University of South Florida Showcase

Date (local and GMT), Time (local and GMT), Latitude (decimal degrees N), Longitude (decimal degrees W), Station, Depth of images (m), Volume filtered (m^3), Marine snow particle size (area mm2), Abundance (count/m^3). Files names are CruiseDate (Month/Year)_Station: WB0810_DSH08. SIPPER used a high speed Dalsa Piranha-2 line-scan camera and a pseudo-collimated LED generated light sheet to image the shadows and outlines of resolvable particles that passed through a 100 cm2 field of view. The operational optical resolution of the system is ~65 um. SIPPER was towed at speeds between 2-3 knots in an oblique profile through the water column, spending approximately equal amounts of time at each meter of depth between the surface and 300 m. At stations with a bottom depth shallower than 300 m, SIPPER was towed within approximately 5 m from the seafloor. Imaging and environmental data were stored internally on a Firewire hard drive and processed upon retrieval of the SIPPER instrument from a deployment using a customized software package called the Plankton Image Classification and Extraction Software (PICES). PICES was used to extract images of interest, classify them using user-specified training libraries and to manage the SIPPER images and environmental data collected. The particle size spectra show the size dependence of particles and follows the methods described in Jackson & Burd (1998) and Jackson & Checkley (2011). A normalized particle volume spectrum (nVd) was calculated, where n is particle abundance, V is particle volume, and d is the median particle diameter within each size bin. Pixel width of images was fixed, while pixel length took into account the flow rate and scan rate. The pixel area of each image was determined, and the equivalent spherical diameter (ESD) and volume (V) were calculated based on the optical cross-section. The ESD is defined as the diameter of a circle having the same area as the projected image of the particle (Billiones et al., 1999). Particle diameter (d) size bins ranged from 0.100 to 105.12 mm, where each bin size increased in a geometric progression by about 6% (bin size scale, k = 21/12) over the previous one, since the number of large particles is rare compared to the abundance of smaller sized particles. Normalizing the abundance data (n) to volume (V) gives more weight to larger sized particles. SIPPER Environmental Sensors. Environmental data were collected simultaneously with the SIPPER imaging system during each deployment. Sensors included a Seabird 19Plus CTD, Seabird SBE43 oxygen sensor, and WET Labs FLNTURTD chlorophyll fluorescence and turbidity, and a transmissometer. AWET Labs CDOM sensor also was used on a few cruises. Sensors were calibrated at Seabird and WET Labs and then integrated into the SIPPER towed platform.

Marine snow images were collected using the towed SIPPER v.3 camera system, which was developed by the College of Marine Science, University of South Florida, to assess the abundance and distribution of particles, phytoplankton, zooplankton, and larval fish (Remsen et al., 2004). Particles were sampled through a square 9.6 cm sampling tube, having a 92.2 cm2 mouth area, which extended in front of the towed platform. As particles passed down the tube they were imaged by a Dalsa Piranha-2 line-scan camera, which imaged 36,000 lines per second and used a pseudo-collimated LED generated light sheet to image the shadows and outlines of resolvable particles. The Plankton Imaging Classification Extraction Software (PICES) was used to extract and classify SIPPER images using a Support Vector Machines (SVM) approach, and to manage the images and environmental data collected (Kramer, 2010). A total of 117,058,275 marine snow images were validated for this project. All particles > 0.053 mm2 (area of 100 pixels) were extracted. This dataset provides measures of marine snow particle shape (elongation) and surface roughness (fractal dimension) during and after the Deepwater Horizon oil spill between May 2010 and August 2014. Elongation ratios (ER) were determined as ER = 1 - aspect ratio, where the aspect ratio is the minor axis length of the particle over the major axis length. Fractal dimension were used to assess the surface roughness or boundary irregularity of particles and were computed using a variation of the box counting method in which the grid comprised rectangular boxes maintaining the same aspect ratio as the particle (Kilps et al. 1994, Brown 1995). File names are: NormalizedDataReport_Cruise name_Station name. Both elongation ratios and fractal dimensions are ratios and therefore do not have units. Depth is in meters; Volume sampled is in m^3; ESDbinstart is the Equivalent Spherical Diameter (ESD) of the particle at the beginning of a size range; ESDbinend is the end of the size range; Particle size is in millimeters; TotalNumberOfParticlesAtDepthESD is the total number of particles in the size range at the listed depth (no units); Elongation is the elongation ratio (no units) of particles in the size range an the listed depth; FractalDim is the fractal dimension of the particles (no units); Abundance is the number of particles in the ESD size range at the listed depth having the listed elongation ratio and fractal dimension.

