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Deep-hole blasting has shown its superior performance in achieving large-scale mining and improving excavation efficiency. While the increasing charge length, coupled with the rising charge weight, prompts a closer examination of the induced vibration and traditional vibration prediction laws. In light of the fact that the induced vibration depends heavily on the evaluation of vibration characteristics of a single long blasthole, a systemic illustration of the wave propagation induced by a long blasthole was first presented, taking into account various conditions. Subsequently, experiments with six charge lengths were conducted in an underground mine to clarify the variation of waveform and peak particle velocity (PPV). Finally, a three-dimensional (3D) finite element model with consideration of the free surface, was developed and validated against the experimental data. The influences of free surface, charge length, detonation position, and velocity of detonation (VoD) were comprehensively investigated to ascertain the vibration characteristics. The experimental and numerical results allow the following conclusions: (1) free surfaces not only increase PPVs but also decrease PPV-attenuation rate, while Mach waves enhance damage and vibration performance in the near-field region. (2) In the presence of a free surface, increasing charge length elevates the PPV level in the near-field and far-field, while it becomes constant in a narrow region of the medium-field. However, as the length exceeds 11.6 m with the effect of Mach waves, the PPV and waveform become independent of the charge length. (3) Different initiation modes mainly affect the near-field vibration; (4) The dominant frequency (DF) falls below 400 Hz with abundant frequency spectra at considerable charge lengths. The results state that the charge weight-scaling laws should be carefully used for long-blasthole vibration. Highlights: Effects of charge length, free surface, detonation position, and VoD are investigated for a long blasthole. The charge weight-scaling laws should be cautiously used for long blasthole vibration. Increasing charge length combined with free surfaces leads to an elevated PPV level in the near- and far- fields, rather than the medium-field. The amplitude is independent of a substantial charge length with the effect of Mach waves. Initiation mode and VoD primarily affect the near-field vibration. [ABSTRACT FROM AUTHOR]

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]

With the continuous expansion of data from different domains, there are lots of inconsistent data in decision tables. Approximations are fundamental notions of rough set theory. However, inconsistent decision tables (IDTs) may update with new coming objects due to the information collection and update. In this circumstance, how to dynamically acquire useful approximations with previous knowledge in IDTs is crucial for data analysis and knowledge discovery. To address this issue, we focus on exploiting incremental approaches for computing approximations when objects are changing in IDTs over time, which is of great significance for gaining decision rules in the context of dynamic environments. First, we introduce the concepts of inconsistent decision tables and approximations. Furthermore, with dynamic changing objects, several principles of updating approximations are investigated according to the positive and boundary regions. Following the proposed principles, incremental algorithms for acquiring approximations in IDTs are presented while the objects change with time. In the end, comparative results are illustrated to verify effectiveness of updating lower and upper approximations in IDTs, displaying the advantage of the proposed approaches.Article Highlights: Incremental principles of obtaining approximations with the changes of objects in IDTs are presented. In line with the principles of knowledge maintenance, the incremental algorithms are designed when adding an object or deleting an object in IDTs. Experimental results on public data sets show that the proposed methods are effective regarding runtime when updating approximations. [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]

Canopy temperature is an important parameter for monitoring the physiological status of maize. The study of its change law can help to improve the growth environment and increase the yield and quality. Based on the RGB, thermal infrared, and multispectral images obtained by an Unmanned Aerial Vehicle (UAV), two peak threshold methods were used in this study to eliminate the soil background and obtain the maize canopy information in the study area. Then, by using the radiation transmission model and Planck's law, the maize canopy temperature was extracted, and the correlation of canopy temperature change with the regional coverage and soil drought degree was analysed. Results indicated that the accuracy of canopy temperature extraction can be effectively enhanced by eliminating the influence of soil background. At jointing and filling stages, the Pearson correlation coefficients r of canopy temperature and coverage were 0.731 and 0.722, respectively, and the slope of the linear fitting model was less than 0, indicating that the coverage has a negative impact on canopy temperature, i.e., the higher the coverage, the lower the canopy temperature, and vice versa. Additionally, statistical analysis of the canopy temperature and temperature vegetation drought index (TVDI) clustering results at the jointing and filling stages demonstrated that the total overlapping rates of the categories were 72.5% and 41.0% respectively, suggesting that maize canopy temperature is related to regional soil drought degree, the change range of canopy temperature increases with the TVDI value, and the soil drought degree at the jointing stage has a large impact on maize canopy temperature change than that at the filling stage. This study provides technical support for extracting and analysing crop canopy temperature change by using UAV thermal infrared technology. [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]

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]

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]

The explosion in foundation poses a significant threat to people and buildings. Currently, a unified empirical prediction formula for crater in calcareous foundation has not been established. In this paper, analyzed the types and sizes of explosion crater with different scaled burial depths through field tests and numerical simulation. In field tests, revealed the influence of scaled burial depth on the type and size of explosion crater and obtained the critical scaled burial depth for three different types of explosion craters, namely ejecta-type crater, collapse-type crater and covert explosion. Through the Smooth Particle Hydrodynamic-Finite Element Method (SPH-FEM) coupling algorithm, studied the movement trajectory of sand particles around the explosive at the moment of explosion in detail. Based on the field tests and numerical simulation results, it was found that calcareous sand has a smaller specific gravity due to its own characteristics, and the size of the explosion crater is larger than that of quartz sand at the same scaled burial depth. Obtained an empirical formula for crater in calcareous sand. Which can quickly predict the size of explosion crater and provide calculation basis for explosion resistant design in calcareous sand foundations. [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]

