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This paper addresses the challenge of indoor space segmentation from 3D point clouds, which is essential for understanding interior layouts, reconstructing 3D structures, and developing indoor navigation maps. While current deep learning-based methods rely on projecting 3D point clouds into 2D for instance extraction, they often fail to capture the local and global 3D features necessary for effectively segmenting complex indoor spaces, such as multi-ring nested structures. These methods also struggle with generalization across different scenes. In response, this paper proposes an efficient indoor space segmentation method that integrates both 2D and 3D geometric constraints. By leveraging the distribution characteristics of point clouds in 2D and the local and global features in 3D, the method achieves reliable extraction of vertical structural information in complex indoor environments. To address under-segmentation in small spaces due to varying scales, the paper introduces an adaptive extraction method for space partition anchors, guided by local features. During instance-level space segmentation, a hierarchical contour tree structure is employed to precisely partition complex indoor spaces, effectively handling circular and composite structures. The proposed approach was tested on 96 RGB-D scans from the Beike dataset and 6 large-scale indoor scenes from the S3DIS dataset, covering a range of complexities, sizes, and structures. The experimental section includes ablation studies and thorough comparisons with existing state-of-the-art spatial partitioning algorithms based on morphology and deep learning. Results demonstrate that the proposed method significantly outperforms existing approaches in terms of accuracy, robustness, and generalization ability, providing a solid foundation for indoor space modeling and robotic navigation. The source code and datasets will be made publicly available via the “EISPGeo” link.

Connectivity enhancement of ecological networks is an efficient and advantageous spatial planning strategy, and it is a hot research topic to analyze ecological networks based on complex systems theory and perform model optimization. As a typical geographic network, the evolution process of ecological network, such as node and edge growth, will be subject to geospatial constraints, and the current research lacks optimization models that take the geospatial constraints into account. This study focused on urban ecological network connectivity and the distribution of nodes as well as structural fractures where the connectivity of the ecological network was hindered were analyzed. Then it introduced a local world network model to optimize network connectivity and designed a network growth mechanism with limited added nodes and optimal connections. An ecological network optimization model considering geospatial constraints was constructed, and the network closeness index was used to fit the objective function curve to select optimization schemes. This study used Shenzhen, China as a case study, and the results indicated that the initial ecological network was significantly fractured by urban construction. In the optimization scenario, the network evolution reached its optimal state at the simulation step size of t = 26. At this point, the closeness index of the optimized network was increased by 15%. The optimization model constructed in this paper emphasizes the importance of functional restoration of existing network nodes, and the results of the study can provide support for ecospatial layout optimization and management.

The under-canopy environment, which is inherently inaccessible to humans, necessitates the use of unmanned aerial vehicles (UAVs) for data collection. The implementation of UAV autonomous navigation in such environments faces challenges, including dense obstacles, GNSS signal interference, and varying lighting conditions. This paper introduces a UAV autonomous navigation method specifically designed for under-canopy environments. Initially, image enhancement techniques are integrated with neural network-based visual feature extraction. Subsequently, employs a high-dimensional error-state optimizer coupled with a low-dimensional height filter to achieve high-precision localization of the UAV in under-canopy environments. Furthermore, proposes a boundary sampling autonomous exploration algorithm and an advanced Rapidly exploring Random Tree (RRT) path planning algorithm. The objective is to enhance the reliability and safety of UAV operations beneath the forest canopy, thereby establishing a technical foundation for surveying vertically stratified natural resources.

This study examines the impact of low-income assistance and electrification programs on a disadvantaged community in Southern California. An urban building energy model is paired with an AC power flow and electric distribution system degradation model to evaluate how the cost of energy, carbon emissions, and pollutant emissions change after applying building weatherization, energy efficiency, and electrification measures to the community. Results show that traditional weatherization and energy efficiency measures (upgrading lighting and appliances, improving insulation to current building code standards) are the most cost-effective, reducing the cost of energy and carbon emissions by 10–20 % for the current community. Heat pump water heaters offer a 40 % average reduction in carbon emissions and almost 50 % decrease in criteria pollutant emissions, but at a cost increase of 17–22 %. Appliance electrification also reduces carbon emissions 5–10 % but increases cost by 7 % to 25 %. For reducing carbon, government programs that support building electrification are most cost-effective when they combine switching from natural gas to electricity with high efficiency system. Electrifying hot water and appliances effectively reduces emissions but must be paired with improved low-income assistance programs to prevent increased energy burden for low-income families. The urban building energy model and electrical distribution simulations used in this study can be replicated in other low-income communities.

This paper presents a novel fully automated approach for generating structured 3D synthetic tree models, addressing the limitations of existing datasets used in applications like digital twin construction, carbon stock calculation, and environmental assessments. The method allows for the automated creation of a large-scale dataset containing 13,000 tree models of ten common species, each featuring a detailed 3D point cloud with hierarchical structures, precise parameters, and separate branch and leaf information. The dataset includes both original and noise-added point clouds to enhance method testing and evaluation. It stands out by providing comprehensive structural data, including branch numbering and detailed tree skeleton information with node hierarchies and radius data. Furthermore, this study introduces randomly distributed batches of tree models within specific terrains. It provides results from airborne laser scanning simulations, which facilitate the individualized segmentation of these tree models. This first-of-its-kind, extensive synthetic dataset is designed for accurate algorithm evaluation in tasks such as branch-leaf separation, 3D reconstruction, individual tree segmentation and carbon stock estimation. The paper validates the dataset’s utility by applying state-of-the-art algorithms to demonstrate its effectiveness in various applications, marking a significant advancement in 3D tree modeling research. The datasets are publicly available, accessible via the ‘‘TreeNet3D Dataset’’ link.

