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Abstract This paper focuses on addressing the limitations of existing mechanical weeding methods for corn plants by introducing a spiral tendon-type precision weeding device specifically designed for corn fields. The study encompasses mechanical design and theoretical analysis to determine the overall structure, component parts, application scenarios, operation modes, and working principles of the device. The force applied to the spiral tendon weeding cutter head, a crucial working component of the device, is analyzed, along with its motion characteristics. This analysis allows for the calculation of the force required for the spiral tendon to penetrate the soil, as well as the determination of its trajectory and speed in the soil. The desired motion pattern of the weeding cutter head is considered in determining the contour shape of the inner cylinder track and its cooperation mode with the roller follower. Furthermore, the design and optimization of the circulation groove track are conducted to derive the key parameters of the track. A prototype is then constructed based on the proposed structural design and parameter optimization scheme. Soil bin tests are performed to evaluate the device’s performance. The optimal combinations of working parameters are determined as follows: a 70-mm depth of the weeding cutter head into the soil, a movement speed of 80 mm/s, and an output thrust of the electric actuator of 180 N. Under these conditions, the device achieves a weed removal rate exceeding 95% with a 3% wounding rate, demonstrating stable operational performance.

Breeding high-photosynthetic-efficiency wheat varieties is a crucial link in safeguarding national food security. Traditional identification methods necessitate laborious on-site observation and measurement, consuming time and effort. Leveraging unmanned aerial vehicle (UAV) remote sensing technology to forecast photosynthetic indices opens up the potential for swiftly discerning high-photosynthetic-efficiency wheat varieties. The objective of this research is to develop a multi-stage predictive model encompassing nine photosynthetic indicators at the field scale for wheat breeding. These indices include soil and plant analyzer development (SPAD), leaf area index (LAI), net photosynthetic rate (Pn), transpiration rate (Tr), intercellular CO2 concentration (Ci), stomatal conductance (Gsw), photochemical quantum efficiency (PhiPS2), PSII reaction center excitation energy capture efficiency (Fv’/Fm’), and photochemical quenching coefficient (qP). The ultimate goal is to differentiate high-photosynthetic-efficiency wheat varieties through model-based predictions. This research gathered red, green, and blue spectrum (RGB) and multispectral (MS) images of eleven wheat varieties at the stages of jointing, heading, flowering, and filling. Vegetation indices (VIs) and texture features (TFs) were extracted as input variables. Three machine learning regression models (Support Vector Machine Regression (SVR), Random Forest (RF), and BP Neural Network (BPNN)) were employed to construct predictive models for nine photosynthetic indices across multiple growth stages. Furthermore, the research conducted principal component analysis (PCA) and membership function analysis on the predicted values of the optimal models for each indicator, established a comprehensive evaluation index for high photosynthetic efficiency, and employed cluster analysis to screen the test materials. The cluster analysis categorized the eleven varieties into three groups, with SH06144 and Yannong 188 demonstrating higher photosynthetic efficiency. The moderately efficient group comprises Liangxing 19, SH05604, SH06085, Chaomai 777, SH05292, Jimai 22, and Guigu 820, totaling seven varieties. Xinmai 916 and Jinong 114 fall into the category of lower photosynthetic efficiency, aligning closely with the results of the clustering analysis based on actual measurements. The findings suggest that employing UAV-based multi-source remote sensing technology to identify wheat varieties with high photosynthetic efficiency is feasible. The study results provide a theoretical basis for winter wheat phenotypic monitoring at the breeding field scale using UAV-based multi-source remote sensing, offering valuable insights for the advancement of smart breeding practices for high-photosynthetic-efficiency wheat varieties.

