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Background/Objectives: The purpose of this study was to develop the gemcitabine-loaded drug-eluting beads (G-DEBs) for transarterial chemoembolization (TACE) in rabbit renal tumors and to evaluate their antitumor effect using 2-deoxy-2-[(18)F]fluoro-D-glucose positron emission tomography/X-ray computed tomography (18F-FDG PET/CT). Methods: DEBs were prepared by polyvinyl alcohol-based macromer crosslinked with N-acryl tyrosine and N,N′-methylenebis(acrylamide). Gemcitabine was loaded through ion change to obtain G-DEBs. Their particle size and drug release profile were characterized. VX2 tumors were implanted in the right kidney of rabbits to establish the renal tumor model. The tumor-bearing rabbits received pre-scan by 18F-FDG PET/CT, followed by targeted transarterial injection of G-DEBs under digital subtraction angiography (DSA) guidance. The rabbits received another 18F-FDG PET/CT scan 10 or 14 days after the treatment. The therapeutic effect was further validated by histopathological analysis of the dissected tumors. Results: The average particle size of the microspheres was 58.06 ± 0.50 µm, and the polydisperse index was 0.26 ± 0.002. The maximum loading rate of G-DEBs was 18.09 ± 0.35%, with almost 100% encapsulation efficiency. Within 24 h, GEM was eluted from G-DEBs rapidly and completely, and more than 20% was released in different media. DSA illustrated that G-DEBs were delivered to rabbit renal tumors. Compared with the untreated control group with increased tumor volume and intense 18F -FDG uptake, the G-DEBs group showed significant reductions in tumor volume and maximum standard uptake value (SUVmax) 10 or 14 days after the treatment. Histopathological analysis confirmed that the proliferating area of tumor cells was significantly reduced in the G-DEBs group. Conclusions: Our results demonstrated that G-DEBs are effective in TACE treatment of rabbit VX2 renal tumors, and 18F-FDG PET/CT provides a non-invasive imaging modality to monitor the antitumor effects of TACE in renal tumors. [ABSTRACT FROM AUTHOR]

The three-dimensional reconstruction of forests is crucial in remote sensing technology, ecological monitoring, and forestry management, as it yields precise forest structure and tree parameters, providing essential data support for forest resource management, evaluation, and sustainable development. Nevertheless, forest 3D reconstruction now encounters obstacles including higher equipment costs, reduced data collection efficiency, and complex data processing. This work introduces a unique deep learning model, CPH-Fmnet, designed to enhance the accuracy and efficiency of 3D reconstruction in intricate forest environments. CPH-Fmnet enhances the FPN Encoder-Decoder Architecture by meticulously incorporating the Channel Attention Mechanism (CA), Path Aggregation Module (PA), and High-Level Feature Selection Module (HFS), alongside the integration of the pre-trained Vision Transformer (ViT), thereby significantly improving the model's global feature extraction and local detail reconstruction abilities. We selected three representative sample plots in Haidian District, Beijing, China, as the study area and took forest stand sequence photos with an iPhone for the research. Comparative experiments with the conventional SfM + MVS and MVSFormer models, along with comprehensive parameter extraction and ablation studies, substantiated the enhanced efficacy of the proposed CPH-Fmnet model in addressing difficult circumstances such as intricate occlusions, poorly textured areas, and variations in lighting. The test results show that the model does better on a number of evaluation criteria. It has an RMSE of 1.353, an MAE of only 5.1%, an r value of 1.190, and a forest reconstruction rate of 100%, all of which are better than current methods. Furthermore, the model produced a more compact and precise 3D point cloud while accurately determining the properties of the forest trees. The findings indicate that CPH-Fmnet offers an innovative approach for forest resource management and ecological monitoring, characterized by cheap cost, high accuracy, and high efficiency. [ABSTRACT FROM AUTHOR]

Using multi-view images of forest plots to reconstruct dense point clouds and extract individual tree parameters enables rapid, high-precision, and cost-effective forest plot surveys. However, images captured at close range face challenges in forest reconstruction, such as unclear canopy reconstruction, prolonged reconstruction times, insufficient accuracy, and issues with tree duplication. To address these challenges, this paper introduces a new image dataset creation process that enhances both the efficiency and quality of image acquisition. Additionally, a block-matching-based multi-view reconstruction algorithm, Forest Multi-View Reconstruction with Enhanced Confidence-Guided Dynamic Domain Propagation (CDP-MVS), is proposed. The CDP-MVS algorithm addresses the issue of canopy and sky mixing in reconstructed point clouds by segmenting the sky in the depth maps and setting its depth value to zero. Furthermore, the algorithm introduces a confidence calculation method that comprehensively evaluates multiple aspects. Moreover, CDP-MVS employs a decentralized dynamic domain propagation sampling strategy, guiding the propagation of the dynamic domain through newly defined confidence measures. Finally, this paper compares the reconstruction results and individual tree parameters of the CDP-MVS, ACMMP, and PatchMatchNet algorithms using self-collected data. Visualization results show that, compared to the other two algorithms, CDP-MVS produces the least sky noise in tree reconstructions, with the clearest and most detailed canopy branches and trunk sections. In terms of parameter metrics, CDP-MVS achieved 100% accuracy in reconstructing tree quantities across the four plots, effectively avoiding tree duplication. The accuracy of breast diameter extraction values of point clouds reconstructed by CDPMVS reached 96.27%, 90%, 90.64%, and 93.62%, respectively, in the four sample plots. The positional deviation of reconstructed trees, compared to ACMMP, was reduced by 0.37 m, 0.07 m, 0.18 m and 0.33 m, with the average distance deviation across the four plots converging within 0.25 m. In terms of reconstruction efficiency, CDP-MVS completed the reconstruction of the four plots in 1.8 to 3.1 h, reducing the average reconstruction time per plot by six minutes compared to ACMMP and by two to three times compared to PatchMatchNet. Finally, the differences in tree height accuracy among the point clouds reconstructed by the different algorithms were minimal. The experimental results demonstrate that CDP-MVS, as a multi-view reconstruction algorithm tailored for forest reconstruction, shows promising application potential and can provide valuable support for forestry surveys. [ABSTRACT FROM AUTHOR]

Geophysical monitoring of CO2 geological sequestration represents a critical technology for ensuring the long-term safe storage of CO2 while verifying its characteristics and dynamic changes. Currently, the primary geophysical monitoring methods employed in CO2 geological sequestration include seismic, fiber optic, and logging technologies. Among these methods, seismic monitoring techniques encompass high-resolution P-Cable three-dimensional seismic systems, delayed vertical seismic profiling technology, and four-dimensional distributed acoustic sensing (DAS). These methods are utilized to monitor interlayer strain induced by CO2 injection, thereby indirectly determining the injection volume, distribution range, and potential diffusion pathways of the CO2 plume. In contrast, fiber optic monitoring primarily involves distributed fiber optic sensing (DFOS), which can be further classified into distributed acoustic sensing (DAS) and distributed temperature sensing (DTS). This technology serves to complement seismic monitoring in observing interlayer strain resulting from CO2 injection. The logging techniques utilized for monitoring CO2 geological sequestration include neutron logging methods, such as thermal neutron imaging and pulsed neutron gamma-ray spectroscopy, which are primarily employed to assess the sequestration volume and state of CO2 plumes within a reservoir. Seismic monitoring technology provides a broader monitoring scale (ranging from dozens of meters to kilometers), while logging techniques operate at centimeter to meter scales; however, their results can be significantly affected by the heterogeneity of a reservoir. [ABSTRACT FROM AUTHOR]

With the development of educational technology, machine learning and deep learning provide technical support for traditional classroom observation assessment. However, in real classroom scenarios, the technique faces challenges such as lack of clarity of raw images, complexity of datasets, multi-target detection errors, and complexity of character interactions. Based on the above problems, a student classroom behavior recognition network incorporating super-resolution and target detection is proposed. To cope with the problem of unclear original images in the classroom scenario, SRGAN (Super Resolution Generative Adversarial Network for Images) is used to improve the image resolution and thus the recognition accuracy. To address the dataset complexity and multi-targeting problems, feature extraction is optimized, and multi-scale feature recognition is enhanced by introducing AKConv and LASK attention mechanisms into the Backbone module of the YOLOv8s algorithm. To improve the character interaction complexity problem, the CBAM attention mechanism is integrated to enhance the recognition of important feature channels and spatial regions. Experiments show that it can detect six behaviors of students—raising their hands, reading, writing, playing on their cell phones, looking down, and leaning on the table—in high-definition images. And the accuracy and robustness of this network is verified. Compared with small-object detection algorithms such as Faster R-CNN, YOLOv5, and YOLOv8s, this network demonstrates good detection performance on low-resolution small objects, complex datasets with numerous targets, occlusion, and overlapping students. [ABSTRACT FROM AUTHOR]