Purpose: Chronic obstructive pulmonary disease (COPD) patients are likely to develop sarcopenia, while the exact mechanism underlying the association between sarcopenia and COPD is still not clear. This cohort study aims to explore the genes, signaling pathways, and transcription factors (TFs) that are related to the molecular pathogenesis of sarcopenia and COPD. Methods: According to the strict inclusion criteria, two gene sets (GSE8479 for sarcopenia and GSE76925 for COPD) were obtained from the Gene Expression Omnibus (GEO) platform. Overlapping differentially expressed genes (DEGs) in sarcopenia and COPD were detected, and comprehensive bioinformatics analysis was conducted, including functional annotation, enrichment analysis of Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG), construction of a protein–protein interaction (PPI) network, co-expression analysis, identification and validation of hub genes, and TFs prediction and verification. Results: In total, 118 downregulated and 92 upregulated common DEGs were detected. Functional analysis revealed that potential pathogenesis involves oxidoreductase activity and ferroptosis. Thirty hub genes were detected, and ATP metabolic process and oxidative phosphorylation were identified to be closely related to the hub genes. Validation analysis revealed that SAA1, C3, and ACSS2 were significantly upregulated, whereas ATF4, PPARGC1A, and MCTS1 were markedly downregulated in both sarcopenia and COPD. In addition, six TFs (NFKB1, RELA, IRF7, SP1, MYC, and JUN) were identified to regulate the expression of these genes, and SAA1 was found to be coregulated by NFKB1 and RELA. Conclusion: This study uncovers potential common mechanisms of COPD complicated by sarcopenia. The hub gene SAA1 and the NF-κB signaling pathway could be involved, and oxidative phosphorylation and ferroptosis might be important contributors to this comorbidity.

The rapid expansion of reservoir fractures and the enlargement of the area affected by working fluids can be accomplished solely through fracturing operations of oilfield working fluids in geological reservoirs. Supercritical CO2 is regarded as an ideal medium for shale reservoir fracturing owing to the inherent advantages of environmental friendliness, excellent capacity, and high stability. However, CO2 gas channeling and complex propagation of fractures in shale reservoirs hindered the commercialization of Supercritical CO2 fracturing technology. Herein, a simulation method for Supercritical CO2 fracturing based on cohesive force units is proposed to investigate the crack propagation behavior of CO2 fracturing technology under different construction parameters. Furthermore, the shale fracture propagation mechanism of Supercritical CO2 fracturing fluid is elucidated. The results indicated that the propagation ability of reservoir fractures and Mises stress are influenced by the fracturing fluid viscosity, fracturing azimuth angle, and reservoir conditions (temperature and pressure). An azimuth angle of 30° can achieve a maximum Mises stress of 3.213 × 107 Pa and a crack width of 1.669 × 10−2 m. However, an apparent viscosity of 14 × 10−6 Pa·s results in a crack width of only 2.227 × 10−2 m and a maximum Mises stress of 4.459 × 107 Pa. Additionally, a weaker fracture propagation ability and reduced Mises stress are exhibited at the fracturing fluid injection rate. As a straightforward model to synergistically investigate the fracture propagation behavior of shale reservoirs, this work provides new insights and strategies for the efficient extraction of shale reservoirs.

Accurate individual tree reconstruction based on laser point clouds is vital for precise biomass estimation, virtual geographic environment modeling, and simulation. However, mobile laser 3D scanning systems often capture tree point clouds obscured by leaves, resulting in missing or incomplete branch data, posing significant challenges to detailed tree reconstruction. In this paper, a novel method for fine-grained 3D tree model reconstruction from incomplete point clouds is proposed. This approach addresses the structural reconstruction of trees, tackling two main problems: reconstructing the main trunk and branches based on data completeness. Initially, a 3D morphological algorithm is employed to separate the main trunk and branches in the point cloud. Next, branch point clouds are node-aggregated using clustering, and the trunk’s skeleton point is computed using a multi-scale curve fitting method. To account for incomplete branch point clouds, the Alpha shape is calculated and used as a constraint for the growth model of the L-system, enabling the automatic generation of branch structures. Finally, a morphologically constrained multi-level trunk fusion method is utilized to achieve complete tree model reconstruction. To validate the effectiveness of this method, five trees with varying structures and levels of complexity were selected for structural reconstruction and compared with state-of-the-art (SOTA) methods. Experimental results evince that the method delineated in this study exhibits an aptitude for adeptly approximating tree nodes, even in scenarios characterized by incomplete point cloud data. This approach transcends the reconstruction accuracy manifested by SOTA algorithms specific to three-dimensional tree modeling, simultaneously demonstrating a pronounced resilience to noise and enhanced robustness.

Street tree extraction based on the 3-D mobile mapping point cloud plays an important role in building smart cities and creating highly accurate urban street maps. Existing methods are often over- or under-segmented when segmenting overlapping street tree canopies and extracting geometrically complex trees. To address this problem, we propose a method based on improved 3-D morphological analysis for extracting street trees from mobile laser scanner (MLS) point clouds. First, the 3-D semantic point cloud segmentation framework based on deep learning is used for preclassification of the original point cloud to obtain the vegetation point cloud in the scene. Considering the influence of terrain unevenness, the vegetation point cloud is deterraformed and slice point cloud containing tree trunks is obtained through spatial filtering on height. On this basis, a voxel-based region growing method constrained with the changing rate of convex area is used to locate the stree trees. Then we propose a progressive tree crown segmentation method, which first completed the preliminary individual segmentation of the tree crown point cloud based on the voxel-based region growth constrained by the minimum increment rule, and then optimizes the crown edges by “valley” structure-based clustering. In this article, the proposed method is validated and the accuracy is evaluated using three sets of MLS datasets collected from different scenarios. The experimental results show that the method can effectively identify and localize street trees with different geometries and has a good segmentation effect for street trees with large adhesion between canopies. The accuracy and recall of tree localization are higher than 96.08% and 95.83%, respectively, and the average precision and recall of instance segmentation in three datasets are higher than 93.23% and 95.41%, respectively.