To effectively solve the problem that the measurement uncertainty evaluation result is not accurate and the calculation is complicated when the humidity measuring instrument is calibrated. The "Monte Carlo simulation method" (MCM) was proposed to evaluate the measurement uncertainty of humidity sensor calibration results. In this process, firstly, by analyzing the calibration process of humidity sensor, the measurement model that can accurately and completely reflect the actual measurement situation is constructed; then, design a performance testing method for the humidity generator to obtain parameter data that can truly reflect the performance of the current humidity generator; finally, taking the 55%RH calibration point as an example, by using the above measurement model and related parameters, single MCM method and adaptive MCM method were used to evaluate the measurement uncertainty of the humidity sensor calibration results. The evaluation results obtained are the same as: the best estimated value of humidity sensor measurement error ΔH = 0.01%RH, the standard uncertainty u(ΔH) = 0.14%RH, and the shortest coverage interval [ΔHlow, ΔHhigh] = [− 0.24%RH, 0.26%RH] when the coverage probability is 95%. Through this application experiment on the MCM method, it was found that compared to the GUM method, the MCM method can effectively improve the credibility of the measurement uncertainty results of the humidity sensor. Moreover, when the adaptive MCM method is applied to evaluate the measurement uncertainty of the humidity sensor, compared to the single MCM method, it can effectively reduce simulation times, reduce storage space resources, and improve evaluation efficiency. Prioritizing the adaptive MCM method in practical operation is recommended. [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 Bayer red mud (RM) and phosphogypsum (PG) accumulation have caused significant environmental contamination. However, practical and effective resource utilization technologies are still lacking currently. This work aims to develop ferric sulfoaluminate cement (FSAC) employing low-cost materials including Bayer red mud, phosphogypsum, and other materials. This method effectively improves the utilization rate of Bayer red mud and phosphogypsum. Under the premise of ensuring the performance of FSAC, the utilization rate of solid waste can reach up to 48.56%. The effects of different red mud dosages on cement mineral formation, workability, and mechanical properties are investigated. Then, untreated phosphogypsum is adopted as a retarder for FSAC, and the hydration process, working properties, mechanical properties, types of hydration products, and morphology of FSAC are explored. The results suggest that the crystal transformation of Ye'elemite C 4 A 3 S ¯ is promoted with the increase of Bayer red mud content. Cubic crystal system Ye'elemite C 4 A 3 S ¯ - c with higher hydration activity is generated, which increases the early strength of cement but greatly reduces the setting time, hindering the later strength growth. Untreated phosphogypsum can effectively delay the early hydration process of FSAC, prolong the setting time of cement, and increase the strength of FSAC in the later stage. When the dosage of Bayer red mud and phosphogypsum is 17.64% and 9.21%, respectively, with phosphogypsum dosage of 20%, the prepared FSAC has satisfactory mechanical properties, and the 3-day and 90-day compressive strengths are 34.6 MPa and 57.1 MPa, respectively. In addition, the study of heavy metal leaching indicates that the FSAC prepared by Bayer red mud, phosphogypsum, and other raw materials will generate no environment pollution, and the solidification of heavy metal elements in the cement slurry is superior. [ABSTRACT FROM AUTHOR]

Maintaining the balance and stability of the gut microbiota is crucial for the gut health and growth development of humans and animals. Bacillus licheniformis (B. licheniformis) has been reported to be beneficial to the gut health of humans and animals, whereas the probiotic effects of a new strain, B. licheniformis HD173, remain uncertain. In this study, nursery piglets were utilized as animal models to investigate the extensive impact of B. licheniformis HD173 on gut microbiota, metabolites, and host health. The major findings were that this probiotic enhanced the growth performance and improved the health status of the nursery piglets. Specifically, it reduced the level of pro-inflammatory cytokines IL-1β and TNF-α in the serum while increasing the level of IL-10 and SOD. In the gut, B. licheniformis HD173 reduced the abundance of pathogenic bacteria such as Mycoplasma, Vibrio, and Vibrio metschnikovii, while it increased the abundance of butyrate-producing bacteria, including Oscillospira, Coprococcus, and Roseburia faecis, leading to an enhanced production of butyric acid. Furthermore, B. licheniformis HD173 effectively improved the gut metabolic status, enabling the gut microbiota to provide the host with stronger metabolic abilities for nutrients. In summary, these findings provide scientific evidence for the utilization of B. licheniformis HD173 in the development and production of probiotic products for maintaining gut health in humans and animals. [ABSTRACT FROM AUTHOR]

Objective: To determine the optimal scan duration for ultrafast DCE-MRI in effectively differentiating benign from malignant breast lesions. Methods: The study prospectively recruited participants who underwent breast ultrafast DCE-MRI from September 2021 to March 2023. A 30-phase breast ultrafast DCE-MRI on a 3.0-T MRI system was conducted with a 4.5-s temporal resolution. Scan durations ranged from 40.5 s to 135.0 s, during which the analysis is performed at three-phase intervals, forming eight dynamic sets (scan duration [SD]40.5s: 40.5 s, SD54s: 54.0 s, SD67.5s: 67.5 s, SD81s: 81.0 s, SD94.5s: 94.5 s, SD108s: 108.0 s, SD121.5s: 121.5 s, and SD135s: 135.0 s). Two ultrafast DCE-MRI parameters, maximum slope (MS) and initial area under the curve in 60 s (iAUC), were calculated for each dynamic set and compared between benign and malignant lesions. Areas under the receiver operating characteristic curve (AUCs) were used to assess their diagnostic performance. Results: A total of 140 women (mean age, 47 ± 11 years) with 151 lesions were included. MS and iAUC from eight dynamic sets exhibited significant differences between benign and malignant lesions (all p < 0.05), except iAUC at SD40.5s. The AUC of MS (AUC = 0.804) and iAUC (AUC = 0.659) at SD67.5s were significantly higher than their values at SD40.5s (AUC = 0.606 and 0.516; corrected p < 0.05). No significant differences in AUCs for MS and iAUC were observed from SD67.5s to SD135s (all corrected p > 0.05). Conclusions: Ultrafast DCE-MRI with a 67.5-s scan duration appears optimal for effectively differentiating malignant from benign breast lesions. Critical relevance statement: By evaluating scan durations (40.5–135 s) and analyzing two ultrafast DCE-MRI parameters, we found a scan duration of 67.5 s optimal for discriminating between these lesions and offering a balance between acquisition time and diagnostic efficacy. Key Points: Ultrafast DCE-MRI can effectively differentiate malignant from benign breast lesions. A minimum of 67.5-sec ultrafast DCE-MRI scan duration is required to differentiate benign and malignant lesions. Extending the scan duration beyond 67.5 s did not significantly improve diagnostic accuracy. [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]

