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Abstract 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.

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.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.

Laser powder bed fusion (LPBF) Invar 36 alloy has attracted plenty of attention in cryogenic components or precision instruments due to its low coefficient of thermal expansion in a wide range of temperature. However, the LPBF Invar 36 has a significant anisotropic mechanical property due to the unique columnar grain morphology and pore feature. In this study, the influence of scanning speed on the anisotropic tensile properties were investigated, and the melt pool dynamics were analyzed by the numerical simulation. In this respect, tensile tests of specimens parallel and perpendicular to the building direction with a scanning speed from 200 to 1400 mm/s of LPBF Invar 36 alloy were conducted, and the microstructural influential factors on the anisotropic tensile properties were analyzed. Results show that as the volumetric energy density (VED) increases, the inhomogeneity of grain morphology decreases, while the pore feature changes from the lack-of-fusion (LOF) pore perpendicular to the building direction to the bubble-shaped spherical pore, which is resulted from the collapse of the key-holes. In this case, the grain orientation and size, as well as the dislocation density in the vicinity of pores are influenced by the pore morphology due to different thermal history. On the whole, the combined morphology of dislocation and stress concentration affected by the pore morphology are the main reason of the increased level of anisotropy in tensile properties.

Abstract 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.

BackgroundCyadox (CYA) is a derivative of quinoxaline 1,4-dioxide and a safe and effective synthetic antibacterial agent.ObjectiveThis study aimed to explore the drug transport in blood, distribution, depletion and hepatotoxicity of drugs in animals.MethodsThe 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.ResultsDuring 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.ConclusionThis 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.

IntroductionCrop height and above-ground biomass (AGB) serve as crucial indicators for monitoring crop growth and estimating grain yield. Timely and accurate acquisition of wheat crop height and AGB data is paramount for guiding agricultural production. However, traditional data acquisition methods suffer from drawbacks such as time-consuming, laborious and destructive sampling.MethodsThe current approach to estimating AGB using unmanned aerial vehicles (UAVs) remote sensing relies solely on spectral data, resulting in low accuracy in estimation. This method fails to address the ill-posed inverse problem of mapping from two-dimensional to three-dimensional and issues related to spectral saturation. To overcome these challenges, RGB and multispectral sensors mounted on UAVs were employed to acquire spectral image data. The five-directional oblique photography technique was utilized to construct the three-dimensional point cloud for extracting crop height.Results and DiscussionThis study comparatively analyzed the potential of the mean method and the Accumulated Incremental Height (AIH) method in crop height extraction. Utilizing Vegetation Indices (VIs), AIH and their feature combinations, models including Random Forest Regression (RFR), eXtreme Gradient Boosting (XGBoost), Gradient Boosting Regression Trees (GBRT), Support Vector Regression (SVR) and Ridge Regression (RR) were constructed to estimate winter wheat AGB. The research results indicated that the AIH method performed well in crop height extraction, with minimal differences between 95% AIH and measured crop height values were observed across various growth stages of wheat, yielding R2 ranging from 0.768 to 0.784. Compared to individual features, the combination of multiple features significantly improved the model’s estimate accuracy. The incorporation of AIH features helps alleviate the effects of spectral saturation. Coupling VIs with AIH features, the model’s R2 increases from 0.694-0.885 with only VIs features to 0.728-0.925. In comparing the performance of five machine learning algorithms, it was discovered that models constructed based on decision trees were superior to other machine learning algorithms. Among them, the RFR algorithm performed optimally, with R2 ranging from 0.9 to 0.93.ConclusionIn conclusion, leveraging multi-source remote sensing data from UAVs with machine learning algorithms overcomes the limitations of traditional crop monitoring methods, offering a technological reference for precision agriculture management and decision-making.

Abstract 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). 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. Graphical Abstract

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.

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.