Abstract Modern urban development urgently requires a new management concept and operational mechanism to encourage the exploration of frameworks for cognizing and studying urban characteristics. In the present study, modern cities are first understood from the perspective of their basic theoretical evolution. Each modern city is seen as a complex system of organic life forms. Urban information science propels modern urban research in the direction of rationality. This paper also presents the new characteristics of modern cities (and how they have changed) in relation to external structure and internal functions. It examines the generation of urban problems and governance adaptability. On this basis, this paper proposes a cognitive model for studying modern cities, integrating basic theoretical, methodological support, and governance systems. It discusses the basic rationale and core idea for constructing each of these three systems. The research aims to guide and implement modern urban construction and sustainable development in a more effective way.

Objective: Primary pulmonary synovial sarcoma (PPSS) is extremely rare. This study aims to identify the clinicopathologic and therapeutic factors determining survival in PPSS. Methods: We performed a retrospective analysis of 121 patients from the Surveillance, Epidemiology, and End Results Database as well as 12 patients from our own institution diagnosed with PPSS. Patient survival was evaluated using the Kaplan-Meier method. Results: The median survival time for 12 PPSS patients in our institution was 78 months. Postoperative chemotherapy (P = .027 for overall survival and P = .035 for disease-specific survival) was associated with superior survival, whereas pneumonectomy (P = .011 for overall survival and P = .006 for disease-specific survival) was associated with worse survival. Single lobe involvement (P = .022) and the absence of lymph node involvement (P = .045) were associated with improved disease-specific survival and overall survival, respectively. In the Surveillance, Epidemiology, and End Results Database, the median survival time was 23 months. Significantly superior survival was observed in patients with earlier American Joint Committee on Cancer stage (Ⅰ-Ⅱ) (P

Understanding the spatial differences and evolutionary characteristics of urban economy and exploring the impact of industrial agglomeration and industrial proximity on urban economic convergence are the bases for scientifically formulating policies for coordinated regional economic development. This study used QGIS 3.10.10 software and the Theil index to analyze the spatial distribution characteristics and regional disparities of urban economy. Then, a spatial econometric model was constructed to analyze the convergence and influencing factors of Guangdong’s urban economy. The results indicate that from 2006 to 2020, Guangdong’s urban economy grew rapidly and the degree of economic agglomeration gradually weakened, but its economic pattern always maintained the “Core-Edge” structural feature. The interval disparities between the Pearl River Delta Urban Agglomeration (PRD) and the edge area have always been greater than the intra-regional disparities, so they are main source of disparities in Guangdong. In Guangdong’s urban economy, σ-convergence and β-convergence coexist. The conditional β-convergence rate is 0.96~1.53%, and the half-life cycle is 45.4~72.36 years. Compared to the PRD, the economic disparities in the edge area are smaller but the convergence speed is faster and the half-life cycle is shorter. Both industrial agglomeration and industrial proximity have a significant impact on the economic convergence of Guangdong’s cities. Among them, industrial agglomeration has a positive impact, while industrial proximity has a negative impact. There is spatial heterogeneity in the impact of industries on economic development. Industrial agglomeration has a positive impact on the overall economic development of Guangdong, but it is not significant within the regions. Industrial proximity has significant negative externalities in the PRD region, and its impact is not significant in the edge area.

Background Adrenocortical carcinoma (ACC) is extremely rare in elderly patients. Thus, this study aimed to identify the incidence rate and develop nomogram models for predicting survival in elderly ACC patients. Methods Data of ACC patients aged >60 years from 1975 to 2016 were obtained from the Surveillance, Epidemiology, and End Results dataset. The national incidence rate was estimated, and survival was subjected to Kaplan–Meier analysis. A multivariate Cox regression model was used to identify predictors of survival. Nomograms were generated to predict survival, calibrated and internally validated. Results We identified 583 cases. Univariate analysis showed that patients with younger age (≤67 years), female sex, lower tumor grade, surgical treatment performed, and earlier European Network for the Study of Adrenal Tumors (ENSAT) stage had a better survival (P

Background: Adrenocortical carcinoma (ACC) is an uncommon endocrine malignancy associated with poor clinical outcome. As a novel form of cell death, ferroptosis is reliant on the accumulation of iron and reactive oxygen species and is involved in the pathogenesis of various tumors, including ACC. Our study aimed to identify and characterize the prognostic ferroptosis-related lncRNA signature (FerRLSig) in ACC.Methods: A regulatory network of ferroptosis-related lncRNAs (FerRLs) and mRNAs was constructed based on The Cancer Genome Atlas (TCGA). Univariate and multivariate Cox regression assays were performed to construct the FerRLSig.Results: Twenty-four FerRLs were identified in the prognostic model, and the high-risk FerRLSig was related to the worse overall survival (OS) in ACC [hazard ratio (HR): 1.936 (1.484–2.526), p < 0.001]. The area under the curve (AUC) value of the FerRLSig was 0.936 according to the receiver operating characteristic (ROC) analyses, superior to other traditional clinicopathological features, further supported the utility in prognosis prediction of ACC. We further established a prognostic nomogram combining clinical factors with the FerRLSig, which showed favorable efficacy for survival prediction. Next, gene set enrichment analysis (GSEA) revealed that gene sets were involved in many immune regulatory biological processes related to malignancies. T-cell function of type II INF response and the immune checkpoints, including CD40, CD276, IDO2, NRP1, and CD80, were expressed with a significant difference between the low- and high-risk groups.Conclusion: This study offered new insights into the pathogenesis of ACC. The novel FerRLSig could be useful in predicting survival and may provide information of immunological research and treatment for ACC patients.

