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Forest carbon stock is an important indicator reflecting a forest ecosystem’s structures and functions. Its spatial distribution is valuable for managing natural resources, protecting ecosystems and biodiversity, and further promoting sustainability, but accurately mapping the forest carbon stock distribution in a large area is a challenging task. This study selected Changting County, Fujian Province, as a case study to explore a method to map the forest carbon stock distribution using the integration of airborne Lidar, Sentinel-2, and ancillary data in 2022. The Bayesian hierarchical modeling approach was used to estimate the local forest carbon stock based on airborne Lidar data and field measurements, and then the random forest approach was used to develop a regional forest carbon stock estimation model based on the Sentinel-2 and ancillary data. The results indicated that the Lidar-based carbon stock distribution effectively provided sample plots with good spatial representativeness for modeling regional carbon stock with a coefficient of determination (R2) of 0.7 and root mean square error (RMSE) of 12.94 t/ha. The average carbon stocks were 48.55 t/ha, 55.51 t/ha, and 57.04 t/ha for Masson pine, Chinese fir, and broadleaf forests, respectively. The carbon stock in non-conservation regions was 15.2–16.1 t/ha higher than that in conservation regions. This study provides a promising method through the use of airborne Lidar data as a linkage between sample plots and Sentinel-2 data to map the regional carbon stock distribution in those subtropical regions where serious soil erosion has led to a relatively sparse forest canopy density. The results are valuable for local government to make scientific decisions for promoting ecosystem restoration due to water and soil erosion.

Forest canopy height (FCH) is an important variable for estimating forest biomass and ecosystem carbon sequestration. Spaceborne LiDAR data have been used to create wall-to-wall FCH maps, such as the forest tree height map of China (FCHChina), Global Forest Canopy Height 2020 (GFCH2020), and Global Forest Canopy Height 2019 (GFCH2019). However, these products lack comprehensive assessment. This study used airborne LiDAR data from various topographies (e.g., plain, hill, and mountain) to assess the impacts of different topographical and vegetation characteristics on spaceborne LiDAR-derived FCH products. The results show that GEDI–FCH demonstrates better accuracy in plain and hill regions, while ICESat-2 ATLAS–FCH shows superior accuracy in the mountainous region. The difficulty in accurately capturing photons from sparse tree canopies by ATLAS and the geolocation errors of GEDI has led to partial underestimations of FCH products in plain areas. Spaceborne LiDAR FCH retrievals are more accurate in hilly regions, with a root mean square error (RMSE) of 4.99 m for ATLAS and 3.85 m for GEDI. GEDI–FCH is significantly affected by slope in mountainous regions, with an RMSE of 13.26 m. For wall-to-wall FCH products, the availability of FCH data is limited in plain areas. Optimal accuracy is achieved in hilly regions by FCHChina, GFCH2020, and GFCH2019, with RMSEs of 5.52 m, 5.07 m, and 4.85 m, respectively. In mountainous regions, the accuracy of wall-to-wall FCH products is influenced by factors such as tree canopy coverage, forest cover types, and slope. However, some of these errors may stem from directly using current ATL08 and GEDI L2A FCH products for mountainous FCH estimation. Introducing accurate digital elevation model (DEM) data can improve FCH retrieval from spaceborne LiDAR to some extent. This research improves our understanding of the existing FCH products and provides valuable insights into methods for more effectively extracting accurate FCH from spaceborne LiDAR data. Further research should focus on developing suitable approaches to enhance the FCH retrieval accuracy from spaceborne LiDAR data and integrating multi-source data and modeling algorithms to produce accurate wall-to-wall FCH distribution in a large area.

