Association of Caveolin-1 gene polymorphism with kidney transplant fibrosis and allograft failure

Context: Caveolin-1 (CAV1) is an inhibitor of tissue fibrosis.
Objective: To study the association of CAV1 gene variation with kidney transplant outcome, using kidney transplantation as a model of accelerated fibrosis.
Design, Setting, and Patients: Candidate gene association and validation study. Genomic DNA from 785 white kidney transplant donors and their respective recipients (transplantations in Birmingham, England, between 1996 and 2006; median followup, 81 months) were analyzed for common variation in CAV1 using a singlenucleotide polymorphism (SNP) tagging approach. Validation of positive findings was sought in an independent kidney transplant donor-recipient cohort (transplantations in Belfast, Northern Ireland, between 1986 and 2005; n=697; median follow-up, 69 months). Association between genotype and allograft failure was initially assessed by Kaplan-Meier analysis, then in an adjusted Cox model.
Main Outcome Measure: Death-censored allograft failure, defined as a return to dialysis or retransplantation.
Results: The presence of donor AA genotype for the CAV1 rs4730751 SNP was associated with increased risk of allograft failure in the Birmingham group (donor AA vs non-AA genotype in adjusted Cox model, hazard ratio [HR], 1.97; 95% confidence interval [CI], 1.29-3.16; P=.002). No other tag SNPs showed a significant association. This finding was validated in the Belfast cohort (in adjusted Cox model, HR, 1.56; 95% CI, 1.07-2.27; P=.02). Overall graft failure rates were as follows: for the Birmingham cohort, donor genotype AA, 22 of 57 (38.6%); genotype CC, 96 of 431 (22.3%); and genotype AC, 66 of 297 (22.2%); and for the Belfast cohort, donor genotype AA, 32 of 48 (67%); genotype CC, 150 of 358 (42%); and genotype AC, 119 of 273 (44%).
Conclusion: Among kidney transplant donors, the CAV1 rs4730751 SNP was significantly associated with allograft failure in 2 independent cohorts.

Dynamic power- and failure-aware cloud resources allocation for Sets of independent tasks

Cloud computing is increasingly being adopted in different scenarios, like social networking, business applications, scientific experiments, etc. Relying in virtualization technology, the construction of these computing environments targets improvements in the infrastructure, such as power-efficiency and fulfillment of users’ SLA specifications. The methodology usually applied is packing all the virtual machines on the proper physical servers. However, failure occurrences in these networked computing systems can induce substantial negative impact on system performance, deviating the system from ours initial objectives. In this work, we propose adapted algorithms to dynamically map virtual machines to physical hosts, in order to improve cloud infrastructure power-efficiency, with low impact on users’ required performance. Our decision making algorithms leverage proactive fault-tolerance techniques to deal with systems failures, allied with virtual machine technology to share nodes resources in an accurately and controlled manner. The results indicate that our algorithms perform better targeting power-efficiency and SLA fulfillment, in face of cloud infrastructure failures.

Enhanced Failure Detection Mechanism in MapReduce

The popularity of MapReduce programming model has increased interest in the research community for its improvement. Among the other directions, the point of fault tolerance, concretely the failure detection issue seems to be a crucial one, but that until now has not reached its satisfying level. Motivated by this, I decided to devote my main research during this period into having a prototype system architecture of MapReduce framework with a new failure detection service, containing both analytical (theoretical) and implementation part. I am confident that this work should lead the way for further contributions in detecting failures to any NoSQL App frameworks, and cloud storage systems in general.

Case study in failure analysis of accelerated life tests (ALT) on III-V commercial triple-junction concentrator solar cells

In this work the failure analysis carried out in III-V concentrator multijunction solar cells after a temperature accelerated life test is presented. All the failures appeared have been catastrophic since all the solar cells turned into low shunt resistances. A case study in failure analysis based on characterization by optical microscope, SEM, EDX, EQE and XPS is presented in this paper, revealing metal deterioration in the bus bar and fingers as well as cracks in the semiconductor structure beneath or next to the bus bar. In fact, in regions far from the bus bar the semiconductor structure seems not to be damaged. SEM images have dismissed the presence of metal spikes inside the solar cell structure. Therefore, we think that for these particular solar cells, failures appear mainly as a consequence of a deficient electrolytic growth of the front metallization which also results in failures in the semiconductor structure close to the bus bars.

Comparison of 3D finite element analysis derived stiffness and BMD to determine the failure load of the excised proximal femur

Introduction: Bone mineral density (BMD) is currently the preferred surrogate for bone strength in clinical practice. Finite element analysis (FEA) is a computer simulation technique that can predict the deformation of a structure when a load is applied, providing a measure of stiffness (Nmm−1). Finite element analysis of X-ray images (3D-FEXI) is a FEA technique whose analysis is derived froma single 2D radiographic image. Methods: 18 excised human femora had previously been quantitative computed tomography scanned, from which 2D BMD-equivalent radiographic images were derived, and mechanically tested to failure in a stance-loading configuration. A 3D proximal femur shape was generated from each 2D radiographic image and used to construct 3D-FEA models. Results: The coefficient of determination (R2%) to predict failure load was 54.5% for BMD and 80.4% for 3D-FEXI. Conclusions: This ex vivo study demonstrates that 3D-FEXI derived from a conventional 2D radiographic image has the potential to significantly increase the accuracy of failure load assessment of the proximal femur compared with that currently achieved with BMD. This approach may be readily extended to routine clinical BMD images derived by dual energy X-ray absorptiometry. Crown Copyright © 2009 Published by Elsevier Ltd on behalf of IPEM. All rights reserved