Unlocking the Potential: Predicting Redox Behavior of Organic Molecules, from Linear Fits to Neural Networks - Fedorov, Rostislav; Gryn'ova, Ganna (Journal of Chemical Theory and Computation, 2023)
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.

Graph neural networks for materials science and chemistry - Reiser, Patrick; Neubert, Marlen; Eberhard, André; Torresi, Luca; Zhou, Chen; Shao, Chen; Metni, Houssam; van Hoesel, Clint; Schopmans, Henrik; Sommer, Timo; Friederich, Pascal (Communications Materials, 2022)
Abstract Machine learning plays an increasingly important role in many areas of chemistry and materials science, being used to predict materials properties, accelerate simulations, design new structures, and predict synthesis routes of new materials. Graph neural networks (GNNs) are one of the fastest growing classes of machine learning models. They are of particular relevance for chemistry and materials science, as they directly work on a graph or structural representation of molecules and materials and therefore have full access to all relevant information required to characterize materials. In this Review, we provide an overview of the basic principles of GNNs, widely used datasets, and state-of-the-art architectures, followed by a discussion of a wide range of recent applications of GNNs in chemistry and materials science, and concluding with a road-map for the further development and application of GNNs.

Theory and Approximate Solvers for Branched Optimal Transport with Multiple Sources - Lippmann, Peter; Sanmartín, Enrique Fita; Hamprecht, Fred A. (arXiv,cs.LG,2210.07702, 2022)
Branched Optimal Transport (BOT) is a generalization of optimal transport in which transportation costs along an edge are subadditive. This subadditivity models an increase in transport efficiency when shipping mass along the same route, favoring branched transportation networks. We here study the NP-hard optimization of BOT networks connecting a finite number of sources and sinks in ℝ2. First, we show how to efficiently find the best geometry of a BOT network for many sources and sinks, given a topology. Second, we argue that a topology with more than three edges meeting at a branching point is never optimal. Third, we show that the results obtained for the Euclidean plane generalize directly to optimal transportation networks on two-dimensional Riemannian manifolds. Finally, we present a simple but effective approximate BOT solver combining geometric optimization with a combinatorial optimization of the network topology.

Molecular Dynamics Simulations of the Human Ecto-5′-Nucleotidase (h-ecto-5′-NT, CD73): Insights into Protein Flexibility and Binding Site Dynamics - Viviani, Lucas G.; Kokh, Daria B.; Wade, Rebecca C.; T.-do Amaral, Antonia (Journal of Chemical Information and Modeling, 2023)

Comparing trained and untrained probabilistic ensemble forecasts of COVID-19 cases and deaths in the United States - Ray, Evan L.; Brooks, Logan C.; Bien, Jacob; Biggerstaff, Matthew; Bosse, Nikos I.; Bracher, Johannes; Cramer, Estee Y.; Funk, Sebastian; Gerding, Aaron; Johansson, Michael A.; Rumack, Aaron; Wang, Yijin; et al. (International Journal of Forecasting, 2023)

Comparative evaluation of point process forecasts - Brehmer, Jonas R.; Gneiting, Tilmann; Herrmann, Marcus; Marzocchi, Warner; Schlather, Martin; Strokorb, Kirstin (Annals of the Institute of Statistical Mathematics, 2024)

SN 2021gno: a Calcium-rich transient with double-peaked light curves - Ertini, K.; Folatelli, G.; Martinez, L.; Bersten, M. C.; Anderson, J. P.; Ashall, C.; Baron, E.; Bose, S.; Brown, P. J.; Burns, C.; DerKacy, J. M.; Ferrari, L.; et al. (\mnras, 2023)

Unprecedented early flux excess in the hybrid 02es-like type Ia supernova 2022ywc indicates interaction with circumstellar material - Srivastav, Shubham; Moore, T.; Nicholl, M.; Magee, M. R.; Smartt, S. J.; Fulton, M. D.; Sim, S. A.; Pollin, J. M.; Galbany, L.; Inserra, C.; Kozyreva, Alexandra; Moriya, Takashi J.; et al. (arXiv e-prints, 2023)