Soil and water erosion has long been regarded as a serious environmental problem in the world. Thus, research on reducing soil erosion has received continuous attention. Different conservation measures such as restoring low-function forests, closing hillsides for afforestation, planting trees and grass, and constructing terraces on slope land have been implemented for controlling soil erosion problems and promoting vegetation cover change. One important task is to understand the effects of different conservation measures on reducing water and soil erosion problems. However, directly conducting the evaluation of soil erosion reduction is difficult. One solution is to evaluate the patterns and magnitudes of vegetation cover change due to implementing these measures. Therefore, this research selected Changting County, Fujian Province as a case study to examine the effects of implementing conservation measures on vegetation cover change based on time series Landsat images and field survey data. Landsat images between 1986 and 2021 were used to produce time series vegetation cover data using the Google Earth Engine. Sentinel-2 images acquired in 2021 and Landsat images in 2010 were separately used to develop land cover maps using the random forest method. The spatial distribution of different conservation measures was linked to annual vegetation cover and land cover change data to examine the effects on the change in vegetation cover. The results showed a significant reduction in bare lands and increase in pine forests. The vegetation coverage increased from 42% in 1986 to 79% in 2021 in the conservation region compared with an increase from 73% to 87% in the non-conservation region during the same period. Of the different conservation measures, the change magnitude was 0.44 for restoring low-function forests and closing hillsides for afforestation and 0.65 for multiple control measures. This research provides new insights in terms of understanding the effects of taking proper measures for reducing soil and water erosion problems and provides scientific results for decisionmaking for soil erosion controls. The strategy and method used in this research are valuable for other regions in understanding the roles of different conservation measures on vegetation cover change and soil erosion reduction through employing remote sensing technologies.

Cell lysis is a key step for studying the structure and function of proteins in cells and an important intermediate step in drug screening, cancer diagnosis, and genome analysis. The current cell lysis methods still suffer from limitations, such as the need for large instruments, a long and time-consuming process, a large sample volume, chemical reagent contamination, and their unsuitability for the small amount of bacteria lysis required for point-of-care testing (POCT) devices. Therefore, a fast, chemical-free, portable, and non-invasive device needs to be developed. In the present study, we designed an integrated microfluidic chip to achieve E. coli lysis by applying an alternating current (AC) electric field and investigated the effects of voltage, frequency, and flow rate on the lysis. The results showed that the lysis efficiency of the bacteria was increased with a higher voltage, lower frequency, and lower flow rate. When the voltage was at 10 Vp-p, the lysis efficiency was close to 100%. The study provided a simple, rapid, reagent-free, and high-efficiency cleavage method for biology and biomedical applications involving bacteria lysis.

Inflammation plays a decisive role in inducing tumorigenesis, promoting tumor development, tumor invasion and migration. The interaction of cancer cells with their surrounding stromal cells and inflammatory cells further forms an inflammatory tumor microenvironment (TME). The large number of cells present within the TME, such as mesenchymal stem cells (MSCs), macrophages, neutrophils, etc., play different roles in the changing TME. Exosomes, extracellular vesicles released by various types of cells, participate in a variety of inflammatory diseases and tumor-related inflammation. As an important communication medium between cells, exosomes continuously regulate the inflammatory microenvironment. In this review, we focused on the role of exosomes in inflammatory diseases and tumor-related inflammation. In addition, we also summarized the functions of exosomes released by various cells in inflammatory diseases and in the TME during the transformation of inflammatory diseases to tumors. We discussed in depth the potential of exosomes as targets and tools to treat inflammatory diseases and tumor-related inflammation.

Coarse cereals are rich in flavonoids, which are bioactive substances with a wide range of functions. Biotransformation is considered an emerging approach to methylate flavonoids, displaying prominent regio- and stereoselectivity. In the current study, liquiritigenin, naringenin, and hesperidin flavonoids were biotransformed using O-methyltransferases that were heterologously expressed in Saccharomyces cerevisiae BJ5464-NpgA. Nuclear magnetic resonance (NMR) spectroscopy was used together with high-resolution mass spectroscopy analysis to determine the structures of the resulting methylated transformants, and their antimicrobial and antiproliferation activities were also characterized. Among the five methylated flavonoids obtained, 7-methoxy-liquiritigenin had the strongest inhibitory effect on Candida albicans SC5314 (C. albicans SC5314), Staphylococcus aureus ATCC6538 (S. aureus ATCC6538), and Escherichia coli ATCC25922 (E. coli ATCC25922), which increased 7.65-, 1.49-, and 0.54-fold in comparison to the values of their unmethylated counterparts at 200, 250, and 400 μM, respectively. The results suggest that 3′-methoxyhesperetin showed the best antiproliferative activity against MCF-7 cells with IC50 values of 10.45 ± 0.45 µM, which was an increase of more than 14.35-fold compared to that of hesperetin. These results indicate that methylation enhances the antimicrobial activities and antiproliferative effects of flavonoids. The current study provides an experimental basis for further research on flavonoids as well as flavonoid-containing crops in the development of antimicrobial and anti-breast cancer drugs in addition to supplementary and health foods. The biotransformation method is ideal, as it represents a means for the sustainable production of bioactive flavonoids.