Identifying the key climate variables affecting optical saturation values (OSVs) in forest aboveground biomass (AGB) estimation using optical remote sensing is crucial for analyzing OSV changes. This can improve AGB estimation accuracy by addressing the uncertainties associated with optical saturation. In this study, Pinus yunnanensis forests and Landsat 8 OLI imagery from Yunnan were used as case studies to explain this issue. The spherical model was applied to determine the OSVs using specific spectral bands (Blue, Green, Red, Near-Infrared (NIR), and Short-Wave Infrared Band 2 (SWIR2)) derived from Landsat 8 OLI imagery. Canonical correlation analysis (CCA) uncovered the intricate relationships between climatic variables and OSV variations. The results reveal the following: (1) All Landsat 8 OLI spectral bands showed a negative correlation with the Pinus yunnanensis forest AGB, with OSVs ranging from 104.42 t/ha to 209.11 t/ha, peaking in the southwestern region and declining to the lowest levels in the southeastern region. (2) CCA effectively explained 93.2% of the OSV variations, identifying annual mean temperature (AMT) as the most influential climatic factor. Additionally, the mean temperature of the wettest quarter (MTQ) and annual precipitation (ANP) were significant secondary determinants, with higher OSV values observed in warmer, more humid areas. These findings offer important insights into climate-driven OSV variations, reducing uncertainty in forest AGB estimation and enhancing the precision of AGB estimations in future research. [ABSTRACT FROM AUTHOR]

A differentiated creative search algorithm with multi-strategy improvement (MSDCS) is proposed for the path planning problem for agricultural UAVs under different complicated situations. First, the good point set and oppositional learning strategies are used to effectively improve the quality of population diversity; the adaptive fitness–distance balance reset strategy is proposed to motivate the low performers to move closer to the region near the optimal solution and find the potential optimal solution; and the vertical and horizontal crossover strategy with random dimensions is proposed to improve the computational accuracy of the algorithm and the ability to jump out of the local optimum. Second, the MSDCS is compared to different algorithms using the IEEE_CEC2017 test set, which consists of 29 test functions. The results demonstrate that the MSDCS achieves the optimal value in 23 test functions, surpassing the comparison algorithms in terms of convergence accuracy, speed, and stability by at least one order of magnitude difference, and it is ranked No. 1 in terms of comprehensive performance. Finally, the enhanced algorithm was employed to address the issue of path planning for agricultural UAVs. The experimental results demonstrate that the MSDCS outperforms comparison algorithms in path planning across various contexts. Consequently, the MSDCS can generate optimal pathways that are both rational and safe for agricultural UAV operations. [ABSTRACT FROM AUTHOR]

Cold-temperate forests (CTFs) are not only an important source of wood but also provide significant carbon storage in China. However, under the increasing pressure of human activities and climate change, CTFs are experiencing severe disturbances, such as logging, fires, and pest infestations, leading to evident degradation trends. Though these disturbances impact both regional and global carbon budgets and their assessments, the disturbance patterns in CTFs in northern China remain poorly understood. In this paper, the Genhe forest area, which is a typical CTF region located in the Inner Mongolia Autonomous Region, Northeast China (with an area of about 2.001 × 104 km2), was selected as the study area. Based on Landsat historical archived data on the Google Earth Engine (GEE) platform, we used the continuous change detection and classification (CCDC) algorithm and considered seasonal features to detect forest disturbances over nearly 30 years. First, we created six inter-annual time series seasonal vegetation index datasets to map forest coverage using the maximum between-class variance algorithm (OTSU). Second, we used the CCDC algorithm to extract disturbance information. Finally, by using the ECMWF climate reanalysis dataset, MODIS C6, the snow phenology dataset, and forestry department records, we evaluated how disturbances relate to climate and human activities. The results showed that the disturbance map generated using summer (June–August) imagery and the enhanced vegetation index (EVI) had the highest overall accuracy (88%). Forests have been disturbed to the extent of 12.65% (2137.31 km2) over the last 30 years, and the disturbed area generally showed a trend toward reduction, especially after commercial logging activities were banned in 2015. However, there was an unusual increase in the number of disturbed areas in 2002 and 2003 due to large fires. The monitoring of potential widespread forest disturbance due to extreme drought and fire events in the context of climate change should be strengthened in the future, and preventive and salvage measures should be taken in a timely manner. Our results demonstrate that CTF disturbance can be robustly mapped by using the CCDC algorithm based on Landsat time series seasonal imagery in areas with complex meteorological conditions and spatial heterogeneity, which is essential for understanding forest change processes. [ABSTRACT FROM AUTHOR]

Interfacial engineering is of great concern in photovoltaic devices. Metal halide perovskite solar cells (PSCs) have garnered much attention due to their impressive development in power conversion efficiencies (PCEs). Benefiting from high electron mobility and good energy-level alignment with perovskite, aqueous SnO2 as an electron transport layer has been widely used in n-i-p perovskite solar cells. However, the interfacial engineering of an aqueous SnO2 layer on PSCs is still an obscure and confusing process. Herein, we proposed the preparation of n-i-p perovskite solar cells with different concentrations of SnO2 as electron transport layers and achieved optimized PCE with an efficiency of 20.27%. I Interfacial engineering with regard to the SnO2 layer is investigated by observing the surface morphology, space charge-limited current (SCLC) with the use of an electron-only device, and time-resolved photoluminescence (TRPL) of perovskite films. [ABSTRACT FROM AUTHOR]

It is natural and efficient to use human natural language (NL) directly to instruct robot task executions without prior user knowledge of instruction patterns. Currently, NL-instructed robot execution (NLexe) is employed in various robotic scenarios, including manufacturing, daily assistance, and health caregiving. It is imperative to summarize the current NLexe systems and discuss future development trends to provide valuable insights for upcoming NLexe research. This review categorizes NLexe systems into four types based on the robot's cognition level during task execution: NL-based execution control systems, NL-based execution training systems, NL-based interactive execution systems, and NL-based social execution systems. For each type of NLexe system, typical application scenarios with advantages, disadvantages, and open problems are introduced. Then, typical implementation methods and future research trends of NLexe systems are discussed to guide the future NLexe research. [ABSTRACT FROM AUTHOR]

Rice (Oryza sativa L.) cultivation using direct seeding is susceptible to chilling stress, particularly during seed germination and early seedling growth in the early season of a double cropping system. Alternatively, seed priming with various plant growth-promoting hormones is an effective technique to promote rapid and uniform emergence under chilling stress. Therefore, we evaluated the impact of gibberellin A3 (GA3) and brassinolide (BR) priming on rice seed emergence, examining their proteomic responses under low-temperature conditions. Results indicated that GA3 and BR increased the seed germination rate by 22.67% and 7.33% at 72 h and 35% and 15% at 96 h compared to the control (CK), respectively. Furthermore, proteomic analysis identified 2551, 2614, and 2592 differentially expressed proteins (DEPs) in GA, BR, and CK, respectively. Among them, GA exhibited 84 upregulated and 260 downregulated DEPs, while BR showed 112 upregulated and 102 downregulated DEPs, and CK had 123 upregulated and 81 downregulated DEPs. Notably, under chilling stress, both GA3 and BR are involved in peroxide metabolism, phenylpropanoid biosynthesis, and inositol phosphate metabolism, enhancing antioxidant capacity and providing energy substances for germination. In addition, GA3 triggers the specific regulation of stress responsive protein activation, GTP activation, and ascorbic acid biosynthesis and promotes the stability and integrity of cell membranes, as well as the synthesis of cell walls, providing physical defense for seeds to resist low temperatures. At the same time, BR triggers specific involvement in ribosome synthesis and amino acid synthesis, promoting biosynthetic ability and metabolic regulation to maintain plant life activities under low-temperature stress. Furthermore, the various genes' expression (OsJ_16716, OsPAL1, RINO1) confirmed GA3 and BR involved in peroxide metabolism, phenylpropanoid biosynthesis, and inositol phosphate metabolism, enhancing antioxidant capacity and providing energy substances for germination. This study provides valuable insights into how rice seed embryo responds to and tolerates chilling stress with GA3 seed priming. [ABSTRACT FROM AUTHOR]

This study aimed to assess the effects of Chinese Baijiu with different flavors as supplementary material on microbial communities and flavor formation during inoculated fermentation of Chinese Dongbei Suancai. The results showed that the addition of Fen flavor Baijiu significantly increased the relative abundance of Candida, Luzhou flavor Baijiu increased the relative abundance of Pedobacter and Hannaella, while Maotai flavor Baijiu increased the Chryseobacterium and Kazachstania. A total of 226 volatile metabolites were detected in Suancai fermented when adding different flavors of Baijiu. Furthermore, the significantly upregulated metabolites (p < 0.01) of Suancai after adding Baijiu increased by 328.57%, whereas the significantly downregulated metabolites decreased by 74.60%. Simultaneously, the addition of Baijiu promoted the synthesis and decomposition of amino acids and short-chain fatty acids in the early and middle stages of fermentation. Further, Maotai flavor Baijiu improved the diversification of metabolic pathways in the late stage of Suancai fermentation. The E-nose response showed that sulfur-organic, broad-alcohol, sulfur-chlor was the principal differential flavor in Suancai caused by adding Baijiu with different flavors. Simultaneously, Fen flavor Baijiu and Luzhou flavor Baijiu accelerated the formation of the Suancai flavor. These results indicated that Baijiu with different flavors had significant effects on the flavor formation of inoculated fermented Suancai. [ABSTRACT FROM AUTHOR]

