As a key technology for large-scale graph data analysis, attributed graph clustering has received extensive attention in recent years. In particular, attributed graph clustering models based on deep graph neural networks have advantages in clustering accuracy. However, the deep graph neural network model has a complex structure and a large number of parameters, which can easily lead to overfitting, and its assumption that the attributes of adjacent nodes are similar is often not satisfied in the real world. To this end, this paper proposes an attributed graph subspace clustering algorithm with residual compensation guided by adaptive dual manifold regularization (ADMRGC). On the basis of the low-rank representation subspace clustering (LRR) model, ADMRGC introduces attributed manifold regularization, topological manifold regularization and residual compensation, which make ADMRGC possible to utilize attributed similarity and topological similarity at the same time, thus solving the problem that traditional subspace clustering only considers attributed information. In addition, ADMRGC balances the contribution of node attributes and topology by adaptively weighting the dual manifold regularization, and uses the residual self-expressive matrix to describe the difference between node attribute similarity and topological neighbor relationship. The attributed graph subspace clustering model ADMRGC proposed in this paper extends the traditional subspace clustering methods which are only applicable to a single structure to an attributed graph with a double structure, and avoids the limitation of graph neural network models assuming that the attributes of adjacent nodes are similar. Experimental results on 7 publicly available graph datasets show that ADMRGC can achieve the best clustering performance on both high-homogeneity and low-homogeneity datasets. Especially, for datasets with low homogeneity, ADMRGC as a shallow model can also achieve better clustering performance than state-of-the-art deep neural network models.
Digital PCR is the most advanced PCR technology. However, due to the high price of the digital PCR analysis instrument, this powerful nucleic acid detection technology is still difficult to be popularized in the general biochemistry laboratory. Moreover, one of the biggest disadvantages of commercial digital PCR systems is the poor versatility of reagents: each instrument can only be used for a few customized kits. Herein, we built a low-cost digital PCR system. The system only relies on low-cost traditional flat-panel PCR equipment to provide temperature conditions for commercial dPCR chips, and the self-made fluorescence detection system is designed and optically optimized to meet a wide range of reagent requirements. More importantly, our system not only has a low cost (<8000 US dollars) but also has a much higher universality for nucleic acid detection reagents than the traditional commercial digital PCR system. In this study, several samples were tested. The genes used in the experiment were plasmids containing UPE-1a fragment, TP53 reference DNA, hepatitis B virus DNA, leukemia sample, SARS-COV-2 DNA, and SARS-COV-2 RNA. Under the condition that DNA can be amplified normally, the function of the dPCR system can be realized with simpler and low-price equipment. Some DNA cannot be detected by using the commercial dPCR system because of the special formula when it is configured as the reaction solution, but these DNA fluorescence signals can be clearly detected by our system, and the concentration can be calculated. Our system is more applicable than the commercial dPCR system to form a new dPCR system that is smaller and more widely applicable than commercially available machinery.
Using method of pymgrallol autoxidation to detect SOD activity in vegetables and flowers.The method is simple,good precision and high sencitivity,the variance coefficient is 2.94 %,the recovery is 97.7%.This method may be widely applied SOD activity in vegetables and flowers.
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