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In this paper, we investigate positive solutions of boundary value problems for a general second-order nonlinear difference equation, which includes a Jacobi operator and a parameter λ. Based on the critical point theory, we obtain the existence of three solutions for the boundary value problem. Then, we establish a strong maximum principle for this problem and obtain some determined open intervals of the parameter λ for the existence of at least two positive solutions. In the end, we give two examples to illustrate our main results.

The implementation of China’s stringent farmland protection policy has resulted in the compulsory removal of trees from farmland, which has significantly impacted farmers’ willingness to transfer their land. To explain the impact of cutting trees on farmers’ land transfer, this study conducted a two-way fixed-effect difference-in-differences (DID) model based on a survey of 163 households in Daxiapo Village in China during 2020–2023. The results show that cutting trees significantly promotes farmland transfer among farmers. Moreover, the promotion effect of cutting trees is more pronounced when transferring land in than it is when transferring land out. For land plots with poor accessibility and fertility, the effects of transferring land out are more significant than for plots that are fertile and conveniently located. As a result, the prohibition of forestation on farmland has increased the contiguity of some land plots and improved the spatial configuration of farmland, but land fragmentation is still prominent on land owned by large-scale farmers. Therefore, this paper proposes two optimization scenarios to address the issue of farmland fragmentation and compares the feasibility of these plans in the short and long term. This paper suggests that short-term policies prohibiting forestation can trigger abrupt changes in farmland transfer patterns and drive further changes in the spatial configuration of farmland. Though some large-scale farming households were established through land transfer, the problem of plot fragmentation must be solved. This article presents several possible scenarios to aid in the design of more systematic policy systems to balance the protection of cultivated land, farmers’ willingness, and the spatial contiguity of cultivated land.

Rainstorms and flooding are among the most common natural disasters, which have a number of impacts on the transport system. This reality highlights the importance of understanding resilience—the ability of a system to resist disruptions and quickly recover to operational status after damage. However, current resilience assessments often overlook transport network functions and lack dynamic spatiotemporal analysis, posing challenges for comprehensive disaster impact evaluations. This study proposes an SR-PR-FR comprehensive resilience evaluation model from three dimensions: structure resilience (SR), performance resilience (PR), and function resilience (FR). Moreover, a simulation model based on Geographic Information System (GIS) and Simulation of Urban MObility (SUMO) is developed to analyze the dynamic spatial–temporal effects of a rainstorm on traffic during Xi’an’s evening rush hour. The results reveal that the southwest part of Xi’an is most prone to being congested and slower to recover, while downtown flooding is the deepest, severely affecting emergency services’ efficiency. In addition, the road network resilience returns to 70% of the normal values only before the morning rush the next day. These research results are presented across both temporal and spatial dimensions, which can help managers propose more targeted recommendations for strengthening urban risk management.

This paper presents a rapidly exploring random tree (RRT) algorithm with an effective post waypoint shift, which is suitable for the path planning of a wheeled mobile robot under kinematic constraints. In the growth of the exploring tree, the nearest node that satisfies the kinematic constraints is selected as the parent node. Once the distance between the new node and the target is within a certain threshold, the tree growth stops and a target connection based on minimum turning radius arc is proposed to generate an initial complete random path. The most significant difference from traditional RRT-based methods is that the proposed method optimizes the path based on Dubins curves through a post waypoint shift after a random path is generated, rather than through parent node selection and rewiring during the exploring tree growth. Then, it is proved that the method can obtain an optimal path in terms of the shortest length. The optimized path has good convergence and almost does not depend on the state of the initial random path. The comparative test results show that the proposed method has significant advantages over traditional RRT-based methods in terms of the sampling point number, the tree node number, and the path node number. Subsequently, an efficient method is further proposed to avoid unknown obstacles, which utilizes the original path information and thus effectively improves the new path planning efficiency. Simulations and real-world tests are carried out to demonstrate the effectiveness of this method.

This study was conducted to investigate the impact of EGCG on antioxidant stress, immune response, and intestinal microbiota flora in post-weaning rabbits. A total of 144 40 d Ira rabbits (equally divided by sex), were randomly allocated to six treatments. with five groups receiving doses of 200, 400, 600, 800, and 1000 mg/kg of EGCG, while one group served as a control without EGCG. Over 48 days, this study the assessed growth performance, antioxidant capacity, immune system, intestinal morphology, and cecal microbiota in the rabbits. The results showed that EGCG did not affect growth performance; however, significant linear and quadratic correlations were observed between the MDA, T-AOC, and GSH-Px activities in the liver and jejunum (p < 0.05). Quadratic effects were observed for the spleen and thymus indexes and serum IgG levels with increasing EGCG dosages (p < 0.05). Additionally, positive linear and quadratic effects were found on the ileal villus height and the villus height/crypt depth ratio. The relative abundances of Euryarchaeota, Patescibacteria, and Synergistota were significantly enriched in rabbits fed with high dosages (600–1000 mg/kg) of EGCG. Conclusively, the addition of large doses of EGCG (400–800 mg/kg) can effectively suppress oxidative stress and alleviate weaning stress, thereby contributing to the protection of post-weaning rabbits.

The Model for Prediction Across Scales-Atmosphere (MPAS) was used to simulate meteorological conditions for a two-week winter episode during 10–23 January 2013, and a two-week summer episode during 18–31 July 2016, using both as a global model and a regional model with a focus on California. The results of both global and regional applications of MPAS were compared against the surface and upper air rawinsonde observations while the variations of characteristic meteorological variables and modeling errors were evaluated in space, time, and statistical sense. The results of the Advanced Weather Research and Forecast (WRF-ARW, hereafter WRF) model simulations for the same episodes were also used to evaluate the results of both applications of MPAS. The temporal analyses performed at surface stations indicate that both global and regional applications of MPAS and WRF model predict the diurnal evolution of characteristic meteorological parameters reasonably well in both winter and summer episodes studied here. The average diurnal bias in predicting 2 m temperature by MPAS and WRF are about the same with a maximum of 2 °C in winter and 1 °C in summer while that of 2 m mixing ratio is within 1 g/kg for all three modeling applications. The rawinsonde profiles of temperature, dew point temperature, and wind direction agree reasonably well with observations while wind speed is underestimated by all three applications. The comparisons of the spatial distribution of anomaly correlation and mean bias errors calculated from each model results for 2 m temperature, 2 m water vapor mixing ratio, 10 m wind speed and wind direction indicate that all three models have similar magnitudes of agreement with observations as well as errors away from observations throughout California.

