Background. Common cold viruses create significant health and financial burdens, and understanding key loci of transmission would help focus control strategies. This study (1) examines factors that influence when individuals transition from a negative to positive test (acquisition) or a positive to negative test (loss) of rhinovirus (HRV) and other respiratory tract viruses in 26 households followed weekly for one year, (2) investigates evidence for intrahousehold and interhousehold transmission and the characteristics of individuals implicated in transmission, and (3) builds data-based simulation models to identify factors that most strongly affect patterns of prevalence. Methods. We detected HRV, coronavirus, paramyxovirus, influenza and bocavirus with the FilmArray polymerase chain reaction (PCR) platform (BioFire Diagnostics, LLC). We used logistic regression to find covariates affecting acquisition or loss of HRV including demographic characteristics of individuals, their household, their current infection status, and prevalence within their household and across the population. We apply generalized linear mixed models to test robustness of results. Results. Acquisition of HRV was less probable in older individuals and those infected with a coronavirus, and higher with a higher proportion of other household members infected. Loss of HRV is reduced with a higher proportion of other household members infected. Within households, only children and symptomatic individuals show evidence for transmission, while between households only a higher number of infected older children (ages 5-19) increases the probability of acquisition. Coronaviruses, paramyxoviruses and bocavirus also show evidence of intrahousehold transmission. Simulations show that age-dependent susceptibility and transmission have the largest effects on mean HRV prevalence. Conclusions. Children are most likely to acquire and most likely to transmit HRV both within and between households, with infectiousness concentrated in symptomatic children. Simulations predict that the spread of HRV and other respiratory tract viruses can be reduced but not eliminated by practices within the home.
Background: The objective of this study was to evaluate the effect of utilising larger lens cubes on phacoemulsification efficiency and chatter using 3 tips of different sizes and 2 ultrasound (US) approaches.
Methods: This was an in vitro laboratory study conducted at the John A. Moran Eye Center Laboratory, University of Utah, Salt Lake City, UT, USA. Porcine lens nuclei were formalin-soaked for 2 hours, then divided into either 2.0 mm or 3.0 mm cubes. 30 degree bent 19 G, 20 G, and 21 G tips were used with a continuous torsional US system; and straight 19 G, 20 G, and 21 G tips were used with a micropulse longitudinal US system. Efficiency and chatter were determined.
Results: Mean phacoemulsification removal time was higher with the 3.0 mm lens cube for all US variations and tip sizes. There were statistically significant differences between the 19 G and 21 G tips with micropulse longitudinal US using the 2.0 mm lens cube and the 3.0 mm lens cube, as well as with continuous transversal US using the 2.0 mm lens cube and the 3.0 mm lens cube. There was no significant difference between 19 G and 20 G tips with either lens cube size in either US approach. However, using both US approaches, trends were identical for both lens cube sizes in which the 19 G tips performed better than the 20 G and 21 G tips.
Conclusion: Regardless of lens size, the 19 G needle was the most efficient, with the fewest outliers and smallest standard deviations.
Light-scattering spectroscopy (LSS) is an established optical approach for nondestructive characterization of biological tissues. Here, we investigated the capabilities of LSS and convolutional neural networks (CNNs) to quantitatively characterize the composition and arrangement of cardiac tissues. We assembled tissue constructs from 200 μm thick sections of fixed myocardium and aortic wall. Thickness of the tissue constructs was similar to the thickness of atrial free wall. In the assembled constructs, the aortic sections represented fibrotic tissue and the depth, volume fraction, and arrangement of these fibrotic insets were varied. We gathered spectra with wavelengths from 500-1100 nm from the constructs at multiple locations relative to a light source. We used single and combinations of two spectra for training of CNNs. With independently measured spectra, we assessed the accuracy of the trained CNNs for classification of tissue constructs from single spectra and combined spectra. In general, classification accuracy with single spectra was smaller than with combined spectra. Combined spectra including spectra from fibers distal from the illumination fiber typically yielded a higher accuracy than proximal single collection fibers. Maximal classification accuracy of depth detection, volume fraction and permutated arrangements was (mean±stddev) 88.97±2.49%, 76.33±1.51% and 84.25±1.88%, respectively. Our studies demonstrate the reliability of quantitative characterization of tissue composition and arrangements using a combination of LSS and CNNs. Potential clinical applications of the developed approach include intraoperative quantification and mapping of atrial fibrosis as well as assessment of ablation lesions.
The data set includes individual images of mouse cochleae, both scanning electron micrographs and fluorescent micrographs, used to generate aggregated data described in Pecha PP, Almishaal AA, Mathur PD, et al. Role of Free Radical Formation in Murine Cytomegalovirus–Induced Hearing Loss. Otolaryngology–Head and Neck Surgery. 2020;162(5):709-717. doi:10.1177/0194599820901485 and Objectives
The goal of the study was to determine whether reactive oxygen species (ROS) mediates cytomegalovirus (CMV)–induced labyrinthitis.
Study Design
Murine model of CMV infection.
Subjects and Methods
Nrf2 knockout mice were inoculated with murine CMV. Auditory brainstem response (ABR) and distortion product otoacoustic emissions (DPOAEs) were then performed on these and uninfected controls. BALB/c mice were inoculated with murine CMV to determine whether a marker for ROS production, dihydroethidium (DHE), is expressed 7 days after inoculation. Finally, 2 antioxidants—D-methionine and ACE-Mg (vitamins A, C, and E with magnesium)—were administered 1 hour before and after infection in inoculated mice for 14 days. Temporal bones were harvested at postnatal day 10 for DHE detection. ABR and DPOAE testing was done at postnatal day 30. Scanning electron microscopy was also performed at postnatal day 30 to evaluate outer hair cell integrity.
