Extensive Analysis on Generation and Consensus Mechanisms of Clustering Ensemble: A Survey

Data analysis plays a prominent role in interpreting various phenomena. Data mining is the process to hypothesize useful knowledge from the extensive data. Based upon the classical statistical prototypes the data can be exploited beyond the storage and management of the data. Cluster analysis a primary investigation with little or no prior knowledge, consists of research and development across a wide variety of communities. Cluster ensembles are melange of individual solutions obtained from different clusterings to produce final quality clustering which is required in wider applications. The method arises in the perspective of increasing robustness, scalability and accuracy. This paper gives a brief overview of the generation methods and consensus functions included in cluster ensemble. The survey is to analyze the various techniques and cluster ensemble methods

TGF‐β1‐activated type 2 dendritic cells promote wound healing and induce fibroblasts to express tenascin c following corneal full‐thickness hydrogel transplantation

ACKNOWLEDGEMENTS We are grateful to the Iain Fraser Flow Cytometry Centre, the Microscopy and Histology Facility, the Quantitative PCR Facility, and the Medical Research Facility at the University of Aberdeen. This work was supported by the Royal College of Surgeons of Edinburgh, UK and Saving Sight in Grampian/Development Trust of the University of Aberdeen, UK. Funding Information: Saving Sight in Grampian/Development Trust of the University of Aberdeen, UK Royal College of Surgeons of Edinburgh, UKPeer reviewedPostprin

Liver Perilipin 5 Expression Worsens Hepatosteatosis But Not Insulin Resistance in High Fat-Fed Mice

Perilipin 5 (PLIN5) is a lipid droplet (LD) protein highly expressed in oxidative tissues, including the fasted liver. However, its expression also increases in nonalcoholic fatty liver. To determine whether PLIN5 regulates metabolic phenotypes of hepatosteatosis under nutritional excess, liver targeted overexpression of PLIN5 was achieved using adenoviral vector (Ad-PLIN5) in male C57BL/6J mice fed high-fat diet. Mice treated with adenovirus expressing green fluorescent protein (GFP) (Ad-GFP) served as control. Ad-PLIN5 livers increased LD in the liver section, and liquid chromatography with tandem mass spectrometry revealed increases in lipid classes associated with LD, including triacylglycerol, cholesterol ester, and phospholipid classes, compared with Ad-GFP liver. Lipids commonly associated with hepatic lipotoxicity, diacylglycerol, and ceramides, were also increased in Ad-PLIN5 liver. The expression of genes in lipid metabolism regulated by peroxisome proliferator-activated receptor-alpha was reduced suggestive of slower mobilization of stored lipids in Ad-PLIN5 mice. However, the increase of hepatosteatosis by PLIN5 overexpression did not worsen glucose homeostasis. Rather, serum insulin levels were decreased, indicating better insulin sensitivity in Ad-PLIN5 mice. Moreover, genes associated with liver injury were unaltered in Ad-PLIN5 steatotic liver compared with Ad-GFP control. Phosphorylation of protein kinase B was increased in Ad-PLIN5-transduced AML12 hepatocyte despite of the promotion of fatty acid incorporation to triacylglycerol as well. Collectively, our data indicates that the increase in liver PLIN5 during hepatosteatosis drives further lipid accumulation but does not adversely affect hepatic health or insulin sensitivity

Potential of Big Data Analytics in Bio-medical and Health Care Arena: An Exploratory Study

With the leveraging emerging big Data in every industry Big Data can amalgamate all data related to patient to get a complete view of patient to analyze and predict the outcomes Using big data analytics as tools It can enhance development in new drugs health care financing process and clinical approaches and extends a lots of benefits such as better health care quality and efficiency fraud detection and early disease detection by means of analytics of big data This paper provides a general survey of current progress and advances in research arena of big data bio-medical and health care and some major challenges of big data concept and characteristics this concerns includes big data from bio-medical and health care arena benefits of big data its applications and opportunities Methods and technology progress about big data in bio-medical and health care and challenges of big data in both bio-medical and healthcare are also discusse

Extensive Analysis on Generation and Consensus Mechanisms of Clustering Ensemble

Data analysis plays a prominent role in interpreting various phenomena. Data mining is the process to hypothesize useful knowledge from the extensive data. Based upon the classical statistical prototypes the data can be exploited beyond the storage and management of the data. Cluster analysis a primary investigation with little or no prior knowledge, consists of research and development across a wide variety of communities. Cluster ensembles are melange of individual solutions obtained from different clusterings to produce final quality clustering which is required in wider applications. The method arises in the perspective of increasing robustness, scalability and accuracy. This paper gives a brief overview of the generation methods and consensus functions included in cluster ensemble. The survey is to analyze the various techniques and cluster ensemble methods

Advances in Machine Learning Techniques for Penaeid Shrimp Disease Detection: A Survey

Shrimps are affecting with various diseases. In particular, white spot disease of the shrimp is a very dangerous disease which causes huge loss to Aqua Farmers. This disease affects more into species like penaeus monodon and penaeus Vennammei. There are many Computer vision techniques are there to identify white spot disease of a shrimp. This study helps in finding effective solution through various image processing and neural network techniques to identify the white spot disease of a shrimp which helps aqua farmers in effective decision making to prevent from virus to spread among other ponds thereby increasing shrimp yield