To investigate the effects and mechanisms of deer antler total polypeptides (VAP-T) and its active component Y (VAP-Y) in treating depression in CUMS mice.VAP-T improved depression-like behavior in CUMS mice, reduced microglial activation, and tissue inflammation. VAP-Y showed better improvement in depression-like behavior in CUMS mice compared to VAP-T, significantly inhibiting microglial activation and tissue inflammation. Chromatographic analysis of VAP-Y revealed that short peptides had good binding activity with TREM2. VAP-T and VAP-Y have significant improvement effects on depression-like behavior in CUMS mice, which is related to the inhibition of TREM2-mediated microglial activation. VAP-Y has better activity than the total polypeptides and is a polypeptide with potential for treating depression.

This paper proposes a method based on image processing algorithms for ground penetrating radar (GPR) to locate hidden defects in tunnel linings.Firstly, the fast synthetic aperture focusing imaging (Fast-SAFI) algorithm is used to accurately identify the morphology of tunnel-lining defects.Secondly, an iterative algorithm is used to determine the connected regions on the binary image, exclude background noise interference, and locate the centroid and vertices of the correct target connected regions to achieve the positioning of the depth of tunnel-lining defects.To verify the feasibility of the proposed positioning algorithm, a verification experiment was conducted on the experimental wall of the China Academy of Railway Sciences.The experimental results show that the proposed positioning algorithm is reliable and rapid for identifying and locating the morphology and depth of tunnel-lining defects.

To investigate the mechanism by which swertiamarin (swertianin, SWE) regulates the polarization of tumor microenvironment-associated macrophages to M1 phenotype, thereby exerting anti-tumor effects.SWE promoted the formation of M1 cells and increased the proportion of CD86 + cells in both RAW264.7 and primary monocyte-derived macrophages, while activating the STING-NF-κB pathway. When STING or P65 was knocked out, the effects of SWE were antagonized, inhibiting the formation of CD86 + M1 cells. At the animal level, SWE inhibited tumor growth, activated STING-NF-κB, and promoted the formation of CD86 + cells. STING-KO inhibited the effects of SWE.SWE can activate the STING-NF-κB signal to promote macrophage M1 polarization, playing an anti-tumor role.

In the field of facial expression recognition (FER), two main trends point to the data-driven FER and feature-driven FER exist. The former focused on the data problems (e.g., sample imbalance and multimodal fusion), while the latter explored the facial expression features. As the feature-driven FER is more important than the data-driven FER, for deeper mining of facial features, we propose an expression recognition model based on Local–Global information Reasoning and Landmark Spatial Distributions. Particularly to reason local–global information, multiple attention mechanisms with the modified residual module are designed for the Res18-LG module. In addition, taking the spatial topology of facial landmarks into account, a topological relationship graph of landmarks and a two-layer graph neural network are introduced to extract spatial distribution features. Finally, the experiment results on FERPlus and RAF-DB datasets demonstrate that our model outperforms the state-of-the-art methods.

Aim: We analyzed the role and mechanism of dihydromyricetin (DHM) in suppressing inflammatory injury in microglial cells via targeting MD2.Methods: In vitro, BV2 cells were used as the objects of study to induce inflammatory injury with LPS + ATP, then the cell apoptosis level was identified, inflammatory factor levels were measured by ELISA, TLR4 and MD2 were stained with fluorescence staining, and protein expression was determined using Western-blot (WB) assay. Af-terwards, MD2 expression was knocked down n BV2 cells to construct the BV2-MD2-/-cell line, so as to detect the role of DHM on BV2-MD2-/-. Moreover, the binding of DHM to MD2 was analyzed via mall molecule-protein docking and pull-down assays. In-vivo, wild-type (WT) C67BL/6 mice and APP/PS1 (AD) mice were used as the objects of study, which were intervened with DHM to detect the changes in mouse cognition. In addition, the pathological changes of brain tissues were analyzed with H&E staining. In addition, the inflammatory factor and protein levels in brain tissues were also detected.Results: DHM suppressed inflammatory injury in BV2 cells, reduced the cell apoptosis rate and inflammatory factor levels, and suppressed the level of TLR4 and MD2. After MD2 knockdown, DHM was unable to further suppress BV2 cell injury. Results of small molecule-protein docking and pull-down assays suggested that DHM bound to MD2 to suppress the formation of TLR4 complex. In AD mice, DHM improved the cognitive disorder in mice, suppressed inflammatory injury in brain tissues and lowered the expression of TLR4 protein.Conclusion: DHM targeted MD2 to suppress the formation of TLR4 protein complex, thereby suppressing in-flammatory injury in microglial cells and improving the cognition in AD mice.

