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BACKGROUND Glioblastoma multiforme (GBM) is the most common malignant and lethal type of primary central nervous system tumor in humans. In spite of its high lethality, a small percentage of patients have a relatively good prognosis, with median survival times of 36 months or longer. The identification of clinical subsets of GBM associated with distinct molecular genetic profiles has made it possible to design therapies tailored to treat individual patients. METHODS We compared microarray data sets from long-term survivors (LTSs) and short-term survivors (STSs) to screen for prognostic biomarkers in GBM patients using the WebArrayDB platform. We focused on FBLN4, IGFBP-2, and CHI3L1, all members of a group of 10 of the most promising, differentially regulated gene candidates. Using formalin-fixed paraffin-embedded GBM samples, we corroborated the relationship between these genes and patient outcomes using methylation-specific polymerase chain reaction (PCR) for MGMT methylation status and quantitative reverse transcription PCR for expression of these genes. RESULTS Expression levels of the mRNAs of these 3 genes were higher in the GBM samples than in normal brain samples and these 3 genes were significantly upregulated in STSs compared to the levels in LTS samples (P

The automatic feature extracting and matching for large amount of linear pushbroom imagery with higher and higher resolution is urgent and challenging in three dimensional reconstructions, remote sensing and mapping. Affine & scale-invariant heterogeneous pyramid feature is proposed in this paper, along with the new scale-invariant analysis method, the detecting of the key points, the affine & scale-invariant descriptor, the steering method of the matching, and the quasi-dense matching algorithm based on the extensive epipolar geometry. The automatic matching is devised for the linear pushbroom imagery. The whole process is executed on lunar images of the highest resolution of ~7 m/pixel (or ~1 m/pixel in the lower orbits) from the Chinese Chang’e 2 satellite, it runs robustly at present, and resulting in large amounts of well-distributed-correspondences with accuracy of 0.3 pixels and excellent reliability, which gives great support for the further exploration both on the Moon and the Earth.

Data transfer is very important in cloud computing, minimum the time cost is an effective way to improve the system performance. The purpose of the study was to model the data transfer process in the private cloud and shrink the time cost. Depending on the communication pattern of the model, the transfer algorithm was proposed for single and multi cluster. Experiments illustrated that the model proposed in this paper was feasible for the RS cloud data transfer.

Remote sensing image processing is a part of the digital photogrammetric system, and data management is very important in this system. The aim of this paper was to build a distributed file system for massive remote sensing image management in the processing. The proposed distributed file system, RSI-DFS, has a flexible namespace mechanism, takes normal file as data model, takes TCP as data transfer model. Experiments indicates that the proposed distributed file system has a stable read and write performance compared with the existing systems.

With the high volume, velocity and variety characteristics, remote sensing(RS) data has been regarded as a typical big data. Cloud computing is a powerful technology in big data era. The Huge computing power of cloud computing could be leveraged in RS image data processing. This paper proposed a preliminary private cloud for RS image processing. The two cornerstone components of the private cloud was private cloud storage engine(PCSE) and private cloud computing engine(PCCE). The PCSE was responsible for the data storage while the PCCE is responsible for data processing. The private cloud had been implemented and used in our RS image processing project. Experiments results illustrated that the private cloud was effective for RS image processing.