Modelling infant mortality rate in Central Java, Indonesia use generalized poisson regression method

NASA Astrophysics Data System (ADS)

Prahutama, Alan; Sudarno

2018-05-01

The infant mortality rate is the number of deaths under one year of age occurring among the live births in a given geographical area during a given year, per 1,000 live births occurring among the population of the given geographical area during the same year. This problem needs to be addressed because it is an important element of a country’s economic development. High infant mortality rate will disrupt the stability of a country as it relates to the sustainability of the population in the country. One of regression model that can be used to analyze the relationship between dependent variable Y in the form of discrete data and independent variable X is Poisson regression model. Recently The regression modeling used for data with dependent variable is discrete, among others, poisson regression, negative binomial regression and generalized poisson regression. In this research, generalized poisson regression modeling gives better AIC value than poisson regression. The most significant variable is the Number of health facilities (X1), while the variable that gives the most influence to infant mortality rate is the average breastfeeding (X9).

[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.

Stagnation in Mortality Decline among Elders in the Netherlands

ERIC Educational Resources Information Center

Janssen, Fanny; Nusselder, Wilma J.; Looman, Caspar W. N.; Mackenbach, Johan P.; Kunst, Anton E.

2003-01-01

Purpose: This study assesses whether the stagnation of old-age (80+) mortality decline observed in The Netherlands in the 1980s continued in the 1990s and determines which factors contributed to this stagnation. Emphasis is on the role of smoking. Design and Methods: Poisson regression analysis with linear splines was applied to total and…

A generalized right truncated bivariate Poisson regression model with applications to health data.

PubMed

Islam, M Ataharul; Chowdhury, Rafiqul I

2017-01-01

A generalized right truncated bivariate Poisson regression model is proposed in this paper. Estimation and tests for goodness of fit and over or under dispersion are illustrated for both untruncated and right truncated bivariate Poisson regression models using marginal-conditional approach. Estimation and test procedures are illustrated for bivariate Poisson regression models with applications to Health and Retirement Study data on number of health conditions and the number of health care services utilized. The proposed test statistics are easy to compute and it is evident from the results that the models fit the data very well. A comparison between the right truncated and untruncated bivariate Poisson regression models using the test for nonnested models clearly shows that the truncated model performs significantly better than the untruncated model.

A generalized right truncated bivariate Poisson regression model with applications to health data

PubMed Central

Islam, M. Ataharul; Chowdhury, Rafiqul I.

2017-01-01

A generalized right truncated bivariate Poisson regression model is proposed in this paper. Estimation and tests for goodness of fit and over or under dispersion are illustrated for both untruncated and right truncated bivariate Poisson regression models using marginal-conditional approach. Estimation and test procedures are illustrated for bivariate Poisson regression models with applications to Health and Retirement Study data on number of health conditions and the number of health care services utilized. The proposed test statistics are easy to compute and it is evident from the results that the models fit the data very well. A comparison between the right truncated and untruncated bivariate Poisson regression models using the test for nonnested models clearly shows that the truncated model performs significantly better than the untruncated model. PMID:28586344

Understanding poisson regression.

PubMed

Hayat, Matthew J; Higgins, Melinda

2014-04-01

Nurse investigators often collect study data in the form of counts. Traditional methods of data analysis have historically approached analysis of count data either as if the count data were continuous and normally distributed or with dichotomization of the counts into the categories of occurred or did not occur. These outdated methods for analyzing count data have been replaced with more appropriate statistical methods that make use of the Poisson probability distribution, which is useful for analyzing count data. The purpose of this article is to provide an overview of the Poisson distribution and its use in Poisson regression. Assumption violations for the standard Poisson regression model are addressed with alternative approaches, including addition of an overdispersion parameter or negative binomial regression. An illustrative example is presented with an application from the ENSPIRE study, and regression modeling of comorbidity data is included for illustrative purposes. Copyright 2014, SLACK Incorporated.

Analyzing hospitalization data: potential limitations of Poisson regression.

PubMed

Weaver, Colin G; Ravani, Pietro; Oliver, Matthew J; Austin, Peter C; Quinn, Robert R

2015-08-01

Poisson regression is commonly used to analyze hospitalization data when outcomes are expressed as counts (e.g. number of days in hospital). However, data often violate the assumptions on which Poisson regression is based. More appropriate extensions of this model, while available, are rarely used. We compared hospitalization data between 206 patients treated with hemodialysis (HD) and 107 treated with peritoneal dialysis (PD) using Poisson regression and compared results from standard Poisson regression with those obtained using three other approaches for modeling count data: negative binomial (NB) regression, zero-inflated Poisson (ZIP) regression and zero-inflated negative binomial (ZINB) regression. We examined the appropriateness of each model and compared the results obtained with each approach. During a mean 1.9 years of follow-up, 183 of 313 patients (58%) were never hospitalized (indicating an excess of 'zeros'). The data also displayed overdispersion (variance greater than mean), violating another assumption of the Poisson model. Using four criteria, we determined that the NB and ZINB models performed best. According to these two models, patients treated with HD experienced similar hospitalization rates as those receiving PD {NB rate ratio (RR): 1.04 [bootstrapped 95% confidence interval (CI): 0.49-2.20]; ZINB summary RR: 1.21 (bootstrapped 95% CI 0.60-2.46)}. Poisson and ZIP models fit the data poorly and had much larger point estimates than the NB and ZINB models [Poisson RR: 1.93 (bootstrapped 95% CI 0.88-4.23); ZIP summary RR: 1.84 (bootstrapped 95% CI 0.88-3.84)]. We found substantially different results when modeling hospitalization data, depending on the approach used. Our results argue strongly for a sound model selection process and improved reporting around statistical methods used for modeling count data. © The Author 2015. Published by Oxford University Press on behalf of ERA-EDTA. All rights reserved.

Functional linear models for zero-inflated count data with application to modeling hospitalizations in patients on dialysis.

PubMed

Sentürk, Damla; Dalrymple, Lorien S; Nguyen, Danh V

2014-11-30

We propose functional linear models for zero-inflated count data with a focus on the functional hurdle and functional zero-inflated Poisson (ZIP) models. Although the hurdle model assumes the counts come from a mixture of a degenerate distribution at zero and a zero-truncated Poisson distribution, the ZIP model considers a mixture of a degenerate distribution at zero and a standard Poisson distribution. We extend the generalized functional linear model framework with a functional predictor and multiple cross-sectional predictors to model counts generated by a mixture distribution. We propose an estimation procedure for functional hurdle and ZIP models, called penalized reconstruction, geared towards error-prone and sparsely observed longitudinal functional predictors. The approach relies on dimension reduction and pooling of information across subjects involving basis expansions and penalized maximum likelihood techniques. The developed functional hurdle model is applied to modeling hospitalizations within the first 2 years from initiation of dialysis, with a high percentage of zeros, in the Comprehensive Dialysis Study participants. Hospitalization counts are modeled as a function of sparse longitudinal measurements of serum albumin concentrations, patient demographics, and comorbidities. Simulation studies are used to study finite sample properties of the proposed method and include comparisons with an adaptation of standard principal components regression. Copyright © 2014 John Wiley & Sons, Ltd.

Introduction to the use of regression models in epidemiology.

PubMed

Bender, Ralf

2009-01-01

Regression modeling is one of the most important statistical techniques used in analytical epidemiology. By means of regression models the effect of one or several explanatory variables (e.g., exposures, subject characteristics, risk factors) on a response variable such as mortality or cancer can be investigated. From multiple regression models, adjusted effect estimates can be obtained that take the effect of potential confounders into account. Regression methods can be applied in all epidemiologic study designs so that they represent a universal tool for data analysis in epidemiology. Different kinds of regression models have been developed in dependence on the measurement scale of the response variable and the study design. The most important methods are linear regression for continuous outcomes, logistic regression for binary outcomes, Cox regression for time-to-event data, and Poisson regression for frequencies and rates. This chapter provides a nontechnical introduction to these regression models with illustrating examples from cancer research.

Impact of a New Law to Reduce the Legal Blood Alcohol Concentration Limit - A Poisson Regression Analysis and Descriptive Approach.

PubMed

Nistal-Nuño, Beatriz

2017-03-31

In Chile, a new law introduced in March 2012 lowered the blood alcohol concentration (BAC) limit for impaired drivers from 0.1% to 0.08% and the BAC limit for driving under the influence of alcohol from 0.05% to 0.03%, but its effectiveness remains uncertain. The goal of this investigation was to evaluate the effects of this enactment on road traffic injuries and fatalities in Chile. A retrospective cohort study. Data were analyzed using a descriptive and a Generalized Linear Models approach, type of Poisson regression, to analyze deaths and injuries in a series of additive Log-Linear Models accounting for the effects of law implementation, month influence, a linear time trend and population exposure. A review of national databases in Chile was conducted from 2003 to 2014 to evaluate the monthly rates of traffic fatalities and injuries associated to alcohol and in total. It was observed a decrease by 28.1 percent in the monthly rate of traffic fatalities related to alcohol as compared to before the law (P<0.001). Adding a linear time trend as a predictor, the decrease was by 20.9 percent (P<0.001).There was a reduction in the monthly rate of traffic injuries related to alcohol by 10.5 percent as compared to before the law (P<0.001). Adding a linear time trend as a predictor, the decrease was by 24.8 percent (P<0.001). Positive results followed from this new 'zero-tolerance' law implemented in 2012 in Chile. Chile experienced a significant reduction in alcohol-related traffic fatalities and injuries, being a successful public health intervention.

Conditional Poisson models: a flexible alternative to conditional logistic case cross-over analysis.

PubMed

Armstrong, Ben G; Gasparrini, Antonio; Tobias, Aurelio

2014-11-24

The time stratified case cross-over approach is a popular alternative to conventional time series regression for analysing associations between time series of environmental exposures (air pollution, weather) and counts of health outcomes. These are almost always analyzed using conditional logistic regression on data expanded to case-control (case crossover) format, but this has some limitations. In particular adjusting for overdispersion and auto-correlation in the counts is not possible. It has been established that a Poisson model for counts with stratum indicators gives identical estimates to those from conditional logistic regression and does not have these limitations, but it is little used, probably because of the overheads in estimating many stratum parameters. The conditional Poisson model avoids estimating stratum parameters by conditioning on the total event count in each stratum, thus simplifying the computing and increasing the number of strata for which fitting is feasible compared with the standard unconditional Poisson model. Unlike the conditional logistic model, the conditional Poisson model does not require expanding the data, and can adjust for overdispersion and auto-correlation. It is available in Stata, R, and other packages. By applying to some real data and using simulations, we demonstrate that conditional Poisson models were simpler to code and shorter to run than are conditional logistic analyses and can be fitted to larger data sets than possible with standard Poisson models. Allowing for overdispersion or autocorrelation was possible with the conditional Poisson model but when not required this model gave identical estimates to those from conditional logistic regression. Conditional Poisson regression models provide an alternative to case crossover analysis of stratified time series data with some advantages. The conditional Poisson model can also be used in other contexts in which primary control for confounding is by fine stratification.

Weather variability and the incidence of cryptosporidiosis: comparison of time series poisson regression and SARIMA models.

PubMed

Hu, Wenbiao; Tong, Shilu; Mengersen, Kerrie; Connell, Des

2007-09-01

Few studies have examined the relationship between weather variables and cryptosporidiosis in Australia. This paper examines the potential impact of weather variability on the transmission of cryptosporidiosis and explores the possibility of developing an empirical forecast system. Data on weather variables, notified cryptosporidiosis cases, and population size in Brisbane were supplied by the Australian Bureau of Meteorology, Queensland Department of Health, and Australian Bureau of Statistics for the period of January 1, 1996-December 31, 2004, respectively. Time series Poisson regression and seasonal auto-regression integrated moving average (SARIMA) models were performed to examine the potential impact of weather variability on the transmission of cryptosporidiosis. Both the time series Poisson regression and SARIMA models show that seasonal and monthly maximum temperature at a prior moving average of 1 and 3 months were significantly associated with cryptosporidiosis disease. It suggests that there may be 50 more cases a year for an increase of 1 degrees C maximum temperature on average in Brisbane. Model assessments indicated that the SARIMA model had better predictive ability than the Poisson regression model (SARIMA: root mean square error (RMSE): 0.40, Akaike information criterion (AIC): -12.53; Poisson regression: RMSE: 0.54, AIC: -2.84). Furthermore, the analysis of residuals shows that the time series Poisson regression appeared to violate a modeling assumption, in that residual autocorrelation persisted. The results of this study suggest that weather variability (particularly maximum temperature) may have played a significant role in the transmission of cryptosporidiosis. A SARIMA model may be a better predictive model than a Poisson regression model in the assessment of the relationship between weather variability and the incidence of cryptosporidiosis.

Non-Poisson Processes: Regression to Equilibrium Versus Equilibrium Correlation Functions

DTIC Science & Technology

2004-07-07

ARTICLE IN PRESSPhysica A 347 (2005) 268–2880378-4371/$ - doi:10.1016/j Correspo E-mail adwww.elsevier.com/locate/physaNon- Poisson processes : regression...05.40.a; 89.75.k; 02.50.Ey Keywords: Stochastic processes; Non- Poisson processes ; Liouville and Liouville-like equations; Correlation function...which is not legitimate with renewal non- Poisson processes , is a correct property if the deviation from the exponential relaxation is obtained by time

Modeling health survey data with excessive zero and K responses.

PubMed

Lin, Ting Hsiang; Tsai, Min-Hsiao

2013-04-30

Zero-inflated Poisson regression is a popular tool used to analyze data with excessive zeros. Although much work has already been performed to fit zero-inflated data, most models heavily depend on special features of the individual data. To be specific, this means that there is a sizable group of respondents who endorse the same answers making the data have peaks. In this paper, we propose a new model with the flexibility to model excessive counts other than zero, and the model is a mixture of multinomial logistic and Poisson regression, in which the multinomial logistic component models the occurrence of excessive counts, including zeros, K (where K is a positive integer) and all other values. The Poisson regression component models the counts that are assumed to follow a Poisson distribution. Two examples are provided to illustrate our models when the data have counts containing many ones and sixes. As a result, the zero-inflated and K-inflated models exhibit a better fit than the zero-inflated Poisson and standard Poisson regressions. Copyright © 2012 John Wiley & Sons, Ltd.

Application of the Hyper-Poisson Generalized Linear Model for Analyzing Motor Vehicle Crashes.

PubMed

Khazraee, S Hadi; Sáez-Castillo, Antonio Jose; Geedipally, Srinivas Reddy; Lord, Dominique

2015-05-01

The hyper-Poisson distribution can handle both over- and underdispersion, and its generalized linear model formulation allows the dispersion of the distribution to be observation-specific and dependent on model covariates. This study's objective is to examine the potential applicability of a newly proposed generalized linear model framework for the hyper-Poisson distribution in analyzing motor vehicle crash count data. The hyper-Poisson generalized linear model was first fitted to intersection crash data from Toronto, characterized by overdispersion, and then to crash data from railway-highway crossings in Korea, characterized by underdispersion. The results of this study are promising. When fitted to the Toronto data set, the goodness-of-fit measures indicated that the hyper-Poisson model with a variable dispersion parameter provided a statistical fit as good as the traditional negative binomial model. The hyper-Poisson model was also successful in handling the underdispersed data from Korea; the model performed as well as the gamma probability model and the Conway-Maxwell-Poisson model previously developed for the same data set. The advantages of the hyper-Poisson model studied in this article are noteworthy. Unlike the negative binomial model, which has difficulties in handling underdispersed data, the hyper-Poisson model can handle both over- and underdispersed crash data. Although not a major issue for the Conway-Maxwell-Poisson model, the effect of each variable on the expected mean of crashes is easily interpretable in the case of this new model. © 2014 Society for Risk Analysis.

Method selection and adaptation for distributed monitoring of infectious diseases for syndromic surveillance.

PubMed

Xing, Jian; Burkom, Howard; Tokars, Jerome

2011-12-01

Automated surveillance systems require statistical methods to recognize increases in visit counts that might indicate an outbreak. In prior work we presented methods to enhance the sensitivity of C2, a commonly used time series method. In this study, we compared the enhanced C2 method with five regression models. We used emergency department chief complaint data from US CDC BioSense surveillance system, aggregated by city (total of 206 hospitals, 16 cities) during 5/2008-4/2009. Data for six syndromes (asthma, gastrointestinal, nausea and vomiting, rash, respiratory, and influenza-like illness) was used and was stratified by mean count (1-19, 20-49, ≥50 per day) into 14 syndrome-count categories. We compared the sensitivity for detecting single-day artificially-added increases in syndrome counts. Four modifications of the C2 time series method, and five regression models (two linear and three Poisson), were tested. A constant alert rate of 1% was used for all methods. Among the regression models tested, we found that a Poisson model controlling for the logarithm of total visits (i.e., visits both meeting and not meeting a syndrome definition), day of week, and 14-day time period was best. Among 14 syndrome-count categories, time series and regression methods produced approximately the same sensitivity (<5% difference) in 6; in six categories, the regression method had higher sensitivity (range 6-14% improvement), and in two categories the time series method had higher sensitivity. When automated data are aggregated to the city level, a Poisson regression model that controls for total visits produces the best overall sensitivity for detecting artificially added visit counts. This improvement was achieved without increasing the alert rate, which was held constant at 1% for all methods. These findings will improve our ability to detect outbreaks in automated surveillance system data. Published by Elsevier Inc.

State Estimation for Linear Systems Driven Simultaneously by Wiener and Poisson Processes.

DTIC Science & Technology

1978-12-01

The state estimation problem of linear stochastic systems driven simultaneously by Wiener and Poisson processes is considered, especially the case...where the incident intensities of the Poisson processes are low and the system is observed in an additive white Gaussian noise. The minimum mean squared

A quasi-Monte-Carlo comparison of parametric and semiparametric regression methods for heavy-tailed and non-normal data: an application to healthcare costs.

PubMed

Jones, Andrew M; Lomas, James; Moore, Peter T; Rice, Nigel

2016-10-01

We conduct a quasi-Monte-Carlo comparison of the recent developments in parametric and semiparametric regression methods for healthcare costs, both against each other and against standard practice. The population of English National Health Service hospital in-patient episodes for the financial year 2007-2008 (summed for each patient) is randomly divided into two equally sized subpopulations to form an estimation set and a validation set. Evaluating out-of-sample using the validation set, a conditional density approximation estimator shows considerable promise in forecasting conditional means, performing best for accuracy of forecasting and among the best four for bias and goodness of fit. The best performing model for bias is linear regression with square-root-transformed dependent variables, whereas a generalized linear model with square-root link function and Poisson distribution performs best in terms of goodness of fit. Commonly used models utilizing a log-link are shown to perform badly relative to other models considered in our comparison.

Models for forecasting the flowering of Cornicabra olive groves.

PubMed

Rojo, Jesús; Pérez-Badia, Rosa

2015-11-01

This study examined the impact of weather-related variables on flowering phenology in the Cornicabra olive tree and constructed models based on linear and Poisson regression to forecast the onset and length of the pre-flowering and flowering phenophases. Spain is the world's leading olive oil producer, and the Cornicabra variety is the second largest Spanish variety in terms of surface area. However, there has been little phenological research into this variety. Phenological observations were made over a 5-year period (2009-2013) at four sampling sites in the province of Toledo (central Spain). Results showed that the onset of the pre-flowering phase is governed largely by temperature, which displayed a positive correlation with the temperature in the start of dormancy (November) and a negative correlation during the months prior to budburst (January, February and March). A similar relationship was recorded for the onset of flowering. Other weather-related variables, including solar radiation and rainfall, also influenced the succession of olive flowering phenophases. Linear models proved the most suitable for forecasting the onset and length of the pre-flowering period and the onset of flowering. The onset and length of pre-flowering can be predicted up to 1 or 2 months prior to budburst, whilst the onset of flowering can be forecast up to 3 months beforehand. By contrast, a nonlinear model using Poisson regression was best suited to predict the length of the flowering period.

Mean-square state and parameter estimation for stochastic linear systems with Gaussian and Poisson noises

NASA Astrophysics Data System (ADS)

Basin, M.; Maldonado, J. J.; Zendejo, O.

2016-07-01

This paper proposes new mean-square filter and parameter estimator design for linear stochastic systems with unknown parameters over linear observations, where unknown parameters are considered as combinations of Gaussian and Poisson white noises. The problem is treated by reducing the original problem to a filtering problem for an extended state vector that includes parameters as additional states, modelled as combinations of independent Gaussian and Poisson processes. The solution to this filtering problem is based on the mean-square filtering equations for incompletely polynomial states confused with Gaussian and Poisson noises over linear observations. The resulting mean-square filter serves as an identifier for the unknown parameters. Finally, a simulation example shows effectiveness of the proposed mean-square filter and parameter estimator.

Zero-inflated Conway-Maxwell Poisson Distribution to Analyze Discrete Data.

PubMed

Sim, Shin Zhu; Gupta, Ramesh C; Ong, Seng Huat

2018-01-09

In this paper, we study the zero-inflated Conway-Maxwell Poisson (ZICMP) distribution and develop a regression model. Score and likelihood ratio tests are also implemented for testing the inflation/deflation parameter. Simulation studies are carried out to examine the performance of these tests. A data example is presented to illustrate the concepts. In this example, the proposed model is compared to the well-known zero-inflated Poisson (ZIP) and the zero- inflated generalized Poisson (ZIGP) regression models. It is shown that the fit by ZICMP is comparable or better than these models.

Statistical guides to estimating the number of undiscovered mineral deposits: an example with porphyry copper deposits

USGS Publications Warehouse

Singer, Donald A.; Menzie, W.D.; Cheng, Qiuming; Bonham-Carter, G. F.

2005-01-01

Estimating numbers of undiscovered mineral deposits is a fundamental part of assessing mineral resources. Some statistical tools can act as guides to low variance, unbiased estimates of the number of deposits. The primary guide is that the estimates must be consistent with the grade and tonnage models. Another statistical guide is the deposit density (i.e., the number of deposits per unit area of permissive rock in well-explored control areas). Preliminary estimates and confidence limits of the number of undiscovered deposits in a tract of given area may be calculated using linear regression and refined using frequency distributions with appropriate parameters. A Poisson distribution leads to estimates having lower relative variances than the regression estimates and implies a random distribution of deposits. Coefficients of variation are used to compare uncertainties of negative binomial, Poisson, or MARK3 empirical distributions that have the same expected number of deposits as the deposit density. Statistical guides presented here allow simple yet robust estimation of the number of undiscovered deposits in permissive terranes. 

A test of inflated zeros for Poisson regression models.

PubMed

He, Hua; Zhang, Hui; Ye, Peng; Tang, Wan

2017-01-01

Excessive zeros are common in practice and may cause overdispersion and invalidate inference when fitting Poisson regression models. There is a large body of literature on zero-inflated Poisson models. However, methods for testing whether there are excessive zeros are less well developed. The Vuong test comparing a Poisson and a zero-inflated Poisson model is commonly applied in practice. However, the type I error of the test often deviates seriously from the nominal level, rendering serious doubts on the validity of the test in such applications. In this paper, we develop a new approach for testing inflated zeros under the Poisson model. Unlike the Vuong test for inflated zeros, our method does not require a zero-inflated Poisson model to perform the test. Simulation studies show that when compared with the Vuong test our approach not only better at controlling type I error rate, but also yield more power.

A quantile count model of water depth constraints on Cape Sable seaside sparrows

USGS Publications Warehouse

Cade, B.S.; Dong, Q.

2008-01-01

1. A quantile regression model for counts of breeding Cape Sable seaside sparrows Ammodramus maritimus mirabilis (L.) as a function of water depth and previous year abundance was developed based on extensive surveys, 1992-2005, in the Florida Everglades. The quantile count model extends linear quantile regression methods to discrete response variables, providing a flexible alternative to discrete parametric distributional models, e.g. Poisson, negative binomial and their zero-inflated counterparts. 2. Estimates from our multiplicative model demonstrated that negative effects of increasing water depth in breeding habitat on sparrow numbers were dependent on recent occupation history. Upper 10th percentiles of counts (one to three sparrows) decreased with increasing water depth from 0 to 30 cm when sites were not occupied in previous years. However, upper 40th percentiles of counts (one to six sparrows) decreased with increasing water depth for sites occupied in previous years. 3. Greatest decreases (-50% to -83%) in upper quantiles of sparrow counts occurred as water depths increased from 0 to 15 cm when previous year counts were 1, but a small proportion of sites (5-10%) held at least one sparrow even as water depths increased to 20 or 30 cm. 4. A zero-inflated Poisson regression model provided estimates of conditional means that also decreased with increasing water depth but rates of change were lower and decreased with increasing previous year counts compared to the quantile count model. Quantiles computed for the zero-inflated Poisson model enhanced interpretation of this model but had greater lack-of-fit for water depths > 0 cm and previous year counts 1, conditions where the negative effect of water depths were readily apparent and fitted better with the quantile count model.

Accuracy assessment of the linear Poisson-Boltzmann equation and reparametrization of the OBC generalized Born model for nucleic acids and nucleic acid-protein complexes.

PubMed

Fogolari, Federico; Corazza, Alessandra; Esposito, Gennaro

2015-04-05

The generalized Born model in the Onufriev, Bashford, and Case (Onufriev et al., Proteins: Struct Funct Genet 2004, 55, 383) implementation has emerged as one of the best compromises between accuracy and speed of computation. For simulations of nucleic acids, however, a number of issues should be addressed: (1) the generalized Born model is based on a linear model and the linearization of the reference Poisson-Boltmann equation may be questioned for highly charged systems as nucleic acids; (2) although much attention has been given to potentials, solvation forces could be much less sensitive to linearization than the potentials; and (3) the accuracy of the Onufriev-Bashford-Case (OBC) model for nucleic acids depends on fine tuning of parameters. Here, we show that the linearization of the Poisson Boltzmann equation has mild effects on computed forces, and that with optimal choice of the OBC model parameters, solvation forces, essential for molecular dynamics simulations, agree well with those computed using the reference Poisson-Boltzmann model. © 2015 Wiley Periodicals, Inc.

Poisson Coordinates.

PubMed

Li, Xian-Ying; Hu, Shi-Min

2013-02-01

Harmonic functions are the critical points of a Dirichlet energy functional, the linear projections of conformal maps. They play an important role in computer graphics, particularly for gradient-domain image processing and shape-preserving geometric computation. We propose Poisson coordinates, a novel transfinite interpolation scheme based on the Poisson integral formula, as a rapid way to estimate a harmonic function on a certain domain with desired boundary values. Poisson coordinates are an extension of the Mean Value coordinates (MVCs) which inherit their linear precision, smoothness, and kernel positivity. We give explicit formulas for Poisson coordinates in both continuous and 2D discrete forms. Superior to MVCs, Poisson coordinates are proved to be pseudoharmonic (i.e., they reproduce harmonic functions on n-dimensional balls). Our experimental results show that Poisson coordinates have lower Dirichlet energies than MVCs on a number of typical 2D domains (particularly convex domains). As well as presenting a formula, our approach provides useful insights for further studies on coordinates-based interpolation and fast estimation of harmonic functions.

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.

Assessment of Poisson, probit and linear models for genetic analysis of presence and number of black spots in Corriedale sheep.

PubMed

Peñagaricano, F; Urioste, J I; Naya, H; de los Campos, G; Gianola, D

2011-04-01

Black skin spots are associated with pigmented fibres in wool, an important quality fault. Our objective was to assess alternative models for genetic analysis of presence (BINBS) and number (NUMBS) of black spots in Corriedale sheep. During 2002-08, 5624 records from 2839 animals in two flocks, aged 1 through 6 years, were taken at shearing. Four models were considered: linear and probit for BINBS and linear and Poisson for NUMBS. All models included flock-year and age as fixed effects and animal and permanent environmental as random effects. Models were fitted to the whole data set and were also compared based on their predictive ability in cross-validation. Estimates of heritability ranged from 0.154 to 0.230 for BINBS and 0.269 to 0.474 for NUMBS. For BINBS, the probit model fitted slightly better to the data than the linear model. Predictions of random effects from these models were highly correlated, and both models exhibited similar predictive ability. For NUMBS, the Poisson model, with a residual term to account for overdispersion, performed better than the linear model in goodness of fit and predictive ability. Predictions of random effects from the Poisson model were more strongly correlated with those from BINBS models than those from the linear model. Overall, the use of probit or linear models for BINBS and of a Poisson model with a residual for NUMBS seems a reasonable choice for genetic selection purposes in Corriedale sheep. © 2010 Blackwell Verlag GmbH.

Modeling animal-vehicle collisions using diagonal inflated bivariate Poisson regression.

PubMed

Lao, Yunteng; Wu, Yao-Jan; Corey, Jonathan; Wang, Yinhai

2011-01-01

Two types of animal-vehicle collision (AVC) data are commonly adopted for AVC-related risk analysis research: reported AVC data and carcass removal data. One issue with these two data sets is that they were found to have significant discrepancies by previous studies. In order to model these two types of data together and provide a better understanding of highway AVCs, this study adopts a diagonal inflated bivariate Poisson regression method, an inflated version of bivariate Poisson regression model, to fit the reported AVC and carcass removal data sets collected in Washington State during 2002-2006. The diagonal inflated bivariate Poisson model not only can model paired data with correlation, but also handle under- or over-dispersed data sets as well. Compared with three other types of models, double Poisson, bivariate Poisson, and zero-inflated double Poisson, the diagonal inflated bivariate Poisson model demonstrates its capability of fitting two data sets with remarkable overlapping portions resulting from the same stochastic process. Therefore, the diagonal inflated bivariate Poisson model provides researchers a new approach to investigating AVCs from a different perspective involving the three distribution parameters (λ(1), λ(2) and λ(3)). The modeling results show the impacts of traffic elements, geometric design and geographic characteristics on the occurrences of both reported AVC and carcass removal data. It is found that the increase of some associated factors, such as speed limit, annual average daily traffic, and shoulder width, will increase the numbers of reported AVCs and carcass removals. Conversely, the presence of some geometric factors, such as rolling and mountainous terrain, will decrease the number of reported AVCs. Published by Elsevier Ltd.

Analysis of Blood Transfusion Data Using Bivariate Zero-Inflated Poisson Model: A Bayesian Approach.

PubMed

Mohammadi, Tayeb; Kheiri, Soleiman; Sedehi, Morteza

2016-01-01

Recognizing the factors affecting the number of blood donation and blood deferral has a major impact on blood transfusion. There is a positive correlation between the variables "number of blood donation" and "number of blood deferral": as the number of return for donation increases, so does the number of blood deferral. On the other hand, due to the fact that many donors never return to donate, there is an extra zero frequency for both of the above-mentioned variables. In this study, in order to apply the correlation and to explain the frequency of the excessive zero, the bivariate zero-inflated Poisson regression model was used for joint modeling of the number of blood donation and number of blood deferral. The data was analyzed using the Bayesian approach applying noninformative priors at the presence and absence of covariates. Estimating the parameters of the model, that is, correlation, zero-inflation parameter, and regression coefficients, was done through MCMC simulation. Eventually double-Poisson model, bivariate Poisson model, and bivariate zero-inflated Poisson model were fitted on the data and were compared using the deviance information criteria (DIC). The results showed that the bivariate zero-inflated Poisson regression model fitted the data better than the other models.

Analysis of Blood Transfusion Data Using Bivariate Zero-Inflated Poisson Model: A Bayesian Approach

PubMed Central

Mohammadi, Tayeb; Sedehi, Morteza

2016-01-01

Recognizing the factors affecting the number of blood donation and blood deferral has a major impact on blood transfusion. There is a positive correlation between the variables “number of blood donation” and “number of blood deferral”: as the number of return for donation increases, so does the number of blood deferral. On the other hand, due to the fact that many donors never return to donate, there is an extra zero frequency for both of the above-mentioned variables. In this study, in order to apply the correlation and to explain the frequency of the excessive zero, the bivariate zero-inflated Poisson regression model was used for joint modeling of the number of blood donation and number of blood deferral. The data was analyzed using the Bayesian approach applying noninformative priors at the presence and absence of covariates. Estimating the parameters of the model, that is, correlation, zero-inflation parameter, and regression coefficients, was done through MCMC simulation. Eventually double-Poisson model, bivariate Poisson model, and bivariate zero-inflated Poisson model were fitted on the data and were compared using the deviance information criteria (DIC). The results showed that the bivariate zero-inflated Poisson regression model fitted the data better than the other models. PMID:27703493

Multi-Parameter Linear Least-Squares Fitting to Poisson Data One Count at a Time

NASA Technical Reports Server (NTRS)

Wheaton, W.; Dunklee, A.; Jacobson, A.; Ling, J.; Mahoney, W.; Radocinski, R.

1993-01-01

A standard problem in gamma-ray astronomy data analysis is the decomposition of a set of observed counts, described by Poisson statistics, according to a given multi-component linear model, with underlying physical count rates or fluxes which are to be estimated from the data.

Simulation Methods for Poisson Processes in Nonstationary Systems.

DTIC Science & Technology

1978-08-01

for simulation of nonhomogeneous Poisson processes is stated with log-linear rate function. The method is based on an identity relating the...and relatively efficient new method for simulation of one-dimensional and two-dimensional nonhomogeneous Poisson processes is described. The method is

Duality and integrability: Electromagnetism, linearized gravity, and massless higher spin gauge fields as bi-Hamiltonian systems

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

Barnich, Glenn; Troessaert, Cedric

2009-04-15

In the reduced phase space of electromagnetism, the generator of duality rotations in the usual Poisson bracket is shown to generate Maxwell's equations in a second, much simpler Poisson bracket. This gives rise to a hierarchy of bi-Hamiltonian evolution equations in the standard way. The result can be extended to linearized Yang-Mills theory, linearized gravity, and massless higher spin gauge fields.

Normal forms for Poisson maps and symplectic groupoids around Poisson transversals

NASA Astrophysics Data System (ADS)

Frejlich, Pedro; Mărcuț, Ioan

2018-03-01

Poisson transversals are submanifolds in a Poisson manifold which intersect all symplectic leaves transversally and symplectically. In this communication, we prove a normal form theorem for Poisson maps around Poisson transversals. A Poisson map pulls a Poisson transversal back to a Poisson transversal, and our first main result states that simultaneous normal forms exist around such transversals, for which the Poisson map becomes transversally linear, and intertwines the normal form data of the transversals. Our second result concerns symplectic integrations. We prove that a neighborhood of a Poisson transversal is integrable exactly when the Poisson transversal itself is integrable, and in that case we prove a normal form theorem for the symplectic groupoid around its restriction to the Poisson transversal, which puts all structure maps in normal form. We conclude by illustrating our results with examples arising from Lie algebras.

Normal forms for Poisson maps and symplectic groupoids around Poisson transversals.

PubMed

Frejlich, Pedro; Mărcuț, Ioan

2018-01-01

Poisson transversals are submanifolds in a Poisson manifold which intersect all symplectic leaves transversally and symplectically. In this communication, we prove a normal form theorem for Poisson maps around Poisson transversals. A Poisson map pulls a Poisson transversal back to a Poisson transversal, and our first main result states that simultaneous normal forms exist around such transversals, for which the Poisson map becomes transversally linear, and intertwines the normal form data of the transversals. Our second result concerns symplectic integrations. We prove that a neighborhood of a Poisson transversal is integrable exactly when the Poisson transversal itself is integrable, and in that case we prove a normal form theorem for the symplectic groupoid around its restriction to the Poisson transversal, which puts all structure maps in normal form. We conclude by illustrating our results with examples arising from Lie algebras.

A generalized Poisson and Poisson-Boltzmann solver for electrostatic environments.

PubMed

Fisicaro, G; Genovese, L; Andreussi, O; Marzari, N; Goedecker, S

2016-01-07

The computational study of chemical reactions in complex, wet environments is critical for applications in many fields. It is often essential to study chemical reactions in the presence of applied electrochemical potentials, taking into account the non-trivial electrostatic screening coming from the solvent and the electrolytes. As a consequence, the electrostatic potential has to be found by solving the generalized Poisson and the Poisson-Boltzmann equations for neutral and ionic solutions, respectively. In the present work, solvers for both problems have been developed. A preconditioned conjugate gradient method has been implemented for the solution of the generalized Poisson equation and the linear regime of the Poisson-Boltzmann, allowing to solve iteratively the minimization problem with some ten iterations of the ordinary Poisson equation solver. In addition, a self-consistent procedure enables us to solve the non-linear Poisson-Boltzmann problem. Both solvers exhibit very high accuracy and parallel efficiency and allow for the treatment of periodic, free, and slab boundary conditions. The solver has been integrated into the BigDFT and Quantum-ESPRESSO electronic-structure packages and will be released as an independent program, suitable for integration in other codes.

A generalized Poisson and Poisson-Boltzmann solver for electrostatic environments

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

Fisicaro, G., E-mail: giuseppe.fisicaro@unibas.ch; Goedecker, S.; Genovese, L.

2016-01-07

The computational study of chemical reactions in complex, wet environments is critical for applications in many fields. It is often essential to study chemical reactions in the presence of applied electrochemical potentials, taking into account the non-trivial electrostatic screening coming from the solvent and the electrolytes. As a consequence, the electrostatic potential has to be found by solving the generalized Poisson and the Poisson-Boltzmann equations for neutral and ionic solutions, respectively. In the present work, solvers for both problems have been developed. A preconditioned conjugate gradient method has been implemented for the solution of the generalized Poisson equation and themore » linear regime of the Poisson-Boltzmann, allowing to solve iteratively the minimization problem with some ten iterations of the ordinary Poisson equation solver. In addition, a self-consistent procedure enables us to solve the non-linear Poisson-Boltzmann problem. Both solvers exhibit very high accuracy and parallel efficiency and allow for the treatment of periodic, free, and slab boundary conditions. The solver has been integrated into the BigDFT and Quantum-ESPRESSO electronic-structure packages and will be released as an independent program, suitable for integration in other codes.« less

Prevalence of vitamin D deficiency and associated factors in women and newborns in the immediate postpartum period

PubMed Central

do Prado, Mara Rúbia Maciel Cardoso; Oliveira, Fabiana de Cássia Carvalho; Assis, Karine Franklin; Ribeiro, Sarah Aparecida Vieira; do Prado, Pedro Paulo; Sant'Ana, Luciana Ferreira da Rocha; Priore, Silvia Eloiza; Franceschini, Sylvia do Carmo Castro

2015-01-01

Abstract Objective: To assess the prevalence of vitamin D deficiency and its associated factors in women and their newborns in the postpartum period. Methods: This cross-sectional study evaluated vitamin D deficiency/insufficiency in 226 women and their newborns in Viçosa (Minas Gerais, BR) between December 2011 and November 2012. Cord blood and venous maternal blood were collected to evaluate the following biochemical parameters: vitamin D, alkaline phosphatase, calcium, phosphorus and parathyroid hormone. Poisson regression analysis, with a confidence interval of 95%, was applied to assess vitamin D deficiency and its associated factors. Multiple linear regression analysis was performed to identify factors associated with 25(OH)D deficiency in the newborns and women from the study. The criteria for variable inclusion in the multiple linear regression model was the association with the dependent variable in the simple linear regression analysis, considering p<0.20. Significance level was α <5%. Results: From 226 women included, 200 (88.5%) were 20-44 years old; the median age was 28 years. Deficient/insufficient levels of vitamin D were found in 192 (85%) women and in 182 (80.5%) neonates. The maternal 25(OH)D and alkaline phosphatase levels were independently associated with vitamin D deficiency in infants. Conclusions: This study identified a high prevalence of vitamin D deficiency and insufficiency in women and newborns and the association between maternal nutritional status of vitamin D and their infants' vitamin D status. PMID:26100593

Identification d’une Classe de Processus de Poisson Filtres (Identification of a Class of Filtered Poisson Processes).

DTIC Science & Technology

1983-05-20

Poisson processes is introduced: the amplitude has a law which is spherically invariant and the filter is real, linear and causal. It is shown how such a model can be identified from experimental data. (Author)

Solution of the nonlinear Poisson-Boltzmann equation: Application to ionic diffusion in cementitious materials

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

Arnold, J.; Kosson, D.S., E-mail: david.s.kosson@vanderbilt.edu; Garrabrants, A.

2013-02-15

A robust numerical solution of the nonlinear Poisson-Boltzmann equation for asymmetric polyelectrolyte solutions in discrete pore geometries is presented. Comparisons to the linearized approximation of the Poisson-Boltzmann equation reveal that the assumptions leading to linearization may not be appropriate for the electrochemical regime in many cementitious materials. Implications of the electric double layer on both partitioning of species and on diffusive release are discussed. The influence of the electric double layer on anion diffusion relative to cation diffusion is examined.

Fuzzy classifier based support vector regression framework for Poisson ratio determination

NASA Astrophysics Data System (ADS)

Asoodeh, Mojtaba; Bagheripour, Parisa

2013-09-01

Poisson ratio is considered as one of the most important rock mechanical properties of hydrocarbon reservoirs. Determination of this parameter through laboratory measurement is time, cost, and labor intensive. Furthermore, laboratory measurements do not provide continuous data along the reservoir intervals. Hence, a fast, accurate, and inexpensive way of determining Poisson ratio which produces continuous data over the whole reservoir interval is desirable. For this purpose, support vector regression (SVR) method based on statistical learning theory (SLT) was employed as a supervised learning algorithm to estimate Poisson ratio from conventional well log data. SVR is capable of accurately extracting the implicit knowledge contained in conventional well logs and converting the gained knowledge into Poisson ratio data. Structural risk minimization (SRM) principle which is embedded in the SVR structure in addition to empirical risk minimization (EMR) principle provides a robust model for finding quantitative formulation between conventional well log data and Poisson ratio. Although satisfying results were obtained from an individual SVR model, it had flaws of overestimation in low Poisson ratios and underestimation in high Poisson ratios. These errors were eliminated through implementation of fuzzy classifier based SVR (FCBSVR). The FCBSVR significantly improved accuracy of the final prediction. This strategy was successfully applied to data from carbonate reservoir rocks of an Iranian Oil Field. Results indicated that SVR predicted Poisson ratio values are in good agreement with measured values.

Predicting spatio-temporal failure in large scale observational and micro scale experimental systems

NASA Astrophysics Data System (ADS)

de las Heras, Alejandro; Hu, Yong

2006-10-01

Forecasting has become an essential part of modern thought, but the practical limitations still are manifold. We addressed future rates of change by comparing models that take into account time, and models that focus more on space. Cox regression confirmed that linear change can be safely assumed in the short-term. Spatially explicit Poisson regression, provided a ceiling value for the number of deforestation spots. With several observed and estimated rates, it was decided to forecast using the more robust assumptions. A Markov-chain cellular automaton thus projected 5-year deforestation in the Amazonian Arc of Deforestation, showing that even a stable rate of change would largely deplete the forest area. More generally, resolution and implementation of the existing models could explain many of the modelling difficulties still affecting forecasting.

A regularization corrected score method for nonlinear regression models with covariate error.

PubMed

Zucker, David M; Gorfine, Malka; Li, Yi; Tadesse, Mahlet G; Spiegelman, Donna

2013-03-01

Many regression analyses involve explanatory variables that are measured with error, and failing to account for this error is well known to lead to biased point and interval estimates of the regression coefficients. We present here a new general method for adjusting for covariate error. Our method consists of an approximate version of the Stefanski-Nakamura corrected score approach, using the method of regularization to obtain an approximate solution of the relevant integral equation. We develop the theory in the setting of classical likelihood models; this setting covers, for example, linear regression, nonlinear regression, logistic regression, and Poisson regression. The method is extremely general in terms of the types of measurement error models covered, and is a functional method in the sense of not involving assumptions on the distribution of the true covariate. We discuss the theoretical properties of the method and present simulation results in the logistic regression setting (univariate and multivariate). For illustration, we apply the method to data from the Harvard Nurses' Health Study concerning the relationship between physical activity and breast cancer mortality in the period following a diagnosis of breast cancer. Copyright © 2013, The International Biometric Society.

Independence of the effective dielectric constant of an electrolytic solution on the ionic distribution in the linear Poisson-Nernst-Planck model.

PubMed

Alexe-Ionescu, A L; Barbero, G; Lelidis, I

2014-08-28

We consider the influence of the spatial dependence of the ions distribution on the effective dielectric constant of an electrolytic solution. We show that in the linear version of the Poisson-Nernst-Planck model, the effective dielectric constant of the solution has to be considered independent of any ionic distribution induced by the external field. This result follows from the fact that, in the linear approximation of the Poisson-Nernst-Planck model, the redistribution of the ions in the solvent due to the external field gives rise to a variation of the dielectric constant that is of the first order in the effective potential, and therefore it has to be neglected in the Poisson's equation that relates the actual electric potential across the electrolytic cell to the bulk density of ions. The analysis is performed in the case where the electrodes are perfectly blocking and the adsorption at the electrodes is negligible, and in the absence of any ion dissociation-recombination effect.

Influenza vaccine coverage, influenza-associated morbidity and all-cause mortality in Catalonia (Spain).

PubMed

Muñoz, M Pilar; Soldevila, Núria; Martínez, Anna; Carmona, Glòria; Batalla, Joan; Acosta, Lesly M; Domínguez, Angela

2011-07-12

The objective of this work was to study the behaviour of influenza with respect to morbidity and all-cause mortality in Catalonia, and their association with influenza vaccination coverage. The study was carried out over 13 influenza seasons, from epidemiological week 40 of 1994 to week 20 of 2007, and included confirmed cases of influenza and all-cause mortality. Two generalized linear models were fitted: influenza-associated morbidity was modelled by Poisson regression and all-cause mortality by negative binomial regression. The seasonal component was modelled with the periodic function formed by the sum of the sinus and cosines. Expected influenza mortality during periods of influenza virus circulation was estimated by Poisson regression and its confidence intervals using the Bootstrap approach. Vaccination coverage was associated with a reduction in influenza-associated morbidity (p<0.001), but not with a reduction in all-cause mortality (p=0.149). In the case of influenza-associated morbidity, an increase of 5% in vaccination coverage represented a reduction of 3% in the incidence rate of influenza. There was a positive association between influenza-associated morbidity and all-cause mortality. Excess mortality attributable to influenza epidemics was estimated as 34.4 (95% CI: 28.4-40.8) weekly deaths. In conclusion, all-cause mortality is a good indicator of influenza surveillance and vaccination coverage is associated with a reduction in influenza-associated morbidity but not with all-cause mortality. Copyright © 2011 Elsevier Ltd. All rights reserved.

Evaluation of Shiryaev-Roberts procedure for on-line environmental radiation monitoring.

PubMed

Watson, Mara M; Seliman, Ayman F; Bliznyuk, Valery N; DeVol, Timothy A

2018-04-30

Water can become contaminated as a result of a leak from a nuclear facility, such as a waste facility, or from clandestine nuclear activity. Low-level on-line radiation monitoring is needed to detect these events in real time. A Bayesian control chart method, Shiryaev-Roberts (SR) procedure, was compared with classical methods, 3-σ and cumulative sum (CUSUM), for quantifying an accumulating signal from an extractive scintillating resin flow-cell detection system. Solutions containing 0.10-5.0 Bq/L of 99 Tc, as T99cO 4 - were pumped through a flow cell packed with extractive scintillating resin used in conjunction with a Beta-RAM Model 5 HPLC detector. While T99cO 4 - accumulated on the resin, time series data were collected. Control chart methods were applied to the data using statistical algorithms developed in MATLAB. SR charts were constructed using Poisson (Poisson SR) and Gaussian (Gaussian SR) probability distributions of count data to estimate the likelihood ratio. Poisson and Gaussian SR charts required less volume of radioactive solution at a fixed concentration to exceed the control limit in most cases than 3-σ and CUSUM control charts, particularly solutions with lower activity. SR is thus the ideal control chart for low-level on-line radiation monitoring. Once the control limit was exceeded, activity concentrations were estimated from the SR control chart using the control chart slope on a semi-logarithmic plot. A linear regression fit was applied to averaged slope data for five activity concentration groupings for Poisson and Gaussian SR control charts. A correlation coefficient (R 2 ) of 0.77 for Poisson SR and 0.90 for Gaussian SR suggest this method will adequately estimate activity concentration for an unknown solution. Copyright © 2018 Elsevier Ltd. All rights reserved.

Response analysis of a class of quasi-linear systems with fractional derivative excited by Poisson white noise

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

Yang, Yongge; Xu, Wei, E-mail: weixu@nwpu.edu.cn; Yang, Guidong

The Poisson white noise, as a typical non-Gaussian excitation, has attracted much attention recently. However, little work was referred to the study of stochastic systems with fractional derivative under Poisson white noise excitation. This paper investigates the stationary response of a class of quasi-linear systems with fractional derivative excited by Poisson white noise. The equivalent stochastic system of the original stochastic system is obtained. Then, approximate stationary solutions are obtained with the help of the perturbation method. Finally, two typical examples are discussed in detail to demonstrate the effectiveness of the proposed method. The analysis also shows that the fractionalmore » order and the fractional coefficient significantly affect the responses of the stochastic systems with fractional derivative.« less

A comparison between Poisson and zero-inflated Poisson regression models with an application to number of black spots in Corriedale sheep

PubMed Central

Naya, Hugo; Urioste, Jorge I; Chang, Yu-Mei; Rodrigues-Motta, Mariana; Kremer, Roberto; Gianola, Daniel

2008-01-01

Dark spots in the fleece area are often associated with dark fibres in wool, which limits its competitiveness with other textile fibres. Field data from a sheep experiment in Uruguay revealed an excess number of zeros for dark spots. We compared the performance of four Poisson and zero-inflated Poisson (ZIP) models under four simulation scenarios. All models performed reasonably well under the same scenario for which the data were simulated. The deviance information criterion favoured a Poisson model with residual, while the ZIP model with a residual gave estimates closer to their true values under all simulation scenarios. Both Poisson and ZIP models with an error term at the regression level performed better than their counterparts without such an error. Field data from Corriedale sheep were analysed with Poisson and ZIP models with residuals. Parameter estimates were similar for both models. Although the posterior distribution of the sire variance was skewed due to a small number of rams in the dataset, the median of this variance suggested a scope for genetic selection. The main environmental factor was the age of the sheep at shearing. In summary, age related processes seem to drive the number of dark spots in this breed of sheep. PMID:18558072

Estimating the Depth of the Navy Recruiting Market

DTIC Science & Technology

2016-09-01

recommend that NRC make use of the Poisson regression model in order to determine high-yield ZIP codes for market depth. 14. SUBJECT...recommend that NRC make use of the Poisson regression model in order to determine high-yield ZIP codes for market depth. vi THIS PAGE INTENTIONALLY LEFT...DEPTH OF THE NAVY RECRUITING MARKET by Emilie M. Monaghan September 2016 Thesis Advisor: Lyn R. Whitaker Second Reader: Jonathan K. Alt

[Prevalence of vitamin D deficiency and associated factors in women and newborns in the immediate postpartum period].

PubMed

do Prado, Mara Rúbia Maciel Cardoso; Oliveira, Fabiana de Cássia Carvalho; Assis, Karine Franklin; Ribeiro, Sarah Aparecida Vieira; do Prado Junior, Pedro Paulo; Sant'Ana, Luciana Ferreira da Rocha; Priore, Silvia Eloiza; Franceschini, Sylvia do Carmo Castro

2015-01-01

To assess the prevalence of vitamin D deficiency and its associated factors in women and their newborns in the postpartum period. This cross-sectional study evaluated vitamin D deficiency/insufficiency in 226 women and their newborns in Viçosa (Minas Gerais, BR) between December 2011 and November 2012. Cord blood and venous maternal blood were collected to evaluate the following biochemical parameters: vitamin D, alkaline phosphatase, calcium, phosphorus and parathyroid hormone. Poisson regression analysis, with a confidence interval of 95% was applied to assess vitamin D deficiency and its associated factors. Multiple linear regression analysis was performed to identify factors associated with 25(OH)D deficiency in the newborns and women from the study. The criteria for variable inclusion in the multiple linear regression model was the association with the dependent variable in the simple linear regression analysis, considering p<0.20. Significance level was α<5%. From 226 women included, 200 (88.5%) were 20 to 44 years old; the median age was 28 years. Deficient/insufficient levels of vitamin D were found in 192 (85%) women and in 182 (80.5%) neonates. The maternal 25(OH)D and alkaline phosphatase levels were independently associated with vitamin D deficiency in infants. This study identified a high prevalence of vitamin D deficiency and insufficiency in women and newborns and the association between maternal nutritional status of vitamin D and their infants' vitamin D status. Copyright © 2015 Sociedade de Pediatria de São Paulo. Publicado por Elsevier Editora Ltda. All rights reserved.

Estimating the intensity of a cyclic Poisson process in the presence of additive and multiplicative linear trend

NASA Astrophysics Data System (ADS)

Wayan Mangku, I.

2017-10-01

In this paper we survey some results on estimation of the intensity function of a cyclic Poisson process in the presence of additive and multiplicative linear trend. We do not assume any parametric form for the cyclic component of the intensity function, except that it is periodic. Moreover, we consider the case when there is only a single realization of the Poisson process is observed in a bounded interval. The considered estimators are weakly and strongly consistent when the size of the observation interval indefinitely expands. Asymptotic approximations to the bias and variance of those estimators are presented.

PB-AM: An open-source, fully analytical linear poisson-boltzmann solver

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

Felberg, Lisa E.; Brookes, David H.; Yap, Eng-Hui

2016-11-02

We present the open source distributed software package Poisson-Boltzmann Analytical Method (PB-AM), a fully analytical solution to the linearized Poisson Boltzmann equation. The PB-AM software package includes the generation of outputs files appropriate for visualization using VMD, a Brownian dynamics scheme that uses periodic boundary conditions to simulate dynamics, the ability to specify docking criteria, and offers two different kinetics schemes to evaluate biomolecular association rate constants. Given that PB-AM defines mutual polarization completely and accurately, it can be refactored as a many-body expansion to explore 2- and 3-body polarization. Additionally, the software has been integrated into the Adaptive Poisson-Boltzmannmore » Solver (APBS) software package to make it more accessible to a larger group of scientists, educators and students that are more familiar with the APBS framework.« less

Identification of a Class of Filtered Poisson Processes.

DTIC Science & Technology

1981-01-01

LD-A135 371 IDENTIFICATION OF A CLASS OF FILERED POISSON PROCESSES I AU) NORTH CAROLINA UNIV AT CHAPEL HIL DEPT 0F STATISTICS D DE RRUC ET AL 1981...STNO&IO$ !tt ~ 4.s " . , ".7" -L N ~ TITLE :IDENTIFICATION OF A CLASS OF FILTERED POISSON PROCESSES Authors : DE BRUCQ Denis - GUALTIEROTTI Antonio...filtered Poisson processes is intro- duced : the amplitude has a law which is spherically invariant and the filter is real, linear and causal. It is shown

Assessment of Linear Finite-Difference Poisson-Boltzmann Solvers

PubMed Central

Wang, Jun; Luo, Ray

2009-01-01

CPU time and memory usage are two vital issues that any numerical solvers for the Poisson-Boltzmann equation have to face in biomolecular applications. In this study we systematically analyzed the CPU time and memory usage of five commonly used finite-difference solvers with a large and diversified set of biomolecular structures. Our comparative analysis shows that modified incomplete Cholesky conjugate gradient and geometric multigrid are the most efficient in the diversified test set. For the two efficient solvers, our test shows that their CPU times increase approximately linearly with the numbers of grids. Their CPU times also increase almost linearly with the negative logarithm of the convergence criterion at very similar rate. Our comparison further shows that geometric multigrid performs better in the large set of tested biomolecules. However, modified incomplete Cholesky conjugate gradient is superior to geometric multigrid in molecular dynamics simulations of tested molecules. We also investigated other significant components in numerical solutions of the Poisson-Boltzmann equation. It turns out that the time-limiting step is the free boundary condition setup for the linear systems for the selected proteins if the electrostatic focusing is not used. Thus, development of future numerical solvers for the Poisson-Boltzmann equation should balance all aspects of the numerical procedures in realistic biomolecular applications. PMID:20063271

Does Group-Level Commitment Predict Employee Well-Being?: A Prospective Analysis.

PubMed

Clausen, Thomas; Christensen, Karl Bang; Nielsen, Karina

2015-11-01

To investigate the links between group-level affective organizational commitment (AOC) and individual-level psychological well-being, self-reported sickness absence, and sleep disturbances. A total of 5085 care workers from 301 workgroups in the Danish eldercare services participated in both waves of the study (T1 [2005] and T2 [2006]). The three outcomes were analyzed using linear multilevel regression analysis, multilevel Poisson regression analysis, and multilevel logistic regression analysis, respectively. Group-level AOC (T1) significantly predicted individual-level psychological well-being, self-reported sickness absence, and sleep disturbances (T2). The association between group-level AOC (T1) and psychological well-being (T2) was fully mediated by individual-level AOC (T1), and the associations between group-level AOC (T1) and self-reported sickness absence and sleep disturbances (T2) were partially mediated by individual-level AOC (T1). Group-level AOC is an important predictor of employee well-being in contemporary health care organizations.

An overall strategy based on regression models to estimate relative survival and model the effects of prognostic factors in cancer survival studies.

PubMed

Remontet, L; Bossard, N; Belot, A; Estève, J

2007-05-10

Relative survival provides a measure of the proportion of patients dying from the disease under study without requiring the knowledge of the cause of death. We propose an overall strategy based on regression models to estimate the relative survival and model the effects of potential prognostic factors. The baseline hazard was modelled until 10 years follow-up using parametric continuous functions. Six models including cubic regression splines were considered and the Akaike Information Criterion was used to select the final model. This approach yielded smooth and reliable estimates of mortality hazard and allowed us to deal with sparse data taking into account all the available information. Splines were also used to model simultaneously non-linear effects of continuous covariates and time-dependent hazard ratios. This led to a graphical representation of the hazard ratio that can be useful for clinical interpretation. Estimates of these models were obtained by likelihood maximization. We showed that these estimates could be also obtained using standard algorithms for Poisson regression. Copyright 2006 John Wiley & Sons, Ltd.

Application of the Conway-Maxwell-Poisson generalized linear model for analyzing motor vehicle crashes.

PubMed

Lord, Dominique; Guikema, Seth D; Geedipally, Srinivas Reddy

2008-05-01

This paper documents the application of the Conway-Maxwell-Poisson (COM-Poisson) generalized linear model (GLM) for modeling motor vehicle crashes. The COM-Poisson distribution, originally developed in 1962, has recently been re-introduced by statisticians for analyzing count data subjected to over- and under-dispersion. This innovative distribution is an extension of the Poisson distribution. The objectives of this study were to evaluate the application of the COM-Poisson GLM for analyzing motor vehicle crashes and compare the results with the traditional negative binomial (NB) model. The comparison analysis was carried out using the most common functional forms employed by transportation safety analysts, which link crashes to the entering flows at intersections or on segments. To accomplish the objectives of the study, several NB and COM-Poisson GLMs were developed and compared using two datasets. The first dataset contained crash data collected at signalized four-legged intersections in Toronto, Ont. The second dataset included data collected for rural four-lane divided and undivided highways in Texas. Several methods were used to assess the statistical fit and predictive performance of the models. The results of this study show that COM-Poisson GLMs perform as well as NB models in terms of GOF statistics and predictive performance. Given the fact the COM-Poisson distribution can also handle under-dispersed data (while the NB distribution cannot or has difficulties converging), which have sometimes been observed in crash databases, the COM-Poisson GLM offers a better alternative over the NB model for modeling motor vehicle crashes, especially given the important limitations recently documented in the safety literature about the latter type of model.

Particle trapping: A key requisite of structure formation and stability of Vlasov–Poisson plasmas

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

Schamel, Hans, E-mail: hans.schamel@uni-bayreuth.de

2015-04-15

Particle trapping is shown to control the existence of undamped coherent structures in Vlasov–Poisson plasmas and thereby affects the onset of plasma instability beyond the realm of linear Landau theory.

Atmospheric pollutants and hospital admissions due to pneumonia in children

PubMed Central

Negrisoli, Juliana; Nascimento, Luiz Fernando C.

2013-01-01

OBJECTIVE: To analyze the relationship between exposure to air pollutants and hospitalizations due to pneumonia in children of Sorocaba, São Paulo, Brazil. METHODS: Time series ecological study, from 2007 to 2008. Daily data were obtained from the State Environmental Agency for Pollution Control for particulate matter, nitric oxide, nitrogen dioxide, ozone, besides air temperature and relative humidity. The data concerning pneumonia admissions were collected in the public health system of Sorocaba. Correlations between the variables of interest using Pearson cofficient were calculated. Models with lags from zero to five days after exposure to pollutants were performed to analyze the association between the exposure to environmental pollutants and hospital admissions. The analysis used the generalized linear model of Poisson regression, being significant p<0.05. RESULTS: There were 1,825 admissions for pneumonia, with a daily mean of 2.5±2.1. There was a strong correlation between pollutants and hospital admissions, except for ozone. Regarding the Poisson regression analysis with the multi-pollutant model, only nitrogen dioxide was statistically significant in the same day (relative risk - RR=1.016), as well as particulate matter with a lag of four days (RR=1.009) after exposure to pollutants. CONCLUSIONS: There was an acute effect of exposure to nitrogen dioxide and a later effect of exposure to particulate matter on children hospitalizations for pneumonia in Sorocaba. PMID:24473956

Non-linear properties of metallic cellular materials with a negative Poisson's ratio

NASA Technical Reports Server (NTRS)

Choi, J. B.; Lakes, R. S.

1992-01-01

Negative Poisson's ratio copper foam was prepared and characterized experimentally. The transformation into re-entrant foam was accomplished by applying sequential permanent compressions above the yield point to achieve a triaxial compression. The Poisson's ratio of the re-entrant foam depended on strain and attained a relative minimum at strains near zero. Poisson's ratio as small as -0.8 was achieved. The strain dependence of properties occurred over a narrower range of strain than in the polymer foams studied earlier. Annealing of the foam resulted in a slightly greater magnitude of negative Poisson's ratio and greater toughness at the expense of a decrease in the Young's modulus.

Low-Dose N,N-Dimethylformamide Exposure and Liver Injuries in a Cohort of Chinese Leather Industry Workers.

PubMed

Qi, Cong; Gu, Yiyang; Sun, Qing; Gu, Hongliang; Xu, Bo; Gu, Qing; Xiao, Jing; Lian, Yulong

2017-05-01

We assessed the risk of liver injuries following low doses of N,N-dimethylformamide (DMF) below threshold limit values (20 mg/m) among leather industry workers and comparison groups. A cohort of 429 workers from a leather factory and 466 non-exposed subjects in China were followed for 4 years. Poisson regression and piece-wise linear regression were used to examine the relationship between DMF and liver injury. Workers exposed to a cumulative dose of DMF were significantly more likely than non-exposed workers to develop liver injury. A nonlinear relationship between DMF and liver injury was observed, and a threshold of the cumulative DMF dose for liver injury was 7.30 (mg/m) year. The findings indicate the importance of taking action to reduce DMF occupational exposure limits for promoting worker health.

Utility of correlation techniques in gravity and magnetic interpretation

NASA Technical Reports Server (NTRS)

Chandler, V. W.; Koski, J. S.; Braice, L. W.; Hinze, W. J.

1977-01-01

Internal correspondence uses Poisson's Theorem in a moving-window linear regression analysis between the anomalous first vertical derivative of gravity and total magnetic field reduced to the pole. The regression parameters provide critical information on source characteristics. The correlation coefficient indicates the strength of the relation between magnetics and gravity. Slope value gives delta j/delta sigma estimates of the anomalous source. The intercept furnishes information on anomaly interference. Cluster analysis consists of the classification of subsets of data into groups of similarity based on correlation of selected characteristics of the anomalies. Model studies are used to illustrate implementation and interpretation procedures of these methods, particularly internal correspondence. Analysis of the results of applying these methods to data from the midcontinent and a transcontinental profile shows they can be useful in identifying crustal provinces, providing information on horizontal and vertical variations of physical properties over province size zones, validating long wavelength anomalies, and isolating geomagnetic field removal problems.

Linear stability analysis of the Vlasov-Poisson equations in high density plasmas in the presence of crossed fields and density gradients

NASA Technical Reports Server (NTRS)

Kaup, D. J.; Hansen, P. J.; Choudhury, S. Roy; Thomas, Gary E.

1986-01-01

The equations for the single-particle orbits in a nonneutral high density plasma in the presence of inhomogeneous crossed fields are obtained. Using these orbits, the linearized Vlasov equation is solved as an expansion in the orbital radii in the presence of inhomogeneities and density gradients. A model distribution function is introduced whose cold-fluid limit is exactly the same as that used in many previous studies of the cold-fluid equations. This model function is used to reduce the linearized Vlasov-Poisson equations to a second-order ordinary differential equation for the linearized electrostatic potential whose eigenvalue is the perturbation frequency.

A Fast Solver for Implicit Integration of the Vlasov--Poisson System in the Eulerian Framework

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

Garrett, C. Kristopher; Hauck, Cory D.

In this paper, we present a domain decomposition algorithm to accelerate the solution of Eulerian-type discretizations of the linear, steady-state Vlasov equation. The steady-state solver then forms a key component in the implementation of fully implicit or nearly fully implicit temporal integrators for the nonlinear Vlasov--Poisson system. The solver relies on a particular decomposition of phase space that enables the use of sweeping techniques commonly used in radiation transport applications. The original linear system for the phase space unknowns is then replaced by a smaller linear system involving only unknowns on the boundary between subdomains, which can then be solvedmore » efficiently with Krylov methods such as GMRES. Steady-state solves are combined to form an implicit Runge--Kutta time integrator, and the Vlasov equation is coupled self-consistently to the Poisson equation via a linearized procedure or a nonlinear fixed-point method for the electric field. Finally, numerical results for standard test problems demonstrate the efficiency of the domain decomposition approach when compared to the direct application of an iterative solver to the original linear system.« less

A Fast Solver for Implicit Integration of the Vlasov--Poisson System in the Eulerian Framework

DOE PAGES

Garrett, C. Kristopher; Hauck, Cory D.

2018-04-05

In this paper, we present a domain decomposition algorithm to accelerate the solution of Eulerian-type discretizations of the linear, steady-state Vlasov equation. The steady-state solver then forms a key component in the implementation of fully implicit or nearly fully implicit temporal integrators for the nonlinear Vlasov--Poisson system. The solver relies on a particular decomposition of phase space that enables the use of sweeping techniques commonly used in radiation transport applications. The original linear system for the phase space unknowns is then replaced by a smaller linear system involving only unknowns on the boundary between subdomains, which can then be solvedmore » efficiently with Krylov methods such as GMRES. Steady-state solves are combined to form an implicit Runge--Kutta time integrator, and the Vlasov equation is coupled self-consistently to the Poisson equation via a linearized procedure or a nonlinear fixed-point method for the electric field. Finally, numerical results for standard test problems demonstrate the efficiency of the domain decomposition approach when compared to the direct application of an iterative solver to the original linear system.« less

Bayesian semi-parametric analysis of Poisson change-point regression models: application to policy making in Cali, Colombia.

PubMed

Park, Taeyoung; Krafty, Robert T; Sánchez, Alvaro I

2012-07-27

A Poisson regression model with an offset assumes a constant baseline rate after accounting for measured covariates, which may lead to biased estimates of coefficients in an inhomogeneous Poisson process. To correctly estimate the effect of time-dependent covariates, we propose a Poisson change-point regression model with an offset that allows a time-varying baseline rate. When the nonconstant pattern of a log baseline rate is modeled with a nonparametric step function, the resulting semi-parametric model involves a model component of varying dimension and thus requires a sophisticated varying-dimensional inference to obtain correct estimates of model parameters of fixed dimension. To fit the proposed varying-dimensional model, we devise a state-of-the-art MCMC-type algorithm based on partial collapse. The proposed model and methods are used to investigate an association between daily homicide rates in Cali, Colombia and policies that restrict the hours during which the legal sale of alcoholic beverages is permitted. While simultaneously identifying the latent changes in the baseline homicide rate which correspond to the incidence of sociopolitical events, we explore the effect of policies governing the sale of alcohol on homicide rates and seek a policy that balances the economic and cultural dependencies on alcohol sales to the health of the public.

A flexible count data regression model for risk analysis.

PubMed

Guikema, Seth D; Coffelt, Jeremy P; Goffelt, Jeremy P

2008-02-01

In many cases, risk and reliability analyses involve estimating the probabilities of discrete events such as hardware failures and occurrences of disease or death. There is often additional information in the form of explanatory variables that can be used to help estimate the likelihood of different numbers of events in the future through the use of an appropriate regression model, such as a generalized linear model. However, existing generalized linear models (GLM) are limited in their ability to handle the types of variance structures often encountered in using count data in risk and reliability analysis. In particular, standard models cannot handle both underdispersed data (variance less than the mean) and overdispersed data (variance greater than the mean) in a single coherent modeling framework. This article presents a new GLM based on a reformulation of the Conway-Maxwell Poisson (COM) distribution that is useful for both underdispersed and overdispersed count data and demonstrates this model by applying it to the assessment of electric power system reliability. The results show that the proposed COM GLM can provide as good of fits to data as the commonly used existing models for overdispered data sets while outperforming these commonly used models for underdispersed data sets.

Adjusting for overdispersion in piecewise exponential regression models to estimate excess mortality rate in population-based research.

PubMed

Luque-Fernandez, Miguel Angel; Belot, Aurélien; Quaresma, Manuela; Maringe, Camille; Coleman, Michel P; Rachet, Bernard

2016-10-01

In population-based cancer research, piecewise exponential regression models are used to derive adjusted estimates of excess mortality due to cancer using the Poisson generalized linear modelling framework. However, the assumption that the conditional mean and variance of the rate parameter given the set of covariates x i are equal is strong and may fail to account for overdispersion given the variability of the rate parameter (the variance exceeds the mean). Using an empirical example, we aimed to describe simple methods to test and correct for overdispersion. We used a regression-based score test for overdispersion under the relative survival framework and proposed different approaches to correct for overdispersion including a quasi-likelihood, robust standard errors estimation, negative binomial regression and flexible piecewise modelling. All piecewise exponential regression models showed the presence of significant inherent overdispersion (p-value <0.001). However, the flexible piecewise exponential model showed the smallest overdispersion parameter (3.2 versus 21.3) for non-flexible piecewise exponential models. We showed that there were no major differences between methods. However, using a flexible piecewise regression modelling, with either a quasi-likelihood or robust standard errors, was the best approach as it deals with both, overdispersion due to model misspecification and true or inherent overdispersion.

Poisson-Based Inference for Perturbation Models in Adaptive Spelling Training

ERIC Educational Resources Information Center

Baschera, Gian-Marco; Gross, Markus

2010-01-01

We present an inference algorithm for perturbation models based on Poisson regression. The algorithm is designed to handle unclassified input with multiple errors described by independent mal-rules. This knowledge representation provides an intelligent tutoring system with local and global information about a student, such as error classification…

Evaluating the double Poisson generalized linear model.

PubMed

Zou, Yaotian; Geedipally, Srinivas Reddy; Lord, Dominique

2013-10-01

The objectives of this study are to: (1) examine the applicability of the double Poisson (DP) generalized linear model (GLM) for analyzing motor vehicle crash data characterized by over- and under-dispersion and (2) compare the performance of the DP GLM with the Conway-Maxwell-Poisson (COM-Poisson) GLM in terms of goodness-of-fit and theoretical soundness. The DP distribution has seldom been investigated and applied since its first introduction two decades ago. The hurdle for applying the DP is related to its normalizing constant (or multiplicative constant) which is not available in closed form. This study proposed a new method to approximate the normalizing constant of the DP with high accuracy and reliability. The DP GLM and COM-Poisson GLM were developed using two observed over-dispersed datasets and one observed under-dispersed dataset. The modeling results indicate that the DP GLM with its normalizing constant approximated by the new method can handle crash data characterized by over- and under-dispersion. Its performance is comparable to the COM-Poisson GLM in terms of goodness-of-fit (GOF), although COM-Poisson GLM provides a slightly better fit. For the over-dispersed data, the DP GLM performs similar to the NB GLM. Considering the fact that the DP GLM can be easily estimated with inexpensive computation and that it is simpler to interpret coefficients, it offers a flexible and efficient alternative for researchers to model count data. Copyright © 2013 Elsevier Ltd. All rights reserved.

