scholarly journals Isolation Prevalence of Pulmonary Nontuberculous Mycobacteria in Ontario in 2007

2011 ◽  
Vol 18 (1) ◽  
pp. 19-24 ◽  
Author(s):  
Mohammed Al Houqani ◽  
Frances Jamieson ◽  
Pamela Chedore ◽  
Mauli Mehta ◽  
Kevin May ◽  
...  
Keyword(s):  
Generalized Linear Models ◽  
Linear Models ◽  
Negative Binomial ◽  
Nontuberculous Mycobacteria ◽  
Statistical Significance ◽  
Population Changes ◽  
Annual Increase ◽  
Rapidly Growing Mycobacteria ◽  
Mycobacterium Xenopi ◽  
Published Research

BACKGROUND: The reported prevalence of pulmonary nontuberculous mycobacteria (NTM) infections is increasing.OBJECTIVE: To determine the ‘isolation prevalence’ of NTM in 2007 and compare it with previously published research that examined the increasing rates of isolation of NTM from clinical pulmonary specimens between 1997 and 2003.METHODS: Isolation prevalence was investigated retrospectively by reviewing a cohort of all positive pulmonary NTM culture results from the Tuberculosis and Mycobacteriology Laboratory, Public Health Laboratory (Toronto, Ontario) in 2007, which identifies at least 95% of NTM isolates in Ontario. Isolation prevalence was calculated as the number of persons with a pulmonary isolate in a calendar year divided by the contemporary population and expressed per 100,000 population. Changes in isolation prevalence from previous years were assessed for statistical significance using generalized linear models with a negative binomial distribution.RESULTS: In 2007, 4160 pulmonary isolates of NTM were collected from 2463 patients. The isolation prevalence of all species (excludingMycobacterium gordonae) was 19 per 100,000 population in 2007 – an increase from previous observations reported for Ontario – corresponding to an average annual increase of 8.5% from 1997 to 2007 (P<0.0001). Average annual increases in isolation prevalence ofMycobacterium aviumcomplex (8.8%, P<0.0001) andMycobacterium xenopi(7.3%, P=0.0005) were largely responsible for the overall increase, while prevalence rates of rapidly growing mycobacteria remained relatively stable.CONCLUSION: The isolation prevalence of pulmonary NTM continues to increase significantly in Ontario, supporting the belief that pulmonary NTM disease is increasingly common.

Modeling of the spatial distribution of Pacific spiny dogfish (Squalus suckleyi) in the Gulf of Alaska using generalized additive and generalized linear models

2013 ◽  
Vol 70 (9) ◽  
pp. 1372-1385 ◽  
Author(s):  
Jason R. Gasper ◽  
Gordon H. Kruse
Keyword(s):  
Spatial Distribution ◽  
Generalized Linear Models ◽  
Linear Models ◽  
Negative Binomial ◽  
Fish Habitat ◽  
Additive Model ◽  
Gulf Of Alaska ◽  
Spiny Dogfish ◽  
Essential Fish Habitat ◽  
Mortality Estimation

The Pacific spiny dogfish (Squalus suckleyi) is a common bycatch species in the Gulf of Alaska. Their spatial distribution is poorly understood, as most catch is discarded at sea. We analyzed spiny dogfish spatial distribution from fishery-dependent and -independent observations of longline gear between 1996 and 2008 using generalized additive and generalized linear models. Poisson, negative binomial, and quasi-Poisson error structures were investigated; the quasi-Poisson generalized additive model fit best. Models showed that spiny dogfish catches were concentrated east of Kodiak Island in waters ≤100 m deep. Results facilitate design of future spiny dogfish assessment surveys and identification of areas in which to focus at-sea observations for fishing mortality estimation, and provide the basis for first-ever designation of spiny dogfish essential fish habitat, despite US legal requirements for essential fish habitat designations since 1996. Identified areas of high bycatch may expedite spatial management by indicating areas in which directed spiny dogfish fisheries could be focused or, conversely, areas in which heightened conservation and catch accounting efforts would be most effective to prevent overfishing of this long-lived, late-maturing species.

scholarly journals Chilling to the bone: Lower temperatures increase vertebrate predation by Tonatia bidens (Chiroptera: Phyllostomidae)

2020 ◽  
Vol 37 ◽  
pp. 1-5
Author(s):  
Fernando Carvalho ◽  
Daniela A.S. Bôlla ◽  
Viviane Mottin ◽  
Suelen Zonta Kiem ◽  
Jairo J. Zocche ◽  
...  
Keyword(s):  
Environmental Factors ◽  
Generalized Linear Models ◽  
Negative Binomial Distribution ◽  
Diet Composition ◽  
Linear Models ◽  
Negative Binomial ◽  
Meteorological Station ◽  
Foraging Patterns ◽  
Influence Of Temperature ◽  
The North

