The Effect of Osteoprotectin (OPG)/Receptor Activator of Nuclear Factor-κB Ligand (RANKL)/Receptor Activator of Nuclear Factor-κB (RANK) Gene Methylation on Aortic Valve Calcified.

To evaluate the effect of the methylation of osteoprotectin (OPG)/receptor activator of nuclear factor-κB ligand (RANKL)/receptor activator of nuclear factor-κB (RANK) pathway on aortic valve calcification, the aortic valve tissue was collected from 38 aortic stenosis (AS) patients who underwent valve replacement. OPG and RANKL gene methylation, RT-PCR, and ELISA were performed. Hematoxylin-eosin staining (HE), alizarin red-S staining, and immunohistochemically staining of OPG, RANKL, and CD68 were simultaneously performed. The patients were divided into noncalcified group (n = 21) and calcified group (n = 17). The methylation rate of OPG gene in noncalcified group was higher than that in calcified group (P = 0.027). The methylation degree of RANKL gene was generally lower, but the noncalcified group was still higher than that in the calcified group (P = 0.025). RT-PCR analysis showed that the mRNA expression of OPG and RANKL was higher in calcified group than in noncalcified group (P = 0.007 and P = 0.036, respectively), and the mRNA expression was negatively correlated with the gene methylation rate. The protein expression of OPG and RANKL was detected by immunohistochemistry and ELISA, showing significantly increased in calcified group (P = 0.004 and P = 0.042, respectively). Soluble RANKL (sRANKL) in CD68-positive group was significantly different from that in negative group (0.1243 ± 0.0321 vs 0.0984 ± 0.0218 pg/mL, P = 0.007). There was no significant difference in OPG value between positive group (1.9411 ± 0.4554 ng/mL) and negative group (1.8422 ± 0.5218 ng/mL, P = 0.587). In conclusion, the degree of methylation of OPG and RANKL genes may play an important role in regulating valve calcification in AS patients.

A Memetic Algorithm Based on Probability Learning for Solving the Multidimensional Knapsack Problem.

The multidimensional knapsack problem (MKP) is a well-known combinatorial optimization problem with many real-life applications. In this article, a memetic algorithm based on probability learning (MA/PL) is proposed to solve MKP. The main highlights of this article are two-fold: 1) problem-dependent heuristics for MKP and 2) a novel framework of MA/PL. For the problem-dependent heuristics, we first propose two kinds of logarithmic utility functions (LUFs) based on the special structure of MKP, in which the profit value and weight vector of each item are considered simultaneously. Then, LUFs are applied to effectively guide the repair operator for infeasible solutions and the local search operator. For the framework of MA/PL, we propose two problem-dependent probability distributions to extract the special knowledge of MKP, that is, the marginal probability distribution (MPD) of each item and the joint probability distribution (JPD) of two conjoint items. Next, learning rules for MPD and JPD, which borrow ideas from competitive learning and binary Markov chain, are proposed. Thereafter, we generate MA/PL's offspring by integrating MPD and JPD, such that the univariate probability information of each item as well as the dependency of conjoint items can be sufficiently used. Results of experiments on 179 benchmark instances and a real-life case study demonstrate the effectiveness and practical values of the proposed MKP.

Extracting chemical-induced disease relation by integrating a hierarchical concentrative attention and a hybrid graph-based neural network.

Extracting the chemical-induced disease relation from literatures is important for biomedical research. On one hand, it is challenging to capture the interactions among remote words and the long-distance information is not adequately exploited by existing systems for document-level relation extraction. On the other hand, there is some information particularly important to the target relations in documents, which should attract more attention than the less relevant information for the relation extraction. However, this issue is not well addressed in existing methods. In this paper, we present a method that integrates a hybrid graph and a hierarchical concentrative attention to overcome these problems. The hybrid graph is constructed by synthesizing the syntactic graph and Abstract Meaning Representation graph to acquire the long-distance information for document-level relation extraction. Meanwhile, the concentrative attention is used to focus on the most important information, and alleviate the disturbance brought by the less relevant items in the document. The experimental results demonstrate that our model yields competitive performance on the dataset of chemical-induced disease relations.

Direct observation of multiple rotational stacking faults coexisting in freestanding bilayer MoS2.

Electronic properties of two-dimensional (2D) MoS2 semiconductors can be modulated by introducing specific defects. One important type of defect in 2D layered materials is known as rotational stacking fault (RSF), but the coexistence of multiple RSFs with different rotational angles was not directly observed in freestanding 2D MoS2 before. In this report, we demonstrate the coexistence of three RSFs with three different rotational angles in a freestanding bilayer MoS2 sheet as directly observed using an aberration-corrected transmission electron microscope (TEM). Our analyses show that these RSFs originate from cracks and dislocations within the bilayer MoS2. First-principles calculations indicate that RSFs with different rotational angles change the electronic structures of bilayer MoS2 and produce two new symmetries in their bandgaps and offset crystal momentums. Therefore, employing RSFs and their coexistence is a promising route in defect engineering of MoS2 to fabricate suitable devices for electronics, optoelectronics, and energy conversion.