In obesity, the expression level of thyroid stimulating hormone receptor in adipose tissue is reduced and the levels of thyroid stimulating hormone (TSH) are often elevated within the normal range. To investigate the role of TSHR in brown and white adipose tissue (AT) using TSHR knockout (KO) mice and the physiological phenotypes affected by the TSHR knockout. AT-specific TSHR KO male mice and wild type (WT) controls were given a high-fat diet (HFD) or a control diet (CD). Body weights and food consumption were recorded for 20weeks and body temperatures for the first 3weeks. At termination, white and brown adipocytes were isolated. Gene expressios was investigated using real-time PCR. In a subgroup of female KO mice, glucose tolerance was investigated. TSHR were partially knocked out in KO mice, which gained more weight than WT mice when fed both a CD (p=.03) and HFD (p=.003). Body temperatures were lower in KO mice on CD (p<.001) and on HFD (p<.001) than WT controls. This was in agreement with reduced gene expression of UCP1 in brown adipocytes in the KO mice. Taselisib Glucose tolerance was significantly impaired in KO mice on CD mice before termination (p<.01). Expression of adipogenic and lipolytic genes were reduced in KO mice, which was exacerbated by HFD. The mRNA levels of adipokines including ADIPOQ and LEP were altered in white adipocytes of KO mice. TSHR KO led to dysfunction of both white and brown AT and predisposition to excess body weight gain in mice. Our data show that TSHR in AT regulates glucose tolerance, lipid metabolism, adipokine profile, and thermogenesis.TSHR KO led to dysfunction of both white and brown AT and predisposition to excess body weight gain in mice. Our data show that TSHR in AT regulates glucose tolerance, lipid metabolism, adipokine profile, and thermogenesis. This study aimed to identify the influencing factors associated with long onset-to-door time and establish predictive models that could help to assess the probability of prehospital delay in populations with a high risk for stroke. Patients who were diagnosed with acute ischemic stroke (AIS) and hospitalized between 1 November 2018 and 31 July 2019 were interviewed, and their medical records were extracted for data analysis. Two machine learning algorithms (support vector machine and Bayesian network) were applied in this study, and their predictive performance was compared with that of the classical logistic regression models after using several variable selection methods. Timely admission (onset-to-door time<3hr) and prehospital delay (onset-to-door time≥3hr) were the outcome variables. We computed the area under curve (AUC) and the difference in the mean AUC values between the models. A total of 450 patients with AIS were enrolled; 57 (12.7%) with timely admission and 393 (87.3%) patients with prehospital delay. All models, both those constructed by logistic regression and those by machine learning, performed well in predicting prehospital delay (range mean AUC 0.800-0.846). The difference in the mean AUC values between the best performing machine learning model and the best performing logistic regression model was negligible (0.014; 95% CI 0.013-0.015). Machine learning algorithms were not inferior to logistic regression models for prediction of prehospital delay after stroke. All models provided good discrimination, thereby creating valuable diagnostic programs for prehospital delay prediction.Machine learning algorithms were not inferior to logistic regression models for prediction of prehospital delay after stroke. All models provided good discrimination, thereby creating valuable diagnostic programs for prehospital delay prediction.Sensorimotor transformations require spatiotemporal coordination of signals, that is, through both time and space. For example, the gaze control system employs signals that are time-locked to various sensorimotor events, but the spatial content of these signals is difficult to assess during ordinary gaze shifts. In this review, we describe the various models and methods that have been devised to test this question, and their limitations. We then describe a new method that can (a) simultaneously test between all of these models during natural, head-unrestrained conditions, and (b) track the evolving spatial continuum from target (T) to future gaze coding (G, including errors) through time. We then summarize some applications of this technique, comparing spatiotemporal coding in the primate frontal eye field (FEF) and superior colliculus (SC). The results confirm that these areas preferentially encode eye-centered, effector-independent parameters, and show-for the first time in ordinary gaze shifts-a spatial transformation between visual and motor responses from T to G coding. We introduce a new set of spatial models (T-G continuum) that revealed task-dependent timing of this transformation progressive during a memory delay between vision and action, and almost immediate without such a delay. We synthesize the results from our studies and supplement it with previous knowledge of anatomy and physiology to propose a conceptual model where cumulative transformation noise is realized as inaccuracies in gaze behavior. We conclude that the spatiotemporal transformation for gaze is both local (observed within and across neurons in a given area) and distributed (with common signals shared across remote but interconnected structures).Despite the obvious power of the money motive, money has received little attention from psychologists' writing about human motivation. Considering China's transition from its high material deprivation of several decades ago to its current rapidly developing economy, this study aimed to examine how the motives for making money affect life satisfaction through the analysis of a sample of 5,911 participants. The results were somewhat different from previous studies all the basic motives, self-integrated motives, and non-integrated motives for making money were positively correlated with life satisfaction. We argue that, as the tool/drug theory of money states, money gives people immediate reward, like a drug. More precisely, our findings suggest that belongingness and meaningfulness of life mediate the relationship between motives for making money and life satisfaction. Based on the tool/drug theory, money can be exchanged for social status and can be used to pursue social display and social protection. Motives for making money can bring a sense of belongingness and meaningfulness of life in a large pay-disparity context.