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Abstract Nanoscale optoelectrodes hold the potential to stimulate optically individual neurons and intracellular organelles, a challenge that demands both a high-density of photoelectron storage and significant charge injection. Here, we report that zinc porphyrin, commonly used in dye-sensitized solar cells, can be self-assembled into nanorods and then coated by TiO2. The J-aggregated zinc porphyrin array enables long-range exciton diffusion and allows for fast electron transfer into TiO2. The formation of TiO2(e−) attracts positive charges around the neuron membrane, contributing to the induction of action potentials. Far-field cranial irradiation of the motor cortex using a 670 nm laser or an 850 nm femtosecond laser can modulate local neuronal firing and trigger motor responses in the hind limb of mice. The pulsed photoelectrical stimulation of neurons in the subthalamic nucleus alleviates parkinsonian symptoms in mice, improving abnormal stepping and enhancing the activity of dopaminergic neurons. Our results suggest injectable nanoscopic optoelectrodes for optical neuromodulation with high efficiency and negligible side effects.

The durability of reinforced concrete structures is significantly influenced by the effectiveness of small molecular inhibitors in preventing the corrosion of steel reinforcements. In a concrete environment, the passive film on steel bars serves as a critical protective component. In this study, a molecular dynamics (MD) simulation is used to study the inhibition mechanism of chloride ions by common corrosion inhibitors (2-Amino-2-thiazoline) in concrete in an excess chloride solution. The results reveal that inhibitors adsorb onto the steel surface primarily through van der Waals forces, with more than 90% of the adsorption occurring vertically. Despite this strong adsorption, inhibitors alone do not form a protective film. In the presence of chloride ions, which frequently penetrate concrete, the coverage rate of inhibitors on the steel surface decreases from 74% to 64%. Nevertheless, inhibitor molecules still provide substantial protection in chloride-rich concrete environments. Further analysis indicates that inhibitor molecules inhibit chloride ions in two ways. Corrosion inhibitor molecules actively desorb from the steel surface to capture chloride ions and prevent them from approaching. Additionally, inhibitors form a multi-electron layer on the steel surface to enhance passive film protection and hinder chloride ion diffusion through Coulombic interactions.

Species recognition is a crucial part of understanding the abundance and distribution of various organisms and is important for biodiversity conservation and management. Traditional vision-based deep learning-driven species recognition requires large amounts of well-labeled, high-quality image data, the collection of which is challenging for rare and endangered species. In addition, recognition methods designed based on specific species have poor generalization ability and are difficult to adapt to new species recognition scenarios. To address these issues, zero-shot species recognition based on Contrastive Language–Image Pre-training (CLIP) has become a research hotspot. However, previous studies have primarily utilized visual descriptive information and taxonomic information of species to improve zero-shot recognition performance, and the use of geographic distribution characteristics of species to improve zero-shot recognition performance has not been explored. To fill this gap, we proposed a CLIP-driven zero-shot species recognition method that incorporates knowledge of the geographic distribution of species. First, we designed three prompts based on the species geographic distribution statistical data. Then, the latitude and longitude coordinate information attached to each image in the species dataset was converted into addresses, and they were integrated together to form the geographical distribution knowledge of each species. Finally, species recognition results were derived by calculating the similarity after acquiring features by the trained CLIP image encoder and text encoder. We conducted extensive experiments on multiple species datasets from the iNaturalist 2021 dataset, where the zero-shot recognition accuracies of mammals, mollusks, reptiles, amphibians, birds, and insects were 44.96%, 15.27%, 17.51%, 9.47%, 28.35%, and 7.03%, an improvement of 2.07%, 0.48%, 0.35%, 1.12%, 1.64%, and 0.61%, respectively, as compared to CLIP with default prompt. The experimental results show that the fusion of geographic distribution statistical data can effectively improve the performance of zero-shot species recognition, which provides a new way to utilize species domain knowledge.

Automatic recognition of species is important for the conservation and management of biodiversity. However, since closely related species are visually similar, it is difficult to distinguish them by images alone. In addition, traditional species-recognition models are limited by the size of the dataset and face the problem of poor generalization ability. Visual-language models such as Contrastive Language-Image Pretraining (CLIP), obtained by training on large-scale datasets, have excellent visual representation learning ability and demonstrated promising few-shot transfer ability in a variety of few-shot species recognition tasks. However, limited by the dataset on which CLIP is trained, the performance of CLIP is poor when used directly for few-shot species recognition. To improve the performance of CLIP for few-shot species recognition, we proposed a few-shot species-recognition method incorporating geolocation information. First, we utilized the powerful feature extraction capability of CLIP to extract image features and text features. Second, a geographic feature extraction module was constructed to provide additional contextual information by converting structured geographic location information into geographic feature representations. Then, a multimodal feature fusion module was constructed to deeply interact geographic features with image features to obtain enhanced image features through residual connection. Finally, the similarity between the enhanced image features and text features was calculated and the species recognition results were obtained. Extensive experiments on the iNaturalist 2021 dataset show that our proposed method can significantly improve the performance of CLIP’s few-shot species recognition. Under ViT-L/14 and 16-shot training species samples, compared to Linear probe CLIP, our method achieved a performance improvement of 6.22% (mammals), 13.77% (reptiles), and 16.82% (amphibians). Our work provides powerful evidence for integrating geolocation information into species-recognition models based on visual-language models.

Abstract Chronic pain is a major cause of suffering that often accompanies diseases and therapies, affecting approximately 20% of individuals at some point in their lives. However, current treatment modalities, such as anesthetic and antipyretic analgesics, have limitations in terms of efficacy and side effects. Nanomedical technology offers a promising avenue to overcome these challenges and introduce new therapeutic mechanisms. This article reviews the recent research on nanomedicine analgesics, integrating analyses of neuroplasticity changes in neurons and pathways related to the transition from acute to chronic pain. Furthermore, it explores potential future strategies using nanomaterials, aiming to provide a roadmap for new analgesic development and improved clinical pain management. By leveraging nanotechnology, these approaches hold the potential to revolutionize pain treatment by delivering targeted and effective relief while minimizing side effects.

Abstract The treatment of bone defects remains a substantial clinical challenge due to the lack of spatiotemporal management of the immune microenvironment, revascularization, and osteogenic differentiation. Herein, deferoxamine (DFO)‐loaded black phosphorus nanosheets decorated by polydopamine layer are prepared (BPPD) and compounded into gelatin methacrylate/sodium alginate methacrylate (GA) hybrid hydrogel as a smart‐responsive therapeutic system (GA/BPPD) for accelerated bone regeneration. The BPPD nanocomposites served as bioactive components and near‐infrared (NIR) photothermal agents, which conferred the hydrogel with excellent NIR/pH dual‐responsive properties, realizing the stimuli‐responsive release of DFO and PO43 − during bone regeneration. Under the action of NIR‐triggered mild photothermal therapy, the GA/BPPD hydrogel exhibited a positive effect on promoting osteogenesis and angiogenesis, eliminating excessive reactive oxygen species, and inducing macrophage polarization to the M2 phenotype. More significantly, through macrophage M2 polarization‐induced osteoimmune microenvironment, this hydrogel platform could also drive functional cytokine secretion for enhanced angiogenesis and osteogenesis. In vivo experiments further demonstrated that the GA/BPPD system could facilitate bone healing by attenuating the local inflammatory response, increasing the secretion of pro‐healing factors, stimulating endogenous cell recruitment, and accelerating revascularization. Collectively, the proposed intelligent photothermal hydrogel platform provides a promising strategy to reshape the damaged tissue microenvironment for augmented bone regeneration.