An investigation is undertaken to explore a sudden quasi-linear precipitation and gale event that transpired in the afternoon of 30 May 2024 over Beijing. It was situated at the southwestern periphery of a double-center low-vortex system, where a moisture-rich belt efficiently channeled abundant warm, humid air northward from the south. The interplay between dynamical lifting, convergent airflow-induced uplift, and the amplifying effects of the northern mountainous terrain’s topography creates favorable conditions that support the development and persistence of quasi-linear convective precipitation, accompanied by gale-force winds at the surface. The study also analyzes the impacts of five microphysics schemes (Lin, WSM6, Goddard, Morrison, and WDM6) employed in a weather research and forecasting (WRF) numerical model, with which the simulated rainfall and radar reflectivity are compared against ground-based rain gauge network and weather radar observations, respectively. Simulations with the five microphysics schemes demonstrate commendable skills in replicating the macroscopic quasi-linear pattern of the event. Among the schemes assessed, the WSM6 scheme exhibits its superior agreement with radar observations. The Morrison scheme demonstrates superior performance in predicting cumulative rainfall. Nevertheless, five microphysics schemes exhibit limitations in predicting the rainfall amount, the rainfall duration, and the rainfall area, with a discernible lag of approximately 30 min in predicting precipitation onset, indicating a tendency to forecast peak rainfall events slightly posterior to their true occurrence. Furthermore, substantial disparities emerge in the simulation of the vertical distribution of hydrometeors, underscoring the intricacies of microphysical processes.

The radial distribution of material feeding onto a screen surface is an important factor affecting vibration screening performance, and it is also the main basis for the optimization of the operating parameters of a vibration screening system. In this paper, based on near-infrared properties, a real-time measurement method for the mass flow rate of grain vibration feeding was proposed. A laser emitter and a silicon photocell were used as the measuring components, and the corresponding signal processing circuit mainly composed of a T-type I/V convertor, a voltage follower, a low-pass filter, and a setting circuit in series was designed. Calibration test results showed that the relationship between grain mass flow rate and output voltage could be described using the Gaussian regression model, and the coefficient of determination was greater than 0.98. According to the working principle of the grain cleaning system of combine harvesters, the dynamic characteristics of grain vibration feeding were analyzed using discrete element method (DEM) simulations, and the monitoring range of the sensor was determined. Finally, grain mass flow rate measurement tests were carried out on a vibration feeding test rig. The results indicated that the grain mass measurement error could be controlled within 5.0% with the average grain mass flow rate in the range of 3.0–5.0 g/mm·s. The proposed measurement method has potential application value in the uniform feeding control systems of vibration feeders.

The first version operational Hurricane Analysis and Forecast System (HAFS) implemented the Vortex Initialization (VI) technique to optimize tropical cyclone structure and intensity, which was adopted from the Hurricane Weather Research and Forecasting system (HWRF) and does not initialize cloud hydrometeors and vertical velocity. This limitation in the VI caused the inconsistency issue between hurricane vortex and its cloud in the model initial condition. A new VI, which can relocate or cycle cloud hydrometeors and vertical velocity, has been developed to solve this issue. For the cold start, the VI simply relocates the cloud and vertical velocity fields of Global Forecasting System (GFS) analysis; for the warm start, the cloud and vertical velocity associated with a hurricane in the GFS analysis are replaced by the fields extracted from the 6 h HAFS forecast of a previous cycle. This new VI has been tested for the 2023 HAFS-A real-time experiment configuration, and another sensitivity experiment without relocating or cycling both cloud and vertical velocity is conducted to examine the effect of the new VI. A comparison of the results reveals that the new VI improves the intensity forecast and generates a very realistic initial cloud field in correct position. Validating the model initial conditions with observed radar data reveals that the new VI captures the secondary eyewall of major hurricanes and asymmetric convective structure of weak tropical storms. This improvement of the cloud field in the model initial condition through the new VI expects to provide a better background for further data assimilation. Additional sensitivity experiment that only relocates or cycles cloud hydrometeors without correcting the vertical velocity field results in poorer intensity forecasts, which highlights the importance of vertical velocity in the model initial condition.

Numerous researchers have endeavored to amalgamate critical transit-oriented development (TOD) indicators, such as development density, walkability, and diversity, into a single TOD index to assess TOD performance. However, implementing TOD in megacities necessitates a more comprehensive selection of indicators, an objective calculation methodology, and accessible calculation data for the TOD index. This study introduces a method based on multi-indicator TOD performance assessment using multi-source urban big data; it uses Shanghai as a case study to evaluate and analyze the impact of site characteristics on performance. The method constructs the Comprehensive Socio-Economic Development Index (CSEDI) based on four indicators of TOD site operations. It establishes a multivariate regression model utilizing principal component analysis to extract 22 leading component indicators as independent variables from 71 indicators associated with TOD. Within the sample space of 77 rail transit TOD sites in Shanghai, the CSEDI exhibited a robust correlation with the independent variables. The evaluation results of the case study demonstrate consistency with the development characteristics of the city and the sites, indicating that the evaluation method can guide the renovation of existing sites and the development of new sites.

As the editor of the Special Issue on “Exploring IoT Sensors and Their Applications: Advancements, Challenges, and Opportunities in Smart Environments”, I am delighted to present this collection of groundbreaking research that addresses the emerging needs and challenges in the field of IoT sensors and smart environments [...]

This paper investigates the role of chaotic analysis and deep learning models in combustion instability predictions. To detect the precursors of impending thermoacoustic instability (TAI) in a swirled combustor with various fuel injection strategies, a data-driven framework is proposed in this study. Based on chaotic analysis, a recurrence matrix derived from combustion system is used in deep learning models, which are able to detect precursors of TAI. More specifically, the ResNet-18 network model is trained to predict the proximity of unstable operation conditions when the combustion system is still stable. The proposed framework achieved state-of-the-art 91.06% accuracy in prediction performance. The framework has potential for practical applications to avoid an unstable operation domain in active combustion control systems and, thus, can offer on-line information on the margin of the combustion instability.

Monitoring the long-term changes in antibody and cellular immunity following Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) infection is crucial for understanding immune mechanisms that prevent reinfection. In March 2023, we recruited 167 participants from the Changning District, Shanghai, China. A subset of 66 participants that were infected between November 2022 and January 2023 was selected for longitudinal follow-up. The study aimed to investigate the dynamics of the immune response, including neutralizing antibodies (NAbs), anti-spike (S)-immunoglobulin G (IgG), anti-S-IgM, and lymphocyte profiles, by analyzing peripheral blood samples collected three to seven months post infection. A gradual decrease in NAbs and IgG levels were observed from three to seven months post infection. No significant differences in NAbs and IgG titers were found across various demographics, including age, sex, occupation, and symptomatic presentation, across five follow-up assessments. Additionally, a strong correlation between NAbs and IgG levels was identified. Lymphocyte profiles showed a slight change at five months but had returned to baseline levels by seven months post infection. Notably, healthcare workers exhibited lower B-cell levels compared to police officers. Our study demonstrated that the immune response to SARS-CoV-2 infection persisted for at least seven months. Similar patterns in the dynamics of antibody responses and cellular immunity were observed throughout this period.

