Independent Distribution Regularization for Private Graph Embedding

Learning graph embeddings is a crucial task in graph mining tasks. An effective graph embedding model can learn low-dimensional representations from graph-structured data for data publishing benefiting various downstream applications such as node classification, link prediction, etc. However, recent studies have revealed that graph embeddings are susceptible to attribute inference attacks, which allow attackers to infer private node attributes from the learned graph embeddings. To address these concerns, privacy-preserving graph embedding methods have emerged, aiming to simultaneously consider primary learning and privacy protection through adversarial learning. However, most existing methods assume that representation models have access to all sensitive attributes in advance during the training stage, which is not always the case due to diverse privacy preferences. Furthermore, the commonly used adversarial learning technique in privacy-preserving representation learning suffers from unstable training issues. In this paper, we propose a novel approach called Private Variational Graph AutoEncoders (PVGAE) with the aid of independent distribution penalty as a regularization term. Specifically, we split the original variational graph autoencoder (VGAE) to learn sensitive and non-sensitive latent representations using two sets of encoders. Additionally, we introduce a novel regularization to enforce the independence of the encoders. We prove the theoretical effectiveness of regularization from the perspective of mutual information. Experimental results on three real-world datasets demonstrate that PVGAE outperforms other baselines in private embedding learning regarding utility performance and privacy protection.Comment: Accepted by CIKM 202

Genome-wide analyses identify KLF4 as an important negative regulator in T-cell acute lymphoblastic leukemia through directly inhibiting T-cell associated genes

é 2015 Li et al. Background: Kruppel-like factor 4 (KLF4) induces tumorigenesis or suppresses tumor growth in a tissue-dependent manner. However, the roles of KLF4 in hematological malignancies and the mechanisms of action are not fully understood. Methods: Inducible KLF4-overexpression Jurkat cell line combined with mouse models bearing cell-derived xenografts and primary T-cell acute lymphoblastic leukemia (T-ALL) cells from four patients were used to assess the functional role of KLF4 in T-ALL cells in vitro and in vivo. A genome-wide RNA-seq analysis was conducted to identify genes regulated by KLF4 in T-ALL cells. Chromatin immunoprecipitation (ChIP) PCR was used to determine direct binding sites of KLF4 in T-ALL cells. Results: Here we reveal that KLF4 induced apoptosis through the BCL2/BCLXL pathway in human T-ALL cell lines and primary T-ALL specimens. In consistence, mice engrafted with KLF4-overexpressing T-ALL cells exhibited prolonged survival. Interestingly, the KLF4-induced apoptosis in T-ALL cells was compromised in xenografts but the invasion capacity of KLF4-expressing T-ALL cells to hosts was dramatically dampened. We found that KLF4 overexpression inhibited T cell-associated genes including NOTCH1, BCL11B, GATA3, and TCF7. Further mechanistic studies revealed that KLF4 directly bound to the promoters of NOTCH1, BCL2, and CXCR4 and suppressed their expression. Additionally, KLF4 induced SUMOylation and degradation of BCL11B. Conclusions: These results suggest that KLF4 as a major transcription factor that suppresses the expression of T-cell associated genes, thus inhibiting T-ALL progression.Link_to_subscribed_fulltex

Calibration of an Indexing Table Using a High Precision Angle Comparator

The calibration of indexing table is important to ensure high accuracy and reliability in angle metrology. In this paper, we present calibrations of an indexing table and uncertainty analysis to evaluate the performance of the high precision angle comparator. The configuration of the angle comparator is explained. In the calibration, a complete closure method is applied to obtain the angle comparator’s position error and the indexing table’s angular deviation simultaneously based on the least square method. The position error of the angle comparator is evaluated to be about 0.08". The calibration result of an indexing table is compatible with that of the manufacturer and National Institute of Metrology of China within the expanded uncertainty of calibration, 0.04"

