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Accurate estimation of forest terrain and canopy height is crucial for timely understanding of forest growth. Gao Fen-7 (GF-7) Satellite is China’s first sub-meter-level three-dimensional (3D) mapping satellite for civilian use, which was equipped with a two-line-array stereo mapping camera and a laser altimeter system that can provide stereo images and full waveform LiDAR data simultaneously. Most of the existing studies have concentrated on evaluating the accuracy of GF-7 for topographic survey in bare land, but few have in-depth studied its ability to measure forest terrain elevation and canopy height. The purpose of this study is to evaluate the potential of GF-7 LiDAR and stereo image for forest terrain and height measurement. The Airborne Laser Scanning (ALS) data were utilized to generate reference terrain and forest vertical information. The validation test was conducted in Pu’er City, Yunnan Province of China, and encouraging results have obtained. The GF-7 LiDAR data obtained the accuracy of forest terrain elevation with RMSE of 8.01 m when 21 available laser footprints were used for results verification; meanwhile, when it was used to calculate the forest height, R2 of 0.84 and RMSE of 3.2 m were obtained although only seven effective footprints were used for result verification. The canopy height values obtained from GF-7 stereo images have also been proven to have high accuracy with the resolution of 20 m × 20 m compared with ALS data (R2 = 0.88, RMSE = 2.98 m). When the results were verified at the forest sub-compartment scale that taking into account the forest types, further higher accuracy (R2 = 0.96, RMSE = 1.23 m) was obtained. These results show that GF-7 has considerable application potential in forest resources monitoring.

Abstract Objective To investigate the correlation between anthropometric indexes [cardiometabolic index (CMI), lipid accumulation products (LAP), waist triglyceride index (WTI), and body mass index (BMI)] and acute pancreatitis (AP) in a Chinese adult population. Methodology The present investigation consisted of a prospective group including 117,326 subjects who were enrolled in the Kailuan investigation. The individuals were categorized into quartiles based on their baseline levels of CMI, LAP, and WIT. BMI was categorized into three distinctive groups: normal weight group (BMI

Identifying the spatiotemporal distributions and phenotypic characteristics of understory saplings is beneficial in exploring the internal mechanisms of plant regeneration and providing technical assistances for continues cover forest management. However, it is challenging to detect the understory saplings using 2-dimensional (2D) spectral information produced by conventional optical remotely sensed data. This study proposed an automatic method to detect the regenerated understory saplings based on the 3D structural information from aerial laser scanning (ALS) data. By delineating individual tree crown using the improved spectral clustering algorithm, we successfully removed the overstory canopy and associated trunk points. Then, individual understory saplings were segmented using an adaptive-mean-shift-based clustering algorithm. This method was tested in an experimental forest farm of North China. Our results showed that the detection rates of understory saplings ranged from 94.41% to 152.78%, and the matching rates increased from 62.59% to 95.65% as canopy closure went down. The ALS-based sapling heights well captured the variations of field measurements [R2 = 0.71, N = 3,241, root mean square error (RMSE) = 0.26 m, P < 0.01] and terrestrial laser scanning (TLS)-based measurements (R2 = 0.78, N =443, RMSE = 0.23 m, P < 0.01). The ALS-based sapling crown width was comparable with TLS-based measurements (R2 = 0.64, N = 443, RMSE = 0.24 m). This study provides a solution for the quantification of understory saplings, which can be used to improve forest ecosystem resilence through regulating the dynamics of forest gaps to better utilize light resources.

Light-induced phase segregation, particularly when incorporating bromine to widen the bandgap, presents significant challenges to the stability and commercialization of perovskite solar cells. This study explores the influence of hole transport layers, specifically poly[bis(4-phenyl)(2,4,6-trimethylphenyl)amine (PTAA) and [4-(3,6-dimethyl-9H-carbazol-9-yl)butyl]phosphonic acid (Me-4PACz), on the dynamics of phase segregation. Through detailed characterization of the buried interface, we demonstrate that Me-4PACz enhances perovskite photostability, surpassing the performance of PTAA. Nanoscale analyses using in situ Kelvin probe force microscopy and quantitative nanomechanical mapping techniques elucidate defect distribution at the buried interface during phase segregation, highlighting the critical role of substrate wettability in perovskite growth and interface integrity. The integration of these characterization techniques provides a thorough understanding of the impact of the buried bottom interface on perovskite growth and phase segregation.