Research on wildlife monitoring methods is a crucial tool for the conservation of rare wildlife in China. However, the fact that rare wildlife monitoring images in field scenes are easily affected by complex scene information, poorly illuminated, obscured, and blurred limits their use. This often results in unstable recognition and low accuracy levels. To address this issue, this paper proposes a novel wildlife identification model for rare animals in Giant Panda National Park (GPNP). We redesigned the C3 module of YOLOv5 using NAMAttention and the MemoryEfficientMish activation function to decrease the weight of field scene features. Additionally, we integrated the WIoU boundary loss function to mitigate the influence of low-quality images during training, resulting in the development of the NMW-YOLOv5 model. Our model achieved 97.3% for mAP50 and 83.3% for mAP50:95 in the LoTE-Animal dataset. When comparing the model with some classical YOLO models for the purpose of conducting comparison experiments, it surpasses the current best-performing model by 1.6% for mAP50:95, showcasing a high level of recognition accuracy. In the generalization ability test, the model has a low error rate for most rare wildlife species and is generally able to identify wildlife in the wild environment of the GPNP with greater accuracy. It has been demonstrated that NMW-YOLOv5 significantly enhances wildlife recognition accuracy in field environments by eliminating irrelevant features and extracting deep, effective features. Furthermore, it exhibits strong detection and recognition capabilities for rare wildlife in GPNP field environments. This could offer a new and effective tool for rare wildlife monitoring in GPNP.

With the progressively deteriorating global ecological environment and the gradual escalation of human activities, the survival of wildlife has been severely impacted. Hence, a rapid, precise, and reliable method for detecting wildlife holds immense significance in safeguarding their existence and monitoring their status. However, due to the rare and concealed nature of wildlife activities, the existing wildlife detection methods face limitations in efficiently extracting features during real-time monitoring in complex forest environments. These models exhibit drawbacks such as slow speed and low accuracy. Therefore, we propose a novel real-time monitoring model called WL-YOLO, which is designed for lightweight wildlife detection in complex forest environments. This model is built upon the deep learning model YOLOv5s. In WL-YOLO, we introduce a novel and lightweight feature extraction module. This module is comprised of a deeply separable convolutional neural network integrated with compression and excitation modules in the backbone network. This design is aimed at reducing the number of model parameters and computational requirements, while simultaneously enhancing the feature representation of the network. Additionally, we introduced a CBAM attention mechanism to enhance the extraction of local key features, resulting in improved performance of WL-YOLO in the natural environment where wildlife has high concealment and complexity. This model achieved a mean accuracy (mAP) value of 97.25%, an F1-score value of 95.65%, and an accuracy value of 95.14%. These results demonstrated that this model outperforms the current mainstream deep learning models. Additionally, compared to the YOLOv5m base model, WL-YOLO reduces the number of parameters by 44.73% and shortens the detection time by 58%. This study offers technical support for detecting and protecting wildlife in intricate environments by introducing a highly efficient and advanced wildlife detection model.

The Amur tiger is an important endangered species in the world, and its re-identification (re-ID) plays an important role in regional biodiversity assessment and wildlife resource statistics. This paper focuses on the task of Amur tiger re-ID based on visible light images from screenshots of surveillance videos or camera traps, aiming to solve the problem of low accuracy caused by camera perspective, noisy background noise, changes in motion posture, and deformation of Amur tiger body patterns during the re-ID process. To overcome this challenge, we propose a serial multi-scale feature fusion and enhancement re-ID network of Amur tiger for this task, in which global and local branches are constructed. Specifically, we design a global inverted pyramid multi-scale feature fusion method in the global branch to effectively fuse multi-scale global features and achieve high-level, fine-grained, and deep semantic feature preservation. We also design a local dual-domain attention feature enhancement method in the local branch, further enhancing local feature extraction and fusion by dividing local feature blocks. Based on the above model structure, we evaluated the effectiveness and feasibility of the model on the public dataset of the Amur Tiger Re-identification in the Wild (ATRW), and achieved good results on mAP, Rank-1, and Rank-5, demonstrating a certain competitiveness. In addition, since our proposed model does not require the introduction of additional expensive annotation information and does not incorporate other pre-training modules, it has important advantages such as strong transferability and simple training.

BackgroundThe most prevalent cancer and the second-leading cause of cancer-related mortality in women is breast cancer. Growing interest has been shown in recent years in learning more about the processes behind the development of breast cancer. It has been shown that persistent inflammation may play a significant role in the advancement of breast cancer. However, a comprehensive and objective analysis on the state of inflammation in breast cancer research is still lacking. This study was aim to undertake a bibliometric analysis of breast cancer research associated with inflammation between 2013 and 2022 in order to identify the trends, dynamics, and scientific outputs in the field.MethodsFrom 2013 to 2022, original and review publications on breast cancer and inflammation-associated research were retrieved from the Web of Science Core Collection (WOSCC) database. To examine the position of yearly publications, journals, nations, institutions, and authors, we employed two bibliometric tools (CiteSpace and VOSviewer). After that, by examining keyword visualization and keyword bursts, we determined the hot research fields related to inflammation in breast cancer.Resultswe discovered 6902 publications regarding inflammation in breast cancer by using our retrieval approach. In terms of the number of publications, The United States ranked first in the global study, followed by China and Italy. In terms of institutions, the University of Texas System, UT MD Anderson Cancer Center, and University of California System are in the top 3 for the quantity of publications published. The most popular journal for this field research is “CANCERS.” Ueno NT, Woodward WA, Cristofanilli M, and others have made significant contributions to the understanding of inflammation in breast cancer. In the end, we conducted a biclustering analysis on keywords and discovered three clusters that represent research hotspots.ConclusionAccording to the global trend, the research output of inflammation in breast cancer is increasing. The information provided in this article, including the cooperation network information of authors, nations, journals, and institutions, may help researchers to better understand hotspots and developing patterns in this discipline. At present, the focus of study gradually shifts from “phenotype study” to “therapeutic research”. It is recommended to pay attention to the latest hot spots, such as targeted therapy, antimicrobial activity and nanoparticle.

