Depression is immensely attributed to the overactivation of N-methyl-d-aspartic acid (NMDA) receptor in the brains. As regulatory binding partners of NMDA receptor, both Zn2+ and H+ are intimately interrelated to NMDA receptor’s activity. Therefore, exploring synergistic changes on the levels of Zn2+ and H+ in brains will promote the knowledge and treatment of depression. However, the lack of efficient, appropriate imaging tools limits simultaneously tracking Zn2+ and H+ in living mouse brains. Thus, a well-designed dual-color fluorescent probe (DNP) was fabricated for the simultaneous monitoring of Zn2+ and H+ in the brains of mice with depression. Encountering Zn2+, the probe evoked bright blue fluorescence at 460 nm. Meanwhile, the red fluorescence at 680 nm was decreased with H+ addition. With blue/red dual fluorescence signal of DNP, we observed the synchronous increased Zn2+ and H+ in PC12 cells under oxidative stress. Notably, in vivo imaging for the first time revealed the simultaneous reduction of Zn2+ and pH in brains of mice with depression-like behaviors. Further results implied that the NMDA receptor might be responsible for the coinstantaneous fluctuation of Zn2+ and H+ during depression. Altogether, this work is conducive to the knowledge of neural signal transduction mechanisms, advancing our understanding of the pathogenesis in depression.

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The clustering algorithm plays an important role in data mining and image processing. The breakthrough of algorithm precision and method directly affects the direction and progress of the following research. At present, types of clustering algorithms are mainly divided into hierarchical, density-based, grid-based and model-based ones. This paper mainly studies the Clustering by Fast Search and Find of Density Peaks (CFSFDP) algorithm, which is a new clustering method based on density. The algorithm has the characteristics of no iterative process, few parameters and high precision. However, we found that the clustering algorithm did not consider the original topological characteristics of the data. We also found that the clustering data is similar to the social network nodes mentioned in DeepWalk, which satisfied power-law distribution. In this study, we tried to consider the topological characteristics of the graph in the clustering algorithm. Based on previous studies, we propose a clustering algorithm that adds the topological characteristics of original data on the basis of the CFSFDP algorithm. Our experimental results show that the clustering algorithm with topological features significantly improves the clustering effect and proves that the addition of topological features is effective and feasible.Electronics 2020, 9, 459 2 of 16The K-means algorithm is an algorithm that adopts the alternative minimization method to solve non-convex optimization problems [11,12] and it is a representative of the prototype-based clustering method of objective functions. It divides a given data set into K clusters designated by users and has a high execution efficiency. However, the K-means algorithm needs to specify the number of clustering centers in advance. Different K values have a great impact on the experimental results and, in the actual operation, multiple attempts are required to obtain the optimal K value. Firstly, the objectivity of the K value makes the clustering results mostly different. Secondly, this algorithm is not friendly to non-convex data sets and it is difficult to obtain satisfactory clustering results.The AP clustering algorithm [13,14] is an unsupervised clustering algorithm. The algorithm realizes the clustering process through the interaction between data points. The advantages of the algorithm are that there is no need to define the number of classes in advance, the clustering center can be obtained adaptively through the iterative process, and the location and number of class centers can be identified automatically from the data points, so as to maximize the similarity sum of all similar points. At the same time, the algorithm has disadvantages. Since the AP algorithm needs to calculate the similarity between each pair of data objects in advance, when there are many data objects, the memory footprint will be high. Moreover, its high time complexity O(N3) is one of its shortcomings.The DBSCAN algorithm [15,16] is a clustering algorithm based on data point density. It gets rid of the constraint o...