Different responses of weather factors on hand, foot and mouth disease in three different climate areas of Gansu, China.

PubMed

Gou, Faxiang; Liu, Xinfeng; He, Jian; Liu, Dongpeng; Cheng, Yao; Liu, Haixia; Yang, Xiaoting; Wei, Kongfu; Zheng, Yunhe; Jiang, Xiaojuan; Meng, Lei; Hu, Wenbiao

2018-01-08

To determine the linear and non-linear interacting relationships between weather factors and hand, foot and mouth disease (HFMD) in children in Gansu, China, and gain further traction as an early warning signal based on weather variability for HFMD transmission. Weekly HFMD cases aged less than 15 and meteorological information from 2010 to 2014 in Jiuquan, Lanzhou and Tianshu, Gansu, China were collected. Generalized linear regression models (GLM) with Poisson link and classification and regression trees (CART) were employed to determine the combined and interactive relationship of weather factors and HFMD in both linear and non-linear ways. GLM suggested an increase in weekly HFMD of 5.9% [95% confidence interval (CI): 5.4%, 6.5%] in Tianshui, 2.8% [2.5%, 3.1%] in Lanzhou and 1.8% [1.4%, 2.2%] in Jiuquan in association with a 1 °C increase in average temperature, respectively. And 1% increase of relative humidity could increase weekly HFMD of 2.47% [2.23%, 2.71%] in Lanzhou and 1.11% [0.72%, 1.51%] in Tianshui. CART revealed that average temperature and relative humidity were the first two important determinants, and their threshold values for average temperature deceased from 20 °C of Jiuquan to 16 °C in Tianshui; and for relative humidity, threshold values increased from 38% of Jiuquan to 65% of Tianshui. Average temperature was the primary weather factor in three areas, more sensitive in southeast Tianshui, compared with northwest Jiuquan; Relative humidity's effect on HFMD showed a non-linear interacting relationship with average temperature.

Center of Excellence in Theoretical Geoplasma Research

DTIC Science & Technology

1993-08-31

of the Balescu -Lenard-Poisson ecluations for collisional plasmas were reported by J.R. Jasperse of the Geophysics Directorate. Discussions at the...the Chairperson: W. Burke (AFGL) 15:00 - 16:30 1. "Solutions of the linearized Balescu -Lenard-Poisson Equations for a Weakly-Collisional Plasma: Some

Center of Excellence in Theoretical Geoplasma Research

DTIC Science & Technology

1989-11-10

iii) First results of closed-form solutions of the3 Balescu -Lenard-Poisson equations for collisional plasmas were reported I REPORT November 10, 1989...Basu, "Solutions of the Linearized Balescu -Lenard-Poisson Equations for a Weakly-Collisional Plasma: Some New Results". [511 American Geophysical Union

Simulation on Poisson and negative binomial models of count road accident modeling

NASA Astrophysics Data System (ADS)

Sapuan, M. S.; Razali, A. M.; Zamzuri, Z. H.; Ibrahim, K.

2016-11-01

Accident count data have often been shown to have overdispersion. On the other hand, the data might contain zero count (excess zeros). The simulation study was conducted to create a scenarios which an accident happen in T-junction with the assumption the dependent variables of generated data follows certain distribution namely Poisson and negative binomial distribution with different sample size of n=30 to n=500. The study objective was accomplished by fitting Poisson regression, negative binomial regression and Hurdle negative binomial model to the simulated data. The model validation was compared and the simulation result shows for each different sample size, not all model fit the data nicely even though the data generated from its own distribution especially when the sample size is larger. Furthermore, the larger sample size indicates that more zeros accident count in the dataset.

Evolution of deep gray matter volume across the human lifespan.

PubMed

Narvacan, Karl; Treit, Sarah; Camicioli, Richard; Martin, Wayne; Beaulieu, Christian

2017-08-01

Magnetic resonance imaging of subcortical gray matter structures, which mediate behavior, cognition and the pathophysiology of several diseases, is crucial for establishing typical maturation patterns across the human lifespan. This single site study examines T1-weighted MPRAGE images of 3 healthy cohorts: (i) a cross-sectional cohort of 406 subjects aged 5-83 years; (ii) a longitudinal neurodevelopment cohort of 84 subjects scanned twice approximately 4 years apart, aged 5-27 years at first scan; and (iii) a longitudinal aging cohort of 55 subjects scanned twice approximately 3 years apart, aged 46-83 years at first scan. First scans from longitudinal subjects were included in the cross-sectional analysis. Age-dependent changes in thalamus, caudate, putamen, globus pallidus, nucleus accumbens, hippocampus, and amygdala volumes were tested with Poisson, quadratic, and linear models in the cross-sectional cohort, and quadratic and linear models in the longitudinal cohorts. Most deep gray matter structures best fit to Poisson regressions in the cross-sectional cohort and quadratic curves in the young longitudinal cohort, whereas the volume of all structures except the caudate and globus pallidus decreased linearly in the longitudinal aging cohort. Males had larger volumes than females for all subcortical structures, but sex differences in trajectories of change with age were not significant. Within subject analysis showed that 65%-80% of 13-17 year olds underwent a longitudinal decrease in volume between scans (∼4 years apart) for the putamen, globus pallidus, and hippocampus, suggesting unique developmental processes during adolescence. This lifespan study of healthy participants will form a basis for comparison to neurological and psychiatric disorders. Hum Brain Mapp 38:3771-3790, 2017. © 2017 Wiley Periodicals, Inc. © 2017 Wiley Periodicals, Inc.

Questionable Validity of Poisson Assumptions in a Combined Loglinear/MDS Mapping Model.

ERIC Educational Resources Information Center

Gleason, John M.

1993-01-01

This response to an earlier article on a combined log-linear/MDS model for mapping journals by citation analysis discusses the underlying assumptions of the Poisson model with respect to characteristics of the citation process. The importance of empirical data analysis is also addressed. (nine references) (LRW)

Transport of Multivalent Electrolyte Mixtures in Micro- and Nanochannels

DTIC Science & Technology

2013-11-08

equations for this process are the unsteady Navier-Stokes equations along with continuity and the Poisson- Nernst -Planck system for the electro- static part...about five times the Debye screening length D (the 1/e lengthscale for the potential from the solution of the linearized Poisson- Boltzmann equation

Crystalline silica exposure and lung cancer mortality in diatomaceous earth industry workers: a quantitative risk assessment.

PubMed

Rice, F L; Park, R; Stayner, L; Smith, R; Gilbert, S; Checkoway, H

2001-01-01

To use various exposure-response models to estimate the risk of mortality from lung cancer due to occupational exposure to respirable crystalline silica dust. Data from a cohort mortality study of 2342 white male California diatomaceous earth mining and processing workers exposed to crystalline silica dust (mainly cristobalite) were reanalyzed with Poisson regression and Cox's proportional hazards models. Internal and external adjustments were used to control for potential confounding from the effects of time since first observation, calendar time, age, and Hispanic ethnicity. Cubic smoothing spline models were used to assess the fit of the models. Exposures were lagged by 10 years. Evaluations of the fit of the models were performed by comparing their deviances. Lifetime risks of lung cancer were estimated up to age 85 with an actuarial approach that accounted for competing causes of death. Exposure to respirable crystalline silica dust was a significant predictor (p<0.05) in nearly all of the models evaluated and the linear relative rate model with a 10 year exposure lag seemed to give the best fit in the Poisson regression analysis. For those who died of lung cancer the linear relative rate model predicted rate ratios for mortality from lung cancer of about 1.6 for the mean cumulative exposure to respirable silica compared with no exposure. The excess lifetime risk (to age 85) of mortality from lung cancer for white men exposed for 45 years and with a 10 year lag period at the current Occupational Safety and Health Administration (OSHA) standard of about 0.05 mg/m(3) for respirable cristobalite dust is 19/1000 (95% confidence interval (95% CI) 5/1000 to 46/1000). There was a significant risk of mortality from lung cancer that increased with cumulative exposure to respirable crystalline silica dust. The predicted number of deaths from lung cancer suggests that current occupational health standards may not be adequately protecting workers from the risk of lung cancer.

Geographically weighted poisson regression semiparametric on modeling of the number of tuberculosis cases (Case study: Bandung city)

NASA Astrophysics Data System (ADS)

Octavianty, Toharudin, Toni; Jaya, I. G. N. Mindra

2017-03-01

Tuberculosis (TB) is a disease caused by a bacterium, called Mycobacterium tuberculosis, which typically attacks the lungs but can also affect the kidney, spine, and brain (Centers for Disease Control and Prevention). Indonesia had the largest number of TB cases after India (Global Tuberculosis Report 2015 by WHO). The distribution of Mycobacterium tuberculosis genotypes in Indonesia showed the high genetic diversity and tended to vary by geographic regions. For instance, in Bandung city, the prevalence rate of TB morbidity is quite high. A number of TB patients belong to the counted data. To determine the factors that significantly influence the number of tuberculosis patients in each location of the observations can be used statistical analysis tool that is Geographically Weighted Poisson Regression Semiparametric (GWPRS). GWPRS is an extension of the Poisson regression and GWPR that is influenced by geographical factors, and there is also variables that influence globally and locally. Using the TB Data in Bandung city (in 2015), the results show that the global and local variables that influence the number of tuberculosis patients in every sub-district.

Efficient Levenberg-Marquardt minimization of the maximum likelihood estimator for Poisson deviates

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

Laurence, T; Chromy, B

2009-11-10

Histograms of counted events are Poisson distributed, but are typically fitted without justification using nonlinear least squares fitting. The more appropriate maximum likelihood estimator (MLE) for Poisson distributed data is seldom used. We extend the use of the Levenberg-Marquardt algorithm commonly used for nonlinear least squares minimization for use with the MLE for Poisson distributed data. In so doing, we remove any excuse for not using this more appropriate MLE. We demonstrate the use of the algorithm and the superior performance of the MLE using simulations and experiments in the context of fluorescence lifetime imaging. Scientists commonly form histograms ofmore » counted events from their data, and extract parameters by fitting to a specified model. Assuming that the probability of occurrence for each bin is small, event counts in the histogram bins will be distributed according to the Poisson distribution. We develop here an efficient algorithm for fitting event counting histograms using the maximum likelihood estimator (MLE) for Poisson distributed data, rather than the non-linear least squares measure. This algorithm is a simple extension of the common Levenberg-Marquardt (L-M) algorithm, is simple to implement, quick and robust. Fitting using a least squares measure is most common, but it is the maximum likelihood estimator only for Gaussian-distributed data. Non-linear least squares methods may be applied to event counting histograms in cases where the number of events is very large, so that the Poisson distribution is well approximated by a Gaussian. However, it is not easy to satisfy this criterion in practice - which requires a large number of events. It has been well-known for years that least squares procedures lead to biased results when applied to Poisson-distributed data; a recent paper providing extensive characterization of these biases in exponential fitting is given. The more appropriate measure based on the maximum likelihood estimator (MLE) for the Poisson distribution is also well known, but has not become generally used. This is primarily because, in contrast to non-linear least squares fitting, there has been no quick, robust, and general fitting method. In the field of fluorescence lifetime spectroscopy and imaging, there have been some efforts to use this estimator through minimization routines such as Nelder-Mead optimization, exhaustive line searches, and Gauss-Newton minimization. Minimization based on specific one- or multi-exponential models has been used to obtain quick results, but this procedure does not allow the incorporation of the instrument response, and is not generally applicable to models found in other fields. Methods for using the MLE for Poisson-distributed data have been published by the wider spectroscopic community, including iterative minimization schemes based on Gauss-Newton minimization. The slow acceptance of these procedures for fitting event counting histograms may also be explained by the use of the ubiquitous, fast Levenberg-Marquardt (L-M) fitting procedure for fitting non-linear models using least squares fitting (simple searches obtain {approx}10000 references - this doesn't include those who use it, but don't know they are using it). The benefits of L-M include a seamless transition between Gauss-Newton minimization and downward gradient minimization through the use of a regularization parameter. This transition is desirable because Gauss-Newton methods converge quickly, but only within a limited domain of convergence; on the other hand the downward gradient methods have a much wider domain of convergence, but converge extremely slowly nearer the minimum. L-M has the advantages of both procedures: relative insensitivity to initial parameters and rapid convergence. Scientists, when wanting an answer quickly, will fit data using L-M, get an answer, and move on. Only those that are aware of the bias issues will bother to fit using the more appropriate MLE for Poisson deviates. However, since there is a simple, analytical formula for the appropriate MLE measure for Poisson deviates, it is inexcusable that least squares estimators are used almost exclusively when fitting event counting histograms. There have been ways found to use successive non-linear least squares fitting to obtain similarly unbiased results, but this procedure is justified by simulation, must be re-tested when conditions change significantly, and requires two successive fits. There is a great need for a fitting routine for the MLE estimator for Poisson deviates that has convergence domains and rates comparable to the non-linear least squares L-M fitting. We show in this report that a simple way to achieve that goal is to use the L-M fitting procedure not to minimize the least squares measure, but the MLE for Poisson deviates.« less

Relative age effect in elite soccer: More early-born players, but no better valued, and no paragon clubs or countries

PubMed Central

Doyle, John R.

2018-01-01

The paper analyses two datasets of elite soccer players (top 1000 professionals and UEFA Under-19 Youth League). In both, we find a Relative Age Effect (RAE) for frequency, but not for value. That is, while there are more players born at the start of the competition year, their transfer values are no higher, nor are they given more game time. We use Poisson regression to derive a transparent index of the discrimination present in RAE. Also, because Poisson is valid for small frequency counts, it supports analysis at the disaggregated levels of country and club. From this, we conclude there are no paragon clubs or countries immune to RAE; that is clubs and countries do not differ systematically in the RAE they experience; also, that Poisson regression is a powerful and flexible method of analysing RAE data. PMID:29420576

Collisional effects on the numerical recurrence in Vlasov-Poisson simulations

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

Pezzi, Oreste; Valentini, Francesco; Camporeale, Enrico

The initial state recurrence in numerical simulations of the Vlasov-Poisson system is a well-known phenomenon. Here, we study the effect on recurrence of artificial collisions modeled through the Lenard-Bernstein operator [A. Lenard and I. B. Bernstein, Phys. Rev. 112, 1456–1459 (1958)]. By decomposing the linear Vlasov-Poisson system in the Fourier-Hermite space, the recurrence problem is investigated in the linear regime of the damping of a Langmuir wave and of the onset of the bump-on-tail instability. The analysis is then confirmed and extended to the nonlinear regime through an Eulerian collisional Vlasov-Poisson code. It is found that, despite being routinely used,more » an artificial collisionality is not a viable way of preventing recurrence in numerical simulations without compromising the kinetic nature of the solution. Moreover, it is shown how numerical effects associated to the generation of fine velocity scales can modify the physical features of the system evolution even in nonlinear regime. This means that filamentation-like phenomena, usually associated with low amplitude fluctuations contexts, can play a role even in nonlinear regime.« less

Regression: The Apple Does Not Fall Far From the Tree.

PubMed

Vetter, Thomas R; Schober, Patrick

2018-05-15

Researchers and clinicians are frequently interested in either: (1) assessing whether there is a relationship or association between 2 or more variables and quantifying this association; or (2) determining whether 1 or more variables can predict another variable. The strength of such an association is mainly described by the correlation. However, regression analysis and regression models can be used not only to identify whether there is a significant relationship or association between variables but also to generate estimations of such a predictive relationship between variables. This basic statistical tutorial discusses the fundamental concepts and techniques related to the most common types of regression analysis and modeling, including simple linear regression, multiple regression, logistic regression, ordinal regression, and Poisson regression, as well as the common yet often underrecognized phenomenon of regression toward the mean. The various types of regression analysis are powerful statistical techniques, which when appropriately applied, can allow for the valid interpretation of complex, multifactorial data. Regression analysis and models can assess whether there is a relationship or association between 2 or more observed variables and estimate the strength of this association, as well as determine whether 1 or more variables can predict another variable. Regression is thus being applied more commonly in anesthesia, perioperative, critical care, and pain research. However, it is crucial to note that regression can identify plausible risk factors; it does not prove causation (a definitive cause and effect relationship). The results of a regression analysis instead identify independent (predictor) variable(s) associated with the dependent (outcome) variable. As with other statistical methods, applying regression requires that certain assumptions be met, which can be tested with specific diagnostics.

Nonlinear Poisson Equation for Heterogeneous Media

PubMed Central

Hu, Langhua; Wei, Guo-Wei

2012-01-01

The Poisson equation is a widely accepted model for electrostatic analysis. However, the Poisson equation is derived based on electric polarizations in a linear, isotropic, and homogeneous dielectric medium. This article introduces a nonlinear Poisson equation to take into consideration of hyperpolarization effects due to intensive charges and possible nonlinear, anisotropic, and heterogeneous media. Variational principle is utilized to derive the nonlinear Poisson model from an electrostatic energy functional. To apply the proposed nonlinear Poisson equation for the solvation analysis, we also construct a nonpolar solvation energy functional based on the nonlinear Poisson equation by using the geometric measure theory. At a fixed temperature, the proposed nonlinear Poisson theory is extensively validated by the electrostatic analysis of the Kirkwood model and a set of 20 proteins, and the solvation analysis of a set of 17 small molecules whose experimental measurements are also available for a comparison. Moreover, the nonlinear Poisson equation is further applied to the solvation analysis of 21 compounds at different temperatures. Numerical results are compared to theoretical prediction, experimental measurements, and those obtained from other theoretical methods in the literature. A good agreement between our results and experimental data as well as theoretical results suggests that the proposed nonlinear Poisson model is a potentially useful model for electrostatic analysis involving hyperpolarization effects. PMID:22947937

Application of zero-inflated poisson mixed models in prognostic factors of hepatitis C.

PubMed

Akbarzadeh Baghban, Alireza; Pourhoseingholi, Asma; Zayeri, Farid; Jafari, Ali Akbar; Alavian, Seyed Moayed

2013-01-01

In recent years, hepatitis C virus (HCV) infection represents a major public health problem. Evaluation of risk factors is one of the solutions which help protect people from the infection. This study aims to employ zero-inflated Poisson mixed models to evaluate prognostic factors of hepatitis C. The data was collected from a longitudinal study during 2005-2010. First, mixed Poisson regression (PR) model was fitted to the data. Then, a mixed zero-inflated Poisson model was fitted with compound Poisson random effects. For evaluating the performance of the proposed mixed model, standard errors of estimators were compared. The results obtained from mixed PR showed that genotype 3 and treatment protocol were statistically significant. Results of zero-inflated Poisson mixed model showed that age, sex, genotypes 2 and 3, the treatment protocol, and having risk factors had significant effects on viral load of HCV patients. Of these two models, the estimators of zero-inflated Poisson mixed model had the minimum standard errors. The results showed that a mixed zero-inflated Poisson model was the almost best fit. The proposed model can capture serial dependence, additional overdispersion, and excess zeros in the longitudinal count data.

Quantization with maximally degenerate Poisson brackets: the harmonic oscillator!

NASA Astrophysics Data System (ADS)

Nutku, Yavuz

2003-07-01

Nambu's construction of multi-linear brackets for super-integrable systems can be thought of as degenerate Poisson brackets with a maximal set of Casimirs in their kernel. By introducing privileged coordinates in phase space these degenerate Poisson brackets are brought to the form of Heisenberg's equations. We propose a definition for constructing quantum operators for classical functions, which enables us to turn the maximally degenerate Poisson brackets into operators. They pose a set of eigenvalue problems for a new state vector. The requirement of the single-valuedness of this eigenfunction leads to quantization. The example of the harmonic oscillator is used to illustrate this general procedure for quantizing a class of maximally super-integrable systems.

A Tutorial on Multilevel Survival Analysis: Methods, Models and Applications

PubMed Central

Austin, Peter C.

2017-01-01

Summary Data that have a multilevel structure occur frequently across a range of disciplines, including epidemiology, health services research, public health, education and sociology. We describe three families of regression models for the analysis of multilevel survival data. First, Cox proportional hazards models with mixed effects incorporate cluster-specific random effects that modify the baseline hazard function. Second, piecewise exponential survival models partition the duration of follow-up into mutually exclusive intervals and fit a model that assumes that the hazard function is constant within each interval. This is equivalent to a Poisson regression model that incorporates the duration of exposure within each interval. By incorporating cluster-specific random effects, generalised linear mixed models can be used to analyse these data. Third, after partitioning the duration of follow-up into mutually exclusive intervals, one can use discrete time survival models that use a complementary log–log generalised linear model to model the occurrence of the outcome of interest within each interval. Random effects can be incorporated to account for within-cluster homogeneity in outcomes. We illustrate the application of these methods using data consisting of patients hospitalised with a heart attack. We illustrate the application of these methods using three statistical programming languages (R, SAS and Stata). PMID:29307954

A Tutorial on Multilevel Survival Analysis: Methods, Models and Applications.

PubMed

Austin, Peter C

2017-08-01

Data that have a multilevel structure occur frequently across a range of disciplines, including epidemiology, health services research, public health, education and sociology. We describe three families of regression models for the analysis of multilevel survival data. First, Cox proportional hazards models with mixed effects incorporate cluster-specific random effects that modify the baseline hazard function. Second, piecewise exponential survival models partition the duration of follow-up into mutually exclusive intervals and fit a model that assumes that the hazard function is constant within each interval. This is equivalent to a Poisson regression model that incorporates the duration of exposure within each interval. By incorporating cluster-specific random effects, generalised linear mixed models can be used to analyse these data. Third, after partitioning the duration of follow-up into mutually exclusive intervals, one can use discrete time survival models that use a complementary log-log generalised linear model to model the occurrence of the outcome of interest within each interval. Random effects can be incorporated to account for within-cluster homogeneity in outcomes. We illustrate the application of these methods using data consisting of patients hospitalised with a heart attack. We illustrate the application of these methods using three statistical programming languages (R, SAS and Stata).

Oscillatory Reduction in Option Pricing Formula Using Shifted Poisson and Linear Approximation

NASA Astrophysics Data System (ADS)

Nur Rachmawati, Ro'fah; Irene; Budiharto, Widodo

2014-03-01

Option is one of derivative instruments that can help investors improve their expected return and minimize the risks. However, the Black-Scholes formula is generally used in determining the price of the option does not involve skewness factor and it is difficult to apply in computing process because it produces oscillation for the skewness values close to zero. In this paper, we construct option pricing formula that involve skewness by modified Black-Scholes formula using Shifted Poisson model and transformed it into the form of a Linear Approximation in the complete market to reduce the oscillation. The results are Linear Approximation formula can predict the price of an option with very accurate and successfully reduce the oscillations in the calculation processes.

[Trend in mortality from external causes in pregnant and postpartum women and its relationship to socioeconomic factors in Colombia, 1998-2010].

PubMed

Salazar, Edwin; Buitrago, Carolina; Molina, Federico; Alzate, Catalina Arango

2015-05-01

Determine the trend in mortality from external causes in pregnant and postpartum women and its relationship to socioeconomic factors. Descriptive study, based on the official registries of deaths reported by the National Statistics Agency, 1998-2010. The trend was analyzed using Poisson regressions. Bivariate correlations and multiple linear regression models were constructed to explore the relationship between mortality and socioeconomic factors: human development index, Gini index, gross domestic product, unsatisfied basic needs, unemployment rate, poverty, extreme poverty, quality of life index, illiteracy rate, and percentage of affiliation to the Social Security System. A total of 2 223 female deaths from external causes were recorded, of which 1 429 occurred during pregnancy and 794 in the postpartum period. The gross mortality rate dropped from 30.7 per 100 000 live births plus fetal deaths in 1998 to 16.7 in 2010. A downward curve with no significant inflection points was shown in the risk of dying from this cause. The multiple linear regression model showed a correlation between mortality and extreme poverty and the illiteracy rate, suggesting that these indicators could explain 89.4% of the change in mortality from external causes in pregnant and postpartum women each year in Colombia. Mortality from external causes in pregnant and postpartum women showed a significant downward trend that may be explained by important socioeconomic changes in the country, including a decrease in extreme poverty and in the illiteracy rate.

Understanding the density of nonprofit organizations across Los Angeles neighborhoods: Does concentrated disadvantage and violent crime matter?

PubMed

Wo, James C

2018-03-01

Although some urban sociology perspectives suggest how certain sociodeomgraphic characteristics influence nonprofit development, there is a dearth of empirical research to assess neighborhood differences in nonprofit organizations. The goal of the current study is to build upon the extant literature by examining how both concentrated disadvantage and violent crime impact nonprofit density across neighborhoods. Using data from Los Angeles census tracts from 2010 to 2012, I test for linear and nonlinear influences that these two neighborhood factors might exert on nonprofit density. Poisson regression models show that concentrated disadvantage has a nonlinear (U-shaped) effect on all forms of nonprofit density, whereas violent crime has a linear and deleterious effect on all forms of nonprofit density. These results provide important new insights for urban sociology and policy; most importantly, the extent to which neighborhoods with ongoing social problems can later respond to such problems via access to nonprofit organizations. Copyright © 2018 Elsevier Inc. All rights reserved.

A Negative Binomial Regression Model for Accuracy Tests

ERIC Educational Resources Information Center

Hung, Lai-Fa

2012-01-01

Rasch used a Poisson model to analyze errors and speed in reading tests. An important property of the Poisson distribution is that the mean and variance are equal. However, in social science research, it is very common for the variance to be greater than the mean (i.e., the data are overdispersed). This study embeds the Rasch model within an…

Structural interactions in ionic liquids linked to higher-order Poisson-Boltzmann equations

NASA Astrophysics Data System (ADS)

Blossey, R.; Maggs, A. C.; Podgornik, R.

2017-06-01

We present a derivation of generalized Poisson-Boltzmann equations starting from classical theories of binary fluid mixtures, employing an approach based on the Legendre transform as recently applied to the case of local descriptions of the fluid free energy. Under specific symmetry assumptions, and in the linearized regime, the Poisson-Boltzmann equation reduces to a phenomenological equation introduced by Bazant et al. [Phys. Rev. Lett. 106, 046102 (2011)], 10.1103/PhysRevLett.106.046102, whereby the structuring near the surface is determined by bulk coefficients.

PB-AM: An open-source, fully analytical linear poisson-boltzmann solver.

PubMed

Felberg, Lisa E; Brookes, David H; Yap, Eng-Hui; Jurrus, Elizabeth; Baker, Nathan A; Head-Gordon, Teresa

2017-06-05

We present the open source distributed software package Poisson-Boltzmann Analytical Method (PB-AM), a fully analytical solution to the linearized PB equation, for molecules represented as non-overlapping spherical cavities. The PB-AM software package includes the generation of outputs files appropriate for visualization using visual molecular dynamics, a Brownian dynamics scheme that uses periodic boundary conditions to simulate dynamics, the ability to specify docking criteria, and offers two different kinetics schemes to evaluate biomolecular association rate constants. Given that PB-AM defines mutual polarization completely and accurately, it can be refactored as a many-body expansion to explore 2- and 3-body polarization. Additionally, the software has been integrated into the Adaptive Poisson-Boltzmann Solver (APBS) software package to make it more accessible to a larger group of scientists, educators, and students that are more familiar with the APBS framework. © 2016 Wiley Periodicals, Inc. © 2016 Wiley Periodicals, Inc.

A comparative study of generalized linear mixed modelling and artificial neural network approach for the joint modelling of survival and incidence of Dengue patients in Sri Lanka

NASA Astrophysics Data System (ADS)

Hapugoda, J. C.; Sooriyarachchi, M. R.

2017-09-01

Survival time of patients with a disease and the incidence of that particular disease (count) is frequently observed in medical studies with the data of a clustered nature. In many cases, though, the survival times and the count can be correlated in a way that, diseases that occur rarely could have shorter survival times or vice versa. Due to this fact, joint modelling of these two variables will provide interesting and certainly improved results than modelling these separately. Authors have previously proposed a methodology using Generalized Linear Mixed Models (GLMM) by joining the Discrete Time Hazard model with the Poisson Regression model to jointly model survival and count model. As Aritificial Neural Network (ANN) has become a most powerful computational tool to model complex non-linear systems, it was proposed to develop a new joint model of survival and count of Dengue patients of Sri Lanka by using that approach. Thus, the objective of this study is to develop a model using ANN approach and compare the results with the previously developed GLMM model. As the response variables are continuous in nature, Generalized Regression Neural Network (GRNN) approach was adopted to model the data. To compare the model fit, measures such as root mean square error (RMSE), absolute mean error (AME) and correlation coefficient (R) were used. The measures indicate the GRNN model fits the data better than the GLMM model.

Datamining approaches for modeling tumor control probability.

PubMed

Naqa, Issam El; Deasy, Joseph O; Mu, Yi; Huang, Ellen; Hope, Andrew J; Lindsay, Patricia E; Apte, Aditya; Alaly, James; Bradley, Jeffrey D

2010-11-01

Tumor control probability (TCP) to radiotherapy is determined by complex interactions between tumor biology, tumor microenvironment, radiation dosimetry, and patient-related variables. The complexity of these heterogeneous variable interactions constitutes a challenge for building predictive models for routine clinical practice. We describe a datamining framework that can unravel the higher order relationships among dosimetric dose-volume prognostic variables, interrogate various radiobiological processes, and generalize to unseen data before when applied prospectively. Several datamining approaches are discussed that include dose-volume metrics, equivalent uniform dose, mechanistic Poisson model, and model building methods using statistical regression and machine learning techniques. Institutional datasets of non-small cell lung cancer (NSCLC) patients are used to demonstrate these methods. The performance of the different methods was evaluated using bivariate Spearman rank correlations (rs). Over-fitting was controlled via resampling methods. Using a dataset of 56 patients with primary NCSLC tumors and 23 candidate variables, we estimated GTV volume and V75 to be the best model parameters for predicting TCP using statistical resampling and a logistic model. Using these variables, the support vector machine (SVM) kernel method provided superior performance for TCP prediction with an rs=0.68 on leave-one-out testing compared to logistic regression (rs=0.4), Poisson-based TCP (rs=0.33), and cell kill equivalent uniform dose model (rs=0.17). The prediction of treatment response can be improved by utilizing datamining approaches, which are able to unravel important non-linear complex interactions among model variables and have the capacity to predict on unseen data for prospective clinical applications.

Nonlinear Poisson equation for heterogeneous media.

PubMed

Hu, Langhua; Wei, Guo-Wei

2012-08-22

The Poisson equation is a widely accepted model for electrostatic analysis. However, the Poisson equation is derived based on electric polarizations in a linear, isotropic, and homogeneous dielectric medium. This article introduces a nonlinear Poisson equation to take into consideration of hyperpolarization effects due to intensive charges and possible nonlinear, anisotropic, and heterogeneous media. Variational principle is utilized to derive the nonlinear Poisson model from an electrostatic energy functional. To apply the proposed nonlinear Poisson equation for the solvation analysis, we also construct a nonpolar solvation energy functional based on the nonlinear Poisson equation by using the geometric measure theory. At a fixed temperature, the proposed nonlinear Poisson theory is extensively validated by the electrostatic analysis of the Kirkwood model and a set of 20 proteins, and the solvation analysis of a set of 17 small molecules whose experimental measurements are also available for a comparison. Moreover, the nonlinear Poisson equation is further applied to the solvation analysis of 21 compounds at different temperatures. Numerical results are compared to theoretical prediction, experimental measurements, and those obtained from other theoretical methods in the literature. A good agreement between our results and experimental data as well as theoretical results suggests that the proposed nonlinear Poisson model is a potentially useful model for electrostatic analysis involving hyperpolarization effects. Copyright © 2012 Biophysical Society. Published by Elsevier Inc. All rights reserved.

Finite-dimensional integrable systems: A collection of research problems

NASA Astrophysics Data System (ADS)

Bolsinov, A. V.; Izosimov, A. M.; Tsonev, D. M.

2017-05-01

This article suggests a series of problems related to various algebraic and geometric aspects of integrability. They reflect some recent developments in the theory of finite-dimensional integrable systems such as bi-Poisson linear algebra, Jordan-Kronecker invariants of finite dimensional Lie algebras, the interplay between singularities of Lagrangian fibrations and compatible Poisson brackets, and new techniques in projective geometry.

Use of instrumental variables in the analysis of generalized linear models in the presence of unmeasured confounding with applications to epidemiological research.

PubMed

Johnston, K M; Gustafson, P; Levy, A R; Grootendorst, P

2008-04-30

A major, often unstated, concern of researchers carrying out epidemiological studies of medical therapy is the potential impact on validity if estimates of treatment are biased due to unmeasured confounders. One technique for obtaining consistent estimates of treatment effects in the presence of unmeasured confounders is instrumental variables analysis (IVA). This technique has been well developed in the econometrics literature and is being increasingly used in epidemiological studies. However, the approach to IVA that is most commonly used in such studies is based on linear models, while many epidemiological applications make use of non-linear models, specifically generalized linear models (GLMs) such as logistic or Poisson regression. Here we present a simple method for applying IVA within the class of GLMs using the generalized method of moments approach. We explore some of the theoretical properties of the method and illustrate its use within both a simulation example and an epidemiological study where unmeasured confounding is suspected to be present. We estimate the effects of beta-blocker therapy on one-year all-cause mortality after an incident hospitalization for heart failure, in the absence of data describing disease severity, which is believed to be a confounder. 2008 John Wiley & Sons, Ltd

Bayesian Poisson hierarchical models for crash data analysis: Investigating the impact of model choice on site-specific predictions.

PubMed

Khazraee, S Hadi; Johnson, Valen; Lord, Dominique

2018-08-01

The Poisson-gamma (PG) and Poisson-lognormal (PLN) regression models are among the most popular means for motor vehicle crash data analysis. Both models belong to the Poisson-hierarchical family of models. While numerous studies have compared the overall performance of alternative Bayesian Poisson-hierarchical models, little research has addressed the impact of model choice on the expected crash frequency prediction at individual sites. This paper sought to examine whether there are any trends among candidate models predictions e.g., that an alternative model's prediction for sites with certain conditions tends to be higher (or lower) than that from another model. In addition to the PG and PLN models, this research formulated a new member of the Poisson-hierarchical family of models: the Poisson-inverse gamma (PIGam). Three field datasets (from Texas, Michigan and Indiana) covering a wide range of over-dispersion characteristics were selected for analysis. This study demonstrated that the model choice can be critical when the calibrated models are used for prediction at new sites, especially when the data are highly over-dispersed. For all three datasets, the PIGam model would predict higher expected crash frequencies than would the PLN and PG models, in order, indicating a clear link between the models predictions and the shape of their mixing distributions (i.e., gamma, lognormal, and inverse gamma, respectively). The thicker tail of the PIGam and PLN models (in order) may provide an advantage when the data are highly over-dispersed. The analysis results also illustrated a major deficiency of the Deviance Information Criterion (DIC) in comparing the goodness-of-fit of hierarchical models; models with drastically different set of coefficients (and thus predictions for new sites) may yield similar DIC values, because the DIC only accounts for the parameters in the lowest (observation) level of the hierarchy and ignores the higher levels (regression coefficients). Copyright © 2018. Published by Elsevier Ltd.

Piecewise exponential survival times and analysis of case-cohort data.

PubMed

Li, Yan; Gail, Mitchell H; Preston, Dale L; Graubard, Barry I; Lubin, Jay H

2012-06-15

Case-cohort designs select a random sample of a cohort to be used as control with cases arising from the follow-up of the cohort. Analyses of case-cohort studies with time-varying exposures that use Cox partial likelihood methods can be computer intensive. We propose a piecewise-exponential approach where Poisson regression model parameters are estimated from a pseudolikelihood and the corresponding variances are derived by applying Taylor linearization methods that are used in survey research. The proposed approach is evaluated using Monte Carlo simulations. An illustration is provided using data from the Alpha-Tocopherol, Beta-Carotene Cancer Prevention Study of male smokers in Finland, where a case-cohort study of serum glucose level and pancreatic cancer was analyzed. Copyright © 2012 John Wiley & Sons, Ltd.

Is long-term exposure to traffic pollution associated with mortality? A small-area study in London.

PubMed

Halonen, Jaana I; Blangiardo, Marta; Toledano, Mireille B; Fecht, Daniela; Gulliver, John; Ghosh, Rebecca; Anderson, H Ross; Beevers, Sean D; Dajnak, David; Kelly, Frank J; Wilkinson, Paul; Tonne, Cathryn

2016-01-01

Long-term exposure to primary traffic pollutants may be harmful for health but few studies have investigated effects on mortality. We examined associations for six primary traffic pollutants with all-cause and cause-specific mortality in 2003-2010 at small-area level using linear and piecewise linear Poisson regression models. In linear models most pollutants showed negative or null association with all-cause, cardiovascular or respiratory mortality. In the piecewise models we observed positive associations in the lowest exposure range (e.g. relative risk (RR) for all-cause mortality 1.07 (95% credible interval (CI) = 1.00-1.15) per 0.15 μg/m(3) increase in exhaust related primary particulate matter ≤2.5 μm (PM2.5)) whereas associations in the highest exposure range were negative (corresponding RR 0.93, 95% CI: 0.91-0.96). Overall, there was only weak evidence of positive associations with mortality. That we found the strongest positive associations in the lowest exposure group may reflect residual confounding by unmeasured confounders that varies by exposure group. Copyright © 2015 The Authors. Published by Elsevier Ltd.. All rights reserved.

Marginalized zero-inflated negative binomial regression with application to dental caries

PubMed Central

Preisser, John S.; Das, Kalyan; Long, D. Leann; Divaris, Kimon

2015-01-01

The zero-inflated negative binomial regression model (ZINB) is often employed in diverse fields such as dentistry, health care utilization, highway safety, and medicine to examine relationships between exposures of interest and overdispersed count outcomes exhibiting many zeros. The regression coefficients of ZINB have latent class interpretations for a susceptible subpopulation at risk for the disease/condition under study with counts generated from a negative binomial distribution and for a non-susceptible subpopulation that provides only zero counts. The ZINB parameters, however, are not well-suited for estimating overall exposure effects, specifically, in quantifying the effect of an explanatory variable in the overall mixture population. In this paper, a marginalized zero-inflated negative binomial regression (MZINB) model for independent responses is proposed to model the population marginal mean count directly, providing straightforward inference for overall exposure effects based on maximum likelihood estimation. Through simulation studies, the finite sample performance of MZINB is compared to marginalized zero-inflated Poisson, Poisson, and negative binomial regression. The MZINB model is applied in the evaluation of a school-based fluoride mouthrinse program on dental caries in 677 children. PMID:26568034

Non-linear effects of the built environment on automobile-involved pedestrian crash frequency: A machine learning approach.

PubMed

Ding, Chuan; Chen, Peng; Jiao, Junfeng

2018-03-01

Although a growing body of literature focuses on the relationship between the built environment and pedestrian crashes, limited evidence is provided about the relative importance of many built environment attributes by accounting for their mutual interaction effects and their non-linear effects on automobile-involved pedestrian crashes. This study adopts the approach of Multiple Additive Poisson Regression Trees (MAPRT) to fill such gaps using pedestrian collision data collected from Seattle, Washington. Traffic analysis zones are chosen as the analytical unit. The effects of various factors on pedestrian crash frequency investigated include characteristics the of road network, street elements, land use patterns, and traffic demand. Density and the degree of mixed land use have major effects on pedestrian crash frequency, accounting for approximately 66% of the effects in total. More importantly, some factors show clear non-linear relationships with pedestrian crash frequency, challenging the linearity assumption commonly used in existing studies which employ statistical models. With various accurately identified non-linear relationships between the built environment and pedestrian crashes, this study suggests local agencies to adopt geo-spatial differentiated policies to establish a safe walking environment. These findings, especially the effective ranges of the built environment, provide evidence to support for transport and land use planning, policy recommendations, and road safety programs. Copyright © 2018 Elsevier Ltd. All rights reserved.

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.

Decoding and modelling of time series count data using Poisson hidden Markov model and Markov ordinal logistic regression models.

PubMed

Sebastian, Tunny; Jeyaseelan, Visalakshi; Jeyaseelan, Lakshmanan; Anandan, Shalini; George, Sebastian; Bangdiwala, Shrikant I

2018-01-01

Hidden Markov models are stochastic models in which the observations are assumed to follow a mixture distribution, but the parameters of the components are governed by a Markov chain which is unobservable. The issues related to the estimation of Poisson-hidden Markov models in which the observations are coming from mixture of Poisson distributions and the parameters of the component Poisson distributions are governed by an m-state Markov chain with an unknown transition probability matrix are explained here. These methods were applied to the data on Vibrio cholerae counts reported every month for 11-year span at Christian Medical College, Vellore, India. Using Viterbi algorithm, the best estimate of the state sequence was obtained and hence the transition probability matrix. The mean passage time between the states were estimated. The 95% confidence interval for the mean passage time was estimated via Monte Carlo simulation. The three hidden states of the estimated Markov chain are labelled as 'Low', 'Moderate' and 'High' with the mean counts of 1.4, 6.6 and 20.2 and the estimated average duration of stay of 3, 3 and 4 months, respectively. Environmental risk factors were studied using Markov ordinal logistic regression analysis. No significant association was found between disease severity levels and climate components.

Prediction of forest fires occurrences with area-level Poisson mixed models.

PubMed

Boubeta, Miguel; Lombardía, María José; Marey-Pérez, Manuel Francisco; Morales, Domingo

2015-05-01

The number of fires in forest areas of Galicia (north-west of Spain) during the summer period is quite high. Local authorities are interested in analyzing the factors that explain this phenomenon. Poisson regression models are good tools for describing and predicting the number of fires per forest areas. This work employs area-level Poisson mixed models for treating real data about fires in forest areas. A parametric bootstrap method is applied for estimating the mean squared errors of fires predictors. The developed methodology and software are applied to a real data set of fires in forest areas of Galicia. Copyright © 2015 Elsevier Ltd. All rights reserved.

Effect of Poisson's loss factor of rubbery material on underwater sound absorption of anechoic coatings

NASA Astrophysics Data System (ADS)

Zhong, Jie; Zhao, Honggang; Yang, Haibin; Yin, Jianfei; Wen, Jihong

2018-06-01

Rubbery coatings embedded with air cavities are commonly used on underwater structures to reduce reflection of incoming sound waves. In this paper, the relationships between Poisson's and modulus loss factors of rubbery materials are theoretically derived, the different effects of the tiny Poisson's loss factor on characterizing the loss factors of shear and longitudinal moduli are revealed. Given complex Young's modulus and dynamic Poisson's ratio, it is found that the shear loss factor has almost invisible variation with the Poisson's loss factor and is very close to the loss factor of Young's modulus, while the longitudinal loss factor almost linearly decreases with the increase of Poisson's loss factor. Then, a finite element (FE) model is used to investigate the effect of the tiny Poisson's loss factor, which is generally neglected in some FE models, on the underwater sound absorption of rubbery coatings. Results show that the tiny Poisson's loss factor has a significant effect on the sound absorption of homogeneous coatings within the concerned frequency range, while it has both frequency- and structure-dependent influence on the sound absorption of inhomogeneous coatings with embedded air cavities. Given the material parameters and cavity dimensions, more obvious effect can be observed for the rubbery coating with a larger lattice constant and/or a thicker cover layer.

A semi-nonparametric Poisson regression model for analyzing motor vehicle crash data.

PubMed

Ye, Xin; Wang, Ke; Zou, Yajie; Lord, Dominique

2018-01-01

This paper develops a semi-nonparametric Poisson regression model to analyze motor vehicle crash frequency data collected from rural multilane highway segments in California, US. Motor vehicle crash frequency on rural highway is a topic of interest in the area of transportation safety due to higher driving speeds and the resultant severity level. Unlike the traditional Negative Binomial (NB) model, the semi-nonparametric Poisson regression model can accommodate an unobserved heterogeneity following a highly flexible semi-nonparametric (SNP) distribution. Simulation experiments are conducted to demonstrate that the SNP distribution can well mimic a large family of distributions, including normal distributions, log-gamma distributions, bimodal and trimodal distributions. Empirical estimation results show that such flexibility offered by the SNP distribution can greatly improve model precision and the overall goodness-of-fit. The semi-nonparametric distribution can provide a better understanding of crash data structure through its ability to capture potential multimodality in the distribution of unobserved heterogeneity. When estimated coefficients in empirical models are compared, SNP and NB models are found to have a substantially different coefficient for the dummy variable indicating the lane width. The SNP model with better statistical performance suggests that the NB model overestimates the effect of lane width on crash frequency reduction by 83.1%.

Effect of motivational interviewing on rates of early childhood caries: a randomized trial.

PubMed

Harrison, Rosamund; Benton, Tonya; Everson-Stewart, Siobhan; Weinstein, Phil

2007-01-01

The purposes of this randomized controlled trial were to: (1) test motivational interviewing (MI) to prevent early childhood caries; and (2) use Poisson regression for data analysis. A total of 240 South Asian children 6 to 18 months old were enrolled and randomly assigned to either the MI or control condition. Children had a dental exam, and their mothers completed pretested instruments at baseline and 1 and 2 years postintervention. Other covariates that might explain outcomes over and above treatment differences were modeled using Poisson regression. Hazard ratios were produced. Analyses included all participants whenever possible. Poisson regression supported a protective effect of MI (hazard ratio [HR]=0.54 (95%CI=035-0.84)-that is, the M/ group had about a 46% lower rate of dmfs at 2 years than did control children. Similar treatment effect estimates were obtained from models that included, as alternative outcomes, ds, dms, and dmfs, including "white spot lesions." Exploratory analyses revealed that rates of dmfs were higher in children whose mothers had: (1) prechewed their food; (2) been raised in a rural environment; and (3) a higher family income (P<.05). A motivational interviewing-style intervention shows promise to promote preventive behaviors in mothers of young children at high risk for caries.

A coarse-grid projection method for accelerating incompressible flow computations

NASA Astrophysics Data System (ADS)

San, Omer; Staples, Anne E.

2013-01-01

We present a coarse-grid projection (CGP) method for accelerating incompressible flow computations, which is applicable to methods involving Poisson equations as incompressibility constraints. The CGP methodology is a modular approach that facilitates data transfer with simple interpolations and uses black-box solvers for the Poisson and advection-diffusion equations in the flow solver. After solving the Poisson equation on a coarsened grid, an interpolation scheme is used to obtain the fine data for subsequent time stepping on the full grid. A particular version of the method is applied here to the vorticity-stream function, primitive variable, and vorticity-velocity formulations of incompressible Navier-Stokes equations. We compute several benchmark flow problems on two-dimensional Cartesian and non-Cartesian grids, as well as a three-dimensional flow problem. The method is found to accelerate these computations while retaining a level of accuracy close to that of the fine resolution field, which is significantly better than the accuracy obtained for a similar computation performed solely using a coarse grid. A linear acceleration rate is obtained for all the cases we consider due to the linear-cost elliptic Poisson solver used, with reduction factors in computational time between 2 and 42. The computational savings are larger when a suboptimal Poisson solver is used. We also find that the computational savings increase with increasing distortion ratio on non-Cartesian grids, making the CGP method a useful tool for accelerating generalized curvilinear incompressible flow solvers.

Modeling the number of car theft using Poisson regression

NASA Astrophysics Data System (ADS)

Zulkifli, Malina; Ling, Agnes Beh Yen; Kasim, Maznah Mat; Ismail, Noriszura

2016-10-01

Regression analysis is the most popular statistical methods used to express the relationship between the variables of response with the covariates. The aim of this paper is to evaluate the factors that influence the number of car theft using Poisson regression model. This paper will focus on the number of car thefts that occurred in districts in Peninsular Malaysia. There are two groups of factor that have been considered, namely district descriptive factors and socio and demographic factors. The result of the study showed that Bumiputera composition, Chinese composition, Other ethnic composition, foreign migration, number of residence with the age between 25 to 64, number of employed person and number of unemployed person are the most influence factors that affect the car theft cases. These information are very useful for the law enforcement department, insurance company and car owners in order to reduce and limiting the car theft cases in Peninsular Malaysia.

A decline in the prevalence of injecting drug users in Estonia, 2005–2009

PubMed Central

Uusküla, A; Rajaleid, K; Talu, A; Abel-Ollo, K; Des Jarlais, DC

2013-01-01

Aims and setting Descriptions of behavioural epidemics have received little attention compared with infectious disease epidemics in Eastern Europe. Here we report a study aimed at estimating trends in the prevalence of injection drug use between 2005 and 2009 in Estonia. Design and methods The number of injection drug users (IDUs) aged 15–44 each year between 2005 and 2009 was estimated using capture-recapture methodology based on 4 data sources (2 treatment data bases: drug abuse and non-fatal overdose treatment; criminal justice (drug related offences) and mortality (injection drug use related deaths) data). Poisson log-linear regression models were applied to the matched data, with interactions between data sources fitted to replicate the dependencies between the data sources. Linear regression was used to estimate average change over time. Findings there were 24305, 12292, 238, 545 records and 8100, 1655, 155, 545 individual IDUs identified in the four capture sources (Police, drug treatment, overdose, and death registry, accordingly) over the period 2005 – 2009. The estimated prevalence of IDUs among the population aged 15–44 declined from 2.7% (1.8–7.9%) in 2005 to 2.0% (1.4–5.0%) in 2008, and 0.9% (0.7–1.7%) in 2009. Regression analysis indicated an average reduction of over 1700 injectors per year. Conclusion While the capture-recapture method has known limitations, the results are consistent with other data from Estonia. Identifying the drivers of change in the prevalence of injection drug use warrants further research. PMID:23290632

Crystalline silica exposure and lung cancer mortality in diatomaceous earth industry workers: a quantitative risk assessment

PubMed Central

Rice, F; Park, R; Stayner, L; Smith, R; Gilbert, S; Checkoway, H

2001-01-01

OBJECTIVE—To use various exposure-response models to estimate the risk of mortality from lung cancer due to occupational exposure to respirable crystalline silica dust.
METHODS—Data from a cohort mortality study of 2342 white male California diatomaceous earth mining and processing workers exposed to crystalline silica dust (mainly cristobalite) were reanalyzed with Poisson regression and Cox's proportional hazards models. Internal and external adjustments were used to control for potential confounding from the effects of time since first observation, calendar time, age, and Hispanic ethnicity. Cubic smoothing spline models were used to assess the fit of the models. Exposures were lagged by 10 years. Evaluations of the fit of the models were performed by comparing their deviances. Lifetime risks of lung cancer were estimated up to age 85 with an actuarial approach that accounted for competing causes of death.
RESULTS—Exposure to respirable crystalline silica dust was a significant predictor (p<0.05) in nearly all of the models evaluated and the linear relative rate model with a 10 year exposure lag seemed to give the best fit in the Poisson regression analysis. For those who died of lung cancer the linear relative rate model predicted rate ratios for mortality from lung cancer of about 1.6 for the mean cumulative exposure to respirable silica compared with no exposure. The excess lifetime risk (to age 85) of mortality from lung cancer for white men exposed for 45 years and with a 10 year lag period at the current Occupational Safety and Health Administration (OSHA) standard of about 0.05 mg/m3 for respirable cristobalite dust is 19/1000 (95% confidence interval (95% CI) 5/1000 to 46/1000).
CONCLUSIONS—There was a significant risk of mortality from lung cancer that increased with cumulative exposure to respirable crystalline silica dust. The predicted number of deaths from lung cancer suggests that current occupational health standards may not be adequately protecting workers from the risk of lung cancer.


Keywords: crystalline silica; cristobalite; lung cancer PMID:11119633

AN EFFICIENT HIGHER-ORDER FAST MULTIPOLE BOUNDARY ELEMENT SOLUTION FOR POISSON-BOLTZMANN BASED MOLECULAR ELECTROSTATICS

PubMed Central

Bajaj, Chandrajit; Chen, Shun-Chuan; Rand, Alexander

2011-01-01

In order to compute polarization energy of biomolecules, we describe a boundary element approach to solving the linearized Poisson-Boltzmann equation. Our approach combines several important features including the derivative boundary formulation of the problem and a smooth approximation of the molecular surface based on the algebraic spline molecular surface. State of the art software for numerical linear algebra and the kernel independent fast multipole method is used for both simplicity and efficiency of our implementation. We perform a variety of computational experiments, testing our method on a number of actual proteins involved in molecular docking and demonstrating the effectiveness of our solver for computing molecular polarization energy. PMID:21660123

Modeling Linguistic Variables With Regression Models: Addressing Non-Gaussian Distributions, Non-independent Observations, and Non-linear Predictors With Random Effects and Generalized Additive Models for Location, Scale, and Shape

PubMed Central

Coupé, Christophe

2018-01-01

As statistical approaches are getting increasingly used in linguistics, attention must be paid to the choice of methods and algorithms used. This is especially true since they require assumptions to be satisfied to provide valid results, and because scientific articles still often fall short of reporting whether such assumptions are met. Progress is being, however, made in various directions, one of them being the introduction of techniques able to model data that cannot be properly analyzed with simpler linear regression models. We report recent advances in statistical modeling in linguistics. We first describe linear mixed-effects regression models (LMM), which address grouping of observations, and generalized linear mixed-effects models (GLMM), which offer a family of distributions for the dependent variable. Generalized additive models (GAM) are then introduced, which allow modeling non-linear parametric or non-parametric relationships between the dependent variable and the predictors. We then highlight the possibilities offered by generalized additive models for location, scale, and shape (GAMLSS). We explain how they make it possible to go beyond common distributions, such as Gaussian or Poisson, and offer the appropriate inferential framework to account for ‘difficult’ variables such as count data with strong overdispersion. We also demonstrate how they offer interesting perspectives on data when not only the mean of the dependent variable is modeled, but also its variance, skewness, and kurtosis. As an illustration, the case of phonemic inventory size is analyzed throughout the article. For over 1,500 languages, we consider as predictors the number of speakers, the distance from Africa, an estimation of the intensity of language contact, and linguistic relationships. We discuss the use of random effects to account for genealogical relationships, the choice of appropriate distributions to model count data, and non-linear relationships. Relying on GAMLSS, we assess a range of candidate distributions, including the Sichel, Delaporte, Box-Cox Green and Cole, and Box-Cox t distributions. We find that the Box-Cox t distribution, with appropriate modeling of its parameters, best fits the conditional distribution of phonemic inventory size. We finally discuss the specificities of phoneme counts, weak effects, and how GAMLSS should be considered for other linguistic variables. PMID:29713298

Modeling Linguistic Variables With Regression Models: Addressing Non-Gaussian Distributions, Non-independent Observations, and Non-linear Predictors With Random Effects and Generalized Additive Models for Location, Scale, and Shape.

PubMed

Coupé, Christophe

2018-01-01

As statistical approaches are getting increasingly used in linguistics, attention must be paid to the choice of methods and algorithms used. This is especially true since they require assumptions to be satisfied to provide valid results, and because scientific articles still often fall short of reporting whether such assumptions are met. Progress is being, however, made in various directions, one of them being the introduction of techniques able to model data that cannot be properly analyzed with simpler linear regression models. We report recent advances in statistical modeling in linguistics. We first describe linear mixed-effects regression models (LMM), which address grouping of observations, and generalized linear mixed-effects models (GLMM), which offer a family of distributions for the dependent variable. Generalized additive models (GAM) are then introduced, which allow modeling non-linear parametric or non-parametric relationships between the dependent variable and the predictors. We then highlight the possibilities offered by generalized additive models for location, scale, and shape (GAMLSS). We explain how they make it possible to go beyond common distributions, such as Gaussian or Poisson, and offer the appropriate inferential framework to account for 'difficult' variables such as count data with strong overdispersion. We also demonstrate how they offer interesting perspectives on data when not only the mean of the dependent variable is modeled, but also its variance, skewness, and kurtosis. As an illustration, the case of phonemic inventory size is analyzed throughout the article. For over 1,500 languages, we consider as predictors the number of speakers, the distance from Africa, an estimation of the intensity of language contact, and linguistic relationships. We discuss the use of random effects to account for genealogical relationships, the choice of appropriate distributions to model count data, and non-linear relationships. Relying on GAMLSS, we assess a range of candidate distributions, including the Sichel, Delaporte, Box-Cox Green and Cole, and Box-Cox t distributions. We find that the Box-Cox t distribution, with appropriate modeling of its parameters, best fits the conditional distribution of phonemic inventory size. We finally discuss the specificities of phoneme counts, weak effects, and how GAMLSS should be considered for other linguistic variables.

Increased Use of Care Management Processes and Expanded Health Information Technology Functions by Practice Ownership and Medicaid Revenue.

PubMed

Rodriguez, Hector P; McClellan, Sean R; Bibi, Salma; Casalino, Lawrence P; Ramsay, Patricia P; Shortell, Stephen M

2016-06-01

Practice ownership and Medicaid revenue may affect the use of care management processes (CMPs) for chronic conditions and expansion of health information technology (HIT). Using a national cohort of medical practices, we compared the use of CMPs and HIT from 2006/2008 to 2013 by practice ownership and level of Medicaid revenue. Poisson regression models estimated changes in CMP use, and linear regression estimated changes in HIT, by practice ownership and Medicaid patient revenue, controlling for other practice characteristics. Compared with physician-owned practices, system-owned practices adopted a greater number of CMPs and HIT functions over time (p < .001). High Medicaid revenue (≥30.0%) was associated with less adoption of CMPs (p < .001) and HIT (p < .01). System-owned practices (p < .001) and community health centers (p < .001) with high Medicaid revenue were more likely than physician-owned practices with high Medicaid revenue to adopt CMPs over time. System and community health center ownership appear to help high Medicaid practices overcome CMP adoption constraints. © The Author(s) 2015.

Spatial Bayesian Latent Factor Regression Modeling of Coordinate-based Meta-analysis Data

PubMed Central

Montagna, Silvia; Wager, Tor; Barrett, Lisa Feldman; Johnson, Timothy D.; Nichols, Thomas E.

2017-01-01

Summary Now over 20 years old, functional MRI (fMRI) has a large and growing literature that is best synthesised with meta-analytic tools. As most authors do not share image data, only the peak activation coordinates (foci) reported in the paper are available for Coordinate-Based Meta-Analysis (CBMA). Neuroimaging meta-analysis is used to 1) identify areas of consistent activation; and 2) build a predictive model of task type or cognitive process for new studies (reverse inference). To simultaneously address these aims, we propose a Bayesian point process hierarchical model for CBMA. We model the foci from each study as a doubly stochastic Poisson process, where the study-specific log intensity function is characterised as a linear combination of a high-dimensional basis set. A sparse representation of the intensities is guaranteed through latent factor modeling of the basis coefficients. Within our framework, it is also possible to account for the effect of study-level covariates (meta-regression), significantly expanding the capabilities of the current neuroimaging meta-analysis methods available. We apply our methodology to synthetic data and neuroimaging meta-analysis datasets. PMID:28498564

The Use of Crow-AMSAA Plots to Assess Mishap Trends

NASA Technical Reports Server (NTRS)

Dawson, Jeffrey W.

2011-01-01

Crow-AMSAA (CA) plots are used to model reliability growth. Use of CA plots has expanded into other areas, such as tracking events of interest to management, maintenance problems, and safety mishaps. Safety mishaps can often be successfully modeled using a Poisson probability distribution. CA plots show a Poisson process in log-log space. If the safety mishaps are a stable homogenous Poisson process, a linear fit to the points in a CA plot will have a slope of one. Slopes of greater than one indicate a nonhomogenous Poisson process, with increasing occurrence. Slopes of less than one indicate a nonhomogenous Poisson process, with decreasing occurrence. Changes in slope, known as "cusps," indicate a change in process, which could be an improvement or a degradation. After presenting the CA conceptual framework, examples are given of trending slips, trips and falls, and ergonomic incidents at NASA (from Agency-level data). Crow-AMSAA plotting is a robust tool for trending safety mishaps that can provide insight into safety performance over time.

Predictors of the number of under-five malnourished children in Bangladesh: application of the generalized poisson regression model

PubMed Central

2013-01-01

Background Malnutrition is one of the principal causes of child mortality in developing countries including Bangladesh. According to our knowledge, most of the available studies, that addressed the issue of malnutrition among under-five children, considered the categorical (dichotomous/polychotomous) outcome variables and applied logistic regression (binary/multinomial) to find their predictors. In this study malnutrition variable (i.e. outcome) is defined as the number of under-five malnourished children in a family, which is a non-negative count variable. The purposes of the study are (i) to demonstrate the applicability of the generalized Poisson regression (GPR) model as an alternative of other statistical methods and (ii) to find some predictors of this outcome variable. Methods The data is extracted from the Bangladesh Demographic and Health Survey (BDHS) 2007. Briefly, this survey employs a nationally representative sample which is based on a two-stage stratified sample of households. A total of 4,460 under-five children is analysed using various statistical techniques namely Chi-square test and GPR model. Results The GPR model (as compared to the standard Poisson regression and negative Binomial regression) is found to be justified to study the above-mentioned outcome variable because of its under-dispersion (variance < mean) property. Our study also identify several significant predictors of the outcome variable namely mother’s education, father’s education, wealth index, sanitation status, source of drinking water, and total number of children ever born to a woman. Conclusions Consistencies of our findings in light of many other studies suggest that the GPR model is an ideal alternative of other statistical models to analyse the number of under-five malnourished children in a family. Strategies based on significant predictors may improve the nutritional status of children in Bangladesh. PMID:23297699

Trend in frequency of extreme precipitation events over Ontario from ensembles of multiple GCMs

NASA Astrophysics Data System (ADS)

Deng, Ziwang; Qiu, Xin; Liu, Jinliang; Madras, Neal; Wang, Xiaogang; Zhu, Huaiping

2016-05-01

As one of the most important extreme weather event types, extreme precipitation events have significant impacts on human and natural environment. This study assesses the projected long term trends in frequency of occurrence of extreme precipitation events represented by heavy precipitation days, very heavy precipitation days, very wet days and extreme wet days over Ontario, based on results of 21 CMIP3 GCM runs. To achieve this goal, first, all model data are linearly interpolated onto 682 grid points (0.45° × 0.45°) in Ontario; Next, biases in model daily precipitation amount are corrected with a local intensity scaling method to make the total wet days and total wet day precipitation from each of the GCMs are consistent with that from the climate forecast system reanalysis data, and then the four indices are estimated for each of the 21 GCM runs for 1968-2000, 2046-2065 and 2081-2100. After that, with the assumption that the rate parameter of the Poisson process for the occurrence of extreme precipitation events may vary with time as climate changes, the Poisson regression model which expresses the log rate as a linear function of time is used to detect the trend in frequency of extreme events in the GCMs simulations; Finally, the trends and their uncertainty are estimated. The result shows that in the twenty-first century annual heavy precipitation days, very heavy precipitation days and very wet days and extreme wet days are likely to significantly increase over major parts of Ontario and particularly heavy precipitation days, very wet days are very likely to significantly increase in some sub-regions in eastern Ontario. However, trends of seasonal indices are not significant.

[Influence of humidex on incidence of bacillary dysentery in Hefei: a time-series study].

PubMed

Zhang, H; Zhao, K F; He, R X; Zhao, D S; Xie, M Y; Wang, S S; Bai, L J; Cheng, Q; Zhang, Y W; Su, H

2017-11-10

Objective: To investigate the effect of humidex combined with mean temperature and relative humidity on the incidence of bacillary dysentery in Hefei. Methods: Daily counts of bacillary dysentery cases and weather data in Hefei were collected from January 1, 2006 to December 31, 2013. Then, the humidex was calculated from temperature and relative humidity. A Poisson generalized linear regression combined with distributed lag non-linear model was applied to analyze the relationship between humidex and the incidence of bacillary dysentery, after adjusting for long-term and seasonal trends, day of week and other weather confounders. Stratified analyses by gender, age and address were also conducted. Results: The risk of bacillary dysentery increased with the rise of humidex. The adverse effect of high humidex (90 percentile of humidex) appeared in 2-days lag and it was the largest at 4-days lag ( RR =1.063, 95 %CI : 1.037-1.090). Subgroup analyses indicated that all groups were affected by high humidex at lag 2-5 days. Conclusion: High humidex could significantly increase the risk of bacillary dysentery, and the lagged effects were observed.

Complex wet-environments in electronic-structure calculations

NASA Astrophysics Data System (ADS)

Fisicaro, Giuseppe; Genovese, Luigi; Andreussi, Oliviero; Marzari, Nicola; Goedecker, Stefan

The computational study of chemical reactions in complex, wet environments is critical for applications in many fields. It is often essential to study chemical reactions in the presence of an applied electrochemical potentials, including complex electrostatic screening coming from the solvent. In the present work we present a solver to handle both the Generalized Poisson and the Poisson-Boltzmann equation. A preconditioned conjugate gradient (PCG) method has been implemented for the Generalized Poisson and the linear regime of the Poisson-Boltzmann, allowing to solve iteratively the minimization problem with some ten iterations. On the other hand, a self-consistent procedure enables us to solve the Poisson-Boltzmann problem. The algorithms take advantage of a preconditioning procedure based on the BigDFT Poisson solver for the standard Poisson equation. They exhibit very high accuracy and parallel efficiency, and allow different boundary conditions, including surfaces. The solver has been integrated into the BigDFT and Quantum-ESPRESSO electronic-structure packages and it will be released as a independent program, suitable for integration in other codes. We present test calculations for large proteins to demonstrate efficiency and performances. This work was done within the PASC and NCCR MARVEL projects. Computer resources were provided by the Swiss National Supercomputing Centre (CSCS) under Project ID s499. LG acknowledges also support from the EXTMOS EU project.

Caribou distribution during the post-calving period in relation to infrastructure in the Prudhoe Bay oil field, Alaska

USGS Publications Warehouse

Cronin, Matthew A.; Amstrup, Steven C.; Durner, George M.; Noel, Lynn E.; McDonald, Trent L.; Ballard, Warren B.

1998-01-01

There is concern that caribou (Rangifer tarandus) may avoid roads and facilities (i.e., infrastructure) in the Prudhoe Bay oil field (PBOF) in northern Alaska, and that this avoidance can have negative effects on the animals. We quantified the relationship between caribou distribution and PBOF infrastructure during the post-calving period (mid-June to mid-August) with aerial surveys from 1990 to 1995. We conducted four to eight surveys per year with complete coverage of the PBOF. We identified active oil field infrastructure and used a geographic information system (GIS) to construct ten 1 km wide concentric intervals surrounding the infrastructure. We tested whether caribou distribution is related to distance from infrastructure with a chi-squared habitat utilization-availability analysis and log-linear regression. We considered bulls, calves, and total caribou of all sex/age classes separately. The habitat utilization-availability analysis indicated there was no consistent trend of attraction to or avoidance of infrastructure. Caribou frequently were more abundant than expected in the intervals close to infrastructure, and this trend was more pronounced for bulls and for total caribou of all sex/age classes than for calves. Log-linear regression (with Poisson error structure) of numbers of caribou and distance from infrastructure were also done, with and without combining data into the 1 km distance intervals. The analysis without intervals revealed no relationship between caribou distribution and distance from oil field infrastructure, or between caribou distribution and Julian date, year, or distance from the Beaufort Sea coast. The log-linear regression with caribou combined into distance intervals showed the density of bulls and total caribou of all sex/age classes declined with distance from infrastructure. Our results indicate that during the post-calving period: 1) caribou distribution is largely unrelated to distance from infrastructure; 2) caribou regularly use habitats in the PBOF; 3) caribou often occur close to infrastructure; and 4) caribou do not appear to avoid oil field infrastructure.

Non-Linear Concentration-Response Relationships between Ambient Ozone and Daily Mortality.

PubMed

Bae, Sanghyuk; Lim, Youn-Hee; Kashima, Saori; Yorifuji, Takashi; Honda, Yasushi; Kim, Ho; Hong, Yun-Chul

2015-01-01

Ambient ozone (O3) concentration has been reported to be significantly associated with mortality. However, linearity of the relationships and the presence of a threshold has been controversial. The aim of the present study was to examine the concentration-response relationship and threshold of the association between ambient O3 concentration and non-accidental mortality in 13 Japanese and Korean cities from 2000 to 2009. We selected Japanese and Korean cities which have population of over 1 million. We constructed Poisson regression models adjusting daily mean temperature, daily mean PM10, humidity, time trend, season, year, day of the week, holidays and yearly population. The association between O3 concentration and mortality was examined using linear, spline and linear-threshold models. The thresholds were estimated for each city, by constructing linear-threshold models. We also examined the city-combined association using a generalized additive mixed model. The mean O3 concentration did not differ greatly between Korea and Japan, which were 26.2 ppb and 24.2 ppb, respectively. Seven out of 13 cities showed better fits for the spline model compared with the linear model, supporting a non-linear relationships between O3 concentration and mortality. All of the 7 cities showed J or U shaped associations suggesting the existence of thresholds. The range of city-specific thresholds was from 11 to 34 ppb. The city-combined analysis also showed a non-linear association with a threshold around 30-40 ppb. We have observed non-linear concentration-response relationship with thresholds between daily mean ambient O3 concentration and daily number of non-accidental death in Japanese and Korean cities.

An INAR(1) Negative Multinomial Regression Model for Longitudinal Count Data.

ERIC Educational Resources Information Center

Bockenholt, Ulf

1999-01-01

Discusses a regression model for the analysis of longitudinal count data in a panel study by adapting an integer-valued first-order autoregressive (INAR(1)) Poisson process to represent time-dependent correlation between counts. Derives a new negative multinomial distribution by combining INAR(1) representation with a random effects approach.…

Pick Your Poisson: A Tutorial on Analyzing Counts of Student Victimization Data

ERIC Educational Resources Information Center

Huang, Francis L.; Cornell, Dewey G.

2012-01-01

School violence research is often concerned with infrequently occurring events such as counts of the number of bullying incidents or fights a student may experience. Analyzing count data using ordinary least squares regression may produce improbable predicted values, and as a result of regression assumption violations, result in higher Type I…

[Prediction and spatial distribution of recruitment trees of natural secondary forest based on geographically weighted Poisson model].

PubMed

Zhang, Ling Yu; Liu, Zhao Gang

2017-12-01

Based on the data collected from 108 permanent plots of the forest resources survey in Maoershan Experimental Forest Farm during 2004-2016, this study investigated the spatial distribution of recruitment trees in natural secondary forest by global Poisson regression and geographically weighted Poisson regression (GWPR) with four bandwidths of 2.5, 5, 10 and 15 km. The simulation effects of the 5 regressions and the factors influencing the recruitment trees in stands were analyzed, a description was given to the spatial autocorrelation of the regression residuals on global and local levels using Moran's I. The results showed that the spatial distribution of the number of natural secondary forest recruitment was significantly influenced by stands and topographic factors, especially average DBH. The GWPR model with small scale (2.5 km) had high accuracy of model fitting, a large range of model parameter estimates was generated, and the localized spatial distribution effect of the model parameters was obtained. The GWPR model at small scale (2.5 and 5 km) had produced a small range of model residuals, and the stability of the model was improved. The global spatial auto-correlation of the GWPR model residual at the small scale (2.5 km) was the lowe-st, and the local spatial auto-correlation was significantly reduced, in which an ideal spatial distribution pattern of small clusters with different observations was formed. The local model at small scale (2.5 km) was much better than the global model in the simulation effect on the spatial distribution of recruitment tree number.

Modeling urban coastal flood severity from crowd-sourced flood reports using Poisson regression and Random Forest

NASA Astrophysics Data System (ADS)

Sadler, J. M.; Goodall, J. L.; Morsy, M. M.; Spencer, K.

