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Abstract Giant trees are pivotal in forest ecosystems, yet our current understanding of their significance is constrained primarily by the limited knowledge of their precise locations and structural characteristics. Amidst escalating human‐induced disturbances globally, there is an urgent need to devise a practical approach to discover and measure giant trees accurately and efficiently. Here, we propose a novel light detection and ranging (lidar)‐based framework designed for the discovery and measurement of giant trees. Our framework integrates cutting‐edge lidar platforms, including spaceborne, Unmanned Aerial Vehicle (UAV), and backpack lidar, to create an end‐to‐end workflow. The algorithm involved in the proposed framework was compiled into a code package and made available as open source. The method successfully identified the tallest trees in China, including the tallest tree in Asia, a Cupressus austrotibetica with a height of 102.3 m, discovered in Yarlung Zangbo Grand Canyon in May 2023. This finding has not only established a new record but also demonstrated the efficacy of our proposed framework. Utilising lidar data, we performed meticulous measurements at both individual and stand levels, revealing the unique characteristics of this giant tree. The new framework for the discovery and measurement of giant trees, encompassing detailed procedures and codes, is expected to facilitate the discovery and measurement of giant trees with high efficiency, thus fostering advancements in giant tree ecology.

Abstract China’s extensive planted forests play a crucial role in carbon storage, vital for climate change mitigation. However, the complex spatiotemporal dynamics of China’s planted forest area and its carbon storage remain uncaptured. Here we reveal such changes in China’s planted forests from 1990 to 2020 using satellite and field data. Results show a doubling of planted forest area, a trend that intensified post-2000. These changes lead to China’s planted forest carbon storage increasing from 675.6 ± 12.5 Tg C in 1990 to 1,873.1 ± 16.2 Tg C in 2020, with an average rate of ~ 40 Tg C yr−1. The area expansion of planted forests contributed ~ 53% (637.2 ± 5.4 Tg C) of the total above increased carbon storage in planted forests compared with planted forest growth. This proactive policy-driven expansion of planted forests has catalyzed a swift increase in carbon storage, aligning with China’s Carbon Neutrality Target for 2060.

Abstract Simulating realistic three‐dimensional (3D) forest scenes is useful in understanding the links between forest structure and ecosystem functions (e.g. radiative transfer). Light detection and ranging (LiDAR) technology provides useful 3D data for forest reconstructions since it can characterise 3D structures of individual trees and canopies. High‐density terrestrial LiDAR (terrestrial laser scanning, TLS) is suitable for fine‐scale reconstructions but is limited to smaller forest plots; low‐density airborne LiDAR (airborne laser scanning, ALS) can cover larger areas but is only suitable for coarse‐scale reconstructions. How to take advantage of TLS and ALS to enable fine‐scale forest simulations in large areas needs to be studied. We propose a new voxel‐based method for forest simulations using the integration of TLS and ALS data. TLS data of representative reference trees are used to approximate the detailed architectures of the whole forest scene, with structural information on each individual tree extracted from ALS data. The high‐density point cloud data derived from TLS and ALS data are voxelised using high resolution solid voxels for scene representation. We tested the proposed method using two virtual forests (108 m × 108 m) and a real forest (300 m × 300 m) with conifer and broadleaf species. The physically based ray tracer (PBRT) was used to visualise the true virtual forest scenes, whereas voxel‐based radiative transfer (VBRT) was used to visualise the modelled forest scenes from LiDAR data. For the real forest scene, simulated and real ALS data were compared. Our results demonstrate that the images simulated by VBRT and PBRT are similar in the virtual forest scenes, with average radiance values of 1.02 and 1.72, respectively. In the real forest scene, the distributions of points and individual tree attributes (tree height, crown radius, and tree volume) derived from real and simulated ALS also match well, with Kullback–Leibler divergence ranging from 0.006 to 0.06. We conclude that the new method is capable of modelling fine‐scale 3D forests in large areas (over 1 ha) when TLS and ALS data are available, and it has good potential in studying the process of radiative transfer in conifer and broadleaf forests.

Over the last decade, a number of techniques for individual tree segmentation has been developed for terrestrial laser scanning data. The superpoint segmentation algorithm based on point cloud has been widely used in individual tree segmentation because of its high efficiency and numerous geometric features. However, this algorithm is generally developed for specific tree species and forest types, limiting its universality and performance for different forest types. To handle this problem, a new method based on the topology of tree branches for individual tree segmentation was proposed. Focusing on the general topological structure of trees, the proposed method iteratively assigns each branch based on its directivity to its upper branch at the superpoint level. The proposed method was tested compared with the original superpoint method and an ecological method in six sample plots with different forest conditions. In such plots, the proposed method achieved anticipated performance with an average accuracy of 40% improvements compared with the other two methods, especially in complicated forest conditions. Experimental results also showed an improved average accuracy of 70% compared with the original superpoint method at the point level. This proposed method can effectively expand the universality of the superpoint method to further advance ecological and forest research.

Abstract Increasing drought frequency and severity in a warming climate threaten forest ecosystems with widespread tree deaths. Canopy structure is important in regulating tree mortality during drought, but how it functions remains controversial. Here, we show that the interplay between tree size and forest structure explains drought-induced tree mortality during the 2012-2016 California drought. Through an analysis of over one million trees, we find that tree mortality rate follows a “negative-positive-negative” piecewise relationship with tree height, and maintains a consistent negative relationship with neighborhood canopy structure (a measure of tree competition). Trees overshadowed by tall neighboring trees experienced lower mortality, likely due to reduced exposure to solar radiation load and lower water demand from evapotranspiration. Our findings demonstrate the significance of neighborhood canopy structure in influencing tree mortality and suggest that re-establishing heterogeneity in canopy structure could improve drought resiliency. Our study also indicates the potential of advances in remote-sensing technologies for silvicultural design, supporting the transition to multi-benefit forest management.

BackgroundActive surveillance has been an option for patients with low-risk papillary thyroid carcinoma (PTC). However, whether delayed surgery leads to an increased risk of local tumor metastasis remain unclear. We sought to investigate the impact of observation time on central lymph node metastasis (CLNM) and multifocal disease in patients with low-risk PTC.MethodsPatients who were diagnosed with asymptomatic low-risk PTC, and with a pathological maximum tumor size ≤1.5 cm by were included. The patients were classified into observation group and immediate surgery group, and subgroup analyses were conducted by observation time period. The prevalence of CLNM, lymph node (LN) involved >5, multifocal PTC and bilateral multifocal PTC were considered as outcome variables. The changing trend and risk ratio of prevalence over observation time were evaluated by Mann-Kendall trend test and Logistics regression.ResultsOverall, 3,427 and 1,860 patients were classified to the observation group and immediate surgery group, respectively. Trend tests showed that decreasing trends both on the prevalence of CLNM and LN involved >5 over the observation time, but the difference was not statistically significant, and the prevalence of multifocal PTC and bilateral multifocal PTC showed the significant decreasing trends. After adjustment, multivariate analysis showed no statistically significant difference between observed and immediate surgery groups in the four outcome variables.ConclusionIn patients with subclinical asymptomatic low-risk PTC, observation did not result in an increased incidence of local metastatic disease, nor did the increased surgery extent in patients with delayed surgery compared to immediate surgery. These findings can strengthen the confidence in the active surveillance management for both doctors and patients.

