01) and relative humidity (P=0.06) but no correlation with precipitation and sea level pressure. On multivariate ARIMA analysis, only average monthly temperature was significantly associated with monthly urinary calculi presentation selleck chemicals llc rate among all comers (P<0.01). The crude correlations held true for both sexes with respect to
temperature but not for females in regard to relative humidity. The age groups of 21 to 44 and 45 to 64 had a rate correlation with temperature and all races correlated with temperature, but only Caucasians had a weak correlation with relative humidity.
Conclusions: This is the first study examining the role of climate on stone presentation rate in a large city above the Southern “”stone belt”" states. Temperature has a strong correlation with calculi presentation rate, and relative humidity has a trend toward overall calculi presentation
rate.”
“Population analysis of brain morphology from magnetic resonance images contributes to the study and understanding of neurological diseases. Such analysis typically involves segmentation of a large set of images and comparisons of these segmentations between relevant subgroups of images ( e. g., “”normal”" versus “”diseased”"). The images of each subgroup are usually selected in advance in a supervised way based on clinical knowledge. Their segmentations are typically guided by one or more available atlases, assumed to be suitable for the images at hand. We present a data-driven
probabilistic framework that simultaneously performs atlas-guided segmentation selleck products of a heterogeneous set of brain MR images and clusters the images in homogeneous subgroups, while constructing separate probabilistic atlases for each cluster to guide the segmentation. The main benefits of integrating segmentation, clustering and atlas construction in a single framework are that: 1) our method can handle find more images of a heterogeneous group of subjects and automatically identifies homogeneous subgroups in an unsupervised way with minimal prior knowledge, 2) the subgroups are formed by automatical detection of the relevant morphological features based on the segmentation, 3) the atlases used by our method are constructed from the images themselves and optimally adapted for guiding the segmentation of each subgroup, and 4) the probabilistic atlases represent the morphological pattern that is specific for each subgroup and expose the groupwise differences between different subgroups. We demonstrate the feasibility of the proposed framework and evaluate its performance with respect to image segmentation, clustering and atlas construction on simulated and real data sets including the publicly available BrainWeb and ADNI data. It is shown that combined segmentation and atlas construction leads to improved segmentation accuracy.