This dataset was generated to determine a 2017 water budget. The boundary of the study area extends from Idaho into a portion of Utah.This layer depicts polygons representing land within the Raft River Study area boundary classified as either "irrigated", "non-irrigated" or "semi-irrigated", where the semi-irrigated classification typically depicts residential land. Neither Irrigation status nor line work were verified by ground truthing. Field boundaries were refined using the 2017 Idaho National Agriculture Imagery Program (NAIP) imagery, Digital Ortho Photo Quadrangle (DOQQ) imagery, or other high resolution imagery. Attribute assignments for irrigation status (irrigated, non-irrigated, and semi-irrigated) are determined using available Landsat and/or Sentinel satellite imagery as background reference. Landsat imagery is typically 30-meter (Landsat5) or 15-meter (Landsat7) resolution. Sentinel imagery is 10-meter resolution. National Agriculture Inventory Program (NAIP) imagery, Digital Ortho Photo Quadrangle (DOQQ) imagery, and other in-house, scanned aerial imagery is used for determining irrigation status and refining the polygon geometry. The interpretation and classification process is described in detail in the report, "2006 Irrigated Land Classification for the Eastern Snake Plain Aquifer" archived on the IDWR website: Legal Actions > Delivery Call Actions > SWC > Archived Matters > Technical Working Group Documents (https://idwr.idaho.gov/legal-actions/delivery-call-actions/SWC/archived-matters.html#twg-documents).

Lipid rafts are highly ordered regions of the plasma membrane enriched in signaling proteins and lipids. Their biological potential is realized in exosomes, a subclass of extracellular vesicles (EVs) that originate from the lipid raft domains. Previous studies have shown that EVs derived from human placental mesenchymal stromal cells (PMSCs) possess strong neuroprotective and angiogenic properties. However, clinical translation of EVs is challenged by very low, impure, and heterogeneous yields. Therefore, in this study, lipid rafts are validated as a functional biomaterial that can recapitulate the exosomal membrane and then be synthesized into biomimetic nanovesicles. Lipidomic and proteomic analyses show that lipid raft isolates retain functional lipids and proteins comparable to PMSC-EV membranes. PMSC-derived lipid raft nanovesicles (LRNVs) are then synthesized at high yields using a facile, extrusion-based methodology. Evaluation of biological properties reveals that LRNVs can promote neurogenesis and angiogenesis through modulation of lipid raft-dependent signaling pathways. A proof-of-concept methodology further shows that LRNVs could be loaded with proteins or other bioactive cargo for greater disease-specific functionalities, thus presenting a novel type of biomimetic nanovesicles that can be leveraged as targeted therapeutics for regenerative medicine.

This raster file represents land within the Raft River Study Area classified as either “irrigated” with a cell value of 1 or “non-irrigated” with a cell value of 0 at a 10-meter spatial resolution. These classifications were determined at the pixel level by a Random Forest supervised machine learning methodology. Random Forest models are often used to classify large datasets accurately and efficiently by assigning each pixel to one of a pre-determined set of labels or groups. The model works by using decision trees that split the data based on characteristics that make the resulting groups as different from each other as possible. The model “learns” the characteristics that correlate to each label based on manually classified data points, also known as training data.A variety of data can be supplied as input to the Random Forest model for it to use in making its classification determinations. Irrigation produces distinct signals in observational data that can be identified by machine learning algorithms. Additionally, datasets that provide the model with information on landscape characteristics that often influence whether irrigation is present are also useful. This dataset was classified by the Random Forest model using United States Geological Survey (USGS) Landsat 8 and 9 Level 2, Collection 2, Tier 1 data, Harmonized Sentinel-2 Multispectral Instrument Level-2A data, USGS 3D Elevation Program (USGS 3DEP) data, and Height Above Nearest Drainage (HAND) data. Landsat 8, Landsat 9, and HAND data are at a 30-meter spatial resolution, and the Sentinel-2 and USGS 3DEP data are at a 10-meter spatial resolution. Sentinel-2 Normalized Difference Vegetation Index (NDVI) values and National Agriculture Imagery Program (NAIP) imagery from 2021 (the most recent available) were used to determine irrigation status for the manually classified training data points. Irrigated training point locations were first identified by the NAIP 2021 imagery. Those point locations were then used to sample all available Sentinel-2 NDVI images for the 2022 growing season, and the time series at each point location was reviewed. Only points whose NDVI values remained at or above 0.6 for the majority of the growing season retained their irrigation classification. All non-irrigated training points were reviewed with Sentinel-2 NDVI and false-color imagery to ensure no new crop fields had been established in those locations during the previous year.The final model results were manually reviewed prior to release, however, no extensive ground truthing process was implemented. A wetlands mask was applied using the U.S. Fish and Wildlife Service’s National Wetlands Inventory (FWS NWI) data for areas without overlapping irrigation POUs or locations manually determined to have potential irrigation. “Speckling”, or small areas of incorrectly classified pixels, was reduced by using the Boundary Clean smoothing tool in ArcGIS with a descending sorting type.