Vapor pressure deficit (VPD) shows significant spatial and temporal variability in the context of global climate change, which is important for studying the implications of climate change on the structure and function of ecosystems to analyze the effects of VPD on vegetation dynamics. Spatial patterns of vegetation sensitivity to VPD have been recently investigated, however, the feedback of different vegetation types to VPD may vary depending on physiological characteristics, it is unclear how different types influence the sensitivity to VPD. In this study, the ERA5-Land reanalysis time-series dataset was used to analyze the spatial and temporal trends of VPD under different vegetation types. It was found that VPD showed an increasing trend in vegetated areas over the past 20 years with large spatial heterogeneity, generally increasing with drying conditions. On this basis, the spatial patterns of vegetation sensitivity to VPD and temporal trends in sensitivity were evaluated over the past 20 years in China using the enhanced vegetation index (EVI) and near-infrared reflectance of vegetation (NIRv) which can describe vegetation dynamics. The results show that the sensitivities under the two indices have high spatial consistency, with northeastern and central China showing positive sensitivities and southern China showing negative sensitivities, respectively. The positive sensitivities are relatively high for Deciduous Broadleaf Forests (DBF), Deciduous Needleleaf Forests (DNF), Grasslands (GL), and Croplands (CL) types, while the negative sensitivities are larger for Shrublands (SL) and Savannas (SA) types. Under different climatic zones, the sensitivity of CL and GL are independent of climatic zones (both showing positive), while the sensitivity of SL is negative in the Humid zone and positive in the Semi-Arid zone. Temporally, the sensitivity showed a slow increasing trend over the last 20 years. In terms of vegetation types, sensitivities of Evergreen Broadleaf Forests (EBF), DBF, GL and CL types showed a significant increasing trend (p

Abstract 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.

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.

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.

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.

In this work, the AlCoCrFeNi2.1 eutectic high-entropy alloys (EHEAs) were fabricated by laser additive manufacturing and subsequently heat-treated at various temperatures between 800 and 1200 °C to achieve excellent strength-plasticity synergy. The high solidification rate of selective laser melting (SLM) leads to instability of the eutectic growth front, resulting in a typical melt pool morphology of equiaxed and columnar crystals for AlCoCrFeNi2.1. The body-centered cubic (BCC) content of ∼82% in the SLM specimens leads to a higher tensile strength of ∼1175 MPa and poorer plasticity of ∼6.42%. After heat treatment, the BCC transforms to face-centered cubic (FCC) and the microstructure changes to a dendrite containing eutectic lamellar structure. As the temperature increases, Ni–Al aggregates, B2 phase particles precipitate and grow in the BCC matrix. Meanwhile, the uniform heating mode eliminates the orientation of the SLM melt pool structure, reduces the internal stress and dislocation density of the SLM alloy. In addition, high-temperature heating promotes the stable growth of lamellar structure. These combined effects allow the strength-plasticity trade-off to be achieved in the heat-treated specimen. The HT-1200 sample has a strength of 1065 MPa and a ductility of 21.97%.

The continuous retrieval of clear-sky land surface temperature (LST) holds paramount importance in monitoring vegetation temperature and assessing water stress conditions. Nonetheless, the extensive cloud cover results in a widespread lack of LST data, posing challenges in accurately forecasting LST in regions characterized by diverse vegetation types and complex terrains. Therefore, this paper proposes a synthetic aperture radar (SAR)- and digital elevation model (DEM)-integrated LST reconstruction model (SDX-LST) to generate realistic and high-spatial-resolution (30 m) clear-sky LST data. To assess the practicality and robustness of the SDX-LST model, this paper selects the study areas of Loess Plateau (LP), Qinghai-Tibet Plateau, Northeast China Plain, Nanling Mountains, and North China Plain in China, Desert Rock, Nevada in America, spanning a wide range of longitude and latitude and having obvious differences in topography, landforms, and vegetation. The analysis of the reconstruction results in different spatial location distributions, vegetation cover types, and multidate and time distributions throughout the year indicate that the SDX-LST model achieves excellent performance and high stability (with a mean absolute error lower than 2K). The SDX-LST predictions demonstrate a commendable level of consistency with the ERA5-hourly product and in situ data. We conclude that the integration of SAR and DEM within the SDX-LST model enables precise predictions of LST for various vegetation types and intricate terrains. The study quantitatively analyzes the effects of SAR and DEM on LST and introduces novel insights for exploring SAR-based reconstruction of LST.