Accurate weed detection is essential for the precise control of weeds in wheat fields, but weeds and wheat are sheltered from each other, and there is no clear size specification, making it difficult to accurately detect weeds in wheat. To achieve the precise identification of weeds, wheat weed datasets were constructed, and a wheat field weed detection model, YOLOv8-MBM, based on improved YOLOv8s, was proposed. In this study, a lightweight visual converter (MobileViTv3) was introduced into the C2f module to enhance the detection accuracy of the model by integrating input, local (CNN), and global (ViT) features. Secondly, a bidirectional feature pyramid network (BiFPN) was introduced to enhance the performance of multi-scale feature fusion. Furthermore, to address the weak generalization and slow convergence speed of the CIoU loss function for detection tasks, the bounding box regression loss function (MPDIOU) was used instead of the CIoU loss function to improve the convergence speed of the model and further enhance the detection performance. Finally, the model performance was tested on the wheat weed datasets. The experiments show that the YOLOv8-MBM proposed in this paper is superior to Fast R-CNN, YOLOv3, YOLOv4-tiny, YOLOv5s, YOLOv7, YOLOv9, and other mainstream models in regards to detection performance. The accuracy of the improved model reaches 92.7%. Compared with the original YOLOv8s model, the precision, recall, mAP1, and mAP2 are increased by 10.6%, 8.9%, 9.7%, and 9.3%, respectively. In summary, the YOLOv8-MBM model successfully meets the requirements for accurate weed detection in wheat fields.

An efficient and accurate estimation of wheat growth and yield is important for wheat assessment and field management. To improve the accuracy and stability of wheat growth and yield estimation, an estimation method based on a genetic algorithm-improved support vector regression (GA-SVR) algorithm was proposed in this study. The correlation analysis between vegetation indices calculated from spectral data and wheat growth phenotypes and yields was performed to obtain the optimal combination of vegetation indices with high correlation and good estimation performance. At the same time, the optimal model for wheat growth monitoring was screened and constructed in experiments with 12 wheat varieties and 3 gradient nitrogen fertilizer application levels. Then, the yield estimation model was established and its applicability was verified under different nitrogen fertilizer application levels. The results showed that the constructed models for the leaf area index, plant height, and yield estimation performed well, with coefficients of determination of 0.82, 0.71, and 0.70, and root mean square errors of 0.09, 2.7, and 68.5, respectively. This study provided an effective UAV remote sensing technique for monitoring wheat growth status and estimating yield. This study provides an effective unmanned aerial remote sensing technique for monitoring wheat growth and estimating yield, and provides technical support for wheat yield assessment and field management.

Abstract Background Tetraspanins are members of the 4-transmembrane protein superfamily (TM4SF) that function by recruiting many cell surface receptors and signaling proteins into tetraspanin-enriched microdomains (TEMs) that play vital roles in the regulation of key cellular processes including adhesion, motility, and proliferation. Tetraspanin7 (Tspan7) is a member of this superfamily that plays documented roles in hippocampal neurogenesis, synaptic transmission, and malignant transformation in certain tumor types. How Tspan7 influences the onset or progression of osteosarcoma (OS), however, remains to be defined. Herein, this study aimed to explore the relationship between Tspan7 and the malignant progression of OS, and its underlying mechanism of action. Methods In this study, the levels of Tspan7 expression in human OS cell lines were evaluated via qRT-PCR and western blotting. The effect of Tspan7 on proliferation was examined using CCK-8 and colony formation assays, while metastatic role of Tspan7 was assessed by functional assays both in vitro and in vivo. In addition, mass spectrometry and co-immunoprecipitation were performed to verify the interaction between Tspan7 and β1 integrin, and western blotting was used to explore the mechanisms of Tspan7 in OS progresses. Results We found that Tspan7 is highly expressed in primary OS tumors and OS cell lines. Downregulation of Tspan7 significantly suppressed OS growth, metastasis, and attenuated epithelial-mesenchymal transition (EMT), while its overexpression had the opposite effects in vitro. Furthermore, it exhibited reduced OS pulmonary metastases in Tspan7-deleted mice comparing control mice in vivo. Additionally, we proved that Tspan7 interacted with β1 integrin to facilitate OS metastasis through the activation of integrin-mediated downstream FAK-Src-Ras-ERK1/2 signaling pathway. Conclusion In summary, this study demonstrates for the first time that Tspan7 promotes OS metastasis via interacting with β1 integrin and activating the FAK-Src-Ras-ERK1/2 pathway, which could provide rationale for a new therapeutic strategy for OS.

