Tulsa: a tool for transforming UML to layered queueing networks for performance analysis of data intensive applications

Motivated by the problem of detecting software performance anti-patterns in data-intensive applications (DIAs), we present a tool, Tulsa, for transforming software architecture models specified through UML into Layered Queueing Networks (LQNs), which are analytical performance models used to capture contention across multiple software layers. In particular, we generalize an existing transformation based on the Epsilon framework to generate LQNs from UML models annotated with the DICE profile, which extends UML to modelling DIAs based on technologies such as Apache Storm

High Erk-1 activation and Gadd45a expression as prognostic markers in high risk pediatric haemolymphoproliferative diseases

Studies on activated cell-signaling pathways responsible for neoplastic transformation are numerous in solid tumors and in adult leukemias. Despite of positive results in the evolution of pediatric hematopoietic neoplasias, there are some high-risk subtypes at worse prognosis. The aim of this study was to asses the expression and activation status of crucial proteins involved in cell-signaling pathways in order to identify molecular alterations responsible for the proliferation and/or escape from apoptosis of leukemic blasts. The quantitative and qualitative expression and activation of Erk-1, c-Jun, Caspase8, and Gadd45a was analyzed, by immunocytochemical (ICC) and western blotting methods, in bone marrow blasts of 72 patients affected by acute myeloid leukemia (AML), T-cell acute lymphoblastic leukemia (ALL) and stage IV non-Hodgkin Lymphoma (NHL). We found an upregulation of Erk-1, Caspase8, c-Jun, and Gadd45a proteins with a constitutive activation in 95.8%, 91.7%, 86.2%, 83.4% of analyzed specimens, respectively. It is worth noting that all AML patients showed an upregulation of all proteins studied and the high expression of GADD45a was associated to the lowest DFS median (p = 0.04). On univariate analysis, only Erk-1 phosphorylation status was found to be correlated with a significantly shorter 5-years DFS in all disease subgroups (p = 0.033) and the lowest DFS median in ALL/NHL subgroup (p = 0.04). Moreover, the simultaneous activation of multiple kinases, as we found for c-Jun and Erk-1 (r = 0.26; p = 0.025), might synergistically enhance survival and proliferation potential of leukemic cells. These results demonstrate an involvement of these proteins in survival of blast cells and, consequently, on relapse percentages of the different subgroups of patients

Nrf2-interacting nutrients and COVID-19 : time for research to develop adaptation strategies

There are large between- and within-country variations in COVID-19 death rates. Some very low death rate settings such as Eastern Asia, Central Europe, the Balkans and Africa have a common feature of eating large quantities of fermented foods whose intake is associated with the activation of the Nrf2 (Nuclear factor (erythroid-derived 2)-like 2) anti-oxidant transcription factor. There are many Nrf2-interacting nutrients (berberine, curcumin, epigallocatechin gallate, genistein, quercetin, resveratrol, sulforaphane) that all act similarly to reduce insulin resistance, endothelial damage, lung injury and cytokine storm. They also act on the same mechanisms (mTOR: Mammalian target of rapamycin, PPAR gamma:Peroxisome proliferator-activated receptor, NF kappa B: Nuclear factor kappa B, ERK: Extracellular signal-regulated kinases and eIF2 alpha:Elongation initiation factor 2 alpha). They may as a result be important in mitigating the severity of COVID-19, acting through the endoplasmic reticulum stress or ACE-Angiotensin-II-AT(1)R axis (AT(1)R) pathway. Many Nrf2-interacting nutrients are also interacting with TRPA1 and/or TRPV1. Interestingly, geographical areas with very low COVID-19 mortality are those with the lowest prevalence of obesity (Sub-Saharan Africa and Asia). It is tempting to propose that Nrf2-interacting foods and nutrients can re-balance insulin resistance and have a significant effect on COVID-19 severity. It is therefore possible that the intake of these foods may restore an optimal natural balance for the Nrf2 pathway and may be of interest in the mitigation of COVID-19 severity

Cabbage and fermented vegetables : From death rate heterogeneity in countries to candidates for mitigation strategies of severe COVID-19

Large differences in COVID-19 death rates exist between countries and between regions of the same country. Some very low death rate countries such as Eastern Asia, Central Europe, or the Balkans have a common feature of eating large quantities of fermented foods. Although biases exist when examining ecological studies, fermented vegetables or cabbage have been associated with low death rates in European countries. SARS-CoV-2 binds to its receptor, the angiotensin-converting enzyme 2 (ACE2). As a result of SARS-CoV-2 binding, ACE2 downregulation enhances the angiotensin II receptor type 1 (AT(1)R) axis associated with oxidative stress. This leads to insulin resistance as well as lung and endothelial damage, two severe outcomes of COVID-19. The nuclear factor (erythroid-derived 2)-like 2 (Nrf2) is the most potent antioxidant in humans and can block in particular the AT(1)R axis. Cabbage contains precursors of sulforaphane, the most active natural activator of Nrf2. Fermented vegetables contain many lactobacilli, which are also potent Nrf2 activators. Three examples are: kimchi in Korea, westernized foods, and the slum paradox. It is proposed that fermented cabbage is a proof-of-concept of dietary manipulations that may enhance Nrf2-associated antioxidant effects, helpful in mitigating COVID-19 severity.Peer reviewe

Blending randomness in closed queueing network models

Abstract Random environments are stochastic models used to describe events occurring in the environment a system operates in. The goal is to describe events that affect performance and reliability such as breakdowns, repairs, or temporary degradations of resource capacities due to exogenous factors. Despite having been studied for decades, models that include both random environments and queueing networks remain difficult to analyse. To cope with this problem, we introduce the blending algorithm, a novel approximation for closed queueing network models in random environments. The algorithm seeks to obtain the stationary solution of the model by iteratively evaluating the dynamics of the system in between state changes of the environment. To make the approach scalable, the computation relies on a fluid approximation of the queueing network model. A validation study on 1800 models shows that blending can save a significant amount of time compared to simulation, with an average accuracy that grows with the number of servers in each station. We also give an interpretation of this technique in terms of Laplace transforms and use this approach to determine convergence properties

Performance models of storage contention in cloud environments

Abstract We propose simple models to predict the perfor-mance degradation of disk requests due to storage device contention in consolidated virtualized environments. Model parameters can be deduced from measurements obtained inside Virtual Machines (VMs) from a system where a sin-gle VM accesses a remote storage server. The parameterized model can then be used to predict the effect of storage con-tention when multiple VMs are consolidated on the same server. We first propose a trace-driven approach that eval-uates a queueing network with fair share scheduling using simulation. The model parameters consider Virtual Machine Monitor level disk access optimizations and rely on a cali-bration technique. We further present a measurement-based approach that allows a distinct characterization of read/write performance attributes. In particular, we define simple lin-ear prediction models for I/O request mean response times, throughputs and read/write mixes, as well as a simulation model for predicting response time distributions. We found Communicated by Dr. Raffaela Mirandola and David Lilja