JOHNATHAN O. H. NAPIER

Development and Validation of a Multi-Class Model Defining Molecular Archetypes of Kidney Transplant Rejection

Zhang, H., et al. (2023): https://doi.org/10.1016/j.labinv.2023.100304

Gene expression profiling (GEP) from formalin-fixed paraffin-embedded (FFPE) renal allograft biopsies is a promising approach for feasibly providing a molecular diagnosis of rejection. However, large-scale studies evaluating the performance of models using NanoString platform data to define molecular archetypes of rejection are lacking. We tested a diverse retrospective cohort of over 1400 FFPE biopsy specimens, rescored according to Banff 2019 criteria and representing ten of 11 UNOS regions, using the Banff Human Organ Transplant (B-HOT) panel from NanoString and developed a multi-class model from the gene expression data to assign relative probabilities of four molecular archetypes: No Rejection, Antibody-Mediated Rejection (ABMR), T cell-Mediated Rejection (TCMR), and Mixed Rejection. Using LASSO regularized regression with 10-fold cross-validation fitted to 1050 biopsies in the discovery cohort and technically validated on an additional 345 biopsies, our model achieved overall accuracy of 85% in the discovery cohort and 80% in the validation cohort, with ≥75% positive predictive value (PPV) for each class, except for the Mixed Rejection class in the validation cohort (PPV 53%). This study represents the technical validation of the first model built from a large and diverse sample of diagnostic FFPE biopsy specimens to define and classify molecular archetypes of histologically-defined diagnoses as derived from B-HOT panel GEP data.

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Connectivity of combination puzzles: Using machine learning on groups

Napier, J. O. H. (2021): ProQuest Dissertations & Theses Global​

This study analyzed the structure of the Pocket Cube (2 × 2) and explored machine learning designed to solve the Rubik’s Cube (3 ×3). Categorization of 2 × 2 permutations revealed 158 subtypes of which some were “dead-end” permutations, incapable of being scrambled further despite not being at the furthest scrambled depth of 11. Markovian analyses of the 2 × 2 determined the mixing time (18.74635), mean time between depths, transition probabilities between layers, and the Monkey number (23,332,546). In building neural networks for the 3 × 3, unique methods were developed to enhance machine learning: warm-encoding for training networks with single inputs and multiple outputs, lambda layer mediated outputs to chain cost- functions, and color rotations that identify algorithms of the same conjugacy classes. Further investigation was done on the 3 × 3 by counting the correct number of edges, corners, and stickers for generated permutations. This highlighted the non-separability with respect to depth and its potential problems for machine learning.

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Behavioral and Disease Ecology of Gopher Tortoises (Gopherus polyphemus) Post Exclusion and Relocation with a Novel Approach to Homing Determination

Napier, J. O. H. (2018): University of Central Florida] STARS Electronic Theses and Dissertations

In the wake of human expansion, relocations and the loss of habitat can be stressful to an organism, plausibly leading to population declines. The gopher tortoise (Gopherus polyphemus) is a keystone species that constructs burrows it shares with 362 commensal species. Frequent exclusions and relocations and long generation times have contributed to G. polyphemus being State-designated as Threatened in Florida. Prior studies have indicated that G. polyphemus may possess homing behavior and thus be able to counteract stressors due to relocation and exclusion. I radiotracked a cohort of G. polyphemus for 11 months following excavation, relocation, and exclusion due to a pipeline construction project. In conjunction with analyzing G. polyphemus movement patterns post-release, I developed novel statistical methodologies with broad application for movement analysis and compared them to traditional analyses. I evaluated habitat usage, burrowing behavior, movements, growth, and disease signs among control versus relocated and excluded individuals and among sexes and size classes, forming predictors for behavior and disease risk. I found statistical support that my new methodology is superior to previous statistical tests for movement analyses. I also found that G. polyphemus engages in homing behavior, but only in males. Behavioral differences were also found between the sexes with respect to burrowing behavior. Overall health, disease prevalence, and immune response were unaffected by relocation and exclusion, nor were they statistically correlated. Signs were unreliable as etiological agents, outperformed by serological detection. I determined that the Sabal Trail pipeline as a potential stressor did not affect movement behavior, homing, nor the disease/immune profile of G. polyphemus in this study.