We report silicon nanowire (SiNW) growth with a novel Cu-In bimetallic catalyst using a plasma-enhanced chemical vapor deposition (PECVD) method. We study the structure of the catalyst nanoparticles (NPs) throughout a two-step process that includes a hydrogen plasma pre-treatment at 200 °C and the SiNW growth itself in a hydrogen-silane plasma at 420 °C. We show that the H2-plasma induces a coalescence of the Cu-rich cores of as-deposited thermally evaporated NPs that does not occur when the same annealing is applied without plasma. The SiNW growth process at 420 °C induces a phase transformation of the catalyst cores to Cu7In3; while a hydrogen plasma treatment at 420 °C without silane can lead to the formation of the Cu11In9 phase. In situ transmission electron microscopy experiments show that the SiNWs synthesis with Cu-In bimetallic catalyst NPs follows an essentially vapor-solid–solid process. By adjusting the catalyst composition, we manage to obtain small-diameter SiNWs—below 10 nm—among which we observe the metastable hexagonal diamond phase of Si, which is predicted to have a direct bandgap.

Abstract Background Huntington’s disease (HD) is a neurodegenerative disease that involves a complex combination of psychiatric, cognitive and motor impairments. Synaptic dysfunction has been implicated in HD pathogenesis. However, the mechanisms have not been clearly delineated. Synaptic vesicular zinc is closely linked to modulating synaptic transmission and maintaining cognitive ability. It is significant to assess zinc homeostasis for further revealing the pathogenesis of synaptic dysfunction and cognitive impairment in HD. Results Histochemical staining by autometallography indicated that synaptic vesicular zinc was decreased in the hippocampus, cortex and striatum of N171-82Q HD transgenic mice. Analyses by immunohistochemistry, Western blot and RT-PCR found that the expression of zinc transporter 3 (ZnT3) required for transport of zinc into synaptic vesicles was obviously reduced in these three brain regions of the HD mice aged from 14 to 20 weeks and BHK cells expressing mutant huntingtin. Significantly, dual-luciferase reporter gene and chromatin immunoprecipitation assays demonstrated that transcription factor Sp1 could activate ZnT3 transcription via its binding to the GC boxes in ZnT3 promoter. Moreover, mutant huntingtin was found to inhibit the binding of Sp1 to the promoter of ZnT3 and down-regulate ZnT3 expression, and the decline in ZnT3 expression could be ameliorated through overexpression of Sp1. Conclusions This is first study to reveal a significant loss of synaptic vesicular zinc and a decline in ZnT3 transcriptional activity in the HD transgenic mice. Our work sheds a novel mechanistic insight into pathogenesis of HD that mutant huntingtin down-regulates expression of ZnT3 through inhibiting binding of Sp1 to the promoter of ZnT3 gene, causing disruption of synaptic vesicular zinc homeostasis. Disrupted vesicular zinc ultimately leads to early synaptic dysfunction and cognitive deficits in HD. It is also suggested that maintaining normal synaptic vesicular zinc concentration is a potential therapeutic strategy for HD.

Spatial relationships exist in our daily communication and provide many locality clues, but minimal attention has been devoted to positioning localities indoors with spatial relationships extracted from locality descriptions. Locality description generally contains spatial relationships (i.e., topological, distance, and direction relations) and reference objects (ROs). For locality description positioning, distance and direction relation convey more clue than topology and combined together can provide much clue to positioning. Explicit and implicit features are inherent characteristics of locality descriptions. Based on the analysis of locality descriptions, a classification scheme of indoor locality descriptions is provided. To address the positioning locality with spatial relationships, we propose a two-state method. First, the located region is obtained according to the fuzzy region of spatial relations in locality description. Second, the probability distribution is determined, which is a joint probability function that consists of relative direction and distance (quantitative or qualitative distance) membership functions. The relative direction membership function is conducted based on human's direction cognition is related to angular information. The trapezoid quantitative distance membership function is based on indoor cognitive experiment. The qualitative distance membership function for indoor ROs is based on minimum Euclidean distance and stolen area. From the cognition and computation perspectives, the visible boundary met some restrictions is provided and a sampling method is defined. To obtain the unique positioning locality, a principle is proposed. We evaluate our method by conducting an indoor cognitive experiment and demonstrate that a positioning accuracy of 3.50 m can be achieved with semantically derived spatial relationships.

Abstract Natural extracellular vesicles (EVs) are ideal drug carriers due to their remarkable biocompatibility. Their delivery specificity can be achieved by the conjugation of targeting ligands. However, existing methods to engineer target‐specific EVs are tedious or inefficient, having to compromise between harsh chemical treatments and transient interactions. Here, we describe a novel method for the covalent conjugation of EVs with high copy numbers of targeting moieties using protein ligases. Conjugation of EVs with either an epidermal growth factor receptor (EGFR)‐targeting peptide or anti‐EGFR nanobody facilitates their accumulation in EGFR‐positive cancer cells, both in vitro and in vivo. Systemic delivery of paclitaxel by EGFR‐targeting EVs at a low dose significantly increases drug efficacy in a xenografted mouse model of EGFR‐positive lung cancer. The method is also applicable to the conjugation of EVs with peptides and nanobodies targeting other receptors, such as HER2 and SIRP alpha, and the conjugated EVs can deliver RNA in addition to small molecules, supporting the versatile application of EVs in cancer therapies. This simple, yet efficient and versatile method for the stable surface modification of EVs bypasses the need for genetic and chemical modifications, thus facilitating safe and specific delivery of therapeutic payloads to target cells.

