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Excessive extracellular deposition of amyloid- peptide (Aβ) in the brain is the pathological hallmark of Alzheimer's disease (AD). Cumulative evidence indicates that autophagy is involved in the metabolism of Aβ and pathogenesis of AD. However, the molecular mechanism underlying the pathogenesis of AD is not yet well defined, and there has been no effective treatment for AD. We recently found that long-term treatment with a butyrolactone derivative 3-benzyl-5-((2-nitrophenoxy) methyl)-dihydrofuran- 2(3 H)-one (3BDO) increased levels of insulin-degrading enzyme and neprilysin, suppressed autophagy via an mTOR pathway, lowered levels of Aβ, and prevented AD-like cognitive deficits in the AβPP/PS1 double transgenic mouse model. Therefore, our findings suggest that 3BDO may be beneficial in the prevention and treatment of AD.

The most of traditional change detection methods are adopted to deal with the remote sensing images based on single threshold algorithm, the different threshold change information cannot be absolutely used, so it difficult to detect the complete information. To solve this problem, a new change detection algorithm based on the MRF is proposed in this paper. This method fully integrates the change information of each threshold change image, and the complete and accurate change information can be obtained. The experimental results suggest that the detection accuracy of this method in the paper, which has certain stability and intelligence outperform the traditional change detection methods.

To enhance the security of mobile cloud users, a few proposals have been presented recently. However we argue that most of them are not suitable for mobile cloud where mobile users might join or leave the mobile networks arbitrarily. In this paper, we design a secure mobile user-based data service mechanism (SDSM) to provide confidentiality and fine-grained access control for data stored in the cloud. This mechanism enables the mobile users to enjoy a secure outsourced data services at a minimized security management overhead. The core idea of SDSM is that SDSM outsources not only the data but also the security management to the mobile cloud in a trust way. Our analysis shows that the proposed mechanism has many advantages over the existing traditional methods such as lower overhead and convenient update, which could better cater the requirements in mobile cloud computing scenarios.

Cloud computing becomes a hot research topic in the recent years. In the cloud computing, software applications and databases are moved to the centralized large data centers, which is called cloud. In the cloud, due to lack of physical possession of the data and the machine, the data and computation may not be well managed and fully trusted by cloud users. Existing work on cloud security mainly focuses on cloud storage without taking computation security into consideration. In this paper, we propose SecCloud, a novel auditing scheme to secure cloud computing based on probabilistic sampling technique as well as designated verifier technique, which aims to consider secure data storage, computation and privacy preserving together. We also discuss how to optimize sampling size to minimize the auditing cost. Detailed analysis and simulations have demonstrated the effectiveness and efficiency of the proposed scheme.

An advanced change detection method based on object-oriented classification of multi-band remote sensing data is proposed in this paper. In the first step, a further classification image is obtained based on the processed segment image and the preliminary classified image. Then the change threshold is achieved by an improved adaptive recursive algorithm based on the fusion image. Finally, the change region and change category are obtained by multi-band integration change mask method. The experiments showed that this method is effective, it can improve the accuracy of change detection and determine type of change, and can be used as a robust tool for territorial resources survey.

Due to the lack of support for a high-resolution image in a short time, land cover change detection is always applied on the multitemporal remote-sensed images with different resolutions. The coarse-resolution image contains a large number of mixed pixels, which can seriously limit the utility of the change detection. Soft classification (SC) can be applied to improve this situation through deriving the abundances and generating the fractional change map, but it cannot provide the spatial distribution of the subpixels. Subpixel mapping (SPM) is a potential solution to resolve this problem, and is designed to use the proportions to obtain a sharpened thematic map at a subpixel scale. Based on this thought, the subpixel scale land cover change mapping result can be realized by integrating these two key techniques. However, in practice, there is a serious limitation to the detail and accuracy of the result, because when the scale factor between the different resolution images is large, the subpixel configuration is complex and the data volumes will be amplified. Moreover, with the high proportion of the changed area in the whole image, the change detection process at the subpixel level gets more difficult. The SPM technique is generally performed based only on the abundances of each and the spatial dependence assumption, so it cannot satisfy the demand. In order to overcome this shortcoming, several new reasonable subpixel scale change detection rules are defined in this paper, which dictate the land cover change map must be constructed according to the existing high-resolution image. The output from SC and prior information of the subpixel feature arrangement is applied into a modified cellular automata (CA) model, which can be regarded as a more reasonable tool to monitor the subpixel changes to resolve the big-data problem. Two experiments are performed and the results prove that the proposed method can effectively improve the accuracy of the change detection maps of the spatial distribution in a subpixel scale.

With the development of the satellite sensor, multi-resource observation systems have become widely used. However, there is a huge difference between quantitative remote sensing products because of the different sensing observations and the quantitative retrieval algorithms. In this paper, the quantitative relationships between the normalized difference vegetation index (NDVI), the soil-adjusted vegetation index (SAVI) and the vegetation index based on the universal pattern decomposition method (VIUPD) of Landsat ETM+ and ASTER sensors are investigated. The difference in observations was examined between the two sensors, based on a pair of images. The results showed that: 1) There was a strong correlation between the different vegetation indices for the same sensor, with the coefficient of determination being greater than 0.9. 2) Whether for ASTER or Landsat, the information of VIUPD was richer than that of NDVI and SAVI. Furthermore, in dense vegetation areas, the values of NDVI and SAVI could easily reach saturation. 3) The values of SAVI were higher than NDVI in the areas of water or bare soil, while this was the opposite in areas of lush vegetation.