The results show that the use of ProViM and QUIMBI not only provides a new fast and intuitive visual analysis, but also allows the detection of new co-location patterns in MSI data that are difficult to find with other methods.Lake Baikal is inhabited by more than 300 endemic amphipod species, which are narrowly adapted to certain thermal niches due to the high interspecific competition. In contrast, the surrounding freshwater fauna is commonly represented by species with large-scale distribution and high phenotypic thermal plasticity. https://www.selleckchem.com/products/rg108.html Here, we investigated the thermal plasticity of the energy metabolism in two closely-related endemic amphipod species from Lake Baikal (Eulimnogammarus verrucosus; stenothermal and Eulimnogammarus cyaneus; eurythermal) and the ubiquitous Holarctic amphipod Gammarus lacustris (eurythermal) by exposure to a summer warming scenario (6-23.6 °C; 0.8 °C d-1). In concert with routine metabolic rates, activities of key metabolic enzymes increased strongly with temperature up to 15 °C in E. verrucosus, whereupon they leveled off (except for lactate dehydrogenase). In contrast, exponential increases were seen in E. cyaneus and G. lacustris throughout the thermal trial (Q10-values 1.6-3.7). Cytochrome-c-oxidase, lactate dehydrogenase, and 3-hydroxyacyl-CoA dehydrogenase activities were found to be higher in G. lacustris than in E. cyaneus, especially at the highest experimental temperature (23.6 °C). Decreasing gene expression levels revealed some thermal compensation in E. cyaneus but not in G. lacustris. In all species, shifts in enzyme activities favored glycolytic energy generation in the warmth. The congruent temperature-dependencies of enzyme activities and routine metabolism in E. verrucosus indicate a strong feedback-regulation of enzymatic activities by whole organism responses. The species-specific thermal reaction norms reflect the different ecological niches, including the spatial distribution, distinct thermal behavior such as temperature-dependent migration, movement activity, and mating season.Basal cells are multipotent stem cells responsible for the repair and regeneration of all the epithelial cell types present in the proximal lung. In mice, the elusive origins of basal cells and their contribution to lung development were recently revealed by high-resolution, lineage tracing studies. It however remains unclear if human basal cells originate and participate in lung development in a similar fashion, particularly with mounting evidence for significant species-specific differences in this process. To address this outstanding question, in the last several years differentiation protocols incorporating human pluripotent stem cells (hPSC) have been developed to produce human basal cells in vitro with varying efficiencies. To facilitate this endeavour, we introduced tdTomato into the human TP63 gene, whose expression specifically labels basal cells, in the background of a previously described hPSC line harbouring an NKX2-1GFP reporter allele. The functionality and specificity of the NKX2-1GFP;TP63tdTomato hPSC line was validated by directed differentiation into lung progenitors as well as more specialised lung epithelial subtypes using an organoid platform. This dual fluorescent reporter hPSC line will be useful for tracking, isolating and expanding basal cells from heterogenous differentiation cultures for further study.Alterations in the human microbiome have been observed in a variety of conditions such as asthma, gingivitis, dermatitis and cancer, and much remains to be learned about the links between the microbiome and human health. The fusion of artificial intelligence with rich microbiome datasets can offer an improved understanding of the microbiome's role in human health. To gain actionable insights it is essential to consider both the predictive power and the transparency of the models by providing explanations for the predictions. We combine the collection of leg skin microbiome samples from two healthy cohorts of women with the application of an explainable artificial intelligence (EAI) approach that provides accurate predictions of phenotypes with explanations. The explanations are expressed in terms of variations in the relative abundance of key microbes that drive the predictions. We predict skin hydration, subject's age, pre/post-menopausal status and smoking status from the leg skin microbiome. The changes in microbial composition linked to skin hydration can accelerate the development of personalized treatments for healthy skin, while those associated with age may offer insights into the skin aging process. The leg microbiome signatures associated with smoking and menopausal status are consistent with previous findings from oral/respiratory tract microbiomes and vaginal/gut microbiomes respectively. This suggests that easily accessible microbiome samples could be used to investigate health-related phenotypes, offering potential for non-invasive diagnosis and condition monitoring. Our EAI approach sets the stage for new work focused on understanding the complex relationships between microbial communities and phenotypes. Our approach can be applied to predict any condition from microbiome samples and has the potential to accelerate the development of microbiome-based personalized therapeutics and non-invasive diagnostics.Genomes retain records of demographic changes and evolutionary forces that shape species and populations. Remnant populations of African buffalo (Syncerus caffer) in South Africa, with varied histories, provide an opportunity to investigate signatures left in their genomes by past events, both recent and ancient. Here, we produce 40 low coverage (7.14×) genome sequences of Cape buffalo (S. c. caffer) from four protected areas in South Africa. Genome-wide heterozygosity was the highest for any mammal for which these data are available, while differences in individual inbreeding coefficients reflected the severity of historical bottlenecks and current census sizes in each population. PSMC analysis revealed multiple changes in Ne between approximately one million and 20 thousand years ago, corresponding to paleoclimatic changes and Cape buffalo colonisation of southern Africa. The results of this study have implications for buffalo management and conservation, particularly in the context of the predicted increase in aridity and temperature in southern Africa over the next century as a result of climate change.