Search Results (43)

Our previous study has shown that supplementation of 0.50% benzoic acid (BA) increased growth performance, promoted rumen fermentation, and improved the composition and function of rumen microbiota. This research was designed to conduct a deeper exploration of the impacts of dietary supplementation with BA on the apparent digestibility of nutrients and the composition of rectal microbiota in weaned Holstein dairy calves. Sixteen Holstein heifer calves with similar body weights (91.2 ± 0.7 kg) were selected and randomly allocated into two groups, each comprising eight calves. Calves in the control group (CON group) were fed with a basal diet, while those in the benzoic acid group (BA group) were fed with the basal diet supplemented with 0.50% benzoic acid (on a dry matter basis). The experimental period started at 60 days of age and ended at 102 days of age, lasting for a total of 42 days. The calves were weaned at 60 days of age, with a transition period of 7 days. Feed samples were collected every two weeks, fecal samples were collected from 99 to 101 days of age, and blood samples were collected at 102 days of age. The results showed that supplementation with BA did not influence the digestibility of dry matter, crude protein, ether extract, neutral detergent fiber, acid detergent fiber, calcium, and phosphorus between the two groups. Compared with the CON group, BA supplementation tended to decrease the total cholesterol (TC) in the serum of the calves (p = 0.067). Supplementation with BA increased the relative abundances of the two beneficial bacteria, Bifidobacterium and Bifidobacterium pseudolongum (p < 0.05, LDA > 2), but decreased that of the harmful bacterium, Clostridium sensu stricto 1, in the rectum of dairy calves. The microbial functional prediction revealed that the fecal microbial metabolism involved in primary bile acid biosynthesis was higher in the calves from the BA group. In conclusion, the present study demonstrated that adding 0.50% BA to the diet did not influence the apparent nutrient digestibility, but improved rectal microbiota health, which finally promoted the growth performance in weaned Holstein dairy calves. Full article

River pollution is a major issue in China’s urbanization process. Understanding the effects of river morphology and constructed wetlands on the self-purification capacity is crucial for water quality improvement. This study takes the Shiwuli River, a main tributary of Chaohu Lake, as an example. By monitoring the concentration changes of five water quality indicators—total nitrogen (TN), total phosphorus (TP), ammonia nitrogen (NH₃-N), chemical oxygen demand (COD), and dissolved oxygen (DO)—in the river section for the years 2017 and 2024, we conducted a comparative analysis of the relationship between river morphology and self-purification capacity, as well as influencing factors. The results show that meandering rivers possess self-purification capabilities under natural conditions. There is a positive correlation between river sinuosity and the reduction rates of TP, TN, NH₃-N, and COD, as well as the increase rate of DO—the greater the sinuosity, the stronger the purification capacity. Wetlands enhance both the self-purification capacity and the purification rate of river channels, reducing the required sinuosity for effective self-purification from 1.49 to 1.30. This study also discusses the mechanisms by which meandering rivers influence water self-purification, and proposes that increasing river sinuosity and constructing wetlands can enhance the self-purification capacity. This measure will increase the length and width of the river, prolong the purification time, improve the DO level, and enhance the exchange between the riverbed and groundwater. The findings of this study can provide a reference for river restoration and management in the context of urbanization. Full article

Currently, it is very important to accurately estimate growing stock volumes; it is crucial for quantitatively assessing forest growth and formulating forest management plans. It is convenient and quick to use the Structure from Motion (SfM) algorithm in computer vision to obtain 3D point cloud data from captured highly overlapped stereo photogrammetry images, while the optimal algorithm for estimating growing stock volume varies across different data sources and forest types. In this study, the performance of UAV stereo photogrammetry (USP) in estimating the growing stock volume (GSV) using three machine learning algorithms for a coniferous plantation in Northern China was explored, as well as the impact of point density on GSV estimation. The three machine learning algorithms used were random forest (RF), K-nearest neighbor (KNN), and support vector machine (SVM). The results showed that USP could accurately estimate the GSV with R² = 0.76–0.81, RMSE = 30.11–35.46, and rRMSE = 14.34%–16.78%. Among the three machine learning algorithms, the SVM showed the best results, followed by RF. In addition, the influence of point density on the estimation accuracy for the USP dataset was minimal in terms of R², RMSE, and rRMSE. Meanwhile, the estimation accuracies of the SVM became stable with a point density of 0.8 pts/m² for the USP data. This study evidences that the low-density point cloud data derived from USP may be a good alternative for UAV Laser Scanning (ULS) to estimate the growing stock volume of coniferous plantations in Northern China. Full article