Circulating cfRNA in plasma has emerged as a fascinating area of research with potential applications in disease diagnosis, monitoring, and personalized medicine. Circulating RNA sequencing technology allows for the non-invasive collection of important information about the expression of target genes, eliminating the need for biopsies. This comprehensive review aims to provide a detailed overview of the current knowledge and advancements in the study of plasma cfRNA, focusing on its diverse landscape and biological functions, detection methods, its diagnostic and prognostic potential in various diseases, challenges, and future perspectives. [ABSTRACT FROM AUTHOR]

At present, heavy-metal-free quantum dot light-emitting diodes (QLEDs) have shown great potential as a research hotspot in the field of optoelectronic devices. This article reviews the research on heavy-metal-free quantum dot (QD) materials and light-emitting diode (LED) devices. In the first section, we discussed the hazards of heavy-metal-containing quantum dots (QDs), such as environmental pollution and human health risks. Next, the main representatives of heavy-metal-free QDs were introduced, such as InP, ZnE (E=S, Se and Te), CuInS2, Ag2S, and so on. In the next section, we discussed the synthesis methods of heavy-metal-free QDs, including the hot injection (HI) method, the heat up (HU) method, the cation exchange (CE) method, the successful ionic layer adsorption and reaction (SILAR) method, and so on. Finally, important progress in the development of heavy-metal-free QLEDs was summarized in three aspects (QD emitter layer, hole transport layer, and electron transport layer). [ABSTRACT FROM AUTHOR]

Perovskite solar cells (PSCs) have attracted much attention due to their low cost, high efficiency, and solution processability. With the development of various materials in perovskite solar cells, self-assembled monolayers (SAMs) have rapidly become an important factor in improving power conversion efficiency (PCE) due to their unique physical and chemical properties and better energy level matching. In this topical review, we introduced important categories of self-assembled molecules, energy level modulation strategies, and various characteristics of self-assembled molecules. In addition, we focused on reviewing the application of self-assembled molecules in solar cells, and explained the changes that self-assembled molecules bring to PSCs by introducing the mechanism and effect of self-assembled molecules. Finally, we also elaborated on the challenges currently faced by self-assembled molecules and provided prospects for their applications in other optoelectronic devices. [ABSTRACT FROM AUTHOR]

The investigation of a potential correlation between the filtered-out vegetation index and forest aboveground biomass (AGB) using the conventional variables screening method is crucial for enhancing the estimation accuracy. In this study, we examined the Pinus densata forests in Shangri-La and utilized 31 variables to establish quantile regression models for the AGB across 19 quantiles. The key variables associated with biomass were based on their significant correlation with the AGB in different quantiles, and the QRNN and QRF models were constructed accordingly. Furthermore, the optimal quartile models yielding the minimum mean error were combined as the best QRF (QRFb) and QRNN (QRNNb). The results were as follows: (1) certain bands exhibited significant relationships with the AGB in specific quantiles, highlighting the importance of band selection. (2) The vegetation index involving the band of blue and SWIR was more suitable for estimating the Pinus densata. (3) Both the QRNN and QRF models demonstrated their optimal performance in the 0.5 quantiles, with respective R2 values of 0.68 and 0.7. Moreover, the QRNNb achieved a high R2 value of 0.93, while the QRFb attained an R2 value of 0.86, effectively reducing the underestimation and overestimation. Overall, this research provides valuable insights into the variable screening methods that enhance estimation accuracy and mitigate underestimation and overestimation issues. [ABSTRACT FROM AUTHOR]

Theanine, polyphenols, and caffeine not only affect the flavor of tea, but also play an important role in human health benefits. However, the specific regulatory mechanism of Se NMs on fat-reducing components is still unclear. In this study, the synthesis of fat-reducing components in Fuding Dabai (FDDB) tea was investigated. The results indicated that the 100-bud weight, theanine, EGCG, total catechin, and caffeine contents of tea buds were optimally promoted by 10 mg·L−1 Se NMs in the range of 24.3%, 36.2%, 53.9%, 67.1%, and 30.9%, respectively. Mechanically, Se NMs promoted photosynthesis in tea plants, increased the soluble sugar content in tea leaves (30.3%), and provided energy for the metabolic processes, including the TCA cycle, pyruvate metabolism, amino acid metabolism, and the glutamine/glutamic acid cycle, ultimately increasing the content of amino acids and antioxidant substances (catechins) in tea buds; the relative expressions of key genes for catechin synthesis, CsPAL, CsC4H, CsCHI, CsDFR, CsANS, CsANR, CsLAR, and UGGT, were significantly upregulated by 45.1–619.1%. The expressions of theanine synthesis genes CsTs, CsGs, and CsGOGAT were upregulated by 138.8–693.7%. Moreover, Se NMs promoted more sucrose transfer to the roots, with the upregulations of CsSUT1, CsSUT2, CsSUT3, and CsSWEET1a by 125.8–560.5%. Correspondingly, Se NMs enriched the beneficial rhizosphere microbiota (Roseiarcus, Acidothermus, Acidibacter, Conexicter, and Pedosphaeraceae), enhancing the absorption and utilization of ammonium nitrogen by tea plants, contributing to the accumulation of theanine. This study provides compelling evidence supporting the application of Se NMs in promoting the lipid-reducing components of tea by enhancing its nitrogen metabolism. [ABSTRACT FROM AUTHOR]

The optical saturation problem is one of the main factors causing uncertainty in aboveground biomass (AGB) estimations using optical remote sensing data. It is critical for the improvement in AGB estimation accuracy to clarify the relationships between environmental factors and the variations in optical saturation values (OSVs). In this study, we obtained the OSVs for 20 districts and clarified the individual, interactive, and comprehensive effects of climate, soil, and topographical factors on the OSV variations. The results are as follows: (1) the range of the OSVs was from 104 t/hm2 to 182 t/hm2 for the 20 districts; (2) the soil factor had the greatest (−0.635) influence on the OSVs compared to climate and topography; (3) the highest interaction effect (−0.833) was between climate and soil; (4) the comprehensive effect of the three environmental factors on the OSVs was obvious, and the correlation coefficient was 0.436. Moreover, the mean temperature of the coldest quarter (MCQMean) had the highest effect on the OSVs. The results indicate that environmental factors significantly affect the variation in OSVs through their individual, interactive, and comprehensive effects. Our findings provide a valuable reference for reducing the uncertainty caused by spectral saturation in AGB estimations using optical remote sensing data. [ABSTRACT FROM AUTHOR]

Numerous studies have been conducted to investigate the genomic characterization of bZIP genes and their involvement in the cellular response to endoplasmic reticulum (ER) stress. These studies have provided valuable insights into the coordinated cellular response to ER stress, which is mediated by bZIP transcription factors (TFs). However, a comprehensive and systematic investigations regarding the role of bZIP genes and their involvement in ER stress response in pak choi is currently lacking in the existing literature. To address this knowledge gap, the current study was initiated to elucidate the genomic characteristics of bZIP genes, gain insight into their expression patterns during ER stress in pak choi, and investigate the protein-to-protein interaction of bZIP genes with the ER chaperone BiP. In total, 112 members of the BcbZIP genes were identified through a comprehensive genome-wide analysis. Based on an analysis of sequence similarity, gene structure, conserved domains, and responsive motifs, the identified BcbZIP genes were categorized into 10 distinct subfamilies through phylogenetic analysis. Chromosomal location and duplication events provided insight into their genomic context and evolutionary history. Divergence analysis estimated their evolutionary history with a predicted divergence time ranging from 0.73 to 80.71 million years ago (MYA). Promoter regions of the BcbZIP genes were discovered to exhibit a wide variety of cis-elements, including light, hormone, and stress-responsive elements. GO enrichment analysis further confirmed their roles in the ER unfolded protein response (UPR), while co-expression network analysis showed a strong relationship of BcbZIP genes with ER-stress-responsive genes. Moreover, gene expression profiles and protein–protein interaction with ER chaperone BiP further confirmed their roles and capacity to respond to ER stress in pak choi. [ABSTRACT FROM AUTHOR]

