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.

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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

Cells sense their environment, process information, and continuously react to both internal and external stimuli. Control system theory provides convenient tools and concepts for describing and analyzing complex cell functions. In this paper the authors demonstrates the use of control theory to forwardengineer a complex synthetic gene network constructed from several modular components. Discussion Authors present the design and simulation of a synthetic multi-cellular maze-solving system. In this system, bacterial cells are programmed to use artificial cell-to-cell communication and regulatory feedback in order to illuminate the correct path in a user-defined maze of cells arranged on a surface. They carried out experiments with Escherichia coli to characterize the diffusion properties of artificial cell-to-cell communication based on bacterial quorum sensing systems. A recursive algorithm was designed to find a path between two predefined points in a user-generated maze. Colonies were arranged in the shape of a maze, surrounded by a wall of cells. The colonies along the correct path were programmed to illuminate the solution to the maze as a result of executing the algorithm. The algorithm started with an exogenous signal at the entry of the maze. In response, the first maze colony emitted a pulse signal Signal-1, which diffused to neighboring colonies. The second maze colony then responded to Signal 1 by secreting a pulse of Signal 1. The process continued until Signal 1 reached the End colony, which secreted Signal 2 in response. Signal 2 diffused to all neighboring colonies. Maze colonies that encountered Signal 2 transformed into End colonies, while End colonies encountering Signal 2 remained End colonies. Once this transformation took place, and sufficient degradation of the communication signals had occurred, exogenous Signal 1 could be re-administered to start another iteration of the algorithm. When the number of iterations equaled the number of colonies between the entry and end of the maze, the maze solving is completed, and the path from start to finish was indicated by the positions of End colonies. In the simulation, the End/Maze cells were modeled using a custom application based on MATLAB 7.0 and Simulink 6.0. The components described were designed and analyzed as independent modules with mRNA input/output. Several mazes of varying topologies were simulated with the different set of Hill functions. Conclusion This study shows the first iteration of the engineering process: design, simulation and experimentation. Modeling the biological system as an interconnection of control systems has provided a useful method for simulation and analysis of complex synthetic biological systems. The results from simulation and experimentation can be applied to the next iteration of the rational design process to achieve an actual maze solving system.

Ron Weiss

Papers

Dynamic control in a coordinated multi-cellular maze solving system - Allen Hsu, Vikram Vijayan,