The Etiology of Multiple Sclerosis: Genetic Evidence for the Involvement of the Human Endogenous Retrovirus HERV-Fc1

We have investigated the role of human endogenous retroviruses in multiple sclerosis by analyzing the DNA of patients and controls in 4 cohorts for associations between multiple sclerosis and polymorphisms near viral restriction genes or near endogenous retroviral loci with one or more intact or almost-intact genes. We found that SNPs in the gene TRIM5 were inversely correlated with disease. Conversely, SNPs around one retroviral locus, HERV-Fc1, showed a highly significant association with disease. The latter association was limited to a narrow region that contains no other known genes. We conclude that HERV-Fc1 and TRIM5 play a role in the etiology of multiple sclerosis. If these results are confirmed, they point to new modes of treatment for multiple sclerosis

Genetic risk and a primary role for cell-mediated immune mechanisms in multiple sclerosis.

Multiple sclerosis is a common disease of the central nervous system in which the interplay between inflammatory and neurodegenerative processes typically results in intermittent neurological disturbance followed by progressive accumulation of disability. Epidemiological studies have shown that genetic factors are primarily responsible for the substantially increased frequency of the disease seen in the relatives of affected individuals, and systematic attempts to identify linkage in multiplex families have confirmed that variation within the major histocompatibility complex (MHC) exerts the greatest individual effect on risk. Modestly powered genome-wide association studies (GWAS) have enabled more than 20 additional risk loci to be identified and have shown that multiple variants exerting modest individual effects have a key role in disease susceptibility. Most of the genetic architecture underlying susceptibility to the disease remains to be defined and is anticipated to require the analysis of sample sizes that are beyond the numbers currently available to individual research groups. In a collaborative GWAS involving 9,772 cases of European descent collected by 23 research groups working in 15 different countries, we have replicated almost all of the previously suggested associations and identified at least a further 29 novel susceptibility loci. Within the MHC we have refined the identity of the HLA-DRB1 risk alleles and confirmed that variation in the HLA-A gene underlies the independent protective effect attributable to the class I region. Immunologically relevant genes are significantly overrepresented among those mapping close to the identified loci and particularly implicate T-helper-cell differentiation in the pathogenesis of multiple sclerosis

Stress testing of web applications in public clouds

V magistrski nalogi se ukvarjamo s problemom testiranja skalabilnosti in stroškov povezanih s povečanim obiskom, s katerim se danes srečuje vse več podjetij. Pri testiranju se osredotočimo na javna oblaka Amazon Web Services in Google Compute Platform. K reševanju problema pristopimo z obremenitvenim testiranjem tako, da razvijemo vzorčno aplikacijo in orodja, s katerimi jo obremenitveno testiramo. Vzorčno aplikacijo in orodja razvijemo po standardu TPC-W, ki ga uporabimo tudi za izvajanje obremenitvenega testa. Po zgledu metrik iz standarda TPC-W definiramo dve novi metriki, s katerima merimo in vrednotimo skalabilnost in stroške, povezane s povečanim obiskom. Meritve izvedemo najprej z ročnim skaliranjem, nato še z vklopljenim avtomatskim skaliranjem. Rezultate meritev ovrednotimo z definiranimi metrikami, izdelamo stroškovni model za omenjena oblaka in ju med seboj primerjamo.In this master thesis we are solving the problem of scalability testing and estimating the costs associated with increased load on web application, with which many companies are facing nowadays. We focus on testing of Amazon Web Services and Google Cloud Platform public clouds. Our approach to solving the problem is with load testing, that\u27s why we develop sample web application and tools to execute the load tests. For developing the web application and tools we used TPC-W standard. We also execute the load test according to TPC-W standard. Based on TPC-W metrics, we develop two new metrics that we use for evaluating load tests. First we perform the measurements with manual scaling and then with automated scaling. After that we evaluate the measurements with newly defined metrics, make the cost model for each cloud and compare them