Natural microalgae (NA, cyanobacteria) collected from Taihu Lake (Jiangsu, China) were used for biofuel production through pyrolysis. The microalgae were de-ashed via pretreatment with deionized water and hydrochloric acid, and the samples obtained were noted as 0 M, 0.1 M, 1 M, 2 M, 4 M, 6 M, 8 M, respectively, according to the concentration of hydrochloric acid used in the pretreatment. Pyrolysis experiments were carried out at 500 °C for 2 h. The products were examined by various techniques to identify the influence of the ash on the pyrolysis behavior. The results showed that the ash inhibited the thermal transformation of microalgae. The 2 mol/L hydrochloric acid performed the best in removing ash and the liquid yield increased from 34.4% (NA) to 40.5% (2 M). Metal-oxides (mainly CaO, MgO, Al2O3) in ash promoted the reaction of hexadecanoic acid and NH3 to produce more hexadecanamide, which was further dehydrated to hexadecanenitrile. After acid pretreatment, significant improvement in the selectivity of hexadecanoic acid was observed, ranging from 22.4% (NA) to 58.8% (4 M). The hydrocarbon compounds in the liquid product increased from 12.90% (NA) to 26.67% (2 M). Furthermore, the acid pretreatment enhanced the content of C9–C16 compounds and the HHV values of bio-oil. For natural microalgae, the de-ashing pretreatment before pyrolysis was essential for improving the biocrude yield and quality, as well as the biomass conversion efficiency.

Timely updates of carbon stock distribution are needed to better understand the impacts of deforestation and degradation on forest carbon stock dynamics. This research aimed to explore an approach for estimating aboveground carbon density (ACD) in the Brazilian Amazon through integration of MODIS (moderate resolution imaging spectroradiometer) and a limited number of light detection and ranging (Lidar) data samples using linear regression (LR) and random forest (RF) algorithms, respectively. Airborne LiDAR data at 23 sites across the Brazilian Amazon were collected and used to calculate ACD. The ACD estimation model, which was developed by Longo et al. in the same study area, was used to map ACD distribution in the 23 sites. The LR and RF methods were used to develop ACD models, in which the samples extracted from LiDAR-estimated ACD were used as dependent variables and MODIS-derived variables were used as independent variables. The evaluation of modeling results indicated that ACD can be successfully estimated with a coefficient of determination of 0.67 and root mean square error of 4.18 kg C/m2 using RF based on spectral indices. The mixed pixel problem in MODIS data is a major factor in ACD overestimation, while cloud contamination and data saturation are major factors in ACD underestimation. These uncertainties in ACD estimation using MODIS data make it difficult to examine annual ACD dynamics of degradation and growth, however this method can be used to examine the deforestation-induced ACD loss.

Many studies have investigated the effects of spectral and spatial features of remotely sensed data and topographic characteristics on land-cover and forest classification results, but they are mainly based on individual sensor data. How these features from different kinds of remotely sensed data with various spatial resolutions influence classification results is unclear. We conducted a comprehensively comparative analysis of spectral and spatial features from ZiYuan-3 (ZY-3), Sentinel-2, and Landsat and their fused datasets with spatial resolution ranges from 2 m, 6 m, 10 m, 15 m, and to 30 m, and topographic factors in influencing land-cover classification results in a subtropical forest ecosystem using random forest approach. The results indicated that the combined spectral (fused data based on ZY-3 and Sentinel-2), spatial, and topographical data with 2-m spatial resolution provided the highest overall classification accuracy of 83.5% for 11 land-cover classes, as well as the highest accuracies for almost all individual classes. The improvement of spectral bands from 4 to 10 through fusion of ZY-3 and Sentinel-2 data increased overall accuracy by 14.2% at 2-m spatial resolution, and by 11.1% at 6-m spatial resolution. Textures from high spatial resolution imagery play more important roles than textures from medium spatial resolution images. The incorporation of textural images into spectral data in the 2-m spatial resolution imagery improved overall accuracy by 6.0–7.7% compared to 1.1–1.7% in the 10-m to 30-m spatial resolution images. Incorporation of topographic factors into spectral and textural imagery further improved overall accuracy by 1.2–5.5%. The classification accuracies for coniferous forest, eucalyptus, other broadleaf forests, and bamboo forest can be 85.3–91.1%. This research provides new insights for using proper combinations of spectral bands and textures corresponding to specifically spatial resolution images in improving land-cover and forest classifications in subtropical regions.