Objective: Up to 40% of multiple endocrine neoplasia type 1 (MEN1) patients may have adrenal cortical tumors. However, adrenocortical carcinoma (ACC) is rare. The clinical manifestations, prevalence, inheritance and prognosis of ACC associated with MEN1 remain unclear. Here we report the clinical manifestations and prevalence of ACC in patients with MEN1. Design and methods: A retrospective analysis of ACC associated with MEN1 patients at a single tertiary care center from December 2001 to June 2017. Genetic analysis of MEN1 and other ACC associated genes, loss of heterozygosity (LOH) of MEN1 locus, immunohistochemistry staining of menin, P53 and β-catenin in ACC tissue were performed. Results: Two related patients had ACC associated with MEN1. The father had ENSAT stage IV tumor with excessive production of cortisol; the daughter had nonfunctional ENSAT stage I tumor. Both patients carried novel germline heterozygous mutation (c.400_401insC) of MEN1. The wild-type MEN1 allele was lost in the resected ACC tissue from the daughter with no menin staining. The ACC tissue had nuclear β-catenin staining, with heterozygous CTNNB1 mutation of 357del24 and P53 staining in only 20% cells. Conclusions: ACC associated with MEN1 is rare and may occur in familial aggregates.

RGB-D dense mapping systems are widely used for indoor navigation and augmented reality. However, the drift in egomotion estimation systems accumulates as the systems expand over space and time. In this paper, we develop a vertex-to-edge weighted closed-form algorithm to reduce camera drift for dense RGB-D indoor simultaneous localization and mapping (SLAM). First, due to the high-frame rate of common RGB-D sensors, in our approach, we employ a robust key-frame selection strategy. We then consider the typical depth noise properties of the sensor and exploit both visual and geometric information to determine the camera motion of the key frames. To enforce global consistency, we employ a combination of techniques to enhance the efficiency and accuracy of loop closure detection and then factor graph optimization is used to mitigate trajectory drift; specifically, the graph edges are weighted by residual errors. To demonstrate its accuracy and robustness, our SLAM system is tested on seven sets of sequences from publicly available benchmark datasets collected with Kinect v1 and on three sets of sequences taken with a handheld structure sensor. In a direct comparison, our approach yields significantly less trajectory error than several other state-of-the-art methods.

Accurate combined bundle adjustment (BA) is a fundamental step for the integration of aerial and terrestrial images captured from complementary platforms. In traditional photogrammetry pipelines, self-calibrated bundle adjustment (SCBA) improves the BA accuracy by simultaneously refining the interior orientation parameters (IOPs), including lens distortion parameters, and the exterior orientation parameters (EOPs). Aerial and terrestrial images separately processed through SCBA need to be fused using BA. Thus, the IOPs in the aerial–terrestrial BA must be properly treated. On one hand, the IOPs in one flight should be identical for the same images in physics. On the other hand, the IOP adjustment in the cross-platform-combined BA may mathematically improve the aerial–terrestrial image co-registration degree in 3D space. In this paper, the impacts of self-calibration strategies in combined BA of aerial and terrestrial image blocks on the co-registration accuracy were investigated. To answer this question, aerial and terrestrial images captured from seven study areas were tested under four aerial–terrestrial BA scenarios: the IOPs for both aerial and terrestrial images were fixed; the IOPs for only aerial images were fixed; the IOPs for only terrestrial images were fixed; the IOPs for both images were adjusted. The cross-platform co-registration accuracy for the BA was evaluated according to independent checkpoints that were visible on the two platforms. The experimental results revealed that the recovered IOPs of aerial images should be fixed during the BA. However, when the tie points of the terrestrial images are comprehensively distributed in the image space and the aerial image networks are sufficiently stable, refining the IOPs of the terrestrial cameras during the BA may improve the co-registration accuracy. Otherwise, fixing the IOPs is the best solution.

As an essential part of point cloud processing, autonomous classification is conventionally used in various multifaceted scenes and non-regular point distributions. State-of-the-art point cloud classification methods mostly process raw point clouds, using a single point as the basic unit and calculating point cloud features by searching local neighbors via the k-neighborhood method. Such methods tend to be computationally inefficient and have difficulty obtaining accurate feature descriptions due to inappropriate neighborhood selection. In this paper, we propose a robust and effective point cloud classification approach that integrates point cloud supervoxels and their locally convex connected patches into a random forest classifier, which effectively improves the point cloud feature calculation accuracy and reduces the computational cost. Considering the different types of point cloud feature descriptions, we divide features into three categories (point-based, eigen-based, and grid-based) and accordingly design three distinct feature calculation strategies to improve feature reliability. Two International Society of Photogrammetry and Remote Sensing benchmark tests show that the proposed method achieves state-of-the-art performance, with average F1-scores of 89.16 and 83.58, respectively. The successful classification of point clouds with great variation in elevation also demonstrates the reliability of the proposed method in challenging scenes.

