A Fast Granular-Ball-Based Density Peaks Clustering Algorithm for Large-Scale Data.

Density peaks clustering algorithm (DP) has difficulty in clustering large-scale data, because it requires the distance matrix to compute the density and δ -distance for each object, which has O(n2) time complexity. Granular ball (GB) is a coarse-grained representation of data. It is based on the fact that an object and its local neighbors have similar distribution and they have high possibility of belonging to the same class. It has been introduced into supervised learning by Xia et al. to improve the efficiency of supervised learning, such as support vector machine, k -nearest neighbor classification, rough set, etc. Inspired by the idea of GB, we introduce it into unsupervised learning for the first time and propose a GB-based DP algorithm, called GB-DP. First, it generates GBs from the original data with an unsupervised partitioning method. Then, it defines the density of GBs, instead of the density of objects, according to the centers, radius, and distances between its members and centers, without setting any parameters. After that, it computes the distance between the centers of GBs as the distance between GBs and defines the δ -distance of GBs. Finally, it uses GBs' density and δ -distance to plot the decision graph, employs DP algorithm to cluster them, and expands the clustering result to the original data. Since there is no need to calculate the distance between any two objects and the number of GBs is far less than the scale of a data, it greatly reduces the running time of DP algorithm. By comparing with k -means, ball k -means, DP, DPC-KNN-PCA, FastDPeak, and DLORE-DP, GB-DP can get similar or even better clustering results in much less running time without setting any parameters. The source code is available at https://github.com/DongdongCheng/GB-DP.

Key theories and technologies and implementation mechanism of parallel computing for ternary optical computer.

Ternary Optical Computer (TOC) is more advanced than traditional computer systems in parallel computing, which is characterized by huge amounts of repeated computations. However, the application of the TOC is still limited because of lack of key theories and technologies. In order to make the TOC applicable and advantageous, this paper systematically elaborates the key theories and technologies of parallel computing for the TOC through a programming platform, including reconfigurability and groupable usability of optical processor bits, parallel carry-free optical adder and the TOC's application characteristics, communication file to express user's needs and data organization method of the TOC. Finally, experiments are carried out to show the effectiveness of the present theories and technologies for parallel computing, as well as the feasibility of the implementation method of the programming platform. For a special instance, it is shown that the clock cycle on the TOC is only 0.26% of on a traditional computer, and the computing resource spent on the TOC is 25% of that on a traditional computer. Based on the study of the TOC in this paper, more complex parallel computing can be realized in the future.

K-Means Clustering With Natural Density Peaks for Discovering Arbitrary-Shaped Clusters.

Due to simplicity, K-means has become a widely used clustering method. However, its clustering result is seriously affected by the initial centers and the allocation strategy makes it hard to identify manifold clusters. Many improved K-means are proposed to accelerate it and improve the quality of initialize cluster centers, but few researchers pay attention to the shortcoming of K-means in discovering arbitrary-shaped clusters. Using graph distance (GD) to measure the dissimilarity between objects is a good way to solve this problem, but computing the GD is time-consuming. Inspired by the idea that granular ball uses a ball to represent the local data, we select representatives from a local neighborhood, called natural density peaks (NDPs). On the basis of NDPs, we propose a novel K-means algorithm for identifying arbitrary-shaped clusters, called NDP-Kmeans. It defines neighbor-based distance between NDPs and takes advantage of the neighbor-based distance to compute the GD between NDPs. Afterward, an improved K-means with high-quality initial centers and GD is used to cluster NDPs. Finally, each remaining object is assigned according to its representative. The experimental results show that our algorithms can not only recognize spherical clusters but also manifold clusters. Therefore, NDP-Kmeans has more advantages in detecting arbitrary-shaped clusters than other excellent algorithms.

Histone demethylase KDM4A plays an oncogenic role in nasopharyngeal carcinoma by promoting cell migration and invasion.