Flower color is a major feature of ornamental plants, and the rich flower color of plants is an important factor in determining their ornamental and economic values, so flower color is an important research target for gardening and horticulture breeders at home and abroad. Our research group collected four colors of Impatiens uliginosa (white, pink, red, and deep red) during the collection of germplasm resources in the field. In this study, we analyzed the transcriptomes of the four flower colors of I. uliginosa by using RNA-Seq technology. The transcriptomes were screened to identify candidate genes related to flower color, and the coloring mechanisms of four flower colors were revealed at the molecular level. The main findings were as follows: (1) The number of the four different transcripts ranged from 64,723 to 93,522 and contained a total of 100,705 unigenes. (2) The analysis of differentially expressed genes revealed structural genes including C4H, FLS, PAL, and ANS and transcription factors including MYB, MYB-related, AP2-EREBP, and bHLH. (3) Among the four flower colors of I. uliginosa, the C4H1 gene had the highest expression in pink flowers, and the C4H2 gene had the highest expression in red flowers. This indicated that C4H genes positively regulated the red flower color of I. uliginosa. However, FLS expression was the highest in white flowers, and with deepening flower color, FLS gene expression gradually weakened, acting as a negative regulator. The results of this study could lay the theoretical foundation for investigating the mechanism of coloration and flower color variation in I. uliginosa. [ABSTRACT FROM AUTHOR]

Impatiens uliginosa is a native and potential water body-restoring ornamental plant. In this study, RNA-Seq technology was used to analyze the transcriptome of its floral organs. Candidate genes related to flower color changes under copper stress were investigated through transcriptome screening, and the intrinsic mechanism of the effects of different concentrations of copper on I. uliginosa was revealed at the molecular level. The main findings were as follows: (1) Transcriptome sequencing analysis was performed on the flower organs of I. uliginosa treated with different concentrations of copper (0 mg·L−1, 10 mg·L−1, and 20 mg·L−1). A total of 70,319 transcripts and 39,949 unigenes were obtained. An analysis of differentially expressed genes revealed structural genes including GT, ANS, CHI, and PAL; transcription factors including MYB and WD40; and transport factors including GST and ABC. (2) The gene expression levels of flower color changed in the flowering period of I. uliginosa at different copper concentrations. The expression levels of IuGT and IuGST genes in I. uliginosa were significantly different under different concentrations of copper treatments. Their expression levels were the highest at a copper concentration of 0 mg·L−1 and the lowest at 20 mg·L−1. In summary, the low expression of IuGT and IuGST genes was more conducive to the formation of white flowers of I. uliginosa. [ABSTRACT FROM AUTHOR]

Rice production is the agricultural activity with the highest energy consumption and carbon emission intensity. Water and fertilizer management constitutes an important part of energy input for rice production and a key factor affecting greenhouse gas emissions from paddy fields. Water–fertilizer integration management (AIM) is an automated water and fertilizer management system for large-scale rice production, which can effectively save water and fertilizer resources. At present, the energy utilization and environmental impact of AIM in rice production are not clear. To clarify whether AIM is a water and fertilizer management measure that combines energy conservation and carbon emission reduction, a comparative study between the widely used farmers' enhanced water and fertilizer management (FEM) in China and AIM was conducted in this paper. Field experiments were conducted to evaluate the rice yield, carbon emission, energy utilization, and economic benefits of the two management methods. The results showed that AIM reduced water and fertilizer inputs, energy inputs, and economic costs by 12.18–28.57%, compared to FEM. The energy utilization efficiency, energy profitability, and energy productivity under AIM were improved by 11.30–12.61%. CH4 and N2O emissions and carbon footprint were reduced by 20.79%, 6.51%, and 16.39%, respectively. Compared with FEM, AIM can effectively improve the utilization efficiency of water and fertilizer resources and reduce carbon emissions. This study presents a mechanized water and fertilizer management approach suitable for large-scale rice production systems in China. By analyzing rice yield, resource utilization efficiency, and environmental benefits, AIM can serve as a crucial management strategy for enhancing productivity, economic returns, and environmental conservation within profitable rice production systems. In the future, further investigation into the impact of AIM on the microbial mechanisms underlying rice yield formation and greenhouse gas emissions is warranted. [ABSTRACT FROM AUTHOR]

Improving the precision of aboveground biomass (AGB) estimation in lowland tropical forests is crucial to enhancing our understanding of carbon dynamics and formulating climate change mitigation strategies. This study proposes an AGB estimation method for lowland tropical forests in Xishuangbanna, which include various vegetation types, such as Pinus kesiya var. langbianensis, oak, Hevea brasiliensis, and other broadleaf trees. In this study, 2016 forest management inventory data are integrated with remote sensing variables from Landsat 8 OLI (L8) and Sentinel 2A (S2) imagery to estimate forest AGB. The forest age and aspect were utilized as stratified variables to construct the random forest (RF) models, which may improve the AGB estimation accuracy. The key findings are as follows: (1) through variable screening, elevation was identified as the main factor correlated with the AGB, with texture measures derived from a pixel window size of 7 × 7 perform best for AGB sensitivity, followed by 5 × 5, with 3 × 3 being the least effective. (2) A comparative analysis of imagery groups for the AGB estimation revealed that combining L8 and S2 imagery achieved superior performance over S2 imagery alone, which, in turn, surpassed the accuracy of L8 imagery. (3) Stratified models, which integrated aspect and age variables, consistently outperformed the unstratified models, offering a more refined fit for lowland tropical forest AGB estimation. (4) Among the analyzed forest types, the AGB of P. kesiya var. langbianensis forests was estimated with the highest accuracy, followed by H. brasiliensis, oak, and other broadleaf forests within the RF models. These findings highlight the importance of selecting appropriate variables and sensor combinations in addition to the potential of stratified modeling approaches to improve the precision of forest biomass estimation. Overall, incorporating stratification theory and multi-source data can enhance the AGB estimation accuracy in lowland tropical forests, thus offering crucial insights for refining forest management strategies. [ABSTRACT FROM AUTHOR]

The AlxCoCrFeNi2.1 (x = 0, 0.3, 0.7, 1.0, 1.3) multi-component high-entropy alloy (HEA) was synthesized by mechanical alloying (MA) and Spark Plasma Sintering (SPS), The impact of the percentage of Al on crystal structure transition, microstructure evolution and mechanical properties were studied. Crystal structure was investigated by X-ray Diffraction (XRD) and Scanning Electron Microscopy (SEM). The results show that with the increasing of Al content, the crystal structure of the alloys gradually transformed from a nanocrystalline phase of FCC to a mix of FCC and BCC nanocrystalline. The hardness was found to increase steadily from 433 HV to 565 HV due to the increase in fraction of BCC nanocrystalline phase. Thus, the compressive fracture strength increased from 1702 MPa to 2333 MPa; in contrast, the fracture strain decreased from 39.8% to 15.6%. [ABSTRACT FROM AUTHOR]

In recent years, the emergence of large-scale language models, such as ChatGPT, has presented significant challenges to research on knowledge graphs and knowledge-based reasoning. As a result, the direction of research on knowledge reasoning has shifted. Two critical issues in knowledge reasoning research are the algorithm of the model itself and the selection of paths. Most studies utilize LSTM as the path encoder and memory module. However, when processing long sequence data, LSTM models may encounter the problem of long-term dependencies, where memory units of the model may decay gradually with an increase in time steps, leading to forgetting earlier input information. This can result in a decline in the performance of the LSTM model in long sequence data. Additionally, as the data volume and network depth increase, there is a risk of gradient disappearance. This study improved and optimized the LSTM model to effectively address the problems of gradient explosion and gradient disappearance. An attention layer was employed to alleviate the issue of long-term dependencies, and ConvR embedding was used to guide path selection and action pruning in the reinforcement learning inference model. The overall model achieved excellent reasoning results. [ABSTRACT FROM AUTHOR]

Carbon absorption and storage in forests is one of the important ways to mitigate climate change. Therefore, it is essential to use a variety of remote-sensing resources to accurately estimate forest aboveground biomass (AGB) in dry-hot valley regions. In this study, satellite images from the Sentinel-1A, Sentinel-2A, and Gaofen-2 satellites were utilized to estimate the forest AGB in Yuanmou County, Yunnan Province, China. Different combinations of image data, based on selected variables of stepwise regression and their performance in constructing linear stepwise regression (LSR) and random forest (RF) models, were explored. The results showed that: (1) after adding the polarized values of the synthetic aperture radar backscatter coefficients, the combination fitting effect was significantly improved; (2) the fitting effect of the Sentinel-1A + Sentinel-2A + Gaofen-2 data combination was superior to the other combinations, indicating that the effective extraction of forest horizon and vertical information can improve the estimation effect of the forest AGB; and (3) the RF model exhibited superior fitting performance compared to the LSR model across all permutations of remotely sensed image datasets, with R2 values of 0.71 and 0.65, and RMSE values of 30.67 and 33.79 Mg/ha, respectively. These findings lay the groundwork for enhancing the precision of AGB estimation in dry-hot valley areas by integrating Sentinel-2A, Sentinel-1A, and GF-2 imagery, providing valuable insights for future research and applications. [ABSTRACT FROM AUTHOR]

