BiDAl: Big Data Analyzer for Cluster Traces

Modern data centers that provide Internet-scale services are stadium-size structures housing tens of thousands of heterogeneous devices (server clusters, networking equipment, power and cooling infrastructures) that must operate continuously and reliably. As part of their operation, these devices produce large amounts of data in the form of event and error logs that are essential not only for identifying problems but also for improving data center efficiency and management. These activities employ data analytics and often exploit hidden statistical patterns and correlations among different factors present in the data. Uncovering these patterns and correlations is challenging due to the sheer volume of data to be analyzed. This paper presents BiDAl, a prototype “log-data analysis framework” that incorporates various Big Data technologies to simplify the analysis of data traces from large clusters. BiDAl is written in Java with a modular and extensible architecture so that different storage backends (currently, HDFS and SQLite are supported), as well as different analysis languages (current implementation supports SQL, R and Hadoop MapReduce) can be easily selected as appropriate. We present the design of BiDAl and describe our experience using it to analyze several public traces of Google data clusters for building a simulation model capable of reproducing observed behavior

A Microservice Infrastructure for Distributed Communities of Practice

Non-formal learning in Communities of Practice (CoPs) makes up a significant portion of today’s knowledge gain. However, only little technological support is tailored specifically towards CoPs and their particular strengths and challenges. Even worse, CoPs often do not possess the resources to host or even develop a software ecosystem to support their activities. In this paper, we describe a distributed, microservice-based Web infrastructure for non-formal learning in CoPs. It mitigates the need for central infrastructures, coordination or facilitation and takes into account the constant change of these communities. As a real use case, we implement an inquiry-based learning application on-top of our infrastructure. Our evaluation results indicate the usefulness of this learning application, which shows promise for future work in the domain of community-hosted, microservice-based Web infrastructures for learning outside of formal settings

Microcities: A Platform Based on Microclouds for Neighborhood Services

International audienceThe current datacenter-centralized architecture limits the cloud to the location of the datacenters, generally far from the user. This architecture collides with the latest trend of ubiquity of Cloud computing. Distance leads to increased utilization of the broadband Wide Area Network and poor user experience, especially for interactive applications. A semi-decentralized approach can provide a better Quality of Experience (QoE) in large urban populations in mobile cloud networks, by confining local traffic near the user while maintaining centralized characteristics, running on the users and network devices. In this paper, we propose a novel semi-decentralized cloud architecture based on microclouds. Microclouds are dynamically created and allow users to contribute resources from their computers, mobile and network devices to the cloud. Microclouds provide a dynamic and scalable system without an extra investment in infrastructure. We also provide a description of a realistic mobile cloud use case, and its adaptation to microclouds

The effect of gamma irradiation on selected growth factors and receptors mRNA in glycerol cryopreserved human amniotic membrane

Human amniotic membrane (HAM), due to its high biocompatibility, low immunogenicity, anti-microbial, anti-viral properties as well as the presence of its growth factors, has been used in various clinical applications. These growth factors are key factors in regulating many cellular processes such as cellular growth, proliferation and cellular differentiation. The current study aimed to explore the effect of glycerol cryopreservation and gamma irradiation on the selected growth factors and receptors mRNA present in HAM. Eight growth factors, namely, EGF, HGF, KGF, TGF-α, TGF-β1, TGF-β2, TGF-β3 and bFGF and two growth factor receptors, HGFR and KGFR were evaluated in this study. The total RNA was extracted and converted to complimentary DNA using commercial kits. Subsequently, the mRNA expressions of these growth factors were evaluated using quantitative PCR and the results were statistically analyzed using REST-MCS software. This study indicated the presence of these growth factors and receptors mRNA in fresh, glycerol cryopreserved and irradiated glycerol cryopreserved HAM. In glycerol cryopreserved HAM, the mRNA expression showed up-regulation of HGF and bFGF and down-regulation of the rest of 8 genes which were EGF, HGFR, KGF, KGFR, TGF-α, TGF-β1, TGF-β2 and TGF-β3. Interestingly, the glycerol cryopreserved HAM radiated with 15 kGy showed up-regulation in the mRNA expression of 7 genes, namely, EGF, HGF, KGF, KGFR, TGF-β1, TGF-β2 and TGF-β3 and down-regulated mRNA expression of HGFR, TGF-α and bFGF. However, these mRNA expressions did not show a statistically significant difference compared to control groups. Thus, it can be concluded that the glycerol cryopreservation did not have an effect on the growth factors’ and receptors’ mRNA expression levels in HAM. Similarly, 15 kGy gamma irradiation did not have an effect on the growth factors’ and receptors’ mRNA expression in glycerol cryopreserved HAM. This finding provides a useful information to clinicians and surgeons to choose the best method for HAM preservation that could benefit patients in their treatment