In China, private-owned cooperatives are becoming increasingly involved in agricultural production. In order to find the key characteristics of smallholders’ social networks after the appearance of cooperatives and better organize different farmland operators, this study completed a field survey of 114 smallholders who adopted farmland trusteeship service of a private-owned cooperative in China and applied the social network analysis to reveal the following results. (1) Compared to the theoretical ideal value, smallholders’ social networks showed low network density, efficiency, and little relevancy. (2) In the social network of mechanical-sharing, neighbor, kinship, and labor-sharing relationships, some isolated nodes existed, but no isolated nodes are found in the synthetic network. (3) The mechanical-sharing relationship among smallholders was stronger than the other relationships. (4) Machinery owners, farmers whose plots are on the geometric center and experienced older farmers showed higher centralities in the network, but village cadres did not. (5) The centralities and QAP correlation coefficients among different networks inside the cooperative were lower than that inside a single village. As a result, this paper confirmed that the ability of cooperatives to organize farmers’ social networks is not ideal. Farmers’ trust of farmland to a cross-village cooperatives does not help them to form a larger social network than their villages. In the future, the answer to the question of “who will farm the land” will still lie with the professional farmers and highly autonomous cooperatives.

In this paper, complexes of soluble dietary fiber (SDF) and polyphenols (PPs) isolated from lotus roots were prepared (SDF-PPs), as well as physical mixtures (SDF&PPs), which were given to high-fat-diet (HFD)-fed mice. The results demonstrated that SDF-PPs improve lipid levels and reverse liver injury in hyperlipidemic mice. Western blotting and real-time quantitative Polymerase Chain Reaction (RT-qPCR) results showed that SDF-PPs regulated liver lipids by increasing the phosphorylation of Adenine monophosphate activated protein kinase (AMPK), up-regulating the expression of Carnitine palmitoyltransferase1 (CPT1), and down-regulating the expression of Fatty acid synthase (FAS) and 3-hydroxy-3-methyl glutaryl coenzyme A (HMG-CoA), as well as the transcription factor sterol-regulatory element binding protein (SPEBP-1) and its downstream liposynthesis genes. Additionally, the intervention of SDF-PPs could modulate the composition of intestinal gut microbes, inducing an increase in Lachnospiraceae and a decrease in Desulfovibrionaceae and Prevotellaceae in high-fat-diet-fed mice. Thus, the research provides a theoretical basis for the application of lotus root active ingredients in functional foods and ingredients.

In financial and statistical computations, calculating expectations often requires evaluating integrals with respect to a Gaussian measure. Monte Carlo methods are widely used for this purpose due to their dimension-independent convergence rate. Quasi-Monte Carlo is the deterministic analogue of Monte Carlo and has the potential to substantially enhance the convergence rate. Importance sampling is a widely used variance reduction technique. However, research into the specific impact of importance sampling on the integrand, as well as the conditions for convergence, is relatively scarce. In this study, we combine the randomly shifted lattice rule with importance sampling. We prove that, for unbounded functions, randomly shifted lattice rules combined with a suitably chosen importance density can achieve convergence as quickly as O(N−1+ϵ), given N samples for arbitrary ϵ values under certain conditions. We also prove that the conditions of convergence for Laplace importance sampling are stricter than those for optimal drift importance sampling. Furthermore, using a generalized linear mixed model and Randleman–Bartter model, we provide the conditions under which functions utilizing Laplace importance sampling achieve convergence rates of nearly O(N−1+ϵ) for arbitrary ϵ values.

Automated evaluation of all glomeruli throughout the whole kidney is essential for the comprehensive study of kidney function as well as understanding the mechanisms of kidney disease and development. The emerging large-volume microscopic optical imaging techniques allow for the acquisition of mouse whole-kidney 3D datasets at a high resolution. However, fast and accurate analysis of massive imaging data remains a challenge. Here, we propose a deep learning-based segmentation method called FastCellpose to efficiently segment all glomeruli in whole mouse kidneys. Our framework is based on Cellpose, with comprehensive optimization in network architecture and the mask reconstruction process. By means of visual and quantitative analysis, we demonstrate that FastCellpose can achieve superior segmentation performance compared to other state-of-the-art cellular segmentation methods, and the processing speed was 12-fold higher than before. Based on this high-performance framework, we quantitatively analyzed the development changes of mouse glomeruli from birth to maturity, which is promising in terms of providing new insights for research on kidney development and function.

Selenium polysaccharides have attracted significant interest due to their superior function to that of individual polysaccharides. However, limited research has compared the protective effects of different selenium polysaccharides from different selenization methods on diabetes. This work aims to compare the preventive effects of natural selenium-enriched green tea polysaccharides (NSe-TPS), synthetic selenized green tea polysaccharides (PCSe-TPS), and a mixture of sodium selenite and green tea polysaccharides (ordinary tea polysaccharides (Ord-TPS)+Se) on the development of diabetes. While establishing a diabetes model induced by a high-sugar, high-fat diet combined with streptozotocin, different selenium polysaccharides were administered daily by gavage for nine weeks. Our findings indicate that PCSe-TPS exhibited superior preventive effects on developing type 2 diabetes compared to NSe-TPS and Ord-TPS+Se. PCSe-TPS effectively regulated glucose metabolism and insulin resistance by activating the PI3K/Akt pathway, thereby preventing elevated blood glucose levels. Additionally, PCSe-TPS mitigated oxidative damage and inflammatory responses in liver tissues. Notably, PCSe-TPS intervention reversed the decline in bacterial species richness and the abundance of unclassified_Oscillospiraceae during the development of diabetes in mice. These results provide valuable insights into the protective effects of PCSe-TPS against diabetes development, highlighting its advantages over NSe-TPS and Ord-TPS+Se.

Accurate indoor occupancy information extraction plays a crucial role in building energy conservation. Vision-based methods are popularly used for occupancy information extraction because of their high accuracy. However, previous vision-based methods either only provide 2D occupancy information or require expensive equipment. In this paper, we propose a cost-effective indoor occupancy information extraction system that estimates occupant positions and trajectories in 3D using a single RGB camera. The proposed system provides an inverse proportional model to estimate the distance between a human head and the camera according to pixel-heights of human heads, eliminating the dependence on expensive depth sensors. The 3D position coordinates of human heads are calculated based on the above model. The proposed system also associates the 3D position coordinates of human heads with human tracking results by assigning the 3D coordinates of human heads to the corresponding human IDs from a tracking module, obtaining the 3D trajectory of each person. Experimental results demonstrate that the proposed system successfully calculates accurate 3D positions and trajectories of indoor occupants with only one surveillance camera. In conclusion, the proposed system is a low-cost and high-accuracy indoor occupancy information extraction system that has high potential in reducing building energy consumption.

