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A cellular hierarchy in melanoma uncouples growth and metastasis

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四川生理科学杂志 2022年第9期44卷 1633-1633页

作者： Panagiotis Karras Laboratory for Molecular Cancer Biology Center for Cancer Biology VIB Leuven Belgium Department of Oncology KU Leuven Leuven Belgium Department of Applied Mathematics and Theoretical Physics Centre for Mathematical Sciences University of Cambridge Cambridge UK The Wellcome Trust/CRUK Gurdon Institute University of Cambridge Cambridge UK Department of General Medical Oncology UZ Leuven Leuven Belgium BGI-Shenzhen Shenzhen China VIB BioImaging Core VIB Center for Brain and Disease Research Leuven Belgium VIB Bioimaging Core VIB Center for Inflammation Research Ghent Belgium Department of Biomedical Molecular Biology Ghent University Ghent Belgium FACS Expertise Center Center for Cancer Biology VIB Leuven Belgium Laboratory of Tumor Microenvironment and Therapeutic Resistance Center for Cancer Biology VIB Leuven Belgium Center for Brain & Disease Research VIB-KU Leuven Leuven Belgium Department of Human Genetics KU Leuven Leuven Belgium Data Mining and Modeling for Biomedicine Group VIB Center for Inflammation Research Ghent Belgium Department of Applied Mathematics Computer Science and Statistics Ghent University Ghent Belgium Laboratory for Translational Cell and Tissue Research Department of Imaging and Pathology KU Leuven Leuven Belgium Laboratory of Translational Genetics Center for Cancer Biology VIB Leuven Belgium Laboratory of Translational Genetics Center for Human Genetics KU Leuven Leuven Belgium Herbert Irving Comprehensive Center Columbia University Irving Medical Center New York USA Laboratory of Stem Cells and Cancer Université Libre de Bruxelles (ULB) Brussels Belgium Wellcome Trust-MRC Stem Cell Institute University of Cambridge Cambridge UK

Although melanoma is notorious for its high degree of heterogeneity and plasticity1,2,the origin and magnitude of cell-state diversity remains poorly ***,it is unclear whether growth and metastatic dissemination are supported by overlapping or distinct melanoma ***,by combining mouse genetics,single-cell and spatial transcriptomics,lineage tracing and quantitative modelling,we provide evidence of a hierarchical model of tumour growth that mirrors the cellular and molecular logic underlying the cell-fate specification and differentiation of the embryonic neural *** show that tumorigenic competence is associated with a spatially localized perivascular niche,a phenotype acquired through an intercellular communication pathway established by endothelial *** with a model in which only a fraction of cells are fated to fuel growth,temporal single-cell tracing of a population of melanoma cells with a mesenchymal-like state revealed that these cells do not contribute to primary tumour growth but,instead,constitute a pool of metastatic initiating cells that switch cell identity while disseminating to secondary *** data provide a spatially and temporally resolved map of the diversity and trajectories of melanoma cell states and suggest that the ability to support growth and metastasis are limited to distinct pools of *** observation that these phenotypic competencies can be dynamically acquired after exposure to specific niche signals warrant the development of therapeutic strategies that interfere with the cancer cell reprogramming activity of such microenvironmental cues.

关键词： melanoma metastasis organs

Binarized LSTM language model

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Binarized LSTM language model

2018 Conference of the North American Chapter of the Association for Computational Linguistics: human Language Technologies, NAACL HLT 2018

作者： Liu, Xuan Cao, Di Yu, Kai Key Laboratory of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering Department of Computer Science and Engineering Shanghai China

ISBN: (纸本)9781948087278

The long short-Term memory (LSTM) language model (LM) has been widely investigated for automatic speech recognition (ASR) and natural language processing (NLP). Although excellent performance is obtained for large vocabulary tasks, tremendous memory consumption prohibits the use of LSTM LMs in low-resource devices. The memory consumption mainly comes from the word embedding layer. In this paper, a novel binarized LSTM LM is proposed to address the problem. Words are encoded into binary vectors and other LSTM parameters are further binarized to achieve high memory compression. This is the first effort to investigate binary LSTMs for large vocabulary language modeling. Experiments on both English and Chinese LM and ASR tasks showed that binarization can achieve a compression ratio of 11.3 without any loss of LM and ASR performance and a compression ratio of 31.6 with acceptable minor performance degradation. © 2018 The Association for Computational Linguistics.

