ALGD-ORB: An improved image feature extraction algorithm with adaptive threshold and local gray difference.

Simultaneous Localization and Mapping (SLAM) technology is crucial for achieving spatial localization and autonomous navigation. Finding image features that are representative presents a key challenge in visual SLAM systems. The widely used ORB (Oriented FAST and Rotating BRIEF) algorithm achieves rapid image feature extraction. However, traditional ORB algorithms face issues such as dense, overlapping feature points, and imbalanced distribution, resulting in mismatches and redundancies. This paper introduces an image feature extraction algorithm called Adaptive Threshold and Local Gray Difference-ORB(ALGD-ORB) to address these limitations. Specifically, an adaptive threshold is employed to enhance feature point detection, and an improved quadtree method is used to homogenize feature point distribution. This method combines feature descriptors generated from both gray size and gray difference to enhance feature descriptor distinctiveness. By fusing these descriptors, their effectiveness is improved. Experimental results demonstrate that the ALGD-ORB algorithm significantly enhances the uniformity of feature point distribution compared to other algorithms, while maintaining accuracy and real-time performance.

Prospects and hot spots for mammalian target of rapamycin in the field of neuroscience from 2002 to 2021.

Mammalian target of rapamycin (mTOR) is an important molecule that regulates cell metabolism, growth, and proliferation in the nervous system. This study aimed to present the current study hot spots and predict the future development trend of the mTOR pathway in neurologic diseases using bibliometrics. We referred to the publications in the Web of Science Core Collection database. VOSviewer and CiteSpace programs were used to evaluate countries/regions, institutions, authors, journals, keywords, and citations showing the current study focus and predicting the future trend of mTOR in neuroscience. The search date ended on 19 June 2022, and there were 3,029 articles on mTOR in neuroscience from 2002 to 2021. Visual analysis showed that although the number of publications declined slightly in some years, the number of publications related to mTOR generally showed an upward trend, reaching its peak in 2021. It had the largest number of publications in the United States. Keywords and literature analysis showed that protein synthesis regulation, ischemia, mitochondrial dysfunction, oxidative stress, and neuroinflammation may be hot spots and future directions of the nervous system in mTOR studies. Recently, the most studied neurological diseases are Alzheimer's disease (AD), tuberous sclerosis complex (TSC), and depression, which are still worthy of further studies by researchers in the future. This can provide a useful reference for future researchers to study mTOR further in the field of neuroscience.

Identification of susceptibility modules for coronary artery disease using a genome wide integrated network analysis.

Although recent genome-wide association studies (GWAS) have identified a handful of variants with best significance for coronary artery disease (CAD), it remains a challenge to summarize the underlying biological information from the abundant genotyping data. Here, we propose an integrated network analysis that effectively combines GWAS genotyping dataset, protein-protein interaction (PPI) database, literature and pathway annotation information. This three-step approach was illustrated for a comprehensive network analysis of CAD as the following. First, a network was constructed from PPI database and CAD seed genes mined from the available literatures. Then, susceptibility network modules were captured from the results of gene-based association tests. Finally, susceptibility modules were annotated with potential mechanisms for CAD via the KEGG pathway database. Our network analysis identified four susceptibility modules for CAD including a complex module that consisted of 15 functional inter-connected sub-modules, AGPAT3-AGPAT4-PPAP2B module, ITGA11-ITGB1 module and EMCN-SELL module. MAPK10 and COL4A2 among the top-scored focal adhesion pathway related module were the most significant genes (MAPK10: OR=32.5, P=3.5 × 10(-11); COL4A2: OR=2.7, P=2.8 × 10(-10)). The significance of the two genes were further validated by other two gene-based association tests (MAPK10: P=0.009 and 0.007; COL4A2: P=0.001 and 0.023) and another independent GWAS dataset (MAPK10: P=0.001; COL4A2: P=0.0004). Furthermore, 34 out of 44 previously reported CAD susceptibility genes were captured by our CAD PPI network and 17 of them were also significant genes. The susceptibility modules identified in our study might provide novel clues for the clarification of CAD pathogenesis in the future.

[Advances on gene-based association analysis].

Genome-wide association studies (GWAS) have been widely used for hunting the susceptibility genes for common diseases in the past years; however, the abundant information for the disease mechanism based on the GWAS data has not been fully mined. Recently, some researchers focused on the biological network and pathway analysis for the GWAS data to explore the potential disease mechanism. Since genes are the basic units for the biological network and pathway, the genetic effects from all or part of single nucleotide polymorphisms (SNPs) in the genes should be integrated into genetic scores, which are so-called "gene-based association analysis". Gene-based association analysis takes into account some important factors such as genetic effects of the SNPs, the number of the SNPs in the genes and the linkage disequilibrium structure of the SNPs. In this review, we will focus on the progress, principle and application of gene-based association analysis.