In gravity inversion, traditional inversion methods usually generate smooth inversion results, that is, there are no obvious boundaries between different geological units. Fuzzy C-Means (FCM) algorithm is introduced into the inversion to solve the problem mentioned above to improve the accuracy and spatial resolution of inversion results. However, when the volume of an anomalous body is much smaller than that of the surrounding rock, and the weight coefficient of the FCM clustering term in the objective function is not selected properly, the algorithm is prone to cause uniform shrinkage of the anomaly inversion results, resulting in lower inversion accuracy, or even failure of the inversion.The main reason for the inversion failure is usually because the total volume of the anomalous bodies is much smaller than the volume of the surrounding rock.For this reason, in this paper, the scaling factor is introduced into the FCM clustering term of the objective function to balance the membership degree of the model parameters to each cluster, so as to reduce the influence of small anomalous body volume compared with the surrounding rock volume. By establishing a simple positive correlation between the scaling exponent ek and the distance snormal from the normalized clustering center and the real clustering center, the scaling factor ρk is continuously updated during the inversion process, which significantly reduces the difficulty in selecting the weight coefficient of the FCM clustering term in the objective function, and avoids the problem of volume shrinkage of the inverted anomalous bodies, thus enhancing the stability of the inversion. The numerical experiments of inversion with theoretical gravity anomaly data and actual data inversion show that the improved algorithm has higher inversion stability and accuracy compared with the previous FCM method.

Abstract Niche for stem cells profoundly influences their maintenance and fate during tissue homeostasis and pathological disorders; however, the underlying mechanisms and tissue‐specific features remain poorly understood. Here, it is reported that fatty acid desaturation catabolized by stearoyl‐coenzyme A desaturase 1 (SCD1) regulates hair follicle stem cells (HFSCs) and hair growth by maintaining the bulge, niche for HFSCs. Scd1 deletion in mice results in abnormal hair growth, an effect exerted directly on keratin K14+ keratinocytes rather than on HFSCs. Mechanistically, Scd1 deficiency impairs the level of integrin α6β4 complex and thus the assembly of hemidesmosomes (HDs). The disruption of HDs allows the aberrant activation of focal adhesion kinase and PI3K in K14+ keratinocytes and subsequently their differentiation and proliferation. The overgrowth of basal keratinocytes results in downward extension of the outer root sheath and interruption of bulge formation. Then, inhibition of PI3K signaling in Scd1−/− mice normalizes the bulge, HFSCs, and hair growth. Additionally, supplementation of oleic acid to Scd1−/− mice reestablishes HDs and the homeostasis of bulge niche, and restores hair growth. Thus, SCD1 is critical in regulating hair growth through stabilizing HDs in basal keratinocytes and thus sustaining bulge for HFSC residence and periodic activity.

Assessing the spatiotemporal changes in forest aboveground biomass (AGB) provides crucial insights for effective forest carbon stock management, an accurate estimation of forest carbon uptake and release balance, and a deeper understanding of forest dynamics and climate responses. However, existing research in this field often lacks a comprehensive methodology for capturing tree-level AGB dynamics using multitemporal remote sensing techniques. In this study, we quantitatively characterized spatiotemporal variations of tree-level AGB in boreal natural secondary forests in the Greater Khingan Mountains region using multitemporal light detection and ranging (LiDAR) data acquired in 2012, 2016, and 2022. Our methodology emphasized improving the accuracy of individual tree segmentation algorithms by taking advantage of canopy structure heterogeneity. We introduced a novel three-dimensional metric, similar to crown width, integrated with tree height to calculate tree-level AGB. Moreover, we address the challenge of underestimating tree-level metrics resulting from low pulse density, ensuring accurate monitoring of AGB changes for every two acquisitions. The results showed that the LiDAR-based ΔAGB explained 62% to 70% of the variance of field-measured ΔAGB at the tree level. Furthermore, when aggregating the tree-level AGB estimates to the plot level, the results also exhibited robust and reasonable accuracy. We identified the average annual change in tree-level AGB and tree height across the study region, quantifying them at 2.23 kg and 0.25 m, respectively. Furthermore, we highlighted the importance of the Gini coefficient, which represents canopy structure heterogeneity, as a key environmental factor that explains relative AGB change rates at the plot level. Our contribution lies in proposing a comprehensive framework for analyzing tree-level AGB dynamics using multitemporal LiDAR data, paving the way for a nuanced understanding of fine-scale forest dynamics. We argue that LiDAR technology is becoming increasingly valuable in monitoring tree dynamics, enabling the application of high-resolution ecosystem dynamics products to elucidate ecological issues and address environmental challenges.

