Cost-Efficient Data Backup for Data Center Networks against {\epsilon}-Time Early Warning Disaster

Data backup in data center networks (DCNs) is critical to minimize the data loss under disaster. This paper considers the cost-efficient data backup for DCNs against a disaster with $\varepsilon$ early warning time. Given geo-distributed DCNs and such a $\varepsilon$-time early warning disaster, we investigate the issue of how to back up the data in DCN nodes under risk to other safe DCN nodes within the $\varepsilon$ early warning time constraint, which is significant because it is an emergency data protection scheme against a predictable disaster and also help DCN operators to build a complete backup scheme, i.e., regular backup and emergency backup. Specifically, an Integer Linear Program (ILP)-based theoretical framework is proposed to identify the optimal selections of backup DCN nodes and data transmission paths, such that the overall data backup cost is minimized. Extensive numerical results are also provided to illustrate the proposed framework for DCN data backup

Raman spectroscopy for accurately characterizing biomolecular changes in androgen-independent prostate cancer cells

© 2017 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim Metastatic prostate cancer resistant to hormonal manipulation is considered the advanced stage of the disease and leads to most cancer-related mortality. With new research focusing on modulating cancer growth, it is essential to understand the biochemical changes in cells that can then be exploited for drug discovery and for improving responsiveness to treatment. Raman spectroscopy has a high chemical specificity and can be used to detect and quantify molecular changes at the cellular level. Collection of large data sets generated from biological samples can be employed to form discriminatory algorithms for detection of subtle and early changes in cancer cells. The present study describes Raman finger printing of normal and metastatic hormone-resistant prostate cancer cells including analyses with principal component analysis and linear discrimination. Amino acid-specific signals were identified, especially loss of arginine band. Androgen-resistant prostate cancer cells presented a higher content of phenylalanine, tyrosine, DNA and Amide III in comparison to PNT2 cells, which possessed greater amounts of L-arginine and had a B conformation of DNA. The analysis utilized in this study could reliably differentiate the 2 cell lines (sensitivity 95%; specificity 88%)

DeepDefrag : Spatio-Temporal Defragmentation of Time-Varying Virtual Networks in Computing Power Network based on Model-Assisted Reinforcement Learning

AbstractWe propose DeepDefrag, a model-assisted reinforcement learning for spatio-temporal defragmentation of time-varying virtual networks in a cross-layer optical network testbed, which realizes the efficient utilization of computing nodes and lightpaths by co-optimizing scheduling and embedding with fragment matching, reduces >13.5% cost of computing power network.Abstract We propose DeepDefrag, a model-assisted reinforcement learning for spatio-temporal defragmentation of time-varying virtual networks in a cross-layer optical network testbed, which realizes the efficient utilization of computing nodes and lightpaths by co-optimizing scheduling and embedding with fragment matching, reduces >13.5% cost of computing power network

Detecting failure of climate predictions

The practical consequences of climate change challenge society to formulate responses that are more suited to achieving long-term objectives, even if those responses have to be made in the face of uncertainty. Such a decision-analytic focus uses the products of climate science as probabilistic predictions about the effects of management policies. Here we present methods to detect when climate predictions are failing to capture the system dynamics. For a single model, we measure goodness of fit based on the empirical distribution function, and define failure when the distribution of observed values significantly diverges from the modelled distribution. For a set of models, the same statistic can be used to provide relative weights for the individual models, and we define failure when there is no linear weighting of the ensemble models that produces a satisfactory match to the observations. Early detection of failure of a set of predictions is important for improving model predictions and the decisions based on them. We show that these methods would have detected a range shift in northern pintail 20 years before it was actually discovered, and are increasingly giving more weight to those climate models that forecast a September ice-free Arctic by 2055

Pneumatose kystique intestinale révélée par une sténose d’une anastomose gastrojéjunale : à propos d’un cas

La pneumatose kystique intestinale est la présence de bulles gazeuses dans la paroi et les séreuses du tube digestif. Il s'agit d'une pathologie bénigne, rare, de diagnostic radiologique et de traitement médical. Nous rapportons le cas d'un homme âgé de 42ans, opéré il y a 6ans pour une sténose du bulbe duodénal d'origine ulcéreuse, il avait bénéficié d'une gastro-entéro-anastomose avec bivagotomie tronculaire. Il a été hospitalisé pour des vomissements associés à des épigastralgies. le patient a bénéficié d'une fibroscopie oeso-gastro-duodénale qui a trouvé une stase gastrique gênant toute exploration, ce qui a conduit à la réalisation d'une tomodensitométrie abdominale qui a objectivé un énorme estomac de stase en amont d'une sténose de l'anastomose gastro jéjunale, une pneumatose kystique intestinale et un pneumopéritoine. Le patient a été opéré et l'exploration a trouvé une ascite, un volumineux estomac de stase et des adhérences entre le grêle et le colon droit, sièges de la pneumatose, provoquant un tour de spire (volvulus) de l'ancienne anastomose gastro-jéjunale. L'estomac était atone. Une gastrectomie des 2/3 emportant l'ancienne anastomose suivie d'une anastomose type Finsterer manuelle a été réalisée. Les suites post opératoires étaient simples. La pneumatose kystique intestinale est une affection bénigne, de diagnostic radiologique. Le scanner permet d'étudier la diffusion des gaz dans les séreuses digestives. Son traitement est habituellement médical alors que ses complications peuvent relever d'un traitement chirurgical comme pour notre patient.Key words: Pneumatose kystique intestinale, sténose, anastomose gastro jéjunale, gastrectomie, gastrectomi

