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

Photohärtbare perfluorpolyether zur verwendung als neue werkstoffe in mikrofluidischen vorrichtungen

Toxoplasma gondii, a protozoan parasite, undergoes a complex and poorly understood developmental process that is critical for establishing a chronic infection in its intermediate hosts. Here, we applied single-cell RNA-sequencing (scRNA-seq) on >5,400 Toxoplasma in both tachyzoite and bradyzoite stages using three widely studied strains to construct a comprehensive atlas of cell-cycle and asexual development, revealing hidden states and transcriptional factors associated with each developmental stage. Analysis of SAG1-related sequence (SRS) antigenic repertoire reveals a highly heterogeneous, sporadic expression pattern unexplained by measurement noise, cell cycle, or asexual development. Furthermore, we identified AP2IX-1 as a transcription factor that controls the switching from the ubiquitous SAG1 to rare surface antigens not previously observed in tachyzoites. In addition, comparative analysis between Toxoplasma and Plasmodium scRNA-seq results reveals concerted expression of gene sets, despite fundamental differences in cell division. Lastly, we built an interactive data-browser for visualization of our atlas resource.

https://www.biorxiv.org/content/biorxiv/early/2020/02/16/656165.full.pdf

A single-parasite transcriptional atlas of Toxoplasma gondii reveals novel control of antigen expression

High-density microfluidic chips contain plumbing networks with thousands of micromechanical valves and hundreds of individually addressable chambers. These fluidic devices are analogous to electronic integrated circuits fabricated using large scale integration (LSI). A component of these networks is the fluidic multiplexor, which is a combinatorial array of binary valve patterns that exponentially increases the processing power of a network by allowing complex fluid manipulations with a minimal number of inputs. These integrated microfluidic networks can be used to construct a variety of highly complex microfluidic devices, for example the microfluidic analog of a comparator array, and a microfluidic memory storage device resembling electronic random access memories.

Microfabricated structure having parallel and orthogonal flow channels controlled by row and column multiplexors

L'invention concerne des methodes, des dispositifs, des compositions et des kits permettant d'analyser le microbiome ou ses composants individuels chez un sujet. Les methodes servent a determiner une infection, a analyser la structure du microbiome, a determiner l'immunocompetence d'un sujet, etc. Dans certains modes de realisation de l'invention, le sujet suit un regime therapeutique, par exemple un traitement medicamenteux, un regime alimentaire, une radiotherapie, etc.

Utilisation d'acides nucléiques acellulaires pour l'analyse du microbiome chez l'homme et de ses composants

The present Application provides in one aspect a method of diagnosing a menstrual cycle event in a subject (e.g., a WOI), comprising detecting in a biological sample a gene signature for one or more endometrial cell types (e.g., unciliated epithelial cells). The present Application in another aspect provides a method comprising determining a gene expression profile in each of a plurality of endometrial cells, wherein said endometrial cells are: (a) in an endometrial sample obtained from a subject, and (b) unciliated epithelial cells. In still another aspect, the present Application provides a method for detecting that a subject is within a window of implantation (WOI), the method comprising: (a) determining a level of expression of a gene signature in a sample of endometrial cells obtained from a subject, (b) comparing the determined level of expression of each gene in the gene signature with a control level; and (c) determining whether the subject is within the WOI, wherein the subject is identified as being within the WOI if the level of the expression of the gene signature is higher than a control level.

Stephen R. Quake

Papers

Photohärtbare perfluorpolyether zur verwendung als neue werkstoffe in mikrofluidischen vorrichtungen

A single-parasite transcriptional atlas of Toxoplasma gondii reveals novel control of antigen expression

Microfabricated structure having parallel and orthogonal flow channels controlled by row and column multiplexors

Utilisation d'acides nucléiques acellulaires pour l'analyse du microbiome chez l'homme et de ses composants

Methods, compositions, and kits for assessing endometrial transformation