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

The lion's share of bacteria in various environments cannot be cloned in the laboratory and thus cannot be sequenced using existing technologies. A major goal of single-cell genomics is to complement gene-centric metagenomic data with whole-genome assemblies of uncultivated organisms. Assembly of single-cell data is challenging because of highly non-uniform read coverage as well as elevated levels of sequencing errors and chimeric reads. We describe SPAdes, a new assembler for both single-cell and standard (multicell) assembly, and demonstrate that it improves on the recently released E+V−SC assembler (specialized for single-cell data) and on popular assemblers Velvet and SoapDeNovo (for multicell data). SPAdes generates single-cell assemblies, providing information about genomes of uncultivatable bacteria that vastly exceeds what may be obtained via traditional metagenomics studies. SPAdes is available online (http://bioinf.spbau.ru/spades). It is distributed as open source software.

/pdf/spades-a-new-genome-assembly-algorithm-and-its-applications-35zec7ea02.pdf

SPAdes: A New Genome Assembly Algorithm and Its Applications to Single-Cell Sequencing

Preface to the Princeton Landmarks in Biology Edition vii Preface xi Symbols Used xiii 1. The Importance of Islands 3 2. Area and Number of Speicies 8 3. Further Explanations of the Area-Diversity Pattern 19 4. The Strategy of Colonization 68 5. Invasibility and the Variable Niche 94 6. Stepping Stones and Biotic Exchange 123 7. Evolutionary Changes Following Colonization 145 8. Prospect 181 Glossary 185 References 193 Index 201

The Theory of Island Biogeography

Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

Machine learning

Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Pattern Recognition and Machine Learning

The invention provides methods, devices, compositions and kits for diagnosing or predicting transplant status or outcome in a subject who has received a transplant.

Nucleic acid sequencing apparatus for monitoring status of a transplant recipient

Methods, machine readable media and kits are provided for analyzing an antibody repertoire of a subject to determine the immunocompetence of the subject. The method may include analyzing the subject's antibody repertoire. Analyzing may include identifying the composition of abundant related IgH sequences of the subject's antibody repertoire. The method may further include providing an assessment of the immunocompetence of the subject, based on the analysis.

Monitoring immunocompetance using immune repertoire sequencing

A fluid valve is provided that includes a first planar substrate having a smooth surface or a surface with features, an elastomer disposed on the first substrate, a second planar substrate disposed on another side of the elastomer, where the second substrate has a smooth surface or features, where the first and second substrate are more rigid than the elastomer, where the first substrate, the second substrate or the elastomer has a fluid channel, where the channel is open when the first or second substrate are in a first thermal state or a first compression state, where the channel is closed or partially closed when the first or second substrate are in a second thermal state or a second compression state, where the second thermal state is a different temperature than the first thermal state, where the second compression state is a different pressure than the first compression state.

Elastomeric focusing valves

The present methods are exemplified by a process in which maternal blood containing fetal DNA is diluted to a nominal value of approximately 0.5 genome equivalent of DNA per reaction sample. Digital analysis is then be used to detect aneuploidy, such as the trisomy that causes Down Syndrome. Since aneuploidies do not present a mutational change in sequence, and are merely a change in the number of chromosomes, it has not been possible to detect them in a fetus without resorting to invasive techniques such as amniocentesis or chorionic villi sampling. Digital amplification allows the detection of aneuploidy using massively parallel amplification and detection methods, examining, e.g., 10,000 genome equivalents.

Non-invasive fetal genetic screening by digtal analysis

Tumor infiltrating T-cells play an important role in many cancers, and can improve prognosis and yield therapeutic targets. We characterized T-cells infiltrating both breast cancer tumors and the surrounding normal breast tissue to identify T-cells specific to each, as well as their abundance in peripheral blood. Using immune profiling of the T-cell beta chain repertoire in 16 patients with early stage breast cancer, we show that the clonal structure of the tumor is significantly different from adjacent breast tissue, with the tumor containing approximately 3-fold more T-cells, but with a lower fraction of unique sequences and higher clonality compared to normal breast. The clonal structure of T-cells in blood and normal breast is more similar than between blood and tumor and can be used to distinguish tumor from normal breast tissue in 14 of 16 patients. Many T-cells overlap between tissues from the same patient, including approximately 50% of T-cells between tumor and normal breast. Both solid tissues contain high-abundance "enriched" sequences that are absent or of low abundance in the other tissue. Many of these T-cells are either not detected or detected with very low frequency in the blood, suggesting the existence of separate compartments of T-cells in both tumor and normal breast. Enriched T-cell sequences are typically unique to each patient, but there is a subset of sequences that are shared between many different patients. We show that most of these are commonly generated sequences and thus unlikely to play an important role in the tumor microenvironment.

http://www.biorxiv.org/content/biorxiv/early/2017/08/04/172494.full.pdf

Stephen R. Quake

Papers

Nucleic acid sequencing apparatus for monitoring status of a transplant recipient

Monitoring immunocompetance using immune repertoire sequencing

Elastomeric focusing valves

Non-invasive fetal genetic screening by digtal analysis

T-cell receptor sequencing of early stage breast cancer tumors identifies altered clonal structure of the T-cell repertoire