Author
Ron Weiss
Other affiliations: French Institute for Research in Computer Science and Automation, Google, University of California, San Diego ...read more
Bio: Ron Weiss is an academic researcher from Massachusetts Institute of Technology. The author has contributed to research in topics: Synthetic biology & Speech synthesis. The author has an hindex of 82, co-authored 292 publications receiving 89189 citations. Previous affiliations of Ron Weiss include French Institute for Research in Computer Science and Automation & Google.
Papers published on a yearly basis
Papers
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TL;DR: An in vitro evolution strategy was developed and six mutations in nonstructural proteins of Venezuelan equine encephalitis replicon that promoted subgenome expression in cells were identified that may be useful for improving RNA therapeutics for vaccination, cancer immunotherapy, and gene therapy.
Abstract: Self-replicating (replicon) RNA is a promising new platform for gene therapy, but applications are still limited by short persistence of expression in most cell types and low levels of transgene expression in vivo. To address these shortcomings, we developed an in vitro evolution strategy and identified six mutations in nonstructural proteins (nsPs) of Venezuelan equine encephalitis (VEE) replicon that promoted subgenome expression in cells. Two mutations in nsP2 and nsP3 enhanced transgene expression, while three mutations in nsP3 regulated this expression. Replicons containing the most effective mutation combinations showed enhanced duration and cargo gene expression in vivo. In comparison to wildtype replicon, mutants expressing IL-2 injected into murine B16F10 melanoma showed 5.5-fold increase in intratumoral IL-2 and 2.1-fold increase in infiltrating CD8 T cells, resulting in significantly slowed tumor growth. Thus, these mutant replicons may be useful for improving RNA therapeutics for vaccination, cancer immunotherapy, and gene therapy.
40 citations
Journal Article•
TL;DR: This work describes a specific natural system, the Lux operon of Vibrio fischeri, which exhibits density dependent behavior using a well characterized set of genetic components, and reports on the first efforts to engineer microbial cells to exhibit this kind of multicellular pattern directed behavior.
Abstract: Multicellular organisms create complex patterned structures from identical, unreliable components. Learning how to engineer such robust behavior is important to both an improved understanding of computer science and to a better understanding of the natural developmental process. Earlier work by our colleagues and ourselves on amorphous computing demonstrates in simulation how one might build complex patterned behavior in this way. This work reports on our first efforts to engineer microbial cells to exhibit this kind of multicellular pattern directed behavior. We describe a specific natural system, the Lux operon of Vibrio fischeri, which exhibits density dependent behavior using a well characterized set of genetic components. We have isolated, sequenced, and used these components to engineer intercellular communication mechanisms between living bacterial cells. In combination with digitally controlled intracellular genetic circuits, we believe this work allows us to begin the more difficult process of using these communication mechanisms to perform directed engineering of multicellular structures, using techniques such as chemical diffusion dependent behavior. These same techniques form an essential part of our toolkit for engineering with life, and are widely applicable in the field of microbial robotics, with potential applications in medicine, environmental monitoring and control, engineered crop cultivation, and molecular scale fabrication.
40 citations
Posted Content•
TL;DR: This paper introduces a factorized model for this new task that optimizes the top-ranked items returned for the given query and user and reports empirical results where it outperforms several baselines.
Abstract: Retrieval tasks typically require a ranking of items given a query. Collaborative filtering tasks, on the other hand, learn to model user's preferences over items. In this paper we study the joint problem of recommending items to a user with respect to a given query, which is a surprisingly common task. This setup differs from the standard collaborative filtering one in that we are given a query x user x item tensor for training instead of the more traditional user x item matrix. Compared to document retrieval we do have a query, but we may or may not have content features (we will consider both cases) and we can also take account of the user's profile. We introduce a factorized model for this new task that optimizes the top-ranked items returned for the given query and user. We report empirical results where it outperforms several baselines.
40 citations
TL;DR: This work built an ex-vivo cell microenvironment by culturing primary β-cells in direct contact with ‘synthetic neighbors', cell-sized soft polymer microbeads that were modified with cell-cell signaling factors as well as components from pancreatic-tissue-specific ECMs to promote native cell- cell and cell-ECM interactions.
Abstract: Diabetes is caused by the loss or dysfunction of insulin-secreting β-cells in the pancreas. β-cells reduce their mass and lose insulin-producing ability in vitro, likely due to insufficient cell-cell and cell-extracellular matrix (ECM) interactions as β-cells lose their native microenvironment. Herein, we built an ex-vivo cell microenvironment by culturing primary β-cells in direct contact with ‘synthetic neighbors', cell-sized soft polymer microbeads that were modified with cell-cell signaling factors as well as components from pancreatic-tissue-specific ECMs. This biomimetic 3D microenvironment was able to promote native cell-cell and cell-ECM interactions. We obtained sustained maintenance of β-cell function in vitro enhanced cell viability from the few days usually observed in 2D culture to periods exceeding three weeks, with enhanced β-cell stability and insulin production. Our approach can be extended to create a general 3D culture platform for other cell types.
40 citations
Posted Content•
TL;DR: A sequence-to-sequence neural network which directly generates speech waveforms from text inputs, extending the Tacotron model by incorporating a normalizing flow into the autoregressive decoder loop, enabling parallel training and synthesis.