Astrophysics with the Laser Interferometer Space Antenna - Amaro-Seoane, Pau; Andrews, Jeff; Arca Sedda, Manuel; Askar, Abbas; Baghi, Quentin; Balasov, Razvan; Bartos, Imre; Bavera, Simone S.; Bellovary, Jillian; Berry, Christopher P. L.; Berti, Emanuele; Bianchi, Stefano; et al. (Living Reviews in Relativity, 2023)

Simulations of common-envelope evolution in binary stellar systems: physical models and numerical techniques - Röpke, Friedrich; De Marco, Orsola (Living Reviews in Computational Astrophysics, 2023)

Helium as a signature of the double detonation in Type Ia supernovae - Collins, Christine E.; Sim, Stuart A.; Shingles, Luke J.; Gronow, Sabrina; Röpke, Friedrich; Pakmor, Rüdiger; Seitenzahl, Ivo R.; Kromer, Markus (\mnras, 2023)

Type Ia Supernova Explosions in Binary Systems: A Review - Liu, Zheng-Wei; Röpke, Friedrich; Han, Zhanwen (Research in Astronomy and Astrophysics, 2023)

Modeling and predicting spatio-temporal dynamics of PM_2.5 concentrations through time-evolving covariance models - Qadir, Ghulam A.; Sun, Ying (Statistica Sinica, 2025)

Impact of central mixing scheme and nuclear reaction network on the extent of convective cores - Noll, Anthony; Deheuvels, Sébastien (Astronomy & Astrophysics, 2023)
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.

Revised Extinctions and Radii for 1.5 Million Stars Observed by APOGEE, GALAH, and RAVE - Yu, Jie; Khanna, Shourya; Themeßl, Nathalie; Hekker, Saskia; Dréau, Guillaume; Gizon, Laurent; Bi, Shaolan (The Astrophysical Journal Supplement Series, 2023)
Abstract

Improved asteroseismic inversions for red-giant surface rotation rates - Ahlborn, Felix; Bellinger, E. P.; Hekker, Saskia; Basu, S.; Mokrytska, D. (Astronomy & Astrophysics, 2022)
Context: Asteroseismic observations of internal stellar rotation have indicated a substantial lack of angular momentum transport in theoretical models of subgiant and red-giant stars. Accurate core and surface rotation rate measurements are therefore needed to constrain the internal transport processes included in the models. Aims: We eliminate substantial systematic errors of asteroseismic surface rotation rates found in previous studies. Methods: We propose a new objective function for the optimally localised averages method of rotational inversions for red-giant stars, which results in more accurate envelope rotation rate estimates obtained from the same data. We use synthetic observations from stellar models across a range of evolutionary stages and masses to demonstrate the improvement. Results: We find that our new inversion technique allows us to obtain estimates of the surface rotation rate that are independent of the core rotation. For a star at the base of the red-giant branch, we reduce the systematic error from about 20% to a value close to 0, assuming constant envelope rotation. We also show the equivalence between this method and the method of linearised rotational splittings. Conclusions: Our new rotational inversion method substantially reduces the systematic errors of red-giant surface rotation rates. In combination with independent measures of the surface rotation rate, this will allow better constraints to be set on the internal rotation profile. This will be a very important probe for further constraining the internal angular momentum transport along the lower part of the red-giant branch.

