TaxThemis: Interactive mining and exploration of suspicious tax evasion group

Tax evasion is a serious economic problem for many countries, as it can undermine the government' s tax system and lead to an unfair business competition environment. Recent research has applied data analytics techniques to analyze and detect tax evasion behaviors of individual taxpayers. However, they failed to support the analysis and exploration of the uprising related party transaction tax evasion (RPTTE) behaviors (e.g., transfer pricing), where a group of taxpayers is involved. In this paper, we present TaxThemis, an interactive visual analytics system to help tax officers mine and explore suspicious tax evasion groups through analyzing heterogeneous tax-related data. A taxpayer network is constructed and fused with the trade network to detect suspicious RPTTE groups. Rich visualizations are designed to facilitate the exploration and investigation of suspicious transactions between related taxpayers with profit and topological data analysis. Specifically, we propose a calendar heatmap with a carefully-designed encoding scheme to intuitively show the evidence of transferring revenue through related party transactions. We demonstrate the usefulness and effectiveness of TaxThemis through two case studies on real-world tax-related data, and interviews with domain experts.Comment: 11 pages, 7 figure

Mental health status and associated contributing factors among the Hakka elderly in Fujian, China

PurposeLittle is known about the mental health of the Hakka elderly. This study explores the status of, and factors associated with mental health among Hakka elderly populations from Fujian, China.MethodsThis is a cross-sectional, community-based survey study containing a total of 1,262 valid samples. The Chinese version Symptom Checklist-90-R (SCL-90-R) was used to assess the mental health status of the Hakka elderly. We used t-tests to compare the differences for 10 dimensions of SCL-90-R scores between the Chinese national norm and the Hakka elderly. Univariate and multivariate analysis were performed by using linear regression analysis to identify the main socio-demographic factors that were most predictive of the total score of SCL-90-R in the Hakka elderly.ResultsThe scores of somatization (1.78 ± 0.55 vs. 1.40 ± 0.46, P < 0.001) and phobic anxiety (1.21 ± 0.36 vs. 1.17 ± 0.31, P < 0.001) for the Hakka elderly in Fujian appeared to be significantly higher than the Chinese norm. The higher total scores of SCL-90-R were found among females (β = 0.030, P = 0.044), widowed persons (β = 0.053, P = 0.021), those with parent(s) alive (β = 0.047, P = 0.019), and those with poorer self-rated health status (β = 0.110, P < 0.001). The lower total scores of SCL-90-R were found among those who were currently living in town, those with lower education level, those with higher average annual household incomes, and those who were living with spouse or children.ConclusionThe worse mental health conditions of the Hakka elderly in somatization and phobic anxiety were detected. The overall mental health status was shown to be worse among females, widowed persons, those who were living in village, those with lower education, and those with father or/and mother alive

Engineering oxygen vacancies in hierarchically Li-rich layered oxide porous microspheres for high-rate lithium ion battery cathode

Abstract(#br)Lithium-rich layered oxides always suffer from low initial Coulombic efficiency, poor rate capability and rapid voltage fading. Herein, engineering oxygen vacancies in hierarchically Li 1.2 Mn 0.54 Ni 0.13 Co 0.13 O 2 porous microspheres (L@S) is carried out to suppress the formation of irreversible Li 2 O during the initial discharge process and improve the Li + diffusion kinetics and structural stability of the cathode mateiral. As a result, the prepared L@S cathode delivers high initial Coulombic efficiency of 92.3% and large specific capacity of 292.6 mA h g −1 at 0.1 C. More importantly, a large reversible capacity of 222 mA h g −1 with a capacity retention of 95.7% can be obtained after 100 cycles at 10 C. Even cycled at ultrahigh rate of 20 C, the L@S cathode can..

PMCNA_RS00975 activates NF-κB and ERK1/2 through TLR2 and contributes to the virulence of Pasteurella multocida

IntroductionPasteurella multocida is a pathogenic bacterium known to cause hemorrhagic septicemia and pneumonia in poultry. Reports have indicated that certain proteins, either directly involved in or regulating iron metabolism, are important virulence factors of P. multocida. Therefore, understanding virulent factors and analyzing the role of pro-inflammatory cytokines can help us elucidate the underlying pathogenesis.MethodsIn this study, the PMCNA_RS00975 protein, a putative encapsuling protein encoded by a gene from a specific prophage island of the pathogenic strain C48-1 of P. multocida, was investigated. To further explore the impact of the PMCNA_RS00975 protein on pathogenicity, a PMCNA_RS00975 gene mutant of P. multocida strain C48-1 was constructed using positive selection technology. Subcellular localization was performed to determine the location of the PMCNA_RS00975 protein within P. multocida. The recombinant protein PMCNA_RS00975 of P. multocida was soluble expressed, purified, and its role in pro-inflammatory cytokines was investigated.ResultsThe mutant exhibited significantly reduced pathogenicity in the mice model. Furthermore, subcellular localization indicated that the PMCNA_RS00975 protein was located at the outer membrane and expressed during infection of P. multocida. Additionally, our experiments revealed that recombinant PMCNA_RS00975 protein promotes the secretion of the IL-6 pro-inflammatory cytokines triggered by the TLR2 receptor via NF-κB and ERK1/2 signaling pathways in the macrophages.DiscussionThis study identified a novel virulence factor in the C48-1 strain, providing a basis for understanding the pathogenesis and directions for the development of attenuated vaccines against P. multocida

Unsupervised Possibilistic Clustering Based on Kernel Methods

AbstractA new kernel based unsupervised clustering algorithm has been proposed. The proposed algorithm is called unsupervised kernel possibilistic clustering algorithm (UKPC), which is an extension of the previously proposed clustering algorithm of unsupervised possibilistic clustering algorithm (UPC). In UKPC, the sample points are mapped into the feature space by the introduced kernel function, and the final clustering partition is obtained by optimizing the objective function of UKPC, which adopts the same clustering rule with UPC clustering model. UKPC has the ability of revealing the non-convex cluster structure because the input data are mapped implicitly into a high-dimensional feature space where the nonlinear pattern now appears linear. The contrast experimental results with UPC and other typical fuzzy clustering algorithms show the better performance of the proposed algorithm