Genome-wide analysis of the synonymous codon usage patterns in apple

Apple（Malus×domestica） has been proposed as an important woody plant and the major cultivated fruit trees in temperate regions. Apple whole genome sequencing has been completed, which provided an excellent opportunity for genome-wide analysis of the synonymous codon usage patterns. In this study, a multivariate bioinformatics analysis was performed to reveal the characteristics of synonymous codon usage and the main factors affecting codon bias in apple. The neutrality, correspondence, and correlation analyses were performed by Codon W and SPSS（Statistical Product and Service Solutions） programs, indicating that the apple genome codon usage patterns were affected by mutational pressure and selective constraint. Meanwhile, coding sequence length and the hydrophobicity of proteins could also influence the codon usage patterns. In short, codon usage pattern analysis and determination of optimal codons has laid an important theoretical basis for genetic engineering, gene prediction and molecular evolution studies in apple.

Exploring the user platform preference and user interest preference of Chinese scholarly articles:A comparison based on usage metrics

In order to comparatively investigate usage patterns of journal official sites and pay-for-access platform and then to support decision-making,this study selected academic papers,published during 2014-2015,of 61 Chinese open access journals indexed by CSSCI and CSCD in eight disciplines,"Library,Information and Archival Science","Management Science","Economics","Pedagogy","Computer Science","Earth Science","Math"and"Biology".Chinese usage patterns in user platform preferences and user interest preferences were explored by analyzing usage data from journal official sites and pay-for-access platforms.Operatively,comparisons of user platform preferences were implemented by descriptive statistics and correlation analysis and comparisons of user interest preferences were explored by Jaccard similarity coefficient and co-word analysis.It proved that there were differences in user platform preferences and user interest preferences between journal official sites and pay-for-access platforms.However,factors that affect usage patterns were very complicated.How to probe into the mechanism of contributory factors and then verify them was still an important problem to be solved in further study.

SOCIAL TAGGING DYNAMICS UNDER SYSTEM RECOMMENDATION AND RESOURCE MULTIDIMENSIONALITY

Social tagging systems have attracted plenty of research endeavors recently. The dynamic models of tag generation or tag usage are one of the key subjects of inquiry. However, the existing models do not well explain the ＂staged＂ power-law distribution of tag usage frequencies as observed in various social tagging systems. To cope with this, a new tag-generation model is proposed in this paper, which is based on a preferential selection mechanism influenced by the combinatorial effects of system recommendation and resource multidimensionality. Furthermore, to validate the model, the simulative results under different parameter combinations are compared with the distributions of tag usage frequencies in datasets from three famous social tagging systems, namely Delicious.com, Last.fin and Flickr. For different categories of resources of the three systems, three tag usage patterns can be identified, namely the power-law distribution with two plateaus, the power-law distribution with one plateau, and the standard power-law distribution. All the three patterns can be well fitted and explained by the proposed model.

A scheduling algorithm with dynamic properties in mobile grid

Mobile grid is a branch of grid computing that incorporates mobile devices into the grid infrastructure. It poses new challenges because mobile devices are typically resource-constrained and exhibit unique characteristics such as instability in network connections. New scheduling strate- gies are imperative in mobile grid to efficiently utilize the devices. This paper presents a scheduling algorithm that con- siders dynamic properties of mobile devices such as avail- ability, reliability, maintainability, and usage pattern in mo- bile grid environments. In particular, usage patterns caused by voluntarily or involuntarily losing a connection, such as switching off the device or a network interruption could be important criteria for choosing the best resource to execute a job. The experimental results show that our scheduling al- gorithm provides superior performance in terms of execution time, as compared to the other methods that do not consider usage pattern. Throughout the experiments, we found it es- sential to consider usage pattern for improving performance in the mobile grid.

Storage capacity model for cloud download

With the rise and world wide deployment of cloud utilities, the principle of the cloud download is proposed to provide high-quality file content distribution by using dedicated servers as cloud cache to guarantee the data availability and enhance the data transfer rate. As the system scales up to a large population, how to design appropriate storage capacity of cloud cache is a key challenge for cloud download. In this paper, primary elements impacting on storage capacity are explored through deliberating in large-scale commercial cloud download system, i.e. general user usage pattern and available period. And based on statistical analysis of real-world traces, we formulate storage capacity related to these two elements, which is the original contribution different from all previous works. This model gives guidance of potential system policy design. Finally, the effectivity of this model is demonstrated by simulation results compared with empirical data of practical system.