Cell division cycle 23 (CDC23) is a component of the tetratricopeptide repeat (TPR) subunit in the anaphase-promoting complex or cyclosome (APC/C) complex, which participates in the regulation of mitosis in eukaryotes. However, the regulatory model and mechanism by which the CDC23 gene regulates muscle production in pigs are largely unknown. In this study, we investigated the expression of CDC23 in pigs, and the results indicated that CDC23 is widely expressed in various tissues and organs. In vitro cell experiments have demonstrated that CDC23 promotes the proliferation of myoblasts, as well as significantly positively regulating the differentiation of skeletal muscle satellite cells. In addition, Gene Set Enrichment Analysis (GSEA) revealed a significant downregulation of the cell cycle pathway during the differentiation process of skeletal muscle satellite cells. The protein–protein interaction (PPI) network showed a high degree of interaction between genes related to the cell cycle pathway and CDC23. Subsequently, in differentiated myocytes induced after overexpression of CDC23, the level of CDC23 exhibited a significant negative correlation with the expression of key factors in the cell cycle pathway, suggesting that CDC23 may be involved in the inhibition of the cell cycle signaling pathway in order to promote the differentiation process. In summary, we preliminarily determined the function of CDC23 with the aim of providing new insights into molecular regulation during porcine skeletal muscle development. [ABSTRACT FROM AUTHOR]

Deep neural networks have achieved significant success in the artificial intelligence community and various downstream tasks. They encode images or texts into dense feature representations and are supervised by a large amount of labeled data. Due to the expensiveness of high-quality labeled data, a huge number of easy-to-access instances are collected to conduct supervised learning. However, they have not been annotated by experts and thus can contain numerous noisy instances, which will degrade the performance. To learn robust feature representations despite misleading noisy labels, we employ supervised contrastive learning to directly perform supervision in the hidden space, rather than in the prediction space like the prevalent cross-entropy loss function. However, cutting-edge noisy label learning methods with supervised contrastive learning always discard the data considered to be noisy, and thus cannot tolerate high-ratio noisy datasets. Therefore, we propose a novel training strategy named Supervised Contrastive Learning with Corrected Labels (Scl 2 ) to defend against the attack of noisy labels. Scl 2 corrects the noisy labels with an empirical small-loss assumption and conducts supervised contrastive learning using these corrected data. Specifically, we employ the generated soft labels as supervisory information to facilitate our implementation of supervised contrastive learning. This expansion of contrastive learning ensures the integrity of the supervisory information while effectively enhancing the learning process. In addition, samples sharing the same soft labels are treated as positive sample pairs, while those with different soft labels are considered to be negative sample pairs. With this strategy, the representations from neural networks keep the local discrimination in one mini-batch. Besides, we also employ a prototype contrastive learning technique to ensure global discrimination. Our Scl 2 has demonstrated excellent performance on numerous benchmark datasets, showcasing its effectiveness in various standardized evaluation scenarios. Additionally, our model has proven to be highly valuable when applied to real-world noisy datasets. [ABSTRACT FROM AUTHOR]

Umbilical hernia (UH) is a prevalent congenital disorder in pigs, resulting in considerable economic losses and severe animal welfare issues. In the present study, we conducted a genome‐wide association study (GWAS) using the GeneSeek 50K Chip in 2777 pigs (Duroc, n = 1267; Landrace, n = 696; and Yorkshire, n = 814) to explore the candidate genes underlying the risk of umbilical hernia in pigs. After quality control analyses, 2748 animals and 48 524 single nucleotide polymorphisms (SNPs) were retained for subsequent GWAS analysis using the FarmCPU model. The heritability of umbilical hernias was estimated to 0.51 ± 0.04, indicating a reasonable basis for investigating genetic markers associated with this disorder. We identified 54 SNPs and 517 candidate genes that showed significant associations with susceptibility to umbilical hernia across the combined population of the three pig breeds. Gene enrichment analyses highlighted several crucial pathways for platelet degranulation, inflammatory mediator regulation of TRP channels and ion transport. These findings provide further insights into the underlying genetic architecture of umbilical hernias in pigs. [ABSTRACT FROM AUTHOR]

Traffic sign poles are crucial components of the highway system, and their maintenance mainly relies on the subjective judgment of staff, which is low-efficiency and will lead to mistaken inspections. This paper proposes a convenient and effective method to monitor the health of sign poles by using a mobile phone. It is unknown whether a mobile phone can be used as a conventional acceleration sensor. Therefore, the performance of the mobile phone was initially tested to ensure its suitability for acquiring the acceleration data of the traffic sign pole. The results show that the acceleration sensor of mobile phones is high-performance and can be used as a traditional acceleration sensor under the similar sampling frequency. The mobile phone can measure the 1st, 2nd and 5th natural frequencies of the traffic sign pole. Although the 1st and 5th natural frequencies have a large error with simulation, the 2nd natural frequency is accurate and stable. The looseness of the base connection greatly impacts on the 2nd natural frequency, which can be used as a measure of the loose base connection. The 2nd natural frequency was measured for 21 times and found to conform to a normal distribution. The relationship between the 2nd natural frequency and base connection loosening was investigated, which fitted to the equation. The fitting result is good and can be used to predict the degree of sign pole base connection loosening. Therefore, the mobile phone based convenient health monitoring method for traffic sign poles is feasible. [ABSTRACT FROM AUTHOR]

Accurate and high-throughput identification of the initial anthesis of soybean varieties is important for the breeding and screening of high-quality soybean cultivars in field trials. The objectives of this study were to identify the initial day of anthesis (IADAS) of soybean varieties based on remote sensing multispectral time-series images acquired by unmanned aerial vehicles (UAVs), and analyze the differences in the initial anthesis of the same soybean varieties between two different climatic regions, Shijiazhuang (SJZ) and Xuzhou (XZ). First, the temporal dynamics of several key crop growth indicators and spectral indices were analyzed to find an effective indicator that favors the identification of IADAS, including leaf area index (LAI), above-ground biomass (AGB), canopy height (CH), normalized-difference vegetation index (NDVI), red edge chlorophyll index (CIred edge), green normalized-difference vegetation index (GNDVI), enhanced vegetation index (EVI), two-band enhanced vegetation index (EVI2) and normalized-difference red-edge index (NDRE). Next, this study compared several functions, like the symmetric gauss function (SGF), asymmetric gauss function (AGF), double logistic function (DLF), and fourier function (FF), for time-series curve fitting, and then estimated the IADAS of soybean varieties with the first-order derivative maximal feature (FDmax) of the CIred edge phenology curves. The relative thresholds of the CIred edge curves were also used to estimate IADAS, in two ways: a single threshold for all of the soybean varieties, and three different relative thresholds for early, middle, and late anthesis varieties, respectively. Finally, this study presented the variations in the IADAS of the same soybean varieties between two different climatic regions and discussed the probable causal factors. The results showed that CIred edge was more suitable for soybean IADAS identification compared with the other investigated indicators because it had no saturation during the whole crop lifespan. Compared with DLF, AGF and FF, SGF provided a better fitting of the CIred edge time-series curves without overfitting problems, although the coefficient of determination (R2) and root mean square error (RMSE) were not the best. The FDmax of the SGF-fitted CIred edge curve (SGF_CIred edge) provided good estimates of the IADAS, with an RMSE and mean average error (MAE) of 3.79 days and 3.00 days, respectively. The SGF-fitted_CIred edge curve can be used to group the soybean varieties into early, middle and late groups. Additionally, the accuracy of the IADAS was improved (RMSE = 3.69 days and MAE = 3.09 days) by using three different relative thresholds (i.e., RT50, RT55, RT60) for the three flowering groups compared to when using a single threshold (RT50). In addition, it was found that the IADAS of the same soybean varieties varied greatly when planted in two different climatic regions due to the genotype–environment interactions. Overall, this study demonstrated that the IADAS of soybean varieties can be identified efficiently and accurately based on UAV remote sensing multispectral time-series data. [ABSTRACT FROM AUTHOR]

