Gaussian Adapted Markov Model with Overhauled Fluctuation Analysis-Based Big Data Streaming Model in Cloud.

An accurate resource usage prediction in the big data streaming applications still remains as one of the complex processes. In the existing works, various resource scaling techniques are developed for forecasting the resource usage in the big data streaming systems. However, the baseline streaming mechanisms limit with the issues of inefficient resource scaling, inaccurate forecasting, high latency, and running time. Therefore, the proposed work motivates to develop a new framework, named as Gaussian adapted Markov model (GAMM)-overhauled fluctuation analysis (OFA), for an efficient big data streaming in the cloud systems. The purpose of this work is to efficiently manage the time-bounded big data streaming applications with reduced error rate. In this study, the gating strategy is also used to extract the set of features for obtaining nonlinear distribution of data and fat convergence solution, used to perform the fluctuation analysis. Moreover, the layered architecture is developed for simplifying the process of resource forecasting in the streaming applications. During experimentation, the results of the proposed stream model GAMM-OFA are validated and compared by using different measures.

Paraneoplastic Dermatomyositis.

Dermatomyositis and its association with malignancy is well known. Though many tumors like breast, ovary, lung carcinomas are reported to be associated with it, cervical carcinoma is a very rare one. Tumors in these patients are usually unmasked by abnormal findings in the medical history and physical examination. Here we report case of a postmenopausal female who presented with subacute onset of weakness of all four limbs. Treatment of the underlying tumor relieved the symptoms of dermatomyositis in our patient.

Polycyclic aromatic hydrocarbons (PAHs) biodegradation by basidiomycetes fungi, Pseudomonas isolate, and their cocultures: comparative in vivo and in silico approach.

The polycyclic aromatic hydrocarbons (PAHs) biodegradation potential of the five basidiomycetes' fungal monocultures and their cocultures was compared with that of a Pseudomonas isolate recovered from oil-spilled soil. As utilization of hydrocarbons by the microorganisms is associated with biosurfactant production, the level of biosurfactant production and its composition by the selected microorganisms was also investigated. The Pseudomonas isolate showed higher ability to degrade three of the five PAHs but the isolate did not produce biosurfactant higher than C. versicolor and P. ostreatus. Among the PAHs, the most effective biodegradation of PAH--pyrene (42%)--was obtained with the fungus C. versicolor. Cocultures involving the fungi and Pseudomonas could not significantly degrade the selected PAHs compounds above that degraded by the most efficient monoculture. A slight increase in pyrene degradation was observed in cocultures of C. versicolor and F. palustris (93.7% pyrene). The crude biosurfactant was biochemically characterized as a multicomponent surfactant consisting of protein and polysaccharides. The PAH biodegradation potential of the basidiomycetes fungi positively correlated with their potential to express ligninolytic enzymes such as lignin peroxidase (Lip), manganese peroxidase (Mnp), and laccase. The present study utilized in silico method such as protein-ligand docking using the FRED in Open Eye software as a tool to assess the level of ligninolytic enzymes and PAHs interactions. The in silico analysis using FRED revealed that of the five PAHs, maximum interaction occurred between pyrene and all the three ligninolytic enzymes. The results of the in silico analysis corroborated with our experimental results showing that pyrene was degraded to the maximum extent by species such as C. versicolor and P. ostreatus.