Companions to Kepler giant stars: A long-period eccentric sub-stellar companion to KIC 3526061 and a stellar companion to HD 187878 - Karjalainen, Marie; Karjalainen, Raine; Hatzes, Artie P.; Lehmann, Holger; Kervella, Pierre; Hekker, Saskia; Van Winckel, Hans; Überlauer, Jakub; Vítková, Michaela; Skarka, Marek; Kabáth, Petr; Prins, Saskia; et al. (Astronomy & Astrophysics, 2022)
Context: Our knowledge of populations and the occurrence of planets orbiting evolved intermediate-mass stars is still incomplete. In 2010 we started a planet search programme among 95 giant stars observed by the Kepler mission to increase the sample of giant stars with planets and with reliable estimates of stellar masses and radii. Aims: We present the two systems from our planet search programme whose companions we were able to characterise: KIC 3526061 and HD 187878. Methods: We used precise stellar radial velocity measurements taken with four different echelle spectrographs to derive an orbital solution. We used Gaia astrometric measurements to obtain the inclination of the HD 187878 system and Kepler photometric observations to estimate the stellar mass and radius. Results: We report the discovery of a sub-stellar companion and a stellar companion around two intermediate-mass red giant branch stars. KIC 3526061 b is most likely a brown dwarf with a minimum mass of 18.15 ± 0.44 M Jupiter in a long-period eccentric orbit, with orbital period 3552−135+158d and orbital eccentricity e= 0.85 ± 0.01. It is the most evolved system found having a sub-stellar companion with such a high eccentricity and wide separation. HD 187878 B has a minimum mass of 78.4 ± 2.0 M Jupiter. Combining the spectroscopic orbital parameters with the astrometric proper motion anomaly, we derived an orbital inclination i=9.8−0.6+0.4deg, which corresponds to the companion’s mass in the stellar regime of 0.51−0.02+0.04M⊙. Conclusions: A sub-stellar companion of KIC 3526061 extends the sample of known red giant branch stars with sub-stellar companions on very eccentric wide orbits, and might provide a probe of the dynamical evolution of such systems over time.

TESS asteroseismology of the Kepler red giants - Stello, Dennis; Saunders, Nicholas; Grunblatt, Sam; Hon, Marc; Reyes, Claudia; Huber, Daniel; Bedding, Timothy R; Elsworth, Yvonne; García, Rafael A; Hekker, Saskia; Kallinger, Thomas; Mathur, Savita; et al. (Monthly Notices of the Royal Astronomical Society, 2022)
ABSTRACT Red giant asteroseismology can provide valuable information for studying the Galaxy as demonstrated by space missions like CoRoT and Kepler. However, previous observations have been limited to small data sets and fields of view. The TESS mission provides far larger samples and, for the first time, the opportunity to perform asteroseimic inference from full-frame images full-sky, instead of narrow fields and pre-selected targets. Here, we seek to detect oscillations in TESS data of the red giants in the Kepler field using the 4-yr Kepler results as a benchmark. Because we use 1–2 sectors of observation, our results are representative of the typical scenario from TESS data. We detect clear oscillations in ∼3000 stars with another ∼1000 borderline (low S/N) cases. In comparison, best-case predictions suggest ∼4500 detectable oscillating giants. Of the clear detections, we measure Δν in 570 stars, meaning a ∼20 per cent Δν yield (14 per cent for one sector and 26 per cent for two sectors). These yields imply that typical (1–2 sector) TESS data will result in significant detection biases. Hence, to boost the number of stars, one might need to use only νmax as the seismic input for stellar property estimation. However, we find little bias in the seismic measurements and typical scatter is about 5–6 per cent in νmax and 2–3 per cent in Δν. These values, coupled with typical uncertainties in parallax, Teff, and [Fe/H] in a grid-based approach, would provide internal uncertainties of 3 per cent in inferred stellar radius, 6 per cent in mass, and 20 per cent in age for low-luminosity giant stars. Finally, we find red giant seismology is not significantly affected by seismic signal confusion from blending for stars with Tmag ≲ 12.5.

Exoplanet interior retrievals: core masses and metallicities from atmospheric abundances - Bloot, S; Miguel, Y; Bazot, M; Howard, S (Monthly Notices of the Royal Astronomical Society, 2023)
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.

