Introduction: Physical activity (PA) and sleep are associated with cerebrovascular disease and events like stroke. Though the interrelationships between PA, sleep, and other stroke risk factors have been studied, we are unclear about the associations of different types, frequency and duration of PA, sleep behavioral patterns (short, average and long sleep durations), within the context of stroke-related clinical, behavioral, and socio-demographic risk factors. The current study utilized Bayesian Belief Network analysis, a type of machine learning analysis, to develop profiles of physical activity (duration, intensity, and frequency) and sleep duration associated with or no history of stroke, given the influence of multiple stroke predictors and correlates. Such a model allowed us to develop a predictive classification model of stroke which can be used in post-stroke risk stratification and developing targeted stroke rehabilitation care based on an individual’s profile. Method: Analysis was based on the 2004-2013 National Health Interview Survey (n=288,888). Bayesian Belief Network analysis (BBN) was used to model the omnidirectional relationships of sleep duration and physical activity to history of stroke.There is increasing interest in evaluating home-range overlap (or, otherwise, segregation) between bird species, and between or within bird populations, to inform spatial planning. So far, studies of home-range overlap typically make use of comparisons between pairs of individuals, populations or species, and return a matrix of pairwise overlaps (e.g., percent overlaps). However, when the number of individuals, populations or species to be compared is elevated, an overlarge overlap matrix is difficult to interpret from an ecological viewpoint. We propose here a new, conceptually simple and computationally efficient index (general overlap index GOI) for the ready computation within GIS of home range overlap of an arbitrarily large number (i.e., n ≥ 2) of individuals, populations or species.
Whatever the number of home ranges to be compared, GOI always returns a single score between 0 and 100. As a case study, we applied our index to 24,074 GPS points of 10 Lesser Kestrels ( Falco naumanni) in order to estimate within-colony and between-colony overlaps in two neighboring colonies in Southern Italy. Within-colony overlap was elevated for both colonies (96.41% at Cassano delle Murge, n = 5 individuals 81.38% at Santeramo in Colle, n = 5 individuals), while between-colony overlap was low (19.12% n = 2 colonies) and, after a randomization procedure, more spatially-segregated than expected by chance. Modern biotelemetry offers huge amounts of data describing the space use of animal species. The use of intuitive and straightforward indices, like GOI, can be useful to promptly extract ecological information from such an amount of data (e.g. detecting change in space use over successive years, evaluating the reliability of various home-range estimators). Intraspecific and interspecific interactions impact the extent and spatial patterning of animal home ranges (Adams 2001).
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