A Lattice-Theoretic Approach to Multigranulation Approximation Space

In this paper, we mainly investigate the equivalence between multigranulation approximation space and single-granulation approximation space from the lattice-theoretic viewpoint. It is proved that multigranulation approximation space is equivalent to single-granulation approximation space if and only if the pair of multigranulation rough approximation operators (Σi=1nRi¯,Σi=1nRi_) forms an order-preserving Galois connection, if and only if the collection of lower (resp., upper) definable sets forms an (resp., union) intersection structure, if and only if the collection of multigranulation upper (lower) definable sets forms a distributive lattice when n=2, and if and only if ∀X⊆U,  Σi=1nRi_(X)=∩i=1nRi_(X). The obtained results help us gain more insights into the mathematical structure of multigranulation approximation spaces

Incremental Feature Selection Oriented for Data with Hierarchical Structure

In the big data era, the sample size is becoming increasingly large, the data dimensionality is also becoming extremely high, moreover, there exists hierarchical structure between different class labels. This paper investigates incremental feature selection for hierarchical classification based on the dependency degree of inclusive strategy and solves the hierarchical classification problem where labels are distributed at arbitrary nodes in tree structure. Firstly, the inclusive strategy is used to reduce the negative sample space by exploiting the hierarchical label structure. Secondly, a new fuzzy rough set model is introduced based on inclusive strategy, and a dependency calculation algorithm based on the inclusive strategy and a non-incremental feature selection algorithm are also proposed. Then, the dependency degree based on the inclusive strategy is proposed by adopting the incremental mechanism. Based on these, two incremental feature selection frameworks based on two strategies are designed. Lastly, a comparative study with the method based on the sibling strategy is performed. The?feasibility?and?efficiency?of the proposed algorithms are verified by numerical experiments

Nrf2 signaling pathway: current status and potential therapeutic targetable role in human cancers

Cancer is a borderless global health challenge that continues to threaten human health. Studies have found that oxidative stress (OS) is often associated with the etiology of many diseases, especially the aging process and cancer. Involved in the OS reaction as a key transcription factor, Nrf2 is a pivotal regulator of cellular redox state and detoxification. Nrf2 can prevent oxidative damage by regulating gene expression with antioxidant response elements (ARE) to promote the antioxidant response process. OS is generated with an imbalance in the redox state and promotes the accumulation of mutations and genome instability, thus associated with the establishment and development of different cancers. Nrf2 activation regulates a plethora of processes inducing cellular proliferation, differentiation and death, and is strongly associated with OS-mediated cancer. What’s more, Nrf2 activation is also involved in anti-inflammatory effects and metabolic disorders, neurodegenerative diseases, and multidrug resistance. Nrf2 is highly expressed in multiple human body parts of digestive system, respiratory system, reproductive system and nervous system. In oncology research, Nrf2 has emerged as a promising therapeutic target. Therefore, certain natural compounds and drugs can exert anti-cancer effects through the Nrf2 signaling pathway, and blocking the Nrf2 signaling pathway can reduce some types of tumor recurrence rates and increase sensitivity to chemotherapy. However, Nrf2’s dual role and controversial impact in cancer are inevitable consideration factors when treating Nrf2 as a therapeutic target. In this review, we summarized the current state of biological characteristics of Nrf2 and its dual role and development mechanism in different tumor cells, discussed Keap1/Nrf2/ARE signaling pathway and its downstream genes, elaborated the expression of related signaling pathways such as AMPK/mTOR and NF-κB. Besides, the main mechanism of Nrf2 as a cancer therapeutic target and the therapeutic strategies using Nrf2 inhibitors or activators, as well as the possible positive and negative effects of Nrf2 activation were also reviewed. It can be concluded that Nrf2 is related to OS and serves as an important factor in cancer formation and development, thus provides a basis for targeted therapy in human cancers

Small Tympanic Membrane Perforations in the Inferior Quadrants Do Not Impact the Manubrium Vibration in Guinea Pigs

