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 delivery methods and compositions for antiviral therapeutics. Methods and compositions are provided for targeted delivery of antiviral therapeutics into cells of interest using, for example, viral vectors such as adenovirus, AAV, and replication incompetent HSV. These and other delivery systems can be used as vehicles to deliver DNA vectors encoding a nuclease or a cell-killing gene. These delivery methods can also be used to deliver naked DNA or RNA, protein products, plasmids containing a promoter that is active only in a latent viral state which drives a cell-killing gene, or other therapeutic agents.

Compositions and methods of delivering treatments for latent viral infections

65: Digital PCR enables rapid prenatal diagnosis of fetal aneuploidy

Methods for detection of single nucleotide mutations of autosomal recessive diseases as early as the first trimester of pregnancy are provided. This is of importance for metabolic disorders where early diagnosis can affect management of the disease and reduce complications and anxiety related to invasive testing.

https://www.biorxiv.org/content/biorxiv/early/2017/08/23/179804.full.pdf

Noninvasive prenatal diagnosis of single-gene disorders using droplet digital pcr

1 Department of Chemistry, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA 2 Department of Chemical Engineering, North Carolina State University, Raleigh, NC, 27695, USA 3 Department of Applied Physics, California Institute of Technology, Pasadena, CA, 91125, USA 4 IBM Almaden Research Center, Center for Polymeric Interfaces and Macromolecular Assemblies, 650 Harry Road, San Jose, CA, 95120-6099, USA

Functional perfluoropolyethers as novel materials for microfluidics and soft lithography

Macrophages mediate key antimicrobial responses against intracellular bacterial pathogens, such as Salmonella enterica. Yet, they can also act as a permissive niche for these pathogens to persist in infected tissues within granulomas, which are immunological structures comprised of macrophages and other immune cells. We apply single-cell transcriptomics to investigate macrophage functional diversity during persistent Salmonella enterica serovar Typhimurium (STm) infection in mice. We identify determinants of macrophage heterogeneity in infected spleens and describe populations of distinct phenotypes, functional programming, and spatial localization. Using a STm mutant with impaired ability to polarize macrophage phenotypes, we find that angiotensin converting enzyme (ACE) defines a granuloma macrophage population that is non-permissive for intracellular bacteria and their abundance anticorrelates with tissue bacterial burden. Disruption of pathogen control by neutralizing TNF preferentially depletes ACE+ macrophages in infected tissues. Thus ACE+ macrophages have differential capacity to serve as cellular niche for intracellular bacteria to establish persistent infection. Teaser This study shows that ACE+ granuloma macrophages have restricted capacity to act as a cellular niche that enables intracellular bacterial persistence.

/pdf/single-cell-profiling-identifies-ace-granuloma-macrophages-1l97sfzt.pdf

Stephen R. Quake

Papers

Compositions and methods of delivering treatments for latent viral infections

65: Digital PCR enables rapid prenatal diagnosis of fetal aneuploidy

Noninvasive prenatal diagnosis of single-gene disorders using droplet digital pcr

Functional perfluoropolyethers as novel materials for microfluidics and soft lithography

Single-cell profiling identifies ACE+ granuloma macrophages as a nonpermissive niche for intracellular bacteria during persistent Salmonella infection