Smart fluorescent materials that can respond to environmental stimuli are of great importance in the fields of information encryption and anti‐counterfeiting. However, traditional fluorescent materials usually face problems such as lack of tunable fluorescence and insufficient surface‐adaptive adhesion, hindering their practical applications. Herein, inspired by the glowing sucker octopus, we present a novel strategy to fabricate a reversible fluorescent eutectogel with high transparency, adhesive and self‐healing performance for conformal information encryption and anti‐counterfeiting. Using anthracene as luminescent unit, the eutectogel exhibits photoswitchable fluorescence and can therefore be reversibly written/erased with patterns by non‐contact stimulation. Additionally, different from mechanically irreversible adhesion via glue, the eutectogel can adhere to various irregular substrates over a wide temperature range (−20 to 65 °C) and conformally deform more than 1000 times without peeling off. Furthermore, by exploiting surface‐adaptive adhesion, high transparency and good stretchability of the eutectogel, dual encryption can be achieved under UV and stretching conditions to further improve the security level. This study should provide a promising strategy for the future development of advanced intelligent anti‐counterfeiting materials. [ABSTRACT FROM AUTHOR]

Smart fluorescent materials that can respond to environmental stimuli are of great importance in the fields of information encryption and anti‐counterfeiting. However, traditional fluorescent materials usually face problems such as lack of tunable fluorescence and insufficient surface‐adaptive adhesion, hindering their practical applications. Herein, inspired by the glowing sucker octopus, we present a novel strategy to fabricate a reversible fluorescent eutectogel with high transparency, adhesive and self‐healing performance for conformal information encryption and anti‐counterfeiting. Using anthracene as luminescent unit, the eutectogel exhibits photoswitchable fluorescence and can therefore be reversibly written/erased with patterns by non‐contact stimulation. Additionally, different from mechanically irreversible adhesion via glue, the eutectogel can adhere to various irregular substrates over a wide temperature range (−20 to 65 °C) and conformally deform more than 1000 times without peeling off. Furthermore, by exploiting surface‐adaptive adhesion, high transparency and good stretchability of the eutectogel, dual encryption can be achieved under UV and stretching conditions to further improve the security level. This study should provide a promising strategy for the future development of advanced intelligent anti‐counterfeiting materials. [ABSTRACT FROM AUTHOR]

Soybeans are rich in high-quality protein and raw materials for producing hundreds of chemical products. Consequently, soybean cultivation has gained widespread prevalence across diverse geographic regions. Soybean breeding necessitates the development of early-, standard-, and late-maturing cultivars to accommodate cultivation at various latitudes, thereby optimizing the utilization of solar radiation. In the practical process of determining the maturity of soybean breeding materials within the breeding field, the ripeness is assessed based on three critical criteria: pod moisture content, leaf color, and the degree of leaf shedding. These parameters reflect the crown structure, physicochemical parameters, and reproductive organ changes in soybeans during the maturation process. Therefore, methods for analyzing soybean maturity at the breeding plot scale should match the standards of agricultural experts to the maximum possible extent. This study presents a hyperspectral remote sensing approach for monitoring soybean maturity. We collected five periods of unmanned aerial vehicle (UAV)-based soybean canopy hyperspectral digital orthophoto maps (DOMs) and ground-level measurements of leaf chlorophyll content (LCC), flavonoids (Flav), and the nitrogen balance index (NBI) from a breeding farm. This study explores the following aspects: (1) the correlations between soybean LCC, NBI, Flav, and maturity; (2) the estimation of soybean LCC, NBI, and Flav using Gaussian process regression (GPR), partial least squares regression (PLSR), and random forest (RF) regression techniques; and (3) the application of threshold-based methods in conjunction with normalized difference vegetation index (NDVI)+LCC and NDVI+NBI for soybean maturity monitoring. The results of this study indicate the following: (1) Soybean LCC, NBI, and Flav are associated with maturity. LCC increases during the beginning bloom period (P1) to the beginning seed period (P3) and sharply decreases during the beginning maturity period (P4) stage. Flav continues to increase from P1 to P4. NBI remains relatively consistent from P1 to P3 and then drops rapidly during the P4 stage. (2) The GPR, PLSR, and RF methodologies yield comparable accuracy in estimating soybean LCC (coefficient of determination (R2): 0.737–0.832, root mean square error (RMSE): 3.35–4.202 Dualex readings), Flav (R2: 0.321–0.461, RMSE: 0.13–0.145 Dualex readings), and NBI (R2: 0.758–0.797, RMSE: 2.922–3.229 Dualex readings). (3) The combination of the threshold method with NDVI < 0.55 and NBI < 8.2 achieves the highest classification accuracy (accuracy = 0.934). Further experiments should explore the relationships between crop NDVI, the Chlorophyll Index, LCC, Flav, and NBI and crop maturity for different crops and ecological areas. [ABSTRACT FROM AUTHOR]