2018-04-01

Sea level rise has already caused more frequent and severe coastal flooding and this trend will likely continue. Flood prediction is an essential part of a coastal city's capacity to adapt to and mitigate this growing problem. Complex coastal urban hydrological systems however, do not always lend themselves easily to physically-based flood prediction approaches. This paper presents a method for using a data-driven approach to estimate flood severity in an urban coastal setting using crowd-sourced data, a non-traditional but growing data source, along with environmental observation data. Two data-driven models, Poisson regression and Random Forest regression, are trained to predict the number of flood reports per storm event as a proxy for flood severity, given extensive environmental data (i.e., rainfall, tide, groundwater table level, and wind conditions) as input. The method is demonstrated using data from Norfolk, Virginia USA from September 2010 to October 2016. Quality-controlled, crowd-sourced street flooding reports ranging from 1 to 159 per storm event for 45 storm events are used to train and evaluate the models. Random Forest performed better than Poisson regression at predicting the number of flood reports and had a lower false negative rate. From the Random Forest model, total cumulative rainfall was by far the most dominant input variable in predicting flood severity, followed by low tide and lower low tide. These methods serve as a first step toward using data-driven methods for spatially and temporally detailed coastal urban flood prediction.

77 FR 13691 - Qualification of Drivers; Exemption Applications; Vision

Federal Register 2010, 2011, 2012, 2013, 2014

2012-03-07

..., ocular hypertension, retinal detachment, cataracts and corneal scaring. In most cases, their eye... Application of Multiple Regression Analysis of a Poisson Process,'' Journal of American Statistical...

Poisson image reconstruction with Hessian Schatten-norm regularization.

PubMed

Lefkimmiatis, Stamatios; Unser, Michael

2013-11-01

Poisson inverse problems arise in many modern imaging applications, including biomedical and astronomical ones. The main challenge is to obtain an estimate of the underlying image from a set of measurements degraded by a linear operator and further corrupted by Poisson noise. In this paper, we propose an efficient framework for Poisson image reconstruction, under a regularization approach, which depends on matrix-valued regularization operators. In particular, the employed regularizers involve the Hessian as the regularization operator and Schatten matrix norms as the potential functions. For the solution of the problem, we propose two optimization algorithms that are specifically tailored to the Poisson nature of the noise. These algorithms are based on an augmented-Lagrangian formulation of the problem and correspond to two variants of the alternating direction method of multipliers. Further, we derive a link that relates the proximal map of an l(p) norm with the proximal map of a Schatten matrix norm of order p. This link plays a key role in the development of one of the proposed algorithms. Finally, we provide experimental results on natural and biological images for the task of Poisson image deblurring and demonstrate the practical relevance and effectiveness of the proposed framework.

Time series regression model for infectious disease and weather.

PubMed

Imai, Chisato; Armstrong, Ben; Chalabi, Zaid; Mangtani, Punam; Hashizume, Masahiro

2015-10-01

Time series regression has been developed and long used to evaluate the short-term associations of air pollution and weather with mortality or morbidity of non-infectious diseases. The application of the regression approaches from this tradition to infectious diseases, however, is less well explored and raises some new issues. We discuss and present potential solutions for five issues often arising in such analyses: changes in immune population, strong autocorrelations, a wide range of plausible lag structures and association patterns, seasonality adjustments, and large overdispersion. The potential approaches are illustrated with datasets of cholera cases and rainfall from Bangladesh and influenza and temperature in Tokyo. Though this article focuses on the application of the traditional time series regression to infectious diseases and weather factors, we also briefly introduce alternative approaches, including mathematical modeling, wavelet analysis, and autoregressive integrated moving average (ARIMA) models. Modifications proposed to standard time series regression practice include using sums of past cases as proxies for the immune population, and using the logarithm of lagged disease counts to control autocorrelation due to true contagion, both of which are motivated from "susceptible-infectious-recovered" (SIR) models. The complexity of lag structures and association patterns can often be informed by biological mechanisms and explored by using distributed lag non-linear models. For overdispersed models, alternative distribution models such as quasi-Poisson and negative binomial should be considered. Time series regression can be used to investigate dependence of infectious diseases on weather, but may need modifying to allow for features specific to this context. Copyright © 2015 The Authors. Published by Elsevier Inc. All rights reserved.

Local existence of solutions to the Euler-Poisson system, including densities without compact support

NASA Astrophysics Data System (ADS)

Brauer, Uwe; Karp, Lavi

2018-01-01

Local existence and well posedness for a class of solutions for the Euler Poisson system is shown. These solutions have a density ρ which either falls off at infinity or has compact support. The solutions have finite mass, finite energy functional and include the static spherical solutions for γ = 6/5. The result is achieved by using weighted Sobolev spaces of fractional order and a new non-linear estimate which allows to estimate the physical density by the regularised non-linear matter variable. Gamblin also has studied this setting but using very different functional spaces. However we believe that the functional setting we use is more appropriate to describe a physical isolated body and more suitable to study the Newtonian limit.

Image denoising in mixed Poisson-Gaussian noise.

PubMed

Luisier, Florian; Blu, Thierry; Unser, Michael

2011-03-01

We propose a general methodology (PURE-LET) to design and optimize a wide class of transform-domain thresholding algorithms for denoising images corrupted by mixed Poisson-Gaussian noise. We express the denoising process as a linear expansion of thresholds (LET) that we optimize by relying on a purely data-adaptive unbiased estimate of the mean-squared error (MSE), derived in a non-Bayesian framework (PURE: Poisson-Gaussian unbiased risk estimate). We provide a practical approximation of this theoretical MSE estimate for the tractable optimization of arbitrary transform-domain thresholding. We then propose a pointwise estimator for undecimated filterbank transforms, which consists of subband-adaptive thresholding functions with signal-dependent thresholds that are globally optimized in the image domain. We finally demonstrate the potential of the proposed approach through extensive comparisons with state-of-the-art techniques that are specifically tailored to the estimation of Poisson intensities. We also present denoising results obtained on real images of low-count fluorescence microscopy.

Marginalized zero-inflated Poisson models with missing covariates.

PubMed

Benecha, Habtamu K; Preisser, John S; Divaris, Kimon; Herring, Amy H; Das, Kalyan

2018-05-11

Unlike zero-inflated Poisson regression, marginalized zero-inflated Poisson (MZIP) models for counts with excess zeros provide estimates with direct interpretations for the overall effects of covariates on the marginal mean. In the presence of missing covariates, MZIP and many other count data models are ordinarily fitted using complete case analysis methods due to lack of appropriate statistical methods and software. This article presents an estimation method for MZIP models with missing covariates. The method, which is applicable to other missing data problems, is illustrated and compared with complete case analysis by using simulations and dental data on the caries preventive effects of a school-based fluoride mouthrinse program. © 2018 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Marginal Stability of Ion-Acoustic Waves in a Weakly Collisional Two-Temperature Plasma without a Current.

DTIC Science & Technology

1987-08-06

ABSTRACT (Continue on reverse if necessary and identify by block number) The linearized Balescu -Lenard-Poisson equations are solved in the weakly...free plasma is . unresolved. The purpose of this report is to present a resolution based upon the Balescu -Lenard-Poisson equations. The Balescu -Lenard...acoustic waves become marginally stable. Gur re- sults are based on the closed form solution for the dielectric function for the line- arized Balescu -Lenard

Extracting real-crack properties from non-linear elastic behaviour of rocks: abundance of cracks with dominating normal compliance and rocks with negative Poisson ratios

NASA Astrophysics Data System (ADS)

Zaitsev, Vladimir Y.; Radostin, Andrey V.; Pasternak, Elena; Dyskin, Arcady

2017-09-01

Results of examination of experimental data on non-linear elasticity of rocks using experimentally determined pressure dependences of P- and S-wave velocities from various literature sources are presented. Overall, over 90 rock samples are considered. Interpretation of the data is performed using an effective-medium description in which cracks are considered as compliant defects with explicitly introduced shear and normal compliances without specifying a particular crack model with an a priori given ratio of the compliances. Comparison with the experimental data indicated abundance (˜ 80 %) of cracks with the normal-to-shear compliance ratios that significantly exceed the values typical of conventionally used crack models (such as penny-shaped cuts or thin ellipsoidal cracks). Correspondingly, rocks with such cracks demonstrate a strongly decreased Poisson ratio including a significant (˜ 45 %) portion of rocks exhibiting negative Poisson ratios at lower pressures, for which the concentration of not yet closed cracks is maximal. The obtained results indicate the necessity for further development of crack models to account for the revealed numerous examples of cracks with strong domination of normal compliance. Discovering such a significant number of naturally auxetic rocks is in contrast to the conventional viewpoint that occurrence of a negative Poisson ratio is an exotic fact that is mostly discussed for artificial structures.

Fast and Accurate Poisson Denoising With Trainable Nonlinear Diffusion.

PubMed

Feng, Wensen; Qiao, Peng; Chen, Yunjin; Wensen Feng; Peng Qiao; Yunjin Chen; Feng, Wensen; Chen, Yunjin; Qiao, Peng

2018-06-01

The degradation of the acquired signal by Poisson noise is a common problem for various imaging applications, such as medical imaging, night vision, and microscopy. Up to now, many state-of-the-art Poisson denoising techniques mainly concentrate on achieving utmost performance, with little consideration for the computation efficiency. Therefore, in this paper we aim to propose an efficient Poisson denoising model with both high computational efficiency and recovery quality. To this end, we exploit the newly developed trainable nonlinear reaction diffusion (TNRD) model which has proven an extremely fast image restoration approach with performance surpassing recent state-of-the-arts. However, the straightforward direct gradient descent employed in the original TNRD-based denoising task is not applicable in this paper. To solve this problem, we resort to the proximal gradient descent method. We retrain the model parameters, including the linear filters and influence functions by taking into account the Poisson noise statistics, and end up with a well-trained nonlinear diffusion model specialized for Poisson denoising. The trained model provides strongly competitive results against state-of-the-art approaches, meanwhile bearing the properties of simple structure and high efficiency. Furthermore, our proposed model comes along with an additional advantage, that the diffusion process is well-suited for parallel computation on graphics processing units (GPUs). For images of size , our GPU implementation takes less than 0.1 s to produce state-of-the-art Poisson denoising performance.

Using multi-year national survey cohorts for period estimates: an application of weighted discrete Poisson regression for assessing annual national mortality in US adults with and without diabetes, 2000-2006.

PubMed

Cheng, Yiling J; Gregg, Edward W; Rolka, Deborah B; Thompson, Theodore J

2016-12-15

Monitoring national mortality among persons with a disease is important to guide and evaluate progress in disease control and prevention. However, a method to estimate nationally representative annual mortality among persons with and without diabetes in the United States does not currently exist. The aim of this study is to demonstrate use of weighted discrete Poisson regression on national survey mortality follow-up data to estimate annual mortality rates among adults with diabetes. To estimate mortality among US adults with diabetes, we applied a weighted discrete time-to-event Poisson regression approach with post-stratification adjustment to national survey data. Adult participants aged 18 or older with and without diabetes in the National Health Interview Survey 1997-2004 were followed up through 2006 for mortality status. We estimated mortality among all US adults, and by self-reported diabetes status at baseline. The time-varying covariates used were age and calendar year. Mortality among all US adults was validated using direct estimates from the National Vital Statistics System (NVSS). Using our approach, annual all-cause mortality among all US adults ranged from 8.8 deaths per 1,000 person-years (95% confidence interval [CI]: 8.0, 9.6) in year 2000 to 7.9 (95% CI: 7.6, 8.3) in year 2006. By comparison, the NVSS estimates ranged from 8.6 to 7.9 (correlation = 0.94). All-cause mortality among persons with diabetes decreased from 35.7 (95% CI: 28.4, 42.9) in 2000 to 31.8 (95% CI: 28.5, 35.1) in 2006. After adjusting for age, sex, and race/ethnicity, persons with diabetes had 2.1 (95% CI: 2.01, 2.26) times the risk of death of those without diabetes. Period-specific national mortality can be estimated for people with and without a chronic condition using national surveys with mortality follow-up and a discrete time-to-event Poisson regression approach with post-stratification adjustment.

Linear and Poisson models for genetic evaluation of tick resistance in cross-bred Hereford x Nellore cattle.

PubMed

Ayres, D R; Pereira, R J; Boligon, A A; Silva, F F; Schenkel, F S; Roso, V M; Albuquerque, L G

2013-12-01

Cattle resistance to ticks is measured by the number of ticks infesting the animal. The model used for the genetic analysis of cattle resistance to ticks frequently requires logarithmic transformation of the observations. The objective of this study was to evaluate the predictive ability and goodness of fit of different models for the analysis of this trait in cross-bred Hereford x Nellore cattle. Three models were tested: a linear model using logarithmic transformation of the observations (MLOG); a linear model without transformation of the observations (MLIN); and a generalized linear Poisson model with residual term (MPOI). All models included the classificatory effects of contemporary group and genetic group and the covariates age of animal at the time of recording and individual heterozygosis, as well as additive genetic effects as random effects. Heritability estimates were 0.08 ± 0.02, 0.10 ± 0.02 and 0.14 ± 0.04 for MLIN, MLOG and MPOI models, respectively. The model fit quality, verified by deviance information criterion (DIC) and residual mean square, indicated fit superiority of MPOI model. The predictive ability of the models was compared by validation test in independent sample. The MPOI model was slightly superior in terms of goodness of fit and predictive ability, whereas the correlations between observed and predicted tick counts were practically the same for all models. A higher rank correlation between breeding values was observed between models MLOG and MPOI. Poisson model can be used for the selection of tick-resistant animals. © 2013 Blackwell Verlag GmbH.

Seasonality in trauma admissions - Are daylight and weather variables better predictors than general cyclic effects?

PubMed

Røislien, Jo; Søvik, Signe; Eken, Torsten

2018-01-01

Trauma is a leading global cause of death, and predicting the burden of trauma admissions is vital for good planning of trauma care. Seasonality in trauma admissions has been found in several studies. Seasonal fluctuations in daylight hours, temperature and weather affect social and cultural practices but also individual neuroendocrine rhythms that may ultimately modify behaviour and potentially predispose to trauma. The aim of the present study was to explore to what extent the observed seasonality in daily trauma admissions could be explained by changes in daylight and weather variables throughout the year. Retrospective registry study on trauma admissions in the 10-year period 2001-2010 at Oslo University Hospital, Ullevål, Norway, where the amount of daylight varies from less than 6 hours to almost 19 hours per day throughout the year. Daily number of admissions was analysed by fitting non-linear Poisson time series regression models, simultaneously adjusting for several layers of temporal patterns, including a non-linear long-term trend and both seasonal and weekly cyclic effects. Five daylight and weather variables were explored, including hours of daylight and amount of precipitation. Models were compared using Akaike's Information Criterion (AIC). A regression model including daylight and weather variables significantly outperformed a traditional seasonality model in terms of AIC. A cyclic week effect was significant in all models. Daylight and weather variables are better predictors of seasonality in daily trauma admissions than mere information on day-of-year.

Morphology and linear-elastic moduli of random network solids.

PubMed

Nachtrab, Susan; Kapfer, Sebastian C; Arns, Christoph H; Madadi, Mahyar; Mecke, Klaus; Schröder-Turk, Gerd E

2011-06-17

The effective linear-elastic moduli of disordered network solids are analyzed by voxel-based finite element calculations. We analyze network solids given by Poisson-Voronoi processes and by the structure of collagen fiber networks imaged by confocal microscopy. The solid volume fraction ϕ is varied by adjusting the fiber radius, while keeping the structural mesh or pore size of the underlying network fixed. For intermediate ϕ, the bulk and shear modulus are approximated by empirical power-laws K(phi)proptophin and G(phi)proptophim with n≈1.4 and m≈1.7. The exponents for the collagen and the Poisson-Voronoi network solids are similar, and are close to the values n=1.22 and m=2.11 found in a previous voxel-based finite element study of Poisson-Voronoi systems with different boundary conditions. However, the exponents of these empirical power-laws are at odds with the analytic values of n=1 and m=2, valid for low-density cellular structures in the limit of thin beams. We propose a functional form for K(ϕ) that models the cross-over from a power-law at low densities to a porous solid at high densities; a fit of the data to this functional form yields the asymptotic exponent n≈1.00, as expected. Further, both the intensity of the Poisson-Voronoi process and the collagen concentration in the samples, both of which alter the typical pore or mesh size, affect the effective moduli only by the resulting change of the solid volume fraction. These findings suggest that a network solid with the structure of the collagen networks can be modeled in quantitative agreement by a Poisson-Voronoi process. Copyright © 2011 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

The Kontsevich tetrahedral flow revisited

NASA Astrophysics Data System (ADS)

Bouisaghouane, A.; Buring, R.; Kiselev, A.

2017-09-01

We prove that the Kontsevich tetrahedral flow P ˙ =Qa:b(P) , the right-hand side of which is a linear combination of two differential monomials of degree four in a bi-vector P on an affine real Poisson manifold Nn, does infinitesimally preserve the space of Poisson bi-vectors on Nn if and only if the two monomials in Qa:b(P) are balanced by the ratio a : b = 1 : 6. The proof is explicit; it is written in the language of Kontsevich graphs.

(Where) Is Functional Decline Isolating? Disordered Environments and the Onset of Disability.

PubMed

Schafer, Markus H

2018-03-01

The onset of disability is believed to undermine social connectedness and raise the risk of social isolation, yet spatial environments are seldom considered in this process. This study examines whether unruly home and neighborhood conditions intensify the association between disability onset and several dimensions of social connectedness. I incorporate longitudinal data from the National Social Life, Health, and Aging Project, which contains environmental evaluations conducted by trained observers ( N = 1,558). Results from Poisson, ordinal logistic, and linear regression models reveal heterogeneous consequences of disablement: disability onset was associated with reduced core network size, fewer friends, lower likelihood of social interaction, and less overall social connectedness-though mainly when accompanied by higher levels of household disorder. There was limited evidence that neighborhood disorder moderated consequences of disability. Findings point to the importance of the home as an environmental resource and underscore important contextual contingencies in the isolating consequences of disability.

Radon exposure and leukaemia in adulthood.

PubMed

Viel, J F

1993-08-01

Positive associations between leukaemia and radon concentrations have been observed in England, Scotland and Wales, and Canada. Results of a similar study for the populations of 41 French administrative areas ('départements') are reported for 1984-1986. The average indoor radon and gamma ray concentrations per 'département' range from 12 to 147 Bq.m-3 and from 28 to 142 nG.h-1, respectively. Acute lymphoid leukaemia mortality rate is similar to the national level, whereas an excess of acute myeloid leukaemia deaths is observed. According to Poisson regression models and modified tests for partial correlation, acute myeloid leukaemia mortality is significantly and positively related to indoor radon concentration whether or not adjustment is made for indoor gamma ray dose, socioeconomic status and linear gradient. This result reinforces the evidence that indoor exposure to high levels of radon is a leukaemic environmental hazard.

Inverse sequential procedures for the monitoring of time series

NASA Technical Reports Server (NTRS)

Radok, Uwe; Brown, Timothy J.

1995-01-01

When one or more new values are added to a developing time series, they change its descriptive parameters (mean, variance, trend, coherence). A 'change index (CI)' is developed as a quantitative indicator that the changed parameters remain compatible with the existing 'base' data. CI formulate are derived, in terms of normalized likelihood ratios, for small samples from Poisson, Gaussian, and Chi-Square distributions, and for regression coefficients measuring linear or exponential trends. A substantial parameter change creates a rapid or abrupt CI decrease which persists when the length of the bases is changed. Except for a special Gaussian case, the CI has no simple explicit regions for tests of hypotheses. However, its design ensures that the series sampled need not conform strictly to the distribution form assumed for the parameter estimates. The use of the CI is illustrated with both constructed and observed data samples, processed with a Fortran code 'Sequitor'.

Exploring the Association of Homicides in Northern Mexico and Healthcare Access for US Residents.

PubMed

Geissler, Kimberley H; Becker, Charles; Stearns, Sally C; Thirumurthy, Harsha; Holmes, George M

2015-08-01

Many legal residents in the United States (US)-Mexico border region cross from the US into Mexico for medical treatment and pharmaceuticals. We analyzed whether recent increases in homicides in Mexico are associated with reduced healthcare access for US border residents. We used data on healthcare access, legal entries to the US from Mexico, and Mexican homicide rates (2002-2010). Poisson regression models estimated associations between homicide rates and total legal US entries. Multivariate difference-in-difference linear probability models evaluated associations between Mexican homicide rates and self-reported measures of healthcare access for US residents. Increased homicide rates were associated with decreased legal entries to the US from Mexico. Contrary to expectations, homicides did not have significant associations with healthcare access measures for legal residents in US border counties. Despite a decrease in border crossings, increased violence in Mexico did not appear to negatively affect healthcare access for US border residents.

Poisson sigma models, reduction and nonlinear gauge theories

NASA Astrophysics Data System (ADS)

Signori, Daniele

This dissertation comprises two main lines of research. Firstly, we study non-linear gauge theories for principal bundles, where the structure group is replaced by a Lie groupoid. We follow the approach of Moerdijk-Mrcun and establish its relation with the existing physics literature. In particular, we derive a new formula for the gauge transformation which closely resembles and generalizes the classical formulas found in Yang Mills gauge theories. Secondly, we give a field theoretic interpretation of the of the BRST (Becchi-Rouet-Stora-Tyutin) and BFV (Batalin-Fradkin-Vilkovisky) methods for the reduction of coisotropic submanifolds of Poisson manifolds. The generalized Poisson sigma models that we define are related to the quantization deformation problems of coisotropic submanifolds using homotopical algebras.

Effects of greening and community reuse of vacant lots on crime

PubMed Central

Kondo, Michelle; Hohl, Bernadette; Han, SeungHoon; Branas, Charles

2016-01-01

The Youngstown Neighborhood Development Corporation initiated a ‘Lots of Green’ programme to reuse vacant land in 2010. We performed a difference-in-differences analysis of the effects of this programme on crime in and around newly treated lots, in comparison to crimes in and around randomly selected and matched, untreated vacant lot controls. The effects of two types of vacant lot treatments on crime were tested: a cleaning and greening ‘stabilisation’ treatment and a ‘community reuse’ treatment mostly involving community gardens. The combined effects of both types of vacant lot treatments were also tested. After adjustment for various sociodemographic factors, linear and Poisson regression models demonstrated statistically significant reductions in all crime classes for at least one lot treatment type. Regression models adjusted for spatial autocorrelation found the most consistent significant reductions in burglaries around stabilisation lots, and in assaults around community reuse lots. Spill-over crime reduction effects were found in contiguous areas around newly treated lots. Significant increases in motor vehicle thefts around both types of lots were also found after they had been greened. Community-initiated vacant lot greening may have a greater impact on reducing more serious, violent crimes. PMID:28529389

Spatial Bayesian latent factor regression modeling of coordinate-based meta-analysis data.

PubMed

Montagna, Silvia; Wager, Tor; Barrett, Lisa Feldman; Johnson, Timothy D; Nichols, Thomas E

2018-03-01

Now over 20 years old, functional MRI (fMRI) has a large and growing literature that is best synthesised with meta-analytic tools. As most authors do not share image data, only the peak activation coordinates (foci) reported in the article are available for Coordinate-Based Meta-Analysis (CBMA). Neuroimaging meta-analysis is used to (i) identify areas of consistent activation; and (ii) build a predictive model of task type or cognitive process for new studies (reverse inference). To simultaneously address these aims, we propose a Bayesian point process hierarchical model for CBMA. We model the foci from each study as a doubly stochastic Poisson process, where the study-specific log intensity function is characterized as a linear combination of a high-dimensional basis set. A sparse representation of the intensities is guaranteed through latent factor modeling of the basis coefficients. Within our framework, it is also possible to account for the effect of study-level covariates (meta-regression), significantly expanding the capabilities of the current neuroimaging meta-analysis methods available. We apply our methodology to synthetic data and neuroimaging meta-analysis datasets. © 2017, The International Biometric Society.

surrosurv: An R package for the evaluation of failure time surrogate endpoints in individual patient data meta-analyses of randomized clinical trials.

PubMed

Rotolo, Federico; Paoletti, Xavier; Michiels, Stefan

2018-03-01

Surrogate endpoints are attractive for use in clinical trials instead of well-established endpoints because of practical convenience. To validate a surrogate endpoint, two important measures can be estimated in a meta-analytic context when individual patient data are available: the R indiv 2 or the Kendall's τ at the individual level, and the R trial 2 at the trial level. We aimed at providing an R implementation of classical and well-established as well as more recent statistical methods for surrogacy assessment with failure time endpoints. We also intended incorporating utilities for model checking and visualization and data generating methods described in the literature to date. In the case of failure time endpoints, the classical approach is based on two steps. First, a Kendall's τ is estimated as measure of individual level surrogacy using a copula model. Then, the R trial 2 is computed via a linear regression of the estimated treatment effects; at this second step, the estimation uncertainty can be accounted for via measurement-error model or via weights. In addition to the classical approach, we recently developed an approach based on bivariate auxiliary Poisson models with individual random effects to measure the Kendall's τ and treatment-by-trial interactions to measure the R trial 2 . The most common data simulation models described in the literature are based on: copula models, mixed proportional hazard models, and mixture of half-normal and exponential random variables. The R package surrosurv implements the classical two-step method with Clayton, Plackett, and Hougaard copulas. It also allows to optionally adjusting the second-step linear regression for measurement-error. The mixed Poisson approach is implemented with different reduced models in addition to the full model. We present the package functions for estimating the surrogacy models, for checking their convergence, for performing leave-one-trial-out cross-validation, and for plotting the results. We illustrate their use in practice on individual patient data from a meta-analysis of 4069 patients with advanced gastric cancer from 20 trials of chemotherapy. The surrosurv package provides an R implementation of classical and recent statistical methods for surrogacy assessment of failure time endpoints. Flexible simulation functions are available to generate data according to the methods described in the literature. Copyright © 2017 Elsevier B.V. All rights reserved.

Matrix decomposition graphics processing unit solver for Poisson image editing

NASA Astrophysics Data System (ADS)

Lei, Zhao; Wei, Li

2012-10-01

In recent years, gradient-domain methods have been widely discussed in the image processing field, including seamless cloning and image stitching. These algorithms are commonly carried out by solving a large sparse linear system: the Poisson equation. However, solving the Poisson equation is a computational and memory intensive task which makes it not suitable for real-time image editing. A new matrix decomposition graphics processing unit (GPU) solver (MDGS) is proposed to settle the problem. A matrix decomposition method is used to distribute the work among GPU threads, so that MDGS will take full advantage of the computing power of current GPUs. Additionally, MDGS is a hybrid solver (combines both the direct and iterative techniques) and has two-level architecture. These enable MDGS to generate identical solutions with those of the common Poisson methods and achieve high convergence rate in most cases. This approach is advantageous in terms of parallelizability, enabling real-time image processing, low memory-taken and extensive applications.

Quantification of integrated HIV DNA by repetitive-sampling Alu-HIV PCR on the basis of poisson statistics.

PubMed

De Spiegelaere, Ward; Malatinkova, Eva; Lynch, Lindsay; Van Nieuwerburgh, Filip; Messiaen, Peter; O'Doherty, Una; Vandekerckhove, Linos

2014-06-01

Quantification of integrated proviral HIV DNA by repetitive-sampling Alu-HIV PCR is a candidate virological tool to monitor the HIV reservoir in patients. However, the experimental procedures and data analysis of the assay are complex and hinder its widespread use. Here, we provide an improved and simplified data analysis method by adopting binomial and Poisson statistics. A modified analysis method on the basis of Poisson statistics was used to analyze the binomial data of positive and negative reactions from a 42-replicate Alu-HIV PCR by use of dilutions of an integration standard and on samples of 57 HIV-infected patients. Results were compared with the quantitative output of the previously described Alu-HIV PCR method. Poisson-based quantification of the Alu-HIV PCR was linearly correlated with the standard dilution series, indicating that absolute quantification with the Poisson method is a valid alternative for data analysis of repetitive-sampling Alu-HIV PCR data. Quantitative outputs of patient samples assessed by the Poisson method correlated with the previously described Alu-HIV PCR analysis, indicating that this method is a valid alternative for quantifying integrated HIV DNA. Poisson-based analysis of the Alu-HIV PCR data enables absolute quantification without the need of a standard dilution curve. Implementation of the CI estimation permits improved qualitative analysis of the data and provides a statistical basis for the required minimal number of technical replicates. © 2014 The American Association for Clinical Chemistry.

Impact of temperature on mortality in Hubei, China: a multi-county time series analysis

NASA Astrophysics Data System (ADS)

Zhang, Yunquan; Yu, Chuanhua; Bao, Junzhe; Li, Xudong

2017-03-01

We examined the impact of extreme temperatures on mortality in 12 counties across Hubei Province, central China, during 2009-2012. Quasi-Poisson generalized linear regression combined with distributed lag non-linear model was first applied to estimate county-specific relationship between temperature and mortality. A multivariable meta-analysis was then used to pool the estimates of county-specific mortality effects of extreme cold temperature (1st percentile) and hot temperature (99th percentile). An inverse J-shaped relationship was observed between temperature and mortality at the provincial level. Heat effect occurred immediately and persisted for 2-3 days, whereas cold effect was 1-2 days delayed and much longer lasting. Higher mortality risks were observed among females, the elderly aged over 75 years, persons dying outside the hospital and those with high education attainment, especially for cold effects. Our data revealed some slight differences in heat- and cold- related mortality effects on urban and rural residents. These findings may have important implications for developing locally-based preventive and intervention strategies to reduce temperature-related mortality, especially for those susceptible subpopulations. Also, urbanization should be considered as a potential influence factor when evaluating temperature-mortality association in future researches.

Evolutionary inference via the Poisson Indel Process.

PubMed

Bouchard-Côté, Alexandre; Jordan, Michael I

2013-01-22

We address the problem of the joint statistical inference of phylogenetic trees and multiple sequence alignments from unaligned molecular sequences. This problem is generally formulated in terms of string-valued evolutionary processes along the branches of a phylogenetic tree. The classic evolutionary process, the TKF91 model [Thorne JL, Kishino H, Felsenstein J (1991) J Mol Evol 33(2):114-124] is a continuous-time Markov chain model composed of insertion, deletion, and substitution events. Unfortunately, this model gives rise to an intractable computational problem: The computation of the marginal likelihood under the TKF91 model is exponential in the number of taxa. In this work, we present a stochastic process, the Poisson Indel Process (PIP), in which the complexity of this computation is reduced to linear. The Poisson Indel Process is closely related to the TKF91 model, differing only in its treatment of insertions, but it has a global characterization as a Poisson process on the phylogeny. Standard results for Poisson processes allow key computations to be decoupled, which yields the favorable computational profile of inference under the PIP model. We present illustrative experiments in which Bayesian inference under the PIP model is compared with separate inference of phylogenies and alignments.

Evolutionary inference via the Poisson Indel Process

PubMed Central

Bouchard-Côté, Alexandre; Jordan, Michael I.

2013-01-01

We address the problem of the joint statistical inference of phylogenetic trees and multiple sequence alignments from unaligned molecular sequences. This problem is generally formulated in terms of string-valued evolutionary processes along the branches of a phylogenetic tree. The classic evolutionary process, the TKF91 model [Thorne JL, Kishino H, Felsenstein J (1991) J Mol Evol 33(2):114–124] is a continuous-time Markov chain model composed of insertion, deletion, and substitution events. Unfortunately, this model gives rise to an intractable computational problem: The computation of the marginal likelihood under the TKF91 model is exponential in the number of taxa. In this work, we present a stochastic process, the Poisson Indel Process (PIP), in which the complexity of this computation is reduced to linear. The Poisson Indel Process is closely related to the TKF91 model, differing only in its treatment of insertions, but it has a global characterization as a Poisson process on the phylogeny. Standard results for Poisson processes allow key computations to be decoupled, which yields the favorable computational profile of inference under the PIP model. We present illustrative experiments in which Bayesian inference under the PIP model is compared with separate inference of phylogenies and alignments. PMID:23275296

A coarse-grid projection method for accelerating incompressible flow computations

NASA Astrophysics Data System (ADS)

San, Omer; Staples, Anne

2011-11-01

We present a coarse-grid projection (CGP) algorithm for accelerating incompressible flow computations, which is applicable to methods involving Poisson equations as incompressibility constraints. CGP methodology is a modular approach that facilitates data transfer with simple interpolations and uses black-box solvers for the Poisson and advection-diffusion equations in the flow solver. Here, we investigate a particular CGP method for the vorticity-stream function formulation that uses the full weighting operation for mapping from fine to coarse grids, the third-order Runge-Kutta method for time stepping, and finite differences for the spatial discretization. After solving the Poisson equation on a coarsened grid, bilinear interpolation is used to obtain the fine data for consequent time stepping on the full grid. We compute several benchmark flows: the Taylor-Green vortex, a vortex pair merging, a double shear layer, decaying turbulence and the Taylor-Green vortex on a distorted grid. In all cases we use either FFT-based or V-cycle multigrid linear-cost Poisson solvers. Reducing the number of degrees of freedom of the Poisson solver by powers of two accelerates these computations while, for the first level of coarsening, retaining the same level of accuracy in the fine resolution vorticity field.

A statistical model to estimate the impact of a hepatitis A vaccination programme.

PubMed

Oviedo, Manuel; Pilar Muñoz, M; Domínguez, Angela; Borras, Eva; Carmona, Gloria

2008-11-11

A program of routine hepatitis A+B vaccination in preadolescents was introduced in 1998 in Catalonia, a region situated in the northeast of Spain. The objective of this study was to quantify the reduction in the incidence of hepatitis A in order to differentiate the natural reduction of the incidence of hepatitis A from that produced due to the vaccination programme and to predict the evolution of the disease in forthcoming years. A generalized linear model (GLM) using negative binomial regression was used to estimate the incidence rates of hepatitis A in Catalonia by year, age group and vaccination. Introduction of the vaccine reduced cases by 5.5 by year (p-value<0.001), but there was a significant interaction between the year of report and vaccination that smoothed this reduction (p-value<0.001). The reduction was not equal in all age groups, being greater in the 12-18 years age group, which fell from a mean rate of 8.15 per 100,000 person/years in the pre-vaccination period (1992-1998) to 1.4 in the vaccination period (1999-2005). The model predicts the evolution accurately for the group of vaccinated subjects. Negative binomial regression is more appropriate than Poisson regression when observed variance exceeds the observed mean (overdispersed count data), can cause a variable apparently contribute more on the model of what really makes it.

Modelling female fertility traits in beef cattle using linear and non-linear models.

PubMed

Naya, H; Peñagaricano, F; Urioste, J I

2017-06-01

Female fertility traits are key components of the profitability of beef cattle production. However, these traits are difficult and expensive to measure, particularly under extensive pastoral conditions, and consequently, fertility records are in general scarce and somehow incomplete. Moreover, fertility traits are usually dominated by the effects of herd-year environment, and it is generally assumed that relatively small margins are kept for genetic improvement. New ways of modelling genetic variation in these traits are needed. Inspired in the methodological developments made by Prof. Daniel Gianola and co-workers, we assayed linear (Gaussian), Poisson, probit (threshold), censored Poisson and censored Gaussian models to three different kinds of endpoints, namely calving success (CS), number of days from first calving (CD) and number of failed oestrus (FE). For models involving FE and CS, non-linear models overperformed their linear counterparts. For models derived from CD, linear versions displayed better adjustment than the non-linear counterparts. Non-linear models showed consistently higher estimates of heritability and repeatability in all cases (h 2  < 0.08 and r < 0.13, for linear models; h 2  > 0.23 and r > 0.24, for non-linear models). While additive and permanent environment effects showed highly favourable correlations between all models (>0.789), consistency in selecting the 10% best sires showed important differences, mainly amongst the considered endpoints (FE, CS and CD). In consequence, endpoints should be considered as modelling different underlying genetic effects, with linear models more appropriate to describe CD and non-linear models better for FE and CS. © 2017 Blackwell Verlag GmbH.

Acceleration of Linear Finite-Difference Poisson-Boltzmann Methods on Graphics Processing Units.

PubMed

Qi, Ruxi; Botello-Smith, Wesley M; Luo, Ray

2017-07-11

Electrostatic interactions play crucial roles in biophysical processes such as protein folding and molecular recognition. Poisson-Boltzmann equation (PBE)-based models have emerged as widely used in modeling these important processes. Though great efforts have been put into developing efficient PBE numerical models, challenges still remain due to the high dimensionality of typical biomolecular systems. In this study, we implemented and analyzed commonly used linear PBE solvers for the ever-improving graphics processing units (GPU) for biomolecular simulations, including both standard and preconditioned conjugate gradient (CG) solvers with several alternative preconditioners. Our implementation utilizes the standard Nvidia CUDA libraries cuSPARSE, cuBLAS, and CUSP. Extensive tests show that good numerical accuracy can be achieved given that the single precision is often used for numerical applications on GPU platforms. The optimal GPU performance was observed with the Jacobi-preconditioned CG solver, with a significant speedup over standard CG solver on CPU in our diversified test cases. Our analysis further shows that different matrix storage formats also considerably affect the efficiency of different linear PBE solvers on GPU, with the diagonal format best suited for our standard finite-difference linear systems. Further efficiency may be possible with matrix-free operations and integrated grid stencil setup specifically tailored for the banded matrices in PBE-specific linear systems.

Nonlocal and nonlinear electrostatics of a dipolar Coulomb fluid.

PubMed

Sahin, Buyukdagli; Ralf, Blossey

2014-07-16

We study a model Coulomb fluid consisting of dipolar solvent molecules of finite extent which generalizes the point-like dipolar Poisson-Boltzmann model (DPB) previously introduced by Coalson and Duncan (1996 J. Phys. Chem. 100 2612) and Abrashkin et al (2007 Phys. Rev. Lett. 99 077801). We formulate a nonlocal Poisson-Boltzmann equation (NLPB) and study both linear and nonlinear dielectric response in this model for the case of a single plane geometry. Our results shed light on the relevance of nonlocal versus nonlinear effects in continuum models of material electrostatics.

Maslov indices, Poisson brackets, and singular differential forms

NASA Astrophysics Data System (ADS)

Esterlis, I.; Haggard, H. M.; Hedeman, A.; Littlejohn, R. G.

2014-06-01

Maslov indices are integers that appear in semiclassical wave functions and quantization conditions. They are often notoriously difficult to compute. We present methods of computing the Maslov index that rely only on typically elementary Poisson brackets and simple linear algebra. We also present a singular differential form, whose integral along a curve gives the Maslov index of that curve. The form is closed but not exact, and transforms by an exact differential under canonical transformations. We illustrate the method with the 6j-symbol, which is important in angular-momentum theory and in quantum gravity.

Equivalence of MAXENT and Poisson point process models for species distribution modeling in ecology.

PubMed

Renner, Ian W; Warton, David I

2013-03-01

Modeling the spatial distribution of a species is a fundamental problem in ecology. A number of modeling methods have been developed, an extremely popular one being MAXENT, a maximum entropy modeling approach. In this article, we show that MAXENT is equivalent to a Poisson regression model and hence is related to a Poisson point process model, differing only in the intercept term, which is scale-dependent in MAXENT. We illustrate a number of improvements to MAXENT that follow from these relations. In particular, a point process model approach facilitates methods for choosing the appropriate spatial resolution, assessing model adequacy, and choosing the LASSO penalty parameter, all currently unavailable to MAXENT. The equivalence result represents a significant step in the unification of the species distribution modeling literature. Copyright © 2013, The International Biometric Society.

A multiscale filter for noise reduction of low-dose cone beam projections.

PubMed

Yao, Weiguang; Farr, Jonathan B

2015-08-21

The Poisson or compound Poisson process governs the randomness of photon fluence in cone beam computed tomography (CBCT) imaging systems. The probability density function depends on the mean (noiseless) of the fluence at a certain detector. This dependence indicates the natural requirement of multiscale filters to smooth noise while preserving structures of the imaged object on the low-dose cone beam projection. In this work, we used a Gaussian filter, exp(-x2/2σ(2)(f)) as the multiscale filter to de-noise the low-dose cone beam projections. We analytically obtained the expression of σ(f), which represents the scale of the filter, by minimizing local noise-to-signal ratio. We analytically derived the variance of residual noise from the Poisson or compound Poisson processes after Gaussian filtering. From the derived analytical form of the variance of residual noise, optimal σ(2)(f)) is proved to be proportional to the noiseless fluence and modulated by local structure strength expressed as the linear fitting error of the structure. A strategy was used to obtain the reliable linear fitting error: smoothing the projection along the longitudinal direction to calculate the linear fitting error along the lateral direction and vice versa. The performance of our multiscale filter was examined on low-dose cone beam projections of a Catphan phantom and a head-and-neck patient. After performing the filter on the Catphan phantom projections scanned with pulse time 4 ms, the number of visible line pairs was similar to that scanned with 16 ms, and the contrast-to-noise ratio of the inserts was higher than that scanned with 16 ms about 64% in average. For the simulated head-and-neck patient projections with pulse time 4 ms, the visibility of soft tissue structures in the patient was comparable to that scanned with 20 ms. The image processing took less than 0.5 s per projection with 1024   ×   768 pixels.

Performance of Nonlinear Finite-Difference Poisson-Boltzmann Solvers

PubMed Central

Cai, Qin; Hsieh, Meng-Juei; Wang, Jun; Luo, Ray

2014-01-01

We implemented and optimized seven finite-difference solvers for the full nonlinear Poisson-Boltzmann equation in biomolecular applications, including four relaxation methods, one conjugate gradient method, and two inexact Newton methods. The performance of the seven solvers was extensively evaluated with a large number of nucleic acids and proteins. Worth noting is the inexact Newton method in our analysis. We investigated the role of linear solvers in its performance by incorporating the incomplete Cholesky conjugate gradient and the geometric multigrid into its inner linear loop. We tailored and optimized both linear solvers for faster convergence rate. In addition, we explored strategies to optimize the successive over-relaxation method to reduce its convergence failures without too much sacrifice in its convergence rate. Specifically we attempted to adaptively change the relaxation parameter and to utilize the damping strategy from the inexact Newton method to improve the successive over-relaxation method. Our analysis shows that the nonlinear methods accompanied with a functional-assisted strategy, such as the conjugate gradient method and the inexact Newton method, can guarantee convergence in the tested molecules. Especially the inexact Newton method exhibits impressive performance when it is combined with highly efficient linear solvers that are tailored for its special requirement. PMID:24723843

Study of non-Hodgkin's lymphoma mortality associated with industrial pollution in Spain, using Poisson models

PubMed Central

Ramis, Rebeca; Vidal, Enrique; García-Pérez, Javier; Lope, Virginia; Aragonés, Nuria; Pérez-Gómez, Beatriz; Pollán, Marina; López-Abente, Gonzalo

2009-01-01

Background Non-Hodgkin's lymphomas (NHLs) have been linked to proximity to industrial areas, but evidence regarding the health risk posed by residence near pollutant industries is very limited. The European Pollutant Emission Register (EPER) is a public register that furnishes valuable information on industries that release pollutants to air and water, along with their geographical location. This study sought to explore the relationship between NHL mortality in small areas in Spain and environmental exposure to pollutant emissions from EPER-registered industries, using three Poisson-regression-based mathematical models. Methods Observed cases were drawn from mortality registries in Spain for the period 1994–2003. Industries were grouped into the following sectors: energy; metal; mineral; organic chemicals; waste; paper; food; and use of solvents. Populations having an industry within a radius of 1, 1.5, or 2 kilometres from the municipal centroid were deemed to be exposed. Municipalities outside those radii were considered as reference populations. The relative risks (RRs) associated with proximity to pollutant industries were estimated using the following methods: Poisson Regression; mixed Poisson model with random provincial effect; and spatial autoregressive modelling (BYM model). Results Only proximity of paper industries to population centres (>2 km) could be associated with a greater risk of NHL mortality (mixed model: RR:1.24, 95% CI:1.09–1.42; BYM model: RR:1.21, 95% CI:1.01–1.45; Poisson model: RR:1.16, 95% CI:1.06–1.27). Spatial models yielded higher estimates. Conclusion The reported association between exposure to air pollution from the paper, pulp and board industry and NHL mortality is independent of the model used. Inclusion of spatial random effects terms in the risk estimate improves the study of associations between environmental exposures and mortality. The EPER could be of great utility when studying the effects of industrial pollution on the health of the population. PMID:19159450

DL_MG: A Parallel Multigrid Poisson and Poisson-Boltzmann Solver for Electronic Structure Calculations in Vacuum and Solution.

PubMed

Womack, James C; Anton, Lucian; Dziedzic, Jacek; Hasnip, Phil J; Probert, Matt I J; Skylaris, Chris-Kriton

2018-03-13

The solution of the Poisson equation is a crucial step in electronic structure calculations, yielding the electrostatic potential-a key component of the quantum mechanical Hamiltonian. In recent decades, theoretical advances and increases in computer performance have made it possible to simulate the electronic structure of extended systems in complex environments. This requires the solution of more complicated variants of the Poisson equation, featuring nonhomogeneous dielectric permittivities, ionic concentrations with nonlinear dependencies, and diverse boundary conditions. The analytic solutions generally used to solve the Poisson equation in vacuum (or with homogeneous permittivity) are not applicable in these circumstances, and numerical methods must be used. In this work, we present DL_MG, a flexible, scalable, and accurate solver library, developed specifically to tackle the challenges of solving the Poisson equation in modern large-scale electronic structure calculations on parallel computers. Our solver is based on the multigrid approach and uses an iterative high-order defect correction method to improve the accuracy of solutions. Using two chemically relevant model systems, we tested the accuracy and computational performance of DL_MG when solving the generalized Poisson and Poisson-Boltzmann equations, demonstrating excellent agreement with analytic solutions and efficient scaling to ∼10 9 unknowns and 100s of CPU cores. We also applied DL_MG in actual large-scale electronic structure calculations, using the ONETEP linear-scaling electronic structure package to study a 2615 atom protein-ligand complex with routinely available computational resources. In these calculations, the overall execution time with DL_MG was not significantly greater than the time required for calculations using a conventional FFT-based solver.

The analysis of incontinence episodes and other count data in patients with overactive bladder by Poisson and negative binomial regression.

PubMed

Martina, R; Kay, R; van Maanen, R; Ridder, A

2015-01-01

Clinical studies in overactive bladder have traditionally used analysis of covariance or nonparametric methods to analyse the number of incontinence episodes and other count data. It is known that if the underlying distributional assumptions of a particular parametric method do not hold, an alternative parametric method may be more efficient than a nonparametric one, which makes no assumptions regarding the underlying distribution of the data. Therefore, there are advantages in using methods based on the Poisson distribution or extensions of that method, which incorporate specific features that provide a modelling framework for count data. One challenge with count data is overdispersion, but methods are available that can account for this through the introduction of random effect terms in the modelling, and it is this modelling framework that leads to the negative binomial distribution. These models can also provide clinicians with a clearer and more appropriate interpretation of treatment effects in terms of rate ratios. In this paper, the previously used parametric and non-parametric approaches are contrasted with those based on Poisson regression and various extensions in trials evaluating solifenacin and mirabegron in patients with overactive bladder. In these applications, negative binomial models are seen to fit the data well. Copyright © 2014 John Wiley & Sons, Ltd.

Validation of Statistical Models for Estimating Hospitalization Associated with Influenza and Other Respiratory Viruses

PubMed Central

Chan, King-Pan; Chan, Kwok-Hung; Wong, Wilfred Hing-Sang; Peiris, J. S. Malik; Wong, Chit-Ming

2011-01-01

Background Reliable estimates of disease burden associated with respiratory viruses are keys to deployment of preventive strategies such as vaccination and resource allocation. Such estimates are particularly needed in tropical and subtropical regions where some methods commonly used in temperate regions are not applicable. While a number of alternative approaches to assess the influenza associated disease burden have been recently reported, none of these models have been validated with virologically confirmed data. Even fewer methods have been developed for other common respiratory viruses such as respiratory syncytial virus (RSV), parainfluenza and adenovirus. Methods and Findings We had recently conducted a prospective population-based study of virologically confirmed hospitalization for acute respiratory illnesses in persons <18 years residing in Hong Kong Island. Here we used this dataset to validate two commonly used models for estimation of influenza disease burden, namely the rate difference model and Poisson regression model, and also explored the applicability of these models to estimate the disease burden of other respiratory viruses. The Poisson regression models with different link functions all yielded estimates well correlated with the virologically confirmed influenza associated hospitalization, especially in children older than two years. The disease burden estimates for RSV, parainfluenza and adenovirus were less reliable with wide confidence intervals. The rate difference model was not applicable to RSV, parainfluenza and adenovirus and grossly underestimated the true burden of influenza associated hospitalization. Conclusion The Poisson regression model generally produced satisfactory estimates in calculating the disease burden of respiratory viruses in a subtropical region such as Hong Kong. PMID:21412433

Systematic review of treatment modalities for gingival depigmentation: a random-effects poisson regression analysis.

PubMed

Lin, Yi Hung; Tu, Yu Kang; Lu, Chun Tai; Chung, Wen Chen; Huang, Chiung Fang; Huang, Mao Suan; Lu, Hsein Kun

2014-01-01

Repigmentation variably occurs with different treatment methods in patients with gingival pigmentation. A systemic review was conducted of various treatment modalities for eliminating melanin pigmentation of the gingiva, comprising bur abrasion, scalpel surgery, cryosurgery, electrosurgery, gingival grafts, and laser techniques, to compare the recurrence rates (Rrs) of these treatment procedures. Electronic databases, including PubMed, Web of Science, Google, and Medline were comprehensively searched, and manual searches were conducted for studies published from January 1951 to June 2013. After applying inclusion and exclusion criteria, the final list of articles was reviewed in depth to achieve the objectives of this review. A Poisson regression was used to analyze the outcome of depigmentation using the various treatment methods. The systematic review was based on case reports mainly. In total, 61 eligible publications met the defined criteria. The various therapeutic procedures showed variable clinical results with a wide range of Rrs. A random-effects Poisson regression showed that cryosurgery (Rr = 0.32%), electrosurgery (Rr = 0.74%), and laser depigmentation (Rr = 1.16%) yielded superior result, whereas bur abrasion yielded the highest Rr (8.89%). Within the limit of the sampling level, the present evidence-based results show that cryosurgery exhibits the optimal predictability for depigmentation of the gingiva among all procedures examined, followed by electrosurgery and laser techniques. It is possible to treat melanin pigmentation of the gingiva with various methods and prevent repigmentation. Among those treatment modalities, cryosurgery, electrosurgery, and laser surgery appear to be the best choices for treating gingival pigmentation. © 2014 Wiley Periodicals, Inc.

Disability rates for cardiovascular and psychological disorders among autoworkers by job category, facility type, and facility overtime hours.

PubMed

Landsbergis, Paul A; Janevic, Teresa; Rothenberg, Laura; Adamu, Mohammed T; Johnson, Sylvia; Mirer, Franklin E

2013-07-01

We examined the association between long work hours, assembly line work and stress-related diseases utilizing objective health and employment data from an employer's administrative databases. A North American automobile manufacturing company provided data for claims for sickness, accident and disability insurance (work absence of at least 4 days) for cardiovascular disease (CVD), hypertension and psychological disorders, employee demographics, and facility hours worked per year for 1996-2001. Age-adjusted claim rates and age-adjusted rate ratios were calculated using Poisson regression, except for comparisons between production and skilled trades workers owing to lack of age denominator data by job category. Associations between overtime hours and claim rates by facility were examined by Poisson regression and multi-level Poisson regression. Claims for hypertension, coronary heart disease, CVD, and psychological disorders were associated with facility overtime hours. We estimate that a facility with 10 more overtime hours per week than another facility would have 4.36 more claims for psychological disorders, 2.33 more claims for CVD, and 3.29 more claims for hypertension per 1,000 employees per year. Assembly plants had the highest rates of claims for most conditions. Production workers tended to have higher rates of claims than skilled trades workers. Data from an auto manufacturer's administrative databases suggest that autoworkers working long hours, and assembly-line workers relative to skilled trades workers or workers in non-assembly facilities, have a higher risk of hypertension, CVD, and psychological disorders. Occupational disease surveillance and disease prevention programs need to fully utilize such administrative data. Copyright © 2013 Wiley Periodicals, Inc.

Accounting for autocorrelation in multi-drug resistant tuberculosis predictors using a set of parsimonious orthogonal eigenvectors aggregated in geographic space.

PubMed

Jacob, Benjamin J; Krapp, Fiorella; Ponce, Mario; Gottuzzo, Eduardo; Griffith, Daniel A; Novak, Robert J

2010-05-01

Spatial autocorrelation is problematic for classical hierarchical cluster detection tests commonly used in multi-drug resistant tuberculosis (MDR-TB) analyses as considerable random error can occur. Therefore, when MDRTB clusters are spatially autocorrelated the assumption that the clusters are independently random is invalid. In this research, a product moment correlation coefficient (i.e., the Moran's coefficient) was used to quantify local spatial variation in multiple clinical and environmental predictor variables sampled in San Juan de Lurigancho, Lima, Peru. Initially, QuickBird 0.61 m data, encompassing visible bands and the near infra-red bands, were selected to synthesize images of land cover attributes of the study site. Data of residential addresses of individual patients with smear-positive MDR-TB were geocoded, prevalence rates calculated and then digitally overlaid onto the satellite data within a 2 km buffer of 31 georeferenced health centers, using a 10 m2 grid-based algorithm. Geographical information system (GIS)-gridded measurements of each health center were generated based on preliminary base maps of the georeferenced data aggregated to block groups and census tracts within each buffered area. A three-dimensional model of the study site was constructed based on a digital elevation model (DEM) to determine terrain covariates associated with the sampled MDR-TB covariates. Pearson's correlation was used to evaluate the linear relationship between the DEM and the sampled MDR-TB data. A SAS/GIS(R) module was then used to calculate univariate statistics and to perform linear and non-linear regression analyses using the sampled predictor variables. The estimates generated from a global autocorrelation analyses were then spatially decomposed into empirical orthogonal bases using a negative binomial regression with a non-homogeneous mean. Results of the DEM analyses indicated a statistically non-significant, linear relationship between georeferenced health centers and the sampled covariate elevation. The data exhibited positive spatial autocorrelation and the decomposition of Moran's coefficient into uncorrelated, orthogonal map pattern components revealed global spatial heterogeneities necessary to capture latent autocorrelation in the MDR-TB model. It was thus shown that Poisson regression analyses and spatial eigenvector mapping can elucidate the mechanics of MDR-TB transmission by prioritizing clinical and environmental-sampled predictor variables for identifying high risk populations.

Zero-inflated Poisson regression models for QTL mapping applied to tick-resistance in a Gyr × Holstein F2 population

PubMed Central

Silva, Fabyano Fonseca; Tunin, Karen P.; Rosa, Guilherme J.M.; da Silva, Marcos V.B.; Azevedo, Ana Luisa Souza; da Silva Verneque, Rui; Machado, Marco Antonio; Packer, Irineu Umberto

2011-01-01

Now a days, an important and interesting alternative in the control of tick-infestation in cattle is to select resistant animals, and identify the respective quantitative trait loci (QTLs) and DNA markers, for posterior use in breeding programs. The number of ticks/animal is characterized as a discrete-counting trait, which could potentially follow Poisson distribution. However, in the case of an excess of zeros, due to the occurrence of several noninfected animals, zero-inflated Poisson and generalized zero-inflated distribution (GZIP) may provide a better description of the data. Thus, the objective here was to compare through simulation, Poisson and ZIP models (simple and generalized) with classical approaches, for QTL mapping with counting phenotypes under different scenarios, and to apply these approaches to a QTL study of tick resistance in an F2 cattle (Gyr × Holstein) population. It was concluded that, when working with zero-inflated data, it is recommendable to use the generalized and simple ZIP model for analysis. On the other hand, when working with data with zeros, but not zero-inflated, the Poisson model or a data-transformation-approach, such as square-root or Box-Cox transformation, are applicable. PMID:22215960

Logistic quantile regression provides improved estimates for bounded avian counts: a case study of California Spotted Owl fledgling production

Treesearch

Brian S. Cade; Barry R. Noon; Rick D. Scherer; John J. Keane

2017-01-01

Counts of avian fledglings, nestlings, or clutch size that are bounded below by zero and above by some small integer form a discrete random variable distribution that is not approximated well by conventional parametric count distributions such as the Poisson or negative binomial. We developed a logistic quantile regression model to provide estimates of the empirical...

Multiparameter linear least-squares fitting to Poisson data one count at a time

NASA Technical Reports Server (NTRS)

Wheaton, Wm. A.; Dunklee, Alfred L.; Jacobsen, Allan S.; Ling, James C.; Mahoney, William A.; Radocinski, Robert G.

1995-01-01

A standard problem in gamma-ray astronomy data analysis is the decomposition of a set of observed counts, described by Poisson statistics, according to a given multicomponent linear model, with underlying physical count rates or fluxes which are to be estimated from the data. Despite its conceptual simplicity, the linear least-squares (LLSQ) method for solving this problem has generally been limited to situations in which the number n(sub i) of counts in each bin i is not too small, conventionally more than 5-30. It seems to be widely believed that the failure of the LLSQ method for small counts is due to the failure of the Poisson distribution to be even approximately normal for small numbers. The cause is more accurately the strong anticorrelation between the data and the wieghts w(sub i) in the weighted LLSQ method when square root of n(sub i) instead of square root of bar-n(sub i) is used to approximate the uncertainties, sigma(sub i), in the data, where bar-n(sub i) = E(n(sub i)), the expected value of N(sub i). We show in an appendix that, avoiding this approximation, the correct equations for the Poisson LLSQ (PLLSQ) problems are actually identical to those for the maximum likelihood estimate using the exact Poisson distribution. We apply the method to solve a problem in high-resolution gamma-ray spectroscopy for the JPL High-Resolution Gamma-Ray Spectrometer flown on HEAO 3. Systematic error in subtracting the strong, highly variable background encountered in the low-energy gamma-ray region can be significantly reduced by closely pairing source and background data in short segments. Significant results can be built up by weighted averaging of the net fluxes obtained from the subtraction of many individual source/background pairs. Extension of the approach to complex situations, with multiple cosmic sources and realistic background parameterizations, requires a means of efficiently fitting to data from single scans in the narrow (approximately = 1.2 keV, HEAO 3) energy channels of a Ge spectrometer, where the expected number of counts obtained per scan may be very low. Such an analysis system is discussed and compared to the method previously used.

Symmetries of the Space of Linear Symplectic Connections

NASA Astrophysics Data System (ADS)

Fox, Daniel J. F.

2017-01-01

There is constructed a family of Lie algebras that act in a Hamiltonian way on the symplectic affine space of linear symplectic connections on a symplectic manifold. The associated equivariant moment map is a formal sum of the Cahen-Gutt moment map, the Ricci tensor, and a translational term. The critical points of a functional constructed from it interpolate between the equations for preferred symplectic connections and the equations for critical symplectic connections. The commutative algebra of formal sums of symmetric tensors on a symplectic manifold carries a pair of compatible Poisson structures, one induced from the canonical Poisson bracket on the space of functions on the cotangent bundle polynomial in the fibers, and the other induced from the algebraic fiberwise Schouten bracket on the symmetric algebra of each fiber of the cotangent bundle. These structures are shown to be compatible, and the required Lie algebras are constructed as central extensions of their! linear combinations restricted to formal sums of symmetric tensors whose first order term is a multiple of the differential of its zeroth order term.

Impact of the 1990 Hong Kong legislation for restriction on sulfur content in fuel.

PubMed

Wong, Chit-Ming; Rabl, Ari; Thach, Thuan Q; Chau, Yuen Kwan; Chan, King Pan; Cowling, Benjamin J; Lai, Hak Kan; Lam, Tai Hing; McGhee, Sarah M; Anderson, H Ross; Hedley, Anthony J

2012-08-01

After the implementation of a regulation restricting sulfur to 0.5% by weight in fuel on July 1, 1990, in Hong Kong, sulfur dioxide (SO2*) levels fell by 45% on average and as much as 80% in the most polluted districts (Hedley et al. 2002). In addition, a reduction of respiratory symptoms and an improvement in bronchial hyperresponsiveness in children were observed (Peters et al. 1996; Wong et al. 1998). A recent time-series study (Hedley et al. 2002) found an immediate reduction in mortality during the cool season at six months after the intervention, followed by an increase in cool-season mortality in the second and third years, suggesting that the reduction in pollution was associated with a delay in mortality. Proportional changes in mortality trends between the 5-year periods before and after the intervention were measured as relative risks and used to assess gains in life expectancy using the life table method (Hedley et al. 2002). To further explore the relation between changes in pollution-related mortality before and after the intervention, our study had three objectives: (1) to evaluate the short-term effects on mortality of changes in the pollutant mix after the Hong Kong sulfur intervention, particularly with changes in the particulate matter (PM) chemical species; (2) to improve the methodology for assessment of the health impact in terms of changes in life expectancy using linear regression models; and (3) to develop an approach for analyzing changes in life expectancy from Poisson regression models. A fourth overarching objective was to determine the relation between short- and long-term benefits due to an improvement in air quality. For an assessment of the short-term effects on mortality due to changes in the pollutant mix, we developed Poisson regression Core Models with natural spline smoothers to control for long-term and seasonal confounding variations in the mortality counts and with covariates to adjust for temperature (T) and relative humidity (RH). We assessed the adequacy of the Core Models by evaluating the results against the Akaike Information Criterion, which stipulates that, at a minimum, partial autocorrelation plots should be between -0.1 and 0.1, and by examining the residual plots to make sure they were free from patterns. We assessed the effects for gaseous pollutants (NO2, SO2, and O3), PM with an aerodynamic diameter < or = 10 microm (PM10), and its chemical species (aluminum [Al], iron [Fe], manganese [Mn], nickel [Ni], vanadium [V], lead [Pb], and zinc [Zn]) using the Core Models, which were developed for the periods 5 years (or 2 years in the case of the sensitivity analysis) before and 5 years after the intervention, as well as in the10-year (or 7-year in the case of the sensitivity analysis) period pre- and post-intervention. We also included an indicator to separate the pre- and post-intervention periods, as well as the product of the indicator with an air pollution concentration variable. The health outcomes were mortality for all natural causes and for cardiovascular and respiratory causes, at all ages and in the 65 years or older age group. To assess the short- and long-term effects, we developed two methods: one using linear regression models reflecting the age-standardized mortality rate D(j) at day j, divided by a reference D(ref); and the other using Poisson regression models with daily mortality counts as the outcome variables. We also used both models to evaluate the relation between outcome variables and daily air pollution concentrations in the current day up to all previous days in the past 3 to 4 years. In the linear regression approach, we adjusted the data for temperature and relative humidity. We then removed season as a potential confounder, or deseasonalized them, by calculating a standard seasonal mortality rate profile, normalized to an annual average of unity, and dividing the mortality rates by this profile. Finally, to correct for long-term trends, we calculated a reference mortality rate D(ref)(j) as a moving average of the corrected and deseasonalized D(j) over the observation window. Then we regressed the outcome variable D(j)/D(ref) on an entire exposure sequence {c(i)} with lags up to 4 years in order to obtain impact coefficient f(i) from the regression model shown below: deltaD(j)/D (ref) = i(max)sigma f(i) c(j - i)(i = 0). The change in life expectancy (LE) for a change of units (deltac) in the concentration of pollutants on T(day)--representing the short interval (i.e., a day)--was calculated from the following equation (deltaL(pop) = average loss in life expectancy of an entire population): deltaL(pop) = -deltac T(day) infinity sigma (j = 0) infinity sigma f(i) (i = 0). In the Poisson regression approach, we fitted a distributed-lag model for exposure to previous days of up to 4 years in order to obtain the cumulative lag effect sigma beta(i). We fit the linear regression model of log(LE*/LE) = gamma(SMR - 1) + alpha to estimate the parameter gamma by gamma, where LE* and LE are life expectancy for an exposed and an unexposed population, respectively, and SMR represents the standardized mortality ratio. The life expectancy change per Ac increase in concentration is LE {exp[gamma delta c(sigma beta(i))]-1}. In our assessment of the changes in pollutant levels, the mean levels of SO2, Ni, and V showed a statistically significant decline, particularly in industrial areas. Ni and V showed the greatest impact on mortality, especially for respiratory diseases in the 5-year pre-intervention period for both the all-ages and 65+ groups among all chemical species. There were decreases in excess risks associated with Ni and V after the intervention, but they were nonsignificant. Using the linear regression approach, with a window of 1095 days (3 years), the losses in life expectancy with a 10-microg/m3 increase in concentrations, using two methods of estimation (one with adjustment for temperature and RH before the regression against pollutants, the other with adjustment for temperature and RH within the regression against pollutants), were 19.2 days (95% CI, 12.5 to 25.9) and 31.4 days (95% CI, 25.6 to 37.2) for PM10; and 19.7 days (95% CI, 15.2 to 24.2) and 12.8 days (95% CI, 8.9 to 16.8) for SO2. The losses in life expectancy in the current study were smaller than the ones implied by Elliott and colleagues (2007) and Pope and colleagues (2002) as expected since the observation window in our study was only 3 years whereas these other studies had windows of 16 years. In particular, the coefficients used by Elliott and colleagues (2007) for windows of 12 and 16 years were non-zero, which suggests that our window of at most 3 years cannot capture the full life expectancy loss and the effects were most likely underestimated. Using the Poisson regression approach, with a window of 1461 days (4 years), we found that a 10-microg/m3 increase in concentration of PM10 was associated with a change in life expectancy of -69 days (95% CI, -140 to 1) and a change of -133 days (95% CI, -172 to -94) for the same increase in SO2. The effect estimates varied as expected according to most variations in the sensitivity analysis model, specifically in terms of the Core Model definition, exposure windows, constraint of the lag effect pattern, and adjustment for smoking prevalence or socioeconomic status. Our results on the excess risks of mortality showed exposure to chemical species to be a health hazard. However, the statistical power was not sufficient to detect the differences between the pre- and post-intervention periods in Hong Kong due to the data limitations (specifically, the chemical species data were available only once every 6 days, and data were not available from some monitoring stations). Further work is needed to develop methods for maximizing the information from the data in order to assess any changes in effects due to the intervention. With complete daily air pollution and mortality data over a long period, time-series analysis methods can be applied to assess the short- and long-term effects of air pollution, in terms of changes in life expectancy. Further work is warranted to assess the duration and pattern of the health effects from an air pollution pulse (i.e., an episode of a rapid rise in air pollution) so as to determine an appropriate length and constraint on the distributed-lag assessment model.

A stochastic model for stationary dynamics of prices in real estate markets. A case of random intensity for Poisson moments of prices changes

NASA Astrophysics Data System (ADS)

Rusakov, Oleg; Laskin, Michael

2017-06-01

We consider a stochastic model of changes of prices in real estate markets. We suppose that in a book of prices the changes happen in points of jumps of a Poisson process with a random intensity, i.e. moments of changes sequently follow to a random process of the Cox process type. We calculate cumulative mathematical expectations and variances for the random intensity of this point process. In the case that the process of random intensity is a martingale the cumulative variance has a linear grows. We statistically process a number of observations of real estate prices and accept hypotheses of a linear grows for estimations as well for cumulative average, as for cumulative variance both for input and output prises that are writing in the book of prises.

Statistical analysis of excitation energies in actinide and rare-earth nuclei

NASA Astrophysics Data System (ADS)

Levon, A. I.; Magner, A. G.; Radionov, S. V.

2018-04-01

Statistical analysis of distributions of the collective states in actinide and rare-earth nuclei is performed in terms of the nearest-neighbor spacing distribution (NNSD). Several approximations, such as the linear approach to the level repulsion density and that suggested by Brody to the NNSDs were applied for the analysis. We found an intermediate character of the experimental spectra between the order and the chaos for a number of rare-earth and actinide nuclei. The spectra are closer to the Wigner distribution for energies limited by 3 MeV, and to the Poisson distribution for data including higher excitation energies and higher spins. The latter result is in agreement with the theoretical calculations. These features are confirmed by the cumulative distributions, where the Wigner contribution dominates at smaller spacings while the Poisson one is more important at larger spacings, and our linear approach improves the comparison with experimental data at all desired spacings.

Poisson, Poisson-gamma and zero-inflated regression models of motor vehicle crashes: balancing statistical fit and theory.

PubMed

Lord, Dominique; Washington, Simon P; Ivan, John N

2005-01-01

There has been considerable research conducted over the last 20 years focused on predicting motor vehicle crashes on transportation facilities. The range of statistical models commonly applied includes binomial, Poisson, Poisson-gamma (or negative binomial), zero-inflated Poisson and negative binomial models (ZIP and ZINB), and multinomial probability models. Given the range of possible modeling approaches and the host of assumptions with each modeling approach, making an intelligent choice for modeling motor vehicle crash data is difficult. There is little discussion in the literature comparing different statistical modeling approaches, identifying which statistical models are most appropriate for modeling crash data, and providing a strong justification from basic crash principles. In the recent literature, it has been suggested that the motor vehicle crash process can successfully be modeled by assuming a dual-state data-generating process, which implies that entities (e.g., intersections, road segments, pedestrian crossings, etc.) exist in one of two states-perfectly safe and unsafe. As a result, the ZIP and ZINB are two models that have been applied to account for the preponderance of "excess" zeros frequently observed in crash count data. The objective of this study is to provide defensible guidance on how to appropriate model crash data. We first examine the motor vehicle crash process using theoretical principles and a basic understanding of the crash process. It is shown that the fundamental crash process follows a Bernoulli trial with unequal probability of independent events, also known as Poisson trials. We examine the evolution of statistical models as they apply to the motor vehicle crash process, and indicate how well they statistically approximate the crash process. We also present the theory behind dual-state process count models, and note why they have become popular for modeling crash data. A simulation experiment is then conducted to demonstrate how crash data give rise to "excess" zeros frequently observed in crash data. It is shown that the Poisson and other mixed probabilistic structures are approximations assumed for modeling the motor vehicle crash process. Furthermore, it is demonstrated that under certain (fairly common) circumstances excess zeros are observed-and that these circumstances arise from low exposure and/or inappropriate selection of time/space scales and not an underlying dual state process. In conclusion, carefully selecting the time/space scales for analysis, including an improved set of explanatory variables and/or unobserved heterogeneity effects in count regression models, or applying small-area statistical methods (observations with low exposure) represent the most defensible modeling approaches for datasets with a preponderance of zeros.

The Poisson model limits in NBA basketball: Complexity in team sports

NASA Astrophysics Data System (ADS)

Martín-González, Juan Manuel; de Saá Guerra, Yves; García-Manso, Juan Manuel; Arriaza, Enrique; Valverde-Estévez, Teresa

2016-12-01

Team sports are frequently studied by researchers. There is presumption that scoring in basketball is a random process and that can be described using the Poisson Model. Basketball is a collaboration-opposition sport, where the non-linear local interactions among players are reflected in the evolution of the score that ultimately determines the winner. In the NBA, the outcomes of close games are often decided in the last minute, where fouls play a main role. We examined 6130 NBA games in order to analyze the time intervals between baskets and scoring dynamics. Most numbers of baskets (n) over a time interval (ΔT) follow a Poisson distribution, but some (e.g., ΔT = 10 s, n > 3) behave as a Power Law. The Poisson distribution includes most baskets in any game, in most game situations, but in close games in the last minute, the numbers of events are distributed following a Power Law. The number of events can be adjusted by a mixture of two distributions. In close games, both teams try to maintain their advantage solely in order to reach the last minute: a completely different game. For this reason, we propose to use the Poisson model as a reference. The complex dynamics will emerge from the limits of this model.

An examination of sources of sensitivity of consumer surplus estimates in travel cost models.

