Upregulated MT1-MMP/TIMP-2 axis in the TSU-Pr1-B1/B2 model of metastatic progression in transitional cell carcinoma of the bladder

Muscle invasive transitional cell carcinoma (TCC) of the bladder is associated with a high frequency of metastasis, resulting in poor prognosis for patients presenting with this disease. Models that capture and demonstrate step-wise enhancement of elements of the human metastatic cascade on a similar genetic background are useful research tools. We have utilized the transitional cell carcinoma cell line TSU-Pr1 to develop an in vivo experimental model of bladder TCC metastasis. TSU-Pr1 cells were inoculated into the left cardiac ventricle of SCID mice and the development of bone metastases was monitored using high resolution X-ray. Tumor tissue from a single bone lesion was excised and cultured in vitro to generate the TSU-Pr1-B1 subline. This cycle was repeated with the TSU-Pr1-B1 cells to generate the successive subline TSU-Pr1-B2. DNA profiling and karyotype analysis confirmed the genetic relationship of these three cell lines. In vitro, the growth rate of these cell lines was not significantly different. However, following intracardiac inoculation TSU-Pr1, TSU-Pr1-B1 and TSU-Pr1-B2 exhibited increasing metastatic potential with a concomitant decrease in time to the onset of radiologically detectable metastatic bone lesions. Significant elevations in the levels of mRNA expression of the matrix metalloproteases (MMPs) membrane type 1-MMP (MT1-MMP), MT2-MMP and MMP-9, and their inhibitor, tissue inhibitor of metalloprotease-2 (TIMP-2), across the progressively metastatic cell lines, were detected by quantitative PCR. Given the role of MT1-MMP and TIMP-2 in MMP-2 activation, and the upregulation of MMP-9, these data suggest an important role for matrix remodeling, particularly basement membrane, in this progression. The TSU-Pr1-B1/B2 model holds promise for further identification of important molecules.

Constructing the identity of a nation : the tourist gaze at the Museum of Scotland

In an era of global instability and crises of national identity, the role of heritage tourism in creating images of national identity has become an important area for research. This article considers the role of heritage tourism in constructing national identity in the nation of Scotland through the lens of the Museum of Scotland. It describes the findings of qualitative research undertaken with potential and actual target consumers to the Museum of Scotland. Three research questions were addressed: Does the Museum of Scotland construct (1) a vision of a `new' Scotland? (2) a symbol of a `real' Scotland? (3) a collective identity of Scotland? The findings suggest that heritage visitors actively identify through their gaze, constructing multifarious meanings of national identity that are dynamic rather than static.

A secure cloud computing based framework for big data information management of smart grid

Smart grid is a technological innovation that improves efficiency, reliability, economics, and sustainability of electricity services. It plays a crucial role in modern energy infrastructure. The main challenges of smart grids, however, are how to manage different types of front-end intelligent devices such as power assets and smart meters efficiently; and how to process a huge amount of data received from these devices. Cloud computing, a technology that provides computational resources on demands, is a good candidate to address these challenges since it has several good properties such as energy saving, cost saving, agility, scalability, and flexibility. In this paper, we propose a secure cloud computing based framework for big data information management in smart grids, which we call 'Smart-Frame.' The main idea of our framework is to build a hierarchical structure of cloud computing centers to provide different types of computing services for information management and big data analysis. In addition to this structural framework, we present a security solution based on identity-based encryption, signature and proxy re-encryption to address critical security issues of the proposed framework.

A clustering-based multi-layer distributed ensemble for neurological diagnostics in cloud services

This paper investigates the problem of minimizing data transfer between different data centers of the cloud during the neurological diagnostics of cardiac autonomic neuropathy (CAN). This problem has never been considered in the literature before. All classifiers considered for the diagnostics of CAN previously assume complete access to all data, which would lead to enormous burden of data transfer during training if such classifiers were deployed in the cloud. We introduce a new model of clustering-based multi-layer distributed ensembles (CBMLDE). It is designed to eliminate the need to transfer data between different data centers for training of the classifiers. We conducted experiments utilizing a dataset derived from an extensive DiScRi database. Our comprehensive tests have determined the best combinations of options for setting up CBMLDE classifiers. The results demonstrate that CBMLDE classifiers not only completely eliminate the need in patient data transfer, but also have significantly outperformed all base classifiers and simpler counterpart models in all cloud frameworks.

An algorithm for finding the minimum cost of storing and regenerating datasets in multiple clouds

The proliferation of cloud computing allows users to flexibly store, re-compute or transfer large generated datasets with multiple cloud service providers. However, due to the pay-As-you-go model, the total cost of using cloud services depends on the consumption of storage, computation and bandwidth resources which are three key factors for the cost of IaaS-based cloud resources. In order to reduce the total cost for data, given cloud service providers with different pricing models on their resources, users can flexibly choose a cloud service to store a generated dataset, or delete it and choose a cloud service to regenerate it whenever reused. However, finding the minimum cost is a complicated yet unsolved problem. In this paper, we propose a novel algorithm that can calculate the minimum cost for storing and regenerating datasets in clouds, i.e. whether datasets should be stored or deleted, and furthermore where to store or to regenerate whenever they are reused. This minimum cost also achieves the best trade-off among computation, storage and bandwidth costs in multiple clouds. Comprehensive analysis and rigid theorems guarantee the theoretical soundness of the paper, and general (random) simulations conducted with popular cloud service providers' pricing models demonstrate the excellent performance of our approach.

Converged network-cloud service composition with end-to-end performance guarantee

The crucial role of networking in Cloud computing calls for federated management of both computing and networkin resources for end-To-end service provisioning. Application of the Service-Oriented Architecture (SOA) in both Cloud computing an networking enables a convergence of network and Cloud service provisioning. One of the key challenges to high performanc converged network-Cloud service provisioning lies in composition of network and Cloud services with end-To-end performanc guarantee. In this paper, we propose a QoS-Aware service composition approach to tackling this challenging issue. We first present system model for network-Cloud service composition and formulate the service composition problem as a variant of Multi-Constraine Optimal Path (MCOP) problem. We then propose an approximation algorithm to solve the problem and give theoretical analysis o properties of the algorithm to show its effectiveness and efficiency for QoS-Aware network-Cloud service composition. Performanc of the proposed algorithm is evaluated through extensive experiments and the obtained results indicate that the proposed metho achieves better performance in service composition than the best current MCOP approaches Service (QoS).