Graph Convolutional Network Based on Manifold Similarity Learning

In the area of large-scale graph data representation and semi-supervised learning, deep graph-based convolutional neural networks have been widely applied. However, typical graph convolutional network (GCN) aggregates information of neighbor nodes based on binary neighborhood similarity (adjacency matrix). It treats all neighbor nodes of one node equally, which does not suppress the influence of dissimilar neighbor nodes. In this paper, we investigate GCN based on similarity matrix instead of adjacency matrix of graph nodes. Gaussian heat kernel similarity in Euclidean space is first adopted, which is named EGCN. Then biologically inspired manifold similarity is trained in reproducing kernel Hilbert space (RKHS), based on which a manifold GCN (named MGCN) is proposed for graph data representation and semi-supervised learning with four different kernel types. The proposed method is evaluated with extensive experiments on four benchmark document citation network datasets. The objective function of manifold similarity learning converges very quickly on different datasets using various kernel functions. Compared with state-of-the-art methods, our method is very competitive in terms of graph node recognition accuracy. In particular, the recognition rates of MGCN (Gaussian kernel) and MGCN (Polynomial Kernel) outperform that of typical GCN about 3.8% on Cora dataset, 3.5% on Citeseer dataset, 1.3% on Pubmed dataset and 4% on Cora_ML dataset, respectively. Although the proposed MGCN is relatively simple and easy to implement, it can discover local manifold structure by manifold similarity learning and suppress the influence of dissimilar neighbor nodes, which shows the effectiveness of the proposed MGCN.

     

Peripheral whole blood FOXP3 TSDR methylation: a potential marker in severity assessment of autoimmune diseases and chronic infections

Immune dysregulation is a cardinal feature of autoimmune diseases and chronic microbial infections. In particular, regulatory T cells are downregulated in autoimmune diseases while upregulated in chronic microbial infections. FOXP3 is the master regulator of Treg development. Treg-specific demethylated region (TSDR) is a highly conserved locus on the FOXP3 gene that is fully demethylated in natural Tregs but methylated in effector T cells. In our study, we used high resolution melt-polymerase chain reaction (HRM-PCR) to determine the FOXP3 TSDR methylation status in autoimmune diseases and chronic microbial infections. We found that FOXP3 TSDR to have the highest mean melting temperature (highly methylated) in active SLE patients compared to all the other groups (p?<?0.001). The psoriasis group also had a significantly high mean melting temperature (78.62?±?0.20) when compared with the inactive SLE group (78.49?±?0.29, p?<?0.05) and control group (78.44?±?0.25, p?<?0.01). There was no significant difference in melting temperature between inactive SLE and healthy controls. Disease activity in SLE was directly associated with methylation of the FOXP3 TSDR. On the other hand, patients with chronic microbial infections had significantly lower FOXP3 TSDR mean melting temperature (demethylated) when compared with healthy controls (78.28?±?0.21 vs 78.44?±?0.25, p?<?0.05). Our results suggest that the use of HRM-PCR to detect FOXP3 TSDR methylation status is a reliable and easy method to predict natural regulatory T cell levels in peripheral blood in different disease conditions. Determining FOXP3 TSDR methylation status can be a useful tool in diagnosis, and monitoring the severity of autoimmune diseases and chronic microbial infections.     

ABCB1基因多态性对乳腺癌紫杉醇治疗反应的影响

化学治疗是乳腺癌综合治疗的重要组成部分,其中以紫杉醇类药物为基础的化疗方案已被广泛应用,能有效降低复发率并延长患者生存期。但部分患者对紫杉醇类药物反应不佳,紫杉醇类药物诱发的不良反应不仅影响患者的生活质量,而且会导致化疗的延误,甚至中止,均会影响治疗效果。ABCB1基因编码的P-糖蛋白是紫杉醇类药物体内转运途径的关键酶。大量研究表明ABCB1基因单核苷酸多态性可能会改变P-糖蛋白的结构,从而影响其功能活性,继而对紫杉醇类药物清除和细胞内外转运过程产生影响,产生个体化差异的化疗反应。这不仅与紫杉醇类诱导的不良反应相关,并且能够影响患者的远期生存。因此,ABCB1基因多态性具有预测紫杉醇类药物化疗效果和不良反应的潜在价值,对于个体化的药物应用具有重要意义。     

Effects of Cinnamomum camphora Leaves Extracts-Flocculants Composite Algaecide on Microcystis aeruginosa Growth and Microcystins Release

Bulletin of Environmental Contamination and Toxicology - In this study, a composite algaecide containing flocculants and Cinnamomum. camphora leaves extracts (CCCLE) were synthesized. The...     

ELAM-1在鼻咽癌裸鼠移植瘤中的表达及其与转移的关系

目的 建立裸鼠鼻咽癌转移模型并探讨 E-选择素（ELAM-1）与鼻咽癌转移的相关性。方法 将鼻咽癌5-8F细胞悬液注射于裸鼠左后肢爪垫，观察裸鼠状态、成瘤情况并测量裸鼠体重及移植瘤长短径；采用连续病理切片苏木精-伊红染色观察移植瘤及转移情况，将16只人鼻咽癌荷瘤裸鼠分为转移组和非转移组；采用免疫组织化学法检测两组移植瘤组织中ELAM-1的表达。 结果 16只裸鼠均成瘤，成瘤率为100.0%，其中10只裸鼠出现转移瘤，转移率为62.5%。建模前，两组裸鼠体重差异无统计学意义［（13.83±0.56）g vs （14.62±0.30） g，t=1.026，P=0.071］。建模后4~7周，裸鼠瘤体体积呈指数增长，且转移组移植瘤增长速度较非转移组快，非转移组裸鼠瘤体体积小于转移组［（198.91 ± 163.29） mm³ vs （268.76 ±174.31） mm³，t=4.376，P=0.005］。ELAM-1在鼻咽癌裸鼠移植瘤、淋巴结转移灶及远处转移灶中的表达均为阳性，主要表达于细胞膜。转移组移植瘤光密度值高于非转移组（0.4497±0.0705 vs 0.0435±0.0082，t=4.388，P＝0.001）。结论 本研究成功构建稳定性好、转移率高的鼻咽癌裸鼠移植瘤转移模型，且ELAM-1在裸鼠移植瘤中高表达，可促进鼻咽癌裸鼠移植瘤生长和转移。