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Abstract Enzymes are making a significant impact on chemical synthesis. However, the range of chemical products achievable through biocatalysis is still limited compared to the vast array of products possible with organic synthesis. For instance, azoxy products have rarely been synthesized using enzyme catalysts. In this study, we discovered that fungal unspecific peroxygenases are promising catalysts for synthesizing azoxy products from simple aniline starting materials. The catalytic features (up to 48,450 turnovers and a turnover frequency of 6.7 s–1) and substrate transformations (up to 99% conversion with 98% chemoselectivity) highlight the synthetic potential. We propose a mechanism where peroxygenase-derived hydroxylamine and nitroso compounds spontaneously (non-enzymatically) form the desired azoxy products. This work expands the reactivity repertoire of biocatalytic transformations in the underexplored field of azoxy compound formation reactions.

Abstract The performance improvement of CMOS computer fails to meet the enormous data processing requirement of artificial intelligence progressively. The memristive neural network is one of the most promising circuit hardwares to make a breakthrough. This paper proposes a novel memristive synaptic circuit that is composed of four MOS transistors and one memristor (4T1M). The 4T1M synaptic circuit provides flexible control strategies to change memristance or respond to the input signal. Applying the 4T1M synaptic circuit as the cell of memristive crossbar array, based on the structure and algorithm of the back-propagation (BP) neural network, this paper proposes circuit design of the memristive crossbar-based BP neural network. By reusing the 4T1M memristive crossbar array, the computations in the forward-propagation process and back-propagation process of BP neural network are accomplished on the memristive crossbar-based circuit to accelerate the computing speed. The 4T1M memristive crossbar array can change all the cells’ memristance at a time, accordingly, the memristive crossbar-based BP neural network can realize synchronous memristance adjustment. The proposed memristive crossbar-based BP neural network is then evaluated through experiments involving XOR logic operation, iris classification, and MNIST handwritten digit recognition. The experimental results present fewer iterations or higher classification accuracies. Further, the comprehensive comparisons with the existing memristive BP neural networks highlight the advantages of the proposed memristive crossbar-based BP neural network, which achieves the fastest memristance adjustment speed using relatively few components.

Multimodal physiological signals play a pivotal role in drivers’ perception of work stress. However, the scarcity of labels and the multitude of modalities render the utilization of physiological signals for driving cognitive alertness detection challenging. We thus propose a multimodal physiological signal detection model based on self-supervised learning. First, in order to mine the intrinsic information of data and enable data to highlight effective information, we introduce a multiscale entropy (MSE) evoked attention mechanism. Secondly, the multimodal patches undergo processing through a novel cascaded attention mechanism. This attention mechanism is rooted in patch-level interactions within each modality, progressively integrating and interacting with other modalities in a cascading manner, thereby mitigating computational complexity. Moreover, a multimodal uncertainty-aware module is devised to effectively cope with intricate variations in the data. This module enhances its generalization ability through the incorporation of uncertain resampling. Experiments were conducted on the DriveDB dataset and the CogPilot dataset with both the linear probing and the fine-tuning evaluation protocols. Experimental results in subject-dependent setting show that our model significantly outperforms previous competitive baselines. In the linear probing evaluation, our model achieves on average 6.26%, 6.64%, and 7.75% improvements in Accuracy (Acc), Recall (Rec), and F1 Score. It also outperforms other models by 7.96% in Acc, 9.13% in Rec, and 9.2% in F1 using the fine-tuning evaluation. Furthermore, our model also demonstrates robust performance in subject-independent setting.