The reconstruction of large bone defects by bioactive materials without exogenous cells, cytokines or growth factors remains a substantial clinical challenge. Herein, we present the rational design of injectable and adhesive (GMAD/LP) hydrogels composed of gelatin-methacryloyl (GelMA), dopamine-grafted alginate (AD), and polydopamine-functionalized Laponite (Lap@PDA) nanosheets that can act as osteoimmune regulators to engineer the pro-regenerative microenvironment for personalized bone regeneration. The as-fabricated GMAD/LP hydrogel with excellent tissue adhesion, self-healing, injectability, and improved mechanical strength can fill irregular bone defects in a minimally invasive manner. Moreover, the hydrogel exhibited outstanding osteogenic potential and osteoimmunomodulatory functions in vitro, significantly promoting the adhesion, proliferation, spreading, and osteogenic differentiation of bone mesenchymal stem cells (BMSCs), which was further augmented by hydrogel-induced macrophage M2-phenotype polarization and effective M2 macrophage-BMSC crosstalk. In vivo experiments in a cranial defect model demonstrated that the GMAD/LP hydrogel induced a reparative microenvironment similar to that in normal cranium, as characterized by an increased percentage of anti-inflammatory M2 macrophages and endogenous stem cells and high-level neovascularization, consequently accelerating bone healing. This strategy not only achieves a bioactive hydrogel for bone defect reconstruction but also provides a feasible methodology to recover injured bone tissue through integrated design with diverse functions.

Abstract Osteogenesis and osteoclastogenesis are closely associated during the bone regeneration process. The development of multifunctional bone repair scaffolds with dual therapeutic actions (pro-osteogenesis and anti-osteoclastogenesis) is still a challenging task for bone tissue engineering applications. Herein, through a facile surface coating process, mussel-inspired polydopamine (PDA) is adhered to the surface of a biocompatible porous scaffold followed by the immobilization of a small-molecule activator (LYN-1604 (LYN)) and the subsequent in situ coprecipitation of hydroxyapatite (HA) nanocrystals. PDA, acting as an intermediate bridge, can provide strong LYN immobilization and biomineralization ability, while LYN targets osteoclast precursor cells to inhibit osteoclastic differentiation and functional activity, which endows LYN/HA-coated hybrid scaffolds with robust anti-osteoclastogenesis ability. Due to the synergistic effects of the LYN and HA components, the obtained three-dimensional hybrid scaffolds exhibited the dual effects of osteoclastic inhibition and osteogenic stimulation, thereby promoting bone tissue repair. Systematic characterization experiments confirmed the successful fabrication of LYN/HA-coated hybrid scaffolds, which exhibited an interconnected porous structure with nanoroughened surface topography, favorable hydrophilicity, and improved mechanical properties, as well as the sustained sequential release of LYN and Ca ions. In vitro experiments demonstrated that LYN/HA-coated hybrid scaffolds possessed satisfactory cytocompatibility, effectively promoting cell adhesion, spreading, proliferation, alkaline phosphatase activity, matrix mineralization, and osteogenesis-related gene and protein secretion, as well as stimulating angiogenic differentiation of endothelial cells. In addition to osteogenesis, the engineered scaffolds also significantly reduced osteoclastogenesis, such as tartrate-resistant acid phosphatase activity, F-actin ring staining, and osteoclastogenesis-related gene and protein secretion. More importantly, in a rat calvarial defect model, the newly developed hybrid scaffolds significantly promoted bone repair and regeneration. Microcomputed tomography, histological, and immunohistochemical analyses all revealed that the LYN/HA-coated hybrid scaffolds possessed not only reliable biosafety but also excellent osteogenesis-inducing and osteoclastogenesis-inhibiting effects, resulting in faster and higher-quality bone tissue regeneration. Taken together, this study offers a powerful and promising strategy to construct multifunctional nanocomposite scaffolds by promoting osteo/angiogenesis and suppressing osteoclastogenesis to accelerate bone regeneration.

Highlights DECT mono+ optimal energy level can preoperatively determine the position of the IMA root. DECT mono+ can preoperatively evaluate the anatomical characteristics of the IMA. DECT mono+ can preoperatively determine the positional relationship between the LCA and IMV at the IMA root level.

Concrete mixture design has been a key focus in concrete research. This study presents a new method for concrete mixture design by combining artificial neural networks (ANN), genetic algorithms (GA), and Scipy libraries for hybrid intelligent modeling. This method enables the prediction of concrete mechanical properties and the optimization of mix proportions with single or multi-objective goals. The GA is used to optimize the structure and weight parameters of ANN to improve prediction accuracy and generalization ability (R2 > 0.95, RMSE and MAE < 10). Then, the Scipy library combined with GA-ANN is used for the multi-objective optimization of concrete mix proportions to balance the compressive strength and costs of concrete. Moreover, an AI-based concrete mix proportion design system is developed, utilizing a user-friendly GUI to meet specific strength requirements and adapt to practical needs. This system enhances optimization design capabilities and sets the stage for future advancements. Overall, this study focuses on optimizing concrete mixture design using hybrid intelligent modeling and multi-objective optimization, which contributes to providing a novel and practical solution for improving the efficiency and accuracy of concrete mixture design in the construction industry.

Eucalyptus plantation forests in southern China provide not only the economic value of producing timber, but also the ecological value service of absorbing carbon dioxide and releasing oxygen. Based on the theory of spatial colonial modeling, this paper proposes a new method for 3D reconstruction of tree terrestrial LiDAR point clouds for determining the aboveground carbon stock of eucalyptus monocotyledons, which consists of the main steps of tree branch and trunk separation, skeleton extraction and optimization, 3D reconstruction, and carbon stock calculation. The main trunk and branches of the tree point clouds are separated using a layer-by-layer judgment and clustering method, which avoids errors in judgment caused by sagging branches. By optimizing and adjusting the skeleton to remove small redundant branches, the near-parallel branches belonging to the same tree branch are fused. The missing parts of the skeleton point clouds were complemented using the cardinal curve interpolation algorithm, and finally a real 3D structural model was generated based on the complemented and smoothed tree skeleton expansion. The bidirectional Hausdoff distance, average Hausdoff distance, and F distance were used as evaluation indexes, which were reduced by 0.7453 m, 0.0028 m, and 0.0011 m, respectively, and the improved spatial colonization algorithm enhanced the accuracy of the reconstructed tree 3D structural model. To verify the accuracy of our method to determine the carbon stock and its related parameters, we cut down 41 eucalyptus trees and destructively sampled the measurement data as reference values. The R2 of the linear fit between the reconstructed single-tree aboveground carbon stock estimates and the reference values was 0.96 with a CV(RMSE) of 16.23%, the R2 of the linear fit between the trunk volume estimates and the reference values was 0.94 with a CV(RMSE) of 19.00%, and the R2 of the linear fit between the branch volume estimates and the reference values was 0.95 with a CV(RMSE) of 38.84%. In this paper, a new method for reconstructing eucalyptus carbon stocks based on TLS point clouds is proposed, which can provide decision support for forest management and administration, forest carbon sink trading, and emission reduction policy formulation.