IntroductionObtaining wheat growth information accurately and efficiently is the key to estimating yields and guiding agricultural development.MethodsThis paper takes the precision agriculture demonstration area of Jiaozuo Academy of Agriculture and Forestry in Henan Province as the research area to obtain data on wheat biomass, nitrogen content, chlorophyll content, and leaf area index. By using the coefficient of variation method, a Comprehensive Growth Monitoring Indicator (CGMI) was constructed to perform fractional derivative processing on drone spectral data, and correlation analysis was performed on the fractional derivative spectra with a single indicator and CGMI, respectively. Then, grey correlation analysis was carried out on differential spectral bands with high correlation, the grey correlation coefficients between differential spectral bands were calculated, and spectral bands with high correlation were screened and taken as input variables for the model. Next, ridge regression, random forest, and XGboost models were used to establish a wheat CGMI inversion model, and the coefficient of determination (R2) and root mean squared error (RMSE) were adopted for accuracy evaluation to optimize the wheat optimal growth inversion model.Results and discussionThe results of the study show that: using the data of wheat biomass, nitrogen content, chlorophyll content and leaf area index to construct the comprehensive growth monitoring indicators, the correlation between the wheat growth monitoring indicators and the spectra was calculated, and the results showed that the correlation between the comprehensive growth monitoring indicators and the single indicator correlation had different degrees of increase, and the growth rate could reach 82.22%. The correlation coefficient between the comprehensive growth monitoring indexes and the differential spectra reached 0.92 at the flowering stage, and compared with the correlation coefficient with the original spectra at the same period, the correlation coefficients increased to different degrees, which indicated that the differential processing of spectral data could effectively enhance the spectral correlation. The three models of Random Forest, Ridge Regression and XGBoost were used to construct the wheat growth inversion model with the best effect at the flowering stage, and the XGBoost model had the highest inversion accuracy when comparing in the same period, with the training and test sets reaching 0.904 and 0.870, and the RMSEs were 0.050 and 0.079, so that the XGBoost model can be used as an effective method of monitoring the growth of wheat. To sum up, this study demonstrates that the combination of constructing comprehensive growth monitoring indicators and differential processing spectra can effectively improve the accuracy of wheat growth monitoring, bringing new methods for precision agriculture management.

Long non-coding RNAs (lncRNAs) are involved in the process of muscle cell differentiation and play an important role. Previous studies have shown that lncRNA-MEG3 promotes the differentiation of porcine skeletal muscle satellite cells (PSCs), but the regulatory mechanism of MEG3 interaction with target protein has not been well studied. We demonstrated that MEG3 can bind dihydrolipoamide succinyltransferase (DLST) by RNA pull down and RIP-qPCR. Subsequently, knockdown and overexpression experiments showed that DLST promotes PSCs differentiation. Rescue experiments showed that the expression of DLST protein was significantly increased with MEG3 overexpression and decreased with MEG3 knockdown, while its mRNA expression was not changed. Furthermore, we have successfully predicted and validated that the transcription factor myogenic differentiation (MYOD) binds to the MEG3 core promoter though utilizing chromatin immunoprecipitation (CHIP) and luciferase reporter assays. The results indicated that MYOD acts as a transcription factor of MEG3 to promote MEG3 transcription. Knockdown of MEG3 in vivo indicated that MEG3 is involved in skeletal muscle regeneration. It is concluded that MYOD acts as a transcription factor to induce MEG3 expression. MEG3 acts as a molecular scaffold to bind and promote DLST protein expression. This paper provides a new molecular mechanism for MEG3 to promote the differentiation of PSCs.

Abstract 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.

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.

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.

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.

