University of South Florida Showcase

Supplementary Videos

Peak moments for 8 cadaveric specimens. Each specimen was tested in 3 planes (flex/ext, lateral flexion, rotation) and under 5 compression loads (0, 100, 250, 400, 500 N). There were 3 total conditions (Native, Vertiwedge full configuration, and Vertiwedge screw only configuration).

This file includes the microbial community composition, community change, and predicted function for the paper titled "Simultaneous N and P removal in sequencing batch biofilm reactors (SBBRs) with sphalerite-oyster shell media."

This data supports the findings in the manuscript titled "Predicting pH at in-situ temperature for aquatic environments". Experiments were performed to analyze how the pH of seawater changes with changing temperature conditions (∆pH/∆T) across a range of salinities. Data was collected by measuring pH spectrophotometrically with simultaneous temperature measurements via a mini surface temperature probe. The total alkalinity (AT) of the seawater sample and salinity were measured for each sample to fully define the marine CO2 system. ∆pH/∆T was determined for a pH (at 25°C) range of 7.2–8.2, a temperature range of 15–40°C and salinities 10.1, 20.2, 30.2, and 36.1.

RWLC-7 Metal Organic Frameowrk Photophysical Data

This dataset contains Fe stable isotope ratios and trace element concentrations of aerosols and seawater collected during Japan GEOTRACES cruise GP02 in 2017 in the subarctic North Pacific in order to clarify the Fe source of aerosols and surface seawater using Fe isotope ratios. The bulk and six size-fractionated aerosols dataset consist of trace element concentrations, some major ion concentrations, enrichment factor, fractional Fe solubility, Fe species, and Fe stable isotope ratios. Seawater data consist of hydrological parameters (temperature, salinity, chlorophyll-a concentration, nitrate+nitrite concentration), dissolved Fe concentration, and dissolved Fe isotope ratios. Aerosol trace element concentrations were measured using ICP-MS (NexION350s, Perkin Elmer) after mixed acid digestion. Fe species were determined by Fe K-edge X-ray absorption near-edge structure (XANES) analysis. Fe isotope ratios were determined by isotope dilution MC-ICP-MS (Neptune plus, Thermo Fisher Scientific) after trace element preconcentration and Fe separation with an anion exchange resin. The dataset contains several data tabs: bulk aerosols, size-fractionated aerosols, size-fractionated aerosol XANES data, seawater data, and other data shown in tables and figures, including accuracy of the double spike analysis, XANES spectra, reference material data, aerosol filter blank, seawater Fe isotope data, and correlation coefficients. The main tabs contain the following information: (1) Bulk aerosols: The data on total suspended aerosol particles (not size-fractionated), including trace element concentrations, sodium and sulfate concentrations, enrichment factors, water-soluble Fe concentrations, Fe stable isotope ratios, contribution of combustion Fe, . (2) Size-fractionated aerosols: The data of six size-fractionated aerosol particles, including trace element concentrations, water-soluble Al and Fe concentrations, enrichment factors, and Fe stable isotope ratios. (3) Aerosol Fe species: The data of six-size fractionated aerosol particles. The relative abundance of different Fe species is shown. (4) Seawater data: The data include salinity, temperature, chlorophyll a, nitrate+nitrite, dissolved Fe concentration, and Fe stable isotope ratios of dissolved Fe.

This dataset contains measurements of fecal indicator bacteria (Escherichia coli and enterococci), microbial source tracking markers (HF183 and GFD), nutrients, and environmental parameters from two freshwater Florida streams. Water and sediment was collected over a 26-month period. Fecal indicator bacteria were cultured from water and sediment, microbial source tracking markers were assessed in water by qPCR, and nutrients were measured in water and sediment.

Data and code used in the publication in the journal "Med" titled: "Closed-Loop AUtomated Drug Infusion regulAtor: a clinically translatable, closed-loop drug delivery system" (doi.org/10.1016/j.medj.2024.03.020). The code includes: SIMULINK model of the system used in controller development (adapted from Mage, P., Ferguson, B., Maliniak, D. et al. Closed-loop control of circulating drug levels in live animals. Nat Biomed Eng 1, 0070 (2017). https://doi.org/10.1038/s41551-017-0070), Python code to implement the controller, Python code for data analysis and figure creation, and the TreeAge software used for the cost effectiveness analysis. We also upload the datafiles for the data used to create the figures in the manuscript.

Data collected from on-line survey of Haitian American participants reviewing ad concepts created to promote COVID-19 vaccination as well as student ratings of ads using cultural resonance checklist.

The dataset presented in this repository was created using de-identified historical surgical data (2012-2021) from 10 regional health services in Chile. To request access to the historical information, follow this link: https://www.portaltransparencia.cl/PortalPdT/ Data: The dataset below covers the future predicted surgical demand (2022-2031) obtained through 100 ARIMA models considering the following specialties in each of the ten regions: Cardiology, head and neck, ear nose and throat, plastic surgery, gastroenterology, gynecology and obstetrics, neurology and neurosurgery, ophthalmology, traumatology, and urology and nephrology. Furthermore, three different demand scenarios are provided per region, specialty, and year. The scenarios are low, medium, and high demand, considering a 95% prediction interval. The attached data table (8x1001) has the following structure: Column 1 presents a numerical regional health service ID. Column 2 presents a numerical specialty ID. Column 3 presents the year of prediction. Column 4 presents the predicted medium demand scenario. Column 5 presents the predicted low-demand scenario. Column 6 presents the predicted high-demand scenario. Column 7 presents the regional health service name. Column 8 presents the specialty name. Feel free to contact us if you have any questions about the dataset.