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BackgroundThe efficacy and acupoint selection of acupuncture in treating chemotherapy-induced peripheral neuropathy (CIPN) remain controversial. This study aims to explore the specific efficacy and acupoint selection of acupuncture in treating CIPN through a meta-analysis and data mining.MethodsSearching for clinical trials on acupuncture treatment for CIPN in 8 databases, evaluating its efficacy and safety through a meta-analysis, and exploring its acupoint selection through data mining.ResultsThe meta-analysis included 21 studies and 2,121 patients, showing that compared with the control group, the acupuncture group could significantly improve neuropathic pain intensity (SMD = −0.66, 95% CI [−1.07, −0.25], p = 0.002), significantly reduce the NCI-CTCAE (MD = −0.29, 95%CI [−0.50, −0.08], p

[Objective] The spatiotemporal characteristics of soil erosion were analyzed in order to provide a reference for soil erosion control and evaluation of soil and water conservation benefits. [Methods] Based on the RUSLE soil erosion equation, the spatial-temporal evolution of soil erosion in the typical black soil region of Binxian County, Heilongjiang Province from 2000 to 2020 was analyzed with the support of RS and GIS. The effects of topographic factors and land use on soil erosion were also investigated. [Results] ① The average modulus of soil erosion in 2000, 2010, and 2020 was 893.02 t/(km2·yr), 499.84 t/(km2·yr), and 1 561.02 t/(km2·yr), respectively. The soil erosion intensity was mainly classified as slight and mild. ② The low intensity erosion was distributed across the entire region, and the high intensity erosion was mainly located in the southern mountainous area. ③ Erosion mainly occurred at elevations of 100 to 200 m. Soil erosion area was inversely proportional to slope. 0°—5° slope was the main erosion gradient zone. Soil erosion area in the direction of the north slope was larger than in the direction of the south slope; ④ The soil erosion modulus and the area of sloping farmland in the study area were larger. This area is the key area for soil erosion control in Binxian County, Heilongjiang Province. [Conclusion] From 2000 to 2020, the average soil erosion modulus initially increased and then decreased, showing a significant spatiotemporal differentiation. Topographic factors and land use change had significant driving effects on soil erosion in the study area. It can be used as a reference for soil erosion control and soil and water conservation benefit evaluation in black soil region.

To realize the safe operation of electric power site, this paper proposes an intelligent recognition technology to automatically identify violations. This study successfully constructs a face detection model for power operation sites by combining deep convolutional neural networks and target detection algorithms. A three-way connected feature pyramid structure containing a neuron self-processing module is adopted, and an accuracy test is completed using a Tri-FPN-based target detection network, significantly improving recognition accuracy. In this paper, we also utilized the on-site images collected by video surveillance equipment, combined with CNN algorithm and HOG feature extraction technology to effectively identify the violations and provide early warning of the breaches of the personnel at the power operation site. MAP curves evaluated the detection performance, and the results showed that the head recognition rate was up to 0.9913, and the accuracy rate of all violations exceeded 0.9350.The high accuracy of CNN-based feature fusion extraction algorithm in the recognition of violations of personnel at the site of electric power operation provides effective technical support to ensure personnel safety.

In the face of increasing demands for tasks such as mountain rescue, geological exploration, and military operations in complex wilderness environments, planning an efficient walking route is crucial. To address the inefficiency of traditional two-dimensional path planning, this paper proposes a two-stage path planning algorithm. First, an improved Probabilistic Roadmap (PRM) algorithm is used to quickly and roughly determine the initial path. Then, the morphological dilation is applied to process the grid points of the initial path, retaining the surrounding area of the initial path for a precise positioning of the search range. Finally, the idea of the A∗ algorithm is applied to achieve precise path planning in the refined search range. During the process of constructing the topology map, we utilized parallelization acceleration strategies to expedite the graph construction. In order to verify the effectiveness of the algorithm, we used terrain data to construct a wilderness environment model, and tests were conducted on off-road path planning tasks with different terrains and distances. The experimental results show a substantial enhancement in the computational efficiency of the proposed algorithm relative to the conventional A∗ algorithm by 30 to 60 times.