Testing angular momentum transport processes with asteroseismology of solar-type main-sequence stars - Bétrisey, J.; Eggenberger, P.; Buldgen, G.; Benomar, O.; Bazot, M. (Astronomy & Astrophysics, 2023)
Context: Thanks to the so-called photometry revolution with the space-based missions CoRoT, Kepler, and TESS, asteroseismology has become a powerful tool to study the internal rotation of stars. The rotation rate depends on the efficiency of the angular momentum (AM) transport inside the star, and its study allows to constrain the internal AM transport processes, as well as improve our understanding of their physical nature. Aims: We compared the ratio of the rotation rate predicted by asteroseismology and starspot measurements of solar-type stars considering different AM transport prescriptions and investigated whether some of these prescriptions can be ruled out observationally. Methods: We conducted a two-step modelling procedure of four main-sequence stars from the Kepler LEGACY sample, which consists of an asteroseismic characterisation that serves as a guide for a modelling with rotating models, including a detailed and coherent treatment of the AM transport. The rotation profiles derived with this procedure were used to estimate the ratio of the mean asteroseismic rotation rate with the surface rotation rate from starspot measurements for each AM transport prescriptions. Comparisons between the models were then conducted. Results: In the hotter part of the Hertzsprung-Russell (HR) diagram (masses typically above ∼1.2 M⊙ at solar metallicity), models with only hydrodynamic transport processes and models with additional transport by magnetic instabilities are found to be consistent with previous measurements that observed a low degree (below 30%) of radial differential rotation between the radiative and convective zones. For these stars, which constitute a significant fraction of the Kepler LEGACY sample, a combination of asteroseismic constraints from the splitting of pressure modes and of the surface rotation rate does not allow us to conclude that an efficient AM transport is required in addition to transport by meridional circulation and shear instability alone. Even a model assuming local AM conservation cannot be ruled out. In the colder part of the HR diagram, the situation is different because of the efficient braking of the stellar surface by magnetised winds. We find a clear disagreement between the rotational properties of models that only include hydrodynamic processes and asteroseismic constraints, while models with magnetic fields correctly reproduce the observations, similarly to the solar case. Conclusions: This shows the existence of a mass regime corresponding to main-sequence F-type stars for which it is difficult to constrain the AM transport processes, unlike for hotter, Gamma Dor stars or colder, less massive solar analogues. The comparison between asteroseismic measurements and surface rotation rates enables us to easily rule out models with an inefficient transport of AM in the colder part of the HR diagram.

Jupiter’s interior from Juno: Equation-of-state uncertainties and dilute core extent - Howard, S.; Guillot, T.; Bazot, M.; Miguel, Y.; Stevenson, D. J.; Galanti, E.; Kaspi, Y.; Hubbard, W. B.; Militzer, B.; Helled, R.; Nettelmann, N.; Idini, B.; et al. (Astronomy & Astrophysics, 2023)
Context: The Juno mission has provided measurements of Jupiter’s gravity field with an outstanding level of accuracy, leading to better constraints on the interior of the planet. Improving our knowledge of the internal structure of Jupiter is key to understanding its formation and evolution but is also important in the framework of exoplanet exploration. Aims: In this study, we investigated the differences between the state-of-the-art equations of state and their impact on the properties of interior models. Accounting for uncertainty on the hydrogen and helium equation of state, we assessed the span of the interior features of Jupiter. Methods: We carried out an extensive exploration of the parameter space and studied a wide range of interior models using Markov chain Monte Carlo simulations. To consider the uncertainty on the equation of state, we allowed for modifications of the equation of state in our calculations. Results: Our models harbour a dilute core and indicate that Jupiter’s internal entropy is higher than what is usually assumed from the Galileo probe measurements. We obtain solutions with extended dilute cores, but contrary to other recent interior models of Jupiter, we also obtain models with small dilute cores. The dilute cores in such solutions extend to ~20% of Jupiter’s mass, leading to better agreement with formation–evolution models. Conclusions: We conclude that the equations of state used in Jupiter models have a crucial effect on the inferred structure and composition. Further explorations of the behaviour of hydrogen–helium mixtures at the pressure and temperature conditions in Jupiter will help to constrain the interior of the planet, and therefore its origin.

