Response Time Approximations in Fork-Join Queues

Fork-join queueing networks model a network of parallel servers in which an arriving job splits into a number of subtasks that are serviced in parallel. Fork-join queues can be used to model disk arrays. A response time approximation of the fork-join queue is presented that attempts to comply with the additional constraints of modelling a disk array. This approximation is compared with existing analytical approximations of the fork-join queueing network

Validation of Large Zoned RAID Systems

Building on our prior work we present an improved model for for large partial stripe following full stripe writes in RAID 5. This was necessary because we observed that our previous model tended to underestimate measured results. To date, we have only validated these models against RAID systems with at most four disks. Here we validate our improved model, and also our existing models for other read and write configurations, against measurements taken from an eight disk RAID array

Standard and inverse site percolation of straight rigid rods on triangular lattices: Isotropic and nematic deposition/removal

Numerical simulations and finite-size scaling analysis have been carried out to study standard and inverse percolation of straight rigid rods on triangular lattices. In the case of standard (inverse) percolation, the lattice is initially empty(occupied) and linear $k$-mers ($k$ linear consecutive sites) are randomly and sequentially deposited on(removed from) the lattice, considering an isotropic and nematic scheme. The study is conducted by following the behavior of four critical concentrations with the size $k$, determined for a wide range of $k$ : $(i)$[$(ii)$] standard isotropic[nematic] percolation threshold $\theta_{c,k}$[$\vartheta_{c,k}$], and $(iii)$[$(iv)$] inverse isotropic[nematic] percolation threshold $\theta^i_{c,k}$[$\vartheta^i_{c,k}$]. The obtained results indicate that: $(1)$ $\theta_{c,k}$[$\theta^i_{c,k}$] exhibits a non-monotonous dependence with $k$. It decreases[increases], goes through a minimum[maximum] around $k = 11$, then increases and asymptotically converges towards a definite value for large $k$ $\theta_{c,k \rightarrow \infty}=0.500(2)$[$\theta^i_{c,k \rightarrow \infty}=0.500(1)$]; $(2)$ $\vartheta_{c,k}$[$\vartheta^i_{c,k}$] rapidly increases[decreases] and asymptotically converges towards a definite value for infinitely long $k$-mers $\vartheta_{c,k \rightarrow \infty}=0.5334(6)$[$\vartheta^i_{c,k \rightarrow \infty}=0.4666(6)$]; $(3)$ for both models, the curves of standard and inverse percolation thresholds are symmetric with respect to $\theta = 0.5$. Thus, a complementary property is found $\theta_{c,k} + \theta^i_{c,k} = 1$ (and $\vartheta_{c,k} + \vartheta^i_{c,k} = 1$), which has not been observed in other regular lattices. This condition is analytically validated by using exact enumeration of configurations for small systems; and $(4)$ in all cases, the model presents percolation transition for the whole range of $k$

Characterization of Anti-ECA Antibodies in Rabbit Antiserum Against Rough Yersinia enterocolitica O:3

Peer reviewe

Computable bounds in fork-join queueing systems

In a Fork-Join (FJ) queueing system an upstream fork station splits incoming jobs into N tasks to be further processed by N parallel servers, each with its own queue; the response time of one job is determined, at a downstream join station, by the maximum of the corresponding tasks' response times. This queueing system is useful to the modelling of multi-service systems subject to synchronization constraints, such as MapReduce clusters or multipath routing. Despite their apparent simplicity, FJ systems are hard to analyze. This paper provides the first computable stochastic bounds on the waiting and response time distributions in FJ systems. We consider four practical scenarios by combining 1a) renewal and 1b) non-renewal arrivals, and 2a) non-blocking and 2b) blocking servers. In the case of non blocking servers we prove that delays scale as O(logN), a law which is known for first moments under renewal input only. In the case of blocking servers, we prove that the same factor of log N dictates the stability region of the system. Simulation results indicate that our bounds are tight, especially at high utilizations, in all four scenarios. A remarkable insight gained from our results is that, at moderate to high utilizations, multipath routing 'makes sense' from a queueing perspective for two paths only, i.e., response times drop the most when N = 2; the technical explanation is that the resequencing (delay) price starts to quickly dominate the tempting gain due to multipath transmissions

Long-term outcome prediction by clinicopathological risk classification algorithms in node-negative breast cancer—comparison between Adjuvant!, St Gallen, and a novel risk algorithm used in the prospective randomized Node-Negative-Breast Cancer-3 (NNBC-3) trial

Background: Defining risk categories in breast cancer is of considerable clinical significance. We have developed a novel risk classification algorithm and compared its prognostic utility to the Web-based tool Adjuvant! and to the St Gallen risk classification. Patients and methods: After a median follow-up of 10 years, we retrospectively analyzed 410 consecutive node-negative breast cancer patients who had not received adjuvant systemic therapy. High risk was defined by any of the following criteria: (i) age 2 cm. All patients were also characterized using Adjuvant! and the St Gallen 2007 risk categories. We analyzed disease-free survival (DFS) and overall survival (OS). Results: The Node-Negative-Breast Cancer-3 (NNBC-3) algorithm enlarged the low-risk group to 37% as compared with Adjuvant! (17%) and St Gallen (18%), respectively. In multivariate analysis, both Adjuvant! [P = 0.027, hazard ratio (HR) 3.81, 96% confidence interval (CI) 1.16-12.47] and the NNBC-3 risk classification (P = 0.049, HR 1.95, 95% CI 1.00-3.81) significantly predicted OS, but only the NNBC-3 algorithm retained its prognostic significance in multivariate analysis for DFS (P < 0.0005). Conclusion: The novel NNBC-3 risk algorithm is the only clinicopathological risk classification algorithm significantly predicting DFS as well as O