Xiaochaihu granules (XCHG) are extensively used to treat fever. Nevertheless, the underlying mechanism remains elusive. This study aimed to explore the potential of XCHG in mitigating yeast-induced fever and the underlying metabolic pathways. The chemical composition of XCHG was ascertained using ultra-fast liquid chromatography/quadrupole-time-of-flight tandem mass spectrometry (UFLC-Q-TOF-MS/MS), followed by integrated network analysis to predict potential targets. We then conducted experimental validation using pharmacological assays and metabolomics analysis in a yeast-induced mouse fever model. The study identified 133 compounds in XCHG, resulting in the development of a comprehensive network of herb–compound–biological functional modules. Subsequently, molecular dynamic (MD) simulations confirmed the stability of the complexes, including γ-aminobutyric acid B receptor 2 (GABBR2)–saikosaponin C, prostaglandin endoperoxide synthases (PTGS2)–lobetyolin, and NF-κB inhibitor IκBα (NFKBIA)–glycyrrhizic acid. Animal experiments demonstrated that XCHG reduced yeast-induced elevation in NFKBIA's downstream regulators [interleukin (IL)-1β and IL-8], inhibited PTGS2 activity, and consequently decreased prostaglandin E2 (PGE2) levels. XCHG also downregulated the levels of 5-hydroxytryptamine (5-HT), γ-aminobutyric acid (GABA), corticotropin releasing hormone (CRH), and adrenocorticotrophin (ACTH). These corroborated the network analysis results indicating XCHG's effectiveness against fever in targeting NFKBIA, PTGS2, and GABBR2. The hypothalamus metabolomics analysis identified 14 distinct metabolites as potential antipyretic biomarkers of XCHG. In conclusion, our findings suggest that XCHG alleviates yeast-induced fever by regulating inflammation/immune responses, neuromodulation, and metabolism modules, providing a scientific basis for the anti-inflammatory and antipyretic properties of XCHG. [ABSTRACT FROM AUTHOR]

The pollution caused by nitrogen dioxide is a major environmental problem in China. This study introduces a new type of atmospheric trace gas remote-sensing instrument, an airborne fiber imaging spectrometer. This spectrometer has a spectral range of 300–410 nm and works in push-broom mode with a 30° field of view on a flight path. Flight experiments were conducted on 30 December 2022 and 5 January 2023, covering heavily polluted areas east of Beijing and Tangshan. This equipment obtained the density distribution of NO2 over the flight area. The results showed that pollution was mainly concentrated in the Caofeidian area and at the power station in the north, and the main source of pollution was anthropogenic. Satellite and airborne data near the pollution points were compared, and the two datasets showed a positive correlation with a correlation coefficient of 0.78 and 0.7, on the two days, respectively. This study demonstrates the capability of an airborne fiber imaging spectrometer for NO2 regional emission remote sensing and identifying the pollution points. [ABSTRACT FROM AUTHOR]

Predicting the health index of turbofan engines is critical in reducing downtime and ensuring aircraft safety. This study introduces the elite snake optimizer-back propagation (ESO-BP) model to address the challenges of low accuracy and poor stability in predicting the health index of turbofan engines through neural networks. Firstly, the snake optimizer (SO) was improved into the elite snake optimizer (ESO) through an elite-guided strategy and a reverse learning mechanism. The performance improvement was validated using benchmark functions. Additionally, feature importance was introduced as a feature selection method. Finally, the optimization results of the ESO were employed to set the initial weights and biases of the BP neural network, preventing convergence to local optima. The prediction performance of the ESO-BP model was validated using the C-MAPSS datasets. The ESO-BP model was compared with the CNN, RNN, LSTM, baseline BP, and unimproved SO-BP models. The results demonstrated that the ESO-BP model has a superior accuracy with an impressive R-squared ( R 2 ) value of 0.931 and a root mean square error ( R M S E ) of 0.060 on the FD001 sub-dataset. Furthermore, the ESO-BP model exhibited lower standard deviations of evaluation metrics on 100 trials. According to the study, ESO-BP demonstrated a greater prediction accuracy and stability when compared to commonly used models such as CNN, RNN, LSTM, and BP. [ABSTRACT FROM AUTHOR]

To enhance forest carbon sequestration capacity, it is important to optimize forest structure by revealing the spatial effects of the aboveground biomass of individual trees, with particular emphasis on stand origin and tree size. Here, 0.3 ha clear-cut plots of Pinus kesiya var. langbianensis forest were selected in a typical plantation and natural stand. Then, the ordinary least squares model and spatial regression models were used to analyze the different responses between spatial position and individual tree biomass based on the stand origin and diameter at breast height (DBH) of the tree. Our study shows the following: (1) The spatial effect produced a stronger response in the natural stand than in the plantation. The amount of change in the adjusted R-squared (ΔRadj2) of tree component totaled 0.34 and 0.57 for Pinus kesiya var. langbianensis and other trees in the natural stand, compared to only 0.2 and 0.42 in the plantation; (2) Spatial effects had a stronger impact on the accuracy of the fit for the crown (ΔRadj2 = 0.52) compared to the wood and bark (ΔRadj2 = 0.03) in the plantation, and there were no significant differences in the natural stand (ΔRadj2 = 0.42, ΔRadj2 = 0.43); (3) When DBH reached a certain size, the impact of spatial effect for the crown showed a significant change from positive to negative. The sizes of DBH were 19.5 cm, 14 cm and 34.6 cm, 19 cm for branches of Pinus kesiya var. langbianensis and other tree species in the plantation and natural stand, and were 20.3 cm and 31.4 cm for the foliage of Pinus kesiya var. langbianensis. Differences in stand structure led to varied responses in the biomass of tree components to spatial effects. [ABSTRACT FROM AUTHOR]

Reservoir operation rule curves are crucial for managing water supply and power generation in reservoirs. As the number of objectives and management requirements increase, there is a growing demand for optimized operation rule curves. The objective of this study is to explore the most effective forms of reservoir operation rule curves, focusing on the case of the Nierji Reservoir and considering the dual objectives of water supply and power generation. The parameter–simulation–optimization framework, specifically employing the NSGA-II algorithm, was used to analyze and compare two basic forms of operation rule curves: the shared type and independent type. The impact of these curves on water supply potential and multi-objective optimization results with various water demand scenarios was assessed. The analysis revealed that the choice of operation rule curve form can influence the maximum water supply potential of the reservoir to some extent. The independent type operation rule curve was significantly more effective in enhancing the water supply potential for industrial and domestic users, resulting in a notable increase of 3.5 × 108 m3. Additionally, it also proved beneficial for environmental users, with an increase of 1 × 108 m3. Conversely, the shared type operation rule curve demonstrated similar functionality to the independent type curve with fewer decision variables, particularly when the water demand was relatively low. In scenarios with high water demand, the independent type curve outperformed the shared type curve by generating 6549 superior, non-dominated solutions for multi-objective optimization, specifically focused on maximizing reservoir operation benefits. In conclusion, selecting the appropriate form of reservoir operation rule curve is crucial to balance different reservoir functional objectives and achieve optimal results. Further research could focus on quantifying the specific benefits and trade-offs associated with each type of curve in order to provide more robust evidence for the advantages of a complex reservoir system. [ABSTRACT FROM AUTHOR]

Efficient and precise forest surveys are crucial for in-depth understanding of the present state of forest resources and conducting scientific forest management. Close-range photogrammetry (CRP) technology enables the convenient and fast collection of highly overlapping sequential images, facilitating the reconstruction of 3D models of forest scenes, which significantly improves the efficiency of forest surveys and holds great potential for forestry visualization management. However, in practical forestry applications, CRP technology still presents challenges, such as low image quality and low reconstruction rates when dealing with complex undergrowth vegetation or forest terrain scenes. In this study, we utilized an iPad Pro device equipped with high-resolution cameras to collect sequential images of four plots in Gaofeng Forest Farm in Guangxi and Genhe Nature Reserve in Inner Mongolia, China. First, we compared the image enhancement effects of two algorithms: histogram equalization (HE) and median–Gaussian filtering (MG). Then, we proposed a deep learning network model called SA-Pmnet based on self-attention mechanisms for 3D reconstruction of forest scenes. The performance of the SA-Pmnet model was compared with that of the traditional SfM+MVS algorithm and the Patchmatchnet network model. The results show that histogram equalization significantly increases the number of matched feature points in the images and improves the uneven distribution of lighting. The deep learning networks demonstrate better performance in complex environmental forest scenes. The SA-Pmnet network, which employs self-attention mechanisms, improves the 3D reconstruction rate in the four plots to 94%, 92%, 94%, and 96% by capturing more details and achieves higher extraction accuracy of diameter at breast height (DBH) with values of 91.8%, 94.1%, 94.7%, and 91.2% respectively. These findings demonstrate the potential of combining of the image enhancement algorithm with deep learning models based on self-attention mechanisms for 3D reconstruction of forests, providing effective support for forest resource surveys and visualization management. [ABSTRACT FROM AUTHOR]

