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Fatty acyl‐CoA reductase (FAR) is one of the key enzymes, which catalyses the conversion of fatty acyl‐CoA to the corresponding alcohols. Among the FAR family members in the brown planthopper (Nilaparvata lugens), NlFAR7 plays a pivotal role in both the synthesis of cuticular hydrocarbons and the waterproofing of the cuticle. However, the precise mechanism by which NlFAR7 influences the formation of the cuticle structure in N. lugens remains unclear. Therefore, this paper aims to investigate the impact of NlFAR7 through RNA interference, transmission electron microscope, focused ion beam scanning electron microscopy (FIB‐SEM) and lipidomics analysis. FIB‐SEM is employed to reconstruct the three‐dimensional (3D) architecture of the pore canals and related cuticle structures in N. lugens subjected to dsNlFAR7 and dsGFP treatments, enabling a comprehensive assessment of changes in the cuticle structures. The results reveal a reduction in the thickness of the cuticle and disruptions in the spiral structure of pore canals, accompanied by widened base and middle diameters. Furthermore, the lipidomics comparison analysis between dsNlFAR7‐ and dsGFP‐treated N. lugens demonstrated that there were 25 metabolites involved in cuticular lipid layer synthesis, including 7 triacylglycerols (TGs), 5 phosphatidylcholines (PCs), 3 phosphatidylethanolamines (PEs) and 2 diacylglycerols (DGs) decreased, and 4 triacylglycerols (TGs) and 4 PEs increased. In conclusion, silencing NlFAR7 disrupts the synthesis of overall lipids and destroys the cuticular pore canals and related structures, thereby disrupting the secretion of cuticular lipids, thus affecting the cuticular waterproofing of N. lugens. These findings give significant attention with reference to further biochemical researches on the substrate specificity of FAR protein, and the molecular regulation mechanisms during N. lugens life cycle. [ABSTRACT FROM AUTHOR]

Neuromorphic computing based on spiking neural networks (SNNs) has attracted significant research interest due to its low energy consumption and high similarity to biological neural systems. The artificial spiking afferent neuron (ASAN) system is the essential component of neuromorphic computing system to interact with the environment. This work presents an ASAN system with simple structure by employing a new architecture of one VO2 Mott memristor and one resistive sensor (1M1S). The Mott memristors show the bidirectional Mott transition, good endurance (> $1.3\times10$ 9), and high uniformity. By incorporating a flexible pressure sensor into the 1M1S architecture, a tactile ASAN system is realized with the pressure stimuli converted into rate-coded spikes. Using a $3\times3$ array of the tactile ASAN systems, different pressure stimulus patterns can be well recognized. The strong adaptability of the proposed system will enable it to convert lots of environmental stimuli through the widely used resistive sensors into rate-coded spikes as the inputs of neuromorphic computing based on SNNs. [ABSTRACT FROM AUTHOR]

Inspired by biological mechanoreceptors, we present a flexible artificial mechanoreceptor (FAM) based on high-performance VO 2 insulator-metal transition (IMT) memristor with the functions of sensing, spikes coding and information fusion for the anthropomorphic neurorobotics. The flexible VO 2 IMT memristors with the structure of Ti/Pt/VO 2 /Pt via-hole show bi-directional threshold switching performance, good endurance (>2 ×109) and excellent flexibility (>103 bending cycles). The FAM comprised of three receptors (flexible pressure sensors) and one flexible VO 2 IMT memristor is able to detect, code and fuse the pressure information from different receptors into spike trains similar to the biological SA-Ⅰ mechanoreceptors, laying the foundation for the flexible electronic skin of anthropomorphic neurorobotics in the future. • The presented artificial mechanoreceptor is fully flexible, satisfying the needs of flexible electronic skins of neurorobots. • The artificial mechanoreceptor can sense, code, and fuse the spatio-temporal pressure information into spike trains. • The VO 2 IMT memristors show high performance with good endurance (>2 ×109) and excellent flexibility (>103 bending cycles). [ABSTRACT FROM AUTHOR]