Backgroud: Hydroxysafflor yellow A (HSYA) has a certain improvement effect on Alzheimer’s disease (AD) rats, but its specific mechanism is still unclear. The purpose of this study was to observe the regulatory effect of HSYA on learning and memory ability of AD rats induced by Aβ1-42.Materials and methods: Morris water maze test was used to evaluate the effect of HSYA on the learning and memory ability of AD model rats. To explore the effective targets and potential molecular mechanisms of HSYA in AD treatment based on quantitative proteomics.Results: Through the Morris water maze experiment, we found that after HSYA treatment, the learning ability of rats in the model group has been significantly improved. Quantitative proteomics results showed that among the 11 common differential proteins between the “model/sham operation” comparison group and the “HSYA treatment/model” comparison group, the cholesterol synthesis rate-limiting enzyme mevalonate decarboxylase (Mvd) Western Blot results are consistent with the results of quantitative proteomics analysis. We found that HSYA can inhibit the expression of BACE protein in hippocampus of AD rats and decrease the level of Aβ1-42. Besides, HSYA could also reduce cholesterol levels in serum and hippocampus.Conclusion: In summary, HSYA can effectively improve learning and memory disorders in AD rats, and exert neuroprotective effects by effectively controlling serum and brain cholesterol to down-regulate the expression of BACE and thus reduce the content of Aβ1-42.

This paper presents a detection method of DCAM-YOLOv5 for ground penetrating radar (GPR) to address the difficulty of identifying complex and multi-type defects in tunnel linings.The diversity of tunnel-lining defects and the multiple reflections and scattering caused by water-bearing defects make GPR images quite complex.Although existing methods can identify the position of underground defects from B-scans, their classification accuracy is not high.The DCAM-YOLOv5 adopts YOLOv5 as the baseline model and integrates deformable convolution and convolutional block attention module (CBAM) without adding a large number of parameters to improve the adaptive learning ability for irregular geometric shapes and boundary fuzzy defects.In this study, dielectric constant models of tunnel linings are established based on the electromagnetic simulation software (GPRMAX), including rebar and various structural defects.The simulated and field GPR B-scan images show that the DCAM-YOLOv5 method has better results for detecting different types of defects than other methods, which validates the effectiveness of the proposed detection method.

To better understand the correlation between structure and properties in thermoplastic starch biopolymer blend films, the effects of amylose content, chain length distribution of amylopectin and molecular orientation of thermoplastic sweet potato starch (TSPS) and thermoplastic pea starch (TPES) on microstructure and functional properties of thermoplastic starch biopolymer blend films were studied. After thermoplastic extrusion, the amylose contents of TSPS and TPES decreased by 16.10 % and 13.13 %, respectively. The proportion of the chains with the degree of polymerization between 9 and 24 of amylopectin in TSPS and TPES increased from 67.61 % to 69.50 %, and from 69.51 % to 71.06 %, respectively. As a result, the degree of crystallinity and molecular orientation of TSPS and TPES films increased as compared to sweet potato starch and pea starch films. The thermoplastic starch biopolymer blend films possessed a more homogeneous and compacter network. The tensile strength and water resistance of thermoplastic starch biopolymer blend films increased significantly, whereas thickness and elongation at break of thermoplastic starch biopolymer blend films decreased significantly.

OBJECTIVE:To investigate the regulatory role and mechanism of betulinic acid (BET) in tumor-associated M2 macrophage polarization.METHODS:For in vitro experiments, RAW246.7 and J774A.1 cells were used, and differentiation of M2 macrophages was induced using recombinant interleukin-4/13. The levels of M2 cell marker cytokines were measured, and the proportion of F4/80+CD206+ cells was evaluated using flow cytometry. Furthermore, STAT6 signaling was detected, and H22 and RAW246.7 cells were cocultured to assess the effect of BET on M2 macrophage polarization. Changes in the malignant behavior of H22 cells after coculturing were observed and a tumor-bearing mouse model was constructed to determine CD206 cell infiltration after BET intervention.RESULTS:In vitro experiments showed that BET inhibited M2 macrophage polarization and phospho-STAT6 signal modification. Moreover, the ability to promote the malignant behavior of H22 cells was reduced in BET-treated M2 macrophages. Furthermore, in vivo experiments indicated that BET decreased M2 macrophage polarization and infiltration in the microenvironment of liver cancer. BET was noted to predominantly bind to the STAT6 site to inhibit STAT6 phosphorylation.CONCLUSION:BET bound chiefly to STAT6 to inhibit STAT6 phosphorylation and decrease M2 polarization in the microenvironment of liver cancer. These findings suggest that BET exerts an antitumor effect by modulating M2 macrophage function.