Jupiter’s inhomogeneous envelope - Miguel, Y.; Bazot, M.; Guillot, T.; Howard, S.; Galanti, E.; Kaspi, Y.; Hubbard, W. B.; Militzer, B.; Helled, R.; Atreya, S. K.; Connerney, J. E. P.; Durante, D.; et al. (Astronomy & Astrophysics, 2022)
Context: While Jupiter’s massive gas envelope consists mainly of hydrogen and helium, the key to understanding Jupiter’s formation and evolution lies in the distribution of the remaining (heavy) elements. Before the Juno mission, the lack of high-precision gravity harmonics precluded the use of statistical analyses in a robust determination of the heavy-element distribution in Jupiter’s envelope. Aims: In this paper, we assemble the most comprehensive and diverse collection of Jupiter interior models to date and use it to study the distribution of heavy elements in the planet’s envelope. Methods: We apply a Bayesian statistical approach to our interior model calculations, reproducing the Juno gravitational and atmospheric measurements and constraints from the deep zonal flows. Results: Our results show that the gravity constraints lead to a deep entropy of Jupiter corresponding to a 1 bar temperature that is 515 K higher than traditionally assumed. We also find that uncertainties in the equation of state are crucial when determining the amount of heavy elements in Jupiter’s interior. Our models put an upper limit to the inner compact core of Jupiter of 7 M_Earth, independently of the structure model (with or without a dilute core) and the equation of state considered. Furthermore, we robustly demonstrate that Jupiter’s envelope is inhomogeneous, with a heavy-element enrichment in the interior relative to the outer envelope. This implies that heavy-element enrichment continued through the gas accretion phase, with important implications for the formation of giant planets in our Solar System and beyond.

BioModelsML: Building a FAIR and reproducible collection of machine learning models in life sciences and medicine for easy reuse - Tiwari, Divyang Deep; Hoffmann, Nils; Didi, Kieran; Deshpande, Sumukh; Ghosh, Sucheta; Nguyen, Tung V. N.; Raman, Karthik; Hermjakob, Henning; Sheriff, Rahuman (2023)
Abstract Machine learning (ML) models are widely used in life sciences and medicine; however, they are scattered across various platforms and there are several challenges that hinder their accessibility, reproducibility and reuse. In this manuscript, we present the formalisation and pilot implementation of community protocol to enable FAIReR (Findable, Accessible, Interoperable, Reusable, and Reproducible) sharing of ML models. The protocol consists of eight steps, including sharing model training code, dataset information, reproduced figures, model evaluation metrics, trained models, Dockerfiles, model metadata, and FAIR dissemination. Applying these measures we aim to build and share a comprehensive public collection of FAIR ML models in the BioModels repository through incentivized community curation. In a pilot implementation, we curated diverse ML models to demonstrate the feasibility of our approach and we discussed the current challenges. Building a FAIReR collection of ML models will directly enhance the reproducibility and reusability of ML models, minimising the effort needed to reimplement models, maximising the impact on the application and significantly accelerating the advancement in the field of life science and medicine.

The role of flow in the self-assembly of dragline spider silk proteins - Herrera-Rodriguez, Ana; Dasanna, Anil Kumar; Daday, Csaba; Cruz-Chú, Eduardo R.; Aponte-Santamaría, Camilo; Schwarz, Ulrich S.; Gräter, Frauke (Biophysical Journal, 2023)
Hydrodynamic flow in the spider duct induces conformational changes in dragline spider silk proteins (spidroins) and drives their assembly, but the underlying physical mechanisms are still elusive. Here we address this challenging multiscale problem with a complementary strategy of atomistic and coarse-grained molecular dynamics simulations with uniform flow. The conformational changes at the molecular level were analyzed for single-tethered spider silk peptides. Uniform flow leads to coiled-to-stretch transitions and pushes alanine residues into β sheet and poly-proline II conformations. Coarse-grained simulations of the assembly process of multiple semi-flexible block copolymers using multi-particle collision dynamics reveal that the spidroins aggregate faster but into low-order assemblies when they are less extended. At medium-to-large peptide extensions (50%–80%), assembly slows down and becomes reversible with frequent association and dissociation events, whereas spidroin alignment increases and alanine repeats form ordered regions. Our work highlights the role of flow in guiding silk self-assembly into tough fibers by enhancing alignment and kinetic reversibility, a mechanism likely relevant also for other proteins whose function depends on hydrodynamic flow.

