No abstract

Failures are normal rather than exceptional in the cloud computing environments. To improve system availability, replicating the popular data to multiple suitable locations is an advisable choice, as users can access the data from a nearby site. This is, however, not the case for replicas which must have a fixed number of copies on several locations. How to decide a reasonable number and right locations for replicas has become a challenge in the cloud computing. In this paper, a dynamic data replication strategy is put forward with a brief survey of replication strategy suitable for distributed computing environments. It includes: 1) analyzing and modeling the relationship between system availability and the number of replicas; 2) evaluating and identifying the popular data and triggering a replication operation when the popularity data passes a dynamic threshold; 3) calculating a suitable number of copies to meet a reasonable system byte effective rate requirement and placing replicas among data nodes in a balanced way; 4) designing the dynamic data replication algorithm in a cloud. Experimental results demonstrate the efficiency and effectiveness of the improved system brought by the proposed strategy in a cloud.

Prediction is one of the most attractive aspects in data mining. Link prediction has recently attracted the attention of many researchers as an effective technique to be used in graph based models in general and in particular for social network analysis due to the recent popularity of the field. Link prediction helps to understand associations between nodes in social communities. Existing link prediction-related approaches described in the literature are limited to predict links that are anticipated to exist in the future. To the best of our knowledge, none of the previous works in this area has explored the prediction of links that could disappear in the future. We argue that the latter set of links are important to know about; they are at least equally important as and do complement the positive link prediction process in order to plan better for the future. In this paper, we propose a link prediction model which is capable of predicting both links that might exist and links that may disappear in the future. The model has been successfully applied in two different though very related domains, namely health care and gene expression networks. The former application concentrates on physicians and their interactions while the second application covers genes and their interactions. We have tested our model using different classifiers and the reported results are encouraging. Finally, we compare our approach with the internal links approach and we reached the conclusion that our approach performs very well in both bipartite and non-bipartite graphs.

No abstract

Online scheduling plays a key role for big data streaming applications in a big data stream computing environment, as the arrival rate of high velocity continuous data stream might fluctuate over time. In this paper, an elastic online scheduling framework for big data streaming applications (E-Stream) is proposed, exhibiting the following features. (1) Profile mathematical relationships between system response time, multiple application fairness, and online features of high velocity continuous stream. (2) Scale out or scale in a data stream graph by quantifying computation and communication cost, and the vertex semantics for arrival rate of data stream, and adjust the degree of parallelism of vertices in the graph. Sub-graph is further constructed to minimize data dependencies among the sub-graphs. (3) Elastically schedule a graph by a priority based earliest finish time first online scheduling strategy, and schedule multiple graphs by a max-min fairness strategy. (4) Evaluate the low system response time and acceptable applications fairness objectives in a realworld big data stream computing environment. Experimental results conclusively demonstrate that the proposed E-Stream provides better system response time and applications fairness compared to the existing Storm framework.

Abstract. Overexpression of breast cancer resistance protein, the ATP-binding cassette, subfamily G, member2 (BCRP/ABCG2), confers multidrug resistance to tumor cells and often limits the efficacy of chemotherapy. The aim of this study was to investigate the expression and functional activity of ABCG2 in head and neck squamous cell carcinoma (HNSCC) and corresponding cell lines. Immunohistochemistry was performed to investigate the presence of the ABCG2 transporter in HNSCC tissues. Expression of ABCG2 in the Hep-2, Hep-2T, CNE and FaDu cell lines was analyzed by real-time quantitative reverse transcription-polymerase chain reaction and Western blotting at the levels of messenger RNA (mRNA) and protein, respectively. The drug sensitivity of the above four cell lines to mitoxantrone was detected using MTT, and the drug accumulation of mitoxantrone was analyzed by flow cytometry. Positive expression of ABCG2 was detected in 52.04% of the laryngeal cancer samples from 98 patients, in 65% of the 40 hypopharyngeal cancer samples and in 58.82% of the 34 nasopharyngeal cancer samples. The level of expression was found to be correlated with tumor TNM stage (P<0.05) and lymph node metastasis (P<0.01). All four HNSCC cell lines expressed ABCG2 at the mRNA and protein levels. The levels of ABCG2 expression in the four cell lines were significantly correlated with the function and sensitivity to mitoxantrone. The addition of fumitremorgin C at a concentration of 5 µM to mitoxantrone treatment caused a varied increase in mitoxantrone accumulation of 1.09-fold, 1.33-fold (P<0.01), 1.4-fold (P<0.01) and 1-fold in the Hep-2, Hep-2T, CNE and FaDu cells, respectively. Expression of ABCG2 varied among the different types of carcinoma tissues and each HNSCC cell line, and it induced multidrug resistance and separation of cancer stem cells attributing to its efflux pump function. Thus, ABCG2 expression may be an unfavorable prognostic factor for HNSCC. Due to the negligible expression and function of ABCG2, we suggest that the FaDu cell line is suitable to be a negative control in studies involving HNSCC. Taken together, ABCG2 is a promising universal biomarker of cancer stem cells and a target gene for HNSCC chemotherapy.

BackgroundBreast cancer is a serious disease which affects many women and may lead to death. It has received considerable attention from the research community. Thus, biomedical researchers aim to find genetic biomarkers indicative of the disease. Novel biomarkers can be elucidated from the existing literature. However, the vast amount of scientific publications on breast cancer make this a daunting task. This paper presents a framework which investigates existing literature data for informative discoveries. It integrates text mining and social network analysis in order to identify new potential biomarkers for breast cancer.ResultsWe utilized PubMed for the testing. We investigated gene–gene interactions, as well as novel interactions such as gene-year, gene-country, and abstract-country to find out how the discoveries varied over time and how overlapping/diverse are the discoveries and the interest of various research groups in different countries.ConclusionsInteresting trends have been identified and discussed, e.g., different genes are highlighted in relationship to different countries though the various genes were found to share functionality. Some text analysis based results have been validated against results from other tools that predict gene–gene relations and gene functions.Electronic supplementary materialThe online version of this article (doi:10.1186/s13104-016-2023-5) contains supplementary material, which is available to authorized users.

A dye-sensitized solar cell (DSC) was fabricated using a new β-diketonato ruthenium(II) sensitizer with a broad absorption range and a high molar extinction coefficient of 11.3×103 M-1 cm-1 at 584 nm. Efficient sensitization of nanocrystalline TiO2 film was observed across the whole visible range and into the near-IR region as far as 1000 nm. The incident photon-to-current conversion efficiency (IPCE) spectrum was consistent with the absorption spectrum and resulted in a high short-circuit photocurrent density (Jsc) of 19 mA cm-2.