Combining data sharing with the master–worker paradigm in the common component architecture

Software component technologies are being accepted as an adequate solution for handling the complexity of applications. However, existing software component models tend to be specialized to some types of resource architectures (e.g. in-process, distributed environments, etc.) and/or do not provide a very high level of abstraction. This paper focuses on handling data sharing on operation invocations between components as a solution allowing applications to be efficiently executed on all kinds of resources. In particular, the data sharing pattern appears in master–worker applications, when workers need to access only a part of a large piece of data, either in read or write mode. This approach is applied to the Common Component Architecture model. Its benefits are discussed using an image rendering application.

Scientific discovery as a combinatorial optimisation problem: How best to navigate the landscape of possible experiments?

A considerable number of areas of bioscience, including gene and drug discovery, metabolic engineering for the biotechnological improvement of organisms, and the processes of natural and directed evolution, are best viewed in terms of a 'landscape' representing a large search space of possible solutions or experiments populated by a considerably smaller number of actual solutions that then emerge. This is what makes these problems 'hard', but as such these are to be seen as combinatorial optimisation problems that are best attacked by heuristic methods known from that field. Such landscapes, which may also represent or include multiple objectives, are effectively modelled in silico, with modern active learning algorithms such as those based on Darwinian evolution providing guidance, using existing knowledge, as to what is the 'best' experiment to do next. An awareness, and the application, of these methods can thereby enhance the scientific discovery process considerably. This analysis fits comfortably with an emerging epistemology that sees scientific reasoning, the search for solutions, and scientific discovery as Bayesian processes.     

Membrane computing: brief introduction, recent results and applications

The internal organization and functioning of living cells, as well as their cooperation in tissues and higher order structures, can be a rich source of inspiration for computer science, not fully exploited at the present date. Membrane computing is an answer to this challenge, well developed at the theoretical (mathematical and computability theory) level, already having several applications (via usual computers), but without having yet a bio-lab implementation. After briefly discussing some general issues related to natural computing, this paper provides an informal introduction to membrane computing, focused on the main ideas, the main classes of results and of applications. Then, three recent achievements, of three different types, are briefly presented, with emphasis on the usefulness of membrane computing as a framework for devising models of interest for biological and medical research.     

A reliability analysis of cardiac repolarization time markers

Only a limited number of studies have addressed the reliability of extracellular markers of cardiac repolarization time, such as the classical marker RT_eg defined as the time of maximum upslope of the electrogram T wave. This work presents an extensive three-dimensional simulation study of cardiac repolarization time, extending the previous one-dimensional simulation study of a myocardial strand by Steinhaus [B.M. Steinhaus, Estimating cardiac transmembrane activation and recovery times from unipolar and bipolar extracellular electrograms: a simulation study, Circ. Res. 64 (3) (1989) 449]. The simulations are based on the bidomain - Luo-Rudy phase I system with rotational fiber anisotropy and homogeneous or heterogeneous transmural intrinsic membrane properties. The classical extracellular marker RT_eg is compared with the gold standard of fastest repolarization time RT_tap, defined as the time of minimum derivative during the downstroke of the transmembrane action potential (TAP). Additionally, a new extracellular marker RT90_eg is compared with the gold standard of late repolarization time RT90_tap, defined as the time when the TAP reaches 90% of its resting value. The results show a good global match between the extracellular and transmembrane repolarization markers, with small relative mean discrepancy (?1.6%) and high correlation coefficients (?0.92), ensuring a reasonably good global match between the associated repolarization sequences. However, large local discrepancies of the extracellular versus transmembrane markers may ensue in regions where the curvature of the repolarization front changes abruptly (e.g. near front collisions) or is negligible (e.g. where repolarization proceeds almost uniformly across fiber). As a consequence, the spatial distribution of activation-recovery intervals (ARI) may provide an inaccurate estimate of (and weakly correlated with) the spatial distribution of action potential durations (APD).     

