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Metabolic diseases have gradually become one of the most significant global medical burdens. Diseases such as obesity, diabetes, and metabolic syndrome, along with their complications, are clinically categorized as metabolic diseases. Long-term oral medication significantly reduces patient compliance and quality of life. Therefore, alternative therapies that intervene at the cellular level or target the root causes of metabolic diseases might help change this predicament. Research has found that extracellular vesicles derived from adipose macrophages can effectively regulate metabolic diseases by influencing the disease’s development. This regulation is likely related to the role of these extracellular vesicles as important mediators in modulating adipose tissue function and insulin sensitivity, and their involvement in the crosstalk between adipocytes and macrophages. This review aims to describe the regulation of metabolic diseases mediated by adipose macrophage-derived extracellular vesicles, with a focus on their involvement in adipocyte crosstalk, the regulation of metabolism-related autoimmunity, and their potential as therapeutic agents for metabolic diseases, providing new avenues for diagnosis and treatment.

Visual features play a key role in indoor positioning and navigation services as the main semantic information to help people understand the environment. However, insufficient semantic constraint information and mismatching localization without building map have hindered the ubiquitous application services. To address the problem, we propose a smartphone indoor scene features recognition localization method with building map semantic constraints. First, based on Geographic Information System and Building Information Modeling techniques, a geocoded entity library of building Map Location Anchor (MLA) is constructed, which is able to provide users with “immersive” meta-building-map and semantic anchor constraints for mobile phone positioning when map matching. Second, using the MYOLOv5s deep learning model improved on indoor location scenario, the nine types of ubiquitous anchor features in building scenes are recognized in real time by acquiring video frames from the smartphone camera. Lastly, the spatial locations of the ubiquitous indoor facilities obtained using smartphone video recognition are matched with the MLA P3P algorithm to achieve real-time positioning and navigation. The experimental results show that the MLA recognition accuracy of the improved MYOLOv5s is 97.2%, and the maximum localization error is within the range of 0.775 m and confined to the interval of 0.5 m after applying the Building Information Modeling based Positioning and Navigation road network step node constraint, which can effectively achieve high positioning accuracy in the building indoor scenarios with adequate MLA and road network constraint.

Objective To investigate the risk factors of vasovagal reactions(VVR) related to plasma donation, so as to put forward clinical suggestions for early identification and accurate intervention of high-risk groups to ensure the safety of plasma donation. Methods The demographic characteristics(i.e. gender, age, weight) and records of plasma donors(donation history, pulse before plasma donation, duration of collection, etc.) were collected from July to December 2019 in a region of Sichuan. Based on logistic regression analysis, the correlation between these factors and the risk of VVR was explored. Results The information of 69 172 donors was collected, and the incidence of VVR was 7.04‰. The risk of VVR was reduced by 99% in the group with plasma collection duration less than 30 minutes compared with the group with plasma collection duration more than 50 minutes(OR, 0.01; 95% CI, 0.00~0.01; P

Abstract With a wide range of applications in autonomous driving and robotics, semantic segmentation for large‐scale outdoor point clouds is a critical and challenging issue. Due to the large number and irregular arrangement of point clouds, it is difficult to balance the efficiency and effectiveness. In this paper, we propose LessNet, a lightweight and efficient voxel‐based method for LiDAR‐only semantic segmentation, taking advantage of cylindrical partition and intra‐voxel feature fusion. Specifically, we use a cylindrical partition method to distribute the outdoor point clouds more evenly in voxels. To better encode the voxel features, we adopt an intra‐voxel aggregation method without querying neighbours. The voxel features are further input into a lightweight and effective 3D U‐net to aggregate local features and dilate the receptive field. Extensive experiments prove the satisfied semantic segmentation performance and the improvement of each component in our proposed framework. Our method is capable of processing more than one million point clouds at a time while retaining low latency and few parameters. Moreover, our method achieves comparable performance with state‐of‐the‐art approaches and outperforms all projection‐based methods on the SemanticKITTI benchmark.

Abstract Economists have used the mechanism of interjurisdictional competition to explain how decentralization affects the degree of property rights protection. This fails however to account for another significant question: In China, can the local decentralization from the provincial level to the prefectural (county) level be more effective in protecting the private property rights of investors, especially when goods and production factors cannot flow freely among regions? The social perspective of property rights can help answer this question. In this study, we find that within the governance structure of decentralization, the mechanism of vertical constraints is more significant than the mechanism of interjurisdictional competition in protecting the private property rights of investors. However, the effectiveness of the vertical constraints mechanism depends on the resistance costs of discontented investors. Decentralizing to the prefectural level, in comparison to the provincial level, lowers these resistance costs for investors while strengthening the mechanism of vertical constraints, thereby improving the degree of property rights protection for private investors.