Protein lactylation is a newly discovered posttranslational modification (PTM) and is involved in multiple biological processes, both in mammalian cells and rice grains. However, the function of lysine lactylation remains unexplored in wheat. In this study, we performed the first comparative proteomes and lysine lactylomes during seed germination of wheat. In total, 8000 proteins and 927 lactylated sites in 394 proteins were identified at 0 and 12 h after imbibition (HAI). Functional enrichment analysis showed that glycolysis- and TCA-cycle-related proteins were significantly enriched, and more differentially lactylated proteins were enriched in up-regulated lactylated proteins at 12 HAI vs. 0 HAI through the KEGG pathway and protein domain enrichment analysis compared to down-regulated lactylated proteins. Meanwhile, ten particularly preferred amino acids near lactylation sites were found in the embryos of germinated seeds: AA*KlaT, A***KlaD********A, KlaA**T****K, K******A*Kla, K*Kla********K, KlaA******A, Kla*A, KD****Kla, K********Kla and KlaG. These results supplied a comprehensive profile of lysine lactylation of wheat and indicated that protein lysine lactylation played important functions in several biological processes.

The flowering period is one of the important indexes of wheat breeding. The early or late flowering affects the final yield and character stability of wheat. In order to solve the problem that it is difficult to accurately and quickly detect the flowering period of a large number of wheat breeding materials, a determination method of flowering period for field wheat based on the improved You Only Look Once (YOLO) v5s model was proposed. Firstly, a feature fusion (FF) method combing RGB images and corresponding comprehensive color features was proposed to highlight more texture features and reduce the distortion caused by light on the extracted feature images. Second, the YOLOv5s model was selected as a base version of the improved model and the convolutional block attention model (CBAM) was adopted into the feature fusion layer of YOLOV5s model. Florets and spikelets were given greater weight along the channel and spatial dimensions to further refine their effective feature information. At the same time, an integrated Transformer small-target detection head (TSDH) was added to solve the high miss rate of small targets in wheat population images. The accurate and rapid detection of florets and spikelets was realized, and the flowering period was determined according to the proportion of florets and spikelets. The experimental results showed that the average computing time of the proposed method was 11.5ms, and the average recognition accuracy of florets and spikelets was 88.9% and 96.8%, respectively. The average difference between the estimated flowering rate and the actual flowering rate was within 5%, and the determination accuracy of the flowering period reached 100%, which met the basic requirements of the flowering period determination of wheat population in the field.

Based on the mechanical test (shear test, compression test), the bond model of corn kernel and straw was established to explore the rolling and crushing effect of different crushing rollers. The type of crushing roller is different. The material crushing process by the force (extrusion and kneading) is different. The mechanical analysis of the crushing process reveals that the disc crushing roller (DCR) has the characteristics of large unit-length kneading area; the spiral-notched serrated crushing roller (SNSCR) has transverse shearing effect on the material; and they affect the crushing effect of the material. By means of discrete element method and simulation test, multiple regression method and variance analysis method are used to systematically analyze the data. The optimal working parameters of each roll (crushing roll speed, crushing clearance, differential ratio) were obtained. The simulation test and bench test of the crushing process of materials with different roll shapes were carried out under the optimal working parameters. The crushing effect was evaluated with a Binzhou screen and a corn silage grain-crushing score screen. The crushed materials of corn kernel can be divided into three categories according to the size (broken grains passed through 2 mm sieve; broken grains passed through 4.75 mm sieve; and broken grains that cannot pass through 4.75 mm sieve), and the crushed materials of corn stalk can be divided into four categories according to the size and thickness (broken straw through 4 mm sieve; broken straw through 8 mm sieve; broken straw through 19 mm sieve; and broken straw that cannot pass 19 mm sieve). The crushing effect and crushing classification of the simulation test and bench test were basically consistent. The results showed that the disc crushing roller group had the highest comprehensive score with straw rolling rate of 89.1% and grain crushing rate of 87.7%, which was the most suitable for harvesting whole-plant silage maize (WSM).

