Feature Influence Based ETL for Efficient Big Data Management

The increased volume of big data introduces various challenges for its maintenance and analysis. There exist various approaches to the problem, but they fail to achieve the expected results. To improve the big data management performance, an efficient real time feature influence analysis based Extraction, Transform, and Loading (ETL) framework is presented in this article. The model fetches the big data and analyses the features to find noisy records by preprocessing the data set. Further, the method performs feature extraction and applies feature influence analysis to various data nodes and the data present in the data nodes. The method estimates Feature Specific Informative Influence (FSII) and Feature Specific Supportive Influence (FSSI). The value of FSII and FSSI are measured with the support of a data dictionary. The class ontology belongs to various classes of data. The value of FSII is measured according to the presence of a concrete feature on a tuple towards any data node, whereas the value of FSSI is measured based on the appearance of supportive features on any data point towards the data node. Using these measures, the method computes the Node Centric Transformation Score (NCTS). Based on the value of NCTS the method performs map reduction and merging of data nodes. The NCTS_FIA method achieves higher performance in the ETL process. By adapting feature influence analysis in big data management, the ETL performance is improved with the least amount of time complexity

An international collaborative evaluation of central serous chorioretinopathy: different therapeutic approaches and review of literature. The European Vitreoretinal Society central serous chorioretinopathy study

Purpose: To study and compare the efficacy of different therapeutic options for the treatment of central serous chorioretinopathy (CSCR). Methods: This is a nonrandomized, international multicentre study on 1719 patients (1861 eyes) diagnosed with CSCR, from 63 centres (24 countries). Reported data included different methods of treatment and both results of diagnostic examinations [fluorescein angiography and/or optical coherent tomography (OCT)] and best-corrected visual acuity (BCVA) before and after therapy. The duration of observation had a mean of 11 months but was extended in a minority of cases up to 7 years. The aim of this study is to evaluate the efficacy of the different therapeutic options of CSCR in terms of both visual (BCVA) and anatomic (OCT) improvement. Results: One thousand seven hundred nineteen patients (1861 eyes) diagnosed with CSCR were included. Treatments performed were nonsteroidal anti-inflammatory eye drops, laser photocoagulation, micropulse diode laser photocoagulation, photodynamic therapy (PDT; Standard PDT, Reduced-dose PDT, Reduced-fluence PDT), intravitreal (IVT) antivascular endothelial growth factor injection (VEGF), observation and other treatments. The list of the OTHERS included both combinations of the main proposed treatments or a variety of other treatments such as eplerenone, spironolactone, acetazolamide, beta-blockers, anti-anxiety drugs, aspirin, folic acid, methotrexate, statins, vitis vinifera extract medication and pars plana vitrectomy. The majority of the patients were men with a prevalence of 77%. The odds ratio (OR) showed a partial or complete resolution of fluid on OCT with any treatment as compared with observation. In univariate analysis, the anatomical result (improvement in subretinal fluid using OCT at 1 month) was favoured by age <60 years (p < 0.005), no previous observation (p < 0.0002), duration less than 3 months (p < 0.0001), absence of CSCR in the fellow eye (p = 0.04), leakage outside of the arcade (p = 0.05) and fluid height >500 \u3bcm (p = 0.03). The OR for obtaining partial or complete resolution showed that anti-VEGF and eyedrops were not statistically significant; whereas PDT (8.5), thermal laser (11.3) and micropulse laser (8.9) lead to better anatomical results with less variability. In univariate analysis, the functional result at 1 month was favoured by first episode (p = 0.04), height of subretinal fluid >500 \u3bcm (p < 0.0001) and short duration of observation (p = 0.02). Finally, there was no statistically significant difference among the treatments at 12 months. Conclusion: Spontaneous resolution has been described in a high percentage of patients. Laser (micropulse and thermal) and PDT seem to lead to significant early anatomical improvement; however, there is little change beyond the first month of treatment. The real visual benefit needs further clarification