Rational design of efficient and durable non-noble transition metal doped carbon-based oxygen reduction electrocatalysts is the key to reduce catalyst cost and optimize fuel cell performance. Herein, hierarchically porous Fe/N co-doped porous carbon on N doped carbon nanotubes (Fe/N-C@NCNTs) are prepared via the pyrolysis of Fe-doped ZIF-8 embedded on polypyrrole nanotubes (PNTs). The as-prepared Fe/N-C@NCNTs electrocatalyst exhibits the limit current density of 5.7 mA cm-2 and the half-wave potential of 0.84 V (vs. RHE), indicating the excellent oxygen reduction performance. Furthermore, the electrocatalytic activity of Fe/N-C@NCNTs is only reduced by 7% after 650 min of the stability test. The remarkable oxygen reduction performance is attributed to the synergistic effects of abundant Fe-Nx-C active sites, high graphitization degree and hierarchically porous structure of electrocatalyst. (c) 2022 Hydrogen Energy Publications LLC. Published by Elsevier Ltd. All rights reserved.

The tunnel lining structure images formed based on ground-penetrating radar reflection signals contain steel structures and disease shapes, which are not recognized correctly, slow and workloady when manual recognition is used. To this end, this paper proposes a YOLOv3-based target detection method for tunnel lining structure images. Firstly, a tunnel lining structure dataset including rebar and hollow is constructed from field detection data and data generated from forward simulation, and secondly, the YOLOv3 model performance evaluation index is determined. The experimental results show that the method proposed in this paper can successfully identify the rebar body and hollow defects in ground-penetrating radar images, and the identification speed is high, which can effectively reduce the labor intensity of staff.

This paper presents a new algorithm of the singular value decomposition (SVD) in the wavelet domain for ground penetrating radar (GPR) to remove direct coupled waves. In fact, direct coupled waves commonly disturb the reflecting waves from underground targets. Besides, the amplitude and energy of direct coupled waves are large, which reduces the resolution of the images to the targets and adversely affects the subsequent image interpretation work. The GPR signal is decomposed into several levels by Wavelet to obtain approximation components and detailed components of each level. The information of targets is contained in big eigenvalues of detail components, while the direct coupled waves are contained in small ones. Therefore, the SVD in the wavelet domain can reduce the misjudgment of effective signals and improve the signal to noise ratio (SNR) of GPR signals. The simulated and field GPR data show that the SVD in the wavelet domain denoising method has better results for direct coupled wave removal than the traditional methods, which validates the effectiveness of the proposed denoising method.

Recently, the prevalence of diseases such as myopia, hunchback and lumbar herniation caused by the long-term bad sitting posture of young people is increasing. For a healthy life, we propose a human sitting posture detection model based on the human skeleton key point detection algorithm, AlphaPose, and the integrated learning method Stacking. The model extracts frames at intervals from the video stream obtained by the camera, and uses the AlphaPose algorithm to detect the extracted images to obtain the corresponding human skeleton key point coordinate information in the image. A high-dimensional sitting posture feature vector is constructed. finally, a two-layer Stacking classification model is applied to identify various bad sitting postures. In our experiment, a human sitting pose dataset of scale 6913 is constructed, and the proposed ensemble classification model is trained and tested on this dataset. The experimental results show that the average classification accuracy of the method for 9 sitting postures reaches 98.55%, which is higher than some state-of-art methods, and the implementation cost is low, which has high value in practical application.

In public transport, the OD (origin-destination) matrix of each passenger's starting and ending points is of great importance for bus scheduling and network optimization. The difficulty is matching passengers getting off at the back door with those getting on at the front door in real-time. The leading technology used here is re-identification. The current re-identification method is mainly used in the security field of pedestrian re-identification, vehicle filling discrimination, and although the accuracy is high, the real-time performance is poor. In this paper, the classical method FastReID is improved by changing its attention module using GCNet to improve accuracy and by adding a fully connected layer to the BNNeck structure to classify other essential attributes of pedestrians while calculating the distance between the same passenger attributes; in the loss function, constraints are applied according to the distance between this attributes to reduce the intra-class distance. Finally, passenger features are stored in categories by attributes to improve the final retrieval efficiency. Experiments show that the method in this paper improves accuracy by 3.2 % and detection speed by 14.73% (69.58ms) per 100 retrievals compared to FastReID.

