GOLM1 Stimulation of Glutamine Metabolism Promotes Osteoporosis via Inhibiting Osteogenic Differentiation of BMSCs

Background/Aims: Bone marrow mesenchymal stem cells (BMSCs) play an essential role in osteoporosis. However, the molecular mechanisms and the involvement of glutamine metabolism in osteogenic BMSCs differentiation and osteoporosis remain largely unclear. In this study, we investigated the role of Golgi membrane protein 1 (GOLM1) and glutamine metabolism in BMSCs differentiation and osteoporosis. Methods: Osteogenic differentiation-inducing media (Odi) was used to induce the osteogenic differentiation of BMSCs. The mRNA expression of GOLM1, ALP, Runx2, Osx, BSP and OCN was determined by qRT-PCR assay. Western blot assay was used to analyze GOLM1, p-mTOR, mTOR, p-S6 and S6 abundance in GOLM1 silencing and over-expressed BMSCs. Glutamine uptake, intracellular glutamine, glutamate and α-KG level was detected using indicated Kits. GOLM1 antibody, glutamine metabolism inhibitors EGCG and BPTES were used to treat ovariectomy (OVX)-induced osteoporosis. Bone mineral density and bone volume relative to tissue volume (%) were analyzed by micro-CT. Serum was collected from osteoporosis patients and healthy participants and subjected to GOLM1 determination using ELISA Kit. Results: GOLM1 expression and glutamine metabolism were suppressed by Odi. GOLM1 blockage or inhibition of glutamine metabolism promoted the osteogenic differentiation of BMSCs induced by Odi. GOLM1 activated glutamine metabolism depending on the mTOR signaling pathway. In vivo, GOLM1 antibody or combination of glutamine inhibitor EGCG and BPTES rescued the osteoporosis in an OVX-operated mouse model. Serum GOLM1 level was increased in the patients of osteoporosis compared with healthy people. Conclusion: GOLM1 stimulates glutamine metabolism to suppress the osteogenic differentiation of BMSCs and to promote osteoporosis. Therefore, GOLM1 activation of glutamine metabolism is a potential target for osteoporosis

Prevalence and Determinants of Metabolic Syndrome among Women in Chinese Rural Areas

BACKGROUND AND AIMS: Metabolic syndrome (MS) is prevalent in recent years but few data is reported in the rural areas in China. The aim of this study was to examine MS prevalence and its risk factors among women in rural China. METHODS AND RESULTS: The Nantong Metabolic Syndrome Study (NMSS), a population based cross-sectional study, was conducted during 2007-2008 in Nantong, China. In person interviews, blood glucose and lipid measurements were completed for 13,505 female participants aged 18-74 years. The International Diabetes Federation (IDF), the US Third Report of the National Cholesterol Education Program, the Adult Treatment Panel (ATPIII) and modified ATPIII for Asian population has determined three criteria of MS. These criteria for MS were used and compared in this study. The prevalence of MS was 22.0%, 16.9% and 23.3% according to IDF, ATPIII and ATPIII-modified criteria, respectively. Levels of agreement of these criteria for MS were above 0.75. We found that vigorous-intensity of occupational physical activity was associated with a low prevalence of MS with OR of 0.76 (95% confidence interval (CI): 0.63-0.91). Rice wine drinkers (alcohol >12.8 g/day) had about 34% low risks of developing MS with OR of 0.66 (95% CI: 0.48-0.91), compared with non-drinkers. Odds ratio of MS was 1.81 (95% CI: 1.15-2.84) in women who smoked more than 20 pack-years, compared to non-smokers. Odds ratio of MS was 1.56 (95% CI: 1.25-1.95) in women who had familial history of diseases, including hypertension, diabetes and stroke, compared to women without familial history of those diseases. CONCLUSION: MS is highly prevalent among women in rural China. Both physical activity and rice wine consumption play a protective role, while family history and smoking are risk factors in MS development. Educational programs should be established for promoting healthy lifestyles and appropriate interventions in rural China

Application of Artificial Intelligence in the MRI Classification Task of Human Brain Neurological and Psychiatric Diseases: A Scoping Review

Artificial intelligence (AI) for medical imaging is a technology with great potential. An in-depth understanding of the principles and applications of magnetic resonance imaging (MRI), machine learning (ML), and deep learning (DL) is fundamental for developing AI-based algorithms that can meet the requirements of clinical diagnosis and have excellent quality and efficiency. Moreover, a more comprehensive understanding of applications and opportunities would help to implement AI-based methods in an ethical and sustainable manner. This review first summarizes recent research advances in ML and DL techniques for classifying human brain magnetic resonance images. Then, the application of ML and DL methods to six typical neurological and psychiatric diseases is summarized, including Alzheimer’s disease (AD), Parkinson’s disease (PD), major depressive disorder (MDD), schizophrenia (SCZ), attention-deficit/hyperactivity disorder (ADHD), and autism spectrum disorder (ASD). Finally, the limitations of the existing research are discussed, and possible future research directions are proposed