Long-term outcome prediction by clinicopathological risk classification algorithms in node-negative breast cancer--comparison between Adjuvant!, St Gallen, and a novel risk algorithm used in the prospective randomized Node-Negative-Breast Cancer-3 (NNBC-3) trial.

Defining risk categories in breast cancer is of considerable clinical significance. We have developed a novel risk classification algorithm and compared its prognostic utility to the Web-based tool Adjuvant! and to the St Gallen risk classification. After a median follow-up of 10 years, we retrospectively analyzed 410 consecutive node-negative breast cancer patients who had not received adjuvant systemic therapy. High risk was defined by any of the following criteria: (i) age &lt;35 years, (ii) grade 3, (iii) human epithelial growth factor receptor-2 positivity, (iv) vascular invasion, (v) progesterone receptor negativity, (vi) grade 2 tumors &gt;2 cm. All patients were also characterized using Adjuvant! and the St Gallen 2007 risk categories. We analyzed disease-free survival (DFS) and overall survival (OS). The Node-Negative-Breast Cancer-3 (NNBC-3) algorithm enlarged the low-risk group to 37% as compared with Adjuvant! (17%) and St Gallen (18%), respectively. In multivariate analysis, both Adjuvant! [P = 0.027, hazard ratio (HR) 3.81, 96% confidence interval (CI) 1.16-12.47] and the NNBC-3 risk classification (P = 0.049, HR 1.95, 95% CI 1.00-3.81) significantly predicted OS, but only the NNBC-3 algorithm retained its prognostic significance in multivariate analysis for DFS (P &lt; 0.0005). The novel NNBC-3 risk algorithm is the only clinicopathological risk classification algorithm significantly predicting DFS as well as OS

Tear fluid biomarkers in ocular and systemic disease: potential use for predictive, preventive and personalised medicine

In the field of predictive, preventive and personalised medicine, researchers are keen to identify novel and reliable ways to predict and diagnose disease, as well as to monitor patient response to therapeutic agents. In the last decade alone, the sensitivity of profiling technologies has undergone huge improvements in detection sensitivity, thus allowing quantification of minute samples, for example body fluids that were previously difficult to assay. As a consequence, there has been a huge increase in tear fluid investigation, predominantly in the field of ocular surface disease. As tears are a more accessible and less complex body fluid (than serum or plasma) and sampling is much less invasive, research is starting to focus on how disease processes affect the proteomic, lipidomic and metabolomic composition of the tear film. By determining compositional changes to tear profiles, crucial pathways in disease progression may be identified, allowing for more predictive and personalised therapy of the individual. This article will provide an overview of the various putative tear fluid biomarkers that have been identified to date, ranging from ocular surface disease and retinopathies to cancer and multiple sclerosis. Putative tear fluid biomarkers of ocular disorders, as well as the more recent field of systemic disease biomarkers, will be shown

High-dose clevudine impairs mitochondrial function and glucose-stimulated insulin secretion in INS-1E cells

<p>Abstract</p> <p>Background</p> <p>Clevudine is a nucleoside analog reverse transcriptase inhibitor that exhibits potent antiviral activity against hepatitis B virus (HBV) without serious side effects. However, mitochondrial myopathy has been observed in patients with chronic HBV infection taking clevudine. Moreover, the development of diabetes was recently reported in patients receiving long-term treatment with clevudine. In this study, we investigated the effects of clevudine on mitochondrial function and insulin release in a rat clonal β-cell line, INS-1E.</p> <p>Methods</p> <p>The mitochondrial DNA (mtDNA) copy number and the mRNA levels were measured by using quantitative PCR. MTT analysis, ATP/lactate measurements, and insulin assay were performed.</p> <p>Results</p> <p>Both INS-1E cells and HepG2 cells, which originated from human hepatoma, showed dose-dependent decreases in mtDNA copy number and cytochrome c oxidase-1 (Cox-1) mRNA level following culture with clevudine (10 μM-1 mM) for 4 weeks. INS-1E cells treated with clevudine had reduced total mitochondrial activities, lower cytosolic ATP contents, enhanced lactate production, and more lipid accumulation. Insulin release in response to glucose application was markedly decreased in clevudine-treated INS-1E cells, which might be a consequence of mitochondrial dysfunction.</p> <p>Conclusions</p> <p>Our data suggest that high-dose treatment with clevudine induces mitochondrial defects associated with mtDNA depletion and impairs glucose-stimulated insulin secretion in insulin-releasing cells. These findings partly explain the development of diabetes in patients receiving clevudine who might have a high susceptibility to mitochondrial toxicity.</p