The greater round-eared bat, Tonatia bidens (Spix, 1823), is a medium-sized phyllostomid bat distributed in the north of Argentina, Paraguay and Brazil. The diet and foraging patterns of this species are poorly known. We analyzed the composition of the diet of a population of T. bidens and how the temperature influences the consumption of vertebrates and invertebrates. To describe diet composition, we conducted weekly collections of food scrap from two monospecific night-perches. Data of temperature for the study period were taken from the meteorological station installed 300 m from the collection perches. The influence of temperature was evaluated using generalized linear models (GLMs) with negative binomial distribution. Tonatia bidens consumed 28 taxons (204 records), being at least 17 Artropods and 11 Passeriformes birds. Temperature explained a greater proportion of vertebrate abundance (R2 = 0.23) than invertebrate (R2 = 0.16) or to both pooled (R2 = 0.11). The relation with temperature was positive with invertebrates and negative with the vertebrates. The diet of the population of T. bidens comprised mainly invertebrates, which were the most frequent and diverse taxa. Data suggests that T. bidens has a diverse diet, with proportion of the item’s consumption varying temporally. Environmental factors, such as the temperature presented on this work, seems to be good proxies for the dietary traits of this species.

scholarly journals eNetXplorer: an R package for the quantitative exploration of elastic net families for generalized linear models

2018 ◽  
Author(s):  
Julián Candia ◽  
John S. Tsang
Keyword(s):  
Generalized Linear Models ◽  
Linear Models ◽  
Statistical Significance ◽  
Model Fitting ◽  
Predictive Performance ◽  
R Package ◽  
Research Area ◽  
Elastic Net ◽  
Feature Identification ◽  
Practical Applications

AbstractBackgroundRegularized generalized linear models (GLMs) are popular regression methods in bioinformatics, particularly useful in scenarios with fewer observations than parameters/features or when many of the features are correlated. In both ridge and lasso regularization, feature shrinkage is controlled by a penalty parameter λ. The elastic net introduces a mixing parameter α to tune the shrinkage continuously from ridge to lasso. Selecting α objectively and determining which features contributed significantly to prediction after model fitting remain a practical challenge given the paucity of available software to evaluate performance and statistical significance.ResultseNetXplorer builds on top of glmnet to address the above issues for linear (Gaussian), binomial (logistic), and multinomial GLMs. It provides new functionalities to empower practical applications by using a cross validation framework that assesses the predictive performance and statistical significance of a family of elastic net models (as α is varied) and of the corresponding features that contribute to prediction. The user can select which quality metrics to use to quantify the concordance between predicted and observed values, with defaults provided for each GLM. Statistical significance for each model (as defined by α) is determined based on comparison to a set of null models generated by random permutations of the response; the same permutation-based approach is used to evaluate the significance of individual features. In the analysis of large and complex biological datasets, such as transcriptomic and proteomic data, eNetXplorer provides summary statistics, output tables, and visualizations to help assess which subset(s) of features have predictive value for a set of response measurements, and to what extent those subset(s) of features can be expanded or reduced via regularization.ConclusionsThis package presents a framework and software for exploratory data analysis and visualization. By making regularized GLMs more accessible and interpretable, eNetXplorer guides the process to generate hypotheses based on features significantly associated with biological phenotypes of interest, e.g. to identify biomarkers for therapeutic responsiveness. eNetXplorer is also generally applicable to any research area that may benefit from predictive modeling and feature identification using regularized GLMs.Availability and implementationThe package is available under GPL-3 license at the CRAN repository, https://CRAN.R-project.org/package=eNetXplorer

scholarly journals On the Trends of Registered Birth and Death Rates in Nigeria: Evidence from Generalized Linear Models

2019 ◽  
Author(s):  
Monday Osagie Adenomon ◽  
Emmanuel Chukwuma Anikweze
Keyword(s):  
Generalized Linear Models ◽  
Regression Models ◽  
Linear Models ◽  
Negative Binomial ◽  
Negative Binomial Regression ◽  
Natural Increase ◽  
Negative Binomial Regression Model ◽  
Annual Data ◽  
Death Rates ◽  
Binomial Regression

This study investigated the trends of registered Death and Birth in Nigeria using Generalized Linear Models. Annual data on Death and Birth was collected from National Population Commission for the period of 2004 to 2017. The Natural increase calculated revealed a positive trend in the natural increase in Nigeria from 2004 to 2017. Evidence from summary statistics revealed some level of over dispersion (variance &gt; mean). This study explored Poisson Regression Models and Negative Binomial Regression Models using two links (identity and log). The results revealed a positive increase in registration of birth and death rates in Nigeria and among the competing the models, Negative Binomial regression model with identity link emerged as the best model for modeling birth and death rates registration in Nigeria. Data on numbers of deaths and causes of death are essential if countries are to determine priorities, formulate and monitor policies for public health care as well as other government policies that may be based on such data

