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Introduction: In the context of climate variability, rapid and accurate estimation of winter wheat yield is essential for agricultural policymaking and food security. With advancements in remote sensing technology and deep learning, methods utilizing remotely sensed data are increasingly being employed for large-scale crop growth monitoring and yield estimation. Methods: Solar-induced chlorophyll fluorescence (SIF) is a new remote sensing metric that is closely linked to crop photosynthesis and has been applied to crop growth and drought monitoring. However, its effectiveness for yield estimation under various data fusion conditions has not been thoroughly explored. This study developed a deep learning model named BO-CNN-BiLSTM (BCBL), combining the feature extraction capabilities of a convolutional neural network (1DCNN) with the time-series memory advantages of a bidirectional long short-term memory network (BiLSTM). The Bayesian Optimization (BOM) method was employed to determine the optimal hyperparameters for model parameter optimization. Traditional remote sensing variables (TS), such as the Enhanced Vegetation Index (EVI) and Leaf Area Index (LAI), were fused with the SIF and climate data to estimate the winter wheat yields in Henan Province, exploring the SIF's estimation capabilities using various datasets. Results and Discussion: The results demonstrated that the BCBL model, integrating TS, climate, and SIF data, outperformed other models (e.g., LSTM, Transformer, RF, and XGBoost) in the estimation accuracy, with R²=0.81, RMSE=616.99 kg/ha, and MRE=7.14%. Stepwise sensitivity analysis revealed that the BCBL model reliably identified the critical stage of winter wheat yield formation (early March to early May) and achieved high yield estimation accuracy approximately 25 d before harvest. Furthermore, the BCBL model exhibited strong stability and generalization across different climatic conditions. Conclusion: Thus, the BCBL model combined with SIF data can offer reliable winter wheat yield estimates, hold significant potential for application, and provide valuable insights for agricultural policymaking and field management. [ABSTRACT FROM AUTHOR]

Actuators based on stimulus‐responsive soft materials have attracted widespread attention due to their significant advantages in flexibility and structural adaptability over traditional rigid actuators. However, the development of fast‐actuating, mechanical robust and self‐healing actuators without compromising flexibility remains an ongoing challenge. Here, this study presents a photo‐responsive flexible actuator by incorporating core–shell liquid metal nano‐assemblies into a polyurethane matrix to construct a dynamic supramolecular interface. The nano‐assemblies are endowed with adaptive characteristics to external loading, being expected to dissipate energy via the reversible reconstruction of hydrogen bonding and deformation of nano‐assemblies. The obtained composites exhibit excellent mechanical strength (31 MPa), low modulus (2.02 MPa), high stretchability (1563.95%), autonomous self‐healing (92.5%), and NIR‐responsive actuation properties. Furthermore, the combination of high cohesive energy but fluxible nano‐liquid metal core, and strong dynamic interface not only achieves the balance of high tensile strength and high flexibility but also endows the actuators with outstanding notch‐resistant performance (fracture energy≈58.8 kJ m−2) through multiphase energy dissipation. This work provides a promising strategy for developing soft yet tough self‐healing materials in the fields of flexible robotics and artificial muscles. [ABSTRACT FROM AUTHOR]

The rapid advancement in attractive platforms such as biomedicine and human‐machine interaction has generated urgent demands for intelligent materials with high strength, flexibility, and self‐healing capabilities. However, existing self‐healing ability materials are challenged by a trade‐off between high strength, low elastic modulus, and healing ability due to the inherent low strength of noncovalent bonding. Here, drawing inspiration from human fibroblasts, a monomer trapping synthesis strategy is presented based on the dissociation and reconfiguration in amphiphilic ionic restrictors (7000‐times volume monomer trapping) to develop a eutectogel. Benefiting from the nanoconfinement and dynamic interfacial interactions, the molecular chain backbone of the formed confined domains is mechanically reinforced while preserving soft movement capabilities. The resulting eutectogels demonstrate superior mechanical properties (1799% and 2753% higher tensile strength and toughness than pure polymerized deep eutectic solvent), excellent self‐healing efficiency (>90%), low tangential modulus (0.367 MPa during the working stage), and the ability to sensitively monitor human activities. This strategy is poised to offer a new perspective for developing high strength, low modulus, and self‐healing wearable electronics tailored to human body motion. [ABSTRACT FROM AUTHOR]