Results
Nrf2-infected mice had worse hearing than uninfected mice (P < .001). A statistically significant increase in DHE fluorescence was detected in BALB/c-infected mice as compared with uninfected mice 7 days after inoculation. D-methionine- and ACE-Mg-treated mice demonstrated an attenuation of the DHE fluorescence and a significant improvement in ABR and DPOAE thresholds when compared with untreated infected controls (P < .0001). Scanning electron microscopy demonstrated less outer hair cell loss in the treated versus untreated infected controls.
Conclusion
These results demonstrate for the first time that excessive ROS mediates CMV-induced hearing loss in a mouse model.
We apply Bayesian inference to instrument calibration and experimental-data uncertainty analysis for the specific application of measuring radiative intensity with a narrow-angle radiometer. We develop a physics-based instrument model that describes temporally varying radiative intensity, the indirectly measured quantity of interest, as a function of scenario and model parameters. We identify a set of five uncertain parameters, find their probability distributions (the posterior or inverse problem) given the calibration data by applying Bayes’ Theorem, and employ a local linearization to marginalize the nuisance parameters resulting from errors-in-variables. We then apply the instrument model to a new scenario that is the intended use of the instrument, a 1.5 MW coal-fired furnace. Unlike standard error propagation, this Bayesian method infers values for the five uncertain parameters by sampling from the posterior distribution and then computing the intensity with quantifiable uncertainty at the point of a new, in-situ furnace measurement (the posterior predictive or forward problem). Given the instrument-model context of this analysis, the propagated uncertainty provides a significant proportion of the measurement error for each in-situ furnace measurement. With this approach, we produce uncertainties at each temporal measurement of the radiative intensity in the furnace, successfully identifying temporal variations that were otherwise indistinguishable from measurement uncertainty.
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 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.
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.
The dataset contains Gas Chromatography (GC) data pertaining to the bulk electrolytic experiments, biocatalytic, organocatalytic reactions, and standards used in the study. The standard GC files calibrate the sensitivity of the column in the Gas Chromatograph to 1-heptanol, heptanal, and the corresponding alpha-hydrazino aldehyde. This information is used to quantify the peaks of 1-heptanol and heptanal obtained in the bulk electrolytic experiments and the alpha-hydrazino aldehyde obtained in the organocatalytic step.
The objective of this study was to determine the influence of face shields on the concentration of respirable aerosols in the breathing zone of the wearer. The experimental approach involved the generation of poly-dispersed respirable test dust aerosol in a low-speed wind tunnel over 15 minutes, with a downstream breathing mannequin. Aerosol concentrations were measured in the breathing zone of the mannequin and at an upstream location using two laser spectrophotometers that measured particle number concentration over the range 0.25-31 µm. Three face shield designs were tested (A, B and C), and were positioned on the mannequin operated at a high and low breathing rate. Efficiency – the reduction in aerosol concentration in the breathing zone – was calculated as a function of particle size and overall, for each face shield. Face shield A, a bucket hat with flexible shield, had the highest efficiency, approximately 95%, while more traditional face shield designs had efficiency 53-78%, depending on face shield and breathing rate. Efficiency varied by particle size, but the pattern differed among face shield designs. Face shields decreased the concentration of respirable aerosols in the breathing zone, when aerosols were carried perpendicular to the face. Additional research is needed to understand the impact of face shield position relative to the source.
This dataset accompanies the research article entitled, "Etiology-Specific Remodeling in Ventricular Tissue of Heart Failure Patients and its Implications for Computational Modeling of Electrical Conduction," where we quantified fibrosis and performed electrophysiological simulation to investigate electrical propagation in etiologically varied heart failure tissue samples. Included are raw confocal microscopic images, data for extracting and processing the raw images and script to analyze fibrosis and generate meshes for simulation.
This study aims to quantify rare earth element enrichment within coal and coal-adjacent strata in the Uinta Region of Utah and western Colorado. Rare earth elements are a subset of critical minerals used for renewable energy technology in the transition toward carbon-neutral energy. This data contains samples from seven active mines and seven stratigraphically complete cores within the Uinta Region, geochemically evaluated via portable X-ray fluorescence (n=3,113) and inductively coupled plasma-mass spectrometry (n=143) elemental abundance methods. Historical evaluations of geochemical data on Uinta Region coal and coal-adjacent data are sparse, emphasizing the statistical significance of this study’s analyses. These results support the utilization of active mines and coal processing waste piles for the future of domestic rare earth element extraction, offering economic and environmental solutions to pressing global demands.
A comprehensive geochemical and stratigraphic study of Cretaceous coal-bearing strata in Utah and western Colorado was performed to evaluate geologic trends in REE-enrichment, as well as elucidate enrichment mechanisms. Preliminary portable X-ray fluorescence (pXRF) analyses (n = 5659) was combined with Inductively Coupled Plasma-Mass Spectrometry (ICP-MS) analyses (n = 135) on particularly REE-enriched samples. Sampling and analyses from active and historic mines as well as nearby cores and outcrops were performed with an emphasis on sedimentary, stratigraphic, geographic, and mining context.