic regression and decision tree analysis. Prediction model that includes tumour locations, total radiation dose and post-surgery had a good predictive ability, with the area under receiver operating characteristic curve 0.79 (95% CI: 0.71-0.88) and 0.78 (95% CI: 0.69-0.86) for decision tree and logistic regression model, respectively. Cancer patients with head and neck tumour location, total radiation dose ≥70 Gy and without post-surgery were at higher risk of critical weight loss during particle therapy, and early intensive nutrition counselling or intervention should be target at this population. © The Author 2017. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

Geospatial techniques for allocating vulnerability zoning of geohazards along the Karakorum Highway, Gilgit-Baltistan-Pakistan

NASA Astrophysics Data System (ADS)

Khan, K. M.; Rashid, S.; Yaseen, M.; Ikram, M.

2016-12-01

The Karakoram Highway (KKH) 'eighth wonder of the world', constructed and completed by the consent of Pakistan and China in 1979 as a Friendship Highway. It connect Gilgit-Baltistan, a strategically prominent region of Pakistan, with Xinjiang region in China. Due to manifold geology/geomorphology, soil formation, steep slopes, climate change well as unsustainable anthropogenic activities, still, KKH is remarkably vulnerable to natural hazards i.e. land subsistence, landslides, erosion, rock fall, floods, debris flows, cyclical torrential rainfall and snowfall, lake outburst etc. Most of the time these geohazard's damaging effects jeopardized the life in the region. To ascertain the nature and frequency of the disaster and vulnerability zoning, a rating and management (logistic) analysis were made to investigate the spatiotemporal sharing of the natural hazard. The substantial dynamics of the physiograpy, geology, geomorphology, soils and climate were carefully understand while slope, aspect, elevation, profile curvature and rock hardness was calculated by different techniques. To assess the nature and intensity geospatial analysis were conducted and magnitude of every factor was gauged by using logistic regression. Moreover, ever relative variable was integrated in the evaluation process. Logistic regression and geospatial techniques were used to map the geohazard vulnerability zoning (GVZ). The GVZ model findings were endorsed by the reviews of documented hazards in the current years and the precision was realized more than 88.1 %. The study has proved the model authentication by highlighting the comfortable indenture among the vulnerability mapping and past documented hazards. By using a receiver operating characteristic curve, the logistic regression model made satisfactory results. The outcomes will be useful in sustainable land use and infrastructure planning, mainly in high risk zones for reduceing economic damages and community betterment.

Fast detection and visualization of minced lamb meat adulteration using NIR hyperspectral imaging and multivariate image analysis.

PubMed

Kamruzzaman, Mohammed; Sun, Da-Wen; ElMasry, Gamal; Allen, Paul

2013-01-15

Many studies have been carried out in developing non-destructive technologies for predicting meat adulteration, but there is still no endeavor for non-destructive detection and quantification of adulteration in minced lamb meat. The main goal of this study was to develop and optimize a rapid analytical technique based on near-infrared (NIR) hyperspectral imaging to detect the level of adulteration in minced lamb. Initial investigation was carried out using principal component analysis (PCA) to identify the most potential adulterate in minced lamb. Minced lamb meat samples were then adulterated with minced pork in the range 2-40% (w/w) at approximately 2% increments. Spectral data were used to develop a partial least squares regression (PLSR) model to predict the level of adulteration in minced lamb. Good prediction model was obtained using the whole spectral range (910-1700 nm) with a coefficient of determination (R(2)(cv)) of 0.99 and root-mean-square errors estimated by cross validation (RMSECV) of 1.37%. Four important wavelengths (940, 1067, 1144 and 1217 nm) were selected using weighted regression coefficients (Bw) and a multiple linear regression (MLR) model was then established using these important wavelengths to predict adulteration. The MLR model resulted in a coefficient of determination (R(2)(cv)) of 0.98 and RMSECV of 1.45%. The developed MLR model was then applied to each pixel in the image to obtain prediction maps to visualize the distribution of adulteration of the tested samples. The results demonstrated that the laborious and time-consuming tradition analytical techniques could be replaced by spectral data in order to provide rapid, low cost and non-destructive testing technique for adulterate detection in minced lamb meat. Copyright © 2012 Elsevier B.V. All rights reserved.

Estimated prevalence of erosive tooth wear in permanent teeth of children and adolescents: an epidemiological systematic review and meta-regression analysis.

PubMed

Salas, M M S; Nascimento, G G; Huysmans, M C; Demarco, F F

2015-01-01

The main purpose of this systematic review was to estimate the prevalence of dental erosion in permanent teeth of children and adolescents. An electronic search was performed up to and including March 2014. Eligibility criteria included population-based studies in permanent teeth of children and adolescents aged 8-19-year-old reporting the prevalence or data that allowed the calculation of prevalence rates of tooth erosion. Data collection assessed information regarding geographic location, type of index used for clinical examination, sample size, year of publication, age, examined teeth and tissue exposure. The estimated prevalence of erosive wear was determined, followed by a meta-regression analysis. Twenty-two papers were included in the systematic review. The overall estimated prevalence of tooth erosion was 30.4% (95%IC 23.8-37.0). In the multivariate meta-regression model use of the Tooth Wear Index for clinical examination, studies with sample smaller than 1000 subjects and those conducted in the Middle East and Africa remained associated with higher dental erosion prevalence rates. Our results demonstrated that the estimated prevalence of erosive wear in permanent teeth of children and adolescents is 30.4% with high heterogeneity between studies. Additionally, the correct choice of a clinical index for dental erosion detection and the geographic location play an important role for the large variability of erosive tooth wear in permanent teeth of children and adolescents. The prevalence of tooth erosion observed in permanent teeth of children and adolescents was considerable high. Our results demonstrated that prevalence rate of erosive wear was influenced by methodological and diagnosis factors. When tooth erosion is assessed, the clinical index should be considered. Copyright © 2014 Elsevier Ltd. All rights reserved.

Impact of Delayed Time to Advanced Imaging on Missed Appointments Across Different Demographic and Socioeconomic Factors.

PubMed

Daye, Dania; Carrodeguas, Emmanuel; Glover, McKinley; Guerrier, Claude Emmanuel; Harvey, H Benjamin; Flores, Efrén J

2018-05-01

The aim of this study was to investigate the impact of wait days (WDs) on missed outpatient MRI appointments across different demographic and socioeconomic factors. An institutional review board-approved retrospective study was conducted among adult patients scheduled for outpatient MRI during a 12-month period. Scheduling data and demographic information were obtained. Imaging missed appointments were defined as missed scheduled imaging encounters. WDs were defined as the number of days from study order to appointment. Multivariate logistic regression was applied to assess the contribution of race and socioeconomic factors to missed appointments. Linear regression was performed to assess the relationship between missed appointment rates and WDs stratified by race, income, and patient insurance groups with analysis of covariance statistics. A total of 42,727 patients met the inclusion criteria. Mean WDs were 7.95 days. Multivariate regression showed increased odds ratio for missed appointments for patients with increased WDs (7-21 days: odds ratio [OR], 1.39; >21 days: OR, 1.77), African American patients (OR, 1.71), Hispanic patients (OR, 1.30), patients with noncommercial insurance (OR, 2.00-2.55), and those with imaging performed at the main hospital campus (OR, 1.51). Missed appointment rate linearly increased with WDs, with analysis of covariance revealing underrepresented minorities and Medicaid insurance as significant effect modifiers. Increased WDs for advanced imaging significantly increases the likelihood of missed appointments. This effect is most pronounced among underrepresented minorities and patients with lower socioeconomic status. Efforts to reduce WDs may improve equity in access to and utilization of advanced diagnostic imaging for all patients. Copyright © 2018. Published by Elsevier Inc.

Examining geological controls on baseflow index (BFI) using regression analysis: An illustration from the Thames Basin, UK

NASA Astrophysics Data System (ADS)

Bloomfield, J. P.; Allen, D. J.; Griffiths, K. J.

2009-06-01

SummaryLinear regression methods can be used to quantify geological controls on baseflow index (BFI). This is illustrated using an example from the Thames Basin, UK. Two approaches have been adopted. The areal extents of geological classes based on lithostratigraphic and hydrogeological classification schemes have been correlated with BFI for 44 'natural' catchments from the Thames Basin. When regression models are built using lithostratigraphic classes that include a constant term then the model is shown to have some physical meaning and the relative influence of the different geological classes on BFI can be quantified. For example, the regression constants for two such models, 0.64 and 0.69, are consistent with the mean observed BFI (0.65) for the Thames Basin, and the signs and relative magnitudes of the regression coefficients for each of the lithostratigraphic classes are consistent with the hydrogeology of the Basin. In addition, regression coefficients for the lithostratigraphic classes scale linearly with estimates of log 10 hydraulic conductivity for each lithological class. When a regression is built using a hydrogeological classification scheme with no constant term, the model does not have any physical meaning, but it has a relatively high adjusted R2 value and because of the continuous coverage of the hydrogeological classification scheme, the model can be used for predictive purposes. A model calibrated on the 44 'natural' catchments and using four hydrogeological classes (low-permeability surficial deposits, consolidated aquitards, fractured aquifers and intergranular aquifers) is shown to perform as well as a model based on a hydrology of soil types (BFIHOST) scheme in predicting BFI in the Thames Basin. Validation of this model using 110 other 'variably impacted' catchments in the Basin shows that there is a correlation between modelled and observed BFI. Where the observed BFI is significantly higher than modelled BFI the deviations can be explained by an exogenous factor, catchment urban area. It is inferred that this is may be due influences from sewage discharge, mains leakage, and leakage from septic tanks.

Bayesian logistic regression in detection of gene-steroid interaction for cancer at PDLIM5 locus.

PubMed

Wang, Ke-Sheng; Owusu, Daniel; Pan, Yue; Xie, Changchun

2016-06-01

The PDZ and LIM domain 5 (PDLIM5) gene may play a role in cancer, bipolar disorder, major depression, alcohol dependence and schizophrenia; however, little is known about the interaction effect of steroid and PDLIM5 gene on cancer. This study examined 47 single-nucleotide polymorphisms (SNPs) within the PDLIM5 gene in the Marshfield sample with 716 cancer patients (any diagnosed cancer, excluding minor skin cancer) and 2848 noncancer controls. Multiple logistic regression model in PLINK software was used to examine the association of each SNP with cancer. Bayesian logistic regression in PROC GENMOD in SAS statistical software, ver. 9.4 was used to detect gene- steroid interactions influencing cancer. Single marker analysis using PLINK identified 12 SNPs associated with cancer (P< 0.05); especially, SNP rs6532496 revealed the strongest association with cancer (P = 6.84 × 10⁻³); while the next best signal was rs951613 (P = 7.46 × 10⁻³). Classic logistic regression in PROC GENMOD showed that both rs6532496 and rs951613 revealed strong gene-steroid interaction effects (OR=2.18, 95% CI=1.31-3.63 with P = 2.9 × 10⁻³ for rs6532496 and OR=2.07, 95% CI=1.24-3.45 with P = 5.43 × 10⁻³ for rs951613, respectively). Results from Bayesian logistic regression showed stronger interaction effects (OR=2.26, 95% CI=1.2-3.38 for rs6532496 and OR=2.14, 95% CI=1.14-3.2 for rs951613, respectively). All the 12 SNPs associated with cancer revealed significant gene-steroid interaction effects (P < 0.05); whereas 13 SNPs showed gene-steroid interaction effects without main effect on cancer. SNP rs4634230 revealed the strongest gene-steroid interaction effect (OR=2.49, 95% CI=1.5-4.13 with P = 4.0 × 10⁻⁴ based on the classic logistic regression and OR=2.59, 95% CI=1.4-3.97 from Bayesian logistic regression; respectively). This study provides evidence of common genetic variants within the PDLIM5 gene and interactions between PLDIM5 gene polymorphisms and steroid use influencing cancer.