The timely and precise estimation of stem biomass is critical for monitoring the crop growing status. Optical remote sensing is limited by the penetration of sunlight into the canopy depth, and thus directly estimating winter wheat stem biomass via canopy spectra remains a difficult task. There is a stable linear relationship between the stem dry biomass (SDB) and leaf dry biomass (LDB) of winter wheat during the entire growth stage. Therefore, this study comprehensively considered remote sensing and crop phenology, as well as biomass allocation laws, to establish a novel two-component (LDB, SDB) and two-parameter (phenological variables, spectral vegetation indices) stratified model (Tc/Tp-SDB) to estimate SDB across the growth stages of winter wheat. The core of the Tc/Tp-SDB model employed phenological variables (e.g., effective accumulative temperature, EAT) to correct the SDB estimations determined from the LDB. In particular, LDB was estimated using spectral vegetation indices (e.g., red-edge chlorophyll index, CIred edge). The results revealed that the coefficient values (β0 and β1) of ordinary least squares regression (OLSR) of SDB with LDB had a strong relationship with phenological variables. These coefficient (β0 and β1) relationships were used to correct the OLSR model parameters based on the calculated phenological variables. The EAT and CIred edge were determined as the optimal parameters for predicting SDB with the novel Tc/Tp-SDB model, with r, RMSE, MAE, and distance between indices of simulation and observation (DISO) values of 0.85, 1.28 t/ha, 0.95 t/ha, and 0.31, respectively. The estimation error of SDB showed an increasing trend from the jointing to flowering stages. Moreover, the proposed model showed good potential for estimating SDB from UAV hyperspectral imagery. This study demonstrates the ability of the Tc/Tp-SDB model to accurately estimate SDB across different growing seasons and growth stages of winter wheat. [ABSTRACT FROM AUTHOR]

Background: Cyadox (CYA) is a derivative of quinoxaline 1,4-dioxide and a safe and effective synthetic antibacterial agent. Objective: This study aimed to explore the drug transport in blood, distribution, depletion and hepatotoxicity of drugs in animals. Methods: The transport of CYA in blood was studied using fluorescence, circular dichroism (CD) and molecular docking methods. Tissue distribution and depletion of CYA in rats were evaluated following oral administration of [3H]-CYA at different doses. Hepatotoxicity of drugs evaluated by transcriptomics. Results: During transport in the bloodstream, the drug binds to bovine serum albumin (BSA) by hydrogen bonding and has only one binding site. Hydrogen bonds were formed between O (2) of CYA and ARG208, O (3) of CYA and LEU480, VAL481. The secondary protein conformation of BSA changed after binding with an increase in α-helix and a decrease in β-strand. After a single oral administration of [3H]-CYA, it was excreted rapidly within 7 days, with 34.81% from the urine and 60.25% from the feces. Higher and sustained levels of radioactivity were detected in the liver during the post-dose period, suggesting that the drug may concentrate in the liver. The transcriptomic data indicates that CYA exhibits low hepatotoxicity. However, there are indications that it may have an impact on steroid biosynthesis. Conclusion: This study could serve as a basis for conducting further studies on the use of CYA in food animals and improving the pharmacologic, pharmacokinetic, and toxicologic effects of CYA on food animals. [ABSTRACT FROM AUTHOR]