This study has designed and developed a smart data glove based on five-channel flexible capacitive stretch sensors and a six-axis inertial measurement unit (IMU) to recognize 25 static hand gestures and ten dynamic hand gestures for amphibious communication. The five-channel flexible capacitive sensors are fabricated on a glove to capture finger motion data in order to recognize static hand gestures and integrated with six-axis IMU data to recognize dynamic gestures. This study also proposes a novel amphibious hierarchical gesture recognition (AHGR) model. This model can adaptively switch between large complex and lightweight gesture recognition models based on environmental changes to ensure gesture recognition accuracy and effectiveness. The large complex model is based on the proposed SqueezeNet-BiLSTM algorithm, specially designed for the land environment, which will use all the sensory data captured from the smart data glove to recognize dynamic gestures, achieving a recognition accuracy of 98.21%. The lightweight stochastic singular value decomposition (SVD)-optimized spectral clustering gesture recognition algorithm for underwater environments that will perform direct inference on the glove-end side can reach an accuracy of 98.35%. This study also proposes a domain separation network (DSN)-based gesture recognition transfer model that ensures a 94% recognition accuracy for new users and new glove devices.

Analyzing the influencing factors of PM2.5 concentration, scenario simulations, and countermeasure research to address the problem of PM2.5 pollution in Guangdong Province is of great significance for governments at all levels for formulating relevant policies. In this study, the ChinaHighPM2.5 dataset and economic and social statistics for Guangdong Province from 2010 to 2019 were selected, and a PM2.5 pollution management compliance path formulation method based on the multi-scenario simulation was proposed by combining the differences in city types and PM2.5 concentration prediction. Based on the prediction model of PM2.5 concentration constructed by the Ridge and SVM models and facing the PM2.5 pollution control target in 2025, the urban PM2.5 pollution control scenario considering the characteristics of urban development was constructed. According to the scenario simulation results of the PM2.5 prediction model, the PM2.5 pollution control path suitable for Guangdong Province during the 14th Five-Year Plan period was explored. The coupling coordination model was used to explore the spatial and temporal pattern evolution of PM2.5 pollution collaborative governance in various prefecture-level cities under the standard path, and the policy recommendations for PM2.5 pollution control during the 14th Five-Year Plan period are proposed. The results showed the following: ① in the case of small samples, the model can provide effective simulation predictions for the study of urban pollutant management compliance pathways. ② Under the scenario of PM2.5 management meeting the standard, in 2025, the annual average mass concentration of PM2.5 in all prefecture-level cities in Guangdong Province will be lower than 22 μg/m3, and the annual average concentration of PM2.5 in the whole province will drop from 25.91 μg/m3 to 21.04 μg/m3, which will fulfil the goal of reducing the annual average concentration of PM2.5 in the whole province to below 22 μg/m3, as set out in the 14th Five-Year Plan for the Ecological Environmental Protection of Guangdong Province. ③ Under the path of PM2.5 control and attainment, the regional coordination relationship among prefecture-level cities in Guangdong Province is gradually optimized, the number of intermediate-level coordinated cities will increase, and the overall spatial distribution pattern will be low in the middle and high in the surrounding area. Based on the characteristics of the four city types, it is recommended that a staggered development strategy be implemented to achieve synergy between economic development and environmental quality. Urban type I should focus on restructuring freight transportation to reduce urban pollutant emissions. City type II should focus on urban transportation and greening. For city type III, the focus should be on optimizing the industrial structure, adjusting the freight structure, and increasing the greening rate of the city. For city type IV, industrial upgrading, energy efficiency, freight structure, and management of industrial pollutant emissions should be strengthened.

The paper aims to study a discrete boundary value problem of the Kirchhoff type based on the critical point theory and the strong maximum principle. Compared to the existing literature, the existence and multiplicity of positive solutions to the problem are considered according to the behavior of the nonlinear term f in some points between the zero and positive infinity, which is a new attempt. Under different assumptions of the nonlinear term f, we obtain the determined open intervals of the parameter λ, such that the problem has at least three positive solutions or at least two positive solutions in different intervals. In the end, two concrete examples are used to illustrate our main conclusions.

In recent years, high-fidelity three-dimensional (3D) oracle bone models (3D-OBMs) have received extensive attention from oracle bone experts due to their unparalleled reducibility to real oracle bone. In the research process of 3D-OBMs, the first procedure is to extract oracle bone inscriptions (OBIs) from the model to form individual oracle bone characters (OBCs). However, the manual extraction of OBIs is a time-consuming and labor-intensive task that relies heavily on oracle bone knowledge. To address these problems, we propose a texture-mapping-based OBI extractor (tm-OBIE), which leverages the symmetrical characteristics of the texture mapping process and is able to extract 3D-OBIs from 3D-OBMs saved as a wavefront file. The OBIs in the texture file were first located using a trained 2D object detector. After that, the 3D mesh area, where the OBIs are located, was obtained using an inverse texture mapping method. Thirdly, a specific 2D plane was fitted using the centroid of triangular faces in the flat regions of the mesh via a singular value decomposition (SVD) method. Finally, by measuring the distances between the triangle meshes and the fitted plane, the meshes of the 3D-OBIs were obtained. This paper verifies the feasibility of this method via experiments and analyzes the possibility of using the algorithm framework for extracting other ancient characters from their corresponding 3D models.

The high-quality development of new agricultural business entities (NABEs) is an important driving force for realizing rural revitalization and accelerating the modernization of agriculture and rural areas. The main purpose of the study is to investigate the spatial distribution pattern, aggregation scales, development mechanism, and internal differences of various types of NABEs in different regions. It provides targeted ideas for alleviating regional differences in the development of NABEs in different agricultural regions. Kernel density estimation, nearest neighbor distance analysis, Tyson’s polygon coefficient of variation, and Ripley’s K function are used to study the spatial and temporal evolution, spatial aggregation, and scale divergence of various types of NABEs, and Pearson correlation analysis is incorporated to explore the specific factors affecting the development of various types of NABEs. The study results: First, family farms are the most widely distributed, and agricultural enterprises are the most sparsely distributed, being distributed “more in the southeast and less in the northwest” in all three categories. Second, the strongest aggregation scales of different NABEs are increasing, and the strongest aggregation scales of agricultural enterprises are larger than those of family farms and cooperatives in all agricultural areas. Third, the development of specialized farmers’ cooperatives (abbreviated as ‘cooperatives’) is more constrained by traditional agricultural inputs and is a kind of agricultural input-oriented development. Family farms are more constrained by the living standards of rural residents in the region and are a kind of rural economy-oriented development. Agricultural enterprises are more subject to the economic level of the region, which is a kind of market economy-oriented development. Finally, in the process of developing NABEs, regional differences should be emphasized, and a small number of agriculturally leading enterprises and model cooperatives should drive a large number of small-scale family farms and smallholder farmers in order to become a characteristic path for China’s agricultural development.