This raster file represents land within the Raft River Study Area classified as either “irrigated” with a cell value of 1 or “non-irrigated” with a cell value of 0 at a 30-meter spatial resolution. These classifications were determined at the pixel level by a Random Forest supervised machine learning methodology. Random Forest models are often used to classify large datasets accurately and efficiently by assigning each pixel to one of a pre-determined set of labels or groups. The model works by using decision trees that split the data based on characteristics that make the resulting groups as different from each other as possible. The model “learns” the characteristics that correlate to each label based on manually classified data points, also known as training data.A variety of data can be supplied as input to the Random Forest model for it to use in making its classification determinations. Irrigation produces distinct signals in observational data that can be identified by machine learning algorithms. Additionally, datasets that provide the model with information on landscape characteristics that often influence whether irrigation is present are also useful. This dataset was classified by the Random Forest model using Level 2 (surface reflectance), Collection 2, Tier 1 data from Landsat 5 and Landsat 7, Mapping Evapotranspiration with Internalized Calibration (METRIC) data produced by IDWR, United States Geological Survey National Elevation Dataset (USGS NED) data, and Height Above Nearest Drainage (HAND) data. Landsat 5, Landsat 7, and HAND data are at a 30-meter spatial resolution, and the USGS NED data are at a 10-meter spatial resolution. The National Land Cover Dataset (NLCD) from the USGS, National Agriculture Imagery Program (NAIP) data from the USDA Farm Service Agency (FSA), Utah Water Related Land Use data from the Utah Division of Water Resources, Mapping Evapotranspiration with Internalized Calibration (METRIC) data (where available), and water rights data from IDWR were also used in determining irrigation status for the manually classified training data points but were not used for the machine learning model predictions. The final model results were manually reviewed prior to release, however, no extensive ground truthing process was implemented. “Speckling”, or small areas of incorrectly classified pixels, was reduced by masking all pixels with a slope value of 10% or greater as “non-irrigated”, regardless of the status they were assigned by the Random Forest model. Speckling within irrigated areas was reduced by a boundary clean smoothing technique.

Supplementary information files for article Crosslinked p(MMA) particles by RAFT emulsion polymerisation: tuning size and stability

The controlled synthesis of amphiphilic di-block copolymers allows a large array of nanostructures to be created, including block copolymer particles, which have proved valuable for biomedical applications. Despite progress in targeting specific block copolymer architectures, control over the size and stability of spherical particles is less well explored. Here, we report the use of RAFT emulsion polymerisation to synthesise a library of p(MMA) particles, crosslinked with ethylene glycol dimethacrylate and stabilised by brush-like poly(ethylene glycol)-based polymers. We successfully synthesised a range of block copolymer particles, offering stability up to p(MMA)1000, with DLS reporting stable particle diameters of 33–176 nm and PDI < 0.2. DLS and AFM studies showed a general increase in particle diameter with increasing amounts of p(MMA). The use of a PEG methacrylate monomer with a methyl ether end group resulted in more well defined and stable particles than those with hydroxyl end groups. The copolymerisation of a suitably functionalized Gd(III) complex into the shell of the spherical p(MMA) particles resulted in Gd-loaded particles that were investigated in detail by 1H NMR relaxometry, demonstrating that the Gd complex was successfully incorporated into the particles. This study will inform the synthesis of core–shell particles with optimised stability and targeted sizes, and show a simple method to incorporate a molecular sensor, generating a macromolecular imaging agent.