Research on Methods of Active Steering Control Based on Receding Horizon Control

Active steering technology is a key technology for automatic driving vehicles to achieve route tracking and obstacle avoidance and risk avoidance, and its performance will affect the stability control of the vehicle. For solving the stability control issues of vehicles, which have uncertainty in model and robustness in system, this paper proposes an active steering control method based on the receding horizon control model. It calculates the optimal control law by this method by using the real-time vehicle state so that it can compensate for the uncertainty caused by model mismatch, interference, etc. The design of the controller is implemented by using the yaw rate deviation of the vehicle as the input of the receding horizon linear quadratic controller model and then inputting the calculated superposition angle into the vehicle model in real time. We built a Simulink control model to implement co-simulation with CarSim to verify the control effect of the controller. In addition, we built a steering hardware-in-the-loop platform based on the LabVIEW RT system. The experimental results show that the active steering system adopting a receding horizon control method had better system robustness and robust stability

Mechanism of ozone adsorption and activation on B-, N-, P-, and Si-doped graphene: A DFT study

The detailed evolution mechanism of O-3 into Reactive oxygen species (ROS) is of paramount importance but remains elusive in catalytic ozonation. Herein, we report a density functional theory study to comprehensively reveal the specific evolution processes of O-3 into ROS on the B-, N-, P-, and Si-doped graphene, including the adsorption, decomposition and ROS generation. In contrast to some previous reports that O-3 would directly decompose into effective ROS on catalysts, our results indicate that after O-3 adsorption, the decomposition products are ground state O-2 and the adsorbed oxygen species (O-ads). The O-ads is more likely to act as a crucial intermediate for generating other ROS instead of directly attacking the organics. The type of the ROS and generation efficiency vary with the doped heteroatoms, and the heteroatoms of B, P and Si, or the neighboring C of N, would serve as active sites for O-3 adsorption and decomposition. The N-and P-doped graphene are predicted to have the superior performance in ROS generation and catalytic stability. Finally, twenty representative descriptors were adopted to build the quantitative structure-activity relationship (QSAR) with the activation energy barrier of O-3 decomposition. The result indicates that condensed dual descriptor (CDD) could be useful for preliminarily selecting the modified graphene catalysts, since it shows a very good linear relation with the activation energy barrier. This contribution provides an alternative way to gain fundamental insights into the mechanism of catalytic ozonation at the molecular level, and could be helpful for designing more-efficient catalysts in environmental remediation

Methylation of bone SOST impairs SP7, RUNX2, and ERα transactivation in patients with postmenopausal osteoporosis

Sclerostin (SOST), a glycoprotein predominantly secreted by bone tissue osteocytes, is an important regulator of bone formation, and loss of SOST results in Van Buchem disease. DNA methylation regulates SOST expression in human osteocytes, although the detailed underlying mechanisms remain unknown. In this study, we compared 12 patients with bone fractures and postmenopausal osteoporosis with eight patients without postmenopausal osteoporosis to understand the mechanisms via which SOST methylation affects osteoporosis. Serum and bone SOST expression was reduced in patients with osteoporosis. Bisulfite sequencing-polymerase chain reaction (PCR) revealed that the methylation rate was higher in patients with osteoporosis. We identified osterix (SP7), Runt-related transcription factor 2 (RUNX2), and estrogen receptor  (ER) as candidate transcription factors activating SOST expression. Increased SOST methylation impaired the transactivation function of SP7, RUNX2, and ER in MG-63 cells. AzadC treatment and SOST overexpression in MG-63 cells altered cell proliferation and apoptosis. Chromatin immunoprecipitation showed that higher methylation was associated with reduced SP7, RUNX2, and ER binding to the SOST promoter in patients with osteoporosis. Our studies provide new insight into the role of SOST methylation in osteoporosis.The accepted manuscript in pdf format is listed with the files at the bottom of this page. The presentation of the authors' names and (or) special characters in the title of the manuscript may differ slightly between what is listed on this page and what is listed in the pdf file of the accepted manuscript; that in the pdf file of the accepted manuscript is what was submitted by the author

EPS Current Tracking Method Research Based on Hybrid Sensitivity H∞ Control Algorithm

