Colorectal cancer (CRC) is one of the most common digestive tract malignancies and inflammation and gut microbiota are well-known key factors to influence CRC development. Akkermansia mucinipila is an important gram-negative anaerobic bacterium that can degrade mucin in gut. Previous studies suggested that A. muciniphila may affect inflammation and cell proliferation, but the relationship between A. muciniphila and CRC is not clarified. Here C57BL/6 mice were administrated with A. muciniphila or PBS and then treated with azoxymethane (AOM)/dextran sodium sulphate (DSS) to induce CRC. The mice receiving A. muciniphila administration had more serious weight loss, shorter colon length and more intestinal tumors than those receiving PBS administration after AOM/DSS treatment. More colon damage and less goblet cells were also observed in A. muciniphila treated mice. Furthermore, A. muciniphila administration induced more Ki67 + proliferating cells, higher PCNA expression and elevated gene expression of proliferation-associated molecules including Snrpd1, Dbf4 or S100A9. At early stage of CRC development, in comparison with controls, the mice receiving A. muciniphila administration also had more body weight loss and shorter colon length, as well as higher gene expression of inflammatory cytokines. Furthermore, the in vitro experimental results showed that the co-culture of colon epithelial cells with A. muciniphila enhanced the cell proliferation and gene expression of proliferation-associated molecules. Therefore, A. mucinipila may promote the formation of CRC in mice through increasing the early level of inflammation and the proliferation of intestinal epithelial cells.

This paper studies the synthesis of high-dimensional datasets with differential privacy (DP). The state-of-the-art solution addresses this problem by first generating a set M of noisy low-dimensional marginals of the input data D , and then use them to approximate the data distribution in D for synthetic data generation. However, it imposes several constraints on M that considerably limits the choices of marginals. This makes it difficult to capture all important correlations among attributes, which in turn degrades the quality of the resulting synthetic data. To address the above deficiency, we propose PrivMRF, a method that (i) also utilizes a set M of low-dimensional marginals for synthesizing high-dimensional data with DP, but (ii) provides a high degree of flexibility in the choices of marginals. The key idea of PrivMRF is to select an appropriate M to construct a Markov random field (MRF) that models the correlations among the attributes in the input data, and then use the MRF for data synthesis. Experimental results on four benchmark datasets show that PrivMRF consistently outperforms the state of the art in terms of the accuracy of counting queries and classification tasks conducted on the synthetic data generated.

TANK-binding kinase 1 (TBK1)/IκB kinase-ε (IKKε) mediates robust production of type I interferons (IFN-I) and proinflammatory cytokines to restrict the spread of invading viruses. However, excessive or prolonged production of IFN-I is harmful to the host by causing autoimmune disorders.

Answering database queries while preserving privacy is an important problem that has attracted considerable research attention in recent years. A canonical approach to this problem is to use synthetic data. That is, we replace the input database R with a synthetic database R* that preserves the characteristics of R, and use R* to answer queries. Existing solutions for relational data synthesis, however, either fail to provide strong privacy protection, or assume that R contains a single relation. In addition, it is challenging to extend the existing single-relation solutions to the case of multiple relations, because they are unable to model the complex correlations induced by the foreign keys. Therefore, multi-relational data synthesis with strong privacy guarantees is an open problem. In this paper, we address the above open problem by proposing PrivLava, the first solution for synthesizing relational data with foreign keys under differential privacy, a rigorous privacy framework widely adopted in both academia and industry. The key idea of PrivLava is to model the data distribution in R using graphical models, with latent variables included to capture the inter-relational correlations caused by foreign keys. We show that PrivLava supports arbitrary foreign key references that form a directed acyclic graph, and is able to tackle the common case when R contains a mixture of public and private relations. Extensive experiments on census data sets and the TPC-H benchmark demonstrate that PrivLava significantly outperforms its competitors in terms of the accuracy of aggregate queries processed on the synthetic data.