The spectral clustering method has notable advantages in segmentation. But the high computational complexity and time consuming limit its application in large-scale and dense airborne Light Detection and Ranging (LiDAR) point cloud data. We proposed the Nyström-based spectral clustering (NSC) algorithm to decrease the computational burden. This novel NSC method showed accurate and rapid in individual tree segmentation using point cloud data. The K-nearest neighbour-based sampling (KNNS) was proposed for the Nyström approximation of voxels to improve the efficiency. The NSC algorithm showed good performance for 32 plots in China and Europe. The overall matching rate and extraction rate of proposed algorithm reached 69% and 103%. For all trees located by Global Navigation Satellite System (GNSS) calibrated tape-measures, the tree height regression of the matching results showed an value of 0.88 and a relative root mean square error (RMSE) of 5.97%. For all trees located by GNSS calibrated total-station measures, the values were 0.89 and 4.49%. The method also showed good performance in a benchmark dataset with an improvement of 7% for the average matching rate. The results demonstrate that the proposed NSC algorithm provides an accurate individual tree segmentation and parameter estimation using airborne LiDAR point cloud data.

Abstract Mesenchymal stromal cells (MSCs) function as a formidable regulator of inflammation and tissue homeostasis and expanded MSCs are shown to be effective in treating various inflammatory diseases. Their therapeutic effects require the existence of certain inflammatory cytokines. However, in the absence of sufficient proinflammatory stimuli or in the presence of anti‐inflammatory medications, MSCs are animated to promote immune responses and unable to alleviate inflammatory disorders. In this study, it is demonstrated that steroid co‐administration interferes the efficacy of MSCs in treating acute graft‐versus‐host disease (aGvHD). Molecular analysis reveals that vascular endothelial growth factor C (VEGF‐C) is highly induced in MSCs by steroids and TNFα and VEGF‐C in turn promotes CD8+ T cell response. This immune promoting effect is abolished by blockade or specific genetic ablation of VEGFR3 in CD8+ T cells. Additionally, administration of VEGF‐C alone exacerbates aGvHD progression through eliciting more vigorous CD8+ T cell activation and proliferation. Further studies demonstrate that VEGF‐C augments the PI3K/AKT signaling process and the expression of downstream genes, such as Cyclin D1. Thus, the data demonstrate that steroids can reverse the immunosuppressive effect of MSCs via promoting VEGF‐C‐augmented CD8+ T cell response and provide novel information for designing efficacious MSC‐based therapies.

With the increasing demand for mineral resources, there is an inevitable trend to carry out deep prospecting in existing old mines to find a second or even third mining space. Deep prospecting is also an affordable and practical way to prolong the lives of mines and provide a sustainable supply of mineral resources. The magnetic survey is arguably the most effective method for iron ore prospecting. In this paper, a high resolution airborne magnetic (HRAM) survey for deep iron prospecting in the Jinling iron ore cluster (JIOC) was carried out in 2018, which renewed the field magnetic data of the JIOC obtained in the 1980s. From previous studies, almost all iron deposits in the JIOC are spatially distributed in the contact zone between the intrusive rocks and the surrounding rocks. The key prospecting areas were inferred by delineating intrusive rock boundaries via boundary enhancement and edge detection methods, and one of the areas was verified by drilling.

Efficient, high-precision, and automatic measurement of tunnel structural changes is the key to ensuring the safe operation of subways. Conventional manual, static, and discrete measurements cannot meet the requirements of rapid and full-section detection in subway construction and operation. Mobile laser scanning technology is the primary method for tunnel detection. Herein, we propose a method to calculate shield tunnel displacements of a full cross-section tunnel. The point cloud data, obtained via a mobile tunnel deformation detection system, were fitted, projected, and interpolated to generate an orthophoto image. Combined with the cumulative characteristics of the tunnel gray gradient, the longitudinal ring seam of the tunnel was identified, while the Canny algorithm and Hough line detection algorithm identified the transverse seam. The symmetrical vertical foot method and cross-section superposition analysis were used to calculate the circumferential and radial displacements, respectively. The proposed displacement calculation method achieves automatic recognition of a ring seam, reduces human–computer interaction, and is fast, intelligent, and accurate. Furthermore, the description of the tunnel deformation location and deformation amount is more quantitative and specific. These results confirm the significance of shield tunnel displacement monitoring based on mobile monitoring systems in tunnel disease monitoring.