IDI1 inhibits the cGAS-Sting signaling pathway in hepatocellular carcinoma

Metabolic reprogramming is one of the prominent features that distinguishes tumor cells from normal cells. The role of metabolic abnormalities in regulating innate immunity is poorly understood. In this study, we found that IDI1 is significantly upregulated in liver cancer. IDI1 has no significant effect on the growth or invasion of liver cancer cells but significantly promotes liver cancer development in mice. Through molecular mechanism studies, we found that IDI1 interacts with the important regulator of innate immunity cGAS and recruits the E3 ligase TRIM41 to promote cGAS ubiquitination and degradation, inhibiting the cGAS-Sting signaling pathway. IDI1 inhibits the phosphorylation of TBK1 and the downstream factor IRF3 as well as the expression of CCL5 and CXCL10. In summary, this study revealed the important role of the metabolic enzyme IDI1 in the regulation of innate immunity, suggesting that it may be a potential target for liver cancer treatment

Calibration of an Indexing Table Using a High Precision Angle Comparator

The calibration of indexing table is important to ensure high accuracy and reliability in angle metrology. In this paper, we present calibrations of an indexing table and uncertainty analysis to evaluate the performance of the high precision angle comparator. The configuration of the angle comparator is explained. In the calibration, a complete closure method is applied to obtain the angle comparator’s position error and the indexing table’s angular deviation simultaneously based on the least square method. The position error of the angle comparator is evaluated to be about 0.08". The calibration result of an indexing table is compatible with that of the manufacturer and National Institute of Metrology of China within the expanded uncertainty of calibration, 0.04"

Combining Machine Learning and Crowdsourcing for Better Understanding Commodity Reviews

In e-commerce systems, customer reviews are important information for understanding market feedbacks on certain commodities. However, accurate analyzing reviews is challenging due to the complexity of natural language processing and informal descriptions in reviews. Existing methods mainly focus on studying efficient algorithms that cannot guarantee the accuracy for review analysis. Crowdsourcing can improve the accuracy of review analysis while it is subject to extra costs and low response time. In this work, we combine machine learning and crowdsourcing together for better understanding customer reviews. First, we collectively use multiple machine learning algorithms to pre-process review classification. Second, we select the reviews on which all machine learning algorithms cannot agree and assign them to humans to process. Third, the results from machine learning and crowdsourcing are aggregated to be the final analysis results. Finally, we perform real experiments with practical review data to confirm the effectiveness of our method

The Feature of Distribution and Clonality of TCR γ/δ Subfamilies T Cells in Patients with B-Cell Non-Hodgkin Lymphoma

Restricted T-cell receptor (TCR) Vα/Vβ repertoire expression and clonal expansion of αβ T cells especially for putative tumor-associated antigens were observed in patients with hematological malignancies. To further characterize the γδ T-cell immune status in B-cell non-Hodgkin lymphoma (B-NHL), we investigated the distribution and clonality of TCR Vγ/Vδ repertoire in peripheral blood (PB), bone marrow (BM), and lymph node (LN) from patients with B-NHL. Four newly diagnosed B-NHL cases, including three with diffuse large B-cell lymphoma (DLBCL) and one with small lymphocytic lymphoma (SLL), were enrolled. The restrictive expression of TCR Vγ/Vδ subfamilies with different distribution patterns could be detected in PB, BM, or LN from all of four patients, and partial subfamily T cells showed clonal proliferation. At least one clonally expanded Vδ subfamily member was found in PB from each patient. However, the expression pattern and clonality of TCR Vγ/Vδ changed in different immune organs and showed individual feature in different patients. The clonally expanded Vδ5, Vδ6, and Vδ8 were detected only in PB but neither in BM nor LN while clonally expanded Vδ2 and Vδ3 could be detected in both PB and BM/LN. In conclusion, the results provide a preliminary profile of distribution and clonality of TCR γ/δ subfamilies T cells in PB, BM, and LN from B-NHL; similar clonally expanded Vδ subfamily T cells in PB and BM may be related to the same B-cell lymphoma-associated antigens, while the different reactive clonally expanded Vγ/Vδ T cells may be due to local immune response