Melanosome (MS) is a special membrane organelle which synthesizes, stores and transports melanin in melanocytes (MC). The diversity of human skin color lies on the type, quantity, distribution and maturation of MS. The transport of MS in MC is the result of the interaction between three kinesins and two kinds of cytoskeletons, which are affected by many regulatory factors. A variety of pigmented diseases are strongly associated with MS transport malfunction. A deeper understanding of the mechanism for MS transport may improve the cognition of MS transport-related diseases and potentially provide a novel strategy for clinical treatment and pharmaceutical development.

Cross-view geo-localization aims to locate street-view images by matching them with a collection of GPS-tagged remote sensing (RS) images. Due to the significant viewpoint and appearance differences between street-view images and RS images, this task is highly challenging. While deep learning-based methods have shown their dominance in the cross-view geo-localization task, existing models have difficulties in extracting comprehensive meaningful features from both domains of images. This limitation results in not establishing accurate and robust dependencies between street-view images and the corresponding RS images. To address the aforementioned issues, this paper proposes a novel and lightweight neural network for cross-view geo-localization. Firstly, in order to capture more diverse information, we propose a module for extracting multi-scale features from images. Secondly, we introduce contrastive learning and design a contrastive loss to further enhance the robustness in extracting and aligning meaningful multi-scale features. Finally, we conduct comprehensive experiments on two open benchmarks. The experimental results have demonstrated the superiority of the proposed method over the state-of-the-art methods.

In order to study rural landscape resources and analyze landscape problems, the effect evaluation method of rural landscape design based on hierarchical analysis method and artificial intelligence technology is proposed. Taking a rural landscape as an example, the evaluation index system of rural landscape design effect is constructed through hierarchical analysis and the index weight is determined. BP neural network based on artificial intelligence technology is used to construct the rural landscape design effect evaluation model, and the rural landscape design effect is evaluated from three aspects: target layer, project layer and index layer. The results show that this method can effectively evaluate the design effect of rural landscape in the study area, in which the index layer is between 0.4 and 0.64, the comprehensive evaluation is excellent, the comprehensive evaluation score of the target layer is 0.60, the design effect is excellent, the bearing capacity and attraction are excellent, indicating that the research area is sufficient; the error trend is stable and the overall relative error is low, indicating that the method has high accuracy and ideal, and lays a foundation for the analysis and evaluation of rural landscape resources.

Objective: To compare the performance of cryogenic refrigeration system using LNG wasted cold and ammonia cooling. Methods: The thermodynamic and economic models of the two systems were established by MATLAB. Under the basic parameters, the working medium concentration suitable for LNG low-temperature refrigeration system was selected, and the cold storage temperature and refrigeration capacity were changed respectively. The thermal economy of the two systems was compared and analyzed. Results: With the increase of ethylene glycol concentration, LNG mass flow, system power consumption and cold storage capacity increased, COP decreased, but ethylene glycol mass flow remained unchanged; With the increase of cold storage temperature, the system COP increased, while the working medium mass flow and power consumption decrease. Similarly, with the increase of refrigeration capacity, the mass flow and power consumption increase, while the COP of ammonia low-temperature refrigeration system remained unchanged and the COP of LNG low-temperature refrigeration system increased. In the range of 5~20 kW, the COP of LNG low-temperature refrigeration system was lower than that of ammonia low-temperature refrigeration system, and the opposite was true in the range of 20~30 kW. The comparison of economic performance showed that the investment cost of LNG cryogenic refrigeration system was much lower than that of ammonia cryogenic refrigeration system. Conclusion: Under the same working conditions, the thermodynamic and economic performance of the low-temperature refrigeration system using LNG wasted cold is better than that of the traditional ammonia low-temperature refrigeration system.