Abstract: We describe a sequence-to-sequence neural network which directly generates speech waveforms from text inputs. The architecture extends the Tacotron model by incorporating a normalizing flow into the autoregressive decoder loop. Output waveforms are modeled as a sequence of non-overlapping fixed-length blocks, each one containing hundreds of samples. The interdependencies of waveform samples within each block are modeled using the normalizing flow, enabling parallel training and synthesis. Longer-term dependencies are handled autoregressively by conditioning each flow on preceding blocks.This model can be optimized directly with maximum likelihood, with-out using intermediate, hand-designed features nor additional loss terms. Contemporary state-of-the-art text-to-speech (TTS) systems use a cascade of separately learned models: one (such as Tacotron) which generates intermediate features (such as spectrograms) from text, followed by a vocoder (such as WaveRNN) which generates waveform samples from the intermediate features. The proposed system, in contrast, does not use a fixed intermediate representation, and learns all parameters end-to-end. Experiments show that the proposed model generates speech with quality approaching a state-of-the-art neural TTS system, with significantly improved generation speed.
40 citations
Cited by
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28 Jul 2005
TL;DR: PfPMP1）与感染红细胞、树突状组胞以及胎盘的单个或多个受体作用，在黏附及免疫逃避中起关键的作�ly.
Abstract: 抗原变异可使得多种致病微生物易于逃避宿主免疫应答。表达在感染红细胞表面的恶性疟原虫红细胞表面蛋白1（PfPMP1）与感染红细胞、内皮细胞、树突状细胞以及胎盘的单个或多个受体作用，在黏附及免疫逃避中起关键的作用。每个单倍体基因组var基因家族编码约60种成员，通过启动转录不同的var基因变异体为抗原变异提供了分子基础。
18,940 citations
13 Aug 2016
TL;DR: XGBoost as discussed by the authors proposes a sparsity-aware algorithm for sparse data and weighted quantile sketch for approximate tree learning to achieve state-of-the-art results on many machine learning challenges.
Abstract: Tree boosting is a highly effective and widely used machine learning method. In this paper, we describe a scalable end-to-end tree boosting system called XGBoost, which is used widely by data scientists to achieve state-of-the-art results on many machine learning challenges. We propose a novel sparsity-aware algorithm for sparse data and weighted quantile sketch for approximate tree learning. More importantly, we provide insights on cache access patterns, data compression and sharding to build a scalable tree boosting system. By combining these insights, XGBoost scales beyond billions of examples using far fewer resources than existing systems.
14,872 citations
01 Apr 1998
TL;DR: This paper provides an in-depth description of Google, a prototype of a large-scale search engine which makes heavy use of the structure present in hypertext and looks at the problem of how to effectively deal with uncontrolled hypertext collections where anyone can publish anything they want.
Abstract: In this paper, we present Google, a prototype of a large-scale search engine which makes heavy use of the structure present in hypertext. Google is designed to crawl and index the Web efficiently and produce much more satisfying search results than existing systems. The prototype with a full text and hyperlink database of at least 24 million pages is available at http://google.stanford.edu/. To engineer a search engine is a challenging task. Search engines index tens to hundreds of millions of web pages involving a comparable number of distinct terms. They answer tens of millions of queries every day. Despite the importance of large-scale search engines on the web, very little academic research has been done on them. Furthermore, due to rapid advance in technology and web proliferation, creating a web search engine today is very different from three years ago. This paper provides an in-depth description of our large-scale web search engine -- the first such detailed public description we know of to date. Apart from the problems of scaling traditional search techniques to data of this magnitude, there are new technical challenges involved with using the additional information present in hypertext to produce better search results. This paper addresses this question of how to build a practical large-scale system which can exploit the additional information present in hypertext. Also we look at the problem of how to effectively deal with uncontrolled hypertext collections where anyone can publish anything they want.
14,696 citations
Proceedings Article•
11 Nov 1999
TL;DR: This paper describes PageRank, a mathod for rating Web pages objectively and mechanically, effectively measuring the human interest and attention devoted to them, and shows how to efficiently compute PageRank for large numbers of pages.
Abstract: The importance of a Web page is an inherently subjective matter, which depends on the readers interests, knowledge and attitudes. But there is still much that can be said objectively about the relative importance of Web pages. This paper describes PageRank, a mathod for rating Web pages objectively and mechanically, effectively measuring the human interest and attention devoted to them. We compare PageRank to an idealized random Web surfer. We show how to efficiently compute PageRank for large numbers of pages. And, we show how to apply PageRank to search and to user navigation.
14,400 citations
TL;DR: This paper proposes a novel sparsity-aware algorithm for sparse data and weighted quantile sketch for approximate tree learning and provides insights on cache access patterns, data compression and sharding to build a scalable tree boosting system called XGBoost.
Abstract: Tree boosting is a highly effective and widely used machine learning method. In this paper, we describe a scalable end-to-end tree boosting system called XGBoost, which is used widely by data scientists to achieve state-of-the-art results on many machine learning challenges. We propose a novel sparsity-aware algorithm for sparse data and weighted quantile sketch for approximate tree learning. More importantly, we provide insights on cache access patterns, data compression and sharding to build a scalable tree boosting system. By combining these insights, XGBoost scales beyond billions of examples using far fewer resources than existing systems.
13,333 citations