We present new calibrations of the near-infrared surface brightness fluctuation (SBF) distance method for the F110W (J 110 ) and F160W (H 160 ) bandpasses of the Wide Field Camera 3 Infrared Channel (WFC3/IR) on the Hubble Space Telescope. The calibrations are based on data for 16 early-type galaxies in the Virgo and Fornax clusters observed with WFC3/IR and are provided as functions of both the optical (g 475 −z 850 ) and nearinfrared (J 110 −H 160 ) colors. The scatter about the linear calibration relations for the luminous red galaxies in the sample is approximately 0.10 mag, corresponding to a statistical error of 5% in distance. Our results imply that the distance to any suitably bright elliptical galaxy can be measured with this precision out to about 80 Mpc in a single-orbit observation with WFC3/IR, making this a remarkably powerful instrument for extragalactic distances. The calibration sample also includes much bluer and lower-luminosity galaxies than previously used for IR SBF studies, revealing interesting population differences that cause the calibration scatter to increase for dwarf galaxies. Comparisons with single-burst population models show that, as expected, the redder early-type galaxies contain old, metal-rich populations, while the bluer dwarf ellipticals contain a wider range of ages and lower metallicities than their more massive counterparts. Radial SBF gradients reveal that IR color gradients are largely an age effect; the bluer dwarfs typically have their youngest populations near their centers, while the redder giant ellipticals show only weak trends and in the opposite sense. Because of the population variations among bluer galaxies, distance measurements in the near-IR are best limited to red early-type galaxies. We conclude with some practical guidelines for using WFC3/IR to measure reliable SBF distances.

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The media chosen to couple the PEA stack (electrode/sample/sensor/backing) can affect the spatial resolution and shape of the response from a Pulsed Electroacoustic (PEA) system significantly. The PEA stack layers must be electrically and acoustically coupled to optimize the amplitude, quality, and spatial resolution of the PEA measurements. Various coupling layer materials were used with 250 µm thick polymethylmethacrylate (PMMA) samples and a standard ~10 μm thick PVDF sensor. Coupling layers tested in this study include no media (with substantial pressure applied), light machine oil, silicone oil, and cyanoacrylate (super glue). Pulse amplitudes of 2000 V and 5 ns width were used. Static 8 kV DC bias was applied to the sample in order to detect a signal, as the samples were initially free of charge, and to see the interfaces more clearly and showcase the differences in response from the various coupling media. The best option was found to be a single layer of cyanoacrylate at the ground electrodesample interface; this is the only viable option for in vacuo PEA measurements of the media tested.

68 Background: Artificial intelligence (AI) can potentially improve patient care by assisting physicians as demonstrated with the recent approvals for technologies that detect intracranial hemorrhage on CT exams of the head. AI also has potential for assisting early detection of colorectal cancer (CRC) on routine CT abdomen and pelvis (CTAP). We assessed the difference in detection of abnormal colonic findings between an expert reader (JC) and amateur readers (ARs). Methods: ARs consisting of two third year medical students (RB - AR1, ZG - AR2) studied 20 CTAP for tracing the colon and identifying pathology. Their search pattern and assessment of the colon was then evaluated by an expert radiologist (JC). They then spent two hours reviewing abnormalities in 10 scans with JC, who highlighted suspicious neoplastic findings such as colonic wall thickening, fat stranding, edema, masses, and abnormal lymph nodes. The ARs then individually read 203 CTAP scans to assess for these suspicious findings. The studies were from a single institution and were reported in a prior study in 2019 GI-ASCO. The findings of the ARs were then compared to those of the expert reader and the initial reader for each study. Data was analyzed using t-test with 2 tails. Results: The incidence of suspicious neoplastic findings was 87% and 81% for AR1 and AR2, respectively, compared to 18% in the initial reads and 33% for expert reader (p=0.01). Greatest discordance were 94% and 87% between AR1 and AR2 to the initial reads. Additionally, the incidence of suspicious findings between the first and last 20 cases (p=0.03 and 0.17) examined by ARs declined from 79 to 40% for AR1 and 69 to 55% for AR2. Conclusions: ARs are capable of detecting CRC features on CTAP from ED, but with higher false-positive (FP) rate than trained experts. The FP rate decreases with increasing experience. ARs learning course simulates AI which will likely yield high FP rate with initial training, but with improving FP with deep training, especially with larger volume of normal variants.

The understanding of charge dynamics in dielectric materials is paramount in mitigating electrostatic discharge events for spacecraft. The most critical spacecraft charging events are found to result from incident electrons in the energy range of 10 keV to 50 keV. The charge embedded in dielectric materials in this energy range are deposited a distance into the material on the order of a few to tens of microns. One way to measure and understand the deposited charge is via pulsed electroacoustic measurements (PEA). However, the typical PEA spatial resolution of ~ 10 μm is not sufficient to resolve or discern charge deposited at the lower end of this incident electron energy range, where deposited charge distributions are obscured by the superposition of the signal originating from induced mirror charge on the electrode of the pulsed electroacoustic system. A simple method is proposed and demonstrated in which reference measurements from a pristine sample are used to separate the effect of the induced mirror charge from the measured embedded charge to obtain a more accurate determination of the deposited charge distribution.