Understanding metric-related pitfalls in image analysis validation

Validation metrics are key for the reliable tracking of scientific progress and for bridging the current chasm between artificial intelligence (AI) research and its translation into practice. However, increasing evidence shows that particularly in image analysis, metrics are often chosen inadequately in relation to the underlying research problem. This could be attributed to a lack of accessibility of metric-related knowledge: While taking into account the individual strengths, weaknesses, and limitations of validation metrics is a critical prerequisite to making educated choices, the relevant knowledge is currently scattered and poorly accessible to individual researchers. Based on a multi-stage Delphi process conducted by a multidisciplinary expert consortium as well as extensive community feedback, the present work provides the first reliable and comprehensive common point of access to information on pitfalls related to validation metrics in image analysis. Focusing on biomedical image analysis but with the potential of transfer to other fields, the addressed pitfalls generalize across application domains and are categorized according to a newly created, domain-agnostic taxonomy. To facilitate comprehension, illustrations and specific examples accompany each pitfall. As a structured body of information accessible to researchers of all levels of expertise, this work enhances global comprehension of a key topic in image analysis validation.Comment: Shared first authors: Annika Reinke, Minu D. Tizabi; shared senior authors: Paul F. J\"ager, Lena Maier-Hei

Feature Influence Based ETL for Efficient Big Data Management

1310-1316The increased volume of big data introduces various challenges for its maintenance and analysis. There exist various approaches to the problem, but they fail to achieve the expected results. To improve the big data management performance, an efficient real time feature influence analysis based Extraction, Transform, and Loading (ETL) framework is presented in this article. The model fetches the big data and analyses the features to find noisy records by preprocessing the data set. Further, the method performs feature extraction and applies feature influence analysis to various data nodes and the data present in the data nodes. The method estimates Feature Specific Informative Influence (FSII) and Feature Specific Supportive Influence (FSSI). The value of FSII and FSSI are measured with the support of a data dictionary. The class ontology belongs to various classes of data. The value of FSII is measured according to the presence of a concrete feature on a tuple towards any data node, whereas the value of FSSI is measured based on the appearance of supportive features on any data point towards the data node. Using these measures, the method computes the Node Centric Transformation Score (NCTS). Based on the value of NCTS the method performs map reduction and merging of data nodes. The NCTS_FIA method achieves higher performance in the ETL process. By adapting feature influence analysis in big data management, the ETL performance is improved with the least amount of time complexity

Crystal structures of Trypanosoma brucei sterol 14alpha-demethylase and implications for selective treatment of human infections.

Sterol 14alpha-demethylase (14DM, the CYP51 family of cytochrome P450) is an essential enzyme in sterol biosynthesis in eukaryotes. It serves as a major drug target for fungal diseases and can potentially become a target for treatment of human infections with protozoa. Here we present 1.9 A resolution crystal structures of 14DM from the protozoan pathogen Trypanosoma brucei, ligand-free and complexed with a strong chemically selected inhibitor N-1-(2,4-dichlorophenyl)-2-(1H-imidazol-1-yl)ethyl)-4-(5-phenyl-1,3,4-oxadi-azol-2-yl)benzamide that we previously found to produce potent antiparasitic effects in Trypanosomatidae. This is the first structure of a eukaryotic microsomal 14DM that acts on sterol biosynthesis, and it differs profoundly from that of the water-soluble CYP51 family member from Mycobacterium tuberculosis, both in organization of the active site cavity and in the substrate access channel location. Inhibitor binding does not cause large scale conformational rearrangements, yet induces unanticipated local alterations in the active site, including formation of a hydrogen bond network that connects, via the inhibitor amide group fragment, two remote functionally essential protein segments and alters the heme environment. The inhibitor binding mode provides a possible explanation for both its functionally irreversible effect on the enzyme activity and its selectivity toward the 14DM from human pathogens versus the human 14DM ortholog. The structures shed new light on 14DM functional conservation and open an excellent opportunity for directed design of novel antiparasitic drugs.Journal ArticleResearch Support, N.I.H. ExtramuralResearch Support, Non-U.S. Gov'tSCOPUS: ar.jinfo:eu-repo/semantics/publishe