ObjectAt present, there are few effective treatment options available to patients suffering from osteosarcoma (OS). Clarifying the signaling pathways that govern OS oncogenesis may highlight novel approaches to treating this deadly form of cancer. Recent experimental evidence suggests that the transmembrane protein tetraspanin-9 (Tspan9) plays a role in tumor development. This study was thus formulated to assess the molecular role of Tspan9 as a regulator of OS cell metastasis.MethodsGene expression in OS cell lines was evaluated via qRT-PCR, while CCK-8, colony formation, Transwell, and wound healing assays were used to explore the in vitro proliferative, invasive, and migratory activities of OS cells. The relationship between Tspan9 and in vivo OS cell metastasis was assessed by injecting these cells into the tail vein of nude mice. Interactions between the Tspan9 and integrin β1 proteins were explored through mass spectrometric and co-immunoprecipitation, and Western blotting to assess the functional mechanisms whereby Tspan9 shapes OS pathogenesis.ResultsBoth primary OS tumors and OS cell lines commonly exhibited Tspan9 upregulation, and the knockdown of this tetraspanin suppressed the migration, invasion, and epithelial-mesenchymal transition (EMT) activity in OS cells, whereas Tspan9 overexpression resulted in opposite phenotypes. Tumor lung metastasis were significantly impaired in mice implanted with HOS cells in which Tspan9 was downregulated as compared to mice implanted with control HOS cells. Tspan9 was also found to interact with β1 integrin and to contribute to OS metastasis via the amplification of integrin-mediated downstream FAK/Ras/ERK1/2 signaling pathway.ConclusionThese data suggest that Tspan9 can serve as a promising therapeutic target in OS.

Early prediction of the growth and development of plants is important for the intelligent breeding process, yet accurate prediction and simulation of plant phenotypes is difficult. In this work, a prediction model of plant growth and development based on spatiotemporal long short-term memory (ST-LSTM) and memory in memory network (MIM) was proposed to predict the image sequences of future growth and development including plant organs such as ears. A novel dataset of wheat growth and development was also compiled. The performance of the prediction model of plant growth and development was evaluated by calculating structural similarity index measure (SSIM), mean square error (MSE), and peak signal to noise ratio (PSNR) between the predicted and real plant images. Moreover, the optimal number of time steps and the optimal time interval between steps were determined for the proposed model on the wheat growth and development dataset. Under the optimal setting, the SSIM values surpassed 84% for all time steps. The mean of MSE values was 46.11 and the MSE values were below 68 for all time steps. The mean of PSNR values was 30.67. When the number of prediction steps was set to eight, the prediction model had the best prediction performance on the public Panicoid Phenomap-1 dataset. The SSIM values surpassed 78% for all time steps. The mean of MSE values was 77.78 and the MSE values were below 118 for all time steps. The mean of PSNR values was 29.03. The results showed a high degree of similarity between the predicted images and the real images of plant growth and development and verified the validity, reliability, and feasibility of the proposed model. The study shows the potential to provide the plant phenotyping community with an efficient tool that can perform high-throughput phenotyping and predict future plant growth.

Abstract Chronic hepatitis B infection remains a serious public health issue worldwide. Hepatitis B virus (HBV) reactivation is commonly reported in patients receiving anticancer therapy, immunosuppressive therapy, or organ and tissue transplantation. However, the precise mechanisms underlying chemotherapeutic agent-related HBV reactivation remain unclear. Here, we report that peroxisome proliferator-activated receptor gamma coactivator 1 alpha (PGC-1α) plays a central role in cisplatin-induced HBV transcription and replication. First, cisplatin treatment upregulated the expression levels of PGC-1α and hepatocyte nuclear factor 4 alpha (HNF-4α) in both HBV-replicating cells and an HBV-transgenic mouse model. PGC-1α coactivates with HNF-4α, which interacts with a core promoter and enhancer II region of HBV genome, thereby promoting HBV production. In contrast, knockdown of PGC-1α and HNF-4α by RNA interference in hepatoma cells reversed HBV activation in response to cisplatin. Additionally, PGC-1α upregulation depended on cisplatin-mediated endoplasmic reticulum (ER) stress. We further observed that the recruitment of cyclic AMP-responsive element-binding protein plays a crucial role for PGC-1α transcriptional activation in cisplatin-treated cells. Finally, pharmacologic inhibition of ER stress impaired PGC-1α upregulation and HBV production induced by cisplatin treatment. These findings demonstrate novel molecular mechanisms indicating that ER stress-PGC1α signaling pathway plays a critical role in cisplatin-evoked HBV reactivation.