Detection and counting of wheat heads are crucial for wheat yield estimation. To address the issues of overlapping and small volumes of wheat heads on complex backgrounds, this paper proposes the YOLOv7-MA model. By introducing micro-scale detection layers and the convolutional block attention module, the model enhances the target information of wheat heads and weakens the background information, thereby strengthening its ability to detect small wheat heads and improving the detection performance. Experimental results indicate that after being trained and tested on the Global Wheat Head Dataset 2021, the YOLOv7-MA model achieves a mean average precision (MAP) of 93.86% with a detection speed of 35.93 frames per second (FPS), outperforming Faster-RCNN, YOLOv5, YOLOX, and YOLOv7 models. Meanwhile, when tested under the three conditions of low illumination, blur, and occlusion, the coefficient of determination (R2) of YOLOv7-MA is respectively 0.9895, 0.9872, and 0.9882, and the correlation between the predicted wheat head number and the manual counting result is stronger than others. In addition, when the YOLOv7-MA model is transferred to field-collected wheat head datasets, it maintains high performance with MAP in maturity and filling stages of 93.33% and 93.03%, respectively, and R2 values of 0.9632 and 0.9155, respectively, demonstrating better performance in the maturity stage. Overall, YOLOv7-MA has achieved accurate identification and counting of wheat heads in complex field backgrounds. In the future, its application with unmanned aerial vehicles (UAVs) can provide technical support for large-scale wheat yield estimation in the field. [ABSTRACT FROM AUTHOR]

Above-ground biomass (AGB) serves as an indicator of crop growth status, and acquiring timely AGB information is crucial for estimating crop yield and determining appropriate water and fertilizer inputs. Unmanned Aerial Vehicles (UAVs) equipped with RGB cameras offer an affordable and practical solution for efficiently obtaining crop AGB. However, traditional vegetation indices (VIs) alone are insufficient in capturing crop canopy structure, leading to poor estimation accuracy. Moreover, different flight heights and machine learning algorithms can impact estimation accuracy. Therefore, this study aims to enhance wheat AGB estimation accuracy by combining VIs, crop height, and texture features while investigating the influence of flight height and machine learning algorithms on estimation. During the heading and grain-filling stages of wheat, wheat AGB data and UAV RGB images were collected at flight heights of 30 m, 60 m, and 90 m. Machine learning algorithms, including Random Forest Regression (RFR), Gradient Boosting Regression Trees (GBRT), Ridge Regression (RR), Least Absolute Shrinkage and Selection Operator (Lasso) and Support Vector Regression (SVR), were utilized to construct wheat AGB estimation models. The research findings are as follows: (1) Estimation accuracy using VIs alone is relatively low, with R2 values ranging from 0.519 to 0.695. However, combining VIs with crop height and texture features improves estimation accuracy, with R2 values reaching 0.845 to 0.852. (2) Estimation accuracy gradually decreases with increasing flight height, resulting in R2 values of 0.519–0.852, 0.438–0.837, and 0.445–0.827 for flight heights of 30 m, 60 m, and 90 m, respectively. (3) The choice of machine learning algorithm significantly influences estimation accuracy, with RFR outperforming other machine learnings. In conclusion, UAV RGB images contain valuable crop canopy information, and effectively utilizing this information in conjunction with machine learning algorithms enables accurate wheat AGB estimation, providing a new approach for precision agriculture management using UAV remote sensing technology. [ABSTRACT FROM AUTHOR]

Crop nitrogen content inversion based on UAV (Unmanned Aerial Vehicle) hyperspectral data is vital for addressing global food supply challenges. Many studies have estimated nitrogen content via simple linear regression using a single vegetation index, multiple vegetation indices, hyperspectral representation, and simple structural transformation. Utilizing UAV hyperspectral data of the winter wheat canopy and leveraging the benefits of fractional differential processing to enhance spectral details, traditional hyperspectral vegetation indices were established, and characteristic parameters derived from spectral position and canopy area were extracted. Then, winter wheat plant nitrogen content models with different spectral information characteristics were highlighted, optimally selected and verified. Results reveal that when the original canopy spectrum is processed using the fractional differential, the association between hyperspectral reflectance and nitrogen levels in winter wheat plants can be effectively enhanced. Fractional differential spectra represent outstanding effects on refining spectral details. The findings provide valuable insights into the potential of hyperspectral fractional differential spectra to enhance the precision of nitrogen estimation in winter wheat. [ABSTRACT FROM AUTHOR]

Crop chlorophyll content measuring plays a vital role in monitoring crop growth and optimizing agricultural inputs such as water and fertilizer. However, traditional methods for measuring chlorophyll content primarily rely on labor-intensive chemical analysis. These methods not only involve destructive sampling but also are time-consuming, often resulting in obtaining monitoring results after the optimal growth period of crops. Unmanned aerial vehicle (UAV) remote sensing technology offers the potential for rapidly acquiring chlorophyll content estimations over large areas. Currently, most studies only utilize single features from UAV data and employ traditional machine learning algorithms to estimate chlorophyll content, while the potential of multisource feature fusion and stacking ensemble learning in chlorophyll content estimation research remains largely unexplored. Therefore, this study collected UAV spectral features, thermal features, structural features, as well as chlorophyll content data during maize jointing, trumpet, and big trumpet stages, creating a multisource feature dataset. Subsequently, chlorophyll content estimation models were built based on four machine learning algorithms, namely, ridge regression (RR), light gradient boosting machine (LightGBM), random forest regression (RFR), and stacking ensemble learning. The research results demonstrate that (1) the multisource feature fusion approach achieves higher estimation accuracy compared to the single-feature method, with R2 ranging from 0.699 to 0.754 and rRMSE ranging from 8.36% to 9.47%; and (2) the stacking ensemble learning outperforms traditional machine learning algorithms in chlorophyll content estimation accuracy, particularly when combined with multisource feature fusion, resulting in the best estimation results. In summary, this study proves the effective improvement in chlorophyll content estimation accuracy through multisource feature fusion and stacking ensemble learning. The combination of these methods provides reliable estimation of chlorophyll content using UAV remote sensing technology and brings new insights to precision agriculture management in this field. [ABSTRACT FROM AUTHOR]

Loess environmental parameters reflect regional environmental evolution and provide basis for regional environmental change studies. Loess samples from different soil layers in Zhengzhou were collected, and their hyperspectral data measured. The characteristics of the original spectra, first-order differential spectra, second-order differential spectra, de-enveloping line spectra, and reciprocal logarithm spectra were analysed and correlated with environmental parameters, including mean particle size, standard deviation, Fe2O3, CaO, MgO, and total organic carbon content, based on partial least squares regression. Inversion models were constructed. After transformation, the correlations between the various spectra and the environmental parameters were enhanced. Among them, the first-order and second-order differential spectra showed the greatest correlation with average particle size (correlation coefficient: 0.74). The correlation coefficients of the order differential spectrum with standard deviation, Fe2O3, and CaO were − 0.69, 0.67, and 0.75, respectively; the accuracy grade of the original spectrum inversion model was D. After spectral transformation, it was mostly improved; the best inversion model accuracy grade for standard deviation was C, that of the other parameters was B. Partial transformation is the optimal spectral transformation to establish an inversion model for these environmental parameters. This provides a new soil parameter inversion and monitoring method using hyperspectral remote sensing technology. [ABSTRACT FROM AUTHOR]

