University of South Florida Showcase

The Landscape Assessment of Nutrient Loading to Waterbodies (LANLoad) is a geospatial screening tool designed to facilitate water quality management decisions. It provides an estimate of the relative likelihood that nutrient inputs applied to locations on land will impact surface water quality. LANLoad is based solely on physical landscape characteristics and may be used independently or with other relevant datasets such as those that reflect nutrient application rates. This dataset includes LANLoad for the Northern Everglades and Estuaries Protection Program (NEEPP) region. The dataset is available as a single comprehensive file "LANLoad_NEEPP_Overall.tif.zip" and as subsets corresponding to intersections between NEEPP and 15 FL counties. These raster dataset consist of cells (10m x 10m) ranked to reflect the likelihood that nutrients applied to a given terrestrial location will reach the nearest downgradient surface waterbody. Possible ranks are from 1 to 9 with values increasing as the likelihood increases that mobile nutrients would reach a downgradient surface waterbody. Ranks are based on 6 physical landscape parameters selected by Subject Matter Experts (SMEs) who also assigned relative weights of each parameter using the Analytical Hierarchy Process (AHP). During this exercise, the focal location for SMEs was the pilot study area, St Lucie County, FL, and the focal nutrient source was Onsite Sewage and Treatment Disposal Systems. The resulting AHP model demonstrated high internal consistency (Consistency Ratio: 0.01) and resulted in the following parameter weights, in order of importance: • Distance to Waterbody, 30.0%; • Depth to Water, 21.6%; • Hydraulic Conductivity, 20.7%; • Potential for Flooding, 10.9%; • Slope, 9.8%; and • Surficial Karstic Deposits, 7.0%. Geospatial datasets representative of these parameters were acquired in 2024 and combined using a weighted overlay to produce LANLoad NEEPP. LANLoad NEEPP performance was evaluated at multiple locations (selected through a random stratified process) within the NEEPP region by classifying LANLoad ranks less than or equal to 4 as “lower” and those more than or equal to 6 as “higher”. Then, two assessment methods were applied, both conducted blind: 1) SME Review: SMEs were provided with input datasets corresponding to 30 locations and asked to assign a classification of lower or higher. There was 92 % consistency between classifications assigned by LANLoad NEEPP and those assigned by SMEs. 2) Numerical modeling: Using ArcNLET-Py, nutrient loading to surface waters from uniform inputs was modeled in 10 locations, each containing 50 model points. Classifications assigned by LANLoad were 100% consistent with those assigned through ArcNLET-Py model results, i.e., locations classified by LANload as “higher” also had the highest ArcNLET-Py modeled nutrient loads while those classified as “lower” had the lowest modeled nutrient loads. Contact: Kai Rains – krains@usf.edu

The data for this project consists of curriculum materials developed for a vocabulary intervention. Two parallel sets of instructional products were created: one monolingual (English-only) and one bilingual (English-Spanish). Each curriculum set contains materials designed for structured delivery of explicit vocabulary instruction over six weeks. Types of materials include: * Teacher lesson cards: Step-by-step guidance for instruction, including pre-teaching of vocabulary, purpose-setting for reading, read-aloud prompts, comprehension checks, and closing activities. * Student reading passages: Thematic texts that embed target vocabulary words in context. * Vocabulary cards: Word-by-word instructional supports including (a) the focal word, (b) child-friendly definitions, (c) contextual examples, (d) visual supports (images), and (e) oral repetition prompts. * Vocabulary review cards: Summative review prompts with definitions, images, and opportunities for repetition and extension questions.

Background: Acute kidney injury (AKI) causes rapid loss of renal function and leads to high morbidity and mortality. Our previous research has shown that neuronal nitric oxide synthase (NOS1) influences nitric oxide (NO)-mediated dilation of the afferent arteriole, thereby inhibiting tubuloglomerular feedback (TGF), which plays a critical role in glomerular filtration rate (GFR). Methods: We generated inducible macula densa–specific NOS1 knockout mice (NKCC2-Cre- NOS1 flox/flox) and introduced AKI by 18 min bilateral renal ischemia at 37 °C, followed by 48 h reperfusion. The kidney injury was assessed by measuring GFR, plasma creatinine, histology, cytokines, apoptosis, fibrotic factors, and proteomics. Results: Deletion of NOS1 was confirmed through immunofluorescence double staining of NKCC2 and NOS1. The results showed that crossing NKCC2 cre line with NOS1 flox line induces a complete deletion of NOS1 from the macula densa cells. In response to IR-AKI, compared with wild-type controls, NOS1 knockouts showed a dramatic GFR decline (236 ± 66 to 24 ± 22 µL/min) and elevated creatinine, alongside more severe tubular damages evidenced by H&E staining. Cytokine array analysis showed chemokines such as MCP-1, CXCL1 and macrophage marker CD68 were significantly increased; Western blot analysis showed cleaved caspase-3 levels were significantly increased, indicating enhanced apoptosis. Additionally, fibrosis markers TIMP1, collagen-3, and α-SMA were significantly upregulated at both mRNA and protein levels. We further observed increased hypoxia marker HIF-1α in MD-NOS1 KO mice. Global label-free proteomic profiling with targeted validation identified genotype-dependent responses involving haptoglobin, Tacstd2, and Cyp20a1, linking NOS1 deficiency to exaggerated inflammatory, fibrotic, and metabolic pathways. Conclusions: These findings highlight a novel role of NOS1 in AKI pathophysiology and suggest targeting NOS1 could be a therapeutic strategy to mitigate AKI severity, identified Hp as a downstream plasma signal of NOS1-dependent AKI responses, suggesting potential translational value pending human validation. 