The subspace clustering methods for motion segmentation are widely used in the field of computer vision. However, the existing methods ignore the low-rank property of motion trajectory with nonlinear structure and are sensitive to non-Gaussian noise. To this end, we seek to improve the performance of motion segmentation by effectively modeling some important characteristics of the motion trajectories, such as nonlinear structure and contained non-Gaussian noise. Specifically, we propose to use kernel function to model motion trajectory, design a variant of the correntropy-induced metric to measure noise, and integrate the block diagonal regularizer into the kernel subspace clustering to strengthen the block diagonal structure of the learned affinity matrix. More importantly, we propose a unified rank-constrained block diagonal subspace clustering method for motion segmentation, which can handle not only rigid body motion segmentation, but also non-rigid motion segmentation. And we further extend this method to deal with various noises in motion data, such as missing trajectories, corrupted trajectory and outlying trajectory. An effective algorithm HQ& AM, which is integrated by Half-quadratic theory and alternating minimization, is designed to optimize these models. Experimental results on several commonly used motion datasets indicate the effectualness and robustness of our methods.

When ground-penetrating radar detects complex and diverse geological structures in the subsurface, the returned detection signals are easily affected by various types of environmental noise, which brings serious interference to the interpretation targets. This paper proposes a ground-penetrating radar data processing method based on the combination of empirical modal decomposition of complementary sets and permutation entropy, which can decompose the ground-penetrating radar data into several IMF components and determine the separation threshold between the target signal and the noise signal through the calculation of the permutation entropy of these components, so as to achieve the effect of noise removal, and the effectiveness of the method is demonstrated through relevant experiments.

This research aims to develop an optimization model for optimizing demand-responsive transit (DRT) services. These services can not only direct passengers to reach their nearest bus stops but also transport them to connecting stops on major transit systems at selected bus stops. The proposed methodology is characterized by service time windows and selected metro schedules when passengers place a personalized travel order. In addition, synchronous transfers between shuttles and feeder buses were fully considered regarding transit problems. Aiming at optimizing the total travel time of passengers, a mixed-integer linear programming model was established, which includes vehicle ride time from pickup locations to drop-off locations and passenger wait time during transfer travels. Since this model is commonly known as an NP-hard problem, a new two-stage heuristic using the ant colony algorithm (ACO) was developed in this study to efficiently achieve the meta-optimal solution of the model within a reasonable time. Furthermore, a case study in Chongqing, China, shows that compared with conventional models, the developed model was more efficient formaking passenger, route and operation plans, and it could reduce the total travel time of passengers.

This paper conducts the research on a Chinese character recognition framework based on the multi-dimensional image information. Since the font data storage structure is designed by the user and is not in the directly addressable space of the code, it is necessary to call the external memory access interface function. Therefore, it is designed as a callback function to copy the font to the buffe. Hence, the pattern analysis model is then integrated. Therefore, the method of fuzzy mathematics is proposed and studied here, that is, the method of fuzzy pattern recognition is used to select the best expression of spatial entities. Furthermore, the novel image recognition algorithm is designed for the scenario of the Chinese character recognition. Through the experimental result, the performance is validated.

Radar and AIS (Automatic Identification System, AIS) are two types of safety navigational aids that ships must be equipped with. Due to the complementarity and redundancy of the data collected by the two, the fusion method of AIS and radar information can be used to improve the accuracy of the track. In view of the fact that the distributed Kalman filtering method that has emerged in recent years fails to consider the impact of ship speed changes on the fusion accuracy, this paper puts forward a ship track fusion model based on distributed extended Kalman filter. The correctness and effectiveness of the proposed method are illustrated through the analysis of evaluation indicators and experiments.

Waxy maize starch (WMS) was modified using sequential α-amylase and transglucosidase to create enzyme-treated waxy maize starch (EWMS) with higher branching degree and lower viscosity as an ideal healing agent. Self-healing properties of retrograded starch films with microcapsules containing WMS (WMC) and EWMS (EWMC) were investigated. The results indicated that EWMS-16 had the maximum branching degree of 21.88 % after transglucosidase treatment time of 16 h, and A chain of 12.89 %, B1 chain of 60.76 %, B2 chain of 18.82 % and B3 chain of 7.52 %. The particle sizes of EWMC ranged from 2.754 to 5.754 μm. The embedding rate of EWMC was 50.08 %. Compared to retrograded starch films with WMC, water vapor transmission coefficients of retrograded starch films with EWMC were lower, while tensile strength and elongation at break values of retrograded starch films were almost similar. Retrograded starch films with EWMC had higher healing efficiency of 58.33 % as compared to that Retrograded starch films retrograded starch films with WMC was 44.65 %.