Marine Diel Vertical Migration (DVM), perhaps the largest movement of animals on Earth, is composed of mesopelagic species migrating vertically every night to feed in epipelagic depths and return to deeper water during the day. The objective of this study infers potential taxonomic identity of organisms in the DVM via their association with bacterioplankton signatures.   High   throughput   sequencing   of   the   16S   rRNA   V4   region   and   the use of bioinformatics and statistics provided evidence to which bacteria appeared associated with either upward or downward vertical migration during two cycles.

Seawater samples were collected during a DEEPEND (Deependconsortium.org) research cruise in May 2017 using a niskin bottle array from 0-326 meters depth. Real-time acoustic echosounder data was used to direct sample collection in order to capture seawater samples above, during, and below the DVM. DNA was extracted from the water samples and sequenced. Data analysis was performed using R Studio software.

Our results confirm the vertical movement of bacterial taxa throughout the pelagic depths. The most abundant bacteria present during the Vertical Migration were of Genera Marinobacter, Alteromonas, Prochlorococcus, and class Gammaproteobacteria. These taxa occurred at depth of 320 meters which is the mesopelagic zone. Proteobacteria was only found during the vertical migration at mesopelagic depths, whereas Cyanobacteria was only found during the vertical migration at epipelagic depths. This indicates that these two phyla of bacteria are distinct to their respective zones.

Cancer describes a category of diseases which involve the uncontrolled growth of abnormal cells and the spread or metastasis of those cells to other sites in the body. Natural products derived from plants have been shown to be valuable sources for anticancer drug discovery. The long term goal of this project is to isolate potential anticancer compounds from medicinal plants through bioassay guided fractionation. Towards this purpose, we commenced our research by performing cytotoxicity assays on chemical extracts obtained from plants with medicinal properties or health benefits. The plants included in this study are commonly known as muscadine, scarlet bush, Brazilian pepper, anamu, moringa, guanabana, oyster plant, and Okinawa spinach. Plant extracts, prepared with aqueous and/or organic solvents (including dimethyl sulfoxide, ethanol and hexane), were tested on MCF-7 breast cancer cells cultured in vitro. Methylthiazol tetrazolium (MTT) assays were used to quantify cytotoxicity. Preliminary data indicated the extracts were not cytotoxic at the concentrations tested. Indeed, extracts from each type of plant improved cell viability. These data provide valuable dosing information regarding extract concentrations for upcoming experiments. In the future, extracts will be tested on other human cancer cell lines, as well as in cell-invasion assays, which model metastatic processes.

The exposure of plants to drought conditions inhibits shoot growth, increases production of toxic reactive oxygen species (ROS), and negatively affects photosynthesis and carbohydrate metabolism. Drought susceptibility in potato impacts all stages of the crop, from emergence to tuber initiation. Although there have been various studies of the global changes in gene expression profiles during drought conditions in potatoes, few studies have been based on native potato species. Because of their high genetic diversity, native potatoes from the Andean regions of Peru, Ecuador and Bolivia, are well adapted to the harsh environmental conditions that prevail in the Andes, including drought. This makes them ideal candidates for gene expression studies associated with drought tolerance. The identification of drought tolerance traits and genes for potato will facilitate breeding for high yield stability under drought conditions. The first phase of this study consisted on a comparative RNA sequencing analysis between drought-tolerant and drought-susceptible native potato cultivars. During the second phase, a drought experiment with both tolerant and susceptible native potato species, using an aeroponics growth system was conducted. Selected drought-associated candidate genes from the RNA sequence analysis were used in primer design and quantitative RT-PCR analysis. Differential gene expression in tolerant vs. susceptible cultivars has been confirmed for two heat shock proteins and for a triacyl glycerol lipase. Additional candidate genes are being tested. In conclusion, native potatoes are providing important information on genes affected by drought conditions.

R, an open source statistical programming language, can be used to gather information from the social media platform Twitter, from which tweets are collected from various news sources, celebrities, political figures, and some official colleges accounts. Other information such as screen names, number of tweets, number of followers, list of friends, and locations can be collected using the twitteR package in combination with the Twitter application programming interface (Twitter API). After collecting this data, one can perform text mining by counting the word frequency in news sources' tweets, creating data visualizations to represent frequency of words, and conduct a sentiment analysis to understand and measure the impact of certain topics and opinions expressed in this social media venue. Spatial visualizations are also created in the form of interactive maps using the location data collected from different Twitter accounts. This project explores the various ways that Twitter can be used to gather information on certain topics and how this data could be used to help predict some of the behaviors and characteristics on how people communicate through this social media source, as well as how different topics are perceived by society.

