Advanced search options

Advanced Search Options 🞨

Browse by author name (“Author name starts with…”).

Find ETDs with:

in
/  
in
/  
in
/  
in

Written in Published in Earliest date Latest date

Sorted by

Results per page:

Sorted by: relevance · author · university · dateNew search

You searched for +publisher:"Kansas State University" +contributor:("Nora M. Bello"). Showing records 1 – 2 of 2 total matches.

Search Limiters

Last 2 Years | English Only

No search limiters apply to these results.

▼ Search Limiters


Kansas State University

1. Chitakasempornkul, Kessinee. Addressing challenges of hierarchical structural equation modeling in animal agriculture.

Degree: PhD, Department of Statistics, 2019, Kansas State University

Feeding a world population of 9 billion people by 2050 is a fundamental challenge of our generation. Animal agriculture is positioned to play a major role on this challenge by ensuring a safe and secure supply of animal protein for food. Thus motivated, an understanding of the mechanistic interconnections between multiple outcomes in agricultural production systems is critical. Structural equation models (SEM) are being increasingly used for investigating directionality in the associations between outcomes in the system. Agricultural data pose peculiar challenges to the implementation of SEM, among them its structured architecture and its multidimensional heterogeneity. For example, observations on a given outcome collected at the animal level are often not mutually independent but rather likely to have correlation patterns due to clustering within pens or cohorts, which in turn may be subjected to common management or business practices defined at the level of a commercial operation. Also, agricultural outcomes of interest are often correlated and at multiple levels. Furthermore, a key assumption underlying SEM is that of a causal homogeneity, whereby the structural coefficients defining functional links in a network are assumed homogeneous and impervious to environmental conditions or management factors. This assumption seems particularly questionable in the context of animal agriculture, where production systems are regularly subjected to explicit interventions intended to optimize the necessary trade-offs between efficacy and efficiency of production. Despite the recent extension of SEM to a mixed-models framework, inferential issues related to hierarchical data architecture in the context of designed experiments and observational studies are not well understood. Hence, my dissertation work investigates the importance of properly specifying data structure in a hierarchical SEM. My research further develops methodological extensions to SEM to account for heterogeneity in the structural coefficients of a network and characterizes problems to be expected otherwise. Throughout my PhD dissertation, I implemented SEM in a hierarchical Bayesian framework, and I used as motivator an observational dataset in beef cattle feedlot production and a dataset from a designed experiment in swine reproduction. I first evaluated the inferential implications of properly accounting for (or ignoring) existent correlation structure due to data architecture when modeling feedlot data with SEM. Results indicated impaired model fit, biased estimation and precision loss for SEM parameters when data architecture was mispecified or ignored. I then investigated potential causal interconnections between reproductive performance outcomes in swine, for which I leveraged the mixed-models adapted inductive causation algorithm to search for and infer upon causal links. Results indicated reproductive networks distinctive by parity groups, thereby suggesting potential network heterogeneity; this finding was in direct conflict with the standard… Advisors/Committee Members: Nora M. Bello.

Subjects/Keywords: Animal agriculture; Data architecture; Hierarchcial Bayesian models; Heterogeneous structural coefficients; Multi-level correlation; Structural equation models

Record DetailsSimilar RecordsGoogle PlusoneFacebookTwitterCiteULikeMendeleyreddit

APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Chitakasempornkul, K. (2019). Addressing challenges of hierarchical structural equation modeling in animal agriculture. (Doctoral Dissertation). Kansas State University. Retrieved from http://hdl.handle.net/2097/39490

Chicago Manual of Style (16th Edition):

Chitakasempornkul, Kessinee. “Addressing challenges of hierarchical structural equation modeling in animal agriculture.” 2019. Doctoral Dissertation, Kansas State University. Accessed September 18, 2019. http://hdl.handle.net/2097/39490.

MLA Handbook (7th Edition):

Chitakasempornkul, Kessinee. “Addressing challenges of hierarchical structural equation modeling in animal agriculture.” 2019. Web. 18 Sep 2019.

