MicroRNA Let-7a Inhibits Proliferation of Human Prostate Cancer Cells In Vitro and In Vivo by Targeting E2F2 and CCND2

Previous work has shown reduced expression levels of let-7 in lung tumors. But little is known about the expression or mechanisms of let-7a in prostate cancer. In this study, we used in vitro and in vivo approaches to investigate whether E2F2 and CCND2 are direct targets of let-7a, and if let-7a acts as a tumor suppressor in prostate cancer by down-regulating E2F2 and CCND2.Findings Real-time RT-PCR demonstrated that decreased levels of let-7a are present in resected prostate cancer samples and prostate cancer cell lines. Cellular proliferation was inhibited in PC3 cells and LNCaP cells after transfection with let-7a. Cell cycle analysis showed that let-7a induced cell cycle arrest at the G1/S phase. A dual-luciferase reporter assay demonstrated that the 3′UTR of E2F2 and CCND2 were directly bound to let-7a and western blotting analysis further indicated that let-7a down-regulated the expression of E2F2 and CCND2. Our xenograft models of prostate cancer confirmed the capability of let-7a to inhibit prostate tumor development in vivo.These findings help to unravel the anti-proliferative mechanisms of let-7a in prostate cancer. Let-7a may also be novel therapeutic candidate for prostate cancer given its ability to induce cell-cycle arrest and inhibit cell growth, especially in hormone-refractory prostate cancer

Modeling Graph Neural Networks and Dynamic Role Sorting for Argument Extraction in Documents

The existing methods for document-level event extraction mainly face two challenges. The first challenge is effectively capturing event information that spans across sentences. The second challenge is using predefined orders to extract event arguments while disregarding the dynamic adjusting of the order according to the importance of argument roles. To address these issues, we propose a model based on graph neural networks which realizes the semantic interaction among documents, sentences, and entities. Additionally, our model adopts a dynamic argument detection strategy, extracting arguments depending on their number in correspondence with each role. The experimental results confirm the outperformance of our model, which surpasses previous methods by 7% and 1.9% in terms of an F1 score

Security Risk Level Prediction of Carbofuran Pesticide Residues in Chinese Vegetables Based on Deep Learning

The supervision of security risk level of carbofuran pesticide residues can guarantee the food quality and security of residents effectively. In order to predict the potential key risk vegetables and regions, this paper constructs a security risk assessment model, combined with the k-means++ algorithm, to establish the risk security level. Then the evaluation index value of the security risk model is predicted to determine the security risk level based on the deep learning model. The model consists of a convolutional neural network (CNN) and a long short-term memory network (LSTM) optimized by an arithmetic optimization algorithm (AOA), namely, CNN-AOA-LSTM. In this paper, a comparative experiment is conducted on a small sample data set of independently constructed security risk assessment indicators. Experimental results show that the accuracy of the CNN-AOA-LSTM prediction model based on attention mechanism is 6.12% to 18.99% higher than several commonly used deep neural network models (gated recurrent unit, LSTM, and recurrent neural networks). The prediction model proposed in this paper provides scientific reference to establish the priority order of supervision, and provides forward-looking supervision for the government

Security Risk Level Prediction of Carbofuran Pesticide Residues in Chinese Vegetables Based on Deep Learning

The supervision of security risk level of carbofuran pesticide residues can guarantee the food quality and security of residents effectively. In order to predict the potential key risk vegetables and regions, this paper constructs a security risk assessment model, combined with the k-means++ algorithm, to establish the risk security level. Then the evaluation index value of the security risk model is predicted to determine the security risk level based on the deep learning model. The model consists of a convolutional neural network (CNN) and a long short-term memory network (LSTM) optimized by an arithmetic optimization algorithm (AOA), namely, CNN-AOA-LSTM. In this paper, a comparative experiment is conducted on a small sample data set of independently constructed security risk assessment indicators. Experimental results show that the accuracy of the CNN-AOA-LSTM prediction model based on attention mechanism is 6.12% to 18.99% higher than several commonly used deep neural network models (gated recurrent unit, LSTM, and recurrent neural networks). The prediction model proposed in this paper provides scientific reference to establish the priority order of supervision, and provides forward-looking supervision for the government