The microcomputer scientific software series 2: general linear model--regression.

Treesearch

Harold M. Rauscher

1983-01-01

The general linear model regression (GLMR) program provides the microcomputer user with a sophisticated regression analysis capability. The output provides a regression ANOVA table, estimators of the regression model coefficients, their confidence intervals, confidence intervals around the predicted Y-values, residuals for plotting, a check for multicollinearity, a...

Soil Particle Size Analysis by Laser Diffractometry: Result Comparison with Pipette Method

NASA Astrophysics Data System (ADS)

Šinkovičová, Miroslava; Igaz, Dušan; Kondrlová, Elena; Jarošová, Miriam

2017-10-01

Soil texture as the basic soil physical property provides a basic information on the soil grain size distribution as well as grain size fraction representation. Currently, there are several methods of particle dimension measurement available that are based on different physical principles. Pipette method based on the different sedimentation velocity of particles with different diameter is considered to be one of the standard methods of individual grain size fraction distribution determination. Following the technical advancement, optical methods such as laser diffraction can be also used nowadays for grain size distribution determination in the soil. According to the literature review of domestic as well as international sources related to this topic, it is obvious that the results obtained by laser diffractometry do not correspond with the results obtained by pipette method. The main aim of this paper was to analyse 132 samples of medium fine soil, taken from the Nitra River catchment in Slovakia, from depths of 15-20 cm and 40-45 cm, respectively, using laser analysers: ANALYSETTE 22 MicroTec plus (Fritsch GmbH) and Mastersizer 2000 (Malvern Instruments Ltd). The results obtained by laser diffractometry were compared with pipette method and the regression relationships using linear, exponential, power and polynomial trend were derived. Regressions with the three highest regression coefficients (R2) were further investigated. The fit with the highest tightness was observed for the polynomial regression. In view of the results obtained, we recommend using the estimate of the representation of the clay fraction (<0.01 mm) polynomial regression, to achieve a highest confidence value R2 at the depths of 15-20 cm 0.72 (Analysette 22 MicroTec plus) and 0.95 (Mastersizer 2000), from a depth of 40-45 cm 0.90 (Analysette 22 MicroTec plus) and 0.96 (Mastersizer 2000). Since the percentage representation of clayey particles (2nd fraction according to the methodology of Complex Soil Survey done in Slovakia) in soil is the determinant for soil type specification, we recommend using the derived relationships in soil science when the soil texture analysis is done according to laser diffractometry. The advantages of laser diffraction method comprise the short analysis time, usage of small sample amount, application for the various grain size fraction and soil type classification systems, and a wide range of determined fractions. Therefore, it is necessary to focus on this issue further to address the needs of soil science research and attempt to replace the standard pipette method with more progressive laser diffraction method.

USAF (United States Air Force) Stability and Control DATCOM (Data Compendium)

DTIC Science & Technology

1978-04-01

regression analysis involves the study of a group of variables to determine their effect on a given parameter. Because of the empirical nature of this...regression analysis of mathematical statistics. In general, a regression analysis involves the study of a group of variables to determine their effect on a...Excperiment, OSR TN 58-114, MIT Fluid Dynamics Research Group Rapt. 57-5, 1957. (U) 90. Kennet, H., and Ashley, H.: Review of Unsteady Aerodynamic Studies in

A Method of Calculating Functional Independence Measure at Discharge from Functional Independence Measure Effectiveness Predicted by Multiple Regression Analysis Has a High Degree of Predictive Accuracy.

PubMed

Tokunaga, Makoto; Watanabe, Susumu; Sonoda, Shigeru

2017-09-01

Multiple linear regression analysis is often used to predict the outcome of stroke rehabilitation. However, the predictive accuracy may not be satisfactory. The objective of this study was to elucidate the predictive accuracy of a method of calculating motor Functional Independence Measure (mFIM) at discharge from mFIM effectiveness predicted by multiple regression analysis. The subjects were 505 patients with stroke who were hospitalized in a convalescent rehabilitation hospital. The formula "mFIM at discharge = mFIM effectiveness × (91 points - mFIM at admission) + mFIM at admission" was used. By including the predicted mFIM effectiveness obtained through multiple regression analysis in this formula, we obtained the predicted mFIM at discharge (A). We also used multiple regression analysis to directly predict mFIM at discharge (B). The correlation between the predicted and the measured values of mFIM at discharge was compared between A and B. The correlation coefficients were .916 for A and .878 for B. Calculating mFIM at discharge from mFIM effectiveness predicted by multiple regression analysis had a higher degree of predictive accuracy of mFIM at discharge than that directly predicted. Copyright © 2017 National Stroke Association. Published by Elsevier Inc. All rights reserved.

Use of Multiple Regression and Use-Availability Analyses in Determining Habitat Selection by Gray Squirrels (Sciurus Carolinensis)

Treesearch

John W. Edwards; Susan C. Loeb; David C. Guynn

1994-01-01

Multiple regression and use-availability analyses are two methods for examining habitat selection. Use-availability analysis is commonly used to evaluate macrohabitat selection whereas multiple regression analysis can be used to determine microhabitat selection. We compared these techniques using behavioral observations (n = 5534) and telemetry locations (n = 2089) of...

A simple linear regression method for quantitative trait loci linkage analysis with censored observations.

PubMed

Anderson, Carl A; McRae, Allan F; Visscher, Peter M

2006-07-01

Standard quantitative trait loci (QTL) mapping techniques commonly assume that the trait is both fully observed and normally distributed. When considering survival or age-at-onset traits these assumptions are often incorrect. Methods have been developed to map QTL for survival traits; however, they are both computationally intensive and not available in standard genome analysis software packages. We propose a grouped linear regression method for the analysis of continuous survival data. Using simulation we compare this method to both the Cox and Weibull proportional hazards models and a standard linear regression method that ignores censoring. The grouped linear regression method is of equivalent power to both the Cox and Weibull proportional hazards methods and is significantly better than the standard linear regression method when censored observations are present. The method is also robust to the proportion of censored individuals and the underlying distribution of the trait. On the basis of linear regression methodology, the grouped linear regression model is computationally simple and fast and can be implemented readily in freely available statistical software.

Functional disability and its predictors in systemic sclerosis: a study from the DeSScipher project within the EUSTAR group.

PubMed

Jaeger, Veronika K; Distler, Oliver; Maurer, Britta; Czirják, Laszlo; Lóránd, Veronika; Valentini, Gabriele; Vettori, Serena; Del Galdo, Francesco; Abignano, Giuseppina; Denton, Christopher; Nihtyanova, Svetlana; Allanore, Yannick; Avouac, Jerome; Riemekasten, Gabriele; Siegert, Elise; Huscher, Dörte; Matucci-Cerinic, Marco; Guiducci, Serena; Frerix, Marc; Tarner, Ingo H; Garay Toth, Beata; Fankhauser, Beat; Umbricht, Jörg; Zakharova, Anastasia; Mihai, Carina; Cozzi, Franco; Yavuz, Sule; Hunzelmann, Nicolas; Rednic, Simona; Vacca, Alessandra; Schmeiser, Tim; Riccieri, Valeria; García de la Peña Lefebvre, Paloma; Gabrielli, Armando; Krummel-Lorenz, Brigitte; Martinovic, Duska; Ancuta, Codrina; Smith, Vanessa; Müller-Ladner, Ulf; Walker, Ulrich A

2018-03-01

The multisystem manifestations of SSc can greatly impact patients' quality of life. The aim of this study was to identify factors associated with disability in SSc. SSc patients from the prospective DeSScipher cohort who had completed the scleroderma health assessment questionnaire (SHAQ), a disability score that combines the health assessment questionnaire and five visual analogue scales, were included in this analysis. The effect of factors possibly associated with disability was analysed with multiple linear regressions. The mean SHAQ and HAQ scores of the 944 patients included were 0.87 (s.d. = 0.66) and 0.92 (s.d. = 0.78); 59% of the patients were in the mild to moderate difficulty SHAQ category (0 ⩽ SHAQ < 1), 34% in the moderate to severe disability category (1 ⩽ SHAQ < 2) and 7% in the severe to very severe disability category (2 ⩽ SHAQ ⩽ 3). The means of the visual analogue scales scores were in order of magnitude: overall disease severity (37 mm), RP (31 mm), pulmonary symptoms (24 mm), gastrointestinal symptoms (20 mm) and digital ulcers (19 mm). In multiple regression, the main factors associated with high SHAQ scores were the presence of dyspnoea [modified New York Heart Association (NYHA) class IV (regression coefficient B = 0.62), modified NYHA class III (B = 0.53) and modified NYHA class II (B = 0.21; all vs modified NYHA class I)], FM (B = 0.37), muscle weakness (B = 0.27), digital ulcers (B = 0.20) and gastrointestinal symptoms (oesophageal symptoms, B = 0.16; stomach symptoms, B = 0.15; intestinal symptoms, B = 0.15). SSc patients perceive dyspnoea, pain, digital ulcers, muscle weakness and gastrointestinal symptoms as the main factors driving their level of disability, unlike physicians who emphasize objective measures of disability. © The Author 2017. Published by Oxford University Press on behalf of the British Society for Rheumatology. All rights reserved. For Permissions, please email: journals.permissions@oup.com

Investigating the Determinants of Toxoplasma gondii Prevalence in Meat: A Systematic Review and Meta-Regression.

PubMed

Belluco, Simone; Mancin, Marzia; Conficoni, Daniele; Simonato, Giulia; Pietrobelli, Mario; Ricci, Antonia

2016-01-01

Toxoplasma gondii is one of the most widespread parasites in humans and can cause severe illness in immunocompromised individuals. However, its role in healthy people is probably under-appreciated. The complex epidemiology of this protozoan recognizes several infection routes but consumption of contaminated food is likely to be the predominant one. Among food, consumption of raw and undercooked meat is a relevant route of transmission, but the role of different meat producing animal species and meats thereof is controversial. The aim of the present work is to summarize and analyse literature data reporting prevalence estimates of T. gondii in meat animals/meats. We searched Medline, Web of Science, Science Direct (last update 31/03/2015). Relevant papers should report data from primary studies dealing with the prevalence of T. gondii in meat from livestock species as obtained through direct detection methods. Meta-analysis and meta-regression were performed. Of 1915 papers screened, 69 papers were included, dealing mainly with cattle, pigs and sheep. Pooled prevalences, based on random-effect models, were 2.6% (CI95 [0.5-5.8]) for cattle, 12.3% (CI95 [7.6-17.8]) for pigs and 14.7% (CI95 [8.9-21.5]) for sheep. Due to the high heterogeneity observed, univariable and multivariable meta-regression models were fitted showing that the geographic area for cattle (p = 0.032), the farming type for pigs (p = 0.0004) and the sample composition for sheep (p = 0.03) had significant effects on the prevalences of Toxoplasma detected/estimated. Moreover, the role of different animal species was dependent on the geographic location of animals' origin. Limitations were due mainly to a possible publication bias. The present work confirms the role of meat, including beef, as T. gondii sources, and highlights the need for a control system for this parasite to be implemented along the meat production chain. Moreover, consumer knowledge should be strengthened in order to reduce the impact of disease.