Nitrogen content is a crucial index for crop growth diagnosis and the exact estimation of nitrogen content is of great significance for grasping crop growth status in real-time. This paper takes winter wheat as the study object and the precision agriculture demonstration area of the Jiaozuo Academy of Agricultural and Forestry Sciences in Henan Province as the research area. The hyperspectral reflectance data of the wheat canopy in different growth periods are obtained with the ASD ground object hyperspectral instrument, and the original canopy spectral data are preprocessed by fractional differential and continuous wavelet transform; then, the vegetation indices are established, correlation analysis with nitrogen content is conducted, and the fractional differential spectra are selected; finally, based on the wavelet energy coefficient and the vegetation indices with strong correlations, the methods of support vector machine (SVM), ridge regression, stepwise regression, Gaussian process regression (GPR), and the BP neural network are used to construct the estimation model for nitrogen content in wheat at different growth stages. By adopting the  R 2  and root mean square error (RMSE) indices, the best nitrogen content estimation model at every growth stage is selected. The overall analysis of the nitrogen content estimation effect indicated that for the four growth periods, the maximum modeling and validation  R 2  of the nitrogen content estimation models of the SVM, ridge regression, stepwise regression, GPR, and BP neural network models reached 0.95 and 0.93, the average reached 0.76 and 0.71, and the overall estimation effect was good. The average values of the modeling and validation  R 2  of the nitrogen content estimation model at the flag picking stage were 0.85 and 0.81, respectively, which were 37.10% and 44.64%, 1.19% and 3.85%, and 14.86% and 17.39% higher than those at the jointing stage, flowering stage, and filling stage, respectively. Therefore, the model of the flag picking stage has higher estimation accuracy and a better estimation effect on the nitrogen content. For the different growth stages, the optimal estimation models of nitrogen content were different. Among them, continuous wavelet transform combined with the BP neural network model can be the most effective method for estimating the N content in wheat at the flagging stage. The paper provides an effective method for estimating the nitrogen content in wheat and a new idea for crop growth monitoring. [ABSTRACT FROM AUTHOR]

The in situ passivation is considered a feasible and effective remediation for moderately and lowly heavy-metal-polluted soil. Under natural precipitation, the continuous leaching characteristics of heavy metals with the immobilizers are unclear and require more study for practical applications. In this work, calcium superphosphate (CS) and activated carbon (AC) were added as stabilizers to passivate Cd in hydromorphic paddy (HP) and gray fluvo-aqvic (GF) soils. Simulated acid precipitation at different pH and salt concentrations were used as eluents. The leachate and soil were collected to analyze the stability and fraction changes of Cd. The results showed that with the eluents from 120–200 mL to 200–250 mL, the leached Cd increased and reached the highest concentration and then gradually decreased. Comparative analysis showed that the two passivators in GF soil had higher application values than those in HP soil, while AC showed 3–77 times the capacity of CS in multiple conditions. The addition of AC conversed the exchangeable and oxidized states of Cd to the residual and reduced states, while the addition of CS conversed the exchangeable and reduced states of Cd to the residual and oxidized states. The above results can provide important references for the immobilization of heavy metal cations in soil and the sustainable utilization of soil. [ABSTRACT FROM AUTHOR]

Myogenic differentiation is a complex biological process that is regulated by multiple factors, among which long noncoding RNAs (lncRNAs) play an essential role. However, in-depth studies on the regulatory mechanisms of long noncoding RNAs (lncRNAs) in myogenic differentiation are limited. In this study, we characterized the role of the novel lncRNA TCONS_00323213, which is upregulated during porcine skeletal muscle satellite cell (PSC) differentiation in myogenesis. We found that TCONS_00323213 affected the proliferation and differentiation of PSC in vitro. We performed quantitative polymerase chain reaction (qPCR), 5-ethynyl-20-deoxyuridine (EdU), western blotting, immunofluorescence staining, pull-down assays, and cleavage under targets and tagmentation (CUT and Tag) assays to clarify the effects and action mechanisms of TCONS_00323213. LncRNA TCONS_00323213 inhibited myoblast proliferation based on analyses of cell survival rates during PSC proliferation. Functional analyses revealed that TCONS_00323213 promotes cell differentiation and enhances myogenin (MyoG), myosin heavy chain (MyHC), and myocyte enhancer factor 2 (MEF2C) during myoblast differentiation. As determined by pull-down and RNA immunoprecipitation (RIP) assays, the lncRNA TCONS_00323213 interacted with PBX/Knotted Homeobox 2 (PKNOX2). CUT and Tag assays showed that PKNOX2 was significantly enriched on the MyoG promoter after lncRNA TCONS_00323213 knockdown. Our findings demonstrate that the interaction between lncRNA TCONS_00323213 and PKNOX2 relieves the inhibitory effect of PKNOX2 on the MyoG promoter, increases its expression, and promotes PSC differentiation. This novel role of lncRNA TCONS_00323213 sheds light on the molecular mechanisms by which lncRNAs regulate porcine myogenesis. [ABSTRACT FROM AUTHOR]

Purpose: D-dimer levels are associated with tumor progression and prognosis in various cancers. However, there are few research about the relationship between D-dimer and neuroblastoma (NB). This study assessed the relationships of D-dimer levels with clinical features and overall survival (OS) in patients with NB. Methods: Information about the clinical features of 365 patients and the prognosis of 301 patients was collected. The relationship between D-dimer levels and clinical features or OS was analyzed. We constructed the risk score based on Cox regression analysis and verified the predictive efficacy of the model through ROC curve and calibration curve. Results: The results showed that D-dimer levels were significantly increased in patients with nonmediastinal tumor, tumor larger than 10 cm, stage 3–4 disease, bone marrow metastasis, unfavorable histology, bone metastasis, NMYC amplification, and the high-risk group (all P < 0.05). The Kaplan–Meier survival analysis showed that there were significant differences in 3- and 5-year OS (87.4% vs. 32.3%, 79.3% vs. 32.3%, P < 0.0001) between the low D-dimer and high D-dimer groups. In the high-risk group, the OS of high D-dimer was significantly lower than that of low D-dimer (P < 0.0001). All cases were divided into the training cohort (N = 211) and the validation cohort (N = 90). Multivariate analysis further suggested that D-dimer level, bone metastasis, and NMYC status were independent prognostic factors for OS (all P < 0.05). Based on the above three factors, we constructed the risk score in the training cohort. Survival analysis showed that compared with the other groups, the group with 11 scores had the worst prognosis (3-year OS 0%, P < 0.0001). The time-dependent ROC analysis and calibration curve indicated that the risk score had good accuracy. Conclusions: Patients with high D-dimer levels tended to have unfavorable clinical characteristics and poor prognosis. [ABSTRACT FROM AUTHOR]