Species-rich subtropical forests have high carbon sequestration capacity and play important roles in regional and global carbon regulation and climate changes. A timely investigation of the spatial distribution characteristics of subtropical forest aboveground biomass (AGB) is essential to assess forest carbon stocks. Lidar (light detection and ranging) is regarded as the most reliable data source for accurate estimation of forest AGB. However, previous studies that have used lidar data have often beenbased on a single model developed from the relationships between lidar-derived variables and AGB, ignoring the variability of this relationship in different forest types. Although stratification of forest types has been proven to be effective for improving AGB estimation, how to stratify forest types and how many strata to use are still unclear. This research aims to improve forest AGB estimation through exploring suitable stratification approaches based on lidar and field survey data. Different stratification schemes including non-stratification and stratifications based on forest types and forest stand structures were examined. The AGB estimation models were developed using linear regression (LR) and random forest (RF) approaches. The results indicate the following: (1) Proper stratifications improved AGB estimation and reduced the effect of under- and overestimation problems; (2) the finer forest type strata generated higher accuracy of AGB estimation but required many more sample plots, which were often unavailable; (3) AGB estimation based on stratification of forest stand structures was similar to that based on five forest types, implying that proper stratification reduces the number of sample plots needed; (4) the optimal AGB estimation model and stratification scheme varied, depending on forest types; and (5) the RF algorithm provided better AGB estimation for non-stratification than the LR algorithm, but the LR approach provided better estimation with stratification. Results from this research provide new insights on how to properly conduct forest stratification for AGB estimation modeling, which is especially valuable in tropical and subtropical regions with complex forest types.

Data saturation in optical sensor data has long been recognized as a major factor that causes underestimation of aboveground biomass (AGB) for forest sites having high AGB, but there is a lack of suitable approaches to solve this problem. The objective of this research was to understand how incorporation of forest canopy features into high spatial resolution optical sensor data improves forest AGB estimation. Therefore, we explored the use of ZiYuan-3 (ZY-3) satellite imagery, including multispectral and stereo data, for AGB estimation of larch plantations in North China. The relative canopy height (RCH) image was calculated from the difference of digital surface model (DSM) data at leaf-on and leaf-off seasons, which were extracted from the ZY-3 stereo images. Image segmentation was conducted using eCognition on the basis of the fused ZY-3 multispectral and panchromatic data. Spectral bands, vegetation indices, textural images, and RCH-based variables based on this segment image were extracted. Linear regression was used to develop forest AGB estimation models, where the dependent variable was AGB from sample plots, and explanatory variables were from the aforementioned remote-sensing variables. The results indicated that incorporation of RCH-based variables and spectral data considerably improved AGB estimation performance when compared with the use of spectral data alone. The RCH-variable successfully reduced the data saturation problem. This research indicated that the combined use of RCH-variables and spectral data provided more accurate AGB estimation for larch plantations than the use of spectral data alone. Specifically, the root mean squared error (RMSE), relative RMSE, and mean absolute error values were 33.89 Mg/ha, 29.57%, and 30.68 Mg/ha, respectively, when using the spectral-only model, but they become 24.49 Mg/ha, 21.37%, and 20.37 Mg/ha, respectively, when using the combined model with RCH variables and spectral band. This proposed approach provides a new insight in reducing the data saturation problem.

Glycosylation is an efficient strategy to modulate the solubility, stability, bioavailability and bioactivity of drug-like natural products. Biological methods, such as whole-cell biocatalyst, promise a simple but highly effective approach to glycosylate biologically active small molecules with remarkable regio- and stereo-selectivity. Herein, we use the entomopathogenic filamentous fungus Isaria fumosorosea ACCC 37814 to biotransform a panel of phenolic natural products, including flavonoids and anthraquinone, into their glycosides. Six new flavonoid (4-O-methyl)glucopyranosides are obtained and structurally characterized using high resolution mass and nuclear magnetic resonance spectroscopic techniques. These compounds further expand the structural diversity of flavonoid glycosides and may be used in biological study.