Is there a correlation between right bronchus length and diameter with age?

PubMed

Otoch, José Pinhata; Minamoto, Hélio; Perini, Marcos; Carneiro, Fred Olavo; de Almeida Artifon, Everson Luiz

2013-06-01

Right main bronchial anatomy knowledge is essential to guide endoscopic stent placement in modern era. The aim is to describe right bronchial anatomy, cross-area and its relation with the right pulmonary artery and patient's age. One hundred thirty four cadaveric specimens were studied after approval by the Research and Ethics Committee at the University of São Paulo Medical School and Medical Forensic Institute of São Paulo. All necropsies were performed in natura after 24 hours of death and patients with previous pulmonary disease were excluded. Landmarks to start measurement were the first tracheal ring, vertex of carina, first right bronchial ring, and right pulmonary artery area over the right main bronchus. After mobilization, the specimens were measured using a caliper and measurement of distances was recorded in centimeters at landmarks points. All the measures (distances, cross sectional area and planes) were performed by three independent observers and recorded as mean, standard error and ranges. Student t test was used to compare means and linear regression was applied to correlate the measurements. From 134 specimens studied, 34 were excluded (10 with previous history of pulmonary diseases, surgery or deformities and 24 of female gender). Linear regression showed proportionality between tracheal length and right bronchus length; with the area at first tracheal ring and carina and also between the cross sectional area at these points. Linear regression analysis between tracheal length and age (R=0.593 P<0.005), right bronchus length and age (R=0.523, P<0.005), area of contact between right bronchus and right pulmonary artery and age (R=0.35, P<0.005). We can conclude that large airways grow progressively with increasing age in male gender. There was a direct correlation between age and tracheal length; as has age and right bronchus length. There was a direct correlation between age and the area of the right bronchus covered by the right pulmonary artery.

Kepler AutoRegressive Planet Search (KARPS)

NASA Astrophysics Data System (ADS)

Caceres, Gabriel

2018-01-01

One of the main obstacles in detecting faint planetary transits is the intrinsic stellar variability of the host star. The Kepler AutoRegressive Planet Search (KARPS) project implements statistical methodology associated with autoregressive processes (in particular, ARIMA and ARFIMA) to model stellar lightcurves in order to improve exoplanet transit detection. We also develop a novel Transit Comb Filter (TCF) applied to the AR residuals which provides a periodogram analogous to the standard Box-fitting Least Squares (BLS) periodogram. We train a random forest classifier on known Kepler Objects of Interest (KOIs) using select features from different stages of this analysis, and then use ROC curves to define and calibrate the criteria to recover the KOI planet candidates with high fidelity. These statistical methods are detailed in a contributed poster (Feigelson et al., this meeting).These procedures are applied to the full DR25 dataset of NASA’s Kepler mission. Using the classification criteria, a vast majority of known KOIs are recovered and dozens of new KARPS Candidate Planets (KCPs) discovered, including ultra-short period exoplanets. The KCPs will be briefly presented and discussed.

Drying kinetics and characteristics of combined infrared-vacuum drying of button mushroom slices

NASA Astrophysics Data System (ADS)

Salehi, Fakhreddin; Kashaninejad, Mahdi; Jafarianlari, Ali

2017-05-01

Infrared-vacuum drying characteristics of button mushroom ( Agaricus bisporus) were evaluated in a combined dryer system. The effects of drying parameters, including infrared radiation power (150-375 W), system pressure (5-15 kPa) and time (0-160 min) on the drying kinetics and characteristics of button mushroom slices were investigated. Both the infrared lamp power and vacuum pressure influenced the drying time of button mushroom slices. The rate constants of the nine different kinetic's models for thin layer drying were established by nonlinear regression analysis of the experimental data which were found to be affected mainly by the infrared power level while system pressure had a little effect on the moisture ratios. The regression results showed that the Page model satisfactorily described the drying behavior of button mushroom slices with highest R value and lowest SE values. The effective moisture diffusivity increases as power increases and range between 0.83 and 2.33 × 10-9 m2/s. The rise in infrared power has a negative effect on the ΔE and with increasing in infrared radiation power it was increased.

Urban change analysis and future growth of Istanbul.

PubMed

Akın, Anıl; Sunar, Filiz; Berberoğlu, Süha

2015-08-01

This study is aimed at analyzing urban change within Istanbul and assessing the city's future growth potential using appropriate approach modeling for the year 2040. Urban growth is a major driving force of land-use change, and spatial and temporal components of urbanization can be identified through accurate spatial modeling. In this context, widely used urban modeling approaches, such as the Markov chain and logistic regression based on cellular automata (CA), were used to simulate urban growth within Istanbul. The distance from each pixel to the urban and road classes, elevation, and slope, together with municipality and land use maps (as an excluded layer), were identified as factors. Calibration data were obtained from remotely sensed data recorded in 1972, 1986, and 2013. Validation was performed by overlaying the simulated and actual 2013 urban maps, and a kappa index of agreement was derived. The results indicate that urban expansion will influence mainly forest areas during the time period of 2013-2040. The urban expansion was predicted as 429 and 327 km(2) with the Markov chain and logistic regression models, respectively.

Message frames interact with motivational systems to determine depth of message processing.

PubMed

Shen, Lijiang; Dillard, James Price

2009-09-01

Although several theoretical perspectives predict that negatively framed messages will be processed more deeply than positively framed messages, a recent meta-analysis found no such difference. In this article, the authors explore 2 explanations for this inconsistency. One possibility is methodological: the statistics used in the primary studies underestimated framing effects on depth of message processing because the data were maldistributed. The other is theoretical: the absence of a main effect is veridical, but framing interacts with individual differences that predispose individuals to greater or lesser depth of processing. Data from 2 experiments (Ns = 286 and 252) were analyzed via tobit regression, a technique designed to overcome the limitations of maldistributed data. One study showed the predicted main effect for framing, but the other did not. Both studies showed the anticipated interaction: Depth of processing correlated positively with a measure of the behavioral activation system in the advantage framing condition, whereas depth of processing correlated positively with the behavioral inhibition system in the disadvantage framing condition.

GENETIC AND ENVIRONMENTAL CONTRIBUTIONS TO THE CO-OCCURRENCE OF DEPRESSIVE PERSONALITY DISORDER AND DSM-IV PERSONALITY DISORDERS

PubMed Central

Ørstavik, Ragnhild E.; Kendler, Kenneth S.; Røysamb, Espen; Czajkowski, Nikolai; Tambs, Kristian; Reichborn-Kjennerud, Ted

2012-01-01

One of the main controversies with regard to depressive personality disorder (DPD) concerns the co-occurrence with the established DSM-IV personality disorders (PDs). The main aim of this study was to examine to what extent DPD and the DSM-IV PDs share genetic and environmental risk factors, using multivariate twin modeling. The DSM-IV Structured Interview for Personality was applied to 2,794 young adult twins. Paranoid PD from Cluster A, borderline PD from Cluster B, and all three PDs from Cluster C were independently and significantly associated with DPD in multiple regression analysis. The genetic correlations between DPD and the other PDs were strong (.53–.83), while the environmental correlations were moderate (.36–.40). Close to 50% of the total variance in DPD was disorder specific. However, only 5% was due to disorder-specific genetic factors, indicating that a substantial part of the genetic vulnerability to DPD also increases the vulnerability to other PDs. PMID:22686231

Genetic and environmental contributions to the co-occurrence of depressive personality disorder and DSM-IV personality disorders.

PubMed

Ørstavik, Ragnhild E; Kendler, Kenneth S; Røysamb, Espen; Czajkowski, Nikolai; Tambs, Kristian; Reichborn-Kjennerud, Ted

2012-06-01

One of the main controversies with regard to depressive personality disorder (DPD) concerns the co-occurrence with the established DSM-IV personality disorders (PDs). The main aim of this study was to examine to what extent DPD and the DSM-IV PDs share genetic and environmental risk factors, using multivariate twin modeling. The DSM-IV Structured Interview for Personality was applied to 2,794 young adult twins. Paranoid PD from Cluster A, borderline PD from Cluster B, and all three PDs from Cluster C were independently and significantly associated with DPD in multiple regression analysis. The genetic correlations between DPD and the other PDs were strong (.53-.83), while the environmental correlations were moderate (.36-.40). Close to 50% of the total variance in DPD was disorder specific. However, only 5% was due to disorder-specific genetic factors, indicating that a substantial part of the genetic vulnerability to DPD also increases the vulnerability to other PDs.

Relations between episodic memory, suggestibility, theory of mind, and cognitive inhibition in the preschool child.

PubMed

Melinder, Annika; Endestad, Tor; Magnussen, Svein

2006-12-01

The development of episodic memory, its relation to theory of mind (ToM), executive functions (e.g., cognitive inhibition), and to suggestibility was studied. Children (n= 115) between 3 and 6 years of age saw two versions of a video film and were tested for their memory of critical elements of the videos. Results indicated similar developmental trends for all memory measures, ToM, and inhibition, but ToM and inhibition were not associated with any memory measures. Correlations involving source memory was found in relation to specific questions, whereas inhibition and ToM were significantly correlated to resistance to suggestions. A regression analysis showed that age was the main contributor to resistance to suggestions, to correct source monitoring, and to correct responses to specific questions. Inhibition was also a significant main predictor of resistance to suggestive questions, whereas the relative contribution of ToM was wiped out when an extended model was tested.

Family socialization of adolescent's self-reported cigarette use: the role of parents' history of regular smoking and parenting style.

PubMed

Foster, Sarah E; Jones, Deborah J; Olson, Ardis L; Forehand, Rex; Gaffney, Cecelia A; Zens, Michael S; Bau, J J

2007-05-01

To examine the main and interactive effects of parental history of regular cigarette smoking and parenting style on adolescent self-reported cigarette use. Predictors of adolescent self-reported cigarette use, including parents' history of regular cigarette smoking and two dimensions of parenting behavior, were analyzed in a sample of 934 predominately Caucasian (96.3%) parent-adolescent dyads. Families were drawn from the control group of a randomized control trial aimed at preventing adolescent substance use. In addition to the main effects of parents' history of regular smoking and parental warmth, logistic regression analysis revealed that the interaction of these two variables was associated with adolescent self-reported cigarette use. Parental warmth was associated with a decreased likelihood of the adolescent ever having smoked a cigarette; however, this was true only if neither parent had a history of regular cigarette smoking. Findings suggest that adolescent smoking prevention programs may be more efficacious if they address both parental history of regular smoking and parenting behavior.

On the Validity of Validity Scales: The Importance of Defensive Responding in the Prediction of Institutional Misconduct

ERIC Educational Resources Information Center

Edens, John F.; Ruiz, Mark A.

2006-01-01

This study examined the effects of defensive responding on the prediction of institutional misconduct among male inmates (N = 349) who completed the Personality Assessment Inventory (L. C. Morey, 1991). Hierarchical logistic regression analyses demonstrated significant main effects for the Antisocial Features (ANT) scale as well as main effects…

Clinical verification of a theory for predicting side branch stenosis after main vessel stenting in coronary bifurcation lesions.