A rapid and accurate determination of large-scale winter wheat yield is significant for food security and policy formulation. In this study, meteorological data and enhanced vegetation index (EVI) were used to estimate the winter wheat yield in Henan Province, China, by constructing a deep learning model. The deep learning model combines CNN feature extraction and makes full use of the sequence data processing capability of the LSTM and a multi-head attention mechanism to develop a novel CNN–MALSTM estimation model, which can capture the information of input sequences in different feature subspaces to enhance the expressiveness of the model. A CNN–LSTM baseline model was also constructed for comparison. Compared with the baseline model (R2 = 0.75, RMSE = 646.53 kg/ha, and MAPE = 8.82%), the proposed CNN–MALSTM model (R2 = 0.79, RMSE = 576.01 kg/ha, MAPE = 7.29%) could more accurately estimate the yield. Based on the cross-validation with one year of left-out data and the input of the fertility period by fertility period to explore the sensitivity of the model to data from different fertility periods to the final yield, an annual yield distribution map of Henan Province was constructed. Through cross-validation, the stability of the model in different years was assessed. The results showed that the model could obtain the best prediction of the yield approximately 20 days in advance. In terms of the spatial distribution of the yield in Henan Province on a yearly basis, the estimated yield showed an overall uptrend from west to east, consistent with the trend in the statistical yearbook of the yield for Henan Province. Thus, it can be concluded that the proposed CNN–MALSTM model can provide stable yield estimation results. [ABSTRACT FROM AUTHOR]

A d10-Cd cluster containing sandwich-type arsenotungstate [C3H12N2]6[Cd4Cl2(B-α-AsW9O34)2] was synthesized and its structure characterized through elemental analyses, X-ray powder diffraction (XRPD), IR spectroscopy, X-ray photoelectron spectroscopy (XPS), and single-crystal X-ray diffraction. The X-ray analysis revealed that the molecular unit of the compound consists of a captivating tetra-Cd-substituted sandwich-type polyoxoanion, accompanied by six elegantly protonated 1,2-diaminopropane as counter ions. The further novelty of the tetranuclear cadmium cluster lies in its occupied chlorine atom sites. This makes it highly susceptible to coordinate reactions with nitrogen on polycyclic aromatic hydrocarbons, thereby exhibiting different fluorescent signals that facilitate the identification and detection of these carcinogenic substances in methanol. [ABSTRACT FROM AUTHOR]

Assessing the quality of maize seedlings is crucial for field management and germplasm evaluation. Traditional methods for evaluating seedling quality mainly rely on manual field surveys, which are not only inefficient but also highly subjective, while large-scale satellite detection often lacks sufficient accuracy. To address these issues, this study proposes an innovative approach that combines the YOLO v8 object detection algorithm with Voronoi spatial analysis to rapidly evaluate maize seedling quality based on high-resolution drone imagery. The YOLO v8 model provides the maize coordinates, which are then used for Voronoi segmentation of the field after applying the Convex Hull difference method. From the generated Voronoi diagram, three key indicators are extracted: Voronoi Polygon Uniformity Index (VPUI), missing seedling rate, and repeated seedling rate to comprehensively evaluate maize seedling quality. The results show that this method effectively extracts the VPUI, missing seedling rate, and repeated seedling rate of maize in the target area. Compared to the traditional plant spacing variation coefficient, VPUI performs better in representing seedling uniformity. Additionally, the R2 for the estimated missing seedling rate and replanting rate based on the Voronoi method were 0.773 and 0.940, respectively. Compared to using the plant spacing method, the R2 increased by 0.09 and 0.544, respectively. The maize seedling quality evaluation method proposed in this study provides technical support for precision maize planting management and is of great significance for improving agricultural production efficiency and reducing labor costs. [ABSTRACT FROM AUTHOR]