关键词： Long short-term memory

Reference language based unsupervised neural machine translation

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arXiv 2020年

作者： Li, Zuchao Zhao, Hai Wang, Rui Utiyama, Masao Sumita, Eiichiro Department of Computer Science and Engineering Shanghai Jiao Tong University Key Laboratory of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering Shanghai Jiao Tong University Shanghai China MoE Key Lab of Artificial Intelligence AI Institute Shanghai Jiao Tong University Kyoto Japan

Exploiting common language as an auxiliary for better translation has a long tradition in machine translation, which lets supervised learning based machine translation enjoy the enhancement delivered by the well-used pivot language, in case that the prerequisite of parallel corpus from source language to target language cannot be fully satisfied. The rising of unsupervised neural machine translation (UNMT) seems completely relieving the parallel corpus curse, though still subject to unsatisfactory performance so far due to vague clues available used for its core back-translation training. Further enriching the idea of pivot translation by freeing the use of parallel corpus other than its specified source and target, we propose a new reference language based UNMT framework, in which the reference language only shares parallel corpus with the source, indicating clear enough signal to help the reconstruction training of UNMT through a proposed reference agreement mechanism. Experimental results show that our methods improve the quality of UNMT over that of a strong baseline in terms of only one auxiliary language, demonstrating the usefulness of the proposed reference language based UNMT with a good start. Copyright © 2020, The Authors. All rights reserved.

关键词： Neural machine translation

Multi-choice Dialogue-Based Reading Comprehension with Knowledge and Key Turns

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arXiv 2020年

作者： Li, Junlong Zhang, Zhuosheng Zhao, Hai Department of Computer Science and Engineering Shanghai Jiao Tong University Key Laboratory of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering Shanghai Jiao Tong University Shanghai China MoE Key Lab of Artificial Intelligence AI Institute Shanghai Jiao Tong University Shanghai China

Multi-choice machine reading comprehension (MRC) requires models to choose the correct answer from candidate options given a passage and a question. Our research focuses dialogue-based MRC, where the passages are multi-turn dialogues. It suffers from two challenges, the answer selection decision is made without support of latently helpful commonsense, and the multi-turn context may hide considerable irrelevant information. This work thus makes the first attempt to tackle those two challenges by extracting substantially important turns and utilizing external knowledge to enhance the representation of context. In this paper, the relevance of each turn to the question are calculated to choose key turns. Besides, terms related to the context and the question in a knowledge graph are extracted as external knowledge. The original context, question and external knowledge are encoded with the pre-trained language model, then the language representation and key turns are combined together with a will-designed mechanism to predict the answer. Experimental results on a DREAM dataset show that our proposed model achieves great improve- Copyright © 2020, The Authors. All rights reserved.

关键词： Knowledge graph

Learning universal representations from word to sentence

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arXiv 2020年

作者： Li, Yian Zhao, Hai Department of Computer Science and Engineering Shanghai Jiao Tong University China Key Laboratory of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering Shanghai Jiao Tong University Shanghai China MoE Key Lab of Artificial Intelligence AI Institute Shanghai Jiao Tong University China

Despite the well-developed cut-edge representation learning for language, most language representation models usually focus on specific level of linguistic unit, which cause great inconvenience when being confronted with handling multiple layers of linguistic objects in a unified way. Thus this work introduces and explores the universal representation learning, i.e., embeddings of different levels of linguistic unit in a uniform vector space through a task-independent evaluation. We present our approach of constructing analogy datasets in terms of words, phrases and sentences and experiment with multiple representation models to examine geometric properties of the learned vector space. Then we empirically verify that well pre-trained Transformer models incorporated with appropriate training settings may effectively yield universal representation. Especially, our implementation of fine-tuning ALBERT on NLI and PPDB datasets achieves the highest accuracy on analogy tasks in different language levels. Further experiments on the insurance FAQ task show effectiveness of universal representation models in real-world applications. Copyright © 2020, The Authors. All rights reserved.

关键词： Linguistics

Semantics-Aware Inferential Network for Natural Language Understanding

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arXiv 2020年

作者： Zhang, Shuailiang Zhao, Hai Zhou, Junru Department of Computer Science and Engineering Shanghai Jiao Tong University Key Laboratory of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering Shanghai Jiao Tong University Shanghai China MoE Key Lab of Artificial Intelligence AI Institute Shanghai Jiao Tong University Shanghai China

For natural language understanding tasks, either machine reading comprehension or natural language inference, both semantics-aware and inference are favorable features of the concerned modeling for better understanding performance. Thus we propose a Semantics-Aware Inferential Network (SAIN) to meet such a motivation. Taking explicit contextualized semantics as a complementary input, the inferential module of SAIN enables a series of reasoning steps over semantic clues through an attention mechanism. By stringing these steps, the inferential network effectively learns to perform iterative reasoning which incorporates both explicit semantics and contextualized representations. In terms of well pre-trained language models as front-end encoder, our model achieves significant improvement on 11 tasks including machine reading comprehension and natural language inference. Copyright © 2020, The Authors. All rights reserved.

