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Hierarchical quotient space structure (HQSS), as a typical description of granular computing (GrC), focuses on hierarchically granulating fuzzy data and mining hidden knowledge. The key step of constructing HQSS is to transform the fuzzy similarity relation into fuzzy equivalence relation. However, on one hand, the transformation process has high time complexity. On the other hand, it is difficult to mine knowledge directly from fuzzy similarity relation due to its information redundancy, i.e., sparsity of effective information. Therefore, this article mainly focuses on proposing an efficient granulation approach for constructing HQSS by quickly extracting the effective value of fuzzy similarity relation. First, the effective value and effective position of fuzzy similarity relation are defined according to whether they could be retained in fuzzy equivalence relation. Second, the number and composition of effective values are presented to confirm that which elements are effective values. Based on these above theories, redundant information and sparse effective information in fuzzy similarity relation could be completely distinguished. Next, both isomorphism and similarity between two fuzzy similarity relations are researched based on the effective value. The isomorphism between two fuzzy equivalence relations is discussed based on the effective value. Then, the algorithm with low time complexity for extracting effective values of fuzzy similarity relation is introduced. On the basis, the algorithm for constructing HQSS is presented to realize efficient granulation of fuzzy data. The proposed algorithms could accurately extract effective information from the fuzzy similarity relation and construct the same HQSS with the fuzzy equivalence relation while greatly reducing the time complexity. Finally, relevant experiments on 15 UCI datasets, 3 UKB datasets, and 5 image datasets are shown and analyzed to verify the effectiveness and efficiency of the proposed algorithm.

This paper presents a strong data-mining method based on a rough set, which can simultaneously realize feature selection, classification, and knowledge representation. Although a rough set, a popular method for feature selection, has good interpretability, it is not sufficiently efficient and accurate to deal with large-scale datasets with high dimensions, which prevents it from being immediately applied to real-world scenarios. To address the efficiency issue of a rough set, we discover the stability of the local redundancy (SLR) of attributes and propose a theorem to prove it rigorously. Based on SLR, only the parts of objects in the boundary region are partitioned when calculating outer significance, which further improves the efficiency of the rough set. With regard to the accuracy issue, we show that overfitting may lead to ineffectiveness of the rough set, especially when processing noise attributes. We then propose relative importance, a robust measurement for an attribute, to alleviate such overfitting issues. In this paper, we propose a novel rough-set framework that significantly improves the efficiency and accuracy of existing rough-set methods. We further develop our rough set framework by proposing a “rough concept tree” for knowledge representation and classification. Experimental results on public benchmark datasets show that our proposed framework achieves higher accuracy than seven state-of-the-art feature-selection methods. All the codes are available at https://github.com/syxiaa/powerroughset.

Compromised autophagy and defective lysosomal clearance significantly contribute to impaired neuronal proteostasis, which represents a hallmark of Alzheimer's disease (AD) and other age-related neurodegenerative disorders. Growing evidence has implicated that modulating autophagic flux, instead of inducing autophagosome formation alone, would be more reliable to rescue neuronal proteostasis. Concurrently, selectively enhancing drug concentrations in the leision areas, instead of the whole brain, will maximize therapeutic efficacy while reduing non-selective autophagy induction. Herein, we design a ROS-responsive targeted micelle system (TT-NM/Rapa) to enhance the delivery efficiency of rapamycin to neurons in AD lesions guided by the fusion peptide TPL, and facilitate its intracellular release via ROS-mediated disassembly of micelles, thereby maximizing autophagic flux modulating efficacy of rapamycin in neurons. Consequently, it promotes the efficient clearance of intracellular neurotoxic proteins, β-amyloid and hyperphosphorylated tau proteins, and ameliorates memory defects and neuronal damage in 3×Tg-AD transgenic mice. Our studies demonstrate a promising strategy to restore autophagic flux and improve neuronal proteostasis by rationally-engineered nano-systems for delaying the progression of AD.