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Schematic illustration of the synthesis and application of A-GQDs and WP5/A-GQDs. [Display omitted] • Green fluorescence A-GQDs were prepared using one-step hydrothermal process. • A-GQDs and WP5/A-GQDs composites all exhibit low cytotoxicity and good biocompatibility. • A-GQDs have a bright fluorescence imaging on the cell membranes of cancer cells and normal cells. • A-GQDs modified by WP5 have good penetration into cell membranes and excellent fluorescence performance. Amphiphilic graphene quantum dots (A-GQDs) reveal prodigious potential application in bioimaging, resulting from their hydrophilic and lipophilic properties, as well as their good biocompatibility and negligible cytotoxicity. Herein, green fluorescent A-GQDs with single-layer graphene structure were prepared by one step of hydrothermal method. The A-GQDs with the average size of 3.5 nm displayed excitation-independent emission due to uniform size. A-GQDs emitted bright fluorescence on the surface of the cell membrane of MCF-7 cells when they were co-cultured with MCF-7 cells, which was similar to that of QSG-7701 cells. The water-soluble pillar[5]arene (WP5) was then incorporated on to the A-GQDs via π-π interaction and electrostatic interaction. There was different labeling imaging effect of MCF-7 and QSG-7701 cells achieved by combining the merits of WP5 and A-GQDs compared with individual A-GQDs. The synthesized composites not only had a certain enhancement in fluorescence, but also constructed a penetrating channel through the cell membrane, so that the entire cell had a distinct imaging. Therefore, these composites might be a potential candidate for applications in visualization of cell membrane and bioimaging. [ABSTRACT FROM AUTHOR]

[Display omitted] • The carbon dots were synthesized from the raw materials of citric acid, phenylalanine and anthraquinone. Among them, we selected anthraquinones (1-Aminoanthraquinone or 1, 5-Diaminoanthraquinone) with different numbers of amino groups to explore the effect of the number of amino groups in the raw materials on the carbon dots. The generated CDs had good biocompatibility, and phenylalanine group could bind to the amino acid transporter (LAT1) on the surface of cancer cells which would transport CDs into cancer cells and CDs fluoresced in cancer cell, while it was difficult for CDs to enter normal cells through LAT1. Therefore 1,5-CDs had been used by us to distinguish cancer cells from normal cells. After that, we studied some factors that affected the fluorescence of CDs, and we found that the amino and carboxyl groups on the surface of CDs had certain effects on their fluorescence. When we reduced the amino and carboxyl groups on the surface of CDs, the fluorescence intensity decreased. Moreover, the as-prepared CDs had solvent effects, and the emission peaks of CDs shifted due to the change of solvent polarity. Based on this, we developed a method for the detection of water content in ethanol using CDs as ratiometric probes. The change in the intensity ratio of the two emission peaks corresponded to the change of water content, and the detection linearity was good. • In other studies, carbon dots usually had only one emission peak, and were more susceptible to other environmental factors in practical applications, resulting in large deviations in the results. The phenylalanine-bound CDs prepared in this study had double emission peaks, and we also explored the factors that affected their fluorescence. And phenylalanine-bound CDs could effectively distinguish cancer cells from normal cells and had potential application in detection of H 2 O. Herein, phenylalanine-bound carbon dots (CDs) with dual fluorescence emission peak behavior were reported, one of which was excitation-dependent and the other was not. The amino and carboxyl groups on the surface of CDs had a certain influence on their fluorescence. When the amino and carboxyl groups on the surface of CDs were removed, their fluorescence was significantly weakened. The prepared CDs had solvent effect, and the emission peak of CDs shifted according to the change of the polarity of the solvent. Based on this, a method was developed for detecting water content in ethanol using CDs as a ratio probe. The change in the ratio of the intensity of the two emission peaks corresponded to the change of water content in ethanol. The detection linearity was good. In addition, the resulting CDs had good biocompatibility and could be enormously transported into cancer cells by the amino acid transporter on the surface of cancer cells, while normal cells only had a small amount of CDs through endocytosis, which laid the base for the distinguishing cancer cells from normal cells. The unique fluorescence properties of the prepared CDs achieved their applications in both water detection and cell differentiation. [ABSTRACT FROM AUTHOR]

Counting pedestrians in surveillance videos has become an urgent safety concern in critical areas. However, surveillance videos of subway spaces suffer from severe crowd occlusion and perspective distortion. In this paper, a novel double-region learning algorithm is presented to overcome these challenges. The main idea of this algorithm is to identify the best two-region boundary and then design a reasonable pedestrian-counting method in each separated region. First, a separate line is obtained via possibility learning, and each frame is divided into a nearby region and a distant region to eliminate the influence of perspective distortion. Second, in the nearby region, we apply the improved aggregate channel feature detection to count the number of pedestrians N 1 . In the distant region, we employ the Extreme Learning Machine and Gaussian Process regression methods to estimate the number of pedestrians N 2 . Finally, the total number of pedestrians in each frame can be obtained with high accuracy according to N 1 and N 2 . We establish a subway pedestrian video dataset about several typical subway stations in Shanghai to validate the algorithm performance. Various experimental results demonstrate that the accuracy of the proposed approach surpasses that of compared methods, which means that our algorithm can meet the management requirements of subway stations. [ABSTRACT FROM AUTHOR]

Wireless sensor networks have been widely used to achieve fine-grained information collection. However, numerous data acquisition and processing of sensors bring some privacy issues. Federated learning is a promising and privacy-friendly framework that trains a model across multiple devices or edge nodes holding local data samples without transferring their data to the server. It is not enough to protect privacy only by maintaining data locality, so differential privacy technology is often used to protect privacy in federated learning. However, different users have different privacy requirements, so it is inappropriate to use the same privacy protection scheme, assuming that all users trust or distrust the server. The former has poor accuracy, while the latter has poor privacy. This paper proposes a secure and reliable federated learning algorithm by integrating hybrid differential privacy into federated learning. We divide users into two categories according to their different privacy needs. In addition, we analyze the convergence and privacy bounds of the proposed algorithm and propose an adaptive gradient clip scheme and improved composition method to reduce the effects of noise and clip, respectively. The validity of the algorithm is verified by theoretical analysis and experimental evaluation on real-world datasets. [ABSTRACT FROM AUTHOR]