Future affective technology for autism and emotion communication

People on the autism spectrum often experience states of emotional or cognitive overload that pose challenges to their interests in learning and communicating. Measurements taken from home and school environments show that extreme overload experienced internally, measured as autonomic nervous system (ANS) activation, may not be visible externally: a person can have a resting heart rate twice the level of non-autistic peers, while outwardly appearing calm and relaxed. The chasm between what is happening on the inside and what is seen on the outside, coupled with challenges in speaking and being pushed to perform, is a recipe for a meltdown that may seem to come ‘out of the blue’, but in fact may have been steadily building. Because ANS activation both influences and is influenced by efforts to process sensory information, interact socially, initiate motor activity, produce meaningful speech and more, deciphering the dynamics of ANS states is important for understanding and helping people on the autism spectrum. This paper highlights advances in technology that can comfortably sense and communicate ANS arousal in daily life, allowing new kinds of investigations to inform the science of autism while also providing personalized feedback to help individuals who participate in the research.     

Discrimination of riboflavin producing Bacillus subtilis strains based on their fed-batch process performances on a millilitre scale

Forty-eight single-use stirred tank bioreactors on a 10-mL scale operated in a magnetically inductive driven bioreaction block and automated with a liquid handler were applied for discrimination of different riboflavin producing Bacillus subtilis strains based on their performances in the parallel fed-batch processes. It was shown that a discrimination of the B. subtilis riboflavin producer strains can efficiently be achieved within one parallel fermentation run based on the integral riboflavin yield after 48 h. The possibility to perform replicates within the parallel fermentation run allows for a robust statistical analysis and is a prerequisite for the discrimination of producer strains under fed-batch process conditions. Within the estimation error, all of the riboflavin producing B. subtilis strains under study showed the same fed-batch process performances on the litre scale compared to the millilitre scale.     

不同压缩程序对海量生物信息数据压缩效率的比较分析

海量生物信息数据的不断涌现迫切需要在数据压缩技术方面进行更多研究,以减轻服务器存储压力和提高网络传输及数据分析的效率。目前虽然已开发出大量数据压缩软件,但对于海量生物信息数据而言,应该选用何种软件和方法进行数据压缩,尚缺乏详细的综合比较分析。本文选择生物信息学领域中GenBank数据库中的典型核酸和蛋白质序列数据库以及典型生物信息软件Blast和EMBOSS为例,采用不同数据压缩软件进行综合比较分析,结果发现经典压缩软件compress的总体压缩效率很高,除压缩比率可接受之外,其压缩时间相对其他软件而言显著减少,甚至比并行化的hzip2（pbzip2）和gzip（pigz）软件的时间还少很多,故可优先考虑使用。7-Zip软件虽然具有最高的压缩比率,但压缩过程十分耗时,可用于数据的长期储存;而采用bzip2、rar以及gzip等软件压缩的文件,虽然压缩比率较7-Zip的偏低,但压缩过程相对而言还比较快速。具体应用中推荐使用经典压缩软件compress以及并行化运行的pbzip2和pigz软件,三者可作为同时兼顾压缩比率和压缩时间的优选。     

In vitro molecular pattern classification via DNA-based weighted-sum operation

Recent progress in molecular computation suggests the possibility of pattern classification in vitro. Weighted sum is a primitive operation required by many pattern classification problems. Here we present a DNA-based molecular computation method for implementing the weighted-sum operation and its use for molecular pattern classification in a test tube. The weights of the classifier are encoded as the mixing ratios of the differentially labeled probe DNA molecules, which are competitively hybridized with the input-encoding target molecules to compute the decision boundary of classification. The computation result is detected by fluorescence signals. We experimentally verify the underlying weight encoding scheme and demonstrate successful discrimination of two-group labels of synthetic DNA mixture patterns. The method can be used for direct computation on biomolecular data in a liquid state.     

A high-accuracy protein structural class prediction algorithm using predicted secondary structural information

One major problem with the existing algorithm for the prediction of protein structural classes is low accuracies for proteins from α/β and α+β classes. In this study, three novel features were rationally designed to model the differences between proteins from these two classes. In combination with other rational designed features, an 11-dimensional vector prediction method was proposed. By means of this method, the overall prediction accuracy based on 25PDB dataset was 1.5% higher than the previous best-performing method, MODAS. Furthermore, the prediction accuracy for proteins from α+β class based on 25PDB dataset was 5% higher than the previous best-performing method, SCPRED. The prediction accuracies obtained with the D675 and FC699 datasets were also improved.