Markov Random Field (MRF) has been successfully used in community detection recently. However, existing MRF methods only utilize the network topology while ignore the semantic attributes. A straightforward way to combine the two types of information is that, one can first use a topic clustering model (e.g. LDA) to derive group membership of nodes by using the semantic attributes, then take this result as a prior to define the MRF model. In this way, however, the parameters of the two models cannot be adjusted by each other, preventing it from really realizing the complementation of the advantages of the two. This paper integrates LDA into MRF to form an end-to-end learning system where their parameters can be trained jointly. However, LDA is a directed graphic model whereas MRF is undirected, making their integration a challenge. To handle this problem, we first transform LDA and MRF into a unified factor graph framework, allowing sharing the parameters of the two models. We then derive an efficient belief propagation algorithm to train their parameters simultaneously, enabling our approach to take advantage of the strength of both LDA and MRF. Empirical results show that our approach compares favorably with the state-of-the-art methods.
CAR-T therapy is a new clinical treatment option. It is the focus of an increasing number of researches, all of which suggested that it has a beneficial therapeutic effect on a variety of diseases, especially blood cancer. In this paper, clinic applications of CAR-T therapy for many diseases are listed, including B-cell acute lymphoblastic leukemia, Hepatitis B, and Human Immunodeficiency Virus. The differences between CAR-T therapy and other cancer treatments like tumor-infiltrating lymphocyte and T cell receptor therapy were discussed, standard biological medicines, and antibody-mediated anti-cancer drugs. The study also looks at the limitations and side-effects of CAR-T therapy, such as toxicity, and missing the target. The disadvantages, constraints, and options for improvement were also discussed in the paper. To summarize, CAR-T therapy has a good therapeutic function on some illnesses, although it is still in the experimental stage and is not commonly used in the clinic. In the near future, CAR-T therapy is likely to be used in a rising range of therapeutic therapies. In general, this paper can help get a better knowledge of CAR-T treatment, as well as a more exact comprehension of its future evolution.
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