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Fall accidents can cause severe impacts on the physical health and the quality of life of those who suffer limb diseases or injuries, the elderly, and their caregivers. Moreover, the later the accident is discovered, the lower the chance of recovery of the injured one. In order to detect accidents earlier, we propose a data-driven human fall detection framework. By combining the sensing mechanism of a commercialized webcam and an ultrasonic sensor array, we develop a probability model for automatic human fall monitoring. The webcam and ultrasonic array respectively collect the transverse and longitudinal time-series signals from a moving subject, and then these signals are assembled as a three-dimensional (3D) movement trajectory map. We also use two different detection-tracking algorithms for recognizing the tracked subjects. The mean height of the subjects is 164.2 ± 12 cm. Based on the data density functional theory (DDFT), we use the 3D motion data to estimate the cluster numbers and their cluster boundaries. We also employ the Gaussian mixture model as the DDFT kernel. Then, we utilize those features to build a probabilistic model of human falling. The model visually exhibits three possible states of human motions normal motion, transition, and falling. 1-Naphthyl PP1 solubility dmso The acceptable detection accuracy and the small model size reveals the feasibility of the proposed hybridized platform. The time from starting the alarm to an actual fall is on average about 0.7 s in our platform. The proposed sensing mechanisms offer 90% accuracy, 90% sensitivity, and 95% precision in the data validation. Then these vital results validate that the proposed framework has comparable performance to the contemporary methods.Currently utilized antidepressants have limited effectiveness and frequently incur undesired effects. Most antidepressants are thought to act via the inhibition of monoamine reuptake; however, direct binding to monoaminergic receptors has been proposed to contribute to both their clinical effectiveness and their side effects, or lack thereof. Among the target receptors of antidepressants, α1‑adrenergic receptors (ARs) have been implicated in depression etiology, antidepressant action, and side effects. However, differences in the direct effects of antidepressants on signaling from the three subtypes of α1-ARs, namely, α1A-, α1B- and α1D‑ARs, have been little explored. We utilized cell lines overexpressing α1A-, α1B- or α1D-ARs to investigate the effects of the antidepressants imipramine (IMI), desipramine (DMI), mianserin (MIA), reboxetine (REB), citalopram (CIT) and fluoxetine (FLU) on noradrenaline-induced second messenger generation by those receptors. We found similar orders of inhibition at α1A-AR (IMI less then DMI less then CIT less then MIA less then REB) and α1D‑AR (IMI = DMI less then CIT less then MIA), while the α1B-AR subtype was the least engaged subtype and was inhibited with low potency by three drugs (MIA less then IMI = DMI). In contrast to their direct antagonistic effects, prolonged incubation with IMI and DMI increased the maximal response of the α1B-AR subtype, and the CIT of both the α1A- and the α1B-ARs. Our data demonstrate a complex, subtype-specific modulation of α1-ARs by antidepressants of different groups.Background Promotion of a healthy lifestyle is considered a good strategy for dealing with chronic diseases. Mobile-based lifestyle interventions have shown beneficial effects in the control and treatment of chronic diseases such as diabetes, obesity and metabolic syndrome. Current clinical trials for mobile-based lifestyle intervention were mainly conducted among non-elderly populations, thus well-designed trials performed among the elderly who are more susceptible to chronic diseases are needed. The study aims to assess the effect of the mobile-based lifestyle intervention on the improvement of body weight, glucose and lipid metabolism among overweight and obese elderly adults in China. Materials and Methods Participants aged 60-80 years who are overweight or obese will be randomly assigned to receive mobile-based nutrition and exercise intervention, mobile-based exercise intervention and no intervention for 3 months. Before the intervention, participants will receive the training of the mobile application and sports bracelet. The primary outcome will be the between-group (three groups) difference in body mass index at the end of intervention. The secondary outcomes will include body composition, parameters of glucose and lipid metabolism, blood pressure, dietary data and physical activity data. All these outcomes will be assessed at baseline, day 45 and day 90. Ethics and dissemination The trial has been approved by the Ethics Committee of Peking University Health Science Center (IRB00001052-18039).The oxalate-carbonate pathway (OCP) is a biogeochemical process linking oxalate oxidation and carbonate precipitation. Currently, this pathway is described as a tripartite association involving oxalogenic plants, oxalogenic fungi, and oxalotrophic bacteria. While the OCP has recently received increasing interest given its potential for capturing carbon in soils, there are still many unknowns, especially regarding the taxonomic and functional diversity of the fungi involved in this pathway. To fill this gap, we described an active OCP site in Madagascar, under the influence of the oxalogenic tree Tamarindus indica, and isolated, identified, and characterized 50 fungal strains from the leaf litter. The fungal diversity encompassed three phyla, namely Mucoromycota, Ascomycota, and Basidiomycota, and 23 genera. Using various media, we further investigated their functional potential. Most of the fungal strains produced siderophores and presented proteolytic activities. The majority were also able to decompose cellulose and xylan, but only a few were able to solubilize inorganic phosphate. Regarding oxalate metabolism, several strains were able to produce calcium oxalate crystals while others decomposed calcium oxalate. These results challenge the current view of the OCP by indicating that fungi are both oxalate producers and degraders. Moreover, they strengthen the importance of the role of fungi in C, N, Ca, and Fe cycles.