The worldwide outbreak of severe acute respiratory syndrome (SARS) in 2003 had caused a high rate of mortality. Main protease (M) of SARS-associated coronavirus (SARS-CoV) is an important target to discover pharmaceutical compounds for the therapy of this life-threatening disease. During the course of screening new anti-SARS agents, we have identified that a series of unsymmetrical aromatic disulfides inhibited SARS-CoV M significantly for the first time. Herein, 40 novel unsymmetrical aromatic disulfides were synthesized chemically and their biological activities were evaluated in vitro against SARS-CoV M. These novel compounds displayed excellent IC data in the range of 0.516-5.954 μM. Preliminary studies indicated that these disulfides are reversible and mpetitive inhibitors. A possible binding mode was generated via molecular docking simulation and a comparative field analysis (CoMFA) model was constructed to understand the structure-activity relationships. The present research therefore has provided some meaningful guidance to design and identify anti-SARS drugs with totally new chemical structures.

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Inspired by the working modes of the human brain, the spiking neuron, as the basic computing unit, plays an important role in artificial perception and neuromorphic computing systems. However, the neuron circuit based on complementary metal-oxide-semiconductor technology has a complex structure, high power consumption, and limited flexibility. These features are not conducive to the large-scale integration and the application of flexible sensing systems compatible with the human body. The flexible memristor prepared in this paper shows stable threshold switching characteristics and excellent mechanical bending characteristics with bending radius up to 1.5 mm and bending times up to 10⁴ times. The compact neuron circuit constructed based on this device shows the key features of the neuron, such as threshold-driven spiking, all-or-nothing, refractory period, and strength-modulated frequency response. The frequency-input voltage relationship of the neuron shows the similarity of the rectified linear unit, which can be used to simulate the function of rectified linear unit in spiking neural networks. In addition, based on the electron transport mechanism, a core-shell model is introduced to analyze the working mechanism of the flexible memristor and explain the output characteristics of the neuron. In this model, the shell region consisting of NbO₂ is subject to ohmic conduction, while the core region consisting of Nb₂O_5-<em>x</em> is dominated by Poole-Frenkel conduction. These two mechanisms, combined with Newton's law of cooling, dominated the threshold switching behavior of flexible memristor device. Furthermore, the threshold switching characteristic of the memristor is simulated, verifying the rationality of the working mechanism of the flexible memristor. Concerning that the threshold voltage decreases with the increasing temperature, a correction term is added to the temperature of the shell region. Subsequently, the output characteristics of the neuron regulated by the input voltage are simulated. The simulation results show that the frequency increases with the input voltage and the threshold voltage decrease with the input voltage, which consistent with the experimental results. The introduction of the correction term confirms the influence of the thermal accumulation effect of the flexible substrate on neuron output characteristics. Finally, we built a spiking neural network based on memristive spiking neurons to implement handwriting recognition, achieving a 95.6% recognition rate, which is comparable to the ideal result of the artificial neural network (96%). This result shows the potential of the memristive spiking neurons for neuromorphic computing. In this paper, the study of flexible neurons can guide the design of neuromorphic sensing and computing systems.