The global availability of high spatial resolution images makes mapping tree species distribution possible for better management of forest resources. Previous research mainly focused on mapping single tree species, but information about the spatial distribution of all kinds of trees, especially plantations, is often required. This research aims to identify suitable variables and algorithms for classifying land cover, forest, and tree species. Bi-temporal ZiYuan-3 multispectral and stereo images were used. Spectral responses and textures from multispectral imagery, canopy height features from bi-temporal stereo imagery, and slope and elevation from the stereo-derived digital surface model data were examined through comparative analysis of six classification algorithms including maximum likelihood classifier (MLC), k-nearest neighbor (kNN), decision tree (DT), random forest (RF), artificial neural network (ANN), and support vector machine (SVM). The results showed that use of multiple source data—spectral bands, vegetation indices, textures, and topographic factors—considerably improved land-cover and forest classification accuracies compared to spectral bands alone, which the highest overall accuracy of 84.5% for land cover classes was from the SVM, and, of 89.2% for forest classes, was from the MLC. The combination of leaf-on and leaf-off seasonal images further improved classification accuracies by 7.8% to 15.0% for land cover classes and by 6.0% to 11.8% for forest classes compared to single season spectral image. The combination of multiple source data also improved land cover classification by 3.7% to 15.5% and forest classification by 1.0% to 12.7% compared to the spectral image alone. MLC provided better land-cover and forest classification accuracies than machine learning algorithms when spectral data alone were used. However, some machine learning approaches such as RF and SVM provided better performance than MLC when multiple data sources were used. Further addition of canopy height features into multiple source data had no or limited effects in improving land-cover or forest classification, but improved classification accuracies of some tree species such as birch and Mongolia scotch pine. Considering tree species classification, Chinese pine, Mongolia scotch pine, red pine, aspen and elm, and other broadleaf trees as having classification accuracies of over 92%, and larch and birch have relatively low accuracies of 87.3% and 84.5%. However, these high classification accuracies are from different data sources and classification algorithms, and no one classification algorithm provided the best accuracy for all tree species classes. This research implies the same data source and the classification algorithm cannot provide the best classification results for different land cover classes. It is necessary to develop a comprehensive classification procedure using an expert-based approach or hierarchical-based classification approach that can employ specific data variables and algorithm for each tree species class.

Increasing agroforestry areas with improper management has produced serious environmental problems, such as soil erosion. It is necessary to rapidly predict the spatial distribution of such erosion risks in a large area, but there is a lack of approaches that are suitable for mountainous regions. The objective of this research was to develop an approach that can effectively employ remotely-sensed and ancillary data, to map soil erosion risks in an agroforestry ecosystem in a mountainous region. This research employed field survey data, soil-type maps, digital elevation model data, weather station data, and Landsat imagery, for extraction of potential variables. It used the random forest approach to identify eight key variables—slope, slope of slope, normalized difference greenness index at leaf-on season, soil organic matter, fractional vegetation at leaf-on season, fractional soil at leaf-off season, precipitation in June, and percent of soil clay—for mapping soil erosion risk distribution in hickory plantations in Western Zhejiang Province, China. The results showed that an overall accuracy of 89.8% was obtained for three levels of soil erosion risk. Approximately one-fourth of hickory plantations were at high-risk, requiring the owners or decision makers to take proper measures to reduce the soil erosion problem. This research provides a new approach to predict soil erosion risk, based on the primary variables that can be extracted directly from remotely-sensed data and ancillary data. This proposed approach will be valuable for other agroforestry and plantations, such as Torreya grandis, eucalyptus, and the rubber tree, that are playing important roles in improving economic conditions for the local farmers but face soil erosion problems.