PubMed

Blaine, Thomas W; Lichtkoppler, Frank R; Bader, Timothy J; Hartman, Travis J; Lucente, Joseph E

2015-03-15

We examine sensitivity of estimates of recreation demand using the Travel Cost Method (TCM) to four factors. Three of the four have been routinely and widely discussed in the TCM literature: a) Poisson verses negative binomial regression; b) application of Englin correction to account for endogenous stratification; c) truncation of the data set to eliminate outliers. A fourth issue we address has not been widely modeled: the potential effect on recreation demand of the interaction between income and travel cost. We provide a straightforward comparison of all four factors, analyzing the impact of each on regression parameters and consumer surplus estimates. Truncation has a modest effect on estimates obtained from the Poisson models but a radical effect on the estimates obtained by way of the negative binomial. Inclusion of an income-travel cost interaction term generally produces a more conservative but not a statistically significantly different estimate of consumer surplus in both Poisson and negative binomial models. It also generates broader confidence intervals. Application of truncation, the Englin correction and the income-travel cost interaction produced the most conservative estimates of consumer surplus and eliminated the statistical difference between the Poisson and the negative binomial. Use of the income-travel cost interaction term reveals that for visitors who face relatively low travel costs, the relationship between income and travel demand is negative, while it is positive for those who face high travel costs. This provides an explanation of the ambiguities on the findings regarding the role of income widely observed in the TCM literature. Our results suggest that policies that reduce access to publicly owned resources inordinately impact local low income recreationists and are contrary to environmental justice. Copyright © 2014 Elsevier Ltd. All rights reserved.

Remote Sensing-Driven Climatic/Environmental Variables for Modelling Malaria Transmission in Sub-Saharan Africa.

PubMed

Ebhuoma, Osadolor; Gebreslasie, Michael

2016-06-14

Malaria is a serious public health threat in Sub-Saharan Africa (SSA), and its transmission risk varies geographically. Modelling its geographic characteristics is essential for identifying the spatial and temporal risk of malaria transmission. Remote sensing (RS) has been serving as an important tool in providing and assessing a variety of potential climatic/environmental malaria transmission variables in diverse areas. This review focuses on the utilization of RS-driven climatic/environmental variables in determining malaria transmission in SSA. A systematic search on Google Scholar and the Institute for Scientific Information (ISI) Web of Knowledge(SM) databases (PubMed, Web of Science and ScienceDirect) was carried out. We identified thirty-five peer-reviewed articles that studied the relationship between remotely-sensed climatic variable(s) and malaria epidemiological data in the SSA sub-regions. The relationship between malaria disease and different climatic/environmental proxies was examined using different statistical methods. Across the SSA sub-region, the normalized difference vegetation index (NDVI) derived from either the National Oceanic and Atmospheric Administration (NOAA) Advanced Very High Resolution Radiometer (AVHRR) or Moderate-resolution Imaging Spectrometer (MODIS) satellite sensors was most frequently returned as a statistically-significant variable to model both spatial and temporal malaria transmission. Furthermore, generalized linear models (linear regression, logistic regression and Poisson regression) were the most frequently-employed methods of statistical analysis in determining malaria transmission predictors in East, Southern and West Africa. By contrast, multivariate analysis was used in Central Africa. We stress that the utilization of RS in determining reliable malaria transmission predictors and climatic/environmental monitoring variables would require a tailored approach that will have cognizance of the geographical/climatic setting, the stage of malaria elimination continuum, the characteristics of the RS variables and the analytical approach, which in turn, would support the channeling of intervention resources sustainably.

Remote Sensing-Driven Climatic/Environmental Variables for Modelling Malaria Transmission in Sub-Saharan Africa

PubMed Central

Ebhuoma, Osadolor; Gebreslasie, Michael

2016-01-01

Malaria is a serious public health threat in Sub-Saharan Africa (SSA), and its transmission risk varies geographically. Modelling its geographic characteristics is essential for identifying the spatial and temporal risk of malaria transmission. Remote sensing (RS) has been serving as an important tool in providing and assessing a variety of potential climatic/environmental malaria transmission variables in diverse areas. This review focuses on the utilization of RS-driven climatic/environmental variables in determining malaria transmission in SSA. A systematic search on Google Scholar and the Institute for Scientific Information (ISI) Web of KnowledgeSM databases (PubMed, Web of Science and ScienceDirect) was carried out. We identified thirty-five peer-reviewed articles that studied the relationship between remotely-sensed climatic variable(s) and malaria epidemiological data in the SSA sub-regions. The relationship between malaria disease and different climatic/environmental proxies was examined using different statistical methods. Across the SSA sub-region, the normalized difference vegetation index (NDVI) derived from either the National Oceanic and Atmospheric Administration (NOAA) Advanced Very High Resolution Radiometer (AVHRR) or Moderate-resolution Imaging Spectrometer (MODIS) satellite sensors was most frequently returned as a statistically-significant variable to model both spatial and temporal malaria transmission. Furthermore, generalized linear models (linear regression, logistic regression and Poisson regression) were the most frequently-employed methods of statistical analysis in determining malaria transmission predictors in East, Southern and West Africa. By contrast, multivariate analysis was used in Central Africa. We stress that the utilization of RS in determining reliable malaria transmission predictors and climatic/environmental monitoring variables would require a tailored approach that will have cognizance of the geographical/climatic setting, the stage of malaria elimination continuum, the characteristics of the RS variables and the analytical approach, which in turn, would support the channeling of intervention resources sustainably. PMID:27314369

Modeling the Non-Linear Response of Fiber-Reinforced Laminates Using a Combined Damage/Plasticity Model

NASA Technical Reports Server (NTRS)

Schuecker, Clara; Davila, Carlos G.; Pettermann, Heinz E.

2008-01-01

The present work is concerned with modeling the non-linear response of fiber reinforced polymer laminates. Recent experimental data suggests that the non-linearity is not only caused by matrix cracking but also by matrix plasticity due to shear stresses. To capture the effects of those two mechanisms, a model combining a plasticity formulation with continuum damage has been developed to simulate the non-linear response of laminates under plane stress states. The model is used to compare the predicted behavior of various laminate lay-ups to experimental data from the literature by looking at the degradation of axial modulus and Poisson s ratio of the laminates. The influence of residual curing stresses and in-situ effect on the predicted response is also investigated. It is shown that predictions of the combined damage/plasticity model, in general, correlate well with the experimental data. The test data shows that there are two different mechanisms that can have opposite effects on the degradation of the laminate Poisson s ratio which is captured correctly by the damage/plasticity model. Residual curing stresses are found to have a minor influence on the predicted response for the cases considered here. Some open questions remain regarding the prediction of damage onset.

Environmental pollutants and stroke-related hospital admissions.

PubMed

Nascimento, Luiz Fernando Costa; Francisco, Juliana B; Patto, Marielle Beatriz R; Antunes, Angélica M

2012-07-01

Some effects of environmental pollution on human health are known, especially those affecting the respiratory and cardiovascular systems. The current study aimed to estimate these effects on the production of hospital admissions for stroke. This was an ecological study using hospital admissions data in São José dos Campos, São Paulo State, Brazil, with diagnosis of stroke, from January 1, 2007, to April 30, 2008. The target pollutants were particulate matter, sulfur dioxide, and ozone. Use of a Poisson linear regression model showed that same-day exposure to particulate matter was associated with hospitalization for stroke (RR = 1.013; 95%CI: 1.001-1.025). An increase of 10 µg/m(3) in this pollutant increased the risk of hospitalization by 12% (RR = 1.137; 95%CI: 1.014-1.276). In the multi-pollutant model, it was thus possible to identify particulate matter as associated with hospitalization for stroke in a medium-sized city like São José dos Campos.

The effectiveness of insurer-supported safety and health engineering controls in reducing workers' compensation claims and costs.

PubMed

Wurzelbacher, Steven J; Bertke, Stephen J; Lampl, Michael P; Bushnell, P Timothy; Meyers, Alysha R; Robins, David C; Al-Tarawneh, Ibraheem S

2014-12-01

This study evaluated the effectiveness of a program in which a workers' compensation (WC) insurer provided matching funds to insured employers to implement safety/health engineering controls. Pre- and post-intervention WC metrics were compiled for the employees designated as affected by the interventions within 468 employers for interventions occurring from 2003 to 2009. Poisson, two-part, and linear regression models with repeated measures were used to evaluate differences in pre- and post-data, controlling for time trends independent of the interventions. For affected employees, total WC claim frequency rates (both medical-only and lost-time claims) decreased 66%, lost-time WC claim frequency rates decreased 78%, WC paid cost per employee decreased 81%, and WC geometric mean paid claim cost decreased 30% post-intervention. Reductions varied by employer size, specific industry, and intervention type. The insurer-supported safety/health engineering control program was effective in reducing WC claims and costs for affected employees. © 2014 Wiley Periodicals, Inc.

Exploring the association of homicides in northern Mexico and healthcare access for US residents

PubMed Central

Geissler, Kimberley; Becker, Charles; Stearns, Sally; Thirumurthy, Harsha; Holmes, George M.

2016-01-01

Background Many legal residents in the United States (US)-Mexico border region cross from the US into Mexico for medical treatment and pharmaceuticals. We analyzed whether recent increases in homicides in Mexico are associated with reduced healthcare access for US border residents. Methods We used data on healthcare access, legal entries to the US from Mexico, and Mexican homicide rates (2002–2010). Poisson regression models estimated associations between homicide rates and total legal US entries. Multivariate difference-in-difference linear probability models evaluated associations between Mexican homicide rates and self-reported measures of healthcare access for US residents. Results Increased homicide rates were associated with decreased legal entries to the US from Mexico. Contrary to expectations, homicides did not have significant associations with healthcare access measures for legal residents in US border counties. Conclusions Despite a decrease in border crossings, increased violence in Mexico did not appear to negatively affect access for US border residents. PMID:24917240

Testing healthy immigrant effects among late life immigrants in the United States: using multiple indicators.

PubMed

Choi, Sunha H

2012-04-01

This study tested a healthy immigrant effect (HIE) and postimmigration health status changes among late life immigrants. Using three waves of the Second Longitudinal Study of Aging (1994-2000) and the linked mortality file through 2006, this study compared (a) chronic health conditions, (b) longitudinal trajectories of self-rated health, (c) longitudinal trajectories of functional impairments, and (d) mortality between three groups (age 70+): (i) late life immigrants with less than 15 years in the United States (n = 133), (ii) longer term immigrants (n = 672), and (iii) U.S.-born individuals (n = 8,642). Logistic and Poisson regression, hierarchical generalized linear modeling, and survival analyses were conducted. Late life immigrants were less likely to suffer from cancer, had lower numbers of chronic conditions at baseline, and displayed lower hazards of mortality during the 12-year follow-up. However, their self-rated health and functional status were worse than those of their counterparts over time. A HIE was only partially supported among older adults.

Global climate change: impact of diurnal temperature range on mortality in Guangzhou, China.

PubMed

Yang, Jun; Liu, Hua-Zhang; Ou, Chun-Quan; Lin, Guo-Zhen; Zhou, Qin; Shen, Gi-Chuan; Chen, Ping-Yan; Guo, Yuming

2013-04-01

Diurnal temperature range (DTR) is an important meteorological indicator associated with global climate change, but little is known about the effects of DTR on mortality. We examined the effects of DTR on cause-/age-/education-specific mortality in Guangzhou, a subtropical city in China during 2003-2010. A quasi-Poisson regression model combined with distributed lag non-linear model was used to examine the effects of DTR, after controlling for daily mean temperature, air pollutants, season and day of the week. A 1 °C increase in DTR at lag 0-4 days was associated with a 0.47% (95% confidence interval: 0.01%-0.93%) increase in non-accidental mortality. Stroke mortality was most sensitive to DTR. Female, the elderly and those with low education were more susceptible to DTR than male, the youth and those with high education, respectively. Our findings suggest that vulnerable subpopulations should pay more attention to protect themselves from unstable daily weather. Copyright © 2012 Elsevier Ltd. All rights reserved.

Potential impacts of climate variability on respiratory morbidity in children, infants, and adults.

PubMed

Souza, Amaury de; Fernandes, Widinei Alves; Pavão, Hamilton Germano; Lastoria, Giancarlo; Albrez, Edilce do Amaral

2012-01-01

To determine whether climate variability influences the number of hospitalizations for respiratory diseases in infants, children, and adults in the city of Campo Grande, Brazil. We used daily data on admissions for respiratory diseases, precipitation, air temperature, humidity, and wind speed for the 2004-2008 period. We calculated the thermal comfort index, effective temperature, and effective temperature with wind speed (wind-chill or heat index) using the meteorological data obtained. Generalized linear models, with Poisson multiple regression, were used in order to predict hospitalizations for respiratory disease. The variables studied were (collectively) found to show relatively high correlation coefficients in relation to hospital admission for pneumonia in children (R² = 68.4%), infants (R² = 71.8%), and adults (R² = 81.8%). Our results indicate a quantitative risk for an increase in the number of hospitalizations of children, infants, and adults, according to the increase or decrease in temperature, humidity, precipitation, wind speed, and thermal comfort index in the city under study.

The Effectiveness of Insurer-Supported Safety and Health Engineering Controls in Reducing Workers’ Compensation Claims and Costs

PubMed Central

Wurzelbacher, Steven J.; Bertke, Stephen J.; Lampl, Michael P.; Bushnell, P. Timothy; Meyers, Alysha R.; Robins, David C.; Al-Tarawneh, Ibraheem S.

2015-01-01

Background This study evaluated the effectiveness of a program in which a workers’ compensation (WC) insurer provided matching funds to insured employers to implement safety/health engineering controls. Methods Pre- and post-intervention WC metrics were compiled for the employees designated as affected by the interventions within 468 employers for interventions occurring from 2003 to 2009. Poisson, two-part, and linear regression models with repeated measures were used to evaluate differences in pre- and post-data, controlling for time trends independent of the interventions. Results For affected employees, total WC claim frequency rates (both medical-only and lost-time claims) decreased 66%, lost-time WC claim frequency rates decreased 78%, WC paid cost per employee decreased 81%, and WC geometric mean paid claim cost decreased 30% post-intervention. Reductions varied by employer size, specific industry, and intervention type. Conclusions The insurer-supported safety/health engineering control program was effective in reducing WC claims and costs for affected employees. PMID:25223846

Analysis strategies for longitudinal attachment loss data.

PubMed

Beck, J D; Elter, J R

2000-02-01

The purpose of this invited review is to describe and discuss methods currently in use to quantify the progression of attachment loss in epidemiological studies of periodontal disease, and to make recommendations for specific analytic methods based upon the particular design of the study and structure of the data. The review concentrates on the definition of incident attachment loss (ALOSS) and its component parts; measurement issues including thresholds and regression to the mean; methods of accounting for longitudinal change, including changes in means, changes in proportions of affected sites, incidence density, the effect of tooth loss and reversals, and repeated events; statistical models of longitudinal change, including the incorporation of the time element, use of linear, logistic or Poisson regression or survival analysis, and statistical tests; site vs person level of analysis, including statistical adjustment for correlated data; the strengths and limitations of ALOSS data. Examples from the Piedmont 65+ Dental Study are used to illustrate specific concepts. We conclude that incidence density is the preferred methodology to use for periodontal studies with more than one period of follow-up and that the use of studies not employing methods for dealing with complex samples, correlated data, and repeated measures does not take advantage of our current understanding of the site- and person-level variables important in periodontal disease and may generate biased results.

Investigating the relationship between jobs-housing balance and traffic safety.

PubMed

Xu, Chengcheng; Li, Haojie; Zhao, Jingya; Chen, Jun; Wang, Wei

2017-10-01

This study aimed to investigate the effects of jobs-housing balance on traffic safety. The crash, demographic characteristics, employment, road network, household characteristics and traffic data were collected from the Los Angeles in 2010. One-way ANOVA tests indicated that the jobs-housing ratio significantly affects traffic safety in terms of crash frequency at traffic analysis zone (TAZ). To quantify the safety impacts of jobs-housing balance, the semi-parametric geographically weighted Poisson regression (S-GWPR) was further used to link crash frequency at TAZ with jobs-housing ratio and other contributing factors. The S-GWPR provides better fitness to the data than do the generalized linear regression, as the S-GWPR accounts for the spatial heterogeneity. The S-GWPR results showed that the jobs-housing relationship has a significant association with crash frequency at TAZ when the factors of traffic, network, and household characteristics are controlled. Crash frequency at TAZ level increases with an increase in the jobs-housing ratio. To further investigate the interactive effects between jobs-housing ratio and other factors, a comparative analysis was conducted to compare the variable elasticities under different jobs-housing ratios. The results indicate considerable interactive effects that traffic conditions and road network characteristics have different effects on crash frequency under various jobs-housing ratios. Copyright © 2017 Elsevier Ltd. All rights reserved.

100 years of mortality due to chronic obstructive pulmonary disease in Australia: the role of tobacco consumption.

PubMed

Adair, T; Hoy, D; Dettrick, Z; Lopez, A D

2012-12-01

Global studies of the long-term association between tobacco consumption and chronic obstructive pulmonary disease (COPD) have relied upon descriptions of trends. To statistically analyse the relationship of tobacco consumption with data on mortality due to COPD over the past 100 years in Australia. Tobacco consumption was reconstructed back to 1887. Log-linear Poisson regression models were used to analyse cumulative cohort and lagged time-specific smoking data and its relationship with COPD mortality. Age-standardised COPD mortality, although likely misclassified with other diseases, decreased for males and females from 1907 until the start of the Second World War in contrast to steadily rising tobacco consumption. Thereafter, COPD mortality rose sharply in line with trends in smoking, peaking in the early 1970s for males and over 20 years later for females, before falling again. Regression models revealed both cumulative and time-specific tobacco consumption to be strongly predictive of COPD mortality, with a time lag of 15 years for males and 20 years for females. Sharp falls in COPD mortality before the Second World War were unrelated to tobacco consumption. Smoking was the primary driver of post-War trends, and the success of anti-smoking campaigns has sharply reduced COPD mortality levels.

Differences in passenger car and large truck involved crash frequencies at urban signalized intersections: an exploratory analysis.

PubMed

Dong, Chunjiao; Clarke, David B; Richards, Stephen H; Huang, Baoshan

2014-01-01

The influence of intersection features on safety has been examined extensively because intersections experience a relatively large proportion of motor vehicle conflicts and crashes. Although there are distinct differences between passenger cars and large trucks-size, operating characteristics, dimensions, and weight-modeling crash counts across vehicle types is rarely addressed. This paper develops and presents a multivariate regression model of crash frequencies by collision vehicle type using crash data for urban signalized intersections in Tennessee. In addition, the performance of univariate Poisson-lognormal (UVPLN), multivariate Poisson (MVP), and multivariate Poisson-lognormal (MVPLN) regression models in establishing the relationship between crashes, traffic factors, and geometric design of roadway intersections is investigated. Bayesian methods are used to estimate the unknown parameters of these models. The evaluation results suggest that the MVPLN model possesses most of the desirable statistical properties in developing the relationships. Compared to the UVPLN and MVP models, the MVPLN model better identifies significant factors and predicts crash frequencies. The findings suggest that traffic volume, truck percentage, lighting condition, and intersection angle significantly affect intersection safety. Important differences in car, car-truck, and truck crash frequencies with respect to various risk factors were found to exist between models. The paper provides some new or more comprehensive observations that have not been covered in previous studies. Copyright © 2013 Elsevier Ltd. All rights reserved.

Geographical variation in the incidence of childhood leukaemia in Manitoba.

PubMed

Torabi, Mahmoud; Singh, Harminder; Galloway, Katie; Israels, Sara J

2015-11-01

Identification of geographical areas and ecological factors associated with higher incidence of childhood leukaemias can direct further study for preventable factors and location of health services to manage such individuals. The aim of this study was to describe the geographical variation and the socio-demographic factors associated with childhood leukaemia in Manitoba. Information on childhood leukaemia incidence between 1992 and 2008 was obtained from the Canadian Cancer Registry and the socio-demographic characteristics for the area of residence from the 2006 Canadian Census. Bayesian spatial Poisson mixed models were used to describe the geographical variation of childhood leukaemia and to determine the association between childhood leukaemia and socio-demographic factors. The south-eastern part of the province had a higher incidence of childhood leukaemia than other parts of the province. In the age and sex-adjusted Poisson regression models, areas with higher proportions of visible minorities and immigrant residents had higher childhood leukaemia incidence rate ratios. In the saturated Poisson regression model, the childhood leukaemia rates were higher in areas with higher proportions of immigrant residents. Unemployment rates were not a significant factor in leukaemia incidence. In Manitoba, areas with higher proportions of immigrants experience higher incidence rates of childhood leukaemia. We have identified geographical areas with higher incidence, which require further study and attention. © 2015 The Authors. Journal of Paediatrics and Child Health © 2015 Paediatrics and Child Health Division (Royal Australasian College of Physicians).

Daily temperature change in relation to the risk of childhood bacillary dysentery among different age groups and sexes in a temperate city in China.

PubMed

Li, K; Zhao, K; Shi, L; Wen, L; Yang, H; Cheng, J; Wang, X; Su, H

2016-02-01

In recent years, many studies have found that ambient temperature is significantly associated with bacillary dysentery (BD). However, there is limited evidence on the relationship between temperature and childhood BD in temperate areas. To investigate the relationship between daily mean temperature (MT) and childhood BD in China. Data on daily MT and childhood BD between 2006 and 2012 were collected from the Bureau of Meteorology and the Centre for Disease Control and Prevention in Hefei, Anhui Province, China. A Poisson generalized linear regression model combined with a distributed lag non-linear model was used to analyse the effects of temperature on childhood BD across different age and sex subgroups. An increase in temperature was significantly associated with childhood BD, and each 1 °C increase corresponded to an increase of 1.58% [95% confidence interval (CI) 0.46-2.71%] in the number of cases of BD. Children aged 0-5 years and girls were particularly sensitive to the effects of temperature. High temperatures may increase the risk of childhood BD in Hefei. Children aged 0-5 years and girls appear to be particularly sensitive to the effects of high temperature. Copyright © 2015 The Royal Society for Public Health. Published by Elsevier Ltd. All rights reserved.

Does attitude matter in computer use in Australian general practice? A zero-inflated Poisson regression analysis.

PubMed

Khan, Asaduzzaman; Western, Mark

The purpose of this study was to explore factors that facilitate or hinder effective use of computers in Australian general medical practice. This study is based on data extracted from a national telephone survey of 480 general practitioners (GPs) across Australia. Clinical functions performed by GPs using computers were examined using a zero-inflated Poisson (ZIP) regression modelling. About 17% of GPs were not using computer for any clinical function, while 18% reported using computers for all clinical functions. The ZIP model showed that computer anxiety was negatively associated with effective computer use, while practitioners' belief about usefulness of computers was positively associated with effective computer use. Being a female GP or working in partnership or group practice increased the odds of effectively using computers for clinical functions. To fully capitalise on the benefits of computer technology, GPs need to be convinced that this technology is useful and can make a difference.

A novel method to predict current voltage characteristics of positive corona discharges based on a perturbation technique. I. Local analysis

NASA Astrophysics Data System (ADS)

Shibata, Hisaichi; Takaki, Ryoji

2017-11-01

A novel method to compute current-voltage characteristics (CVCs) of direct current positive corona discharges is formulated based on a perturbation technique. We use linearized fluid equations coupled with the linearized Poisson's equation. Townsend relation is assumed to predict CVCs apart from the linearization point. We choose coaxial cylinders as a test problem, and we have successfully predicted parameters which can determine CVCs with arbitrary inner and outer radii. It is also confirmed that the proposed method essentially does not induce numerical instabilities.

A multiscale filter for noise reduction of low-dose cone beam projections

NASA Astrophysics Data System (ADS)

Yao, Weiguang; Farr, Jonathan B.

2015-08-01

The Poisson or compound Poisson process governs the randomness of photon fluence in cone beam computed tomography (CBCT) imaging systems. The probability density function depends on the mean (noiseless) of the fluence at a certain detector. This dependence indicates the natural requirement of multiscale filters to smooth noise while preserving structures of the imaged object on the low-dose cone beam projection. In this work, we used a Gaussian filter, \\text{exp}≤ft(-{{x}2}/2σ f2\\right) as the multiscale filter to de-noise the low-dose cone beam projections. We analytically obtained the expression of {σf} , which represents the scale of the filter, by minimizing local noise-to-signal ratio. We analytically derived the variance of residual noise from the Poisson or compound Poisson processes after Gaussian filtering. From the derived analytical form of the variance of residual noise, optimal σ f2 is proved to be proportional to the noiseless fluence and modulated by local structure strength expressed as the linear fitting error of the structure. A strategy was used to obtain the reliable linear fitting error: smoothing the projection along the longitudinal direction to calculate the linear fitting error along the lateral direction and vice versa. The performance of our multiscale filter was examined on low-dose cone beam projections of a Catphan phantom and a head-and-neck patient. After performing the filter on the Catphan phantom projections scanned with pulse time 4 ms, the number of visible line pairs was similar to that scanned with 16 ms, and the contrast-to-noise ratio of the inserts was higher than that scanned with 16 ms about 64% in average. For the simulated head-and-neck patient projections with pulse time 4 ms, the visibility of soft tissue structures in the patient was comparable to that scanned with 20 ms. The image processing took less than 0.5 s per projection with 1024   ×   768 pixels.

Advanced statistics: linear regression, part I: simple linear regression.

PubMed

Marill, Keith A

2004-01-01

Simple linear regression is a mathematical technique used to model the relationship between a single independent predictor variable and a single dependent outcome variable. In this, the first of a two-part series exploring concepts in linear regression analysis, the four fundamental assumptions and the mechanics of simple linear regression are reviewed. The most common technique used to derive the regression line, the method of least squares, is described. The reader will be acquainted with other important concepts in simple linear regression, including: variable transformations, dummy variables, relationship to inference testing, and leverage. Simplified clinical examples with small datasets and graphic models are used to illustrate the points. This will provide a foundation for the second article in this series: a discussion of multiple linear regression, in which there are multiple predictor variables.

The biomechanical modelling of non-ballistic skin wounding: blunt-force injury.

PubMed

Whittle, Kelly; Kieser, Jules; Ichim, Ionut; Swain, Michael; Waddell, Neil; Livingstone, Vicki; Taylor, Michael

2008-01-01

Knowledge of the biomechanical dynamics of blunt force trauma is indispensable for forensic reconstruction of a wounding event. In this study, we describe and interpret wound features on a synthetic skin model under defined laboratory conditions. To simulate skin and the sub-dermal tissues we used open-celled polyurethane sponge (foam), covered by a silicone layer. A drop tube device with three tube lengths (300, 400, and 500 mm), each secured to a weighted steel scaffold and into which a round, 5-kg Federal dumbbell of length 180 mm and diameter 8 cm was placed delivered blows of known impact. To calculate energy and velocity at impact the experimental set-up was replicated using rigid-body dynamics and motion simulation software. We soaked each foam square in 500 mL water, until fully saturated, immediately before placing it beneath the drop tube. We then recorded and classified both external and internal lacerations. The association between external wounding rates and the explanatory variables sponge type, sponge thickness, and height were investigated using Poisson regression. Tears (lacerations) of the silicone skin layer resembled linear lacerations seen in the clinical literature and resulted from only 48.6% of impacts. Poisson regression showed there was no significant difference between the rate of external wounding for different sponge types (P = 0.294) or different drop heights (P = 0.276). Most impacts produced "internal wounds" or subsurface cavitation (96%). There were four internal "wound" types; Y-shape (53%), linear (25%), stellate (16%), and double crescent (6%). The two-way interaction height by sponge type was statistically significant in the analysis of variance model (P = 0.035). The other two-way interactions; height by thickness and sponge type by thickness, were also bordering on statistical significance (P = 0.061 and P = 0.071, respectively). The observation that external wounds were present for less than half of impacts only, but that nearly all impacts resulted in internal wounds, might explain the observed haematoma formation and contusions so often associated with blunt-force injuries. Our study also confirms the key role of hydrodynamic pressure changes in the actual tearing of subcutaneous tissue. At the moment and site of impact, transferred kinetic energy creates a region of high pressure on the fluid inside the tissue. As a result of the incompressibility of the fluid, this will be displaced away from the impact at a rate that depends on the velocity (or kinetic energy) of impact and the permeability and stiffness of the polymeric foam and skin layer.

Detecting isotopic ratio outliers

NASA Astrophysics Data System (ADS)

Bayne, C. K.; Smith, D. H.

An alternative method is proposed for improving isotopic ratio estimates. This method mathematically models pulse-count data and uses iterative reweighted Poisson regression to estimate model parameters to calculate the isotopic ratios. This computer-oriented approach provides theoretically better methods than conventional techniques to establish error limits and to identify outliers.

Study on longitudinal dispersion relation in one-dimensional relativistic plasma: Linear theory and Vlasov simulation

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

Zhang, H.; Wu, S. Z.; Zhou, C. T.

2013-09-15

The dispersion relation of one-dimensional longitudinal plasma waves in relativistic homogeneous plasmas is investigated with both linear theory and Vlasov simulation in this paper. From the Vlasov-Poisson equations, the linear dispersion relation is derived for the proper one-dimensional Jüttner distribution. Numerically obtained linear dispersion relation as well as an approximate formula for plasma wave frequency in the long wavelength limit is given. The dispersion of longitudinal wave is also simulated with a relativistic Vlasov code. The real and imaginary parts of dispersion relation are well studied by varying wave number and plasma temperature. Simulation results are in agreement with establishedmore » linear theory.« less

Poisson structure of dynamical systems with three degrees of freedom

NASA Astrophysics Data System (ADS)

Gümral, Hasan; Nutku, Yavuz

1993-12-01

It is shown that the Poisson structure of dynamical systems with three degrees of freedom can be defined in terms of an integrable one-form in three dimensions. Advantage is taken of this fact and the theory of foliations is used in discussing the geometrical structure underlying complete and partial integrability. Techniques for finding Poisson structures are presented and applied to various examples such as the Halphen system which has been studied as the two-monopole problem by Atiyah and Hitchin. It is shown that the Halphen system can be formulated in terms of a flat SL(2,R)-valued connection and belongs to a nontrivial Godbillon-Vey class. On the other hand, for the Euler top and a special case of three-species Lotka-Volterra equations which are contained in the Halphen system as limiting cases, this structure degenerates into the form of globally integrable bi-Hamiltonian structures. The globally integrable bi-Hamiltonian case is a linear and the SL(2,R) structure is a quadratic unfolding of an integrable one-form in 3+1 dimensions. It is shown that the existence of a vector field compatible with the flow is a powerful tool in the investigation of Poisson structure and some new techniques for incorporating arbitrary constants into the Poisson one-form are presented herein. This leads to some extensions, analogous to q extensions, of Poisson structure. The Kermack-McKendrick model and some of its generalizations describing the spread of epidemics, as well as the integrable cases of the Lorenz, Lotka-Volterra, May-Leonard, and Maxwell-Bloch systems admit globally integrable bi-Hamiltonian structure.

Radiation dose and cataract surgery incidence in atomic bomb survivors, 1986-2005.

PubMed

Neriishi, Kazuo; Nakashima, Eiji; Akahoshi, Masazumi; Hida, Ayumi; Grant, Eric J; Masunari, Naomi; Funamoto, Sachiyo; Minamoto, Atsushi; Fujiwara, Saeko; Shore, Roy E

2012-10-01

To examine the incidence of clinically important cataracts in relation to lens radiation doses between 0 and approximately 3 Gy to address risks at relatively low brief doses. Informed consent was obtained, and human subjects procedures were approved by the ethical committee at the Radiation Effects Research Foundation. Cataract surgery incidence was documented for 6066 atomic bomb survivors during 1986-2005. Sixteen risk factors for cataract, such as smoking, hypertension, and corticosteroid use, were not confounders of the radiation effect on the basis of Cox regression analysis. Radiation dose-response analyses were performed for cataract surgery incidence by using Poisson regression analysis, adjusting for demographic variables and diabetes mellitus, and results were expressed as the excess relative risk (ERR) and the excess absolute risk (EAR) (ie, measures of how much radiation multiplies [ERR] or adds to [EAR] the risk in the unexposed group). Of 6066 atomic bomb survivors, 1028 underwent a first cataract surgery during 1986-2005. The estimated threshold dose was 0.50 Gy (95% confidence interval [CI]: 0.10 Gy, 0.95 Gy) for the ERR model and 0.45 Gy (95% CI: 0.10 Gy, 1.05 Gy) for the EAR model. A linear-quadratic test for upward curvature did not show a significant quadratic effect for either the ERR or EAR model. The linear ERR model for a 70-year-old individual, exposed at age 20 years, showed a 0.32 (95% CI: 0.09, 0.53) [corrected] excess risk at 1 Gy. The ERR was highest for those who were young at exposure. These data indicate a radiation effect for vision-impairing cataracts at doses less than 1 Gy. The evidence suggests that dose standards for protection of the eye from brief radiation exposures should be 0.5 Gy or less. © RSNA, 2012.

Elliptic Euler-Poisson-Darboux equation, critical points and integrable systems

NASA Astrophysics Data System (ADS)

Konopelchenko, B. G.; Ortenzi, G.

2013-12-01

The structure and properties of families of critical points for classes of functions W(z,{\\overline{z}}) obeying the elliptic Euler-Poisson-Darboux equation E(1/2, 1/2) are studied. General variational and differential equations governing the dependence of critical points in variational (deformation) parameters are found. Explicit examples of the corresponding integrable quasi-linear differential systems and hierarchies are presented. There are the extended dispersionless Toda/nonlinear Schrödinger hierarchies, the ‘inverse’ hierarchy and equations associated with the real-analytic Eisenstein series E(\\beta ,{\\overline{\\beta }};1/2) among them. The specific bi-Hamiltonian structure of these equations is also discussed.

Protein-ion binding process on finite macromolecular concentration. A Poisson-Boltzmann and Monte Carlo study.

PubMed

de Carvalho, Sidney Jurado; Fenley, Márcia O; da Silva, Fernando Luís Barroso

2008-12-25

Electrostatic interactions are one of the key driving forces for protein-ligands complexation. Different levels for the theoretical modeling of such processes are available on the literature. Most of the studies on the Molecular Biology field are performed within numerical solutions of the Poisson-Boltzmann Equation and the dielectric continuum models framework. In such dielectric continuum models, there are two pivotal questions: (a) how the protein dielectric medium should be modeled, and (b) what protocol should be used when solving this effective Hamiltonian. By means of Monte Carlo (MC) and Poisson-Boltzmann (PB) calculations, we define the applicability of the PB approach with linear and nonlinear responses for macromolecular electrostatic interactions in electrolyte solution, revealing some physical mechanisms and limitations behind it especially due the raise of both macromolecular charge and concentration out of the strong coupling regime. A discrepancy between PB and MC for binding constant shifts is shown and explained in terms of the manner PB approximates the excess chemical potentials of the ligand, and not as a consequence of the nonlinear thermal treatment and/or explicit ion-ion interactions as it could be argued. Our findings also show that the nonlinear PB predictions with a low dielectric response well reproduce the pK shifts calculations carried out with an uniform dielectric model. This confirms and completes previous results obtained by both MC and linear PB calculations.

Influence of the nucleus area distribution on the survival fraction after charged particles broad beam irradiation.

PubMed

Wéra, A-C; Barazzuol, L; Jeynes, J C G; Merchant, M J; Suzuki, M; Kirkby, K J

2014-08-07

It is well known that broad beam irradiation with heavy ions leads to variation in the number of hit(s) received by each cell as the distribution of particles follows the Poisson statistics. Although the nucleus area will determine the number of hit(s) received for a given dose, variation amongst its irradiated cell population is generally not considered. In this work, we investigate the effect of the nucleus area's distribution on the survival fraction. More specifically, this work aims to explain the deviation, or tail, which might be observed in the survival fraction at high irradiation doses. For this purpose, the nucleus area distribution was added to the beam Poisson statistics and the Linear-Quadratic model in order to fit the experimental data. As shown in this study, nucleus size variation, and the associated Poisson statistics, can lead to an upward survival trend after broad beam irradiation. The influence of the distribution parameters (mean area and standard deviation) was studied using a normal distribution, along with the Linear-Quadratic model parameters (α and β). Finally, the model proposed here was successfully tested to the survival fraction of LN18 cells irradiated with a 85 keV µm(- 1) carbon ion broad beam for which the distribution in the area of the nucleus had been determined.

Blind beam-hardening correction from Poisson measurements

NASA Astrophysics Data System (ADS)

Gu, Renliang; Dogandžić, Aleksandar

2016-02-01

We develop a sparse image reconstruction method for Poisson-distributed polychromatic X-ray computed tomography (CT) measurements under the blind scenario where the material of the inspected object and the incident energy spectrum are unknown. We employ our mass-attenuation spectrum parameterization of the noiseless measurements and express the mass- attenuation spectrum as a linear combination of B-spline basis functions of order one. A block coordinate-descent algorithm is developed for constrained minimization of a penalized Poisson negative log-likelihood (NLL) cost function, where constraints and penalty terms ensure nonnegativity of the spline coefficients and nonnegativity and sparsity of the density map image; the image sparsity is imposed using a convex total-variation (TV) norm penalty term. This algorithm alternates between a Nesterov's proximal-gradient (NPG) step for estimating the density map image and a limited-memory Broyden-Fletcher-Goldfarb-Shanno with box constraints (L-BFGS-B) step for estimating the incident-spectrum parameters. To accelerate convergence of the density- map NPG steps, we apply function restart and a step-size selection scheme that accounts for varying local Lipschitz constants of the Poisson NLL. Real X-ray CT reconstruction examples demonstrate the performance of the proposed scheme.

Estimation of parameters in Shot-Noise-Driven Doubly Stochastic Poisson processes using the EM algorithm--modeling of pre- and postsynaptic spike trains.

PubMed

Mino, H

2007-01-01

To estimate the parameters, the impulse response (IR) functions of some linear time-invariant systems generating intensity processes, in Shot-Noise-Driven Doubly Stochastic Poisson Process (SND-DSPP) in which multivariate presynaptic spike trains and postsynaptic spike trains can be assumed to be modeled by the SND-DSPPs. An explicit formula for estimating the IR functions from observations of multivariate input processes of the linear systems and the corresponding counting process (output process) is derived utilizing the expectation maximization (EM) algorithm. The validity of the estimation formula was verified through Monte Carlo simulations in which two presynaptic spike trains and one postsynaptic spike train were assumed to be observable. The IR functions estimated on the basis of the proposed identification method were close to the true IR functions. The proposed method will play an important role in identifying the input-output relationship of pre- and postsynaptic neural spike trains in practical situations.

Design studies of the Ku-band, wide-band Gyro-TWT amplifier

NASA Astrophysics Data System (ADS)

Jung, Sang Wook; Lee, Han Seul; Jang, Kwong Ho; Choi, Jin Joo; Hong, Yong Jun; Shin, Jin Woo; So, Jun Ho; Won, Jong Hyo

2014-02-01

This paper reports a Ku-band, wide band Gyrotron-Traveling-wave-tube(Gyro-TWT) that is currently being developed at Kwangwoon University. The Gyro-TWT has a two stage linear tapered interaction circuit to obtain a wide operating bandwidth. The linearly-tapered interaction circuit and nonlinearly-tapered magnetic field gives the Gyro-TWT a wide operating bandwidth. The Gyro-TWT bandwidth is 23%. The 2d-Particle-in-cell(PIC) and MAGIC2d code simulation results are 17.3 dB and 24.34 kW, respectively for the maximum saturated output power. A double anode MIG was simulated with E-Gun code. The results were 0.7 for the transvers to the axial beam velocity ratio (=alpha) and a 2.3% axial velocity spread at 50 kV and 4 A. A magnetic field profile simulation was performed by using the Poisson code to obtain the grazing magnetic field of the entire interaction circuit with Poisson code.

Estimating False Positive Contamination in Crater Annotations from Citizen Science Data

NASA Astrophysics Data System (ADS)

Tar, P. D.; Bugiolacchi, R.; Thacker, N. A.; Gilmour, J. D.

2017-01-01

Web-based citizen science often involves the classification of image features by large numbers of minimally trained volunteers, such as the identification of lunar impact craters under the Moon Zoo project. Whilst such approaches facilitate the analysis of large image data sets, the inexperience of users and ambiguity in image content can lead to contamination from false positive identifications. We give an approach, using Linear Poisson Models and image template matching, that can quantify levels of false positive contamination in citizen science Moon Zoo crater annotations. Linear Poisson Models are a form of machine learning which supports predictive error modelling and goodness-of-fits, unlike most alternative machine learning methods. The proposed supervised learning system can reduce the variability in crater counts whilst providing predictive error assessments of estimated quantities of remaining true verses false annotations. In an area of research influenced by human subjectivity, the proposed method provides a level of objectivity through the utilisation of image evidence, guided by candidate crater identifications.

Generation of Plausible Hurricane Tracks for Preparedness Exercises

DTIC Science & Technology

2017-04-25

wind extents are simulated by Poisson regression and temporal filtering . The un-optimized MATLAB code runs in less than a minute and is integrated into...of real hurricanes. After wind radii have been simulated for the entire track, median filtering , attenuation over land, and smoothing clean up the wind

Inhalant Use among Indiana School Children, 1991-2004

ERIC Educational Resources Information Center

Ding, Kele; Torabi, Mohammad R.; Perera, Bilesha; Jun, Mi Kyung; Jones-McKyer, E. Lisako

2007-01-01

Objective: To examine the prevalence and trend of inhalant use among Indiana public school students. Methods: The Alcohol, Tobacco, and Other Drug Use among Indiana Children and Adolescents surveys conducted annually between 1991 and 2004 were reanalyzed using 2-way moving average, Poisson regression, and ANOVA tests. Results: The prevalence had…

Seasonally adjusted birth frequencies follow the Poisson distribution.

PubMed

Barra, Mathias; Lindstrøm, Jonas C; Adams, Samantha S; Augestad, Liv A

2015-12-15

Variations in birth frequencies have an impact on activity planning in maternity wards. Previous studies of this phenomenon have commonly included elective births. A Danish study of spontaneous births found that birth frequencies were well modelled by a Poisson process. Somewhat unexpectedly, there were also weekly variations in the frequency of spontaneous births. Another study claimed that birth frequencies follow the Benford distribution. Our objective was to test these results. We analysed 50,017 spontaneous births at Akershus University Hospital in the period 1999-2014. To investigate the Poisson distribution of these births, we plotted their variance over a sliding average. We specified various Poisson regression models, with the number of births on a given day as the outcome variable. The explanatory variables included various combinations of years, months, days of the week and the digit sum of the date. The relationship between the variance and the average fits well with an underlying Poisson process. A Benford distribution was disproved by a goodness-of-fit test (p < 0.01). The fundamental model with year and month as explanatory variables is significantly improved (p < 0.001) by adding day of the week as an explanatory variable. Altogether 7.5% more children are born on Tuesdays than on Sundays. The digit sum of the date is non-significant as an explanatory variable (p = 0.23), nor does it increase the explained variance. INERPRETATION: Spontaneous births are well modelled by a time-dependent Poisson process when monthly and day-of-the-week variation is included. The frequency is highest in summer towards June and July, Friday and Tuesday stand out as particularly busy days, and the activity level is at its lowest during weekends.

Marginal regression models for clustered count data based on zero-inflated Conway-Maxwell-Poisson distribution with applications.

PubMed

Choo-Wosoba, Hyoyoung; Levy, Steven M; Datta, Somnath

2016-06-01

Community water fluoridation is an important public health measure to prevent dental caries, but it continues to be somewhat controversial. The Iowa Fluoride Study (IFS) is a longitudinal study on a cohort of Iowa children that began in 1991. The main purposes of this study (http://www.dentistry.uiowa.edu/preventive-fluoride-study) were to quantify fluoride exposures from both dietary and nondietary sources and to associate longitudinal fluoride exposures with dental fluorosis (spots on teeth) and dental caries (cavities). We analyze a subset of the IFS data by a marginal regression model with a zero-inflated version of the Conway-Maxwell-Poisson distribution for count data exhibiting excessive zeros and a wide range of dispersion patterns. In general, we introduce two estimation methods for fitting a ZICMP marginal regression model. Finite sample behaviors of the estimators and the resulting confidence intervals are studied using extensive simulation studies. We apply our methodologies to the dental caries data. Our novel modeling incorporating zero inflation, clustering, and overdispersion sheds some new light on the effect of community water fluoridation and other factors. We also include a second application of our methodology to a genomic (next-generation sequencing) dataset that exhibits underdispersion. © 2015, The International Biometric Society.

Impact of previous ART and of ART initiation on outcome of HIV-associated tuberculosis.

PubMed

Girardi, Enrico; Palmieri, Fabrizio; Angeletti, Claudio; Vanacore, Paola; Matteelli, Alberto; Gori, Andrea; Carbonara, Sergio; Ippolito, Giuseppe

2012-01-01

Combination antiretroviral therapy (cART) has progressively decreased mortality of HIV-associated tuberculosis .To date, however, limited data on tuberculosis treatment outcomes among coinfected patients who are not ART-naive at the time of tuberculosis diagnosis are available. A multicenter, observational study enrolled 246 HIV-infected patients diagnosed with tuberculosis, in 96 Italian infectious diseases hospital units, who started tuberculosis treatment. A polytomous logistic regression model was used to identify baseline factors associated with the outcome. A Poisson regression model was used to explain the effect of ART during tuberculosis treatment on mortality, as a time-varying covariate, adjusting for baseline characteristics. Outcomes of tuberculosis treatment were as follows: 130 (52.8%) were successfully treated, 36 (14.6%) patients died in a median time of 2 months (range: 0-16), and 80 (32.6%) had an unsuccessful outcome. Being foreign born or injecting drug users was associated with unsuccessful outcomes. In multivariable Poisson regression, cART during tuberculosis treatment decreased the risk of death, while this risk increased for those who were not ART-naive at tuberculosis diagnosis. ART during tuberculosis treatment is associated with a substantial reduction of death rate among HIV-infected patients. However, patients who are not ART-naive when they develop tuberculosis remain at elevated risk of death.

Characterizing the effect of summer temperature on heatstroke-related emergency ambulance dispatches in the Kanto area of Japan

NASA Astrophysics Data System (ADS)

Ng, Chris Fook Sheng; Ueda, Kayo; Ono, Masaji; Nitta, Hiroshi; Takami, Akinori

2014-07-01

Despite rising concern on the impact of heat on human health, the risk of high summer temperature on heatstroke-related emergency dispatches is not well understood in Japan. A time-series study was conducted to examine the association between apparent temperature and daily heatstroke-related ambulance dispatches (HSAD) within the Kanto area of Japan. A total of 12,907 HSAD occurring from 2000 to 2009 in five major cities—Saitama, Chiba, Tokyo, Kawasaki, and Yokohama—were analyzed. Generalized additive models and zero-inflated Poisson regressions were used to estimate the effects of daily maximum three-hour apparent temperature (AT) on dispatch frequency from May to September, with adjustment for seasonality, long-term trend, weekends, and public holidays. Linear and non-linear exposure effects were considered. Effects on days when AT first exceeded its summer median were also investigated. City-specific estimates were combined using random effects meta-analyses. Exposure-response relationship was found to be fairly linear. Significant risk increase began from 21 °C with a combined relative risk (RR) of 1.22 (95 % confidence interval, 1.03-1.44), increasing to 1.49 (1.42-1.57) at peak AT. When linear exposure was assumed, combined RR was 1.43 (1.37-1.50) per degree Celsius increment. Overall association was significant the first few times when median AT was initially exceeded in a particular warm season. More than two-thirds of these initial hot days were in June, implying the harmful effect of initial warming as the season changed. Risk increase that began early at the fairly mild perceived temperature implies the need for early precaution.

Characterizing the effect of summer temperature on heatstroke-related emergency ambulance dispatches in the Kanto area of Japan.

PubMed

Ng, Chris Fook Sheng; Ueda, Kayo; Ono, Masaji; Nitta, Hiroshi; Takami, Akinori

2014-07-01

Despite rising concern on the impact of heat on human health, the risk of high summer temperature on heatstroke-related emergency dispatches is not well understood in Japan. A time-series study was conducted to examine the association between apparent temperature and daily heatstroke-related ambulance dispatches (HSAD) within the Kanto area of Japan. A total of 12,907 HSAD occurring from 2000 to 2009 in five major cities-Saitama, Chiba, Tokyo, Kawasaki, and Yokohama-were analyzed. Generalized additive models and zero-inflated Poisson regressions were used to estimate the effects of daily maximum three-hour apparent temperature (AT) on dispatch frequency from May to September, with adjustment for seasonality, long-term trend, weekends, and public holidays. Linear and non-linear exposure effects were considered. Effects on days when AT first exceeded its summer median were also investigated. City-specific estimates were combined using random effects meta-analyses. Exposure-response relationship was found to be fairly linear. Significant risk increase began from 21 °C with a combined relative risk (RR) of 1.22 (95% confidence interval, 1.03-1.44), increasing to 1.49 (1.42-1.57) at peak AT. When linear exposure was assumed, combined RR was 1.43 (1.37-1.50) per degree Celsius increment. Overall association was significant the first few times when median AT was initially exceeded in a particular warm season. More than two-thirds of these initial hot days were in June, implying the harmful effect of initial warming as the season changed. Risk increase that began early at the fairly mild perceived temperature implies the need for early precaution.

Modeling Tetanus Neonatorum case using the regression of negative binomial and zero-inflated negative binomial

NASA Astrophysics Data System (ADS)

Amaliana, Luthfatul; Sa'adah, Umu; Wayan Surya Wardhani, Ni

2017-12-01

Tetanus Neonatorum is an infectious disease that can be prevented by immunization. The number of Tetanus Neonatorum cases in East Java Province is the highest in Indonesia until 2015. Tetanus Neonatorum data contain over dispersion and big enough proportion of zero-inflation. Negative Binomial (NB) regression is an alternative method when over dispersion happens in Poisson regression. However, the data containing over dispersion and zero-inflation are more appropriately analyzed by using Zero-Inflated Negative Binomial (ZINB) regression. The purpose of this study are: (1) to model Tetanus Neonatorum cases in East Java Province with 71.05 percent proportion of zero-inflation by using NB and ZINB regression, (2) to obtain the best model. The result of this study indicates that ZINB is better than NB regression with smaller AIC.

Paramedic-Initiated Home Care Referrals and Use of Home Care and Emergency Medical Services.

PubMed

Verma, Amol A; Klich, John; Thurston, Adam; Scantlebury, Jordan; Kiss, Alex; Seddon, Gayle; Sinha, Samir K

2018-01-01

We examined the association between paramedic-initiated home care referrals and utilization of home care, 9-1-1, and Emergency Department (ED) services. This was a retrospective cohort study of individuals who received a paramedic-initiated home care referral after a 9-1-1 call between January 1, 2011 and December 31, 2012 in Toronto, Ontario, Canada. Home care, 9-1-1, and ED utilization were compared in the 6 months before and after home care referral. Nonparametric longitudinal regression was performed to assess changes in hours of home care service use and zero-inflated Poisson regression was performed to assess changes in the number of 9-1-1 calls and ambulance transports to ED. During the 24-month study period, 2,382 individuals received a paramedic-initiated home care referral. After excluding individuals who died, were hospitalized, or were admitted to a nursing home, the final study cohort was 1,851. The proportion of the study population receiving home care services increased from 18.2% to 42.5% after referral, representing 450 additional people receiving services. In longitudinal regression analysis, there was an increase of 17.4 hours in total services per person in the six months after referral (95% CI: 1.7-33.1, p = 0.03). The mean number of 9-1-1 calls per person was 1.44 (SD 9.58) before home care referral and 1.20 (SD 7.04) after home care referral in the overall study cohort. This represented a 10% reduction in 9-1-1 calls (95% CI: 7-13%, p < 0.001) in Poisson regression analysis. The mean number of ambulance transports to ED per person was 0.91 (SD 8.90) before home care referral and 0.79 (SD 6.27) after home care referral, representing a 7% reduction (95% CI: 3-11%, p < 0.001) in Poisson regression analysis. When only the participants with complete paramedic and home care records were included in the analysis, the reductions in 9-1-1 calls and ambulance transports to ED were attenuated but remained statistically significant. Paramedic-initiated home care referrals in Toronto were associated with improved access to and use of home care services and may have been associated with reduced 9-1-1 calls and ambulance transports to ED.

Accident prediction model for public highway-rail grade crossings.

PubMed

Lu, Pan; Tolliver, Denver

2016-05-01

Considerable research has focused on roadway accident frequency analysis, but relatively little research has examined safety evaluation at highway-rail grade crossings. Highway-rail grade crossings are critical spatial locations of utmost importance for transportation safety because traffic crashes at highway-rail grade crossings are often catastrophic with serious consequences. The Poisson regression model has been employed to analyze vehicle accident frequency as a good starting point for many years. The most commonly applied variations of Poisson including negative binomial, and zero-inflated Poisson. These models are used to deal with common crash data issues such as over-dispersion (sample variance is larger than the sample mean) and preponderance of zeros (low sample mean and small sample size). On rare occasions traffic crash data have been shown to be under-dispersed (sample variance is smaller than the sample mean) and traditional distributions such as Poisson or negative binomial cannot handle under-dispersion well. The objective of this study is to investigate and compare various alternate highway-rail grade crossing accident frequency models that can handle the under-dispersion issue. The contributions of the paper are two-fold: (1) application of probability models to deal with under-dispersion issues and (2) obtain insights regarding to vehicle crashes at public highway-rail grade crossings. Copyright © 2016 Elsevier Ltd. All rights reserved.

Statistical modeling of dental unit water bacterial test kit performance.

PubMed

Cohen, Mark E; Harte, Jennifer A; Stone, Mark E; O'Connor, Karen H; Coen, Michael L; Cullum, Malford E

2007-01-01

While it is important to monitor dental water quality, it is unclear whether in-office test kits provide bacterial counts comparable to the gold standard method (R2A). Studies were conducted on specimens with known bacterial concentrations, and from dental units, to evaluate test kit accuracy across a range of bacterial types and loads. Colony forming units (CFU) were counted for samples from each source, using R2A and two types of test kits, and conformity to Poisson distribution expectations was evaluated. Poisson regression was used to test for effects of source and device, and to estimate rate ratios for kits relative to R2A. For all devices, distributions were Poisson for low CFU/mL when only beige-pigmented bacteria were considered. For higher counts, R2A remained Poisson, but kits exhibited over-dispersion. Both kits undercounted relative to R2A, but the degree of undercounting was reasonably stable. Kits did not grow pink-pigmented bacteria from dental-unit water identified as Methylobacterium rhodesianum. Only one of the test kits provided results with adequate reliability at higher bacterial concentrations. Undercount bias could be estimated for this device and used to adjust test kit results. Insensitivity to methylobacteria spp. is problematic.

Correlation and simple linear regression.

PubMed

Eberly, Lynn E

2007-01-01

This chapter highlights important steps in using correlation and simple linear regression to address scientific questions about the association of two continuous variables with each other. These steps include estimation and inference, assessing model fit, the connection between regression and ANOVA, and study design. Examples in microbiology are used throughout. This chapter provides a framework that is helpful in understanding more complex statistical techniques, such as multiple linear regression, linear mixed effects models, logistic regression, and proportional hazards regression.

On the origin of dual Lax pairs and their r-matrix structure

NASA Astrophysics Data System (ADS)

Avan, Jean; Caudrelier, Vincent

2017-10-01

We establish the algebraic origin of the following observations made previously by the authors and coworkers: (i) A given integrable PDE in 1 + 1 dimensions within the Zakharov-Shabat scheme related to a Lax pair can be cast in two distinct, dual Hamiltonian formulations; (ii) Associated to each formulation is a Poisson bracket and a phase space (which are not compatible in the sense of Magri); (iii) Each matrix in the Lax pair satisfies a linear Poisson algebra a la Sklyanin characterized by the same classical r matrix. We develop the general concept of dual Lax pairs and dual Hamiltonian formulation of an integrable field theory. We elucidate the origin of the common r-matrix structure by tracing it back to a single Lie-Poisson bracket on a suitable coadjoint orbit of the loop algebra sl(2 , C) ⊗ C(λ ,λ-1) . The results are illustrated with the examples of the nonlinear Schrödinger and Gerdjikov-Ivanov hierarchies.

Effect of latitude on the rate of change in incidence of Lyme disease in the United States

PubMed Central

Tuite, Ashleigh R.; Greer, Amy L.

2013-01-01

Background Tick-borne illnesses represent an important class of emerging zoonoses, with climate change projected to increase the geographic range within which tick-borne zoonoses might become endemic. We evaluated the impact of latitude on the rate of change in the incidence of Lyme disease in the United States, using publicly available data. Methods We estimated state-level year-on-year incidence rate ratios (IRRs) for Lyme disease for the period 1993 to 2007 using Poisson regression methods. We evaluated between-state heterogeneity in IRRs using a random-effects meta-analytic approach. We identified state-level characteristics associated with increasing incidence using random-effects meta-regression. Results The incidence of Lyme disease in the US increased by about 80% between 1993 and 2007 (IRR per year 1.049, 95% CI [confidence interval] 1.048 to 1.050). There was marked between-state heterogeneity in the average incidence of Lyme disease, ranging from 0.008 per 100 000 person-years in Colorado to 75 per 100 000 in Connecticut, and significant between-state heterogeneity in temporal trends (p < 0.001). In multivariable meta-regression models, increasing incidence showed a linear association with state latitude and population density. These 2 factors explained 27% of the between-state variation in IRRs. No independent association was identified for other state-level characteristics. Interpretation Lyme disease incidence increased in the US as a whole during the study period, but the changes were not uniform. Marked increases were identified in northern-most states, whereas southern states experienced stable or declining rates of Lyme disease. PMID:25077101

Conservative management or gamma knife radiosurgery for vestibular schwannoma: tumor growth, symptoms, and quality of life.

PubMed

Breivik, Cathrine Nansdal; Nilsen, Roy Miodini; Myrseth, Erling; Pedersen, Paal Henning; Varughese, Jobin K; Chaudhry, Aqeel Asghar; Lund-Johansen, Morten

2013-07-01

There are few reports about the course of vestibular schwannoma (VS) patients following gamma knife radiosurgery (GKRS) compared with the course following conservative management (CM). In this study, we present prospectively collected data of 237 patients with unilateral VS extending outside the internal acoustic canal who received either GKRS (113) or CM (124). The aim was to measure the effect of GKRS compared with the natural course on tumor growth rate and hearing loss. Secondary end points were postinclusion additional treatment, quality of life (QoL), and symptom development. The patients underwent magnetic resonance imaging scans, clinical examination, and QoL assessment by SF-36 questionnaire. Statistics were performed by using Spearman correlation coefficient, Kaplan-Meier plot, Poisson regression model, mixed linear regression models, and mixed logistic regression models. Mean follow-up time was 55.0 months (26.1 standard deviation, range 10-132). Thirteen patients were lost to follow-up. Serviceable hearing was lost in 54 of 71 (76%) (CM) and 34 of 53 (64%) (GKRS) patients during the study period (not significant, log-rank test). There was a significant reduction in tumor volume over time in the GKRS group. The need for treatment following initial GKRS or CM differed at highly significant levels (log-rank test, P < .001). Symptom and QoL development did not differ significantly between the groups. In VS patients, GKRS reduces the tumor growth rate and thereby the incidence rate of new treatment about tenfold. Hearing is lost at similar rates in both groups. Symptoms and QoL seem not to be significantly affected by GKRS.

Poisson structure on a space with linear SU(2) fuzziness

NASA Astrophysics Data System (ADS)

Khorrami, Mohammad; Fatollahi, Amir H.; Shariati, Ahmad

2009-07-01

The Poisson structure is constructed for a model in which spatial coordinates of configuration space are noncommutative and satisfy the commutation relations of a Lie algebra. The case is specialized to that of the group SU(2), for which the counterpart of the angular momentum as well as the Euler parametrization of the phase space are introduced. SU(2)-invariant classical systems are discussed, and it is observed that the path of particle can be obtained by the solution of a first-order equation, as the case with such models on commutative spaces. The examples of free particle, rotationally invariant potentials, and specially the isotropic harmonic oscillator are investigated in more detail.

Linear-Nonlinear-Poisson Models of Primate Choice Dynamics

ERIC Educational Resources Information Center

Corrado, Greg S.; Sugrue, Leo P.; Seung, H. Sebastian; Newsome, William T.

2005-01-01

The equilibrium phenomenon of matching behavior traditionally has been studied in stationary environments. Here we attempt to uncover the local mechanism of choice that gives rise to matching by studying behavior in a highly dynamic foraging environment. In our experiments, 2 rhesus monkeys ("Macacca mulatta") foraged for juice rewards by making…

AFMPB: An adaptive fast multipole Poisson-Boltzmann solver for calculating electrostatics in biomolecular systems

NASA Astrophysics Data System (ADS)

Lu, Benzhuo; Cheng, Xiaolin; Huang, Jingfang; McCammon, J. Andrew

2010-06-01

A Fortran program package is introduced for rapid evaluation of the electrostatic potentials and forces in biomolecular systems modeled by the linearized Poisson-Boltzmann equation. The numerical solver utilizes a well-conditioned boundary integral equation (BIE) formulation, a node-patch discretization scheme, a Krylov subspace iterative solver package with reverse communication protocols, and an adaptive new version of fast multipole method in which the exponential expansions are used to diagonalize the multipole-to-local translations. The program and its full description, as well as several closely related libraries and utility tools are available at http://lsec.cc.ac.cn/~lubz/afmpb.html and a mirror site at http://mccammon.ucsd.edu/. This paper is a brief summary of the program: the algorithms, the implementation and the usage. Program summaryProgram title: AFMPB: Adaptive fast multipole Poisson-Boltzmann solver Catalogue identifier: AEGB_v1_0 Program summary URL:http://cpc.cs.qub.ac.uk/summaries/AEGB_v1_0.html Program obtainable from: CPC Program Library, Queen's University, Belfast, N. Ireland Licensing provisions: GPL 2.0 No. of lines in distributed program, including test data, etc.: 453 649 No. of bytes in distributed program, including test data, etc.: 8 764 754 Distribution format: tar.gz Programming language: Fortran Computer: Any Operating system: Any RAM: Depends on the size of the discretized biomolecular system Classification: 3 External routines: Pre- and post-processing tools are required for generating the boundary elements and for visualization. Users can use MSMS ( http://www.scripps.edu/~sanner/html/msms_home.html) for pre-processing, and VMD ( http://www.ks.uiuc.edu/Research/vmd/) for visualization. Sub-programs included: An iterative Krylov subspace solvers package from SPARSKIT by Yousef Saad ( http://www-users.cs.umn.edu/~saad/software/SPARSKIT/sparskit.html), and the fast multipole methods subroutines from FMMSuite ( http://www.fastmultipole.org/). Nature of problem: Numerical solution of the linearized Poisson-Boltzmann equation that describes electrostatic interactions of molecular systems in ionic solutions. Solution method: A novel node-patch scheme is used to discretize the well-conditioned boundary integral equation formulation of the linearized Poisson-Boltzmann equation. Various Krylov subspace solvers can be subsequently applied to solve the resulting linear system, with a bounded number of iterations independent of the number of discretized unknowns. The matrix-vector multiplication at each iteration is accelerated by the adaptive new versions of fast multipole methods. The AFMPB solver requires other stand-alone pre-processing tools for boundary mesh generation, post-processing tools for data analysis and visualization, and can be conveniently coupled with different time stepping methods for dynamics simulation. Restrictions: Only three or six significant digits options are provided in this version. Unusual features: Most of the codes are in Fortran77 style. Memory allocation functions from Fortran90 and above are used in a few subroutines. Additional comments: The current version of the codes is designed and written for single core/processor desktop machines. Check http://lsec.cc.ac.cn/~lubz/afmpb.html and http://mccammon.ucsd.edu/ for updates and changes. Running time: The running time varies with the number of discretized elements ( N) in the system and their distributions. In most cases, it scales linearly as a function of N.

The Effectiveness of an Electronic Security Management System in a Privately Owned Apartment Complex

ERIC Educational Resources Information Center

Greenberg, David F.; Roush, Jeffrey B.

2009-01-01

Poisson and negative binomial regression methods are used to analyze the monthly time series data to determine the effects of introducing an integrated security management system including closed-circuit television (CCTV), door alarm monitoring, proximity card access, and emergency call boxes to a large privately-owned complex of apartment…

Hospitalizations for primary care-sensitive conditions in Pelotas, Brazil: 1998 to 2012.

PubMed

Costa, Juvenal Soares Dias da; Teixeira, Ana Maria Ferreira Borges; Moraes, Mauricio; Strauch, Eliane Schneider; Silveira, Denise Silva da; Carret, Maria Laura Vidal; Fantinel, Everton

2017-01-01

To verify the hospitalization trend for primary care sensitive-conditions in Pelotas, Rio Grande do Sul, Brazil from 1998 to 2012. An ecological study compared hospitalizations rates of the city of Pelotas with the rest of state of Rio Grande do Sul. Analysis was conducted using direct standardization of rates, coefficients were stratified by sex and the Poisson regression was used. Hospitalizations for sensitive conditions decreased in Pelotas and Rio Grande do Sul. In Pelotas, a 63.8% decrease was detected in the period observed, and there was a 43.1% decrease in the state of Rio Grande do Sul. Poisson regression coefficients showed a decrease of 7% in Pelotas and of 4% in the rest of Rio Grande do Sul each year. During the study period, several changes were introduced in the Brazilian Unified Health System ("Sistema Único de Saúde") that may have influenced the results, including changes in administration, health funding, and a complete reworking of primary care through the creation of the Family Health Strategy program ("Estratégia Saúde da Família").

The Use of Illegal Drugs and Infectious Contagious Diseases: Knowledge and Intervention among Dockworkers

PubMed Central

Cezar-Vaz, Marta Regina; Bonow, Clarice Alves; da Silva, Mara Regina Santos; de Farias, Francisca Lucélia Ribeiro; de Almeida, Marlise Capa Verde

2016-01-01

This study’s objective was to analyze the use of illegal drugs by dockworkers and provide risk communication regarding the use of illegal drugs and test for infectious contagious diseases among dockworkers. This cross-sectional study including an intervention addressed to 232 dockworkers, who were individually interviewed, as well as communication of risk with testing for infectious contagious diseases for 93 dockworkers from a city in the interior of Rio Grande do Sul, Brazil. Poisson regression analysis was used. Twenty-nine workers reported the use of illegal drugs. Poisson regression indicated that being a wharfage worker, smoker, having a high income, and heavier workload increases the prevalence of the use of illegal drugs. During risk communication, two workers were diagnosed with hepatitis B (2.2%), three (3.2%) with hepatitis C, two (2.2%) with syphilis. None of the workers, though, had HIV. This study provides evidence that can motivate further research on the topic and also lead to treatment of individuals to improve work safety, productivity, and the health of workers. PMID:26771625

Modeling both of the number of pausibacillary and multibacillary leprosy patients by using bivariate poisson regression

NASA Astrophysics Data System (ADS)

Winahju, W. S.; Mukarromah, A.; Putri, S.

2015-03-01

Leprosy is a chronic infectious disease caused by bacteria of leprosy (Mycobacterium leprae). Leprosy has become an important thing in Indonesia because its morbidity is quite high. Based on WHO data in 2014, in 2012 Indonesia has the highest number of new leprosy patients after India and Brazil with a contribution of 18.994 people (8.7% of the world). This number makes Indonesia automatically placed as the country with the highest number of leprosy morbidity of ASEAN countries. The province that most contributes to the number of leprosy patients in Indonesia is East Java. There are two kind of leprosy. They consist of pausibacillary and multibacillary. The morbidity of multibacillary leprosy is higher than pausibacillary leprosy. This paper will discuss modeling both of the number of multibacillary and pausibacillary leprosy patients as responses variables. These responses are count variables, so modeling will be conducted by using bivariate poisson regression method. Unit experiment used is in East Java, and predictors involved are: environment, demography, and poverty. The model uses data in 2012, and the result indicates that all predictors influence significantly.

The Use of Illegal Drugs and Infectious Contagious Diseases: Knowledge and Intervention among Dockworkers.

PubMed

Cezar-Vaz, Marta Regina; Bonow, Clarice Alves; da Silva, Mara Regina Santos; de Farias, Francisca Lucélia Ribeiro; de Almeida, Marlise Capa Verde

2016-01-12

This study's objective was to analyze the use of illegal drugs by dockworkers and provide risk communication regarding the use of illegal drugs and test for infectious contagious diseases among dockworkers. This cross-sectional study including an intervention addressed to 232 dockworkers, who were individually interviewed, as well as communication of risk with testing for infectious contagious diseases for 93 dockworkers from a city in the interior of Rio Grande do Sul, Brazil. Poisson regression analysis was used. Twenty-nine workers reported the use of illegal drugs. Poisson regression indicated that being a wharfage worker, smoker, having a high income, and heavier workload increases the prevalence of the use of illegal drugs. During risk communication, two workers were diagnosed with hepatitis B (2.2%), three (3.2%) with hepatitis C, two (2.2%) with syphilis. None of the workers, though, had HIV. This study provides evidence that can motivate further research on the topic and also lead to treatment of individuals to improve work safety, productivity, and the health of workers.

Enhanced Night Vision Via a Combination of Poisson Interpolation and Machine Learning

DTIC Science & Technology

2006-02-01

of 0-255, they are mostly similar. The right plot shows a family of m(x, ψ) curves of ψ=2 (the most linear) through ψ=1024 (the most curved ...complicating low-light imaging. Nayar and Branzoi [04] later suggested a second variant using a DLP micromirror array to modulate the exposure, via time...255, they are mostly similar. The right plot shows a family of m(x, ψ) curves of ψ=2 (the most linear) through ψ=1024 (the most curved

Modeling Stochastic Variability in the Numbers of Surviving Salmonella enterica, Enterohemorrhagic Escherichia coli, and Listeria monocytogenes Cells at the Single-Cell Level in a Desiccated Environment

PubMed Central

Koyama, Kento; Hokunan, Hidekazu; Hasegawa, Mayumi; Kawamura, Shuso

2016-01-01

ABSTRACT Despite effective inactivation procedures, small numbers of bacterial cells may still remain in food samples. The risk that bacteria will survive these procedures has not been estimated precisely because deterministic models cannot be used to describe the uncertain behavior of bacterial populations. We used the Poisson distribution as a representative probability distribution to estimate the variability in bacterial numbers during the inactivation process. Strains of four serotypes of Salmonella enterica, three serotypes of enterohemorrhagic Escherichia coli, and one serotype of Listeria monocytogenes were evaluated for survival. We prepared bacterial cell numbers following a Poisson distribution (indicated by the parameter λ, which was equal to 2) and plated the cells in 96-well microplates, which were stored in a desiccated environment at 10% to 20% relative humidity and at 5, 15, and 25°C. The survival or death of the bacterial cells in each well was confirmed by adding tryptic soy broth as an enrichment culture. Changes in the Poisson distribution parameter during the inactivation process, which represent the variability in the numbers of surviving bacteria, were described by nonlinear regression with an exponential function based on a Weibull distribution. We also examined random changes in the number of surviving bacteria using a random number generator and computer simulations to determine whether the number of surviving bacteria followed a Poisson distribution during the bacterial death process by use of the Poisson process. For small initial cell numbers, more than 80% of the simulated distributions (λ = 2 or 10) followed a Poisson distribution. The results demonstrate that variability in the number of surviving bacteria can be described as a Poisson distribution by use of the model developed by use of the Poisson process. IMPORTANCE We developed a model to enable the quantitative assessment of bacterial survivors of inactivation procedures because the presence of even one bacterium can cause foodborne disease. The results demonstrate that the variability in the numbers of surviving bacteria was described as a Poisson distribution by use of the model developed by use of the Poisson process. Description of the number of surviving bacteria as a probability distribution rather than as the point estimates used in a deterministic approach can provide a more realistic estimation of risk. The probability model should be useful for estimating the quantitative risk of bacterial survival during inactivation. PMID:27940547

Modeling Stochastic Variability in the Numbers of Surviving Salmonella enterica, Enterohemorrhagic Escherichia coli, and Listeria monocytogenes Cells at the Single-Cell Level in a Desiccated Environment.

