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Summary: Clonal reproduction through seeds, also termed apomixis, has the potential to revolutionize agriculture by allowing hybrid crops to be clonally propagated. Although apomixis has been introduced into rice through de novo engineering in recent years, the poor fertility and low-frequency clonal reproduction of synthetic apomicts hinder the application of apomixis in crop breeding. Here, in elite hybrid rice, we generated many apomicts, which produced clonal progeny with frequencies of > 95.0% and had high even normal fertility through combining enhanced rice BABYBOOM1 (BBM1)-induced parthenogenesis with the simultaneous inactivation of PAIR1, REC8, and OSD1. These synthetic apomicts maintained the agronomic traits of the elite hybrid rice. Our results indicate that fertility-normal hybrids with high penetrance of apomixis can be generated in rice, thereby laying an important foundation for the application of synthetic apomixis in crop breeding.

Spatiotemporal (ST) remote sensing satellites (RSSs) play essential roles in numerous fields including environmental monitoring, agriculture, urban planning, disaster management, and scientific research. The Fengyun III E satellite, also known as FY-3E, is dedicated to advancing numerical weather prediction and offers distinctive capabilities in weather forecasting, tropical cyclone, and other extreme meteorological event warnings, climate monitoring, as well as solar and space weather observation. Given the escalating concerns surrounding global warming, high-temperature predictions (HTPs) have emerged as a pressing issue. Nonetheless, ongoing challenges persist, encompassing inaccurate identification of satellite images, sluggish HTP speeds, heightened risks linked to meteorological information fusion, and disparities in interpreting conflicting ST RSS data from various satellites. To address these challenges, an FY-3E RSS data-based HTP model grounded in multigranularity (MG) picture fuzzy (PF) probabilistic rough sets (PRSs) is proposed, complemented by an improved sixfold strategy consensus-reaching process (6FS-CRP) to facilitate meteorologists in consensus reaching. Initially, the concept of adjustable MG PF PRSs is introduced by integrating picture fuzzy sets with PRSs. Subsequently, a clustering algorithm based on trust similarity analysis is employed as the clustering method within the proposed framework, alongside the incorporation of 6FS-CRP. Following this, the 6FS-CRP algorithm is seamlessly integrated into the adjustable MG PF PRS model, thereby introducing the PF 6FS-CRP application on HTPs. Finally, leveraging satellite data from the China Meteorological Data Service Centre, the proposed model is applied to satellite image analysis, weather forecasting, and HTPs, thereby validating its feasibility and effectiveness.

As mining activities extend deeper, intense heat dissipation from surrounding rock has become the most significant factor leading to heat hazards in deep mines. Laying the thermal insulation layer (TIL) along the roadway walls is an effective measure to reduce heat dissipation. This study is to investigate the temperature field and heat dissipation evolution of roadway surrounding rock after applying thermal insulation layer (TIL) by numerical simulation and similar experiment. The study shows that (i) The simulation curves agree well with the experimental data, which confirms the reliability of the mathematical model and numerical simulator; (ii) The application of TIL slows down the cooling of surrounding rock temperature field, the cooling ring radius decreased by 8.13 % after 10 years of ventilation; (iii) The effect of TIL on the equivalent thermal resistance (ETR) is most significant in early ventilation, and the thermal insulation effect decreases with time; (iv) The heat insulation rate is positively correlated with the TIL thickness and the convective heat transfer coefficient, and negatively with the thermal conductivity of TIL. This study provides the theoretical basis for the use of thermal insulation materials in underground mines.

