Search

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]

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]

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]

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]

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]

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]

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 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]

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]

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]

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]

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]

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]

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]

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]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Parthenogenesis activation (PA), as an important artificial breeding method, can stably preserve the dominant genotype of a species. However, the delayed development of PA embryos is still overly severe and largely leads to pre-implantation failure in pigs. The mechanisms underlying the deficiencies of PA embryos have not been completely understood. For further understanding of the molecular mechanism behind PA embryo failure, we performed transcriptome analysis among pig oocytes (meiosis II, MII) and early embryos at three developmental stages (zygote, morula, and blastocyst) in vitro fertilization (IVF) and PA group. Totally, 11,110 differentially expressed genes (DEGs), 4694 differentially expressed lincRNAs (DELs) were identified, and most DEGs enriched the regulation of apoptotic processes. Through cis- and trans-manner functional prediction, we found that hub lincRNAs were mostly involved in abnormal parthenogenesis embryonic development. In addition, twenty DE imprinted genes showed that some paternally imprinted genes in IVF displayed higher expression than that in PA. Notably, we identified that three DELs of imprinted genes (MEST, PLAGL1, and DIRAS3) were up regulated in IVF, and there was no significant change in PA group. Disordered expression of key genes for embryonic development might play key roles in abnormal parthenogenesis embryonic development. Our study indicates that embryos derived from different production techniques have varied in vitro development to the blastocyst stage, and they also affect the transcription level of corresponding genes, such as imprinted genes. This work will help future research on these genes and molecular-assisted breeding for pig parthenotes. [ABSTRACT FROM AUTHOR]

The number of wheat ears is an essential indicator for wheat production and yield estimation, but accurately obtaining wheat ears requires expensive manual cost and labor time. Meanwhile, the characteristics of wheat ears provide less information, and the color is consistent with the background, which can be challenging to obtain the number of wheat ears required. In this paper, the performance of Faster regions with convolutional neural networks (Faster R-CNN) and RetinaNet to predict the number of wheat ears for wheat at different growth stages under different conditions is investigated. The results show that using the Global WHEAT dataset for recognition, the RetinaNet method, and the Faster R-CNN method achieve an average accuracy of 0.82 and 0.72, with the RetinaNet method obtaining the highest recognition accuracy. Secondly, using the collected image data for recognition, the R2 of RetinaNet and Faster R-CNN after transfer learning is 0.9722 and 0.8702, respectively, indicating that the recognition accuracy of the RetinaNet method is higher on different data sets. We also tested wheat ears at both the filling and maturity stages; our proposed method has proven to be very robust (the R2 is above 90). This study provides technical support and a reference for automatic wheat ear recognition and yield estimation. [ABSTRACT FROM AUTHOR]

Grain yield is increasingly affected by climate factors such as drought and heat. To develop resilient and high-yielding cultivars, high-throughput phenotyping (HTP) techniques are essential for precise decisions in wheat breeding. The ability of unmanned aerial vehicle (UAV)-based multispectral imaging and ensemble learning methods to increase the accuracy of grain yield prediction in practical breeding work is evaluated in this study. For this, 211 winter wheat genotypes were planted under full and limited irrigation treatments, and multispectral data were collected at heading, flowering, early grain filling (EGF), and mid-grain filling (MGF) stages. Twenty multispectral vegetation indices (VIs) were estimated, and VIs with heritability greater than 0.5 were selected to evaluate the models across the growth stages under both irrigation treatments. A framework for ensemble learning was developed by combining multiple base models such as random forest (RF), support vector machine (SVM), Gaussian process (GP), and ridge regression (RR). The R2 values between VIs and grain yield for individual base models were ranged from 0.468 to 0.580 and 0.537 to 0.598 for grain yield prediction in full and limited irrigation treatments across growth stages, respectively. The prediction results of ensemble models were ranged from 0.491 to 0.616 and 0.560 to 0.616 under full and limited irrigation treatments respectively, and were higher than that of the corresponding base learners. Moreover, the grain yield prediction results were observed high at mid grain filling stage under both full (R2 = 0.625) and limited (R2 = 0.628) irrigation treatments through ensemble learning based stacking of four base learners. Further improvements in ensemble learning models can accelerate the use of UAV-based multispectral data for accurate predictions of complex traits like grain yield in wheat. [ABSTRACT FROM AUTHOR]