As the world’s largest developing country, China first proposed the construction of a Beautiful China initiative in 2012, with the aim of exploring Chinese solutions for sustainable regional development. The construction of a beautiful China is based on the guiding ideology of the Five-sphere Integrated Plan in China, that is, the overall plan for building socialism with Chinese characteristics, including economic construction, political construction, cultural construction, social construction and ecological civilization construction. This paper aims to understand the coupling relationship, as well as the spatial and temporal changes, between China’s sustainable development under the Beautiful China initiative and tourism. Using data from the China Statistical Yearbook database, we constructed an evaluation index system to measure both the construction of beautiful China and tourism development using a literature review, statistical analysis, the entropy method and GIS-based spatial analysis methods. Furthermore, using the 31 Chinese provinces as the research subject, we further analyzed the state of Beautiful China construction and tourism development, as well as their coupling relationships of the two systems. Our results show that firstly, the economic “hard power” plays the most prominent role in the process of building a beautiful China under the sustainable development regime, while the status of cultural “soft power” has also been well reflected. Secondly, the weight ranking of tourism evaluation indicators and the spatial distribution of tourism development levels both reflect the central and fundamental role of tourism market demand in tourism development. Third, the weight ranking of tourism evaluation indicators ranks the highest in the mean value of the coupling coordination degree of society, ecology and tourism in the Beautiful China subsystem, which reflects the harmony between society and ecology and the significant livelihood function of tourism as a happiness industry in the new era. Fourth, the spatial and temporal relationship between the coupled and coordinated development of the Beautiful China and tourism systems varies, indicating that there is a regional imbalance in China’s sustainable development. This further indicates the need to adapt to local conditions, and to build on strengths and avoid weaknesses to achieve regional sustainable development. The study highlights China’s contribution to global sustainable development. It also provides theoretical and practical guidance for the promotion of the coordinated development of both Beautiful China and tourism.

The loss rate is an important index to evaluate the harvesting performance of white radish. To reduce the loss rate, it is necessary to analyze the pulling dynamic characteristics of white radish and then optimize the structure and operating parameters of the harvesting device. In this paper, according to the growth characteristics of white radish in the field, the discrete element method (DEM) was used to simulate the pulling process. The pulling force was calculated using the Edinburgh elasto-plastic adhesion model (EEPA), and the effects of soil bed compactness, pulling speed and angle on the pulling force were analyzed. The tests on pulling mechanics were carried out in the laboratory to verify the accuracy of DEM simulation results. The results showed that in the soft soil bed with compactness less than 2.8 MPa, the pulling force of radish is generally smaller than the leaf breaking force, and it is feasible to pull the radish out directly. While in a soil bed with high compactness, it is necessary to install a loosening shovel to reduce the pulling force thus reducing the loss rate due to leaf breakage. The structure and operating parameters of the harvesting device were designed according to the pulling dynamic characteristics, and the white radish harvesting tests were carried out in different fields. Statistical results show that when the soil compaction was increased from 1.47 MPa to 2.21 MPa, the average loss rate increased from 0.68% to 1.75%, and the average damage rate increased from 2.41% to 2.70%. Similarly, when the forward speed was increased from 0.18 to 0.47 m/s, the average loss rate increased from 1.08% to 1.30%, and the average damage rate increased from 2.34% to 2.74%. Overall, the maximum loss rate and the maximum damage rate could be controlled below 2.0% and 3.0%, respectively. In the hard soil bed, the loss rate can be effectively reduced from 15% to 2.5% by installing a loosening shovel.

An airborne anemometer, which monitors wind on the basis of Meteorological Multi-rotor UAVs (Unmanned Aerial Vehicles), is important for the prevention of catastrophe. However, its performance will be affected by the self-excited air turbulence generated by UAV rotors. In this paper, for the purpose of the correction of an error, we developed a method for the elimination of the influence of air turbulence on wind speed measurement. The corresponding correction model is obtained according to the CFD (Computational Fluid Dynamics) simulation of a six-rotor UAV which is carried out with the sliding grid method and the S-A turbulence model. Then, the model is applied to the developed prototype by adding the angle of attack compensation model of the airborne anemometer. It is shown by the actual application that the airborne anemometer can maintain the original measurement accuracy at different ascent speeds.

Osteoarthritis (OA) is the most common degenerative bone and joint disease that can lead to disability and severely affect the quality of life of patients. However, its etiology and pathogenesis remain unclear. It is currently believed that articular cartilage lesions are an important marker of the onset and development of osteoarthritis. Long noncoding RNAs (lncRNAs) are a class of multifunctional regulatory RNAs that are involved in various physiological functions. There are many differentially expressed lncRNAs between osteoarthritic and normal cartilage tissues that play multiple roles in the pathogenesis of OA. Here, we reviewed lncRNAs that have been reported to play regulatory roles in the pathological changes associated with osteoarthritic cartilage and their potential as biomarkers and a therapeutic target in OA to further elucidate the pathogenesis of OA and provide insights for the diagnosis and treatment of OA.

The causes of sore throat are complex. It can be caused by diseases of the pharynx, adjacent organs of the pharynx, or even systemic diseases. Therefore, a lack of medical knowledge and experience may cause misdiagnoses or missed diagnoses in sore throat diagnoses, especially for general practitioners in primary hospitals. This study aims to develop a computer-aided diagnostic system to assist clinicians in the differential diagnoses of sore throat. The computer-aided system is developed based on the Dynamic Uncertain Causality Graph (DUCG) theory. We cooperated with medical specialists to establish a sore throat DUCG model as the diagnostic knowledge base. The construction of the model integrates epidemiological data, knowledge, and clinical experience of medical specialists. The chain reasoning algorithm of the DUCG is used for the differential diagnoses of sore throat. The system can diagnose 27 sore throat-related diseases. The model builder initially tests it with 81 cases, and all cases are correctly diagnosed. Then the system is verified by the third-party hospital, and the diagnostic accuracy is 98%. Now, the system has been applied in hundreds of primary hospitals in Jiaozhou City, China, and the degree of recognition for doctors to the diagnostic results of the system is more than 99.9%. It is feasible to use DUCG for the differential diagnoses of sore throat, which can assist primary doctors in clinical diagnoses and the diagnostic results are acceptable to clinicians.