During the breeding seasons of 2010-2016, we have found seven nests of the Booted Eagle (Hieraaetus pennatus) in Xinjiang, the west of China. We used a method of focal sampling and infrared cameras to continually observe behaviors and nestlings’ growth. Nestling behaviors were different between nestling period and post-nestling period. Attendance at the nests by both adults decreased as the nestling aged. The female brooded significantly more than the male did during daylight hours (P=0.016, F= 8.38, df =1). The daily mean number of food items delivered to the nests by adults was 3.2 times/day in nestling period, and 0.96/day in post-nestling period. Seven orders of wild birds, three orders of mammals and domestic poultry were documented as prey.

Eggplant (Solanum melongena L.) is an economically and nutritionally important fruit crop of the Solanaceae family, which was domesticated in India and southern China. However, the genome regions subjected to selective sweeps in eggplant remain unknown. In the present study, we performed comparative transcriptome analysis of cultivated and wild eggplant species with emphasis on the selection pattern during domestication. In total, 44,073 (S. sisymbriifolium) to 58,677 (S. melongena cultivar S58) unigenes were generated for the six eggplant accessions with total lengths of 36.6-46 Mb. The orthologous genes were assessed using the ratio of nonsynonymous (Ka) to synonymous (Ks) nucleotide substitutions to characterize selective patterns during eggplant domestication. We identified 19 genes under positive selection across the phylogeny that were classified into four groups. The gene (OG12205) under positive selection was possibly associated with fruit-related traits in eggplant, which may have resulted from human manipulation. Eight positive selected genes were potentially involved in stress tolerance or disease resistance, suggesting that environmental changes and biotic stresses were important selective pressures in eggplant domestication. Taken together, our results shed light on the effects of artificial and natural selection on the transcriptomes of eggplant and its wild relatives. Identification of the selected genes will facilitate the understanding of genetic architecture of domesticated-related traits and provide resources for resistant breeding in eggplant.

Osteoblasts and adipocytes are derived from a common precursor, mesenchymal stem cells (MSCs). Alterations in the normal fate of differentiating MSCs are involved in the development of obesity and osteoporosis. Here, we report that viable motheaten (mev) mice, which are deficient in the SH2-domain-containing phosphatase-1 (SHP1), develop osteoporosis spontaneously. Consistently, MSCs from mev/mev mice exhibit significantly reduced osteogenic potential and greatly increased adipogenic potential. When MSCs were transplanted into nude mice, SHP1-deficient MSCs resulted in diminished bone formation compared with wild-type MSCs. SHP1 was found to bind to GSK3β and suppress its kinase activity by dephosphorylating pY216, thus resulting in β-catenin stabilization. Mice, in which SHP1 was deleted in MSCs using SHP1fl/flDermo1-cre, displayed significantly decreased bone mass and increased adipose tissue. Taken together, these results suggest a possible role for SHP1 in controlling tissue homeostasis through modulation of MSC differentiation via Wnt signaling regulation.

The common statistical methods for rail tunnel deformation and disease detection usually require a large amount of equipment and manpower to achieve full section detection, which are time consuming and inefficient. The development trend in the industry is to use laser scanning for full section detection. In this paper, a design scheme for a tunnel monitoring and measuring system with laser scanning as the main sensor for tunnel environmental disease and deformation analysis is proposed. The system provides functions such as tunnel point cloud collection, section deformation analysis, dislocation analysis, disease extraction, tunnel and track image generation, roaming video generation, etc. Field engineering indicated that the repeatability of the convergence diameter detection of the system can reach ±2 mm, dislocation repeatability can reach ±3 mm, the image resolution is about 0.5 mm/pixel in the ballast part, and the resolution of the inner wall of the tunnel is about 1.5 mm/pixel. The system can include human−computer interaction to extract and label diseases or appurtenances and support the generation of thematic disease maps. The developed system can provide important technical support for deformation and disease detection of rail transit tunnels.

