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The client components in the case study were implemented in Java, which means that they can be downloaded from a web server. Updating the copy on the web server implements migration to new versions of the client. Moreover, Java was chosen in order to ensure that the clients run on a number of different platforms, including web browsers and set-top boxes. The server objects were implemented in C , mainly because they had to perform efficiently on Unix servers. Hence, the video-on-demand system is a distributed system consisting of heterogeneous components. The video-on-demand system uses an object-oriented middleware. We will revisit this form of middleware throughout the book. It supports the definition of interfaces of the server objects in a way that is independent of programming language, network and hardware. It manages the communication between client and server objects that is necessaryto locate the server that holds a desired video and to select the video to be downloaded. The system exhibits all of the characteristics of distributed systems that we discussed in the previous section. The components are autonomous and heterogeneous: client components are written in Java. They operate in web browsers or set-top boxes. The servers operate on Unix hosts. The servers are shared components while each customer has an unshared client object. The system is inherently concurrent, given that there are 90,000 potential users. Although it is unlikelythat all subscribers will decide to watch a video at the same time, there are probably several hundred concurrent users at any point in time. Finally, there are also multiple points of failure as each of the set-top boxes/web browsers, the network link or the servers might be overloaded or fail.



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Taking into consideration the necessary functional components as presented in Figure 6.2, we adopted the decision to make a physical architecture consisting in a one-to-one relationship between functional and physical components; that is, each function is implemented by means of one component. What follows here is an account of the relations of the service layer domain components so far introduced, including the relevant information they exchange, that is interface descriptions. The interface descriptions are ordered to mirror the service life cycle and therefore they consist of the implementation of the reference points considered in the Service Layer Reference Points of Figure 3.4. An overview of the listed interfaces is shown in Figure 6.3. 6.2.3.1. CAS Authoring (Input) System Capabilities: The available system capabilities for constructing Context-aware Services, based on the CAS modeling approach. (Input) CAS Author: The input from the person responsible for de ning services. (Output) Service De nition Document: An implementation technology-independent document describing all details required for a service s creation and provisioning. 6.2.3.2. Service Customization (Input) Consumer request: A consumer s request for a service, containing all the necessary service customizations representing the consumer s individual needs. (Input) Customizations Interface Con gurations: A document specifying the customizations interface exposed to the consumers for the speci c service this document is based on the capabilities of the front-end technology implementing the





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Non-parametric ( model-free ) methods of linkage analysis are used for complex traits because this method does not require prior knowledge about the inheritance of a disease. In non-parametric linkage analysis the segregation of variants of anonymous, highly variable deoxyribonucleic acid (DNA) loci (marker alleles) is examined in affected family member pairs, e.g. affected sister pairs. Marker alleles shared by affected relative pairs more often than would be expected by chance provide evidence that there is a linkage between the marker and the disease under investigation, which can either be a dichotomous complex trait or a quantitative trait. The affected sib-pair design is the most widely used approach. Linkage analysis is a statistical approach involving the calculation of LOD scores, which are the logarithms to the base 10 of the likelihood ratios for linkage verses non-linkage. For complex diseases LOD scores >3.3 are considered the most appropriate threshold for evidence of signi cant linkage (Lander & Kruglyak, 1995). This threshold for signi cance minimizes the risk of false positive results.

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1. Jemal A, Siegel R, Ward E, et al. Cancer statistics, 2008. CA Cancer J Clin. 2008;58:71 96. 2. Wong AS, Auersperg N. Ovarian surface epithelium: family history and early events in ovarian cancer. Reprod Biol Endocrinol. 2003;1:70. 3. Amos CI, Shaw GL, Tucker MA, Hartge P. Age at onset for familial epithelial ovarian cancer. JAMA. 1992;268:1896 1899. 4. Vergote I, Trimbos BJ. Treatment of patients with early epithelial ovarian cancer. Curr Opin Oncol. 2003;15:452 455. 5. Changes in de nitions of clinical staging for carcinoma of the cervix and ovary: International Federation of Gynecology and Obstetrics. Am J Obstet Gynecol. 1987;156:263 264. 6. Landen CN, Jr., Birrer MJ, Sood AK. Early events in the pathogenesis of epithelial ovarian cancer. J Clin Oncol. 2008;26:995 1005. 7. Kaku T, Ogawa S, Kawano Y, et al. Histological classi cation of ovarian cancer. Med Electron Microsc. 2003;36:9 17. 8. Seidman JD, Horkayne-Szakaly I, Haib M, et al. The histologic type and stage distribution of ovarian carcinomas of surface epithelial origin. Int J Gynecol Pathol. 2004;23:41 44. 9. Malpica A, Deavers MT, Tornos C, et al. Interobserver and intraobserver variability of a two-tier system for grading ovarian serous carcinoma. Am J Surg Pathol. 2007;31:1168 1174. 10. Kurman RJ, Visvanathan K, Roden R, Wu TC, Shih Ie M. Early detection and treatment of ovarian cancer: shifting from early stage to minimal volume of disease based on a new model of carcinogenesis. Am J Obstet Gynecol. 2008;198:351 356.

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