Applications of Improved Ant Colony Optimization Clustering Algorithm in Image Segmentation

When expressing the data feature extraction of the interesting objectives, image segmentation is to transform the data set of the features of the original image into more tight and general data set. This paper explores the image segmentation technology based on ant colony optimization clustering algorithm and proposes an improved ant colony clustering algorithm (ACCA). It improves and analyzes the computational formula of the similarity function and improves parameter selection and setting by setting ant clustering rules. Through this algorithm, it can not only accelerate the clustering speed, but it can also have a better clustering partitioning result. The experimental result shows that the method of this paper is better than the original OTSU image segmentation method in accuracy, rapidity and stability

A statistical normalization method and differential expression analysis for RNA-seq data between different species

Background: High-throughput techniques bring novel tools but also statistical challenges to genomic research. Identifying genes with differential expression between different species is an effective way to discover evolutionarily conserved transcriptional responses. To remove systematic variation between different species for a fair comparison, the normalization procedure serves as a crucial pre-processing step that adjusts for the varying sample sequencing depths and other confounding technical effects. Results: In this paper, we propose a scale based normalization (SCBN) method by taking into account the available knowledge of conserved orthologous genes and hypothesis testing framework. Considering the different gene lengths and unmapped genes between different species, we formulate the problem from the perspective of hypothesis testing and search for the optimal scaling factor that minimizes the deviation between the empirical and nominal type I errors. Conclusions: Simulation studies show that the proposed method performs significantly better than the existing competitor in a wide range of settings. An RNA-seq dataset of different species is also analyzed and it coincides with the conclusion that the proposed method outperforms the existing method. For practical applications, we have also developed an R package named "SCBN" and the software is available at http://www.bioconductor.org/packages/devel/bioc/html/SCBN.html

Light Field Depth Estimation Based on Stitched-EPI

Depth estimation is one of the most essential problems for light field applications. In EPI-based methods, the slope computation usually suffers low accuracy due to the discretization error and low angular resolution. In addition, recent methods work well in most regions but often struggle with blurry edges over occluded regions and ambiguity over texture-less regions. To address these challenging issues, we first propose the stitched-EPI and half-stitched-EPI algorithms for non-occluded and occluded regions, respectively. The algorithms improve slope computation by shifting and concatenating lines in different EPIs but related to the same point in 3D scene, while the half-stitched-EPI only uses non-occluded part of lines. Combined with the joint photo-consistency cost proposed by us, the more accurate and robust depth map can be obtained in both occluded and non-occluded regions. Furthermore, to improve the depth estimation in texture-less regions, we propose a depth propagation strategy that determines their depth from the edge to interior, from accurate regions to coarse regions. Experimental and ablation results demonstrate that the proposed method achieves accurate and robust depth maps in all regions effectively.Comment: 15 page

An Investigation of Distribution Alignment in Multi-Genre Speaker Recognition

Multi-genre speaker recognition is becoming increasingly popular due to its ability to better represent the complexities of real-world applications. However, a major challenge is the significant shift in the distribution of speaker vectors across different genres. While distribution alignment is a common approach to address this challenge, previous studies have mainly focused on aligning a source domain with a target domain, and the performance of multi-genre data is unknown. This paper presents a comprehensive study of mainstream distribution alignment methods on multi-genre data, where multiple distributions need to be aligned. We analyze various methods both qualitatively and quantitatively. Our experiments on the CN-Celeb dataset show that within-between distribution alignment (WBDA) performs relatively better. However, we also found that none of the investigated methods consistently improved performance in all test cases. This suggests that solely aligning the distributions of speaker vectors may not fully address the challenges posed by multi-genre speaker recognition. Further investigation is necessary to develop a more comprehensive solution.Comment: submitted to ICASSP 202

A Review of Active Management for Distribution Networks: Current Status and Future Development Trends

Driven by smart distribution technologies, by the widespread use of distributed generation sources, and by the injection of new loads, such as electric vehicles, distribution networks are evolving from passive to active. The integration of distributed generation, including renewable distributed generation changes the power flow of a distribution network from unidirectional to bi-directional. The adoption of electric vehicles makes the management of distribution networks even more challenging. As such, an active network management has to be fulfilled by taking advantage of the emerging techniques of control, monitoring, protection, and communication to assist distribution network operators in an optimal manner. This article presents a short review of recent advancements and identifies emerging technologies and future development trends to support active management of distribution networks

2-Amino-nicotinamide induces apoptosis of prostate cancer cells via inhibition of PI3K/AKT and phosphorylation of STA3/JAK2

Purpose: To study the cytotoxicity of 2-amino-nicotinamide against prostate cancer (PCa) cells, and the underlying molecular mechanism.Methods: The effect of 2-amino-nicotinamide on cell viability and apoptosis was determined by 3-(4,5- dimethylthiazol-2-yl)-2,5-diphenyltetrazolium (MTT) and flow cytometry, respectively, while its effect on cellular production of fluorescent-oxidized product from DCFH-DA was measured using flow cytometry. Apoptosis-related protein expressions were evaluated by western blot assay.Results: 2-Amino-nicotinamide produced cytotoxicity against MCF-7, SGC7901, PCa 22Rv1 and LNCaP cancer cell lines (p < 0.05). Mechanistic data revealed that 2-amino-nicotinamide activated apoptosis, and enhanced cleavage of PARP and caspase-3 in PCa 22Rv1 and LNCaP cells. In PCa 22Rv1 and LNCaP cell lines, cytochrome C and Bax levels were enhanced by treatment with 2-aminonicotinamide, while Bcl-2 protein level was suppressed (p < 0.05). Activated expressions of PI3K, Akt and ERK in PCa 22Rv1 and LNCaP cells were down-regulated, while p38 expression was increased.Moreover, 2-amino-nicotinamide suppressed the activation of JAK2 and STAT3, but did not alter total JAK2 and STAT3 levels in PCa 22Rv1 and LNCaP cells (p < 0.05).Conclusion: 2-Amino-nicotinamide exerts cytotoxic effects on prostate carcinoma cells via activation of apoptosis and down-regulation of PI3K/AKT and STA3/JAK2. Thus, 2-amino nicotinamide is a potential bioactive agent for prostate cancer management. Keywords: 2-Amino-nicotinamide, Apoptosis, Fluorescent-oxidized, Cytotoxicit