PubMed

Koyama, Kento; Hokunan, Hidekazu; Hasegawa, Mayumi; Kawamura, Shuso; Koseki, Shigenobu

2017-02-15

Despite effective inactivation procedures, small numbers of bacterial cells may still remain in food samples. The risk that bacteria will survive these procedures has not been estimated precisely because deterministic models cannot be used to describe the uncertain behavior of bacterial populations. We used the Poisson distribution as a representative probability distribution to estimate the variability in bacterial numbers during the inactivation process. Strains of four serotypes of Salmonella enterica, three serotypes of enterohemorrhagic Escherichia coli, and one serotype of Listeria monocytogenes were evaluated for survival. We prepared bacterial cell numbers following a Poisson distribution (indicated by the parameter λ, which was equal to 2) and plated the cells in 96-well microplates, which were stored in a desiccated environment at 10% to 20% relative humidity and at 5, 15, and 25°C. The survival or death of the bacterial cells in each well was confirmed by adding tryptic soy broth as an enrichment culture. Changes in the Poisson distribution parameter during the inactivation process, which represent the variability in the numbers of surviving bacteria, were described by nonlinear regression with an exponential function based on a Weibull distribution. We also examined random changes in the number of surviving bacteria using a random number generator and computer simulations to determine whether the number of surviving bacteria followed a Poisson distribution during the bacterial death process by use of the Poisson process. For small initial cell numbers, more than 80% of the simulated distributions (λ = 2 or 10) followed a Poisson distribution. The results demonstrate that variability in the number of surviving bacteria can be described as a Poisson distribution by use of the model developed by use of the Poisson process. We developed a model to enable the quantitative assessment of bacterial survivors of inactivation procedures because the presence of even one bacterium can cause foodborne disease. The results demonstrate that the variability in the numbers of surviving bacteria was described as a Poisson distribution by use of the model developed by use of the Poisson process. Description of the number of surviving bacteria as a probability distribution rather than as the point estimates used in a deterministic approach can provide a more realistic estimation of risk. The probability model should be useful for estimating the quantitative risk of bacterial survival during inactivation. Copyright © 2017 Koyama et al.

Economic Adversity Transitions From Childhood to Older Adulthood Are Differentially Associated With Later-Life Physical Performance Measures in Men and Women in Middle and High-Income Sites.

PubMed

Hwang, Phoebe W; Dos Santos Gomes, Cristiano; Auais, Mohammad; Braun, Kathryn L; Guralnik, Jack M; Pirkle, Catherine M

2017-10-01

This study examines the relationship between economic adversity transitions from childhood to older adulthood and older adulthood physical performance among 1,998 community-dwelling older adults from five demographically diverse sites from middle and high-income countries. The principal exposure variable was economic adversity transition. No adversity encompassed not experiencing poverty in both childhood and older adulthood, improved described having only experienced poverty in childhood, worsened captured having experienced poverty in older adulthood, and severe is having experienced poverty in both childhood and older adulthood. The short physical performance battery (SPPB) was used for outcome measures. Analyses of the continuous SPPB score used linear regression, while analysis of a binary outcome (SPPB < 8 vs. ≥8) used Poisson regression models with robust error variance, both adjusting for sex, education, and site location. In sex-stratified models, the SPPB < 8 prevalence rate ratio (PRR) was higher for the severe (PRR: 2.80, 95% confidence interval [CI] = [1.70, 4.61]), worsened (PRR: 2.40, 95% CI = [1.41, 4.09]), and improved (PRR: 1.82, 95% CI = [1.11, 3.01]) groups, compared with those with no adversity in childhood or as adults, but only for females. Findings from this study indicate that persistent economic adversity has a negative effect on older adult physical performance, especially among women.

Sexual Orientation Disparities in Weight Status in Adolescence: Findings From a Prospective Study

PubMed Central

Austin, S. Bryn; Ziyadeh, Najat J.; Corliss, Heather L.; Haines, Jess; Rockett, Helaine; Wypij, David; Field, Alison E.

2009-01-01

A growing number of studies among adult women have documented disparities in overweight adversely affecting lesbian and bisexual women, but few studies have examined sexual orientation-related patterns in weight status among men or adolescents. We examined sexual orientation group trends in body mass index (BMI; kg/m2), BMI Z-scores, and overweight using 56,990 observations from 13,785 adolescent females and males in the Growing Up Today Study, a large prospective cohort of U.S. youth. Participants provided self-reported information from six waves of questionnaire data collection from 1998 to 2005. Gender-stratified linear regression models were used to estimate BMI and BMI Z-score and modified Poisson regression models to estimate risk ratios (RR) for overweight, controlling for age and race/ethnicity, with heterosexuals as the referent group. Among females, we observed fairly consistently elevated BMI in all sexual orientation minority groups relative to heterosexual peers. In contrast, among males we documented a sexual-orientation-by-age interaction indicating steeper increases in BMI with age from early to late adolescence in heterosexuals relative to sexual orientation minorities. Additional prospective research is needed to understand the determinants of observed sexual orientation disparities and to inform appropriate preventive and treatment interventions. The long-term health consequences of overweight are well-documented and over time are likely to exact a high toll on populations with elevated rates. PMID:19300430

Comparison Between Linear and Non-parametric Regression Models for Genome-Enabled Prediction in Wheat

PubMed Central

Pérez-Rodríguez, Paulino; Gianola, Daniel; González-Camacho, Juan Manuel; Crossa, José; Manès, Yann; Dreisigacker, Susanne

2012-01-01

In genome-enabled prediction, parametric, semi-parametric, and non-parametric regression models have been used. This study assessed the predictive ability of linear and non-linear models using dense molecular markers. The linear models were linear on marker effects and included the Bayesian LASSO, Bayesian ridge regression, Bayes A, and Bayes B. The non-linear models (this refers to non-linearity on markers) were reproducing kernel Hilbert space (RKHS) regression, Bayesian regularized neural networks (BRNN), and radial basis function neural networks (RBFNN). These statistical models were compared using 306 elite wheat lines from CIMMYT genotyped with 1717 diversity array technology (DArT) markers and two traits, days to heading (DTH) and grain yield (GY), measured in each of 12 environments. It was found that the three non-linear models had better overall prediction accuracy than the linear regression specification. Results showed a consistent superiority of RKHS and RBFNN over the Bayesian LASSO, Bayesian ridge regression, Bayes A, and Bayes B models. PMID:23275882

Comparison between linear and non-parametric regression models for genome-enabled prediction in wheat.

PubMed

Pérez-Rodríguez, Paulino; Gianola, Daniel; González-Camacho, Juan Manuel; Crossa, José; Manès, Yann; Dreisigacker, Susanne

2012-12-01

In genome-enabled prediction, parametric, semi-parametric, and non-parametric regression models have been used. This study assessed the predictive ability of linear and non-linear models using dense molecular markers. The linear models were linear on marker effects and included the Bayesian LASSO, Bayesian ridge regression, Bayes A, and Bayes B. The non-linear models (this refers to non-linearity on markers) were reproducing kernel Hilbert space (RKHS) regression, Bayesian regularized neural networks (BRNN), and radial basis function neural networks (RBFNN). These statistical models were compared using 306 elite wheat lines from CIMMYT genotyped with 1717 diversity array technology (DArT) markers and two traits, days to heading (DTH) and grain yield (GY), measured in each of 12 environments. It was found that the three non-linear models had better overall prediction accuracy than the linear regression specification. Results showed a consistent superiority of RKHS and RBFNN over the Bayesian LASSO, Bayesian ridge regression, Bayes A, and Bayes B models.

Gender Differences in the Effects of the Frequency of Physical Activity on the Incidence of Metabolic Syndrome: Results from a Middle-Aged Community Cohort in Taiwan.

PubMed

Chen, Sheng-Pyng; Chang, Huan-Cheng; Hsiao, Tien-Mu; Yeh, Chih-Jung; Yang, Hao-Jan

2018-06-01

Little is known about how the frequency of physical activity in adults influences the occurrence of metabolic syndrome (MetS), and whether there are gender differences within these effects. In this study, 3368 residents from the established "Landseed Cohort" underwent three waves of health examinations, and those who did not have MetS at baseline were selected and analyzed using a multiple Poisson regression model. By calculating the adjusted relative risk (ARR), the linear and nonlinear relationships between the frequency of physical activity and risk of developing MetS were examined for male and female participants. The prevalence of MetS was fairly stable across the three waves (ranging from 16.24% to 16.82%), but the incidence dropped from 7.11% to 4.52%. The risk of MetS in women was 10 times higher than that in men (ARR = 10.06; 95% CI = 6.60-15.33), and frequent exercise was shown to help prevent it. The frequency of exercise had a linear dose-response effect in females and an exponential protective effect in males on the occurrence of MetS. Exercising more than four times a week for females and twice or more a week for males effectively reduced the risk of developing MetS. The frequency of physical activity in adults was negatively related to the risk of developing MetS, and this relationship differed based on gender. The protective effect of physical activity on MetS was linear in females and exponential in males.

The Equivalence of Information-Theoretic and Likelihood-Based Methods for Neural Dimensionality Reduction

PubMed Central

Williamson, Ross S.; Sahani, Maneesh; Pillow, Jonathan W.

2015-01-01

Stimulus dimensionality-reduction methods in neuroscience seek to identify a low-dimensional space of stimulus features that affect a neuron’s probability of spiking. One popular method, known as maximally informative dimensions (MID), uses an information-theoretic quantity known as “single-spike information” to identify this space. Here we examine MID from a model-based perspective. We show that MID is a maximum-likelihood estimator for the parameters of a linear-nonlinear-Poisson (LNP) model, and that the empirical single-spike information corresponds to the normalized log-likelihood under a Poisson model. This equivalence implies that MID does not necessarily find maximally informative stimulus dimensions when spiking is not well described as Poisson. We provide several examples to illustrate this shortcoming, and derive a lower bound on the information lost when spiking is Bernoulli in discrete time bins. To overcome this limitation, we introduce model-based dimensionality reduction methods for neurons with non-Poisson firing statistics, and show that they can be framed equivalently in likelihood-based or information-theoretic terms. Finally, we show how to overcome practical limitations on the number of stimulus dimensions that MID can estimate by constraining the form of the non-parametric nonlinearity in an LNP model. We illustrate these methods with simulations and data from primate visual cortex. PMID:25831448

Isolated and synergistic effects of PM10 and average temperature on cardiovascular and respiratory mortality.

PubMed

Pinheiro, Samya de Lara Lins de Araujo; Saldiva, Paulo Hilário Nascimento; Schwartz, Joel; Zanobetti, Antonella

2014-12-01

OBJECTIVE To analyze the effect of air pollution and temperature on mortality due to cardiovascular and respiratory diseases. METHODS We evaluated the isolated and synergistic effects of temperature and particulate matter with aerodynamic diameter < 10 µm (PM10) on the mortality of individuals > 40 years old due to cardiovascular disease and that of individuals > 60 years old due to respiratory diseases in Sao Paulo, SP, Southeastern Brazil, between 1998 and 2008. Three methodologies were used to evaluate the isolated association: time-series analysis using Poisson regression model, bidirectional case-crossover analysis matched by period, and case-crossover analysis matched by the confounding factor, i.e., average temperature or pollutant concentration. The graphical representation of the response surface, generated by the interaction term between these factors added to the Poisson regression model, was interpreted to evaluate the synergistic effect of the risk factors. RESULTS No differences were observed between the results of the case-crossover and time-series analyses. The percentage change in the relative risk of cardiovascular and respiratory mortality was 0.85% (0.45;1.25) and 1.60% (0.74;2.46), respectively, due to an increase of 10 μg/m3 in the PM10 concentration. The pattern of correlation of the temperature with cardiovascular mortality was U-shaped and that with respiratory mortality was J-shaped, indicating an increased relative risk at high temperatures. The values for the interaction term indicated a higher relative risk for cardiovascular and respiratory mortalities at low temperatures and high temperatures, respectively, when the pollution levels reached approximately 60 μg/m3. CONCLUSIONS The positive association standardized in the Poisson regression model for pollutant concentration is not confounded by temperature, and the effect of temperature is not confounded by the pollutant levels in the time-series analysis. The simultaneous exposure to different levels of environmental factors can create synergistic effects that are as disturbing as those caused by extreme concentrations.

The Use of a Poisson Regression to Evaluate Antihistamines and Fatal Aircraft Mishaps in Instrument Meteorological Conditions.

PubMed

Gildea, Kevin M; Hileman, Christy R; Rogers, Paul; Salazar, Guillermo J; Paskoff, Lawrence N

2018-04-01

Research indicates that first-generation antihistamine usage may impair pilot performance by increasing the likelihood of vestibular illusions, spatial disorientation, and/or cognitive impairment. Second- and third-generation antihistamines generally have fewer impairing side effects and are approved for pilot use. We hypothesized that toxicological findings positive for second- and third-generation antihistamines are less likely to be associated with pilots involved in fatal mishaps than first-generation antihistamines. The evaluated population consisted of 1475 U.S. civil pilots fatally injured between September 30, 2008, and October 1, 2014. Mishap factors evaluated included year, weather conditions, airman rating, recent airman flight time, quarter of year, and time of day. Due to the low prevalence of positive antihistamine findings, a count-based model was selected, which can account for rare outcomes. The means and variances were close for both regression models supporting the assumption that the data follow a Poisson distribution; first-generation antihistamine mishap airmen (N = 582, M = 0.17, S2 = 0.17) with second- and third-generation antihistamine mishap airmen (N = 116, M = 0.20, S2 = 0.18). The data indicate fewer airmen with second- and third-generation antihistamines than first-generation antihistamines in their system are fatally injured while flying in IMC conditions. Whether the lower incidence is a factor of greater usage of first-generation antihistamines versus second- and third-generation antihistamines by the pilot population or fewer deleterious side effects with second- and third-generation antihistamines is unclear. These results engender cautious optimism, but additional research is necessary to determine why these differences exist.Gildea KM, Hileman CR, Rogers P, Salazar GJ, Paskoff LN. The use of a Poisson regression to evaluate antihistamines and fatal aircraft mishaps in instrument meteorological conditions. Aerosp Med Hum Perform. 2018; 89(4):389-395.

Fine particulate air pollution and all-cause mortality within the Harvard Six-Cities Study: variations in risk by period of exposure.

PubMed

Villeneuve, Paul J; Goldberg, Mark S; Krewski, Daniel; Burnett, Richard T; Chen, Yue

2002-11-01

We used Poisson regression methods to examine the relation between temporal changes in the levels of fine particulate air pollution (PM(2.5)) and the risk of mortality among participants of the Harvard Six Cities longitudinal study. Our analyses were based on 1430 deaths that occurred between 1974 and 1991 in a cohort that accumulated 105,714 person-years of follow-up. For each city, indices of PM(2.5) were derived using daily samples. Individual level data were collected on several risk factors including: smoking, education, body mass index (BMI), and occupational exposure to dusts. Time-dependent indices of PM(2.5) were created across 13 calendar periods (< 1979, 1979, 1980, em leader, 1989, >/= 1990) to explore whether recent or chronic exposures were more important predictors of mortality. The relative risk (RR) of mortality calculated using Poisson regression based on average city-specific exposures that remained constant during follow-up was 1.31 [95% confidence interval (CI) = 1.12-1.52] per 18.6 microg/m(3) of PM(2.5). This result was similar to the risk calculated using the Cox model (RR = 1.26, 95% CI = 1.08-1.46). The RR of mortality was attenuated when the Poisson regression model included a time-dependent estimate of exposure (RR = 1.19, 95% CI = 1.04-1.36). There was little variation in RR across time-dependent indices of PM(2.5). The attenuated risk of mortality that was observed with a time-dependent index of PM(2.5) is due to the combined influence of city-specific variations in mortality rates and decreasing levels of air pollution that occurred during follow-up. The RR of mortality associated with PM(2.5) did not depend on when exposure occurred in relation to death, possibly because of little variation between the time-dependent city-specific exposure indices.

Technical report. The application of probability-generating functions to linear-quadratic radiation survival curves.

PubMed

Kendal, W S

2000-04-01

To illustrate how probability-generating functions (PGFs) can be employed to derive a simple probabilistic model for clonogenic survival after exposure to ionizing irradiation. Both repairable and irreparable radiation damage to DNA were assumed to occur by independent (Poisson) processes, at intensities proportional to the irradiation dose. Also, repairable damage was assumed to be either repaired or further (lethally) injured according to a third (Bernoulli) process, with the probability of lethal conversion being directly proportional to dose. Using the algebra of PGFs, these three processes were combined to yield a composite PGF that described the distribution of lethal DNA lesions in irradiated cells. The composite PGF characterized a Poisson distribution with mean, chiD+betaD2, where D was dose and alpha and beta were radiobiological constants. This distribution yielded the conventional linear-quadratic survival equation. To test the composite model, the derived distribution was used to predict the frequencies of multiple chromosomal aberrations in irradiated human lymphocytes. The predictions agreed well with observation. This probabilistic model was consistent with single-hit mechanisms, but it was not consistent with binary misrepair mechanisms. A stochastic model for radiation survival has been constructed from elementary PGFs that exactly yields the linear-quadratic relationship. This approach can be used to investigate other simple probabilistic survival models.

Nonparametric Inference of Doubly Stochastic Poisson Process Data via the Kernel Method

PubMed Central

Zhang, Tingting; Kou, S. C.

2010-01-01

Doubly stochastic Poisson processes, also known as the Cox processes, frequently occur in various scientific fields. In this article, motivated primarily by analyzing Cox process data in biophysics, we propose a nonparametric kernel-based inference method. We conduct a detailed study, including an asymptotic analysis, of the proposed method, and provide guidelines for its practical use, introducing a fast and stable regression method for bandwidth selection. We apply our method to real photon arrival data from recent single-molecule biophysical experiments, investigating proteins' conformational dynamics. Our result shows that conformational fluctuation is widely present in protein systems, and that the fluctuation covers a broad range of time scales, highlighting the dynamic and complex nature of proteins' structure. PMID:21258615

Nonparametric Inference of Doubly Stochastic Poisson Process Data via the Kernel Method.

PubMed

Zhang, Tingting; Kou, S C

2010-01-01

Doubly stochastic Poisson processes, also known as the Cox processes, frequently occur in various scientific fields. In this article, motivated primarily by analyzing Cox process data in biophysics, we propose a nonparametric kernel-based inference method. We conduct a detailed study, including an asymptotic analysis, of the proposed method, and provide guidelines for its practical use, introducing a fast and stable regression method for bandwidth selection. We apply our method to real photon arrival data from recent single-molecule biophysical experiments, investigating proteins' conformational dynamics. Our result shows that conformational fluctuation is widely present in protein systems, and that the fluctuation covers a broad range of time scales, highlighting the dynamic and complex nature of proteins' structure.

Heatwaves differentially affect risk of Salmonella serotypes.

PubMed

Milazzo, Adriana; Giles, Lynne C; Zhang, Ying; Koehler, Ann P; Hiller, Janet E; Bi, Peng

2016-09-01

Given increasing frequency of heatwaves and growing public health concerns associated with foodborne disease, we examined the relationship between heatwaves and salmonellosis in Adelaide, Australia. Poisson regression analysis with Generalised Estimating Equations was used to estimate the effect of heatwaves and the impact of intensity, duration and timing on salmonellosis and specific serotypes notified from 1990 to 2012. Distributed lag non-linear models were applied to assess the non-linear and delayed effects of temperature during heatwaves on Salmonella cases. Salmonella typhimurium PT135 notifications were sensitive to the effects of heatwaves with a twofold (IRR 2.08, 95% CI 1.14-3.79) increase in cases relative to non-heatwave days. Heatwave intensity had a significant effect on daily counts of overall salmonellosis with a 34% increase in risk of infection (IRR 1.34, 95% CI 1.01-1.78) at >41 °C. The effects of temperature during heatwaves on Salmonella cases and serotypes were found at lags of up to 14 days. This study confirms heatwaves have a significant effect on Salmonella cases, and for the first time, identifies its impact on specific serotypes and phage types. These findings will contribute to the understanding of the impact of heatwaves on salmonellosis and provide insights that could mitigate their impact. Copyright © 2016 The British Infection Association. Published by Elsevier Ltd. All rights reserved.

Impact of temperature variation between adjacent days on childhood hand, foot and mouth disease during April and July in urban and rural Hefei, China

NASA Astrophysics Data System (ADS)

Cheng, Jian; Zhu, Rui; Xu, Zhiwei; Wu, Jinju; Wang, Xu; Li, Kesheng; Wen, Liying; Yang, Huihui; Su, Hong

2016-06-01

Previous studies have found that both high temperature and low temperature increase the risk of childhood hand, foot and mouth disease (HFMD). However, little is known about whether temperature variation between neighboring days has any effects on childhood HFMD. A Poisson generalized linear regression model, combined with a distributed lag non-linear model, was applied to examine the relationship between temperature change and childhood HFMD in Hefei, China, from 1st January 2010 to 31st December 2012. Temperature change was defined as the difference of current day's mean temperature and previous day's mean temperature. Late spring and early summer (April-July) were chosen as the main study period due to it having the highest childhood HFMD incidence. There was a statistical association between temperature change between neighboring days and childhood HFMD. The effects of temperature change on childhood HFMD increased below a temperature change of 0 °C (temperature drop). The temperature change has the greatest adverse effect on childhood HFMD at 7 days lag, with 4 % (95 % confidence interval 2-7 %) increase per 3 °C drop of temperature. Male children and urban children appeared to be more vulnerable to the effects of temperature change. Temperature change between adjacent days might be an alternative temperature indictor for exploring the temperature-HFMD relationship.

Impact of temperature variation between adjacent days on childhood hand, foot and mouth disease during April and July in urban and rural Hefei, China.

PubMed

Cheng, Jian; Zhu, Rui; Xu, Zhiwei; Wu, Jinju; Wang, Xu; Li, Kesheng; Wen, Liying; Yang, Huihui; Su, Hong

2016-06-01

Previous studies have found that both high temperature and low temperature increase the risk of childhood hand, foot and mouth disease (HFMD). However, little is known about whether temperature variation between neighboring days has any effects on childhood HFMD. A Poisson generalized linear regression model, combined with a distributed lag non-linear model, was applied to examine the relationship between temperature change and childhood HFMD in Hefei, China, from 1st January 2010 to 31st December 2012. Temperature change was defined as the difference of current day's mean temperature and previous day's mean temperature. Late spring and early summer (April-July) were chosen as the main study period due to it having the highest childhood HFMD incidence. There was a statistical association between temperature change between neighboring days and childhood HFMD. The effects of temperature change on childhood HFMD increased below a temperature change of 0 °C (temperature drop). The temperature change has the greatest adverse effect on childhood HFMD at 7 days lag, with 4 % (95 % confidence interval 2-7 %) increase per 3 °C drop of temperature. Male children and urban children appeared to be more vulnerable to the effects of temperature change. Temperature change between adjacent days might be an alternative temperature indictor for exploring the temperature-HFMD relationship.

Effects of weather variability and air pollutants on emergency admissions for cardiovascular and cerebrovascular diseases.

PubMed

Hori, Aya; Hashizume, Masahiro; Tsuda, Yoko; Tsukahara, Teruomi; Nomiyama, Tetsuo

2012-01-01

We examined the effect of ambient temperature, air pressure and air pollutants on daily emergency admissions by identifying the cause of admission for each type of stroke and cardiovascular disease using generalized linear Poisson regression models allowing for overdispersion, and controlling for seasonal and inter-annual variations, days of the week and public holidays, levels of influenza and respiratory syncytial viruses. Every 1°C decrease in mean temperature was associated with an increase in the daily number of emergency admissions by 7.83% (95% CI 2.06-13.25) for acute coronary syndrome (ACS) and heart failure, by 35.57% (95% CI 15.59-59.02) for intracerebral haemorrhage (ICH) and by 11.71% (95% CI 4.1-19.89) for cerebral infarction. An increase of emergency admissions due to ICH (3.25% (95% CI 0.94-5.51)), heart failure (3.56% (95% CI 1.09-5.96)) was observed at every 1 hPa decrease in air pressure from the previous days. We found stronger detrimental effect of cold on stroke than cardiovascular disease.

Body mass index, body esteem, and unprotected receptive anal intercourse among young men who have sex with men who seek partners online.

PubMed

Meanley, Steven; Hickok, Andrew; Johns, Michelle Marie; Pingel, Emily S; Bauermeister, José A

2014-05-01

Research examining the relationship between body mass index (BMI) and sexual risk outcomes among men who have sex with men (MSM) has yielded inconsistent results. Using a web-based survey, single-identified (e.g., not in a relationship) young MSM (N = 431) between the ages of 18 and 24 years who sought romantic partners online were asked to respond to items regarding their BMI, body image (e.g., attribution, dissatisfaction, and pride), and sexual risk behaviors. We used Poisson regressions to examine the relationships between BMI, body image, and the number of unprotected receptive anal intercourse (URAI) occasions and partners in the past 2 months. We found a curvilinear relationship between BMI and URAI occasions, and a linear relationship between BMI and URAI partners. These relationships persisted after accounting for body image. Further, we found that body attribution served as a protective factor whereas body pride served as a risk factor. We discuss the implications of our findings for sexual health education and HIV prevention.

Short-term association between air pollution and emergency room admissions for chronic obstructive pulmonary disease in Nis, Serbia.

PubMed

Milutinović, Suzana; Nikić, Dragana; Stosić, Ljiljana; Stanković, Aleksandra; Bogdanović, Dragan

2009-03-01

The present study assesses the short-term association between black smoke (BS) and sulphur dioxide (SO2) levels in urban air and the daily number of emergency room admissions for chronic obstructive pulmonary disease (COPD) in Nis, Serbia. Generalised linear models extending Poisson regression were fitted controlling for time trend, seasonal variations, days of the week, temperature, relative humidity, air pressure, precipitation, rainfall, snowfall, overcast, and wind velocity. The emergency room admissions for all ages for COPD were significantly associated with previous-day level of BS and lag 0-2 (1,60% and 2,26% increase per 10 microg/m3, respectively). After controlling for SO2, single lagged (lag 1 and lag 2) as well as mean lagged values of BS (up to lag 0-3) were significantly associated with COPD emergencies. No effect was found for SO2, even after controlling for black smoke. The present findings support the conclusion that current levels of ambient BS may have an effect on the respiratory health of susceptible persons.

Control of diabetes and fibrinogen levels as well as improvement in health care might delay low cognitive performance in societies aging progressively.

PubMed

Lopes, Daniele Almeida; Moraes, Suzana Alves de; Freitas, Isabel Cristina Martins de

2015-01-01

To know the prevalence and factors associated to low cognitive performance in a representative sample of the adult population in a society aging progressively. Cross-sectional population-based study carried out in a three-stage sampling: 81 census tracts (primary sampling unity) were randomly selected, followed by 1,672 households and 2,471 participants (weighted sample) corresponding to the second and third stages, respectively. The outcome prevalence was calculated according sociodemographic, behavioral and health related variables. Crude and adjusted prevalence ratios were estimated using Poisson regression. The prevalence of low cognitive performance was high, mainly among females, and indicated linear trends into categories of age, schooling, income, plasma fibrinogen and self-reported health status. In multivariate models, gender, diabetes, fibrinogen and self-reported health status presented positive associations, while schooling, employment and sitting time presented negative associations with the outcome. Interventions related to diabetes and fibrinogen levels control as well as improvement in health care might delay low cognitive performance in societies aging progressively as such the study population.

Multidisciplinary perspective intervention with community involvement to decrease antibiotic sales in village groceries in Thailand.

PubMed

Arparsrithongsagul, Somsak; Kulsomboon, Vithaya; Zuckerman, Ilene H

2015-03-01

In Thailand, antibiotics are rampantly available in village groceries, despite the fact that it is illegal to sell antibiotics without a pharmacy license. This study implemented a multidisciplinary perspectives intervention with community involvement (MPI&CI), which was developed based on information obtained from focus groups that included multidisciplinary stakeholders. Community leaders in the intervention group were trained to implement MPI&CI in their villages. A quasi-experiment with a pretest-posttest design was conducted. Data were collected from 20 villages in Mahasarakham Province (intervention group) along with another 20 villages (comparison group). Using a generalized linear mixed model Poisson regression with repeated measures, groceries in the intervention group had 87% fewer antibiotics available at postintervention compared with preintervention (relative rate = 0.13; 95% confidence interval = 0.07-0.23), whereas the control group had only an 8% reduction in antibiotic availability (relative rate = 0.92; 95% confidence interval = 0.88-0.97) between the 2 time periods. Further study should be made to assess the sustainability and long-term effectiveness of MPI&CI. © 2013 APJPH.

Promoting physical activity and quality of life in Vitoria, Brazil: evaluation of the Exercise Orientation Service (EOS) program.

PubMed

Reis, Rodrigo S; Hino, Adriano Akira F; Cruz, Danielle K; da Silva Filho, Lourival Espiridião; Malta, Deborah C; Domingues, Marlos R; Hallal, Pedro C

2014-01-01

The purpose of this study was to evaluate associations between exposure to the Exercise Orientation Service (EOS) program and physical activity (PA) and quality of life (QoL) in adults from Vitoria, Brazil. A phone survey was conducted with 2023 randomly selected participants (≥ 18 years) to measure awareness about the program, participation in the program, PA levels, and QoL. The associations were tested using Poisson and Linear regression models. 31.5% reported awareness about the program, 1.5% reported current participation, and 5.8% reported previous participation. Participation was higher among women (2.1%), older subjects (2.8%), and those reporting morbidities (2.4%). Awareness was higher among middle-aged persons (36.0%) and highly educated participants (37.1%). Current participation (PR = 2.22; 95% CI = 1.65-2.99) and awareness (PR = 1.15; 95% CI = 1.02-1.30) were associated with leisure-time PA (LTPA). Exposure to the program was not associated with QoL but was consistently associated with sufficient levels of LTPA among adults from Vitoria, Brazil.

Reanalysis of the occurrence of back pain among construction workers: modelling for the interdependent effects of heavy physical work, earlier back accidents, and aging.

PubMed

Nurminen, M

1997-11-01

To re-examine the relation between heavy physical work and the occurrence of sciatic pain among construction workers reported previously to be absent in an epidemiological study. METHODS-Poisson log linear regression was used to model for the frequency of sciatic pain among concrete reinforcement workers and maintenance house painters with adjustment for the interactive effects of earlier back accidents and aging that modified the relation. Concrete reinforcement work not only had a direct effect on the frequency of sciatic pain, but it also contributed significantly to the risk indirectly through earlier back accidents. The risk of sciatic pain increased from age 25 to 54 in a different manner for a worker depending on his occupational group and record of back accidents. Epidemiological studies on low back pain need to be analysed with sound methodology. This is important in view of future meta-analyses that will be performed for the purpose of providing guidelines on the prevention of back disorders in heavy physical work.

Genetic parameters and signatures of selection in two divergent laying hen lines selected for feather pecking behaviour.

PubMed

Grams, Vanessa; Wellmann, Robin; Preuß, Siegfried; Grashorn, Michael A; Kjaer, Jörgen B; Bessei, Werner; Bennewitz, Jörn

2015-09-30

Feather pecking (FP) in laying hens is a well-known and multi-factorial behaviour with a genetic background. In a selection experiment, two lines were developed for 11 generations for high (HFP) and low (LFP) feather pecking, respectively. Starting with the second generation of selection, there was a constant difference in mean number of FP bouts between both lines. We used the data from this experiment to perform a quantitative genetic analysis and to map selection signatures. Pedigree and phenotypic data were available for the last six generations of both lines. Univariate quantitative genetic analyses were conducted using mixed linear and generalized mixed linear models assuming a Poisson distribution. Selection signatures were mapped using 33,228 single nucleotide polymorphisms (SNPs) genotyped on 41 HFP and 34 LFP individuals of generation 11. For each SNP, we estimated Wright's fixation index (FST). We tested the null hypothesis that FST is driven purely by genetic drift against the alternative hypothesis that it is driven by genetic drift and selection. The mixed linear model failed to analyze the LFP data because of the large number of 0s in the observation vector. The Poisson model fitted the data well and revealed a small but continuous genetic trend in both lines. Most of the 17 genome-wide significant SNPs were located on chromosomes 3 and 4. Thirteen clusters with at least two significant SNPs within an interval of 3 Mb maximum were identified. Two clusters were mapped on chromosomes 3, 4, 8 and 19. Of the 17 genome-wide significant SNPs, 12 were located within the identified clusters. This indicates a non-random distribution of significant SNPs and points to the presence of selection sweeps. Data on FP should be analysed using generalised linear mixed models assuming a Poisson distribution, especially if the number of FP bouts is small and the distribution is heavily peaked at 0. The FST-based approach was suitable to map selection signatures that need to be confirmed by linkage or association mapping.

Gestational diabetes in the United States: temporal changes in prevalence rates between 1979 and 2010.

PubMed

Lavery, J A; Friedman, A M; Keyes, K M; Wright, J D; Ananth, C V

2017-04-01

To examine age-period-cohort effects on trends in gestational diabetes mellitus (GDM) prevalence in the US, and to evaluate how these trends have affected the rates of stillbirth and large for gestational age (LGA)/macrosomia. Retrospective cohort study. USA, 1979-2010. Over 125 million pregnancies (3 337 284 GDM cases) associated with hospitalisations. Trends in GDM prevalence were examined via weighted Poisson models to parse out the extent to which GDM trends can be attributed to maternal age, period of delivery, and maternal birth cohort. Multilevel models were used to assess the contribution of population effects to the rate of GDM. Log-linear Poisson regression models were used to estimate the contributions of the increasing GDM rates to changes in the rates of LGA and stillbirth between 1979-81 and 2008-10. Rates and rate ratios (RRs). Compared with 1979-1980 (0.3%), the rate of GDM has increased to 5.8% in 2008-10, indicating a strong period effect. Substantial age and modest cohort effects were evident. The period effect is partly explained by period trends in body mass index (BMI), race, and maternal smoking. The increasing prevalence of GDM is associated with a 184% (95% CI 180-188%) decline in the rate of LGA/macrosomia and a 0.75% (95% CI 0.74-0.76) increase in the rate of stillbirths for 2008-10, compared with 1979-81. The temporal increase in GDM can be attributed to period of pregnancy and age. Increasing BMI appears to partially contribute to the GDM increase in the US. The increasing prevalence of GDM can be attributed to period of delivery and increasing maternal age. © 2016 Royal College of Obstetricians and Gynaecologists.

Chronic Exposure to Fine Particles and Mortality: An Extended Follow-up of the Harvard Six Cities Study from 1974 to 2009

PubMed Central

Laden, Francine; Dockery, Douglas; Schwartz, Joel

2012-01-01

Background: Epidemiologic studies have reported associations between fine particles (aerodynamic diameter ≤ 2.5 µm; PM2.5) and mortality. However, concerns have been raised regarding the sensitivity of the results to model specifications, lower exposures, and averaging time. Objective: We addressed these issues using 11 additional years of follow-up of the Harvard Six Cities study, incorporating recent lower exposures. Methods: We replicated the previously applied Cox regression, and examined different time lags, the shape of the concentration–response relationship using penalized splines, and changes in the slope of the relation over time. We then conducted Poisson survival analysis with time-varying effects for smoking, sex, and education. Results: Since 2001, average PM2.5 levels, for all six cities, were < 18 µg/m3. Each increase in PM2.5 (10 µg/m3) was associated with an adjusted increased risk of all-cause mortality (PM2.5 average on previous year) of 14% [95% confidence interval (CI): 7, 22], and with 26% (95% CI: 14, 40) and 37% (95% CI: 7, 75) increases in cardiovascular and lung-cancer mortality (PM2.5 average of three previous years), respectively. The concentration–response relationship was linear down to PM2.5 concentrations of 8 µg/m3. Mortality rate ratios for PM2.5 fluctuated over time, but without clear trends despite a substantial drop in the sulfate fraction. Poisson models produced similar results. Conclusions: These results suggest that further public policy efforts that reduce fine particulate matter air pollution are likely to have continuing public health benefits. PMID:22456598

Nonlinear and anisotropic tensile properties of graft materials used in soft tissue applications.

PubMed

Yoder, Jonathon H; Elliott, Dawn M

2010-05-01

The mechanical properties of extracellular matrix grafts that are intended to augment or replace soft tissues should be comparable to the native tissue. Such grafts are often used in fiber-reinforced tissue applications that undergo multi-axial loading and therefore knowledge of the anisotropic and nonlinear properties are needed, including the moduli and Poisson's ratio in two orthogonal directions within the plane of the graft. The objective of this study was to measure the tensile mechanical properties of several marketed grafts: Alloderm, Restore, CuffPatch, and OrthADAPT. The degree of anisotropy and non-linearity within each graft was evaluated from uniaxial tensile tests and compared to their native tissue. The Alloderm graft was anisotropic in both the toe- and linear-region of the stress-strain response, was highly nonlinear, and generally had low properties. The Restore and CuffPatch grafts had similar stress-strain responses, were largely isotropic, had a linear-region modulus of 18MPa, and were nonlinear. OrthADAPT was anisotropic in the linear-region (131 MPA vs 47MPa in the toe-region) and was highly nonlinear. The Poisson ratio for all grafts was between 0.4 and 0.7, except for the parallel orientation of Restore which was greater than 1.0. Having an informed understanding of how the available grafts perform mechanically will allow for better assessment by the physician for which graft to apply depending upon its application. Copyright 2010 Elsevier Ltd. All rights reserved.

Parallel SOR methods with a parabolic-diffusion acceleration technique for solving an unstructured-grid Poisson equation on 3D arbitrary geometries

NASA Astrophysics Data System (ADS)

Zapata, M. A. Uh; Van Bang, D. Pham; Nguyen, K. D.

2016-05-01

This paper presents a parallel algorithm for the finite-volume discretisation of the Poisson equation on three-dimensional arbitrary geometries. The proposed method is formulated by using a 2D horizontal block domain decomposition and interprocessor data communication techniques with message passing interface. The horizontal unstructured-grid cells are reordered according to the neighbouring relations and decomposed into blocks using a load-balanced distribution to give all processors an equal amount of elements. In this algorithm, two parallel successive over-relaxation methods are presented: a multi-colour ordering technique for unstructured grids based on distributed memory and a block method using reordering index following similar ideas of the partitioning for structured grids. In all cases, the parallel algorithms are implemented with a combination of an acceleration iterative solver. This solver is based on a parabolic-diffusion equation introduced to obtain faster solutions of the linear systems arising from the discretisation. Numerical results are given to evaluate the performances of the methods showing speedups better than linear.

Single-Specimen Technique to Establish the J-Resistance of Linear Viscoelastic Solids with Constant Poisson's Ratio

NASA Technical Reports Server (NTRS)

Gutierrez-Lemini, Danton; McCool, Alex (Technical Monitor)

2001-01-01

A method is developed to establish the J-resistance function for an isotropic linear viscoelastic solid of constant Poisson's ratio using the single-specimen technique with constant-rate test data. The method is based on the fact that, for a test specimen of fixed crack size under constant rate, the initiation J-integral may be established from the crack size itself, the actual external load and load-point displacement at growth initiation, and the relaxation modulus of the viscoelastic solid, without knowledge of the complete test record. Since crack size alone, of the required data, would be unknown at each point of the load-vs-load-point displacement curve of a single-specimen test, an expression is derived to estimate it. With it, the physical J-integral at each point of the test record may be established. Because of its basis on single-specimen testing, not only does the method not require the use of multiple specimens with differing initial crack sizes, but avoids the need for tracking crack growth as well.

Occurrence of Conotruncal Heart Birth Defects in Texas: A Comparison of Urban/Rural Classifications

ERIC Educational Resources Information Center

Langlois, Peter H.; Jandle, Leigh; Scheuerle, Angela; Horel, Scott A.; Carozza, Susan E.

2010-01-01

Purpose: (1) Determine if there is an association between 3 conotruncal heart birth defects and urban/rural residence of mother. (2) Compare results using different methods of measuring urban/rural status. Methods: Data were taken from the Texas Birth Defects Registry, 1999-2003. Poisson regression was used to compare crude and adjusted birth…

Pseudo-second order models for the adsorption of safranin onto activated carbon: comparison of linear and non-linear regression methods.

PubMed

Kumar, K Vasanth

2007-04-02

Kinetic experiments were carried out for the sorption of safranin onto activated carbon particles. The kinetic data were fitted to pseudo-second order model of Ho, Sobkowsk and Czerwinski, Blanchard et al. and Ritchie by linear and non-linear regression methods. Non-linear method was found to be a better way of obtaining the parameters involved in the second order rate kinetic expressions. Both linear and non-linear regression showed that the Sobkowsk and Czerwinski and Ritchie's pseudo-second order models were the same. Non-linear regression analysis showed that both Blanchard et al. and Ho have similar ideas on the pseudo-second order model but with different assumptions. The best fit of experimental data in Ho's pseudo-second order expression by linear and non-linear regression method showed that Ho pseudo-second order model was a better kinetic expression when compared to other pseudo-second order kinetic expressions.

Dynamic spatiotemporal analysis of indigenous dengue fever at street-level in Guangzhou city, China

PubMed Central

Xia, Yao; Zhang, Yingtao; Huang, Xiaodong; Huang, Jiawei; Nie, Enqiong; Jing, Qinlong; Wang, Guoling; Yang, Zhicong; Hu, Wenbiao

2018-01-01

Background This study aimed to investigate the spatiotemporal clustering and socio-environmental factors associated with dengue fever (DF) incidence rates at street level in Guangzhou city, China. Methods Spatiotemporal scan technique was applied to identify the high risk region of DF. Multiple regression model was used to identify the socio-environmental factors associated with DF infection. A Poisson regression model was employed to examine the spatiotemporal patterns in the spread of DF. Results Spatial clusters of DF were primarily concentrated at the southwest part of Guangzhou city. Age group (65+ years) (Odd Ratio (OR) = 1.49, 95% Confidence Interval (CI) = 1.13 to 2.03), floating population (OR = 1.09, 95% CI = 1.05 to 1.15), low-education (OR = 1.08, 95% CI = 1.01 to 1.16) and non-agriculture (OR = 1.07, 95% CI = 1.03 to 1.11) were associated with DF transmission. Poisson regression results indicated that changes in DF incidence rates were significantly associated with longitude (β = -5.08, P<0.01) and latitude (β = -1.99, P<0.01). Conclusions The study demonstrated that social-environmental factors may play an important role in DF transmission in Guangzhou. As geographic range of notified DF has significantly expanded over recent years, an early warning systems based on spatiotemporal model with socio-environmental is urgently needed to improve the effectiveness and efficiency of dengue control and prevention. PMID:29561835

Dynamic spatiotemporal analysis of indigenous dengue fever at street-level in Guangzhou city, China.

PubMed

Liu, Kangkang; Zhu, Yanshan; Xia, Yao; Zhang, Yingtao; Huang, Xiaodong; Huang, Jiawei; Nie, Enqiong; Jing, Qinlong; Wang, Guoling; Yang, Zhicong; Hu, Wenbiao; Lu, Jiahai

2018-03-01

This study aimed to investigate the spatiotemporal clustering and socio-environmental factors associated with dengue fever (DF) incidence rates at street level in Guangzhou city, China. Spatiotemporal scan technique was applied to identify the high risk region of DF. Multiple regression model was used to identify the socio-environmental factors associated with DF infection. A Poisson regression model was employed to examine the spatiotemporal patterns in the spread of DF. Spatial clusters of DF were primarily concentrated at the southwest part of Guangzhou city. Age group (65+ years) (Odd Ratio (OR) = 1.49, 95% Confidence Interval (CI) = 1.13 to 2.03), floating population (OR = 1.09, 95% CI = 1.05 to 1.15), low-education (OR = 1.08, 95% CI = 1.01 to 1.16) and non-agriculture (OR = 1.07, 95% CI = 1.03 to 1.11) were associated with DF transmission. Poisson regression results indicated that changes in DF incidence rates were significantly associated with longitude (β = -5.08, P<0.01) and latitude (β = -1.99, P<0.01). The study demonstrated that social-environmental factors may play an important role in DF transmission in Guangzhou. As geographic range of notified DF has significantly expanded over recent years, an early warning systems based on spatiotemporal model with socio-environmental is urgently needed to improve the effectiveness and efficiency of dengue control and prevention.

Lifestyle and clinical factors associated with elevated C-reactive protein among newly diagnosed Type 2 diabetes mellitus patients: a cross-sectional study from the nationwide DD2 cohort.

PubMed

Svensson, Elisabeth; Mor, Anil; Rungby, Jørgen; Berencsi, Klara; Nielsen, Jens Steen; Stidsen, Jacob V; Friborg, Søren; Brandslund, Ivan; Christiansen, Jens Sandahl; Beck-Nielsen, Henning; Sørensen, Henrik Toft; Thomsen, Reimar W

2014-08-28

We aimed to examine the prevalence of and modifiable factors associated with elevated C-reactive Protein (CRP), a marker of inflammation, in men and women with newly diagnosed Type 2 Diabetes mellitus (DM) in a population-based setting. CRP was measured in 1,037 patients (57% male) with newly diagnosed Type 2 DM included in the prospective nationwide Danish Centre for Strategic Research in Type 2 Diabetes (DD2) project. We assessed the prevalence of elevated CRP and calculated relative risks (RR) examining the association of CRP with lifestyle and clinical factors by Poisson regression, stratified by gender. We used linear regression to examine the association of CRP with other biomarkers. The median CRP value was 2.1 mg/L (interquartile range, 1.0 - 4.8 mg/L). In total, 405 out of the 1,037 Type 2 DM patients (40%) had elevated CRP levels (>3.0 mg/L). More women (46%) than men (34%) had elevated CRP. Among women, a lower risk of elevated CRP was observed in patients receiving statins (adjusted RR (aRR) 0.7 (95% confidence interval (CI) 0.6-0.9)), whereas a higher risk was seen in patients with central obesity (aRR 2.3 (95% CI 1.0-5.3)). For men, CRP was primarily elevated among patients with no regular physical activity (aRR 1.5 (95% CI 1.1-1.9)), previous cardiovascular disease (aRR1.5 (95% CI 1.2-1.9) and other comorbidity. For both genders, elevated CRP was 1.4-fold increased in those with weight gain >30 kg since age 20 years. Sensitivity analyses showed consistent results with the full analysis. The linear regression analysis conveyed an association between high CRP and increased fasting blood glucose. Among newly diagnosed Type 2 DM patients, 40% had elevated CRP levels. Important modifiable risk factors for elevated CRP may vary by gender, and include low physical activity for men and central obesity and absence of statin use for women.

Beta-Poisson model for single-cell RNA-seq data analyses.

PubMed

Vu, Trung Nghia; Wills, Quin F; Kalari, Krishna R; Niu, Nifang; Wang, Liewei; Rantalainen, Mattias; Pawitan, Yudi

2016-07-15

Single-cell RNA-sequencing technology allows detection of gene expression at the single-cell level. One typical feature of the data is a bimodality in the cellular distribution even for highly expressed genes, primarily caused by a proportion of non-expressing cells. The standard and the over-dispersed gamma-Poisson models that are commonly used in bulk-cell RNA-sequencing are not able to capture this property. We introduce a beta-Poisson mixture model that can capture the bimodality of the single-cell gene expression distribution. We further integrate the model into the generalized linear model framework in order to perform differential expression analyses. The whole analytical procedure is called BPSC. The results from several real single-cell RNA-seq datasets indicate that ∼90% of the transcripts are well characterized by the beta-Poisson model; the model-fit from BPSC is better than the fit of the standard gamma-Poisson model in > 80% of the transcripts. Moreover, in differential expression analyses of simulated and real datasets, BPSC performs well against edgeR, a conventional method widely used in bulk-cell RNA-sequencing data, and against scde and MAST, two recent methods specifically designed for single-cell RNA-seq data. An R package BPSC for model fitting and differential expression analyses of single-cell RNA-seq data is available under GPL-3 license at https://github.com/nghiavtr/BPSC CONTACT: yudi.pawitan@ki.se or mattias.rantalainen@ki.se Supplementary data are available at Bioinformatics online. © The Author 2016. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

Localization of intense electromagnetic waves in a relativistically hot plasma.

PubMed

Shukla, P K; Eliasson, B

2005-02-18

We consider nonlinear interactions between intense short electromagnetic waves (EMWs) and a relativistically hot electron plasma that supports relativistic electron holes (REHs). It is shown that such EMW-REH interactions are governed by a coupled nonlinear system of equations composed of a nonlinear Schro dinger equation describing the dynamics of the EMWs and the Poisson-relativistic Vlasov system describing the dynamics of driven REHs. The present nonlinear system of equations admits both a linearly trapped discrete number of eigenmodes of the EMWs in a quasistationary REH and a modification of the REH by large-amplitude trapped EMWs. Computer simulations of the relativistic Vlasov and Maxwell-Poisson system of equations show complex interactions between REHs loaded with localized EMWs.

Measures of clustering and heterogeneity in multilevel Poisson regression analyses of rates/count data

PubMed Central

Austin, Peter C.; Stryhn, Henrik; Leckie, George; Merlo, Juan

2017-01-01

Multilevel data occur frequently in many research areas like health services research and epidemiology. A suitable way to analyze such data is through the use of multilevel regression models. These models incorporate cluster‐specific random effects that allow one to partition the total variation in the outcome into between‐cluster variation and between‐individual variation. The magnitude of the effect of clustering provides a measure of the general contextual effect. When outcomes are binary or time‐to‐event in nature, the general contextual effect can be quantified by measures of heterogeneity like the median odds ratio or the median hazard ratio, respectively, which can be calculated from a multilevel regression model. Outcomes that are integer counts denoting the number of times that an event occurred are common in epidemiological and medical research. The median (incidence) rate ratio in multilevel Poisson regression for counts that corresponds to the median odds ratio or median hazard ratio for binary or time‐to‐event outcomes respectively is relatively unknown and is rarely used. The median rate ratio is the median relative change in the rate of the occurrence of the event when comparing identical subjects from 2 randomly selected different clusters that are ordered by rate. We also describe how the variance partition coefficient, which denotes the proportion of the variation in the outcome that is attributable to between‐cluster differences, can be computed with count outcomes. We illustrate the application and interpretation of these measures in a case study analyzing the rate of hospital readmission in patients discharged from hospital with a diagnosis of heart failure. PMID:29114926

Spatiotemporal analysis of the relationship between socioeconomic factors and stroke in the Portuguese mainland population under 65 years old.

PubMed

Oliveira, André; Cabral, António J R; Mendes, Jorge M; Martins, Maria R O; Cabral, Pedro

2015-11-04

Stroke risk has been shown to display varying patterns of geographic distribution amongst countries but also between regions of the same country. Traditionally a disease of older persons, a global 25% increase in incidence instead was noticed between 1990 and 2010 in persons aged 20-≤64 years, particularly in low- and medium-income countries. Understanding spatial disparities in the association between socioeconomic factors and stroke is critical to target public health initiatives aiming to mitigate or prevent this disease, including in younger persons. We aimed to identify socioeconomic determinants of geographic disparities of stroke risk in people <65 years old, in municipalities of mainland Portugal, and the spatiotemporal variation of the association between these determinants and stroke risk during two study periods (1992-1996 and 2002-2006). Poisson and negative binomial global regression models were used to explore determinants of disease risk. Geographically weighted regression (GWR) represents a distinctive approach, allowing estimation of local regression coefficients. Models for both study periods were identified. Significant variables included education attainment, work hours per week and unemployment. Local Poisson GWR models achieved the best fit and evidenced spatially varying regression coefficients. Spatiotemporal inequalities were observed in significant variables, with dissimilarities between men and women. This study contributes to a better understanding of the relationship between stroke and socioeconomic factors in the population <65 years of age, one age group seldom analysed separately. It can thus help to improve the targeting of public health initiatives, even more in a context of economic crisis.

WE-G-18A-02: Calibration-Free Combined KV/MV Short Scan CBCT

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

Wu, M; Loo, B; Bazalova, M

Purpose: To combine orthogonal kilo-voltage (kV) and Mega-voltage (MV) projection data for short scan cone-beam CT to reduce imaging time on current radiation treatment systems, using a calibration-free gain correction method. Methods: Combining two orthogonal projection data sets for kV and MV imaging hardware can reduce the scan angle to as small as 110° (90°+fan) such that the total scan time is ∼18 seconds, or within a breath hold. To obtain an accurate reconstruction, the MV projection data is first linearly corrected using linear regression using the redundant data from the start and end of the sinogram, and then themore » combined data is reconstructed using the FDK method. To correct for the different changes of attenuation coefficients in kV/MV between soft tissue and bone, the forward projection of the segmented bone and soft tissue from the first reconstruction in the redundant region are added to the linear regression model. The MV data is corrected again using the additional information from the segmented image, and combined with kV for a second FDK reconstruction. We simulated polychromatic 120 kVp (conventional a-Si EPID with CsI) and 2.5 MVp (prototype high-DQE MV detector) projection data with Poisson noise using the XCAT phantom. The gain correction and combined kV/MV short scan reconstructions were tested with head and thorax cases, and simple contrast-to-noise ratio measurements were made in a low-contrast pattern in the head. Results: The FDK reconstruction using the proposed gain correction method can effectively reduce artifacts caused by the differences of attenuation coefficients in the kV/MV data. The CNRs of the short scans for kV, MV, and kV/MV are 5.0, 2.6 and 3.4 respectively. The proposed gain correction method also works with truncated projections. Conclusion: A novel gain correction and reconstruction method was developed to generate short scan CBCT from orthogonal kV/MV projections. This work is supported by NIH Grant 5R01CA138426-05.« less

A Technique of Fuzzy C-Mean in Multiple Linear Regression Model toward Paddy Yield

NASA Astrophysics Data System (ADS)

Syazwan Wahab, Nur; Saifullah Rusiman, Mohd; Mohamad, Mahathir; Amira Azmi, Nur; Che Him, Norziha; Ghazali Kamardan, M.; Ali, Maselan

2018-04-01

In this paper, we propose a hybrid model which is a combination of multiple linear regression model and fuzzy c-means method. This research involved a relationship between 20 variates of the top soil that are analyzed prior to planting of paddy yields at standard fertilizer rates. Data used were from the multi-location trials for rice carried out by MARDI at major paddy granary in Peninsular Malaysia during the period from 2009 to 2012. Missing observations were estimated using mean estimation techniques. The data were analyzed using multiple linear regression model and a combination of multiple linear regression model and fuzzy c-means method. Analysis of normality and multicollinearity indicate that the data is normally scattered without multicollinearity among independent variables. Analysis of fuzzy c-means cluster the yield of paddy into two clusters before the multiple linear regression model can be used. The comparison between two method indicate that the hybrid of multiple linear regression model and fuzzy c-means method outperform the multiple linear regression model with lower value of mean square error.

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.

Linear regression crash prediction models : issues and proposed solutions.

DOT National Transportation Integrated Search

2010-05-01

The paper develops a linear regression model approach that can be applied to : crash data to predict vehicle crashes. The proposed approach involves novice data aggregation : to satisfy linear regression assumptions; namely error structure normality ...

A linear model of population dynamics

NASA Astrophysics Data System (ADS)

Lushnikov, A. A.; Kagan, A. I.

2016-08-01

The Malthus process of population growth is reformulated in terms of the probability w(n,t) to find exactly n individuals at time t assuming that both the birth and the death rates are linear functions of the population size. The master equation for w(n,t) is solved exactly. It is shown that w(n,t) strongly deviates from the Poisson distribution and is expressed in terms either of Laguerre’s polynomials or a modified Bessel function. The latter expression allows for considerable simplifications of the asymptotic analysis of w(n,t).

Analysis and control of hourglass instabilities in underintegrated linear and nonlinear elasticity

NASA Technical Reports Server (NTRS)

Jacquotte, Olivier P.; Oden, J. Tinsley

1994-01-01

Methods are described to identify and correct a bad finite element approximation of the governing operator obtained when under-integration is used in numerical code for several model problems: the Poisson problem, the linear elasticity problem, and for problems in the nonlinear theory of elasticity. For each of these problems, the reason for the occurrence of instabilities is given, a way to control or eliminate them is presented, and theorems of existence, uniqueness, and convergence for the given methods are established. Finally, numerical results are included which illustrate the theory.

Comparison between Linear and Nonlinear Regression in a Laboratory Heat Transfer Experiment

ERIC Educational Resources Information Center

Gonçalves, Carine Messias; Schwaab, Marcio; Pinto, José Carlos

2013-01-01

In order to interpret laboratory experimental data, undergraduate students are used to perform linear regression through linearized versions of nonlinear models. However, the use of linearized models can lead to statistically biased parameter estimates. Even so, it is not an easy task to introduce nonlinear regression and show for the students…

Beyond Poisson-Boltzmann: Fluctuation effects and correlation functions

NASA Astrophysics Data System (ADS)

Netz, R. R.; Orland, H.

2000-02-01

We formulate the exact non-linear field theory for a fluctuating counter-ion distribution in the presence of a fixed, arbitrary charge distribution. The Poisson-Boltzmann equation is obtained as the saddle-point of the field-theoretic action, and the effects of counter-ion fluctuations are included by a loop-wise expansion around this saddle point. The Poisson equation is obeyed at each order in this loop expansion. We explicitly give the expansion of the Gibbs potential up to two loops. We then apply our field-theoretic formalism to the case of a single impenetrable wall with counter ions only (in the absence of salt ions). We obtain the fluctuation corrections to the electrostatic potential and the counter-ion density to one-loop order without further approximations. The relative importance of fluctuation corrections is controlled by a single parameter, which is proportional to the cube of the counter-ion valency and to the surface charge density. The effective interactions and correlation functions between charged particles close to the charged wall are obtained on the one-loop level.

A Hierarchical Poisson Log-Normal Model for Network Inference from RNA Sequencing Data

PubMed Central

Gallopin, Mélina; Rau, Andrea; Jaffrézic, Florence

2013-01-01

Gene network inference from transcriptomic data is an important methodological challenge and a key aspect of systems biology. Although several methods have been proposed to infer networks from microarray data, there is a need for inference methods able to model RNA-seq data, which are count-based and highly variable. In this work we propose a hierarchical Poisson log-normal model with a Lasso penalty to infer gene networks from RNA-seq data; this model has the advantage of directly modelling discrete data and accounting for inter-sample variance larger than the sample mean. Using real microRNA-seq data from breast cancer tumors and simulations, we compare this method to a regularized Gaussian graphical model on log-transformed data, and a Poisson log-linear graphical model with a Lasso penalty on power-transformed data. For data simulated with large inter-sample dispersion, the proposed model performs better than the other methods in terms of sensitivity, specificity and area under the ROC curve. These results show the necessity of methods specifically designed for gene network inference from RNA-seq data. PMID:24147011

Overdispersion of the Molecular Clock: Temporal Variation of Gene-Specific Substitution Rates in Drosophila

PubMed Central

Hartl, Daniel L.

2008-01-01

Simple models of molecular evolution assume that sequences evolve by a Poisson process in which nucleotide or amino acid substitutions occur as rare independent events. In these models, the expected ratio of the variance to the mean of substitution counts equals 1, and substitution processes with a ratio greater than 1 are called overdispersed. Comparing the genomes of 10 closely related species of Drosophila, we extend earlier evidence for overdispersion in amino acid replacements as well as in four-fold synonymous substitutions. The observed deviation from the Poisson expectation can be described as a linear function of the rate at which substitutions occur on a phylogeny, which implies that deviations from the Poisson expectation arise from gene-specific temporal variation in substitution rates. Amino acid sequences show greater temporal variation in substitution rates than do four-fold synonymous sequences. Our findings provide a general phenomenological framework for understanding overdispersion in the molecular clock. Also, the presence of substantial variation in gene-specific substitution rates has broad implications for work in phylogeny reconstruction and evolutionary rate estimation. PMID:18480070

Hydrodynamic representation of the Klein-Gordon-Einstein equations in the weak field limit: General formalism and perturbations analysis

NASA Astrophysics Data System (ADS)

Suárez, Abril; Chavanis, Pierre-Henri

2015-07-01

Using a generalization of the Madelung transformation, we derive the hydrodynamic representation of the Klein-Gordon-Einstein equations in the weak field limit. We consider a complex self-interacting scalar field with a λ |φ |4 potential. We study the evolution of the spatially homogeneous background in the fluid representation and derive the linearized equations describing the evolution of small perturbations in a static and in an expanding Universe. We compare the results with simplified models in which the gravitational potential is introduced by hand in the Klein-Gordon equation, and assumed to satisfy a (generalized) Poisson equation. Nonrelativistic hydrodynamic equations based on the Schrödinger-Poisson equations or on the Gross-Pitaevskii-Poisson equations are recovered in the limit c →+∞. We study the evolution of the perturbations in the matter era using the nonrelativistic limit of our formalism. Perturbations whose wavelength is below the Jeans length oscillate in time while perturbations whose wavelength is above the Jeans length grow linearly with the scale factor as in the cold dark matter model. The growth of perturbations in the scalar field model is substantially faster than in the cold dark matter model. When the wavelength of the perturbations approaches the cosmological horizon (Hubble length), a relativistic treatment is mandatory. In that case, we find that relativistic effects attenuate or even prevent the growth of perturbations. This paper exposes the general formalism and provides illustrations in simple cases. Other applications of our formalism will be considered in companion papers.

The Application of the Cumulative Logistic Regression Model to Automated Essay Scoring

ERIC Educational Resources Information Center

Haberman, Shelby J.; Sinharay, Sandip

2010-01-01

Most automated essay scoring programs use a linear regression model to predict an essay score from several essay features. This article applied a cumulative logit model instead of the linear regression model to automated essay scoring. Comparison of the performances of the linear regression model and the cumulative logit model was performed on a…

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

Moradi, Afshin, E-mail: a.moradi@kut.ac.ir

We develop the Maxwell-Garnett theory for the effective medium approximation of composite materials with metallic nanoparticles by taking into account the quantum spatial dispersion effects in dielectric response of nanoparticles. We derive a quantum nonlocal generalization of the standard Maxwell-Garnett formula, by means the linearized quantum hydrodynamic theory in conjunction with the Poisson equation as well as the appropriate additional quantum boundary conditions.

Meta-analysis of haplotype-association studies: comparison of methods and empirical evaluation of the literature

PubMed Central

2011-01-01

Background Meta-analysis is a popular methodology in several fields of medical research, including genetic association studies. However, the methods used for meta-analysis of association studies that report haplotypes have not been studied in detail. In this work, methods for performing meta-analysis of haplotype association studies are summarized, compared and presented in a unified framework along with an empirical evaluation of the literature. Results We present multivariate methods that use summary-based data as well as methods that use binary and count data in a generalized linear mixed model framework (logistic regression, multinomial regression and Poisson regression). The methods presented here avoid the inflation of the type I error rate that could be the result of the traditional approach of comparing a haplotype against the remaining ones, whereas, they can be fitted using standard software. Moreover, formal global tests are presented for assessing the statistical significance of the overall association. Although the methods presented here assume that the haplotypes are directly observed, they can be easily extended to allow for such an uncertainty by weighting the haplotypes by their probability. Conclusions An empirical evaluation of the published literature and a comparison against the meta-analyses that use single nucleotide polymorphisms, suggests that the studies reporting meta-analysis of haplotypes contain approximately half of the included studies and produce significant results twice more often. We show that this excess of statistically significant results, stems from the sub-optimal method of analysis used and, in approximately half of the cases, the statistical significance is refuted if the data are properly re-analyzed. Illustrative examples of code are given in Stata and it is anticipated that the methods developed in this work will be widely applied in the meta-analysis of haplotype association studies. PMID:21247440

Physical Function, Hyperuricemia, and Gout in Older Adults.

PubMed

Burke, Bridget Teevan; Köttgen, Anna; Law, Andrew; Windham, Beverly Gwen; Segev, Dorry; Baer, Alan N; Coresh, Josef; McAdams-DeMarco, Mara A

2015-12-01

Gout prevalence is high in older adults and those affected are at risk of physical disability, yet it is unclear whether they have worse physical function. We studied gout, hyperuricemia, and physical function in 5,819 older adults (age ≥65 years) attending the 2011-2013 Atherosclerosis Risk in Communities Study visit, a prospective US population-based cohort. Differences in lower extremity function (Short Physical Performance Battery [SPPB] and 4-meter walking speed) and upper extremity function (grip strength) by gout status and by hyperuricemia prevalence were estimated in adjusted ordinal logistic regression (SPPB) and linear regression (walking speed and grip strength) models. Lower scores or times signify worse function. The prevalence of poor physical performance (first quartile) by gout and hyperuricemia was estimated using adjusted modified Poisson regression. Ten percent of participants reported a history of gout and 21% had hyperuricemia. There was no difference in grip strength by history of gout (P = 0.77). Participants with gout performed worse on the SPPB test; they had 0.77 times (95% confidence interval [95% CI] 0.65, 0.90, P = 0.001) the prevalence odds of a 1-unit increase in SPPB score and were 1.18 times (95% CI 1.07, 1.32, P = 0.002) more likely to have poor SPPB performance. Participants with a history of gout had slower walking speed (mean difference -0.03; 95% CI -0.05, -0.01, P < 0.001) and were 1.19 times (95% CI 1.06, 1.34, P = 0.003) more likely to have poor walking speed. Similarly, SPPB score and walking speed, but not grip strength, were worse in participants with hyperuricemia. Older adults with gout and hyperuricemia are more likely to have worse lower extremity, but not upper extremity, function. © 2015, American College of Rheumatology.

The impact of sleep disorders on driving safety-findings from the Second Strategic Highway Research Program naturalistic driving study.

PubMed

Liu, Shu-Yuan; Perez, Miguel A; Lau, Nathan

2018-04-01

This study investigated the association between driving safety and seven sleep disorders amongst 3541 participants of the Second Strategic Highway Research Program (SHRP 2) naturalistic driving study. SHRP 2 collected naturalistic driving data from participants between 16 and 98 years old by instrumenting participants' vehicles. The analyses used logistic regression to determine the likelihood of crash or near-crash involvement, Poisson log-linear regression to assess crash or near-crash rate, and ordinal logistic regression to assess driver maneuver appropriateness and crash or near-crash severity. These analyses did not account for any medical treatments for the sleep disorders. Females with restless legs syndrome/Willis-Ekbom disease (RLS/WED), drivers with insomnia or narcolepsy, are associated with significantly higher risk of crash or near-crash. Drivers with shift work sleep disorder (SWSD) are associated with significantly increased crash or near-crash rate. Females with RLS/WED or sleep apnea and drivers with SWSD are associated with less safe driver maneuver and drivers with periodic limb movement disorder are associated with more severe events. The four analyses provide no evidence of safety decrements associated with migraine. This study is the first examination on the association between seven sleep disorders and different measures of driving risk using large-scale naturalistic driving study data. The results corroborate much of the existing simulator and epidemiological research related to sleep-disorder patients and their driving safety, but add ecological validity to those findings. These results contribute to the empirical basis for medical professionals, policy makers, and employers in making decisions to aid individuals with sleep disorders in balancing safety and personal mobility.

Rates of Femicide in Women of Different Races, Ethnicities, and Places of Birth: Massachusetts, 1993-2007

ERIC Educational Resources Information Center

Azziz-Baumgartner, Eduardo; McKeown, Loreta; Melvin, Patrice; Dang, Quynh; Reed, Joan

2011-01-01

To describe the epidemiology of intimate partner violence (IPV) homicide in Massachusetts, an IPV mortality data set developed by the Massachusetts Department of Public Health was analyzed. The rates of death were estimated by dividing the number of decedents over the aged-matched population and Poisson regression was used to estimate the…

Growth Curve Models for Zero-Inflated Count Data: An Application to Smoking Behavior

ERIC Educational Resources Information Center

Liu, Hui; Powers, Daniel A.

2007-01-01

This article applies growth curve models to longitudinal count data characterized by an excess of zero counts. We discuss a zero-inflated Poisson regression model for longitudinal data in which the impact of covariates on the initial counts and the rate of change in counts over time is the focus of inference. Basic growth curve models using a…

Relations Between Residential Proximity to EPA-Designated Toxic Release Sites and Diffuse Large B-Cell Lymphoma Incidence.

PubMed

Bulka, Catherine; Nastoupil, Loretta J; Koff, Jean L; Bernal-Mizrachi, Leon; Ward, Kevin C; Williams, Jessica N; Bayakly, A Rana; Switchenko, Jeffrey M; Waller, Lance A; Flowers, Christopher R

2016-10-01

Examining the spatial patterns of diffuse large B-cell lymphoma (DLBCL) incidence and residential proximity to toxic release locations may provide insight regarding environmental and sociodemographic risk factors. We linked and geocoded cancer incidence data for the period 1999-2008 from the Georgia Comprehensive Cancer Registry with population data from the US Census and the Environmental Protection Agency's Toxics Release Inventory. We conducted cluster analyses and constructed Poisson regression models to assess DLBCL incidence as a function of mean distance to the toxic release sites. In total, 3851 incident DLBCL cases occurred among adults residing in Georgia between 1999 and 2008. Significant focal clustering was observed around 57% of ethylene oxide sites, 5% of benzene sites, 9% of tetrachloroethylene sites, 7% of styrene sites, 10% of formaldehyde sites, 5% of trichloroethylene sites, and 10% of all release sites. Mean distance to sites was significantly associated with DLBCL risk for all chemicals. Proximity to Toxics Release Inventory sites can be linked to increased DLBCL risk as assessed through focal clustering and Poisson regression, and confirmatory studies using geospatial mapping can aid in further specifying risk factors for DLBCL.

Prediction of Short-Distance Aerial Movement of Phakopsora pachyrhizi Urediniospores Using Machine Learning.

PubMed

Wen, L; Bowen, C R; Hartman, G L

2017-10-01

Dispersal of urediniospores by wind is the primary means of spread for Phakopsora pachyrhizi, the cause of soybean rust. Our research focused on the short-distance movement of urediniospores from within the soybean canopy and up to 61 m from field-grown rust-infected soybean plants. Environmental variables were used to develop and compare models including the least absolute shrinkage and selection operator regression, zero-inflated Poisson/regular Poisson regression, random forest, and neural network to describe deposition of urediniospores collected in passive and active traps. All four models identified distance of trap from source, humidity, temperature, wind direction, and wind speed as the five most important variables influencing short-distance movement of urediniospores. The random forest model provided the best predictions, explaining 76.1 and 86.8% of the total variation in the passive- and active-trap datasets, respectively. The prediction accuracy based on the correlation coefficient (r) between predicted values and the true values were 0.83 (P < 0.0001) and 0.94 (P < 0.0001) for the passive and active trap datasets, respectively. Overall, multiple machine learning techniques identified the most important variables to make the most accurate predictions of movement of P. pachyrhizi urediniospores short-distance.

Predictors for the Number of Warning Information Sources During Tornadoes.

PubMed

Cong, Zhen; Luo, Jianjun; Liang, Daan; Nejat, Ali

2017-04-01

People may receive tornado warnings from multiple information sources, but little is known about factors that affect the number of warning information sources (WISs). This study examined predictors for the number of WISs with a telephone survey on randomly sampled residents in Tuscaloosa, Alabama, and Joplin, Missouri, approximately 1 year after both cities were struck by violent tornadoes (EF4 and EF5) in 2011. The survey included 1006 finished interviews and the working sample included 903 respondents. Poisson regression and Zero-Inflated Poisson regression showed that older age and having an emergency plan predicted more WISs in both cities. Education, marital status, and gender affected the possibilities of receiving warnings and the number of WISs either in Joplin or in Tuscaloosa. The findings suggest that social disparity affects the access to warnings not only with respect to the likelihood of receiving any warnings but also with respect to the number of WISs. In addition, historical and social contexts are important for examining predictors for the number of WISs. We recommend that the number of WISs should be regarded as an important measure to evaluate access to warnings in addition to the likelihood of receiving warnings. (Disaster Med Public Health Preparedness. 2017;11:168-172).

Association between large strongyle genera in larval cultures--using rare-event poisson regression.