Arthrospira platensis (A. platensis) is a species of cyanobacteria with high economic value; the species is commercially well known as Spirulina platensis, and A. platensis was used in this paper. Its high adaptability, high photosynthetic efficiency, and fast growth rate make it one of the few cyanobacteria that can be cultivated on a large scale. Therefore, using the selenium enrichment property of A. platensis to cultivate selenium-enriched A. platensis will not only enhance the physiological efficacy of A. platensis but also increase its economic value significantly. In this study, we investigated the effects of sodium selenite on the growth and photosynthetic performance of A. platensis selenium by setting different amounts and methods of sodium selenite addition, and we explored the optimal culture conditions of the best dosage and method of sodium selenite addition. The results showed that the experimental group treated with sodium selenite at 700 μmol/L had the fastest growth, and the contents of soluble protein, phycocyanin C, and chlorophyll a increased by approximately 67.9%, 1.44 times, and 38.8% compared to the control group, respectively. Superoxide dismutase (SOD) and catalase (CAT) activity increased by 1.88-fold and 65%, respectively, and malondialdehyde (MDA) levels were reduced by 62% compared to the control group. The results of the OJIP assay showed that the J and I points were significantly higher at the batch addition and treatment concentration of 700 μmol/L, with the rate of QA being reduced and the proportion of the slowly reduced PQ pool being increased. The values of the maximum light energy conversion efficiency (Fv/Fm) per unit of reaction center were higher in both sodium selenite treatment groups than in the control group, indicating that the light energy conversion efficiency of A. platensis was promoted under all concentration treatment conditions. The batch addition of sodium selenite at concentrations less than 700 μmol/L resulted in significantly higher ABS/RC values than the control, and they were far superior to the one-time addition method. The reason for this may have been that the batch addition of sodium selenite at low concentrations increased the light absorption capacity of the unit reaction center of PSII, resulting in a rise in captured light energy, a rise in the energy captured by the reaction center for electron transfer (ETo/RC), a decrease in the energy dissipated in the absorption of light energy by the reaction center (DIo/RC), and an increase in the photosynthetic performance index (PI abs). [ABSTRACT FROM AUTHOR]

Electromagnetic wave pollution has become a significant harm posed to human health and precision instruments. To shelter such instruments from electromagnetic radiation, high-frequency electromagnetic interference (EMI) shielding materials are extremely desirable. The focus of this research is lightweight, high-absorption EMI shielding composites. Simple aqueous dispersion and drying procedures were used to prepare cotton fiber (CF)-based sheets combined with various zinc oxide (ZnO) contents. These composites were carbonated in a high-temperature furnace at 800 °C for two hours. The obtained CF/ZnO samples have densities of 1.02–1.08 g/cm3. The EMI shielding effectiveness of CF-30% ZnO, CF-50% ZnO, and CF-70% ZnO reached 32.06, 38.08, and 34.69 dB, respectively, to which more than 80% of absorption is attributed. The synergetic effects of carbon networks and surface structures are responsible for the high EMI shielding performance; various reflections inside the interconnected networks may also help in improving their EMI shielding performance. [ABSTRACT FROM AUTHOR]

Using various biomass factors, such as biomass expansion factor (BEF) and biomass conversion and expansion factor (BCEF), yields different results for estimating forest biomass. Therefore, ensuring compatibility between total biomass and its components when employing different biomass factors is crucial for developing a set of rapid and efficient models for large-scale biomass calculation. In this study, allometric equations were utilized to construct independent models and the proportional values (root-to-shoot ratio (Rra), crown-to-stem ratio (Rcs), bark-to-wood ratio (Rbw), foliage-to-bark ratio (Rfb), and wood biomass-to-wood volume (ρ)) by using the mean height (Hm) and the mean diameter at breast height (Dg) of 98 Pinus densata plots in Shangri-La, Yunnan province, China. The compatible methods were applied to reveal the compatibility between the total biomass and each component's biomass. The results showed the following: (1) Both the independent model and compatible model had a higher accuracy. The values were greater than 0.7 overall, but the foliage biomass accuracy was only 0.2. The total biomass and the component biomass showed compatibility. (2) The accuracy of BEF and BCEF exceeded 0.87 and the total error was less than 0.1 for most components. (3) The mean BEF (1.6) was greater than that of the Intergovernmental Panel on Climate Change (IPCC) (M = 1.3), and the mean BCEF was smaller than that of the IPCC; the values were 0.6 and 0.7, respectively. The range of BEF (1.4–2.1) and BCEF (0.44–0.89) were all within the range of the IPCC (1.15–3.2, 0.4–1.0). This study provides a more convenient and accurate method for calculating conversion coefficients (BEF and BCEF), especially when only Rcs data is available. [ABSTRACT FROM AUTHOR]

Perovskites have been recognized as a class of promising materials for optoelectronic devices. We intentionally include excessive Cs+ cations in precursors in the synthesis of perovskite CsPbBr3 nanocrystals and investigate how the Cs+ cations influence the lattice strain in these perovskite nanocrystals. Upon light illumination, the lattice strain due to the addition of alkali metal Cs+ cations can be compensated by light–induced lattice expansion. When the Cs+ cation in precursors is about 10% excessive, the electron–phonon coupling strength can be reduced by about 70%, and the carrier cooling can be slowed down about 3.5 times in lead halide perovskite CsPbBr3 nanocrystals. This work reveals a new understanding of the role of Cs+ cations, which take the A–site in ABX3 perovskite and provide a new way to improve the performance of perovskites and their practical devices further. [ABSTRACT FROM AUTHOR]

Automatic, efficient, and accurate individual tree species identification and crown parameters extraction is of great significance for biodiversity conservation and ecosystem function assessment. UAV multispectral data have the advantage of low cost and easy access, and hyperspectral data can finely characterize spatial and spectral features. As such, they have attracted extensive attention in the field of forest resource investigation, but their applicability for end-to-end individual tree species identification is unclear. Based on the Mask R-CNN instance segmentation model, this study utilized UAV hyperspectral images to generate spectral thinning data, spectral dimensionality reduction data, and simulated multispectral data, thereby evaluating the importance of high-resolution spectral information, the effectiveness of PCA dimensionality reduction processing of hyperspectral data, and the feasibility of multispectral data for individual tree identification. The results showed that the individual tree species identification accuracy of spectral thinning data was positively correlated with the number of bands, and full-band hyperspectral data were better than other hyperspectral thinning data and PCA dimensionality reduction data, with Precision, Recall, and F1-score of 0.785, 0.825, and 0.802, respectively. The simulated multispectral data are also effective in identifying individual tree species, among which the best result is realized through the combination of Green, Red, and NIR bands, with Precision, Recall, and F1-score of 0.797, 0.836, and 0.814, respectively. Furthermore, by using Green–Red–NIR data as input, the tree crown area and width are predicted with an RMSE of 3.16m2 and 0.51m, respectively, along with an rRMSE of 0.26 and 0.12. This study indicates that the Mask R-CNN model with UAV optical images is a novel solution for identifying individual tree species and extracting crown parameters, which can provide practical technical support for sustainable forest management and ecological diversity monitoring. [ABSTRACT FROM AUTHOR]

Lanzhou is the only provincial capital city in Northwest China where the main stream of the Yellow River and its tributaries flow through the city. Due to its geographical location and the influence of various factors, it is difficult to evaluate and simulate the climatic, hydrological, and ecological processes of the main stream of the Yellow River and its tributaries in the region. In this study, the Wanchuan River basin, currently undergoing ecological restoration, was selected as the study area. Seasonal backscatter differences generated using Sentinel-1/2 (S1/S2) data and the CD algorithm were used to reduce the effects of surface roughness; vegetation indices, soils, and field measurements were used to jointly characterize the vegetation contribution and soil contribution. Then, SM maps with a grid spacing of 10 m × 10 m were generated in the Wanchuan River basin, covering an area of 1767.78 km2. To validate the results, optimal factors were selected, and a training set and validation set were constructed. The results indicated a high level of the coefficient of determination (R2) of 0.78 and the root mean square error (RMSE) of 0.08 for the comparison of measured and inverted water contents, indicating that the algorithm retrieved the SM values of the study area well. Furthermore, Box line plots with ERA5-Land and GLDAS confirmed that the algorithm is in good agreement with current SM products and feasibility for soil water content inversion work in the Wanchuan River basin. [ABSTRACT FROM AUTHOR]

