Lentiviral-Mediated shRNA Silencing of PDE4D Gene Inhibits Platelet-Derived Growth Factor-Induced Proliferation and Migration of Rat Aortic Smooth Muscle Cells

Phosphodiesterase 4D (PDE4D) is a member of the large superfamily of phosphodiesterases. PDE4D polymorphisms have been found to associate with ischemic stroke. Proliferation and migration of vascular smooth muscle cells (VSMCs) play a critical role in the pathogenesis of atherosclerosis. In this study, infection of VSMCs with lentivrius particles carrying shRNA direct against PDE4D significantly inhibited platelet-derived growth factor-induced VSMC proliferation and migration, and the inhibitory effects were not associated with global intracellular cAMP level. Our results implicate that PDE4D has an important role in VSMC proliferation and migration which may explain its genetic susceptibility to ischemic stroke

Surface characterization, mechanical properties and corrosion behaviour of ternary based ZneZnOeSiO2composite coating of mild steel

Zinc coatings are obtained either from cyanide, non-cyanide alkaline or acid solutions. Because of the pollution and high cost associated with cyanide, deposition from other baths is gaining importance. In order to develop a bath with additive that could produce a quality coating is the motivation behind this present work which is surface modification of Zne8ZnOeSiO2 nano composite coating on mild steel surface by electrodeposition route. The influence of SiO2 on Zne8ZnO sulphate electrolyte on the properties and microstructure of the produced nano-coatings were investigated. The SiO2 was varied from 0 to 16wt%. The microstructure characteristics of these produced series composites coating were investigated using scanning electron microscopy couple with energy dispersive spectroscopy (SEM/EDS), X-ray diffraction and atomic force microscopy (AFM). The corrosion degradation properties in 3.65% NaCl medium were studied using potentiodynamic polarization technique and characterized by high resolution optical microscope (HR-OPM). The hardness and wear of the composite coating were measured with high diamond microhardness tester and dry abrasive MTR-300 testers respectively. The results showed that average hardness value of 142.5 and 251.2HV and corrosion rate of 0.13088 and 0.00122 mm/yr were obtained for the 0 and 16wt% SiO2 in Zne8ZnO. The work have established that upto 16% SiO2 in Zne8ZnO composite coating on mild steel can be used in improving the microhardness, wear loss and corrosion resistance of mild stee

Preparation and properties of electroceramics films using the metallo-organic decomposition process

The application of metallo-organic decomposition (MOD) technology to the fabrication of useful thin films in a wide range of materials systems was investigated. The materials properties were studied closely and correlated with crystal structures, microstructures and the materials processing. MOD Pt films 50 nm thick on various substrates were formed at temperatures as low as 350\sp\circC. Films fired at 600\sp\circC showed a texture structure with grains oriented with their $\lbrack111\rbrack$ axis perpendicular to the substrate surface. The electrical resistivity measured by the Van der Pauw method was 30 $\mu\Omega$cm. MOD ITO transparent conductor films with composition In\sb{1.91}Sn\sb{0.09}O\sb3 were shown to be n-type semiconductors. The energy gap was estimated to be 6.2 meV from the measurements of the temperature dependence of charge density and the optical spectrum. The relatively low Hall mobility (about 2cm\sp2V\sp{-1}s\sp{-1}) at room temperature in the fine grain size films (10-20 nm) was related to grain boundary scattering. Ferroelectric BaTiO\sb3, PbTiO\sb3 and (PbSr)TiO\sb3 films from MOD technology were studied. It was found that in this category the basic materials properties such as polarization and its reversal, which were mostly explained by their crystal structure, were closely linked with film\u27s microstructures, which were in turn controlled by the materials processing. Both crystal structure changes due to the smaller grain size and the presence of an amorphous phase inside the films affected the properties of these ferroelectric films. MOD PLZT (8/65/35) films (0.4 $\mu$m thickness) on sapphire showed a birefringence shift of 0.0014 at an applied electric field of 2kV/cm. The hysteresis loop of the $\Delta$($\Delta n$)-E plot also indicated the presence of memory characteristics. The quadratic electro-optic coefficient was 0.5 $\times$ 10\sp{-16}(m/V)\sp2, and the linear electro-optic coefficient at 632.8 nm was 0.30 $\times$ 10\sp{-10}m/V, which is larger than that of LiNbO\sb3 commonly used for electrooptic waveguiding devices. MOD YBa\sb2Cu\sb3O\sb{\rm 7-x} superconducting films on sapphire showed a wide transition temperature of 30K (from onset to zero resistivity temperature). The TEM study indicated that there was a micro-segregation of chemical composition inside the films. Producing films with more uniform distribution of composition will improve the electrical properties of MOD superconducting films

