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Physiological performance in fish peaks within a well-defined range of temperatures, which is distinct for each species. Species-specific thermal responses for growth, survival, and reproduction are most commonly quantified directly through laboratory experiment or field observation, with a focus on six specific metrics: optimum growth temperature and final temperature preferendum (growth), upper incipient lethal temperature and critical thermal maximum (survival), and optimum spawning temperature and optimum egg development temperature (reproduction). These values remain unknown for many North American freshwater fish species. In this paper, we present a new statistical method (Bayesian phylogenetic regression) that uses relationships between these metrics and phenetic relatedness to estimate unknown metric values. The reliability of these estimates was compared with those derived from models incorporating taxonomic family and models without any taxonomic information. Overall, incorporating taxonomic family relatedness improved estimation accuracy across all metrics. For Salmonidae and Cyprinidae, estimates derived from Bayesian phylogenetic regression typically had the highest expected reliability. We used our methods to generate 274 estimates of unknown metric values for over 100 North American freshwater fish species. La performance physiologique chez les poissons atteint un maximum dans une fourchette bien definie de temperatures, qui varie selon l'espece. Les reactions thermiques propres a l'espece en matiere de croissance, de survie et de reproduction sont le plus souvent quantifiees directement par des experiences en laboratoire ou des observations de terrain, en mettant l'accent sur six parametres, a savoir : la temperature de croissance optimale et le preferendum de la temperature finale (croissance), la temperature superieure du debut de la letalite et la temperature critique maximum (survie) et la temperature de frai optimale et la temperature de developpement des reufs optimale (reproduction). Ces valeurs demeurent inconnues pour de nombreuses especes de poissons d'eau douce nord-americaines. Nous presentons une nouvelle methode statistique (regression phylogenetique bayesienne) qui fait appel aux relations entre ces parametres et l'affinite phenetique pour estimer des valeurs de parametre inconnues. La fiabilite de ces estimations est comparee a celles d'estimations obtenues de modeles integrant la famille taxonomique et de modeles n'integrant aucune information taxonomique. Globalement, l'integration de l'affinite taxonomique familiale ameliore l'exactitude des estimations pour tous les parametres. Pour les salmonides et les cyprinides, les estimations obtenues par regression phylogenetique bayesienne presentent typiquement la plus grande fiabilite attendue. Nous avons utilise nos methodes pour produire 274 estimations de valeurs de parametre inconnues pour plus de 100 especes de poissons d'eau douce nord-americaines. [Traduit par la Redaction], Introduction Temperature affects all biochemical, physiological, and life history processes in fish. The external thermal environment regulates almost all internal processes, from protein synthesis and active metabolic rate to swimming [...]

Objective: To understand how pre-injury health status present five-years preceding traumatic brain injury (TBI) affects direct medical cost two years post-injury. Methods: Patients age ≥19 years in the emergency department (ED) or acute care for a TBI between April 1, 2007 and March 31, 2014 in Ontario, Canada (N = 55,669) were identified from population-based health administrative data. Forty-three factors of pre-injury health status (i.e., comorbidities and personal, social, and environmental factors) that were internally validated for the TBI population were assessed in this study. The outcome of interest was direct medical cost within two years of discharge. Sex-specific multivariable linear regressions were conducted to understand the associations between direct medical cost within two years of discharge and pre-injury health status. Results: Patients who received care in the ED (81.9% of total sample) incurred a median cost of $2,492/male patient (average $12,342/patient) and $3,508/female patient (average $65,285/patient) within two years of injury; 37 pre-injury factors were significantly associated with increased direct medical costs. Patients who first received care for their TBI in acute care (18.1%) incurred a median cost of $25,081/male patient (average $63,060/patient) and $30,277/female patient (average $65,285/patient) within two years of injury; 21 factors were significantly associated with increased direct medical costs. Among more prevalent factors, those associated with increased medical cost by at least 50% included mental health disorders, substance abuse, disorders or medical conditions frequently observed among the elderly, cardiovascular disorders, stroke and emergencies involving the brain, metabolic disorders and abdominal symptoms, conditions and symptoms of abdomen and pelvis, genitourinary disorders and disorders of prostate, and pulmonary abdominal and other emergencies. Conclusions: Direct medical costs two years post-TBI differed significantly between patients with and without adverse pre-existing health status. Interdisciplinary teams to promote early identification of pre-existing health conditions and appropriate management and integration of these conditions in TBI care across the continuum of healthcare may be opportunities to reduce direct medical costs post-injury. [ABSTRACT FROM AUTHOR]