PubMed

Cao, X; Vidyashankar, A N; Nielsen, M K

2013-09-01

Decades of intensive anthelmintic treatment has caused equine large strongyles to become quite rare, while the cyathostomins have developed resistance to several drug classes. The larval culture has been associated with low to moderate negative predictive values for detecting Strongylus vulgaris infection. It is unknown whether detection of other large strongyle species can be statistically associated with presence of S. vulgaris. This remains a statistical challenge because of the rare occurrence of large strongyle species. This study used a modified Poisson regression to analyse a dataset for associations between S. vulgaris infection and simultaneous occurrence of Strongylus edentatus and Triodontophorus spp. In 663 horses on 42 Danish farms, the individual prevalences of S. vulgaris, S. edentatus and Triodontophorus spp. were 12%, 3% and 12%, respectively. Both S. edentatus and Triodontophorus spp. were significantly associated with S. vulgaris infection with relative risks above 1. Further, S. edentatus was associated with use of selective therapy on the farms, as well as negatively associated with anthelmintic treatment carried out within 6 months prior to the study. The findings illustrate that occurrence of S. vulgaris in larval cultures can be interpreted as indicative of other large strongyles being likely to be present.

A Time Series Analysis: Weather Factors, Human Migration and Malaria Cases in Endemic Area of Purworejo, Indonesia, 2005–2014

PubMed Central

REJEKI, Dwi Sarwani Sri; NURHAYATI, Nunung; AJI, Budi; MURHANDARWATI, E. Elsa Herdiana; KUSNANTO, Hari

2018-01-01

Background: Climatic and weather factors become important determinants of vector-borne diseases transmission like malaria. This study aimed to prove relationships between weather factors with considering human migration and previous case findings and malaria cases in endemic areas in Purworejo during 2005–2014. Methods: This study employed ecological time series analysis by using monthly data. The independent variables were the maximum temperature, minimum temperature, maximum humidity, minimum humidity, precipitation, human migration, and previous malaria cases, while the dependent variable was positive malaria cases. Three models of count data regression analysis i.e. Poisson model, quasi-Poisson model, and negative binomial model were applied to measure the relationship. The least Akaike Information Criteria (AIC) value was also performed to find the best model. Negative binomial regression analysis was considered as the best model. Results: The model showed that humidity (lag 2), precipitation (lag 3), precipitation (lag 12), migration (lag1) and previous malaria cases (lag 12) had a significant relationship with malaria cases. Conclusion: Weather, migration and previous malaria cases factors need to be considered as prominent indicators for the increase of malaria case projection. PMID:29900134

Reanalysis of cancer mortality in Japanese A-bomb survivors exposed to low doses of radiation: bootstrap and simulation methods

PubMed Central

2009-01-01

Background The International Commission on Radiological Protection (ICRP) recommended annual occupational dose limit is 20 mSv. Cancer mortality in Japanese A-bomb survivors exposed to less than 20 mSv external radiation in 1945 was analysed previously, using a latency model with non-linear dose response. Questions were raised regarding statistical inference with this model. Methods Cancers with over 100 deaths in the 0 - 20 mSv subcohort of the 1950-1990 Life Span Study are analysed with Poisson regression models incorporating latency, allowing linear and non-linear dose response. Bootstrap percentile and Bias-corrected accelerated (BCa) methods and simulation of the Likelihood Ratio Test lead to Confidence Intervals for Excess Relative Risk (ERR) and tests against the linear model. Results The linear model shows significant large, positive values of ERR for liver and urinary cancers at latencies from 37 - 43 years. Dose response below 20 mSv is strongly non-linear at the optimal latencies for the stomach (11.89 years), liver (36.9), lung (13.6), leukaemia (23.66), and pancreas (11.86) and across broad latency ranges. Confidence Intervals for ERR are comparable using Bootstrap and Likelihood Ratio Test methods and BCa 95% Confidence Intervals are strictly positive across latency ranges for all 5 cancers. Similar risk estimates for 10 mSv (lagged dose) are obtained from the 0 - 20 mSv and 5 - 500 mSv data for the stomach, liver, lung and leukaemia. Dose response for the latter 3 cancers is significantly non-linear in the 5 - 500 mSv range. Conclusion Liver and urinary cancer mortality risk is significantly raised using a latency model with linear dose response. A non-linear model is strongly superior for the stomach, liver, lung, pancreas and leukaemia. Bootstrap and Likelihood-based confidence intervals are broadly comparable and ERR is strictly positive by bootstrap methods for all 5 cancers. Except for the pancreas, similar estimates of latency and risk from 10 mSv are obtained from the 0 - 20 mSv and 5 - 500 mSv subcohorts. Large and significant cancer risks for Japanese survivors exposed to less than 20 mSv external radiation from the atomic bombs in 1945 cast doubt on the ICRP recommended annual occupational dose limit. PMID:20003238

Transmission of linear regression patterns between time series: From relationship in time series to complex networks

NASA Astrophysics Data System (ADS)

Gao, Xiangyun; An, Haizhong; Fang, Wei; Huang, Xuan; Li, Huajiao; Zhong, Weiqiong; Ding, Yinghui

2014-07-01

The linear regression parameters between two time series can be different under different lengths of observation period. If we study the whole period by the sliding window of a short period, the change of the linear regression parameters is a process of dynamic transmission over time. We tackle fundamental research that presents a simple and efficient computational scheme: a linear regression patterns transmission algorithm, which transforms linear regression patterns into directed and weighted networks. The linear regression patterns (nodes) are defined by the combination of intervals of the linear regression parameters and the results of the significance testing under different sizes of the sliding window. The transmissions between adjacent patterns are defined as edges, and the weights of the edges are the frequency of the transmissions. The major patterns, the distance, and the medium in the process of the transmission can be captured. The statistical results of weighted out-degree and betweenness centrality are mapped on timelines, which shows the features of the distribution of the results. Many measurements in different areas that involve two related time series variables could take advantage of this algorithm to characterize the dynamic relationships between the time series from a new perspective.

Transmission of linear regression patterns between time series: from relationship in time series to complex networks.

PubMed

Gao, Xiangyun; An, Haizhong; Fang, Wei; Huang, Xuan; Li, Huajiao; Zhong, Weiqiong; Ding, Yinghui

2014-07-01

The linear regression parameters between two time series can be different under different lengths of observation period. If we study the whole period by the sliding window of a short period, the change of the linear regression parameters is a process of dynamic transmission over time. We tackle fundamental research that presents a simple and efficient computational scheme: a linear regression patterns transmission algorithm, which transforms linear regression patterns into directed and weighted networks. The linear regression patterns (nodes) are defined by the combination of intervals of the linear regression parameters and the results of the significance testing under different sizes of the sliding window. The transmissions between adjacent patterns are defined as edges, and the weights of the edges are the frequency of the transmissions. The major patterns, the distance, and the medium in the process of the transmission can be captured. The statistical results of weighted out-degree and betweenness centrality are mapped on timelines, which shows the features of the distribution of the results. Many measurements in different areas that involve two related time series variables could take advantage of this algorithm to characterize the dynamic relationships between the time series from a new perspective.

An implicit boundary integral method for computing electric potential of macromolecules in solvent

NASA Astrophysics Data System (ADS)

Zhong, Yimin; Ren, Kui; Tsai, Richard

2018-04-01

A numerical method using implicit surface representations is proposed to solve the linearized Poisson-Boltzmann equation that arises in mathematical models for the electrostatics of molecules in solvent. The proposed method uses an implicit boundary integral formulation to derive a linear system defined on Cartesian nodes in a narrowband surrounding the closed surface that separates the molecule and the solvent. The needed implicit surface is constructed from the given atomic description of the molecules, by a sequence of standard level set algorithms. A fast multipole method is applied to accelerate the solution of the linear system. A few numerical studies involving some standard test cases are presented and compared to other existing results.

Linear frictional forces cause orbits to neither circularize nor precess

NASA Astrophysics Data System (ADS)

Hamilton, B.; Crescimanno, M.

2008-06-01

For the undamped Kepler potential the lack of precession has historically been understood in terms of the Runge-Lenz symmetry. For the damped Kepler problem this result may be understood in terms of the generalization of Poisson structure to damped systems suggested recently by Tarasov (2005 J. Phys. A: Math. Gen. 38 2145). In this generalized algebraic structure the orbit-averaged Runge-Lenz vector remains a constant in the linearly damped Kepler problem to leading order in the damping coefficient. Beyond Kepler, we prove that, for any potential proportional to a power of the radius, the orbit shape and precession angle remain constant to leading order in the linear friction coefficient.

Occupational dust and radiation exposure and mortality from stomach cancer among German uranium miners, 1946-2003.

PubMed

Kreuzer, M; Straif, K; Marsh, J W; Dufey, F; Grosche, B; Nosske, D; Sogl, M

2012-03-01

'Dusty occupations' and exposure to low-dose radiation have been suggested as potential risk factors for stomach cancer. Data from the German uranium miner cohort study are used to further evaluate this topic. The cohort includes 58 677 miners with complete information on occupational exposure to dust, arsenic and radiation dose based on a detailed job-exposure matrix. A total of 592 stomach cancer deaths occurred in the follow-up period from 1946 to 2003. A Poisson regression model stratified by age and calendar year was used to calculate the excess relative risk (ERR) per unit of cumulative exposure to fine dust or from cumulative absorbed dose to stomach from α or low-LET (low linear energy transfer) radiation. For arsenic exposure, a binary quadratic model was applied. After adjustment for each of the three other variables, a statistically non-significant linear relationship was observed for absorbed dose from low-LET radiation (ERR/Gy=0.30, 95% CI -1.26 to 1.87), α radiation (ERR/Gy=22.5, 95% CI -26.5 to 71.5) and fine dust (ERR/dust-year=0.0012, 95% CI -0.0020 to 0.0043). The relationship between stomach cancer and arsenic exposure was non-linear with a 2.1-fold higher RR (95% CI 0.9 to 3.3) in the exposure category above 500 compared with 0 dust-years. Positive statistically non-significant relationships between stomach cancer and arsenic dust, fine dust and absorbed dose from α and low-LET radiation were found. Overall, low statistical power due to low doses from radiation and dust are of concern.

Global variation in the effects of ambient temperature on mortality: a systematic evaluation

PubMed Central

Guo, Yuming; Gasparrini, Antonio; Armstrong, Ben; Li, Shanshan; Tawatsupa, Benjawan; Tobias, Aurelio; Lavigne, Eric; de Sousa Zanotti Stagliorio Coelho, Micheline; Leone, Michela; Pan, Xiaochuan; Tong, Shilu; Tian, Linwei; Kim, Ho; Hashizume, Masahiro; Honda, Yasushi; Guo, Yue-Liang Leon; Wu, Chang-Fu; Punnasiri, Kornwipa; Yi, Seung-Muk; Michelozzi, Paola; Saldiva, Paulo Hilario Nascimento; Williams, Gail

2014-01-01

Background Studies have examined the effects of temperature on mortality in a single city, country or region. However, less evidence is available on the variation in the associations between temperature and mortality in multiple countries, analyzed simultaneously. Methods We obtained daily data on temperature and mortality in 306 communities from 12 countries/regions (Australia, Brazil, Thailand, China, Taiwan, Korea, Japan, Italy, Spain, United Kingdom, United States and Canada). Two-stage analyses were used to assess the non-linear and delayed relationship between temperature and mortality. In the first stage, a Poisson regression allowing over-dispersion with distributed lag non-linear model was used to estimate the community-specific temperature-mortality relationship. In the second stage, a multivariate meta-analysis was used to pool the non-linear and delayed effects of ambient temperature at the national level, in each country. Results The temperatures associated with the lowest mortality were around the 75th percentile of temperature in all the countries/regions, ranging from 66th (Taiwan) to 80th (UK) percentiles. The estimated effects of cold and hot temperatures on mortality varied by community and country. Meta-analysis results show that both cold and hot temperatures increased the risk of mortality in all the countries/regions. Cold effects were delayed and lasted for many days, while hot effects appeared quickly and did not last long. Conclusions People have some ability to adapt to their local climate type, but both cold and hot temperatures are still associated with the risk of mortality. Public health strategies to alleviate the impact of ambient temperatures are important, in particular in the context of climate change. PMID:25166878

Skin cancer incidence among atomic bomb survivors from 1958 to 1996.

PubMed

Sugiyama, Hiromi; Misumi, Munechika; Kishikawa, Masao; Iseki, Masachika; Yonehara, Shuji; Hayashi, Tomayoshi; Soda, Midori; Tokuoka, Shoji; Shimizu, Yukiko; Sakata, Ritsu; Grant, Eric J; Kasagi, Fumiyoshi; Mabuchi, Kiyohiko; Suyama, Akihiko; Ozasa, Kotaro

2014-05-01

The radiation risk of skin cancer by histological types has been evaluated in the atomic bomb survivors. We examined 80,158 of the 120,321 cohort members who had their radiation dose estimated by the latest dosimetry system (DS02). Potential skin tumors diagnosed from 1958 to 1996 were reviewed by a panel of pathologists, and radiation risk of the first primary skin cancer was analyzed by histological types using a Poisson regression model. A significant excess relative risk (ERR) of basal cell carcinoma (BCC) (n = 123) was estimated at 1 Gy (0.74, 95% confidence interval (CI): 0.26, 1.6) for those age 30 at exposure and age 70 at observation based on a linear-threshold model with a threshold dose of 0.63 Gy (95% CI: 0.32, 0.89) and a slope of 2.0 (95% CI: 0.69, 4.3). The estimated risks were 15, 5.7, 1.3 and 0.9 for age at exposure of 0-9, 10-19, 20-39, over 40 years, respectively, and the risk increased 11% with each one-year decrease in age at exposure. The ERR for squamous cell carcinoma (SCC) in situ (n = 64) using a linear model was estimated as 0.71 (95% CI: 0.063, 1.9). However, there were no significant dose responses for malignant melanoma (n = 10), SCC (n = 114), Paget disease (n = 10) or other skin cancers (n = 15). The significant linear radiation risk for BCC with a threshold at 0.63 Gy suggested that the basal cells of the epidermis had a threshold sensitivity to ionizing radiation, especially for young persons at the time of exposure.

Short-term effects of meteorological factors on hand, foot and mouth disease among children in Shenzhen, China: Non-linearity, threshold and interaction.

PubMed

Zhang, Zhen; Xie, Xu; Chen, Xiliang; Li, Yuan; Lu, Yan; Mei, Shujiang; Liao, Yuxue; Lin, Hualiang

2016-01-01

Various meteorological factors have been associated with hand, foot and mouth disease (HFMD) among children; however, fewer studies have examined the non-linearity and interaction among the meteorological factors. A generalized additive model with a log link allowing Poisson auto-regression and over-dispersion was applied to investigate the short-term effects daily meteorological factors on children HFMD with adjustment of potential confounding factors. We found positive effects of mean temperature and wind speed, the excess relative risk (ERR) was 2.75% (95% CI: 1.98%, 3.53%) for one degree increase in daily mean temperature on lag day 6, and 3.93% (95% CI: 2.16% to 5.73%) for 1m/s increase in wind speed on lag day 3. We found a non-linear effect of relative humidity with thresholds with the low threshold at 45% and high threshold at 85%, within which there was positive effect, the ERR was 1.06% (95% CI: 0.85% to 1.27%) for 1 percent increase in relative humidity on lag day 5. No significant effect was observed for rainfall and sunshine duration. For the interactive effects, we found a weak additive interaction between mean temperature and relative humidity, and slightly antagonistic interaction between mean temperature and wind speed, and between relative humidity and wind speed in the additive models, but the interactions were not statistically significant. This study suggests that mean temperature, relative humidity and wind speed might be risk factors of children HFMD in Shenzhen, and the interaction analysis indicates that these meteorological factors might have played their roles individually. Copyright © 2015 Elsevier B.V. All rights reserved.

Concentration–Response Function for Ozone and Daily Mortality: Results from Five Urban and Five Rural U.K. Populations

PubMed Central

Yu, Dahai; Armstrong, Ben G.; Pattenden, Sam; Wilkinson, Paul; Doherty, Ruth M.; Heal, Mathew R.; Anderson, H. Ross

2012-01-01

Background: Short-term exposure to ozone has been associated with increased daily mortality. The shape of the concentration–response relationship—and, in particular, if there is a threshold—is critical for estimating public health impacts. Objective: We investigated the concentration–response relationship between daily ozone and mortality in five urban and five rural areas in the United Kingdom from 1993 to 2006. Methods: We used Poisson regression, controlling for seasonality, temperature, and influenza, to investigate associations between daily maximum 8-hr ozone and daily all-cause mortality, assuming linear, linear-threshold, and spline models for all-year and season-specific periods. We examined sensitivity to adjustment for particles (urban areas only) and alternative temperature metrics. Results: In all-year analyses, we found clear evidence for a threshold in the concentration–response relationship between ozone and all-cause mortality in London at 65 µg/m3 [95% confidence interval (CI): 58, 83] but little evidence of a threshold in other urban or rural areas. Combined linear effect estimates for all-cause mortality were comparable for urban and rural areas: 0.48% (95% CI: 0.35, 0.60) and 0.58% (95% CI: 0.36, 0.81) per 10-µg/m3 increase in ozone concentrations, respectively. Seasonal analyses suggested thresholds in both urban and rural areas for effects of ozone during summer months. Conclusions: Our results suggest that health impacts should be estimated across the whole ambient range of ozone using both threshold and nonthreshold models, and models stratified by season. Evidence of a threshold effect in London but not in other study areas requires further investigation. The public health impacts of exposure to ozone in rural areas should not be overlooked. PMID:22814173

Are all quantitative postmarketing signal detection methods equal? Performance characteristics of logistic regression and Multi-item Gamma Poisson Shrinker.

PubMed

Berlin, Conny; Blanch, Carles; Lewis, David J; Maladorno, Dionigi D; Michel, Christiane; Petrin, Michael; Sarp, Severine; Close, Philippe

2012-06-01

The detection of safety signals with medicines is an essential activity to protect public health. Despite widespread acceptance, it is unclear whether recently applied statistical algorithms provide enhanced performance characteristics when compared with traditional systems. Novartis has adopted a novel system for automated signal detection on the basis of disproportionality methods within a safety data mining application (Empirica™ Signal System [ESS]). ESS uses two algorithms for routine analyses: empirical Bayes Multi-item Gamma Poisson Shrinker and logistic regression (LR). A model was developed comprising 14 medicines, categorized as "new" or "established." A standard was prepared on the basis of safety findings selected from traditional sources. ESS results were compared with the standard to calculate the positive predictive value (PPV), specificity, and sensitivity. PPVs of the lower one-sided 5% and 0.05% confidence limits of the Bayes geometric mean (EB05) and of the LR odds ratio (LR0005) almost coincided for all the drug-event combinations studied. There was no obvious difference comparing the PPV of the leading Medical Dictionary for Regulatory Activities (MedDRA) terms to the PPV for all terms. The PPV of narrow MedDRA query searches was higher than that for broad searches. The widely used threshold value of EB05 = 2.0 or LR0005 = 2.0 together with more than three spontaneous reports of the drug-event combination produced balanced results for PPV, sensitivity, and specificity. Consequently, performance characteristics were best for leading terms with narrow MedDRA query searches irrespective of applying Multi-item Gamma Poisson Shrinker or LR at a threshold value of 2.0. This research formed the basis for the configuration of ESS for signal detection at Novartis. Copyright © 2011 John Wiley & Sons, Ltd.

Preconditioner and convergence study for the Quantum Computer Aided Design (QCAD) nonlinear poisson problem posed on the Ottawa Flat 270 design geometry.

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

Kalashnikova, Irina

2012-05-01

A numerical study aimed to evaluate different preconditioners within the Trilinos Ifpack and ML packages for the Quantum Computer Aided Design (QCAD) non-linear Poisson problem implemented within the Albany code base and posed on the Ottawa Flat 270 design geometry is performed. This study led to some new development of Albany that allows the user to select an ML preconditioner with Zoltan repartitioning based on nodal coordinates, which is summarized. Convergence of the numerical solutions computed within the QCAD computational suite with successive mesh refinement is examined in two metrics, the mean value of the solution (an L{sup 1} norm)more » and the field integral of the solution (L{sup 2} norm).« less

Least median of squares and iteratively re-weighted least squares as robust linear regression methods for fluorimetric determination of α-lipoic acid in capsules in ideal and non-ideal cases of linearity.

PubMed

Korany, Mohamed A; Gazy, Azza A; Khamis, Essam F; Ragab, Marwa A A; Kamal, Miranda F

2018-06-01

This study outlines two robust regression approaches, namely least median of squares (LMS) and iteratively re-weighted least squares (IRLS) to investigate their application in instrument analysis of nutraceuticals (that is, fluorescence quenching of merbromin reagent upon lipoic acid addition). These robust regression methods were used to calculate calibration data from the fluorescence quenching reaction (∆F and F-ratio) under ideal or non-ideal linearity conditions. For each condition, data were treated using three regression fittings: Ordinary Least Squares (OLS), LMS and IRLS. Assessment of linearity, limits of detection (LOD) and quantitation (LOQ), accuracy and precision were carefully studied for each condition. LMS and IRLS regression line fittings showed significant improvement in correlation coefficients and all regression parameters for both methods and both conditions. In the ideal linearity condition, the intercept and slope changed insignificantly, but a dramatic change was observed for the non-ideal condition and linearity intercept. Under both linearity conditions, LOD and LOQ values after the robust regression line fitting of data were lower than those obtained before data treatment. The results obtained after statistical treatment indicated that the linearity ranges for drug determination could be expanded to lower limits of quantitation by enhancing the regression equation parameters after data treatment. Analysis results for lipoic acid in capsules, using both fluorimetric methods, treated by parametric OLS and after treatment by robust LMS and IRLS were compared for both linearity conditions. Copyright © 2018 John Wiley & Sons, Ltd.

Breastfeeding associated with higher lung function in African American youths with asthma.

PubMed

Oh, Sam S; Du, Randal; Zeiger, Andrew M; McGarry, Meghan E; Hu, Donglei; Thakur, Neeta; Pino-Yanes, Maria; Galanter, Joshua M; Eng, Celeste; Nishimura, Katherine Keiko; Huntsman, Scott; Farber, Harold J; Meade, Kelley; Avila, Pedro; Serebrisky, Denise; Bibbins-Domingo, Kirsten; Lenoir, Michael A; Ford, Jean G; Brigino-Buenaventura, Emerita; Rodriguez-Cintron, William; Thyne, Shannon M; Sen, Saunak; Rodriguez-Santana, Jose R; Williams, Keoki; Kumar, Rajesh; Burchard, Esteban G

2017-10-01

In the United States, Puerto Ricans and African Americans have lower prevalence of breastfeeding and worse clinical outcomes for asthma compared with other racial/ethnic groups. We hypothesize that the history of breastfeeding is associated with increased forced expiratory volume in 1 second (FEV 1 ) % predicted and reduced asthma exacerbations in Latino and African American youths with asthma. As part of the Genes-environments & Admixture in Latino Americans (GALA II) Study and the Study of African Americans, asthma, Genes & Environments (SAGE II), we conducted case-only analyses in children and adolescents aged 8-21 years with asthma from four different racial/ethnic groups: African Americans (n = 426), Mexican Americans (n = 424), mixed/other Latinos (n = 255), and Puerto Ricans (n = 629). We investigated the association between any breastfeeding in infancy and FEV 1 % predicted using multivariable linear regression; Poisson regression was used to determine the association between breastfeeding and asthma exacerbations. Prevalence of breastfeeding was lower in African Americans (59.4%) and Puerto Ricans (54.9%) compared to Mexican Americans (76.2%) and mixed/other Latinos (66.9%; p < 0.001). After adjusting for covariates, breastfeeding was associated with a 3.58% point increase in FEV 1 % predicted (p = 0.01) and a 21% reduction in asthma exacerbations (p = 0.03) in African Americans only. Breastfeeding was associated with higher FEV 1 % predicted in asthma and reduced number of asthma exacerbations in African American youths, calling attention to continued support for breastfeeding.

Testing anti-smoking messages for Air Force trainees

PubMed Central

Popova, Lucy; Linde, Brittany D.; Bursac, Zoran; Talcott, G. Wayne; Modayil, Mary V.; Little, Melissa A.; Ling, Pamela M.; Glantz, Stanton A.; Klesges, Robert C.

2015-01-01

Introduction Young adults in the military are aggressively targeted by tobacco companies and are at high risk of tobacco use. Existing anti-smoking advertisements developed for the general population might be effective in educating young adults in the military. This study evaluated the effects of different themes of existing anti-smoking advertisements on perceived harm and intentions to use cigarettes and other tobacco products among Air Force trainees. Methods In a pretest-posttest experiment, 782 Airmen were randomized to view anti-smoking advertisements in one of six conditions: anti-industry, health effects+anti-industry, sexual health, secondhand smoke, environment+anti-industry, or control. We assessed the effect of different conditions on changes in perceived harm and intentions to use cigarettes, electronic cigarettes (e-cigarettes), smokeless tobacco, hookah and cigarillos from pretest to posttest with multivariable linear regression models (perceived harm) and zero-inflated Poisson regression model (intentions). Results Anti-smoking advertisements increased perceived harm of various tobacco products and reduced intentions to use. Advertisements featuring negative effects of tobacco on health and sexual performance coupled with revealing tobacco industry manipulations had the most consistent pattern of effects on perceived harm and intentions. Conclusion Anti-smoking advertisements produced for the general public might also be effective with a young adult military population and could have spillover effects on perceptions of harm and intentions to use other tobacco products besides cigarettes. Existing anti-smoking advertising may be a cost-effective tool to educate young adults in the military. PMID:26482786

A retrospective survey of the quality of reports and their correlates among randomized controlled trials of immunotherapy for Guillain-Barré syndrome.

PubMed

Lu, Liming; Luo, Gaoquan; Xiao, Fang

2013-08-01

This study aims to assess the quality of reports and their correlates in randomized controlled trials (RCTs) of immunotherapy for Guillain-Barré syndrome (GBS). A search was performed in multiple databases of reports published between April 1992 and November 2012. Reporting quality was assessed by items of the Consolidated Standards of Reporting Trials (CONSORT) 2010 Statement. An overall quality score (OQS) and a key methodological index score (MIS) were calculated for each trial. Factors associated with OQS and MIS were then identified. A total of 19 RCTs were included in the full text. The median OQS was 7.0, with a range of 1-10. However, the quality of reporting in items of 'flow chart' and 'ancillary analyses' was poor with a positive rate of less than 40%. The median MIS was 0 with a range of 0-2. Twelve (63.2%) did not report any of the three key methodological items. Specifically, the mean OQS increased by approximately 2.73 for manuscripts published in the New England Journal of Medicine, The Lancet, Pediatrics and Neurology (95% CI: 0.35-5.12; p < 0.05). Multivariate linear regression and the Poisson regression model could not be presented as the number of included trials was too small. The reporting quality in RCTs on immunotherapy for GBS was poor, which indicated that reporting in RCTs of immunotherapy for GBS needed substantial improvement in order to meet the guideline of the CONSORT Statement.

Mental Illness-Related Stigma in Canadian Military and Civilian Populations: A Comparison Using Population Health Survey Data.

PubMed

Weeks, Murray; Zamorski, Mark A; Rusu, Corneliu; Colman, Ian

2017-07-01

This study sought to compare the prevalence and impacts of mental illness-related stigma among Canadian Armed Forces personnel and Canadian civilians. Data were from two highly comparable, population-based, cross-sectional surveys of Canadian military personnel and Canadian civilians: the 2013 Canadian Forces Mental Health Survey (N=6,696) and the 2012 Canadian Community Health Survey-Mental Health (N=25,113), respectively. Perceived stigma was assessed among those who reported care seeking for a mental health problem in the past 12 months. Follow-up questions assessed the impact of stigma in various domains. Modified Poisson regression and linear regression were used to examine population differences (military versus civilian) in terms of care seeking, stigma, and stigma impact, with adjustments for sociodemographic characteristics and the need for care. Military personnel were significantly more likely than civilians to have perceived stigma (adjusted prevalence ratio [PR]=1.70, 95% confidence interval [CI]=1.11-2.60). Stigma had a greater impact on military personnel, particularly in terms of work or school life (b=1.01, CI=.57-1.47). However, military personnel were also significantly more likely than civilians to have sought care (PR=1.86, CI=1.53-2.25). Military personnel reported a disproportionate amount of mental illness-related stigma, compared with Canadian civilians, and a greater impact of stigma. Nevertheless, military personnel were more likely to seek care, pointing to a complex relationship between stigma and care seeking in the military.

Calcitonin gene-related peptide: neuroendocrine communication between the pancreas, gut, and brain in regulation of blood glucose.

PubMed

Pendharkar, Sayali A; Walia, Monika; Drury, Marie; Petrov, Maxim S

2017-11-01

Calcitonin gene-related peptide (CGRP), a ubiquitous neuropeptide, plays a diverse and intricate role in chronic low-grade inflammation, including conditions such as obesity, type 2 diabetes, and diabetes of the exocrine pancreas. Diabetes of exocrine pancreas is characterised by chronic hyperglycemia and is associated with persistent low-grade inflammation and altered secretion of certain pancreatic and gut hormones. While CGRP may regulate glucose homeostasis and the secretion of pancreatic and gut hormones, its role in chronic hyperglycemia after acute pancreatitis (CHAP) is not known. The aim of this study was to investigate the association between CGRP and CHAP. Fasting blood samples were collected to measure insulin, HbA1c, CGRP, amylin, C-peptide, glucagon, pancreatic polypeptide (PP), somatostatin, gastric inhibitory peptide, glicentin, glucagon-like peptide-1 and 2, and oxyntomodulin. Modified Poisson regression analysis and linear regression analyses were conducted. Five statistical models were used to adjust for demographic, metabolic, and pancreatitis-related risk factors. A total of 83 patients were recruited. CGRP was significantly associated with CHAP in all five models (P-trend <0.005). Further, it was significantly associated with oxyntomodulin (P<0.005) and glucagon (P<0.030). Oxyntomodulin and glucagon independently contributed 9.7% and 7%, respectively, to circulating CGRP variance. Other pancreatic and gut hormones were not significantly associated with CGRP. CGRP is involved in regulation of blood glucose in individuals after acute pancreatitis. This may have translational implications in prevention and treatment of diabetes of the exocrine pancreas.

Use of ACE-inhibitors and falls in patients with Parkinson's disease.

PubMed

Laudisio, Alice; Lo Monaco, Maria Rita; Silveri, Maria Caterina; Bentivoglio, Anna Rita; Vetrano, Davide L; Pisciotta, Maria Stella; Brandi, Vincenzo; Bernabei, Roberto; Zuccalà, Giuseppe

2017-05-01

Falls represent a major concern in patients with Parkinson's disease (PD); however, currently acknowledged treatments for PD are not effective in reducing the risk of falling. The aim was to assess the association of use of ACE-inhibitors (ACEIs) and angiotensin receptor blockers (ARBs) with falls among patients with PD. We analysed data of 194 elderly with PD attending a geriatric Day Hospital. Self-reported history of falls that occurred over the last year, as well as use of drugs, including ACEIs and angiotensin II receptor blockers (ARBs) were recorded. The association of the occurrence of any falls with use of ACEIs, and ARBs was assessed by logistic regression analysis. The association between the number of falls and use of ACEIs, and ARBs was assessed according to Poisson regression. In logistic regression, after adjusting for potential confounders, use of ACEIs was associated with a reduced probability of falling over the last year (OR=0.15, 95% CI=0.03-0.81; P=0.028). This association did not vary with blood pressure levels (P for the interaction term=0.528). Also, using Poisson regression, use of ACEIs predicted a reduced number of falls among participants who fell (PR=0.31; 95% CI=0.10-0.94; P=0.039). No association was found between use of ARBs and falls. Our results indicate that use of ACEIs might be independently associated with reduced probability, and a reduced number of falls among patients with PD. Dedicated studies are needed to define the single agents and dosages that might most effectively reduce the risk of falling in clinical practice. Copyright © 2017 Elsevier B.V. All rights reserved.

Trends in Mortality After Primary Cytoreductive Surgery for Ovarian Cancer: A Systematic Review and Metaregression of Randomized Clinical Trials and Observational Studies.

PubMed

Di Donato, Violante; Kontopantelis, Evangelos; Aletti, Giovanni; Casorelli, Assunta; Piacenti, Ilaria; Bogani, Giorgio; Lecce, Francesca; Benedetti Panici, Pierluigi

2017-06-01

Primary cytoreductive surgery (PDS) followed by platinum-based chemotherapy is the cornerstone of treatment and the absence of residual tumor after PDS is universally considered the most important prognostic factor. The aim of the present analysis was to evaluate trend and predictors of 30-day mortality in patients undergoing primary cytoreduction for ovarian cancer. Literature was searched for records reporting 30-day mortality after PDS. All cohorts were rated for quality. Simple and multiple Poisson regression models were used to quantify the association between 30-day mortality and the following: overall or severe complications, proportion of patients with stage IV disease, median age, year of publication, and weighted surgical complexity index. Using the multiple regression model, we calculated the risk of perioperative mortality at different levels for statistically significant covariates of interest. Simple regression identified median age and proportion of patients with stage IV disease as statistically significant predictors of 30-day mortality. When included in the multiple Poisson regression model, both remained statistically significant, with an incidence rate ratio of 1.087 for median age and 1.017 for stage IV disease. Disease stage was a strong predictor, with the risk estimated to increase from 2.8% (95% confidence interval 2.02-3.66) for stage III to 16.1% (95% confidence interval 6.18-25.93) for stage IV, for a cohort with a median age of 65 years. Metaregression demonstrated that increased age and advanced clinical stage were independently associated with an increased risk of mortality, and the combined effects of both factors greatly increased the risk.

Effects of diurnal temperature range on mortality in Hefei city, China

NASA Astrophysics Data System (ADS)

Tang, Jing; Xiao, Chang-chun; Li, Yu-rong; Zhang, Jun-qing; Zhai, Hao-yuan; Geng, Xi-ya; Ding, Rui; Zhai, Jin-xia

2017-12-01

Although several studies indicated an association between diurnal temperature range (DTR) and mortality, the results about modifiers are inconsistent, and few studies were conducted in developing inland country. This study aims to evaluate the effects of DTR on cause-specific mortality and whether season, gender, or age might modify any association in Hefei city, China, during 2007-2016. Quasi-Poisson generalized linear regression models combined with a distributed lag non-linear model (DLNM) were applied to evaluate the relationships between DTR and non-accidental, cardiovascular, and respiratory mortality. We observed a J-shaped relationship between DTR and cause-specific mortality. With a DTR of 8.3 °C as the reference, the cumulative effects of extremely high DTR were significantly higher for all types of mortality than effects of lower or moderate DTR in full year. When stratified by season, extremely high DTR in spring had a greater impact on all cause-specific mortality than other three seasons. Male and the elderly (≥ 65 years) were consistently more susceptible to extremely high DTR effect than female and the youth (< 65 years) for non-accidental and cardiovascular mortality. To the contrary, female and the youth were more susceptible to extremely high DTR effect than male and the elderly for respiratory morality. The study suggests that extremely high DTR is a potential trigger for non-accidental mortality in Hefei city, China. Our findings also highlight the importance of protecting susceptible groups from extremely high DTR especially in the spring.

Impact of temperature variability on childhood hand, foot and mouth disease in Huainan, China.

PubMed

Xu, J; Zhao, D; Su, H; Xie, M; Cheng, J; Wang, X; Li, K; Yang, H; Wen, L; Wang, B

2016-05-01

The short-term temperature variation has been shown to be significantly associated with human health. However, little is known about whether temperature change between neighbouring days (TCN) and diurnal temperature range (DTR) have any effect on childhood hand, foot and mouth disease (HFMD). This study aims to explore whether temperature variability has any effect on childhood HFMD. Ecological study. The association between meteorological variables and HFMD cases in Huainan, China, from January 1st 2012 to December 31st 2014 was analysed using Poisson generalized linear regression combined with distributed lag non-linear model (DLNM) after controlling for long-term trend and seasonality, mean temperature and relative humidity. An adverse effect of TCN on childhood HFMD was observed, and the impact of TCN was the greatest at five days lag, with a 10% (95% CI: 4%-15%) increase of daily number of HFMD cases per 3 °C (10th percentile) decrease of TCN. Male children, children aged 0-5 years, scattered children and children in high-risk areas appeared to be more vulnerable to the TCN effect than others. However, there was no significant association between DTR and childhood HFMD. Our findings indicate that TCN drops may increase the incidence of childhood HFMD in Huainan, highlighting the importance of protecting children from forthcoming TCN drops, particularly for those who are male, young, scattered and from high-risk areas. Copyright © 2015 The Royal Society for Public Health. Published by Elsevier Ltd. All rights reserved.

Effects of diurnal temperature range on mortality in Hefei city, China

NASA Astrophysics Data System (ADS)

Tang, Jing; Xiao, Chang-chun; Li, Yu-rong; Zhang, Jun-qing; Zhai, Hao-yuan; Geng, Xi-ya; Ding, Rui; Zhai, Jin-xia

2018-05-01

Although several studies indicated an association between diurnal temperature range (DTR) and mortality, the results about modifiers are inconsistent, and few studies were conducted in developing inland country. This study aims to evaluate the effects of DTR on cause-specific mortality and whether season, gender, or age might modify any association in Hefei city, China, during 2007-2016. Quasi-Poisson generalized linear regression models combined with a distributed lag non-linear model (DLNM) were applied to evaluate the relationships between DTR and non-accidental, cardiovascular, and respiratory mortality. We observed a J-shaped relationship between DTR and cause-specific mortality. With a DTR of 8.3 °C as the reference, the cumulative effects of extremely high DTR were significantly higher for all types of mortality than effects of lower or moderate DTR in full year. When stratified by season, extremely high DTR in spring had a greater impact on all cause-specific mortality than other three seasons. Male and the elderly (≥ 65 years) were consistently more susceptible to extremely high DTR effect than female and the youth (< 65 years) for non-accidental and cardiovascular mortality. To the contrary, female and the youth were more susceptible to extremely high DTR effect than male and the elderly for respiratory morality. The study suggests that extremely high DTR is a potential trigger for non-accidental mortality in Hefei city, China. Our findings also highlight the importance of protecting susceptible groups from extremely high DTR especially in the spring.

Digital Image Restoration Under a Regression Model - The Unconstrained, Linear Equality and Inequality Constrained Approaches

DTIC Science & Technology

1974-01-01

REGRESSION MODEL - THE UNCONSTRAINED, LINEAR EQUALITY AND INEQUALITY CONSTRAINED APPROACHES January 1974 Nelson Delfino d’Avila Mascarenha;? Image...Report 520 DIGITAL IMAGE RESTORATION UNDER A REGRESSION MODEL THE UNCONSTRAINED, LINEAR EQUALITY AND INEQUALITY CONSTRAINED APPROACHES January...a two- dimensional form adequately describes the linear model . A dis- cretization is performed by using quadrature methods. By trans

Element enrichment factor calculation using grain-size distribution and functional data regression.

PubMed

Sierra, C; Ordóñez, C; Saavedra, A; Gallego, J R

2015-01-01

In environmental geochemistry studies it is common practice to normalize element concentrations in order to remove the effect of grain size. Linear regression with respect to a particular grain size or conservative element is a widely used method of normalization. In this paper, the utility of functional linear regression, in which the grain-size curve is the independent variable and the concentration of pollutant the dependent variable, is analyzed and applied to detrital sediment. After implementing functional linear regression and classical linear regression models to normalize and calculate enrichment factors, we concluded that the former regression technique has some advantages over the latter. First, functional linear regression directly considers the grain-size distribution of the samples as the explanatory variable. Second, as the regression coefficients are not constant values but functions depending on the grain size, it is easier to comprehend the relationship between grain size and pollutant concentration. Third, regularization can be introduced into the model in order to establish equilibrium between reliability of the data and smoothness of the solutions. Copyright © 2014 Elsevier Ltd. All rights reserved.

Who Will Win?: Predicting the Presidential Election Using Linear Regression

ERIC Educational Resources Information Center

Lamb, John H.

2007-01-01

This article outlines a linear regression activity that engages learners, uses technology, and fosters cooperation. Students generated least-squares linear regression equations using TI-83 Plus[TM] graphing calculators, Microsoft[C] Excel, and paper-and-pencil calculations using derived normal equations to predict the 2004 presidential election.…

Research in Stochastic Processes.

DTIC Science & Technology

1983-10-01

increases. A more detailed investigation for the exceedances themselves (rather than Just the cluster centers) was undertaken, together with J. HUsler and...J. HUsler and M.R. Leadbetter, Compoung Poisson limit theorems for high level exceedances by stationary sequences, Center for Stochastic Processes...stability by a random linear operator. C.D. Hardin, General (asymmetric) stable variables and processes. T. Hsing, J. HUsler and M.R. Leadbetter, Compound

Using a Modification of the Capture-Recapture Model To Estimate the Need for Substance Abuse Treatment.

ERIC Educational Resources Information Center

Maxwell, Jane Carlisle; Pullum, Thomas W.

2001-01-01

Applied the capture-recapture model, through a Poisson regression to a time series of data for admissions to treatment from 1987 to 1996 to estimate the number of heroin addicts in Texas who are "at-risk" for treatment. The entire data set produced estimates that were lower and more plausible than those produced by drawing samples,…

Predicting Hospital Admissions With Poisson Regression Analysis

DTIC Science & Technology

2009-06-01

East and Four West. Four East is where bariatric , general, neurologic, otolaryngology (ENT), ophthalmologic, orthopedic, and plastic surgery ...where care is provided for cardiovascular, thoracic, and vascular surgery patients. Figure 1 shows a bar graph for each unit, giving the proportion of...provided at NMCSD, or a study could be conducted on the amount of time that patients generally wait for elective surgeries . There is also the

Continuum description of ionic and dielectric shielding for molecular-dynamics simulations of proteins in solution

NASA Astrophysics Data System (ADS)

Egwolf, Bernhard; Tavan, Paul

2004-01-01

We extend our continuum description of solvent dielectrics in molecular-dynamics (MD) simulations [B. Egwolf and P. Tavan, J. Chem. Phys. 118, 2039 (2003)], which has provided an efficient and accurate solution of the Poisson equation, to ionic solvents as described by the linearized Poisson-Boltzmann (LPB) equation. We start with the formulation of a general theory for the electrostatics of an arbitrarily shaped molecular system, which consists of partially charged atoms and is embedded in a LPB continuum. This theory represents the reaction field induced by the continuum in terms of charge and dipole densities localized within the molecular system. Because these densities cannot be calculated analytically for systems of arbitrary shape, we introduce an atom-based discretization and a set of carefully designed approximations. This allows us to represent the densities by charges and dipoles located at the atoms. Coupled systems of linear equations determine these multipoles and can be rapidly solved by iteration during a MD simulation. The multipoles yield the reaction field forces and energies. Finally, we scrutinize the quality of our approach by comparisons with an analytical solution restricted to perfectly spherical systems and with results of a finite difference method.

A novel multitarget model of radiation-induced cell killing based on the Gaussian distribution.

PubMed

Zhao, Lei; Mi, Dong; Sun, Yeqing

2017-05-07

The multitarget version of the traditional target theory based on the Poisson distribution is still used to describe the dose-survival curves of cells after ionizing radiation in radiobiology and radiotherapy. However, noting that the usual ionizing radiation damage is the result of two sequential stochastic processes, the probability distribution of the damage number per cell should follow a compound Poisson distribution, like e.g. Neyman's distribution of type A (N. A.). In consideration of that the Gaussian distribution can be considered as the approximation of the N. A. in the case of high flux, a multitarget model based on the Gaussian distribution is proposed to describe the cell inactivation effects in low linear energy transfer (LET) radiation with high dose-rate. Theoretical analysis and experimental data fitting indicate that the present theory is superior to the traditional multitarget model and similar to the Linear - Quadratic (LQ) model in describing the biological effects of low-LET radiation with high dose-rate, and the parameter ratio in the present model can be used as an alternative indicator to reflect the radiation damage and radiosensitivity of the cells. Copyright © 2017 Elsevier Ltd. All rights reserved.

A coarse-grid-projection acceleration method for finite-element incompressible flow computations

NASA Astrophysics Data System (ADS)

Kashefi, Ali; Staples, Anne; FiN Lab Team

2015-11-01

Coarse grid projection (CGP) methodology provides a framework for accelerating computations by performing some part of the computation on a coarsened grid. We apply the CGP to pressure projection methods for finite element-based incompressible flow simulations. Based on it, the predicted velocity field data is restricted to a coarsened grid, the pressure is determined by solving the Poisson equation on the coarse grid, and the resulting data are prolonged to the preset fine grid. The contributions of the CGP method to the pressure correction technique are twofold: first, it substantially lessens the computational cost devoted to the Poisson equation, which is the most time-consuming part of the simulation process. Second, it preserves the accuracy of the velocity field. The velocity and pressure spaces are approximated by Galerkin spectral element using piecewise linear basis functions. A restriction operator is designed so that fine data are directly injected into the coarse grid. The Laplacian and divergence matrices are driven by taking inner products of coarse grid shape functions. Linear interpolation is implemented to construct a prolongation operator. A study of the data accuracy and the CPU time for the CGP-based versus non-CGP computations is presented. Laboratory for Fluid Dynamics in Nature.

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.

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...

Modeling motor vehicle crashes using Poisson-gamma models: examining the effects of low sample mean values and small sample size on the estimation of the fixed dispersion parameter.

PubMed

Lord, Dominique

2006-07-01

There has been considerable research conducted on the development of statistical models for predicting crashes on highway facilities. Despite numerous advancements made for improving the estimation tools of statistical models, the most common probabilistic structure used for modeling motor vehicle crashes remains the traditional Poisson and Poisson-gamma (or Negative Binomial) distribution; when crash data exhibit over-dispersion, the Poisson-gamma model is usually the model of choice most favored by transportation safety modelers. Crash data collected for safety studies often have the unusual attributes of being characterized by low sample mean values. Studies have shown that the goodness-of-fit of statistical models produced from such datasets can be significantly affected. This issue has been defined as the "low mean problem" (LMP). Despite recent developments on methods to circumvent the LMP and test the goodness-of-fit of models developed using such datasets, no work has so far examined how the LMP affects the fixed dispersion parameter of Poisson-gamma models used for modeling motor vehicle crashes. The dispersion parameter plays an important role in many types of safety studies and should, therefore, be reliably estimated. The primary objective of this research project was to verify whether the LMP affects the estimation of the dispersion parameter and, if it is, to determine the magnitude of the problem. The secondary objective consisted of determining the effects of an unreliably estimated dispersion parameter on common analyses performed in highway safety studies. To accomplish the objectives of the study, a series of Poisson-gamma distributions were simulated using different values describing the mean, the dispersion parameter, and the sample size. Three estimators commonly used by transportation safety modelers for estimating the dispersion parameter of Poisson-gamma models were evaluated: the method of moments, the weighted regression, and the maximum likelihood method. In an attempt to complement the outcome of the simulation study, Poisson-gamma models were fitted to crash data collected in Toronto, Ont. characterized by a low sample mean and small sample size. The study shows that a low sample mean combined with a small sample size can seriously affect the estimation of the dispersion parameter, no matter which estimator is used within the estimation process. The probability the dispersion parameter becomes unreliably estimated increases significantly as the sample mean and sample size decrease. Consequently, the results show that an unreliably estimated dispersion parameter can significantly undermine empirical Bayes (EB) estimates as well as the estimation of confidence intervals for the gamma mean and predicted response. The paper ends with recommendations about minimizing the likelihood of producing Poisson-gamma models with an unreliable dispersion parameter for modeling motor vehicle crashes.

Ambient temperature and FIT performance in the Emilia-Romagna colorectal cancer screening programme.

PubMed

De Girolamo, Gianfranco; Goldoni, Carlo A; Corradini, Rossella; Giuliani, Orietta; Falcini, Fabio; Sassoli De'Bianchi, Priscilla; Naldoni, Carlo; Zauli Sajani, Stefano

2016-12-01

To assess the impact of ambient temperature on faecal immunochemical test (FIT) performance in the colorectal cancer screening programme of Emilia-Romagna (Italy). A population-based retrospective cohort study on data from 2005 to 2011. Positive rate, detection rate, and positive predictive value rate for cancers and adenomas, and incidence rate of interval cancers after negative tests were analysed using Poisson regression models. In addition to ambient temperature, gender, age, screening history, and Local Health Unit were also considered. In 1,521,819 tests analysed, the probability of a positive result decreased linearly with increasing temperature. Point estimates and 95% Confidence Intervals were estimated for six temperature classes (<5, 5 |-10, 10 |-15, 15 |-20, 20|-25 and ≥25℃), and referred to the 5|-10℃ class. The positive rate ratio was significantly related to temperature increase: 0.99 (0.97-1.02), 1, 0.98 (0.96-1.00), 0.96 (0.94-0.99), 0.93 (0.91-0.96), 0.92 (0.89-0.95). A linear trend was also evident for advanced adenoma detection rate ratio: 1.00 (0.96-1.04), 1, 0.98 (0.93-1.02), 0.96 (0.92-1.00), 0.92 (0.88-0.96), 0.94 (0.88-1.01). The effect was less linear, but still important, for cancer detection rates: 0.95 (0.85-1.06), 1, 1.00 (0.90-1.10), 0.94 (0.85-1.05), 0.81 (0.72-0.92), 0.93 (0.80-1.09). No association or linear trend was found for positive predictive values or risk of interval cancer, despite an excess of +16% in the highest temperature class for interval cancer. Ambient temperatures can affect screening performance. Continued monitoring is needed to verify the effect of introducing FIT tubes with a new buffer, which should guarantee a higher stability of haemoglobin. © The Author(s) 2016.

Cardiovascular diseases and air pollution in Novi Sad, Serbia.

PubMed

Jevtić, Marija; Dragić, Nataša; Bijelović, Sanja; Popović, Milka

2014-04-01

A large body of evidence has documented that air pollutants have adverse effect on human health as well as on the environment. The aim of this study was to determine whether there was an association between outdoor concentrations of sulfur dioxide (SO2) and nitrogen dioxide (NO2) and a daily number of hospital admissions due to cardiovascular diseases (CVD) in Novi Sad, Serbia among patients aged above 18. The investigation was carried out during over a 3-year period (from January 1, 2007 to December 31, 2009) in the area of Novi Sad. The number (N = 10 469) of daily CVD (ICD-10: I00-I99) hospital admissions was collected according to patients' addresses. Daily mean levels of NO2 and SO2, measured in the ambient air of Novi Sad via a network of fixed samplers, have been used to put forward outdoor air pollution. Associations between air pollutants and hospital admissions were firstly analyzed by the use of the linear regression in a single polluted model, and then trough a single and multi-polluted adjusted generalized linear Poisson model. The single polluted model (without confounding factors) indicated that there was a linear increase in the number of hospital admissions due to CVD in relation to the linear increase in concentrations of SO2 (p = 0.015; 95% confidence interval (95% CI): 0.144-1.329, R(2) = 0.005) and NO2 (p = 0.007; 95% CI: 0.214-1.361, R(2) = 0.007). However, the single and multi-polluted adjusted models revealed that only NO2 was associated with the CVD (p = 0.016, relative risk (RR) = 1.049, 95% CI: 1.009-1.091 and p = 0.022, RR = 1.047, 95% CI: 1.007-1.089, respectively). This study shows a significant positive association between hospital admissions due to CVD and outdoor NO2 concentrations in the area of Novi Sad, Serbia.

Tackle-related injury rates and nature of injuries in South African Youth Week tournament rugby union players (under-13 to under-18): an observational cohort study.

PubMed

Burger, Nicholas; Lambert, Mike I; Viljoen, Wayne; Brown, James C; Readhead, Clint; Hendricks, Sharief

2014-08-12

The tackle situation is most often associated with the high injury rates in rugby union. Tackle injury epidemiology in rugby union has previously been focused on senior cohorts but less is known about younger cohorts. The aim of this study was to report on the nature and rates of tackle-related injuries in South African youth rugby union players representing their provinces at national tournaments. Observational cohort study. Four South African Youth Week tournaments (under-13 Craven Week, under-16 Grant Khomo Week, under-18 Academy Week, under-18 Craven Week). Injury data were collected from 3652 youth rugby union players (population at risk) in 2011 and 2012. Tackle-related injury severity ('time-loss' and 'medical attention'), type and location, injury rate per 1000 h (including 95% CIs). Injury rate ratios (IRR) were calculated and modelled using a Poisson regression. A χ(2) analysis was used to detect linear trends between injuries and increasing match quarters. The 2012 under-13 Craven Week had a significantly greater 'time-loss' injury rate when compared with the 2012 under-18 Academy Week (IRR=4.43; 95% CI 2.13 to 9.21, p<0.05) and under-18 Craven Week (IRR=3.52; 95% CI 1.54 to 8.00, p<0.05). The Poisson regression also revealed a higher probability of 'overall' ('time-loss' and 'medical attention' combined) and 'time-loss' tackle-related injuries occurring at the under-13 Craven Week. The proportion of 'overall' and 'time-loss' injuries increased significantly with each quarter of the match when all four tournaments were combined (p<0.05). There was a difference in the tackle-related injury rate between the under-13 tournament and the two under-18 tournaments, and the tackle-related injury rate was higher in the final quarter of matches. Ongoing injury surveillance is required to better interpret these findings. Injury prevention strategies targeting the tackle may only be effective once the rate and nature of injuries have been accurately determined. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://group.bmj.com/group/rights-licensing/permissions.

Tackle-related injury rates and nature of injuries in South African Youth Week tournament rugby union players (under-13 to under-18): an observational cohort study

PubMed Central

Burger, Nicholas; Lambert, Mike I; Viljoen, Wayne; Brown, James C; Readhead, Clint; Hendricks, Sharief

2014-01-01

Objectives The tackle situation is most often associated with the high injury rates in rugby union. Tackle injury epidemiology in rugby union has previously been focused on senior cohorts but less is known about younger cohorts. The aim of this study was to report on the nature and rates of tackle-related injuries in South African youth rugby union players representing their provinces at national tournaments. Design Observational cohort study. Setting Four South African Youth Week tournaments (under-13 Craven Week, under-16 Grant Khomo Week, under-18 Academy Week, under-18 Craven Week). Participants Injury data were collected from 3652 youth rugby union players (population at risk) in 2011 and 2012. Outcome measures Tackle-related injury severity (‘time-loss’ and ‘medical attention’), type and location, injury rate per 1000 h (including 95% CIs). Injury rate ratios (IRR) were calculated and modelled using a Poisson regression. A χ2 analysis was used to detect linear trends between injuries and increasing match quarters. Results The 2012 under-13 Craven Week had a significantly greater ‘time-loss’ injury rate when compared with the 2012 under-18 Academy Week (IRR=4.43; 95% CI 2.13 to 9.21, p<0.05) and under-18 Craven Week (IRR=3.52; 95% CI 1.54 to 8.00, p<0.05). The Poisson regression also revealed a higher probability of ‘overall’ (‘time-loss’ and ‘medical attention’ combined) and ‘time-loss’ tackle-related injuries occurring at the under-13 Craven Week. The proportion of ‘overall’ and ‘time-loss’ injuries increased significantly with each quarter of the match when all four tournaments were combined (p<0.05). Conclusions There was a difference in the tackle-related injury rate between the under-13 tournament and the two under-18 tournaments, and the tackle-related injury rate was higher in the final quarter of matches. Ongoing injury surveillance is required to better interpret these findings. Injury prevention strategies targeting the tackle may only be effective once the rate and nature of injuries have been accurately determined. PMID:25116454

Modeling salt-mediated electrostatics of macromolecules: the discrete surface charge optimization algorithm and its application to the nucleosome.

PubMed

Beard, D A; Schlick, T

2001-01-01

Much progress has been achieved on quantitative assessment of electrostatic interactions on the all-atom level by molecular mechanics and dynamics, as well as on the macroscopic level by models of continuum solvation. Bridging of the two representations-an area of active research-is necessary for studying integrated functions of large systems of biological importance. Following perspectives of both discrete (N-body) interaction and continuum solvation, we present a new algorithm, DiSCO (Discrete Surface Charge Optimization), for economically describing the electrostatic field predicted by Poisson-Boltzmann theory using a discrete set of Debye-Hückel charges distributed on a virtual surface enclosing the macromolecule. The procedure in DiSCO relies on the linear behavior of the Poisson-Boltzmann equation in the far zone; thus contributions from a number of molecules may be superimposed, and the electrostatic potential, or equivalently the electrostatic field, may be quickly and efficiently approximated by the summation of contributions from the set of charges. The desired accuracy of this approximation is achieved by minimizing the difference between the Poisson-Boltzmann electrostatic field and that produced by the linearized Debye-Hückel approximation using our truncated Newton optimization package. DiSCO is applied here to describe the salt-dependent electrostatic environment of the nucleosome core particle in terms of several hundred surface charges. This representation forms the basis for modeling-by dynamic simulations (or Monte Carlo)-the folding of chromatin. DiSCO can be applied more generally to many macromolecular systems whose size and complexity warrant a model resolution between the all-atom and macroscopic levels. Copyright 2000 John Wiley & Sons, Inc.

A prospective cohort study of postoperative complications in the management of perforated peptic ulcer.

PubMed

Sharma, Smita S; Mamtani, Manju R; Sharma, Mamta S; Kulkarni, Hemant

2006-06-16

With dwindling rates of postoperative mortality in perforated peptic ulcer that is attributable to H2-receptor blocker usage, there is a need to shift the focus towards the prevention of postoperative morbidity. Further, the simultaneous contribution of several putative clinical predictors to this postoperative morbidity is not fully appreciated. Our objective was to assess the predictors of the risk, rate and number of postoperative complications in surgically treated patients of perforated peptic ulcer. In a prospective cohort study of 96 subjects presenting as perforated peptic ulcer and treated using Graham's omentoplatsy patch or gastrojejunostomy (with total truncal vagotomy), we assessed the association of clinical predictors with three domains of postoperative complications: the risk of developing a complication, the rate of developing the first complication and the risk of developing higher number of complications. We used multiple regression methods - logistic regression, Cox proportional hazards regression and Poisson regression, respectively - to examine the association of the predictors with these three domains. We observed that the risk of developing a postoperative complication was significantly influenced by the presence of a concomitant medical illness [odds ratio (OR) = 8.9, p = 0.001], abdominal distension (3.8, 0.048) and a need of blood transfusion (OR = 8.2, p = 0.027). Using Poisson regression, it was observed that the risk for a higher number of complications was influenced by the same three factors [relative risk (RR) = 2.6, p = 0.015; RR = 4.6, p < 0.001; and RR = 2.4, p = 0.002; respectively]. However, the rate of development of complications was influenced by a history suggestive of shock [relative hazards (RH) = 3.4, p = 0.002] and A- blood group (RH = 4.7, p = 0.04). Abdominal distension, presence of a concomitant medical illness and a history suggestive of shock at the time of admission warrant a closer and alacritous postoperative management in patients of perforated peptic ulcer.

[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

[Ultrasonic measurements of fetal thalamus, caudate nucleus and lenticular nucleus in prenatal diagnosis].

PubMed

Yang, Ruiqi; Wang, Fei; Zhang, Jialing; Zhu, Chonglei; Fan, Limei

2015-05-19

To establish the reference values of thalamus, caudate nucleus and lenticular nucleus diameters through fetal thalamic transverse section. A total of 265 fetuses at our hospital were randomly selected from November 2012 to August 2014. And the transverse and length diameters of thalamus, caudate nucleus and lenticular nucleus were measured. SPSS 19.0 statistical software was used to calculate the regression curve of fetal diameter changes and gestational weeks of pregnancy. P < 0.05 was considered as having statistical significance. The linear regression equation of fetal thalamic length diameter and gestational week was: Y = 0.051X+0.201, R = 0.876, linear regression equation of thalamic transverse diameter and fetal gestational week was: Y = 0.031X+0.229, R = 0.817, linear regression equation of fetal head of caudate nucleus length diameter and gestational age was: Y = 0.033X+0.101, R = 0.722, linear regression equation of fetal head of caudate nucleus transverse diameter and gestational week was: R = 0.025 - 0.046, R = 0.711, linear regression equation of fetal lentiform nucleus length diameter and gestational week was: Y = 0.046+0.229, R = 0.765, linear regression equation of fetal lentiform nucleus diameter and gestational week was: Y = 0.025 - 0.05, R = 0.772. Ultrasonic measurement of diameter of fetal thalamus caudate nucleus, and lenticular nucleus through thalamic transverse section is simple and convenient. And measurements increase with fetal gestational weeks and there is linear regression relationship between them.

Local Linear Regression for Data with AR Errors.

PubMed

Li, Runze; Li, Yan

2009-07-01

In many statistical applications, data are collected over time, and they are likely correlated. In this paper, we investigate how to incorporate the correlation information into the local linear regression. Under the assumption that the error process is an auto-regressive process, a new estimation procedure is proposed for the nonparametric regression by using local linear regression method and the profile least squares techniques. We further propose the SCAD penalized profile least squares method to determine the order of auto-regressive process. Extensive Monte Carlo simulation studies are conducted to examine the finite sample performance of the proposed procedure, and to compare the performance of the proposed procedures with the existing one. From our empirical studies, the newly proposed procedures can dramatically improve the accuracy of naive local linear regression with working-independent error structure. We illustrate the proposed methodology by an analysis of real data set.

Spirituality and Resilience Among Mexican American IPV Survivors.

PubMed

de la Rosa, Iván A; Barnett-Queen, Timothy; Messick, Madeline; Gurrola, Maria

2016-12-01

Women with abusive partners use a variety of coping strategies. This study examined the correlation between spirituality, resilience, and intimate partner violence using a cross-sectional survey of 54 Mexican American women living along the U.S.-Mexico border. The meaning-making coping model provides the conceptual framework to explore how spirituality is used as a copying strategy. Multiple ordinary least squares (OLS) regression results indicate women who score higher on spirituality also report greater resilient characteristics. Poisson regression analyses revealed that an increase in level of spirituality is associated with lower number of types of abuse experienced. Clinical, programmatic, and research implications are discussed. © The Author(s) 2015.

Social stressors and alcohol use among immigrant sexual and gender minority Latinos in a nontraditional settlement state.

PubMed

Gilbert, Paul A; Perreira, Krista; Eng, Eugenia; Rhodes, Scott D

2014-09-01

We sought to quantify the association of social stressors with alcohol use among immigrant sexual and gender minority Latinos in North Carolina (n = 190). We modeled any drinking in past year using logistic regression and heavy episodic drinking in past 30 days using Poisson regression. Despite a large proportion of abstainers, there were indications of hazardous drinking. Among current drinkers, 63% reported at least one heavy drinking episode in past 30 days. Ethnic discrimination increased, and social support decreased, odds of any drinking in past year. Social support moderated the associations of English use and ethnic discrimination with heavy episodic drinking.

Orthogonal Regression: A Teaching Perspective

ERIC Educational Resources Information Center

Carr, James R.

2012-01-01

A well-known approach to linear least squares regression is that which involves minimizing the sum of squared orthogonal projections of data points onto the best fit line. This form of regression is known as orthogonal regression, and the linear model that it yields is known as the major axis. A similar method, reduced major axis regression, is…

Prescription-induced jump distributions in multiplicative Poisson processes.

PubMed

Suweis, Samir; Porporato, Amilcare; Rinaldo, Andrea; Maritan, Amos

2011-06-01

Generalized Langevin equations (GLE) with multiplicative white Poisson noise pose the usual prescription dilemma leading to different evolution equations (master equations) for the probability distribution. Contrary to the case of multiplicative Gaussian white noise, the Stratonovich prescription does not correspond to the well-known midpoint (or any other intermediate) prescription. By introducing an inertial term in the GLE, we show that the Itô and Stratonovich prescriptions naturally arise depending on two time scales, one induced by the inertial term and the other determined by the jump event. We also show that, when the multiplicative noise is linear in the random variable, one prescription can be made equivalent to the other by a suitable transformation in the jump probability distribution. We apply these results to a recently proposed stochastic model describing the dynamics of primary soil salinization, in which the salt mass balance within the soil root zone requires the analysis of different prescriptions arising from the resulting stochastic differential equation forced by multiplicative white Poisson noise, the features of which are tailored to the characters of the daily precipitation. A method is finally suggested to infer the most appropriate prescription from the data.

Prescription-induced jump distributions in multiplicative Poisson processes

NASA Astrophysics Data System (ADS)

Suweis, Samir; Porporato, Amilcare; Rinaldo, Andrea; Maritan, Amos

2011-06-01

Generalized Langevin equations (GLE) with multiplicative white Poisson noise pose the usual prescription dilemma leading to different evolution equations (master equations) for the probability distribution. Contrary to the case of multiplicative Gaussian white noise, the Stratonovich prescription does not correspond to the well-known midpoint (or any other intermediate) prescription. By introducing an inertial term in the GLE, we show that the Itô and Stratonovich prescriptions naturally arise depending on two time scales, one induced by the inertial term and the other determined by the jump event. We also show that, when the multiplicative noise is linear in the random variable, one prescription can be made equivalent to the other by a suitable transformation in the jump probability distribution. We apply these results to a recently proposed stochastic model describing the dynamics of primary soil salinization, in which the salt mass balance within the soil root zone requires the analysis of different prescriptions arising from the resulting stochastic differential equation forced by multiplicative white Poisson noise, the features of which are tailored to the characters of the daily precipitation. A method is finally suggested to infer the most appropriate prescription from the data.

Multiscale modeling of a rectifying bipolar nanopore: Comparing Poisson-Nernst-Planck to Monte Carlo

NASA Astrophysics Data System (ADS)

Matejczyk, Bartłomiej; Valiskó, Mónika; Wolfram, Marie-Therese; Pietschmann, Jan-Frederik; Boda, Dezső

2017-03-01

In the framework of a multiscale modeling approach, we present a systematic study of a bipolar rectifying nanopore using a continuum and a particle simulation method. The common ground in the two methods is the application of the Nernst-Planck (NP) equation to compute ion transport in the framework of the implicit-water electrolyte model. The difference is that the Poisson-Boltzmann theory is used in the Poisson-Nernst-Planck (PNP) approach, while the Local Equilibrium Monte Carlo (LEMC) method is used in the particle simulation approach (NP+LEMC) to relate the concentration profile to the electrochemical potential profile. Since we consider a bipolar pore which is short and narrow, we perform simulations using two-dimensional PNP. In addition, results of a non-linear version of PNP that takes crowding of ions into account are shown. We observe that the mean field approximation applied in PNP is appropriate to reproduce the basic behavior of the bipolar nanopore (e.g., rectification) for varying parameters of the system (voltage, surface charge, electrolyte concentration, and pore radius). We present current data that characterize the nanopore's behavior as a device, as well as concentration, electrical potential, and electrochemical potential profiles.

Multiscale modeling of a rectifying bipolar nanopore: Comparing Poisson-Nernst-Planck to Monte Carlo.

PubMed

Matejczyk, Bartłomiej; Valiskó, Mónika; Wolfram, Marie-Therese; Pietschmann, Jan-Frederik; Boda, Dezső

2017-03-28

In the framework of a multiscale modeling approach, we present a systematic study of a bipolar rectifying nanopore using a continuum and a particle simulation method. The common ground in the two methods is the application of the Nernst-Planck (NP) equation to compute ion transport in the framework of the implicit-water electrolytemodel. The difference is that the Poisson-Boltzmann theory is used in the Poisson-Nernst-Planck (PNP) approach, while the Local Equilibrium Monte Carlo (LEMC) method is used in the particle simulation approach (NP+LEMC) to relate the concentration profile to the electrochemical potential profile. Since we consider a bipolar pore which is short and narrow, we perform simulations using two-dimensional PNP. In addition, results of a non-linear version of PNP that takes crowding of ions into account are shown. We observe that the mean field approximation applied in PNP is appropriate to reproduce the basic behavior of the bipolar nanopore (e.g., rectification) for varying parameters of the system (voltage, surface charge,electrolyte concentration, and pore radius). We present current data that characterize the nanopore's behavior as a device, as well as concentration, electrical potential, and electrochemical potential profiles.

Associations between extreme precipitation and acute gastro-intestinal illness due to cryptosporidiosis and giardiasis in an urban Canadian drinking water system (1997-2009).

PubMed

Chhetri, Bimal K; Takaro, Tim K; Balshaw, Robert; Otterstatter, Michael; Mak, Sunny; Lem, Marcus; Zubel, Marc; Lysyshyn, Mark; Clarkson, Len; Edwards, Joanne; Fleury, Manon D; Henderson, Sarah B; Galanis, Eleni

2017-10-01

Drinking water related infections are expected to increase in the future due to climate change. Understanding the current links between these infections and environmental factors is vital to understand and reduce the future burden of illness. We investigated the relationship between weekly reported cryptosporidiosis and giardiasis (n = 7,422), extreme precipitation (>90th percentile), drinking water turbidity, and preceding dry periods in a drinking water system located in greater Vancouver, British Columbia, Canada (1997-2009) using distributed lag non-linear Poisson regression models adjusted for seasonality, secular trend, and the effect of holidays on reporting. We found a significant increase in cryptosporidiosis and giardiasis 4-6 weeks after extreme precipitation. The effect was greater following a dry period. Similarly, extreme precipitation led to significantly increased turbidity only after prolonged dry periods. Our results suggest that the risk of cryptosporidiosis and giardiasis increases with extreme precipitation, and that the effects are more pronounced after a prolonged dry period. Given that extreme precipitation events are expected to increase with climate change, it is important to further understand the risks from these events, develop planning tools, and build resilience to these future risks.

Factors associated with physical inactivity in transportation in Brazilian adults living in a low socioeconomic area.

PubMed

Sa, Thiago Herick; Salvador, Emanuel Péricles; Florindo, Alex Antonio

2013-08-01

Physical inactivity in transportation is negatively related to many health outcomes. However, little is known about the correlates of this condition among people living in regions of low socioeconomic level. Cross-sectional study aimed to assess factors associated with physical inactivity in transportation among adults in the Eastern Zone of São Paulo, Brazil. Home-based interviews were conducted between May 2007 and January 2008 on a probabilistic sample of the adult population (≥18 years), totaling 368 men and 522 women. Factors associated with physical inactivity in transportation (less than 10 minutes per week of walking or cycling) were assessed using multivariate Poisson regression with hierarchical selection of variables. Physical inactivity in transportation was associated with the presence of vehicles in the household in men (PR = 2.96) and women (PR = 2.42), with linear trend for both sexes (P < .001 and P = .004, respectively), even after adjusting for age, schooling level and chronic diseases (this last factor, only among women). Presence of vehicles in the household was associated positively with physical inactivity in transportation, both for men and for women. This should be taken into consideration in drawing up public policies for promoting physical activity.

Long-wavelength Magnetic and Gravity Anomaly Correlations of Africa and Europe

NASA Technical Reports Server (NTRS)

Vonfrese, R. R. B.; Hinze, W. J. (Principal Investigator); Olivier, R.

1984-01-01

Preliminary MAGSAT scalar magnetic anomaly data were compiled for comparison with long-wavelength-pass filtered free-air gravity anomalies and regional heat-flow and tectonic data. To facilitate the correlation analysis at satellite elevations over a spherical-Earth, equivalent point source inversion was used to differentially reduce the magnetic satellite anomalies to the radial pole at 350 km elevation, and to upward continue the first radial derivative of the free-air gravity anomalies. Correlation patterns between these regional geopotential anomaly fields are quantitatively established by moving window linear regression based on Poisson's theorem. Prominent correlations include direct correspondences for the Baltic Shield, where both anomalies are negative, and the central Mediterranean and Zaire Basin where both anomalies are positive. Inverse relationships are generally common over the Precambrian Shield in northwest Africa, the Basins and Shields in southern Africa, and the Alpine Orogenic Belt. Inverse correlations also presist over the North Sea Rifts, the Benue Rift, and more generally over the East African Rifts. The results of this quantitative correlation analysis support the general inverse relationships of gravity and magnetic anomalies observed for North American continental terrain which may be broadly related to magnetic crustal thickness variations.

Long-wavelength magnetic and gravity anomaly correlations on Africa and Europe

NASA Technical Reports Server (NTRS)

Vonfrese, R. R. B.; Olivier, R.; Hinze, W. J.