Studies on molecular mechanisms of the persist infection of hepatitis B virus have been hampered by a lack of a robust animal model. We successfully established a simple, versatile, and reproducible HBV persist infection model in vitro and in vivo with the circularized HBV DNA. The cells and mice were transfected or injected with circularized HBV DNA and pAAV/HBV1.2, respectively. At the indicated time, the cells, supernatants, serum samples, and liver tissues were collected for virological and serological detection. Both in vitro and in vivo, the circularized HBV DNA and pAAV/HBV1.2 could replicate and transcribe efficiently, but the infection effect of the former was superior to the latter (p < 0.05). The injection of circularized HBV genome DNA into the mice robustly supported HBV infection and approximately 80% of HBV infected mice established persistent infection for at least 10 weeks. This study demonstrated that the infection efficiency and replication ability of the circularized structure of HBV DNA overmatched that of the expression plasmid containing the linear structure of HBV DNA in vitro and in vivo. Meanwhile, this research results could provide useful tools and methodology for further study of pathogenic mechanisms and potential antiviral treatments of human chronic HBV infection in vitro and in vivo.

Polyhedral oligomeric silsesquioxane (POSS)-derived Si@C anode material is prepared by the copolymerization of octavinyl-polyhedral oligomeric silsesquioxane (octavinyl-POSS) and styrene. Octavinyl-polyhedral oligomeric silsesquioxane has an inorganic core (-Si8O12) and an organic vinyl shell. Carbonization of the core-shell structured organic-inorganic hybrid precursor results in the formation of carbon protected Si-based anode material applicable for lithium ion battery. The initial discharge capacity of the battery based on the as-obtained Si@C material Si reaches 1500 mAh g−1. After 550 charge-discharge cycles, a high capacity of 1430 mAh g−1 was maintained. A combined XRD, XPS and TEM analysis was performed to investigate the variation of the discharge performance during the cycling experiments. The results show that the decrease in discharge capacity in the first few cycles is related to the formation of solid electrolyte interphase (SEI). The subsequent rise in the capacity can be ascribed to the gradual morphology evolution of the anode material and the loss of capacity after long-term cycles is due to the structural pulverization of silicon within the electrode. Our results not only show the high potential of the novel electrode material but also provide insight into the dynamic features of the material during battery cycling, which is useful for the future design of high-performance electrode material.

We construct a deep neural network model named ECGResNet. This model can diagnosis diseases based on 12-lead ECG data of eight common cardiovascular diseases with a high accuracy. We chose the 16 Blocks of ResNet50 as the main body of the model and added the Squeeze-and-Excitation module to learn the data information between channels adaptively. We modified the first convolutional layer of ResNet50 which has a convolutional kernel of 7 to a superposition of convolutional kernels of 8 and 16 as our feature extraction method. This way allows the model to focus on the overall trend of the ECG signal while also noticing subtle changes. The model further improves the accuracy of cardiovascular and cerebrovascular disease classification by using a fully connected layer that integrates factors such as gender and age. The ECGResNet model adds Dropout layers to both the residual block and SE module of ResNet50, further avoiding the phenomenon of model overfitting. The model was eventually trained using a five-fold cross-validation and Flooding training method, with an accuracy of 95% on the test set and an F1-score of 0.841.We design a new deep neural network, innovate a multi-scale feature extraction method, and apply the SE module to extract features of ECG data. [ABSTRACT FROM AUTHOR]

Automatic weed identification and detection are crucial for precision weeding operations. In recent years, deep learning (DL) has gained widespread attention for its potential in crop weed identification. This paper provides a review of the current research status and development trends of weed identification in crop fields based on DL. Through an analysis of relevant literature from both within and outside of China, the author summarizes the development history, research progress, and identification and detection methods of DL-based weed identification technology. Emphasis is placed on data sources and DL models applied to different technical tasks. Additionally, the paper discusses the challenges of time-consuming and laborious dataset preparation, poor generality, unbalanced data categories, and low accuracy of field identification in DL for weed identification. Corresponding solutions are proposed to provide a reference for future research directions in weed identification. [ABSTRACT FROM AUTHOR]