This dataset contains room occupancy during the study period at University of Utah hospital. Admission, Discharge, and Transfer (ADT) data is captured in participating hospitals to characterize room occupancy and non-occupancy in wards. These data are pulled from multiple sources collected during the study by study staff as well as harvested EHR data. Data were adjudicated and compiled into one comprehensive file. Data manipulation included redaction of dates, replaced with study days 1-n, as well as transformation from long format to wide for ease of use.
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. This dataset contains cleaned sensor pings of RFD reads between healthcare worker worn sensors and environmental sensors placed in facility using methods described in the "Data Cleaning Steps" section.
The microbiology data represents the microorganisms recovered during the study period at the University of Utah hospital from samples collected from patients, environmental surfaces, and healthcare personnel (HCP) hands using premoistened sponges. Patient samples were collected daily from the axilla, groin, and perianal areas or stool. Environmental samples were collected daily from room surfaces and unit common areas (such as bed rails, overbed tables, door handles, computer keyboards, and other high-touch areas). HCP hands were periodically sampled upon HCP exit from a patient room after engaging in health care activities. Samples were collected from the 20-bed University of Utah Hospital Cardiovascular ICU (CVICU) over a 54 day period. The information from these datasets can be used to understand how different organisms appear and move throughout a hospital ward over a period of time.
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.
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.
This study of the role and impact of the subject selector in academic libraries is unique and long overdue. We focused on the Pac-12 university libraries, a representative sample of nationwide academic libraries. The strength of our investigation is this small, focused sample size and unique statistical analysis of subject specialists. There is a wide variety among these libraries with respect to the hiring requirements for MLIS, the MLIS with an additional advanced-subject master’s degree, and those libraries who hire non-MLIS librarians. This investigation has the possibility of promoting greater awareness for the future of subject specialists in academic libraries.
Significance: Current medical imaging systems have many limitations for applications in cardiovascular diseases. New technologies may overcome these limitations. Particularly interesting are technologies for diagnosis of cardiac diseases, e.g. fibrosis, myocarditis, and transplant rejection.
Aim: To introduce and assess a new optical system capable of assessing cardiac muscle tissue using light-scattering spectroscopy (LSS) in conjunction with machine learning.
Approach: We applied an ovine model to investigate if the new LSS system is capable of estimating densities of cell nuclei in cardiac tissue. We measured the nuclear density using fluorescent labeling, confocal microscopy, and image processing. Spectra acquired from the same cardiac tissues were analyzed with spectral clustering and convolutional neural networks to assess feasibility and reliability of density quantification.
Results: Spectral clustering revealed distinct groups of spectra correlated to ranges of nuclear density. Convolutional neural networks correctly classified 3 groups of spectra with low, medium, or high nuclear density with 95.00±11.77% (mean and standard deviation) accuracy. The analysis revealed sensitivity of the accuracy to wavelength range and subsampling of spectra.
Conclusions: LSS and machine learning are capable of assessing nuclear density in cardiac tissues. The approach could be useful for diagnosis of cardiac diseases associated with an increase of nuclei.
Environmental noise may affect hearing and a variety of non-auditory disease processes. There is some evidence that, like other environmental hazards, noise may be differentially distributed across communities based on socioeconomic status. We aimed to a) predict daytime noise pollution levels and b) assess disparities in daytime noise exposure in Chicago, Illinois. We measured 5-minute daytime noise levels (Leq, 5-min) at 75 randomly selected sites in Chicago in March, 2019. Geographically-based variables thought to be associated with noise were obtained, and used to fit a noise land-use regression model to estimate the daytime environmental noise level at the centroid of the census blocks. Demographic and socioeconomic data were obtained from the City of Chicago for the 77 community areas, and associations with daytime noise levels were assessed using spatial autoregressive models. Mean sampled noise level (Leq, 5-min) was 60.6 dBA. The adjusted R2 and root mean square error of the noise land use regression model and the validation model were 0.60 and 4.67 dBA and 0.51 and 5.90 dBA, respectively. Nearly 75% of city blocks and 85% of city communities have predicted daytime noise level higher than 55 dBA. Of the socioeconomic variables explored, only community per capita income was associated with mean community predicted noise levels, and was highest for communities with incomes in the 2nd quartile. Both the noise measurements and land-use regression modeling demonstrate that Chicago has levels of environmental noise likely contributing to the total burden of environmental stressors. Noise is not uniformly distributed across Chicago; it is associated with proximity to roads and public transportation, and is higher among communities with mid-to-low incomes per capita, which highlights how socially and economically disadvantaged communities may be disproportionately impacted by this environmental exposure.
This dataset comprises MODTRAN radiative transfer simulations used to determine scene-specific enhancement spectra for matched filter retrieval of CH4 and CO2 concentrations from imaging spectroscopy data. An example implementation to generate a enhancement spectrum is also included.
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.