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As a commonly used physical intervention, electrical stimulation (ES) has been demonstrated to be effective in the treatment of central nervous system disorders. Currently, researchers are studying the effects of electrical stimulation on individual neurons and neural networks, which are dependent on factors such as stimulation intensity, duration, location, and neuronal properties. However, the exact mechanism of action of electrical stimulation remains unclear. In some cases, repeated or prolonged electrical stimulation can lead to changes in the morphology or function of the neuron. In this study, immunofluorescence staining and Sholl analysis are used to assess changes in the neurite number and axon length to determine the optimal pattern and stimulation parameters of ES for neurons. Neuronal death and plasticity are detected by TUNEL staining and microelectrode array assays, respectively. mRNA sequencing and bioinformatics analysis are applied to predict the key targets of the action of ES on neurons, and the identified targets are validated by western blot analysis and qRT-PCR. The effects of alternating current stimulation (ACS) on neurons are more significant than those of direct current stimulation (DCS), and the optimal parameters are 3 μA and 20 min. ACS stimulation significantly increases the number of neurites, the length of axons and the spontaneous electrical activity of neurons, significantly elevates the expression of growth-associated protein-43 (GAP-43) without significant changes in the expression of neurotrophic factors. Furthermore, application of PI3K/AKT-specific inhibitors significantly abolishes the beneficial effects of ACS on neurons, confirming that the PI3K/AKT pathway is an important potential signaling pathway in the action of ACS.

In this research, we introduce a deep-learning-based framework designed for the prediction of transonic flow through a linear cascade utilizing large-scale point-cloud data. In our experimental cases, the predictions demonstrate a nearly four-fold speed improvement compared to traditional CFD calculations while maintaining a commendable level of accuracy. Taking advantage of a multilayer graph structure, the framework can extract both global and local information from the cascade flow field simultaneously and present prediction over unstructured data. In line with the results obtained from the test datasets, we conducted an in-depth analysis of the geometric attributes of the cascades reconstructed using our framework, considering adjustments made to the geometric information of the point cloud. We fine-tuned the input using 1603 data points and quantified the contribution of each point. The outcomes reveal that variations in the suction side of the cascade have a significantly more substantial influence on the field results compared to the pressure side and explain the way graph neural networks work for cascade flow-field prediction, enhancing the comprehension of graph-based flow-field prediction among developers and proves the potential of graph neural networks in flow-field prediction on large-scale point clouds and design. [ABSTRACT FROM AUTHOR]

As a core component of an aero-engine, the aerodynamic performance of the nacelle is essential for the overall performance of an aircraft. However, the direct design of a three-dimensional (3D) nacelle is limited by the complex design space consisting of different cross-section profiles and irregular circumferential curves. The deep manifold learning-assisted geometric multiple dimensionality reduction method combines autoencoders (AE) with strong capabilities for non-linear data dimensionality reduction and class function/shape function transformation (CST). A novel geometric dimensionality reduction method is developed to address the typical constraints of nacelle parameterization. Low-dimensional latent variables are extracted from the high-dimensional design space to achieve a parametric representation of 3D nacelle manifolds. Compared with traditional parametric methods, the proposed geometric dimensionality reduction method improves the accuracy and efficiency of geometric reconstruction and aerodynamic evaluation. A multi-objective optimization framework is proposed based on deep manifold learning to increase the efficiency of 3D nacelle design. The Pareto front curves under drag divergence constraints reveal the correlation between the geometry distribution and the surface isentropic Mach number distribution of 3D nacelles. This paper demonstrates the feasibility of the proposed geometric dimensionality reduction method for direct multi-objective optimization of 3D nacelles. [ABSTRACT FROM AUTHOR]

The multi-objective optimization of compressor cascade rotor blade is important for aero engine design. Many conventional approaches are thus proposed; however, they lack a methodology for utilizing existing design data/experiences to guide actual design. Therefore, the conventional methods require and consume large computational resources due to their need for large numbers of stochastic cases for determining optimization direction in the design space of problem. This paper proposed a Reinforcement Learning method as a new approach for compressor blade multi-objective optimization. By using Deep Deterministic Policy Gradient (DDPG), the approach modifies the blade profile as an intelligent designer according to the design policy: it learns the design experience of cascade blade as accumulated knowledge from interaction with the computation-based environment; the design policy can thus be updated. The accumulated computational data is therefore transformed into design experience and policies, which are directly applied to the cascade optimization, and the good-performance profiles can be thus approached. In a case study provided in this paper, the proposed approach is applied on a blade profile, which is thus optimized in terms of total pressure loss and laminar flow area. Compared with the initial profile, the total pressure loss coefficient is reduced by 3.59 % , and the relative laminar flow area at the suction surface is improved by 25.4 % . [ABSTRACT FROM AUTHOR]

