Publications

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1687 Publications visible to you, out of a total of 1687

Abstract (Expand)

The Metadata Schema of the NFDI4Health and the NFDI4Health Task Force COVID-19 (Metadata Schema) contains a list of properties that describe a resource to be registered in the German Central Health Study Hub. Currently, two main types of resources are distinguished: a) study descriptions (i.e., metadata set describing a study) and b) study documents. However, due to the generic character of the Metadata Schema, other types of resources may also be described and registered. The metadata properties are divided into mandatory and recommended ones. Along with bibliographic information such as title and description of the resource, the related persons and organizations contributing to the development of the resource can also be specified. The results of studies published in journal articles or other text publications can be linked too. For studies, information about study design and accessibility of the collected data should be additionally provided. The Metadata Schema consists mainly of properties adapted from established standards and models such as DataCite Metadata Schema 4.4, data models of the ClinicalTrials.gov, German Clinical Trials Register, International Clinical Trials Registry, HL7® FHIR, MIABIS, Maelstrom Research cataloguing toolkit and DDI Controlled Vocabularies. This is an updated version V3_2 of the Metadata Schema, which improves the modules of the previous version via refined description texts and added, deleted, moved, or renamed items. Additional use case-specific requirements, particularly for the chronic diseases and record linkage modules, have also been considered in this new version along with updating the list of sources. The undertaken changes are described within the document.

Authors: Haitham Abaza, A. Shutsko, Martin Golebiewski, Sophie Klopfenstein, Carsten Oliver Schmidt, Carina Vorisek, NFDI4Health Task Force COVID-19, NFDI4Health, C. Brünings-Kuppe, V. Clemens, J. Darms, S. Hanß, T. Intemann, F. Jannasch, E. Kasbohm, Birte Lindstädt, Matthias Löbe, E. Orban, I. Perrar, M. Peters, U. Sax, M. Schulze, C. Schupp, F. Schwarz, C. Schwedhelm, S. Strathmann, Dagmar Waltemath, H. Wünsche, A. Zeleke

Date Published: 14th Aug 2023

Publication Type: Misc

Abstract

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Authors: Daniel Wolffram, Sam Abbott, Matthias an der Heiden, Sebastian Funk, Felix Günther, Davide Hailer, Stefan Heyder, Thomas Hotz, Jan van de Kassteele, Helmut Küchenhoff, Sören Müller-Hansen, Diellë Syliqi, Alexander Ullrich, Maximilian Weigert, Melanie Schienle, Johannes Bracher

Date Published: 11th Aug 2023

Publication Type: Journal

Abstract (Expand)

Redox-active organic molecules, i.e., molecules that can relatively easily accept and/or donate electrons, are ubiquitous in biology, chemical synthesis, and electronic and spintronic devices, such as solar cells and rechargeable batteries, etc. Choosing the best candidates from an essentially infinite chemical space for experimental testing in a target application requires efficient screening approaches. In this Review, we discuss modern in silico techniques for predicting reduction and oxidation potentials of organic molecules that go beyond conventional first-principles computations and thermodynamic cycles. Approaches ranging from simple linear fits based on molecular orbital energy approximation and energy difference approximation to advanced regression and neural network machine learning algorithms employing complex descriptors of molecular compositions, geometries, and electronic structures are examined in conjunction with relevant literature examples. We discuss the interplay between ab initio data and machine learning (ML), i.e., whether it is better to base predictions on low-level quantum-chemical results corrected with ML or to bypass first-principles computations entirely and instead rely on elaborate deep learning architectures. Finally, we list currently available data sets of redox-active organic molecules and their experimental and/or computed properties to facilitate the development of screening platforms and rational design of redox-active organic molecules.

Authors: Rostislav Fedorov, Ganna Gryn’ova

Date Published: 8th Aug 2023

Publication Type: Journal

Abstract

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Authors: Shubham Srivastav, T. Moore, M. Nicholl, M. R. Magee, S. J. Smartt, M. D. Fulton, S. A. Sim, J. M. Pollin, L. Galbany, C. Inserra, A. Kozyreva, Takashi J. Moriya, F. P. Callan, X. Sheng, K. W. Smith, J. S. Sommer, J. P. Anderson, M. Deckers, M. Gromadzki, T. E. Müller-Bravo, G. Pignata, A. Rest, D. R. Young

Date Published: 1st Aug 2023

Publication Type: Journal

Abstract (Expand)

Convective cores are the hydrogen reservoirs of main sequence stars that are more massive than around 1.2 solar masses. The characteristics of the cores have a strong impact on the evolution and structure of the star. However, such results rely on stellar evolution codes, in which simplistic assumptions are often made on the physics in the core. Indeed, mixing is commonly considered to be instantaneous and the most basic nuclear networks assume beryllium at its equilibrium abundance. Those assumptions lead to significant differences in the central composition of the elements for which the timescale to reach nuclear equilibrium is lower than the convective timescale. In this work, we show that those discrepancies impact the nuclear energy production and, therefore, the size of convective cores in models computed with overshoot. We find that cores computed with instantaneous mixing are up to 30% bigger than those computed with diffusive mixing. Similar differences are found when using basic nuclear networks. Additionally, we observed an extension of the duration of the main sequence due to those core size differences. We then investigated the impact of those structural differences on the seismic modeling of solar-like oscillators. Modeling two stars observed by Kepler, we find that the overshoot parameter of the best models computed with a basic nuclear network is significantly lower, compared to models computed with a full nuclear network. This work is a necessary step in improving the modeling of convective cores, which is key to determining accurate ages in the framework of future space missions such as Plato.

Authors: Anthony Noll, Sébastien Deheuvels

Date Published: 1st Aug 2023

Publication Type: Journal

Abstract (Expand)

ABSTRACT The mass and distribution of metals in the interiors of exoplanets are essential for constraining their formation and evolution processes. Never the less, with only masses and radii measured, the determination of exoplanet interior structures is degenerate, and so far simplified assumptions have mostly been used to derive planetary metallicities. In this work, we present a method based on a state-of-the-art interior code, recently used for Jupiter, and a Bayesian framework, to explore the possibility of retrieving the interior structure of exoplanets. We use masses, radii, equilibrium temperatures, and measured atmospheric metallicities to retrieve planetary bulk metallicities and core masses. Following results on the giant planets in the Solar system and recent development in planet formation, we implement two interior structure models: one with a homogeneous envelope and one with an inhomogeneous one. Our method is first evaluated using a test planet and then applied to a sample of 37 giant exoplanets with observed atmospheric metallicities from the pre-JWST era. Although neither internal structure model is preferred with the current data, it is possible to obtain information on the interior properties of the planets, such as the core mass, through atmospheric measurements in both cases. We present updated metal mass fractions, in agreement with recent results on giant planets in the Solar system.

Authors: S Bloot, Y Miguel, M Bazot, S Howard

Date Published: 1st Aug 2023

Publication Type: Journal

Abstract

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Editor:

Date Published: 1st Aug 2023

Publication Type: Journal

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