Abstract Background Iron accumulation in basal ganglia accompanies neuronal loss in Huntington’s disease (HD) patients and mouse disease models. Disruption of HD brain iron homeostasis occurs before the onset of clinical signs. Therefore, investigating the mechanism of iron accumulation is essential to understanding its role in disease pathogenesis. Methods N171-82Q HD transgenic mice brain iron was detected by using Diaminobenzidine-enhanced Perls’ stain. Iron homeostatic proteins including iron response protein 1 (IRP1), transferrin (Tf), ferritin and transferrin receptor (TfR) were determined by using western blotting and immunohistochemistry, and their relative expression levels of RNA were measured by RT-PCR in both N171-82Q HD transgenic mice and HEK293 cells expressing N-terminal of huntingtin. Results Iron was increased in striatum and cortex of N171-82Q HD transgenic mice. Analysis of iron homeostatic proteins revealed increased expression of IRP1, Tf, ferritin and TfR in N171-82Q mice striatum and cortex. The same results were obtained in HEK293 cells expressing N-terminal of mutant huntingtin containing 160 CAG repeats. Conclusion We conclude that mutant huntingtin may cause abnormal iron homeostatic pathways by increasing IRP1 expression in Huntington’s disease, suggesting potential therapeutic target.

Papillary thyroid carcinomas are often found with coincidental benign thyroid nodules. Here, the authors provide genomic evidence that papillary thyroid carcinomas and coincidental benign thyroid nodules originate and evolve independently.

Urban modeling and visualization are highly useful in the development of smart cities. Buildings are the most prominent features in the urban environment, and are necessary for urban decision support; thus, buildings should be modeled effectively and efficiently in three dimensions (3D). In this study, with the help of Gaofen-7 (GF-7) high-resolution stereo mapping satellite double-line camera (DLC) images and multispectral (MUX) images, the boundary of a building is segmented via a multilevel features fusion network (MFFN). A digital surface model (DSM) is generated to obtain the elevation of buildings. The building vector with height information is processed using a 3D modeling tool to create a white building model. The building model, DSM, and multispectral fused image are then imported into the Unreal Engine 4 (UE4) to complete the urban scene level, vividly rendered with environmental effects for urban visualization. The results of this study show that high accuracy of 95.29% is achieved in building extraction using our proposed method. Based on the extracted building vector and elevation information from the DSM, building 3D models can be efficiently created in Level of Details 1 (LOD1). Finally, the urban scene is produced for realistic 3D visualization. This study shows that high-resolution stereo mapping satellite images are useful in 3D modeling for urban buildings and can support the generation and visualization of urban scenes in a large area for different applications.

Estimating the potential achievable solar energy in urban buildings is significantly important for the long-term planning and development of environmental protection strategies. Nevertheless, conventional methods based on LiDAR data are often costly and require more than one platform to obtain complete building surface information. The development of oblique photogrammetry technology enables fast and accurate acquiring of the 3D information of the surface. In this paper, we propose an efficient method to estimate the potential achievable solar energy of building surfaces based on photogrammetric mesh models. In this method, we use photogrammetric mesh models as the input data and then propose a hierarchical algorithm for shadow analysis. Combined with the solar radiation model, we then obtain the potential achievable solar energy of the building surface. We further investigate the performance of the proposed method and it is shown that this method outperforms the multi-source LiDAR data.

Three-dimensional (3D) building models play an important role in digital cities and have numerous potential applications in environmental studies. In recent years, the photogrammetric point clouds obtained by aerial oblique images have become a major source of data for 3D building reconstruction. Aiming at reconstructing a 3D building model at Level of Detail (LoD) 2 and even LoD3 with preferred geometry accuracy and affordable computation expense, in this paper, we propose a novel method for the efficient reconstruction of building models from the photogrammetric point clouds which combines the rule-based and the hypothesis-based method using a two-stage topological recovery process. Given the point clouds of a single building, planar primitives and their corresponding boundaries are extracted and regularized to obtain abstracted building counters. In the first stage, we take advantage of the regularity and adjacency of the building counters to recover parts of the topological relationships between different primitives. Three constraints, namely pairwise constraint, triplet constraint, and nearby constraint, are utilized to form an initial reconstruction with candidate faces in ambiguous areas. In the second stage, the topologies in ambiguous areas are removed and reconstructed by solving an integer linear optimization problem based on the initial constraints while considering data fitting degree. Experiments using real datasets reveal that compared with state-of-the-art methods, the proposed method can efficiently reconstruct 3D building models in seconds with the geometry accuracy in decimeter level.

Indoor positioning is of great importance in the era of mobile computing. Currently, considerable focus has been on RSS-based locations because they can provide position information without additional equipment. However, this method suffers from two challenges: (1) fingerprint ambiguity and (2) labour-intensive fingerprint collection. To overcome these drawbacks, we provide a near relation-based indoor positioning method under a sparse Wi-Fi fingerprint. To effectively obtain the fingerprint database, certain interpolation methods are used to enrich sparse Wi-Fi fingerprints. A near relation boundary is provided, and Wi-Fi fingerprints are constrained to this region to reduce fingerprint ambiguity, which can also improve the efficiency of fingerprint matching. Extensive experiments show that the kriging interpolation method performs well, and a positioning accuracy of 2.86 m can be achieved with a near relation under a 1 m interpolation density.

