A Role of the Bile Salt Receptor FXR in Atherosclerosis

This study reviews current insights into the role of bile salts and bile salt receptors on the progression and regression of atherosclerosis. Bile salts have emerged as important modifiers of lipid and energy metabolism. At the molecular level, bile salts regulate lipid and energy homeostasis mainly via the bile salt receptors FXR and TGR5. Activation of FXR has been shown to improve plasma lipid profiles, whereas Fxr(-/-) mice have increased plasma triglyceride and very-low-density lipoprotein levels. Nevertheless, high-density lipoprotein cholesterol levels are increased in these mice, suggesting that FXR has both anti-and proatherosclerotic properties. Interestingly, there is increasing evidence for a role of FXR in "nonclassical" bile salt target tissues, eg, vasculature and macrophages. In these tissues, FXR has been shown to influence vascular tension and regulate the unloading of cholesterol from foam cells, respectively. Recent publications have provided insight into the antiinflammatory properties of FXR in atherosclerosis. Bile salt signaling via TGR5 might regulate energy homeostasis, which could serve as an attractive target to increase energy expenditure and weight loss. Interventions aiming to increase cholesterol turnover (eg, by bile salt sequestration) significantly improve plasma lipid profiles and diminish atherosclerosis in animal models. Bile salt metabolism and bile salt signaling pathways represent attractive therapeutic targets for the treatment of atherosclerosi

Towards accurate spatial prediction of Glossina pallidipes relative densities at country-scale in Kenya

Vector-borne diseases, like those transmitted by tsetse flies, pose a significant global public health threat. Reducing vector populations is a promising strategy for disease control, especially in the case of tsetse-transmitted African trypanosomiasis. However, the cost-effective implementation of large-scale vector surveillance and control measures face challenges due to the lack of spatially explicit and reliable maps identifying vector hotspots. In this study, we assessed the accuracy of predicting Glossina pallidipes relative densities across Kenya by linking constrained in-situ tsetse catch data from 660 traps across three Kenyan regions with readily available gridded satellite information (human population, land cover, soil properties, elevation, precipitation, and land surface temperature) using a classical random forest algorithm. To enhance predictive performance, we employed two feature elimination techniques specifically designed for machine learning algorithms, i.e., Recursive Feature Elimination (RFE) and Variable Selection Using Random Forests (VSURF). For each set of retained variables, we trained a Random Forest model using a spatial cross-validation technique. Our findings showed that tsetse fly relative densities decreased with mean annual precipitation, and soil moisture, and conversely increased with higher tree cover. Based on the cross-validated R2, 41% of the spatial variability in relative densities of tsetse flies could be explained. For spatial extrapolation, only the set of predictors retained by VSURF closely matched known tsetse fly distributions in Kenya. This more accurate performance of VSURF may be attributed to its approach of assessing variables for both importance and their contribution to reducing prediction error. Our study demonstrates the potential of using a random forest method to upscale tsetse relative abundance predictions to the national level. However, the reliability of the current extrapolated map remains uncertain. We recommend: 1) increasing tsetse fly sampling efforts, particularly in the data-limited northern and eastern regions of Kenya, and 2) developing a more precise and accurate land cover map with classes that directly associate with known habitat characteristics of the target tsetse species

Climatic niche breadth can explain variation in geographical range size of alpine and subalpine plants

Understanding the environmental factors determining the distribution of species with different range sizes can provide valuable insights for evolutionary ecology and conservation biology in the face of expected climate change. However, little is known about what determines the variation in geographical and elevational ranges of alpine and subalpine plant species. Here, we examined the relationship between geographical and elevational range sizes for 80 endemic rhododendron species in China using Spearman’s rank-order correlation. We ran the species distribution model – maximum entropy modelling (MaxEnt) – with 27 environmental variables. The importance of each variable to the model prediction was compared for species groups with different geographical and elevational range sizes. Our results showed that the correlation between geographical and elevational range sizes of rhododendron species was not significant. Climate-related variables were found to be the most important factors in shaping the distributional ranges of alpine and subalpine plant species across China. Species with geographically and elevationally narrow ranges had distinct niche requirements. For geographical ranges, the narrow-ranged species showed less tolerance to niche conditions than the wide-ranged species. For elevational ranges, compared with the wide-ranged species, the narrow-ranged species showed an equivalent niche breadth, but occurred at different niche position along the environmental gradient. Our findings suggest that over large spatial extents the elevational range size can be a complementary trait of alpine and subalpine plant species to geographical range size. Climatic niche breadth, especially the range of seasonal variability, can explain species’ geographical range sizes. Changes in climate may influence the distribution of rhododendrons, with the effects likely being felt most by species with either a narrow geographical or narrow elevational range