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11,260 results on '"WANG Zhiqiang"'

253. 3D-organoid culture supports differentiation of human CAR+ iPSCs into highly functional CAR T cells

Author

Wang, Zhiqiang, McWilliams-Koeppen, Helen P, Reza, Hernan, Ostberg, Julie R, Chen, Wuyang, Wang, Xiuli, Huynh, Christian, Vyas, Vibhuti, Chang, Wen-Chung, Starr, Renate, Wagner, Jamie R, Aguilar, Brenda, Yang, Xin, Wu, Xiwei, Wang, Jinhui, Chen, Wei, Koelker-Wolfe, Ellery, Seet, Christopher S, Montel-Hagen, Amélie, Crooks, Gay M, Forman, Stephen J, and Brown, Christine E

Subjects
Biomedical and Clinical Sciences, Oncology and Carcinogenesis, Immunology, Stem Cell Research - Induced Pluripotent Stem Cell, Stem Cell Research - Induced Pluripotent Stem Cell - Human, Biotechnology, Regenerative Medicine, Stem Cell Research, Vaccine Related, Immunization, Underpinning research, Development of treatments and therapeutic interventions, 1.1 Normal biological development and functioning, 5.2 Cellular and gene therapies, Animals, Cell Differentiation, Humans, Immunotherapy, Immunotherapy, Adoptive, Induced Pluripotent Stem Cells, Mice, Organoids, Receptors, Chimeric Antigen, 3D-organoid culture, CAR, PSC-ATO, chimeric antigen receptor T cells, human iPSC, immunotherapy, off-the-shelf, pluripotent stem cell-artificial thymic organoid culture, Biological Sciences, Medical and Health Sciences, Developmental Biology, Biological sciences, Biomedical and clinical sciences
Abstract

Unlimited generation of chimeric antigen receptor (CAR) T cells from human-induced pluripotent stem cells (iPSCs) is an attractive approach for "off-the-shelf" CAR T cell immunotherapy. Approaches to efficiently differentiate iPSCs into canonical αβ T cell lineages, while maintaining CAR expression and functionality, however, have been challenging. We report that iPSCs reprogramed from CD62L+ naive and memory T cells followed by CD19-CAR engineering and 3D-organoid system differentiation confers products with conventional CD8αβ-positive CAR T cell characteristics. Expanded iPSC CD19-CAR T cells showed comparable antigen-specific activation, degranulation, cytotoxicity, and cytokine secretion compared with conventional CD19-CAR T cells and maintained homogeneous expression of the TCR derived from the initial clone. iPSC CD19-CAR T cells also mediated potent antitumor activity in vivo, prolonging survival of mice with CD19+ human tumor xenografts. Our study establishes feasible methodologies to generate highly functional CAR T cells from iPSCs to support the development of "off-the-shelf" manufacturing strategies.

Published
2022

258. On a class of self-similar sets which contain finitely many common points

Author

Jiang, Kan, Kong, Derong, Li, Wenxia, and Wang, Zhiqiang

Subjects
Mathematics - Dynamical Systems, Mathematics - Classical Analysis and ODEs, Primary: 28A78, Secondary: 28A80, 37B10
Abstract

For $\lambda\in(0,1/2]$ let $K_\lambda \subset\mathbb{R}$ be a self-similar set generated by the iterated function system $\{\lambda x, \lambda x+1-\lambda\}$. Given $x\in(0,1/2)$, let $\Lambda(x)$ be the set of $\lambda\in(0,1/2]$ such that $x\in K_\lambda$. In this paper we show that $\Lambda(x)$ is a topological Cantor set having zero Lebesgue measure and full Hausdorff dimension. Furthermore, we show that for any $y_1,\ldots, y_p\in(0,1/2)$ there exists a full Hausdorff dimensional set of $\lambda\in(0,1/2]$ such that $y_1,\ldots, y_p \in K_\lambda$., Comment: 21 pages, 1 figure, final version

Published
2021
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259. ContextNet: A Click-Through Rate Prediction Framework Using Contextual information to Refine Feature Embedding

Author

Wang, Zhiqiang, She, Qingyun, Zhang, PengTao, and Zhang, Junlin

Subjects
Computer Science - Information Retrieval, Computer Science - Artificial Intelligence
Abstract

Click-through rate (CTR) estimation is a fundamental task in personalized advertising and recommender systems and it's important for ranking models to effectively capture complex high-order features.Inspired by the success of ELMO and Bert in NLP field, which dynamically refine word embedding according to the context sentence information where the word appears, we think it's also important to dynamically refine each feature's embedding layer by layer according to the context information contained in input instance in CTR estimation tasks. We can effectively capture the useful feature interactions for each feature in this way. In this paper, We propose a novel CTR Framework named ContextNet that implicitly models high-order feature interactions by dynamically refining each feature's embedding according to the input context. Specifically, ContextNet consists of two key components: contextual embedding module and ContextNet block. Contextual embedding module aggregates contextual information for each feature from input instance and ContextNet block maintains each feature's embedding layer by layer and dynamically refines its representation by merging contextual high-order interaction information into feature embedding. To make the framework specific, we also propose two models(ContextNet-PFFN and ContextNet-SFFN) under this framework by introducing linear contextual embedding network and two non-linear mapping sub-network in ContextNet block. We conduct extensive experiments on four real-world datasets and the experiment results demonstrate that our proposed ContextNet-PFFN and ContextNet-SFFN model outperform state-of-the-art models such as DeepFM and xDeepFM significantly., Comment: arXiv admin note: text overlap with arXiv:2102.07619

Published
2021

260. Leaf-FM: A Learnable Feature Generation Factorization Machine for Click-Through Rate Prediction

Author

She, Qingyun, Wang, Zhiqiang, and Zhang, Junlin

Subjects
Computer Science - Information Retrieval, Computer Science - Artificial Intelligence
Abstract

Click-through rate (CTR) prediction plays important role in personalized advertising and recommender systems. Though many models have been proposed such as FM, FFM and DeepFM in recent years, feature engineering is still a very important way to improve the model performance in many applications because using raw features can rarely lead to optimal results. For example, the continuous features are usually transformed to the power forms by adding a new feature to allow it to easily form non-linear functions of the feature. However, this kind of feature engineering heavily relies on peoples experience and it is both time consuming and labor consuming. On the other side, concise CTR model with both fast online serving speed and good model performance is critical for many real life applications. In this paper, we propose LeafFM model based on FM to generate new features from the original feature embedding by learning the transformation functions automatically. We also design three concrete Leaf-FM models according to the different strategies of combing the original and the generated features. Extensive experiments are conducted on three real-world datasets and the results show Leaf-FM model outperforms standard FMs by a large margin. Compared with FFMs, Leaf-FM can achieve significantly better performance with much less parameters. In Avazu and Malware dataset, add version Leaf-FM achieves comparable performance with some deep learning based models such as DNN and AutoInt. As an improved FM model, Leaf-FM has the same computation complexity with FM in online serving phase and it means Leaf-FM is applicable in many industry applications because of its better performance and high computation efficiency.

Published
2021

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