Abstract Fast and selective fluorescence imaging for a biomarker to related‐disease diagnosis remains a significant challenge due to complex physical environment. Human carboxylesterase (CE) is expected to be a potential biomarker of hepatocellular carcinoma (HCC) to improve the accuracy of diagnosis. However, existing probes for CE has slow response rate and low selectivity. Herein, the amide group is selected as CE‐responsive sites based on the “substrate‐hydrolysis enzymatic reaction” approach. From a series of off–on probes with leave groups in the amide unit, probe JFast is screened with the optimal combination of rapid response rate and high selectivity toward CE. JFast requires only 150 s to reach the maximum fluorescence at 676 nm in the presence of CE and free from the interference of other esterase. Computational docking simulations indicate the shortest distance between the CE and active site of JFast. Cell and in vivo imaging present that the probe can turn on the liver cancer cells and tumor region precisely. Importantly, JFast is allowed to specifically image orthotopic liver tumor rather than metastatic tumor and distinguish human primary liver cancer tissue from adjacent ones. This study provides a new tool for CE detection and promotes advancements in accurate HCC diagnosis.

Objective To investigate the effect of miRNA-495 on the proliferation and apoptosis of acute myeloid leukemia (AML) cells with mixed lineage leukemia (MLL) gene rearrangement. Methods Ten patients diagnosed with AML with MLL-AF9 fusion gene positive (6 males, 4 females, mean age of 39.37±2.71 years), and 10 cases of AML (5 males, 5 females, mean age of 41.12±1.92 years) admitted in our hospital from August 2017 to May 2019 were enrolled in this study, and another 10 patients (6 males, 4 females, mean age of 42.31±3.21 years) who attended the surgical outpatient department during the same period were also collected as the control. THP-1 cells (containing MLL-AF9 fusion gene) were divided into miRNA-495 and control groups based on transfection with miRNA-495 or not, and the expression of miRNA-495 in each group was detected by RT-qPCR; CCK-8 assay was used to determine the proliferation of transfected THP-1 cells in each group; RT-qPCR and Western blotting were performed to measure the expression of proliferation-related factors (Ki-67 and PCNA) and apoptosis-related factors (Bcl-2, Bax and Caspase-3). The target genes of miRNA-495 were predicted by TargetScan and then verified by dual-luciferase reporter assay. Finally, RT-qPCR and Western blotting were adopted to detect the expression of serine/threonine/tyrosine interacting protein (STYX) in groups as follows: miRNA-495 group, control group, STYX group (transfected with STYX) and miRNA-495+STYX group (transfected with miRNA-495 and STYX). Results MiRNA-495 expression was down-regulated in AML patients with MLL-AF9 fusion gene, with relative expression of 0.25±0.08 (95%CI=-0.86~-0.65, P < 0.001); miRNA-495 inhibited cell proliferation (P < 0.001) and induced apoptosis of THP-1 cells, and it targetedly bound to STYX (P < 0.001). In addition, STYX overexpression reversed the effects of miRNA-495 on THP-1 cell proliferation and apoptosis. Conclusion MiRNA-495 regulates the proliferation and apoptosis of AML cells with MLL gene rearrangement by targeting STYX.

Road extraction from a remote sensing image is a research hotspot due to its broad range of applications. Despite recent advancements, achieving precise road extraction remains challenging. Since a road is thin and long, roadside objects and shadows cause occlusions, thus influencing the distinguishment of the road. Masked image modeling reconstructs masked areas from unmasked areas, which is similar to the process of inferring occluded roads from nonoccluded areas. Therefore, we believe that mask image modeling is beneficial for indicating occluded areas from other areas, thus alleviating the occlusion issue in remote sensing image road extraction. In this paper, we propose a remote sensing image road extraction network named RemainNet, which is based on mask image modeling. RemainNet consists of a backbone, image prediction module, and semantic prediction module. An image prediction module reconstructs a masked area RGB value from unmasked areas. Apart from reconstructing original remote sensing images, a semantic prediction module of RemainNet also extracts roads from masked images. Extensive experiments are carried out on the Massachusetts Roads dataset and DeepGlobe Road Extraction dataset; the proposed RemainNet improves 0.82–1.70% IoU compared with other state-of-the-art road extraction methods.