The screen surface particle distribution is an important factor affecting screening performance. A vibrating screen with an adjustable horizontal attitude angle was used, the non-uniform feeding and horizontal attitude angles were used as variables and the screening of rice particles was simulated by the discrete element method. The screen surface distribution and movement speed of the rice particles were analyzed based on the influence of the variables on screening performance. The results indicated that the material distribution became more unbalanced with the increase in non-uniform feeding, and the particles’ speed increased with the increase in attitude angle on the y-axis. The particles experienced accelerated dispersion, which improved the unbalanced distribution of the particles and screening performance. According to the loss rate, the horizontal attitude angle adjustment model was established and optimized under non-uniform feeding. The reliability of the model was verified by simulation. A bench test was carried out to verify the simulation. The optimization model can reduce the loss rate, improve the screening performance of non-uniform feeding, and provide a reference for the material screening of non-uniform feeding.

Neurocognitive impairment (NCI) associated with HIV infection of the brain impacts a large proportion of people with HIV (PWH) regardless of antiretroviral therapy (ART). While the number of PWH and severe NCI has dropped considerably with the introduction of ART, the sole use of ART is not sufficient to prevent or arrest NCI in many PWH. As the HIV field continues to investigate cure strategies, adjunctive therapies are greatly needed. HIV imaging, cerebrospinal fluid, and pathological studies point to the presence of continual inflammation, and the presence of HIV RNA, DNA, and proteins in the brain despite ART. Clinical trials exploring potential adjunctive therapeutics for the treatment of HIV NCI over the last few decades have had limited success. Ideally, future research and development of novel compounds need to address both the HIV replication and neuroinflammation associated with HIV infection in the brain. Brain mononuclear phagocytes (MPs) are the primary instigators of inflammation and HIV protein expression; therefore, adjunctive treatments that act on MPs, such as immunomodulating agents, look promising. In this review, we will highlight recent developments of innovative therapies and discuss future approaches for HIV NCI treatment.

β2 integrins are transmembrane receptors that exist widely in human immune cells and participate in pathological processes such as chronic inflammation, thrombosis, and malignant tumor formation. They mainly mediate intercellular adhesion, coordinate the ingestion of extracellular matrix components, and regulate cytoskeleton formation, thereby regulating cell signaling. Osteoarthritis (OA) is a chronic joint disease that causes joint pain and increases disease burden; it has a high prevalence among populations worldwide. Previous studies have reported that β2 integrins are overexpressed in OA and may play an essential role in the occurrence of OA. The important roles of β2 integrins in the maturation and differentiation of osteoclasts, the regulation of bone homeostasis, and the polarization and migration of macrophages have also been reported. The present review aims to highlight the role of β2 integrins in OA pathogenesis and outline their potential for serving as therapeutic targets.

As a country with a vast area and complex terrain, the differentiation between paddy field and dryland under different topographic gradients in China is difficult. Based on a land-use grid data set with an accuracy of 1 km, this study applied the Topographic Potential Index and used land-use transition matrices and landscape analysis to compare the change in dryland and paddy field in China from 1990 to 2020 at different elevations, slopes, and slope aspects. The results indicate that paddy field and dryland were mostly distributed in areas with better photothermal conditions. However, in recent years, the paddy field and dryland on the “sunny” slope decreased. Specifically, the area of paddy field and dryland on the southeast, south, and southwest slopes decreased, while they increased on the northwest, north, and northeast slopes. From 1990 to 2020, land conversion among paddy field, dryland, and other land use was mostly concentrated in the third ladder (

In this paper, we study the existence and multiplicity of solutions for the discrete Dirichlet boundary value problem of the Kirchhoff type, which has a symmetric structure. By using the critical point theory, we establish the existence of infinitely many solutions under appropriate assumptions on the nonlinear term. Moreover, we obtain the existence of infinitely many positive solutions via the strong maximum principle. Finally, we take two examples to verify our results.

Rejoining oracle bone fragments plays an import role in studying the history and culture of the Shang dynasty by its characters. However, current computer vision technology has a low accuracy in judging whether the texture of oracle bone fragment image pairs can be put back together. When rejoining fragment images, the coordinate sequence and texture features of edge pixels from original and target fragment images form a continuous symmetrical structure, so we put forward an internal similarity network (ISN) to rejoin the fragment image automatically. Firstly, an edge equidistant matching (EEM) algorithm was given to search similar coordinate sequences of edge segment pairs on the fragment image contours and to locally match the edge coordinate sequence of an oracle bone fragment image. Then, a target mask-based method was designed in order to put two images into a whole and to cut a local region image by the local matching edge. Next, we calculated a convolution feature gradient map (CFGM) of the local region image texture, and an internal similarity pooling (ISP) layer was proposed to compute the internal similarity of the convolution feature gradient map. Finally, ISN was contributed in order to evaluate a similarity score of a local region image texture and to determine whether two fragment images are a coherent whole. The experiments show that the correct judgement probability of ISN is higher than 90% in actual rejoining work and that our method searched 37 pairs of correctly rejoined oracle bone fragment images that have not been discovered by archaeologists.

Respiration monitoring is vital for human health assessment. Humidity sensing is a promising way to establish a relationship between human respiration and electrical signal. This paper presents a polyimide-based film bulk acoustic resonator (PI-FBAR) humidity sensor operating in resonant frequency and reflection coefficient S11 dual-parameter with high sensitivity and stability, and it is applied in real-time human respiration monitoring for the first time. Both these two parameters can be used to sense different breathing conditions, such as normal breathing and deep breathing, and breathing with different rates such as normal breathing, slow breathing, apnea, and fast breathing. Experimental results also indicate that the proposed humidity sensor has potential applications in predicting the fitness of individual and in the medical field for detecting body fluids loss and daily water intake warning. The respiratory rates measured by our proposed PI-FBAR humidity sensor operating in frequency mode and S11 mode have Pearson correlation of up to 0.975 and 0.982 with that measured by the clinical monitor, respectively. Bland–Altman method analysis results further revealed that both S11 and frequency response are in good agreement with clinical monitor. The proposed sensor combines the advantages of non-invasiveness, high sensitivity and high stability, and it has great potential in human health monitoring.