Reliable image matching is the basis of image-based three-dimensional (3D) reconstruction. This study presents a quasi-dense matching method based on triangulation constraint and propagation as applied to different types of close-range image matching, such as illumination change, large viewpoint, and scale change. The method begins from a set of sparse matched points that are used to construct an initial Delaunay triangulation. Edge-to-edge matching propagation is then conducted for the point matching. Two types of matching primitives from the edges of triangles with areas larger than a given threshold in the reference image, that is, the midpoints of edges and the intersections between the edges and extracted line segments, are used for the matching. A hierarchical matching strategy is adopted for the above-mentioned primitive matching. The points that cannot be matched in the first stage, specifically those that failed in a gradient orientation descriptor similarity constraint, are further matched in the second stage. The second stage combines the descriptor and the Mahalanobis distance constraints, and the optimal matching subpixel is determined according to an overall similarity score defined for the multiple constraints with different weights. Subsequently, the triangulation is updated using the newly matched points, and the aforementioned matching is repeated iteratively until no new matching points are generated. Twelve sets of close-range images are considered for the experiment. Results reveal that the proposed method has high robustness for different images and can obtain reliable matching results.

A 3-D Shenzhen City Web platform based on the Web virtual reality geographical information system is presented. A 3-D global browser is employed to load multiple types of demand data from the city, such as 3-D building model data, residents' information, and real-time and historical traffic data. Using these data, a 3-D analysis and visualization of the city's information are conducted on a platform. The amount of information that can be visualized with this platform is very large, and the GIS-based navigational scheme enables great flexibility to access different available data sources. All the presented functions of the platform are extracted from the customers' practical demand. The system design considers some existing geographic human-computer interaction research results.

This paper presents an automatic building extraction method which utilizes a photogrammetric digital surface model (DSM) and digital orthophoto map (DOM) with the help of historical digital line graphic (DLG) data. To reduce the need for manual labeling, the initial labels were automatically obtained from historical DLGs. Nonetheless, a proportion of these labels are incorrect due to changes (e.g., new constructions, demolished buildings). To select clean samples, an iterative method using random forest (RF) classifier was proposed in order to remove some possible incorrect labels. To get effective features, deep features extracted from normalized DSM (nDSM) and DOM using the pre-trained fully convolutional networks (FCN) were combined. To control the computation cost and alleviate the burden of redundancy, the principal component analysis (PCA) algorithm was applied to reduce the feature dimensions. Three data sets in two areas were employed with evaluation in two aspects. In these data sets, three DLGs with 15%, 65%, and 25% of noise were applied. The results demonstrate the proposed method could effectively select clean samples, and maintain acceptable quality of extracted results in both pixel-based and object-based evaluations.

Lampposts, traffic lights, traffic signs, utility poles and so forth are important road furniture in urban areas. The fast and accurate localization and extraction of this type of furniture is urgent for the construction and updating of infrastructure databases in cities. This paper proposes a pipeline for mobile laser scanning data processing to locate and extract road poles. The proposed method is based on the vertical continuity with isolation feature of the pole part and the overall roughness feature of the attachment part of road poles. The isolation feature of the pole part is analysed by constructing two concentric cylinders from bottom to top and there should be no or a limited number of, points between these two cylinders. After splitting up the pole part and the attachment part of a road pole, the roughness of the candidate attachment points is computed and the attachment is obtained by performing region growing method based on roughness values. By applying the proposed pipeline to different situations in two datasets, the proposed method proves to be efficient not only in simple scenes but also in cluttered scenes.

The Lianjiang Plain in China and ancient villages distributed within the plain are under the potential threat of surface motion change, but no effective monitoring strategy currently exists. Distributed Scatterer InSAR (DSInSAR) provides a new high-resolution method for the precise detection of surface motion change. In contrast to the first-generation of time-series InSAR methodology, the distributed scatterer-based method focuses both on pointwise targets with high phase stability and distributed targets with moderate coherence, the latter of which is more suitable for the comprehensive environment of the Lianjiang Plain. In this paper, we present the first study of surface motion change detection in the Lianjiang Plain, China. Two data stacks, including 54 and 29 images from Sentinel-1A adjacent orbits, are used to retrieve time-series surface motion changes for the Lianjiang Plain from 2015 to 2018. The consistency of measurement has been cross-validated between adjacent orbit results with a statistically significant determination coefficient of 0.92. The temporal evolution of representative measuring points indicates three subzones with varied surface patterns: Eastern Puning (Zone A) in a slight elastic rebound phase with a moderate deformation rate (0–40 mm/yr), Chaonan (Zone B) in a substantial subsidence phase with a strong deformation rate (−140–0 mm/yr), and Chaoyang (Zone C) in a homogeneous and stable situation (−10–10 mm/yr). The spatial distribution of these zones suggests a combined change dynamic and a strong concordance of factors impacting surface motion change. Human activities, especially groundwater exploitation, dominate the subsidence pattern, and natural conditions act as a supplementary inducement by providing a hazard-prone environment. The qualitative and quantitative analysis of spatial and temporal details in this study provides a basis for systematic surface motion monitoring, cultural heritage protection and groundwater resources management.

Semantically rich indoor models are increasingly used throughout a facility’s life cycle for different applications. With the decreasing price of 3D sensors, it is convenient to acquire point cloud data from consumer-level scanners. However, most existing methods in 3D indoor reconstruction from point clouds involve a tedious manual or interactive process due to line-of-sight occlusions and complex space structures. Using the multiple types of data obtained by RGB-D devices, this paper proposes a fast and automatic method for reconstructing semantically rich indoor 3D building models from low-quality RGB-D sequences. Our method is capable of identifying and modelling the main structural components of indoor environments such as space, wall, floor, ceilings, windows, and doors from the RGB-D datasets. The method includes space division and extraction, opening extraction, and global optimization. For space division and extraction, rather than distinguishing room spaces based on the detected wall planes, we interactively define the start-stop position for each functional space (e.g., room, corridor, kitchen) during scanning. Then, an interior elements filtering algorithm is proposed for wall component extraction and a boundary generation algorithm is used for space layout determination. For opening extraction, we propose a new noise robustness method based on the properties of convex hull, octrees structure, Euclidean clusters and the camera trajectory for opening generation, which is inapplicable to the data collected in the indoor environments due to inevitable occlusion. A global optimization approach for planes is designed to eliminate the inconsistency of planes sharing the same global plane, and maintain plausible connectivity between the walls and the relationships between the walls and openings. The final model is stored according to the CityGML3.0 standard. Our approach allows for the robust generation of semantically rich 3D indoor models and has strong applicability and reconstruction power for complex real-world datasets.