The raw data of research article "ACSL3 is a promising therapeutic target for alleviating anxiety and depression in Alzheimer's disease"

The NSF 3D Wetlands project produced 2-meter spatial resolution maps of elevation and habitat cover for all states bordering the Gulf of (Mexico||America). A Digital Elevation Model (DEM) was generated from a compilation of LiDAR datasets. Habitat cover was generated using a machine learning model run on MAXA Worldview 2 and 3 multiband imagery.

This dataset contains the data that corresponds to the information included in 'Ampicillin- and Multidrug-Resistant Escherichia coli and Enterococcus spp. in Costa Rican Wastewater and Surface Water' (in review). The data include measurements of concentrations of total and ampicillin-resistant fecal indicator bacteria (E. coli and enterococci) from four sites in and near a wastewater treatment plant in Puntarenas, Costa Rica. The four sites at which samples were collected were the influent from hospital wastewater, influent from residential wastewater, treated wastewater effluent, and the estuary in which treated effluent is discharged. Frequency of ampicillin resistance is recorded, and ampicillin-resistant isolates were confirmed to species or genus before further testing. Multidrug resistance testing was conducted via the Kirby Bauer assay, and the zones of inhibition for each isolate, as well as the interpretation (resistant, intermediate, and sensitive), are provided. Samples were collected from each site during four sampling events from October to November 2019.

This archive contains four data files providing different calculations of steric (SSL), thermometric (SSL), and ocean heat content (OHC) anomalies from 2003 through 2023: 1) a text file of SSL, TSL, and OHC anomalies for individual Argo float profiles at specific longitude, latitude, and times. 2) a netCDF file of monthly longwave SSL maps (1° resolution) computed by combining Argo floats with satellite altimetry and gravimetry (along with standard errors) 3) a netCDF file of monthly longwave TSL maps (1° resolution) computed by combining Argo floats with satellite altimetry and gravimetry (along with standard errors). 4) a netCDF file of monthly longwave OHC maps (1° resolution) computed by combining Argo floats with satellite altimetry and gravimetry (along with standard errors).

This dataset contains the training and validation data used to develop and evaluate a ResUNet deep learning (DL) segmentation model for detecting floating algae from MODIS/Aqua imagery at the global scale. The model was trained using inputs including MODIS Rayleigh corrected reflectance (Rrc) in 7 spectral bands and the Alternative Floating Algae Index (AFAI), and is capable of identifying both microalgae (phytoplankton) scums and macroalgae (seaweed) mats. These include Trichodesmium, Noctiluca, Dinoflagellates, Cyanobacteria, Sargassum, and Ulva.

This is a detailed treatment protocol for PATS: Program for Advanced Treatment of Stuttering (copyright: Nathan Maxfield).

This dataset provides monthly mean Sargassum wet biomass estimates (in million metric tons) from March 2000 to February 2024, derived from MODIS observations using the methodology described in Hu et al. (2023). The data cover several subregions, including the North Sargasso Sea (NSS), South Sargasso Sea (SSS), Gulf Stream Region (GSR), Antilles Current Region (ACR), Great Atlantic Sargassum Belt (GASB), and the Northwestern Gulf of Mexico (NW_GoM). The locations of these subregions are provided in the attached READ ME file. Briefly, each image pixel is classified into one of the three types using a deep-learning computer model: Sargassum containing, Sargassum free, and invalid observation (clouds, cloud shadows, strong sun glint, etc). Then the Sargassum containing pixels are further spectrally unmixed to determine the subpixel percent (%) cover within each pixel. For a pre-gridded Sargassum map, the mean Sargassum percent cover in each 0.5-degree grid is calculated as the arithmetic average of all image pixels (both Sargassum containing and Sargassum free) in that grid collected by the satellite during that calendar month. Such a mean percent cover is converted to Sargassum wet biomass using a calibration constant of 3.34 kg per square meter of Sargassum determined from field measurements (Wang et al., 2018). Integration of the grid-specific biomass across all grids within a subregion leads to the mean Sargassum biomass for that subregion. These steps were applied to MODIS/Terra (2000 – 2024) and MODIS/Aqua (2002 – 2024) separately, with the final maps being the arithmetic average between the two. In the above steps, all MODIS data were downloaded from NASA OB.DAAC (https://oceancolor.gsfc.nasa.gov) and processed using the NASA software SeaDAS (version 8.0). The deep-learning model and spectral unmixing model as well as the method to calculate monthly means were all developed at the Optical Oceanography Lab of the University of South Florida using computer codes developed in house. The daily and weekly Sargassum maps have been made available through their Sargassum Watch System (SaWS) website: https://optics.marine.usf.edu/projects/saws.html. Hu, C., Zhang, S., Barnes, B.B., Xie, Y., Wang, M., Cannizzaro, J.P., & English, D.C. (2023). Mapping and quantifying pelagic Sargassum in the Atlantic Ocean using multi‐band medium‐resolution satellite data and deep learning. Remote Sensing of Environment, 289, 113515. https://doi.org/10.1016/j.rse.2023.113515. Wang, M., Hu, C., Cannizzaro, J., English, D., Han, X., Naar, D., Lapointe, B., Brewton, R. and Hernandez, F. (2018). Remote sensing of Sargassum biomass, nutrients, and pigments. Geophysical Research Letters, 45(22), pp.12-359. https://doi.org/10.1029/2018GL078858.