Intramuscular fat (IMF) content is closely related to various meat traits, such as tenderness, juiciness, and flavor. The IMF content varies considerably among pig breeds with different genetic backgrounds. Long intergenic non-coding RNAs (lincRNAs) have been widely identified in many species and found to be an important class of regulators that can participate in multiple biological processes. However, the mechanism behind lincRNAs regulation of pig IMF content remains unknown and requires further study. In our study, we identified a total of 156 lincRNAs in the longissimus dorsi muscle of Wei (fat-type) and Yorkshire (lean-type) pigs using previously published data. These identified lincRNAs have shorter transcript length, longer exon length, lower exon number, and lower expression level as compared with protein-coding transcripts. We predicted potential target genes (PTGs) that are potentially regulated by lincRNAs in cis or trans regulation. Gene ontology and pathway analyses indicated that many potential lincRNAs target genes are involved in IMF-related processes or pathways, such as fatty acid catabolic process and adipocytokine signaling pathway. In addition, we analyzed quantitative trait locus (QTL) sites that differentially expressed lincRNAs (DE lincRNAs) between Wei and Yorkshire pigs co-localized. The QTL sites where DE lincRNAs co-localize are mostly related to IMF content. Furthermore, we constructed a co-expressed network between DE lincRNAs and their differentially expressed PTGs (DEPTGs). On the basis of their expression levels, we suggest that many DE lincRNAs can affect IMF development by positively or negatively regulating their PTGs. This study identified and analyzed some lincRNAs- and PTGs-related IMF development of the two pig breeds and provided new insight into research on the roles of lincRNAs in the two types of breeds. [ABSTRACT FROM AUTHOR]

Long non-coding RNAs (lncRNAs) have been implicated in fundamental and diverse biological processes, including myogenesis. However, the molecular mechanisms involved in this process remain largely unexplored. This study found that H19 affected the differentiation of porcine satellite cells (PSCs) by directly binding to the DNA/RNA-binding protein TDP43. Functional analyses showed that TDP43 knockdown decreased PSC differentiation, whereas TDP43 overexpression exerted opposite effects in vitro. Furthermore, rescue experiments demonstrated that TDP43 can rescue the decrease in PSC differentiation caused by H19 knockdown. Mechanistically, H19 may act as a scaffold to recruit TDP43 to the promoters of MYOD and thereby activate the transcription of MYOD, leading to PSC differentiation. In summary, we elucidate the molecular mechanism by which H19 and TDP43 regulate myogenesis. [ABSTRACT FROM AUTHOR]

Although thousands of long noncoding RNAs (lncRNAs) have been identified in porcine growth and development, the regulation mechanisms of functional lncRNAs have not been well explored. In this study, using 5′- and 3′-rapid amplification of cDNA ends (RACE) assays, we obtained two different variants of lncRNA maternally expressed gene 3 (MEG3), namely, MEG3 v1 and MEG3 v2, that were both highly expressed in porcine skeletal muscle and in the early stage of the differentiation of porcine satellite cells. Moreover, we identified the core transcript MEG3 v2. Functional analyses showed that MEG3 overexpression could effectively arrest myoblasts in the G1 phase, inhibit DNA replication, and promote myoblast differentiation, whereas MEG3 knockdown resulted in the opposite effects. Interestingly, the expression of serum response factor (SRF), a crucial transcription factor for myogenesis process, remarkably increased and decreased in mRNA and protein levels with the respective overexpression and knockdown of MEG3. Dual luciferase reporter assay showed that MEG3 could attenuate the decrease of luciferase activity of SRF induced by miR-423-5p in a dose-dependent manner. MEG3 overexpression could relieve the inhibitory effect on SRF and myoblast differentiation induced by miR-423-5p. In addition, results of RNA immunoprecipitation analysis suggested that MEG3 could act as a ceRNA for miR-423-5p. Our findings initially established a novel connection among MEG3, miR-423-5p, and SRF in porcine satellite cell differentiation. This novel role of MEG3 may shed new light on understanding of molecular regulation of lncRNA in porcine myogenesis. [ABSTRACT FROM AUTHOR]