Analysis of factors affecting superovulatory responses in ruminants

1995 ◽  
Vol 124 (1) ◽  
pp. 61-70 ◽  
Author(s):  
J. A. Woolliams ◽  
Z. W. Luo ◽  
B. Villanueva ◽  
D. Waddington ◽  
P. J. Broadbent ◽  
...  
Keyword(s):  
Generalized Linear Models ◽  
Linear Models ◽  
Negative Binomial ◽  
Commercial Application ◽  
Data Set ◽  
Factors Affecting ◽  
Binomial Distributions ◽  
Breeding Schemes ◽  
Over Dispersion ◽  
Mean Variance

SUMMARYData on ovulation rate and numbers of ova and transferable embryos recovered from superovulated cattle and sheep were analysed using generalized linear models, quasi-likelihood, restricted maximum likelihood (REML) and generalized linear mixed models (GLMMS). The data pertained to the operation of nucleus breeding schemes in cattle and the commercial application of embryo transfer in sheep.Results of the analyses showed that generalized linear models involving Poisson and Binomial distributions were inappropriate because of over-dispersion, and that analyses using quasi-likelihood to model negative binomial and β-binomial distributions were more suitable. Factors identified as important in determining the results in cattle were the number of previous superovulations (a higher proportion of transferable embryos were obtained in the initial flush compared to subsequent recoveries in two out of three sets of data), the donor (significant in all analyses with repeated recoveries) and its mate (significant in some analyses). In sheep, the use of pFSH or hMG for superovulation increased embryo yields above those obtained with PMSG + GnRH. Analyses of a further data set for sheep showed the effect of breed was ambiguous.The effects of donors and their mates were treated as random effects in analyses involving REML and GLMMS. Results showed that the repeatability of the number of transferable embryos produced per donor ranged between 0·13 and 0·23 in three sets of data and was significant in all cases. In these analyses the variance among mates was not significantly different from zero.The results of analyses were used to develop a random generator to simulate the numbers of ova and embryos recovered from a cow following superovulation. By sampling from negative binomial distributions where the scale factor used for each cow was a normally distributed deviate, distributions were obtained which had the same mean, variance and repeatability as those observed.

scholarly journals Robustness of zero-augmented models over generalized linear models in analysing fertility data in Nigeria

2019 ◽  
Vol 12 (1) ◽  
Author(s):  
Yusuf Olushola Kareem ◽  
Imran O. Morhason-Bello ◽  
Ayo Stephen Adebowale ◽  
Joshua Odunayo Akinyemi ◽  
Oyindamola Bidemi Yusuf
Keyword(s):  
Generalized Linear Models ◽  
Linear Models ◽  
Negative Binomial ◽  
Information Criteria ◽  
North Central ◽  
North West ◽  
Number Of Zeros ◽  
North East ◽  
Fertility Data ◽  
Central South

Abstract Objective Fertility is a count data usually rightly skewed and exhibiting large number of zeros than the distributional assumption of the generalized linear models (GLMs). This study examined the robustness of zero-augmented models over GLMs to fit fertility data across regions in Nigeria. The 2013 Nigeria Demographic and Health Survey data were used. The fertility models fitted included: Poisson, negative binomial, zero-inflated Poisson, zero-inflated negative binomial, hurdle Poisson and hurdle negative binomial. Akaike Information Criteria (AIC) and Bayesian Information Criteria (BIC) were used to identify the model with best fit (α = 0.05). Results The percentage of zero count in the fertility responses were 21.3, 23.9, 31.1, 30.7, 37.6 and 42.4 in North West, North East, North Central, South West, South South and South East regions respectively. In all the six regions in Nigeria, the zero-augmented models were better than the generalized linear models except for North Central. Extensively, the zero-augmented negative binomial based models were of better fit than their Poisson based counterparts; or in rare cases maybe indistinguishable. However, specific family of zero-augmented model is recommended for each region in Nigeria.

Generalized Linear Models (GLM)

2021 ◽  
pp. 181-208
Author(s):  
Justin C. Touchon
Keyword(s):  
Linear Model ◽  
Generalized Linear Models ◽  
Linear Models ◽  
Negative Binomial ◽  
Error Distribution ◽  
Distributed Data ◽  
Diagnostic Plots ◽  
Error Distributions ◽  
Parametric Statistics ◽  
Competing Models

Chapter 7 introduces one of the most useful statistical frameworks for the modern life scientist: the generalized linear model (GLM). GLMs extend the linear model to an array of non-normally distributed data such as Poisson, negative binomial, binomial, and Gamma distributed data. These models dramatically improve the breadth of data that can be properly analysed without resorting to non-parametric statistics. Using the same RxP dataset, readers learn how to assess the error distribution of their data and evaluate competing models to achieve the best, most robust analysis possible. Diagnostic plots and assessing model fit is continually taught as is how to interpret the model output and calculate summary statistics. Plotting non-normal error distributions with ggplot2 is taught, as is using the predict() function.

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