Three-dimensional (3D) phenotyping is important for studying plant structure and function. Light detection and ranging (LiDAR) has gained prominence in 3D plant phenotyping due to its ability to collect 3D point clouds. However, organ-level branch detection remains challenging due to small targets, sparse points, and low signal-to-noise ratios. In addition, extracting biologically relevant angle traits is difficult. In this study, we developed a stratified, clustered, and growing-based algorithm (SCAG) for soybean branch detection and branch angle calculation from LiDAR data, which is heuristic, open-source, and expandable. SCAG achieved high branch detection accuracy (F-score = 0.77) and branch angle calculation accuracy (r = 0.84) when evaluated on 152 diverse soybean varieties. Meanwhile, the SCAG outperformed 2 other classic algorithms, the support vector machine (F-score = 0.53) and density-based methods (F-score = 0.55). Moreover, after applying the SCAG to 405 soybean varieties over 2 consecutive years, we quantified various 3D traits, including canopy width, height, stem length, and average angle. After data filtering, we identified novel heritable and repeatable traits for evaluating soybean density tolerance potential, such as the ratio of average angle to height and the ratio of average angle to stem length, which showed greater potential than the well-known ratio of canopy width to height trait. Our work demonstrates remarkable advances in 3D phenotyping and plant architecture screening. The algorithm can be applied to other crops, such as maize and tomato. Our dataset, scripts, and software are public, which can further benefit the plant science community by enhancing plant architecture characterization and ideal variety selection.

Tree height composition describes the relative abundance of trees in different height levels and performs as a critical characteristic for community ecology. The recent launched full-waveform spaceborne LiDAR (Light Detection and Ranging), i.e., Global Ecosystem Dynamics Investigation (GEDI), can map canopy height, but whether this observation reflects tree height composition remains untested. In this study, we firstly conduct numerical simulations to explore to what extent tree height composition can be obtained from GEDI waveform signals. We simulate waveforms for diverse forest scenarios using GEDI simulator coupled with LESS (LargE-Scale remote sensing data and image Simulation), a state-of-the-art radiative transfer model. We devise a minimalistic model, Tree generation based on Asymmetric generalized Gaussian (TAG), for customizing tree objects to accelerate forest scene creation. The results demonstrate that tree objects generated by TAG perform similarly in LiDAR simulation with objects from commercial 3-dimensional software. Results of simulated GEDI waveforms reasonably respond to the variation of crown architectures in even-aged forests. GEDI waveforms have an acceptable ability to identify different height layers within multi-layer forests, except for fir forests with a cone-shaped crown. The shape metric of waveforms reflects the height of each layer, while retrieval accuracy decreases with the increases in height variations within each layer. A 5-m interval between layers is the minimum requirement so that the different height layers can be separated. A mixture of different tree species reduces the retrieval accuracy of tree height layers. We also utilize real GEDI observations to retrieve tree heights in multi-height-layer forests. The findings indicate that GEDI waveforms are also efficient in identifying tree height composition in practical forest scenarios. Overall, results from this study demonstrate that GEDI waveforms can reflect the height composition within typical forest stands.

Highlights The effects of canopy height, leaf area index, and growth stage on the accurate monitoring of canopy height with different 3D sensors were systematically evaluated. Field-measured canopy height may not be as accurate as believed, especially in the plots with higher canopy height and at later growth stages. 3D sensing methods achieved higher heritable canopy height estimation than field measurement.

Abstract Background Management of the inferior parathyroid gland using total thyroidectomy (TT) with central lymph node dissection (CLND) is still controversial. Therefore, we evaluated the safety and effectiveness of single inferior parathyroid autotransplantation. Methods The clinical data of patients with papillary thyroid carcinoma (PTC) who underwent TT with bilateral CLND from January 2018 to December 2020 were collected. Quality of life (QoL) was assessed using the Chinese version of the EORTC QLQ-C30 and THYCA-QOL. The patients were divided into an autotransplantation group and a preservation group according to whether a single inferior parathyroid gland was transplanted. The incidence of permanent hypoparathyroidism, the number of resected central lymph nodes (CLNs), the rate of recurrence reoperation, the rate of radioactive iodine (RAI) treatment, and the QoL score were compared between the two groups. Results A total of 296 patients were included in the study; there were 99 patients in the autotransplantation group and 197 in the preservation group. The incidence of permanent hypoparathyroidism was 3.0% (3/99) and 4.6% (9/197) in the autotransplantation and preservation groups, respectively (P = 0.532). The median number of resected CLNs was 12 (8–17) and 10 (6–14) in the autotransplantation and preservation groups, respectively (P = 0.015). No reoperations were performed for patients with CLN recurrence, and the rates of lateral lymph node (LLN) recurrence reoperation were 2.0% (2/99) and 3.6% (7/197) in the autotransplantation and preservation groups, respectively (P = 0.473). The RAI treatment rates were 12.1% (12/99) and 22.3% (44/197) in the autotransplantation and preservation groups, respectively (P = 0.034). A total of 276 questionnaires were recovered, including 84 in the autotransplantation group and 192 in the preservation group. The QoL of the two groups of patients is similar (P > 0.05). Conclusion Single inferior parathyroid autotransplantation during thyroidectomy can be used to prevent permanent hypoparathyroidism and can enable more extensive CLND.

Grassland is one of the largest terrestrial biomes, providing critical ecosystem services such as food production, biodiversity conservation, and climate change mitigation. Global climate change and land-use intensification have been causing grassland degradation and desertification worldwide. As one of the primary medium for ecosystem energy flow and biogeochemical cycling, grassland carbon (C) cycling is the most fundamental process for maintaining ecosystem services. In this review, we first summarize recent advances in our understanding of the mechanisms underpinning spatial and temporal patterns of the grassland C cycle, discuss the importance of grasslands in regulating inter- and intra-annual variations in global C fluxes, and explore the previously unappreciated complexity in abiotic processes controlling the grassland C balance, including soil inorganic C accumulation, photochemical and thermal degradation, and wind erosion. We also discuss how climate and land-use changes could alter the grassland C balance by modifying the water budget, nutrient cycling and additional plant and soil processes. Further, we examine why and how increasing aridity and improper land use may induce significant losses in grassland C stocks. Finally, we identify several priorities for future grassland C research, including improving understanding of abiotic processes in the grassland C cycle, strengthening monitoring of grassland C dynamics by integrating ground inventory, flux monitoring, and modern remote sensing techniques, and selecting appropriate plant species combinations with suitable traits and strong resistance to climate fluctuations, which would help design sustainable grassland restoration strategies in a changing climate.

Grasslands are one of the largest coupled human-nature terrestrial ecosystems on Earth, and severe anthropogenic-induced grassland ecosystem function declines have been reported recently. Understanding factors influencing grassland ecosystem functions is critical for making sustainable management policies. Canopy structure is an important factor influencing plant growth through mediating within-canopy microclimate (e.g., light, water, and wind), and it is found coordinating tightly with plant species diversity to influence forest ecosystem functions. However, the role of canopy structure in regulating grassland ecosystem functions along with plant species diversity has been rarely investigated. Here, we investigated this problem by collecting field data from 170 field plots distributed along an over 2000 km transect across the northern agro-pastoral ecotone of China. Aboveground net primary productivity (ANPP) and resilience, two indicators of grassland ecosystem functions, were measured from field data and satellite remote sensing data. Terrestrial laser scanning data were collected to measure canopy structure (represented by mean height and canopy cover). Our results showed that plant species diversity was positively correlated to canopy structural traits, and negatively correlated to human activity intensity. Canopy structure was a significant indicator for ANPP and resilience, but their correlations were inconsistent under different human activity intensity levels. Compared to plant species diversity, canopy structural traits were better indicators for grassland ecosystem functions, especially for ANPP. Through structure equation modeling analyses, we found that plant species diversity did not have a direct influence on ANPP under human disturbances. Instead, it had a strong indirect effect on ANPP by altering canopy structural traits. As to resilience, plant species diversity had both a direct positive contribution and an indirect contribution through mediating canopy cover. This study highlights that canopy structure is an important intermediate factor regulating grassland diversity-function relationships under human disturbances, which should be included in future grassland monitoring and management.

