The COVID-19 pandemic disrupted scientific research, teaching, and learning in higher education and forced many institutions to explore new modalities in response to the abrupt shift to remote learning. Accordingly, many colleges and universities struggled to provide the training, technology, and best practices to support faculty and students, especially those at historically disadvantaged and underrepresented institutions. In this study we investigate different remote learning modalities to improve and enhance research education training for faculty and students. We specifically focus on Responsible and Ethical Conduct of Research (RECR) and Research Mentoring content to help address the newly established requirements of the National Science Foundation for investigators. To address this need we conducted a workshop to determine the effectiveness of three common research education modalities: Live Lecture, Podcast, and Reading. The Live Lecture sessions provided the most evidence of learning based on the comparison between pre- and post-test results, whereas the Podcast format was well received but produced a slight (and non-significant) decline in scores between the pre- and post-tests. The Reading format showed no significant improvement in learning. The results of our workshop illuminate the effectiveness and obstacles associated with various remote learning modalities, enabling us to pinpoint areas that require additional refinement and effort, including the addition of interactive media in Reading materials.
Abstract from Paper (Lange et. al, 2022): Atypical atrial flutter is seen post-ablation in patients, and it can be challenging to map. These flutters are typically set up around areas of scar in the left atrium. MRI can reliably identify left atrial scar. We propose a personalized computational model using patient specific scar information, to generate a monodomain model. In the model conductivities are adjusted for different tissue regions and flutter was induced with a premature pacing protocol. The model was tested prospectively in patients undergoing atypical flutter ablation. The simulation-predicted flutters were visualized and presented to clinicians. Validation of the computational model was motivated by recording from electroanatomical mapping. These personalized models successfully predicted clinically observed atypical flutter circuits and at times even better than invasive maps leading to flutter termination at isthmus sites predicted by the model.
The objective of using the wireless sensors was to improve understanding of the heterogeneity of healthcare worker (HCW) contact with patients and the physical environment in patients’ rooms. The framework and design were based on contact networks with a) nodes defined by HCW’s, rooms, and items in the room and b) edges defined by HCW’s in the room, near the bed, and touching items. Nodes had characteristics of HCW role and room number. Edges had characteristics of day, start time, and duration. Thus, patterns and heterogeneity could be understood within contexts of time, space, roles, and patient characteristics. At the University of Utah Hospital Cardiovascular ICU (CVICU), a 20-bed unit, we collected data for 54 days. HCW contact with patients was measured using wireless sensors to capture time spent in patient rooms as well as time spent near the patient bed. HCW contact with the physical environment was measured using wireless sensors on the following items in patient rooms: door, sink, toilet, over-bed table, keyboard, vital signs monitor touchscreen, and cart. HCW’s clipped a sensor to their clothing or lanyard.
The data was obtained from the FDTD simulations. For one of the FDTD simulations, the conductivity data for British Columbia was used in order to obtain the simulated data. The data obtained from simulations are post-processed using MATLAB for plotting the figures in the paper.
We determined whether a large, multi-analyte panel of circulating biomarkers can improve detection of early-stage pancreatic ductal adenocarcinoma (PDAC). We defined a biologically relevant subspace of blood analytes based on previous identification in premalignant lesions or early-stage PDAC and evaluated each in pilot studies. The 31 analytes that met minimum diagnostic accuracy were measured in serum of 837 subjects (461 healthy, 194 benign pancreatic disease, 182 early stage PDAC). We used machine learning to develop classification algorithms using the relationship between subjects based on their changes across the predictors. Model performance was subsequently evaluated in an independent validation data set from 186 additional subjects.
This is the IDL code used to create the results published in Mace, G. G., Benson, S., Humphries, R., Gombert P. M., Sterner, E.: Natural marine cloud brightening in the Southern Ocean, Atmospheric Chemistry and Physics. The IDL code processes MOD03 geolocation fields, MOD06_L2 cloud retrievals, MODIS ocean color chlorophyll-a concentrations and CERES shortwave albedo data that is distributed by NASA data archives. It creates statistical results for non-precipitating or weakly precipitating warm, liquid, shallow, marine boundary layer clouds.
The data are bed-scale measurements taken from virtual outcrop models (Morris, E.A., Atlas, C.E., Johnson, C.L., 2023, Architectural analysis of the Panther Tongue - virtual outcrop models) and calibrated with measurements taken at outcrop in the field.
Abstract: Data for Performance evaluation of the Alphasense OPC-N3 and Plantower PMS5003 sensor in measuring dust events in the Salt Lake Valley, Utah
This data file was used to estimate the performance of the Alphasense OPC-N3 and PMS5003 sensor in measuring ambient PM10, especially during dust events, and to obtain correction factors to correct the PMS5003 data. During April 2022, the OPC-N3 and PMS5003 sensors were collocated with federal equivalent method (FEM)at two Utah Division of Air Quality (UDAQ) sites: Hawthorne (HW) station and Environmental Quality (EQ) station. One residential site (RS)was also tested, with OPC-N3 and PMS5003 collocated with GRIMM portable aerosol spectrophotometer. The FEM data (PM2.5 and PM10 concentrations) and meteorological parameters (wind speed, wind direction, relative humidity, and temperature) for the two UDAQ sites were downloaded from the EPA website. The Excel sheet contained all the raw data and the processed data. The FEM, OPC-N3, and PMS5003 measurements were labeled as FEM-YYY, OPC-YYY, and PMS-YYY, where YYY represents the sites nomenclature, i.e., HW, EQ, and RS. The sheet labeled “HW”, “RS”, and,” EQ” contained the raw measurements (meteorological, PM10, and PM2.5 (whenever applicable)) for the sites. The sheet” PM-ratio-based correlation” provided the data used to get the PM-ratio-based correlation. Briefly, based on the ratio of FEM-HW PM2.5/PM10, the FEM-HW and PMS-HW PM10 measurements were segregated into six bins: PM2.5/PM10: <0.2, 0.2-0.3, 0.3-0.4, 0.4-0.5, 0.5-0.7, and >0.7. For each bin, the co-located PMS-HW PM10 concentrations were linearly regressed against the FEM-HW PM10 concentrations to obtain correction factors (slope and intercept). These correction factors were later used to correct the PMS PM10 concentrations at the other two locations (RS and EQ), presented in the sheets with labels “RS correction using GRIMM ratio”, “RS correction using opc ratio” and “EQ corrected using EQ ratio”. Each sheet also includes the calculation of RMSE and NRMSE of OPC-YYY and PMS-YYY against FEM-YYY, with YYY as the site nomenclature.