Spatio-temporal association analysis has attracted attention in various fields, such as urban computing and crime analysis. The proliferation of positioning technology and location-based services has facilitated the expansion of association analysis across spatio-temporal scales. However, existing methods inadequately consider the scale differences among spatio-temporal objects during analysis, leading to suboptimal precision in association analysis results. To remedy this issue, we propose a multiscale spatio-temporal object representation method, STO2Vec, for association analysis. This method comprises of two parts: graph construction and embedding. For graph construction, we introduce an adaptive hierarchical discretization method to distinguish the varying scales of local features. Then, we merge the embedding method for spatio-temporal objects with that for discrete units, establishing a heterogeneous graph. For embedding, to enhance embedding quality for homogeneous and heterogeneous data, we use biased sampling and unsupervised models to capture the association strengths between spatio-temporal objects. Empirical results using real-world open-source datasets show that STO2Vec outperforms other models, improving accuracy by 16.25% on average across diverse applications. Further case studies indicate STO2Vec effectively detects association relationships between spatio-temporal objects in a range of scenarios and is applicable to tasks such as moving object behavior pattern mining and trajectory semantic annotation.

Geographic meshing system is an essential technology in the digital earth framework and has essential applications in the integration and organization of heterogeneous spatial data, along with corresponding coding method. But current meshing and coding methods show unsatisfying locality and performance. To make an improvement, an adaptive Hilbert-Geohash meshing and coding method called AHG is proposed, which could represent both the location and the approximate size of the coded object directly by the meshing hierarchy and the corresponding coding length. This unique feature helps to accelerate the spatial range query and neighbor query. By simple string operations, many candidate objects that do not meet the query criteria can be quickly filtered out without precise spatial calculation. In addition, AHG code can also support spatial size query by finding objects whose size is within a certain interval quickly, without calculating the precise size of each object, which brings great convenience to spatial size statistics of a massive spatial dataset. Demonstrated by experiments over different types of the spatial dataset in a common PC environment, AHG shows favorable stability and scalability besides its capability in accelerating spatial query. The method is now applied successfully in several spatial query tools in a high-performance geographic information system called HiGIS.

Online visualization and query of massive geo-spatial data are facing increasing challenges with the explosive growth of location-based spatial datasets. In the practical scenario, online visualization is carried out in a progressive way, namely, a sketchy view map is first presented, and more detailed view maps are produced gradually as the viewport scale goes deeper. One approach is to use the multi-scale spatial index technique. However, it loses the original data attribute and cannot provide spatial statistics information. The paper is to provide an improved index structure, the Geo-Gap tree, which aims to enhance online interactive access to large spatial datasets, as well as enable one to compute statistical attributes like aggregation at the coarse level. Therefore, the first focus of Geo-Gap tree is improving the efficiency of tree building. For this purpose, an adaptive geohash coding is introduced to reduce the computing of neighboring objects. And, this phase can be improved in parallel once objects are partitioned. Compare to Gap tree, the cost of building the Geo-Gap tree can be greatly reduced. The second contribution is to choose data at different level based on sampling so that a sample for each level can be served as a progressive query result. The third contribution is an estimation of progressive query results, which ensure that progressive query accuracy can be controlled within the range of theoretical analysis. With the query continuing to execute, the query results become more and more accurate. The method is now integrated successfully into a high-performance geographic information system called HiGIS.

Objective The aim of this study was to predict disease-free (DFS) and overall (OS) survival of cancer patients through expression of CUE domain containing 2 (CUEDC2) protein. Methods In this retrospective study, we investigated CUEDC2 expression in 75 serous ovarian cancer tissues and 34 tubal fimbria tissues by immunohistochemistry. Chemoresistance was analyzed using clinical follow-up data. Results CUEDC2 expression scores were 1.35 ± 0.60, 1.54 ± 0.57, 1.78 ± 0.71, and 2.13 ± 0.27 for International Federation of Gynecology and Obstetrics (FIGO) stages I, II, III, and IV tissues, respectively, indicating that CUEDC2 expression increased with stage and that scores differed between patients with early and advanced cancers. We found no differences in CUEDC2 expression for tissues with low, medium, and high differentiation. CUEDC2 expression was unrelated to patient age, pathological grade, or presence or absence of lymph node metastasis, but was related to tumor stage. For CUEDC2-positive patients, median DFS and OS survival were 32.6 and 54.3 months, respectively. For CUEDC2-negative patients, median DFS and OS were 51.9 and 63.5 months, respectively. Expression of CUEDC2 was correlated with DFS but not OS. Conclusion CUEDC2 is highly expressed in ovarian cancer tissues and is related to tumor stage and DFS.