Neuroblastoma(NB) is the most common extracranial solid tumor in childhood, and it is now believed that some patients with NB have an underlying genetic susceptibility, which may be one of the reasons for the multiplicity of NB patients within a family line. Even within the same family, the samples show great variation and can present as ganglioneuroblastoma or even benign ganglioneuroma. The genomics of NB is still unclear and more in-depth studies are needed to reveal its key components. We first performed single-cell RNA sequencing(sc-RNAseq) analysis on clinical specimens of two family neuroblastoma(FNB) and four sporadic NB cases. A complete transcriptional profile of FNB was constructed from 18,394 cells from FNB, and we found that SDHD may be genetically associated with FNB and identified a prognostic related CAF subtype in FNB: Fib-4. Single-cell flux estimation analysis (scFEA) results showed that malignant cells were associated with arginine spermine, oxaloacetate and hypoxanthine, and that malignant cells metabolize lactate at lower levels than T cells. Our study provides new resources and ideas for the development of the genomics of family NB, and the mechanisms of cell-to-cell interactions and communication and the metabolic landscape will provide new therapeutic targets.

Objective To assess the efficacy and safety of dual ultrasound-guided (DUG) totally implantable venous access port (TIVAP) implantation (namely, using ultrasound-guided percutaneous puncture with transesophageal echocardiography-guided catheterization) via the right internal jugular vein (IJV) in pediatric patients with cancer.Methods Fifty-five children with cancer requiring chemotherapy underwent DUG-TIVAP implantation via the right IJV. Clinical data were recorded, including the procedure success rate, first attempt success rate, and perioperative and postoperative complications.Results All 55 cases were successfully operated on. The first puncture success rate was 100%. The operation time was 22–41 min, with a mean time of 30.8±5.5 min. The mean TIVAP implantation time was 253±145 days (range 42–520 days). There were no perioperative complications. The postoperative complication rate was 5.4% (3/55), including skin infections around the port in one case, catheter-related infection in one case, and fibrin sheath formation in one case. The ports were all preserved after anti-infection or thrombolytic therapy. No unplanned port withdrawal was recorded in this study.Conclusions DUG-TIVAP implantation is a technique with a high success rate and a low complication rate; therefore, it provides an alternative for children with cancer. Further randomized controlled studies are needed to confirm the efficacy and safety of DUG-TIVAP via the right IJV in children.

Abstract The study of target genes for the spontaneous regression phenomenon of neuroblastoma (NB) is still unclear. Common differentially expressed genes (DEGs) were identified by differential expression analysis in both public databases for the stage 4 death group and stage 4S survival group. The DEGs were ranked by constructing protein–protein interaction (PPI) network as well as calculating betweenness centrality (BC) values, and the relationship with NB prognosis was determined by performing univariate analysis, multifactor analysis, and lasso regression analysis on the top 10 genes and possibly correlated with spontaneous regression of NB. We identified a total of 173 DEGs, including 143 upregulated genes and 30 downregulated genes. PPI network showed a rich interaction between DEGs. We ranked the DEGs by calculating BC values and showed the top 10 genes, followed by univariate and multifactorial analyses, which showed that GRIA2, NTRK1, SCN9A, SLC18A2, CNR1, PIK3R1, and DGKB were associated with good prognosis, and CNR1 was the most closely associated with prognosis among them. By lasso regression analysis, we constructed a four‐gene risk score formula. We drew a nomogram to use in clinical work and two newly identified genes associated with good prognosis in NB: CNR1 and GIRA2.

Web-building spiders that build detritus-based bell-shaped cobwebs are model organisms for studies on behavioral plasticity because their web architecture components are easily quantified and behavioral investments are clearly separated. We investigated the web architectures and behavioral investments of the cobwebs built by Campanicola campanulata under different weight (heavy, medium, and light) detritus to research its cobweb architecture variation and analyzed the investment trade-off between foraging and defense. The results showed that spiders could actively choose lighter detritus to build retreats to reduce material and energy cost. There was a clear trade-off between defense and foraging investment of spiders choosing different weight detritus for their webs. The total length of gumfooted lines (foraging investment) was longer for the spiders that chose lighter detritus, but the energy expenditure during web-building (defense investment) was higher for the spiders that chose heavier detritus.