In order to further improve the energy utilization of the range extender electric vehicle (REEV), the energy management strategy of the REEV under different heat demands and operating temperatures is studied. Firstly, for reducing the extra fuel consumption caused by engine operating temperature, the influence of engine lubricating oil temperature on the equivalence factor is analyzed, and a novel method is proposed, which can determine the equivalence factor by three parameters, including engine temperature, operating condition type, and battery state of charge (SOC). Secondly, to reduce the influence of frequent engine start/stop on fuel consumption, a method to establish the penalty factor based on battery SOC is proposed. Finally, the engine temperature rise law under low-temperature environment and the compressor drive mode determination method under high-temperature environment are studied according to the heat demand under different ambient temperatures. An adaptive equivalent consumption minimization strategy (ECMS) considering the effect of temperature under different heat demands is proposed on the basis of the above research. The simulation under the worldwide harmonized light vehicle test cycle (WLTC) drive cycle reveals that by using the adaptive ECMS, the vehicle operating cost is reduced by 5.69% in normal temperature environment, 0.53%-2.39% in a low-temperature environment, and 0.2%-2.01% in a high-temperature environment. [ABSTRACT FROM AUTHOR]

The correction notice for the article "Extraction of maize canopy temperature and variation factor analysis based on multi-source unmanned aerial vehicle remote sensing data" in Earth Science Informatics addresses an error in the work unit/affiliation of Liting Zhai. The correction clarifies that Liting Zhai is affiliated with Guangdong Aerospace Hongtu Information Technology Co., Ltd., Guangzhou 510000, China, and her affiliation link should be updated accordingly. The correction has been made to the original article, and Springer Nature maintains neutrality regarding jurisdictional claims and institutional affiliations. [Extracted from the article]

Leaf water content (LWC) is one of the important indicators of crop health. It plays an important role in the physiological process of leaves, participates in almost all physiological processes of crops, and is of great significance to the survival and growth of crops. Based on the hyperspectral (350–1350 nm) and LWC data (jointing, booting, flowering, and filling periods) of winter wheat in 2020 and 2021, this work proposed to transform and process the hyperspectral data by adopting fractional order differential and continuous wavelet transform, and took a differential spectrum, wavelet coefficients, and mixed variables (differential spectrum and wavelet coefficients) as input variables of the model and adopted Gaussian process regression (GPR), classification and regression decision tree (CART), and artificial neural network (ANN) methods to estimate the LWC of wheat in different growth periods. The results indicated that fractional differential and continuous wavelet transform could highlight the spectral characteristics of winter wheat canopy and improve its correlation with LWC. The three model variables had the best estimation effect on LWC in the flowering period, and the average values of R2 were 0.86 and 0.87 in modeling and verification, which indicated that the flowering period could be used as the best estimation period for LWC. Compared with the differential spectrum and wavelet coefficients, LWC estimation based on mixed variables performed best. The average values of R2 in modeling and verification were 0.78 and 0.79. Among them, the ANN model had the highest estimation accuracy, and the R2 in modeling and verification could reach 0.92 and 0.91. This showed that fractional differential and continuous wavelet transform could effectively promote the sensitivity of spectral information to LWC and enhance the prediction ability and stability of wheat LWC. The outcomes of the present study have the potential to provide new ideas for the water monitoring of crops. [ABSTRACT FROM AUTHOR]

As predators, the macronutrients spiders extract from their prey play important roles in their mating and reproduction. Previous studies of macronutrients on spider mating and reproduction focus on protein, the potential impact of prey lipid content on spider mating and reproduction remains largely unexplored. Here, we tested the influence of prey varying in lipid content on female mating, sexual cannibalism, reproduction, and offspring fitness in the wolf spider Pardosa pseudoannulata. We acquired 2 groups of fruit fly Drosophila melanogaster that differed significantly in lipid but not protein content by supplementing cultural media with a high or low dose of sucrose on which the fruit flies were reared (HL: high lipid and LL: low lipid). Subadult (i.e. 1 molt before adult) female spiders that fed HL flies matured with significantly higher lipid content than those fed LL flies. We found that the mated females fed with HL flies significantly shortened pre-oviposition time and resulted in a significantly higher fecundity. However, there was no significant difference in female spiders varying in lipid content on other behaviors and traits, including the latency to courtship, courtship duration, mating, copulation duration, sexual cannibalism, offspring body size, and survival. Hence, our results suggest that the lipid content of prey may be a limiting factor for female reproduction, but not for other behavioral traits in the wolf spiders P. pseudoannulata. [ABSTRACT FROM AUTHOR]

Nitrogen (N) is an important factor limiting crop productivity, and accurate estimation of the N content in winter wheat can effectively monitor the crop growth status. The objective of this study was to evaluate the ability of the unmanned aerial vehicle (UAV) platform with multiple sensors to estimate the N content of winter wheat using machine learning algorithms; to collect multispectral (MS), red-green-blue (RGB), and thermal infrared (TIR) images to construct a multi-source data fusion dataset; to predict the N content in winter wheat using random forest regression (RFR), support vector machine regression (SVR), and partial least squares regression (PLSR). The results showed that the mean absolute error (MAE) and relative root-mean-square error (rRMSE) of all models showed an overall decreasing trend with an increasing number of input features from different data sources. The accuracy varied among the three algorithms used, with RFR achieving the highest prediction accuracy with an MAE of 1.616 mg/g and rRMSE of 12.333%. For models built with single sensor data, MS images achieved a higher accuracy than RGB and TIR images. This study showed that the multi-source data fusion technique can enhance the prediction of N content in winter wheat and provide assistance for decision-making in practical production. [ABSTRACT FROM AUTHOR]