The vertical structure of forest ecosystems influences and reflects ecosystem functioning. Terrestrial laser scanning (TLS) enables the rapid acquisition of 3D forest information and subsequent reconstruction of the vertical structure, which provides new support for acquiring forest vertical structure information. We focused on artificial forest sample plots in the north-central of Nanning, Guangxi, China as the research area. Forest sample point cloud data were obtained through TLS. By accurately capturing the gradient information of the forest vertical structure, a classification boundary was delineated. A complex forest vertical structure segmentation method was proposed based on the Forest-PointNet model. This method comprehensively utilized the spatial and shape features of the point cloud. The study accurately segmented four types of vertical structure features in the forest sample location cloud data: ground, bushes, trunks, and leaves. With optimal training, the average classification accuracy reaches 90.98%. The results indicated that segmentation errors are mainly concentrated at the branch intersections of the canopy. Our model demonstrates significant advantages, including effective segmentation of vertical structures, strong generalization ability, and feature extraction capability.

Recently, strategies that focus on biofunctionalized implant surfaces to enhance bone defect healing through the synergistic regulation of osteogenesis, angiogenesis, and osteoclastogenesis have attracted increasing attention in the bone tissue engineering field. Studies have shown that the Wnt/β-catenin signaling pathway has an imperative effect of promoting osteogenesis and angiogenesis while reducing osteoclastogenesis. However, how to prepare biofunctionalized bone implants with balanced osteogenesis, angiogenesis, and osteoclastogenesis by activating the Wnt/β-catenin pathway has seldom been investigated. Herein, through a bioinspired dopamine chemistry and self-assembly method, BML-284 (BML), a potent and highly selective Wnt signaling activator, was loaded on a mussel-inspired polydopamine (PDA) layer that had been immobilized on the porous beta-tricalcium calcium phosphate (β-TCP) scaffold surface and subsequently modified by a biocompatible carboxymethyl chitosan hydrogel to form a sandwich-like hybrid surface. β-TCP provides a biomimetic three-dimensional porous microenvironment similar to that of natural cancellous bone, and the BML-loaded sandwich-like hybrid surface endows the scaffold with multifunctional properties for potential application in bone regeneration. The results show that the sustained release of BML from the sandwich-like hybrid surface significantly facilitates the adhesion, migration, proliferation, spreading, and osteogenic differentiation of MC3T3-E1 cells as well as the angiogenic activity of human umbilical vein endothelial cells. In addition to osteogenesis and angiogenesis, the hybrid surface also exerts critical roles in suppressing osteoclastic activity. Remarkably, in a critical-sized cranial defect model, the biofunctionalized β-TCP scaffold could potentially trigger a chain of biological events: stimulating the polarization of M2 macrophages, recruiting endogenous stem cells and endothelial cells at the injury site to enable a favorable microenvironment for greatly accelerating bone ingrowth and angiogenesis while compromising osteoclastogenesis, thereby promoting bone healing. Therefore, these surface-biofunctionalized β-TCP implants, which regulate the synergies of osteogenesis, angiogenesis, and anti-osteoclastogenesis, indicate strong potential for clinical application as advanced orthopedic implants.

Peripheral nerve regeneration requires stepwise and well-organized establishment of microenvironment. Since local delivery of VEGF-A in peripheral nerve repair is expected to promote angiogenesis in the microenvironment and Schwann cells (SCs) play critical role in nerve repair, combination of VEGF and Schwann cells may lead to efficient peripheral nerve regeneration. VEGF-A overexpressing Schwann cells were established and loaded into the inner wall of hydroxyethyl cellulose/soy protein isolate/polyaniline sponge (HSPS) conduits. When HSPS is mechanically distorted, it still has high durability of strain strength, thus, can accommodate unexpected strain of nerve tissues in motion. A 10 mm nerve defect rat model was used to test the repair performance of the HSPS-SC (VEGF) conduits, meanwhile the HSPS, HSPS-SC, HSPS-VEGF conduits and autografts were worked as controls. The immunofluorescent co-staining of GFP/VEGF-A, Ki67 and MBP showed that the VEGF-A overexpressing Schwann cells could promote the proliferation, migration and differentiation of Schwann cells as the VEGF-A was secreted from the VEGF-A overexpressing Schwann cells. The nerve repair performance of the multifunctional and flexible conduits was examined though rat behavioristics, electrophysiology, nerve innervation to gastrocnemius muscle (GM), toluidine blue (TB) staining, transmission electron microscopy (TEM) and NF200/S100 double staining in the regenerated nerve. The results displayed that the effects on the repair of peripheral nerves in HSPS-SC (VEGF) group was the best among the conduits groups and closed to autografts. HSPS-SC (VEGF) group exhibited notably increased CD31+ endothelial cells and activation of VEGFR2/ERK signaling pathway in the regenerated nerve tissues, which probably contributed to the improved nerve regeneration. Altogether, the comprehensive strategy including VEGF overexpressing Schwann cells-mediated and HSPS conduit-guided peripheral nerve repair provides a new avenue for nerve tissue engineering.

Objective: Metastatic colon cancer (mCC) poses a great threat to the survival of patients suffering from it. In the past decade, many clinical trials have been carried out to improve the prognosis of patients with mCC. Numerous treatments have emerged, and satisfactory efficacy has been demonstrated in randomized phase III trials in highly selective patients with mCC. Our present study aims to investigate whether these therapeutic advances can be reflected to the broader mCC patients who performed cytoreductive colectomy.Method: General and prognostic data for patients diagnosed with mCC who underwent cytoreductive colectomy between 2004–2018 were extracted from the Surveillance, Epidemiology, and End Results (SEER) database. Survival was analyzed using the Kaplan-Meier method and Cox proportional hazards model. The hazard ratio (HR) and its 95% confidence interval (CI) were used to evaluate the influence of risk factors on prognosis.Results: A total of 26,301 patients diagnosed with mCC treated with cytoreductive colectomy were included in this study. The median overall survival was 19 months (range, 17–23). The good prognosis was associated with patients diagnosed at the most recent year, younger age, non-black race, female, married, without previous history of malignancy, no second malignancy onset, descending/sigmoid/splenic flexure colon tumor, normal CEA levels at diagnosis, low primary tumor burden, T1/T2 stage, N0 stage, single organ metastasis, underwent surgical resection of synchronous distant metastatic lymph nodes or organs, a high number of lymph-node examinations, low positive lymph-node ratio and received adjuvant chemotherapy. The proportion of patients surviving for ≥24 months increased from 37% in 2004 to 44.2% in 2016 (p < 0.001), especially in ≤49 years patients [46.8% in 2004 to 57.8% in 2016 (p < 0.001)]. The percentage of patients who died within 3 months decreased between 2004 and 2018 (from 19.6% to 15.7%; p < 0.001).Conclusion: Over a span of 15 years, the long-term survival has improved in real-world mCC patients who were treated with cytoreductive colectomy, especially among younger patients. However, the median overall survival remains not substantial.

