dc.contributor.author	Cowan, Matthew L.
dc.date.accessioned	2022-05-25T14:02:53Z
dc.date.available	2022-05-25T14:02:53Z
dc.date.issued	2022-04
dc.identifier.other	FC2323D9-6C23-0BAB-43BE-C953E6F8B5EF	en_US
dc.identifier.uri	https://hdl.handle.net/10428/5432
dc.description.abstract	Research scientists, medical professionals, and the academic community publish their findings every year culminating in time series publication numbers of data that form nonlinear trends over time. Understanding these trends would allow researchers to predict future levels of need and interest in specific research areas within their discipline. The problem with studying these trends is defining exactly what their quantitative behavior will be in the future. Trends in publication frequency can be described by plotting sub-discipline publication numbers over time. In this study, we assign specific sigmoidal equations to each sub-discipline studied by doing a Boolean search of PubMed for publication numbers on research topics related to six molecules, ten cell types, and four organ types all related to immunology. Our approach was to transform the original data by reduction of the x-axis and then curve fit the original data set to the best fitting curve which could be analyzed by non-linear regression. This technique was essential to arriving at an accurate prediction of the expected number of publications. Our findings are immunological publication numbers of cells, molecules, and organ types in immunology have exhibited significant trends that give R2 values higher than 0.95 and that in our areas of study only sigmoidal trend behaviors are observed. We propose that demonstrated trends in publications counts will be informative to researchers allowing prediction of growth of interest in their respective fields of study in immunology. Also, we affirm that any predictions made from our research can be verified by chi-square analysis. Keywords: time series, publication numbers, publication frequency, sigmoidal, immunology, prediction	en_US
dc.description.tableofcontents	Chapter I: INTRODUCTION 1 -- Chapter II: LITERATURE REVIEW 3 -- Immunology History 3 -- Modern Immunological Techniques 4 -- Database Storage 4 -- Data Analysis 5 -- Chapter III: MATERIALS AND METHODS 6 -- PubMed Search 6 -- Chapter IV: RESULTS 8 -- Sigmoidal Occurrence 8 -- R2 Table 9 -- Chapter V: DISCUSSION 10 -- Sigmoidal Behavior In Scientific Community 10 -- Behavior of PubMed Cataloging 10 -- Indexing of Publications Over Time 10 -- Future Prediction of Immunology 11 -- REFERENCE 12 -- APPENDIX A: Nonlinear Regression Analysis of Cell Types: Regression Analysis ran using Sigmoidal, Two-Sigmoidal, 6 Parameter and Sigmoidal, Sigmoid, 3 Parameter 16 -- APPENDIX B: Nonlinear Regression Analysis of Molecule Types: Regression Analysis ran using Sigmoidal, Two-Sigmoidal, 6 Parameter and Sigmoidal, Sigmoid, 3 Parameter 27 -- APPENDIX C: Nonlinear Regression Analysis of Organ Types: Regression Analysis ran using Sigmoidal, Two-Sigmoidal, 6 Parameter and Sigmoidal, Sigmoid, 3 Parameter 34	en_US
dc.format.extent	1 electronic document and derivatives, 49 pages. 598412 bytes.	en_US
dc.format.mimetype	application/pdf	en_US
dc.language.iso	en_US	en_US
dc.rights	This dissertation is protected by the Copyright Laws of the United States (Public Law 94-553, revised in 1976). Consistent with fair use as defined in the Copyright Laws, brief quotations from this material are allowed with proper acknowledgement. Use of the materials for financial gain with the author's expressed written permissions is not allowed.	en_US
dc.subject	Academic theses	en_US
dc.subject	Biology	en_US
dc.subject	Immunology	en_US
dc.subject	Time-series analysis	en_US
dc.subject	Scholarly publishing	en_US
dc.title	Quantitative Analysis of Publication Numbers Over Time for Immunology Research Topics in Molecules, Cells, and Organs	en_US
dc.type	Thesis	en_US
dc.contributor.department	Department of Biology of the College of Science and Mathematics	en_US
dc.description.advisor	Kang, Jonghoon
dc.description.committee	Elder, John F.
dc.description.committee	James, Christine
dc.description.degree	M.S.	en_US
dc.description.major	Biology	en_US