Cauliflower is a nutritious vegetable with inflorescences that are specialized to form the edible organs called curds. Uncovering key genes underlying important traits is crucial for the genetic improvement of this important crop. However, the genetic basis of many important agronomic traits, including curd performance and plant architecture in cauliflower, remains unclear. GWASs have proved to be powerful tools to study agronomic traits in many crops. To reveal the genetic basis of four important agronomic traits, namely, the main stem height (MSH), purplish curd (PC), external leaf wing (ELW) and weight of a single curd (WSC), we selected 220 core accessions of loose-curd cauliflower for resequencing, phenotypic investigation and GWAS. The approach revealed significant novel loci. We detected several significant associations: on C02 for MSH and PC, on C06 for ELW and on C01 for WSC. More interestingly, we identified a significant single-peak signal for the weight of a single curd (WSC), an important yield trait, and within this signal interval, we identified the BOB01G136670 gene with five SNPs encoding nonsynonymous mutations in the CDS region; these mutations resulted in two haplotypes with significant differences in curd weight. The weight of a single curd was significantly increased in the varieties with the BOB01G136670 Hap1 allele compared to those with BOB01G136670 Hap2. BOB01G136670 was highly conserved with the homologous genes that encode serine carboxypeptidase and belong to the S10 family in other species, including GS5, which functions as a positive regulator of grain size in rice, wheat and maize. Additionally, BOB01G136670 was highly expressed specifically at the curd enlargement stage, with low or even no expression at all in other tissues and stages, indicating that BOB01G136670 is a plausible candidate gene for WSC. Overall, this study identified genomic loci for four important agronomic traits that are relevant for accelerating biological breeding and the improvement of cauliflower varieties. [ABSTRACT FROM AUTHOR]

Comparing algorithms are crucial for enhancing the accuracy of remote sensing estimations of forest biomass in regions with high heterogeneity. Herein, Sentinel 2A, Sentinel 1A, Landsat 8 OLI, and Digital Elevation Model (DEM) were selected as data sources. A total of 12 algorithms, including 7 types of learners, were utilized for estimating the aboveground biomass (AGB) of Pinus yunnanensis forest. The results showed that: (1) The optimal algorithm (Extreme Gradient Boosting, XGBoost) was selected as the meta-model (referred to as XGBoost-stacking) of the stacking ensemble algorithm, which integrated 11 other algorithms. The R2 value was improved by 0.12 up to 0.61, and RMSE was decreased by 4.53 Mg/ha down to 39.34 Mg/ha compared to the XGBoost. All algorithms consistently showed severe underestimation of AGB in the Pinus yunnanensis forest of Yunnan Province when AGB exceeded 100 Mg/ha. (2) XGBoost-Stacking, XGBoost, BRNN (Bayesian Regularized Neural Network), RF (Random Forest), and QRF (Quantile Random Forest) have good sensitivity to forest AGB. QRNN (Quantile Regression Neural Network), GP (Gaussian Process), and EN (Elastic Network) have more outlier data and their robustness was poor. SVM-RBF (Radial Basis Function Kernel Support Vector Machine), k-NN (K Nearest Neighbors), and SGB (Stochastic Gradient Boosting) algorithms have good robustness, but their sensitivity was poor, and QRF algorithms and BRNN algorithm can estimate low values with higher accuracy. In conclusion, the XGBoost-stacking, XGBoost, and BRNN algorithms have shown promising application prospects in remote sensing estimation of forest biomass. This study could provide a reference for selecting the suitable algorithm for forest AGB estimation. [ABSTRACT FROM AUTHOR]

The transformer-based U-Net network structure has gained popularity in the field of medical image segmentation. However, most networks overlook the impact of the distance between each patch on the encoding process. This paper proposes a novel GC-TransUnet for medical image segmentation. The key innovation is that it takes into account the relationships between patch blocks based on their distances, optimizing the encoding process in traditional transformer networks. This optimization results in improved encoding efficiency and reduced computational costs. Moreover, the proposed GC-TransUnet is combined with U-Net to accomplish the segmentation task. In the encoder part, the traditional vision transformer is replaced by the global context vision transformer (GC-VIT), eliminating the need for the CNN network while retaining skip connections for subsequent decoders. Experimental results demonstrate that the proposed algorithm achieves superior segmentation results compared to other algorithms when applied to medical images. [ABSTRACT FROM AUTHOR]

It is important to improve the accuracy of models estimating aboveground biomass (AGB) in large areas with complex geography and high forest heterogeneity. In this study, k-nearest neighbors (k-NN), gradient boosting machine (GBM), random forest (RF), quantile random forest (QRF), regularized random forest (RRF), and Bayesian regularization neural network (BRNN) machine learning algorithms were constructed to estimate the AGB of four forest types based on environmental factors and the variables selected by the Boruta algorithm in Yunnan Province and using integrated Landsat 8 OLI and Sentinel 2A images. The results showed that (1) DEM was the most important variable for estimating the AGB of coniferous forests, evergreen broadleaved forests, deciduous broadleaved forests, and mixed forests; while the vegetation index was the most important variable for estimating deciduous broadleaved forests, the climatic factors had a higher variable importance for estimating coniferous and mixed forests, and texture features and vegetation index had a higher variable importance for estimating evergreen broadleaved forests. (2) In terms of specific model performance for the four forest types, RRF was the best model both in estimating the AGB of coniferous forests and mixed forests; the R2 and RMSE for coniferous forests were 0.63 and 43.23 Mg ha−1, respectively, and the R2 and RMSE for mixed forests were 0.56 and 47.79 Mg ha−1, respectively. BRNN performed the best in estimating the AGB of evergreen broadleaved forests; the R2 was 0.53 and the RMSE was 68.16 Mg ha−1. QRF was the best in estimating the AGB of deciduous broadleaved forests, with R2 of 0.43 and RMSE of 45.09 Mg ha−1. (3) RRF was the best model for the four forest types according to the mean values, with R2 and RMSE of 0.503 and 52.335 Mg ha−1, respectively. In conclusion, different variables and suitable models should be considered when estimating the AGB of different forest types. This study could provide a reference for the estimation of forest AGB based on remote sensing in complex terrain areas with a high degree of forest heterogeneity. [ABSTRACT FROM AUTHOR]

Cultivated peanut (Arachis hypogaea L.) is one of the most important oilseed crops worldwide. Pod-related traits, including pod length (PL), pod width (PW), ratio of PL to PW (PL/W) and 100-pod weight (100-PW), are crucial factors for pod yield and are key target traits for selection in peanut breeding. However, the studies on the natural variation and genetic mechanism of pod-related traits are not clear in peanut. In this study, we phenotyped 136 peanut accessions for four pod-related traits in two consecutive years and genotyped the population using a re-sequencing technique. Based on 884,737 high-quality single nucleotide polymorphisms (SNPs), genome-wide association studies (GWAS) were conducted for four pod-related traits using a fixed and random model uniform cyclic probability (FarmCPU) model. The results showed that a total of 36 SNPs were identified by GWAS, among which twenty-one, fourteen and one SNPs were significantly associated with PL, PL/W and 100-PW, respectively. The candidate regions where the four peak SNPs (10_76084075, 11_138356586, 16_64420451, and 18_126782541) were located were used for searching genes, and nineteen candidate genes for pod-related traits were preliminarily predicted based on functional annotations. In addition, we also compared the expression patterns of these nineteen candidate genes in different tissues of peanut, and we found that eight genes were specifically highly expressed in tender fruit, immature pericarp, or seed, so we considered these genes to be the potential candidate genes for pod-related traits. These results enriched the understanding of the genetic basis of pod-related traits and provided an important theoretical basis for subsequent gene cloning and marker-assisted selection (MAS) breeding in peanut. [ABSTRACT FROM AUTHOR]

The pine wood nematode (PWN; Bursaphelenchus xylophilus) is a major invasive species in China, causing huge economic and ecological damage to the country due to the absence of natural enemies and the extremely rapid rate of infection and spread. Accurate monitoring of pine wilt disease (PWD) is a prerequisite for timely and effective disaster prevention and control. UAVs can carry hyperspectral sensors for near-ground remote sensing observations, which can obtain rich spatial and spectral information and have the potential for infected tree identification. Deep learning techniques can use rich multidimensional data to mine deep features in order to achieve tasks such as classification and target identification. Therefore, we propose an improved Mask R-CNN instance segmentation method and an integrated approach combining a prototypical network classification model with an individual tree segmentation algorithm to verify the possibility of deep learning models and UAV hyperspectral imagery for identifying infected individual trees at different stages of PWD. The results showed that both methods achieved good performance for PWD identification: the overall accuracy of the improved Mask R-CNN with the screened bands as input data was 71%, and the integrated method combining prototypical network classification model with individual tree segmentation obtained an overall accuracy of 83.51% based on the screened bands data, in which the early infected pine trees were identified with an accuracy of 74.89%. This study indicates that the improved Mask R-CNN and integrated prototypical network method are effective and practical for PWD-infected individual trees identification using UAV hyperspectral data, and the proposed integrated prototypical network enables early identification of PWD, providing a new technical guidance for early monitoring and control of PWD. [ABSTRACT FROM AUTHOR]

