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The Land Use Change Alert (LUCA) dataset was developed for effective and timely monitoring of global forest changes that are mostly associated with human activities. Near- real-time changes of forest land use are mapped at 0.05 ha minimum mapping unit for all forest types across the Earth's ecoregions, every two weeks. LUCA is based on Sentinel-1 cloud penetrating synthetic aperture radar (SAR) observations to circumvent limitations of optical imagery from pervasive cloud cover over forested areas globally, and especially in the tropics. The methodology is based on a combination of time-series change detection and machine learning analytics to achieve high accuracy of alerts across all ecoregions and landscapes globally with an average area-adjusted users and producers accuracy of 83% and 63%, respectively. The bi-weekly global alert maps capture forest clearing associated with deforestation and industrial timber harvesting, along with forest degradation associated with selective logging, fragmentation, fire, and roads. The product was developed and released publicly through Google Earth Engine to allow for the rapid assessment of land use change activities, quantifying patterns and processes driving forest change and dynamics across forest ecoregions. LUCA is designed to help monitor a variety of emission reduction programs at the local to regional scales and play a key role in implementing regulations on deforestation-free products. [ABSTRACT FROM AUTHOR]

Aims: The relationship between accelerometry data and changes in Kansas City Cardiomyopathy Questionnaire‐Physical Limitation Score (KCCQ‐PLS) or 6 min walk test (6MWT) is not well understood. Methods and results: VITALITY‐HFpEF accelerometry substudy (n = 69) data were assessed at baseline and 24 weeks. Ordinal logistic regression models were used to assess the association between accelerometry activity and deterioration, improved, or unchanged KCCQ‐PLS (≥8.33 and ≤ −4.17 points) and 6MWT (≥32 vs. ≤ −32 m). KCCQ‐PLS score deteriorated in 16 patients, improved in 34, and was unchanged in 19. 6MWT deteriorated in 8 patients, improved in 21, and was unchanged in 19. Mean accelerometer wear was 21.4 (±2.1) h/day. Changes in hours active from baseline to 24 weeks were not significantly different among patients who exhibited deterioration, improvement, or no change in KCCQ‐PLS [odds ratio (OR) 0.91, 95% confidence interval (CI) 0.71–1.18; P = 0.48] or 6MWT (OR 1.21, 95% CI 0.91–1.60; P = 0.18). Similar lack of association was observed for other accelerometry metrics and change in KCCQ and 6MWT. These findings were unaffected when KCCQ and 6MWT were examined as continuous variables. Conclusions: Accelerometer‐based activity measures did not correlate with subjective or objective standard measures of health status and functional capacity in heart failure with preserved ejection fraction. Further investigation of their relationships to clinical outcomes is required. [ABSTRACT FROM AUTHOR]

OBJECTIVES: The Centers for Disease Control and Prevention recommend that schools can offer severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) diagnostic (on-demand) testing for students and staff with coronavirus disease 2019 symptoms or exposures. Data related to the uptake, implementation, and effect of school-associated on-demand diagnostic testing have not been described. METHODS: The Rapid Acceleration of Diagnostics Underserved Populations Return to School program provided resources to researchers to implement on-demand SARS-CoV-2 testing in schools. This study describes the strategies used and uptake among the different testing programs. Risk of positivity was compared for symptomatic and exposure testing during the d and o variant periods. We estimated the number of school absence days saved with school- based diagnostic testing. RESULTS: Of the 16 eligible programs, 7 provided school-based on-demand testing. The number of persons that participated in these testing programs is 8281, with 4134 (49.9%) receiving >1 test during the school year. Risk of positivity was higher for symptomatic testing compared with exposure testing and higher during the o variant predominant period compared with the d variant predominant period. Overall, access to testing saved an estimated 13 806 absent school days. CONCLUSIONS: School-based on-demand SARS-CoV-2 testing was used throughout the school year, and nearly half the participants accessed testing on more than 1 occasion. Future studies should work to understand participant preferences around school-based testing and how these strategies can be used both during and outside of pandemics. [ABSTRACT FROM AUTHOR]

The Kagome antiferromagnet Mn 3 Sn has garnered much attention due to the presence of exciting properties such as anomalous Hall and Nernst effects. This paper discusses the synthesis of crystalline Mn 3 Sn thin films, prepared on Al 2 O 3 (0001) substrates at 453 ± 5 ° C using molecular beam epitaxy. The growth is monitored in situ using reflection high energy electron diffraction and measured ex situ using x-ray diffraction, Rutherford back-scattering, and cross-sectional scanning transmission electron microscopy. Our analysis shows the in-plane lattice constants of a 1 , M = 4.117 ± 0.027 Å and a 2 , M = 4.943 ± 0.033 Å, which is a very unexpected result when compared to the bulk a -plane Mn 3 Sn. This indicates a strain in the film and makes it challenging to provide a straightforward explanation. In an effort to explain our results, we discuss two possible orientation relationships between the Mn 3 Sn films and the sapphire substrates. Samples prepared under these conditions appear to have smooth surfaces locally, but overall the film has a 3D island morphology. First-principles calculations provide atomic models of the Mn 3 Sn (11 2 ¯ 0) lattice on Al 2 O 3 (0001) high symmetry sites, indicating that the L3-R90 ° is the most stable configuration. A detailed discussion of the experimental data and theoretical results, as well as strain effects, is provided. [ABSTRACT FROM AUTHOR]