Hemorrhage, infection, and frequent replacement of dressings bring great clinical challenges to wound healing. In this work, Flammulina velutipes extract (FV) and hydroxyethyl cellulose (HEC) were chemically cross-linked and freeze-dried to obtain novel HFV cryogels (named HFVn, with n = 10, 40, or 70 corresponding to the weight percentage of the FV content), which were constructed for wound hemostasis and full-thickness skin defect repair. Systematic characterization experiments were performed to assess the morphology, mechanical properties, hydrophilic properties, and degradation rate of the cryogels. The results indicated that HFV70 showed a loose interconnected-porous structure and exhibited the highest porosity (95%) and water uptake ratio (over 2,500%) with a desirable degradation rate and shape memory properties. In vitro cell culture and hemocompatibility experiments indicated that HFV70 showed improved cytocompatibility and hemocompatibility. It can effectively mimic the extracellular matrix microenvironment and support the adhesion and proliferation of L929 cells, and its hemolysis rate in vitro was less than 5%. Moreover, HFV70 effectively induced tube formation in HUVEC cells in vitro. The results of the bacteriostatic annulus confirmed that HFV70 significantly inhibited the growth of Gram-negative E. coli and Gram-positive S. aureus. In addition, HFV70 showed ideal antioxidant properties, with the DPPH scavenging rate in vitro reaching 74.55%. In vivo rat liver hemostasis experiments confirmed that HFV70 showed rapid and effective hemostasis, with effects comparable to those of commercial gelatin sponges. Furthermore, when applied to the repair of full-thickness skin defects in a rat model, HFV70 significantly promoted tissue regeneration. Histological analysis further confirmed the improved pro-angiogenic and anti-inflammatory activity of HFV70 in vivo. Collectively, our results demonstrated the potential of HFV70 in the treatment of full-thickness skin defects and rapid hemostasis.

Bioinspired materials with dual capabilities of pro-osteogenesis and pro-angiogenesis have attracted increasing scientific interest in the field of orthopedics. Herein, inspired by the mineralization process of natural bone, a biomimetic strategy is presented to fabricate highly bioactive hybrid nanocomposite scaffolds via impregnation of Si-containing polymer-induced liquid precursor (PILP) into biocompatible porous gelatin scaffolds followed by in situ free radial polymerization. The well-designed biohybrid scaffold exhibited not only a micro/nano fiber-like porous architecture (pore size: 100–200 μm; porosity: 80–90 %), high specific surface area (≈20 m2/g), vastly improved mechanical performance, tunable swelling and degradation behaviors but also a sustained release profile of bioactive Si ions up to 21 days. In vitro experiments revealed the excellent biological performance of the resulting biohybrid scaffold, including good biomineralization capacity and biocompatibility, establishing a favorable microenvironment for facilitating cell adhesion, proliferation, migration, osteogenic differentiation, and vascularization. In rat calvarial critical-size defect models, the newly developed biohybrid scaffold could potentially trigger a chain of biological events: stimulating the polarization of M2 macrophages, recruiting endogenous stem cells and endothelial cells at the injury site to enable a suitable regenerative microenvironment for accelerating coupled osteogenesis and angiogenesis, and eventually promoting vascularized bone regeneration.

Complexity of forest structure is an important factor contributing to uncertainty in aboveground biomass estimates. In this study, we present a new method for reducing uncertainty in forest aboveground biomass (AGB) estimation based on plot-level terrestrial laser scanner (TLS) point clouds reconstruction. The method estimates the total AGB of plots with complex structures after automatically performing the steps of ground point filtering, single tree segmentation, and three-dimensional (3D) structure reconstruction. We used plot data from temperate and tropical forest ecosystems to verify the effectiveness of the method, reconstructing a 1300 m2 temperate plantation plot and a 5000 m2 mingled forest plot, respectively. The total biomass of 153 trees in the plantation plot was overestimated by 17.12 %, and the total biomass of 61 trees in the mingled forest plot was underestimated by 10.88 %. We found that the uncertainty of aboveground biomass estimation in tropical forests with more complex structures is not necessarily greater than in plantations. Therefore, in large-scale remote sensing observations of forest biomass, the number or area of plots can be increased to reduce the uncertainty of the results caused by the complex structure. The focus of this study is to explore TLS point clouds modeling methods to reduce the uncertainty in AGB estimation caused by the complexity of forest structures, and to provide reference cases for plot-level point clouds reconstruction methods. Forest ecologists can use this method to regularly observe forest growth and obtain indicators related to forest ecology without destroying trees.

Lithological mapping is a crucial tool for exploring minerals, reconstructing geological formations, and interpreting geological evolution. The study aimed to investigate the application of the back propagation neural network (BPNN) and particle swarm optimization (PSO) algorithm in lithological mapping. The study area is the Beiliutumiao map-sheet (No. K49E011021) in Inner Mongolia, China. This area was divided into two parts, with the left side used for training and the right side used for validation. Fifteen geological relevant factors, including geochemistry (1:200,000-scale) and geophysics (1:50,000-scale), were used as predictor variables. Taking one lithology as an example, the lithological binary mapping method was introduced in detail, and then the complete lithology was mapped. The model was compared with commonly used spatial data mining methods using the E-measure, S-measure, and Weighted F-measure values. In diorite testing, the accuracy and kappa of the optimized model were 92.11% and 0.81, respectively. The validation results showed that our method outperformed the traditional BPNN and weights-of-evidence approaches. In the extension of the complete lithological mapping, the accuracy, recall, and F1-score were 82.66%, 74.54%, and 0.76, respectively. Thus, the proposed method is useful for predicting the distribution of one lithology and completing the whole lithological mapping at a fine scale. In addition, the trained network can be extended to an adjacent area with similar lithological features.

Abstract Background The Interaction between severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) spike protein with Angiotensin converting enzyme 2 (ACE2) on the host cells is a crucial step for the viral entry and infection. Therefore, investigating the molecular mechanism underlying the interaction is of great importance for the prevention of the infection of SARS-CoV-2. In this study, we aimed to establish a virus-free in vitro system to study the interaction between the spike protein and host cells of SARS-CoV-2. Results Our results show that ACE2-overexpressing HEK293T cells are captured by immobilized spike S1 protein, and the cell capturing process can be inhibited by the receptor binding domain of the spike protein or antibodies against S protein. Furthermore, spike S1 protein variant with D614G mutant show a higher cell capturing ability than wild type spike S1 protein and stronger binding capacity of its receptor ACE2. In addition, the captured cells can be eluted as living cells for further investigation. Conclusions This study provides a new in vitro system for investigating the interaction between SARS-CoV-2 and host cells and purifying ACE2-expressing cells.

Abstract Peripheral nerve injury is a serious health problem and repairing long nerve deficits remains a clinical challenge nowadays. Nerve guidance conduit (NGC) serves as the most promising alternative therapy strategy to autografts but its repairing efficiency needs improvement. In this study, we investigated whether modulating the immune microenvironment by Interleukin-17F (IL-17F) could promote NGC mediated peripheral nerve repair. Chitosan conduits were used to bridge sciatic nerve defect in IL-17F knockout mice and wild-type mice with autografts as controls. Our data revealed that IL-17F knockout mice had improved functional recovery and axonal regeneration of sciatic nerve bridged by chitosan conduits comparing to the wild-type mice. Notably, IL-17F knockout mice had enhanced anti-inflammatory macrophages in the NGC repairing microenvironment. In vitro data revealed that IL-17F knockout peritoneal and bone marrow derived macrophages had increased anti-inflammatory markers after treatment with the extracts from chitosan conduits, while higher pro-inflammatory markers were detected in the Raw264.7 macrophage cell line, wild-type peritoneal and bone marrow derived macrophages after the same treatment. The biased anti-inflammatory phenotype of macrophages by IL-17F knockout probably contributed to the improved chitosan conduit guided sciatic nerve regeneration. Additionally, IL-17F could enhance pro-inflammatory factors production in Raw264.7 cells and wild-type peritoneal macrophages. Altogether, IL-17F may partially mediate chitosan conduit induced pro-inflammatory polarization of macrophages during nerve repair. These results not only revealed a role of IL-17F in macrophage function, but also provided a unique and promising target, IL-17F, to modulate the microenvironment and enhance the peripheral nerve regeneration.