Abstract Purpose To summarize the treatment experience of single-center children with PTC and to explore the influence of initial surgery on the recurrence/metastasis of papillary thyroid carcinoma (PTC) in children. Methods A retrospective analysis of PTC case data of children (≤ 18 years old) who were admitted to and received surgical treatment in the First Affiliated Hospital of Kunming Medical University from January 2012 to December 2020. Results A total of 64 children with PTC were included, including 45 cases (70.31%) with a single lesion, and 19 cases (29.69%) with multiple lesions (≥ 2 lesions). Fifteen patients relapsed. Univariate analysis found that gender, thyroidectomy scope, central lymph node dissection, and lateral lymph node dissection were risk factors affecting reoperation; multi-factor analysis showed that central lymph node dissection was an independent risk factor affecting reoperation. According to Kaplan–Meier analysis, central lymph node dissection, total thyroidectomy (TT), lobectomy (LT), and disease-free survival (DFS) were statistically significant (p = 0.000, p = 0.000). Conclusion At the time of diagnosis of PTC in children, the rate of lymph node metastasis in the central and lateral cervical regions is high. The vast majority of children with PTC should be treated with TT, and LT is chosen for a small number of patients. CND should be routinely lined.

Background and objective microRNAs play an important role in the development and biological phenotype of lung cancer. The present study was to investigate miR-367-3p level in non-small cell lung cancer (NSCLC) patients and its biological function of NSCLC cells. Methods Twenty-two patients with NSCLC (13 cases of adenocarcinoma and 9 cases of squamous carcinoma) admitted to our hospital and treated by surgery were included. During the operation, cancer tissue, paracancerous tissue and 5 mL peripheral blood were collected. Meanwhile, 22 healthy controls were selected and 5 mL peripheral blood was taken. Real-time PCR was applied to detected the expression of miR-367-3p in cancer tissues, peripheral blood of patients with NSCLC and healthy controls. miR-367-3p was detected in lung cancer cell lines (A549) and normal bronchial epithelial cells (BEAS-2B). The proliferation and invasion ability of A549 cells before and after infection were detected by MTT and Transwell assay after transfection with exogenous miR-367-3p. The downstream target gene of miR-367-3p was analyzed by bioinformatics. Zinc finger E-box binding homeobox 2 (ZEB2) was detected by Real-time PCR and Western blot. Results miR-367-3p in cancer tissues of 22 NSCLC patients was lower than corresponding normal tissues (P0.05). ZEB2 expression was positively correlated with infiltration of B lymphocytes (r=0.32, P

Accurate, efficient, and timely yield estimation is critical for crop variety breeding and management optimization. However, the contributions of proximal sensing data characteristics (spectral, temporal, and spatial) to yield estimation have not been systematically evaluated. We collected long-term, hyper-temporal, and large-volume light detection and ranging (LiDAR) and multispectral data to (i) identify the best machine learning method and prediction stage for wheat yield estimation, (ii) characterize the contribution of multisource data fusion and the dynamic importance of structural and spectral traits to yield estimation, and (iii) elucidate the contribution of time-series data fusion and 3D spatial information to yield estimation. Wheat yield could be accurately (R2 = 0.891) and timely (approximately-two months before harvest) estimated from fused LiDAR and multispectral data. The artificial neural network model and the flowering stage were always the best method and prediction stage, respectively. Spectral traits (such as CIgreen) dominated yield estimation, especially in the early stage, whereas the contribution of structural traits (such as height) was more stable in the late stage. Fusing spectral and structural traits increased estimation accuracy at all growth stages. Better yield estimation was realized from traits derived from complete 3D points than from canopy surface points and from integrated multi-stage (especially from jointing to heading and flowering stages) data than from single-stage data. We suggest that this study offers a novel perspective on deciphering the contributions of spectral, structural, and time-series information to wheat yield estimation and can guide accurate, efficient, and timely estimation of wheat yield.

High-throughput maize phenotyping at both organ and plant levels plays a key role in molecular breeding for increasing crop yields. Although the rapid development of light detection and ranging (LiDAR) provides a new way to characterize three-dimensional (3D) plant structure, there is a need to develop robust algorithms for extracting 3D phenotypic traits from LiDAR data to assist in gene identification and selection. Accurate 3D phenotyping in field environments remains challenging, owing to difficulties in segmentation of organs and individual plants in field terrestrial LiDAR data. We describe a two-stage method that combines both convolutional neural networks (CNNs) and morphological characteristics to segment stems and leaves of individual maize plants in field environments. It initially extracts stem points using the PointCNN model and obtains stem instances by fitting 3D cylinders to the points. It then segments the field LiDAR point cloud into individual plants using local point densities and 3D morphological structures of maize plants. The method was tested using 40 samples from field observations and showed high accuracy in the segmentation of both organs (F-score =0.8207) and plants (F-score =0.9909). The effectiveness of terrestrial LiDAR for phenotyping at organ (including leaf area and stem position) and individual plant (including individual height and crown width) levels in field environments was evaluated. The accuracies of derived stem position (position error =0.0141 m), plant height (R2 >0.99), crown width (R2 >0.90), and leaf area (R2 >0.85) allow investigating plant structural and functional phenotypes in a high-throughput way. This CNN-based solution overcomes the major challenges in organ-level phenotypic trait extraction associated with the organ segmentation, and potentially contributes to studies of plant phenomics and precision agriculture.

Lung cancer is one of the malignant tumors with the highest morbidity and mortality in the world. The low early diagnosis rate and poor prognosis of patients have caused serious social burden. Regular screening of high-risk population by low-dose spiral computed tomography (LDCT) can significantly improve the early diagnosis rate of lung cancer and bring new opportunities for the diagnosis and treatment of lung cancer. In recent years, LDCT lung cancer screening programs have been carried out in many countries around the world and achieved good results, but there are still some controversies in the selection of screening subjects, screening frequency, cost effectiveness and other aspects. In this paper, the key factors of LDCT lung cancer screening, screening effect, pulmonary nodule management and artificial intelligence contribution to the development of LDCT will be reviewed, and the application progress of LDCT in lung cancer screening will be discussed.

Small cell lung cancer (SCLC) is a malignant tumor with strong invasiveness and high mortality. It has the characteristics of easy metastasis, fast growth, high degree of malignancy and strong invasiveness. The prognosis of patients is generally poor. The current clinical diagnosis of SCLC is mainly based on tissue biopsy, which is invasive, long cycle time and high cost. In recent years, liquid biopsy has been gradually applied because of its non-invasive, comprehensive and real-time characteristics that traditional tissue biopsy does not have. The main detection objects of liquid biopsy include circulating tumor DNA (ctDNA), circulating tumor cells (CTCs) and exosomes in peripheral blood. The application of liquid biopsy in the clinical treatment of SCLC will help clinicians to improve the detailed diagnosis of SCLC patients, as well as the timely control and response to the treatment response of patients.

Objective: In 2015, American Thyroid Association (ATA) issued the first version of Management Guidelines for Children with Thyroid Nodules and Differentiated Thyroid Cancer. The purpose of this study is to evaluate whether the ATA pediatric guidelines recommended surgical approach for the patient can be applied to surgical treatment of pediatric PTC in China. Method: From April 2012 to December 2020, clinical data of children (≤18 years) with PTC consecutively admitted and treated with initial surgery in the study's department were retrospectively reviewed. Results: The authors found that the central lymph node metastasis (CLNM) rate was significantly higher than that in the lateral neck (83.33 % vs 62.96%, χ2 = 5.704, p = 0.017) .The lymph node metastasis rate was significantly lower in cN1b (-) patients than in cN1b (+) patient (55.00% vs 100.00%, χ2 = 15.263, p = 0.000); Meanwhile, the CLNM and LLNM rates of ipsilateral were significantly higher than those of contralateral central compartment (83.33༅vs 57.41༅%, χ2 = 8.704, p = 0.003). Lymph nodes of 51 lateral lymph node dissection (LND) were analyzed, which revealed the LNM rate of cN1b (-) patients was significantly lower than that of cN1b (+) patients (55.00% vs. 100.00%, χ2 = 15.263, p = 0.000). Conclusion: Children and adolescents have a higher rate of lymph node metastasis at the time of diagnosis. TT should be conducted in the majority of children with PTC. CND should be routinely performed; therapeutic LND is recommended for children with cN1b (+).

