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Background: Due to its effective osteogenic ability, BMP9 is a promising candidate for bone regeneration medicine. Whereas, BMP9 can also induce adipogenesis simultaneously. LCN2 is a cytokine associated with osteogenesis and adipogenesis. Reducing the adipogenic potential may be a feasible measure to enhance the osteogenic capability of BMP9. Objective: The objective of the study was to explore the role of LCN2 in regulating the BMP9-initialized osteogenic and adipogenic differentiation in mouse embryonic fibroblasts (MEFs), and clarify the possible underlying mechanism. Methods: Histochemical stain, western blot, real-time PCR, laser confocal, immunoprecipitation, cranial defect repair, and fetal limb culture assays were used to evaluate the effects of LCN2 on BMP9-induced osteogenic and adipogenic differentiation, as well as Wnt/β-catenin signaling. Results: LCN2 was down-regulated by BMP9. The BMP9-induced osteogenic markers were inhibited by LCN2 overexpression, but the adipogenic markers were increased; LCN2 knockdown exhibited opposite effects. Similar results were found in bone defect repair and fetal limb culture tests. The level of β-catenin nucleus translocation was found to be reduced by LCN2 overexpression, but increased by LCN2 knockdown. The inhibitory effect of LCN2 overexpression on the osteogenic capability of BMP9 was reversed by β-catenin overexpression; whereas, the effect of LCN2 knockdown on promoting BMP9 osteogenic potential was almost eliminated by β-catenin knockdown. LCN2 could bind with LRP6 specifically, and the inhibitory effect of LCN2 on the osteogenic potential of BMP9 could not be enhanced by LRP6 knockdown. Conclusion: LCN2 inhibits the BMP9-induced osteogenic differentiation but promotes its adipogenic potential in MEFs, which may be partially mediated by reducing Wnt/β-catenin signaling via binding with LRP6.

With the rapid development of information technology, medical treatments are gradually transforming into digitalization and informatization. The diverse sources and scopes of the medical data are sensitive and significant. As important data assets, medical data are constantly being analyzed and mined. Because of the huge commercial value of medical data, the problems of privacy leakage exist in the centralized information system. They cannot adapt to complex medical environment. Most of the current authentication mechanisms use passwords for verification. It is not easy for users to remember all usernames and passwords. Based on these problems, we design single-sign-on (called SSO) authentication (Password Single-Sign-On Authentication scheme, PSSOA) and blockchain (Improved Cross-Blockchain based Privacy-Preserving Date Sharing scheme, ICPDS) fusion schemes. Users are verified by multiple identity servers in this scheme. Then identity servers issue authentication tokens with threshold manners. The protected identity servers regularly update their master secret shares to defend against malicious adversaries. &nbsp

This letter introduces ERRA, an embodied learning architecture that enables robots to jointly obtain three fundamental capabilities (reasoning, planning, and interaction) for solving long-horizon language-conditioned manipulation tasks. ERRA is based on tightly-coupled probabilistic inferences at two granularity levels. Coarse-resolution inference is formulated as sequence generation through a large language model, which infers action language from natural language instruction and environment state. The robot then zooms to the fine-resolution inference part to perform the concrete action corresponding to the action language. Fine-resolution inference is constructed as a Markov decision process, which takes action language and environmental sensing as observations and outputs the action. The results of action execution in environments provide feedback for subsequent coarse-resolution reasoning. Such coarse-to-fine inference allows the robot to decompose and achieve long-horizon tasks interactively. In extensive experiments, we show that ERRA can complete various long-horizon manipulation tasks specified by abstract language instructions. We also demonstrate successful generalization to the novel but similar natural language instructions., Comment: Accepted to IEEE Robotics and Automation Letters (RA-L)

Bacteroides intestinalis (B. intestinalis) is an abundant gastrointestinal commensal bacterium and is able to produce secondary bile acids (BAs) among other important metabolic functions. However, deoxycholic acid (DCA) is known to suppress Bacteroides, suggesting differential molecular impact of different BA species on Bacteroides. Among major human gastrointestinal BA components, we first demonstrated that DCA and chenodeoxycholic acid (CDCA) and their taurine-conjugated species at 1 mM showed significantly higher inhibitory effects on the growth of B. intestinalis than cholic acid (CA) and lithocholic acid (LCA) and their taurine-conjugated species. Then, high-throughput proteomic strategy was used to show that both TCDCA and TDCA caused more proteome-wide modulation than TCA and TLCA. In response to incremental BA toxicity, the main functional changes of B. intestinalis include enhanced protein synthesis, DNA integrity maintenance, and suppressed central metabolic activities. Importantly, key energy and BA metabolism enzymes of B. intestinalis were inhibited by TCDCA and TDCA. These findings provide a basis for future studies to explore how Bacteroides respond to bile stress and how BA composition modulate gut microbiome homeostasis.

In this work, 1200 V SiC JMOS devices with different Wsch (2 μm, 2.5 μm and 3 μm) are fabricated. The single unclamped inductive switching (UIS) tests under different Vgs_off (-5 V and 0 V) are carried out to investigate the avalanche capability. The avalanche robustness among various Wsch under same Vgs_off is also compared and analyzed by simulation. The different failure mechanisms between different Vgs_off are studied by failure analysis and simulation. The method of improving avalanche ruggedness of JMOS is proposed.

High purity copper and copper alloy targets are the key supporting materials for the interconnection of integrated circuits in advanced processes. In this article, the chemical composition and microstructure of Cu-0.6wt%Mn alloy were characterized by means of Glow Discharge Mass Spectrometry, Inductive Coupled Plasma Emission Spectrometer, Optical Microscope and Scanning Electron Microscope. The results show that the total impurity content for Cu-0.69wt%Mn alloy is less than 1 ppm. The three key impurities contents of Ag and Fe and Si could meet the requirement of electronic materials for integrated circuits by use of high purity raw material and appropriate melting and casting methods. Mn content at different positions along the diameter direction fluctuates slightly between 0.66~0.72 wt%, and completely distributed uniformly in the Cu matrix without any trace of aggregation. Due to the influence of raw materials and casting technology, defects such as porosity and carbon inclusion are easy to appear in as-cast microstructure. Therefore, it is necessary to develop new casting mould and casting processing to improve the quality of ingots.

Aim: This article aims to quantitatively analyze the growth trend of listed pharmaceutical companies in the US and China by a machine learning algorithm. Background: In the last two decades, the global pharmaceutical industry has faced the dilemma of low research & development (R&D) success rate. The US is the world's largest pharmaceutical market, while China is the largest emerging market. Objective: To collect data from the database and apply machine learning to build the model. Method: LightGBM algorithm was used to build the model and identify the factor important to the performance of pharmaceutical companies. Results: The prediction accuracy for US companies was 80.3%, while it was 64.9% for Chinese companies. The feature importance shows that the net profit growth rate and debt liability ratio are significant in financial indicators. The results indicated that the US may continue to dominate the global pharmaceutical industry, while several Chinese pharmaceutical companies rose sharply after 2015 with the narrowing gap between the Chinese and US pharmaceutical industries. Conclusion: In summary, our research quantitatively analyzed the growth trend of listed pharmaceutical companies in the US and China by a machine learning algorithm, which provide a novel perspective for the global pharmaceutical industry. According to the R&D capability and profitability, 141 US-listed and 129 China-listed pharmaceutical companies were divided into four levels to evaluate the growth trend of pharmaceutical firms.