Hydrogels with adhesive properties have the potential for rapid haemostasis and wound healing in uncontrolled non-pressurized surface bleeding. Herein, a typical hydrogen bond-crosslinked hydrogel with the above functions was constructed by directly mixing solutions of humic acid (HA) and polyvinylpyrrolidone (PVP), in which the HA worked as a crosslinking agent to form hydrogen bonds with the PVP. By altering the concentration of HA, a cluster of stable and uniform hydrogels were prepared within 10 s. The dynamic and reversible nature of the hydrogen bonds gave the HA/PVP complex (HPC) hydrogels injectability and good flexibility, as well as a self-healing ability. Moreover, the numerous functional groups in the hydrogels enhanced the cohesion strength and interaction on the interface between the hydrogel and the substrate, endowing them with good adhesion properties. The unique chemical composition and cross-linking mechanism gave the HPC hydrogel good biocompatibility. Taking advantage of all these features, the HPC hydrogels obtained in this work were broadly applied as haemostatic agents and showed a good therapeutic effect. This work might lead to an improvement in the development of multifunctional non-covalent hydrogels for application to biomaterials.

The recognition and classification of rock lithology is an extremely important task of geological surveys. This paper proposes a new method for quickly identifying multiple types of rocks suitable for geological survey work field. Based on the two lightweight convolutional neural networks (CNNs), SqueezeNet and MobileNets, and combined with the transfer learning method, a rock lithology recognition model was established. The model was embedded into a smart phone for testing. This method was used to identify and classify the images of 28 kinds of rocks. Through a comprehensive comparison of the two models, the accuracy of SqueezeNet and MobileNets in the test dataset is 94.55% and 93.27%, respectively. Via the two models, the average recognition time of a single rock image is 557 and 836 milliseconds, and rock images with a recognition accuracy of over 96% accounted for 95% and 93% of the entire test dataset. Compared with the classification method based on rock thin section images, this method does not need to make rock thin sections. This paper meets the requirements of workers to quickly and accurately identify rocks in the work field, which improves the work efficiency and limits identification costs.

There have been some limitations in acquiring an accurate representation of remotely sensed data-derived soil moisture. Here, we propose a simplified thermal inertia model (STIM). This model requires only the albedo and surface maximum temperature easily obtained from satellite imagery, such as that of the moderate resolution imaging spectroradiometer (MODIS). In this study, we defined a soil moisture index (SMI) using the MODIS imagery to obtain a simplified thermal inertia-ratio vegetation index spectral feature space. SMI results from STIM were validated at several locations in the study area of western Inner Mongolia and compared with those from the apparent thermal inertia (ATI) model. Our results showed that our SMI model could explain 71% of the variance in the surface soil moisture, approximately 5% higher than that of the ATI model. In a comparison of field-measured soil moisture data with data simulated using two methods, the SMI and ATI, the SMI showed better retrieval accuracy by lessening the effective error due to the vegetation by 4.2%-10.8%, whereas soil moisture data simulated with ATI showed an effective error of 4.5%-17.0%. The SMI model was also used to map soil moisture; a relative root-mean-square error of 7.67% was recorded for the region, implying the ability of the model to map soil moisture over large areas. Here, the proposed SMI model was proven to be more suitable for estimating soil moisture in locations in which the vegetation index values ranged from 0 to 3. Thus, the proposed SMI model provides a new approach using remote sensing thermal inertia methods to quantify soil moisture at the regional scale.

Au metal nanoparticles as artificial nanozymes have attracted wide interest in biotechnology due to high stability and easy synthesis. Unfortunately, its catalytic activity is limited by the uniform surface electron distribution, fundamentally affecting the oxidation efficiency of glucose. Here, we synthesized AuPt bimetallic nanoparticles with unique surface electron structure due to the coupling effect of the two metal components, achieving improved glucose catalytic oxidase. Because of the effective work function difference between the two metals in AuPt, the electrons will transfer from Au to accumulate on Pt, simultaneously contributing to the substantial enhancement of Au-induced glucose oxidase and Pt-induced catalase performance. We systematically studied the enzyme-catalytic efficiency of AuPt with varied two metal proportions, in which Au:Pt at 3:1 showed the highest catalytic efficiency of glucose oxidase in solution. The AuPt nanoparticles were further co-cultured with cells and also showed excellent biological activity for glucose oxidase. This work demonstrates that the physicochemical properties between different metals can be exploited for engineering high-performance metal nanoparticle-based nanozymes, which opens up a new way to rationally design and optimize artificial nanozymes to mimic natural enzymes.

Biomimetic organic-inorganic nanocomposite scaffolds represent a tremendous opportunity to accelerate bone regeneration due to their excellent structural and biological cues. However, the tradeoffs between biodegradability, mechanical strength, porosity and pore size, and bioactivity of the tissue-engineered nanocomposites remain a challenge. To this end, we constructed a novel biodegradable nanocomposite scaffold by chemical crosslinking of hydroxyethyl cellulose (HEC)/soy protein isolate (SPI) and montmorillonite (MMT) modification. Owing to the strong interfacial interaction between nanoclay and polymer chains, the prepared nanocomposites showed enhanced mechanical and physical properties, within the ideal range for bone tissue engineering. Furthermore, sustained sequential release of Ca and Mg ions during the degradation of the nanocomposites provided essential signaling cues for guiding bone regeneration. It was verified in vitro that the engineered nanocomposites possessed satisfactory cytocompatibility, offering a highly desirable microenvironment for cellular behaviors, including adhesion, proliferation, and spreading, as well as promoting the mineralization and osteoblastic differentiation of rat bone marrow stem cells. Notably, when employed to repair critical-sized cranial defects in rat models, the nanocomposites demonstrated not only reliable biosafety but also significant osteointegration and bone-forming ability in vivo. Collectively, our results confirmed that the incorporation of inorganic MMT nanosheets into biocompatible HEC/SPI scaffolds is a viable strategy for fabricating a suitable biomaterial that enhances the regeneration of large bone defects.