Lane Change Trajectory Planning Based on Quadratic Programming in Rainy Weather

To enhance the safety and stability of lane change maneuvers for autonomous vehicles in adverse weather conditions, this paper proposes a quadratic programming−based trajectory planning algorithm for lane changing in rainy weather. Initially, in order to mitigate the risk of potential collisions on wet and slippery road surfaces, we incorporate the concept of road adhesion coefficients and delayed reaction time to refine the establishment of the minimum safety distance. This augmentation establishes constraints on lane change safety distances and delineates the boundaries of viable lane change domains within inclement weather contexts. Subsequently, adopting a hierarchical trajectory planning framework, we incorporate visibility cost functions and safety distance constraints during dynamic programming sampling to ensure the safety of vehicle operation. Furthermore, the vehicle lane change sideslip phenomenon is considered, and the optimal lane change trajectory is obtained based on the quadratic programming algorithm by introducing the maneuverability objective function. In conclusion, to verify the effectiveness of the algorithm, lateral linear quadratic regulator (LQR) and longitudinal double proportional−integral−derivative (DPID) controllers are designed for trajectory tracking. The results demonstrate the algorithm’s capability to produce continuous, stable, and collision−free trajectories. Moreover, the lateral acceleration varies within the range of ±1.5 m/s2, the center of mass lateral deflection angle varies within the range of ±0.15°, and the yaw rate remains within the ±0.1°/s range

Microbial Community Structure in the Sediments and Its Relation to Environmental Factors in Eutrophicated Sancha Lake

To study the microbial community structure in sediments and its relation to eutrophication environment factors, the sediments and the overlying water of Sancha Lake were collected in the four seasons. MiSeq high-throughput sequencing was conducted for the V3–V4 hypervariable regions of the 16S rRNA gene and was used to analyze the microbial community structure in sediments. Pearson correlation and redundancy analysis (RDA) were conducted to determine the relation between microbial populations and eutrophic factors. The results demonstrated four main patterns: (1) in the 36 samples that were collected, the classification annotation suggested 64 phyla, 259 classes, 476 orders, 759 families, and 9325 OTUs; (2) The diversity indices were ordered according to their values as with summer > winter > autumn > spring; (3) The microbial populations in the four seasons belonged to two distinct characteristic groups; (4) pH, dissolved oxygen (DO), total phosphorus (TP), and total nitrogen (TN) had significant effects on the community composition and structure, which further affected the dissolved total phosphorus (DTP) significantly. The present study demonstrates that the microbial communities in Sancha Lake sediments are highly diverse, their compositions and distributions are significantly different between spring and non-spring, and Actinobacteria and Cyanobacteria may be the key populations or indicator organisms for eutrophication

Fast Stepwise Inertial Control Scheme of a DFIG for Reducing Second Frequency Drop

With the fast growth in the penetration of wind power, doubly fed induction generators (DFIGs) are recommended for their ability to enforce grid codes that provide inertial control services by releasing rotational energy. However, after supporting the system frequency, a second frequency drop (SFD) is prone to occurring to regain the rotor speed caused by the sudden reduction in output. In this article, we propose a torque limit-based fast stepwise inertial control scheme of a DFIG using a piecewise reference function for reducing the SFD while preserving the frequency nadir (FN) with less rotor energy released. To achieve the first objective, the power reference increases to the torque limit and then decays with the rotor speed toward the preset operating point. To achieve the second objective, the power reference smoothly lessens over time based on the exponential function. The performance of the proposed stepwise inertial control strategy was studied under various scenarios, including constant wind speed and ramp down wind speed conditions. The test results demonstrated that the frequency stability is preserved during the frequency support phase, while the second frequency drop and mechanical stress on the wind turbine reduce during the rotor speed restoration phase when the DFIG implements the proposed stepwise inertial control scheme

Driving Decisions for Autonomous Vehicles in Intersection Environments: Deep Reinforcement Learning Approaches with Risk Assessment

Intersection scenarios are one of the most complex and high-risk traffic scenarios. Therefore, it is important to propose a vehicle driving decision algorithm for intersection scenarios. Most of the related studies have focused on considering explicit collision risks while lacking consideration for potential driving risks. Therefore, this study proposes a deep-reinforcement-learning-based driving decision algorithm to address these problems. In this study, a non-deterministic vehicle driving risk assessment method is proposed for intersection scenarios and introduced into a learning-based intelligent driving decision algorithm. In addition, this study proposes an attention network based on state information. In this study, a typical intersection scenario was constructed using simulation software, and experiments were conducted. The experimental results show that the algorithm proposed in this paper can effectively derive a driving strategy with both driving efficiency and driving safety in the intersection driving scenario. It is also demonstrated that the attentional neural network designed in this study helps intelligent vehicles to perceive the surrounding environment more accurately, improves the performance of intelligent vehicles, as well as accelerates the convergence speed