In this study, we examined the folding processes of eight helical proteins (2I9M, TC5B, 1WN8, 1V4Z, 1HO2, 1HLL, 2KFE, and 1YYB) at room temperature using the explicit solvent model under the AMBER14SB force field with the accelerated molecular dynamics (AMD) and traditional molecular dynamics (MD), respectively. We analyzed and compared the simulation results obtained by these two methods based on several aspects, such as root mean square deviation (RMSD), native contacts, cluster analysis, folding snapshots, free energy landscape, and the evolution of the radius of gyration, which showed that these eight proteins were successfully and consistently folded into the corresponding native structures by AMD simulations carried out at room temperature. In addition, the folding occurred in the range of 40~180 ns after starting from the linear structures of the eight proteins at 300 K. By contrast, these stable folding structures were not found when the traditional molecular dynamics (MD) simulation was used. At the same time, the influence of high temperatures (350, 400, and 450 K) is also further investigated. Study found that the simulation efficiency of AMD is higher than that of MD simulations, regardless of the temperature. Of these temperatures, 300 K is the most suitable temperature for protein folding for all systems. To further investigate the efficiency of AMD, another trajectory was simulated for eight proteins with the same linear structure but different random seeds at 300 K. Both AMD trajectories reached the correct folded structures. Our result clearly shows that AMD simulation are a highly efficient and reliable method for the study of protein folding.

Accurate and fast indoor Location-Based Services (LBS) is very important for daily life and emergency response. Indoor map is the basis of indoor LBS. The model construction and data organization of indoor map are the key scientific problems that urgently need to be solved in the current indoor LBS application. In recent years, hybrid models have been used widely in the research of indoor map, because they can balance the limitations of single models. However, the current studies about hybrid model pay more attention to the model accuracy and modeling algorithm, while ignoring its relationship between positioning and navigation and its practicality in mobile indoor LBS applications. This paper addresses a new indoor map model, named Building Information Modeling based Positioning and Navigation (BIMPN), which is based on the entity model and the network model. The highlight of BIMPN is that it proposes a concept of Step Node (SN) to assist indoor positioning and navigation function. We developed the Mobile Indoor Positioning and Navigation System (MIPNS) to verify the practicability of BIMPN. Results indicate that the BIMPN can effectively organize the characteristics of indoor spaces and the building features, and assist indoor positioning and navigation. The BIMPN proposed in this paper can be used for the construction of indoor maps and it is suitable for mobile indoor positioning and navigation systems.

High spatial resolution remote sensing image (HSRRSI) data provide rich texture, geometric structure, and spatial distribution information for surface water bodies. The rich detail information provides better representation of the internal components of each object category and better reflects the relationships between adjacent objects. In this context, recognition methods such as geographic object-based image analysis (GEOBIA) have improved significantly. However, these methods focus mainly on bottom-up classifications from visual features to semantic categories, but ignore top-down feedback which can optimize recognition results. In recent years, deep learning has been applied in the field of remote sensing measurements because of its powerful feature extraction ability. A special convolutional neural network (CNN) based region proposal generation and object detection integrated framework has greatly improved the performance of object detection for HSRRSI, which provides a new method for water body recognition based on remote sensing data. This study uses the excellent “self-learning ability” of deep learning to construct a modified structure of the Mask R-CNN method which integrates bottom-up and top-down processes for water recognition. Compared with traditional methods, our method is completely data-driven without prior knowledge, and it can be regarded as a novel technical procedure for water body recognition in practical engineering application. Experimental results indicate that the method produces accurate recognition results for multi-source and multi-temporal water bodies, and can effectively avoid confusion with shadows and other ground features.

At present, the calculated binding free energy obtained using the molecular mechanics/Poisson-Boltzmann (Generalized-Born) surface area (MM/PB(GB)SA) method is overestimated due to the lack of knowledge of suitable interior dielectric constants in the simulation on the interaction of Human Immunodeficiency Virus (HIV-1) protease systems with inhibitors. Therefore, the impact of different values of the interior dielectric constant and the entropic contribution when using the MM/PB(GB)SA method to calculate the binding free energy was systemically evaluated. Our results show that the use of higher interior dielectric constants (1.4–2.0) can clearly improve the predictive accuracy of the MM/PBSA and MM/GBSA methods, and computational errors are significantly reduced by including the effects of electronic polarization and using a new highly efficient interaction entropy (IE) method to calculate the entropic contribution. The suitable range for the interior dielectric constant is 1.4–1.6 for the MM/PBSA method; within this range, the correlation coefficient fluctuates around 0.84, and the mean absolute error fluctuates around 2 kcal/mol. Similarly, an interior dielectric constant of 1.8–2.0 produces a correlation coefficient of approximately 0.76 when using the MM/GBSA method. In addition, the entropic contribution of each individual residue was further calculated using the IE method to predict hot-spot residues, and the detailed binding mechanisms underlying the interactions of the HIV-1 protease, its inhibitors, and bridging water molecules were investigated. In this study, the use of a higher interior dielectric constant and the IE method can improve the calculation accuracy of the HIV-1 system.