PubMed

Vassilev, Dobrin; Gil, Robert

2008-12-01

To verify in a clinical scenario a theory for predicting side branch (SB) stenosis after main vessel stent implantation in coronary bifurcation lesions. Many unresolved issues remain regarding SB compromise when the parent vessel is stented. Bifurcation lesions (all Medina types) were subjected to angiographic analysis to determine the angle, defined as alpha, between the axes of the parent vessel and the SB. Using the prediction that the percent diameter stenosis (%DS) is equal to the cosine of angle alpha and relating it to a formula to determine the minimal lumen diameter (MLD) led to the following equation: MLD = ds x (1 -cos alpha); ds refers to the diameter of the SB. The predicted and observed SB stenosis values following angiography were compared. Fifty-two patients with 57 lesions were included in the analysis. Patient demographics and characteristics were similar to those in previous studies. There was a high coefficient of determination between the predicted and observed values of %DS (r(2)= 0.82, P < 0.001) and MLD (r(2)= 0.86, P < 0.001). We determined a cutoff value of 70% for predicted %DS for SB closure. When using multivariate regression analysis, the only predictor of SB ostial stenosis after stenting was alpha angle, whereas the predictors of MLD included the angle alpha and the RVD of the SB. Our analysis shows that the most powerful independent predictor of SB compromise is a new variable angle alpha.

Prediction by regression and intrarange data scatter in surface-process studies

USGS Publications Warehouse

Toy, T.J.; Osterkamp, W.R.; Renard, K.G.

1993-01-01

Modeling is a major component of contemporary earth science, and regression analysis occupies a central position in the parameterization, calibration, and validation of geomorphic and hydrologic models. Although this methodology can be used in many ways, we are primarily concerned with the prediction of values for one variable from another variable. Examination of the literature reveals considerable inconsistency in the presentation of the results of regression analysis and the occurrence of patterns in the scatter of data points about the regression line. Both circumstances confound utilization and evaluation of the models. Statisticians are well aware of various problems associated with the use of regression analysis and offer improved practices; often, however, their guidelines are not followed. After a review of the aforementioned circumstances and until standard criteria for model evaluation become established, we recommend, as a minimum, inclusion of scatter diagrams, the standard error of the estimate, and sample size in reporting the results of regression analyses for most surface-process studies. ?? 1993 Springer-Verlag.

Quantile regression for the statistical analysis of immunological data with many non-detects.

PubMed

Eilers, Paul H C; Röder, Esther; Savelkoul, Huub F J; van Wijk, Roy Gerth

2012-07-07

Immunological parameters are hard to measure. A well-known problem is the occurrence of values below the detection limit, the non-detects. Non-detects are a nuisance, because classical statistical analyses, like ANOVA and regression, cannot be applied. The more advanced statistical techniques currently available for the analysis of datasets with non-detects can only be used if a small percentage of the data are non-detects. Quantile regression, a generalization of percentiles to regression models, models the median or higher percentiles and tolerates very high numbers of non-detects. We present a non-technical introduction and illustrate it with an implementation to real data from a clinical trial. We show that by using quantile regression, groups can be compared and that meaningful linear trends can be computed, even if more than half of the data consists of non-detects. Quantile regression is a valuable addition to the statistical methods that can be used for the analysis of immunological datasets with non-detects.

Noninvasive spectral imaging of skin chromophores based on multiple regression analysis aided by Monte Carlo simulation

NASA Astrophysics Data System (ADS)

Nishidate, Izumi; Wiswadarma, Aditya; Hase, Yota; Tanaka, Noriyuki; Maeda, Takaaki; Niizeki, Kyuichi; Aizu, Yoshihisa

2011-08-01

In order to visualize melanin and blood concentrations and oxygen saturation in human skin tissue, a simple imaging technique based on multispectral diffuse reflectance images acquired at six wavelengths (500, 520, 540, 560, 580 and 600nm) was developed. The technique utilizes multiple regression analysis aided by Monte Carlo simulation for diffuse reflectance spectra. Using the absorbance spectrum as a response variable and the extinction coefficients of melanin, oxygenated hemoglobin, and deoxygenated hemoglobin as predictor variables, multiple regression analysis provides regression coefficients. Concentrations of melanin and total blood are then determined from the regression coefficients using conversion vectors that are deduced numerically in advance, while oxygen saturation is obtained directly from the regression coefficients. Experiments with a tissue-like agar gel phantom validated the method. In vivo experiments with human skin of the human hand during upper limb occlusion and of the inner forearm exposed to UV irradiation demonstrated the ability of the method to evaluate physiological reactions of human skin tissue.

Factors influencing psychosocial adjustment and quality of life in Parkinson patients and informal caregivers.

PubMed

Navarta-Sánchez, María Victoria; Senosiain García, Juana M; Riverol, Mario; Ursúa Sesma, María Eugenia; Díaz de Cerio Ayesa, Sara; Anaut Bravo, Sagrario; Caparrós Civera, Neus; Portillo, Mari Carmen

2016-08-01

The influence that social conditions and personal attitudes may have on the quality of life (QoL) of Parkinson's disease (PD) patients and informal caregivers does not receive enough attention in health care, as a result of it not being clearly identified, especially in informal caregivers. The aim of this study was to provide a comprehensive analysis of psychosocial adjustment and QoL determinants in PD patients and informal caregivers. Ninety-one PD patients and 83 caregivers participated in the study. Multiple regression analyses were performed including benefit finding, coping, disease severity and socio-demographic factors, in order to determine how these aspects influence the psychosocial adjustment and QoL in PD patients and caregivers. Regression models showed that severity of PD was the main predictor of psychosocial adjustment and QoL in patients. Nevertheless, multiple regression analyses also revealed that coping was a significant predictor of psychosocial adjustment in patients and caregivers. Furthermore, psychosocial adjustment was significantly related to QoL in patients and caregivers. Also, coping and benefit finding were predictors of QoL in caregivers but not in patients. Multidisciplinary interventions aimed at improving PD patients' QoL may have more effective outcomes if education about coping skills, and how these can help towards a positive psychosocial adjustment to illness, were included, and targeted not only at patients, but also at informal caregivers.

Application of face centred central composite design to optimise compression force and tablet diameter for the formulation of mechanically strong and fast disintegrating orodispersible tablets.

PubMed

Pabari, Ritesh M; Ramtoola, Zebunnissa

2012-07-01

A two factor, three level (3(2)) face centred, central composite design (CCD) was applied to investigate the main and interaction effects of tablet diameter and compression force (CF) on hardness, disintegration time (DT) and porosity of mannitol based orodispersible tablets (ODTs). Tablet diameters of 10, 13 and 15 mm, and CF of 10, 15 and 20 kN were studied. Results of multiple linear regression analysis show that both the tablet diameter and CF influence tablet characteristics. A negative value of regression coefficient for tablet diameter showed an inverse relationship with hardness and DT. A positive value of regression coefficient for CF indicated an increase in hardness and DT with increasing CF as a result of the decrease in tablet porosity. Interestingly, at the larger tablet diameter of 15 mm, while hardness increased and porosity decreased with an increase in CF, the DT was resistant to change. The optimised combination was a tablet of 15 mm diameter compressed at 15 kN showing a rapid DT of 37.7s and high hardness of 71.4N. Using these parameters, ODTs containing ibuprofen showed no significant change in DT (ANOVA; p>0.05) irrespective of the hydrophobicity of the ibuprofen. Copyright © 2012 Elsevier B.V. All rights reserved.

Change in the discontinuation pattern of tumour necrosis factor antagonists in rheumatoid arthritis over 10 years: data from the Spanish registry BIOBADASER 2.0.

PubMed

Gómez-Reino, Juan J; Rodríguez-Lozano, Carlos; Campos-Fernández, Cristina; Montoro, María; Descalzo, Miguel Ángel; Carmona, Loreto

2012-03-01

To investigate in rheumatoid arthritis (RA) the rate and reason of discontinuation of tumour necrosis factor (TNF) antagonists over the past decade. RA patients in BIOBADASER 2.0 were stratified according to the start date of their first TNF antagonist into 2000-3, 2004-6 and 2007-9 interval years. Cumulative incidence function of discontinuation for inefficacy or toxicity was estimated with the alternative reason as competing risk. Competing risks regression models were used to measure the association of study groups with covariates and reasons for discontinuation. Association is expressed as subhazard ratios (SHR). 2907 RA patients were included in the study. Competing risk regression for inefficacy shows larger SHR for patients starting treatment in 2004-6 (SHR 2.57; 95% CI 1.55 to 4.25) and 2007-9 (SHR 3.4; 95% CI 2.08 to 5.55) than for those starting in 2000-3, after adjusting for TNF antagonists, clinical activity and concomitant treatment. Competing risk regression analysis for adverse events revealed no differences across the three time intervals. In RA, the discontinuation rate of TNF antagonists in the first year of treatment is higher more recently than a decade ago, inefficacy being the main reason for the increased rate. The rate of discontinuation for adverse events has remained stable.

CADDIS Volume 4. Data Analysis: PECBO Appendix - R Scripts for Non-Parametric Regressions

EPA Pesticide Factsheets

Script for computing nonparametric regression analysis. Overview of using scripts to infer environmental conditions from biological observations, statistically estimating species-environment relationships, statistical scripts.

Process-based organization design and hospital efficiency.

PubMed

Vera, Antonio; Kuntz, Ludwig

2007-01-01

The central idea of process-based organization design is that organizing a firm around core business processes leads to cost reductions and quality improvements. We investigated theoretically and empirically whether the implementation of a process-based organization design is advisable in hospitals. The data came from a database compiled by the Statistical Office of the German federal state of Rheinland-Pfalz and from a written questionnaire, which was sent to the chief executive officers (CEOs) of all 92 hospitals in this federal state. We used data envelopment analysis (DEA) to measure hospital efficiency, and factor analysis and regression analysis to test our hypothesis. Our principal finding is that a high degree of process-based organization has a moderate but significant positive effect on the efficiency of hospitals. The main implication is that hospitals should implement a process-based organization to improve their efficiency. However, to actually achieve positive effects on efficiency, it is of paramount importance to observe some implementation rules, in particular to mobilize physician participation and to create an adequate organizational culture.

A meta-analysis of interleukin-10-1082 promoter genetic polymorphism associated with atherosclerotic risk.

PubMed

Chao, Li; Lei, Huang; Fei, Jin

2014-01-01

This meta-analysis was conducted to assess the relationship between interleukin-10-1082 G/A single nucleotide polymorphism with atherosclerosis (AS) risk. The databases of PubMed, EMBASE, Chinese National Knowledge Infrastructure and Wan-Fang were searched from January 2000 to January 2014. 16 studies (involving 7779 cases and 7271 controls) were finally included. Each eligible study was scored for quality assessment. We adopted the most probably appropriate genetic model (recessive model) after carefully calculation. Between study heterogeneity was explored by subgroup analysis and publication bias was estimated by Begg's funnel plot and Egger's regression test. Statistically significant association was observed between AA genotype with overall AS risk, being mainly in coronary heart disease and stroke subgroups among Asian population, and peripheral artery disease (PAD) subgroup among Caucasians. Interleukin-10-1082 AA genotype is associated with increased overall AS risk. AA carriers of Asians seem to be more susceptible to coronary artery disease and stroke, and Caucasians are more susceptible to PAD.

