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Inthis paper, we consider the reducibility of a class of nonlinear almost periodic Hamiltonian systems. Under suitable hypothesis of analyticity, non-resonant conditions and non-degeneracy conditions, by using KAM iteration, it is shown that the considered Hamiltonian system is reducible to an almost periodic Hamiltonian system with zero equilibrium points for most small enough parameters. As an example, we discuss the reducibility and stability of an almost periodic Hill's equation. [ABSTRACT FROM AUTHOR]

• The electronic simulation of the from pressure stimuli to nociceptors was conducted. • A bionic sensory system has been developed to present tactile perception. • The piezoelectric film sensor serves as an electronic skin. • The NiAl-LDHs/gelatin heterojunction memristor emulates biological nociceptors. • The essential characteristics of nociceptors, including threshold, no adaptation, relaxation, and sensitization were demonstrated. The sensations of touch and pain are fundamental components of our daily existence, serving as conduits for providing crucial insights into our environment and ensuring the safeguarding of our health. To date, the exploration of artificial systems that combine pain and touch perception has remained very limited. Moreover, memristor based visualization of pain degree is rarely reported. The electronic simulation of the from pressure stimuli to nociceptors was conducted in this work. For this purpose, a bionic sensory system has been developed to present tactile perception. In this system, the piezoelectric film sensor serves as an electronic skin to mimic artificial mechanoreceptors for sensing external pressure stimulation and generating signals. Meanwhile, the Ni-Al layered double hydroxides (LDHs)/gelatin heterojunction memristor emulates biological nociceptors to receive and process receptor signals, with luminous diode used as displays to indicate the degree of artificial contusion. The Ni-Al LDHs/gelatin heterojunction memristor demonstrates essential characteristics of nociceptors, including threshold, no adaptation, relaxation, and sensitization phenomena of allodynia and hyperalgesia. This biomimetic artificial tactile and pain perception system successfully simulates the perception threshold and sensitivity to pressure stimuli. The potential ability of humanoid robots could be demonstrated by utilizing the system as an artificial tactile alarm system. [ABSTRACT FROM AUTHOR]

Predicting interactions between microbes and hosts plays critical roles in microbiome population genetics and microbial ecology and evolution. How to systematically characterize the sophisticated mechanisms and signal interplay between microbes and hosts is a significant challenge for global health risks. Identifying microbe-host interactions (MHIs) can not only provide helpful insights into their fundamental regulatory mechanisms, but also facilitate the development of targeted therapies for microbial infections. In recent years, computational methods have become an appealing alternative due to the high risk and cost of wet-lab experiments. Therefore, in this study, we utilized rich microbial metagenomic information to construct a novel heterogeneous microbial network (HMN)-based model named KGVHI to predict candidate microbes for target hosts. Specifically, KGVHI first built a HMN by integrating human proteins, viruses and pathogenic bacteria with their biological attributes. Then KGVHI adopted a knowledge graph embedding strategy to capture the global topological structure information of the whole network. A natural language processing algorithm is used to extract the local biological attribute information from the nodes in HMN. Finally, we combined the local and global information and fed it into a blended deep neural network (DNN) for training and prediction. Compared to state-of-the-art methods, the comprehensive experimental results show that our model can obtain excellent results on the corresponding three MHI datasets. Furthermore, we also conducted two pathogenic bacteria case studies to further indicate that KGVHI has excellent predictive capabilities for potential MHI pairs. [ABSTRACT FROM AUTHOR]