Background: Alzheimer's disease (AD) is a complex neurodegenerative disorder, with recent research emphasizing the roles of microglia and their secreted extracellular vesicles in AD pathology. However, the involvement of specific molecular pathways contributing to neuronal death in the context of copper toxicity remains largely unexplored. Objective: This study investigates the interaction between pyruvate kinase M2 (PKM2) and dihydrolipoamide S‐acetyltransferase (DLAT), particularly focusing on copper‐induced neuronal death in Alzheimer's disease. Methods: Gene expression datasets were analyzed to identify key factors involved in AD‐related copper toxicity. The role of DLAT was validated using 5xFAD transgenic mice, while in vitro experiments were conducted to assess the impact of microglial exosomes on neuronal PKM2 transfer and DLAT expression. The effects of inhibiting the PKM2 transfer via microglial exosomes on DLAT expression and copper‐induced neuronal death were also evaluated. Results: DLAT was identified as a critical factor in the pathology of AD, particularly in copper toxicity. In 5xFAD mice, increased DLAT expression was linked to hippocampal damage and cognitive decline. In vitro, microglial exosomes were shown to facilitate the transfer of PKM2 to neurons, leading to upregulation of DLAT expression and increased copper‐induced neuronal death. Inhibition of PKM2 transfer via exosomes resulted in a significant reduction in DLAT expression, mitigating neuronal death and slowing AD progression. Conclusion: This study uncovers a novel pathway involving microglial exosomes and the PKM2‐DLAT interaction in copper‐induced neuronal death, providing potential therapeutic targets for Alzheimer's disease. Blocking PKM2 transfer could offer new strategies for reducing neuronal damage and slowing disease progression in AD. [ABSTRACT FROM AUTHOR]

Accurately assessing maize crop height (CH) and aboveground biomass (AGB) is crucial for understanding crop growth and light-use efficiency. Unmanned aerial vehicle (UAV) remote sensing, with its flexibility and high spatiotemporal resolution, has been widely applied in crop phenotyping studies. Traditional canopy height models (CHMs) are significantly influenced by image resolution and meteorological factors. In contrast, the accumulated incremental height (AIH) extracted from point cloud data offers a more accurate estimation of CH. In this study, vegetation indices and structural features were extracted from optical imagery, nadir and oblique photography, and LiDAR point cloud data. Optuna-optimized models, including random forest regression (RFR), light gradient boosting machine (LightGBM), gradient boosting decision tree (GBDT), and support vector regression (SVR), were employed to estimate maize AGB. Results show that AIH99 has higher accuracy in estimating CH. LiDAR demonstrated the highest accuracy, while oblique photography and nadir photography point clouds were slightly less accurate. Fusion of multi-source data achieved higher estimation accuracy than single-sensor data. Embedding structural features can mitigate spectral saturation, with R2 ranging from 0.704 to 0.939 and RMSE ranging from 0.338 to 1.899 t/hm2. During the entire growth cycle, the R2 for LightGBM and RFR were 0.887 and 0.878, with an RMSE of 1.75 and 1.76 t/hm2. LightGBM and RFR also performed well across different growth stages, while SVR showed the poorest performance. As the amount of nitrogen application gradually decreases, the accumulation and accumulation rate of AGB also gradually decrease. This high-throughput crop-phenotyping analysis method offers advantages, such as speed and high accuracy, providing valuable references for precision agriculture management in maize fields. [ABSTRACT FROM AUTHOR]

A timely and comprehensive understanding of winter wheat maturity is crucial for deploying large-scale harvesters within a region, ensuring timely winter wheat harvesting, and maintaining grain quality. Winter wheat maturity prediction is limited by two key issues: accurate extraction of wheat planting areas and effective maturity prediction methods. The primary aim of this study is to propose a method for predicting winter wheat maturity. The method comprises three parts: (i) winter wheat planting area extraction via phenological characteristics across multiple growth stages; (ii) extraction of winter wheat maturity features via vegetation indices (VIs, such as NDVI, NDRE, NDII1, and NDII2) and box plot analysis; and (iii) winter wheat maturity data prediction via the selected VIs. The key findings of this work are as follows: (i) Combining multispectral remote sensing data from the winter wheat jointing-filling and maturity-harvest stages can provide high-precision extraction of winter wheat planting areas (OA = 95.67%, PA = 91.67%, UA = 99.64%, and Kappa = 0.9133). (ii) The proposed method can offer the highest accuracy in predicting maturity at the winter wheat flowering stage (R2 = 0.802, RMSE = 1.56 days), aiding in a timely and comprehensive understanding of winter wheat maturity and in deploying large-scale harvesters within the region. (iii) The study's validation was only conducted for winter wheat maturity prediction in the North China Plain wheat production area, and the accuracy of harvesting progress information extraction for other regions' wheat still requires further testing. The method proposed in this study can provide accurate predictions of winter wheat maturity, helping agricultural management departments adopt information-based measures to improve the efficiency of monitoring winter wheat maturation and harvesting, thus promoting the efficiency of precision agricultural operations and informatization efforts. [ABSTRACT FROM AUTHOR]

