No statistically significant differences were found oil any of the NEO-PI-R domains or facet trait scales There SYN-117 research buy were also no significant differences between groups on the Minnesota Multiphasic Personality Inventory 2 (MMPI-2), a pleasure of psychopathology However, mild elevations were seen in all groups oil clinical
scales related to physical symptoms, health concern, and depression These data suggest there arc no consistent personality or psychopathology differences, as measured by the NEO-PI-R and the MMPI-2, between patients with left temporal, right temporal, and extratemporal epilepsy whose seizures are localized using video/EEG monitoring (C) 2009 Elsevier Inc All rights reserved”
“Background: selleck kinase inhibitor Identification of high-risk malaria foci can help enhance surveillance or control activities in regions where they are most needed. Associations between malaria risk and land-use/land-cover are
well-recognized, but these environmental characteristics are closely interrelated with the land’s topography (e. g., hills, valleys, elevation), which also influences malaria risk strongly. Parsing the individual contributions of land-cover/land-use variables to malaria risk requires examining these associations in the context of their topographic landscape. This study examined whether environmental factors like
land-cover, land-use, and urban density improved malaria risk prediction based solely on the topographically-determined Crenolanib cost context, as measured by the topographic wetness index.
Methods: The topographic wetness index, an estimate of predicted water accumulation in a defined area, was generated from a digital terrain model of the landscape surrounding households in two neighbouring western Kenyan highland communities. Variables determined to best encompass the variance in this topographic wetness surface were calculated at a household level. Land-cover/land-use information was extracted from a high-resolution satellite image using an object-based classification method. Topographic and land-cover variables were used individually and in combination to predict household-level malaria in the communities through an iterative split-sample model fitting and testing procedure. Models with only topographic variables were compared to those with additional predictive factors related to land-cover/land-use to investigate whether these environmental factors improved prediction of malaria based on the shape of the land alone.
Results: Variables related to topographic wetness proved most useful in predicting the households of individuals contracting malaria in this region of rugged terrain.