Polyketide biosynthesis: understanding and exploiting modularity

Polyketide-based pharmaceuticals are some of our most important medicines. They are constructed in micro-organisms (typically bacteria and fungi) by gigantic enzyme catalysts called polyketide synthases (PKSs). The organization of PKSs into molecular assembly lines makes them particularly appealing targets for genetic engineering because, in principle, an alteration in the enzyme organization might translate into a predictable change in polyketide structure. Excitingly, this has been shown repeatedly to work in practice, but the efficiency of the engineered PKSs is frequently too low to be useful for large-scale drug synthesis. To reach this goal, researchers need a deeper understanding of the structure and function of these proteins, which are among the most complex in nature. This review highlights some recent experiments which are providing key information about the molecular organization, mechanism and orchestration of these magnificent catalysts, and opening up fresh prospects of truly combinatorial biosynthesis of novel polyketides as leads in drug discovery.     

Silicate-Containing Sodium Beta-Alumina Solid Electrolytes

Sodium beta-alumina solid electrolytes were prepared containing up to 7.5 wt% SiO₂ and their density, soda content, micro-structure, and ionic resistivity were studied. The resulting microstructures have glassy sodium ahminosilicate phases inhomogeneously distributed. The glassy phase collects preferentially at triple grain junctions, but occasionally penetrates along some grain boundaries as a thin layer (10 to 20 Å). Alpha-alumina is also present, especially with high silica content and when the samples are not protected with β-alumina powder during sintering. The total specific ionic resistivity follows a dependence on silicate content typical of 2-phase mixtures.     

温度测控技术在中药加速仪中的应用

本文介绍了中药加速仪利用单片机和热敏电阻设计低成本温度测控系统的一种温度测量比值查表方法和温度脉冲加热控制法,并对硬件系统原理和软件设计流程作了简要描述。     

Universal Filtering Via Prediction

We consider the filtering problem, where a finite-alphabet individual sequence is corrupted by a discrete memoryless channel, and the goal is to causally estimate each sequence component based on the past and present noisy observations. We establish a correspondence between the filtering problem and the problem of prediction of individual sequences which leads to the following result: Given an arbitrary finite set of filters, there exists a filter which performs, with high probability, essentially as well as the best in the set, regardless of the underlying noiseless individual sequence. We use this relationship between the problems to derive a filter guaranteed of attaining the "finite-state filterability" of any individual sequence by leveraging results from the prediction problem     

Scanning and Sequential Decision Making for Multidimensional Data–Part I: The Noiseless Case

We investigate the problem of scanning and prediction (ldquoscandiction,rdquo for short) of multidimensional data arrays. This problem arises in several aspects of image and video processing, such as predictive coding, for example, where an image is compressed by coding the error sequence resulting from scandicting it. Thus, it is natural to ask what is the optimal method to scan and predict a given image, what is the resulting minimum prediction loss, and whether there exist specific scandiction schemes which are universal in some sense. Specifically, we investigate the following problems: first, modeling the data array as a random field, we wish to examine whether there exists a scandiction scheme which is independent of the field's distribution, yet asymptotically achieves the same performance as if this distribution were known. This question is answered in the affirmative for the set of all spatially stationary random fields and under mild conditions on the loss function. We then discuss the scenario where a nonoptimal scanning order is used, yet accompanied by an optimal predictor, and derive bounds on the excess loss compared to optimal scanning and prediction. This paper is the first part of a two-part paper on sequential decision making for multidimensional data. It deals with clean, noiseless data arrays. The second part deals with noisy data arrays, namely, with the case where the decision maker observes only a noisy version of the data, yet it is judged with respect to the original, clean data.     

Local multi-valued logics in modular expert systems

Abstract

Abstract. In this paper we describe an approach to the problem of dealing with uncertainty by means of finite multi-valued logics in modular expert systems, and the results obtained. The modularity of the systems allows us to address two main characteristics of human problem-solving: the adaptation of general knowledge to particular problems and the dependency of the management of uncertainty on the different subtasks being implemented in the modules of the system, i.e. different modules can have different local multiple-valued logics as part of their local deductive mechanisms. Although the results obtained are general, we use, throughout the paper, examples of a medical expert system that has been designed using a modular language called MILORD-II, that implements them showing the practical interest of the theoretical concepts involved.     

Some results on the improvement of adaptation transients

A technique to improve the adaptation transient in discrete-time model reference adaptive control (MRAC) systems is given on the basis of applying classical optimization techniques to an equivalent near-linear system to the whole adaptive scheme. The resulting optimization is finally translated into modifications of one of the parameters entering the adaptation algorithm. This permits computation of a posteriori values of the adaptive controller parameters before generation of the input to the plant.