Background: Pseudopoda Jäger, 2000 is currently the largest genus in the family Sparassidae Bertkau, 1872, comprising 255 species. Of these, 154 species have been recorded in China, representing 60.4% of the global species. In October 2023, a spider survey was conducted in Shennongjia National Nature Reserve, Hubei Province, China. After examination and morphological comparison, one new species of the genus Pseudopoda was identified and is described here. New information: In this paper, a new species of Pseudopoda collected in Hubei Province, China, is proposed and named Pseudopoda guanmenshan sp. nov. A detailed description, diagnosis, photographs and distribution map of the new species are provided. [ABSTRACT FROM AUTHOR]

Heat shock proteins (HSPs) are molecular chaperones that play essential roles in plant development and in response to various environmental stresses. Understanding R. delavayi HSP genes is of great importance since R. delavayi is severely affected by heat stress. In the present study, a total of 76 RdHSP genes were identified in the R. delavayi genome, which were divided into five subfamilies based on molecular weight and domain composition. Analyses of the chromosome distribution, gene structure, and conserved motif of the RdHSP family genes were conducted using bioinformatics analysis methods. Gene duplication analysis showed that 15 and 8 RdHSP genes were obtained and retained from the WGD/segmental duplication and tandem duplication, respectively. Cis-element analysis revealed the importance of RdHSP genes in plant adaptations to the environment. Moreover, the expression patterns of RdHSP family genes were investigated in R. delavayi treated with high temperature based on our RNA-seq data, which were further verified by qRT-PCR. Further analysis revealed that nine candidate genes, including six RdHSP20 subfamily genes (RdHSP20.4, RdHSP20.8, RdHSP20.6, RdHSP20.3, RdHSP20.10, and RdHSP20.15) and three RdHSP70 subfamily genes (RdHSP70.15, RdHSP70.21, and RdHSP70.16), might be involved in enhancing the heat stress tolerance. The subcellular localization of two candidate RdHSP genes (RdHSP20.8 and RdHSP20.6) showed that two candidate RdHSPs were expressed and function in the chloroplast and nucleus, respectively. These results provide a basis for the functional characterization of HSP genes and investigations on the molecular mechanisms of heat stress response in R. delavayi. [ABSTRACT FROM AUTHOR]

The antioxidant activity of chitosan (CS) and three water-soluble derivatives was analyzed comparatively by in vitro and in vivo experiments, including hydroxypropyl chitosan (HPCS), quaternary ammonium salt of chitosan (HACC), and carboxymethyl chitosan (CMCS). The results show that chitosan and its water-soluble derivatives have a scavenging ability on DPPH radicals, superoxide radicals, and hydroxyl radicals, and a reducing ability. A remarkable difference (p < 0.05) was found for HACC and HPCS compared with CS on DPPH radicals, hydroxyl radicals, and reducing ability. The antioxidant ability of the four chitosan samples was in the order of HPCS > HACC > CMCS > CS. Furthermore, antioxidant activity of all samples increased gradually in a concentration-dependent manner. The in vivo result indicates that oral CS and its derivatives samples result in a decrease in lipid peroxides (LPO) and free fatty acids (FFA) levels in serum with an increase in superoxide dismutase (SOD) activity. Especially for the HPCS and HACC groups, the LPO, FFA, and SOD activity in serum was different significantly in comparison with the high-fat controlgroup (HF) (p < 0.05). These results indicate that chitosan and its derivatives can be used as good antioxidants, and the antioxidant activity might be related to the molecular structure of chitosan derivatives. [ABSTRACT FROM AUTHOR]