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Interestingly, the principal component analysis revealed gender and age-specific behavioral modulations. Moreover, the diet integration with the botanicals did not modify the body weight gain and the daily intake of water. Our results support the use of TTBEs as dietary supplements for anxiolytic purposes and unveil age and gender-dependent responses.Identifying perturbed pathways at an individual level is important to discover the causes of cancer and develop individualized custom therapeutic strategies. Though prognostic gene lists have had success in prognosis prediction, using single genes that are related to the relevant system or specific network cannot fully reveal the process of tumorigenesis. We hypothesize that in individual samples, the disruption of transcription homeostasis can influence the occurrence, development, and metastasis of tumors and has implications for patient survival outcomes. Here, we introduced the individual-level pathway score, which can measure the correlation perturbation of the pathways in a single sample well. We applied this method to the expression data of 16 different cancer types from The Cancer Genome Atlas (TCGA) database. Our results indicate that different cancer types as well as their tumor-adjacent tissues can be clearly distinguished by the individual-level pathway score. Additionally, we found that there was strong heterogeneity among different cancer types and the percentage of perturbed pathways as well as the perturbation proportions of tumor samples in each pathway were significantly different. Finally, the prognosis-related pathways of different cancer types were obtained by survival analysis. We demonstrated that the individual-level pathway score (iPS) is capable of classifying cancer types and identifying some key prognosis-related pathways.To improve the standard point positioning (SPP) accuracy of integrated BDS (BeiDou Navigation Satellite System)/GPS (Global Positioning System) at the receiver end, a novel approach based on Long Short-Term Memory (LSTM) error correction recurrent neural network is proposed and implemented to reduce the error caused by multiple sources. On the basis of the weighted least square (WLS) method and Kalman filter, the proposed LSTM-based algorithms, named WLS-LSTM and Kalman-LSTM error correction methods, are used to predict the positioning error of the next epoch, and the prediction result is used to correct the next epoch error. Based on the measured data, the results of the weighted least square method, the Kalman filter method and the LSTM error correction method were compared and analyzed. The dynamic test was also conducted, and the experimental results in dynamic scenarios were analyzed. From the experimental results, the three-dimensional point positioning error of Kalman-LSTM error correction method is 1.038 m, while the error of weighted least square method, Kalman filter and WLS-LSTM error correction method are 3.498, 3.406 and 1.782 m, respectively. The positioning error is 3.7399 m and the corrected positioning error is 0.7493 m in a dynamic scene. The results show that the LSTM-based error correction method can improve the standard point positioning accuracy of integrated BDS/GPS significantly.Maternal high-fat (HF) is associated with offspring hyperphagia and obesity. We hypothesized that maternal HF alters fetal neuroprogenitor cell (NPC) and hypothalamic arcuate nucleus (ARC) development with preferential differentiation of neurons towards orexigenic (NPY/AgRP) versus anorexigenic (POMC) neurons, leading to offspring hyperphagia and obesity. Furthermore, these changes may involve hypothalamic bHLH neuroregulatory factors (Hes1, Mash1, Ngn3) and energy sensor AMPK. Female mice were fed either a control or a high fat (HF) diet prior to mating, and during pregnancy and lactation. HF male newborns were heavier at birth and exhibited decreased protein expression of hypothalamic bHLH factors, pAMPK/AMPK and POMC with increased AgRP. see more As adults, these changes persisted though with increased ARC pAMPK/AMPK. Importantly, the total NPY neurons were increased, which was consistent with the increased food intake and adult fat mass. Further, NPCs from HF newborn hypothalamic tissue showed similar changes with preferential NPC neuronal differentiation towards NPY. Lastly, the role of AMPK was further confirmed with in vitro treatment of Control NPCs with pharmacologic AMPK modulators. Thus, the altered ARC development of HF offspring results in excess appetite and reduced satiety leading to obesity. The underlying mechanism may involve AMPK/bHLH pathways.Intimate partner violence is a recognized public health and development issue that is consistently and comparatively measured through women's experience of physical and/or sexual acts by their partner. While physical intimate partner violence is covered by a wide range of behaviors, sexual intimate partner violence (SIPV) is often only measured through attempted or completed forced sex, ignoring less obvious forms of sexual intimate partner violence. We explored women's conceptualizations of SIPV by conducting in-depth interviews with 18 Tanzanian women. Using a thematic approach, we identified key features of women's sexual intimate relationships and their perceptions of them. The women clearly defined acts of positive sexual relationships that occurred with mutual consent and seduction and SIPV that included acts of forced sex and sex under the threat of violence. They also identified several acts that were crossing the line, whereby a discrepancy of views existed whether they constituted SIPV, such as having sex when out of the mood, sex being the duty of the wife, sex during the menses, requests for anal sex, having sex to not lose the husband, husband refusing sex and husband having other partners. Women in this study felt violated by a far wider range of sexual acts in their relationships. Future studies need to improve the measurement of sexual intimate partner violence to allow the collection of encompassing, yet comparable, data on this harmful phenomenon.The Internet of Things (IoT) has evolved significantly with advances in gathering data that can be extracted to provide knowledge and facilitate decision-making processes. Currently, IoT data analytics encountered challenges such as growing data volumes collected by IoT devices and fast response requirements for time-sensitive applications in which traditional Cloud-based solution is unable to meet due to bandwidth and high latency limitations. In this paper, we develop a distributed analytics framework for fog-enabled IoT systems aiming to avoid raw data movement and reduce latency. The distributed framework leverages the computational capacities of all the participants such as edge devices and fog nodes and allows them to obtain the global optimal solution locally. To further enhance the privacy of data holders in the system, a privacy-preserving protocol is proposed using cryptographic schemes. Security analysis was conducted and it verified that exact private information about any edge device's raw data would not be inferred by an honest-but-curious neighbor in the proposed secure protocol.