BaTiO₃ (BTO), a lead-free chalcogenide ferroelectric material, has emerged as a promising candidate for ferroelectric memories due to its advantageous ferroelectric properties, notable flexibility, and mechanical stability, along with a high dielectric constant and minimal leakage. These attributes lay a crucial foundation for multi-value storage. In this study, high-quality BaTiO₃ ferroelectric thin films have been successfully prepared on STO substrates by pulsed laser deposition (PLD), and Pt/BaTiO₃/SrRuO₃/SrTiO₃ ferroelectric heterojunctions were finally prepared by a combination of UV lithography and magnetron sputtering. Characterization and performance tests were carried out by AFM, XRD, and a semiconductor analyzer. The results demonstrate that the ferroelectric heterojunction prepared in this study exhibits excellent ferroelectric properties. Furthermore, the device demonstrates fatigue-free operation after 10⁷ bipolar switching cycle tests, and the polarization value exhibits no significant decrease in the holding characteristic test at 10⁴ s, thereby further substantiating its exceptional reliability and durability. These findings underscore the considerable promise of BTO ferroelectric memories for nonvolatile storage applications and lay the foundation for the development in the fields of both in-memory computing systems and neuromorphic computing. Full article

Multifocus microscopy has previously been demonstrated to provide volumetric information from a single shot. However, the practical application of this method is challenging due to its weak optical sectioning and limited spatial resolution. Here, we report on the combination of a distorted diffraction grating and multifocal scanning illumination microscopy to improve spatial resolution and contrast. DG is introduced in the emission path of the multifocal scanning illumination microscopy, which splits the fluorescence signal from different sample layers into different diffraction orders. After postprocessing, super-resolution wide-field images of different sample layers can be reconstructed from single 2D scanning. Full article

Supplementation with benzoic acid (BA) in animal feed can reduce feeds’ acid-binding capacity, inhibit pathogenic bacterial growth, enhance nutrient digestion, and increase intestinal enzyme activities. This study aimed to investigate the effects of different doses of BA on the growth performance, rumen fermentation, and rumen microbiota of weaned Holstein dairy calves. Thirty-two Holstein calves at 60 days of age were randomly assigned into four groups (n = 8): a control group (fed with a basal diet without BA supplementation; CON group) and groups that were supplemented with 0.25% (LBA group), 0.50% (MBA group), and 0.75% (HBA group) BA to the basal diet (dry matter basis), respectively. The experiment lasted for 42 days, starting at 60 days of age and ending at 102 days of age, with weaning occurring at 67 days of age. Supplementation with BA linearly increased the average daily gain of the weaned dairy calves, which was significantly higher in the LBA, MBA, and HBA groups than that in the CON group. The average daily feed intake was quadratically increased with increasing BA supplementation, peaking in the MBA group. Supplementation with BA linearly decreased the feed-to-gain (F/G) ratio, but did not affect rumen fermentation parameters, except for the molar proportion of butyrate and iso-butyrate, which were linearly increased with the dose of BA supplementation. Compared with the CON group, the molar proportions of iso-butyrate in the LBA, MBA, and HBA groups and that of butyrate in the HBA group were significantly higher than those in the CON group. Supplementation with BA had no significant effect on the alpha and beta diversity of the rumen microbiota, but significantly increased the relative abundances of beneficial bacteria, such as Bifidobacterium, and reduced those of the harmful bacteria, such as unclassified_o__Gastranaerophilales and Oscillospiraceae_UCG-002, in the rumen. Functional prediction analysis using the MetaCyc database revealed significant variations in the pathways associated with glycolysis across groups, including the GLYCOLYSIS-TCA-GLYOX-BYPASS, GLYCOL-GLYOXDEG-PWY, and P105-PWY pathways. In conclusion, BA supplementation improved the composition and function of rumen microbiota, elevated the production of butyrate and iso-butyrate, and increased the growth performance of weaned Holstein dairy calves. Full article