Vancouver:

Chitakasempornkul K. Addressing challenges of hierarchical structural equation modeling in animal agriculture. [Internet] [Doctoral dissertation]. Kansas State University; 2019. [cited 2019 Sep 18]. Available from: http://hdl.handle.net/2097/39490.

Council of Science Editors:

Chitakasempornkul K. Addressing challenges of hierarchical structural equation modeling in animal agriculture. [Doctoral Dissertation]. Kansas State University; 2019. Available from: http://hdl.handle.net/2097/39490

2. Raithel, Seth. Inferential considerations for low-count RNA-seq transcripts: a case study on an edaphic subspecies of dominant prairie grass Andropogon gerardii.

Degree: MS, Statistics, 2015, Kansas State University

Big bluestem (Andropogon gerardii) is a wide-ranging dominant prairie grass of ecological and agricultural importance to the US Midwest while edaphic subspecies sand bluestem (A. gerardii ssp. Hallii) grows exclusively on sand dunes. Sand bluestem exhibits phenotypic divergence related to epicuticular properties and enhanced drought tolerance relative to big bluestem. Understanding the mechanisms underlying differential drought tolerance is relevant in the face of climate change. For bluestem subspecies, presence or absence of these phenotypes may be associated with RNA transcripts characterized by low number of read counts. So called low-count transcripts pose particular inferential challenges and are thus usually filtered out at early steps of data management protocols and ignored for analyses. In this study, we use a plasmode-based approach to assess the relative performance of alternative inferential strategies on RNA-seq transcripts, with special emphasis on low-count transcripts as motivated by differential bluestem phenotypes. Our dataset consists of RNA-seq read counts for 25,582 transcripts (60% of which are classified as low-count) collected from leaf tissue of 4 individual plants of big bluestem and 4 of sand bluestem. We also compare alternative ad-hoc data filtering techniques commonly used in RNA-seq pipelines and assess the performance of recently developed statistical methods for differential expression (DE) analysis, namely DESeq2 and edgeR robust. These methods attempt to overcome the inherently noisy behavior of low-count transcripts by either shrinkage or differential weighting of observations, respectively. Our results indicate that proper specification of DE methods can remove the need for ad- hoc data filtering at arbitrary expression threshold, thus allowing for inference on low-count transcripts. Practical recommendations for inference are provided when low-count RNA-seq transcripts are of interest, as is the case in the comparison of subspecies of bluestem grasses. Insights from this study may also be relevant to other applications also focused on transcripts of low expression levels. Advisors/Committee Members: Nora M. Bello.

Subjects/Keywords: RNA-Seq low-count transcripts; Low-count transcripts; Andropogon gerardii; Gene filtering plasmode; Statistics (0463)

Record DetailsSimilar RecordsGoogle PlusoneFacebookTwitterCiteULikeMendeleyreddit

APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Raithel, S. (2015). Inferential considerations for low-count RNA-seq transcripts: a case study on an edaphic subspecies of dominant prairie grass Andropogon gerardii. (Masters Thesis). Kansas State University. Retrieved from http://hdl.handle.net/2097/19712

Chicago Manual of Style (16th Edition):

Raithel, Seth. “Inferential considerations for low-count RNA-seq transcripts: a case study on an edaphic subspecies of dominant prairie grass Andropogon gerardii.” 2015. Masters Thesis, Kansas State University. Accessed September 18, 2019. http://hdl.handle.net/2097/19712.

MLA Handbook (7th Edition):

Raithel, Seth. “Inferential considerations for low-count RNA-seq transcripts: a case study on an edaphic subspecies of dominant prairie grass Andropogon gerardii.” 2015. Web. 18 Sep 2019.

Vancouver:

Raithel S. Inferential considerations for low-count RNA-seq transcripts: a case study on an edaphic subspecies of dominant prairie grass Andropogon gerardii. [Internet] [Masters thesis]. Kansas State University; 2015. [cited 2019 Sep 18]. Available from: http://hdl.handle.net/2097/19712.

Council of Science Editors:

Raithel S. Inferential considerations for low-count RNA-seq transcripts: a case study on an edaphic subspecies of dominant prairie grass Andropogon gerardii. [Masters Thesis]. Kansas State University; 2015. Available from: http://hdl.handle.net/2097/19712

.