Moldy corn produces aflatoxin and gibberellin, which can have adverse effects on human health if consumed. Mold is a significant factor that affects the safe storage of corn. If not detected and controlled in a timely manner, it will result in substantial food losses. Understanding the infection patterns of mold on corn kernels and the changing characteristics of the internal structure of corn kernels after infection is crucial for guiding innovation and optimizing detection methods for moldy corn. This knowledge also helps maintain corn storage and ensure food safety. This study was based on X-ray tomography technology to non-destructively detect changes in the structural characteristics of moldy corn kernels. It used image processing technology and model reconstruction algorithms to obtain the 3D model of the embryo, pores and cracks, endosperm and seed coat, and kernels of moldy corn kernels; qualitative analysis of the characteristic changes of two-dimensional slice grayscale images and 3D models of moldy corn kernels; and quantitative analysis of changes in the volume parameters of corn kernels, embryos, endosperm, and seed coats as a whole. It explored the detection method of moldy corn kernels based on a combination of X-ray tomography technology and deep learning algorithms. The analysis concluded that mold infection in maize begins in the embryo and gradually spreads and that mold damage to the tissue structure of maize kernels is irregular in nature. The overall volume parameter changes of corn kernels, embryos, endosperm, and seed coats in the four stages of 0 d, 5 d, 10 d, and 15 d showed a trend of first increasing and then decreasing. The ResNet50 model was enhanced for detecting mold on maize kernels, achieving an accuracy of over 93% in identifying mold features in sliced images of maize kernels. This advancement enabled the non-destructive detection and classification of the degree of mold in maize kernel samples. This article studies the characterization of the characteristic changes of moldy corn kernels and the detection of mildew, which will provide certain help for optimizing the monitoring of corn kernel mildew and the development of rapid detection equipment.

Abstract Background Variety genuineness and purity are essential indices of maize seed quality that affect yield. However, detection methods for variety genuineness are time-consuming, expensive, require extensive training, or destroy the seeds in the process. Here, we present an accurate, high-throughput, cost-effective, and non-destructive method for screening variety genuineness that uses seed phenotype data with machine learning to distinguish between genetically and phenotypically similar seed varieties. Specifically, we obtained image data of seed morphology and hyperspectral reflectance for Jingke 968 and nine other closely-related varieties (non-Jingke 968). We then compared the robustness of three common machine learning algorithms in distinguishing these varieties based on the phenotypic imaging data. Results Our results showed that hyperspectral imaging (HSI) combined with a multilayer perceptron (MLP) or support vector machine (SVM) model could distinguish Jingke 968 from varieties that differed by as few as two loci, with a 99% or higher accuracy, while machine vision imaging provided ~ 90% accuracy. Through model validation and updating with varieties not included in the training data, we developed a genuineness detection model for Jingke 968 that effectively discriminated between genetically similar and distant varieties. Conclusions This strategy has potential for wide adoption in large-scale variety genuineness detection operations for internal quality control or governmental regulatory agencies, or for accelerating the breeding of new varieties. Besides, it could easily be extended to other target varieties and other crops.

As two commonly used fungicides, carbendazim and tebuconazole are widely found in the environment and in foods. Studies have reported that these fungicides can induce hepatic oxidative stress and other health risks. Nevertheless, the influences of exposure to carbendazim and tebuconazole at their acceptable daily intake (ADI) doses on hepatic oxidative stress, and the residual distributions in mice remain unclear. To fill these gaps, ICR (CD-1) mice were exposed to carbendazim and tebuconazole at their ADI doses by oral administration for 4 weeks in this study. The results showed that tebuconazole accumulated primarily in the epididymal fat of mice (16.84 μg/kg), whereas no significant residues of carbendazim in the tissues were observed. In addition, exposure to ADI doses of tebuconazole significantly reduced liver coefficients and induced hepatic oxidative stress in mice, including elevating the levels of glutathione and malonaldehyde. However, no significant impacts were observed on the hepatic redox homeostasis in mice after exposure to carbendazim at its ADI dose. The results could be helpful for understanding the exposure risks of carbendazim and tebuconazole in terms of low doses and long term.

Achieving a real-time and accurate detection of drones in natural environments is essential for the interception of drones intruding into high-security areas. However, a rapid and accurate detection of drones is difficult because of their small size and fast speed. In this paper a drone detection method as proposed by pruning the convolutional channel and residual structures of YOLOv3-SPP3. First, the k-means algorithm was used to cluster label the boxes. Second, the channel and shortcut layer pruning algorithm was used to prune the model. Third, the model was fine tuned to achieve a real-time detection of drones. The experimental results obtained by using the Ubuntu server under the Python 3.6 environment show that the YOLOv3-SPP3 algorithm is better than YOLOV3, Tiny-YOLOv3, CenterNet, SSD300, and faster R-CNN. There is significant compression in the size, the maximum compression factor is 20.1 times, the maximum detection speed is increased by 10.2 times, the maximum map value is increased by 15.2%, and the maximum precision is increased by 16.54%. The proposed algorithm achieves the mAP score of 95.15% and the detection speed of 112 f/s, which can meet the requirements of the real-time detection of UAVs.