CO 2 on Graphene: Benchmarking Computational Approaches to Noncovalent Interactions - Ehlert, Christopher; Piras, Anna; Gryn'ova, Ganna (ACS Omega, 2023)

Crafting molecular architectures with guided diffusion - Gryn'ova, Ganna (Nature Computational Science, 2023)

Ganna (Anya) Gryn'ova (Helvetica Chimica Acta, 2023)

Comparative analysis of drug-salt-polymer interactions by experiment and molecular simulation improves biopharmaceutical performance - Mukesh, Sumit; Mukherjee, Goutam; Singh, Ridhima; Steenbuck, Nathan; Demidova, Carolina; Joshi, Prachi; Sangamwar, Abhay T.; Wade, Rebecca C. (Communications Chemistry, 2023)
Abstract

Sources of predictability of synoptic‐scale rainfall during the West African summer monsoon - Rasheeda Satheesh, Athul; Knippertz, Peter; Fink, Andreas H.; Walz, Eva-Maria; Gneiting, Tilmann (Quarterly Journal of the Royal Meteorological Society, 2023)

Scoring epidemiological forecasts on transformed scales - Bosse, Nikos I.; Abbott, Sam; Cori, Anne; van Leeuwen, Edwin; Bracher, Johannes; Funk, Sebastian (PLOS Computational Biology, 2023)

Collaborative nowcasting of COVID-19 hospitalization incidences in Germany - Wolffram, Daniel; Abbott, Sam; an der Heiden, Matthias; Funk, Sebastian; Günther, Felix; Hailer, Davide; Heyder, Stefan; Hotz, Thomas; van de Kassteele, Jan; Küchenhoff, Helmut; Müller-Hansen, Sören; Syliqi, Diellë; et al. (PLOS Computational Biology, 2023)

A community effort in SARS‐CoV‐2 drug discovery - Schimunek, Johannes; Seidl, Philipp; Elez, Katarina; Hempel, Tim; Le, Tuan; Noé, Frank; Olsson, Simon; Raich, Lluís; Winter, Robin; Gokcan, Hatice; Gusev, Filipp; Gutkin, Evgeny M.; et al. (Molecular Informatics, 2024)
Abstract The COVID‐19 pandemic continues to pose a substantial threat to human lives and is likely to do so for years to come. Despite the availability of vaccines, searching for efficient small‐molecule drugs that are widely available, including in low‐ and middle‐income countries, is an ongoing challenge. In this work, we report the results of an open science community effort, the “Billion molecules against COVID‐19 challenge”, to identify small‐molecule inhibitors against SARS‐CoV‐2 or relevant human receptors. Participating teams used a wide variety of computational methods to screen a minimum of 1 billion virtual molecules against 6 protein targets. Overall, 31 teams participated, and they suggested a total of 639,024 molecules, which were subsequently ranked to find ‘consensus compounds’. The organizing team coordinated with various contract research organizations (CROs) and collaborating institutions to synthesize and test 878 compounds for biological activity against proteases (Nsp5, Nsp3, TMPRSS2), nucleocapsid N, RdRP (only the Nsp12 domain), and (alpha) spike protein S. Overall, 27 compounds with weak inhibition/binding were experimentally identified by binding‐, cleavage‐, and/or viral suppression assays and are presented here. Open science approaches such as the one presented here contribute to the knowledge base of future drug discovery efforts in finding better SARS‐CoV‐2 treatments.

Modeling graphs beyond hyperbolic: Graph neural networks in symmetric positive definite matrices - Zhao, Wei; López, Federico; Riestenberg, J. Maxwell; Strube, Michael; Taha, Diaaeldin; Trettel, Steve (2023)

Investigating multilingual coreference resolution by universal annotations - Chai, Haixia; Strube, Michael (2023)

HITS at DISRPT 2023: Discourse segmentation, connective detection, and relation classification - Liu, Wei; Fan, Yi; Strube, Michael (2023)

SimCSum: Joint learning of simplification and cross-lingual summarization for cross-lingual science journalism - Fatima, Mehwish; Kolber, Tim; Markert, Katja; Strube, Michael (2023)

