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The cyclic voltammetric responses of individual palladium-coated carbon nanotubes are reported. Upon impact-from the solution phase-with the electrified interface, the nanoparticles act as individual nanoelectrodes catalyzing the hydrogen-oxidation reaction. At high overpotentials the current is shown to reach a quasi-steady-state diffusion limit, allowing determination of the tube length. The electrochemical response of the individual nanotubes also reveals the system to be modulated by the electrical contact between the electrode and carbon nanotube. This modulation presents itself as fluctuations in the recorded Faradaic current.

Honeybee caste development is nutritionally regulated by royal jelly (RJ). Major royal jelly protein 1 (MRJP1), the most abundant glycoprotein among soluble royal jelly proteins, plays pivotal roles in honeybee nutrition and larvae development, and exhibits broad pharmacological activities in humans. However, its structure has long remained unknown. Herein, we identify and report a 16-molecule architecture of native MRJP1 oligomer containing four MRJP1, four apisimin, and eight unanticipated 24-methylenecholesterol molecules at 2.65 Å resolution. MRJP1 has a unique six-bladed β-propeller fold with three disulfide bonds, and it interacts with apisimin mainly by hydrophobic interaction. Every four 24-methylenecholesterol molecules are packaged by two MRJP1 and two apisimin molecules. This assembly dimerizes to form an H-shaped MRJP14-apisimin4-24-methylenecholesterol8 complex via apisimin in a conserved and pH-dependent fashion. Our findings offer a structural basis for understanding the pharmacological effects of MRJPs and 24-methylenecholesterol, and provide insights into their unique physiological roles in bees.

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Cyber-physical systems embed software into the physical world. They appear in a wide range of applications such as smart grids, robotics, and intelligent manufacturing. Cyber-physical systems have proved resistant to modeling due to their intrinsic complexity arising from the combination of physical and cyber components and the interaction between them. This study proposes a general framework for discovering cyber-physical systems directly from data. The framework involves the identification of physical systems as well as the inference of transition logics. It has been applied successfully to a number of real-world examples. The novel framework seeks to understand the underlying mechanism of cyber-physical systems as well as make predictions concerning their state trajectories based on the discovered models. Such information has been proven essential for the assessment of the performance of cyber-physical systems; it can potentially help debug in the implementation procedure and guide the redesign to achieve the required performance.