Although Bayesian Optimization (BO) has been employed for accelerating materials design in computational materials engineering, existing works are restricted to problems with quantitative variables. However, real designs of materials systems involve both qualitative and quantitative design variables representing material compositions, microstructure morphology, and processing conditions. For mixed-variable problems, existing Bayesian Optimization (BO) approaches represent qualitative factors by dummy variables first and then fit a standard Gaussian process (GP) model with numerical variables as the surrogate model. This approach is restrictive theoretically and fails to capture complex correlations between qualitative levels. We present in this paper the integration of a novel latent-variable (LV) approach for mixed-variable GP modeling with the BO framework for materials design. LVGP is a fundamentally different approach that maps qualitative design variables to underlying numerical LV in GP, which has strong physical justification. It provides flexible parameterization and representation of qualitative factors and shows superior modeling accuracy compared to the existing methods. We demonstrate our approach through testing with numerical examples and materials design examples. The chosen materials design examples represent two different scenarios, one on concurrent materials selection and microstructure optimization for optimizing the light absorption of a quasi-random solar cell, and another on combinatorial search of material constitutes for optimal Hybrid Organic-Inorganic Perovskite (HOIP) design. It is found that in all test examples the mapped LVs provide intuitive visualization and substantial insight into the nature and effects of the qualitative factors. Though materials designs are used as examples, the method presented is generic and can be utilized for other mixed variable design optimization problems that involve expensive physics-based simulations.The Jun dimerization protein 2 (Jdp2) is expressed predominantly in granule cell progenitors (GCPs) in the cerebellum, as was shown in Jdp2-promoter-Cre transgenic mice. Cerebellum of Jdp2-knockout (KO) mice contains lower number of Atoh-1 positive GCPs than WT. Primary cultures of GCPs from Jdp2-KO mice at postnatal day 5 were more resistant to apoptosis than GCPs from wild-type mice. In Jdp2-KO GCPs, the levels of both the glutamate‒cystine exchanger Sc7a11 and glutathione were increased; by contrast, the activity of reactive oxygen species (ROS) was decreased; these changes confer resistance to ROS-mediated apoptosis. In the absence of Jdp2, a complex of the cyclin-dependent kinase inhibitor 1 (p21Cip1) and Nrf2 bound to antioxidant response elements of the Slc7a11 promoter and provide redox control to block ROS-mediated apoptosis. These findings suggest that an interplay between Jdp2, Nrf2, and p21Cip1 regulates the GCP apoptosis, which is one of critical events for normal development of the cerebellum.Drug-disease association is an important piece of information which participates in all stages of drug repositioning. Although the number of drug-disease associations identified by high-throughput technologies is increasing, the experimental methods are time consuming and expensive. As supplement to them, many computational methods have been developed for an accurate in silico prediction for new drug-disease associations. In this work, we present a novel computational model combining sparse auto-encoder and rotation forest (SAEROF) to predict drug-disease association. Gaussian interaction profile kernel similarity, drug structure similarity and disease semantic similarity were extracted for exploring the association among drugs and diseases. On this basis, a rotation forest classifier based on sparse auto-encoder is proposed to predict the association between drugs and diseases. In order to evaluate the performance of the proposed model, we used it to implement 10-fold cross validation on two golden standard datasets, Fdataset and Cdataset. As a result, the proposed model achieved AUCs (Area Under the ROC Curve) of Fdataset and Cdataset are 0.9092 and 0.9323, respectively. For performance evaluation, we compared SAEROF with the state-of-the-art support vector machine (SVM) classifier and some existing computational models. Three human diseases (Obesity, Stomach Neoplasms and Lung Neoplasms) were explored in case studies. As a result, more than half of the top 20 drugs predicted were successfully confirmed by the Comparative Toxicogenomics Database(CTD database). This model is a feasible and effective method to predict drug-disease correlation, and its performance is significantly improved compared with existing methods.The black carbon or elemental carbon (EC) content in ice and snow has been a concern in climate change studies, but time-series records have mostly been obtained from glacier ice-core samples in limited geographical locations, such as the Arctic or high mountains. This is the first study to present decade-long records of EC deposition measured at urban (Sapporo) and background (Rishiri Island) sites in Japan, in the mid-latitude zone of the eastern edge of the Asian continent. By using archived membrane filters from an acid rain study, we retrieved monthly EC deposition records of 1993-2012 in Sapporo and intermittent deposition data in Rishiri. Annual EC deposition showed large fluctuations, with a maximum in 2000-2001 and a minor increase in 2010-2011. This interannual change was moderately related to the deposition of non-sea salt SO42- and the collected water volume but did not reflect the estimated emission history of China. High depositions in 2000-2001 were probably caused by the transport of Asian Dust accompanied by air pollutants, which were characteristically active in these years. The findings of this study have implications for the use of observational data in validating global aerosol transport models.This prospective cohort study aims to investigate the incidence, related factors and prognosis of IgG4-related disease (IgG4-RD) with malignancies in the Chinese cohort. We prospectively analyzed the IgG4-RD patients recruited in Peking Union Medical College Hospital from January 2011 to August 2018 and identified patients diagnosed with IgG4-RD complicating malignancies. Data regarding demographics, clinical features, treatment and prognosis of IgG4-RD patients complicating malignancies were collected and compared to those of age- and sex-matched controls.  https://www.selleckchem.com/CDK.html  Among the 587 Chinese patients with IgG4-RD, 17 malignancies were identified. Ten of them developed malignancy after the diagnosis of IgG4-RD, given a standard incidence ratio (SIR) of 2.78 (95%CI 1.33-5.12). Multivariate logistic analysis indicated that autoimmune pancreatitis (OR = 6.230, 95%CI 1.559-24.907, p = 0.010) was positively associated with malignancy, whereas eosinophilia (OR = 0.094, 95%CI 0.010-0.883, p = 0.039) was negatively related with malignancies.