Prediction of Safety Risk Levels of Veterinary Drug Residues in Freshwater Products in China Based on Transformer

Early warning and focused regulation of veterinary drug residues in freshwater products can protect human health and stabilize social development. To improve the prediction accuracy, this paper constructs a Transformer-based model for predicting the safety risk level of veterinary drug residues in freshwater products in China to conduct a comprehensive assessment and prediction of the three veterinary drug residues with the maximum detection rate in freshwater products, including florfenicol, enrofloxacin and sulfonamides. Using the national sampling data and consumption data of freshwater products from 2019 to 2021, this paper constructs a self-built dataset, combined with the k-means algorithm, to establish the risk-level space. Finally, based on a Transformer neural network model, the safety risk assessment index is predicted on a self-built dataset, with the corresponding risk level for prediction. In this paper, comparison experiments are conducted on the self-built dataset. The experimental results show that the prediction model proposed in this paper achieves a recall rate of 94.14%, which is significantly better than other neural network models. The model proposed in this paper provides a scientific basis for the government to implement focused regulation, and it also provides technical support for the government’s intervention regulation

Prediction of Food Safety Risk Level of Wheat in China Based on Pyraformer Neural Network Model for Heavy Metal Contamination

Heavy metal contamination in wheat not only endangers human health, but also causes crop quality degradation, leads to economic losses and affects social stability. Therefore, this paper proposes a Pyraformer-based model to predict the safety risk level of Chinese wheat contaminated with heavy metals. First, based on the heavy metal sampling data of wheat and the dietary consumption data of residents, a wheat risk level dataset was constructed using the risk evaluation method; a data-driven approach was used to classify the dataset into risk levels using the K-Means++ clustering algorithm; and, finally, on the constructed dataset, Pyraformer was used to predict the risk assessment indicator and, thus, the risk level. In this paper, the proposed model was compared to the constructed dataset, and for the dataset with the lowest risk level, the precision and recall of this model still reached more than 90%, which was 25.38–4.15% and 18.42–5.26% higher, respectively. The model proposed in this paper provides a technical means for hierarchical management and early warning of heavy metal contamination of wheat in China, and also provides a scientific basis for dynamic monitoring and integrated prevention of heavy metal contamination of wheat in farmland

Autoformer-Based Model for Predicting and Assessing Wheat Quality Changes of Pesticide Residues during Storage

Proper grain storage plays a critical role in maintaining food quality. Among a variety of grains, wheat has emerged as one of the most important grain reserves globally due to its short growing period, high yield, and storage resistance. To improve the quality assessment of wheat during storage, this study collected and analyzed monitoring data from more than 20 regions in China, including information on storage environmental parameters and changes in wheat pesticide residue concentrations. Based on these factors, an Autoformer-based model was developed to predict the changes in wheat pesticide residue concentrations during storage. A comprehensive wheat quality assessment index Q was set for the predicted and true values of pesticide residue concentrations, then combined with the K-means++ algorithm to assess the quality of wheat during storage. The results of the study demonstrate that the Autoformer model achieved the optimal prediction results and the smallest error values. The mean absolute error (MAE) and the other four error values are 0.11017, 0.01358, 0.04681, 0.11654, and 0.13005. The findings offer technical assistance and a scientific foundation for enhancing the quality of stored wheat

Assessment of Porosity Defects in Ingot Using Machine Learning Methods during Electro Slag Remelting Process