1985-01-01

Preliminary MAGSAT scalar magnetic anomaly data were compiled for comparison with long-wavelength-pass filtered free-air gravity anomalies and regional heat-flow and tectonic data. To facilitate the correlation analysis at satellite elevations over a spherical-Earth, equivalent point source inversion was used to differentially reduce the magnetic satellite anomalies to the radial pole at 350 km elevation, and to upward continue the first radial derivative of the free-air gravity anomalies. Correlation patterns between these regional geopotential anomaly fields are quantitatively established by moving window linear regression based on Poisson's theorem. Prominent correlations include direct correspondences for the Baltic shield, where both anomalies are negative, and the central Mediterranean and Zaire Basin where both anomalies are positive. Inverse relationships are generally common over the Precambrian Shield in northwest Africa, the Basins and Shields in southern Africa, and the Alpine Orogenic Belt. Inverse correlations also presist over the North Sea Rifts, the Benue Rift, and more generally over the East African Rifts. The results of this quantitative correlation analysis support the general inverse relationships of gravity and magnetic anomalies observed for North American continental terrain which may be broadly related to magnetic crustal thickness variations.

Estimating the intensity of ward admission and its effect on emergency department access block.

PubMed

Luo, Wei; Cao, Jiguo; Gallagher, Marcus; Wiles, Janet

2013-07-10

Emergency department access block is an urgent problem faced by many public hospitals today. When access block occurs, patients in need of acute care cannot access inpatient wards within an optimal time frame. A widely held belief is that access block is the end product of a long causal chain, which involves poor discharge planning, insufficient bed capacity, and inadequate admission intensity to the wards. This paper studies the last link of the causal chain-the effect of admission intensity on access block, using data from a metropolitan hospital in Australia. We applied several modern statistical methods to analyze the data. First, we modeled the admission events as a nonhomogeneous Poisson process and estimated time-varying admission intensity with penalized regression splines. Next, we established a functional linear model to investigate the effect of the time-varying admission intensity on emergency department access block. Finally, we used functional principal component analysis to explore the variation in the daily time-varying admission intensities. The analyses suggest that improving admission practice during off-peak hours may have most impact on reducing the number of ED access blocks. Copyright © 2012 John Wiley & Sons, Ltd.

Factoring vs linear modeling in rate estimation: a simulation study of relative accuracy.

PubMed

Maldonado, G; Greenland, S

1998-07-01

A common strategy for modeling dose-response in epidemiology is to transform ordered exposures and covariates into sets of dichotomous indicator variables (that is, to factor the variables). Factoring tends to increase estimation variance, but it also tends to decrease bias and thus may increase or decrease total accuracy. We conducted a simulation study to examine the impact of factoring on the accuracy of rate estimation. Factored and unfactored Poisson regression models were fit to follow-up study datasets that were randomly generated from 37,500 population model forms that ranged from subadditive to supramultiplicative. In the situations we examined, factoring sometimes substantially improved accuracy relative to fitting the corresponding unfactored model, sometimes substantially decreased accuracy, and sometimes made little difference. The difference in accuracy between factored and unfactored models depended in a complicated fashion on the difference between the true and fitted model forms, the strength of exposure and covariate effects in the population, and the study size. It may be difficult in practice to predict when factoring is increasing or decreasing accuracy. We recommend, therefore, that the strategy of factoring variables be supplemented with other strategies for modeling dose-response.

Hyperspectral cytometry.

PubMed

Grégori, Gérald; Rajwa, Bartek; Patsekin, Valery; Jones, James; Furuki, Motohiro; Yamamoto, Masanobu; Paul Robinson, J

2014-01-01

Hyperspectral cytometry is an emerging technology for single-cell analysis that combines ultrafast optical spectroscopy and flow cytometry. Spectral cytometry systems utilize diffraction gratings or prism-based monochromators to disperse fluorescence signals from multiple labels (organic dyes, nanoparticles, or fluorescent proteins) present in each analyzed bioparticle onto linear detector arrays such as multianode photomultipliers or charge-coupled device sensors. The resultant data, consisting of a series of characterizing every analyzed cell, are not compensated by employing the traditional cytometry approach, but rather are spectrally unmixed utilizing algorithms such as constrained Poisson regression or non-negative matrix factorization. Although implementations of spectral cytometry were envisioned as early as the 1980s, only recently has the development of highly sensitive photomultiplier tube arrays led to design and construction of functional prototypes and subsequently to introduction of commercially available systems. This chapter summarizes the historical efforts and work in the field of spectral cytometry performed at Purdue University Cytometry Laboratories and describes the technology developed by Sony Corporation that resulted in release of the first commercial spectral cytometry system-the Sony SP6800. A brief introduction to spectral data analysis is also provided, with emphasis on the differences between traditional polychromatic and spectral cytometry approaches.

Age, period and cohort effects on suicide mortality in Russia, 1956-2005.

PubMed

Jukkala, Tanya; Stickley, Andrew; Mäkinen, Ilkka Henrik; Baburin, Aleksei; Sparén, Pär

2017-03-07

Russian suicide mortality rates changed rapidly over the second half of the twentieth century. This study attempts to differentiate between underlying period and cohort effects in relation to the changes in suicide mortality in Russia between 1956 and 2005. Sex- and age-specific suicide mortality data were analyzed using an age-period-cohort (APC) approach. Descriptive analyses and APC modeling with log-linear Poisson regression were performed. Strong period effects were observed for the years during and after Gorbachev's political reforms (including the anti-alcohol campaign) and for those following the break-up of the Soviet Union. After mutual adjustment, the cohort- and period-specific relative risk estimates for suicide revealed differing underlying processes. While the estimated period effects had an overall positive trend, cohort-specific developments indicated a positive trend for the male cohorts born between 1891 and 1931 and for the female cohorts born between 1891 and 1911, but a negative trend for subsequent cohorts. Our results indicate that the specific life experiences of cohorts may be important for variations in suicide mortality across time, in addition to more immediate effects of changes in the social environment.

A Bayesian approach to modelling the impact of hydrodynamic shear stress on biofilm deformation

PubMed Central

Wilkinson, Darren J.; Jayathilake, Pahala Gedara; Rushton, Steve P.; Bridgens, Ben; Li, Bowen; Zuliani, Paolo

2018-01-01

We investigate the feasibility of using a surrogate-based method to emulate the deformation and detachment behaviour of a biofilm in response to hydrodynamic shear stress. The influence of shear force, growth rate and viscoelastic parameters on the patterns of growth, structure and resulting shape of microbial biofilms was examined. We develop a statistical modelling approach to this problem, using combination of Bayesian Poisson regression and dynamic linear models for the emulation. We observe that the hydrodynamic shear force affects biofilm deformation in line with some literature. Sensitivity results also showed that the expected number of shear events, shear flow, yield coefficient for heterotrophic bacteria and extracellular polymeric substance (EPS) stiffness per unit EPS mass are the four principal mechanisms governing the bacteria detachment in this study. The sensitivity of the model parameters is temporally dynamic, emphasising the significance of conducting the sensitivity analysis across multiple time points. The surrogate models are shown to perform well, and produced ≈ 480 fold increase in computational efficiency. We conclude that a surrogate-based approach is effective, and resulting biofilm structure is determined primarily by a balance between bacteria growth, viscoelastic parameters and applied shear stress. PMID:29649240

Predictive models of safety based on audit findings: Part 2: Measurement of model validity.

PubMed

Hsiao, Yu-Lin; Drury, Colin; Wu, Changxu; Paquet, Victor

2013-07-01

Part 1 of this study sequence developed a human factors/ergonomics (HF/E) based classification system (termed HFACS-MA) for safety audit findings and proved its measurement reliability. In Part 2, we used the human error categories of HFACS-MA as predictors of future safety performance. Audit records and monthly safety incident reports from two airlines submitted to their regulatory authority were available for analysis, covering over 6.5 years. Two participants derived consensus results of HF/E errors from the audit reports using HFACS-MA. We adopted Neural Network and Poisson regression methods to establish nonlinear and linear prediction models respectively. These models were tested for the validity of prediction of the safety data, and only Neural Network method resulted in substantially significant predictive ability for each airline. Alternative predictions from counting of audit findings and from time sequence of safety data produced some significant results, but of much smaller magnitude than HFACS-MA. The use of HF/E analysis of audit findings provided proactive predictors of future safety performance in the aviation maintenance field. Copyright © 2013 Elsevier Ltd and The Ergonomics Society. All rights reserved.

Practical Session: Simple Linear Regression

NASA Astrophysics Data System (ADS)

Clausel, M.; Grégoire, G.

2014-12-01

Two exercises are proposed to illustrate the simple linear regression. The first one is based on the famous Galton's data set on heredity. We use the lm R command and get coefficients estimates, standard error of the error, R2, residuals …In the second example, devoted to data related to the vapor tension of mercury, we fit a simple linear regression, predict values, and anticipate on multiple linear regression. This pratical session is an excerpt from practical exercises proposed by A. Dalalyan at EPNC (see Exercises 1 and 2 of http://certis.enpc.fr/~dalalyan/Download/TP_ENPC_4.pdf).

Logistic quantile regression provides improved estimates for bounded avian counts: A case study of California Spotted Owl fledgling production

USGS Publications Warehouse

Cade, Brian S.; Noon, Barry R.; Scherer, Rick D.; Keane, John J.

2017-01-01

Counts of avian fledglings, nestlings, or clutch size that are bounded below by zero and above by some small integer form a discrete random variable distribution that is not approximated well by conventional parametric count distributions such as the Poisson or negative binomial. We developed a logistic quantile regression model to provide estimates of the empirical conditional distribution of a bounded discrete random variable. The logistic quantile regression model requires that counts are randomly jittered to a continuous random variable, logit transformed to bound them between specified lower and upper values, then estimated in conventional linear quantile regression, repeating the 3 steps and averaging estimates. Back-transformation to the original discrete scale relies on the fact that quantiles are equivariant to monotonic transformations. We demonstrate this statistical procedure by modeling 20 years of California Spotted Owl fledgling production (0−3 per territory) on the Lassen National Forest, California, USA, as related to climate, demographic, and landscape habitat characteristics at territories. Spotted Owl fledgling counts increased nonlinearly with decreasing precipitation in the early nesting period, in the winter prior to nesting, and in the prior growing season; with increasing minimum temperatures in the early nesting period; with adult compared to subadult parents; when there was no fledgling production in the prior year; and when percentage of the landscape surrounding nesting sites (202 ha) with trees ≥25 m height increased. Changes in production were primarily driven by changes in the proportion of territories with 2 or 3 fledglings. Average variances of the discrete cumulative distributions of the estimated fledgling counts indicated that temporal changes in climate and parent age class explained 18% of the annual variance in owl fledgling production, which was 34% of the total variance. Prior fledgling production explained as much of the variance in the fledgling counts as climate, parent age class, and landscape habitat predictors. Our logistic quantile regression model can be used for any discrete response variables with fixed upper and lower bounds.

Unique Zigzag-Shaped Buckling Zn2C Monolayer with Strain-Tunable Band Gap and Negative Poisson Ratio.

PubMed

Meng, Lingbiao; Zhang, Yingjuan; Zhou, Minjie; Zhang, Jicheng; Zhou, Xiuwen; Ni, Shuang; Wu, Weidong

2018-02-19

Designing new materials with reduced dimensionality and distinguished properties has continuously attracted intense interest for materials innovation. Here we report a novel two-dimensional (2D) Zn 2 C monolayer nanomaterial with exceptional structure and properties by means of first-principles calculations. This new Zn 2 C monolayer is composed of quasi-tetrahedral tetracoordinate carbon and quasi-linear bicoordinate zinc, featuring a peculiar zigzag-shaped buckling configuration. The unique coordinate topology endows this natural 2D semiconducting monolayer with strongly strain tunable band gap and unusual negative Poisson ratios. The monolayer has good dynamic and thermal stabilities and is also the lowest-energy structure of 2D space indicated by the particle-swarm optimization (PSO) method, implying its synthetic feasibility. With these intriguing properties the material may find applications in nanoelectronics and micromechanics.

Morse Code, Scrabble, and the Alphabet

ERIC Educational Resources Information Center

Richardson, Mary; Gabrosek, John; Reischman, Diann; Curtiss, Phyliss

2004-01-01

In this paper we describe an interactive activity that illustrates simple linear regression. Students collect data and analyze it using simple linear regression techniques taught in an introductory applied statistics course. The activity is extended to illustrate checks for regression assumptions and regression diagnostics taught in an…

Nonlinear and Anisotropic Tensile Properties of Graft Materials used in Soft Tissue Applications

PubMed Central

Yoder, Jonathon H; Elliott, Dawn M

2010-01-01

Background The mechanical properties of extracellular matrix grafts that are intended to augment or replace soft tissues should be comparable to the native tissue. Such grafts are often used in fiber-reinforced tissue applications that undergo multi-axial loading and therefore knowledge of the anisotropic and nonlinear properties are needed, including the moduli and Poisson's ratio in two orthogonal directions within the plane of the graft. The objective of this study was to measure the tensile mechanical properties of several marketed grafts: Alloderm, Restore, CuffPatch, and OrthADAPT. Methods The degree of anisotropy and nonlinearity within each graft was evaluated from uniaxial tensile tests and compared to their native tissue. Results The Alloderm graft was anisotropic in both the toe and linear-region of the stress-strain response, was highly nonlinear, and generally had low properties. The Restore and CuffPatch grafts had similar stress-strain responses, were largely isotropic, had a linear-region modulus of 18 MPa, and were nonlinear. OrthADAPT was anisotropic in the linear region (131 vs 47 MPa) and was highly nonlinear. The Poisson ratio for all grafts was between 0.4 and 0.7, except for the parallel orientation of Restore which was greater than 1.0. Interpretation Having an informed understanding of how the available grafts perform mechanically will allow for better assessment by the physician for which graft to apply depending upon its application. PMID:20129728

Advanced statistics: linear regression, part II: multiple linear regression.

PubMed

Marill, Keith A

2004-01-01

The applications of simple linear regression in medical research are limited, because in most situations, there are multiple relevant predictor variables. Univariate statistical techniques such as simple linear regression use a single predictor variable, and they often may be mathematically correct but clinically misleading. Multiple linear regression is a mathematical technique used to model the relationship between multiple independent predictor variables and a single dependent outcome variable. It is used in medical research to model observational data, as well as in diagnostic and therapeutic studies in which the outcome is dependent on more than one factor. Although the technique generally is limited to data that can be expressed with a linear function, it benefits from a well-developed mathematical framework that yields unique solutions and exact confidence intervals for regression coefficients. Building on Part I of this series, this article acquaints the reader with some of the important concepts in multiple regression analysis. These include multicollinearity, interaction effects, and an expansion of the discussion of inference testing, leverage, and variable transformations to multivariate models. Examples from the first article in this series are expanded on using a primarily graphic, rather than mathematical, approach. The importance of the relationships among the predictor variables and the dependence of the multivariate model coefficients on the choice of these variables are stressed. Finally, concepts in regression model building are discussed.

Reversed inverse regression for the univariate linear calibration and its statistical properties derived using a new methodology

NASA Astrophysics Data System (ADS)

Kang, Pilsang; Koo, Changhoi; Roh, Hokyu

2017-11-01

Since simple linear regression theory was established at the beginning of the 1900s, it has been used in a variety of fields. Unfortunately, it cannot be used directly for calibration. In practical calibrations, the observed measurements (the inputs) are subject to errors, and hence they vary, thus violating the assumption that the inputs are fixed. Therefore, in the case of calibration, the regression line fitted using the method of least squares is not consistent with the statistical properties of simple linear regression as already established based on this assumption. To resolve this problem, "classical regression" and "inverse regression" have been proposed. However, they do not completely resolve the problem. As a fundamental solution, we introduce "reversed inverse regression" along with a new methodology for deriving its statistical properties. In this study, the statistical properties of this regression are derived using the "error propagation rule" and the "method of simultaneous error equations" and are compared with those of the existing regression approaches. The accuracy of the statistical properties thus derived is investigated in a simulation study. We conclude that the newly proposed regression and methodology constitute the complete regression approach for univariate linear calibrations.

Angiogenic Signaling in Living Breast Tumor Models

DTIC Science & Technology

2007-06-01

Poisson distributed random noise is added in an amount relative to the desired signal to noise ratio. We fit the data using a regressive fitting...AD_________________ Award Number: W81XWH-05-1-0396 TITLE: Angiogenic Signaling in Living Breast...CONTRACT NUMBER Angiogenic Signaling in Living Breast Tumor Models 5b. GRANT NUMBER W81XWH-05-1-0396 5c. PROGRAM ELEMENT NUMBER 6. AUTHOR(S) 5d

The Role of Depressive Symptoms, Family Invalidation and Behavioral Impulsivity in the Occurrence and Repetition of Non-Suicidal Self-Injury in Chinese Adolescents: A 2-Year Follow-Up Study

ERIC Educational Resources Information Center

You, Jianing; Leung, Freedom

2012-01-01

This study used zero-inflated poisson regression analysis to examine the role of depressive symptoms, family invalidation, and behavioral impulsivity in the occurrence and repetition of non-suicidal self-injury among Chinese community adolescents over a 2-year period. Participants, 4782 high school students, were assessed twice during the…

Research in computer science

NASA Technical Reports Server (NTRS)

Ortega, J. M.

1986-01-01

Various graduate research activities in the field of computer science are reported. Among the topics discussed are: (1) failure probabilities in multi-version software; (2) Gaussian Elimination on parallel computers; (3) three dimensional Poisson solvers on parallel/vector computers; (4) automated task decomposition for multiple robot arms; (5) multi-color incomplete cholesky conjugate gradient methods on the Cyber 205; and (6) parallel implementation of iterative methods for solving linear equations.

Long-term sickness absence during pregnancy and the gender balance of workplaces.

PubMed

Melsom, Anne M

2014-11-01

This study addresses how the gender composition of workplaces affects pregnant women's sickness absence. It also assesses whether an observed association may be explaine by differential selection to female- or male-dominated workplaces. The analyses are based on Norwegian registry data from 2003-2011. Using Poisson regressions with detailed control for occupational categories, I examine whether the number of absence days are associated with the proportion of females at the workplace. I address possible selection effects by Poisson regressions with fixed individual effects using only within-individual variation on women with two or more pregnancies during the time window. The analyses indicate a positive and significant relationship between the female proportion in workplaces and sickness absence rates during pregnancy. Analyses limited to within-individual variation also show positive and significant effects of similar strength, indicating that the observed relationship is not due to differential selection of absence-prone pregnant workers to female-dominated workplaces. The proportion of female individuals at workplaces is positively associated with sickness absence rates during pregnancy this association is not likely explained by occupational nor individual characteristics the results are consistent with absence culture theory and more lenient norms concerning sickness absence during pregnancy at female-dominated workplaces. © 2014 the Nordic Societies of Public Health.

Anonymous birth law saves babies--optimization, sustainability and public awareness.

PubMed

Grylli, Chryssa; Brockington, Ian; Fiala, Christian; Huscsava, Mercedes; Waldhoer, Thomas; Klier, Claudia M

2016-04-01

The aims of this study are to assess the impact of Austria's anonymous birth law from the time relevant statistical records are available and to evaluate the use of hatches versus anonymous hospital delivery. This study is a complete census of police-reported neonaticides (1975-2012) as well as anonymous births including baby hatches in Austria during 2002-2012. The time trends of neonaticide rates, anonymous births and baby hatches were analysed by means of Poisson and logistic regression model. Predicted and observed rates were derived and compared using a Bayesian Poisson regression model. Predicted numbers of neonaticides for the period of the active awareness campaign, 2002-2004, were more than three times larger than the observed number (p = 0.0067). Of the 365 women who benefitted from this legislation, only 11.5% chose to put their babies in a baby hatch. Since the law was introduced, a significant decreasing tendency of numbers of anonymous births (p = 047) was observed, while there was significant increase of neonaticide rates (p = 0.0001). The implementation of the anonymous delivery law is associated with a decrease in the number of police-reported neonaticides. The subsequent significantly decreasing numbers of anonymous births with an accompanying increase of neonaticides represents additional evidence for the effectiveness of the measure.

Spatial distribution of psychotic disorders in an urban area of France: an ecological study.

PubMed

Pignon, Baptiste; Schürhoff, Franck; Baudin, Grégoire; Ferchiou, Aziz; Richard, Jean-Romain; Saba, Ghassen; Leboyer, Marion; Kirkbride, James B; Szöke, Andrei

2016-05-18

Previous analyses of neighbourhood variations of non-affective psychotic disorders (NAPD) have focused mainly on incidence. However, prevalence studies provide important insights on factors associated with disease evolution as well as for healthcare resource allocation. This study aimed to investigate the distribution of prevalent NAPD cases in an urban area in France. The number of cases in each neighbourhood was modelled as a function of potential confounders and ecological variables, namely: migrant density, economic deprivation and social fragmentation. This was modelled using statistical models of increasing complexity: frequentist models (using Poisson and negative binomial regressions), and several Bayesian models. For each model, assumptions validity were checked and compared as to how this fitted to the data, in order to test for possible spatial variation in prevalence. Data showed significant overdispersion (invalidating the Poisson regression model) and residual autocorrelation (suggesting the need to use Bayesian models). The best Bayesian model was Leroux's model (i.e. a model with both strong correlation between neighbouring areas and weaker correlation between areas further apart), with economic deprivation as an explanatory variable (OR = 1.13, 95% CI [1.02-1.25]). In comparison with frequentist methods, the Bayesian model showed a better fit. The number of cases showed non-random spatial distribution and was linked to economic deprivation.

Evaluation and comparison of statistical methods for early temporal detection of outbreaks: A simulation-based study

PubMed Central

Le Strat, Yann

2017-01-01

The objective of this paper is to evaluate a panel of statistical algorithms for temporal outbreak detection. Based on a large dataset of simulated weekly surveillance time series, we performed a systematic assessment of 21 statistical algorithms, 19 implemented in the R package surveillance and two other methods. We estimated false positive rate (FPR), probability of detection (POD), probability of detection during the first week, sensitivity, specificity, negative and positive predictive values and F1-measure for each detection method. Then, to identify the factors associated with these performance measures, we ran multivariate Poisson regression models adjusted for the characteristics of the simulated time series (trend, seasonality, dispersion, outbreak sizes, etc.). The FPR ranged from 0.7% to 59.9% and the POD from 43.3% to 88.7%. Some methods had a very high specificity, up to 99.4%, but a low sensitivity. Methods with a high sensitivity (up to 79.5%) had a low specificity. All methods had a high negative predictive value, over 94%, while positive predictive values ranged from 6.5% to 68.4%. Multivariate Poisson regression models showed that performance measures were strongly influenced by the characteristics of time series. Past or current outbreak size and duration strongly influenced detection performances. PMID:28715489

OPLS statistical model versus linear regression to assess sonographic predictors of stroke prognosis.

PubMed

Vajargah, Kianoush Fathi; Sadeghi-Bazargani, Homayoun; Mehdizadeh-Esfanjani, Robab; Savadi-Oskouei, Daryoush; Farhoudi, Mehdi

2012-01-01

The objective of the present study was to assess the comparable applicability of orthogonal projections to latent structures (OPLS) statistical model vs traditional linear regression in order to investigate the role of trans cranial doppler (TCD) sonography in predicting ischemic stroke prognosis. The study was conducted on 116 ischemic stroke patients admitted to a specialty neurology ward. The Unified Neurological Stroke Scale was used once for clinical evaluation on the first week of admission and again six months later. All data was primarily analyzed using simple linear regression and later considered for multivariate analysis using PLS/OPLS models through the SIMCA P+12 statistical software package. The linear regression analysis results used for the identification of TCD predictors of stroke prognosis were confirmed through the OPLS modeling technique. Moreover, in comparison to linear regression, the OPLS model appeared to have higher sensitivity in detecting the predictors of ischemic stroke prognosis and detected several more predictors. Applying the OPLS model made it possible to use both single TCD measures/indicators and arbitrarily dichotomized measures of TCD single vessel involvement as well as the overall TCD result. In conclusion, the authors recommend PLS/OPLS methods as complementary rather than alternative to the available classical regression models such as linear regression.

WE-H-BRA-01: BEST IN PHYSICS (THERAPY): Nano-Dosimetric Kinetic Model for Variable Relative Biological Effectiveness of Proton and Ion Beams

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

Abolfath, R; Bronk, L; Titt, U.

2016-06-15

Purpose: Recent clonogenic cell survival and γH2AX studies suggest proton relative biological effectiveness (RBE) may be a non-linear function of linear energy transfer (LET) in the distal edge of the Bragg peak and beyond. We sought to develop a multiscale model to account for non-linear response phenomena to aid in the optimization of intensity-modulated proton therapy. Methods: The model is based on first-principle simulations of proton track structures, including secondary ions, and an analytical derivation of the dependence on particle LET of the linear-quadratic (LQ) model parameters α and β. The derived formulas are an extension of the microdosimetric kineticmore » (MK) model that captures dissipative track structures and non-Poissonian distribution of DNA damage at the distal edge of the Bragg peak and beyond. Monte Carlo simulations were performed to confirm the non-linear dose-response characteristics arising from the non-Poisson distribution of initial DNA damage. Results: In contrast to low LET segments of the proton depth dose, from the beam entrance to the Bragg peak, strong deviations from non-dissipative track structures and Poisson distribution in the ionization events in the Bragg peak distal edge govern the non-linear cell response and result in the transformation α=(1+c-1 L) α-x+2(c-0 L+c-2 L^2 )(1+c-1 L) β-x and β=(1+c-1 L)^2 β-x. Here L is the charged particle LET, and c-0,c-1, and c-2 are functions of microscopic parameters and can be served as fitting parameters to the cell-survival data. In the low LET limit c-1, and c-2 are negligible hence the linear model proposed and used by Wilkins-Oelfke for the proton treatment planning system can be retrieved. The present model fits well the recent clonogenic survival data measured recently in our group in MDACC. Conclusion: The present hybrid method provides higher accuracy in calculating the RBE-weighted dose in the target and normal tissues.« less

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.

Interpretation of commonly used statistical regression models.

PubMed

Kasza, Jessica; Wolfe, Rory

2014-01-01

A review of some regression models commonly used in respiratory health applications is provided in this article. Simple linear regression, multiple linear regression, logistic regression and ordinal logistic regression are considered. The focus of this article is on the interpretation of the regression coefficients of each model, which are illustrated through the application of these models to a respiratory health research study. © 2013 The Authors. Respirology © 2013 Asian Pacific Society of Respirology.

Zero-Inflated Poisson Modeling of Fall Risk Factors in Community-Dwelling Older Adults.

PubMed

Jung, Dukyoo; Kang, Younhee; Kim, Mi Young; Ma, Rye-Won; Bhandari, Pratibha

2016-02-01

The aim of this study was to identify risk factors for falls among community-dwelling older adults. The study used a cross-sectional descriptive design. Self-report questionnaires were used to collect data from 658 community-dwelling older adults and were analyzed using logistic and zero-inflated Poisson (ZIP) regression. Perceived health status was a significant factor in the count model, and fall efficacy emerged as a significant predictor in the logistic models. The findings suggest that fall efficacy is important for predicting not only faller and nonfaller status but also fall counts in older adults who may or may not have experienced a previous fall. The fall predictors identified in this study--perceived health status and fall efficacy--indicate the need for fall-prevention programs tailored to address both the physical and psychological issues unique to older adults. © The Author(s) 2014.

Use of probabilistic weights to enhance linear regression myoelectric control

NASA Astrophysics Data System (ADS)

Smith, Lauren H.; Kuiken, Todd A.; Hargrove, Levi J.

2015-12-01

Objective. Clinically available prostheses for transradial amputees do not allow simultaneous myoelectric control of degrees of freedom (DOFs). Linear regression methods can provide simultaneous myoelectric control, but frequently also result in difficulty with isolating individual DOFs when desired. This study evaluated the potential of using probabilistic estimates of categories of gross prosthesis movement, which are commonly used in classification-based myoelectric control, to enhance linear regression myoelectric control. Approach. Gaussian models were fit to electromyogram (EMG) feature distributions for three movement classes at each DOF (no movement, or movement in either direction) and used to weight the output of linear regression models by the probability that the user intended the movement. Eight able-bodied and two transradial amputee subjects worked in a virtual Fitts’ law task to evaluate differences in controllability between linear regression and probability-weighted regression for an intramuscular EMG-based three-DOF wrist and hand system. Main results. Real-time and offline analyses in able-bodied subjects demonstrated that probability weighting improved performance during single-DOF tasks (p < 0.05) by preventing extraneous movement at additional DOFs. Similar results were seen in experiments with two transradial amputees. Though goodness-of-fit evaluations suggested that the EMG feature distributions showed some deviations from the Gaussian, equal-covariance assumptions used in this experiment, the assumptions were sufficiently met to provide improved performance compared to linear regression control. Significance. Use of probability weights can improve the ability to isolate individual during linear regression myoelectric control, while maintaining the ability to simultaneously control multiple DOFs.

Examining the spatially non-stationary associations between the second demographic transition and infant mortality: A Poisson GWR approach.

PubMed

Yang, Tse-Chuan; Shoff, Carla; Matthews, Stephen A

2013-01-01

Based on ecological studies, second demographic transition (SDT) theorists concluded that some areas in the US were in vanguard of the SDT compared to others, implying spatial nonstationarity may be inherent in the SDT process. Linking the SDT to the infant mortality literature, we sought out to answer two related questions: Are the main components of the SDT, specifically marriage postponement, cohabitation, and divorce, associated with infant mortality? If yes, do these associations vary across the US? We applied global Poisson and geographically weighted Poisson regression (GWPR) models, a place-specific analytic approach, to county-level data in the contiguous US. After accounting for the racial/ethnic and socioeconomic compositions of counties and prenatal care utilization, we found (1) marriage postponement was negatively related to infant mortality in the southwestern states, but positively associated with infant mortality in parts of Indiana, Kentucky, and Tennessee, (2) cohabitation rates were positively related to infant mortality, and this relationship was stronger in California, coastal Virginia, and the Carolinas than other areas, and (3) a positive association between divorce rates and infant mortality in southwestern and northeastern areas of the US. These spatial patterns suggested that the associations between the SDT and infant mortality were stronger in the areas in vanguard of the SDT than in others. The comparison between global Poisson and GWPR results indicated that a place-specific spatial analysis not only fit the data better, but also provided insights into understanding the non-stationarity of the associations between the SDT and infant mortality.

Simplified large African carnivore density estimators from track indices.

PubMed

Winterbach, Christiaan W; Ferreira, Sam M; Funston, Paul J; Somers, Michael J

2016-01-01

The range, population size and trend of large carnivores are important parameters to assess their status globally and to plan conservation strategies. One can use linear models to assess population size and trends of large carnivores from track-based surveys on suitable substrates. The conventional approach of a linear model with intercept may not intercept at zero, but may fit the data better than linear model through the origin. We assess whether a linear regression through the origin is more appropriate than a linear regression with intercept to model large African carnivore densities and track indices. We did simple linear regression with intercept analysis and simple linear regression through the origin and used the confidence interval for ß in the linear model y  =  αx  + ß, Standard Error of Estimate, Mean Squares Residual and Akaike Information Criteria to evaluate the models. The Lion on Clay and Low Density on Sand models with intercept were not significant ( P  > 0.05). The other four models with intercept and the six models thorough origin were all significant ( P  < 0.05). The models using linear regression with intercept all included zero in the confidence interval for ß and the null hypothesis that ß = 0 could not be rejected. All models showed that the linear model through the origin provided a better fit than the linear model with intercept, as indicated by the Standard Error of Estimate and Mean Square Residuals. Akaike Information Criteria showed that linear models through the origin were better and that none of the linear models with intercept had substantial support. Our results showed that linear regression through the origin is justified over the more typical linear regression with intercept for all models we tested. A general model can be used to estimate large carnivore densities from track densities across species and study areas. The formula observed track density = 3.26 × carnivore density can be used to estimate densities of large African carnivores using track counts on sandy substrates in areas where carnivore densities are 0.27 carnivores/100 km 2 or higher. To improve the current models, we need independent data to validate the models and data to test for non-linear relationship between track indices and true density at low densities.

Logistic regression for dichotomized counts.

PubMed

Preisser, John S; Das, Kalyan; Benecha, Habtamu; Stamm, John W

2016-12-01

Sometimes there is interest in a dichotomized outcome indicating whether a count variable is positive or zero. Under this scenario, the application of ordinary logistic regression may result in efficiency loss, which is quantifiable under an assumed model for the counts. In such situations, a shared-parameter hurdle model is investigated for more efficient estimation of regression parameters relating to overall effects of covariates on the dichotomous outcome, while handling count data with many zeroes. One model part provides a logistic regression containing marginal log odds ratio effects of primary interest, while an ancillary model part describes the mean count of a Poisson or negative binomial process in terms of nuisance regression parameters. Asymptotic efficiency of the logistic model parameter estimators of the two-part models is evaluated with respect to ordinary logistic regression. Simulations are used to assess the properties of the models with respect to power and Type I error, the latter investigated under both misspecified and correctly specified models. The methods are applied to data from a randomized clinical trial of three toothpaste formulations to prevent incident dental caries in a large population of Scottish schoolchildren. © The Author(s) 2014.

[From clinical judgment to linear regression model.

PubMed

Palacios-Cruz, Lino; Pérez, Marcela; Rivas-Ruiz, Rodolfo; Talavera, Juan O

2013-01-01

When we think about mathematical models, such as linear regression model, we think that these terms are only used by those engaged in research, a notion that is far from the truth. Legendre described the first mathematical model in 1805, and Galton introduced the formal term in 1886. Linear regression is one of the most commonly used regression models in clinical practice. It is useful to predict or show the relationship between two or more variables as long as the dependent variable is quantitative and has normal distribution. Stated in another way, the regression is used to predict a measure based on the knowledge of at least one other variable. Linear regression has as it's first objective to determine the slope or inclination of the regression line: Y = a + bx, where "a" is the intercept or regression constant and it is equivalent to "Y" value when "X" equals 0 and "b" (also called slope) indicates the increase or decrease that occurs when the variable "x" increases or decreases in one unit. In the regression line, "b" is called regression coefficient. The coefficient of determination (R 2 ) indicates the importance of independent variables in the outcome.

Impact of hepatitis B vaccination on acute hepatitis B epidemiology in European Union/European Economic Area countries, 2006 to 2014

PubMed Central

Miglietta, Alessandro; Quinten, Chantal; Lopalco, Pier Luigi; Duffell, Erika

2018-01-01

Hepatitis B prevention in European Union/European Economic Area (EU/EEA) countries relies on vaccination programmes. We describe the epidemiology of acute hepatitis B virus (HBV) at country and EU/EEA level during 2006–2014. Using a multi-level mixed-effects Poisson regression model we assessed differences in the acute HBV infection notification rates between groups of countries that started universal HBV vaccination before/in vs after 1995; implemented or not a catch-up strategy; reached a vaccine coverage ≥ 95% vs < 95% and had a hepatitis B surface antigen prevalence ≥ 1% vs < 1%. Joinpoint regression analysis was used to assess trends by groups of countries, and additional Poisson regression models to evaluate the association between three-dose HBV vaccine coverage and acute HBV infection notification rates at country and EU/EEA level. The EU/EEA acute HBV infection notification rate decreased from 1.6 per 100,000 population in 2006 to 0.7 in 2014. No differences (p > 0.05) were found in the acute HBV infection notification rates between groups of countries, while as vaccine coverage increased, such rates decreased (p < 0.01). Countries with universal HBV vaccination before 1995, a catch-up strategy, and a vaccine coverage ≥ 95% had significant decreasing trends (p < 0.01). Ending HBV transmission in Europe by 2030 will require high vaccine coverage delivered through universal programmes, supported, where appropriate, by catch-up vaccination campaigns. PMID:29439751

[Travel time and participation in breast cancer screening in a region with high population dispersion].

PubMed

Borda, Alfredo; Sanz, Belén; Otero, Laura; Blasco, Teresa; García-Gómez, Francisco J; de Andrés, Fuencisla

2011-01-01

To analyze the association between travel time and participation in a breast cancer screening program adjusted for contextual variables in the province of Segovia (Spain). We performed an ecological study using the following data sources: the Breast Cancer Early Detection Program of the Primary Care Management of Segovia, the Population and Housing Census for 2001 and the municipal register for 2006-2007. The study period comprised January 2006 to December 2007. Dependent variables consisted of the municipal participation rate and the desired level of municipal participation (greater than or equal to 70%). The key independent variable was travel time from the municipality to the mammography unit. Covariables consisted of the municipalities' demographic and socioeconomic factors. We performed univariate and multivariate Poisson regression analyses of the participation rate, and logistic regression of the desired participation level. The sample was composed of 178 municipalities. The mean participation rate was 75.2%. The desired level of participation (≥ 70%) was achieved in 119 municipalities (67%). In the multivariate Poisson and logistic regression analyses, longer travel time was associated with a lower participation rate and with lower participation after adjustment was made for geographic density, age, socioeconomic status and dependency ratio, with a relative risk index of 0.88 (95% CI: 0.81-0.96) and an odds ratio of 0.22 (95% CI: 0.1-0.47), respectively. Travel time to the mammography unit may help to explain participation in breast cancer screening programs. Copyright © 2010 SESPAS. Published by Elsevier Espana. All rights reserved.

Identification of the prediction model for dengue incidence in Can Tho city, a Mekong Delta area in Vietnam.

PubMed

Phung, Dung; Huang, Cunrui; Rutherford, Shannon; Chu, Cordia; Wang, Xiaoming; Nguyen, Minh; Nguyen, Nga Huy; Manh, Cuong Do

2015-01-01

The Mekong Delta is highly vulnerable to climate change and a dengue endemic area in Vietnam. This study aims to examine the association between climate factors and dengue incidence and to identify the best climate prediction model for dengue incidence in Can Tho city, the Mekong Delta area in Vietnam. We used three different regression models comprising: standard multiple regression model (SMR), seasonal autoregressive integrated moving average model (SARIMA), and Poisson distributed lag model (PDLM) to examine the association between climate factors and dengue incidence over the period 2003-2010. We validated the models by forecasting dengue cases for the period of January-December, 2011 using the mean absolute percentage error (MAPE). Receiver operating characteristics curves were used to analyze the sensitivity of the forecast of a dengue outbreak. The results indicate that temperature and relative humidity are significantly associated with changes in dengue incidence consistently across the model methods used, but not cumulative rainfall. The Poisson distributed lag model (PDLM) performs the best prediction of dengue incidence for a 6, 9, and 12-month period and diagnosis of an outbreak however the SARIMA model performs a better prediction of dengue incidence for a 3-month period. The simple or standard multiple regression performed highly imprecise prediction of dengue incidence. We recommend a follow-up study to validate the model on a larger scale in the Mekong Delta region and to analyze the possibility of incorporating a climate-based dengue early warning method into the national dengue surveillance system. Copyright © 2014 Elsevier B.V. All rights reserved.

Changes in physical activity and screen time related to psychological well-being in early adolescence: findings from longitudinal study ELANA.

PubMed

Straatmann, Viviane S; Oliveira, Aldair J; Rostila, Mikael; Lopes, Claudia S

2016-09-15

Psychological well-being influences health behaviours differently in adolescent boys and girls. We evaluated the role of psychological well-being in early adolescence in the onset and persistence of insufficient physical activity and exceeding recommended screen time, depending on gender. This work derives from a cohort study called Longitudinal Study of Adolescent Nutritional Assessment conducted among elementary school students from two public and four private schools in Rio de Janeiro, Brazil from 2010-2013. We analysed data from 2010 and 2012 from 526 adolescents. Physical activity was evaluated using the International Physical Activity Questionnaire. Those who performed less than 60 min per day of moderate to vigorous physical activity (MVPA) were classified as insufficiently active. Screen time was evaluated based on daily time spent in front of television, video games, and computers. Those who had 4 h or more screen time per day were classified as exceeding the recommended time. Psychological well-being was assessed using the psychological domain of the KIDSCREEN 27 questionnaire. Linear regression was used to estimate coefficient (β) and r (2) values for continuous variables. Relative risks (RR) and confidence intervals (95 % CI) for onset and persistence of insufficient activity and exceeding recommended screen time were estimated with Poisson regression models. Among girls, linear regression analyses showed a significant inverse association between psychological well-being and screen minutes per day at T2 (r (2) = 0.049/β = -3.81 (95 % CI -7.0, -0.9)), as well as an association between poor psychological well-being and onset of exceeding recommended screen time in categorical analyses (RR crude: 1.3; CI 95 % 1.1, 1.7; RR adjusted: 1.3; CI 95 % 1.0, 1.6). For boys, an association was found between psychological well-being and onset of insufficient activity 2 years later (RR crude: 1.3; CI 95 % 1.2, 1.4; RR adjusted: 1.2; CI 95 % 1.1, 1.4). Adolescence is crucial for the development of unhealthy behaviours related to psychological well-being status in the context of a middle-income country. Gender differences are important because poor psychological well-being seems to affect sedentary behaviour in girls more than in boys, and predicts insufficient activity among boys.

Fourier transform infrared reflectance spectra of latent fingerprints: a biometric gauge for the age of an individual.

PubMed

Hemmila, April; McGill, Jim; Ritter, David

2008-03-01

To determine if changes in fingerprint infrared spectra linear with age can be found, partial least squares (PLS1) regression of 155 fingerprint infrared spectra against the person's age was constructed. The regression produced a linear model of age as a function of spectrum with a root mean square error of calibration of less than 4 years, showing an inflection at about 25 years of age. The spectral ranges emphasized by the regression do not correspond to the highest concentration constituents of the fingerprints. Separate linear regression models for old and young people can be constructed with even more statistical rigor. The success of the regression demonstrates that a combination of constituents can be found that changes linearly with age, with a significant shift around puberty.

Linearity versus Nonlinearity of Offspring-Parent Regression: An Experimental Study of Drosophila Melanogaster

PubMed Central

Gimelfarb, A.; Willis, J. H.

1994-01-01

An experiment was conducted to investigate the offspring-parent regression for three quantitative traits (weight, abdominal bristles and wing length) in Drosophila melanogaster. Linear and polynomial models were fitted for the regressions of a character in offspring on both parents. It is demonstrated that responses by the characters to selection predicted by the nonlinear regressions may differ substantially from those predicted by the linear regressions. This is true even, and especially, if selection is weak. The realized heritability for a character under selection is shown to be determined not only by the offspring-parent regression but also by the distribution of the character and by the form and strength of selection. PMID:7828818

Linear and nonlinear regression techniques for simultaneous and proportional myoelectric control.

PubMed

Hahne, J M; Biessmann, F; Jiang, N; Rehbaum, H; Farina, D; Meinecke, F C; Muller, K-R; Parra, L C

2014-03-01

In recent years the number of active controllable joints in electrically powered hand-prostheses has increased significantly. However, the control strategies for these devices in current clinical use are inadequate as they require separate and sequential control of each degree-of-freedom (DoF). In this study we systematically compare linear and nonlinear regression techniques for an independent, simultaneous and proportional myoelectric control of wrist movements with two DoF. These techniques include linear regression, mixture of linear experts (ME), multilayer-perceptron, and kernel ridge regression (KRR). They are investigated offline with electro-myographic signals acquired from ten able-bodied subjects and one person with congenital upper limb deficiency. The control accuracy is reported as a function of the number of electrodes and the amount and diversity of training data providing guidance for the requirements in clinical practice. The results showed that KRR, a nonparametric statistical learning method, outperformed the other methods. However, simple transformations in the feature space could linearize the problem, so that linear models could achieve similar performance as KRR at much lower computational costs. Especially ME, a physiologically inspired extension of linear regression represents a promising candidate for the next generation of prosthetic devices.

Static behavior and the effects of thermal cycling in hybrid laminates

NASA Technical Reports Server (NTRS)

Liber, T. M.; Daniel, I. M.; Chamis, C. C.

1977-01-01

Static stiffness, strength and ultimate strain after thermal cycling were investigated for graphite/Kevlar 49/epoxy and graphite/S-glass/epoxy angle-ply laminates. Tensile stress-strain curves to failure and uniaxial tensile properties were determined, and theoretical predictions of modulus, Poisson's ratio and ultimate strain, based on linear lamination theory, constituent ply properties and measured strength, were made. No significant influence on tensile stress properties due to stacking sequence variations was observed. In general, specimens containing two 0-degree Kevlar or S-glass plies were found to behave linearly to failure, while specimens containing 4 0-degree Kevlar or S-glass plies showed some nonlinear behavior.

The interplay between screening properties and colloid anisotropy: towards a reliable pair potential for disc-like charged particles.

PubMed

Agra, R; Trizac, E; Bocquet, L

2004-12-01

The electrostatic potential of a highly charged disc (clay platelet) in an electrolyte is investigated in detail. The corresponding non-linear Poisson-Boltzmann (PB) equation is solved numerically, and we show that the far-field behaviour (relevant for colloidal interactions in dilute suspensions) is exactly that obtained within linearized PB theory, with the surface boundary condition of a uniform potential. The latter linear problem is solved by a new semi-analytical procedure and both the potential amplitude (quantified by an effective charge) and potential anisotropy coincide closely within PB and linearized PB, provided the disc bare charge is high enough. This anisotropy remains at all scales; it is encoded in a function that may vary over several orders of magnitude depending on the azimuthal angle under which the disc is seen. The results allow to construct a pair potential for discs interaction, that is strongly orientation dependent.

Iterative algorithms for a non-linear inverse problem in atmospheric lidar

NASA Astrophysics Data System (ADS)

Denevi, Giulia; Garbarino, Sara; Sorrentino, Alberto

2017-08-01

We consider the inverse problem of retrieving aerosol extinction coefficients from Raman lidar measurements. In this problem the unknown and the data are related through the exponential of a linear operator, the unknown is non-negative and the data follow the Poisson distribution. Standard methods work on the log-transformed data and solve the resulting linear inverse problem, but neglect to take into account the noise statistics. In this study we show that proper modelling of the noise distribution can improve substantially the quality of the reconstructed extinction profiles. To achieve this goal, we consider the non-linear inverse problem with non-negativity constraint, and propose two iterative algorithms derived using the Karush-Kuhn-Tucker conditions. We validate the algorithms with synthetic and experimental data. As expected, the proposed algorithms out-perform standard methods in terms of sensitivity to noise and reliability of the estimated profile.

Unitary Response Regression Models

ERIC Educational Resources Information Center

Lipovetsky, S.

2007-01-01

The dependent variable in a regular linear regression is a numerical variable, and in a logistic regression it is a binary or categorical variable. In these models the dependent variable has varying values. However, there are problems yielding an identity output of a constant value which can also be modelled in a linear or logistic regression with…

An Expert System for the Evaluation of Cost Models

DTIC Science & Technology

1990-09-01

contrast to the condition of equal error variance, called homoscedasticity. (Reference: Applied Linear Regression Models by John Neter - page 423...normal. (Reference: Applied Linear Regression Models by John Neter - page 125) Click Here to continue -> Autocorrelation Click Here for the index - Index...over time. Error terms correlated over time are said to be autocorrelated or serially correlated. (REFERENCE: Applied Linear Regression Models by John

Pressure fluctuations and time scales in turbulent channel flow

NASA Astrophysics Data System (ADS)

Septham, Kamthon; Morrison, Jonathan; Diwan, Sourabh

2015-11-01

Pressure fluctuations in turbulent channel flow subjected to globally stabilising linear feedback control are investigated at Reτ = 400 . The passivity-based control is adopted and explained by the conservative characteristics of the nonlinear terms contributing to the Reynolds-Orr equation (Sharma et al. Phys. Fluids 2011). The linear control operates via vU' ; the maximum forcing is located at y+ ~ 20 , corresponding to the location of the maximum in the mean-square pressure gradient. The responses of the rapid (linear) and slow (nonlinear) pressure fluctuations to the linear control are investigated using the Green's function representations. It demonstrates that the linear control operates via the linear source terms of the Poisson equation for pressure fluctuations. Landahl's timescales of the minimal flow unit (MFU) in turbulent channel flow are examined at y+ = 20 . It shows that the timescales of MFU agree well with the theoretical values proposed by Landahl (1993). Therefore, the effectiveness of the linear control to attenuate wall turbulence is explained by Landahl's theory for timescales, in that the control proceeds via the shear interaction timescale which is significantly shorter than both the nonlinear and viscous timescales.

Impact of Availability and Use of ART/PMTCT Services on Fertility Desires of Previously Pregnant Women in Rakai, Uganda: A Retrospective Cohort Study.

PubMed

Litwin, Lindsay E; Makumbi, Frederick E; Gray, Ronald; Wawer, Maria; Kigozi, Godfrey; Kagaayi, Joseph; Nakigozi, Gertrude; Lutalo, Tom; Serwada, David; Brahmbhatt, Heena

2015-07-01

To assess fertility desires by availability and use of antiretroviral therapy and prevention of mother-to-child transmission (ART/PMTCT) services in Rakai, Uganda. Retrospective analyses of longitudinal data from the Rakai Community Cohort Study. Study participants were retrospectively identified and categorized by HIV status. Availability of ART/PMTCT services in Rakai was defined in three periods: (1) pre-ART/PMTCT (<2005), (2) ART/PMTCT rollout (2005-2006), and (3) universal ART/PMTCT (>2006); and use of ART/PMTCT was coded as yes if the woman received services. Trends in fertility desires were assessed by χ. "Modified" Poisson regression was performed using generalized linear models with a log link and Poisson family to estimate prevalence rate ratios (PRRs) and 95% confidence intervals (CIs) of desire for another child among previously and currently pregnant women; PRRs were adjusted for demographic and behavioral factors. A total of 4227 sexually active women in Rakai, including 436 HIV+ women, contributed 13,970 observations over 5 survey rounds. Fertility desires increased in the population in the ART/PMTCT rollout [adjusted (adj.) PRR: 1.08, 95% CI: 1.04 to 1.13] and the universal availability periods (adj. PRR: 1.11, 95% CI: 1.08 to 1.14) compared with pre-ART/PMTCT period. A total of 862 woman observations used ART/PMTCT services. Fertility desires were similar among ART/PMTCT service users and nonusers in cross-sectional analysis (adj. PRR: 0.84, 95% CI: 0.62 to 1.14) and 1 year after ART/PMTCT use (adj. PRR: 1.27, 95% CI: 0.83 to 1.94). Availability of ART/PMTCT may increase fertility desires of previously pregnant women in Rakai, Uganda. Use of ART/PMTCT services was not correlated with fertility desires of previously or current pregnant women.

Low-Rank Correction Methods for Algebraic Domain Decomposition Preconditioners

DOE PAGES

Li, Ruipeng; Saad, Yousef

2017-08-01

This study presents a parallel preconditioning method for distributed sparse linear systems, based on an approximate inverse of the original matrix, that adopts a general framework of distributed sparse matrices and exploits domain decomposition (DD) and low-rank corrections. The DD approach decouples the matrix and, once inverted, a low-rank approximation is applied by exploiting the Sherman--Morrison--Woodbury formula, which yields two variants of the preconditioning methods. The low-rank expansion is computed by the Lanczos procedure with reorthogonalizations. Numerical experiments indicate that, when combined with Krylov subspace accelerators, this preconditioner can be efficient and robust for solving symmetric sparse linear systems. Comparisonsmore » with pARMS, a DD-based parallel incomplete LU (ILU) preconditioning method, are presented for solving Poisson's equation and linear elasticity problems.« less

Low-Rank Correction Methods for Algebraic Domain Decomposition Preconditioners

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

Li, Ruipeng; Saad, Yousef

This study presents a parallel preconditioning method for distributed sparse linear systems, based on an approximate inverse of the original matrix, that adopts a general framework of distributed sparse matrices and exploits domain decomposition (DD) and low-rank corrections. The DD approach decouples the matrix and, once inverted, a low-rank approximation is applied by exploiting the Sherman--Morrison--Woodbury formula, which yields two variants of the preconditioning methods. The low-rank expansion is computed by the Lanczos procedure with reorthogonalizations. Numerical experiments indicate that, when combined with Krylov subspace accelerators, this preconditioner can be efficient and robust for solving symmetric sparse linear systems. Comparisonsmore » with pARMS, a DD-based parallel incomplete LU (ILU) preconditioning method, are presented for solving Poisson's equation and linear elasticity problems.« less

The Teaching of Ethics and Professionalism in Plastic Surgery Residency: A Cross-Sectional Survey.

PubMed

Bennett, Katelyn G; Ingraham, John M; Schneider, Lisa F; Saadeh, Pierre B; Vercler, Christian J

2017-05-01

The ethical practice of medicine has always been of utmost importance, and plastic surgery is no exception. The literature is devoid of information on the teaching of ethics and professionalism in plastic surgery. In light of this, a survey was sent to ascertain the status of ethics training in plastic surgery residencies. A 21-question survey was sent from the American Council of Academic Plastic Surgeons meeting to 180 plastic surgery program directors and coordinators via email. Survey questions inquired about practice environment, number of residents, presence of a formal ethics training program, among others. Binary regression was used to determine if any relationships existed between categorical variables, and Poisson linear regression was used to assess relationships between continuous variables. Statistical significance was set at a P value of 0.05. A total of 104 members responded to the survey (58% response rate). Sixty-three percent were program directors, and most (89%) practiced in academic settings. Sixty-two percent in academics reported having a formal training program, and 60% in private practice reported having one. Only 40% of programs with fewer than 10 residents had ethics training, whereas 78% of programs with more than 20 residents did. The odds of having a training program were slightly higher (odds ratio, 1.1) with more residents (P = 0.17). Despite the lack of information in the literature, formal ethics and professionalism training does exist in many plastic surgery residencies, although barriers to implementation do exist. Plastic surgery leadership should be involved in the development of standardized curricula to help overcome these barriers.

Modeling the spatial distribution of African buffalo (Syncerus caffer) in the Kruger National Park, South Africa

PubMed Central

Hughes, Kristen; Budke, Christine M.; Ward, Michael P.; Kerry, Ruth; Ingram, Ben

2017-01-01

The population density of wildlife reservoirs contributes to disease transmission risk for domestic animals. The objective of this study was to model the African buffalo distribution of the Kruger National Park. A secondary objective was to collect field data to evaluate models and determine environmental predictors of buffalo detection. Spatial distribution models were created using buffalo census information and archived data from previous research. Field data were collected during the dry (August 2012) and wet (January 2013) seasons using a random walk design. The fit of the prediction models were assessed descriptively and formally by calculating the root mean square error (rMSE) of deviations from field observations. Logistic regression was used to estimate the effects of environmental variables on the detection of buffalo herds and linear regression was used to identify predictors of larger herd sizes. A zero-inflated Poisson model produced distributions that were most consistent with expected buffalo behavior. Field data confirmed that environmental factors including season (P = 0.008), vegetation type (P = 0.002), and vegetation density (P = 0.010) were significant predictors of buffalo detection. Bachelor herds were more likely to be detected in dense vegetation (P = 0.005) and during the wet season (P = 0.022) compared to the larger mixed-sex herds. Static distribution models for African buffalo can produce biologically reasonable results but environmental factors have significant effects and therefore could be used to improve model performance. Accurate distribution models are critical for the evaluation of disease risk and to model disease transmission. PMID:28902858

Testing antismoking messages for Air Force trainees.

PubMed

Popova, Lucy; Linde, Brittany D; Bursac, Zoran; Talcott, G Wayne; Modayil, Mary V; Little, Melissa A; Ling, Pamela M; Glantz, Stanton A; Klesges, Robert C

2016-11-01

Young adults in the military are aggressively targeted by tobacco companies and are at high risk of tobacco use. Existing antismoking advertisements developed for the general population might be effective in educating young adults in the military. This study evaluated the effects of different themes of existing antismoking advertisements on perceived harm and intentions to use cigarettes and other tobacco products among Air Force trainees. In a pretest-post-test experiment, 782 Airmen were randomised to view antismoking advertisements in 1 of 6 conditions: anti-industry, health effects+anti-industry, sexual health, secondhand smoke, environment+anti-industry or control. We assessed the effect of different conditions on changes in perceived harm and intentions to use cigarettes, electronic cigarettes, smokeless tobacco, hookah and cigarillos from pretest to post-test with multivariable linear regression models (perceived harm) and zero-inflated Poisson regression model (intentions). Antismoking advertisements increased perceived harm of various tobacco products and reduced intentions to use. Advertisements featuring negative effects of tobacco on health and sexual performance coupled with revealing tobacco industry manipulations had the most consistent pattern of effects on perceived harm and intentions. Antismoking advertisements produced for the general public might also be effective with a young adult military population and could have spillover effects on perceptions of harm and intentions to use other tobacco products besides cigarettes. Existing antismoking advertising may be a cost-effective tool to educate young adults in the military. 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/.

Association between excess weight and beverage portion size consumed in Brazil

PubMed Central

Bezerra, Ilana Nogueira; de Alencar, Eudóxia Sousa

2018-01-01

ABSTRACT OBJECTIVE To describe the beverage portion size consumed and to evaluate their association with excess weight in Brazil. METHODS We used data from the National Dietary Survey, which included individuals with two days of food record aged over 20 years (n = 24,527 individuals). The beverages were categorized into six groups: soft drink, 100% fruit juice, fruit drink, alcoholic beverage, milk, and coffee or tea. We estimated the average portion consumed for each group and we evaluated, using linear regression, the association between portion size per group and the variables of age, sex, income, and nutritional status. We tested the association between portion size and excess weight using Poisson regression, adjusted for age, sex, income, and total energy intake. RESULTS The most frequently consumed beverages in Brazil were coffee and tea, followed by 100% fruit juices, soft drinks, and milk. Alcoholic beverages presented the highest average in the portion size consumed, followed by soft drinks, 100% fruit juice, fruit drink, and milk. Portion size showed positive association with excess weight only in the soft drink (PR = 1.19, 95%CI 1.10–1.27) and alcoholic beverage groups (PR = 1.20, 95%CI, 1.11–1.29), regardless of age, sex, income, and total energy intake. CONCLUSIONS Alcoholic beverages and soft drinks presented the highest averages in portion size and positive association with excess weight. Public health interventions should address the issue of portion sizes offered to consumers by discouraging the consumption of large portions, especially sweetened and low nutritional beverages. PMID:29489988

Gauge Momenta as Casimir Functions of Nonholonomic Systems

NASA Astrophysics Data System (ADS)

García-Naranjo, Luis C.; Montaldi, James

2018-05-01

We consider nonholonomic systems with symmetry possessing a certain type of first integral which is linear in the velocities. We develop a systematic method for modifying the standard nonholonomic almost Poisson structure that describes the dynamics so that these integrals become Casimir functions after reduction. This explains a number of recent results on Hamiltonization of nonholonomic systems, and has consequences for the study of relative equilibria in such systems.

POSTPROCESSING MIXED FINITE ELEMENT METHODS FOR SOLVING CAHN-HILLIARD EQUATION: METHODS AND ERROR ANALYSIS

PubMed Central

Wang, Wansheng; Chen, Long; Zhou, Jie

2015-01-01

A postprocessing technique for mixed finite element methods for the Cahn-Hilliard equation is developed and analyzed. Once the mixed finite element approximations have been computed at a fixed time on the coarser mesh, the approximations are postprocessed by solving two decoupled Poisson equations in an enriched finite element space (either on a finer grid or a higher-order space) for which many fast Poisson solvers can be applied. The nonlinear iteration is only applied to a much smaller size problem and the computational cost using Newton and direct solvers is negligible compared with the cost of the linear problem. The analysis presented here shows that this technique remains the optimal rate of convergence for both the concentration and the chemical potential approximations. The corresponding error estimate obtained in our paper, especially the negative norm error estimates, are non-trivial and different with the existing results in the literatures. PMID:27110063

Compound Identification Using Penalized Linear Regression on Metabolomics

PubMed Central

Liu, Ruiqi; Wu, Dongfeng; Zhang, Xiang; Kim, Seongho

2014-01-01

Compound identification is often achieved by matching the experimental mass spectra to the mass spectra stored in a reference library based on mass spectral similarity. Because the number of compounds in the reference library is much larger than the range of mass-to-charge ratio (m/z) values so that the data become high dimensional data suffering from singularity. For this reason, penalized linear regressions such as ridge regression and the lasso are used instead of the ordinary least squares regression. Furthermore, two-step approaches using the dot product and Pearson’s correlation along with the penalized linear regression are proposed in this study. PMID:27212894

Formulation of the Multi-Hit Model With a Non-Poisson Distribution of Hits

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

Vassiliev, Oleg N., E-mail: Oleg.Vassiliev@albertahealthservices.ca

2012-07-15

Purpose: We proposed a formulation of the multi-hit single-target model in which the Poisson distribution of hits was replaced by a combination of two distributions: one for the number of particles entering the target and one for the number of hits a particle entering the target produces. Such an approach reflects the fact that radiation damage is a result of two different random processes: particle emission by a radiation source and interaction of particles with matter inside the target. Methods and Materials: Poisson distribution is well justified for the first of the two processes. The second distribution depends on howmore » a hit is defined. To test our approach, we assumed that the second distribution was also a Poisson distribution. The two distributions combined resulted in a non-Poisson distribution. We tested the proposed model by comparing it with previously reported data for DNA single- and double-strand breaks induced by protons and electrons, for survival of a range of cell lines, and variation of the initial slopes of survival curves with radiation quality for heavy-ion beams. Results: Analysis of cell survival equations for this new model showed that they had realistic properties overall, such as the initial and high-dose slopes of survival curves, the shoulder, and relative biological effectiveness (RBE) In most cases tested, a better fit of survival curves was achieved with the new model than with the linear-quadratic model. The results also suggested that the proposed approach may extend the multi-hit model beyond its traditional role in analysis of survival curves to predicting effects of radiation quality and analysis of DNA strand breaks. Conclusions: Our model, although conceptually simple, performed well in all tests. The model was able to consistently fit data for both cell survival and DNA single- and double-strand breaks. It correctly predicted the dependence of radiation effects on parameters of radiation quality.« less

MO-G-17A-05: PET Image Deblurring Using Adaptive Dictionary Learning

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

Valiollahzadeh, S; Clark, J; Mawlawi, O

2014-06-15

Purpose: The aim of this work is to deblur PET images while suppressing Poisson noise effects using adaptive dictionary learning (DL) techniques. Methods: The model that relates a blurred and noisy PET image to the desired image is described as a linear transform y=Hm+n where m is the desired image, H is a blur kernel, n is Poisson noise and y is the blurred image. The approach we follow to recover m involves the sparse representation of y over a learned dictionary, since the image has lots of repeated patterns, edges, textures and smooth regions. The recovery is based onmore » an optimization of a cost function having four major terms: adaptive dictionary learning term, sparsity term, regularization term, and MLEM Poisson noise estimation term. The optimization is solved by a variable splitting method that introduces additional variables. We simulated a 128×128 Hoffman brain PET image (baseline) with varying kernel types and sizes (Gaussian 9×9, σ=5.4mm; Uniform 5×5, σ=2.9mm) with additive Poisson noise (Blurred). Image recovery was performed once when the kernel type was included in the model optimization and once with the model blinded to kernel type. The recovered image was compared to the baseline as well as another recovery algorithm PIDSPLIT+ (Setzer et. al.) by calculating PSNR (Peak SNR) and normalized average differences in pixel intensities (NADPI) of line profiles across the images. Results: For known kernel types, the PSNR of the Gaussian (Uniform) was 28.73 (25.1) and 25.18 (23.4) for DL and PIDSPLIT+ respectively. For blinded deblurring the PSNRs were 25.32 and 22.86 for DL and PIDSPLIT+ respectively. NADPI between baseline and DL, and baseline and blurred for the Gaussian kernel was 2.5 and 10.8 respectively. Conclusion: PET image deblurring using dictionary learning seems to be a good approach to restore image resolution in presence of Poisson noise. GE Health Care.« less

Added sugars and ultra-processed foods in Spanish households (1990-2010).

PubMed

Latasa, P; Louzada, M L D C; Martinez Steele, E; Monteiro, C A

2017-12-26

To study the association between ultra-processed foods acquisitions and added sugar content of total food purchases in Spanish households in 2010. Changes over time (1990-2000-2010) in ultra-processed food purchases and added sugars content of total food purchases are also compared. We used data from three nationally representative Household Budget Surveys (HBS) conducted in 1990, 2000 and 2010. Number of studied households was 21,012, 33,730 and 22,116, respectively. Purchased foods and drinks were classified according to NOVA food groups as ultra-processed foods, processed foods, unprocessed or minimally processed foods, or processed culinary ingredients. Linear and Poisson regressions were used to estimate the association between quintiles of energy contribution of ultra-processed foods and added sugars contents of total food purchases in 2010. Changes over time were assessed using tests of linear trend and Student's t test. In 2010, ultra-processed foods represented 31.7% of daily energy acquisitions and 80.4% of all added sugars. Added sugars content of food purchases raised from 7.3% in the lowest to 18.2% in the highest quintiles of energy contribution of ultra-processed foods. The risk of exceeding 10% energy from added sugars quadrupled between the lowest and highest quintiles. The percentage of ultra-processed foods on all food purchases almost tripled between 1990 and 2010 (from 11.0 to 31.7%), paralleling the increase of added sugars content (from 8.4 to 13.0%). Cutting down exceeding added sugars availability in Spain may require a reduction in ultra-processed food purchasing.

Statistical Models for the Analysis of Zero-Inflated Pain Intensity Numeric Rating Scale Data.

PubMed

Goulet, Joseph L; Buta, Eugenia; Bathulapalli, Harini; Gueorguieva, Ralitza; Brandt, Cynthia A

2017-03-01

Pain intensity is often measured in clinical and research settings using the 0 to 10 numeric rating scale (NRS). NRS scores are recorded as discrete values, and in some samples they may display a high proportion of zeroes and a right-skewed distribution. Despite this, statistical methods for normally distributed data are frequently used in the analysis of NRS data. We present results from an observational cross-sectional study examining the association of NRS scores with patient characteristics using data collected from a large cohort of 18,935 veterans in Department of Veterans Affairs care diagnosed with a potentially painful musculoskeletal disorder. The mean (variance) NRS pain was 3.0 (7.5), and 34% of patients reported no pain (NRS = 0). We compared the following statistical models for analyzing NRS scores: linear regression, generalized linear models (Poisson and negative binomial), zero-inflated and hurdle models for data with an excess of zeroes, and a cumulative logit model for ordinal data. We examined model fit, interpretability of results, and whether conclusions about the predictor effects changed across models. In this study, models that accommodate zero inflation provided a better fit than the other models. These models should be considered for the analysis of NRS data with a large proportion of zeroes. We examined and analyzed pain data from a large cohort of veterans with musculoskeletal disorders. We found that many reported no current pain on the NRS on the diagnosis date. We present several alternative statistical methods for the analysis of pain intensity data with a large proportion of zeroes. Published by Elsevier Inc.

Effects of temperature variation between neighbouring days on daily hospital visits for childhood asthma: a time-series analysis.

PubMed

Li, K; Ni, H; Yang, Z; Wang, Y; Ding, S; Wen, L; Yang, H; Cheng, J; Su, H

2016-07-01

To identify the relationship between temperature variation between neighbouring days (TVN) and hospital visits for childhood asthma in age- and sex-specific groups. An ecological design was used to explore the effect of TVN on hospital visits for childhood asthma. A Poisson generalised linear regression model combined with a distributed lag non-linear model was used to analyse the association between TVN and hospital visits for childhood asthma. All hospital visits for childhood asthma from June 2010 to July 2013 were included (n = 17,022). Daily climate data were obtained from Hefei Meteorological Bureau. A significant correlation was found between TVN and hospital visits for childhood asthma in age- and sex-specific groups. For different gender groups, the effect of TVN on childhood asthma was the greatest at 3 and 5 days lag for males and females. For different age groups, the effect of TVN on childhood asthma was the greatest at 1 and 5 days lag for 0-4 years and 5-14 years children, respectively. A 1 °C increase in TVN was associated with a 4.2% (95% confidence interval 0.9-7.6%) increase in hospital visits for childhood asthma. TVN is associated with hospital visits for childhood asthma. Once the temperature change rapidly, guardians will be urged to pay more attention to their children's health, which may reduce the morbidity of childhood asthma. Copyright © 2016 The Royal Society for Public Health. Published by Elsevier Ltd. All rights reserved.

Is greater temperature change within a day associated with increased emergency admissions for schizophrenia?

PubMed

Zhao, Desheng; Zhang, Xulai; Xie, Mingyu; Cheng, Jian; Zhang, Heng; Wang, Shusi; Li, Kesheng; Yang, Huihui; Wen, Liying; Wang, Xu; Su, Hong

2016-10-01

Diurnal temperature range (DTR), as an important index of climate change, has been increasingly used to evaluate the impacts of temperature variability on human health. However, little is known about the effects of DTR on schizophrenia. The present study aims to examine the relationship between DTR and schizophrenia admissions, and further, to explore whether the association varied by individual characteristics and study periods. A Poisson generalized linear regression combined with distributed lag non-linear model (DLNM) was applied to analyze daily DTR and schizophrenia data from Hefei, China during 2005 to 2014, after adjusting for long-term and seasonal trends, mean temperature, relative humidity and other confounding factors. An acute adverse effect of extremely high DTR on schizophrenia was observed, with a 2.7% (95% CI: 1.007-1.047) increase of daily schizophrenia admissions after exposure to extremely high DTR (95th percentile vs. 50th percentile). The risk for schizophrenia onset due to large DTR exposure increased from the first five years (2005-2009) to the second five years (2010-2014). Additionally, the patient aged 15-29 and 50-64years, male patients, patients born in spring/autumn, and married patients appeared to be more vulnerable to DTR effect. However, there was no significant association between moderately high DTR (75th percentile) and schizophrenia. This study suggests that extremely high DTR is a potential trigger for schizophrenia admissions in Hefei, China. Our findings may provide valuable information to decisions-makers and guidance to health practitioners. Copyright © 2016 Elsevier B.V. All rights reserved.

The association between diurnal temperature range and childhood bacillary dysentery

NASA Astrophysics Data System (ADS)

Wen, Li-ying; Zhao, Ke-fu; Cheng, Jian; Wang, Xu; Yang, Hui-hui; Li, Ke-sheng; Xu, Zhi-wei; Su, Hong

2016-02-01

Previous studies have found that mean, maximum, and minimum temperatures were associated with bacillary dysentery (BD). However, little is known about whether the within-day variation of temperature has any impact on bacillary dysentery. The current study aimed to identify the relationship between diurnal temperature range (DTR) and BD in Hefei, China. Daily data on BD counts among children aged 0-14 years from 1 January 2006 to 31 December 2012 were retrieved from Hefei Center for Disease Control and Prevention. Daily data on ambient temperature and relative humidity covering the same period were collected from the Hefei Bureau of Meteorology. A Poisson generalized linear regression model combined with a distributed lag non-linear model (DLNM) was used in the analysis after controlling the effects of season, long-term trends, mean temperature, and relative humidity. The results showed that there existed a statistically significant relationship between DTR and childhood BD. The DTR effect on childhood bacillary dysentery increased when DTR was over 8 °C. And it was greatest at 1-day lag, with an 8 % (95 % CI = 2.9-13.4 %) increase of BD cases per 5 °C increment of DTR. Male children and children aged 0-5 years appeared to be more vulnerable to the DTR effect. The data indicate that large DTR may increase the incidence of childhood BD. Caregivers and health practitioners should be made aware of the potential threat posed by large DTR. Therefore, DTR should be taken into consideration when making targeted health policies and programs to protect children from being harmed by climate impacts.

The association between diurnal temperature range and childhood bacillary dysentery.

