Circadian analysis of myocardial infarction incidence in an Argentine and Uruguayan population

BACKGROUND: The occurrence of variations in the spectrum of cardiovascular disease between different regions of the world and ethnic groups have been the subject of great interest. This study report the 24-h variation of myocardial infarction (MI) occurrence in patients recruited from CCU located in Argentina and Uruguay. METHODS: A cohort of 1063 patients admitted to the CCU within 24 h of the onset of symptoms of an acute MI was examined. MI incidence along the day was computed in 1 h-intervals. RESULTS: A minimal MI incidence between 03:00 and 07:00 h and the occurrence of a first maximum between 08:00 and 12:00 h and a second maximum between 15:00 and 22:00 h were verified. The best fit curve was a 24 h cosinor (acrophase ~ 19:00 h, accounting for 63 % of variance) together with a symmetrical gaussian bell (maximum at ~ 10:00 h, accounting for 37 % of variance). A similar picture was observed for MI frequencies among different excluding subgroups (older or younger than 70 years; with or without previous symptoms; diabetics or non diabetics; Q wave- or non-Q wave-type MI; anterior or inferior MI location). Proportion between cosinor and gaussian probabilities was maintained among most subgroups except for older patients who had more MI at the afternoon and patients with previous symptoms who were equally distributed among the morning and afternoon maxima. CONCLUSION: The results support the existence of two maxima (at morning and afternoon hours) in MI incidence in the Argentine and Uruguayan population

Global sensitivity analysis of stochastic computer models with joint metamodels

The global sensitivity analysis method used to quantify the influence of uncertain input variables on the variability in numerical model responses has already been applied to deterministic computer codes; deterministic means here that the same set of input variables gives always the same output value. This paper proposes a global sensitivity analysis methodology for stochastic computer codes, for which the result of each code run is itself random. The framework of the joint modeling of the mean and dispersion of heteroscedastic data is used. To deal with the complexity of computer experiment outputs, nonparametric joint models are discussed and a new Gaussian process-based joint model is proposed. The relevance of these models is analyzed based upon two case studies. Results show that the joint modeling approach yields accurate sensitivity index estimatiors even when heteroscedasticity is strong

The CNS Stochastically Selects Motor Plan Utilizing Extrinsic and Intrinsic Representations

Traditionally motor studies have assumed that motor tasks are executed according to a single plan characterized by regular patterns, which corresponds to the minimum of a cost function in extrinsic or intrinsic coordinates. However, the novel via-point task examined in this paper shows distinct planning and execution stages in motion production and demonstrates that subjects randomly select from several available motor plans to perform a task. Examination of the effect of pre-training and via-point orientation on subject behavior reveals that the selection of a plan depends on previous movements and is affected by constraints both intrinsic and extrinsic of the body. These results provide new insights into the hierarchical structure of motion planning in humans, which can only be explained if the current models of motor control integrate an explicit plan selection stage

An Attribute-Based Approach to Classifying Community-Based Tourism Networks

This conceptual paper proposes the adoption of a collaborative network approach as a prospective means of improving success in implementing community-based tourism (CBT) initiatives. Drawing upon relevant literature, the researchers identify the key attributes that characterise a network-based approach. By proposing alternatives for each attribute, the research provides CBT practitioners with options for making informed decisions about how to build collaboration connecting individual CBT initiatives in multiple locations. The researchers discuss the implications of different approaches for power relations between stakeholders. The proposed framework provides a means of classifying existing CBT networks and analyses the types of network and the circumstances which lead to better outcomes for community development. Further empirical research is required to test the validity of the key network attributes and to develop a comprehensive classification system of CBT networks.School of Hotel and Tourism Managemen

A Single-Rate Context-Dependent Learning Process Underlies Rapid Adaptation to Familiar Object Dynamics

Motor learning has been extensively studied using dynamic (force-field) perturbations. These induce movement errors that result in adaptive changes to the motor commands. Several state-space models have been developed to explain how trial-by-trial errors drive the progressive adaptation observed in such studies. These models have been applied to adaptation involving novel dynamics, which typically occurs over tens to hundreds of trials, and which appears to be mediated by a dual-rate adaptation process. In contrast, when manipulating objects with familiar dynamics, subjects adapt rapidly within a few trials. Here, we apply state-space models to familiar dynamics, asking whether adaptation is mediated by a single-rate or dual-rate process. Previously, we reported a task in which subjects rotate an object with known dynamics. By presenting the object at different visual orientations, adaptation was shown to be context-specific, with limited generalization to novel orientations. Here we show that a multiple-context state-space model, with a generalization function tuned to visual object orientation, can reproduce the time-course of adaptation and de-adaptation as well as the observed context-dependent behavior. In contrast to the dual-rate process associated with novel dynamics, we show that a single-rate process mediates adaptation to familiar object dynamics. The model predicts that during exposure to the object across multiple orientations, there will be a degree of independence for adaptation and de-adaptation within each context, and that the states associated with all contexts will slowly de-adapt during exposure in one particular context. We confirm these predictions in two new experiments. Results of the current study thus highlight similarities and differences in the processes engaged during exposure to novel versus familiar dynamics. In both cases, adaptation is mediated by multiple context-specific representations. In the case of familiar object dynamics, however, the representations can be engaged based on visual context, and are updated by a single-rate process