Standardized Bronchoscopy Testing for Immunocompromised Patients

Aims for Improvement Determine the consistency in which the order set was followed Determine the length of time from finding pulmonary infiltrate to consultation Determine the length of time from consultation to bronchoscopy Determine whether a follow up note was written by pulmonary Determine whether management is affected based on obtained result

Food Security in the COVID-19 Era

Food insecurity is a national issue, one that affected 10.5% of households during some point of the year 2019. Those affected by food insecurity can have their access to food jeopardized due to financial hardship, eating patterns altered to prolong the food available, or various other adjustments including reliance on low-cost food, skipping meals, etc. The state of Vermont is not immune to food insecurity, with a rate of 11.3% of households in 2018. The Covid-19 pandemic created an unprecedented shift in daily life, with households having to rapidly adapt to meet newly imposed governmental regulations, including stay at home orders, while maintaining access to food essentials. This changed exacerbated food insecurity in already food-insecure households, while simultaneously creating food insecurity for those previously unaffected. A study focusing on food insecurity in Vermont from March to April 2020 found a 32.3% increase in food insecurity, with 35.5% of food-insecure households being previously food-secure. This change highlighted not only the growing incidence of food insecurity, but also acknowledged the demographic change seen by newly food insecure households. While this increase is dramatic and alarming, to our knowledge there is no research looking at the continuation of these trends regarding the impact of the Covid-19 pandemic on food insecurity in Vermont households. This lack of data indicates a need for continued follow up to best inform governmental agencies on both how Vermont households are being affected, and how regulations during summer & fall 2020 impacted the rise in food insecurity. These data will then provide guidance for future action to combat current and future food insecurity.https://scholarworks.uvm.edu/comphp_gallery/1304/thumbnail.jp

Evaluation of 3D Reconstruction Algorithms for a Small Animal PET Camera

The use of paired, opposing position-sensitive phototube scintillation cameras (SCs) operating in coincidence for small animal imaging with positron emitters is currently under study. Because of the low sensitivity of the system even in 3D mode and the need to produce images with high resolution, it was postulated that a 3D expectation maximization (EM) reconstruction algorithm might be well suited for this application. We investigated six reconstruction algorithms for the 3D SC PET camera: 2D filtered back-projection (FBP), 3D reprojection (3DRP), 2D EM, 3D EM, 2D ordered subset EM (OSEM), and 3D OSEM. Noise was assessed for all slices by the coefficient of variation in a simulated uniform cylinder. Resolution was assessed from a simulation of 15 point sources in the warm background of the uniform cylinder. At comparable noise levels, the resolution achieved with EM and OSEM (0.9-mm to 1.2-mm) is significantly better than that obtained with FBP or 3DRP (1.5-mm to 2.0-mm.) Images of a ..

Geophysics Field Camp 2003

Four different geophysical methods were used near Chino Valley, Arizona in order to map a suspected andesite intrusive, identified as plug 15, which had originally been found using aeromagnetic data already acquired over the area. Magnetic, transient electromagnetic, seismic, and gravity measurements were collected between 3/1/2003-3/2/2003 and 3/22/2003-3/23/2003. The surveys were located near the center of section 35, township 17 North, Range 2 West, just north of Chino Valley, AZ. The magnetic and TEM surveys provided the best indication of the location and depth of the plug. The north-south spatial extent of the plug was estimated to be approximately 600 meters. The depth to the top of the plug was found from the TEM survey to be approximately 300 meters at the center of the survey. The seismic survey did not reach deep enough to find the andesite anomaly and the gravity survey did not appear to be affected by the plug. Magnetic, TEM, and seismic surveys were also performed at another site located approximately 1.25 km northeast of plug 15. The seismic survey did not reach deep enough and the magnetic survey was too short to provide a depth interpretation. The single TEM sounding measured a very high resistivity (approximately 900 ohm-m) at this site.The Geophysics Field Camp Reports are made available by the Laboratory for Advanced Subsurface Imaging (LASI) and the University of Arizona Libraries. Visit the LASI website for more information http://www.lasi.arizona.edu

Automatic magnetic resonance prostate segmentation by deep learning with holistically nested networks

© 2017 Society of Photo-Optical Instrumentation Engineers (SPIE). Accurate automatic segmentation of the prostate in magnetic resonance images (MRI) is a challenging task due to the high variability of prostate anatomic structure. Artifacts such as noise and similar signal intensity of tissues around the prostate boundary inhibit traditional segmentation methods from achieving high accuracy. We investigate both patch-based and holistic (image-to-image) deep-learning methods for segmentation of the prostate. First, we introduce a patch-based convolutional network that aims to refine the prostate contour which provides an initialization. Second, we propose a method for end-to-end prostate segmentation by integrating holistically nested edge detection with fully convolutional networks. Holistically nested networks (HNN) automatically learn a hierarchical representation that can improve prostate boundary detection. Quantitative evaluation is performed on the MRI scans of 250 patients in fivefold cross-validation. The proposed enhanced HNN model achieves a mean ± standard deviation. A Dice similarity coefficient (DSC) of 89.77%±3.29% and a mean Jaccard similarity coefficient (IoU) of 81.59%±5.18% are used to calculate without trimming any end slices. The proposed holistic model significantly (p\u3c0.001) outperforms a patch-based AlexNet model by 9% in DSC and 13% in IoU. Overall, the method achieves state-of-the-art performance as compared with other MRI prostate segmentation methods in the literature