Manufacturing enterprises are confronted with the historic opportunity and challenge of balancing green transformation with economic development to achieve the goal of carbon neutrality. Some advanced companies are increasingly pursuing green development and innovation by expanding and optimizing the use of digital technology. In this study, we employ Chinese listed manufacturing companies from 2013–2019 as the research sample and examine the mechanism by which corporate digital optimization affects green technological innovation, as well as the mediating role of green R&D collaboration between the two. We also introduce external environmental orientation as a moderating variable. The results of fixed-effect Poisson model analysis are as follows. First, a positive correlation between digital optimization and green R&D collaboration indicates that scaling up digital optimization promotes green R&D collaboration. Second, we observe an inverted U-shaped relationship between green R&D collaboration and green technological innovation. Third, green R&D collaboration acts as a mediating factor between digital optimization and green technological innovation, and external environmental orientation moderates the relationship between digital optimization and green R&D collaboration. Fourth, the threshold effect results indicate that the optimal value of digital optimization projects is 10.167, with too many or too few projects impairing the effect of digital optimization on green technological innovation. All of the above results passed the robustness test. [ABSTRACT FROM AUTHOR]

Expectation propagation (EP) is a widely successful way to approximate the posteriors of complex Bayesian models. However, it suffers from expensive memory and time overheads, since it involves local approximations with locally specific messages. A recent art, namely averaged EP (AEP), upgrades EP by leveraging the average message effect on the posterior distribution, instead of the locally specific ones, so as to simultaneously reduce memory and time costs. In this paper, we extend AEP to a novel black-box expectation propagation (abbr. BBEP) algorithm, which can be directly applied to many Bayesian models without model-specific derivations. We leverage three ideas of black-box learning, leading to three versions of BBEP, referred to as BBEP m , BBEP g and BBEP o with Monte Carlo moment matching, Monte Carlo gradients and objective of AEP, respectively. For variance reduction, the importance sampling is used, and the proposal distribution selection as well as high dimensionality setting is discussed. Furthermore, we develop online versions of BBEP for optimization speedup given large-scale data sets. We empirically compare BBEP against the state-of-the-art black-box baseline algorithms on both synthetic and real-world data sets. Experimental results demonstrate that BBEP outperforms the baseline algorithms and it is even on a par with analytical solutions in some settings. [ABSTRACT FROM AUTHOR]

Remote sensing estimation of crop nitrogen content allows real-time monitoring of growth to develop scientific methods. However, most of the current remote sensing estimates of crop nitrogen contents have limitations in accurately reflecting the vertical distribution of nutrients in plants. Firstly, the original hyperspectrum is first-order differential (FD), second-order differential (SD), and continuous removal (CR), and the corresponding sensitive bands were screened by correlation analysis in this paper. Then, the spectral reflectance, vegetation indices, and wavelet coefficients were used as input features to construct models for estimating nitrogen content of flag leaf, upper 1 leaf, upper 2 leaf, upper 3 leaf, and upper 4 leaf based on partial least squares regression (PLSR), support vector machine (SVM), random forest (RF), and multiple linear regression (MLR), respectively. The results showed that the accuracy of nitrogen content prediction based on wavelet coefficients was the highest. The combination of MLR and SVM with wavelet coefficients had high accuracy and robustness in the prediction of nitrogen content at different leaf positions. Additionally, the prediction accuracy of nitrogen gradually increased as the leaf position of winter wheat increased. The study can provide technical support for remote sensing estimation of nutrient elements at vertical leaf position of crops. The study can provide a reference for prediction of other crops. [ABSTRACT FROM AUTHOR]

Drainage rigid piles represent a commonly used antiliquefaction treatment for saturated sand foundations. Small-scale model tests on the liquefaction-resistant characteristics of drainage rigid piles subjected to blasting were conducted in this study. The effect of ordinary rigid piles and drainage rigid piles on the disposal of saturated sand foundations was investigated by using carbon dioxide (CO2) fracturing to generate explosive vibratory loads. The test results showed that the blast vibration load increased the relative density of the site by approximately 10% and that the relative density was proportional to the burial depth. The site vertical acceleration attenuations resulting from the two blasts were 35% and 45%, respectively, and the variation pattern of vertical acceleration on both sides of drainage rigid piles and ordinary piles is similar. Drainage rigid piles can dissipate more than 70% of the excess pore water pressure under liquefaction and can quickly accumulate excess pore water pressure and, likewise, quickly dissipate excess pore water pressure. The higher the excess pore water pressure at a site is, the more effective the disposal by a drainage rigid pile is. This field test provides a design reference for the engineering application of drainage rigid piles. [ABSTRACT FROM AUTHOR]

In this study, numerical simulations were carried out to analyze the influence of caves in different positions and shapes, in combination with structural planes, on the stability of the slope and the failure characteristics of a rock slope in a deep foundation pit with high inclination structural planes and cave development. The schemes for substituting a single karst cave for karst caves were constructed. Based on the penetration failure characteristics of karst caves between parallel structural planes, calculation methods for the safety factor of the rock foundation pits and the upper bound of the lateral pressure of supporting structures under the combined influence of the caves and structural planes have been developed, which can be used to assess the safety factor of a rock mass and to calculate the lateral pressure under complex geological conditions. The example of Xuzhou Metro Line 3 Sanhuan South Road Station is taken as an example to verify. The displacement calculation results show that the proposed rock and soil pressure calculation method is close to the actual measured value. [ABSTRACT FROM AUTHOR]

The growing demand for and supply of meat and meat products has led to a proportional increase in cases of meat adulteration. Adulterated meat poses serious economic and health consequences globally. Current laboratory methods for meat species identification require specialized equipment with limited field applications. This study developed an inexpensive, point-of-care Loop-Mediated Isothermal Amplification (LAMP)-CRISPR/Cas12a colorimetric assay to detect meat species using a Texas Red-labelled single-strand (ssDNA) reporter. As low as 1.0 pg/µL of the porcine NADH4, the chicken NADH dehydrogenase subunit 2 (ND2) and the duck D-loop genes was detectable under white, blue and ultraviolet light. The test turnaround time from DNA extraction to visualization was approximately 40 min. The assay accurately detected pure and mixed-meat products in the laboratory (n = 15) and during a pilot point-of-care test (n = 8) in a food processing factory. The results are 100% reproducible using lateral flow detection strips and the real-time PCR detection instrument. This technology is fully deployable and usable in any standard room. Thus, our study demonstrates that this method is a straightforward, specific, sensitive, point-of-care test (POCT) adaptable to various outlets such as customs, quarantine units and meat import/export departments. [ABSTRACT FROM AUTHOR]