Clustering algorithm based on DINNSM and its application in gene expression data analysis.

BACKGROUND: Selecting an appropriate similarity measurement method is crucial for obtaining biologically meaningful clustering modules. Commonly used measurement methods are insufficient in capturing the complexity of biological systems and fail to accurately represent their intricate interactions. OBJECTIVE: This study aimed to obtain biologically meaningful gene modules by using the clustering algorithm based on a similarity measurement method. METHODS: A new algorithm called the Dual-Index Nearest Neighbor Similarity Measure (DINNSM) was proposed. This algorithm calculated the similarity matrix between genes using Pearson's or Spearman's correlation. It was then used to construct a nearest-neighbor table based on the similarity matrix. The final similarity matrix was reconstructed using the positions of shared genes in the nearest neighbor table and the number of shared genes. RESULTS: Experiments were conducted on five different gene expression datasets and compared with five widely used similarity measurement techniques for gene expression data. The findings demonstrate that when utilizing DINNSM as the similarity measure, the clustering results performed better than using alternative measurement techniques. CONCLUSIONS: DINNSM provided more accurate insights into the intricate biological connections among genes, facilitating the identification of more accurate and biological gene co-expression modules.

A PSO-based energy-efficient data collection optimization algorithm for UAV mission planning.

With the development of the Internet of Things (IoT), the use of UAV-based data collection systems has become a very popular research topic. This paper focuses on the energy consumption problem of this system. Genetic algorithms and swarm algorithms are effective approaches for solving this problem. However, optimizing UAV energy consumption remains a challenging task due to the inherent characteristics of these algorithms, which make it difficult to achieve the optimum solution. In this paper, a novel particle swarm optimization (PSO) algorithm called Double Self-Limiting PSO (DSLPSO) is proposed to minimize the energy consumption of the unmanned aerial vehicle (UAV). DSLPSO refers to the operational principle of PSO and incorporates two new mechanisms. The first mechanism is to restrict the particle movement, improving the local search capability of the algorithm. The second mechanism dynamically adjusts the search range, which improves the algorithm's global search capability. DSLPSO employs a variable population strategy that treats the entire population as a single mission plan for the UAV and dynamically adjusts the number of stopping points. In addition, the proposed algorithm was also simulated using public and random datasets. The effectiveness of the proposed DSLPSO and the two new mechanisms has been verified through experiments. The DSLPSO algorithm can effectively improve the lifetime of the UAV, and the two newly proposed mechanisms have potential for optimization work.

The alleles of AGT and HIF1A gene affect the risk of hypertension in plateau residents.

Plateau essential hypertension is a common chronic harmful disease of permanent residents in plateau areas. Studies have shown some single nucleotide polymorphisms (SNPs) associations with hypertension, but few have been verified in plateau area-lived people. In this paper, we examined some hypertension-related gene loci to analyze the relationship between risk SNPs and plateau essential hypertension in residents in Qinghai-Tibet plateau area. We screened hypertension-related SNPs from the literature, Clinvar database, GHR database, GTR database, and GWAS database, and then selected 101 susceptible SNPs for detection. Illumina MiSeq NGS platform was used to perform DNA sequencing on the blood samples from 185 Tibetan dwellings of Qinghai, and bioinformatic tools were used to make genotyping. Genetic models adjusted by gender and age were used to calculate the risk effects of genotypes. Four known SNPs as well as a new locus were found associated with PHE, which were rs2493134 (AGT), rs9349379 (PHACTR1), rs1371182 (CYP2C56P-PRPS1P1), rs567481079 (CYP2C56P-PRPS1P1), and chr14:61734822 (HIF1A). Among them, genotypes of rs2493134, rs9349379, and rs567481079 were risk factors, genotypes of rs1371182 and chr14:61734822 were protective factors. The rs2493134 in AGT was found associated with an increased risk of the plateau essential hypertension by 3.24-, 3.24-, and 2.06-fold in co-dominant, dominant, and Log-additive models, respectively. The rs9349379 in PHACTR1 is associated with a 2.61-fold increased risk of plateau essential hypertension according to the dominant model. This study reveals that the alleles of AGT, HIF1A, and PHACTR1 are closely related to plateau essential hypertension risk in the plateau Tibetan population.

Trust Model of Wireless Sensor Networks and Its Application in Data Fusion.

In order to ensure the reliability and credibility of the data in wireless sensor networks (WSNs), this paper proposes a trust evaluation model and data fusion mechanism based on trust. First of all, it gives the model structure. Then, the calculation rules of trust are given. In the trust evaluation model, comprehensive trust consists of three parts: behavior trust, data trust, and historical trust. Data trust can be calculated by processing the sensor data. Based on the behavior of nodes in sensing and forwarding, the behavior trust is obtained. The initial value of historical trust is set to the maximum and updated with comprehensive trust. Comprehensive trust can be obtained by weighted calculation, and then the model is used to construct the trust list and guide the process of data fusion. Using the trust model, simulation results indicate that energy consumption can be reduced by an average of 15%. The detection rate of abnormal nodes is at least 10% higher than that of the lightweight and dependable trust system (LDTS) model. Therefore, this model has good performance in ensuring the reliability and credibility of the data. Moreover, the energy consumption of transmitting was greatly reduced.

Using Trust to Establish a Secure Routing Model in Cognitive Radio Network.

Specific to the selective forwarding attack on routing in cognitive radio network, this paper proposes a trust-based secure routing model. Through monitoring nodes' forwarding behaviors, trusts of nodes are constructed to identify malicious nodes. In consideration of that routing selection-based model must be closely collaborative with spectrum allocation, a route request piggybacking available spectrum opportunities is sent to non-malicious nodes. In the routing decision phase, nodes' trusts are used to construct available path trusts and delay measurement is combined for making routing decisions. At the same time, according to the trust classification, different responses are made specific to their service requests. By adopting stricter punishment on malicious behaviors from non-trusted nodes, the cooperation of nodes in routing can be stimulated. Simulation results and analysis indicate that this model has good performance in network throughput and end-to-end delay under the selective forwarding attack.