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。

疟原虫var基因转换速率变化导致抗原变异[英]／Paul H, Robert P, Christodoulou Z, et al//Proc Natl Acad Sci U S A

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.

/pdf/xgboost-a-scalable-tree-boosting-system-48ocu6x4c7.pdf

XGBoost: A Scalable Tree Boosting System

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.

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The anatomy of a large-scale hypertextual Web search engine

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.

The PageRank Citation Ranking : Bringing Order to the Web

Laboratory automation now commonly allows high-throughput sample preparation, culturing, and acquisition of microscopy images, but quantitative image analysis is often still a painstaking and subjective process. This is a problem especially significant for work on programmed morphogenesis, where the spatial organization of cells and cell types is of paramount importance. To address the challenges of quantitative analysis for such experiments, we have developed TASBE Image Analytics, a software pipeline for automatically segmenting collections of cells using the fluorescence channels of microscopy images. With TASBE Image Analytics, collections of cells can be grouped into spatially disjoint segments, the movement or development of these segments tracked over time, and rich statistical data output in a standardized format for analysis. Processing is readily configurable, rapid, and produces results that closely match hand annotation by humans for all but the smallest and dimmest segments. TASBE Image Analytics can thus provide the analysis necessary to complete the design-build-test-learn cycle for high-throughput experiments in programmed morphogenesis, as validated by our application of this pipeline to process experiments on shape formation with engineered CHO and HEK293 cells.

TASBE Image Analytics: A Processing Pipeline for Quantifying Cell Organization from Fluorescent Microscopy.

Supervised linear embedding models like WSABIE (Weston et al., 2011) and supervised semantic indexing (Bai et al., 2010) have proven successful at ranking, recommendation and annotation tasks. However, despite being scalable to large datasets they do not take full advantage of the extra data due to their linear nature, and we believe they typically underfit. We propose a new class of models which aim to provide improved performance while retaining many of the benefits of the existing class of embedding models. Our approach works by reweighting each component of the embedding of features and labels with a potentially nonlinear affinity function. We describe several variants of the family, and show its usefulness on several datasets.

/pdf/affinity-weighted-embedding-3rgfv2y9y9.pdf

Affinity Weighted Embedding

In the originally published version of this Article, financial support was not fully acknowledged. The PDF and HTML versions of the Article have now been corrected to include support from the National Science Foundation (NSF), award number 1745645.

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Author Correction: A mixed antagonistic/synergistic miRNA repression model enables accurate predictions of multi-input miRNA sensor activity

Precise control of stem cell differentiation offers tremendous potential for tissue engineering. Synthetic gene networks provide a framework for understanding and engineering life. We propose to use synthetic gene networks to engineer circuits that dictate the cell fate of embryonic stem (ES) cells by controlling gene expression. These networks will be capable of turning on cell fate regulator genes in stem cells at precise times and under well-controlled and well-defined conditions based on external stimuli and the internal state of the cell. The over-expression of these cell fate regulator genes is sufficient to trigger particular differentiation path ways in ES cells. We have implemented a lentivirus delivered 1:2 multiplexer circuit for programmed differentiation. This network uses a transactivator, a repressor and one small molecule input Doxycycline (Dox). Dox selects to activate one of two cell fate regulator gene outputs, thereby pushing the ES cell along one of two differentiation pathways. Preliminary results demonstrate the ability to switch between the expression of two fluorescent proteins - EGFP and DsRed-Express based on the external input Dox. Upon integration of the cell fate regulators MyoD and Nanog into this circuit, ES cells will either differentiate into muscle or maintain their undifferentiated state. Characterization of this simple network in mammalian cells is an important first step as this circuit will serve as a basis for building more complex networks that can select between many outputs using only a few inputs to form structures that resemble complex tissues like the spinal cord.

Engineering a 1:2 bio-multiplexer for controlled stem cell differentiation

Direct Mutual Inhibition is known to be the primary mechanism behind bi-stabile behavior in biological neuronal networks to implement a neuronal toggle switch, similar to flip-flops in electronics. With a view to enabling the implementation of this pervasive behavior in neuromorphic computing, we have previously used computational models to conclude that direct mutual inhibition suffers from a common mode failure, and have proposed an alternative design, staged mutual inhibition, that ensures correct switching. In this work, we provide further analysis for these two designs with respect to switching delay as a performance metric, similar to hold-time considerations in electronics. Our timing analysis shows that staged mutual inhibition incurs slightly larger delay compared with direct mutual inhibition, which is attributed to its more sophisticated design. We perform simulations to validate our quantitative delay analysis.

Ron Weiss

Papers

TASBE Image Analytics: A Processing Pipeline for Quantifying Cell Organization from Fluorescent Microscopy.

Affinity Weighted Embedding

Author Correction: A mixed antagonistic/synergistic miRNA repression model enables accurate predictions of multi-input miRNA sensor activity

Engineering a 1:2 bio-multiplexer for controlled stem cell differentiation

Switching Delay Analysis for Two Neuronal Toggle Switch Designs: Direct and Staged Mutual Inhibition