R. S. Miller

Operational lessons learned from simulating an elimination response to a transboundary animal disease in wild animals

The paper simulates a TAD control effort in wild pigs to identify operational challenges and propose solutions for rapid population reduction during a hypothetical outbreak.

CameraTrapDetectoR: Automatically detect, classify, and count animals in camera trap images using artificial intelligence

CameraTrapDetectoR uses deep learning to detect, classify, and count animals in camera trap images, achieving high accuracy across multiple taxonomic levels.

Transient population dynamics drive the spread of invasive wild pigs in North America

The paper predicts wild pig population growth and spread in North America by incorporating transient population dynamics and environmental factors.

rapidPop: Rapid population assessments of wildlife using camera trap data in R Shiny Applications

The paper introduces an R package for rapid population assessments using camera trap data, enabling occupancy modeling with Shiny applications to analyze density changes.

Improving the accessibility and transferability of machine learning algorithms for identification of animals in camera trap images: MLWIC2

This paper develops two deep learning models for camera trap image classification, achieving high accuracy and transferability across locations, and provides an accessible R package for biologists.

High throughput small molecule screening reveals NRF2-dependent and -independent pathways of cellular stress resistance

This study identifies small molecules that induce cellular stress resistance against paraquat, cadmium, and methyl methanesulfonate, with some compounds activating Nrf2 signaling and others triggering inflammatory and AMPK pathways.

Determinants of invasive species policy: print media and agriculture determine United States invasive wild pig policy

This study identifies factors influencing U.S. invasive wild pig policy by linking congressional activity to print media coverage and agricultural co-occurrence.

Machine learning to classify animal species in camera trap images: applications in ecology

This paper trains a machine learning model to classify animal species in camera trap images, achieving high accuracy and providing an R package for ecological use.