Deep Multiple Description Coding by Learning Scalar Quantization

In this paper, we propose a deep multiple description coding framework, whose quantizers are adaptively learned via the minimization of multiple description compressive loss. Firstly, our framework is built upon auto-encoder networks, which have multiple description multi-scale dilated encoder network and multiple description decoder networks. Secondly, two entropy estimation networks are learned to estimate the informative amounts of the quantized tensors, which can further supervise the learning of multiple description encoder network to represent the input image delicately. Thirdly, a pair of scalar quantizers accompanied by two importance-indicator maps is automatically learned in an end-to-end self-supervised way. Finally, multiple description structural dissimilarity distance loss is imposed on multiple description decoded images in pixel domain for diversified multiple description generations rather than on feature tensors in feature domain, in addition to multiple description reconstruction loss. Through testing on two commonly used datasets, it is verified that our method is beyond several state-of-the-art multiple description coding approaches in terms of coding efficiency.Comment: 8 pages, 4 figures. (DCC 2019: Data Compression Conference). Testing datasets for "Deep Optimized Multiple Description Image Coding via Scalar Quantization Learning" can be found in the website of https://github.com/mdcnn/Deep-Multiple-Description-Codin

Investigating Disease Progression and Therapeutic Targets in Multiple Myeloma Using Single-cell Technologies

Multiple Myeloma (MM) is a highly heterogeneous disease characterized by uncontrolled clonal expansion of plasma cells. Single-cell techniques are advantageous in providing a more granular understanding of inter- and intratumoral genomics and surrounding microenvironments. The high relapse rate and intrinsic complexity of MM make the application of single-cell technologies particularly beneficial. Understanding the concordance of the measurements across single cell techniques in MM is of great interest. In this dissertation, we first integrated three single-cell technologies, namely scRNA-seq, CyTOF, and CITE-seq, to characterize MM immune microenvironment and assess their concordances of measurement. Overall, cell type abundances were relatively consistent, while variations were observed in T cells, macrophages, and monocytes. In addition to immune profiling, we sought to discover tumor specific markers based on single-cell transcriptomic profiling. With better understanding of single cell technologies, we then leveraged a number of scRNA-seq datasets and developed a robust scRNA-seq driven tumor-marker discovery pipeline. In total, we identified 20 MM marker genes encoding cell-surface proteins that are not yet under clinical study. The findings were cross-validated using different methods, including bulk RNA sequencing, flow cytometry, and proteomic mass spectrometry, on both MM cell lines and patient bone marrows. We also used both transcriptomic and immuno-imaging techniques to examine target dynamics and heterogeneity to identify potential combinatorial target partners. Lastly, we further characterized tumor heterogeneity, malignant B cell to plasma cell transitions, lineage compositional changes, and signature genes associated with MM progression by utilizing single-cell RNA sequencing of 361 samples from 263 MM patients in the Multiple Myeloma Research Foundation CoMMpass study. Interestingly, we identified B cell subpopulations as precancerous given their higher mutation burden. Additionally, we observed compositional alterations of immune subsets from baseline to relapse stages and identified differentially expressed genes associated with MM progression. Overall, this dissertation provides a comprehensive interrogation of tumor and the immune microenvironment in MM using single-cell technologies and proteomics, which deepens our understanding of MM disease onset and clinical outcomes and potentially provides novel targets for immunotherapies