Small cell lung cancer (SCLC) is a neuroendocrine tumor with fast progression, high malignancy, easy recurrence, and extremely poor prognosis. In the past 30 years, the clinical treatment strategy of SCLC has been mainly chemotherapy and radiotherapy, but the curative effect is not significant; the current immunotherapy of SCLC has gradually entered the clinic and has made certain progress. Tumor immunotherapy includes immune checkpoint inhibitors, tumor vaccines, cytokines, chimeric antigen receptor T-cell immunotherapy (CAR-T) therapy, etc. Currently, immune checkpoint inhibitors are the most widely used. This article summarizes the principles of immune checkpoint inhibitors and related drugs, summarizes their domestic and foreign clinical trials progress in SCLC treatment, reviews the biomarkers used in the therapy, and discusses its future development direction.

The COVID-19 outbreak triggered a serious and potentially lethal pandemic, resulting in massive health and economic losses worldwide. The most common clinical manifestations of COVID-19 patients are pneumonia and acute respiratory distress syndrome, with a variety of complications. Multiple organ failure and damage, ultimately leading to patient death, are possible as a result of medication combinations, and this is exemplified by DILI. We hope to summarize DILI caused by the antiviral drugs favipiravir, remdesivir, lopinavir/ritonavir, and hydroxychloroquine in COVID-19 patients in this review. The incidence of liver injury in the treatment of COVID-19 patients was searched on PubMed to investigate DILI cases. The cumulative prevalence of acute liver injury was 23.7% (16.1%–33.1%). We discuss the frequency of these events, potential mechanisms, and new insights into surveillance strategies. Furthermore, we also describe medication recommendations aimed at preserving DILI caused by treatment in COVID-19 patients.

Immunotherapy has revolutionized cancer treatment and become one of the five pillars of cancer therapy. The clinical applications of immunotherapy have been adapted to range from the management of melanoma to most tumor types. As the clinical applications of cancer immunotherapies expand, understanding the treatment-related adverse events of these drugs becomes critical in clinical practice. We report a rare case of ocular immune-related side effects associated with camrelizumab that resulted in vision loss. A 56-year-old male patient was diagnosed with small cell lung cancer. The tumor involved the porta pulmonis and mediastinum upon initial diagnosis; therefore, surgery was not possible. Upon receiving the 10th immunotherapy session with camrelizumab 200 mg, the patient’s visual acuity began to decrease in his right eye and a central retinal vein occlusion. Optical coherence tomography revealed significant cystoid exudation in the macular area and vitreous hemorrhage. The patient underwent vitrectomy, phacoemulsification and intraocular lens implantation after symptom onset. Following surgery, the patient’s vision was limitedly restored. This is the first clinical report in China of central retinal vein occlusion and vitreous hemorrhage associated with anti-PD-1 therapy, ultimately leading to blindness. Although rare, clinical practitioners should be concerned about ocular adverse events associated with anti-PD-1 immunotherapy and develop a high index of suspicion for this possibility since ophthalmic manifestations that are rapidly detected, closely monitored, and appropriately managed are treatable.

Small cell lung cancer (SCLC) is a highly aggressive and fatal malignant tumor. It has the characteristics of complex etiology, low differentiation, high malignancy, fast growth, strong invasiveness, early metastasis and acquired drug resistance, resulting in poor prognosis. In recent years, with the gradual deepening understanding on the molecular mechanism of SCLC and multi-omics data, it is proposed that molecular typing can be carried out according to the differential expression of key transcription factors, including SCLC-A, SCLC-N, SCLC-P and SCLC-I subtypes. Molecular typing of SCLC and its clinical application will help doctors to further optimize the detailed diagnosis and treatment plan of SCLC patients, so as to prolong the survival time and improve the quality of life of patients.

Human-scale greenery has been heavily featured in the development of planning-related theories and research. Daily exposure to street greenery is proportionately greater than exposure to parks. Several case studies have estimated the green view index (GVI), which quantifies daily exposure to street-side greenery, at the city-scale. However, the street-side greenery distribution is uneven. The relationship between the GVI and the surrounding environment has rarely been discussed. Here, we investigate the differentiation of street greenery from the physical features of a street and examine how the surrounding environment affects the physical appearance of eye-level greenery. In this work, we analyzed the GVI using the internet data crawling approach and obtained 12,232 panoramic street view images from Baidu Map for the urban zones of Hangzhou, China. A range of 17 surrounding environmental characteristics are integrated with multisource geographic data to analyze their relationship with the GVI at the microscale. Spatial econometric models are explored to provide insights into the underlying mechanisms associated with the GVI. The results showed that the surrounding environment exhibited a strong role in the physical appearance of eye-level greenness. There are two pathways through which surrounding environmental characteristics (the nature of land use and the enclosure of the street) affect the distribution of GVI. Four factors proved to be important: scenic protection policies and ecological renewal projects, vertical and horizontal green characteristics of different land uses, and facade designs dominated by enclosed exterior walls or railing walls. Interesting approaches are presented to rationally implement activities related to these factors. The urban green design process should be facilitated in terms of the trade-off between greenery and optimum land use. These findings can be useful in drafting appropriate policies and increasing eye-level greenery in cities.

Abstract Vegetation community complexity is a critical factor influencing terrestrial ecosystem stability. China, the country leading the world in vegetation greening resulting from human activities, has experienced dramatic changes in vegetation community composition during the past 30 years. However, how China's vegetation community complexity varies spatially and temporally remains unclear. Here, we examined the spatial pattern of China's vegetation community complexity and its temporal changes from the 1980s to 2015 using two vegetation maps of China as well as more than half a million field samples. Spatially, China's vegetation community complexity distribution is primarily dominated by elevation, although temperature and precipitation can be locally more influential than elevation when they become the factors limiting plant growth. Temporally, China's vegetation community complexity shows a significant decreasing trend during the past 30 years, despite the observed vegetation greening trend. Prevailing climate warming across China exhibits a significant negative correlation with the decrease in vegetation community complexity, but this correlation varies with biogeographical regions. The intensity of human activities have an overall negative influence on vegetation community complexity, but vegetation conservation and restoration efforts can have a positive effect on maintaining vegetation composition complexity, informing the critical role of vegetation management policies in achieving the sustainable development goal.

BackgroundActive surveillance (AS) has been considered the first-line management for patients with clinical low-risk papillary thyroid microcarcinoma (PTMC) who often have lymph node micrometastasis (m-LNM) when diagnosed. The “low-risk” and “high prevalence of m-LNM” paradox is a potential barrier to the acceptance of AS for thyroid cancer by both surgeons and patients.MethodsPatients diagnosed with PTMC who underwent thyroidectomy with at least one lymph node (LN) examined were identified from a tertiary center database (n = 5,399). A β-binomial distribution was used to estimate the probability of missing nodal disease as a function of the number of LNs examined. Overall survival (OS) probabilities of groups with adequate and inadequate numbers of LNs examined were estimated using the Kaplan–Meier method in the Surveillance, Epidemiology, and End Results (SEER) database (n = 15,340). A multivariable model with restricted cubic splines was also used to verify the association of OS with the number of LNs examined.ResultsThe risk of residual m-LNM (missed nodal disease) ranged from 31.3% to 10.0% if the number of LNs examined ranged from 1 and 7 in patients with PTMC. With 7 LNs examined serving as the cutoff value, the intergroup comparison showed that residual positive LNs did not affect OS across all patients and patients aged ≥55 years (P = 0.72 and P = 0.112, respectively). After adjusting for patient and clinical characteristics, the multivariate model also showed a slight effect of the number of LNs examined on OS (P = 0.69).ConclusionsEven with the high prevalence, OS is not significantly compromised by persistent m-LNM in the body of patients with low-risk PTMC. These findings suggest that the concerns of LNM should not be viewed as an obstacle to developing AS for thyroid cancer. For patients with PTMC who undergo surgery, prophylactic central LN dissection does not provide a survival benefit.

