The Shuttle Activation Monitor (SAM) experiment was flown on the Space Shuttle Columbia (STS-28) from 8-13 August, 1989 in a 57 degrees, 300 km orbit. One objective of the SAM experiment was to determine the relative effect of different amounts of shielding on the gamma-ray backgrounds measured with similarly configured sodium iodide (NaI) and bismuth germante (BGO) detectors. To achieve this objective twenty-four hours of data were taken with each detector in the middeck of the Shuttle on the ceiling of the airlock (a high-shielding location) as well as on the sleep station wall (a low-shielding location). For the cosmic-ray induced background the results indicate an increased overall count rate in the 0.2 to 10 MeV energy range at the more highly shielded location, while in regions of trapped radiation the low shielding configuration gives higher rates at the low energy end of the spectrum.
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http://dx.doi.org/10.1016/0273-1177(92)90144-m | DOI Listing |
JMIR Form Res
January 2025
Department of Psychology, The University of Texas at San Antonio, San Antonio, TX, United States.
Background: Perception-related errors comprise most diagnostic mistakes in radiology. To mitigate this problem, radiologists use personalized and high-dimensional visual search strategies, otherwise known as search patterns. Qualitative descriptions of these search patterns, which involve the physician verbalizing or annotating the order he or she analyzes the image, can be unreliable due to discrepancies in what is reported versus the actual visual patterns.
View Article and Find Full Text PDFNanoscale
January 2025
Institute Nanoscience - CNR-NANO, Center S3, via G. Campi 213/A, 41125, Modena, Italy.
A multiscale approach is employed to investigate the interaction dynamics between interleukin-6, a key cancer biomarker, and alkyl-functionalized surfaces, with the ultimate goal of guiding biosensor design. The study integrates classical molecular dynamics, Brownian dynamics simulations, and binding experiments to explore the adsorption dynamics and energetics of IL-6 on surfaces modified with self-assembled monolayers (SAMs). The comparative analysis reveals a dramatic effect on the interaction strength of IL-6 with a SAMs comprising a mix of charged and hydrophobic ligands.
View Article and Find Full Text PDFComput Methods Programs Biomed
January 2025
School of Engineering Medicine, Beihang University, Beijing 100191, PR China; Key Laboratory of Big Data-Based Precision Medicine (Beihang University), Ministry of Industry and Information Technology of the People's Republic of China, Beijing 100191, PR China; Beijing Engineering Research Center of Cardiovascular Wisdom Diagnosis and Treatment, Beijing 100029, PR China. Electronic address:
Background And Objective: Single-source domain generalization (SSDG) aims to generalize a deep learning (DL) model trained on one source dataset to multiple unseen datasets. This is important for the clinical applications of DL-based models to breast cancer screening, wherein a DL-based model is commonly developed in an institute and then tested in other institutes. One challenge of SSDG is to alleviate the domain shifts using only one domain dataset.
View Article and Find Full Text PDFbioRxiv
December 2024
Departments of Ophthalmology and Visual Sciences and Genetics, Albert Einstein College of Medicine, Bronx, New York 10461.
Crystallin proteins serve as both essential structural and as well as protective components of the ocular lens and are required for the transparency and light refraction properties of the organ. The mouse lens crystallin proteome is represented by αA-, αB-, βA1-, βA2-, βA3-, βA4-, βB1-, βB2-, βB3-, γA-, γB-, γC-, γD-, γE, γF-, γN-, and γS-crystallin proteins encoded by 16 genes. Their mutations are responsible for lens opacification and early onset cataract formation.
View Article and Find Full Text PDFSensors (Basel)
January 2025
Phillip M. Drayer Electrical Engineering Department, Lamar University, Beaumont, TX 77705, USA.
Automated ultrasonic testing (AUT) is a critical tool for infrastructure evaluation in industries such as oil and gas, and, while skilled operators manually analyze complex AUT data, artificial intelligence (AI)-based methods show promise for automating interpretation. However, improving the reliability and effectiveness of these methods remains a significant challenge. This study employs the Segment Anything Model (SAM), a vision foundation model, to design an AI-assisted tool for weld defect detection in real-world ultrasonic B-scan images.
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