Compelling evidence has indicated the vital role of lysine-specific demethylase 4 A (KDM4A), hypoxia-inducible factor-1α (HIF1α) and the mechanistic target of rapamycin (mTOR) signaling pathway in nasopharyngeal carcinoma (NPC). Therefore, we aimed to investigate whether KDM4A affects NPC progression by regulating the HIF1α/DDIT4/mTOR signaling pathway. First, NPC and adjacent tissue samples were collected, and KDM4A protein expression was examined by immunohistochemistry. Then, the interactions among KDM4A, HIF1α and DDIT4 were assessed. Gain- and loss-of-function approaches were used to alter KDM4A, HIF1α and DDIT4 expression in NPC cells. The mechanism of KDM4A in NPC was evaluated both in vivo and in vitro via RT-qPCR, Western blot analysis, MTT assay, Transwell assay, flow cytometry and tumor formation experiments. KDM4A, HIF1α, and DDIT4 were highly expressed in NPC tissues and cells. Mechanistically, KDM4A inhibited the enrichment of histone H3 lysine 9 trimethylation (H3K9me3) in the HIF1α promoter region and thus inhibited the methylation of HIF1α to promote HIF1α expression, thus upregulating DDIT4 and activating the mTOR signaling pathway. Overexpression of KDM4A, HIF1α, or DDIT4 or activation of the mTOR signaling pathway promoted SUNE1 cell proliferation, migration, and invasion but inhibited apoptosis. KDM4A silencing blocked the mTOR signaling pathway by inhibiting the HIF1α/DDIT4 axis to inhibit the growth of SUNE1 cells in vivo. Collectively, KDM4A silencing could inhibit NPC progression by blocking the activation of the HIF1α/DDIT4/mTOR signaling pathway by increasing H3K9me3, highlighting a promising therapeutic target for NPC.

[The local outliers mining system of celestial body spectrum based on constrained concept lattice].

It is one of the main goals in mankinds universe exploration to find unknown and particular celestial bodies. Data mining is an effective way of finding the spectrum data of unknown and particular celestial body in mass celestial body spectrum data. Constrained concept lattice, with characteristics of higher constructing efficiency, practicability and pertinency, is a new concept lattice structure. For local bias data of celestial body spectrum in characteristic subspace, the local outlier mining system of celestial body spectrum based on constrained concept lattice was designed and implemented by using VC++ 6.0 and Oracle 9i as developing tools. At the same time, its software architecture and function modules were outlined. Such key techniques for preprocessing celestial body spectrum data, the constructing method of constrained concept lattice, and the local outlier mining method were discussed in details. The running results show that the system is feasible and valuable for mining local bias data of celestial body spectrum in low dimensional characteristic subspace. Therefore, the system provides an effective means for finding the unknown and particular celestial bodies.

[Clinical observation on three-combination needling method for treatment of primary trigeminal neuralgia].

OBJECTIVE: To probe into a better therapy for primary trigeminal neuralgia. METHODS: Eighty-six cases were randomly divided into an observation group (n = 46) and a control group (n = 40). The observation group were treated with the three-combination needling method, i. e. acupuncture, acupoint-injection and fire-needle therapy, and the control group with acupuncture and acupoint-injection. After treatment of 2 courses, their therapeutic effects were assessed. RESULTS: The total effective rate of 93.5% and the cured rate of 60.9% in the observation group were better than 65.0% and 22.5% in the control group, with significant differences (both P < 0.01). CONCLUSION: The three-combination needling method has obvious clinical therapeutic effect on primary trigeminal neuralgia.

[Soluble sugar content of clonal plant Neosinocalamus affinis at module and ramet levels].

In this paper, soluble sugar content of Neosinocalamus affinis was measured by anthrone colorimetry at two levels of module and ramet in order to reveal its ecological and physiological adaptability. The results showed that soluble sugar content decreased in the order of leaf > branch > culm. As for soluble sugar content, different modules responded to ramet age and position in different ways. The branch and culm of 1-year-old ramets contented more soluble sugar than those of other four ages, but soluble sugar content in leaf was independent to ramet age. For leaf and culm, lower parts of ramet contented more soluble sugar than middle and upper parts. Under high irradiance, the soluble sugar content of Neosinocalamus affinis leaf was more than that under low irradiance. Seasonal change had significant effect on soluble sugar content. The soluble sugar content of leaf presented a monthly change, with a bottom and upper value in January and in April, respectively, but there was no obvious difference between 2-year-old and 3-year-old ramets. The soluble sugar content of parent ramet leaves was positively correlative to that of daughter ramet in upper and middle parts of ramets. There was no significant difference between ramet ages at ramet level. Hierarchical response of physiological character to environmental changes existed at the module and ramet levels of Neosinocalamus affinis.