Abstract Background Precision agriculture is an emerging research field that relies on monitoring and managing field variability in phenotypic traits. An important phenotypic trait is biomass, a comprehensive indicator that can reflect crop yields. However, non-destructive biomass estimation at fine levels is unknown and challenging due to the lack of accurate and high-throughput phenotypic data and algorithms. Results In this study, we evaluated the capability of terrestrial light detection and ranging (lidar) data in estimating field maize biomass at the plot, individual plant, leaf group, and individual organ (i.e., individual leaf or stem) levels. The terrestrial lidar data of 59 maize plots with more than 1000 maize plants were collected and used to calculate phenotypes through a deep learning-based pipeline, which were then used to predict maize biomass through simple regression (SR), stepwise multiple regression (SMR), artificial neural network (ANN), and random forest (RF). The results showed that terrestrial lidar data were useful for estimating maize biomass at all levels (at each level, R2 was greater than 0.80), and biomass estimation at leaf group level was the most precise (R2 = 0.97, RMSE = 2.22 g) among all four levels. All four regression techniques performed similarly at all levels. However, considering the transferability and interpretability of the model itself, SR is the suggested method for estimating maize biomass from terrestrial lidar-derived phenotypes. Moreover, height-related variables showed to be the most important and robust variables for predicting maize biomass from terrestrial lidar at all levels, and some two-dimensional variables (e.g., leaf area) and three-dimensional variables (e.g., volume) showed great potential as well. Conclusion We believe that this study is a unique effort on evaluating the capability of terrestrial lidar on estimating maize biomass at difference levels, and can provide a useful resource for the selection of the phenotypes and models required to estimate maize biomass in precision agriculture practices.

The therapeutic effects and mechanism of umbilical cord mesenchymal stem cells (UC-MSC) on kidney injury in MRL/Ipr mice were studied. UC-MSC, methylprednisolone (MP), and their combination were used to treat MRL/Ipr mice. The therapeutic effects were evaluated by renal function assessment, and HE, PAS, and Masson staining were carried out on renal tissues and visualized by electron microscopy. Subsequently, podocyte injury was detected by the presence of podocin in renal tissues by immunofluorescence. To further explore the mechanism, serum TGF-β1 was measured, and TGF-β1, p-Smad3, and TRAF6 in the renal tissue were detected by Western blotting. In vitro, TGF-β1 was used to stimulate podocytes, and the podocyte activity and changes in synaptopodin were observed after UC-MSC treatment. Significant improvements in renal function and pathological injury were observed in the UC-MSC group compared to the lupus nephritis (LN) model group. UC-MSC and MP treatment improved podocyte injury in MRL/Ipr mice. Western blot examination showed a significant increase in TGF-β1, p-Smad3, and TRAF6 expression in renal tissues of the LN model group, while significant downregulation of those proteins was observed in the UC-MSC group. After TGF-β1 stimulation in vitro, podocyte activity decreased, and UC-MSC treatment improved podocyte activity and restored synaptopodin expression. UC-MSC therapy could improve the deterioration of renal function and the pathological changes of the renal tissues in MRL/Ipr mice. Our study suggested that UC-MSC may improve kidney injury and podocyte injury in LN mice by inhibiting the TGF-β1 pathway.

Accurate quantification of grassland structural and functional traits is the foundation for grassland management and restoration. Light detection and ranging (lidar), especially the unmanned aerial vehicle (UAV) lidar, has been recognized as an accurate and effective technique for local to regional-scale vegetation structural and functional traits estimation. However, in grassland ecosystems, it is more likely to be influenced by UAV lidar information loss caused by dense vegetation canopies. In this study, we investigated how UAV lidar information loss may occur and how it may influence the estimation accuracy of grassland structural and functional traits by comparing it with terrestrial laser scanning (TLS) and field measurements in a meadow steppe of northern China. Five structural traits (i.e., mean vegetation height, maximum vegetation height, standard deviation of vegetation height, canopy cover, and canopy volume) and one functional trait (i.e., aboveground biomass) were estimated from the UAV lidar data and TLS data for evaluation. The results showed that TLS-derived structural and functional traits had a much higher accuracy than UAV lidar-derived traits. By comparing with TLS data, we found that UAV lidar data had a much more prevailing information loss at canopy tops than at canopy bottoms. The average height loss of UAV lidar at canopy tops reached over 0.30 m, and the average relative height loss reached over 49%, comparing to a value of 0.03 m and 6% at canopy bottoms. Maximum vegetation height, standard deviation, and the distance from the UAV lidar system to the ground were the three most influential factors on UAV lidar information loss at canopy tops, indicating the commonly seen sharp canopy tops of grasslands were prone to be missed by the UAV lidar system. UAV lidar information loss at canopy tops had a much stronger influence on the estimation accuracy of grassland structural and functional traits than that at canopy bottoms. With the decrease of information loss at canopy tops, UAV lidar can be used to extract grassland structural and functional traits with a comparable accuracy to TLS. Among the five grassland traits, aboveground biomass was the least influenced by UAV lidar information loss. This study is a very first evaluation on the UAV lidar information loss in grassland ecosystems and its influence on grassland structural and functional trait estimation, which can provide guidance for UAV lidar data collection and processing in future grassland applications.

Canopy structural complexity is a critical emergent forest attribute, and light detection and ranging (lidar)-based fractal dimension has been recognized as its powerful measure at the individual tree level. However, the current lidar-based estimation method is highly sensitive to data characteristics, and its scalability from individual trees to forest stands remains unclear. This study proposed an improved method to estimate fractal dimension from lidar data by considering Shannon entropy, and evaluated its scalability from individual trees to forest stands through mathematical derivations. Moreover, a total of 280 forest stand scenes simulated from the terrestrial lidar data of 115 trees spanning large variability in canopy structural complexity were used to evaluate the robustness of the proposed method and the scalability of fractal dimension. The results show that the proposed method can significantly improve the robustness of lidar-derived fractal dimensions. Both mathematical derivations and experimental analyses demonstrate that the fractal dimension of a forest stand is equal to that of the tree with the largest fractal dimension in it, manifesting its nonscalability from individual trees to forest stands. The nonscalability of fractal dimension reveals its limited capability in canopy structural complexity quantification and indicates that the power-law scaling theory of a forest stand underlying fractal geometry is determined by its dominant tree instead of the entire community. Nevertheless, we believe that fractal dimension is still a useful indicator of canopy structural complexity at the individual tree level and might be used along with other stand-level indexes to reflect the “tree-to-stand” correlation of canopy structural complexity.

Background: We compared the efficacy, safety, and immunogenicity of MIL60 with reference bevacizumab as first-line treatment in patients with advanced or recurrent non-squamous non-small cell lung cancer (NSCLC) in this phase 3, randomized, double-blind study. Methods: Patients with untreated advanced or recurrent NSCLC were randomized (1:1 ratio) to receive either MIL60 or bevacizumab in combination with paclitaxel/carboplatin. Patients with non-progressive disease continued maintenance single-agent MIL60 until disease progression, or intolerable toxicity. The primary endpoint was the 12-week objective response rates (ORR12) by independent review committee (IRC) using RECIST 1.1. Bioequivalence was established if the ORR ratio located between 0.75 and 1/0.75. The trial was registered with clinicaltrials.gov (NCT03196986). Findings: Between Aug 23, 2017, and May 8, 2019, 517 patients were randomly assigned to MIL60 group (n=257) and bevacizumab group (n=260). In the full analysis set (FAS) population including all randomized and evaluable patients who received at least one dose of MIL60 or bevacizumab, the ORR12 in MIL60 group and bevacizumab group were 48.6% and 43.1%, respectively. The ORR ratio of these two groups were 1.14 (90% CI 0.97-1.33), which fell within the pre-specified equivalence boundaries (0.75-1/0.75). The median DOR was 5.7 months (95% CI 4.5-6.2) for MIL60 and 5.6 months (95% CI 4.3-6.4) for bevacizumab. No significant difference was noted in median PFS (7.2 vs. 8.1 months; HR 1.01, 95% CI 0.78-1.30, p=0.9606) and OS (19.3 vs. 16.3 months; HR 0.81, 95% CI 0.64-1.02, p=0.0755). Safety and tolerability profiles were similar between the two groups. No patient detected positive for Anti-drug antibody (ADA). Interpretation: The efficacy, safety and immunogenicity of MIL60 were similar with bevacizumab, providing an alternative treatment option for advanced or recurrent non-squamous NSCLC. Funding: This study was sponsored by Betta Pharmaceutical Co., Ltd.

