Effect of Baicalin on inflammatory mediator levels and microcirculation disturbance in rats with severe acute pancreatitis

Objective: To investigate the effect of Bacailin on inflammatory mediator levels and microcirculation disturbance in severe acute pancreatitis (SAP) rats and explore its therapeutic mechanism on this disease. Methods: SAP model rats were randomly divided into model control group and Baicalin treated group, 45 rats in each group. The same number of normal rats were included in sham-operated group. These groups were further subdivided into 3 h, 6 h and 12 h subgroups, respectively (15 rats in each subgroup). At 3, 6 and 12 hours after operation, rats were killed to conduct the following experiments: (1) to examine the mortality rates of rats, the ascites volume and pancreatic pathological changes in each group; (2) to determine the contents of amylase, PLA~2~, TXB~2~, PGE~2~, PAF and IL-1[beta]; in blood as well as the changes in blood viscosity.Results: (1) Compared to model control group, treatment with Baicalin is able to improve the pathological damage of the pancreas, lower the contents of amylase and multiple inflammatory mediators in blood, decrease the amount of ascitic fluid and reduce the mortality rates of SAP rats; (2) at 3 hours after operation, the low-shear whole blood viscosity in Baicalin treated group was significantly lower than that in model control group;at 12 hours after operation, both the high-shear and low-shear whole blood viscosity in Baicalin treated group were also significantly lower than those in model control group.Conclusion: Baicalin, as a new drug, has good prospects in the treatment of SAP since it can exert therapeutic effects on this disease through inhibiting the production of inflammatory mediators, lowering blood viscosity, improving microcirculation and mitigating the pathological damage of the pancreas

A facial depression recognition method based on hybrid multi-head cross attention network

IntroductionDeep-learn methods based on convolutional neural networks (CNNs) have demonstrated impressive performance in depression analysis. Nevertheless, some critical challenges need to be resolved in these methods: (1) It is still difficult for CNNs to learn long-range inductive biases in the low-level feature extraction of different facial regions because of the spatial locality. (2) It is difficult for a model with only a single attention head to concentrate on various parts of the face simultaneously, leading to less sensitivity to other important facial regions associated with depression. In the case of facial depression recognition, many of the clues come from a few areas of the face simultaneously, e.g., the mouth and eyes.MethodsTo address these issues, we present an end-to-end integrated framework called Hybrid Multi-head Cross Attention Network (HMHN), which includes two stages. The first stage consists of the Grid-Wise Attention block (GWA) and Deep Feature Fusion block (DFF) for the low-level visual depression feature learning. In the second stage, we obtain the global representation by encoding high-order interactions among local features with Multi-head Cross Attention block (MAB) and Attention Fusion block (AFB).ResultsWe experimented on AVEC2013 and AVEC2014 depression datasets. The results of AVEC 2013 (RMSE = 7.38, MAE = 6.05) and AVEC 2014 (RMSE = 7.60, MAE = 6.01) demonstrated the efficacy of our method and outperformed most of the state-of-the-art video-based depression recognition approaches.DiscussionWe proposed a deep learning hybrid model for depression recognition by capturing the higher-order interactions between the depression features of multiple facial regions, which can effectively reduce the error in depression recognition and gives great potential for clinical experiments

Feature Analysis of Scanning Point Cloud of Structure and Research on Hole Repair Technology Considering Space-Ground Multi-Source 3D Data Acquisition

As one of the best means of obtaining the geometry information of special shaped structures, point cloud data acquisition can be achieved by laser scanning or photogrammetry. However, there are some differences in the quantity, quality, and information type of point clouds obtained by different methods when collecting point clouds of the same structure, due to differences in sensor mechanisms and collection paths. Thus, this study aimed to combine the complementary advantages of multi-source point cloud data and provide the high-quality basic data required for structure measurement and modeling. Specifically, low-altitude photogrammetry technologies such as hand-held laser scanners (HLS), terrestrial laser scanners (TLS), and unmanned aerial systems (UAS) were adopted to collect point cloud data of the same special-shaped structure in different paths. The advantages and disadvantages of different point cloud acquisition methods of special-shaped structures were analyzed from the perspective of the point cloud acquisition mechanism of different sensors, point cloud data integrity, and single-point geometric characteristics of the point cloud. Additionally, a point cloud void repair technology based on the TLS point cloud was proposed according to the analysis results. Under the premise of unifying the spatial position relationship of the three point clouds, the M3C2 distance algorithm was performed to extract the point clouds with significant spatial position differences in the same area of the structure from the three point clouds. Meanwhile, the single-point geometric feature differences of the multi-source point cloud in the area with the same neighborhood radius was calculated. With the kernel density distribution of the feature difference, the feature points filtered from the HLS point cloud and the TLS point cloud were fused to enrich the number of feature points in the TLS point cloud. In addition, the TLS point cloud voids were located by raster projection, and the point clouds within the void range were extracted, or the closest points were retrieved from the other two heterologous point clouds, to repair the top surface and façade voids of the TLS point cloud. Finally, high-quality basic point cloud data of the special-shaped structure were generated