Host–guest interactions in framework materials: Insight from modeling - Ernst, Michelle; Evans, Jack D.; Gryn'ova, Ganna (Chemical Physics Reviews, 2023)
The performance of metal–organic and covalent organic framework materials in sought-after applications—capture, storage, and delivery of gases and molecules, and separation of their mixtures—heavily depends on the host–guest interactions established inside the pores of these materials. Computational modeling provides information about the structures of these host–guest complexes and the strength and nature of the interactions present at a level of detail and precision that is often unobtainable from experiment. In this Review, we summarize the key simulation techniques spanning from molecular dynamics and Monte Carlo methods to correlate ab initio approaches and energy, density, and wavefunction partitioning schemes. We provide illustrative literature examples of their uses in analyzing and designing organic framework hosts. We also describe modern approaches to the high-throughput screening of thousands of existing and hypothetical metal–organic frameworks (MOFs) and covalent organic frameworks (COFs) and emerging machine learning techniques for predicting their properties and performances. Finally, we discuss the key methodological challenges on the path toward computation-driven design and reliable prediction of high-performing MOF and COF adsorbents and catalysts and suggest possible solutions and future directions in this exciting field of computational materials science.

Beyond the imitation game: Quantifying and extrapolating the capabilities of language models - Srivastava, Aarohi; Rastogi, Abhinav; Rao, Abhishek; ...,; Strube, Michael; ..,; and_another_400_something_other_authors, (Transactions of Machine Learning Research, 2023)

Proceedings of the 4th Workshop on Computational Approaches to Discourse - Braud, Chloé; Hardmeier, Christian; Li, Junyi Jessy; Loaciga, Sharid; Strube, Michael; Zeldes, Amir (2023)

High-Throughput Phenotypic Screening and Machine Learning Methods Enabled the Selection of Broad-Spectrum Low-Toxicity Antitrypanosomatidic Agents. - Linciano, P.; Quotadamo, A.; Luciani, R.; Santucci, M.; Zorn, K. M.; Foil, D. H.; Lane, T. R.; Cordeiro da Silva, A.; Santarem, N.; B Moraes, C.; Freitas-Junior, L.; Wittig, U.; et al. (J Med Chem, 2023)
Broad-spectrum anti-infective chemotherapy agents with activity against Trypanosomes, Leishmania, and Mycobacterium tuberculosis species were identified from a high-throughput phenotypic screening program of the 456 compounds belonging to the Ty-Box, an in-house industry database. Compound characterization using machine learning approaches enabled the identification and synthesis of 44 compounds with broad-spectrum antiparasitic activity and minimal toxicity against Trypanosoma brucei, Leishmania Infantum, and Trypanosoma cruzi. In vitro studies confirmed the predictive models identified in compound 40 which emerged as a new lead, featured by an innovative N-(5-pyrimidinyl)benzenesulfonamide scaffold and promising low micromolar activity against two parasites and low toxicity. Given the volume and complexity of data generated by the diverse high-throughput screening assays performed on the compounds of the Ty-Box library, the chemoinformatic and machine learning tools enabled the selection of compounds eligible for further evaluation of their biological and toxicological activities and aided in the decision-making process toward the design and optimization of the identified lead.
Keywords: High-Throughput Screening Assays; Antiparasitic Agents; *Trypanosoma cruzi; *Trypanosoma brucei brucei; *Leishmania infantum

Evaluating probabilistic classifiers: The triptych - Dimitriadis, Timo; Gneiting, Tilmann; Jordan, Alexander I.; Vogel, Peter (International Journal of Forecasting, 2024)