Traditional measurement methods often rely on manual operations, which are not only inefficient but also cause stress to cattle, affecting animal welfare. Currently, non-contact cattle dimension measurement usually involves the use of multi-view images combined with point cloud or 3D reconstruction technologies, which are costly and less flexible in actual farming environments. To address this, this study proposes an automated cattle dimension measurement method based on an improved keypoint detection model combined with unilateral depth imaging. Firstly, YOLOv8-Pose is selected as the keypoint detection model and SimSPPF replaces the original SPPF to optimize spatial pyramid pooling, reducing computational complexity. The CARAFE architecture, which enhances upsampling content-aware capabilities, is introduced at the neck. The improved YOLOv8-pose achieves a mAP of 94.4%, a 2% increase over the baseline model. Then, cattle keypoints are captured on RGB images and mapped to depth images, where keypoints are optimized using conditional filtering on the depth image. Finally, cattle dimension parameters are calculated using the cattle keypoints combined with Euclidean distance, the Moving Least Squares (MLS) method, Radial Basis Functions (RBFs), and Cubic B-Spline Interpolation (CB-SI). The average relative errors for the body height, lumbar height, body length, and chest girth of the 23 measured beef cattle were 1.28%, 3.02%, 6.47%, and 4.43%, respectively. The results show that the method proposed in this study has high accuracy and can provide a new approach to non-contact beef cattle dimension measurement. Full article

As emerging pollutants, organophosphate esters (OPEs) have been reported in coastal environments worldwide. Nevertheless, information on the occurrence and ecological risks of OPEs, especially the related transformation products, in mangrove wetlands is scarce. For the first time, the coexistence and distribution of OP triesters and their transformation products in three mangrove wetlands in the Beibu Gulf were investigated using ultrasonication and solid-phase extraction, followed by UHPLC-MS/MS detection. The studied OPEs widely existed in all the sampling sites, with the total concentrations ranging from 6.43 ng/g dry weight (dw) to 39.96 ng/g dw and from 3.33 ng/g dw to 22.50 ng/g dw for the OP triesters and transformation products, respectively. Mangrove wetlands tend to retain more OPEs than the surrounding coastal environment. Pearson correlation analysis revealed that the TOC was not the sole factor in determining the OPEs’ distribution, and degradation was not the main source of the transformation products in mangrove sediments in the Beibu Gulf. The ecological risks of selected OPEs for different organisms were also assessed, revealing a medium to high risk posed by OP diesters to organisms. The levels or coexistence of OPEs and their metabolites in mangroves need constant monitoring, and more toxicity data should be further studied to assess the effect on normal aquatic organisms. Full article

Information about the distribution of coniferous forests holds significance for enhancing forestry efficiency and making informed policy decisions. Accurately identifying and mapping coniferous forests can expedite the achievement of Sustainable Development Goal (SDG) 15, aimed at managing forests sustainably, combating desertification, halting and reversing land degradation, and halting biodiversity loss. However, traditional methods employed to identify and map coniferous forests are costly and labor-intensive, particularly in dealing with large-scale regions. Consequently, a methodological framework is proposed to identify coniferous forests in northwestern Liaoning, China, in which there are semi-arid and barren environment areas. This framework leverages a multi-classifier fusion algorithm that combines deep learning (U²-Net and Resnet-50) and shallow learning (support vector machines and random forests) methods deployed in the Google Earth Engine. Freely available remote sensing images are integrated from multiple sources, including Gaofen-1 and Sentinel-1, to enhance the accuracy and reliability of the results. The overall accuracy of the coniferous forest identification results reached 97.6%, highlighting the effectiveness of the proposed methodology. Further calculations were conducted to determine the area of coniferous forests in each administrative region of northwestern Liaoning. It was found that the total area of coniferous forests in the study area is about 6013.67 km², accounting for 9.59% of northwestern Liaoning. The proposed framework has the potential to offer timely and accurate information on coniferous forests and holds promise for informed decision making and the sustainable development of ecological environment. Full article