Terrestrial laser scanning (TLS) can provide accurate and detailed three-dimensional (3D) structure information of the forest understory. Segmenting individual trees from disordered, discrete, and high-density TLS point clouds is the premise for obtaining accurate individual tree structure parameters of forest understory, pest control and fine modeling. In this study, we propose a bottom-up method to segment individual trees from TLS forest data based on density-based spatial clustering of applications with noise (DBSCAN). In addition, we also improve the DBSCAN based on the distance distribution matrix (DDM) to automatically and adaptively determine the optimal cluster number and the corresponding input parameters. Firstly, the proposed method is based on the improved DBSCAN to detect the trunks and obtain the initial clustering results. Then, the Hough circle fitting method is used to modify the trunk detection results. Finally, individual tree segmentation is realized based on regional growth layer-by-layer clustering. In this paper, we use TLS multi-station scanning data from Chinese artificial forest and German mixed forest, and then evaluate the efficiency of the method from three aspects: overall segmentation, trunk detection and small tree segmentation. Furthermore, the proposed method is compared with three existing individual tree segmentation methods. The results show that the total recall, precision, and F1-score of the proposed method are 90.84%, 95.38% and 0.93, respectively. Compared with traditional DBSCAN, recall, accuracy and F1-score are increased by 6.96%, 4.14% and 0.06, respectively. The individual tree segmentation result of the proposed method is comparable to those of the existing methods, and the optimal parameters can be automatically extracted and the small trees under tall trees can be accurately segmented.

Peripheral nerve injury is a common clinical neurological disease. In our previous study, highly oriented poly (L-lactic acid) (PLLA)/soy protein isolate (SPI) nanofiber nerve conduits were constructed and exhibited a certain repair capacity for peripheral nerve injury. In order to further improve their nerve repairing efficiency, the bone mesenchymal stem cells (BMSCs) overexpressing brain derived neurotrophic factor (BDNF) and glial cell line-derived neurotrophic factor (GDNF) were introduced into the conduits as seed cells and then were used to repair the 10-mm sciatic nerve defects in rats. The nerve repair efficiency of the functional nerve conduits was evaluated by gait experiment, electrophysiological test, and a series of assays such as hemotoxylin-eosin (HE) staining, immunofluorescence staining, toluidine blue (TB) staining, transmission electron microscopy (TEM) observation of regenerated nerve and Masson’s trichrome staining of gastrocnemius muscle. The results showed that the conduits containing BMSCs overexpressing BDNF and GDNF double-factors group had better nerve repairing efficiency than blank BMSCs and single BDNF or GDNF factor groups, and superior to autografts group in some aspects. These data demonstrated that BDNF and GDNF produced by BMSCs could synergistically promote peripheral nerve repair. This study shed a new light on the conduits and stem cells-based peripheral nerve repair.

Nerve regeneration remains a challenge to the treatment of peripheral nerve injury. Electrical stimulation (ES) is an assistant treatment to enhance recovery from peripheral nerve injury. A conductive nerve guide conduit was prepared from hydroxyethyl cellulose (HEC)/soy protein isolate (SPI)/PANI sponge (HSPS) and then the HSPS conduits were used to repair 10 mm sciatic nerve injury in rat model with or without ES, using HSPS+brain-derived neurotrophic factor (BDNF) and autografts as controls. The nerve repairing capacities were evaluated by animal experiments of behavioristics, electrophysiology, toluidine blue staining, and transmission electron microscopy (TEM) in the regenerated nerves. The results revealed that the nerve regeneration efficiency of HSPS conduits with ES (HSPS+ES) group was the best among the conduit groups but slightly lower than that of autografts group. HSPS+ES group even exhibited notably increased in the BDNF expression of regenerated nerve tissues, which was also confirmed through in vitro experiments that exogenous BDNF could promote Schwann cells proliferation and MBP protein expression. As a result, this work provided a strategy to repair nerve defect using conductive HSPS as nerve guide conduit and using ES as an extrinsic physical cue to promote the expression of endogenous BDNF.

The quantitative structure model (QSM) contains the branch geometry and attributes of the tree. AdQSM is a new, accurate, and detailed tree QSM. In this paper, an automatic modeling method based on AdQSM is developed, and a low-cost technical scheme of tree structure modeling is provided, so that AdQSM can be freely used by more people. First, we used two digital cameras to collect two-dimensional (2D) photos of trees and generated three-dimensional (3D) point clouds of plot and segmented individual tree from the plot point clouds. Then a new QSM-AdQSM was used to construct tree model from point clouds of 44 trees. Finally, to verify the effectiveness of our method, the diameter at breast height (DBH), tree height, and trunk volume were derived from the reconstructed tree model. These parameters extracted from AdQSM were compared with the reference values from forest inventory. For the DBH, the relative bias (rBias), root mean square error (RMSE), and coefficient of variation of root mean square error (rRMSE) were 4.26%, 1.93 cm, and 6.60%. For the tree height, the rBias, RMSE, and rRMSE were—10.86%, 1.67 m, and 12.34%. The determination coefficient (R2) of DBH and tree height estimated by AdQSM and the reference value were 0.94 and 0.86. We used the trunk volume calculated by the allometric equation as a reference value to test the accuracy of AdQSM. The trunk volume was estimated based on AdQSM, and its bias was 0.07066 m3, rBias was 18.73%, RMSE was 0.12369 m3, rRMSE was 32.78%. To better evaluate the accuracy of QSM’s reconstruction of the trunk volume, we compared AdQSM and TreeQSM in the same dataset. The bias of the trunk volume estimated based on TreeQSM was −0.05071 m3, and the rBias was −13.44%, RMSE was 0.13267 m3, rRMSE was 35.16%. At 95% confidence interval level, the concordance correlation coefficient (CCC = 0.77) of the agreement between the estimated tree trunk volume of AdQSM and the reference value was greater than that of TreeQSM (CCC = 0.60). The significance of this research is as follows: (1) The automatic modeling method based on AdQSM is developed, which expands the application scope of AdQSM; (2) provide low-cost photogrammetric point cloud as the input data of AdQSM; (3) explore the potential of AdQSM to reconstruct forest terrestrial photogrammetric point clouds.

This study was conducted to elucidate how changes in critical soil quality characteristics relate to continuous rice cultivation and changes in cropping systems and fertilization in South China over the time span 1980 to 2017. Soil pH, soil organic matter (SOM), total nitrogen (TN), available phosphorus (AP) and potassium (AK) were determined for the samples taken in 2017 and compared to results from the paired samples collected in 1980 by grouping all samples into four cropping systems: continuous paddy fields, new paddy fields developed from uplands, continuous uplands, and new uplands developed from paddy soils. The results show a significant increase in soil pH, AP and AK in all cropping systems, which was, however, coupled with low fertilizer use efficiency. Additionally, a significant increase in SOM came with new paddy soils, whereas a little SOM accumulation and a significantly weakened correlation of TN to SOM occurred in continuous paddy soils. Both low fertilizer use efficiency and deteriorated soil C sequestration function imply a sub-health status of continuous paddy soils. The changes in cropping systems and fertilization, which essentially resulted from expeditious economic growth, should be responsible for the dynamics of C and N and the consequences to soil quality. More experimental studies on balanced fertilization vs. local commonly used fertilization are suggested to probe the mechanisms underlying the C and N dynamics in paddy soils.

