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Abstract Neural topic modeling is a scalable automated technique for text data mining. In various downstream tasks of topic modeling, it is preferred that the discovered topics well align with labels. However, due to the lack of guidance from labels, unsupervised neural topic models are less powerful in this situation. Existing supervised neural topic models often adopt a label-free prior to generate the latent document-topic distributions and use them to predict the labels and thus achieve label-topic alignment indirectly. Such a mechanism faces the following issues: 1) The label-free prior leads to topics blending the latent patterns of multiple labels; and 2) One is unable to intuitively identify the explicit relationships between labels and the discovered topics. To tackle these problems, we develop a novel supervised neural topic model which utilizes a chain-structured graphical model with a label-conditioned prior. Soft indicators are introduced to explicitly construct the label-topic relationships. To obtain well-organized label-topic relationships, we formalize an entropy-regularized optimal transport problem on the embedding space and model them as the transport plan. Moreover, our proposed method can be flexibly integrated with most existing unsupervised neural topic models. Experimental results on multiple datasets demonstrate that our model can greatly enhance the alignment between labels and topics while maintaining good topic quality. 

Abstract Generation of a stable long-lived plasma cell (LLPC) population is the sine qua non of durable antibody responses after vaccination or infection. We studied 20 individuals with a prior coronavirus disease 2019 infection and characterized the antibody response using bone marrow aspiration and plasma samples. We noted deficient generation of spike-specific LLPCs in the bone marrow after severe acute respiratory syndrome coronavirus 2 infection. Furthermore, while the regression model explained 98% of the observed variance in anti-tetanus immunoglobulin G levels based on LLPC enzyme-linked immunospot assay, we were unable to fit the same model with anti-spike antibodies, again pointing to the lack of LLPC contribution to circulating anti-spike antibodies.