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Grid computing provides the infrastructure necessary to access and use distributed resources as part of virtual organizations. When used in this way, Grid computing makes it possible for users to participate in collaborative and distributed applications such as tele-immersion, visualization, and computational simulation. Some of these applications operate in a collaborative mode, requiring data to be stored and delivered in a timely manner. This class of applications must adhere to stringent real-time constraints and Quality-of-Service (QoS) requirements. A QoS management approach is therefore required to orchestrate and guarantee the timely interaction between such applications and services. We discuss the design and a prototype implementation of a QoS system, and demonstrate how we enable Grid applications to become QoS compliant. We validate this approach through a case study of an image processing task derived from a nanoscale structures application.

We compared the performance of several prediction techniques for breast cancer prognosis, based on AU-ROC performance (Area Under ROC) for different prognosis periods. The analyzed dataset contained 1,981 patients and from an initial 25 variables, the 11 most common clinical predictors were retained. We compared eight models from a wide spectrum of predictive models, namely; Generalized Linear Model (GLM), GLM-Net, Partial Least Square (PLS), Support Vector Machines (SVM), Random Forests (RF), Neural Networks, k-Nearest Neighbors (k-NN) and Boosted Trees. In order to compare these models, paired t-test was applied on the model performance differences obtained from data resampling. Random Forests, Boosted Trees, Partial Least Square and GLMNet have superior overall performance, however they are only slightly higher than the other models. The comparative analysis also allowed us to define a relative variable importance as the average of variable importance from the different models. Two sets of variables are identified from this analysis. The first includes number of positive lymph nodes, tumor size, cancer grade and estrogen receptor, all has an important influence on model predictability. The second set incudes variables related to histological parameters and treatment types. The short term vs long term contribution of the clinical variables are also analyzed from the comparative models. From the various cancer treatment plans, the combination of Chemo/Radio therapy leads to the largest impact on cancer prognosis.

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BackgroundKCNH1 encodes a voltage-gated potassium channel that is predominantly expressed in the central nervous system. Mutations in this gene were recently found to be responsible for Temple-Baraitser Syndrome (TMBTS) and Zimmermann-Laband syndrome (ZLS).MethodsHere, we report a new case of TMBTS diagnosed in a Lebanese child. Whole genome sequencing was carried out on DNA samples of the proband and his parents to identify mutations associated with this disease. Sanger sequencing was performed to confirm the presence of detected variants.ResultsWhole genome sequencing revealed three missense mutations in TMBTS patient: c.1042G > A in KCNH1, c.2131 T > C in STK36, and c.726C > A in ZNF517. According to all predictors, mutation in KCNH1 is damaging de novo mutation that results in substitution of Glycine by Arginine, i.e., p.(Gly348Arg). This mutation was already reported in a patient with ZLS that could affect the connecting loop between helices S4-S5 of KCNH1 with a gain of function effect.ConclusionsOur findings demonstrate that KCNH1 mutations cause TMBTS and expand the mutational spectrum of KCNH1 in TMBTS. In addition, all cases of TMBTS were reviewed and compared to ZLS. We suggest that the two syndromes are a continuum and that the variability in the phenotypes is the result of the involvement of genetic modifiers.

SUMMARYSome applications utilizing Grid computing infrastructure require the simultaneous allocation of resources, such as compute servers, networks, memory, disk storage and other specialized resources. Collaborative working and visualization is one example of such applications. In this context, quality of service (QoS) is related to Grid services, and not just to the network connecting these services. With the emerging interest in service-oriented Grids, resources may be advertised and traded as services based on a service level agreement (SLA). Such a SLA must include both general and technical specifications, including pricing policy and properties of the resources required to execute the service, to ensure QoS requirements are satisfied. An approach for QoS adaptation is presented to enable the dynamic adjustment of behavior of an application based on changes in the pre-defined SLA. The approach is particularly useful if workload or network traffic changes in unpredictable ways during an active session.

Arab populations are largely understudied, notably their genetic structure and history. Here we present an in-depth analysis of 6,218 whole genomes from Qatar, revealing extensive diversity as well as genetic ancestries representing the main founding Arab genealogical lineages of Qahtanite (Peninsular Arabs) and Adnanite (General Arabs and West Eurasian Arabs). We find that Peninsular Arabs are the closest relatives of ancient hunter-gatherers and Neolithic farmers from the Levant, and that founder Arab populations experienced multiple splitting events 12–20 kya, consistent with the aridification of Arabia and farming in the Levant, giving rise to settler and nomadic communities. In terms of recent genetic flow, we show that these ancestries contributed significantly to European, South Asian as well as South American populations, likely as a result of Islamic expansion over the past 1400 years. Notably, we characterize a large cohort of men with the ChrY J1a2b haplogroup (n = 1,491), identifying 29 unique sub-haplogroups. Finally, we leverage genotype novelty to build a reference panel of 12,432 haplotypes, demonstrating improved genotype imputation for both rare and common alleles in Arabs and the wider Middle East.

In a clinical setting, DNA sequencing can uncover findings unrelated to the purpose of genetic evaluation. The American College of Medical Genetics and Genomics (ACMG) recommends the evaluation and reporting of 59 genes from clinic genomic sequencing. While the prevalence of secondary findings is available from large population studies, these data lack Arab and other Middle Eastern populations. The Qatar Genome Program (QGP) generates whole-genome sequencing (WGS) data and combines it with phenotypic information to create a comprehensive database for studying the Qatari and wider Arab and Middle Eastern populations at the molecular level. This study identified and analyzed medically actionable variants in the 59 ACMG genes using WGS data from 6045 QGP participants. Our results identified a total of 60 pathogenic and likely pathogenic variants in 25 ACMG genes in 141 unique individuals. Overall, 2.3% of the QGP sequenced participants carried a pathogenic or likely pathogenic variant in one of the 59 ACMG genes. We evaluated the QGP phenotype-genotype association of additional nonpathogenic ACMG variants. These variants were found in patients from the Hamad Medical Corporation or reported incidental findings data in Qatar. We found a significant phenotype association for two variants, c.313+3A>C in LDLR, and c.58C>T (p.Gln20*) in the TPM1.