The ectopic overexpression of foreign genes in animal genomes is an important strategy for gain-of-function study and establishment of transgenic animal models. Previous studies showed that two loci (Rosa26 and pH11) were identified as safe harbor locus in pig genomes, which means foreign genes can be integrated into this locus for stable expression. Moreover, integration of a transgene may interfere with the endogenous gene expression of the target locus after the foreign fragments are inserted. Here, we provide a new strategy for efficient transgene knock-in in the endogenous GAPDH gene via CRISPR/Cas9 mediated homologous recombination. This strategy has no influence on the expression of the endogenous GAPDH gene. Thus, the GAPDH locus is a new alternative safe harbor locus in the pig genome for foreign gene knock-ins. This strategy is promising for agricultural breeding and biomedical model applications. [ABSTRACT FROM AUTHOR]

The liver plays a key role in metabolism and affects pig production. However, the functional annotation of noncoding regions of the pig liver remains poorly understood. We revealed the landscape of cis-regulatory elements and their functional characterization in pig liver. We identified 102,373 cis-regulatory elements in the pig liver, including enhancers, promoters, super-enhancers, and broad H3K4me3 domains, and highlighted 26 core transcription regulatory factors in the pig liver as well. We found similarity of cis-regulatory elements among those of pigs, humans, and cattle. Despite the low proportion of functionally conserved enhancers (~30%) between pig and human liver tissue, ~78% of the pig liver enhancer orthologues sequence could play an enhancer role in other human tissues. Additionally, we observed that the ratio of consistent super-enhancer-associated genes was significantly higher than the ratio of functionally conserved super-enhancers. Approximately 54% of the core regulation factors driven by super-enhancers were consistent across the liver from these three species. Our pig liver annotation and functional characterization studies provide a system and resource for noncoding annotation for future gene regulatory studies in pigs. Furthermore, our study also showed the high level functional conservation of cis-regulatory elements in mammals; it also improved our understanding of regulation function of mammal cis-regulatory elements. [ABSTRACT FROM AUTHOR]

There are major differences between Yorkshire (lean-type) and Wannanhua pig (fat-type) in terms of growth performance and meat quality. Long intergenic noncoding RNAs (lincRNAs) are a class of regulators that are involved in numerous biological processes and widely identified in many species. However, the role of lincRNAs in pig is largely unknown, and the mechanisms by which they affect growth and meat quality are elusive. In this study, we used published data to identify 759 lincRNAs in porcine longissimus dorsi muscle. These putative lincRNAs shared many features with mammalian lincRNAs, such as shorter length and fewer exons. Gene ontology and pathway analysis indicated that many potential target genes (PTGs) of lincRNAs were involved in muscle growth-related and meat quality-related biological processes. Moreover, we constructed a co-expression network between differentially expressed lincRNAs (DELs) and their PTGs, and found a potential mechanism that most DELs can use to upregulate their PTGs, which may finally contribute to the growth and meat quality differences between the two breeds through an unknown manner. This work details some lincRNAs and their PTGs related to muscle growth or meat quality, and facilitates future research on the roles of lincRNAs in these two types of pig, as well as molecular-assisted breeding for pig., Competing Interests: The authors declare no conflict of interest.