Student success is a major focus in the educational system, where a variety of predictors are used to estimate and measure how well students do in their different classes at the end of the academic year. Our research project aims towards proposing a model capable of demonstrating how student success can be predicted based on a series of indicators gathered from work submitted by the student throughout the semester. We studied the student’s performance in the open-source online homework assignment system WeBWorK for a mathematics course, taking into account the final score in a given assignment, and the number of times every problem was tried by the student before obtaining a correct answer. Data from one Pre-Calculus and two Calculus I courses at St Thomas University was used to create a multinomial logistic regression model that takes into account the student’s scores in all assignments during a semester, as well as the student’s “success index” per assignment, a fairly good indicator of how well the student is grasping the concepts evaluated in every assignment.

Gentrification is a process involving the movement of a high-income group to working-class neighborhoods nearby amenities, and that usually results in the displacement of the original inhabitants. Changes like alteration of local services, increasing prices, and unaffordable housing are derived from the process, and the negative repercussions it might have in our community, grant great significance to its forecasting. Our goal for this project was to offer a comprehensive analysis of the main indicators of the gentrification process in Miami. The data that was collected and analyzed, along with the different patterns found for the indicators considered in this study, will help in the development of a forecasting model for gentrification of the neighborhoods in the City of Miami.

Magnetic Nanoparticles Fluid Hyperthermia is called to be a promising treatment method for cancer lesions, constituting an alternative pathway to other medical approaches, as   for example, chemotherapy and radiotherapy. The large surface area to volume ratio of nanoparticles makes them a suitable element to amplify the effect of external fields, in particular, the heat generated by alternating magnetic fields. Despite of these promising possibilities, a critical problem of hyperthermia is the direct control of the heat source and the distribution of nanoparticles in order to induce necrosis within cancerous cells with the minimum negative impact to the surrounding healthy cells. In the current project, the biomedical modeling of the process of hyperthermia is carried on for cancer cells of different geometries appealing to the modified Penne’s bioheat equation and the Finite Element Method (FEM). Special attention was paid to the size and spatial distribution of nanoparticles. The results from numerical solutions have permitted to establish guidance towards optimal conditions for its use. Computations were performed either in Wolfram Mathematica and/or Octave/MATLAB.

Urban meteorology and biometeorology have become very important fields nowadays due to the high rate of urbanization worldwide. The load created within mega-urban centers is leading to the proliferation of many respiratory problems as well as a deterioration of green areas. Motivated by this situation this project is aimed at assessing the microclimatic conditions of St. Thomas University forest and evaluates the impact of canopy on the distribution on weather parameters within and around green areas as well as the extent of dispersal of pollutants from the Palmetto Expressway as a result of automobile exhausts. Observations from both, in campus Automated Weather Station (AWS) running in partnership with Earth-Network (Weatherbug) and mobile sensors (Xplorer from PASCO) are put together and mapped based on GIS information for points of measurements. The statistical analysis was done through the software R-Studio and packages for data visualization. As a result, a full characterization of soil and atmospheric conditions within the forest was done.

Epilepsy is a Central Nervous Disorder that is affecting millions of people with a different degree of severity. Depending on the level the patient is at the moment, seizures can be controlled either with medications or with surgical procedures. However, during surgical procedures many complications might show up, mainly due to the lack of knowledge of functional neuronal networks operating behind epileptic seizures. Therefore, a reliable methodology capable to predict in advance the beginning of seizures would have a tremendous impact on the quality of life of these patients and might prevent further complications with the management of this condition. In this end, EEG provides a reliable method to detect the seizures with very good temporal resolution. Besides that, advances in wearable technologies had lead to the creation of the first prototypes of portable EEG sensors coupled to smart – phones and the further connection to servers. Thus, the signal processing in situ is becoming a must. This project is aimed at studying the feasibility to use the software R for statistical analysis of EEG signals in order to perform statistical forecast of epileptic seizures by constructing functional networks based on the cross-correlation of time series from different electrodes. Such functional associations are a result of an emergent neuronal activity of a large amount of neurons, thus, they will be guidance to physicians. A further understanding of the causes will require a combination of biomedical modeling and sensing with fMRI and EEG combined.

In an effort to understand conditions triggering asthma episodes and therefore create a asthma risk index that might be valuable to both patients and medical practitioners, 6 different counties in Florida were chosen, 3 of them in the southeast region and 3 located in the central region. The number of cases at emergency rooms due to asthma and other respiratory conditions were provided by the Department of Health BRACE project and analyzed statistically looking for potential associations with weather and environmental conditions. Weather information was obtained from airports through Wolfram Language data mining interface, and environmental parameters (Ozone level and particulate matter) from the Environmental Protection Agency measuring stations. Correlation analysis was performed with both, linear and logistic models and using software RStudio. Additionally, ARIMA forecasting modeling was implemented within RStudio to predict future events based on previous records and compared with a Machine Learning approach.