In the Tien-Shan Mountains, Ili Prefecture, Xinjiang, China, the livestock industry has experienced rapid growth in recent decades. However, this expansion has led to increased overgrazing behavior, resulting in the proliferation of grazing paths and a decline in vegetation cover. These factors are considered the main causes of vegetation degradation in the region. To investigate this issue, we conducted a study utilizing unmanned aerial vehicle imagery in the Zollersay Mountains of Ili to examine the distribution of grazing paths and their effects on mountain vegetation, including grassland and Malus sieversii. The results of our study revealed that grazing paths in the area exhibited various formations, including parallel, oblique intersection, and grid. On the hilltop, the grazing paths were not only shorter but also wider, whereas on the hillside, they were denser, indicating a higher concentration of livestock trampling events. It was found that grazing path density played a pivotal role in grassland degradation, with a negative correlation observed between grazing path density and indicators such as the grassland quality index and grass vegetation coverage. As grazing path density increased, the damage inflicted on Malus sieversii by livestock also intensified. However, as the trees grow older, their height surpasses the feeding range of livestock, resulting in reduced grazing impact. The findings of our study carry significant implications for developing scientifically informed livestock policies and promoting the conservation of wild fruit forests. [ABSTRACT FROM AUTHOR]

In the context of global climate change, many studies have focused on the interannual vegetation variation trends and their response to precipitation and temperature, but ignored the effects of seasonal variability. This study explored the relationship between normalized difference vegetation index (NDVI) and seasonal climate elements in the Wuliangsu Lake Basin area from 1990 to 2020, and quantified the impacts of human activities on vegetation dynamics. We used Landsat series data to analyze the spatial and temporal variation of the NDVI using the trend analysis method, the Theil–Sen median, the Mann–Kendall test, and the Hurst index. Then, we used meteorological data and land use data to quantify the effects of human activities using residual analysis, and correlation methods to determine the driving forces of NDVI variations. The results showed that the NDVI changes presented obvious regional characteristics, with a decreasing trend from southeast to northwest in Wuliangsu Lake Basin. Due to global warming, the start of the growing season (SOS) is 4.3 days (2001 to 2010) and 6.8 days (2011 to 2020) earlier compared with 1990 to 2000. The end of the season (EOS) is advanced by 3.6 days (2001 to 2010), and delayed by 8.9 days (2011 to 2020). Seasonal (spring, summer, autumn, and winter) NDVIs with precipitation and temperature show spatial heterogeneity. Further, changes in grasslands and woodlands were vulnerable to climate change and human activities. Since the beginning of the 21st century, human activity was the driving force for vegetation improvement in the Dengkou, west-central, north and southwest regions, where ecological instability is weak. This finding can provide a theoretical basis for the implementation of the same type of ecological restoration projects and the construction of ecological civilization, and contribute to the regional green and sustainable development. [ABSTRACT FROM AUTHOR]

The inspiration for the footed robot was originally derived from biology, and it was an imitation of biological form and movement. In this paper, a bionic-robot dog is designed to reveal the motion characteristics of a quadruped robot mechanism through modeling, model kinematic analysis, and other methods. First, the structural characteristics and movement characteristics of the developed bionic-dog model are studied. The first step is to study the physiological structure of the dog, analyze the function of the dog's limbs, and then use a high-speed camera to capture the motion of the marked bionic-robot dog and shoot motion video of the bionic-robot dog in different motion states. The effective data of the marked points in the video are extracted using PHOTRON 1.0 software, and the extracted data are analyzed and processed in the software MATLAB R2020a, and finally the structural characteristics and motion laws of the bionic-robot dog are obtained. Then, a bionic-robot-dog experimental platform is built to conduct experiments with three planned gaits (dynamic gait, static gait, and gait transition). The experiments showed that the three gaits were consistent with the planned movements and the bionic-robot dog could perform stable fast-gait walking, slow-gait walking, and quickly complete gait transitions. All three gaits were simulated in ADAMS 2019 software, and the simulation results showed that all three gaits caused the bionic dog robot to move smoothly. [ABSTRACT FROM AUTHOR]

SWEET (Sugars Will Eventually be Exported Transporter) genes play essential roles in various biological processes, including phloem loading, sugar efflux, plant development and stress response. In this study, a total of 33 RsSWEET gene members were identified in the radish genome. They could be divided into four subfamilies and are distributed on eight radish chromosomes. Cis-acting regulatory element analysis indicated that these RsSWEET genes were potentially involved in the radish growth and development and stress response process, including circadian control and light response and responses to numerous stresses, including low-temperature and drought stress. Transcriptome data analysis revealed that a number of RsSWEET genes exhibited specific expression patterns in different tissues and developmental stages of radish. Moreover, several RsSWEET genes (e.g., RsSWEET2a, RsSWEET3a, RsSWEET16b and RsSWEET17) showed differential expression profiles under various abiotic stresses, including cold, heat, salt, Cd and Pb stress. Remarkably, the RsSWEET17 was specifically expressed in the cambium of radish. RsSWEET17 was heterologously expressed in yeast strain EBY.VW4000, which suggested that it has the ability to transport sugar. Notably, RsSWEET17-overexpressing Arabidopsis plants exhibited excessive root length, greater fresh weight and higher soluble sugar content (SSC) accumulation compared with wild-type (WT) plants, indicating that RsSWEET17 might positively regulate radish taproot development by strategically manipulating sugar accumulation. Collectively, these results clarify the molecular mechanisms underlying RsSWEET-mediated sugar accumulation and root growth and development in radish. [ABSTRACT FROM AUTHOR]

Simple Summary: Over 90% of the adult population worldwide is infected with the Epstein–Barr virus (EBV). While EBV infection is associated with the development of lymphoproliferative disorders (EBV-LPD) in people with weakened immune systems, only ~20% of immunodeficient individuals develop EBV-LPD. Such clinical heterogeneity may reflect host variables that increase the risk of developing EBV-LPD. Immunodeficient mice engrafted with blood cells from EBV+ individuals develop spontaneous EBV-LPD of human B-cell origin with similar heterogeneity observed in humans. Our study aimed to investigate differences between the model's lymphoma producers (High-Incidence, HI donors) and non-lymphoma producers (No-Incidence, NI donors). HI donors showed high levels of T follicular helper (Tfh), regulatory T cells (Treg), and myeloid-derived suppressor cells compared to NI donors. Depletion of Tfh or Treg subsets delays or prevents EBV-LPD in this model. Our results reveal potential biomarkers that may help classify vulnerable patients at risk for developing EBV-LPD. Epstein–Barr virus (EBV) is a ubiquitous herpes virus associated with various cancers. EBV establishes latency with life-long persistence in memory B-cells and can reactivate lytic infection placing immunocompromised individuals at risk for EBV-driven lymphoproliferative disorders (EBV-LPD). Despite the ubiquity of EBV, only a small percentage of immunocompromised patients (~20%) develop EBV-LPD. Engraftment of immunodeficient mice with peripheral blood mononuclear cells (PBMCs) from healthy EBV-seropositive donors leads to spontaneous, malignant, human B-cell EBV-LPD. Only about 20% of EBV+ donors induce EBV-LPD in 100% of engrafted mice (High-Incidence, HI), while another 20% of donors never generate EBV-LPD (No-Incidence, NI). Here, we report HI donors to have significantly higher basal T follicular helper (Tfh) and regulatory T-cells (Treg), and depletion of these subsets prevents/delays EBV-LPD. Transcriptomic analysis of CD4+ T cells from ex vivo HI donor PBMC revealed amplified cytokine and inflammatory gene signatures. HI vs. NI donors showed a marked reduction in IFNγ production to EBV latent and lytic antigen stimulation. In addition, we observed abundant myeloid-derived suppressor cells in HI donor PBMC that decreased CTL proliferation in co-cultures with autologous EBV+ lymphoblasts. Our findings identify potential biomarkers that may identify individuals at risk for EBV-LPD and suggest possible strategies for prevention. [ABSTRACT FROM AUTHOR]

Nucleotide-binding and oligomerization domain-like receptors (NOD-like receptors, NLRs) can regulate the inflammatory response to eliminate pathogens and maintain the host's homeostasis. In this study, the head kidney macrophages of Siberian sturgeon were treated with lipopolysaccharide (LPS) to induce inflammation by evaluating the expression of cytokines. The high-throughput sequencing for macrophages after 12 h treatment showed that 1224 differentially expressed genes (DEGs), including 779 upregulated and 445 downregulated, were identified. DEGs mainly focus on pattern recognition receptors (PRRs) and the adaptor proteins, cytokines, and cell adhesion molecules. In the NOD-like receptor signaling pathway, multiple NOD-like receptor family CARD domains containing 3-like (NLRC3-like) were significantly downregulated, and pro-inflammatory cytokines were upregulated. Based on the transcriptome database, 19 NLRs with NACHT structural domains were mined and named in Siberian sturgeon, including 5 NLR-A, 12 NLR-C, and 2 other NLRs. The NLR-C subfamily had the characteristics of expansion of the teleost NLRC3 family and lacked the B30.2 domain compared with other fish. This study revealed the inflammatory response mechanism and NLRs family characterization in Siberian sturgeon by transcriptome and provided basic data for further research on inflammation in teleost. [ABSTRACT FROM AUTHOR]