Landslide Susceptibility Mapping with Integrated SBAS-InSAR Technique: A Case Study of Dongchuan District, Yunnan (China)

Landslide susceptibility maps (LSM) are often used by government departments to carry out land use management and planning, which supports decision makers in urban and infrastructure planning. The accuracy of conventional landslide susceptibility maps is often affected by classification errors. Consequently, they become less reliable, which makes it difficult to meet the needs of decision-makers. Therefore, it is proposed in this paper to reduce classification errors and improve LSM reliability by integrating the Small Baseline Subsets-Interferometric Synthetic Aperture Radar (SBAS-InSAR) technique and LSM. By using the logistic regression model (LR) and the support vector machine model (SVM), experiments were conducted to generate LSM in the Dongchuan district. It was classified into five classes: very high susceptibility, high susceptibility, medium susceptibility, low susceptibility, and very low susceptibility. Then, the surface deformation rate of the Dongchuan area was obtained through the ascending and descending orbit sentinel-1A data from January 2018 to January 2021. To correct the classification errors, the SBAS-InSAR technique was integrated into LSM under the optimal model by constructing the contingency matrix. Finally, the LSMs obtained before and after correction were compared. Moreover, the correction results were validated and analyzed by combining remote sensing images, InSAR deformation results, and field surveys. According to the research results, the susceptibility class of 66,094 classification error cells (59.48 km2) was significantly improved in the LSM after the integration of the SBAS-InSAR correction. The enhanced susceptibility classes and the spectral characteristics of remote sensing images are highly consistent with the trends of InSAR cumulative deformation and the results of field investigation. It is suggested that integrating SBAS-InSAR and LSM is effective in correcting classification errors and further improving the reliability of LSM for landslide prediction. The LSM obtained by using this method plays an important role in guiding local government departments on disaster prevention and mitigation, which is conducive to eliminating the risk of landslides

Research on Image Denoising Adaptive Algorithm for UAV Based on Visual Landing

UAV autonomous landing refers to the UAV lands on only depending on airborne navigation equipment and flight control system, and ultimately achieves a safe landing. To achieve self-landing, UAV must have the ability to self-navigation and positioning, so that the high-precision visual navigation positioning technology is the key to achieve UAV self-landing technology.This paper concentrating on the noise effects on the pictures obtained during visual landing process of UAV, it has introduced the gravity of classical physics to image pixel, come up with a mathematical expression for the strength of gravity between pixels, then conformed the adaptive window by the gravity between pixels and performed corresponding filtering processing. It is shown by the experimental results that this algorithm has a great improvement on image denoising and detail preserving when compared with the traditional median filtering and switching median filtering algorithms

Position Clustering for Polygon Object under Dual-constrains

It is a vital research direction for spatial clustering to recognize polygon cluster, but due to the dual-constrains by geometric information of polygons and obstacles, the position similarity of polygon is difficult to calculate accurately and quickly.A polygon clustering algorithm under dual-constrains is proposed by extending the algorithm of multi-scale spatial clustering, and constructing an intensity function to express position aggregation between object and its adjacent object. For further discuss, it takes the same thresholds of intensity function in adjacent scales as convergence condition. Simulated polygons and real data are chosen to perform clustering in experiments to verify the validity of our algorithm. Results show that without predefined parameters, this algorithm can identify variety polygon clusters with different densities, arbitrary shape, bridge and obstacle

Multi-scale Clustering of Points Synthetically Considering Lines and Polygons Distribution

Considering the complexity and discontinuity of spatial data distribution, a clustering algorithm of points was proposed. To accurately identify and express the spatial correlation among points,lines and polygons, a Voronoi diagram that is generated by all spatial features is introduced. According to the distribution characteristics of point's position, an area threshold used to control clustering granularity was calculated. Meanwhile, judging scale convergence by constant area threshold, the algorithm classifies spatial features based on multi-scale, with an <i>O</i>(<i>n</i> log <i>n</i>) running time.Results indicate that spatial scale converges self-adaptively according with distribution of points.Without the custom parameters, the algorithm capable to discover arbitrary shape clusters which be bound by lines and polygons, and is robust for outliers