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1549 Publications visible to you, out of a total of 1549

Abstract (Expand)

As part of the BioHackathon Europe 2023, we here report on the progress of the hacking team preparing a resource index and knowledge graph based on the JSON-LD Bioschemas markup from several resourcesal resources in the life- and natural sciences, predominantly from the fields of plant- and (bio)chemistry research. This preliminary analysis will allow us to better understand how Bioschemas markup is currently used in these two communities, so we can take actions to improve guidelines and validation on the Bioschemas markup and the data providers side. The lessons learnt will be useful for other communities as well. The ultimate goal is facilitating and improving interoperability across resources.

Authors: Daniel Arend, Alessio Del Conte, Manuel Feser, Yojana Gadiya, Alban Gaignard, Leyla Jael Castro, Ivan Mičetić, Sebastien Moretti, Steffen Neumann, Noura Rayya, Ginger Tsueng, Egon Willighagen, Ulrike Wittig

Date Published: 30th Jan 2024

Publication Type: Misc

Abstract (Expand)

The theoretical oscillation frequencies of even the best asteroseismic models of solar-like oscillators show significant differences from observed oscillation frequencies. Structure inversions seek to use these frequency differences to infer the underlying differences in stellar structure. While used extensively to study the Sun, structure inversion results for other stars have so far been limited. Applying sound speed inversions to more stars allows us to probe stellar theory over a larger range of conditions, as well as look for overall patterns that may hint at deficits in our current understanding. To that end, we present structure inversion results for 12 main-sequence solar-type stars with masses between 1 and 1.15M⊙. Our inversions are able to infer differences in the isothermal sound speed in the innermost 30% by radius of our target stars. In half of our target stars, the structure of our best-fit model fully agrees with the observations. In the remainder, the inversions reveal significant differences between the sound speed profile of the star and that of the model. We find five stars where the sound speed in the core of our stellar models is too low and one star showing the opposite behavior. For the two stars in which our inversions reveal the most significant differences, we examine whether changing the microphysics of our models improves them and find that changes to nuclear reaction rates or core opacities can reduce, but do not fully resolve, the differences.

Authors: Lynn Buchele, Earl P. Bellinger, Saskia Hekker, Sarbani Basu, Warrick Ball, Jørgen Christensen-Dalsgaard

Date Published: 26th Jan 2024

Publication Type: Journal

Abstract (Expand)

The ASLLA Symposium focused on accelerating chemical science with AI. Discussions on data, new applications, algorithms, and education were summarized. Recommendations for researchers, educators, andeducators, and academic bodies were provided.

Authors: Seoin Back, Alán Aspuru-Guzik, Michele Ceriotti, Ganna Gryn'ova, Bartosz Grzybowski, Geun Ho Gu, Jason Hein, Kedar Hippalgaonkar, Rodrigo Hormázabal, Yousung Jung, Seonah Kim, Woo Youn Kim, Seyed Mohamad Moosavi, Juhwan Noh, Changyoung Park, Joshua Schrier, Philippe Schwaller, Koji Tsuda, Tejs Vegge, O. Anatole von Lilienfeld, Aron Walsh

Date Published: 17th Jan 2024

Publication Type: Journal

Abstract (Expand)

This document created within the European Coordination and Support Action (CSA) of the EDITH (Ecosystem Digital Twins in Healthcare) project describes the current landscape of formatting and description standards, terminologies and metadata guidelines for virtual human twins (VHTs). It refers to corresponding biomedical data, simulation models and workflows, as well as their metadata relevant for the definition, implementation, and simulation of Digital Twins in Healthcare (DTHs). It comprises both, ISO and community standards and lists the relevant standards and terminologies describing the modelling process, the integration of domain-specific medical research data with routine data from electronic health records, the documentation of data provenance, the validation process for biomedical, physiological, bio-signaling and other healthcare data and models. The document also reveals needs and gaps in the current standards landscape to drive the further development of such standards. Therefore, remarks and comments on how to improve existing standards or on areas for which standards are still missing are very welcome.

Author: Gerhard Mayer, Martin Golebiewski

Date Published: 17th Jan 2024

Publication Type: Tech report

Abstract (Expand)

This document provides a guideline for using and implementing standards, terminologies, and metadata guidelines when setting up, executing, and archiving virtual human twins. It is created within thehe European Coordination and Support Action (CSA) of the EDITH (Ecosystem Digital Twins in Healthcare) project. The aim of this implementation guide is two-fold: First it gives hints to the modelers, which steps they should follow in the model building process and which standards, terminologies, and guidelines (depending on their modelling domain) they should use in defining their biomedical and healthcare models. Second it is intended as a practical guide for implementers giving hints, which standards, terminologies, and guidelines should be supported in the long-term by the simulation environment consisting of the repository, the simulation platform, and the workflow execution engines. Initially it suffices if they support all formats and annotations used by the demonstrator use cases. To get an overview and access information on the standards, terminologies, and metadata guidelines referenced in this document, there also is an EDITH FairSharing collection available: https://fairsharing.org/4787

Author: Gerhard Mayer, Martin Golebiewski

Date Published: 17th Jan 2024

Publication Type: Tech report

Abstract (Expand)

Hydrogen atom transfer (HAT) reactions are important in many biological systems. As these reactions are hard to observe experimentally, it is of high interest to shed light on them using simulations. Here, we present a machine learning model based on graph neural networks for the prediction of energy barriers of HAT reactions in proteins. As input, the model uses exclusively non-optimized structures as obtained from classical simulations. It was trained on more than 17 000 energy barriers calculated using hybrid density functional theory. We built and evaluated the model in the context of HAT in collagen, but we show that the same workflow can easily be applied to HAT reactions in other biological or synthetic polymers. We obtain for relevant reactions (small reaction distances) a model with good predictive power (R2 ∼ 0.9 and mean absolute error of <3 kcal mol−1). As the inference speed is high, this model enables evaluations of dozens of chemical situations within seconds. When combined with molecular dynamics in a kinetic Monte-Carlo scheme, the model paves the way toward reactive simulations.

Authors: Kai Riedmiller, Patrick Reiser, Elizaveta Bobkova, Kiril Maltsev, Ganna Gryn'ova, Pascal Friederich, Frauke Gräter

Date Published: 16th Jan 2024

Publication Type: Journal

Abstract (Expand)

Hydrogen atom transfer (HAT) reactions are important in many biological systems. As these reactions are hard to observe experimentally, it is of high interest to shed light on them using simulations. Here, we present a machine learning model based on graph neural networks for the prediction of energy barriers of HAT reactions in proteins. As input, the model uses exclusively non-optimized structures as obtained from classical simulations. It was trained on more than 17 000 energy barriers calculated using hybrid density functional theory. We built and evaluated the model in the context of HAT in collagen, but we show that the same workflow can easily be applied to HAT reactions in other biological or synthetic polymers. We obtain for relevant reactions (small reaction distances) a model with good predictive power (R2 ∼ 0.9 and mean absolute error of <3 kcal mol−1). As the inference speed is high, this model enables evaluations of dozens of chemical situations within seconds. When combined with molecular dynamics in a kinetic Monte-Carlo scheme, the model paves the way toward reactive simulations.

Authors: Kai Riedmiller, Patrick Reiser, Elizaveta Bobkova, Kiril Maltsev, Ganna Gryn’ova, Pascal Friederich, Frauke Gräter

Date Published: 16th Jan 2024

Publication Type: Journal

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