miR-135a-5p overexpression in peripheral blood-derived exosomes mediates vascular injury in type 2 diabetes patients

ObjectiveDiabetes pathology relies on exosomes (Exos). This study investigated how peripheral blood Exo-containing microRNAs (miRNAs) cause vascular injury in type 2 diabetes (T2D).MethodsWe removed DEmiRNA from T2D chip data from the GEO database. We isolated Exo from 15 peripheral blood samples from T2D patients and 15 healthy controls and measured Exo DEmiRNA levels. We employed the intersection of Geneards and mirWALK database queries to find T2D peripheral blood mRNA-related chip target genes. Next, we created a STRING database candidate target gene interaction network map. Next, we performed GO and KEGG enrichment analysis on T2D-related potential target genes using the ClusterProfiler R package. Finally, we selected T2D vascular damage core genes and signaling pathways using GSEA and PPI analysis. Finally, we used HEK293 cells for luciferase assays, co-cultured T2D peripheral blood-derived Exo with HVSMC, and detected HVSMC movement alterations.ResultsWe found 12 T2D-related DEmiRNAs in GEO. T2D patient-derived peripheral blood Exo exhibited significantly up-regulated miR-135a-3p by qRT-PCR. Next, we projected miR-135a-3p’s downstream target mRNA and screened 715 DEmRNAs to create a regulatory network diagram. DEmRNAs regulated biological enzyme activity and vascular endothelial cells according to GO function and KEGG pathway analysis. ErbB signaling pathway differences stood out. PPI network study demonstrated that DEmRNA ATM genes regulate the ErbB signaling pathway. The luciferase experiment validated miR-135a-3p and ATM target-binding. Co-culture of T2D patient-derived peripheral blood Exo with HVSMC cells increases HVSMC migration, ErbB2, Bcl-2, and VEGF production, and decreases BAX and ATM. However, miR-135a-3p can reverse the production of the aforesaid functional proteins and impair HVSMC cell movement.ConclusionT2D patient-derived peripheral blood Exo carrying miR-135a-3p enter HVSMC, possibly targeting and inhibiting ATM, activating the ErbB signaling pathway, promoting abnormal HVSMC proliferation and migration, and aggravating vascular damage

α-Synuclein: A Multifunctional Player in Exocytosis, Endocytosis, and Vesicle Recycling

α-synuclein (α-Syn) is a presynaptic enriched protein involved in the pathogenesis of Parkinson’s disease. However, the physiological roles of α-Syn remain poorly understood. Recent studies have indicated a critical role of α-Syn in the sensing and generation of membrane curvature during vesicular exocytosis and endocytosis. It has been known to modulate the assembly of SNARE complex during exocytosis including vesicle docking, priming and fusion steps. Growing evidence suggests that α-Syn also plays critical roles in the endocytosis of synaptic vesicles. It also modulates the availability of releasable vesicles by promoting synaptic vesicles clustering. Here, we provide an overview of recent progresses in understanding the function of α-Syn in the regulation of exocytosis, endocytosis, and vesicle recycling under physiological and pathological conditions

Initial Microstructure Effects on Hot Tensile Deformation and Fracture Mechanisms of Ti-5Al-5Mo-5V-1Cr-1Fe Alloy Using In Situ Observation

The hot tensile deformation and fracture mechanisms of a Ti-5Al-5Mo-5V-1Cr-1Fe alloy with bimodal and lamellar microstructures were investigated by in situ tensile tests under scanning electron microscopy (SEM) and electron backscatter diffraction (EBSD). The results show that the main slip deformation modes are prismatic slip ({11¯00}<112¯0>) and pyramidal slip ({11¯01}<112¯0>) under tension at 350 °C. In the bimodal microstructure, several parallel slip bands (SBs) first form within the primary α (αP) phase. As the strain increases, the number of SBs in the αP phase increases significantly and multislip systems are activated to help further coordinate the increasing deformation. Consequently, the microcracks nucleate and generally propagate along the SBs in the αP phase. The direction of propagation of the cracks deflects significantly when it crosses the αP/β interface, resulting in a tortuous crack path. In the lamellar microstructure, many dislocations pile up at the coarse-lath α (αL) phase near the grain boundaries (GBs) due to the strong fencing effect thereof. As a result, SBs develop first; then, microcracks nucleate at the αL phase boundary. During propagation, the cracks tend to propagate along the GB and thus lead to the intergranular fracture of the lamellar microstructure

Design and Validation of Real-Time Optimal Control with ECMS to Minimize Energy Consumption for Parallel Hybrid Electric Vehicles

A real-time optimal control of parallel hybrid electric vehicles (PHEVs) with the equivalent consumption minimization strategy (ECMS) is presented in this paper, whose purpose is to achieve the total equivalent fuel consumption minimization and to maintain the battery state of charge (SOC) within its operation range at all times simultaneously. Vehicle and assembly models of PHEVs are established, which provide the foundation for the following calculations. The ECMS is described in detail, in which an instantaneous cost function including the fuel energy and the electrical energy is proposed, whose emphasis is the computation of the equivalent factor. The real-time optimal control strategy is designed through regarding the minimum of the total equivalent fuel consumption as the control objective and the torque split factor as the control variable. The validation of the control strategy proposed is demonstrated both in the MATLAB/Simulink/Advisor environment and under actual transportation conditions by comparing the fuel economy, the charge sustainability, and parts performance with other three control strategies under different driving cycles including standard, actual, and real-time road conditions. Through numerical simulations and real vehicle tests, the accuracy of the approach used for the evaluation of the equivalent factor is confirmed, and the potential of the proposed control strategy in terms of fuel economy and keeping the deviations of SOC at a low level is illustrated

Research and Implementation of ε-SVR Training Method Based on FPGA

Online training of Support Vector Regression (SVR) in the field of machine learning is a computationally complex algorithm. Due to the need for multiple iterative processing in training, SVR training is usually implemented on computer, and the existing training methods cannot be directly implemented on Field-Programmable Gate Array (FPGA), which restricts the application range. This paper reconstructs the training framework and implementation without precision loss to reduce the total latency required for matrix update, reducing time consumption by 90%. A general ε-SVR training system with low latency is implemented on Zynq platform. Taking the regression of samples in two-dimensional as an example, the maximum acceleration ratio is 27.014× compared with microcontroller platform and the energy consumption is 12.449% of microcontroller. From the experiments for the University of California, Riverside (UCR) time series data set. The regression results obtain excellent regression effects. The minimum coefficient of determination is 0.996, and running time is less than 30 ms, which can meet the requirements of different applications for real-time regression