Oblique photogrammetric point clouds are currently one of the major data sources for the three-dimensional level-of-detail reconstruction of buildings. However, they are severely noise-laden and pose serious problems for the effective and automatic surface extraction of buildings. In addition, conventional methods generally use normal vectors estimated in a local neighborhood, which are liable to be affected by noise, leading to inferior results in successive building reconstruction. In this paper, we propose an intact planar abstraction method for buildings, which explicitly handles noise by integrating information in a larger context through global optimization. The information propagates hierarchically from a local to global scale through the following steps: first, based on voxel cloud connectivity segmentation, single points are clustered into supervoxels that are enforced to not cross the surface boundary; second, each supervoxel is expanded to nearby supervoxels through the maximal support region, which strictly enforces planarity; third, the relationships established by the maximal support regions are injected into a global optimization, which reorients the local normal vectors to be more consistent in a larger context; finally, the intact planar surfaces are obtained by region growing using robust normal and point connectivity in the established spatial relations. Experiments on the photogrammetric point clouds obtained from oblique images showed that the proposed method is effective in reducing the influence of noise and retrieving almost all of the major planar structures of the examined buildings.

Traditionally, visual-based RGB-D SLAM systems only use correspondences with valid depth values for camera tracking, thus ignoring the regions without 3D information. Due to the strict limitation on measurement distance and view angle, such systems adopt only short-range constraints which may introduce larger drift errors during long-distance unidirectional tracking. In this paper, we propose a novel geometric integration method that makes use of both 2D and 3D correspondences for RGB-D tracking. Our method handles the problem by exploring visual features both when depth information is available and when it is unknown. The system comprises two parts: coarse pose tracking with 3D correspondences, and geometric integration with hybrid correspondences. First, the coarse pose tracking generates the initial camera pose using 3D correspondences with frame-by-frame registration. The initial camera poses are then used as inputs for the geometric integration model, along with 3D correspondences, 2D-3D correspondences and 2D correspondences identified from frame pairs. The initial 3D location of the correspondence is determined in two ways, from depth image and by using the initial poses to triangulate. The model improves the camera poses and decreases drift error during long-distance RGB-D tracking iteratively. Experiments were conducted using data sequences collected by commercial Structure Sensors. The results verify that the geometric integration of hybrid correspondences effectively decreases the drift error and improves mapping accuracy. Furthermore, the model enables a comparative and synergistic use of datasets, including both 2D and 3D features.

The collaborative system for power grid operation mode calculation implements data management of multi-level paralleling operated dispatching departments indifferent places and joint operation mode calculation, which is based on the calculated data of power grid operation modes. The transient stability analysis monitors the components of electrical variation curve through the network transmission to the web page. When more users and more curves reach a certain extent, the overall simulation time is longer and the curve is stuck because of the limitation of the transmission bandwidth and the refresh rate of the web page. This paper, The Douglas Peucker algorithm is used in system to solve the data transmission of large amount of data delay problem and achieve the transmission of the large amount of data, the overall simulation time of curve in the web page display is decreased, the curves are refreshed smoothly.

Nature Communications 8: Article number:15533 (2017); Published 5 June 2017; Updated 4 July 2017 The original version of this Article contained an error in the formatting of the author name Yang Shu, which was incorrectly given as Shu Yang. This has now been corrected in both the PDF and HTML versions of the Article.

Mitochondrial dysfunction caused by amyloid β-peptide (Aβ) plays an important role in the pathogenesis of Alzheimer disease (AD). Substantial evidence has indicated that the mitochondrial permeability transition pore (mPTP) opening is involved in Aβ-induced neuronal death and reactive oxygen species (ROS) generation. Astragaloside IV (AS-IV), one of the major active constituents of Astragalus membranaceus, has been reported as an effective anti-oxidant for treating neurodegenerative diseases. However, the molecular mechanisms still need to be clarified. In this study, we investigated whether AS-IV could prevent Aβ1-42-induced neurotoxicity in SK-N-SH cells via inhibiting the mPTP opening. The results showed that pretreatment of AS-IV significantly increased the viability of neuronal cells, reduced apoptosis, decreased the generation of intracellular reactive oxygen species (ROS) and decreased mitochondrial superoxide in the presence of Aβ1-42. In addition, pretreatment of AS-IV inhibited the mPTP opening, rescued mitochondrial membrane potential (ΔΨm), enhanced ATP generation, improved the activity of cytochrome c oxidase and blocked cytochrome c release from mitochondria in Aβ1-42 rich milieu. Moreover, pretreatment of AS-IV reduced the expression of Bax and cleaved caspase-3 and increased the expression of Bcl-2 in an Aβ1-42 rich environment. These data indicate that AS-IV prevents Aβ1-42-induced SK-N-SH cell apoptosis via inhibiting the mPTP opening and ROS generation. These results provide novel insights of AS-IV for the prevention and treatment of neurodegenerative disorders such as AD.