BACKGROUND: It has been believed that location of the perforation has a significant impact on hearing loss. However, recent studies have demonstrated that the perforation sites had no impact on hearing loss. We measured the velocity and pattern of the manubrium vibration in guinea pigs with intact and perforated eardrum using a laser Doppler vibrometer in order to determine the effects of different location perforations on the middle ear transfer functions. METHODS: Two bullas from 2 guinea pigs were used to determine stability of the umbo velocities, and 12 bullas from six guinea pigs to determine the effects of different location perforations on sound transmission. The manubrium velocity was measured at three points on the manubrium in the frequencies of 0.5-8 kHz before and after a perforation was made. The sites of perforations were in anterior-inferior (AI) quadrants of left ears and posterior-inferior (PI) quadrants of right ears. RESULTS: The manubrium vibration velocity losses were noticed in the perforated ears only below 1.5 kHz. The maximum velocity loss was about 7 dB at 500 Hz with the PI perforation. No significant difference in the velocity loss was found between AI and PI perforations. The average ratio of short process velocity to the umbo velocity was approximately 0.5 at all frequencies. No significant differences were found before and after perforation at all frequencies (p>0.05) except 7 kHz (p = 0.004) for both AI and PI perforations. CONCLUSIONS: The manubrium vibration velocity losses from eardrum perforation were frequency-dependent and the largest losses occur at low frequencies. Manubrium velocity losses caused by small acute inferior perforations in guinea pigs have no significant impact on middle ear sound transmission at any frequency tested. The manubrium vibration axis may be perpendicular to the manubrium below 8 kHz in guinea pigs

Hybrid Wireless Sensor Network Coverage Holes Restoring Algorithm

Aiming at the perception hole caused by the necessary movement or failure of nodes in the wireless sensor actuator network, this paper proposed a kind of coverage restoring scheme based on hybrid particle swarm optimization algorithm. The scheme first introduced network coverage based on grids, transformed the coverage restoring problem into unconstrained optimization problem taking the network coverage as the optimization target, and then solved the optimization problem in the use of the hybrid particle swarm optimization algorithm with the idea of simulated annealing. Simulation results show that the probabilistic jumping property of simulated annealing algorithm could make up for the defect that particle swarm optimization algorithm is easy to fall into premature convergence, and the hybrid algorithm can effectively solve the coverage restoring problem

Uncertainty measures for rough formulae in rough logic: An axiomatic approach

AbstractRough set theory, initiated by Pawlak, is a mathematical tool in dealing with inexact and incomplete information. Various types of uncertainty measure such as accuracy measure, roughness measure, etc, which aim to quantify the imprecision of a rough set caused by its boundary region, have been extensively studied in the existing literatures. However, a few of these uncertainty measures are explored from the viewpoint of formal rough set theory, which, however, help to develop a kind of graded reasoning model in the framework of rough logic. To solve such a problem, a framework of uncertainty measure for formulae in rough logic is presented in this paper. Unlike the existing literatures, we adopt an axiomatic approach to study the uncertainty measure in rough logic, concretely, we define the notion of rough truth degree by some axioms, such a notion is demonstrated to be adequate for measuring the extent to which any formula is roughly true. Then based on this fundamental notion, the notions of rough accuracy degree, roughness degree for any formula, and rough inclusion degree, rough similarity degree between any two formulae are also proposed. In addition, their properties are investigated in detail. These obtained results will be used to develop an approximate reasoning model in the framework of rough logic from the axiomatic viewpoint

Research on Multiple Classification Based on Improved SVM Algorithm for Balanced Binary Decision Tree

Support vector machines (SVMs) are designed to solve the binary classification problems at the beginning, but in the real world, there are a lot of multiclassification cases. The multiclassification methods based on SVM are mainly divided into the direct methods and the indirect methods, in which the indirect methods, which consist of multiple binary classifiers integrated in accordance with certain rules to form the multiclassification model, are the most commonly used multiclassification methods at present. In this paper, an improved multiclassification algorithm based on the balanced binary decision tree is proposed, which is called the IBDT-SVM algorithm. In this algorithm, it considers not only the influence of “between-classes distance” and “class variance” in traditional measures of between-classes separability but also takes “between-classes variance” into consideration and proposes a new improved “between-classes separability measure.” Based on the new “between-classes separability measure,” it finds out the two classes with the largest between-classes separability measure and uses them as the positive and negative samples to train and learn the classifier. After that, according to the principle of the class-grouping-by-majority, the remaining classes are close to these two classes and merged into the positive samples and the negative samples to train SVM classifier again. For the samples with uneven distribution or sparse distribution, this method can avoid the error caused by the shortest canter distance classification method and overcome the “error accumulation” problem existing in traditional binary decision tree to the greatest extent so as to obtain a better classifier. According to the above algorithm, each layer node of the decision tree is traversed until the output classification result is a single-class label. The experimental results show that the IBDT-SVM algorithm proposed in this paper can achieve better classification accuracy and effectiveness for multiple classification problems