Abstract 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 Crop growth monitoring and yield estimate information can be obtained via appropriate metrics such as the leaf area index (LAI) and biomass. Such information is crucial for guiding agricultural production, ensuring food security, and maintaining sustainable agricultural development. Traditional methods of field measurement and monitoring typically have low efficiency and can only give limited untimely information. Alternatively, methods based on remote sensing technologies are fast, objective, and nondestructive. Indeed, remote sensing data assimilation and crop growth modeling represent an important trend in crop growth monitoring and yield estimation. In this study, we assimilate the leaf area index retrieved from Sentinel-2 remote sensing data for crop growth model of the simple algorithm for yield estimation (SAFY) in wheat. The SP-UCI optimization algorithm is used for fine-tuning for several SAFY parameters, namely the emergence date (D0), the effective light energy utilization rate (ELUE), and the senescence temperature threshold (STT) which is indicative of biological aging. These three sensitive parameters are set in order to attain the global minimum of an error function between the SAFY model predicted values and the LAI inversion values. This assimilation of remote sensing data into the crop growth model facilitates the LAI, biomass, and yield estimation. The estimation results were validated using data collected from 48 experimental plots during 2014 and 2015. For the 2014 data, the results showed coefficients of determination (R2) of the LAI, biomass and yield of 0.73, 0.83 and 0.49, respectively, with corresponding root-mean-squared error (RMSE) values of 0.72, 1.13 t/ha and 1.14 t/ha, respectively. For the 2015 data, the estimated R2 values of the LAI, biomass, and yield were 0.700, 0.85, and 0.61, respectively, with respective RMSE values of 0.83, 1.22 t/ha, and 1.39 t/ha, respectively. The estimated values were found to be in good agreement with the measured ones. This shows high applicability of the proposed data assimilation scheme in crop monitoring and yield estimation. As well, this scheme provides a reference for the assimilation of remote sensing data into crop growth models for regional crop monitoring and yield estimation.

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.

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.

IntroductionCancer stem cells (CSCs) targeted therapy holds the potential for improving cancer management; identification of stemness-related genes in CSCs is necessary for its development.MethodsThe Cancer Genome Atlas (TCGA) and the Molecular Taxonomy of Breast Cancer International Consortium (METABRIC) datasets were used for survival analysis. ZSCAN1 correlated genes was identified by Spearman correlation analysis. Breast cancer stem-like cells (BCSLCs) were isolated by sorting CD44+CD24- cells from suspension cultured breast cancer (BC) spheroids. The sphere-forming capacity and sphere- and tumor-initiating capacities were determined by sphere formation and limiting dilution assays. The relative gene expression was determined by qRT-PCR, western blot. Lentivirus system was used for gene manipulation. Nuclear run-on assay was employed to examine the levels of nascent mRNAs. DNA pull-down and Chromatin immunoprecipitation (ChIP) assays were used for determining the interaction between protein and target DNA fragments. Luciferase reporter assay was used for evaluating the activity of the promoter.Results and discussionZSCAN1 is aberrantly suppressed in BC, and this suppression indicates a bad prognosis. Ectopic expression of ZSCAN1 inhibited the proliferation, clonogenicity, and tumorigenicity of BC cells. ZSCAN1-overexpressing BCSLCs exhibited weakened stemness properties. Normal human mammary epithelial (HMLE) cells with ZSCAN1 depletion exhibited enhanced stemness properties. Mechanistic studies showed that ZSCAN1 directly binds to -951 ~ -925bp region of WWTR1 (encodes TAZ) promoter, inhibits WWTR1 transcription, thereby inhibiting the stemness of BCSCs. Our work thus revealed ZSCAN1 as a novel stemness-related tumor suppressor and transcriptional repressor in BC.

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.

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.

