Bigtable: A Distributed Storage System for Structured Data

BigTable是一个分布式存储系统，它可以支持扩展到很大尺寸的数据：PB级别的数据，包含几千个商业服务器。Google的许多项目都存储在BigTable中，包括WEB索引、Google Earth 和Google Finance。这些应用对BigTable提出了截然不同的需求，无论是从数据量（从URL到网页到卫星图像）而言，还是从延迟需求（从后端批量处理到实时数据服务）而言。尽管这些不同的需求，BigTable已经为所有的Google产品提供了一个灵活的、高性能的解决方案。本文中，我们描述了BigTable提供的简单数据模型，它允许客户端对数据部署和格式进行动态控制，我们描述了BigTable的设计和实施

Spanner: Google’s Globally-Distributed Database

Spanner是谷歌公司研发的、可扩展的、多版本、全球分布式、同步复制数据库。它是第一个把数据分布在全球范围内的系统，并且支持外部一致性的分布式事务。本文描述了Spanner的架构、特性、不同设计决策的背后机理和一个新的时间API，这个API可以暴露时钟的不确定性。这个API及其实现，对于支持外部一致性和许多强大特性而言，是非常重要的，这些强大特性包括：非阻塞的读、不采用锁机制的只读事务、原子模式变更

Prevention of Cytotoxic T Cell Escape Using a Heteroclitic Subdominant Viral T Cell Determinant

High affinity antigen-specific T cells play a critical role during protective immune responses. Epitope enhancement can elicit more potent T cell responses and can subsequently lead to a stronger memory pool; however, the molecular basis of such enhancement is unclear. We used the consensus peptide-binding motif for the Major Histocompatibility Complex molecule H-2Kb to design a heteroclitic version of the mouse hepatitis virus-specific subdominant S598 determinant. We demonstrate that a single amino acid substitution at a secondary anchor residue (Q to Y at position 3) increased the stability of the engineered determinant in complex with H-2Kb. The structural basis for this enhanced stability was associated with local alterations in the pMHC conformation as a result of the Q to Y substitution. Recombinant viruses encoding this engineered determinant primed CTL responses that also reacted to the wildtype epitope with significantly higher functional avidity, and protected against selection of virus mutated at a second CTL determinant and consequent disease progression in persistently infected mice. Collectively, our findings provide a basis for the enhanced immunogenicity of an engineered determinant that will serve as a template for guiding the development of heteroclitic T cell determinants with applications in prevention of CTL escape in chronic viral infections as well as in tumor immunity

Absolute Motion Parallax Weakly Determines Visual Scale In Real And Virtual Environments

The determinants of visual scale (size and distance) under monocular viewing are still largely unknown. The problem of visual scale under monocular viewing becomes readily apparent when one moves about within a virtual environment. It might be thought that the absolute motion parallax of stationary objects (both in real and virtual environments), under the assumption of their stationarity, would immediately determine their apparent size and distance for an observer who is walking about. We sought to assess the effectiveness of observer-produced motion parallax in scaling apparent size and distance within near space. We had subjects judge the apparent size and distance of real and virtual objects under closely matched conditions. Real and virtual targets were 4 spheres seen in darkness at eye level. The targets ranged in diameter from 3.7 cm to 14.8 cm and were viewed monocularly from different distances, with a subset of the size/distance combinations resulting in projectively equivalent stimuli at the viewing origin. Subjects moved laterally plus and minus 1 m to produce large amounts of motion parallax. When angular size was held constant and motion parallax acted as a differential cue to target size and distance, judged size varied by a factor of 1.67 and 1.18 for the real and virtual environments, respectively, well short of the four-fold change in distal size. Similarly, distance judgments varied by factors of only 1.74 and 1.07, respectively. We conclude that absolute motion parallax only weakly determines the visual scale of nearby objects varying over a four-fold range in size

Bigtable: A distributed storage system for structured data

Bigtable is a distributed storage system for managing structured data that is designed to scale to a very large size: petabytes of data across thousands of commodity servers. Many projects at Google store data in Bigtable, including web indexing, Google Earth, and Google Finance. These applications place very different demands on Bigtable, both in terms of data size (from URLs to web pages to satellite imagery) and latency requirements (from backend bulk processing to real-time data serving). Despite these varied demands, Bigtable has successfully provided a flexible, high-performance solution for all of these Google products. In this paper we describe the simple data model provided by Bigtable, which gives clients dynamic control over data layout and format, and we describe the design and implementation of Bigtable