Airborne laser scanning (ALS) data is one of the most commonly used data for terrain products generation. Filtering ground points is a prerequisite step for ALS data processing. Traditional filtering methods mainly use handcrafted features or predefined classification rules with preprocessing/post-processing operations to filter ground points iteratively, which is empirical and cumbersome. Deep learning provides a new approach to solve classification and segmentation problems because of its ability to self-learn features, which has been favored in many fields, particularly remote sensing. In this article, we proposed a point-based fully convolutional neural network (PFCN) which directly consumed points with only geometric information and extracted both point-wise and tile-wise features to classify each point. The network was trained with 37449157 points from 14 sites and evaluated on 6 sites in various forested environments. Additionally, the method was compared with five widely used filtering methods and one of the best point-based deep learning methods (PointNet++). Results showed that the PFCN achieved the best results in terms of mean omission error (T1 = 1.10%), total error (Te = 1.73%), and Kappa coefficient (93.88%), but ranked second for the root mean square error of the digital Terrain model caused by the worst commission error. Additionally, our method was on par with or even better than PointNet++ in accuracy. Moreover, the method consumes one-third of the computational resource and one-seventh of the training time. We believe that PFCN is a simple and flexible method that can be widely applied for ground point filtering.

Abstract Background Maize (Zea mays L.) is the third most consumed grain in the world and improving maize yield is of great importance of the world food security, especially under global climate change and more frequent severe droughts. Due to the limitation of phenotyping methods, most current studies only focused on the responses of phenotypes on certain key growth stages. Although light detection and ranging (lidar) technology showed great potential in acquiring three-dimensional (3D) vegetation information, it has been rarely used in monitoring maize phenotype dynamics at an individual plant level. Results In this study, we used a terrestrial laser scanner to collect lidar data at six growth stages for 20 maize varieties under drought stress. Three drought-related phenotypes, i.e., plant height, plant area index (PAI) and projected leaf area (PLA), were calculated from the lidar point clouds at the individual plant level. The results showed that terrestrial lidar data can be used to estimate plant height, PAI and PLA at an accuracy of 96%, 70% and 92%, respectively. All three phenotypes showed a pattern of first increasing and then decreasing during the growth period. The high drought tolerance group tended to keep lower plant height and PAI without losing PLA during the tasseling stage. Moreover, the high drought tolerance group inclined to have lower plant area density in the upper canopy than the low drought tolerance group. Conclusion The results demonstrate the feasibility of using terrestrial lidar to monitor 3D maize phenotypes under drought stress in the field and may provide new insights on identifying the key phenotypes and growth stages influenced by drought stress.

We describe a case of recurrent and metastatic radioactive iodine-refractory differentiated thyroid cancer (RAIR-DTC) treated with anlotinib in this report. The patient was randomized to placebo initially, after disease progressed at C8 (C is the treatment cycle), the patient was referred to the open label therapy of anlotinib, 12 mg p.o. daily with a 2-week on/1-week off regimen. Partial response was achieved at C2 with anlotinib treatment. To date, over 37 months of progression-free survival (PFS) has been achieved. Adverse effects were tolerable and manageable in this patient. Molecular characterization revealed coexistent C228T mutation of TERT promoter and BRAFV600E mutations. Favorable clinical outcome in this patient suggests that anlotinib might provide a novel effective therapeutic option for patients with RAIR-DTC. TERT and BRAFV600E mutations may represent as biomarker for predicting salutary effects of anlotinib.

Plant growth rhythm in structural traits is important for better understanding plant response to the ever-changing environment. Terrestrial laser scanning (TLS) is a well-suited tool to study structural rhythm under field conditions. Recent studies have used TLS to describe the structural rhythm of trees, but no consistent patterns have been drawn. Meanwhile, whether TLS can capture structural rhythm in crops is unclear. Here, we aim to explore the seasonal and circadian rhythms in maize structural traits at both the plant and leaf levels from time-series TLS. The seasonal rhythm was studied using TLS data collected at four key growth periods, including jointing, bell-mouthed, heading, and maturity periods. Circadian rhythms were explored by using TLS data acquired around every 2 hours in a whole day under standard and cold stress conditions. Results showed that TLS can quantify the seasonal and circadian rhythm in structural traits at both plant and leaf levels. (1) Leaf inclination angle decreased significantly between the jointing stage and bell-mouthed stage. Leaf azimuth was stable after the jointing stage. (2) Some individual-level structural rhythms (e.g., azimuth and projected leaf area/PLA) were consistent with leaf-level structural rhythms. (3) The circadian rhythms of some traits (e.g., PLA) were not consistent under standard and cold stress conditions. (4) Environmental factors showed better correlations with leaf traits under cold stress than standard conditions. Temperature was the most important factor that significantly correlated with all leaf traits except leaf azimuth. This study highlights the potential of time-series TLS in studying outdoor agricultural chronobiology.

Platelet-rich fibrin (PRF) as a reservoir of various growth factors plays an essential role in wound healing and tissue engineering at present. Electrospinning technology is an efficient approach to acquire artificial scaffold which has large specific surface area and high porosity. The goal of this study was to investigate the potential of electrospinning on the proliferation and osteogenesis of osteogenic precursor cells in vitro, with lyophilized PRF added as a component for electrospinning preparation. The surface structure of lyophilized PRF and nanofibers were investigated, and the proliferation, osteogenesis of MEC3T3-E1 cells with lyophilized PRF or nanofibers extract were studied. The results showed that the diameters of the lyophilized PRF pores were 1.51 ± 0.75 μm, and lyophilized PRF medium promoted the proliferation and osteocalcin (OCN) and osteopontin (OPN) genes expression of MEC3T3-E1 cells. Furthermore, the diameters of the polyvinyl alcohol/sodium alginate/lyophilized PRF (PVA/SA/PRF) fibers were 201.14 ± 40.14 nm. Compared to PVA/SA nanofibers extract and control medium, PVA/SA/PRF nanofibers extract also enhanced the proliferation and mineralization activity of MEC3T3-E1 cells. These results might be instructive to future therapeutics with PVA/SA/PRF electrospinning for bone tissue engineering or other applications.

The mangrove forests of northeast Hainan Island are the most species diverse forests in China and consist of the Dongzhai National Nature Reserve and the Qinglan Provincial Nature Reserve. The former reserve is the first Chinese national nature reserve for mangroves and the latter has the most abundant mangrove species in China. However, to date the aboveground ground biomass (AGB) of this mangrove region has not been quantified due to the high species diversity and the difficulty of extensive field sampling in mangrove habitat. Although three-dimensional point clouds can capture the forest vertical structure, their application to large areas is hindered by the logistics, costs and data volumes involved. To fill the gap and address this issue, this study proposed a novel upscaling method for mangrove AGB estimation using field plots, UAV-LiDAR strip data and Sentinel-2 imagery (named G∼LiDAR∼S2 model) based on a point-line-polygon framework. In this model, the partial-coverage UAV-LiDAR data were used as a linear bridge to link ground measurements to the wall-to-wall coverage Sentinel-2 data. The results showed that northeast Hainan Island has a total mangrove AGB of 312,806.29 Mg with a mean AGB of 119.26 Mg ha−1. The results also indicated that at the regional scale, the proposed UAV-LiDAR linear bridge method (i.e., G∼LiDAR∼S2 model) performed better than the traditional approach, which directly relates field plots to Sentinel-2 data (named the G∼S2 model) (R2 = 0.62 > 0.52, RMSE = 50.36 Mg ha−1