PubMed

Wen, Li-ying; Zhao, Ke-fu; Cheng, Jian; Wang, Xu; Yang, Hui-hui; Li, Ke-sheng; Xu, Zhi-wei; Su, Hong

2016-02-01

Previous studies have found that mean, maximum, and minimum temperatures were associated with bacillary dysentery (BD). However, little is known about whether the within-day variation of temperature has any impact on bacillary dysentery. The current study aimed to identify the relationship between diurnal temperature range (DTR) and BD in Hefei, China. Daily data on BD counts among children aged 0-14 years from 1 January 2006 to 31 December 2012 were retrieved from Hefei Center for Disease Control and Prevention. Daily data on ambient temperature and relative humidity covering the same period were collected from the Hefei Bureau of Meteorology. A Poisson generalized linear regression model combined with a distributed lag non-linear model (DLNM) was used in the analysis after controlling the effects of season, long-term trends, mean temperature, and relative humidity. The results showed that there existed a statistically significant relationship between DTR and childhood BD. The DTR effect on childhood bacillary dysentery increased when DTR was over 8 °C. And it was greatest at 1-day lag, with an 8% (95% CI = 2.9-13.4%) increase of BD cases per 5 °C increment of DTR. Male children and children aged 0-5 years appeared to be more vulnerable to the DTR effect. The data indicate that large DTR may increase the incidence of childhood BD. Caregivers and health practitioners should be made aware of the potential threat posed by large DTR. Therefore, DTR should be taken into consideration when making targeted health policies and programs to protect children from being harmed by climate impacts.

Risk of myelodysplastic syndromes in people exposed to ionizing radiation: a retrospective cohort study of Nagasaki atomic bomb survivors.

PubMed

Iwanaga, Masako; Hsu, Wan-Ling; Soda, Midori; Takasaki, Yumi; Tawara, Masayuki; Joh, Tatsuro; Amenomori, Tatsuhiko; Yamamura, Masaomi; Yoshida, Yoshiharu; Koba, Takashi; Miyazaki, Yasushi; Matsuo, Tatsuki; Preston, Dale L; Suyama, Akihiko; Kodama, Kazunori; Tomonaga, Masao

2011-02-01

The risk of myelodysplastic syndromes (MDS) has not been fully investigated among people exposed to ionizing radiation. We investigate MDS risk and radiation dose-response in Japanese atomic bomb survivors. We conducted a retrospective cohort study by using two databases of Nagasaki atomic bomb survivors: 64,026 people with known exposure distance in the database of Nagasaki University Atomic-Bomb Disease Institute (ABDI) and 22,245 people with estimated radiation dose in the Radiation Effects Research Foundation Life Span Study (LSS). Patients with MDS diagnosed from 1985 to 2004 were identified by record linkage between the cohorts and the Nagasaki Prefecture Cancer Registry. Cox and Poisson regression models were used to estimate relationships between exposure distance or dose and MDS risk. There were 151 patients with MDS in the ABDI cohort and 47 patients with MDS in the LSS cohort. MDS rate increased inversely with exposure distance, with an excess relative risk (ERR) decay per km of 1.2 (95% CI, 0.4 to 3.0; P < .001) for ABDI. MDS risk also showed a significant linear response to exposure dose level (P < .001) with an ERR per Gy of 4.3 (95% CI, 1.6 to 9.5; P < .001). After adjustment for sex, attained age, and birth year, the MDS risk was significantly greater in those exposed when young. A significant linear radiation dose-response for MDS exists in atomic bomb survivors 40 to 60 years after radiation exposure. Clinicians should perform careful long-term follow-up of irradiated people to detect MDS as early as possible.

Control Variate Selection for Multiresponse Simulation.

DTIC Science & Technology

1987-05-01

M. H. Knuter, Applied Linear Regression Mfodels, Richard D. Erwin, Inc., Homewood, Illinois, 1983. Neuts, Marcel F., Probability, Allyn and Bacon...1982. Neter, J., V. Wasserman, and M. H. Knuter, Applied Linear Regression .fodels, Richard D. Erwin, Inc., Homewood, Illinois, 1983. Neuts, Marcel F...Aspects of J%,ultivariate Statistical Theory, John Wiley and Sons, New York, New York, 1982. dY Neter, J., W. Wasserman, and M. H. Knuter, Applied Linear Regression Mfodels

An Investigation of the Fit of Linear Regression Models to Data from an SAT[R] Validity Study. Research Report 2011-3

ERIC Educational Resources Information Center

Kobrin, Jennifer L.; Sinharay, Sandip; Haberman, Shelby J.; Chajewski, Michael

2011-01-01

This study examined the adequacy of a multiple linear regression model for predicting first-year college grade point average (FYGPA) using SAT[R] scores and high school grade point average (HSGPA). A variety of techniques, both graphical and statistical, were used to examine if it is possible to improve on the linear regression model. The results…

High correlations between MRI brain volume measurements based on NeuroQuant® and FreeSurfer.

PubMed

Ross, David E; Ochs, Alfred L; Tate, David F; Tokac, Umit; Seabaugh, John; Abildskov, Tracy J; Bigler, Erin D

2018-05-30

NeuroQuant ® (NQ) and FreeSurfer (FS) are commonly used computer-automated programs for measuring MRI brain volume. Previously they were reported to have high intermethod reliabilities but often large intermethod effect size differences. We hypothesized that linear transformations could be used to reduce the large effect sizes. This study was an extension of our previously reported study. We performed NQ and FS brain volume measurements on 60 subjects (including normal controls, patients with traumatic brain injury, and patients with Alzheimer's disease). We used two statistical approaches in parallel to develop methods for transforming FS volumes into NQ volumes: traditional linear regression, and Bayesian linear regression. For both methods, we used regression analyses to develop linear transformations of the FS volumes to make them more similar to the NQ volumes. The FS-to-NQ transformations based on traditional linear regression resulted in effect sizes which were small to moderate. The transformations based on Bayesian linear regression resulted in all effect sizes being trivially small. To our knowledge, this is the first report describing a method for transforming FS to NQ data so as to achieve high reliability and low effect size differences. Machine learning methods like Bayesian regression may be more useful than traditional methods. Copyright © 2018 Elsevier B.V. All rights reserved.

Quantile Regression in the Study of Developmental Sciences

PubMed Central

Petscher, Yaacov; Logan, Jessica A. R.

2014-01-01

Linear regression analysis is one of the most common techniques applied in developmental research, but only allows for an estimate of the average relations between the predictor(s) and the outcome. This study describes quantile regression, which provides estimates of the relations between the predictor(s) and outcome, but across multiple points of the outcome’s distribution. Using data from the High School and Beyond and U.S. Sustained Effects Study databases, quantile regression is demonstrated and contrasted with linear regression when considering models with: (a) one continuous predictor, (b) one dichotomous predictor, (c) a continuous and a dichotomous predictor, and (d) a longitudinal application. Results from each example exhibited the differential inferences which may be drawn using linear or quantile regression. PMID:24329596

Improving validation methods for molecular diagnostics: application of Bland-Altman, Deming and simple linear regression analyses in assay comparison and evaluation for next-generation sequencing

PubMed Central

Misyura, Maksym; Sukhai, Mahadeo A; Kulasignam, Vathany; Zhang, Tong; Kamel-Reid, Suzanne; Stockley, Tracy L

2018-01-01

Aims A standard approach in test evaluation is to compare results of the assay in validation to results from previously validated methods. For quantitative molecular diagnostic assays, comparison of test values is often performed using simple linear regression and the coefficient of determination (R2), using R2 as the primary metric of assay agreement. However, the use of R2 alone does not adequately quantify constant or proportional errors required for optimal test evaluation. More extensive statistical approaches, such as Bland-Altman and expanded interpretation of linear regression methods, can be used to more thoroughly compare data from quantitative molecular assays. Methods We present the application of Bland-Altman and linear regression statistical methods to evaluate quantitative outputs from next-generation sequencing assays (NGS). NGS-derived data sets from assay validation experiments were used to demonstrate the utility of the statistical methods. Results Both Bland-Altman and linear regression were able to detect the presence and magnitude of constant and proportional error in quantitative values of NGS data. Deming linear regression was used in the context of assay comparison studies, while simple linear regression was used to analyse serial dilution data. Bland-Altman statistical approach was also adapted to quantify assay accuracy, including constant and proportional errors, and precision where theoretical and empirical values were known. Conclusions The complementary application of the statistical methods described in this manuscript enables more extensive evaluation of performance characteristics of quantitative molecular assays, prior to implementation in the clinical molecular laboratory. PMID:28747393

A SEMIPARAMETRIC BAYESIAN MODEL FOR CIRCULAR-LINEAR REGRESSION

EPA Science Inventory

We present a Bayesian approach to regress a circular variable on a linear predictor. The regression coefficients are assumed to have a nonparametric distribution with a Dirichlet process prior. The semiparametric Bayesian approach gives added flexibility to the model and is usefu...

Quantifying biological samples using Linear Poisson Independent Component Analysis for MALDI-ToF mass spectra

PubMed Central

Deepaisarn, S; Tar, P D; Thacker, N A; Seepujak, A; McMahon, A W

2018-01-01

Abstract Motivation Matrix-assisted laser desorption/ionisation time-of-flight mass spectrometry (MALDI) facilitates the analysis of large organic molecules. However, the complexity of biological samples and MALDI data acquisition leads to high levels of variation, making reliable quantification of samples difficult. We present a new analysis approach that we believe is well-suited to the properties of MALDI mass spectra, based upon an Independent Component Analysis derived for Poisson sampled data. Simple analyses have been limited to studying small numbers of mass peaks, via peak ratios, which is known to be inefficient. Conventional PCA and ICA methods have also been applied, which extract correlations between any number of peaks, but we argue makes inappropriate assumptions regarding data noise, i.e. uniform and Gaussian. Results We provide evidence that the Gaussian assumption is incorrect, motivating the need for our Poisson approach. The method is demonstrated by making proportion measurements from lipid-rich binary mixtures of lamb brain and liver, and also goat and cow milk. These allow our measurements and error predictions to be compared to ground truth. Availability and implementation Software is available via the open source image analysis system TINA Vision, www.tina-vision.net. Contact paul.tar@manchester.ac.uk Supplementary information Supplementary data are available at Bioinformatics online. PMID:29091994

Pseudo second order kinetics and pseudo isotherms for malachite green onto activated carbon: comparison of linear and non-linear regression methods.

PubMed

Kumar, K Vasanth; Sivanesan, S

2006-08-25

Pseudo second order kinetic expressions of Ho, Sobkowsk and Czerwinski, Blanachard et al. and Ritchie were fitted to the experimental kinetic data of malachite green onto activated carbon by non-linear and linear method. Non-linear method was found to be a better way of obtaining the parameters involved in the second order rate kinetic expressions. Both linear and non-linear regression showed that the Sobkowsk and Czerwinski and Ritchie's pseudo second order model were the same. Non-linear regression analysis showed that both Blanachard et al. and Ho have similar ideas on the pseudo second order model but with different assumptions. The best fit of experimental data in Ho's pseudo second order expression by linear and non-linear regression method showed that Ho pseudo second order model was a better kinetic expression when compared to other pseudo second order kinetic expressions. The amount of dye adsorbed at equilibrium, q(e), was predicted from Ho pseudo second order expression and were fitted to the Langmuir, Freundlich and Redlich Peterson expressions by both linear and non-linear method to obtain the pseudo isotherms. The best fitting pseudo isotherm was found to be the Langmuir and Redlich Peterson isotherm. Redlich Peterson is a special case of Langmuir when the constant g equals unity.

Comparison of Neural Network and Linear Regression Models in Statistically Predicting Mental and Physical Health Status of Breast Cancer Survivors

DTIC Science & Technology

2015-07-15

Long-term effects on cancer survivors’ quality of life of physical training versus physical training combined with cognitive-behavioral therapy ...COMPARISON OF NEURAL NETWORK AND LINEAR REGRESSION MODELS IN STATISTICALLY PREDICTING MENTAL AND PHYSICAL HEALTH STATUS OF BREAST...34Comparison of Neural Network and Linear Regression Models in Statistically Predicting Mental and Physical Health Status of Breast Cancer Survivors

Prediction of the Main Engine Power of a New Container Ship at the Preliminary Design Stage

NASA Astrophysics Data System (ADS)

Cepowski, Tomasz

2017-06-01

The paper presents mathematical relationships that allow us to forecast the estimated main engine power of new container ships, based on data concerning vessels built in 2005-2015. The presented approximations allow us to estimate the engine power based on the length between perpendiculars and the number of containers the ship will carry. The approximations were developed using simple linear regression and multivariate linear regression analysis. The presented relations have practical application for estimation of container ship engine power needed in preliminary parametric design of the ship. It follows from the above that the use of multiple linear regression to predict the main engine power of a container ship brings more accurate solutions than simple linear regression.

Estimation of Standard Error of Regression Effects in Latent Regression Models Using Binder's Linearization. Research Report. ETS RR-07-09

ERIC Educational Resources Information Center

Li, Deping; Oranje, Andreas

2007-01-01

Two versions of a general method for approximating standard error of regression effect estimates within an IRT-based latent regression model are compared. The general method is based on Binder's (1983) approach, accounting for complex samples and finite populations by Taylor series linearization. In contrast, the current National Assessment of…

Regression assumptions in clinical psychology research practice-a systematic review of common misconceptions.

PubMed

Ernst, Anja F; Albers, Casper J

2017-01-01

Misconceptions about the assumptions behind the standard linear regression model are widespread and dangerous. These lead to using linear regression when inappropriate, and to employing alternative procedures with less statistical power when unnecessary. Our systematic literature review investigated employment and reporting of assumption checks in twelve clinical psychology journals. Findings indicate that normality of the variables themselves, rather than of the errors, was wrongfully held for a necessary assumption in 4% of papers that use regression. Furthermore, 92% of all papers using linear regression were unclear about their assumption checks, violating APA-recommendations. This paper appeals for a heightened awareness for and increased transparency in the reporting of statistical assumption checking.

Regression assumptions in clinical psychology research practice—a systematic review of common misconceptions

PubMed Central

Ernst, Anja F.

2017-01-01

Misconceptions about the assumptions behind the standard linear regression model are widespread and dangerous. These lead to using linear regression when inappropriate, and to employing alternative procedures with less statistical power when unnecessary. Our systematic literature review investigated employment and reporting of assumption checks in twelve clinical psychology journals. Findings indicate that normality of the variables themselves, rather than of the errors, was wrongfully held for a necessary assumption in 4% of papers that use regression. Furthermore, 92% of all papers using linear regression were unclear about their assumption checks, violating APA-recommendations. This paper appeals for a heightened awareness for and increased transparency in the reporting of statistical assumption checking. PMID:28533971

Estimating linear temporal trends from aggregated environmental monitoring data

USGS Publications Warehouse

Erickson, Richard A.; Gray, Brian R.; Eager, Eric A.

2017-01-01

Trend estimates are often used as part of environmental monitoring programs. These trends inform managers (e.g., are desired species increasing or undesired species decreasing?). Data collected from environmental monitoring programs is often aggregated (i.e., averaged), which confounds sampling and process variation. State-space models allow sampling variation and process variations to be separated. We used simulated time-series to compare linear trend estimations from three state-space models, a simple linear regression model, and an auto-regressive model. We also compared the performance of these five models to estimate trends from a long term monitoring program. We specifically estimated trends for two species of fish and four species of aquatic vegetation from the Upper Mississippi River system. We found that the simple linear regression had the best performance of all the given models because it was best able to recover parameters and had consistent numerical convergence. Conversely, the simple linear regression did the worst job estimating populations in a given year. The state-space models did not estimate trends well, but estimated population sizes best when the models converged. We found that a simple linear regression performed better than more complex autoregression and state-space models when used to analyze aggregated environmental monitoring data.

Added sugars and periodontal disease in young adults: an analysis of NHANES III data.

PubMed

Lula, Estevam C O; Ribeiro, Cecilia C C; Hugo, Fernando N; Alves, Cláudia M C; Silva, Antônio A M

2014-10-01

Added sugar consumption seems to trigger a hyperinflammatory state and may result in visceral adiposity, dyslipidemia, and insulin resistance. These conditions are risk factors for periodontal disease. However, the role of sugar intake in the cause of periodontal disease has not been adequately studied. We evaluated the association between the frequency of added sugar consumption and periodontal disease in young adults by using NHANES III data. Data from 2437 young adults (aged 18-25 y) who participated in NHANES III (1988-1994) were analyzed. We estimated the frequency of added sugar consumption by using food-frequency questionnaire responses. We considered periodontal disease to be present in teeth with bleeding on probing and a probing depth ≥3 mm at one or more sites. We evaluated this outcome as a discrete variable in Poisson regression models and as a categorical variable in multinomial logistic regression models adjusted for sex, age, race-ethnicity, education, poverty-income ratio, tobacco exposure, previous diagnosis of diabetes, and body mass index. A high consumption of added sugars was associated with a greater prevalence of periodontal disease in middle [prevalence ratio (PR): 1.39; 95% CI: 1.02, 1.89] and upper (PR: 1.42; 95% CI: 1.08, 1.85) tertiles of consumption in the adjusted Poisson regression model. The upper tertile of added sugar intake was associated with periodontal disease in ≥2 teeth (PR: 1.73; 95% CI: 1.19, 2.52) but not with periodontal disease in only one tooth (PR: 0.85; 95% CI: 0.54, 1.34) in the adjusted multinomial logistic regression model. A high frequency of consumption of added sugars is associated with periodontal disease, independent of traditional risk factors, suggesting that this consumption pattern may contribute to the systemic inflammation observed in periodontal disease and associated noncommunicable diseases. © 2014 American Society for Nutrition.

Temperature dependence of elastic and strength properties of T300/5208 graphite-epoxy

NASA Technical Reports Server (NTRS)

Milkovich, S. M.; Herakovich, C. T.

1984-01-01

Experimental results are presented for the elastic and strength properties of T300/5208 graphite-epoxy at room temperature, 116K (-250 F), and 394K (+250 F). Results are presented for unidirectional 0, 90, and 45 degree laminates, and + or - 30, + or - 45, and + or - 60 degree angle-ply laminates. The stress-strain behavior of the 0 and 90 degree laminates is essentially linear for all three temperatures and that the stress-strain behavior of all other laminates is linear at 116K. A second-order curve provides the best fit for the temperature is linear at 116K. A second-order curve provides the best fit for the temperature dependence of the elastic modulus of all laminates and for the principal shear modulus. Poisson's ratio appears to vary linearly with temperature. all moduli decrease with increasing temperature except for E (sub 1) which exhibits a small increase. The strength temperature dependence is also quadratic for all laminates except the 0 degree - laminate which exhibits linear temperature dependence. In many cases the temperature dependence of properties is nearly linear.

Comparing The Effectiveness of a90/95 Calculations (Preprint)

DTIC Science & Technology

2006-09-01

Nachtsheim, John Neter, William Li, Applied Linear Statistical Models , 5th ed., McGraw-Hill/Irwin, 2005 5. Mood, Graybill and Boes, Introduction...curves is based on methods that are only valid for ordinary linear regression. Requirements for a valid Ordinary Least-Squares Regression Model There... linear . For example is a linear model ; is not. 2. Uniform variance (homoscedasticity

Age and the economics of an emergency medical admission-what factors determine costs?

PubMed

McCabe, J J; Cournane, S; Byrne, D; Conway, R; O'Riordan, D; Silke, B

2017-02-01

The ageing of the population may be anticipated to increase demand on hospital resources. We have investigated the relationship between hospital episode costs and age profile in a single centre. All Emergency Medical admissions (33 732 episodes) to an Irish hospital over a 6-year period, categorized into three age groups, were evaluated against total hospital episode costs. Univariate and adjusted incidence rate ratios (IRRs) were calculated using zero truncated Poisson regression. The total hospital episode cost increased with age ( P < 0.001). The multi-variable Poisson regression model demonstrated that the most important drivers of overall costs were Acute Illness Severity-IRR 1.36 (95% CI: 1.30, 1.41), Sepsis Status -1.46 (95% CI: 1.42, 1.51) and Chronic Disabling Disease Score -1.25 (95% CI: 1.22, 1.27) and the Age Group as exemplified for those 85 years IRR 1.23 (95% CI: 1.15, 1.32). Total hospital episode costs are a product of clinical complexity with contributions from the Acute Illness Severity, Co-Morbidity, Chronic Disabling Disease Score and Sepsis Status. However age is also an important contributor and an increasing patient age profile will have a predictable impact on total hospital episode costs. © The Author 2016. Published by Oxford University Press on behalf of the Association of Physicians. All rights reserved. For Permissions, please email: journals.permissions@oup.com

Methods for analyzing matched designs with double controls: excess risk is easily estimated and misinterpreted when evaluating traffic deaths.

PubMed

Redelmeier, Donald A; Tibshirani, Robert J

2018-06-01

To demonstrate analytic approaches for matched studies where two controls are linked to each case and events are accumulating counts rather than binary outcomes. A secondary intent is to clarify the distinction between total risk and excess risk (unmatched vs. matched perspectives). We review past research testing whether elections can lead to increased traffic risks. The results are reinterpreted by analyzing both the total count of individuals in fatal crashes and the excess count of individuals in fatal crashes, each time accounting for the matched double controls. Overall, 1,546 individuals were in fatal crashes on the 10 election days (average = 155/d), and 2,593 individuals were in fatal crashes on the 20 control days (average = 130/d). Poisson regression of total counts yielded a relative risk of 1.19 (95% confidence interval: 1.12-1.27). Poisson regression of excess counts yielded a relative risk of 3.22 (95% confidence interval: 2.72-3.80). The discrepancy between analyses of total counts and excess counts replicated with alternative statistical models and was visualized in graphical displays. Available approaches provide methods for analyzing count data in matched designs with double controls and help clarify the distinction between increases in total risk and increases in excess risk. Copyright © 2018 Elsevier Inc. All rights reserved.

Epidemiology of occupational injury among cleaners in the healthcare sector.

PubMed

Alamgir, Hasanat; Yu, Shicheng

2008-09-01

The cleaning profession has been associated with multiple ergonomic and chemical hazards which elevate the risk for occupational injury. This study investigated the epidemiology of occupational injury among cleaners in healthcare work settings in the Canadian province of British Columbia. Incidents of occupational injury among cleaners, resulting in lost time from work or medical care, over a period of 1 year in two healthcare regions were extracted from a standardized operational database and with person-years obtained from payroll data. Detailed analysis was conducted using Poisson regression modeling. A total of 145 injuries were identified among cleaners, with an annual incidence rate of 32.1 per 100 person-years. After adjustment for age, gender, subsector, facility, experience and employment status, Poisson regression models demonstrated that a significantly higher relative risk (RR) of all injury, musculoskeletal injury and cuts was associated with cleaning work in acute care facilities, compared with long-term care facilities. Female cleaners were at a higher RR of all injuries and contusions than male cleaners. A lower risk of all injury and allergy and irritation incidents among part-time or casual workers was found. Cleaners with >10 years of experience were at significantly lower risk for all injury, contusion and allergy and irritation incidents. Cleaners were found to be at an elevated risk of all injury categories compared with healthcare workers in general.

WINPEPI updated: computer programs for epidemiologists, and their teaching potential

PubMed Central

2011-01-01

Background The WINPEPI computer programs for epidemiologists are designed for use in practice and research in the health field and as learning or teaching aids. The programs are free, and can be downloaded from the Internet. Numerous additions have been made in recent years. Implementation There are now seven WINPEPI programs: DESCRIBE, for use in descriptive epidemiology; COMPARE2, for use in comparisons of two independent groups or samples; PAIRSetc, for use in comparisons of paired and other matched observations; LOGISTIC, for logistic regression analysis; POISSON, for Poisson regression analysis; WHATIS, a "ready reckoner" utility program; and ETCETERA, for miscellaneous other procedures. The programs now contain 122 modules, each of which provides a number, sometimes a large number, of statistical procedures. The programs are accompanied by a Finder that indicates which modules are appropriate for different purposes. The manuals explain the uses, limitations and applicability of the procedures, and furnish formulae and references. Conclusions WINPEPI is a handy resource for a wide variety of statistical routines used by epidemiologists. Because of its ready availability, portability, ease of use, and versatility, WINPEPI has a considerable potential as a learning and teaching aid, both with respect to practical procedures in the planning and analysis of epidemiological studies, and with respect to important epidemiological concepts. It can also be used as an aid in the teaching of general basic statistics. PMID:21288353

Tuberculosis and the role of war in the modern era.

PubMed

Drobniewski, F A; Verlander, N Q

2000-12-01

Tuberculosis (TB) remains a major global health problem; historically, major wars have increased TB notifications. This study evaluated whether modern conflicts worldwide affected TB notifications between 1975 and 1995. Dates of conflicts were obtained and matched with national TB notification data reported to the World Health Organization. Overall notification rates were calculated pre and post conflict. Poisson regression analysis was applied to all conflicts with sufficient data for detailed trend analysis. Thirty-six conflicts were identified, for which 3-year population and notification data were obtained. Overall crude TB notification rates were 81.9 and 105.1/100,000 pre and post start of conflict in these countries. Sufficient data existed in 16 countries to apply Poisson regression analysis to model 5-year pre and post start of conflict trends. This analysis indicated that the risk of presenting with TB in any country 2.5 years after the outbreak of conflict relative to 2.5 years before the outbreak was 1.016 (95%CI 0.9435-1.095). The modelling suggested that in the modern era war may not significantly damage efforts to control TB in the long term. This might be due to the limited scale of most of these conflicts compared to the large-scale civilian disruption associated with 'world wars'. The management of TB should be considered in planning post-conflict refugee and reconstruction programmes.

Effectiveness of preventive home visits in reducing the risk of falls in old age: a randomized controlled trial

PubMed Central

Luck, Tobias; Motzek, Tom; Luppa, Melanie; Matschinger, Herbert; Fleischer, Steffen; Sesselmann, Yves; Roling, Gudrun; Beutner, Katrin; König, Hans-Helmut; Behrens, Johann; Riedel-Heller, Steffi G

2013-01-01

Background Falls in older people are a major public health issue, but the underlying causes are complex. We sought to evaluate the effectiveness of preventive home visits as a multifactorial, individualized strategy to reduce falls in community-dwelling older people. Methods Data were derived from a prospective randomized controlled trial with follow-up examination after 18 months. Two hundred and thirty participants (≥80 years of age) with functional impairment were randomized to intervention and control groups. The intervention group received up to three preventive home visits including risk assessment, home counseling intervention, and a booster session. The control group received no preventive home visits. Structured interviews at baseline and follow-up provided information concerning falls in both study groups. Random-effects Poisson regression evaluated the effect of preventive home visits on the number of falls controlling for covariates. Results Random-effects Poisson regression showed a significant increase in the number of falls between baseline and follow-up in the control group (incidence rate ratio 1.96) and a significant decrease in the intervention group (incidence rate ratio 0.63) controlling for age, sex, family status, level of care, and impairment in activities of daily living. Conclusion Our results indicate that a preventive home visiting program can be effective in reducing falls in community-dwelling older people. PMID:23788832

Using perinatal morbidity scoring tools as a primary study outcome.

PubMed

Hutcheon, Jennifer A; Bodnar, Lisa M; Platt, Robert W

2017-11-01

Perinatal morbidity scores are tools that score or weight different adverse events according to their relative severity. Perinatal morbidity scores are appealing for maternal-infant health researchers because they provide a way to capture a broad range of adverse events to mother and newborn while recognising that some events are considered more serious than others. However, they have proved difficult to implement as a primary outcome in applied research studies because of challenges in testing if the scores are significantly different between two or more study groups. We outline these challenges and describe a solution, based on Poisson regression, that allows differences in perinatal morbidity scores to be formally evaluated. The approach is illustrated using an existing maternal-neonatal scoring tool, the Adverse Outcome Index, to evaluate the safety of labour and delivery before and after the closure of obstetrical services in small rural communities. Applying the proposed Poisson regression to the case study showed a protective risk ratio for adverse outcome following closures as compared with the original analysis, where no difference was found. This approach opens the door for considerably broader use of perinatal morbidity scoring tools as a primary outcome in applied population and clinical maternal-infant health research studies. © Article author(s) (or their employer(s) unless otherwise stated in the text of the article) 2017. All rights reserved. No commercial use is permitted unless otherwise expressly granted.

Use of Segregation Indices, Townsend Index, and Air Toxics Data to Assess Lifetime Cancer Risk Disparities in Metropolitan Charleston, South Carolina, USA

PubMed Central

Rice, LaShanta J.; Jiang, Chengsheng; Wilson, Sacoby M.; Burwell-Naney, Kristen; Samantapudi, Ashok; Zhang, Hongmei

2014-01-01

Background: Studies have demonstrated a relationship between segregation and level of education, occupational opportunities, and risk behaviors, yet a paucity of research has elucidated the association between racial residential segregation, socioeconomic deprivation, and lifetime cancer risk. Objectives: We examined estimated lifetime cancer risk from air toxics by racial composition, segregation, and deprivation in census tracts in Metropolitan Charleston. Methods: Segregation indices were used to measure the distribution of groups of people from different races within neighborhoods. The Townsend Index was used to measure economic deprivation in the study area. Poisson multivariate regressions were applied to assess the association of lifetime cancer risk with segregation indices and Townsend Index along with several sociodemographic measures. Results: Lifetime cancer risk from all pollution sources was 28 persons/million for half of the census tracts in Metropolitan Charleston. Isolation Index and Townsend Index both showed significant correlation with lifetime cancer risk from different sources. This significance still holds after adjusting for other sociodemographic measures in a Poisson regression, and these two indices have stronger effect on lifetime cancer risk compared to the effects of sociodemographic measures. Conclusions: We found that material deprivation, measured by the Townsend Index and segregation measured by the Isolation index, introduced high impact on lifetime cancer risk by air toxics at the census tract level. PMID:24852759

Correlation and simple linear regression.

PubMed

Zou, Kelly H; Tuncali, Kemal; Silverman, Stuart G

2003-06-01

In this tutorial article, the concepts of correlation and regression are reviewed and demonstrated. The authors review and compare two correlation coefficients, the Pearson correlation coefficient and the Spearman rho, for measuring linear and nonlinear relationships between two continuous variables. In the case of measuring the linear relationship between a predictor and an outcome variable, simple linear regression analysis is conducted. These statistical concepts are illustrated by using a data set from published literature to assess a computed tomography-guided interventional technique. These statistical methods are important for exploring the relationships between variables and can be applied to many radiologic studies.

Electrokinetics Models for Micro and Nano Fluidic Impedance Sensors

DTIC Science & Technology

2010-11-01

primitive Differential-Algebraic Equations (DAEs), used to process and interpret the experimentally measured electrical impedance data (Sun and Morgan...field, and species respectively. A second-order scheme was used to calculate the ionic species distribution. The linearized algebraic equations were...is governed by the Poisson equation 2 0 0 r i i i F z cε ε φ∇ + =∑ where ε0 and εr are, respectively, the electrical permittivity in the vacuum

Fourier analysis of the SOR iteration

NASA Technical Reports Server (NTRS)

Leveque, R. J.; Trefethen, L. N.

1986-01-01

The SOR iteration for solving linear systems of equations depends upon an overrelaxation factor omega. It is shown that for the standard model problem of Poisson's equation on a rectangle, the optimal omega and corresponding convergence rate can be rigorously obtained by Fourier analysis. The trick is to tilt the space-time grid so that the SOR stencil becomes symmetrical. The tilted grid also gives insight into the relation between convergence rates of several variants.

Control of Structure in Turbulent Flows: Bifurcating and Blooming Jets.

DTIC Science & Technology

1987-10-10

injected through computational boundaries. (2) to satisfy no- slip boundary conditions or (3) during ’ grid " refinement when one element may be split...use of fast Poisson solvers on a mesh of M grid points, the operation count for this step can approach 0(M log M). Additional required steps are (1...consider s- three-dimensionai perturbations to the uart vortices. The linear stability calculations ot Pierrehumbert & Widnadl [101 are available for

Statistical Modeling Reveals the Effect of Absolute Humidity on Dengue in Singapore

PubMed Central

Xu, Hai-Yan; Fu, Xiuju; Lee, Lionel Kim Hock; Ma, Stefan; Goh, Kee Tai; Wong, Jiancheng; Habibullah, Mohamed Salahuddin; Lee, Gary Kee Khoon; Lim, Tian Kuay; Tambyah, Paul Anantharajah; Lim, Chin Leong; Ng, Lee Ching

2014-01-01

Weather factors are widely studied for their effects on indicating dengue incidence trends. However, these studies have been limited due to the complex epidemiology of dengue, which involves dynamic interplay of multiple factors such as herd immunity within a population, distinct serotypes of the virus, environmental factors and intervention programs. In this study, we investigate the impact of weather factors on dengue in Singapore, considering the disease epidemiology and profile of virus serotypes. A Poisson regression combined with Distributed Lag Non-linear Model (DLNM) was used to evaluate and compare the impact of weekly Absolute Humidity (AH) and other weather factors (mean temperature, minimum temperature, maximum temperature, rainfall, relative humidity and wind speed) on dengue incidence from 2001 to 2009. The same analysis was also performed on three sub-periods, defined by predominant circulating serotypes. The performance of DLNM regression models were then evaluated through the Akaike's Information Criterion. From the correlation and DLNM regression modeling analyses of the studied period, AH was found to be a better predictor for modeling dengue incidence than the other unique weather variables. Whilst mean temperature (MeanT) also showed significant correlation with dengue incidence, the relationship between AH or MeanT and dengue incidence, however, varied in the three sub-periods. Our results showed that AH had a more stable impact on dengue incidence than temperature when virological factors were taken into consideration. AH appeared to be the most consistent factor in modeling dengue incidence in Singapore. Considering the changes in dominant serotypes, the improvements in vector control programs and the inconsistent weather patterns observed in the sub-periods, the impact of weather on dengue is modulated by these other factors. Future studies on the impact of climate change on dengue need to take all the other contributing factors into consideration in order to make meaningful public policy recommendations. PMID:24786517

A possible dose-response association between distance to farmers' markets and roadside produce stands, frequency of shopping, fruit and vegetable consumption, and body mass index among customers in the Southern United States.

PubMed

Jilcott Pitts, Stephanie B; Hinkley, Jedediah; Wu, Qiang; McGuirt, Jared T; Lyonnais, Mary Jane; Rafferty, Ann P; Whitt, Olivia R; Winterbauer, Nancy; Phillips, Lisa

2017-01-11

The association between farmers' market characteristics and consumer shopping habits remains unclear. Our objective was to examine associations among distance to farmers' markets, amenities within farmers' markets, frequency of farmers' market shopping, fruit and vegetable consumption, and body mass index (BMI). We hypothesized that the relationship between frequency of farmers' market shopping and BMI would be mediated by fruit and vegetable consumption. In 15 farmers' markets in northeastern North Carolina, July-September 2015, we conducted a cross-sectional survey among 263 farmers' market customers (199 provided complete address data) and conducted farmers' market audits. To participate, customers had to be over 18 years of age, and English speaking. Dependent variables included farmers' market shopping frequency, fruit and vegetable consumption, and BMI. Analysis of variance, adjusted multinomial logistic regression, Poisson regression, and linear regression models, adjusted for age, race, sex, and education, were used to examine associations between distance to farmers' markets, amenities within farmers' markets, frequency of farmers' market shopping, fruit and vegetable consumption, and BMI. Those who reported shopping at farmers' markets a few times per year or less reported consuming 4.4 (standard deviation = 1.7) daily servings of fruits and vegetables, and those who reported shopping 2 or more times per week reported consuming 5.5 (2.2) daily servings. There was no association between farmers' market amenities, and shopping frequency or fruit and vegetable consumption. Those who shopped 2 or more times per week had a statistically significantly lower BMI than those who shopped less frequently. There was no evidence of mediation of the relationship between frequency of shopping and BMI by fruit and vegetable consumption. More work should be done to understand factors within farmers' markets that encourage fruit and vegetable purchases.

Statistical modeling reveals the effect of absolute humidity on dengue in Singapore.

PubMed

Xu, Hai-Yan; Fu, Xiuju; Lee, Lionel Kim Hock; Ma, Stefan; Goh, Kee Tai; Wong, Jiancheng; Habibullah, Mohamed Salahuddin; Lee, Gary Kee Khoon; Lim, Tian Kuay; Tambyah, Paul Anantharajah; Lim, Chin Leong; Ng, Lee Ching

2014-05-01

Weather factors are widely studied for their effects on indicating dengue incidence trends. However, these studies have been limited due to the complex epidemiology of dengue, which involves dynamic interplay of multiple factors such as herd immunity within a population, distinct serotypes of the virus, environmental factors and intervention programs. In this study, we investigate the impact of weather factors on dengue in Singapore, considering the disease epidemiology and profile of virus serotypes. A Poisson regression combined with Distributed Lag Non-linear Model (DLNM) was used to evaluate and compare the impact of weekly Absolute Humidity (AH) and other weather factors (mean temperature, minimum temperature, maximum temperature, rainfall, relative humidity and wind speed) on dengue incidence from 2001 to 2009. The same analysis was also performed on three sub-periods, defined by predominant circulating serotypes. The performance of DLNM regression models were then evaluated through the Akaike's Information Criterion. From the correlation and DLNM regression modeling analyses of the studied period, AH was found to be a better predictor for modeling dengue incidence than the other unique weather variables. Whilst mean temperature (MeanT) also showed significant correlation with dengue incidence, the relationship between AH or MeanT and dengue incidence, however, varied in the three sub-periods. Our results showed that AH had a more stable impact on dengue incidence than temperature when virological factors were taken into consideration. AH appeared to be the most consistent factor in modeling dengue incidence in Singapore. Considering the changes in dominant serotypes, the improvements in vector control programs and the inconsistent weather patterns observed in the sub-periods, the impact of weather on dengue is modulated by these other factors. Future studies on the impact of climate change on dengue need to take all the other contributing factors into consideration in order to make meaningful public policy recommendations.

Childhood temperament predictors of adolescent physical activity.

PubMed

Janssen, James A; Kolacz, Jacek; Shanahan, Lilly; Gangel, Meghan J; Calkins, Susan D; Keane, Susan P; Wideman, Laurie

2017-01-05

Physical inactivity is a leading cause of mortality worldwide. Many patterns of physical activity involvement are established early in life. To date, the role of easily identifiable early-life individual predictors of PA, such as childhood temperament, remains relatively unexplored. Here, we tested whether childhood temperamental activity level, high intensity pleasure, low intensity pleasure, and surgency predicted engagement in physical activity (PA) patterns 11 years later in adolescence. Data came from a longitudinal community study (N = 206 participants, 53% females, 70% Caucasian). Parents reported their children's temperamental characteristics using the Child Behavior Questionnaire (CBQ) when children were 4 & 5 years old. Approximately 11 years later, adolescents completed self-reports of PA using the Godin Leisure Time Exercise Questionnaire and the Youth Risk Behavior Survey. Ordered logistic regression, ordinary least squares linear regression, and Zero-inflated Poisson regression models were used to predict adolescent PA from childhood temperament. Race, socioeconomic status, and adolescent body mass index were used as covariates. Males with greater childhood temperamental activity level engaged in greater adolescent PA volume (B = .42, SE = .13) and a 1 SD difference in childhood temperamental activity level predicted 29.7% more strenuous adolescent PA per week. Males' high intensity pleasure predicted higher adolescent PA volume (B = .28, SE = .12). Males' surgency positively predicted more frequent PA activity (B = .47, SE = .23, OR = 1.61, 95% CI: 1.02, 2.54) and PA volume (B = .31, SE = .12). No predictions from females' childhood temperament to later PA engagement were identified. Childhood temperament may influence the formation of later PA habits, particularly in males. Boys with high temperamental activity level, high intensity pleasure, and surgency may directly seek out pastimes that involve PA. Indirectly, temperament may also influence caregivers' perceptions of optimal activity choices for children. Understanding how temperament influences the development of PA patterns has the potential to inform efforts aimed at promoting long-term PA engagement and physical health.

Improving validation methods for molecular diagnostics: application of Bland-Altman, Deming and simple linear regression analyses in assay comparison and evaluation for next-generation sequencing.

PubMed

Misyura, Maksym; Sukhai, Mahadeo A; Kulasignam, Vathany; Zhang, Tong; Kamel-Reid, Suzanne; Stockley, Tracy L

2018-02-01

A standard approach in test evaluation is to compare results of the assay in validation to results from previously validated methods. For quantitative molecular diagnostic assays, comparison of test values is often performed using simple linear regression and the coefficient of determination (R 2 ), using R 2 as the primary metric of assay agreement. However, the use of R 2 alone does not adequately quantify constant or proportional errors required for optimal test evaluation. More extensive statistical approaches, such as Bland-Altman and expanded interpretation of linear regression methods, can be used to more thoroughly compare data from quantitative molecular assays. We present the application of Bland-Altman and linear regression statistical methods to evaluate quantitative outputs from next-generation sequencing assays (NGS). NGS-derived data sets from assay validation experiments were used to demonstrate the utility of the statistical methods. Both Bland-Altman and linear regression were able to detect the presence and magnitude of constant and proportional error in quantitative values of NGS data. Deming linear regression was used in the context of assay comparison studies, while simple linear regression was used to analyse serial dilution data. Bland-Altman statistical approach was also adapted to quantify assay accuracy, including constant and proportional errors, and precision where theoretical and empirical values were known. The complementary application of the statistical methods described in this manuscript enables more extensive evaluation of performance characteristics of quantitative molecular assays, prior to implementation in the clinical molecular laboratory. © 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.

U.S. Army Armament Research, Development and Engineering Center Grain Evaluation Software to Numerically Predict Linear Burn Regression for Solid Propellant Grain Geometries

DTIC Science & Technology

2017-10-01

ENGINEERING CENTER GRAIN EVALUATION SOFTWARE TO NUMERICALLY PREDICT LINEAR BURN REGRESSION FOR SOLID PROPELLANT GRAIN GEOMETRIES Brian...author(s) and should not be construed as an official Department of the Army position, policy, or decision, unless so designated by other documentation...U.S. ARMY ARMAMENT RESEARCH, DEVELOPMENT AND ENGINEERING CENTER GRAIN EVALUATION SOFTWARE TO NUMERICALLY PREDICT LINEAR BURN REGRESSION FOR SOLID

Proposing an Empirically Justified Reference Threshold for Blood Culture Sampling Rates in Intensive Care Units

PubMed Central

Castell, Stefanie; Schwab, Frank; Geffers, Christine; Bongartz, Hannah; Brunkhorst, Frank M.; Gastmeier, Petra; Mikolajczyk, Rafael T.

2014-01-01

Early and appropriate blood culture sampling is recommended as a standard of care for patients with suspected bloodstream infections (BSI) but is rarely taken into account when quality indicators for BSI are evaluated. To date, sampling of about 100 to 200 blood culture sets per 1,000 patient-days is recommended as the target range for blood culture rates. However, the empirical basis of this recommendation is not clear. The aim of the current study was to analyze the association between blood culture rates and observed BSI rates and to derive a reference threshold for blood culture rates in intensive care units (ICUs). This study is based on data from 223 ICUs taking part in the German hospital infection surveillance system. We applied locally weighted regression and segmented Poisson regression to assess the association between blood culture rates and BSI rates. Below 80 to 90 blood culture sets per 1,000 patient-days, observed BSI rates increased with increasing blood culture rates, while there was no further increase above this threshold. Segmented Poisson regression located the threshold at 87 (95% confidence interval, 54 to 120) blood culture sets per 1,000 patient-days. Only one-third of the investigated ICUs displayed blood culture rates above this threshold. We provided empirical justification for a blood culture target threshold in ICUs. In the majority of the studied ICUs, blood culture sampling rates were below this threshold. This suggests that a substantial fraction of BSI cases might remain undetected; reporting observed BSI rates as a quality indicator without sufficiently high blood culture rates might be misleading. PMID:25520442

Strongly nonlinear composite dielectrics: A perturbation method for finding the potential field and bulk effective properties

NASA Astrophysics Data System (ADS)

Blumenfeld, Raphael; Bergman, David J.

1991-10-01

A class of strongly nonlinear composite dielectrics is studied. We develop a general method to reduce the scalar-potential-field problem to the solution of a set of linear Poisson-type equations in rescaled coordinates. The method is applicable for a large variety of nonlinear materials. For a power-law relation between the displacement and the electric fields, it is used to solve explicitly for the value of the bulk effective dielectric constant ɛe to second order in the fluctuations of its local value. A simlar procedure for the vector potential, whose curl is the displacement field, yields a quantity analogous to the inverse dielectric constant in linear dielectrics. The bulk effective dielectric constant is given by a set of linear integral expressions in the rescaled coordinates and exact bounds for it are derived.

Linear regression in astronomy. II

NASA Technical Reports Server (NTRS)

Feigelson, Eric D.; Babu, Gutti J.

1992-01-01

A wide variety of least-squares linear regression procedures used in observational astronomy, particularly investigations of the cosmic distance scale, are presented and discussed. The classes of linear models considered are (1) unweighted regression lines, with bootstrap and jackknife resampling; (2) regression solutions when measurement error, in one or both variables, dominates the scatter; (3) methods to apply a calibration line to new data; (4) truncated regression models, which apply to flux-limited data sets; and (5) censored regression models, which apply when nondetections are present. For the calibration problem we develop two new procedures: a formula for the intercept offset between two parallel data sets, which propagates slope errors from one regression to the other; and a generalization of the Working-Hotelling confidence bands to nonstandard least-squares lines. They can provide improved error analysis for Faber-Jackson, Tully-Fisher, and similar cosmic distance scale relations.

Ionization effects and linear stability in a coaxial plasma device

NASA Astrophysics Data System (ADS)

Kurt, Erol; Kurt, Hilal; Bayhan, Ulku

2009-03-01

A 2-D computer simulation of a coaxial plasma device depending on the conservation equations of electrons, ions and excited atoms together with the Poisson equation for a plasma gun is carried out. Some characteristics of the plasma focus device (PF) such as critical wave numbers a c and voltages U c in the cases of various pressures Pare estimated in order to satisfy the necessary conditions of traveling particle densities ( i.e. plasma patterns) via a linear analysis. Oscillatory solutions are characterized by a nonzero imaginary part of the growth rate Im ( σ) for all cases. The model also predicts the minimal voltage ranges of the system for certain pressure intervals.

A Constrained Linear Estimator for Multiple Regression

ERIC Educational Resources Information Center

Davis-Stober, Clintin P.; Dana, Jason; Budescu, David V.

2010-01-01

"Improper linear models" (see Dawes, Am. Psychol. 34:571-582, "1979"), such as equal weighting, have garnered interest as alternatives to standard regression models. We analyze the general circumstances under which these models perform well by recasting a class of "improper" linear models as "proper" statistical models with a single predictor. We…

On the design of classifiers for crop inventories

NASA Technical Reports Server (NTRS)

Heydorn, R. P.; Takacs, H. C.

1986-01-01

Crop proportion estimators that use classifications of satellite data to correct, in an additive way, a given estimate acquired from ground observations are discussed. A linear version of these estimators is optimal, in terms of minimum variance, when the regression of the ground observations onto the satellite observations in linear. When this regression is not linear, but the reverse regression (satellite observations onto ground observations) is linear, the estimator is suboptimal but still has certain appealing variance properties. In this paper expressions are derived for those regressions which relate the intercepts and slopes to conditional classification probabilities. These expressions are then used to discuss the question of classifier designs that can lead to low-variance crop proportion estimates. Variance expressions for these estimates in terms of classifier omission and commission errors are also derived.

The association of C-reactive protein with subclinical cardiovascular disease in HIV-infected and HIV-uninfected women.

PubMed

Moran, Caitlin A; Sheth, Anandi N; Mehta, C Christina; Hanna, David B; Gustafson, Deborah R; Plankey, Michael W; Mack, Wendy J; Tien, Phyllis C; French, Audrey L; Golub, Elizabeth T; Quyyumi, Arshed; Kaplan, Robert C; Ofotokun, Ighovwerha

2018-05-15

HIV is a cardiovascular disease (CVD) risk factor. However, CVD risk is often underestimated in HIV-infected women. C-reactive protein (CRP) may improve CVD prediction in this population. We examined the association of baseline plasma CRP with subclinical CVD in women with and without HIV. Retrospective cohort study. A total of 572 HIV-infected and 211 HIV-uninfected women enrolled in the Women's Interagency HIV Study underwent serial high-resolution B-mode carotid artery ultrasonography between 2004 and 2013 to assess carotid intima-media thickness (CIMT) and focal carotid artery plaques. We used multivariable linear and logistic regression models to assess the association of baseline high (≥3 mg/l) high-sensitivity (hs) CRP with baseline CIMT and focal plaques, and used multivariable linear and Poisson regression models for the associations of high hsCRP with CIMT change and focal plaque progression. We stratified our analyses by HIV status. Median (interquartile range) hsCRP was 2.2 mg/l (0.8-5.3) in HIV-infected, and 3.2 mg/l (0.9-7.7) in HIV-uninfected, women (P = 0.005). There was no statistically significant association of hsCRP with baseline CIMT [adjusted mean difference -3.5 μm (95% confidence interval:-19.0 to 12.1)] or focal plaques [adjusted odds ratio: 1.31 (0.67-2.67)], and no statistically significant association of hsCRP with CIMT change [adjusted mean difference 11.4 μm (-2.3 to 25.1)]. However, hsCRP at least 3 mg/l was positively associated with focal plaque progression in HIV-uninfected [adjusted rate ratio: 5.97 (1.46-24.43)], but not in HIV-infected [adjusted rate ratio: 0.81 (0.47-1.42)] women (P = 0.042 for interaction). In our cohort of women with similar CVD risk factors, higher baseline hsCRP is positively associated with carotid plaque progression in HIV-uninfected, but not HIV-infected, women, suggesting that subclinical CVD pathogenesis may be different HIV-infected women.

Higher Dialysate Matrix Metalloproteinase-2 Levels Are Associated with Peritoneal Membrane Dysfunction

PubMed Central

Cho, Yeoungjee; Johnson, David W.; Vesey, David A.; Hawley, Carmel M.; Pascoe, Elaine M.; Clarke, Margaret; Topley, Nicholas

2016-01-01

♦ Background: Peritoneal dialysis (PD) patients develop progressive and cumulative peritoneal injury with longer time spent on PD. The present study aimed to a) describe the trend of peritoneal injury biomarkers, matrix metalloproteinase-2 (MMP-2) and tissue inhibitor of metalloproteinase-1 (TIMP-1), in incident PD patients, b) to explore the capacity of dialysate MMP-2 to predict peritoneal solute transport rate (PSTR) and peritonitis, and c) to evaluate the influence of neutral pH, low glucose degradation product (GDP) PD solution on these outcomes. ♦ Methods: The study included 178 participants from the balANZ trial who had at least 1 stored dialysate sample. Changes in PSTR and peritonitis were primary outcome measures, and the utility of MMP-2 in predicting these outcomes was analyzed using multilevel linear regression and multilevel Poisson regression, respectively. ♦ Results: Significant linear increases in dialysate MMP-2 and TIMP-1 concentrations were observed (p < 0.001), but neither was affected by the type of PD solutions received (MMP-2: p = 0.07; TIMP-1: p = 0.63). An increase in PSTR from baseline was associated with higher levels of MMP-2 (p = 0.02), and the use of standard solutions over longer PD duration (p = 0.001). The risk of peritonitis was independently predicted by higher dialysate MMP-2 levels (incidence rate ratio [IRR] per ng/mL 1.01, 95% confidence interval [CI] 1.005 – 1.02, p = 0.002) and use of standard solutions (Biocompatible solution: IRR 0.45, 95% CI 0.24 – 0.85, p = 0.01). ♦ Conclusion: Dialysate MMP-2 and TIMP-1 concentrations increased with longer PD duration. Higher MMP-2 levels were associated with faster PSTR and future peritonitis risk. Administration of biocompatible solutions exerted no significant effect on dialysate levels of MMP-2 or TIMP-1, but did counteract the increase in PSTR and the risk of peritonitis associated with the use of standard PD solutions. This is the first longitudinal study to examine the clinical utility of MMP-2 as a predictor of patient-level outcomes. PMID:25292407

A Legendre–Fourier spectral method with exact conservation laws for the Vlasov–Poisson system

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

Manzini, Gianmarco; Delzanno, Gian Luca; Vencels, Juris

In this study, we present the design and implementation of an L 2-stable spectral method for the discretization of the Vlasov–Poisson model of a collisionless plasma in one space and velocity dimension. The velocity and space dependence of the Vlasov equation are resolved through a truncated spectral expansion based on Legendre and Fourier basis functions, respectively. The Poisson equation, which is coupled to the Vlasov equation, is also resolved through a Fourier expansion. The resulting system of ordinary differential equation is discretized by the implicit second-order accurate Crank–Nicolson time discretization. The non-linear dependence between the Vlasov and Poisson equations ismore » iteratively solved at any time cycle by a Jacobian-Free Newton–Krylov method. In this work we analyze the structure of the main conservation laws of the resulting Legendre–Fourier model, e.g., mass, momentum, and energy, and prove that they are exactly satisfied in the semi-discrete and discrete setting. The L 2-stability of the method is ensured by discretizing the boundary conditions of the distribution function at the boundaries of the velocity domain by a suitable penalty term. The impact of the penalty term on the conservation properties is investigated theoretically and numerically. An implementation of the penalty term that does not affect the conservation of mass, momentum and energy, is also proposed and studied. A collisional term is introduced in the discrete model to control the filamentation effect, but does not affect the conservation properties of the system. Numerical results on a set of standard test problems illustrate the performance of the method.« less

A Legendre–Fourier spectral method with exact conservation laws for the Vlasov–Poisson system

DOE PAGES

Manzini, Gianmarco; Delzanno, Gian Luca; Vencels, Juris; ...

2016-04-22

In this study, we present the design and implementation of an L 2-stable spectral method for the discretization of the Vlasov–Poisson model of a collisionless plasma in one space and velocity dimension. The velocity and space dependence of the Vlasov equation are resolved through a truncated spectral expansion based on Legendre and Fourier basis functions, respectively. The Poisson equation, which is coupled to the Vlasov equation, is also resolved through a Fourier expansion. The resulting system of ordinary differential equation is discretized by the implicit second-order accurate Crank–Nicolson time discretization. The non-linear dependence between the Vlasov and Poisson equations ismore » iteratively solved at any time cycle by a Jacobian-Free Newton–Krylov method. In this work we analyze the structure of the main conservation laws of the resulting Legendre–Fourier model, e.g., mass, momentum, and energy, and prove that they are exactly satisfied in the semi-discrete and discrete setting. The L 2-stability of the method is ensured by discretizing the boundary conditions of the distribution function at the boundaries of the velocity domain by a suitable penalty term. The impact of the penalty term on the conservation properties is investigated theoretically and numerically. An implementation of the penalty term that does not affect the conservation of mass, momentum and energy, is also proposed and studied. A collisional term is introduced in the discrete model to control the filamentation effect, but does not affect the conservation properties of the system. Numerical results on a set of standard test problems illustrate the performance of the method.« less

The effect of atmospheric temperature and pressure on the occurrence of acute myocardial infarction in Kaunas.

PubMed

Radišauskas, Ričardas; Vaičiulis, Vidmantas; Ustinavičienė, Rūta; Bernotienė, Gailutė

2013-01-01

OBJECTIVE. The aim of the study was to evaluate the impact of meteorological variables (atmospheric temperature and pressure) on the daily occurrence of acute myocardial infarction (AMI). MATERIAL AND METHODS. The study used the daily values of atmospheric temperature and pressure in 2000-2007. The meteorological data were obtained from the Lithuanian Hydrometeorological Service for Kaunas. The relative risks of event occurrence were computed for 5°C atmospheric temperature and for 10-hPa atmospheric pressure variations by means of the Poisson regression model. RESULTS. The occurrence of AMI and atmospheric temperature showed an inverse linear relationship, while the occurrence of AMI and atmospheric pressure, a positive linear relationship. Among the youngest subjects (25-44 years old), no relationships were detected. Contrary, among the subjects aged 45-64 years and those aged 65 years and older, the occurrence of AMI significantly decreased with higher temperature (P=0.001 and P=0.002, respectively). A decrease in atmospheric temperature by 10ºC reduced the risk of AMI by 8.7% in the age groups of 45-64 and 65 years and older and by 19% in the age group of 25 years and older. Among the first AMI cases, the risk increased by 7.5% in the age group of 45-64-year olds and by 6.4% in the age group of 25-64-year olds. The relationship between atmospheric temperature and pressure, and AMI occurrence was found to be linear but inverse. An increase in atmospheric pressure by 10 hPa resulted in an increase in risk by 4% among the subjects aged 65 years and more and by 3% among the subjects aged 25 years and more. CONCLUSIONS. Atmospheric temperature and pressure variations had the greatest effect on middle-aged and aging subjects (starting from 45 years). At younger age, the effect of such factors on the AMI risk was considerably lower.

Effects of Extreme Temperatures on Cause-Specific Cardiovascular Mortality in China

PubMed Central

Wang, Xuying; Li, Guoxing; Liu, Liqun; Westerdahl, Dane; Jin, Xiaobin; Pan, Xiaochuan

2015-01-01

Objective: Limited evidence is available for the effects of extreme temperatures on cause-specific cardiovascular mortality in China. Methods: We collected data from Beijing and Shanghai, China, during 2007–2009, including the daily mortality of cardiovascular disease, cerebrovascular disease, ischemic heart disease and hypertensive disease, as well as air pollution concentrations and weather conditions. We used Poisson regression with a distributed lag non-linear model to examine the effects of extremely high and low ambient temperatures on cause-specific cardiovascular mortality. Results: For all cause-specific cardiovascular mortality, Beijing had stronger cold and hot effects than those in Shanghai. The cold effects on cause-specific cardiovascular mortality reached the strongest at lag 0–27, while the hot effects reached the strongest at lag 0–14. The effects of extremely low and high temperatures differed by mortality types in the two cities. Hypertensive disease in Beijing was particularly susceptible to both extremely high and low temperatures; while for Shanghai, people with ischemic heart disease showed the greatest relative risk (RRs = 1.16, 95% CI: 1.03, 1.34) to extremely low temperature. Conclusion: People with hypertensive disease were particularly susceptible to extremely low and high temperatures in Beijing. People with ischemic heart disease in Shanghai showed greater susceptibility to extremely cold days. PMID:26703637

Effects of Extreme Temperatures on Cause-Specific Cardiovascular Mortality in China.

PubMed

Wang, Xuying; Li, Guoxing; Liu, Liqun; Westerdahl, Dane; Jin, Xiaobin; Pan, Xiaochuan

2015-12-21

Limited evidence is available for the effects of extreme temperatures on cause-specific cardiovascular mortality in China. We collected data from Beijing and Shanghai, China, during 2007-2009, including the daily mortality of cardiovascular disease, cerebrovascular disease, ischemic heart disease and hypertensive disease, as well as air pollution concentrations and weather conditions. We used Poisson regression with a distributed lag non-linear model to examine the effects of extremely high and low ambient temperatures on cause-specific cardiovascular mortality. For all cause-specific cardiovascular mortality, Beijing had stronger cold and hot effects than those in Shanghai. The cold effects on cause-specific cardiovascular mortality reached the strongest at lag 0-27, while the hot effects reached the strongest at lag 0-14. The effects of extremely low and high temperatures differed by mortality types in the two cities. Hypertensive disease in Beijing was particularly susceptible to both extremely high and low temperatures; while for Shanghai, people with ischemic heart disease showed the greatest relative risk (RRs = 1.16, 95% CI: 1.03, 1.34) to extremely low temperature. People with hypertensive disease were particularly susceptible to extremely low and high temperatures in Beijing. People with ischemic heart disease in Shanghai showed greater susceptibility to extremely cold days.

Scholarships for scientific initiation encourage post-graduation degree.

PubMed

Pinto, Gabriela S; Nascimento, Gustavo G; Mendes, Matheus S; Ogliari, Fabrício A; Demarco, Flávio F; Correa, Marcos B

2014-01-01

This study aimed to evaluate the factors associated with the decision to attend an academic post-graduation program by dental students. A cross-sectional study was conducted in 2012, last-year undergraduate students from Dental Schools of Southern Brazil. A closed questionnaire was applied including questions grouped in three different blocks: pre-graduate, undergraduate period and future perspectives. The outcome was the decision to pursuit an academic post-graduation degree. Associations were tested using chi-squared test and chi-squared test for linear trends when appropriate. Multivariate Poisson regression was also performed. The sample was composed by 671 students (response rate of 69.9%, n=467). In relation to future perspectives, 68% of the interviewed students intended to attend a post-graduation program, but only 17.5% would choose a program with academic and research post-graduation program (Master and PhD programs). In the final model, students from public universities (PR 2.08, 95%CI 1.41-3.08) and students that received scientific initiation scholarship (PR 1.93 95%CI 1.14-3.27) presented a twice greater prevalence to seek academic post-graduate programs. Students with higher family incomes showed a lower prevalence to seek these programs (PR 0.50, 95%IC 0.28-0.90). Scholarships seem to encourage undergraduate students to pursue stricto sensu post-graduation.

Influence of socioeconomic conditions on air pollution adverse health effects in elderly people: an analysis of six regions in São Paulo, Brazil

PubMed Central

Martins, M; Fatigati, F; Vespoli, T; Martins, L; Pereira, L; Martins, M; Saldiva, P; Braga, A

2004-01-01

Study objective: To evaluate if the effects of particulate matter (PM10) on respiratory mortality of elderly people are affected by socioeconomic status. Design: Time series studies. The daily number of elderly respiratory deaths were modelled in generalised linear Poisson regression models controlling for long term trend, weather, and day of the week, from January 1997 to December 1999, in six different regions of São Paulo City, Brazil. The regions were defined according to the proximity of air pollution monitoring stations. Three socioeconomic indicators were used: college education, monthly income, and housing. Main results: For a 10 µg/m3 increase in PM10, the percentage increase in respiratory mortality varied from 1.4% (95% CI 5.9 to 8.7) to 14.2% (95% CI 0.4 to 28.0). The overall percentage increase in the six regions was 5.4% (95% CI 2.3 to 8.6). The effect of PM10 was negatively correlated with both percentage of people with college education and high family income, and it was positively associated with the percentage of people living in slums. Conclusions: These results suggest that socioeconomic deprivation represents an effect modifier of the association between air pollution and respiratory deaths. PMID:14684725

Physical inactivity and associated factors among women from a municipality in southern Brazil.

PubMed

Marcellino, Cristiano; Henn, Ruth Liane; Olinto, Maria Teresa; Bressan, Ana Weigert; Paniz, Vera Maria; Pattussi, Marcos Pascoal

2014-05-01

Physical inactivity is one of the most important modifiable risk factors that is raising the global burden of chronic diseases. This is a cross-sectional, population-based study of 790 women aged 20 years or older living in the urban area of a municipality in Southern Brazil. The level of physical activity was measured using the International Physical Activity Questionnaire, short form. Inactivity was defined as fewer than 150 min/wk-1 spent in moderate or vigorous physical activities. Prevalence ratios were calculated by robust Poisson regression. The prevalence of physical inactivity was 48.7% (95% CI, 43.3%-54.1%). After adjusting for confounders, we found a linear trend for increasing prevalence of physical inactivity with increasing body mass index (P = .008). Women who were married or in a domestic partnership were 29% less physically active than single women (P = .044). A borderline association was detected between the presence of minor psychiatric disorders (MPD) and physical inactivity (P = .058). There was a high prevalence of inactivity. Obese women, those married or in domestic partnerships and those with MPD were more likely to lead an inactive lifestyle. These results suggest that strategies are required for breaking down barriers to physical activity in this demographic group.

Investigation of cancer mortality inequalities between rural and urban areas in South Korea.

PubMed

Choi, Kyung-Mee

2016-02-01

Little is known about rural-urban cancer disparities, particularly in South Korea, and this study is to identify cancer-specific mortality inequalities between the rural and urban areas of the country. For 11 specific cancer sites, age-standardised mortality rates were analysed for the rural and urban administrative districts of South Korea during 2006-2011. The Poisson log linear regression models were employed to estimate cancer-specific mortality rates, and Bonferroni comparison method was used to identify rural-urban disparities. There were significant rural-urban disparities observed for all cancer sites except prostate, pancreas and leukaemia. The mortality rates of lung, liver and stomach cancers, the three most common cancers in the country, were observed to be significantly higher in rural areas than in metropolitan areas. In contrast, the reverse relationship was observed for the reproductive system (breast and uterus) and colon cancers. Central nervous system cancer mortality was observed to be significantly higher in rural areas than in non-metro urban areas. For the first time ever, significant rural-urban disparity patterns in cancer mortality rates in South Korea have been identified in this paper. Future investigations on cancer risk factors for the country should address these disparity patterns. © 2015 National Rural Health Alliance Inc.

What are hierarchical models and how do we analyze them?

USGS Publications Warehouse

Royle, Andy

2016-01-01

In this chapter we provide a basic definition of hierarchical models and introduce the two canonical hierarchical models in this book: site occupancy and N-mixture models. The former is a hierarchical extension of logistic regression and the latter is a hierarchical extension of Poisson regression. We introduce basic concepts of probability modeling and statistical inference including likelihood and Bayesian perspectives. We go through the mechanics of maximizing the likelihood and characterizing the posterior distribution by Markov chain Monte Carlo (MCMC) methods. We give a general perspective on topics such as model selection and assessment of model fit, although we demonstrate these topics in practice in later chapters (especially Chapters 5, 6, 7, and 10 Chapter 5 Chapter 6 Chapter 7 Chapter 10)

Spatially resolved regression analysis of pre-treatment FDG, FLT and Cu-ATSM PET from post-treatment FDG PET: an exploratory study

PubMed Central

Bowen, Stephen R; Chappell, Richard J; Bentzen, Søren M; Deveau, Michael A; Forrest, Lisa J; Jeraj, Robert

2012-01-01

Purpose To quantify associations between pre-radiotherapy and post-radiotherapy PET parameters via spatially resolved regression. Materials and methods Ten canine sinonasal cancer patients underwent PET/CT scans of [18F]FDG (FDGpre), [18F]FLT (FLTpre), and [61Cu]Cu-ATSM (Cu-ATSMpre). Following radiotherapy regimens of 50 Gy in 10 fractions, veterinary patients underwent FDG PET/CT scans at three months (FDGpost). Regression of standardized uptake values in baseline FDGpre, FLTpre and Cu-ATSMpre tumour voxels to those in FDGpost images was performed for linear, log-linear, generalized-linear and mixed-fit linear models. Goodness-of-fit in regression coefficients was assessed by R2. Hypothesis testing of coefficients over the patient population was performed. Results Multivariate linear model fits of FDGpre to FDGpost were significantly positive over the population (FDGpost~0.17 FDGpre, p=0.03), and classified slopes of RECIST non-responders and responders to be different (0.37 vs. 0.07, p=0.01). Generalized-linear model fits related FDGpre to FDGpost by a linear power law (FDGpost~FDGpre0.93, p<0.001). Univariate mixture model fits of FDGpre improved R2 from 0.17 to 0.52. Neither baseline FLT PET nor Cu-ATSM PET uptake contributed statistically significant multivariate regression coefficients. Conclusions Spatially resolved regression analysis indicates that pre-treatment FDG PET uptake is most strongly associated with three-month post-treatment FDG PET uptake in this patient population, though associations are histopathology-dependent. PMID:22682748

Linear Mechanisms and Pressure Fluctuations in Wall Turbulence

NASA Astrophysics Data System (ADS)

Septham, Kamthon; Morrison, Jonathan

2014-11-01

Full-domain, linear feedback control of turbulent channel flow at Reτ <= 400 via vU' at low wavenumbers is an effective method to attenuate turbulent channel flow such that it is relaminarised. The passivity-based control approach is adopted and explained by the conservative characteristics of the nonlinear terms contributing to the Reynolds-Orr equation (Sharma et al .Phys .Fluids 2011). The linear forcing acts on the wall-normal velocity field and thus the pressure field via the linear (rapid) source term of the Poisson equation for pressure fluctuations, 2U'∂v/∂x . The minimum required spanwise wavelength resolution without losing control is constant at λz+ = 125, based on the wall friction velocity at t = 0 . The result shows that the maximum forcing is located at y+ ~ 20 , corresponding to the location of the maximum in the mean-square pressure gradient. The effectiveness of linear control is qualitatively explained by Landahl's theory for timescales, in that the control proceeds via the shear interaction timescale which is much shorter than both the nonlinear and viscous timescales. The response of the rapid (linear) and slow (nonlinear) pressure fluctuations to the linear control is examined and discussed.

Linear regression analysis of survival data with missing censoring indicators.

PubMed

Wang, Qihua; Dinse, Gregg E

2011-04-01

Linear regression analysis has been studied extensively in a random censorship setting, but typically all of the censoring indicators are assumed to be observed. In this paper, we develop synthetic data methods for estimating regression parameters in a linear model when some censoring indicators are missing. We define estimators based on regression calibration, imputation, and inverse probability weighting techniques, and we prove all three estimators are asymptotically normal. The finite-sample performance of each estimator is evaluated via simulation. We illustrate our methods by assessing the effects of sex and age on the time to non-ambulatory progression for patients in a brain cancer clinical trial.

Multilevel poisson regression modelling for determining factors of dengue fever cases in bandung

NASA Astrophysics Data System (ADS)

Arundina, Davila Rubianti; Tantular, Bertho; Pontoh, Resa Septiani

2017-03-01

Scralatina or Dengue Fever is a kind of fever caused by serotype virus which Flavivirus genus and be known as Dengue Virus. Dengue Fever caused by Aedes Aegipty Mosquito bites who infected by a dengue virus. The study was conducted in 151 villages in Bandung. Health Analysts believes that there are two factors that affect the dengue cases, Internal factor (individual) and external factor (environment). The data who used in this research is hierarchical data. The method is used for hierarchical data modelling is multilevel method. Which is, the level 1 is village and level 2 is sub-district. According exploration data analysis, the suitable Multilevel Method is Random Intercept Model. Penalized Quasi Likelihood (PQL) approach on multilevel Poisson is a proper analysis to determine factors that affecting dengue cases in the city of Bandung. Clean and Healthy Behavior factor from the village level have an effect on the number of cases of dengue fever in the city of Bandung. Factor from the sub-district level has no effect.

Statistical Analyses of Raw Material Data for MTM45-1/CF7442A-36% RW: CMH Cure Cycle

NASA Technical Reports Server (NTRS)

Coroneos, Rula; Pai, Shantaram, S.; Murthy, Pappu

2013-01-01

This report describes statistical characterization of physical properties of the composite material system MTM45-1/CF7442A, which has been tested and is currently being considered for use on spacecraft structures. This composite system is made of 6K plain weave graphite fibers in a highly toughened resin system. This report summarizes the distribution types and statistical details of the tests and the conditions for the experimental data generated. These distributions will be used in multivariate regression analyses to help determine material and design allowables for similar material systems and to establish a procedure for other material systems. Additionally, these distributions will be used in future probabilistic analyses of spacecraft structures. The specific properties that are characterized are the ultimate strength, modulus, and Poisson??s ratio by using a commercially available statistical package. Results are displayed using graphical and semigraphical methods and are included in the accompanying appendixes.

An Analysis of COLA (Cost of Living Adjustment) Allocation within the United States Coast Guard.

DTIC Science & Technology

1983-09-01

books Applied Linear Regression [Ref. 39], and Statistical Methods in Research and Production [Ref. 40], or any other book on regression. In the event...Indexes, Master’s Thesis, Air Force Institute of Technology, Wright-Patterson AFB, 1976. 39. Weisberg, Stanford, Applied Linear Regression , Wiley, 1980. 40

Testing hypotheses for differences between linear regression lines

Treesearch

Stanley J. Zarnoch

2009-01-01

Five hypotheses are identified for testing differences between simple linear regression lines. The distinctions between these hypotheses are based on a priori assumptions and illustrated with full and reduced models. The contrast approach is presented as an easy and complete method for testing for overall differences between the regressions and for making pairwise...

Graphical Description of Johnson-Neyman Outcomes for Linear and Quadratic Regression Surfaces.

ERIC Educational Resources Information Center

Schafer, William D.; Wang, Yuh-Yin

A modification of the usual graphical representation of heterogeneous regressions is described that can aid in interpreting significant regions for linear or quadratic surfaces. The standard Johnson-Neyman graph is a bivariate plot with the criterion variable on the ordinate and the predictor variable on the abscissa. Regression surfaces are drawn…