Genetic diversity studies and identification of SSR markers associated with Fusarium wilt (Fusarium udum) resistance in cultivated pigeonpea (Cajanus cajan).

PubMed

Singh, A K; Rai, V P; Chand, R; Singh, R P; Singh, M N

2013-01-01

Genetic diversity and identification of simple sequence repeat markers correlated with Fusarium wilt resistance was performed in a set of 36 elite cultivated pigeonpea genotypes differing in levels of resistance to Fusarium wilt. Twenty-four polymorphic sequence repeat markers were screened across these genotypes, and amplified a total of 59 alleles with an average high polymorphic information content value of 0.52. Cluster analysis, done by UPGMA and PCA, grouped the 36 pigeonpea genotypes into two main clusters according to their Fusarium wilt reaction. Based on the Kruskal-Wallis ANOVA and simple regression analysis, six simple sequence repeat markers were found to be significantly associated with Fusarium wilt resistance. The phenotypic variation explained by these markers ranged from 23.7 to 56.4%. The present study helps in finding out feasibility of prescreened SSR markers to be used in genetic diversity analysis and their potential association with disease resistance.

Statistical analysis and isotherm study of uranium biosorption by Padina sp. algae biomass.

PubMed

Khani, Mohammad Hassan

2011-06-01

The application of response surface methodology is presented for optimizing the removal of U ions from aqueous solutions using Padina sp., a brown marine algal biomass. Box-Wilson central composite design was employed to assess individual and interactive effects of the four main parameters (pH and initial uranium concentration in solutions, contact time and temperature) on uranium uptake. Response surface analysis showed that the data were adequately fitted to second-order polynomial model. Analysis of variance showed a high coefficient of determination value (R (2)=0.9746) and satisfactory second-order regression model was derived. The optimum pH and initial uranium concentration in solutions, contact time and temperature were found to be 4.07, 778.48 mg/l, 74.31 min, and 37.47°C, respectively. Maximized uranium uptake was predicted and experimentally validated. The equilibrium data for biosorption of U onto the Padina sp. were well represented by the Langmuir isotherm, giving maximum monolayer adsorption capacity as high as 376.73 mg/g.

Hypothyroidism among SLE patients: Case-control study.

PubMed

Watad, Abdulla; Mahroum, Naim; Whitby, Aaron; Gertel, Smadar; Comaneshter, Doron; Cohen, Arnon D; Amital, Howard

2016-05-01

The prevalence of hypothyroidism in SLE patients varies considerably and early reports were mainly based on small cohorts. To investigate the association between SLE and hypothyroidism. Patients with SLE were compared with age and sex-matched controls regarding the proportion of hypothyroidism in a case-control study. Chi-square and t-tests were used for univariate analysis and a logistic regression model was used for multivariate analysis. The study was performed utilizing the medical database of Clalit Health Services. The study included 5018 patients with SLE and 25,090 age and sex-matched controls. The proportion of hypothyroidism in patients with SLE was increased compared with the prevalence in controls (15.58% and 5.75%, respectively, P<0.001). In a multivariate analysis, SLE was associated with hypothyroidism (odds ratio 2.644, 95% confidence interval 2.405-2.908). Patients with SLE have a greater proportion of hypothyroidism than matched controls. Therefore, physicians treating patients with SLE should be aware of the possibility of thyroid dysfunction. Copyright © 2016 Elsevier B.V. All rights reserved.

Tobacco use in popular movies during the past decade

PubMed Central

Mekemson, C; Glik, D; Titus, K; Myerson, A; Shaivitz, A; Ang, A; Mitchell, S

2004-01-01

Objective: The top 50 commercially successful films released per year from 1991 to 2000 were content coded to assess trends in tobacco use over time and attributes of films predictive of higher smoking rates. Design: This observational study used media content analysis methods to generate data about tobacco use depictions in films studied (n = 497). Films are the basic unit of analysis. Once films were coded and preliminary analysis completed, outcome data were transformed to approximate multivariate normality before being analysed with general linear models and longitudinal mixed method regression methods. Main outcome measures: Tobacco use per minute of film was the main outcome measure used. Predictor variables include attributes of films and actors. Tobacco use was defined as any cigarette, cigar, and chewing tobacco use as well as the display of smoke and cigarette paraphernalia such as ashtrays, brand names, or logos within frames of films reviewed. Results: Smoking rates in the top films fluctuated yearly over the decade with an overall modest downward trend (p < 0.005), with the exception of R rated films where rates went up. Conclusions: The decrease in smoking rates found in films in the past decade is modest given extensive efforts to educate the entertainment industry on this issue over the past decade. Monitoring, education, advocacy, and policy change to bring tobacco depiction rates down further should continue. PMID:15564625

Optimization of Extraction Process for Antidiabetic and Antioxidant Activities of Kursi Wufarikun Ziyabit Using Response Surface Methodology and Quantitative Analysis of Main Components.

PubMed

Edirs, Salamet; Turak, Ablajan; Numonov, Sodik; Xin, Xuelei; Aisa, Haji Akber

2017-01-01

By using extraction yield, total polyphenolic content, antidiabetic activities (PTP-1B and α -glycosidase), and antioxidant activity (ABTS and DPPH) as indicated markers, the extraction conditions of the prescription Kursi Wufarikun Ziyabit (KWZ) were optimized by response surface methodology (RSM). Independent variables were ethanol concentration, extraction temperature, solid-to-solvent ratio, and extraction time. The result of RSM analysis showed that the four variables investigated have a significant effect ( p < 0.05) for Y 1 , Y 2 , Y 3 , Y 4 , and Y 5 with R 2 value of 0.9120, 0.9793, 0.9076, 0.9125, and 0.9709, respectively. Optimal conditions for the highest extraction yield of 39.28%, PTP-1B inhibition rate of 86.21%, α -glycosidase enzymes inhibition rate of 96.56%, and ABTS inhibition rate of 77.38% were derived at ethanol concentration 50.11%, extraction temperature 72.06°C, solid-to-solvent ratio 1 : 22.73 g/mL, and extraction time 2.93 h. On the basis of total polyphenol content of 48.44% in this optimal condition, the quantitative analysis of effective part of KWZ was characterized via UPLC method, 12 main components were identified by standard compounds, and all of them have shown good regression within the test ranges and the total content of them was 11.18%.

Nutrition knowledge, and use and understanding of nutrition information on food labels among consumers in the UK.

PubMed

Grunert, Klaus G; Wills, Josephine M; Fernández-Celemín, Laura

2010-10-01

Based on in-store observations in three major UK retailers, in-store interviews (2019) and questionnaires filled out at home and returned (921), use of nutrition information on food labels and its understanding were investigated. Respondents' nutrition knowledge was also measured, using a comprehensive instrument covering knowledge of expert recommendations, nutrient content in different food products, and calorie content in different food products. Across six product categories, 27% of shoppers were found to have looked at nutrition information on the label, with guideline daily amount (GDA) labels and the nutrition grid/table as the main sources consulted. Respondents' understanding of major front-of-pack nutrition labels was measured using a variety of tasks dealing with conceptual understanding, substantial understanding and health inferences. Understanding was high, with up to 87.5% of respondents being able to identify the healthiest product in a set of three. Differences between level of understanding and level of usage are explained by different causal mechanisms. Regression analysis showed that usage is mainly related to interest in healthy eating, whereas understanding of nutrition information on food labels is mainly related to nutrition knowledge. Both are in turn affected by demographic variables, but in different ways.

[Analysis of the prevalence and risk factors of preoperative angiography confirmed coronary artery stenosis in patients with degenerative valvular heart disease].

PubMed

Xu, Z J; Pan, J; Zhou, Q; Wang, D J

2017-10-24

Objective: To estimate the prevalence and the risk factors of preoperative coronary angiography (CAG) confirmed coronary stenosis in patients with degenerative valvular heart disease. Methods: A total of 491 patients who underwent screening CAG before valvular surgery due to degenerative valvular heart disease were enrolled from January 2011 to September 2014 in our hospital, and clinical data were analyzed. According to CAG results, patients were divided into positive CAG result (PCAG) group or negative CAG (NCAG) group. Positive CAG result was defined as stenosis ≥50% of the diameter of the left main coronary artery or stenosis ≥70% of the diameter of left anterior descending, left circumflex artery, and right coronary artery.Risk factors of positive CAG result were analyzed by multivariable logistic regression analysis, and Bootstrap method was used to verify the results. Results: There were 47(9.57%)degenerative valvular heart disease patients with PCAG. Patients were older ((68.0±7.6)years vs.(62.6±7.1)years, P <0.001) and the prevalence of typical angina was significantly higher (14.89%(7/47)vs. 2.03%(9/444), P <0.001)in PCAG group than in NCAG group. Multivariable logistic regression analysis showed that age ( OR =1.118, 95% CI 1.067-1.172, P <0.001), typical angina ( OR =8.970, 95% CI 2.963-27.154, P <0.001), and serum concentration of apolipoprotein B ( OR =20.311, 95% CI 4.774-86.416, P <0.001) were the independent risk factors of PCAG in degenerative valvular heart disease patients. Bootstrap method revealed satisfactory repeatability of multivariable logistic regression analysis results (age: OR =1.118, 95% CI 1.068-1.178, P =0.001; typical angina: OR =8.970, 95% CI 2.338-35.891, P =0.001; serum concentration of apolipoprotein B: OR =20.311, 95% CI 4.639-91.977, P =0.001). Conclusions: A low prevalence of PCAG before valvular surgery is observed in degenerative valvular heart disease patients in this patient cohort. Age, typical angina, and serum concentration of apolipoprotein B are independent risk factors of PCAG in this patient cohort.

Clinical evaluation of a novel population-based regression analysis for detecting glaucomatous visual field progression.

PubMed

Kovalska, M P; Bürki, E; Schoetzau, A; Orguel, S F; Orguel, S; Grieshaber, M C

2011-04-01

The distinction of real progression from test variability in visual field (VF) series may be based on clinical judgment, on trend analysis based on follow-up of test parameters over time, or on identification of a significant change related to the mean of baseline exams (event analysis). The aim of this study was to compare a new population-based method (Octopus field analysis, OFA) with classic regression analyses and clinical judgment for detecting glaucomatous VF changes. 240 VF series of 240 patients with at least 9 consecutive examinations available were included into this study. They were independently classified by two experienced investigators. The results of such a classification served as a reference for comparison for the following statistical tests: (a) t-test global, (b) r-test global, (c) regression analysis of 10 VF clusters and (d) point-wise linear regression analysis. 32.5 % of the VF series were classified as progressive by the investigators. The sensitivity and specificity were 89.7 % and 92.0 % for r-test, and 73.1 % and 93.8 % for the t-test, respectively. In the point-wise linear regression analysis, the specificity was comparable (89.5 % versus 92 %), but the sensitivity was clearly lower than in the r-test (22.4 % versus 89.7 %) at a significance level of p = 0.01. A regression analysis for the 10 VF clusters showed a markedly higher sensitivity for the r-test (37.7 %) than the t-test (14.1 %) at a similar specificity (88.3 % versus 93.8 %) for a significant trend (p = 0.005). In regard to the cluster distribution, the paracentral clusters and the superior nasal hemifield progressed most frequently. The population-based regression analysis seems to be superior to the trend analysis in detecting VF progression in glaucoma, and may eliminate the drawbacks of the event analysis. Further, it may assist the clinician in the evaluation of VF series and may allow better visualization of the correlation between function and structure owing to VF clusters. © Georg Thieme Verlag KG Stuttgart · New York.