Cyclic nucleotide-gated ion channels (CNGCs) remain poorly studied in crop plants, most of which are polyploid. In allotetraploid Upland cotton (Gossypium hirsutum), silencing GhCNGC13 and 32 impaired plant growth and shoot apical meristem (SAM) development, while triggering plant autoimmunity. Both growth hormones (indole-3-acetic acid and gibberellin) and stress hormones (abscisic acid, salicylic acid, and jasmonate) increased, while leaf photosynthesis decreased. The silenced plants exhibited an enhanced resistance to Botrytis cinerea; however, Verticillium wilt resistance was weakened, which was associated with LIPOXYGENASE2 (LOX2) downregulation. Transcriptomic analysis of silenced plants revealed 4835 differentially expressed genes (DEGs) with functional enrichment in immunity and photosynthesis. These DEGs included a set of transcription factors with significant over-representation in the HSF, NAC, and WRKY families. Moreover, numerous members of the GhCNGC family were identified among the DEGs, which may indicate a coordinated action. Collectively, our results suggested that GhCNGC13 and 32 functionally link to photosynthesis, plant growth, and plant immunity. We proposed that GhCNGC13 and 32 play a critical role in the “growth–defense tradeoff” widely observed in crops. Full article

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

Radiometric terrain correction (RTC) is an important preprocessing step for synthetic aperture radar (SAR) data application in mountainous areas. At present, the RTC processing of SAR depends on the Range Doppler (RD) positioning model. However, the solution of this model has a high threshold for ordinary remote sensing technicians. To solve this problem, we propose an RTC method based on the rational polynomial coefficient (RPC) model, which is widely used in optical remote sensing and is simpler and more practical than the RD model. China’s GF-3 polarimetric SAR data were used to verify the proposed method. The experimental results showed that the RTC method based on RPC is effective and can achieve better correction effects on the premise of reducing the complexity of the algorithm. The correction effect based on the RPC model can be similar to that based on the RD model. The proposed approach can realize the correction of 4~5 dB terrain radiation distortion to a 0.5 dB level. Full article

Mapping the distribution of coniferous forests is of great importance to the sustainable management of forests and government decision-making. The development of remote sensing, cloud computing and deep learning has provided the support of data, computing power and algorithms for obtaining large-scale forest parameters. However, few studies have used deep learning algorithms combined with Google Earth Engine (GEE) to extract coniferous forests in large areas and the performance remains unknown. In this study, we thus propose a cloud-enabled deep-learning approach using long-time series Landsat remote sensing images to map the distribution and obtain information on the dynamics of coniferous forests over 35 years (1985–2020) in the northwest of Liaoning, China, through the combination of GEE and U²-Net. Firstly, to assess the reliability of the proposed method, the U²-Net model was compared with three Unet variants (i.e., Resnet50-Unet, Mobile-Unet and U-Net) in coniferous forest extraction. Secondly, we evaluated U²-Net’s temporal transferability of remote sensing images from Landsat-5 TM, Landsat-7 ETM+ and Landsat-8 OLI. Finally, we compared the results obtained by the proposed approach with three publicly available datasets, namely GlobeLand30-2010, GLC_FCS30-2010 and FROM_GLC30-2010. The results show that (1) the cloud-enabled deep-learning approach proposed in this paper that combines GEE and U²-Net achieves a high performance in coniferous forest extraction with an F1 score, overall accuracy (OA), precision, recall and kappa of 95.4%, 94.2%, 96.6%, 95.5% and 94.0%, respectively, outperforming the other three Unet variants; (2) the proposed model trained by the sample blocks collected from a specific time can be applied to predict the coniferous forests in different years with satisfactory precision; (3) Compared with three global land-cover products, the distribution of coniferous forests extracted by U²-Net was most similar to that of actual coniferous forests; (4) The area of coniferous forests in Northwestern Liaoning showed an upward trend in the past 35 years. The area of coniferous forests has grown from 945.64 km² in 1985 to 6084.55 km² in 2020 with a growth rate of 543.43%. This study indicates that the proposed approach combining GEE and U²-Net can extract coniferous forests quickly and accurately, which helps obtain dynamic information and assists scientists in developing sustainable strategies for forest management. Full article

