Carcinomas are complex ecosystems composed of cancer, stromal and immune cells. Communication between these cells and their microenvironments induces cancer progression and causes therapy resistance. In order to improve the treatment of cancers, it is essential to quantify crosstalk between and within various cell types in a tumour microenvironment. Focusing on the coordinated expression patterns of ligands and cognate receptors, cell-cell communication can be inferred through ligand-receptor interactions (LRIs). In this manuscript, we carry out the following work: (i) introduce pipeline for ligand-receptor-mediated intercellular communication estimation from single-cell transcriptomics and list a few available LRI-related databases and visualization tools; (ii) demonstrate seven classical intercellular communication scoring strategies, highlight four types of representative intercellular communication inference methods, including network-based approaches, machine learning-based approaches, spatial information-based approaches and other approaches; (iii) summarize the evaluation and validation avenues for intercellular communication inference and analyze the advantages and limitations for the above four types of cell-cell communication methods; (iv) comment several major challenges while provide further research directions for intercellular communication analysis in the tumour microenvironments. We anticipate that this work helps to better understand intercellular crosstalk and to further develop powerful cell-cell communication estimation tools for tumor-targeted therapy.

In order to obtain high classification accuracy and reduce time consumption for large-scale polarimetric synthetic-aperture radar (PolSAR) data. In this letter, we propose a fast semisupervised classification algorithm using histogram-based density estimation (called FSHDE). First, a noniterative collaborative training using our proposed Wishart-clustering selection strategy is designed to expand the labeled sample set from unlabeled samples. Second, a fast feature mapping based on histogram density estimation is employed to reliably capture the interaction of nonlinear features. Third, submodular optimization is used to select optimal subspace features to reduce feature correlation. Experimental results on synthetic and real PolSAR data indicate that FSHDE greatly reduces the time consumption and improves the accuracy for terrain classification compared with the state-of-the-art methods.

Previous studies usually divided aspect-based sentiment analysis into several subtasks in pipeline, i.e., first aspect term and/or opinion term extraction, then aspect-based sentiment prediction, resulting in error propagation and external resources dependency. To overcome the problems mentioned above, in this work we present a novel one-stop solution on aspect-based sentiment analysis. Specifically, we propose a novel multi-task neural learning framework to jointly tackle aspect extraction and sentiment prediction subtasks at the same time, and leverage attention mechanisms to learn the joint representation of aspect-sentiment relationship. We have conducted extensive comparative experiments on two benchmark datasets from SemEval-2014. The experiment results demonstrate the effectiveness of our proposed solution. Especially, our multi-task model outperforms the state-of-the-art systems on aspect extraction subtask.