Observations of energetic neutral atoms (ENAs) are a useful tool for analyzing ion and neutral abundances in planetary magnetospheres. They are created when hot plasma, originating for example from magnetic reconnection sites, charge-exchanges with the ambient neutral population surrounding the planet. The motion of ENAs is not governed by the magnetic field, allowing remote imaging. During the Cassini mission, the Ion and Neutral CAmera (INCA) of the Magnetosphere Imaging Instrument (MIMI) collected vast amounts of hydrogen and oxygen ENA observations of Saturn's magnetosphere from a variety of different viewing geometries. To enable investigations of the morphology and dynamics of Saturn's ring current, it is useful to re-bin and re-project the camera-like views from the spacecraft-based perspective into a common reference frame. We developed an algorithm projecting INCA's ENA observations into a regular grid in Saturn's equatorial plane. With most neutrals and ions being confined into an equatorial rotating disc, this projection is quite accurate in both spatial location and preservation of ENA intensity, provided the spacecraft is located at large enough elevations. Such projections were performed for all INCA ENA data from the Cassini Saturn tour; the data are available for download together with a Python routine flagging contaminated data and returning detailed spacecraft geometry information. The resulting data set is a good foundation for investigating for example the statistical properties of Saturn's ring current and its complicated dynamics in relation to other remote and in situ observations of, for example, auroral emissions and magnetotail reconnection events.
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http://dx.doi.org/10.1029/2020JA028908 | DOI Listing |
J Chem Inf Model
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Department of Medicinal Chemistry, School of Pharmacy, Fudan University, 826 Zhangheng Road, Shanghai 201203, People's Republic of China.
In recent decades, covalent inhibitors have emerged as a promising strategy for therapeutic development, leveraging their unique mechanism of forming covalent bonds with target proteins. This approach offers advantages such as prolonged drug efficacy, precise targeting, and the potential to overcome resistance. However, the inherent reactivity of covalent compounds presents significant challenges, leading to off-target effects and toxicities.
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Institute of Animal Science, University of Bonn, Bonn, Germany.
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View Article and Find Full Text PDFJ Chem Inf Model
January 2025
Department of Chemistry, New York University, New York, New York 10003, United States.
Molecular Docking is a critical task in structure-based virtual screening. Recent advancements have showcased the efficacy of diffusion-based generative models for blind docking tasks. However, these models do not inherently estimate protein-ligand binding strength thus cannot be directly applied to virtual screening tasks.
View Article and Find Full Text PDFLearn Health Syst
January 2025
Department of Biomedical Informatics University of Arkansas for Medical Sciences, College of Medicine Little Rock Arkansas USA.
Objective: This project demonstrates the feasibility of connecting medical imaging data and features, SARS-CoV-2 genome variants, with clinical data in the National Clinical Cohort Collaborative (N3C) repository to accelerate integrative research on detection, diagnosis, and treatment of COVID-19-related morbidities. The N3C curated a rich collection of aggregated and de-identified electronic health records (EHR) data of over 18 million patients, including 7.5 million COVID-positive patients, seen at hospitals across the United States.
View Article and Find Full Text PDFBiostat Epidemiol
October 2024
Department of Epidemiology and Biostatistics, Indiana University, Bloomington, Indiana, US.
Wearable devices enable the continuous monitoring of physical activity (PA) but generate complex functional data with poorly characterized errors. Most work on functional data views the data as smooth, latent curves obtained at discrete time intervals with some random noise with mean zero and constant variance. Viewing this noise as homoscedastic and independent ignores potential serial correlations.
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