Loss of SOCS3 expression in T cells reveals a regulatory role for interleukin-17 in atherosclerosis

Atherosclerosis is an inflammatory vascular disease responsible for the first cause of mortality worldwide. Recent studies have clearly highlighted the critical role of the immunoinflammatory balance in the modulation of disease development and progression. However, the immunoregulatory pathways that control atherosclerosis remain largely unknown. We show that loss of suppressor of cytokine signaling (SOCS) 3 in T cells increases both interleukin (IL)-17 and IL-10 production, induces an antiinflammatory macrophage phenotype, and leads to unexpected IL-17–dependent reduction in lesion development and vascular inflammation. In vivo administration of IL-17 reduces endothelial vascular cell adhesion molecule–1 expression and vascular T cell infiltration, and significantly limits atherosclerotic lesion development. In contrast, overexpression of SOCS3 in T cells reduces IL-17 and accelerates atherosclerosis. We also show that in human lesions, increased levels of signal transducer and activator of transcription (STAT) 3 phosphorylation and IL-17 are associated with a stable plaque phenotype. These results identify novel SOCS3-controlled IL-17 regulatory pathways in atherosclerosis and may have important implications for the understanding of the increased susceptibility to vascular inflammation in patients with dominant-negative STAT3 mutations and defective Th17 cell differentiation

Performance evaluation of five ELISA kits for detecting anti-SARS-COV-2 IgG antibodies

Objectives: To evaluate and compare the performances of five commercial ELISA assays (EDI, AnshLabs, Dia.Pro, NovaTec, and Lionex) for detecting anti-SARS-CoV-2 IgG. / Methods: Seventy negative control samples (collected before the COVID-19 pandemic) and samples from 101 RT-PCR-confirmed SARS-CoV-2 patients (collected at different time points from symptom onset: ≤7, 8–14 and >14 days) were used to compare the sensitivity, specificity, agreement, and positive and negative predictive values of each assay with RT-PCR. A concordance assessment between the five assays was also conducted. Cross-reactivity with other HCoV, non-HCoV respiratory viruses, non-respiratory viruses, and nuclear antigens was investigated. / Results: Lionex showed the highest specificity (98.6%; 95% CI 92.3–99.8), followed by EDI and Dia.Pro (97.1%; 95% CI 90.2–99.2), NovaTec (85.7%; 95% CI 75.7–92.1), then AnshLabs (75.7%; 95% CI 64.5–84.2). All ELISA kits cross-reacted with one anti-MERS IgG-positive sample, except Lionex. The sensitivity was low during the early stages of the disease but improved over time. After 14 days from symptom onset, Lionex and NovaTec showed the highest sensitivity at 87.9% (95% CI 72.7–95.2) and 86.4% (95% CI 78.5–91.7), respectively. The agreement with RT-PCR results based on Cohen's kappa was as follows: Lionex (0.89) > NovaTec (0.70) > Dia.Pro (0.69) > AnshLabs (0.63) > EDI (0.55). / Conclusion: The Lionex and NovaLisa IgG ELISA kits, demonstrated the best overall performance

Modelling and optimization of factors influencing adsorptive performance of agrowaste-derived Nanocellulose Iron Oxide Nanobiocomposites during remediation of Arsenic contaminated groundwater

Nanocellulose Iron Oxide Nanobiocomposites (NIONs) were synthesized from rice husk and sugarcane bagasse derived nanocelluloses for adsorptive removal of arsenic and associated contaminants present in groundwater samples. These NIONSs were superparamagnetic, hence magnetically recoverable and demonstrated promising recyclability. Synthesis of NIONs was confirmed by Transmission electron microscopy (TEM), X-Ray Diffraction (XRD), Fourier transform infrared spectroscopy (FTIR) and X-ray photoelectron spectroscopic (XPS). FTIR and XPS data together with adsorption kinetics provide insights into probable adsorption mechanism of Arsenic by NIONs. The experimental conditions for 10 different variants were modelled using response surface methodology (RSM) based on central composite design (CCD), considering the parameters; adsorbate dosage, adsorbent dosage, pH and contact time. The results identified the best performing variants and the optimal conditions for maximal absorption (~99%). These results were validated using a three-layer feed-forward Multilayer Perceptron (MLP) based Artificial Neural Network (ANN) model. Both RSM and ANN chemometric models were in close conformity for optimized conditions of highest adsorption by specific variants. The standardized conditions were used to expand the study to field-based arsenic contaminated groundwater samples and their performance to commercial adsorbents. NIONs show promising commercial potential for water remediation applications due to their high adsorptive performance, magnetic recoverability and recyclability