For the mimicry of human visual memory, a prominent challenge is how to detect and store the image information by electronic devices, which demands a multifunctional integration to sense light like eyes and to memorize image information like the brain by transforming optical signals to electrical signals that can be recognized by electronic devices. Although current image sensors can perceive simple images in real time, the image information fades away when the external image stimuli are removed. The deficiency between the state-of-the-art image sensors and visual memory system inspires the logical integration of image sensors and memory devices to realize the sensing and memory process toward light information for the bionic design of human visual memory. Hence, a facile architecture is designed to construct artificial flexible visual memory system by employing an UV-motivated memristor. The visual memory arrays can realize the detection and memory process of UV light distribution with a patterned image for a long-term retention and the stored image information can be reset by a negative voltage sweep and reprogrammed to the same or an other image distribution, which proves the effective reusability. These results provide new opportunities for the mimicry of human visual memory and enable the flexible visual memory device to be applied in future wearable electronics, electronic eyes, multifunctional robotics, and auxiliary equipment for visual handicapped.

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Microelectromechanical Systems (MEMS) Inertial Measurement Unit (IMU) containing a three-orthogonal gyroscope and three-orthogonal accelerometer has been widely utilized in position and navigation, due to gradually improved accuracy and its small size and low cost. However, the errors of a MEMS IMU based standalone Inertial Navigation System (INS) will diverge over time dramatically, since there are various and nonlinear errors contained in the MEMS IMU measurements. Therefore, MEMS INS is usually integrated with a Global Positioning System (GPS) for providing reliable navigation solutions. The GPS receiver is able to generate stable and precise position and time information in open sky environment. However, under signal challenging conditions, for instance dense forests, city canyons, or mountain valleys, if the GPS signal is weak and even is blocked, the GPS receiver will fail to output reliable positioning information, and the integration system will fade to an INS standalone system. A number of effects have been devoted to improving the accuracy of INS, and de-nosing or modelling the random errors contained in the MEMS IMU have been demonstrated to be an effective way of improving MEMS INS performance. In this paper, an Artificial Intelligence (AI) method was proposed to de-noise the MEMS IMU output signals, specifically, a popular variant of Recurrent Neural Network (RNN) Long Short Term Memory (LSTM) RNN was employed to filter the MEMS gyroscope outputs, in which the signals were treated as time series. A MEMS IMU (MSI3200, manufactured by MT Microsystems Company, Shijiazhuang, China) was employed to test the proposed method, a 2 min raw gyroscope data with 400 Hz sampling rate was collected and employed in this testing. The results show that the standard deviation (STD) of the gyroscope data decreased by 60.3%, 37%, and 44.6% respectively compared with raw signals, and on the other way, the three-axis attitude errors decreased by 15.8%, 18.3% and 51.3% individually. Further, compared with an Auto Regressive and Moving Average (ARMA) model with fixed parameters, the STD of the three-axis gyroscope outputs decreased by 42.4%, 21.4% and 21.4%, and the attitude errors decreased by 47.6%, 42.3% and 52.0%. The results indicated that the de-noising scheme was effective for improving MEMS INS accuracy, and the proposed LSTM-RNN method was more preferable in this application.

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Aiming to address the antimicrobial resistance and environmental burdens because of the widespread indulgence in biocides, a synthetic formula of biocides with controllable antibacterial activities and biodegradable characters are encouraged for the emergence of a novel spectrum of green biocides. In this respect, a polymeric antimicrobial catiomer, with the polymeric backbone sequentially attached with the functional cationic moieties and lipophilic moieties is synthesized. The striking antibacterial potency is achieved for treatment of both amoxicillin (AMO)-resistant Gram-positive S. aureus and Gram-negative E. coli. Moreover, the anionic aggregation-induced emission (AIE) compound is strategically introduced into the aforementioned polymeric antibacterial system, consequently employed as a facile reporter for the utility of bacteria detection based on the electrostatic reaction of anionic species in the bacterial membrane with the cationic polymeric antibacterials to liberate the anionic AIE compound. Ultimately, the proposed system is determined to afford appreciable temporal antibacterial potency by virtue of the readily available cleavage of the ester linkage in the presence of lipase, thereby eliminating the possibility of the antimicrobial resistance and environmental burdens. Hence, the proposed system provided a bottom-up approach for construction of an intriguing green antibacterial molecule.

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Mass spectrometry-based platforms have gained increasing success in discovery of ligands bound to therapeutic targets as drug candidates. We established both a nanoelectrospray ionization mass spectrometry (nanoESI-MS) assay and an ultrafiltration liquid chromatography/mass spectrometry (LC/MS) assay to identify new ligands for New Delhi metallo-β-lactamase 1 (NDM-1), responsible for worldwide antibiotic resistance. To alleviate nonspecific binding of hydrophobic compounds and eliminate false positives typically encountered in the indirect LC/MS-based assay, we introduced a blocking protein in the control, which remarkably enhances the selectivity and accuracy of the indirect approach. Side-by-side comparison of the two MS-based approaches for the first time further reveals unique advantages of the indirect approach, including better reproducibility and tolerance of interference. Moreover, the success of fishing out a potent ligand from a mixture of small-molecule fragments demonstrates great potential of the indirect LC/MS-based approach for constructing a robust screening platform against combinatorial libraries or natural product extracts. More importantly, by combining the results of MS-based analyses, enzymatic activity assay, competition experiments, and structural simulation, we discovered a new compound as a promising drug candidate targeting NDM-1.

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