With the improvement of social development level, the development speed of all walks of life has been rapidly improved. This increase in speed makes the industry’s demand for energy also show a growing trend. Based on this, relevant departments have also increased the research on oilfield exploration and reservoir development. Among them, through research, it is found that geological parameters have a very significant impact on the oil reservoir development effect. Only based on geological parameters can the developed oil reservoir have the support of objective data and guarantee the development effect. This paper will analyze and study the influence of geological parameters on reservoir development in Daqing oilfield.

With the development of aerospace technologies, the mission planning of agile earth observation satellites has to consider several objectives simultaneously, such as profit, observation task number, image quality, resource balance, and observation timeliness. In this paper, a five-objective mixed-integer optimization problem is formulated for agile satellite mission planning. Preference-based multiobjective evolutionary algorithms, i.e., T-MOEA/D-TCH, T-MOEA/D-PBI, and T-NSGA-III are applied to solve the problem. Problem-specific coding and decoding approaches are proposed based on heuristic rules. Experiments have shown the advantage of integrating preferences in many-objective satellite mission planning. A comparative study is conducted with other state-of-the-art preference-based methods (T-NSGA-II, T-RVEA, and MOEA/D-c). Results have demonstrated that the proposed T-MOEA/D-TCH has the best performance with regard to IGD and elapsed runtime. An interactive framework is also proposed for the decision maker to adjust preferences during the search. We have exemplified that a more satisfactory solution could be gained through the interactive approach.

Onboard observation task planning plays an essential role in satellite autonomy, which has attracted considerable attention from researchers in recent years. Most of the existing studies solve the satellite onboard observation task planning problem (SOOTP) by searching algorithms. However, the limited computing resources and the changes of onboard condition present a new challenge for these methods. In this paper, we develop a sequential decision-making model and propose a deep learning-based planning method to solve the SOOTP. Instead of generating a short-term or long-term plan in advance, the sequential decisionmaking model enables the satellite to decide the observation task to execute in real-time. In the deep learning-based planning method, a long short-term memory-based encoding network is designed to extract the features and a classification network is used to make such a decision. In the experiment, we compared our method with the gated recurrent unit network and other three searching algorithms based on five scenarios. The experimental results show that our method can solve problems with 90.3%-93.7% accuracy, 2.19%-3.95% profit gap, and 0.004-0.006 s response time per task, which confirms its feasibility.

Semantic segmentation of remote sensing images is always a critical and challenging task. Graph neural networks, which can capture global contextual representations, can exploit long-range pixel dependency, thereby improving semantic segmentation performance. In this paper, a novel self-constructing graph attention neural network is proposed for such a purpose. Firstly, ResNet50 was employed as backbone of a feature extraction network to acquire feature maps of remote sensing images. Secondly, pixel-wise dependency graphs were constructed from the feature maps of images, and a graph attention network is designed to extract the correlations of pixels of the remote sensing images. Thirdly, the channel linear attention mechanism obtained the channel dependency of images, further improving the prediction of semantic segmentation. Lastly, we conducted comprehensive experiments and found that the proposed model consistently outperformed state-of-the-art methods on two widely used remote sensing image datasets.

