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Background Wolfram syndrome (WFS) is a rare, neurodegenerative disease that typically presents with childhood onset insulin dependent diabetes mellitus, followed by optic atrophy, diabetes insipidus, deafness, and neurological and psychiatric dysfunction. There is no cure for the disease, but recent advances in research have improved understanding of the disease course. Measuring disease severity and progression with reliable and validated tools is a prerequisite for clinical trials of any new intervention for neurodegenerative conditions. To this end, we developed the Wolfram Unified Rating Scale (WURS) to measure the severity and individual variability of WFS symptoms. The aim of this study is to develop and test the reliability and validity of the Wolfram Unified Rating Scale (WURS). Methods A rating scale of disease severity in WFS was developed by modifying a standardized assessment for another neurodegenerative condition (Batten disease). WFS experts scored the representativeness of WURS items for the disease. The WURS was administered to 13 individuals with WFS (6-25 years of age). Motor, balance, mood and quality of life were also evaluated with standard instruments. Inter-rater reliability, internal consistency reliability, concurrent, predictive and content validity of the WURS were calculated. Results The WURS had high inter-rater reliability (ICCs>.93), moderate to high internal consistency reliability (Cronbach’s α = 0.78-0.91) and demonstrated good concurrent and predictive validity. There were significant correlations between the WURS Physical Assessment and motor and balance tests (r s >.67, p<.03), between the WURS Behavioral Scale and reports of mood and behavior (r s >.76, p<.04) and between WURS Total scores and quality of life (r s =-.86, p=.001). The WURS demonstrated acceptable content validity (Scale-Content Validity Index=0.83). Conclusions These preliminary findings demonstrate that the WURS has acceptable reliability and validity and captures individual differences in disease severity in children and young adults with WFS.

We created daily concentration estimates for fine particulate matter (PM 2.5 ) at the centroids of each county, ZIP code, and census tract across the western US, from 2008–2018. These estimates are predictions from ensemble machine learning models trained on 24-hour PM 2.5 measurements from monitoring station data across 11 states in the western US. Predictor variables were derived from satellite, land cover, chemical transport model (just for the 2008–2016 model), and meteorological data. Ten-fold spatial and random CV R 2 were 0.66 and 0.73, respectively, for the 2008–2016 model and 0.58 and 0.72, respectively for the 2008–2018 model. Comparing areal predictions to nearby monitored observations demonstrated overall R 2 of 0.70 for the 2008–2016 model and 0.58 for the 2008–2018 model, but we observed higher R 2 (>0.80) in many urban areas. These data can be used to understand spatiotemporal patterns of, exposures to, and health impacts of PM 2.5 in the western US, where PM 2.5 levels have been heavily impacted by wildfire smoke over this time period. Measurement(s) fine respirable suspended particulate matter Technology Type(s) machine learning Factor Type(s) elevation • land cover • vehicle emissions • seasonality • spatiotemporal variation Sample Characteristic - Environment air pollution Sample Characteristic - Location State of Arizona • State of California • State of Colorado • State of Idaho • State of Montana • State of Nevada • State of New Mexico • State of Oregon • State of Utah • State of Washington • State of Wyoming Machine-accessible metadata file describing the reported data: https://doi.org/10.6084/m9.figshare.14161856

We created daily concentration estimates for fine particulate matter (PM ) at the centroids of each county, ZIP code, and census tract across the western US, from 2008-2018. These estimates are predictions from ensemble machine learning models trained on 24-hour PM measurements from monitoring station data across 11 states in the western US. Predictor variables were derived from satellite, land cover, chemical transport model (just for the 2008-2016 model), and meteorological data. Ten-fold spatial and random CV R were 0.66 and 0.73, respectively, for the 2008-2016 model and 0.58 and 0.72, respectively for the 2008-2018 model. Comparing areal predictions to nearby monitored observations demonstrated overall R of 0.70 for the 2008-2016 model and 0.58 for the 2008-2018 model, but we observed higher R (>0.80) in many urban areas. These data can be used to understand spatiotemporal patterns of, exposures to, and health impacts of PM in the western US, where PM levels have been heavily impacted by wildfire smoke over this time period.

Investigating the health impacts of wildfire smoke requires data on people's exposure to fine particulate matter (PM\\(_2.5\\)) across space and time. In recent years, it has become common to use machine learning models to fill gaps in monitoring data. However, it remains unclear how well these models are able to capture spikes in PM\\(_2.5\\) during and across wildfire events. Here, we evaluate the accuracy of two sets of high-coverage and high-resolution machine learning-derived PM\\(_2.5\\) data sets created by Di et al. (2021) and Reid et al. (2021). In general, the Reid estimates are more accurate than the Di estimates when compared to independent validation data from mobile smoke monitors deployed by the US Forest Service. However, both models tend to severely under-predict PM\\(_2.5\\) on high-pollution days. Our findings complement other recent studies calling for increased air pollution monitoring in the western US and support the inclusion of wildfire-specific monitoring observations and predictor variables in model-based estimates of PM\\(_2.5\\). Lastly, we call for more rigorous error quantification of machine-learning derived exposure data sets, with special attention to extreme events.