Introduction of an agent-based multi-scale modular architecture for dynamic knowledge representation of acute inflammation

<p>Abstract</p> <p>Background</p> <p>One of the greatest challenges facing biomedical research is the integration and sharing of vast amounts of information, not only for individual researchers, but also for the community at large. Agent Based Modeling (ABM) can provide a means of addressing this challenge via a unifying translational architecture for dynamic knowledge representation. This paper presents a series of linked ABMs representing multiple levels of biological organization. They are intended to translate the knowledge derived from in vitro models of acute inflammation to clinically relevant phenomenon such as multiple organ failure.</p> <p>Results and Discussion</p> <p>ABM development followed a sequence starting with relatively direct translation from in-vitro derived rules into a cell-as-agent level ABM, leading on to concatenated ABMs into multi-tissue models, eventually resulting in topologically linked aggregate multi-tissue ABMs modeling organ-organ crosstalk. As an underlying design principle organs were considered to be functionally composed of an epithelial surface, which determined organ integrity, and an endothelial/blood interface, representing the reaction surface for the initiation and propagation of inflammation. The development of the epithelial ABM derived from an in-vitro model of gut epithelial permeability is described. Next, the epithelial ABM was concatenated with the endothelial/inflammatory cell ABM to produce an organ model of the gut. This model was validated against in-vivo models of the inflammatory response of the gut to ischemia. Finally, the gut ABM was linked to a similarly constructed pulmonary ABM to simulate the gut-pulmonary axis in the pathogenesis of multiple organ failure. The behavior of this model was validated against in-vivo and clinical observations on the cross-talk between these two organ systems</p> <p>Conclusion</p> <p>A series of ABMs are presented extending from the level of intracellular mechanism to clinically observed behavior in the intensive care setting. The ABMs all utilize cell-level agents that encapsulate specific mechanistic knowledge extracted from in vitro experiments. The execution of the ABMs results in a dynamic representation of the multi-scale conceptual models derived from those experiments. These models represent a qualitative means of integrating basic scientific information on acute inflammation in a multi-scale, modular architecture as a means of conceptual model verification that can potentially be used to concatenate, communicate and advance community-wide knowledge.</p

Genome-wide association analyses of symptom severity among clozapine-treated patients with schizophrenia spectrum disorders

Clozapine is the most effective antipsychotic for patients with treatment-resistant schizophrenia. However, response is highly variable and possible genetic underpinnings of this variability remain unknown. Here, we performed polygenic risk score (PRS) analyses to estimate the amount of variance in symptom severity among clozapine-treated patients explained by PRSs (R2) and examined the association between symptom severity and genotype-predicted CYP1A2, CYP2D6, and CYP2C19 enzyme activity. Genome-wide association (GWA) analyses were performed to explore loci associated with symptom severity. A multicenter cohort of 804 patients (after quality control N = 684) with schizophrenia spectrum disorder treated with clozapine were cross-sectionally assessed using the Positive and Negative Syndrome Scale and/or the Clinical Global Impression-Severity (CGI-S) scale. GWA and PRS regression analyses were conducted. Genotype-predicted CYP1A2, CYP2D6, and CYP2C19 enzyme activities were calculated. Schizophrenia-PRS was most significantly and positively associated with low symptom severity (p = 1.03 × 10−3; R2 = 1.85). Cross-disorder-PRS was also positively associated with lower CGI-S score (p = 0.01; R2 = 0.81). Compared to the lowest tertile, patients in the highest schizophrenia-PRS tertile had 1.94 times (p = 6.84×10−4) increased probability of low symptom severity. Higher genotype-predicted CYP2C19 enzyme activity was independently associated with lower symptom severity (p = 8.44×10−3). While no locus surpassed the genome-wide significance threshold, rs1923778 within NFIB showed a suggestive association (p = 3.78×10−7) with symptom severity. We show that high schizophrenia-PRS and genotype-predicted CYP2C19 enzyme activity are independently associated with lower symptom severity among individuals treated with clozapine. Our findings open avenues for future pharmacogenomic projects investigating the potential of PRS and genotype-predicted CYP-activity in schizophrenia