Urban expansion has long been a research hotspot and is often based on individual cities, but rarely has research conducted a comprehensive comparison between coastal and inland metropoles for understanding different spatial patterns of urban expansions and driving forces. We selected coastal metropoles (Shanghai and Shenzhen in China, and Ho Chi Minh City in Vietnam) and inland metropoles (Ulaanbaatar in Mongolia, Lanzhou in China, and Vientiane in Laos) with various developing stages and physical conditions for examining the spatiotemporal patterns of urban expansions in the past 25 years (1990–2015). Multitemporal Landsat images with 30 m spatial resolution were used to develop urban impervious surface area (ISA) distributions and examine their dynamic changes. The impacts of elevation, slope, and rivers on spatial patterns of urban expansion were examined. This research indicates that ISA is an important variable for examining urban expansion. Coastal metropoles had much faster urbanization rates than inland metropoles. The spatial patterns of urban ISA distribution and expansion are greatly influenced by physical conditions; that is, ISA is mainly distributed in the areas with slopes of less than 10 degrees. Rivers are important geographical factors constraining urban expansion, especially in developing stages, while bridges across the rivers promote urban expansion patterns and rates. The relationships of spatial patterns of urban ISA distribution and dynamics with physical conditions provide scientific data for urban planning, management, and sustainability.

Understanding changes in the distribution and coverage of aquatic vegetation (AV) is of great significance for the restoration of lake ecosystems. In this study, the vegetation and bloom indices (VBI) algorithm were used to interpret submerged aquatic vegetation (SAV), floating/emergent aquatic vegetation (FEAV), and algal bloom (AB). The dynamics of AV and their influencing factors in Bao’ an Lake, in the middle reaches of the Yangtze River in China, were studied from 2000 to 2023. The results showed that (1) the VBI algorithm can accurately distinguish AV and AB of different life forms with an overall accuracy of 93% and a kappa coefficient of 0.86. (2) Macrophyte coverage decreases. AV grew vigorously in spring, and SAV was the dominant type within it, whereas AV coverage was low in summer, and SAV had no summer species for a long time. In 2000, the coverage of AV was the highest, reaching 64.5%, but a gradual decrease that followed in the coming years finally led to a coverage percentage of less than 5% by 2023. (3) The correlation between SAV coverage and total phosphorus (p < 0.01), total nitrogen (p < 0.05), and water depth/transparency (p < 0.05) in Bao’ an Lake were 0.23, 0.28, and 0.32, respectively. (4) The SAV species experienced three stages: richness (before 2003), monotonicity (2004–2020), and final disappearance (2021–present). This study shows that the coverage of AV in Bao’ an Lake is too low and the number of SAV species is one (2010–now). Therefore, it is necessary to implement measures to improve vegetation coverage and diversity.

The red imported fire ant Solenopsis invicta Buren (Hymenoptera: Formicidae) is native to South America; however, its introduction to other countries has caused serious biodiversity, agricultural, and public health problems. As toxic bait is an effective method to control fire ant populations, the aim of this study was to determine the most effective concentration of sulfoxaflor, flupyradifurone, and triflumezopyrim as ingredients for baits against S. invicta under laboratory and field conditions. Sulfoxaflor, flupyradifurone, and triflumezopyrim had no effect on the feeding behavior of the fire ants. However, they significantly reduced the climbing, walking, and arrest abilities of the fire ant workers after 10 days of treatment, and insecticides were horizontally transferred from workers to alates or larvae. Specifically, sulfoxaflor and triflumezopyrim at 0.05% concentration were the most effective in exterminating fire ants. Sulfoxaflor and triflumezopyrim are nonrepellent and effective insecticides against S. invicta.

Phytoplankton plays a key role in the stabilization of aquatic ecosystems. However, there is a lack of research on the structure of phytoplankton communities and their influencing factors in shallow lakes in Southeast Hubei. In this study, four typical lakes were selected in the summer of 2019, and the phytoplankton community structure and its drivers were investigated for each lake. The results showed that the number of phytoplankton species, dominant species, biomass, and abundance varied among lakes. A comprehensive trophic level index (TLI) assessment showed that Lake Xiandao was oligotrophic, Lake Baoan was light eutrophic, and Lake Wang and Lake Ci were moderate eutrophic. The Shannon–Wiener, Margalef, and Pielou indices showed that the phytoplankton communities of Lake Wang and Lake Ci are less stable, and the water bodies are more severely contaminated. In contrast, the communities of the Lake Baoan and Lake Xiandao are more stable and have superior water quality. A redundancy analysis (RDA) indicated that the primary variables influencing phytoplankton community structures were pH and water temperature (WT) (Lake Ci); permanganate index (CODMn) and pH (Lake Xiandao); WT and total nitrogen (TN) (Lake Wang); and WT and total phosphorus (TP) (Lake Baoan).