GWAS Identifies Novel Susceptibility Loci on 6p21.32 and 21q21.3 for Hepatocellular Carcinoma in Chronic Hepatitis B Virus Carriers

Genome-wide association studies (GWAS) have recently identified KIF1B as susceptibility locus for hepatitis B virus (HBV)–related hepatocellular carcinoma (HCC). To further identify novel susceptibility loci associated with HBV–related HCC and replicate the previously reported association, we performed a large three-stage GWAS in the Han Chinese population. 523,663 autosomal SNPs in 1,538 HBV–positive HCC patients and 1,465 chronic HBV carriers were genotyped for the discovery stage. Top candidate SNPs were genotyped in the initial validation samples of 2,112 HBV–positive HCC cases and 2,208 HBV carriers and then in the second validation samples of 1,021 cases and 1,491 HBV carriers. We discovered two novel associations at rs9272105 (HLA-DQA1/DRB1) on 6p21.32 (OR = 1.30, P = 1.13×$10^{−19}$) and rs455804 (GRIK1) on 21q21.3 (OR = 0.84, P = 1.86×$10^{−8}$), which were further replicated in the fourth independent sample of 1,298 cases and 1,026 controls (rs9272105: OR = 1.25, P = 1.71×$10^{−4}$; rs455804: OR = 0.84, P = 6.92×$10^{−3}$). We also revealed the associations of HLA-DRB1*0405 and 0901*0602, which could partially account for the association at rs9272105. The association at rs455804 implicates GRIK1 as a novel susceptibility gene for HBV–related HCC, suggesting the involvement of glutamate signaling in the development of HBV–related HCC

A Cloud Theory-Based Trust Computing Model in Social Networks

How to develop a trust management model and then to efficiently control and manage nodes is an important issue in the scope of social network security. In this paper, a trust management model based on a cloud model is proposed. The cloud model uses a specific computation operator to achieve the transformation from qualitative concepts to quantitative computation. Additionally, this can also be used to effectively express the fuzziness, randomness and the relationship between them of the subjective trust. The node trust is divided into reputation trust and transaction trust. In addition, evaluation methods are designed, respectively. Firstly, the two-dimension trust cloud evaluation model is designed based on node’s comprehensive and trading experience to determine the reputation trust. The expected value reflects the average trust status of nodes. Then, entropy and hyper-entropy are used to describe the uncertainty of trust. Secondly, the calculation methods of the proposed direct transaction trust and the recommendation transaction trust involve comprehensively computation of the transaction trust of each node. Then, the choosing strategies were designed for node to trade based on trust cloud. Finally, the results of a simulation experiment in P2P network file sharing on an experimental platform directly reflect the objectivity, accuracy and robustness of the proposed model, and could also effectively identify the malicious or unreliable service nodes in the system. In addition, this can be used to promote the service reliability of the nodes with high credibility, by which the stability of the whole network is improved

Intrusion Detection Model for Internet of Vehicles Using GRIPCA and OWELM

With the rapid development of the Internet of Vehicles, a large amount of vehicle network data is being generated. The large amount of data presents network communication security challenges. Although intrusion detection technology can assist in safeguarding the system from malicious attacks, the substantial data generated within the vehicle network poses time-consuming detection challenges. Thus, we propose an intrusion detection model for the Internet of Vehicles, utilizing Gaussian random incremental principal component analysis (GRIPCA) and optimal weighted extreme learning machine (OWELM). First, we utilize GRIPCA to reduce data redundancy by projecting high-dimensional data into a low-dimensional space, thus reducing storage costs. Then, we utilize the dynamic inertia weight particle swarm optimization (DPSO) to optimize the parameters of the weighted extreme learning machine (WELM) to achieve the best performance. We utilize the NSL-KDD and CIC-IDS-2017 datasets to perform experiments and compare the results with other techniques. The experimental results show the excellence of the proposed model, achieving an accuracy rate of 91.02% on the NSL KDD dataset and 94.67% on the CIC-IDS-2017 dataset

Negative Emotions Will Be Welcomed: The Effect of Upward Comparison on Counterhedonic Consumption

Upward comparisons are prevalent in life and have a significant influence on consumer psychology and subsequent behavior. Previous research examined the effects of upward comparisons on consumption behavior, mainly focusing on behavior that evokes positive emotions (e.g., donation behavior, sustainable consumption) or behavior that evokes negative emotions (e.g., impulsive consumption, compulsive consumption) and less on behavior that evokes both negative emotions and positive emotions (i.e., counterhedonic consumption). This research examined the effect of upward comparisons on counterhedonic consumption. Five studies (N = 1111) demonstrated that upward comparison (vs. non-upward comparison) leads to counterhedonic consumption, and this effect is mediated by relative deprivation (Studies 2 and 3). In addition, this research showed that the comparison targets moderate the effects of upward comparisons on counterhedonic consumption. Specifically, when the comparison target is a friend, an upward comparison (vs. non-upward comparison) leads to counterhedonic consumption. When the comparison target is a stranger, an upward comparison (vs. non-upward comparison) has no significant influence on counterhedonic consumption (Study 5). Our findings extend the research on upward comparisons, relative deprivation, and counterhedonic consumption