Measuring the similarity between a pair of trajectories is the basis of many spatiotemporal clustering methods and has wide applications in trajectory pattern mining. However, most measures of trajectory similarity in the literature are based on precise models that ignore the inherent uncertainty in trajectory data recorded by sensors. Traditional computing or mining approaches that assume the preciseness and exactness of trajectories therefore risk underperforming or returning incorrect results. To address the problem, we propose an amended ellipse model, which takes both interpolation error and positioning error into account by making use of the motion features of the trajectory to compute the ellipse’s shape parameters. A specialized similarity measure method considering uncertainty called the Uncertain Trajectory Similarity Measure (UTSM) based on the model is also proposed. We validate the approach experimentally on both synthetic and real-world data and show that UTSM is not only more robust to noise and outliers, but also more tolerant of different sample frequencies and asynchronous sampling of trajectories.

Road networks play a significant role in modern city management. It is necessary to continually extract current road structure, as it changes rapidly with the development of the city. Due to the success of semantic segmentation based on deep learning in the application of computer vision, extracting road networks from VHR (Very High Resolution) imagery becomes a method of updating geographic databases. The major shortcoming of deep learning methods for road networks extraction is that they need a massive amount of high quality pixel-wise training datasets, which is hard to obtain. Meanwhile, a large amount of different types of VGI (volunteer geographic information) data including road centerline has been accumulated in the past few decades. However, most road centerlines in VGI data lack precise width information and, therefore, cannot be directly applied to conventional supervised deep learning models. In this paper, we propose a novel weakly supervised method to extract road networks from VHR images using only the OSM (OpenStreetMap) road centerline as training data instead of high quality pixel-wise road width label. Large amounts of paired Google Earth images and OSM data are used to validate the approach. The results show that the proposed method can extract road networks from the VHR images both accurately and effectively without using pixel-wise road training data.

The tremendous advance in information technology has promoted the rapid development of location-based services (LBSs), which play an indispensable role in people’s daily lives. Compared with a traditional LBS based on Point-Of-Interest (POI), which is an isolated location point, an increasing number of demands have concentrated on Region-Of-Interest (ROI) exploration, i.e., geographic regions that contain many POIs and express rich environmental information. The intention behind the POI is to search the geographical regions related to the user’s requirements, which contain some spatial objects, such as POIs and have certain environmental characteristics. In order to achieve effective ROI exploration, we propose an ROI top-k keyword query method that considers the environmental information of the regions. Specifically, the Word2Vec model has been introduced to achieve the distributed representation of POIs and capture their environmental semantics, which are then leveraged to describe the environmental characteristic information of the candidate ROI. Given a keyword query, different query patterns are designed to measure the similarities between the query keyword and the candidate ROIs to find the k candidate ROIs that are most relevant to the query. In the verification step, an evaluation criterion has been developed to test the effectiveness of the distributed representations of POIs. Finally, after generating the POI vectors in high quality, we validated the performance of the proposed ROI top-k query on a large-scale real-life dataset where the experimental results demonstrated the effectiveness of our proposals.

Viewshed analysis is of great interest to location optimization, environmental planning, ecology and tourism. There have been plenty of viewshed analysis methods which are generally time-consuming and among these methods, the XDraw algorithm is one of the fastest algorithms and has been widely adopted in various applications. Unfortunately, XDraw suffers from chunk distortion which greatly lowers the accuracy, which limits the application of XDraw to a certain extent. Previous works failed to remove chunk distortion because they are unaware of the underlying contribution relationship. In this paper, we propose HiXDraw—an improved XDraw algorithm free of chunk distortion. We first uncover the causation of chunk distortion from an innovative contributing perspective. Instead of recording LOS (line-of-sight) height, we use a new auxiliary grid to preserve contributing points. By preventing improper terrain data from contributing to determining the visibility, we significantly improve the accuracy of the outcome viewshed. The experimental results reveal that the error rate largely decreases by 65%. Given the same computing time, HiXDraw is more accurate than previous improvements in XDraw. To validate the removal of chunk distortion, we also present a pillar experiment.