Regional landslide identification is important for the risk management of landslide hazards. The traditional methods of regional landslide identification were mainly conducted by a human being. In previous studies, automatic landslide recognition mainly focused on new landslides distinct from the environment induced by rainfall or earthquake, using the image classification method and semantic segmentation method of deep learning. However, there is a lack of research on the automatic recognition of old loess landslides, which are difficult to distinguish from the environment. Therefore, this study uses the object detection method of deep learning to identify old loess landslides with Google Earth images. At first, a database of loess historical landslide samples was established for deep learning based on Google Earth images. A total of 6111 landslides were interpreted in three landslide areas in Gansu Province, China. Second, three object detection algorithms including the one-stage algorithm RetinaNet and YOLO v3 and the two-stage algorithm Mask R-CNN, were chosen for automatic landslide identification. Mask R-CNN achieved the greatest accuracy, with an AP of 18.9% and F1-score of 55.31%. Among the three landslide areas, the order of identification accuracy from high to low was Site 1, Site 2, and Site 3, with the F1-scores of 62.05%, 61.04% and 50.88%, respectively, which were positively related to their recognition difficulty. The research results proved that the object detection method can be employed for the automatic identification of loess landslides based on Google Earth images.

Filtering of airborne light detection and ranging (LiDAR) data is a challenging task in vegetated mountain areas. Environmental features and LiDAR data characteristics have significant impacts on the performance of filtering algorithms. This study aims to determine the effects of topographic and environmental features such as slope, canopy cover, elevation variability, and LiDAR point density on five widely used filtering algorithms, including multi-scale curvature classification (MCC), interpolation-based filtering (IBF) algorithm, morphological filtering (MF) algorithm, progressive triangulated irregular network densification filtering (PTDF) algorithm, and slope-based filtering (SBF). The results show that the performances of these filtering algorithms are all significantly influenced by the chosen factors, but the dominant influential factor varies with algorithms. The MCC works well in steep and dense forests; IBF and MCC outperform the rest of filtering algorithms in areas with steep terrain but low vegetation coverage; and PTDF is more reliable for low-density LiDAR data. Our results can provide guidance for choosing the appropriate filtering algorithm based on the specific topographic and environmental features of a study area.

Improved monitoring and understanding of tree growth and its responses to controlling factors are important for tree growth modeling. Airborne Laser Scanning (ALS) can be used to enhance the efficiency and accuracy of large-scale forest surveys in delineating three-dimensional forest structures and under-canopy terrains. This study proposed an ALS-based framework to quantify tree growth and competition. Bi-temporal ALS data were used to quantify tree growth in height (ΔH), crown area (ΔA), crown volume (ΔV), and tree competition for 114,000 individual trees in two conifer-dominant Sierra Nevada forests. We analyzed the correlations between tree growth attributes and controlling factors (i.e. tree sizes, competition, forest structure, and topographic parameters) at multiple levels. At the individual tree level, ΔH had no consistent correlations with controlling factors, ΔA and ΔV were positively related to original tree sizes (R > 0.3) and negatively related to competition indices (R 0.7), ΔV was positively related to original tree sizes (|R| > 0.8). Multivariate regression models were simulated at individual tree level for ΔH, ΔA, and ΔV with the R2 ranged from 0.1 to 0.43. The ALS-based tree height estimation and growth analysis results were consistent with field measurements.

Abstract Early preoperative diagnosis of central lymph node metastasis (CNM) is crucial to improve survival rates among patients with papillary thyroid carcinoma (PTC). Here, we analyzed clinical data from 2862 PTC patients and developed a scoring system using multivariable logistic regression and testified by the validation group. The predictive diagnostic effectiveness of the scoring system was evaluated based on consistency, discrimination ability, and accuracy. The scoring system considered seven variables: gender, age, tumor size, microcalcification, resistance index >0.7, multiple nodular lesions, and extrathyroid extension. The area under the receiver operating characteristic curve (AUC) was 0.742, indicating a good discrimination. Using 5 points as a diagnostic threshold, the validation results for validation group had an AUC of 0.758, indicating good discrimination and consistency in the scoring system. The sensitivity of this predictive model for preoperative diagnosis of CNM was 4 times higher than a direct ultrasound diagnosis. These data indicate that the CNM prediction model would improve preoperative diagnostic sensitivity for CNM in patients with papillary thyroid carcinoma.

Forests of the Sierra Nevada (SN) mountain range are valuable natural heritages for the region and the country, and tree height is an important forest structure parameter for understanding the SN forest ecosystem. There is still a need in the accurate estimation of wall-to-wall SN tree height distribution at fine spatial resolution. In this study, we presented a method to map wall-to-wall forest tree height (defined as Lorey’s height) across the SN at 70-m resolution by fusing multi-source datasets, including over 1600 in situ tree height measurements and over 1600 km2 airborne light detection and ranging (LiDAR) data. Accurate tree height estimates within these airborne LiDAR boundaries were first computed based on in situ measurements, and then these airborne LiDAR-derived tree heights were used as reference data to estimate tree heights at Geoscience Laser Altimeter System (GLAS) footprints. Finally, the random forest algorithm was used to model the SN tree height from these GLAS tree heights, optical imagery, topographic data, and climate data. The results show that our fine-resolution SN tree height product has a good correspondence with field measurements. The coefficient of determination between them is 0.60, and the root-mean-squared error is 5.45 m.

Accurate and repeated forest inventory data are critical to understand forest ecosystem processes and manage forest resources. In recent years, unmanned aerial vehicle (UAV)-borne light detection and ranging (lidar) systems have demonstrated effectiveness at deriving forest inventory attributes. However, their high cost has largely prevented them from being used in large-scale forest applications. Here, we developed a very low-cost UAV lidar system that integrates a recently emerged DJI Livox MID40 laser scanner (~$600 USD) and evaluated its capability in estimating both individual tree-level (i.e., tree height) and plot-level forest inventory attributes (i.e., canopy cover, gap fraction, and leaf area index (LAI)). Moreover, a comprehensive comparison was conducted between the developed DJI Livox system and four other UAV lidar systems equipped with high-end laser scanners (i.e., RIEGL VUX-1 UAV, RIEGL miniVUX-1 UAV, HESAI Pandar40, and Velodyne Puck LITE). Using these instruments, we surveyed a coniferous forest site and a broadleaved forest site, with tree densities ranging from 500 trees/ha to 3000 trees/ha, with 52 UAV flights at different flying height and speed combinations. The developed DJI Livox MID40 system effectively captured the upper canopy structure and terrain surface information at both forest sites. The estimated individual tree height was highly correlated with field measurements (coniferous site: R2 = 0.96, root mean squared error/RMSE = 0.59 m; broadleaved site: R2 = 0.70, RMSE = 1.63 m). The plot-level estimates of canopy cover, gap fraction, and LAI corresponded well with those derived from the high-end RIEGL VUX-1 UAV system but tended to have systematic biases in areas with medium to high canopy densities. Overall, the DJI Livox MID40 system performed comparably to the RIEGL miniVUX-1 UAV, HESAI Pandar40, and Velodyne Puck LITE systems in the coniferous site and to the Velodyne Puck LITE system in the broadleaved forest. Despite its apparent weaknesses of limited sensitivity to low-intensity returns and narrow field of view, we believe that the very low-cost system developed by this study can largely broaden the potential use of UAV lidar in forest inventory applications. This study also provides guidance for the selection of the appropriate UAV lidar system and flight specifications for forest research and management.