With the ongoing developments in laser scanning technology, applications for describing tunnel deformation using rich point cloud data have become a significant topic of investigation. This study describes the independently developed a mobile tunnel monitoring system called the second version of Tunnel Scan developed by Capital Normal University (CNU-TS-2) for data acquisition, which has an electric system to control its forward speed and is compatible with various laser scanners such as the Faro and Leica models. A comparison with corresponding data acquired by total station data demonstrates that the data collected by CNU-TS-2 is accurate. Following data acquisition, the overall and local deformation of the tunnel is determined by denoising and 360° deformation analysis of the point cloud data. To enhance the expression of the analysis results, this study proposes an expansion of the tunnel point cloud data into a two-dimensional image via cylindrical projection, followed by an expression of the tunnel deformation through color difference to visualize the deformation. Compared with the three-dimensional modeling method of visualization, this method is easier to implement and facilitates storage. In addition, it is conducive to the performance of comprehensive analysis of problems such as water leakage in the tunnel, thereby achieving the effect of multiple uses for a single image.

It is valuable to study the land use/land cover (LULC) classification for suburbs. The fusion of Light Detection and Ranging (LiDAR) data and aerial imagery is often regarded as an effective method for the LULC classification; however, more in-depth analysis would be required to explore effective information for enhancing the suburban LULC classification. In this study, first, both aerial imageries and point clouds were simultaneously collected. Then, LiDAR-derived models, i.e., normalized digital surface model (nDSM) and surface intensity model (IM), were generated from the elevation and intensity of point clouds. Further, considering the surface characteristics of ground objects in suburb, we proposed a new LiDAR-derived model, namely surface roughness model (RM), to reveal the degree of surface fluctuations. Additionally, various combinations of aerial imageries and the LiDAR-derived data were used to analyze the effects of multi-variable fusion under different scenarios and optimize the multi-variable integration for suburban LULC classification. The mean decrease impurity method was used to identify the importance of variables; three machine learning classifiers, i.e., random forest (RF), k-nearest neighbor (KNN) and artificial neural network (ANN) were adopted in various scenarios. The results were as follows. The fusion of aerial imagery and all the LiDAR-derived models, i.e., nDSM, RM and IM, with RF classifier performs best in the suburban LULC classification (overall accuracy = 84.75%, kappa coefficient = 0.80). Variable importance analysis shows that nDSM has the highest variable importance proportion (VIP) value, followed by RM, IM, and spectral information, indicating the feasibility of this proposed LiDAR-derived model-RM. This research presents effective methods relating to the application of aerial imagery and LiDAR-derived model for the complex suburban surface scenarios.

A rapid, precise and automated means for the regular inspection and maintenance of a large number of tunnels is needed. Based on the depth study of the tunnel monitoring method, the CNU-TS-1 mobile tunnel monitoring system (TS1) is developed and presented. It can efficiently obtain the cross-sections that are orthogonal to the tunnel in a dynamic way, and the control measurements that depend on design data are eliminated. By using odometers to locate the cross-sections and correcting the data based on longitudinal joints of tunnel segment lining, the cost of the system has been significantly reduced, and the interval between adjacent cross-sections can reach 1–2 cm when pushed to collect data at a normal walking speed. Meanwhile, the relative deformation of tunnel can be analyzed by selecting cross-sections from original data. Through the measurement of the actual tunnel, the applicability of the system for tunnel deformation detection is verified, and the system is shown to be 15 times more efficient than that of the total station. The simulation experiment of the tunnel deformation indicates that the measurement accuracy of TS1 for cross-sections is 1.1 mm. Compared with the traditional method, TS1 improves the efficiency as well as increases the density of the obtained points.

The importance of classical CD8+ T cells in tumor eradication is well acknowledged. However, the anti-tumor activity of MHC (major histocompatibility complex) Ib-restricted CD8+ T (Ib-CD8+ T) cells remains obscure. Here, we show that CX3CR1-expressing Ib-CD8+ T cells (Ib-restricted CD8+ T cells) highly express cytotoxic factors, austerely resist exhaustion, and effectively eliminate various tumors. These Ib-CD8+ T cells can be primed by MHC Ia (MHC class Ia molecules) expressed on various cell types for optimal activation in a Tbet-dependent manner. Importantly, MHC Ia does not allogeneically activate Ib-CD8+ T cells, rather, sensitizes these cells for T cell receptor activation. Such effects were observed when MHC Ia+ cells were administered to tumor-bearing Kb−/−Db−/−mice. A similar population of tumoricidal CX3CR1+ CD8+ T cells was identified in wild-type mice and melanoma patients. Adoptive transfer of Ib-CD8+ T cells to wild-type mice inhibited tumor progression without damaging normal tissues. Taken together, we demonstrate that MHC class Ia can prime Ib-CD8+ T cells for robust tumoricidal activities. [ABSTRACT FROM AUTHOR]