Using "Excel" for White's Test--An Important Technique for Evaluating the Equality of Variance Assumption and Model Specification in a Regression Analysis

ERIC Educational Resources Information Center

Berenson, Mark L.

2013-01-01

There is consensus in the statistical literature that severe departures from its assumptions invalidate the use of regression modeling for purposes of inference. The assumptions of regression modeling are usually evaluated subjectively through visual, graphic displays in a residual analysis but such an approach, taken alone, may be insufficient…

Rex fortran 4 system for combinatorial screening or conventional analysis of multivariate regressions

Treesearch

L.R. Grosenbaugh

1967-01-01

Describes an expansible computerized system that provides data needed in regression or covariance analysis of as many as 50 variables, 8 of which may be dependent. Alternatively, it can screen variously generated combinations of independent variables to find the regression with the smallest mean-squared-residual, which will be fitted if desired. The user can easily...

Transmission Risks of Schistosomiasis Japonica: Extraction from Back-propagation Artificial Neural Network and Logistic Regression Model

PubMed Central

Xu, Jun-Fang; Xu, Jing; Li, Shi-Zhu; Jia, Tia-Wu; Huang, Xi-Bao; Zhang, Hua-Ming; Chen, Mei; Yang, Guo-Jing; Gao, Shu-Jing; Wang, Qing-Yun; Zhou, Xiao-Nong

2013-01-01

Background The transmission of schistosomiasis japonica in a local setting is still poorly understood in the lake regions of the People's Republic of China (P. R. China), and its transmission patterns are closely related to human, social and economic factors. Methodology/Principal Findings We aimed to apply the integrated approach of artificial neural network (ANN) and logistic regression model in assessment of transmission risks of Schistosoma japonicum with epidemiological data collected from 2339 villagers from 1247 households in six villages of Jiangling County, P.R. China. By using the back-propagation (BP) of the ANN model, 16 factors out of 27 factors were screened, and the top five factors ranked by the absolute value of mean impact value (MIV) were mainly related to human behavior, i.e. integration of water contact history and infection history, family with past infection, history of water contact, infection history, and infection times. The top five factors screened by the logistic regression model were mainly related to the social economics, i.e. village level, economic conditions of family, age group, education level, and infection times. The risk of human infection with S. japonicum is higher in the population who are at age 15 or younger, or with lower education, or with the higher infection rate of the village, or with poor family, and in the population with more than one time to be infected. Conclusion/Significance Both BP artificial neural network and logistic regression model established in a small scale suggested that individual behavior and socioeconomic status are the most important risk factors in the transmission of schistosomiasis japonica. It was reviewed that the young population (≤15) in higher-risk areas was the main target to be intervened for the disease transmission control. PMID:23556015

Micro-Raman spectroscopy on oral tissues

NASA Astrophysics Data System (ADS)

Zenone, F.; Lepore, M.; Perna, G.; Carmone, P.; Riccio, R.; Gaeta, G. M.; Capozzi, V.

2006-02-01

Micro-Raman Spectroscopy (μ-RS) provides a unique tool in medicine for a not invasive and real time analysis of biological tissue for biopsy and "in vivo" investigation. Based on the evaluation of molecular vibration frequencies, the μ-RS is able to detect the main molecular bonds of protein constituents, as the C-H and C-C ones. Changes in frequency or in the relative intensity of the vibration modes revealed by μ-RS can be related to changes of chemical bond and of protein structure induced by pathology. The μ-RS has been performed on samples of oral tissue from informed patients, affected by pemphigus vulgaris (an oral pathology) in an advanced regression state. The biopsies were thin slices (about 1mm thick) with 6mm diameter. The sample was measured through a 170 μm thick cover-glass. The experimental set-up was mainly composed by a He-Ne laser and a monochromator equipped with a Peltier cell and with a grating of 1800 grooves/mm. The laser light was focused on the sample surface by means of a long focal length 50X optical objective. The main protein bonds are clearly detectable in the considered samples and this give important information on the integrity and on the state of tissue components (lipids and proteins), and consequently on the occurrence of pathology. The potential application of this method for in vivo analysis is an invaluable alternative to biopsy and pathological examinations for many medical application as screening diagnostic, therapy progress examination, and surgical support.

ESTIMATING LOW-FLOW FREQUENCIES OF UNGAGED STREAMS IN NEW ENGLAND.

USGS Publications Warehouse

Wandle, S. William

1987-01-01

Equations to estimate low flows were developed using multiple-regression analysis with a sample of 48 river basins, which were selected from the U. S. Geological Survey's network of gaged river basins in Massachusetts, New Hampshire, Rhode Island, Vermont, and southwestern Maine. Low-flow characteristics are represented by the 7Q2 and 7Q10 (the annual minimum 7-day mean low flow at the 2- and 10-year recurrence intervals). These statistics for each of the 48 basins were determined from a low-flow frequency analysis of streamflow records for 1942-71, or from a graphical or mathematical relationship if the record did not cover this 30-year period. Estimators for the mean and variance of the 7-day low flows at the index and short-term sites were used for two stations where discharge measurements of base flow were available and for two sites where the graphical technique was unsatisfactory.

The Relationship of Gender and Self-Efficacy on Social Physique Anxiety among College Students.

PubMed

Rothberger, Sara M; Harris, Brandonn S; Czech, Daniel R; Melton, Bridget

The anxiety or fear associated with physique evaluation is defined as Social Physique Anxiety (SPA). Numerous studies have examined this construct, yet a gap exists exploring this phenomenon among current college students with SPA, self-efficacy, and gender concurrently. Therefore, the purposes of this study included quantitatively analyzing the association between SPA, gender, and self-efficacy. Participants included 237 students at a Southeastern university participating in jogging, body conditioning, or weight training courses. Analysis of Variance yielded a significant main effect for self-efficacy as well, as those with lower self-efficacy displayed higher levels of SPA ( p < 0.001). Stepwise regression analysis indicated self-efficacy and gender were both significant predictors of SPA. This information could aid in creating interventions designed to decrease the prevalence of SPA and increase levels of self-efficacy among the current college student population.

Epidemiological Distribution and Subtype Analysis of Premenstrual Dysphoric Disorder Syndromes and Symptoms Based on TCM Theories

PubMed Central

Wang, Haijun; Zhan, Xianghong; Liu, Hongqi; Wang, Xiaoyun; Li, Xia; Wang, Xiaoru; Wu, Jibiao

2017-01-01

We performed an epidemiological investigation of subjects with premenstrual dysphoric disorder (PMDD) to identify the clinical distribution of the major syndromes and symptoms. The pathogenesis of PMDD mainly involves the dysfunction of liver conveyance and dispersion. Excessive liver conveyance and dispersion are associated with liver-qi invasion syndrome, while insufficient liver conveyance and dispersion are expressed as liver-qi depression syndrome. Additionally, a nonconditional logistic regression was performed to analyze the symptomatic features of liver-qi invasion and liver-qi depression. As a result of this analysis, two subtypes of PMDD are proposed, namely, excessive liver conveyance and dispersion (liver-qi invasion syndrome) and insufficient liver conveyance and dispersion (liver-qi depression syndrome). Our findings provide an epidemiological foundation for the clinical diagnosis and treatment of PMDD based on the identification of different types. PMID:28698873

Recovery of zinc and manganese from alkaline and zinc-carbon spent batteries

NASA Astrophysics Data System (ADS)

De Michelis, I.; Ferella, F.; Karakaya, E.; Beolchini, F.; Vegliò, F.

This paper concerns the recovery of zinc and manganese from alkaline and zinc-carbon spent batteries. The metals were dissolved by a reductive-acid leaching with sulphuric acid in the presence of oxalic acid as reductant. Leaching tests were realised according to a full factorial design, then simple regression equations for Mn, Zn and Fe extraction were determined from the experimental data as a function of pulp density, sulphuric acid concentration, temperature and oxalic acid concentration. The main effects and interactions were investigated by the analysis of variance (ANOVA). This analysis evidenced the best operating conditions of the reductive acid leaching: 70% of manganese and 100% of zinc were extracted after 5 h, at 80 °C with 20% of pulp density, 1.8 M sulphuric acid concentration and 59.4 g L -1 of oxalic acid. Both manganese and zinc extraction yields higher than 96% were obtained by using two sequential leaching steps.

A comparative analysis of fertility differentials in Ghana and Nigeria.

PubMed

Olatoregun, Oluwaseun; Fagbamigbe, Adeniyi Francis; Akinyemi, Odunayo Joshua; Yusuf, Oyindamola Bidemi; Bamgboye, Elijah Afolabi

2014-09-01

Nigeria and Ghana are the most densely populated countries in the West African sub-region with fertility levels above world average. Our study compared the two countries' fertility levels and their determinants as well as the differentials in the effect of these factors across the two countries. We carried out a retrospective analysis of data from the Nigeria and Ghana Demographic Health Surveys, 2008. The sample of 33,385 and 4,916 women aged 15-49 years obtained in Nigeria and Ghana respectively was stratified into low, medium and high fertility using reported children ever born. Data was summarized using appropriate descriptive statistics. Factors influencing fertility were identified using ordinal logistic regression at 5% significance level. While unemployment significantly lowers fertility in Nigeria, it wasn't significant in Ghana. In both countries, education, age at first marriage, marital status, urban-rural residence, wealth index and use of oral contraception were the main factors influencing high fertility levels.

Water-contact patterns in relation to Schistosoma haematobium infection

PubMed Central

Dalton, P. R.; Pole, D.

1978-01-01

Water-contact observations were carried out on a population exposed to Schistosoma haematobium in a village situated on a man-made lake, Lake Volta, Ghana. The observations were made over a period of 12 months prior to the introduction of control measures. A multiple regression analysis was performed on the results of observations on 132 individuals, with egg output as the dependent variable and various types of water-contact activity, as well as age and sex, as independent variables. In the analysis, specific activities, notably water-contact for domestic purposes and activities associated with fishermen's canoes, were found to be significantly related to schistosomiasis. Age was less important than degree of exposure as a contributory factor to variations in infection rates: the reduced intensity of infection of S. haematobium in the older age groups could be mainly due to a lower level of exposure to the cercarial population. PMID:308406

Factors affecting the retrieval of famous names.

PubMed

Martins, Isabel Pavão; Loureiro, Clara; Rodrigues, Susana; Dias, Beatriz; Slade, Peter

2010-06-01

Tests of famous faces are used to study language and memory. Yet, the effect of stimulus properties on performance has not been fully investigated. To identify factors influencing proper name retrieval and to probe stimulus-specific parameters within proper name lexicon, we analysed the results obtained by 300 healthy participants on a test of famous faces that includes 74 personalities. A factor analysis yielded five main factors that were characterized by language (national or foreign names), epoch of peak popularity (current, recent or past) and occupation (politicians, entertainment and sports) of the personalities. Multiple regression analysis showed that participants' education, age and gender accounted for 10-32% of the variance in factor scores. These results indicate that there are variables of the stimulus and participants' that must be taken into account in proper name testing and in designing tests aimed to differentiate age-associated difficulties from cognitive decline.