Mangrove forest ecosystems are distributed at the land–sea interface in tropical and subtropical regions and play an important role in carbon cycles and biodiversity. Accurately mapping global mangrove aboveground biomass (AGB) will help us understand how mangrove ecosystems are affected by the impacts of climatic change and human activities. Light detection and ranging (LiDAR) techniques have been proven to accurately capture the three-dimensional structure of mangroves and LiDAR can estimate forest AGB with high accuracy. In this study, we produced a global mangrove forest AGB map for 2004 at a 250-m resolution by combining ground inventory data, spaceborne LiDAR, optical imagery, climate surfaces, and topographic data with random forest, a machine learning method. From the published literature and free-access datasets of mangrove biomass, we selected 342 surface observations to train and validate the mangrove AGB estimation model. Our global mangrove AGB map showed that average global mangrove AGB density was 115.23 Mg/ha, with a standard deviation of 48.89 Mg/ha. Total global AGB storage within mangrove forests was 1.52 Pg. Cross-validation with observed data demonstrated that our mangrove AGB estimates were reliable. The adjusted coefficient of determination (R2) and root-mean-square error (RMSE) were 0.48 and 75.85 Mg/ha, respectively. Our estimated global mangrove AGB storage was similar to that predicted by previous remote sensing methods, and remote sensing approaches can overcome overestimates from climate-based models. This new biomass map provides information that can help us understand the global mangrove distribution, while also serving as a baseline to monitor trends in global mangrove biomass.

The rapid development of light detection and ranging (Lidar) provides a promising way to obtain three-dimensional (3D) phenotype traits with its high ability of recording accurate 3D laser points. Recently, Lidar has been widely used to obtain phenotype data in the greenhouse and field with along other sensors. Individual maize segmentation is the prerequisite for high throughput phenotype data extraction at individual crop or leaf level, which is still a huge challenge. Deep learning, a state-of-the-art machine learning method, has shown high performance in object detection, classification, and segmentation. In this study, we proposed a method to combine deep leaning and regional growth algorithms to segment individual maize from terrestrial Lidar data. The scanned 3D points of the training site were sliced row and row with a fixed 3D window. Points within the window were compressed into deep images, which were used to train the Faster R-CNN (region-based convolutional neural network) model to learn the ability of detecting maize stem. Three sites of different planting densities were used to test the method. Each site was also sliced into many 3D windows, and the testing deep images were generated. The detected stem in the testing images can be mapped into 3D points, which were used as seed points for the regional growth algorithm to grow individual maize from bottom to up. The results showed that the method combing deep leaning and regional growth algorithms was promising in individual maize segmentation, and the values of r, p, and F of the three testing sites with different planting density were all over 0.9. Moreover, the height of the truly segmented maize was highly correlated to the manually measured height (R2> 0.9). This work shows the possibility of using deep leaning to solve the individual maize segmentation problem from Lidar data.

The Shuttle Radar Topography Mission (SRTM) digital elevation model (DEM) is one of the most complete and frequently used global-scale DEM products in various applications. However, previous studies have shown that the SRTM DEM is systematically higher than the actual land surface in vegetated mountain areas. The objective of this study is to propose a procedure to calibrate the SRTM DEM over large vegetated mountain areas. Firstly, we developed methods to estimate canopy cover from aerial imagery and tree height from multi-source datasets (i.e., field observations, airborne light detection and ranging (LiDAR) data, Geoscience Laser Altimeter System (GLAS) data, Landsat TM imagery, climate surfaces, and topographic data). Then, the airborne LiDAR derived DEM, covering ~5% of the study area, was used to evaluate the accuracy of the SRTM DEM. Finally, a regression model of the SRTM DEM error depending on tree height, canopy cover, and terrain slope was developed to calibrate the SRTM DEM. Our results show that the proposed procedure can significantly improve the accuracy of the SRTM DEM over vegetated mountain areas. The mean difference between the SRTM DEM and the LiDAR DEM decreased from 12.15 m to −0.82 m, and the standard deviation dropped by 2 m.

Urban areas are one of the most important components of human society. Their extents have been continuously growing during the last few decades. Accurate and timely measurements of the extents of urban areas can help in analyzing population densities and urban sprawls and in studying environmental issues related to urbanization. Urban extents detected from remotely sensed data are usually a by-product of land use classification results, and their interpretation requires a full understanding of land cover types. In this study, for the first time, we mapped urban extents in the continental United States using a novel one-class classification method, i.e., positive and unlabeled learning (PUL), with multi-temporal Moderate Resolution Imaging Spectroradiometer (MODIS) data for the year 2010. The Defense Meteorological Satellite Program Operational Linescan System (DMSP-OLS) night stable light data were used to calibrate the urban extents obtained from the one-class classification scheme. Our results demonstrated the effectiveness of the use of the PUL algorithm in mapping large-scale urban areas from coarse remote-sensing images, for the first time. The total accuracy of mapped urban areas was 92.9% and the kappa coefficient was 0.85. The use of DMSP-OLS night stable light data can significantly reduce false detection rates from bare land and cropland far from cities. Compared with traditional supervised classification methods, the one-class classification scheme can greatly reduce the effort involved in collecting training datasets, without losing predictive accuracy.

Imbalanced learning is a methodological challenge in remote sensing communities, especially in complex areas where the spectral similarity exists between land covers. Obtaining high-confidence classification results for imbalanced class issues is highly important in practice. In this paper, extreme gradient boosting (XGB), a novel tree-based ensemble system, is employed to classify the land cover types in Very-high resolution (VHR) images with imbalanced training data. We introduce an extended margin criterion and disagreement performance to evaluate the efficiency of XGB in imbalanced learning situations and examine the effect of minority class spectral separability on model performance. The results suggest that the uncertainty of XGB associated with correct classification is stable. The average probability-based margin of correct classification provided by XGB is 0.82, which is about 46.30% higher than that by random forest (RF) method (0.56). Moreover, the performance uncertainty of XGB is insensitive to spectral separability after the sample imbalance reached a certain level (minority:majority > 10:100). The impact of sample imbalance on the minority class is also related to its spectral separability, and XGB performs better than RF in terms of user accuracy for the minority class with imperfect separability. The disagreement components of XGB are better and more stable than RF with imbalanced samples, especially for complex areas with more types. In addition, appropriate sample imbalance helps to improve the trade-off between the recognition accuracy of XGB and the sample cost. According to our analysis, this margin-based uncertainty assessment and disagreement performance can help users identify the confidence level and error component in similar classification performance (overall, producer, and user accuracies).

Mapping mangrove extent and species is important for understanding their response to environmental changes and for observing their integrity for providing goods and services. However, accurately mapping mangrove extent and species are ongoing challenges in remote sensing. The newly-launched and freely-available Sentinel-2 (S2) sensor offers a new opportunity for these challenges. This study presents the first study dedicated to the examination of the potential of original bands, spectral indices, and texture information of S2 in mapping mangrove extent and species in the first National Nature Reserve for mangroves in Dongzhaigang, China. To map mangrove extent and species, a three-level hierarchical structure based on the spatial structure of a mangrove ecosystem and geographic object-based image analysis is utilized and modified. During the experiments, to conquer the challenge of optimizing high-dimension and correlated feature space, the recursive feature elimination (RFE) algorithm is introduced. Finally, the selected features from RFE are employed in mangrove species discriminations, based on a random forest algorithm. The results are compared with those of Landsat 8 (L8) and Pléiades-1 (P1) data and show that S2 and L8 could accurately extract mangrove extent, but P1 obviously overestimated it. Regarding mangrove species community levels, the overall classification accuracy of S2 is 70.95%, which is lower than P1 imagery (78.57%) and slightly higher than L8 data (68.57%). Meanwhile, the former difference is statistically significant, and the latter is not. The dominant species is extracted basically in S2 and P1 imagery, but for the occasionally distributed K. candel and the pioneer and fringe mangrove A. marina, S2 performs poorly. Concerning L8, S2, and P1, there are eight (8/126), nine (9/218), and eight (8/73) features, respectively, that are the most important for mangrove species discriminations. The most important feature overall is the red-edge bands, followed by shortwave infrared, near infrared, blue, and other visible bands in turn. This study demonstrates that the S2 sensor can accurately map mangrove extent and basically discriminate mangrove species communities, but for the latter, one should be cautious due to the complexity of mangrove species.