Current Search:  Weight Control (x)

View All Items

  • CSV Spreadsheet
(1 - 2 of 2)
ANATOMICAL AND FUNCTIONAL ASSESSMENT OF PNMT+ NEURONS IN THE MOUSE HYPOTHALAMUS AND CEREBELLUM: POTENTIAL ROLES IN ENERGY METABOLISM AND MOTOR CONTROL
Title
ANATOMICAL AND FUNCTIONAL ASSESSMENT OF PNMT+ NEURONS IN THE MOUSE HYPOTHALAMUS AND CEREBELLUM: POTENTIAL ROLES IN ENERGY METABOLISM AND MOTOR CONTROL.
Creator
Lindo, Lake A, Ebert, Steven, University of Central Florida
Abstract / Description
Phenylethanolamine N-methyltransferase (Pnmt) is the enzyme in the catecholamine pathway responsible for converting norepinephrine to epinephrine. Pnmt is present in numerous areas; however, the scope of its expression in the mouse brain is not fully understood. A genetic mouse model was generated by the Ebert lab that exhibited the selective destruction of all Pnmt+ cells through the induction of apoptosis by Diphtheria Toxin A. Unexpected phenotypic defects arose that are characterized by...
Show morePhenylethanolamine N-methyltransferase (Pnmt) is the enzyme in the catecholamine pathway responsible for converting norepinephrine to epinephrine. Pnmt is present in numerous areas; however, the scope of its expression in the mouse brain is not fully understood. A genetic mouse model was generated by the Ebert lab that exhibited the selective destruction of all Pnmt+ cells through the induction of apoptosis by Diphtheria Toxin A. Unexpected phenotypic defects arose that are characterized by metabolic weight deficits and motor ataxia. The distribution of Pnmt+ neurons was examined throughout the hypothalamus and cerebellum to generate an anatomical map of current and historical Pnmt expression using various histochemical methods. Historical Pnmt expression appears more extensive than current expression levels at the adult stage, indicating that certain cells in the mouse brain may have experienced transient Pnmt expression. The presence of Pnmt in these regions suggests that the destruction of these neurons may play a role in the phenotypic defects observed in the ablation mouse model. Gaining a more comprehensive understanding of the potential role of Pnmt in these areas may elucidate new drug targets or novel methods to treat obesity and motor control disorders such as ataxia.
Show less
Date Issued
2018
Identifier
CFH2000547, ucf:45689
Format
Document (PDF)
PURL
http://purl.flvc.org/ucf/fd/CFH2000547
Cost-Sensitive Learning-based Methods for Imbalanced Classification Problems with Applications
Title
Cost-Sensitive Learning-based Methods for Imbalanced Classification Problems with Applications.
Creator
Razzaghi, Talayeh, Xanthopoulos, Petros, Karwowski, Waldemar, Pazour, Jennifer, Mikusinski, Piotr, University of Central Florida
Abstract / Description
Analysis and predictive modeling of massive datasets is an extremely significant problem that arises in many practical applications. The task of predictive modeling becomes even more challenging when data are imperfect or uncertain. The real data are frequently affected by outliers, uncertain labels, and uneven distribution of classes (imbalanced data). Such uncertainties createbias and make predictive modeling an even more difficult task. In the present work, we introduce a cost-sensitive...
Show moreAnalysis and predictive modeling of massive datasets is an extremely significant problem that arises in many practical applications. The task of predictive modeling becomes even more challenging when data are imperfect or uncertain. The real data are frequently affected by outliers, uncertain labels, and uneven distribution of classes (imbalanced data). Such uncertainties createbias and make predictive modeling an even more difficult task. In the present work, we introduce a cost-sensitive learning method (CSL) to deal with the classification of imperfect data. Typically, most traditional approaches for classification demonstrate poor performance in an environment with imperfect data. We propose the use of CSL with Support Vector Machine, which is a well-known data mining algorithm. The results reveal that the proposed algorithm produces more accurate classifiers and is more robust with respect to imperfect data. Furthermore, we explore the best performance measures to tackle imperfect data along with addressing real problems in quality control and business analytics.
Show less
Date Issued
2014
Identifier
CFE0005542, ucf:50298
Format
Document (PDF)
PURL
http://purl.flvc.org/ucf/fd/CFE0005542