The porosity defects in the ingot, which are caused by moisture absorption in slag during the electroslag remelting process, deserve the researcher’s attention in the summer wet season. The prediction of slag weight gain caused by moisture absorption is critical for developing slag baking and scheduling strategies and can assist workshop managers in making informed decisions during industrial production of electro slag remelting. The moisture absorption in slag under the conditions of different air humidity, experimental time, slag particle size, and CaO content in the slag are investigated by slag weight gain experiments. The purpose of this study is to predict the rate of weight gain in slag using observed weight gain data and machine learning (ML) models. The observation dataset includes features and rate of weight growth, which serve as independent and dependent variables, respectively, for ML models. Four machine learning models: linear regression, support vector regression, random forest regression, and multi-layer perceptron, were employed in this study. Additionally, parameters for machine learning models were selected using 5-fold cross-validation. Support vector regression outperformed the other three machine learning models in terms of root-mean-square errors, mean squared errors, and coefficients of determination. Thus, the ML-based model is a viable and significant method for forecasting the slag weight gain rate, whereas support vector regression can produce results that are competitive and satisfying. The results of slag weight gain data and ML models show that the slag weight gain increases with the increase of air humidity, experimental time, slag particle size, and CaO content in the slag. The porosity defect in the ingot during the ESR process often appears when the moisture in the slag exceeds 0.02%. Considering saving electric energy, the complexity of on-site scheduling, and 4 h of scheduling time, the slag T3 (CaF2:CaO:Al2O3:MgO = 37:28:30:5) is selected to produce H13 steel ESR ingot in the winter, and slag T2 (CaF2:CaO:Al2O3:MgO = 48:17:30:5) is selected to produce H13 steel ESR ingot in the summer

Assessment of Porosity Defects in Ingot Using Machine Learning Methods during Electro Slag Remelting Process

The porosity defects in the ingot, which are caused by moisture absorption in slag during the electroslag remelting process, deserve the researcher’s attention in the summer wet season. The prediction of slag weight gain caused by moisture absorption is critical for developing slag baking and scheduling strategies and can assist workshop managers in making informed decisions during industrial production of electro slag remelting. The moisture absorption in slag under the conditions of different air humidity, experimental time, slag particle size, and CaO content in the slag are investigated by slag weight gain experiments. The purpose of this study is to predict the rate of weight gain in slag using observed weight gain data and machine learning (ML) models. The observation dataset includes features and rate of weight growth, which serve as independent and dependent variables, respectively, for ML models. Four machine learning models: linear regression, support vector regression, random forest regression, and multi-layer perceptron, were employed in this study. Additionally, parameters for machine learning models were selected using 5-fold cross-validation. Support vector regression outperformed the other three machine learning models in terms of root-mean-square errors, mean squared errors, and coefficients of determination. Thus, the ML-based model is a viable and significant method for forecasting the slag weight gain rate, whereas support vector regression can produce results that are competitive and satisfying. The results of slag weight gain data and ML models show that the slag weight gain increases with the increase of air humidity, experimental time, slag particle size, and CaO content in the slag. The porosity defect in the ingot during the ESR process often appears when the moisture in the slag exceeds 0.02%. Considering saving electric energy, the complexity of on-site scheduling, and 4 h of scheduling time, the slag T3 (CaF2:CaO:Al2O3:MgO = 37:28:30:5) is selected to produce H13 steel ESR ingot in the winter, and slag T2 (CaF2:CaO:Al2O3:MgO = 48:17:30:5) is selected to produce H13 steel ESR ingot in the summer

Dynamic investigation on the powder spreading during selective laser melting additive manufacturing

In selective laser melting in additive manufacturing, powder spreading significantly affects the subsequent operating procedure as well as the quality of final products. Compared with large amount of previous work on powder spreading on a flat substrate surface before printing, in this article, 3D particulate scale dynamic simulations were carried out to study the spreading of 316 L stainless steel powder during printing by using discrete element method (DEM). The influences of various factors including processing parameters, blade shape, and the powder size on the quality of the spread powder bed were investigated in terms of both macroscopic packing density/uniformity and microstructure/micro dynamics. And optimized condition was identified. The mechanisms were also analyzed based on the powder behavior and forces caused by cooperative interaction between the formed zone (printed part) and the already packed powder layer. The results show that the blade moving speed can seriously influence the quality of the spread powder bed; normally the smaller the blade moving speed, the higher the powder bed quality, but the lower the working efficiency. Therefore, through comprehensive consideration, the proper blade moving speed is chosen to be 0.1 m/s. Increasing the blade gap height or decreasing the particle size (i.e., D = 30 µm) will increase the average relative packing density and structure uniformity. The angle of 15° for the blade is proved to be optimal for excellent powder spreading. Through simulation under optimized parameters, it can be found that the spread powder layer can be more uniform and much denser with high efficiency. And both macroscopic and microscopic analyzes indicate that the spread powder bed has desired structure and property. These findings can improve the fundamental understanding on the powder spreading and provide valuable references for the formation of high quality powder bed during 3D printing