SUMMARYBulk data transfers, such as backups and propagation of bulky updates, account for a large portion of the inter-datacenter traffic. These bulk transfers consume massive bandwidth and further increase the operational cost of datacenters. The advent of store-and-forward transfer mode offers the opportunity for cloud provider companies to transfer bulk data by utilizing dynamic leftover bandwidth resources. In this paper, we study the multiple bulk data transfers scheduling problem in inter-datacenter networks with dynamic link capacities. To improve the network utilization while guaranteeing fairness among requests, we employ the max-min fairness and aim at computing the lexicographically maximized solution. Leveraging the timeexpanded technique, the problem in dynamic networks is formulated as a static multi-flow model. Then, we devise an optimal algorithm to solve it simultaneously from routing assignments and bandwidth allocation. To further reduce the computational cost, we propose to select an appropriate number of disjoint paths for each request. Extensive simulations are conducted on a real datacenter topology and prove that (i) benefiting from max-min fairness, the network utilization is significantly improved while honoring each individual performance; (ii) a small number of disjoint paths per request are sufficient to obtain the near optimal allocation within practical execution time.
We report preliminary results from a novel method that predicts the value of the RF path loss exponent (PLE) from satellite remote-sensing observations. The value of the PLE is required when designing wireless sensor networks for environmental monitoring. The model was produced by correlating field measurements of path loss to Landsat 8 data for three dates in 2013. The correlations are strong (R 2 > 0.87), and exhibit high statistical significance (p < 0.01). As far as we know, this is the first reported work that links remote sensing observations to field predictions of RF loss.The work reported here is preliminary because we were only able to gather field observations for three dates in 2013. Now that we know the approach holds some promise, we plan to extend the work with a much more aggressive field campaign in the spring and summer of 2014.
Allocryptopine (ALL) is an isoquinoline alkaloid extracted from Macleaya cordata (Willd). R. Br., which has been claimed to have anti-inflammatory and neuroprotection properties. However, the mechanism by which ALL ameliorates inflammatory bowel disease (IBD) remains unclear. Here, we used network pharmacology and quantitative proteomic approaches to investigate the effect of ALL on IBD pathogenesis. Network pharmacology predicted potential targets and signaling pathways of ALL’s anti-IBD effects. As predicted by network pharmacology, gene ontology (GO) analysis, in terms of the proteomic results, showed that the immune response in mucosa and antimicrobial humoral response were enriched. Further study revealed that the ALL-related pathways were the chemokine signaling pathway and apoptosis in the Kyoto Encyclopedia of Genes and Genomes (KEGG). In addition, we identified AKT1 as a hub for the critical pathways through protein–protein interaction (PPI) network analysis. Similar to mesalazine (MES), Western blot verified that ALL downregulated upstream chemokine CX3CL1 and GNB5 content to reduce phosphorylation of AKT and NF-κB, as well as the degree of apoptosis, to improve inflammatory response in the colon. Our research may shed light on the mechanism by which ALL inhibits the CX3CL1/GNB5/AKT2/NF-κB/apoptosis pathway and improves the intestinal barrier to reduce colitis response and act on the CX3CL1–CX3CR1 axis to achieve neuroprotection.
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