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Importantly, these hAFSC-iPSC-CMs demonstrated low major histocompatibility complex (MHC) class I antigen expression and the absence of MHC class II antigens, indicating their low immunogenicity. The intramyocardial transplantation of hAFSC-iPSC-CMs restored cardiac function, partially remuscularized the injured region, and reduced fibrosis in the rat infarcted hearts. Therefore, hAFSC-iPSCs are potential candidates for the repair of infarcted myocardium.Extramammary Paget's disease (EMPD) is a neoplastic skin disease of indeterminate origin with an unknown genetic cause. We performed a comprehensive genetic analysis or targeted gene sequencing in 48 patients with EMPD. Muvalaplin concentration We identified FOXA1 mutations, a GAS6-FOXA1 fusion gene, and somatic hotspot mutations in the FOXA1 promoter region in 11 of the 48 EMPD patients (11/48, 23%). Additional mutations were identified in PIK3CA (six patients) and in HIST1H2BB, HIST1H2BC, and SMARCB1 (one patient each), but none were found in other frequently mutated genes in cancer. A global gene expression analysis using EMPD clinical samples found the upregulation of PI3 kinase-AKT-mTOR signaling. ABCC11, which is specifically expressed in the apocrine secretory cells and is necessary for their sweat secretion, was upregulated in the EMPD samples. This upregulation suggests that Paget cells originate from apocrine secretory cells. Immunohistochemical staining revealed that FOXA1 expression was prevalent in all of the EMPD samples analyzed and was associated with estrogen receptor expression. Our genetic analysis indicates that EMPD frequently involves FOXA1 mutations. FOXA1 is a transcriptional pioneer factor for the estrogen receptor, and the present results suggest that certain treatments for hormone-dependent cancers could be effective for EMPD.This paper presents a reconfigurable time-to-digital converter (TDC) used to quantize the phase of the impedance in electrical impedance spectroscopy (EIS). The TDC in the EIS system must handle a wide input-time range for analysis in the low-frequency range and have a high resolution for analysis in the high-frequency range. The proposed TDC adopts a coarse counter to support a wide input-time range and cascaded time interpolators to improve the time resolution in the high-frequency analysis without increasing the counting clock speed. When the same large interpolation factor is adopted, the cascaded time interpolators have shorter measurement time and smaller chip area than a single-stage time interpolator. A reconfigurable time interpolation factor is adopted to maintain the phase resolution with reasonable measurement time. The fabricated TDC has a peak-to-peak phase error of less than 0.72° over the input frequency range from 1 kHz to 512 kHz and the phase error of less than 2.70° when the range is extended to 2.048 MHz, which demonstrates a competitive performance when compared with previously reported designs.The world demography is continuously changing. During the last decade, we noticed a regular variation in the world demography leading to a nearly balanced society share between the young and aging population. This increasing older adult population is facing many problems. In fact, the transition to the aging period is associated with physical, psychological, cognitive, and societal changes. Negative behavior changes are considered as indicators of older adults' frailty. This is why it is important to detect such behavior changes early in order to prevent isolation, sedentary lifestyle, and even diseases, and therefore delay the frailty period. This paper exhibits a proof-of-concept pilot site deployment of an Internet of Thing (IoT) solution for the continuous monitoring and detection of older adults' behavior changes. The objective is to help geriatricians detect sedentary lifestyle and health-related problems at an early stage.We examined the predictive validity of a newly developed scale-the National Center for Geriatrics and Gerontology Activities of Daily Living (NCGG-ADL)-to measure instrumental activities of daily living (IADL) ability. We tested the scale for detecting new incidences of functional disability among community-dwelling older Japanese adults. Participants were 2708 older adults (mean age = 79.0 years, 51.6% women) living in the community who had no functional decline at baseline. We assessed IADL ability using the NCGG-ADL scale, comprising 13 self-report questions. Next, we assessed their functional disability monthly for 24 months, based on the national long-term care insurance (LTCI) system. Among all participants, 430 (15.9%) had an IADL limitation at baseline, and 289 (10.7%) were newly certified as functionally disabled. Participants scoring ≤ 12 of 13 points in the NCGG-ADL showed a significantly higher risk of functional disability than did those scoring 13 points, even after adjusting for covariates (hazard ratio [95% confidence interval] = 1.58 [1.19-2.09]). We thus validated the NCGG-ADL as a screening tool for assessing the risk of functional disability among community-dwelling older Japanese adults. We conclude that IADL limitations, as measured by the NCGG-ADL, could be useful predictors of functional disability.Autonomous motion planning (AMP) of unmanned aerial vehicles (UAVs) is aimed at enabling a UAV to safely fly to the target without human intervention. Recently, several emerging deep reinforcement learning (DRL) methods have been employed to address the AMP problem in some simplified environments, and these methods have yielded good results. This paper proposes a multiple experience pools (MEPs) framework leveraging human expert experiences for DRL to speed up the learning process. Based on the deep deterministic policy gradient (DDPG) algorithm, a MEP-DDPG algorithm was designed using model predictive control and simulated annealing to generate expert experiences. On applying this algorithm to a complex unknown simulation environment constructed based on the parameters of the real UAV, the training experiment results showed that the novel DRL algorithm resulted in a performance improvement exceeding 20% as compared with the state-of-the-art DDPG. The results of the experimental testing indicate that UAVs trained using MEP-DDPG can stably complete a variety of tasks in complex, unknown environments.