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 Skeletal muscle growth plays a critical role during porcine muscle development stages. Genome-wide transcriptome analysis reveals that long intergenic non-coding RNAs (lincRNAs) are implicated as crucial regulator involving in epigenetic regulation. However, comprehensive analysis of lincRNAs in embryonic muscle development stages remain still elusive. Here, we investigated the transcriptome profiles of Duroc embryonic muscle tissues from days 33, 65, and 90 of gestation using RNA-seq, and 228 putative lincRNAs were identified. Moreover, these lincRNAs exhibit the characteristics of shorter transcripts length, longer exons, less exon numbers and lower expression level compared with protein-coding transcripts. Expression profile analysis showed that a total of 120 lincRNAs and 2638 mRNAs were differentially expressed. In addition, we also performed quantitative trait locus (QTL) mapping analysis for differentially expressed lincRNAs (DE lincRNAs), 113 of 120 DE lincRNAs were localized on 2200 QTLs, we observed many QTLs involved in growth and meat quality traits. Furthermore, we predicted potential target genes of DE lincRNAs in cis or trans regulation. Gene ontology and pathway analysis reveals that potential targets of DE lincRNAs mostly were enriched in the processes and pathways related to tissue development, MAPK signaling pathway, Wnt signaling pathway, TGF-beta signaling pathway and insulin signaling pathway, which involved in skeletal muscle physiological functions. Based on cluster analysis, co-expression network analysis of DE lincRNAs and their potential target genes indicated that DE lincRNAs highly regulated protein-coding genes associated with skeletal muscle development. In this study, many of the DE lincRNAs may play essential roles in pig muscle growth and muscle mass. Our study provides crucial information for further exploring the molecular mechanisms of lincRNAs during skeletal muscle development.

BackgroundGestational hypertension (GH) is a common disease that seriously threatens the safety and health of pregnant women and their newborns. Physical exercise (PE) is widely recognized as a health maintenance method and it has numerous benefits. Studies on the association between PE and the risk of GH in obese and overweight pregnant women have generated controversial findings. This updated meta-analysis was performed to reassess the effects of PE on GH.MethodsThe articles from inception to April 2022, presenting studies investigating exercise intervention and pregnancy outcomes were explored across several online databases. Heterogeneity among the included studies was estimated and tested by Q test and I2 statistic. Risk ratios (RRs) and 95% confidence intervals (CI) were calculated through either random-effect or fixed-effect models. Subgroup analyses, sensitivity analyses, and publication bias diagnoses were also conducted.ResultsTwelve with 1,649 subjects were included. PE was associated with a reduced risk of GH in obese and overweight pregnant women (Pooled RR = 0.58, 95% CI = 0.42–0.81, P = 0.001; I2 = 24.3%). Subgroup analysis found significant trends amongst Eastern countries (RR = 0.59, 95% CI = 0.36–0.96, P = 0.033). Sensitivity analysis suggested the results were stable. No publication bias was detected based on Begg's test and Egger's test.ConclusionPE was associated with reduced risk of GH in obese and overweight pregnant women, especially in Eastern countries. More well-designed studies are still needed to further elaborate on these associations.Systematic review registrationCRD42022326183.

The normalized difference vegetation index (NDVI) is crucial to many sustainable agricultural practices such as vegetation monitoring and health evaluation. However, optical remote sensing data often suffer from a large amount of missing information due to sensor failures and harsh atmospheric conditions. The synthetic aperture radar (SAR) offers a new approach to filling in missing optical data based on its excessive revisit density and its potential to image without interference from clouds and rain. Due to the difference in imaging mechanisms between SAR and optical sensors, it is very difficult to fuse the data. This paper developed an advanced deep learning Spatio-temporal fusion method, i.e., Transformer Temporal-spatial Model (TTSM), to synergize the SAR and optical time-series to reconstruct vegetation NDVI time series in cloudy regions. The proposed multi-head attention and end-to-end architecture achieved satisfactory accuracy (R2 greater than 0.88), outperforming the existing deep learning solutions. Extensive experiments were carried out to evaluate the TTSM method on large-scale areas (the spatial scale of megapixels) in northeast China with the main vegetation types of crops and forests. The R2, SSIM, RMSE, NRMSE, and MAE of our prediction results were 0.88, 0.80, 0.06, 0.16, and 0.05, respectively. The influence of training sample size was investigated through a transfer learning study, and the result indicated that the model had good generalizability. Overall, our proposed method can fill in the gap of optical data at an extensive regional scope over the vegetated area using SAR.

