CEReS Newsletter No.62, January 2011

[掲載記事] 「＜調印式報告＞千葉大学・パジャジャラン大学との大学間交流協定」ほ

Hydrological Characteristics of the Mae Klong River Basin in Thailand

[ABSTRACT] This paper discusses hydrological characteristics in the Mae Klong River Basin in Thailand. Firstly, rainfall distribution characteristics are examined by using daily rainfall data in sixteen rainfall observatories. Secondly, hydrological events of the two dam basins were qualitatively analyzed. It was found that, there was a relationship between the Western Monsoon and the rain occurrences; Si Sawat (Sri Nagarind) zone was draughtier in the Mae Klong River Basin. Si Sawat Dam Basin and Khao Laem Dam Basin, although they are close to each other (50 Km in distance), their differences concerning hydrological characteristics are high

Moderate Traumatic Brain Injury:Clinical Characteristics and a Prognostic Model of 12-Month Outcome

BACKGROUND: Patients with moderate traumatic brain injury (TBI) often are studied together with patients with severe TBI, even though the expected outcome of the former is better. Therefore, we aimed to describe patient characteristics and 12-month outcomes, and to develop a prognostic model based on admission data, specifically for patients with moderate TBI. METHODS: Patients with Glasgow Coma Scale scores of 9-13 and age ≥16 years were prospectively enrolled in 2 level I trauma centers in Europe. Glasgow Outcome Scale Extended (GOSE) score was assessed at 12 months. A prognostic model predicting moderate disability or worse (GOSE score ≤6), as opposed to a good recovery, was fitted by penalized regression. Model performance was evaluated by area under the curve of the receiver operating characteristics curves. RESULTS: Of the 395 enrolled patients, 81% had intracranial lesions on head computed tomography, and 71% were admitted to an intensive care unit. At 12 months, 44% were moderately disabled or worse (GOSE score ≤6), whereas 8% were severely disabled and 6% died (GOSE score ≤4). Older age, lower Glasgow Coma Scale score, no day-of-injury alcohol intoxication, presence of a subdural hematoma, occurrence of hypoxia and/or hypotension, and preinjury disability were significant predictors of GOSE score ≤6 (area under the curve = 0.80). CONCLUSIONS: Patients with moderate TBI exhibit characteristics of significant brain injury. Although few patients died or experienced severe disability, 44% did not experience good recovery, indicating that follow-up is needed. The model is a first step in development of prognostic models for moderate TBI that are valid across centers

Effect of high intensity interval training on cardiac function in children with obesity: a randomised controlled trial

High intensity interval training (HIIT) confers superior cardiovascular health benefits to moderate intensity continuous training (MICT) in adults and may be efficacious for improving diminished cardiac function in obese children. The aim of this study was to compare the effects of HIIT, MICT and nutrition advice interventions on resting left ventricular (LV) peak systolic tissue velocity (S') in obese children.Ninety-nine obese children were randomised into one of three 12-week interventions, 1) HIIT [n = 33, 4 × 4 min bouts at 85-95% maximum heart rate (HR), 3 times/week] and nutrition advice, 2) MICT [n = 32, 44 min at 60-70% HR, 3 times/week] and nutrition advice, and 3) nutrition advice only (nutrition) [n = 34].Twelve weeks of HIIT and MICT were equally efficacious, but superior to nutrition, for normalising resting LV S' in children with obesity (estimated mean difference 1.0 cm/s, 95% confidence interval 0.5 to 1.6 cm/s, P

Stochastic Modelling and Simulation Based Inference of Fish Population Dynamics and Spatial Variation in Disease Risk

We present a non-Gaussian and non-linear state-space model for the population dynamics of cod along the Norwegian Skagerak coast, embedded in the framework of a Bayesian hierarchical model. The model takes into account both process error, representing natural variability in the dynamics of a population, and observational error, reflecting the sampling process relating the observed data to true abundances. The data set on which our study is based, consists of samples of two juvenile age-groups of cod taken by beach seine hauls at a set of sample stations within several fjords along the coast. The age-structure population dynamics model, constituting the prior of the Bayesian model, is specified in terms of the recruitment process and the processes of survival for these two juvenile age-groups and the mature population, for which we have no data. The population dynamics is specified on abundances at the fjord level, and an explicit down-scaling from the fjord level to the level of the monitored stations is included in the likelihood, modelling the sampling process relating the observed counts to the underlying fjord abundances. We take a sampling based approach to parameter estimation using Markov chain Monte Carlo methods. The properties of the model in terms of mixing and convergence of the MCMC algorithm and explored empirically on the basis of a simulated data set, and we show how the mixing properties can be improved by re-parameterisation. Estimation of the model parameters, and not the abundances, is the primary aim of the study, and we also propose an alternative approach to the estimation of the model parameters based on the marginal posterior distribution integrating over the abundances. Based on the estimated model we illustrate how we can simulate the release of juvenile cod, imitating an experiment conducted in the early 20th century to resolve a controversy between a fisherman and a scientist who could not agree on the effect of releasing cod larvae on the mature abundance of cod. This controversy initiated the monitoring programme generating the data used in our study

Efficient Computations for Gaussian Markov Random Field Models with two Applications in Spatial Epidemiology

Gaussian Markov random fields (GMRFs) are frequently used in statistics, and in spatial statistics in particular. The analytical properties of the Gaussian distribution are convenient and the Markov property invaluable when constructing single site Markov chain Monte Carlo algorithms. Rue (2001) demonstrates how numerical methods for sparse matrices can be utilised to construct efficient algorithms for unconditional and various forms for conditional sampling and for the evaluation of the log normalised density. These algorithms allow for constructing block-MCMC algorithms, where all parameters involved, including hyper-parameters, can often be updated jointly in one block. The convergence properties of such algorithms are superior compared to their single-site versions. This paper reviews the basic properties of a GMRF and how to take advantage of sparse matrix algorithms for sampling and evaluation of the log normalised density. We then discuss how to take advantage of more modern techniques for sparse matrices compared to more classical band-matrix methods, and how to sample a GMRF under a soft linear constraint. We apply and illustrate these techniques on two problems in spatial epidemiology. The first is a semi-parametric ecological regression problem presented by Natário and Knorr-Held (2003). The second is concerned with the modelling of a smoothly varying disease risk surface from area-level aggregated disease counts using an underlying Gaussian field model, motivated by the work of Kelsall and Wakefield (2002)