Software defined networking challenges and future direction: A case study of implementing SDN features on OpenStack private cloud

Cloud computing provides services on demand instantly, such as access to network infrastructure consisting of computing hardware, operating systems, network storage, database and applications. Network usage and demands are growing at a very fast rate and to meet the current requirements, there is a need for automatic infrastructure scaling. Traditional networks are difficult to automate because of the distributed nature of their decision making process for switching or routing which are collocated on the same device. Managing complex environments using traditional networks is time-consuming and expensive, especially in the case of generating virtual machines, migration and network configuration. To mitigate the challenges, network operations require efficient, flexible, agile and scalable software defined networks (SDN). This paper discuss various issues in SDN and suggests how to mitigate the network management related issues. A private cloud prototype test bed was setup to implement the SDN on the OpenStack platform to test and evaluate the various network performances provided by the various configurations

Laparoscopic Management of Adrenal Lesions Larger Than 5 cm in Diameter

Introduction: Laparoscopic adrenalectomy remains a controversial procedure for large tumors. The incidence of adrenocortical carcinoma increases and technical difficulty of adrenalectomy increases as the size increases. We examined the outcome and complications of laparoscopic adrenalectomy for such lesions. Materials and Methods: Twenty-nine patients underwent laparoscopic adrenalectomy, of whom 19 had tumors larger than 5 cm in diameter, having a median tumor size of 7.0 cm. They were compared with patients whose adrenal tumors were smaller than 5 cm. Results: Patients with small tumors (< 5 cm) had a significantly shorter median operative time of 90 minutes as compared to 145 minutes in those with large tumors (> 5 cm). There was no significant difference in the median hemoglobin drop (1.05 g/dL versus 1.30 g/dL), time for starting oral intake (24 hours in both groups) or hospital stay (3.5 days versus 4.0 days) between patients with small and large tumors, respectively. There were no intra-operative complications except for 1 incidence of supraventricular tachycardia in a patient with a large pheochromocytoma. There were no major complications seen in any of the patients and no open conversions. Histopathology of large tumors revealed 16 benign tumors (8 pheochromocytomas, 4 adenomas, 2 ganglioneuromas, 1 pseudocyst, and 1 myelolipoma) and 3 malignancies, of which 1 was primary adrenocortical carcinoma and 2 were metastatic renal cell carcinoma. Conclusion: In experienced hands, laparoscopic adrenalectomy is safe and feasible for large functioning adrenal tumors. Large adrenal tumors suspicious of harboring malignancy with no peri-adrenal involvement can be tackled laparoscopically

Predicting functional associations from metabolism using bi-partite network algorithms

<p>Abstract</p> <p>Background</p> <p>Metabolic reconstructions contain detailed information about metabolic enzymes and their reactants and products. These networks can be used to infer functional associations between metabolic enzymes. Many methods are based on the number of metabolites shared by two enzymes, or the shortest path between two enzymes. Metabolite sharing can miss associations between non-consecutive enzymes in a serial pathway, and shortest-path algorithms are sensitive to high-degree metabolites such as water and ATP that create connections between enzymes with little functional similarity.</p> <p>Results</p> <p>We present new, fast methods to infer functional associations in metabolic networks. A local method, the degree-corrected Poisson score, is based only on the metabolites shared by two enzymes, but uses the known metabolite degree distribution. A global method, based on graph diffusion kernels, predicts associations between enzymes that do not share metabolites. Both methods are robust to high-degree metabolites. They out-perform previous methods in predicting shared Gene Ontology (GO) annotations and in predicting experimentally observed synthetic lethal genetic interactions. Including cellular compartment information improves GO annotation predictions but degrades synthetic lethal interaction prediction. These new methods perform nearly as well as computationally demanding methods based on flux balance analysis.</p> <p>Conclusions</p> <p>We present fast, accurate methods to predict functional associations from metabolic networks. Biological significance is demonstrated by identifying enzymes whose strong metabolic correlations are missed by conventional annotations in GO, most often enzymes involved in transport vs. synthesis of the same metabolite or other enzyme pairs that share a metabolite but are separated by conventional pathway boundaries. More generally, the methods described here may be valuable for analyzing other types of networks with long-tailed degree distributions and high-degree hubs.</p

Stable U(IV) Complexes Form at High-Affinity Mineral Surface Sites

Uranium (U) poses a significant contamination hazard to soils, sediments, and groundwater due to its extensive use for energy production. Despite advances in modeling the risks of this toxic and radioactive element, lack of information about the mechanisms controlling U transport hinders further improvements, particularly in reducing environments where UIV predominates. Here we establish that mineral surfaces can stabilize the majority of U as adsorbed UIV species following reduction of UVI. Using X-ray absorption spectroscopy and electron imaging analysis, we find that at low surface loading, UIV forms inner-sphere complexes with two metal oxides, TiO2 (rutile) and Fe3O4 (magnetite) (at <1.3 U nm–2 and <0.037 U nm–2, respectively). The uraninite (UO2) form of UIV predominates only at higher surface loading. UIV–TiO2 complexes remain stable for at least 12 months, and UIV–Fe3O4 complexes remain stable for at least 4 months, under anoxic conditions. Adsorbed UIV results from UVI reduction by FeII or by the reduced electron shuttle AH2QDS, suggesting that both abiotic and biotic reduction pathways can produce stable UIV–mineral complexes in the subsurface. The observed control of high-affinity mineral surface sites on UIV speciation helps explain the presence of nonuraninite UIV in sediments and has important implications for U transport modeling

Prediction and Testing of Biological Networks Underlying Intestinal Cancer

Colorectal cancer progresses through an accumulation of somatic mutations, some of which reside in so-called “driver” genes that provide a growth advantage to the tumor. To identify points of intersection between driver gene pathways, we implemented a network analysis framework using protein interactions to predict likely connections – both precedented and novel – between key driver genes in cancer. We applied the framework to find significant connections between two genes, Apc and Cdkn1a (p21), known to be synergistic in tumorigenesis in mouse models. We then assessed the functional coherence of the resulting Apc-Cdkn1a network by engineering in vivo single node perturbations of the network: mouse models mutated individually at Apc (Apc1638N+/−) or Cdkn1a (Cdkn1a−/−), followed by measurements of protein and gene expression changes in intestinal epithelial tissue. We hypothesized that if the predicted network is biologically coherent (functional), then the predicted nodes should associate more specifically with dysregulated genes and proteins than stochastically selected genes and proteins. The predicted Apc-Cdkn1a network was significantly perturbed at the mRNA-level by both single gene knockouts, and the predictions were also strongly supported based on physical proximity and mRNA coexpression of proteomic targets. These results support the functional coherence of the proposed Apc-Cdkn1a network and also demonstrate how network-based predictions can be statistically tested using high-throughput biological data