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

A stepwise huisgen cycloaddition process: copper(I)-catalyzed regioselective "ligation" of azides and terminal alkynes.

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.

SPAdes, a new genome assembly algorithm and its applications to single-cell sequencing ( 7th Annual SFAF Meeting, 2012)

J. Appl. Cryst.の発刊に際して

http://control.disp.uniroma2.it/galeani/CO/materiale/automatica2000.pdf

Survey Constrained model predictive control: Stability and optimality

Generalized predictive control—Part I. The basic algorithm

https://www.mindmeister.com/generic_files/get_file/3609879?filetype=attachment_file

Elaborating a model of teacher professional growth

Generalized predictive control—Part II. Extensions and interpretations

A strategy for the design of self-tuning controllers of systems with constant but unknown parameters is presented. A cost function which incorporates system input, output and set-point variations is selected, and a control law for a known system is derived. This control law is shown to comprise a least-squares predictor of a function related to the cost function, and the control input is chosen to make the prediction zero. The parameters of the control law for the unknown system are estimated using a recursive-least-squares algorithm, and the optimal parameters are shown to be a fixed point of the algorithm. Whilst retaining their computational simplicity, the proposed method has several advantages over self-tuning-regulator strategies which attempt to minimise the output variance alone: weighting of control is allowed for; set-point variation may be optimally followed; there is no requirement to choose a system-related parameter to ensure convergence; and, for stable but nonminimum phase systems, there is no need to employ time-consuming methods, such as the solution of a Riccati equation. Several simulated examples are used to demonstrate the potential of the method.

Self-tuning controller

Self tuning is an important new branch of control which is attracting increasing theoretical and practical interest. The objective of self tuning is to control systems with unknown constant or slowly varying parameters, so theoretical interest is concerned with the stability, performance and convergence of the recursive algorithms involved, while practical interest derives from its potential as a simple controller commissioning tool, both as a method for controlling time-varying or nonlinear plant over a range of operating points, and for dealing with batch problems where the plant or materials vary over successive batches. This paper summarises and expands previous work on the design of self-tuning controllers. It discusses the closed-loop properties of various classes of self tuner, convergence concepts and results, and some of the technical problems involved with implementing self tuners on small computers or microprocessors.

David Clarke

Papers

Generalized predictive control—Part I. The basic algorithm

Elaborating a model of teacher professional growth

Generalized predictive control—Part II. Extensions and interpretations

Self-tuning controller

Self-tuning control