This study presents an industrial fault diagnosis system based on the cubic dynamic uncertain causality graph (cubic DUCG) used to model and diagnose industrial systems without sufficient data for model training. The system is developed based on cloud native technology. It contains two main parts, the diagnostic knowledge base and the inference method. The knowledge base was built by domain experts modularly based on professional knowledge. It represented the causality between events in the target industrial system in a visual and graphical form. During the inference, the cubic DUCG algorithm could dynamically generate the cubic causal graph according to the real-time data and perform the logic and probability calculations based on the generated cubic DUCG models, visually displaying the dynamic causal evolution of faults. To verify the system’s feasibility, we rebuild a fault-diagnosis model of the secondary circuit system of No. 1 at the Ningde nuclear power plant based on the new system. Twenty-four fault cases were used to test the diagnostic accuracy of the system, and all faults were correctly diagnosed. The results showed that it was feasible to use the cubic DUCG platform for fault diagnosis.

The classification of unmanned aerial vehicle hyperspectral images is of great significance in agricultural monitoring. This paper studied a fine classification method for crops based on feature transform combined with random forest (RF). Aiming at the problem of a large number of spectra and a large amount of calculation, three feature transform methods for dimensionality reduction, minimum noise fraction (MNF), independent component analysis (ICA), and principal component analysis (PCA), were studied. Then, RF was used to finely classify a variety of crops in hyperspectral images. The results showed: (1) The MNF–RF combination was the best ideal classification combination in this study. The best classification accuracies of the MNF–RF random sample set in the Longkou and Honghu areas were 97.18% and 80.43%, respectively; compared with the original image, the RF classification accuracy was improved by 6.43% and 8.81%, respectively. (2) For this study, the overall classification accuracy of RF in the two regions was positively correlated with the number of random sample points. (3) The image after feature transform was less affected by the number of sample points than the original image. The MNF transform curve of the overall RF classification accuracy in the two regions varied with the number of random sample points but was the smoothest and least affected by the number of sample points, followed by the PCA transform and ICA transform curves. The overall classification accuracies of MNF–RF in the Longkou and Honghu areas did not exceed 0.50% and 3.25%, respectively, with the fluctuation of the number of sample points. This research can provide reference for the fine classification of crops based on UAV-borne hyperspectral images.

The mean curvature problem is an important class of problems in mathematics and physics. We consider the existence of homoclinic solutions to a discrete partial mean curvature problem, which is tied to the existence of discrete solitons. Under the assumptions that the potential function is unbounded and that the nonlinear term is superlinear at infinity, we obtain the existence of infinitely many homoclinic solutions to this problem by means of the fountain theorem in the critical point theory. In the end, an example is given to illustrate the applicability of our results.

Objective: Pulmonary function parameters play a pivotal role in the assessment of respiratory diseases. However, the accuracy of the existing methods for the prediction of pulmonary function parameters is low. This study proposes a combination algorithm to improve the accuracy of pulmonary function parameter prediction. Methods: We first established a system to collect volumetric capnography and then processed the data with a combination algorithm to predict pulmonary function parameters. The algorithm consists of three main parts: a medical feature regression structure consisting of support vector machines (SVM) and extreme gradient boosting (XGBoost) algorithms, a sequence feature regression structure consisting of one-dimensional convolutional neural network (1D-CNN), and an error correction structure using improved K-nearest neighbor (KNN) algorithm. Results: The root mean square error (RMSE) of the pulmonary function parameters predicted by the combination algorithm was less than 0.39L and the R2 was found to be greater than 0.85 through a ten-fold cross-validation experiment. Conclusion: Compared with the existing methods for predicting pulmonary function parameters, the present algorithm can achieve a higher accuracy rate. At the same time, this algorithm uses specific processing structures for different features, and the interpretability of the algorithm is ensured while mining the feature depth information.

Pulse wave analysis (PWA) has been widely used in the medical field. A novel multi-channel sensor is employed in arterial pulse acquisition and brings richer physiological information to PWA. However, the noise of this sensor is distributed in the main frequency band of the pulse signal, which seriously interferes with subsequent analyses and is difficult to eliminate by existing methods. This study proposes a cross-channel dynamic weighting robust principal component analysis algorithm. A channel-scaled factor technique is used to manipulate the weighting factors in the nuclear norm. This factor can adaptively adjust the weights among the channels according to the signal pattern of each channel, optimizing the feature extraction in multi-channel signals. A series of performance evaluations were conducted, and four well-known de-noising algorithms were used for comparison. The results reveal that the proposed algorithm achieved one of the best de-noising performances in the time and frequency domains. The mean of h1 in the amplitude relative error (ARE) was 23.4% smaller than for the WRPCA algorithm. Moreover, our algorithm could accelerate convergence and reduce the computational time complexity by approximately 34.6%. These results demonstrate the performance and efficiency of the algorithm. Meanwhile, the idea can be extended to other multi-channel physiological signal de-noising and feature extraction fields.

Plum fruit and kernels offer bioactive material for industrial production. The promising procedure for distinguishing plum kernel cultivars used in this study comprised two stages: image analysis to compute the texture parameters of plum kernels belonging to three cultivars ‘Emper’, ‘Kalipso’, and ‘Polinka’, and discriminant analysis using machine learning algorithms to classify plum kernel cultivars based on selected textures with the highest discriminative power. The discriminative models built separately for sets of textures selected from all color channels L, a, b, R, G, B, U, V, S, X, Y, Z, color space Lab and color channel b using the KStar (Lazy), PART (Rules), and LMT (Trees) classifiers provided the highest average accuracies reaching 98% in the case of the color space Lab and the KStar classifier. In this case, individual cultivars were discriminated with the accuracies of 97% for ‘Emper’ and ‘Kalipso’ to 99% for ‘Polinka’. The values of other performance metrics were also satisfactory, higher than 0.95. The ROC curves were quite smooth and steady with the most satisfactory curve for the ‘Kalipso’ kernels. The present study sheds light on an objective, non-destructive, and inexpensive procedure for cultivar discrimination of plum kernels.

Leaf microorganism communities play significant roles in the process of plant growth, but the microbiome profiling of crop leaves is still a relatively new research area. Here, we used 16S rDNA sequencing to profile the microbiomes of 78 primary dried tobacco leaf samples from 26 locations in eight Chinese provinces. Our analyses revealed that the national leaf microbial communities contain 4473 operational taxonomic units (OTU) representing 1234 species, but there is a small, national core microbiome with only 14 OTU representing nine species. The function of this core microbiome is related to processes including nitrogen fixation, detoxification of diverse pollutants, and heavy-metal reduction. The leaf microorganism communities are obviously affected by local environments but did not exhibit obvious relationships to single ecological factors (e.g., temperature, precipitation). Our findings enhance the understanding of microbial diversity of tobacco leaves, which could be utilized for a variety of bioprocess, agricultural, and environmental detoxification applications.