关键词： Semantics

Bipartite Flat-Graph Network for Nested Named Entity Recognition

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arXiv 2020年

作者： Luo, Ying Zhao, Hai Department of Computer Science and Engineering Shanghai Jiao Tong University Key Laboratory of Shanghai Education Commission for Intelligent Interaction Cognitive Engineering Shanghai Jiao Tong University Shanghai China MoE Key Lab of Artificial Intelligence AI Institute Shanghai Jiao Tong University Shanghai China

In this paper, we propose a novel bipartite flat-graph network (BiFlaG) for nested named entity recognition (NER), which contains two subgraph modules: a flat NER module for outermost entities and a graph module for all the entities located in inner layers. Bidirectional LSTM (BiLSTM) and graph convolutional network (GCN) are adopted to jointly learn flat entities and their inner dependencies. Different from previous models, which only consider the unidirectional delivery of information from innermost layers to outer ones (or outside-to-inside), our model effectively captures the bidirectional interaction between them. We first use the entities recognized by the flat NER module to construct an entity graph, which is fed to the next graph module. The richer representation learned from graph module carries the dependencies of inner entities and can be exploited to improve outermost entity predictions. Experimental results on three standard nested NER datasets demonstrate that our BiFlaG outperforms previous state-of-the-art models. Copyright © 2020, The Authors. All rights reserved.

关键词： Graph neural networks

Topic-aware multi-turn dialogue modeling

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arXiv 2020年

作者： Xu, Yi Zhao, Hai Zhang, Zhuosheng Department of Computer Science and Engineering Shanghai Jiao Tong University China Key Laboratory of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering Shanghai Jiao Tong University Shanghai China MoE Key Lab of Artificial Intelligence AI Institute Shanghai Jiao Tong University China

In the retrieval-based multi-turn dialogue modeling, it remains a challenge to select the most appropriate response according to extracting salient features in context utterances. As a conversation goes on, topic shift at discourse-level naturally happens through the continuous multi-turn dialogue context. However, all known retrieval-based systems are satisfied with exploiting local topic words for context utterance representation but fail to capture such essential global topic-aware clues at discourse-level. Instead of taking topic-agnostic ngram utterance as processing unit for matching purpose in existing systems, this paper presents a novel topic-aware solution for multi-turn dialogue modeling, which segments and extracts topic-aware utterances in an unsupervised way, so that the resulted model is capable of capturing salient topic shift at discourse-level in need and thus effectively track topic flow during multi-turn conversation. Our topic-aware modeling is implemented by a newly proposed unsupervised topic-aware segmentation algorithm and Topic-Aware Dual-attention Matching (TADAM) Network, which matches each topic segment with the response in a dual cross-attention way. Experimental results on three public datasets show TADAM can outperform the state-of-the-art method by a large margin, especially by 3.4% on E-commerce dataset that has an obvious topic shift. Copyright © 2020, The Authors. All rights reserved.

关键词： Large dataset

Dialogue graph modeling for conversational machine reading

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arXiv 2020年

作者： Ouyang, Siru Zhang, Zhuosheng Zhao, Hai Department of Computer Science and Engineering Shanghai Jiao Tong University Key Laboratory of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering Shanghai Jiao Tong University Shanghai China MoE Key Lab of Artificial Intelligence AI Institute Shanghai Jiao Tong University Shanghai China

Conversational Machine Reading (CMR) aims at answering questions in complicated interactive scenarios. Machine needs to answer questions through interactions with users based on given rule document, user scenario and dialogue history, and even initiatively asks questions for clarification if necessary. Namely, the answer to the task needs a machine in the response of either Yes, No, Irrelevant or to raise a follow-up question for further clarification. To effectively capture multiple objects in such a challenging task, graph modeling is supposed to be adopted, though it is surprising that this does not happen until this work proposes a dialogue graph modeling framework by incorporating two complementary graph models, i.e., explicit discourse graph and implicit discourse graph, which respectively capture explicit and implicit interactions hidden in the rule documents. The proposed model is evaluated on the ShARC benchmark and achieves new state-of-the-art by first exceeding the milestone accuracy score of 80%. The source code of our paper is available at https://***/ozyyshr/DGM. Copyright © 2020, The Authors. All rights reserved.

关键词： Graph theory