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With the rapid development of machine learning, artificial intelligence technology has been widely applied across various domains of life. However, concerns regarding the privacy risks associated with machine learning have increased. In response to these concerns, the Personal Information Protection Law of the People's Republic of China was promulgated to regulate the collection, use, and transmission of private information. Despite this, machine learning requires a large amount of data, necessitating the development of privacy protection technologies that allow for the collection and processing of data under legal and compliant conditions. Split learning, a privacy-preserving machine learning technique that enables the training of distributed models among multiple participants without sharing raw data, has emerged as a research focus. It has been recognized that split learning is vulnerable to data privacy attacks, and various attacks along with corresponding defenses have been proposed. However, existing surveys have not discussed and summarized research on data privacy during the training phase of split learning. The comprehensive overview of data privacy attack and defense techniques in the training phase of split learning was offered. Initially, the definition, principles, and classifications of split learning were summarized. Subsequently, two common attacks in split learning, namely the raw data reconstruction attack and the label leakage attack, were introduced. The causes of these attacks in the training phase of split learning were then analyzed, and corresponding defenses were presented. Finally, future research directions in the area of data privacy for split learning were discussed.

Non-aqueous biocatalysis has attracted broad interest recently due to its differences from traditional aqueous catalysis and increased substrate solubility, which reduces feedback inhibition, improving enantiomer selectivity and completing synthesis reactions that cannot be performed in an aqueous solution. This approach shows remarkable application value in producing natural products, chemical products, pharmaceutical intermediates, and foods. This study aims to provide a concise overview of the current state of non-aqueous biocatalysis and its sustainability, summarizing the mechanism of non-aqueous biocatalysis and recent progress using immobilization technology. It includes different non-aqueous systems, such as organic phase systems, two-phase systems, ionic liquid systems, deep eutectic solvent systems, and non-solvent systems. Finally, this manuscript illustrates the challenges of non-aqueous catalysis and the prospects of the future areas of non-aqueous catalysis research. [ABSTRACT FROM AUTHOR]

The secondary and tertiary structures of RNA play a vital role in the regulation of biological reactions. These structures have been experimentally studied through in vivo and in vitro analyses, and in silico models have become increasingly accurate in predicting them. Recent technologies have diversified RNA structure predictions, from the earliest thermodynamic and molecular dynamic-based RNA structure predictions to deep learning-based conformation predictions in the past decade. While most research on RNA structure prediction has focused on short non-coding RNAs, there has been limited research on predicting the conformation of longer mRNAs. Our study introduces a computer simulation model called the Three-dimensional RNA Illustration Program (TRIP). TRIP is based on single-chain models and angle restriction of each bead component from previously reported single-molecule fluorescence in situ hybridization (smFISH) experiments. TRIP is a fast and efficient application that only requires up to three inputs to acquire outputs. It can also provide a rough visualization of the 3D conformation of RNA, making it a valuable tool for predicting RNA end-to-end distance., (© 2024. The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature.)