To understand how health status preceding traumatic brain injury (TBI) affects relative functional gain after inpatient rehabilitation using a data mining approach. Population-based, sex-stratified, retrospective cohort study using health administrative data from Ontario, Canada (39% of the Canadian population). Inpatient rehabilitation. Patients 14 years or older (N=5802; 63.4% male) admitted to inpatient rehabilitation within 1 year of a TBI-related acute care discharge between April 1, 2008, and March 31, 2015. Not applicable. Relative functional gain (RFG) in percentage, calculated as ([discharge FIM−admission FIM]/[126−admission FIM]×100). Health status prior to TBI was identified and internally validated using a data mining approach that categorized all International Classification of Diseases, 10th revision, codes for each patient. The average RFG was 52.8%±27.6% among male patients and 51.6%±27.1% among female patients. Sex-specific Bonferroni adjusted multivariable linear regressions identified 10 factors of preinjury health status related to neurology, emergency medicine, cardiology, psychiatry, geriatrics, and gastroenterology that were significantly associated with reduced RFG in FIM for male patients. Only 1 preinjury health status category, geriatrics, was significantly associated with RFG in female patients. Comorbid health conditions present up to 5 years preceding the TBI event were significantly associated with RFG. These findings should be considered when planning and executing interventions to maximize functional gain and to support an interdisciplinary approach. Best practices guidelines and clinical interventions for older male and female patients with TBI should be developed given the increasingly aging population with TBI. [ABSTRACT FROM AUTHOR]

With the introduction and revisions of geotechnical limit states design (LSD) standards such as Eurocode 7, rock engineering design is moving towards reliability-based design, a method for which statistical characterisation of design parameters is essential. However, the often limited project-specific data in rock engineering do not allow straightforward application of classical statistical analyses, and thus alternative approaches are required. In this paper, hierarchical Bayesian modelling is first introduced as a means of logically combining data from different sources to augment limited project-specific data. A Bayesian hierarchical non-linear regression model for the analysis of rock strength data is then developed and implemented; it is applied to 40 strength data sets of granite retrieved from the literature. In the context of these data, the advantages of the hierarchical model and the improvements in strength parameter estimations brought about by its application are discussed. Also discussed is the goodness-of-fit of the hierarchical model in comparison with more conventional statistical models. The paper concludes with suggestions for further development of the proposed hierarchical model, and the potential of hierarchical modelling as a general approach to statistical modelling of geotechnical data. [ABSTRACT FROM AUTHOR]

In this paper, we present a method to assess differences between microbiome communities that effectively models sparse count data and accounts for presence-absence bias frequently encountered when zeros are present. We assume that the observed data for each operational taxonomic unit is Poisson generated with the rate for each sample originating from an underlying rate distribution. We propose to model this distribution using a mixture model, specifying the components based on the posterior rate distribution of a count and estimating the optimal weights using a least squares objective function. The distribution incorporates varying resolutions of samples, a point mass for differentiating structural and nonstructural zeros, and a truncation point mass to account for high values that are too sparse to model. As mixture component specification is not always straightforward, a method to estimate a joint model from several mixture distributions using minimum distances of bootstrap iterates is proposed. Once the population rate distribution is approximated, we obtain sample-specific distributions by conditioning on the observed operational taxonomic unit count, resolution, and estimated mixture distribution and then use these to estimate pairwise distances for a permutation test. The method gives an accurate estimate of the true proportion of zeros for presence-absence, effectively models the distribution of low counts using the mixture distribution, and achieves good power for detecting differences in a variety of scenarios. The method is tested using a simulation study and applied to two microbiome datasets. In each case, the results are compared with a number of existing methods. [ABSTRACT FROM AUTHOR]

Finite mixture models are flexible parametric models that allow one to describe complex probability distributions as a mixture of a small number of simple probability distributions. Measures of health status are often used to reflect a person's overall health. Such measures may be subject to a ceiling effect, in that the measure is unable to discern gradations in health status above the ceiling. The purpose of this paper is to illustrate the use of finite mixture models to describe the probability distribution of the Health Utilities Index, under the assumption that the HUI is subject to a ceiling effect. Mixture models with two through six components are fit to the HUI. Bayes factors were used to compare the evidence that the Canadian population of non-institutionalized residents is composed of four distinct subpopulations, and that a mixture of six Normal components is required to describe these four subpopulations. [ABSTRACT FROM AUTHOR]

Focuses on a study which examined the prevalence of reading disability in boys and girls. Clinical characteristics of subjects; Criterion measures used; Characteristics of reading disability in the subjects.

Presents a response by Salley E. Shaywitz, Jack M. Fletcher and Michael D. Escobar to a letter to the editor about their article 'Prevalence of Reading Disability in Boys and Girls: Results of the Connecticut Longitudinal Study,' in the 1990 issue of the 'Journal of the American Medical Association.'

Background: Traumatic brain injury (TBI) is a primary cause of injury mortality in developed countries but less is known about the impact of TBI on postacute mortality in large study populations. This study investigates the rate and predictors of postacute mortality (1-9 years after the initial injury) of severely injured persons with TBI in the Province of Ontario from April 1, 1993 to March 31, 1995., Method: Cases were identified (n = 2,721) from the Ontario Trauma Registry Comprehensive Data Set based on lead trauma hospitals in the province which also provided data on predictors. Severely injured patients (n = 557) who had lower extremity injuries during the sample time period formed a control population., Results: Poisson regression modeling showed that having a TBI was a significant predictor of premature death controlling for age and injury severity. Age, the number of comorbidities, injury severity, mechanism of injury, and discharge destination were significant predictors in the multivariate analyses for the TBI population., Conclusions: This research quantifies the elevated risk of premature death in the postacute period for seriously injured adults with TBI and identifies factors most associated with highest mortality rates in this population.