Analysis of albumin Raman scattering in visible and near-infrared ranges

NASA Astrophysics Data System (ADS)

Lykina, Anastasia A.; Artemyev, Dmitry N.

2018-04-01

In this work the analysis of the shape and intensity of albumin Raman signals in visible and near-IR ranges was carried out. The experimental setup using lasers from the visible region first of all excites the fluorescence of the albumin solution, the main contribution to which is produced by sodium chloride, which is a component of the tested sample. At the same time, lasers from the near-infrared range excited the Raman signal of albumin most effectively. It was found that the highest ratio of Raman scattering to autofluorescence intensities in the detected signal was obtained using a laser with a wavelength of 1064 nm. To determine the albumin solution concentration by type of spectrum, a regression approach with the projection to latent structures method was applied. The lowest predicted error of albumin concentration of 2-3 g/l was obtained by using the near-infrared range lasers.

Reliability and validity of the Marijuana Motives Measure among young adult frequent cannabis users and associations with cannabis dependence.

PubMed

Benschop, Annemieke; Liebregts, Nienke; van der Pol, Peggy; Schaap, Rick; Buisman, Renate; van Laar, Margriet; van den Brink, Wim; de Graaf, Ron; Korf, Dirk J

2015-01-01

The Marijuana Motives Measure (MMM) has so far been examined mainly in student populations, often with relatively limited involvement in cannabis use. This study evaluated the factor structure of the MMM in a demographically mixed sample of 600 young adult (18-30 years) frequent (≥ 3 days per week) cannabis users in the Netherlands. Analysis confirmed a five-factor solution, denoting coping, enhancement, social, conformity and expansion motives. Additionally, the original MMM was extended with two items (boredom and habit), which formed a distinct, internally consistent sixth factor labelled routine motives. In a multivariable logistic regression analysis, coping and routine motives showed significant associations with 12-month DSM-IV cannabis dependence. The results suggest general reliability and validity of the MMM in a heterogeneous population of experienced cannabis users. Copyright © 2014 Elsevier Ltd. All rights reserved.

A menu-driven software package of Bayesian nonparametric (and parametric) mixed models for regression analysis and density estimation.

PubMed

Karabatsos, George

2017-02-01

Most of applied statistics involves regression analysis of data. In practice, it is important to specify a regression model that has minimal assumptions which are not violated by data, to ensure that statistical inferences from the model are informative and not misleading. This paper presents a stand-alone and menu-driven software package, Bayesian Regression: Nonparametric and Parametric Models, constructed from MATLAB Compiler. Currently, this package gives the user a choice from 83 Bayesian models for data analysis. They include 47 Bayesian nonparametric (BNP) infinite-mixture regression models; 5 BNP infinite-mixture models for density estimation; and 31 normal random effects models (HLMs), including normal linear models. Each of the 78 regression models handles either a continuous, binary, or ordinal dependent variable, and can handle multi-level (grouped) data. All 83 Bayesian models can handle the analysis of weighted observations (e.g., for meta-analysis), and the analysis of left-censored, right-censored, and/or interval-censored data. Each BNP infinite-mixture model has a mixture distribution assigned one of various BNP prior distributions, including priors defined by either the Dirichlet process, Pitman-Yor process (including the normalized stable process), beta (two-parameter) process, normalized inverse-Gaussian process, geometric weights prior, dependent Dirichlet process, or the dependent infinite-probits prior. The software user can mouse-click to select a Bayesian model and perform data analysis via Markov chain Monte Carlo (MCMC) sampling. After the sampling completes, the software automatically opens text output that reports MCMC-based estimates of the model's posterior distribution and model predictive fit to the data. Additional text and/or graphical output can be generated by mouse-clicking other menu options. This includes output of MCMC convergence analyses, and estimates of the model's posterior predictive distribution, for selected functionals and values of covariates. The software is illustrated through the BNP regression analysis of real data.

Applications of statistics to medical science, III. Correlation and regression.

PubMed

Watanabe, Hiroshi

2012-01-01

In this third part of a series surveying medical statistics, the concepts of correlation and regression are reviewed. In particular, methods of linear regression and logistic regression are discussed. Arguments related to survival analysis will be made in a subsequent paper.

Flood-Frequency Estimates for Streams on Kaua`i, O`ahu, Moloka`i, Maui, and Hawai`i, State of Hawai`i

USGS Publications Warehouse

Oki, Delwyn S.; Rosa, Sarah N.; Yeung, Chiu W.

2010-01-01

This study provides an updated analysis of the magnitude and frequency of peak stream discharges in Hawai`i. Annual peak-discharge data collected by the U.S. Geological Survey during and before water year 2008 (ending September 30, 2008) at stream-gaging stations were analyzed. The existing generalized-skew value for the State of Hawai`i was retained, although three methods were used to evaluate whether an update was needed. Regional regression equations were developed for peak discharges with 2-, 5-, 10-, 25-, 50-, 100-, and 500-year recurrence intervals for unregulated streams (those for which peak discharges are not affected to a large extent by upstream reservoirs, dams, diversions, or other structures) in areas with less than 20 percent combined medium- and high-intensity development on Kaua`i, O`ahu, Moloka`i, Maui, and Hawai`i. The generalized-least-squares (GLS) regression equations relate peak stream discharge to quantified basin characteristics (for example, drainage-basin area and mean annual rainfall) that were determined using geographic information system (GIS) methods. Each of the islands of Kaua`i,O`ahu, Moloka`i, Maui, and Hawai`i was divided into two regions, generally corresponding to a wet region and a dry region. Unique peak-discharge regression equations were developed for each region. The regression equations developed for this study have standard errors of prediction ranging from 16 to 620 percent. Standard errors of prediction are greatest for regression equations developed for leeward Moloka`i and southern Hawai`i. In general, estimated 100-year peak discharges from this study are lower than those from previous studies, which may reflect the longer periods of record used in this study. Each regression equation is valid within the range of values of the explanatory variables used to develop the equation. The regression equations were developed using peak-discharge data from streams that are mainly unregulated, and they should not be used to estimate peak discharges in regulated streams. Use of a regression equation beyond its limits will produce peak-discharge estimates with unknown error and should therefore be avoided. Improved estimates of the magnitude and frequency of peak discharges in Hawai`i will require continued operation of existing stream-gaging stations and operation of additional gaging stations for areas such as Moloka`i and Hawai`i, where limited stream-gaging data are available.

Exploring consumers’ attitudes towards informal patient payments using the combined method of cluster and multinomial regression analysis - the case of Hungary

PubMed Central

2013-01-01

Background Previous studies on informal patient payments have mostly focused on the magnitude and determinants of these payments while the attitudes of health care actors towards these payments are less well known. This study aims to reveal the attitudes of Hungarian health care consumers towards informal payments to provide a better understanding of this phenomenon. Methods For the analysis, we use data from a survey carried out in 2010 in Hungary involving a representative sample of 1037 respondents. We use cluster analysis to identify the main attitude groups related to informal payments based on the respondents’ perception of and behavior related to informal payments. Multinomial logistic regression is applied to examine the differences between these groups in terms of socio-demographic characteristics, as well as past utilization and informal payments paid for health care services. Results We identified three main different attitudes towards informal payments: accepting informal payments, doubting about informal payments and opposing informal payments. Those who accept informal payments (mostly young or elderly people, living in the capital) consider these payments as an expression of gratitude and perceive them as inevitable due to the low funding of the health care system. Those who doubt about informal payments (mostly respondents outside the capital, with higher education and higher household income) are not certain whether these payments are inevitable, perceive them as similar to corruption rather than gratitude, and would rather use private services to avoid these payments. We find that the opposition to informal payments (mostly among men from small households and low income households) can be explained by their lower ability and willingness to pay. Conclusions A large share of Hungarian health care consumers has a rather positive attitude towards informal payments, perceiving them as “inevitable due to the low funding of the health care system”. From a policy point-of-view, the change of this consumer attitude will be essential to deal with these payments in addition to other policy strategies. PMID:23414488

An Analysis of Terrestrial and Aquatic Environmental Controls of Riverine Dissolved Organic Carbon in the Conterminous United States

DOE PAGES

Yang, Qichun; Zhang, Xuesong; Xu, Xingya; ...

2017-05-29

Riverine carbon cycling is an important, but insufficiently investigated component of the global carbon cycle. Analyses of environmental controls on riverine carbon cycling are critical for improved understanding of mechanisms regulating carbon processing and storage along the terrestrial-aquatic continuum. Here, we compile and analyze riverine dissolved organic carbon (DOC) concentration data from 1402 United States Geological Survey (USGS) gauge stations to examine the spatial variability and environmental controls of DOC concentrations in the United States (U.S.) surface waters. DOC concentrations exhibit high spatial variability, with an average of 6.42 ± 6.47 mg C/ L (Mean ± Standard Deviation). In general,more » high DOC concentrations occur in the Upper Mississippi River basin and the Southeastern U.S., while low concentrations are mainly distributed in the Western U.S. Single-factor analysis indicates that slope of drainage areas, wetlands, forests, percentage of first-order streams, and instream nutrients (such as nitrogen and phosphorus) pronouncedly influence DOC concentrations, but the explanatory power of each bivariate model is lower than 35%. Analyses based on the general multi-linear regression models suggest DOC concentrations are jointly impacted by multiple factors. Soil properties mainly show positive correlations with DOC concentrations; forest and shrub lands have positive correlations with DOC concentrations, but urban area and croplands demonstrate negative impacts; total instream phosphorus and dam density correlate positively with DOC concentrations. Notably, the relative importance of these environmental controls varies substantially across major U.S. water resource regions. In addition, DOC concentrations and environmental controls also show significant variability from small streams to large rivers, which may be caused by changing carbon sources and removal rates by river orders. In sum, our results reveal that general multi-linear regression analysis of twenty one terrestrial and aquatic environmental factors can partially explain (56%) the DOC concentration variation. In conclusion, this study highlights the complexity of the interactions among these environmental factors in determining DOC concentrations, thus calls for processes-based, non-linear methodologies to constrain uncertainties in riverine DOC cycling.« less

Optimizing methods for linking cinematic features to fMRI data.

PubMed

Kauttonen, Janne; Hlushchuk, Yevhen; Tikka, Pia

2015-04-15

One of the challenges of naturalistic neurosciences using movie-viewing experiments is how to interpret observed brain activations in relation to the multiplicity of time-locked stimulus features. As previous studies have shown less inter-subject synchronization across viewers of random video footage than story-driven films, new methods need to be developed for analysis of less story-driven contents. To optimize the linkage between our fMRI data collected during viewing of a deliberately non-narrative silent film 'At Land' by Maya Deren (1944) and its annotated content, we combined the method of elastic-net regularization with the model-driven linear regression and the well-established data-driven independent component analysis (ICA) and inter-subject correlation (ISC) methods. In the linear regression analysis, both IC and region-of-interest (ROI) time-series were fitted with time-series of a total of 36 binary-valued and one real-valued tactile annotation of film features. The elastic-net regularization and cross-validation were applied in the ordinary least-squares linear regression in order to avoid over-fitting due to the multicollinearity of regressors, the results were compared against both the partial least-squares (PLS) regression and the un-regularized full-model regression. Non-parametric permutation testing scheme was applied to evaluate the statistical significance of regression. We found statistically significant correlation between the annotation model and 9 ICs out of 40 ICs. Regression analysis was also repeated for a large set of cubic ROIs covering the grey matter. Both IC- and ROI-based regression analyses revealed activations in parietal and occipital regions, with additional smaller clusters in the frontal lobe. Furthermore, we found elastic-net based regression more sensitive than PLS and un-regularized regression since it detected a larger number of significant ICs and ROIs. Along with the ISC ranking methods, our regression analysis proved a feasible method for ordering the ICs based on their functional relevance to the annotated cinematic features. The novelty of our method is - in comparison to the hypothesis-driven manual pre-selection and observation of some individual regressors biased by choice - in applying data-driven approach to all content features simultaneously. We found especially the combination of regularized regression and ICA useful when analyzing fMRI data obtained using non-narrative movie stimulus with a large set of complex and correlated features. Copyright © 2015. Published by Elsevier Inc.

