Biology subjects

Gill, I.

Publications and source records attributed to Gill, I..

3 recordsLinked to original sources

bioRxiv2025

Sampling Aware Ancestral State Inference

In phylogeography, ancestral state inference methods are used to identify the geographic or host species origin of viral or bacterial lineages and reconstruct their transmission histories over time. However, differences in sampling among states can bias these inference methods. We introduce sampling-aware ancestral state inference (SAASI), a method that accounts for sampling differences. Here, we apply SAASI to the multi-host spread of the H5N1 virus in the United States in 2024 and find that the key transmission event from wild birds to cattle is estimated to occur later under lower sampling in wild birds (compared to other species) than when sampling is not accounted for. Using simulation, we find that SAASI infers past viral locations/host species considerably more accurately than standard methods when sampling bias exists, is computationally feasible for large datasets and scales to trees with 100,000 tips.

evolutionary biology↗

bioRxiv2023

Conditional Generative Learning for Medical Image Imputation

Image imputation refers to the task of generating a type of medical image given images of another type. This task becomes challenging when the difference between the available images, and the image to be imputed is large. In this manuscript, one such application, derived from the dynamic contrast enhanced computed tomography (CECT) imaging of the kidneys, is considered: given an incomplete sequence of three CECT images, we are required to the impute the missing image. This task is posed as one of probabilistic inference and a generative algorithm to generate samples of the imputed image, conditioned on the available images, is developed, trained, and tested. The output of this algorithm is the "best guess" of the imputed image, and a pixel-wise image of variance in the imputation. It is demonstrated that this best guess is more accurate than those generated by other, deterministic deep-learning based algorithms, including ones which utilize additional information and more complex loss terms. It is also shown the pixel-wise variance image, which quantifies the confidence in the reconstruction, can be used to determine whether the result of the imputation meets a specified accuracy threshold and is therefore appropriate for a downstream task.

cancer biology↗

bioRxiv2021

Physiological activation of the nephron central command drives endogenous kidney tissue regeneration

Tissue regeneration is limited in several organs including the kidney, contributing to the high prevalence of kidney disease globally. However, evolutionary and physiological adaptive responses and the presence of renal progenitor cells suggest existing remodeling capacity. This study uncovered a novel endogenous tissue remodeling mechanism in the kidney that is activated by the loss of body fluid and salt and involves a unique niche of chief cells called macula densa (MD) that control resident progenitor cells via secreted angiogenic, growth and extracellular matrix remodeling factors, cytokines and chemokines. Serial intravital imaging, MD Wnt mouse models and transcriptome analysis provide functional and molecular characterization of this newly identified MD program for kidney regeneration complemented with human and therapeutic translation. The concept that chief cells responding to organ-specific physiological inputs control local progenitors and direct them to remodel or repair tissues may be applicable to other organs and diverse tissue regenerative therapeutic strategies.

physiology↗