Marine snow images were collected using the towed SIPPER v.3 camera system, which was developed by the College of Marine Science, University of South Florida, to assess the abundance and distribution of particles, phytoplankton, zooplankton, and larval fish (Remsen et al., 2004). Particles were sampled through a square 9.6 cm sampling tube, having a 92.2 cm2 mouth area, which extended in front of the towed platform. As particles passed down the tube they were imaged by a Dalsa Piranha-2 line-scan camera, which imaged 36,000 lines per second and used a pseudo-collimated LED generated light sheet to image the shadows and outlines of resolvable particles. The Plankton Imaging Classification Extraction Software (PICES) was used to extract and classify SIPPER images using a Support Vector Machines (SVM) approach, and to manage the images and environmental data collected (Kramer, 2010). A total of 117,058,275 marine snow images were validated for this project. All particles > 0.053 mm2 (area of 100 pixels) were extracted. This dataset provides measures of marine snow particle shape (elongation). Elongation ratios (ER) were determined as ER = 1 - aspect ratio, where the aspect ratio is the minor axis length of the particle over the major axis length. Elongation ratios do not have units. File names are: NormalizedDataReport_Cruise name_station name. Depth is in meters; Volume sampled is in m^3; ESDbinstart is Equivalent Spherical Diameter (ESD) beginning of a size range; ESDbinend is the end of the size range; Particle size is in millimeters; TotalNumberOfParticlesAtDepthESD is the total number of particles in the size range at the listed depth (no units); Elongation is the elongation ratio (no units); Abundance is the number of particles in the ESD size range at listed depth having the listed elongation ratio.

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.

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 at specific locations on land will impact water quality. LANLoad is based solely on physical characteristics and may be used independently or with other relevant datasets. LANLoad NEEPP is available as a single comprehensive file "LANLoad_NEEPP_Overall" and as subsets corresponding to intersections between NEEPP and 15 FL counties. The datasets consist of cells (10m x 10m) ranked to reflect the likelihood that nutrients applied to a given terrestrial location will reach a downgradient surface waterbody. Possible ranks range from 1 to 9 with values increasing as the likelihood of nutrient transport to downgradient surface waterbodies increases. Ranks are based on 6 physical landscape parameters selected by Subject Matter Experts (SMEs) who also assigned relative weights to each parameter using the Analytical Hierarchy Process (AHP). During this exercise, the location considered by SMEs was the pilot study area, St Lucie County, FL, and the focal nutrient source was Onsite Sewage and Treatment Disposal Systems (OSTDS). Despite the original focus on OSTDS, LANLoad NEEPP can be used to gauge the likelihood of nutrient transport to surface waterbodies from other, similar, nutrient sources. 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 (2024) and combined using a weighted overlay to produce LANLoad NEEPP. Details are available in a report (link below) and publication (in prep as of Jan 2026) LANLoad NEEPP performance was evaluated at multiple locations (selected via a random stratified process) within NEEPP 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

Studies comparing immunogenicity of biosimilars and their reference products that are used for the treatment of plaque psoriasis. (*) indicates when a study reported EU-reference product. ADA percentages indicate the number of patients who developed anti-drug antibodies. NAb percentages indicate the percentage of total patients who developed neutralizing antibodies.

Station locations for SIPPER camera deployments in the NE Gulf of Mexico as part of an investigation into the impact of the Deepwater Horizon Oil Spill on the Lower Trophic Ecosystem.

Applications developed to determine elongation ratios and fractal dimensions of marine snow particles. App and file upload and plotting instructions using MatLab are included.

Table of cruise names, dates, and station names for the SIPPER camera imaging system collections of marine snow during and after the Deepwater Horizon oil spill, May 2010-August 2014, in the NE Gulf of Mexico.

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.