We analyzed the effects of matrix metalloproteinase (MMP)-1, MMP-3, and MMP-12 on the degree of carotid atherosclerosis (CAS) and plaque stability, and investigated their correlations with cardiovascular and cerebrovascular events (CCEs). Two hundred CAS patients were enrolled. Carotid intima-media thickness (IMT) was measured using ultrasonic examination. Patients were divided into the no plaque group (NP group), stable plaque group (SP group), and vulnerable plaque group (VP group). The Crouse method was used for the evaluation of plaque scores. Additionally, 60 healthy subjects were enrolled as the control group. Serum triacylglycerol (TG), total cholesterol (TC), low density lipoprotein cholesterol (LDL-C), and high density lipoprotein cholesterol (HDL-C) were analyzed. The serum protein levels of MMP-1, MMP-3, and MMP-12 were measured by western blotting. The frequency of CCEs within 2 years was recorded, and its correlation with MMP-1, MMP-3, and MMP-12 was analyzed. The CAS plaque scores in the SP and VP groups were significantly increased compared with the NP group, and the difference between the SP and VP groups was significant. The levels of TC, TG, LDL-C, and HDL-C of CAS patients were significantly increased compared with those in the control group, but the differences in these indexes between the patient groups were not significant. Western blotting showed that the levels of MMP-1, MMP-3, and MMP-12 in the patient groups were significantly increased compared with those in the control group, and the protein levels in the VP group were significantly higher than those in the SP and NP groups. Additionally, the levels of MMP-1, MMP-3, and MMP-12 had significantly positive correlations with the occurrence of CCEs in CAS patients. In conclusion, MMP-1, MMP-3, and MMP-12 are positively correlated with CCEs in CAS patients. They can be used as markers for the clinical diagnosis and prognosis of CAS. [ABSTRACT FROM AUTHOR]

Objective: To generate recombinant Bacillus subtilis ( B. subtilis ) engineered for expression of porcine β-defensin-2 (pBD-2) and cecropin P1 (CP1) fusion antimicrobial peptide and investigate their anti-bacterial activity in vitro and their growth-promoting and disease resisting activity in vivo ., Methods: The pBD-2 and CP1 fused gene was synthesized using the main codons of B. subtilis and inserted into plasmid pMK4 vector to construct their expression vector. The fusion peptide-expressing B. subtilis was constructed by transformation with the vector. The expressed fusion peptide was detected with Western blot. The antimicrobial activity of the expressed fusion peptide and the recovered pBD-2 and CP1 by enterokinase digestion in vitro was analyzed by the bacterial growth-inhibitory activity assay. To analyze the engineered B. subtilis on growth promotion and disease resistance, the weaned piglets were fed with basic diet supplemented with the recombinant B. subtilis. Then the piglets were challenged by enteropathogenic Escherichia coli ( E. coli ). The weight gain and diarrhea incidence of piglets were measured after challenge., Results: The recombinant B. subtilis engineered for expression of pBD-2/CP1 fusion peptide was successfully constructed using the main codons of the B. subtilis . Both expressed pBD-2/CP1 fusion peptide and their individual peptides recovered from parental fusion peptide by enterokinase digestion possessed the antimicrobial activities to a variety of the bacteria, including gram-negative bacteria ( E. coli , Salmonella typhimurium , and Haemophilus parasuis ) and gram-positive bacteria ( Staphylococcus aureus ). Supplementing the engineered B. subtilis to the pig feed could significantly promote the piglet growth and reduced diarrhea incidence of the piglets., Conclusion: The generated B. subtilis strain can efficiently express pBD-2/CP1 fusion antimicrobial peptide, the recovered pBD-2 and CP1 peptides possess potent antimicrobial activities to a variety of bacterial species in vitro . Supplementation of the engineered B. subtilis in pig feed obviously promote piglet growth and resistance to the colibacillosis.

Canine parvovirus (CPV) disease is an acute, highly infectious disease threatening the dog-raising industry. So far there are no effective therapeutic strategies to control this disease. Although the canine transferrin receptor (TfR) was identified as a receptor for CPV infection, whether extracellular domain of TfR (called soluble TfR (sTfR)) possesses anti-CPV activities remains elusive. Here, we used the recombinant sTfR prepared from HEK293T cells with codon-optimized gene structure to investigate its anti-CPV activity both in vitro and in vivo. Our results indicated that codon optimization could significantly improve sTfR expression in HEK293T cells. The prepared recombinant sTfR possessed a binding activity to both CPV and CPV VP2 capsid proteins and significantly inhibited CPV infection of cultured feline F81 cells and decreased the mortality of CPV-infected dogs, which indicates that the sTfR has the anti-CPV activity both in vitro and in vivo.