Tree species information is an important factor in forest resource surveys, and light detection and ranging (LiDAR), as a new technical tool for forest resource surveys, can quickly obtain the 3D structural information of trees. In particular, the rapid and accurate classification and identification of tree species information from individual tree point clouds using deep learning methods is a new development direction for LiDAR technology in forest applications. In this study, mobile laser scanning (MLS) data collected in the field are first pre-processed to extract individual tree point clouds. Two downsampling methods, non-uniform grid and farthest point sampling, are combined to process the point cloud data, and the obtained sample data are more conducive to the deep learning model for extracting classification features. Finally, four different types of point cloud deep learning models, including pointwise multi-layer perceptron (MLP) (PointNet, PointNet++, PointMLP), convolution-based (PointConv), graph-based (DGCNN), and attention-based (PCT) models, are used to classify and identify the individual tree point clouds of eight tree species. The results show that the classification accuracy of all models (except for PointNet) exceeded 0.90, where the PointConv model achieved the highest classification accuracy for tree species classification. The streamlined PointMLP model can still achieve high classification accuracy, while the PCT model did not achieve good accuracy in the tree species classification experiment, likely due to the small sample size. We compare the training process and final classification accuracy of the different types of point cloud deep learning models in tree species classification experiments, further demonstrating the advantages of deep learning techniques in tree species recognition and providing experimental reference for related research and technological development. Full article

Forest height is an essential parameter used to derive important information about forest ecosystems, such as forest above-ground biomass. In this article, a forest height estimation approach combining P-band and X-band interferometric synthetic aperture radar (InSAR) was introduced. The forest height was estimated using the difference in the penetration of long- and short-wavelength radars to the forest. That is, the P-band and X-band InSAR data were used to extract the digital terrain model (DTM) and digital surface model (DSM), respectively. For the DTM, an improved time-frequency (TF) analysis method was used to reduce the effect of forest scatterers on the extraction of a pure understory terrain phase based on P-band InSAR. For the DSM, a novel compensation algorithm based on a multi-layer model (MLM) was proposed to remove the penetration bias of the X-band. Compared to the existing method based on the infinitely deep uniform volumes (IDUV) model, the MLM-based method is more in line with the characteristics of forest structure and the scattering mechanism for X-band InSAR. The airborne P-band repeat-pass InSAR and spaceborne X-band (TanDEM-X) single-pass InSAR data were used to verify the proposed method over the study area in the Saihanba Forest Farm in Hebei, China. The results demonstrated that the improved TF method can achieve high-precision DTM extraction based on P-band InSAR data, and the root mean square error (RMSE) was 0.94 m. The proposed MLM-based compensation method of the DSM achieved a smaller error (RMSE: 1.67 m) compared to the IDUV-based method (RMSE: 3.01 m). Under the same DTM extracted by P-band InSAR, the estimation accuracy of forest height based on the MLM method was 86.58% (RMSE: 1.81 m), which was 8.49% higher than that of the IDUV-based method (RMSE: 2.98 m). Full article