Buffer and overlay analysis are fundamental operations which are widely used in Geographic Information Systems (GIS) for resource allocation, land planning, and other relevant fields. Real-time buffer and overlay analysis for large-scale spatial data remains a challenging problem because the computational scales of conventional data-oriented methods expand rapidly with data volumes. In this paper, we present HiBO, a visualization-oriented buffer-overlay analysis model which is less sensitive to data volumes. In HiBO, the core task is to determine the value of pixels for display. Therefore, we introduce an efficient spatial-index-based buffer generation method and an effective set-transformation-based overlay optimization method. Moreover, we propose a fully optimized hybrid-parallel processing architecture to ensure the real-time capability of HiBO. Experiments on real-world datasets show that our approach is capable of handling ten-million-scale spatial data in real time. An online demonstration of HiBO is provided (http://www.higis.org.cn: 8080/hibo).

Buffer analysis, a fundamental function in a geographic information system (GIS), identifies areas by the surrounding geographic features within a given distance. Real-time buffer analysis for large-scale spatial data remains a challenging problem since the computational scales of conventional data-oriented methods expand rapidly with increasing data volume. In this paper, we introduce HiBuffer, a visualization-oriented model for real-time buffer analysis. An efficient buffer generation method is proposed which introduces spatial indexes and a corresponding query strategy. Buffer results are organized into a tile-pyramid structure to enable stepless zooming. Moreover, a fully optimized hybrid parallel processing architecture is proposed for the real-time buffer analysis of large-scale spatial data. Experiments using real-world datasets show that our approach can reduce computation time by up to several orders of magnitude while preserving superior visualization effects. Additional experiments were conducted to analyze the influence of spatial data density, buffer radius, and request rate on HiBuffer performance, and the results demonstrate the adaptability and stability of HiBuffer. The parallel scalability of HiBuffer was also tested, showing that HiBuffer achieves high performance of parallel acceleration. Experimental results verify that HiBuffer is capable of handling 10-million-scale data.

The buffer generation algorithm is a fundamental function in GIS, identifying areas of a given distance surrounding geographic features. Past research largely focused on buffer generation algorithms generated in a stand-alone environment. Moreover, dissolved buffer generation is data- and computing-intensive. In this scenario, the improvement in the stand-alone environment is limited when considering large-scale mass vector data. Nevertheless, recent parallel dissolved vector buffer algorithms suffer from scalability problems, leaving room for further optimization. At present, the prevailing in-memory cluster-computing framework—Spark—provides promising efficiency for computing-intensive analysis; however, it has seldom been researched for buffer analysis. On this basis, we propose a cluster-computing-oriented parallel dissolved vector buffer generating algorithm, called the HPBM, that contains a Hilbert-space-filling-curve-based data partition method, a data skew and cross-boundary objects processing strategy, and a depth-given tree-like merging method. Experiments are conducted in both stand-alone and cluster environments using real-world vector data that include points and roads. Compared with some existing parallel buffer algorithms, as well as various popular GIS software, the HPBM achieves a performance gain of more than 50%.

Soluble epoxide hydrolase (sEH) plays a critical role in inflammation by modulating levels of epoxyeicosatrienoic acids (EETs) and other epoxy fatty acids (EpFAs). Here, we investigate the possible role of sEH in lipopolysaccharide (LPS)-mediated macrophage activation and acute lung injury (ALI). In this study, we found that a small molecule, wedelolactone (WED), targeted sEH and led to macrophage inactivation. Through the molecular interaction with amino acids Phe362 and Gln384, WED suppressed sEH activity to enhance levels of EETs, thus attenuating inflammation and oxidative stress by regulating glycogen synthase kinase 3beta (GSK3β)-mediated nuclear factor-kappa B (NF-κB) and nuclear factor E2-related factor 2 (Nrf2) pathways in vitro. In an LPS-stimulated ALI animal model, pharmacological sEH inhibition by WED or sEH knockout (KO) alleviated pulmonary damage, such as the increase in the alveolar wall thickness and collapse. Additionally, WED or sEH genetic KO both suppressed macrophage activation and attenuated inflammation and oxidative stress in vivo. These findings provided the broader prospects for ALI treatment by targeting sEH to alleviate inflammation and oxidative stress and suggested WED as a natural lead candidate for the development of novel synthetic sEH inhibitors.