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https://www.selleckchem.com/products/bufalin.html Simultaneous sensing of temperature, pH, serotonin, and dopamine enabled integration of physiological and neurochemical data from individual bioelectronic devices.While the analysis of mitochondrial morphology has emerged as a key tool in the study of mitochondrial function, efficient quantification of mitochondrial microscopy images presents a challenging task and bottleneck for statistically robust conclusions. Here, we present Mitochondrial Segmentation Network (MitoSegNet), a pretrained deep learning segmentation model that enables researchers to easily exploit the power of deep learning for the quantification of mitochondrial morphology. We tested the performance of MitoSegNet against three feature-based segmentation algorithms and the machine-learning segmentation tool Ilastik. MitoSegNet outperformed all other methods in both pixelwise and morphological segmentation accuracy. We successfully applied MitoSegNet to unseen fluorescence microscopy images of mitoGFP expressing mitochondria in wild-type and catp-6ATP13A2 mutant C. elegans adults. Additionally, MitoSegNet was capable of accurately segmenting mitochondria in HeLa cells treated with fragmentation inducing reagents. We provide MitoSegNet in a toolbox for Windows and Linux operating systems that combines segmentation with morphological analysis.Xenopus laevis tolerate dehydration when their environments evaporate during summer months. Protein phosphorylation has previously shown to regulate important adaptations in X. laevis, including the transition to anaerobic metabolism. We therefore performed phosphoproteomic analysis of X. laevis to further elucidate the cellular and metabolic responses to dehydration. Phosphoproteins were enriched in cellular functions and pathways related to glycolysis/gluconeogenesis, the TCA cycle, and protein metabolism, among others. The prominence of phosphoproteins related to glucose metabolism led us to discover that the h

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