Forest above-ground biomass (AGB) can be estimated based on light detection and ranging (LiDAR) point clouds. This paper introduces an accurate and detailed quantitative structure model (AdQSM), which can estimate the AGB of large tropical trees. AdQSM is based on the reconstruction of 3D tree models from terrestrial laser scanning (TLS) point clouds. It represents a tree as a set of closed and complete convex polyhedra. We use AdQSM to model 29 trees of various species (total 18 species) scanned by TLS from three study sites (the dense tropical forests of Peru, Indonesia, and Guyana). The destructively sampled tree geometry measurement data is used as reference values to evaluate the accuracy of diameter at breast height (DBH), tree height, tree volume, branch volume, and AGB estimated from AdQSM. After AdQSM reconstructs the structure and volume of each tree, AGB is derived by combining the wood density of the specific tree species from destructive sampling. The AGB estimation from AdQSM and the post-harvest reference measurement data show a satisfying agreement. The coefficient of variation of root mean square error (CV-RMSE) and the concordance correlation coefficient (CCC) are 20.37% and 0.97, respectively. AdQSM provides accurate tree volume estimation, regardless of the characteristics of the tree structure, without major systematic deviations. We compared the accuracy of AdQSM and TreeQSM in modeling the volume of 29 trees. The tree volume from AdQSM is compared with the reference value, and the determination coefficient (R2), relative bias (rBias), and CV-RMSE of tree volume are 0.96, 6.98%, and 22.62%, respectively. The tree volume from TreeQSM is compared with the reference value, and the R2, relative Bias (rBias), and CV-RMSE of tree volume are 0.94, −9.69%, and 23.20%, respectively. The CCCs between the volume estimates based on AdQSM, TreeQSM, and the reference values are 0.97 and 0.96. AdQSM also models the branches in detail. The volume of branches from AdQSM is compared with the destructive measurement reference data. The R2, rBias, and CV-RMSE of the branches volume are 0.97, 12.38%, and 36.86%, respectively. The DBH and height of the harvested trees were used as reference values to test the accuracy of AdQSM’s estimation of DBH and tree height. The R2, rBias, and CV-RMSE of DBH are 0.94, −5.01%, and 9.06%, respectively. The R2, rBias, and CV-RMSE of the tree height were 0.95, 1.88%, and 5.79%, respectively. This paper provides not only a new QSM method for estimating AGB based on TLS point clouds but also the potential for further development and testing of allometric equations.

Tree-level information can be estimated based on light detection and ranging (LiDAR) point clouds. We propose to develop a quantitative structural model based on terrestrial laser scanning (TLS) point clouds to automatically and accurately estimate tree attributes and to detect real trees for the first time. This model is suitable for forest research where branches are involved in the calculation. First, the Adtree method was used to approximate the geometry of the tree stem and branches by fitting a series of cylinders. Trees were represented as a broad set of cylinders. Then, the end of the stem or all branches were closed. The tree model changed from a cylinder to a closed convex hull polyhedron, which was to reconstruct a 3D model of the tree. Finally, to extract effective tree attributes from the reconstructed 3D model, a convex hull polyhedron calculation method based on the tree model was defined. This calculation method can be used to extract wood (including tree stem and branches) volume, diameter at breast height (DBH) and tree height. To verify the accuracy of tree attributes extracted from the model, the tree models of 153 Chinese scholartrees from TLS data were reconstructed and the tree volume, DBH and tree height were extracted from the model. The experimental results show that the DBH and tree height extracted based on this model are in better consistency with the reference value based on field survey data. The bias, RMSE and R2 of DBH were 0.38 cm, 1.28 cm and 0.92, respectively. The bias, RMSE and R2 of tree height were −0.76 m, 1.21 m and 0.93, respectively. The tree volume extracted from the model is in better consistency with the reference value. The bias, root mean square error (RMSE) and determination coefficient (R2) of tree volume were −0.01236 m3, 0.03498 m3 and 0.96, respectively. This study provides a new model for nondestructive estimation of tree volume, above-ground biomass (AGB) or carbon stock based on LiDAR data.

In present forest surveys, some problems occur because of the cost and time required when using external tools to acquire tree measurement. Therefore, it is of great importance to develop a new cost-saving and time-saving ground measurement method implemented in a forest geographic information system (GIS) survey. To obtain a better solution, this paper presents the design and implementation of a new ground measurement tool in which mobile devices play a very important role. Based on terrestrial photogrammetry, location-based services (LBS), and computer vision, the tool assists forest GIS surveys in obtaining important forest structure factors such as tree position, diameter at breast height (DBH), tree height, and tree species. This paper selected two plots to verify the accuracy of the ground measurement tool. Experiments show that the root mean square error (RMSE) of the position coordinates of the trees was 0.222 m and 0.229 m, respectively, and the relative root mean square error (rRMSE) was close to 0. The rRMSE of the DBH measurement was 10.17% and 13.38%, and the relative Bias (rBias) of the DBH measurement was −0.88% and −2.41%. The rRMSE of tree height measurement was 6.74% and 6.69%, and the rBias of tree height measurement was −1.69% and −1.27%, which conforms to the forest investigation requirements. In addition, workers usually make visual observations of trees and then combine their personal knowledge or experience to identify tree species, which may lead to the situations when they cannot distinguish tree species due to insufficient knowledge or experience. Based on MobileNets, a lightweight convolutional neural network designed for mobile phone, a model was trained to assist workers in identifying tree species. The dataset was collected from some forest parks in Beijing. The accuracy of the tree species recognition model was 94.02% on a test dataset and 93.21% on a test dataset in the mobile phone. This provides an effective reference for workers to identify tree species and can assist in artificial identification of tree species. Experiments show that this solution using the ground measurement tool saves time and cost for forest resources GIS surveys.

We obtain some Hermite-Hadamard type inequalities for s-convex functions on the coordinates via Riemann-Liouville integrals. Some integral inequalities with the right-hand side of the fractional Hermite-Hadamard type inequality are also established.

We use basic properties of superquadratic functions to obtain some new Hermite-Hadamard type inequalities via Riemann-Liouville fractional integrals. For superquadratic functions which are also convex, we get refinements of existing results.

We establish a Fejér type inequality for harmonically convex functions. Our results are the generalizations of some known results. Moreover, some properties of the mappings in connection with Hermite-Hadamard and Fejér type inequalities for harmonically convex functions are also considered.

We establish some estimates of the right-hand side of Hermite-Hadamard type inequalities for functions whose derivatives absolute values are harmonically s-convex. Several Hermite-Hadamard type inequalities for products of two harmonically s-convex functions are also considered.

We give some new generalizations of the well-known Hermite-Hadamard inequality for r-convex functions.

We obtain some new Hermite-Hadamard type inequalities for products of convex functions. We conclude that the results obtained in this work are the refinements of the present results.

Some new Kantorovich-type inequalities for Hermitian matrix are proposed in this paper. We consider what happens to these inequalities when the positive definite matrix is allowed to be invertible and provides refinements of the classical results.