The ensuing spread of whirling disease following its initial introduction in the United States in the late 1950s caused drastic declines in populations of Rainbow Trout Oncorhynchus mykiss. The German Rainbow (GR) strain has strong resistance to Myxobolus cerebralis, the parasite causing whirling disease, but also has an extensive history as a domesticated food‐production fish, resulting in traits that are presumed to be maladaptive for survival in the wild. The wild Harrison Lake Rainbow (HAR) strain exhibits reduced infection severity compared with other wild Rainbow Trout strains, but resistance is lower than the GR strain and was considered insufficient to meet management goals. Prior research suggests that crosses of GR with susceptible strains create offspring with Myxobolus cerebralis resistance and survival rates intermediate to the parental strains in lotic environments, but analysis on lentic environments with larger predators has been limited. We evaluated survival rates and infection severities of fingerling Rainbow Trout stocked into a lentic environment from four different strains with theoretically varying resistances to Myxobolus cerebralis: GR, HAR, and an F1 [GR × HAR(50:50)] and F2 [GR × HAR(75:25)] crossed strain between the GR and HAR strains. Using Seber dead‐recovery models, we found that both the HAR and GR × HAR(50:50) strains had the highest survival rate, followed sequentially by the GR × HAR(75:25) and GR. Whirling disease resistance, as estimated from infection probability and severity, increased with increasing GR strain genetic background. To reduce spore loads and increase resistance in managed Rainbow Trout populations, we suggest stocking strains like the GR × HAR(50:50) that have respectable whirling disease resistance and high survival rates, enabling wild recruitment with increased cost‐efficiency of stocking. [ABSTRACT FROM AUTHOR]

The lack of consistent, accurate information on evapotranspiration (ET) and consumptive use of water by irrigated agriculture is one of the most important data gaps for water managers in the western United States (U.S.) and other arid agricultural regions globally. The ability to easily access information on ET is central to improving water budgets across the West, advancing the use of data‐driven irrigation management strategies, and expanding incentive‐driven conservation programs. Recent advances in remote sensing of ET have led to the development of multiple approaches for field‐scale ET mapping that have been used for local and regional water resource management applications by U.S. state and federal agencies. The OpenET project is a community‐driven effort that is building upon these advances to develop an operational system for generating and distributing ET data at a field scale using an ensemble of six well‐established satellite‐based approaches for mapping ET. Key objectives of OpenET include: Increasing access to remotely sensed ET data through a web‐based data explorer and data services; supporting the use of ET data for a range of water resource management applications; and development of use cases and training resources for agricultural producers and water resource managers. Here we describe the OpenET framework, including the models used in the ensemble, the satellite, meteorological, and ancillary data inputs to the system, and the OpenET data visualization and access tools. We also summarize an extensive intercomparison and accuracy assessment conducted using ground measurements of ET from 139 flux tower sites instrumented with open path eddy covariance systems. Results calculated for 24 cropland sites from Phase I of the intercomparison and accuracy assessment demonstrate strong agreement between the satellite‐driven ET models and the flux tower ET data. For the six models that have been evaluated to date (ALEXI/DisALEXI, eeMETRIC, geeSEBAL, PT‐JPL, SIMS, and SSEBop) and the ensemble mean, the weighted average mean absolute error (MAE) values across all sites range from 13.6 to 21.6 mm/month at a monthly timestep, and 0.74 to 1.07 mm/day at a daily timestep. At seasonal time scales, for all but one of the models the weighted mean total ET is within ±8% of both the ensemble mean and the weighted mean total ET calculated from the flux tower data. Overall, the ensemble mean performs as well as any individual model across nearly all accuracy statistics for croplands, though some individual models may perform better for specific sites and regions. We conclude with three brief use cases to illustrate current applications and benefits of increased access to ET data, and discuss key lessons learned from the development of OpenET. [ABSTRACT FROM AUTHOR]

Satellite derived vegetation indices (VIs) are broadly used in ecological research, ecosystem modeling, and land surface monitoring. The Normalized Difference Vegetation Index (NDVI), perhaps the most utilized VI, has countless applications across ecology, forestry, agriculture, wildlife, biodiversity, and other disciplines. Calculating satellite derived NDVI is not always straight-forward, however, as satellite remote sensing datasets are inherently noisy due to cloud and atmospheric contamination, data processing failures, and instrument malfunction. Readily available NDVI products that account for these complexities are generally at coarse resolution; high resolution NDVI datasets are not conveniently accessible and developing them often presents numerous technical and methodological challenges. We address this deficiency by producing a Landsat derived, high resolution (30 m), long-term (30+ years) NDVI dataset for the conterminous United States. We use Google Earth Engine, a planetary-scale cloud-based geospatial analysis platform, for processing the Landsat data and distributing the final dataset. We use a climatology driven approach to fill missing data and validate the dataset with established remote sensing products at multiple scales. We provide access to the composites through a simple web application, allowing users to customize key parameters appropriate for their application, question, and region of interest. [ABSTRACT FROM AUTHOR]