High pressure behavior of a 2D hybrid perovskite semiconductor, (BA)2PbBr4 (BA = benzylammonium), has been systematically studied via synchrotron powder X-ray diffraction over the range of 0-14.22 GPa. (BA)2PbBr4 shows a soft nature with a bulk modulus of 10(1) GPa and anisotropic linear compressibility with βa = 25(6), βb = 5.3(4), and βc = 15.1(8) TPa−1. Experiment-aided density functional theory calculations reveal that the structural changes of (BA)2PbBr4 under compression are attributed to the configurational alterations of BA+ organic cations and synergistic distortion of the (PbBr4)2− inorganic layer. Further extensive theoretical calculations of elastic tensors give full mapping of Young’s moduli, shear moduli, and Poisson’s ratios of (BA)2PbBr4, which confirm its anisotropic nature in elasticity. In addition, the photoluminescence of (BA)2PbBr4 exhibits a significant red-shift and an intensity decrease in response to hydrostatic stress which respectively arise from the contractions of Pb–Br bonds and distortions of PbBr6 octahedra in the inorganic layer mediated by the configurational alterations of BA+ cations upon compression.

The elastic properties of a zeolitic metal-azolate framework, Zn(mtz)2 (MAF-7, mtz− = 3-methyl-1,2,4-triazolate), have been examined from the view point of the first principles calculations and experiments. Our results demonstrate that the three independent elastic constants of MAF-7 are about 5.0–73.3% higher than those of ZIF-8, though they are isomorphic. The electron-donating effect of the nitrogen atom at the 2-position in mtz- ring dominantly accounts for such a prominent difference. The detailed analysis of the full elastic tensors reveals that the volume moduli, shear moduli, and Poisson’s ratios of MAF-7 are about 3.4% to 20.1%, 3.2% to 20.6%, and −30.3% to 12.3% higher than those of ZIF-8. The underlying structural reasons were discussed to explain the anisotropic difference of those properties. Moreover, the conclusion deduced from first-principle calculations was also been verified by nanoindentation and high-pressure synchrotron X-ray diffraction measurements.

Reversible addition fragmentation chain transfer (RAFT) synthesis and self-assembly of free-base porphyrin cored star polymers are reported. The polymerization, in the presence of a free-base porphyrin cored chain transfer agent (CTA-FBP), produced porphyrin star polymers with controlled molecular weights and narrow polydispersities for a number of monomers including N, N-dimethylacrylamide (DMA) and styrene (St). Well-defined amphiphilic star block copolymers, P-(PS-PDMA)4 and P-(PDMA-PS)4 (P: porphyrin), were also prepared and used for self-assembly studies. In methanol, a selective solvent for PDMA, spherical micelles were observed for both block copolymers as characterized by TEM. UV-vis studies suggested star-like micelles were formed from P-(PS-PDMA)4, while P-(PDMA-PS)4 aggregated into flower-like micelles. Spectrophotometric titrations indicated that the optical response of these two micelles to external ions was a function of micellar structures. These structure-related properties will be used for micelle studies and functional material development in the future.

Three methods to reduce the RCS of Fabry-Perot (FP) resonator antenna using diffuse scattering are verified and compared in this paper. They are 1 bit random coding, 2 bit random coding, and 2 bit random phase gradient coding method. In order to realize reflection phase coding, a receiver-transmitter type unit with adjustable reflection phase from top side is proposed. Metasurface (MS) composed of this unit is the best choice to achieve the RCS reduction of FP resonator antenna, because it has the ability to independently control the reflection phase on both sides. By changing the size of radiation patch, two units with 90 reflection phase difference and four units with 90 reflection phase difference from top side can be obtained. They are used to compose MSs with different reflection phase distributions. These MSs can form FP resonator antennas with RCS reduction characteristics. Subsequently, three antennas are fabricated and tested, and the test results are compared. The results show that the FP resonator antenna using 2-bit random phase gradient coding has the best performance. It achieves the wideband RCS reduction of antenna and has the least influence on radiation performance. The proposed antenna A3 achieves an average RCS reduction of 12 dB over the bandwidth range of 7.7-13.7 GHz while maintaining a peak gain of 18 dB and good radiation patterns. [ABSTRACT FROM AUTHOR]