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 R2 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 R2 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 R2 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.

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 The difference between the skeletal muscle growth rates of Western and domestic breeds is remarkable, but the potential regulatory mechanism involved is still unclear. Numerous studies have pointed out that long intergenic noncoding RNA (lincRNA) plays a key role in skeletal muscle development. This study used published Yorkshire (LW) and Tibetan pig (TP) transcriptome data to explore the possible role of lincRNA in the difference in skeletal muscle development between the two breeds. 138 differentially expressed lincRNAs (DELs) were obtained between the two breeds, and their potential target genes (PTGs) were predicted. The results of GO and KEGG analysis revealed that PTGs are involved in multiple biological processes and pathways related to muscle development. The quantitative trait loci (QTLs) of DELs were predicted, and the results showed that most QTLs are related to muscle development. Finally, we constructed a co-expression network between muscle development related PTGs (MDRPTGs) and their corresponding DELs on the basis of their expression levels. The expression of DELs was significantly correlated with the corresponding MDRPTGs. Also, multiple MDRPTGs are involved in the key regulatory pathway of muscle fiber hypertrophy, which is the IGF-1-AKT-mTOR pathway. In summary, multiple lincRNAs that may cause differences in skeletal muscle development between the two breeds were identified, and their possible regulatory roles were explored. The findings of this study may provide a valuable reference for further research on the role of lincRNA in skeletal muscle development.

Abstract Colostrum quality is a vital factor in mortality and growth performance for piglets. Lactoferrin is an immuno-active milk protein that contributes to the formation of a protective layer above intestinal mucosa, possesses the antibacterial and antiviral activities that are favorable for piglet development. However, there is a notable reduction in lactoferrin in sow milk during lactation after the first few days, which causes many piglets to fail to ingest enough colostrum thereby leading to an increase in piglet mortality. In this study, we successfully constructed genome-edited Large-White pigs with marker-free site-specific knock-in of lactoferrin gene in the 3′-end of Casein alpha-s1 via CRISPR/Cas9 mediated homologous recombination. Thus, the lactoferrin protein can be expressed in the mammary gland in the control of Casein alpha-s1 promoter. As expected, the lactoferrin protein in genetically modified pigs sustained high expression in both colostrum and milk when compared with wild-type pigs. Moreover, the bacterial plate assay indicated that the milk from genetically modified pigs showed bacteriostatic effects when compared with control pigs. Taken together, our study demonstrated that the milk from genetically modified pigs had antibacterial activity which may reduce the costs of veterinary drug and improve the surviving rate of piglets, which is promising for pig breeding.

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.

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.

In multigranulation environments, variable precision multigranulation rough set (VPMGRS) is a useful framework that has a tolerance for errors. Approximations are basic concepts for knowledge acquisition and attribute reductions. Accelerating update of approximations can enhance the efficiency of acquiring decision rules by utilizing previously saved information. In this study, we focus on exploiting update mechanisms of approximations in VPMGRS with the addition of granular structures. By analyzing the basic changing trends of approximations in VPMGRS, we develop accelerating update mechanisms for acquiring approximations. In addition, an incremental algorithm to update variable precision multigranulation approximations is proposed when adding multiple granular structures. Finally, extensive comparisons elaborate the efficiency of the incremental algorithm.

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.

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.

