[Progress in the application of artificial intelligence-assisted molecular modification of enzymes].

Natural enzymes are often difficult to meet the needs of application and research in terms of activity, enantiomer selectivity or thermal stability. Therefore, it is an important task of enzyme engineering to explore efficient molecular modification technologies to improve the properties of such enzymes. The molecular modification technologies of enzymes mainly include rational design, directed evolution, and artificial intelligence-assisted design. Directed evolution and rational design are experiment-driven molecular modification approaches of enzymes and have been successfully applied to enzyme engineering. However, due to the huge space sizes of protein sequences and the lack of experimental data, the current modification methods still face major challenges. With the development of next-generation sequencing, high-throughput screening, protein databases, and artificial intelligence (AI), data-driven enzyme engineering is emerging as a promising solution to these challenges. The AI-assisted statistical learning method has been used to establish a model for predicting the sequence/structure-properties of enzymes in a data-driven manner. Excellent mutant enzymes can be selected according to the prediction results, which greatly improve the efficiency of molecular modification. Considering the application requirements of molecular modification of enzymes, this paper reviews the data acquisition methods and application examples of AI-assisted molecular modification of enzymes, with focuses on the convolutional neural network method for predicting protein thermostability, aiming to provide reference for researchers in this field.

Convolutional neural network in rice disease recognition: accuracy, speed and lightweight.

There are many rice diseases, which have very serious negative effects on rice growth and final yield. It is very important to identify the categories of rice diseases and control them. In the past, the identification of rice disease types was completely dependent on manual work, which required a high level of human experience. But the method often could not achieve the desired effect, and was difficult to popularize on a large scale. Convolutional neural networks are good at extracting localized features from input data, converting low-level shape and texture features into high-level semantic features. Models trained by convolutional neural network technology based on existing data can extract common features of data and make the framework have generalization ability. Applying ensemble learning or transfer learning techniques to convolutional neural network can further improve the performance of the model. In recent years, convolutional neural network technology has been applied to the automatic recognition of rice diseases, which reduces the manpower burden and ensures the accuracy of recognition. In this paper, the applications of convolutional neural network technology in rice disease recognition are summarized, and the fruitful achievements in rice disease recognition accuracy, speed, and mobile device deployment are described. This paper also elaborates on the lightweighting of convolutional neural networks for real-time applications as well as mobile deployments, and the various improvements in the dataset and model structure to enhance the model recognition performance.

Machine learning boosts three-dimensional bioprinting.

Three-dimensional (3D) bioprinting is a computer-controlled technology that combines biological factors and bioinks to print an accurate 3D structure in a layer- by-layer fashion. 3D bioprinting is a new tissue engineering technology based on rapid prototyping and additive manufacturing technology, combined with various disciplines. In addition to the problems in in vitro culture process, the bioprinting procedure is also afflicted with a few issues: (1) difficulty in looking for the appropriate bioink to match the printing parameters to reduce cell damage and mortality; and (2) difficulty in improving the printing accuracy in the printing process. Data- driven machine learning algorithms with powerful predictive capabilities have natural advantages in behavior prediction and new model exploration. Combining machine learning algorithms with 3D bioprinting helps to find more efficient bioinks, determine printing parameters, and detect defects in the printing process. This paper introduces several machine learning algorithms in detail, summarizes the role of machine learning in additive manufacturing applications, and reviews the research progress of the combination of 3D bioprinting and machine learning in recent years, especially the improvement of bioink generation, the optimization of printing parameter, and the detection of printing defect.

Quantitative detection of zearalenone in wheat grains based on near-infrared spectroscopy.

Zearalenone (ZEN) can easily contaminate wheat, seriously affecting the quality and safety of wheat grains. In this study, a near-infrared (NIR) spectroscopy detection method for rapid detection of ZEN in wheat grains was proposed. First, the collected original near-infrared spectra were denoised, smoothed and scatter corrected by Savitzky-Golay smoothing (SG-smoothing) and multiple scattering correction (MSC), and then normalized. Three wavelength variable selection algorithms were used to select variables from the preprocessed NIR spectra, which were random frog (RF), successive projections algorithm (SPA), least absolute shrinkage and selection operator (LASSO). Finally, based on the feature variables extracted by the above algorithms, support vector machine (SVM) models were established respectively to realize the quantitative detection of the ZEN in wheat grains. Eventually, the prediction effect of the LASSO-SVM model was the best, the prediction correlation coefficient (RP) was 0.99, the root mean square error of prediction (RMSEP) was 2.1 µg·kg-1, and the residual prediction deviation (RPD) was 6.0. This research shows that the NIR spectroscopy can be used for high-precision quantitative detection of the ZEN in grains, and the research gives a new technical solution for the in-situ detection of mycotoxins in stored grains.