LINE: Large-scale Information Network Embedding

This paper studies the problem of embedding very large information networks into low-dimensional vector spaces, which is useful in many tasks such as visualization, node classification, and link prediction. Most existing graph embedding methods do not scale for real world information networks which usually contain millions of nodes. In this paper, we propose a novel network embedding method called the "LINE," which is suitable for arbitrary types of information networks: undirected, directed, and/or weighted. The method optimizes a carefully designed objective function that preserves both the local and global network structures. An edge-sampling algorithm is proposed that addresses the limitation of the classical stochastic gradient descent and improves both the effectiveness and the efficiency of the inference. Empirical experiments prove the effectiveness of the LINE on a variety of real-world information networks, including language networks, social networks, and citation networks. The algorithm is very efficient, which is able to learn the embedding of a network with millions of vertices and billions of edges in a few hours on a typical single machine. The source code of the LINE is available online.Comment: WWW 201

BPTF promotes tumor growth and predicts poor prognosis in lung adenocarcinomas.

BPTF, a subunit of NURF, is well known to be involved in the development of eukaryotic cell, but little is known about its roles in cancers, especially in non-small-cell lung cancer (NSCLC). Here we showed that BPTF was specifically overexpressed in NSCLC cell lines and lung adenocarcinoma tissues. Knockdown of BPTF by siRNA significantly inhibited cell proliferation, induced cell apoptosis and arrested cell cycle progress from G1 to S phase. We also found that BPTF knockdown downregulated the expression of the phosphorylated Erk1/2, PI3K and Akt proteins and induced the cleavage of caspase-8, caspase-7 and PARP proteins, thereby inhibiting the MAPK and PI3K/AKT signaling and activating apoptotic pathway. BPTF knockdown by siRNA also upregulated the cell cycle inhibitors such as p21 and p18 but inhibited the expression of cyclin D, phospho-Rb and phospho-cdc2 in lung cancer cells. Moreover, BPTF knockdown by its specific shRNA inhibited lung cancer growth in vivo in the xenografts of A549 cells accompanied by the suppression of VEGF, p-Erk and p-Akt expression. Immunohistochemical assay for tumor tissue microarrays of lung tumor tissues showed that BPTF overexpression predicted a poor prognosis in the patients with lung adenocarcinomas. Therefore, our data indicate that BPTF plays an essential role in cell growth and survival by targeting multiply signaling pathways in human lung cancers

SimCalib: Graph Neural Network Calibration based on Similarity between Nodes

Graph neural networks (GNNs) have exhibited impressive performance in modeling graph data as exemplified in various applications. Recently, the GNN calibration problem has attracted increasing attention, especially in cost-sensitive scenarios. Previous work has gained empirical insights on the issue, and devised effective approaches for it, but theoretical supports still fall short. In this work, we shed light on the relationship between GNN calibration and nodewise similarity via theoretical analysis. A novel calibration framework, named SimCalib, is accordingly proposed to consider similarity between nodes at global and local levels. At the global level, the Mahalanobis distance between the current node and class prototypes is integrated to implicitly consider similarity between the current node and all nodes in the same class. At the local level, the similarity of node representation movement dynamics, quantified by nodewise homophily and relative degree, is considered. Informed about the application of nodewise movement patterns in analyzing nodewise behavior on the over-smoothing problem, we empirically present a possible relationship between over-smoothing and GNN calibration problem. Experimentally, we discover a correlation between nodewise similarity and model calibration improvement, in alignment with our theoretical results. Additionally, we conduct extensive experiments investigating different design factors and demonstrate the effectiveness of our proposed SimCalib framework for GNN calibration by achieving state-of-the-art performance on 14 out of 16 benchmarks

Effect of Acorus tatarinowii extract on hyperprolactinemia in rats

Purpose: To determine the mechanism underlying the anti-hyperprolactinemia effect of Acorus tatarinowii extract (ATE) in rats. Methods: Rats were divided into six groups (n =10 each group), viz, healthy control, untreated hyperprolactinemic rats, hyperprolactinemic rats treated with bromocriptine (0.6 mg/kg), and hyperprolactinemic rats treated with ATE (3.2, 6.4, or 12.8 g/kg). After 30 days, the hypothalamic protein levels of dopamine D2 receptor, protein kinase A (PKA), and cyclic adenosine monophosphate (cAMP) were determined. Results: Dopamine D2 receptor levels were lower in untreated hyperprolactinemic rats than in healthy control (p < 0.01), but this decrease was attenuated by ATE (p < 0.05). Elevated PKA levels in untreated hyperprolactinemic rats (0.78 ± 0.03µg/mL, p < 0.01) were decreased by ATE (3.2 g/kg, 0.51 ± 0.02 µg/mL, p < 0.05; 6.4 g/kg, 0.39 ± 0.03 µg/mL, p < 0.01; 12.8 g/kg, 0.24 ± 0.04 µg/mL, p < 0.01). Similarly, elevated cAMP levels in hyperprolactinemic rats (3.1 ± 0.3 ng/mL) were lowered by ATE (3.2 g/kg, 2.2 ± 0.4 ng/mL, p < 0.05; 6.4 g/kg, 1.8 ± 0.3 ng/mL, p < 0.01; 12.8 g/kg, 1.4 ± 0.3 ng/mL, p < 0.01). Conclusion: ATE anti-hyperprolactinemia activity is mediated by dopamine D2 receptor signaling via cAMP/PKA pathway

Effects of maternal enflurane exposure on NR2B expression in the hippocampus of their offspring

Este trabalho objetiva o estudo da patogênese de deficiência no aprendizado e memória de prole de ratos resultante da anestesia maternal por enflurano, por meio da expressão da subunidade 2B do receptor do ácidoN-metil-D-aspártico (NR2B) no hipocampo dos filhotes. Dividiram-se, aleatoriamente, 30 fêmeas de ratos Sprague-Dawley em três grupos: controle (grupo C), exposição ao enflurano por 4 h (grupo E1) e por 8 h (grupo E2). De oito a 10 dias após o início da gravidez, os ratos dos grupos E1 e E2 inalaram enflurano 1,7% em 2 L/min de oxigênio, por 4 h e 8 h, respectivamente. Ratos do grupo C inalaram apenas 2 L/min de oxigênio. O labirinto de água de Morris foi empregado para analisar as funções de aprendizado e memória da cria em 20 e 30 dias após o nascimento. Utilizaram-se ensaios de RT-PCR e de imuno-histoquímica para medir os níveis de mRNA e expressão da proteína do NR2B, respectivamente. Em comparação com os ratos controle do grupo C, aqueles dos grupos E1 e E2 exibiram latências de escape mais longas, menor número de travessias na plataforma e menos tempo gasto no quadrante alvo no teste de exploração espacial (P ; 0.05) in terms of mRNA levels and protein expression of NR2B. The cognitive function of the offspring is impaired when maternal rats are exposed to enflurane during early pregnancy. A possible mechanism of this effect is related to the down-regulation of NR2B expression