The H19 gene promotes skeletal muscle differentiation in mice, but the regulatory models and mechanisms of myogenesis regulated by H19 are largely unknown in pigs. Therefore, the regulatory modes of H19 in the differentiation of porcine skeletal muscle satellite cells (PSCs) need to be determined. We observed that H19 gene silencing could decrease the expressions of the myogenin (MYOG) gene, myogenic differentiation (MYOD), and myosin heavy chain (MYHC) in PSCs. Therefore, we constructed and sequenced 12 cDNA libraries of PSCs after knockdown of H19 at two differentiation time points to analyze the transcriptome differences. A total of 11,419 differentially expressed genes (DEGs) were identified. Among these DEGs, we found through bioinformatics analysis and protein interaction experiment that SRY-box transcription factor 4 (SOX4) and Drebrin 1 (DBN1) were the key genes in H19-regulated PSC differentiation. Functional analysis shows that SOX4 and DBN1 promote PSC differentiation. Mechanistically, H19 regulates PSC differentiation through two different pathways. On the one hand, H19 functions as a molecular sponge of miR-140-5p, which inhibits the differentiation of PSCs, thereby modulating the derepression of SOX4. On the other hand, H19 regulates PSC differentiation through directly binding with DBN1. Furthermore, MYOD binds to the promoters of H19 and DBN1. The knockdown of MYOD inhibits the expression of H19 and DBN1. We determined the function of H19 and provided a molecular model to elucidate H19’s role in regulating PSC differentiation.

The timely and accurate acquisition of winter wheat acreage is crucial for food security. This study investigated the feasibility of extracting the spatial distribution map of winter wheat in Henan Province by using synthetic aperture radar (SAR, Sentinel-1A) and optical (Sentinel-2) images. Firstly, the SAR images were aggregated based on the growth period of winter wheat, and the optical images were aggregated based on the moderate resolution imaging spectroradiometer normalized difference vegetation index (MODIS-NDVI) curve. Then, five spectral features, two polarization features, and four texture features were selected as feature variables. Finally, the Google Earth Engine (GEE) cloud platform was employed to extract winter wheat acreage through the random forest (RF) algorithm. The results show that: (1) aggregated images based on the growth period of winter wheat and sensor characteristics can improve the mapping accuracy and efficiency; (2) the extraction accuracy of using only SAR images was improved with the accumulation of growth period. The extraction accuracy of using the SAR images in the full growth period reached 80.1%; and (3) the identification effect of integrated images was relatively good, which makes up for the shortcomings of SAR and optical images and improves the extraction accuracy of winter wheat.

Crop growth and development is a dynamic and complex process, and the essence of yield formation is the continuous accumulation of photosynthetic products from multiple fertility stages. In this study, a new stacking method for integrating multiple growth stages information was proposed to improve the performance of the winter wheat grain yield (GY) prediction model. For this purpose, crop canopy hyperspectral reflectance and leaf area index (LAI) data were obtained at the jointing, flagging, anthesis and grain filling stages. In this case, 15 vegetation indices and LAI were used as input features of the elastic network to construct GY prediction models for single growth stage. Based on Stacking technique, the GY prediction results of four single growth stages were integrated to construct the ensemble learning framework. The results showed that vegetation indices coupled LAI could effectively overcome the spectral saturation phenomenon, the validated R2 of each growth stage was improved by 10%, 22.5%, 3.6% and 10%, respectively. The stacking method provided more stable information with higher prediction accuracy than the individual fertility results (R2 = 0.74), and the R2 of the model validation phase improved by 236%, 51%, 27.6%, and 12.1%, respectively. The study can provide a reference for GY prediction of other crops.

Leaf area index (LAI) is highly related to crop growth, and the traditional LAI measurement methods are field destructive and unable to be acquired by large-scale, continuous, and real-time means. In this study, fractional order differential and continuous wavelet transform were used to process the canopy hyperspectral reflectance data of winter wheat, the fractional order differential spectral bands and wavelet energy coefficients with more sensitive to LAI changes were screened by correlation analysis, and the optimal subset regression and support vector machine were used to construct the LAI estimation models for different growth stages. The precision evaluation results showed that the LAI estimation models constructed by using wavelet energy coefficients combined with a support vector machine at the jointing stage, fractional order differential combined with support vector machine at the booting stage, and wavelet energy coefficients combined with optimal subset regression at the flowering and filling stages had the best prediction performance. Among these, both flowering and filling stages could be used as the best growth stages for LAI estimation with modeling and validation R2 of 0.87 and 0.71, 0.84 and 0.77, respectively. This study can provide technical reference for LAI estimation of crops based on remote sensing technology.