2 research outputs found
Generative Algorithms for Fusion of Physics-Based Wildfire Spread Models with Satellite Data for Initializing Wildfire Forecasts
Increases in wildfire activity and the resulting impacts have prompted the
development of high-resolution wildfire behavior models for forecasting fire
spread. Recent progress in using satellites to detect fire locations further
provides the opportunity to use measurements to improve fire spread forecasts
from numerical models through data assimilation. This work develops a method
for inferring the history of a wildfire from satellite measurements, providing
the necessary information to initialize coupled atmosphere-wildfire models from
a measured wildfire state in a physics-informed approach. The fire arrival
time, which is the time the fire reaches a given spatial location, acts as a
succinct representation of the history of a wildfire. In this work, a
conditional Wasserstein Generative Adversarial Network (cWGAN), trained with
WRF-SFIRE simulations, is used to infer the fire arrival time from satellite
active fire data. The cWGAN is used to produce samples of likely fire arrival
times from the conditional distribution of arrival times given satellite active
fire detections. Samples produced by the cWGAN are further used to assess the
uncertainty of predictions. The cWGAN is tested on four California wildfires
occurring between 2020 and 2022, and predictions for fire extent are compared
against high resolution airborne infrared measurements. Further, the predicted
ignition times are compared with reported ignition times. An average Sorensen's
coefficient of 0.81 for the fire perimeters and an average ignition time error
of 32 minutes suggest that the method is highly accurate
Crowdsourced assessment of common genetic contribution to predicting anti-TNF treatment response in rheumatoid arthritis
Correction: vol 7, 13205, 2016, doi:10.1038/ncomms13205Rheumatoid arthritis (RA) affects millions world-wide. While anti-TNF treatment is widely used to reduce disease progression, treatment fails in Bone-third of patients. No biomarker currently exists that identifies non-responders before treatment. A rigorous community-based assessment of the utility of SNP data for predicting anti-TNF treatment efficacy in RA patients was performed in the context of a DREAM Challenge (http://www.synapse.org/RA_Challenge). An open challenge framework enabled the comparative evaluation of predictions developed by 73 research groups using the most comprehensive available data and covering a wide range of state-of-the-art modelling methodologies. Despite a significant genetic heritability estimate of treatment non-response trait (h(2) = 0.18, P value = 0.02), no significant genetic contribution to prediction accuracy is observed. Results formally confirm the expectations of the rheumatology community that SNP information does not significantly improve predictive performance relative to standard clinical traits, thereby justifying a refocusing of future efforts on collection of other data.Peer reviewe