Chronic atrophic gastritis in association with hair mercury level.

PubMed

Xue, Zeyun; Xue, Huiping; Jiang, Jianlan; Lin, Bing; Zeng, Si; Huang, Xiaoyun; An, Jianfu

2014-11-01

The objective of this study was to explore hair mercury level in association with chronic atrophic gastritis, a precancerous stage of gastric cancer (GC), and thus provide a brand new angle of view on the timely intervention of precancerous stage of GC. We recruited 149 healthy volunteers as controls and 152 patients suffering from chronic gastritis as cases. The controls denied upper gastrointestinal discomforts, and the cases were diagnosed as chronic superficial gastritis (n=68) or chronic atrophic gastritis (n=84). We utilized Mercury Automated Analyzer (NIC MA-3000) to detect hair mercury level of both healthy controls and cases of chronic gastritis. The statistic of measurement data was expressed as mean ± standard deviation, which was analyzed using Levene variance equality test and t test. Pearson correlation analysis was employed to determine associated factors affecting hair mercury levels, and multiple stepwise regression analysis was performed to deduce regression equations. Statistical significance is considered if p value is less than 0.05. The overall hair mercury level was 0.908949 ± 0.8844490 ng/g [mean ± standard deviation (SD)] in gastritis cases and 0.460198 ± 0.2712187 ng/g (mean±SD) in healthy controls; the former level was significantly higher than the latter one (p=0.000<0.01). The hair mercury level in chronic atrophic gastritis subgroup was 1.155220 ± 0.9470246 ng/g (mean ± SD) and that in chronic superficial gastritis subgroup was 0.604732 ± 0.6942509 ng/g (mean ± SD); the former level was significantly higher than the latter level (p<0.01). The hair mercury level in chronic superficial gastritis cases was significantly higher than that in healthy controls (p<0.05). The hair mercury level in chronic atrophic gastritis cases was significantly higher than that in healthy controls (p<0.01). Stratified analysis indicated that the hair mercury level in healthy controls with eating seafood was significantly higher than that in healthy controls without eating seafood (p<0.01) and that the hair mercury level in chronic atrophic gastritis cases was significantly higher than that in chronic superficial gastritis cases (p<0.01). Pearson correlation analysis indicated that eating seafood was most correlated with hair mercury level and positively correlated in the healthy controls and that the severity of gastritis was most correlated with hair mercury level and positively correlated in the gastritis cases. Multiple stepwise regression analysis indicated that the regression equation of hair mercury level in controls could be expressed as 0.262 multiplied the value of eating seafood plus 0.434, the model that was statistically significant (p<0.01). Multiple stepwise regression analysis also indicated that the regression equation of hair mercury level in gastritis cases could be expressed as 0.305 multiplied the severity of gastritis, the model that was also statistically significant (p<0.01). The graphs of regression standardized residual for both controls and cases conformed to normal distribution. The main positively correlated factor affecting the hair mercury level is eating seafood in healthy people whereas the predominant positively correlated factor affecting the hair mercury level is the severity of gastritis in chronic gastritis patients. That is to say, the severity of chronic gastritis is positively correlated with the level of hair mercury. The incessantly increased level of hair mercury possibly reflects the development of gastritis from normal stomach to superficial gastritis and to atrophic gastritis. The detection of hair mercury is potentially a means to predict the severity of chronic gastritis and possibly to insinuate the environmental mercury threat to human health in terms of gastritis or even carcinogenesis.

The implementation of rare events logistic regression to predict the distribution of mesophotic hard corals across the main Hawaiian Islands.

PubMed

Veazey, Lindsay M; Franklin, Erik C; Kelley, Christopher; Rooney, John; Frazer, L Neil; Toonen, Robert J

2016-01-01

Predictive habitat suitability models are powerful tools for cost-effective, statistically robust assessment of the environmental drivers of species distributions. The aim of this study was to develop predictive habitat suitability models for two genera of scleractinian corals (Leptoserisand Montipora) found within the mesophotic zone across the main Hawaiian Islands. The mesophotic zone (30-180 m) is challenging to reach, and therefore historically understudied, because it falls between the maximum limit of SCUBA divers and the minimum typical working depth of submersible vehicles. Here, we implement a logistic regression with rare events corrections to account for the scarcity of presence observations within the dataset. These corrections reduced the coefficient error and improved overall prediction success (73.6% and 74.3%) for both original regression models. The final models included depth, rugosity, slope, mean current velocity, and wave height as the best environmental covariates for predicting the occurrence of the two genera in the mesophotic zone. Using an objectively selected theta ("presence") threshold, the predicted presence probability values (average of 0.051 for Leptoseris and 0.040 for Montipora) were translated to spatially-explicit habitat suitability maps of the main Hawaiian Islands at 25 m grid cell resolution. Our maps are the first of their kind to use extant presence and absence data to examine the habitat preferences of these two dominant mesophotic coral genera across Hawai'i.

Local spatial variations analysis of smear-positive tuberculosis in Xinjiang using Geographically Weighted Regression model.

PubMed

Wei, Wang; Yuan-Yuan, Jin; Ci, Yan; Ahan, Alayi; Ming-Qin, Cao

2016-10-06

The spatial interplay between socioeconomic factors and tuberculosis (TB) cases contributes to the understanding of regional tuberculosis burdens. Historically, local Poisson Geographically Weighted Regression (GWR) has allowed for the identification of the geographic disparities of TB cases and their relevant socioeconomic determinants, thereby forecasting local regression coefficients for the relations between the incidence of TB and its socioeconomic determinants. Therefore, the aims of this study were to: (1) identify the socioeconomic determinants of geographic disparities of smear positive TB in Xinjiang, China (2) confirm if the incidence of smear positive TB and its associated socioeconomic determinants demonstrate spatial variability (3) compare the performance of two main models: one is Ordinary Least Square Regression (OLS), and the other local GWR model. Reported smear-positive TB cases in Xinjiang were extracted from the TB surveillance system database during 2004-2010. The average number of smear-positive TB cases notified in Xinjiang was collected from 98 districts/counties. The population density (POPden), proportion of minorities (PROmin), number of infectious disease network reporting agencies (NUMagen), proportion of agricultural population (PROagr), and per capita annual gross domestic product (per capita GDP) were gathered from the Xinjiang Statistical Yearbook covering a period from 2004 to 2010. The OLS model and GWR model were then utilized to investigate socioeconomic determinants of smear-positive TB cases. Geoda 1.6.7, and GWR 4.0 software were used for data analysis. Our findings indicate that the relations between the average number of smear-positive TB cases notified in Xinjiang and their socioeconomic determinants (POPden, PROmin, NUMagen, PROagr, and per capita GDP) were significantly spatially non-stationary. This means that in some areas more smear-positive TB cases could be related to higher socioeconomic determinant regression coefficients, but in some areas more smear-positive TB cases were found to do with lower socioeconomic determinant regression coefficients. We also found out that the GWR model could be better exploited to geographically differentiate the relationships between the average number of smear-positive TB cases and their socioeconomic determinants, which could interpret the dataset better (adjusted R 2  = 0.912, AICc = 1107.22) than the OLS model (adjusted R 2  = 0.768, AICc = 1196.74). POPden, PROmin, NUMagen, PROagr, and per capita GDP are socioeconomic determinants of smear-positive TB cases. Comprehending the spatial heterogeneity of POPden, PROmin, NUMagen, PROagr, per capita GDP, and smear-positive TB cases could provide valuable information for TB precaution and control strategies.

[Survival analysis of patients with pneumoconiosis from 1956 to 2010 in Changsha].

PubMed

Xue, Jing; Chen, Lizhang

2012-01-01

To investigate the survival rate and life expectancy of patients with pneumoconiosis and influence factors in Changsha from 1956 to 2010. A total of 3685 patients with pneumoconiosis were diagnosed and reported from 1956 to 2010 in Changsha. The fatality rate and life expectancy were analyzed by life table and the cause of death was analyzed by Kaplan-Meier method and Cox regression model. The death rate increased obviously with age. Age and accumulation death probability showed linearity (Ŷ=1.271+0.041X, r=0.989). The life expectancy was 60.12 years. The first cause of death was pulmonary tuberculosis in patients with pneumoconiosis. Ruling out the influence of pulmonary tuberculosis, pneumoconiosis, and lung source heart disease, the life expectancy of patients with pneumoconiosis averagely extended 0.83, 0.99, and 0.02 years. The death rate of pneumoconiosis-tuberculosis had significant difference with that of the pneumoconiosisnontuberculosis (P<0.01). Cox regression analysis revealed that the main risk factors for the survival of patients with pneumoconiosis included type of work (smashing worker), complication with tuberculosis, type of pneumoconiosis (silicosis). The death hazard ratio or relative risk caused by them was 1.927, 1.749, and 1.609, respectively. Prevention of pneumoconiosis should focus on smashing workers in Changsha, while its the treatment primarily attaches importance to complication of tuberculosis and lung infection.

Prophylaxis of venous thromboembolism in elderly patients with multimorbidity.

PubMed

Marcucci, Maura; Iorio, Alfonso; Nobili, Alessandro; Tettamanti, Mauro; Pasina, Luca; Djade, Codjo Djignefa; Marengoni, Alessandra; Salerno, Francesco; Corrao, Salvatore; Mannucci, Pier Mannuccio

2013-09-01

Pharmacological thromboprophylaxis (TP) is known to reduce venous thromboembolism (VTE) in medical inpatients, but the criteria for risk-driven prescription, safety and impact on mortality are still debated. We analyze data on elderly patients with multimorbidities admitted in the year 2010 to the Italian internal medicine wards participating in the REPOSI registry to investigate the rate of TP during the hospital stay, and analyze the factors that are related to its prescription. Multivariate logistic regression, area under the ROC curve and CART analysis were performed to look for independent predictors of TP prescription. Association between TP and VTE, bleeding and death in hospital and during the 3-month post-discharge follow-up were explored by logistic regression and propensity score analysis. Among the 1,380 patients enrolled, 171 (15.2 %) were on TP during the hospital stay (162 on low molecular weight heparins, 9 on fondaparinux). The disability Barthel index was the main independent predictor of TP prescription. Rate of fatal and non-fatal VTE and bleeding during and after hospitalization did not differ between TP and non-TP patients. In-hospital and post-discharge mortality was significantly higher in patients on TP, that however was not an independent predictor of mortality. Among elderly medical patients there was a relatively low rate of TP, that was more frequently prescribed to patients with a higher degree of disability and who had an overall higher mortality.