For electric power steering system (EPS), road interference, noise of the sensor, and the uncertainty of the steering system may make EPS control effect and the driver's road sense worse. EPS system which takes advantage of good current tracking ability, good anti-interference ability, and good operation stability is becoming more and more important in automotive research. The traditional H∞ control algorithm can solve the system uncertainty theoretically, but it cannot solve the contradiction between robustness and performance without considering the performance of the system. Therefore, this paper proposes a EPS current tracking method based on the hybrid sensitivity H∞ control algorithm, which takes the current tracking performance as one of the control objectives, so that the system can maximize the robustness and performance. Firstly, the dynamic model of EPS is established. Then, the two-degree-of-freedom vehicle model and tire model are introduced. The state space equation of the system is constructed on the basis of the system state space with random disturbance signals, the hybrid sensitivity H∞ controller is designed in the sensitivity index design, and the proposed algorithm can use weighting function to minimize the performance of the current tracking error as well as the robustness of the yaw rate error in response to robustness. Simulation analysis and experimental verification of EPS system are also carried out. The results show that the control method of the hybrid sensitivity H∞ can better achieve EPS target current tracking, effectively suppress the effect of external interference and noise, improve the system performance and robustness, ensure the driver get good road sense, and improve the system of steering stability

Highly efficient Tb-utilization in sintered Nd-Fe-B magnets by Al aided TbH2 grain boundary diffusion

Al aided TbH2 grain boundary diffusion (GBD) was applied to sintered Nd-Fe-B magnet in comparison with the TbH2 GBD magnet. The coercivity was enhanced from 13.73 kOe to 23.29 kOe, higher than the 22.26 kOe of TbH2 GBD magnet. The Tb content in the bulk magnet was reduced to 0.47 wt%, much lower than the 1.85 wt% of the TbH2 GBD magnet. Microstructure analysis showed that continuous grain boundary phases were formed and completely enveloped the grains with thin Tb-rich shells, which can be attributed to the reduced Tb content in diffusion source and the promoted GBD aided by Al. (C) 2018 Acta Materialia Inc. Published by Elsevier Ltd. All rights reserved

Integrated metabolomic and transcriptomic analyses revealed the distribution of saponins in Panax notoginseng

Panax notoginseng is famous for its important therapeutic effects. Saponins are bioactive compounds found in different parts and developmental stages of P. notoginseng plants. Thus, it is urgently to study saponins distribution in different parts and growth ages of P. notoginseng plants. In this study, potential biomarkers were found, and their chemical characteristic differences were revealed through metabolomic analysis. High-performance liquid chromatography data indicated the higher content of saponins (i.e., Rg1, Re, Rd, and Rb1) in the underground parts than that in the aerial parts. 20(S)-Protopanaxadiol saponins were mainly distributed in the aerial parts. Additionally, the total saponin content in the 3-year-old P. notoginseng plant (188.0 mg/g) was 1.4-fold higher than that in 2-year-old plant (130.5 mg/g). The transcriptomic analysis indicated the tissue-specific transcription expression of genes, namely, PnFPS, PnSS, PnSE1, PnSE2, and PnDS, which encoded critical synthases in saponin biosyntheses. These genes showed similar expression patterns among the parts of P. notoginseng plants. The expression levels of these genes in the flowers and leaves were 5.2fold higher than that in the roots and fibrils. These results suggested that saponins might be actively synthesized in the aerial parts and transformed to the underground parts. This study provides insights into the chemical and genetic characteristics of P. notoginseng to facilitate the synthesis of its secondary metabolites and a scientific basis for appropriate collection and rational use of this plant. KEY WORDS: Panax notoginseng, Saponin, Growth years, Metabolomic analyses, Gene expressio

Hierarchical speed control for autonomous electric vehicle through deep reinforcement learning and robust control

Abstract For the speed control system of autonomous electric vehicle (AEV), challenge happens with how to determine an appropriate driving speed to satisfy the dynamic environment while resisting uncertainty and disturbance. Therefore, this paper proposes a robust optimal speed control approach based on hierarchical architecture for AEV through combining deep reinforcement learning (DRL) and robust control. In decision‐making layer, a deep maximum entropy proximal policy optimization (DMEPPO) algorithm is presented to obtain an optimal speed via dynamic environment information, heuristic target entropy and adaptive entropy constraint. In motion control layer, to track the learned optimal speed while resisting uncertainty and disturbance, a robust speed controller is designed by the linear matrix inequality (LMI). Finally, simulation experiment results show that the proposed robust optimal speed control scheme based on hierarchical architecture for AEV is feasible and effective