The HAFS model is an effort under the NGGPS and UFS initiatives to create the next generation of hurricane prediction and analysis system based on FV3-GFS. It has been validated extensively using traditional verification indicators such as tracker error and biases, intensity error and biases, and the radii of gale, damaging and hurricane strength winds. While satellite images have been used to verify hurricane model forecasts, they have not been used on HAFS. The community radiative transfer model CRTM is used to generate model synthetic satellite images from HAFS model forecast state variables. The 24 forecast snapshots in the mature stage of hurricane Dorian in 2019 are used to generate a composite model synthetic GOES-R infrared brightness image. The composite synthetic image is compared to the corresponding composite image generated from the observed GOES-R data, to evaluate the model forecast TC vortex intensity, size, and asymmetric structure. Results show that the HAFS forecast TC Dorian agrees reasonably well with the observation, but the forecast intensity is weaker, its overall vortex size smaller, and the radii of its eye and maximum winds larger than the observed. The evaluation results can be used to further improve the model. While these results are consistent with those obtained by traditional verification methods, evaluations based on composite satellite images provide an additional benefit with richer information because they have near-real-times spatially and temporally continuous high-resolution data with global coverage. Composite satellite infrared images could be used routinely to supplement traditional verification methods in the HAFS and other hurricane model evaluations. Note since this study only evaluated one hurricane, the above conclusions are only applicable to the model behavior of the mature stage of hurricane Dorian in 2019, and caution is needed to extend these conclusions to expect model biases in predicting other TCs. Nevertheless, the consistency between the evaluation using composite satellite images and the traditional metrics, of hurricane Dorian, shows that this method has the potential to be applied to other storms in future studies.

Single-resonator-based (SRB) sensors have thrived in many sensing applications. However, they cannot meet the high-sensitivity requirement of future high-end markets such as ultra-small mass sensors and ultra-low accelerometers, and are vulnerable to environmental influences. It is fortunate that the integration of dual or multiple resonators into a sensor has become an effective way to solve such issues. Studies have shown that dual-resonator-based (DRB) and multiple-resonator-based (MRB) MEMS sensors have the ability to reject environmental influences, and their sensitivity is tens or hundreds of times that of SRB sensors. Hence, it is worth understanding the state-of-the-art technology behind DRB and MRB MEMS sensors to promote their application in future high-end markets.

The novel coronavirus pneumonia (COVID-19) pandemic is a great threat to human society and now is still spreading. Although several vaccines have been authorized for emergency use, only one recombinant subunit vaccine has been permitted for widespread use. More subunit vaccines for COVID-19 should be developed in the future. The receptor binding domain (RBD), located at the S protein of SARS-CoV-2, contains most of the neutralizing epitopes. However, the immunogenicity of RBD monomers is not strong enough. In this study, we fused the RBD-monomer with a modified Fc fragment of human IgG1 to form an RBD-Fc fusion protein. The recombinant vaccine candidate based on the RBD-Fc protein could induce high levels of IgG and neutralizing antibody in mice, and these could last for at least three months. The secretion of IFN-γ, IL-2 and IL-10 in the RBD-stimulated splenocytes of immunized mice also increased significantly. Our results first showed that the RBD-Fc vaccine could induce both humoral and cellular immune responses and might be an optional strategy to control COVID-19.

The performance of voice-controlled systems is usually influenced by accented speech. To make these systems more robust, frontend accent recognition (AR) technologies have received increased attention in recent years. As accent is a high-level abstract feature that has a profound relationship with language knowledge, AR is more challenging than other language-agnostic audio classification tasks. In this paper, we use an auxiliary automatic speech recognition (ASR) task to extract language-related phonetic features. Furthermore, we propose a hybrid structure that incorporates the embeddings of both a fixed acoustic model and a trainable acoustic model, making the language-related acoustic feature more robust. We conduct several experiments on the AESRC dataset. The results demonstrate that our approach can obtain an 8.02% relative improvement compared with the Transformer baseline, showing the merits of the proposed method.

Anti-inflammatory cytokine interleukin (IL)-10 is pivotal for limiting excessive inflammation in the central nervous system. Reports show that lipopolysaccharide (LPS)-induced microglial IL-10 emerges in a delayed manner in vitro and in vivo, lagging behind proinflammatory cytokines to facilitate the resolution of neuroinflammation. We hypothesized that IL-10 releases quite quickly based on our pilot investigation. Here, we uncovered a bimodal expression of microglial IL-10 gene transcription induced by LPS in mouse primary mixed glial cultures. This pattern consisted of a short brief early-phase and a long-lived late-phase, enabling the production of IL-10 protein in a rapid manner. The removal and addition of IL-10 protein assays indicated that early-released IL-10 exerted potent modulatory effects on neuroinflammation at picomolar levels, and IL-10 released at the onset of neuroinflammation is tightly controlled. We further showed that the early-released, but not the late-released, IL-10 was crucial for mediating and potentiating the anti-inflammatory function of a β2-adrenergic receptor agonist salmeterol. This study in vitro highlights the essential role of early-released IL-10 in regulating the appropriate degree of neuroinflammation, overturning the previous notion that microglial IL-10 produces and functions in a delayed manner and providing new insights into anti-inflammatory mechanisms-mediated neuroimmune homeostasis.

The specific recognition of T cell receptors (TCR) and peptides presented by human leukocyte antigens (pHLAs) is the core step for T cell triggering to execute anti-tumor activity. However, TCR assembly and soluble expression are challenging, which precludes the broad use of TCR in tumor therapy. Herein, we used heterodimeric Fc to assist in the correct assembly of TCRs to achieve the stable and soluble expression of several TCRs in mammalian cells, and the soluble TCRs enable us to yield novel bispecific T cell engagers (TCR/aCD3) through pairing them with an anti-CD3 antibody. The NY-ESO-1/LAGE-1 targeted TCR/aCD3 (NY-TCR/aCD3) that we generated can redirect naïve T cells to specific lysis antigen-positive tumor cells, but the potency of the NY-TCR/aCD3 was disappointing. Furthermore, we found that the activation of T cells by NY-TCR/aCD3 was mild and unabiding, and the activity of NY-TCR/aCD3 could be significantly improved when we replaced naïve T cells with pre-activated T cells. Therefore, we employed the robust T cell activation ability of staphylococcal enterotoxin C2 (SEC2) to optimize the activity of NY-TCR/aCD3. Moreover, we found that the secretions of SEC2-activated T cells can promote HLA-I expression and thus increase target levels, which may further contribute to improving the activity of NY-TCR/aCD3. Our study described novel strategies for soluble TCR expression, and the optimization of the generation and potency of TCR/aCD3 provided a representative for us to fully exploit TCRs for the precision targeting of cancers.