S. D. Taylor

Dissecting signaling regulators driving AXL-mediated bypass resistance and associated phenotypes by phosphosite perturbations

The paper uses phosphosite perturbations to identify signaling pathways driving AXL-mediated erlotinib resistance in lung cancer.

HOXD13 is a direct EWS-FLI1 target and moderates fusion-dependent transcriptional states

The paper identifies HOXD13 as a direct target of EWS-FLI1 and shows it modulates transcriptional states in Ewing sarcoma by antagonizing EWS-FLI1-dependent gene repression.

Classification of daily crop phenology in PhenoCams using deep learning and hidden markov models

This paper develops a deep learning and hidden markov model workflow to classify daily crop phenology from PhenoCam images, improving accuracy by accounting for temporal correlations and contextual factors.

Constraints and Opportunities for Detecting Land Surface Phenology in Drylands

This study evaluates the feasibility of detecting land surface phenology in drylands using UAV imagery and simulated data, finding that vegetation cover and VI uncertainty significantly impact detection accuracy.

Structured decomposition improves systems serology prediction and interpretation

This paper introduces TMTF, a tensor factorization method that improves data reduction, prediction, and interpretation in systems serology by capturing structured patterns in HIV humoral immune responses.

Influence of climate forecasts, data assimilation, and uncertainty propagation on the performance of near-term phenology forecasts

This study evaluates a near-term phenology forecast system, finding that temperature data assimilation improves skill while climate forecasts contribute little overall.

Rethinking global carbon storage potential of trees. A comment on Bastin et al. 2019

This paper re-evaluates global carbon storage potential of restored forests, finding it significantly lower than a previous estimate due to flawed assumptions in the original study.

Opportunistically collected photographs can be used to estimate large-scale phenological trends

The paper estimates large-scale phenological trends using iNaturalist photographs and compares them with Phenocam data, showing alignment across years and latitudes.

Automated data-intensive forecasting of plant phenology throughout the United States

The paper develops an automated system for forecasting plant phenology across the U.S., predicting events like budburst and fall colors for 78 species up to 6 months in advance.

Developing an automated iterative near-term forecasting system for an ecological study

This paper develops an automated iterative forecasting system for ecological systems, demonstrating its application to rodent abundance forecasts using monthly data from the Portal Project.

Comparison of large-scale citizen science data and long-term study data for phenology modeling

This paper compares phenology models built from citizen science data and long-term ecological study data, finding that model performance depends on dataset scale and complexity.