Wird KI neue Medikamente gegen Herzkrankheiten hervorbringen? - Glaser, Manuel; Ritterhof, Julia; Most, Patrick; Wade, Rebecca C. (Aktuelle Kardiologie, 2023)
Zusammenfassung Angesichts der umwälzenden Auswirkungen, die künstliche Intelligenz (KI) auf Wissenschaft, Medizin und darüber hinaus hat, betrachten wir hier das Potenzial von KI für die Entdeckung neuer Medikamente gegen Herzkrankheiten. Wir definieren KI im weitesten Sinne als den Einsatz von maschinellem Lernen, einschließlich Statistik und Deep Learning, um Muster in Datensätzen zu erkennen, die für Vorhersagen genutzt werden können. Jüngste Durchbrüche in der Fähigkeit, sehr große Datenmengen zu berücksichtigen, haben einen Boom in der KI-gestützten Arzneimittelentdeckung sowohl in der Wissenschaft als auch in der Industrie ausgelöst. Viele neue Unternehmen verfügen bereits über Arzneimittel-Pipelines, die bis in die klinische Erprobung reichen, aber noch keine Medikamente gegen Herzkrankheiten enthalten. Wir beschreiben hier den Einsatz von KI für die Entdeckung von niedermolekularen Medikamenten und Biologika, einschließlich therapeutischer Peptide, sowie für die Vorhersage von Wirkungen wie Kardiotoxizität. Der konzertierte Einsatz von KI zusammen mit physikbasierten Simulationen und experimentellen Rückkopplungsschleifen wird notwendig sein, um das Potenzial der KI für die Arzneimittelentdeckung und die Entwicklung von Präzisionsarzneimitteln für Herzkrankheiten voll auszuschöpfen.

Simulations of hydrodynamic phenomena in stellar interiors - Horst, Leonhard (2021)
This thesis presents multidimensional hydrodynamic simulations of stellar interiors with a focus on dynamical phenomena at low Mach numbers using the Seven-League Hydro (SLH) code. A better understanding of these phenomena is crucial to improve the modeling of stellar evolution. It is demonstrated that suitable numerical methods are required to avoid that numerical artifacts and spurious dissipation dominate the actual physical flow. Three-dimensional simulations of convective helium shell burning are used to measure the entrainment of mass into the convective region. This aids the parametrization of entrainment in one-dimensional evolution calculations. Furthermore, the excitation of waves by core convection is simulated and the results are analyzed regarding their physical plausibility and agreement with observations. These simulations demonstrate that the current set of numerical methods used by the SLH code is promising regarding future, more realistic simulations of astrophysical flows.

Self-consistent magnetohydrodynamic simulation of jet launching in a neutron star – white dwarf merger - Moran Fraile, Javier; Röpke, Friedrich; Pakmor, R.; Aloy, M. A.; Ohlmann, S. T.; Schneider, Fabian; Leidi, Giovanni (Astronomy & Astrophysics, 2024)
The merger of a white dwarf (WD) and a neutron star (NS) is a relatively common event that will produce an observable electromagnetic signal. Furthermore, the compactness of these stellar objects makes them an interesting candidate for gravitational wave (GW) astronomy, potentially being in the frequency range of LISA and other missions. To date, three-dimensional simulations of these mergers have not fully modelled the WD disruption, or have used lower resolutions and have not included magnetic fields even though they potentially shape the evolution of the merger remnant. In this work, we simulate the merger of a 1.4M_⊙NS with a 1M_⊙carbon oxygen WD in the magnetohydrodynamic moving mesh code \AREPO. We find that the disruption of the WD forms an accretion disk around the NS, and the subsequent accretion by the NS powers the launch of strongly magnetized, mildly relativistic jets perpendicular to the orbital plane. Although the exact properties of the jets could be altered by unresolved physics around the NS, the event could result in a transient with a larger luminosity than kilonovae. We discuss possible connections to fast blue optical transients (FBOTs) and long-duration gamma-ray bursts. We find that the frequency of GWs released during the merger is too high to be detectable by the LISA mission, but suitable for deci-hertz observatories such as LGWA, BBO or DECIGO.

Faint calcium-rich transient from a double detonation of a 0.6 M⊙ carbon-oxygen white dwarf star - Moran Fraile, Javier; Holas, Alexander; Röpke, Friedrich; Pakmor, Rüdiger; Schneider, Fabian (Astronomy & Astrophysics, 2024)
We have computed a three-dimensional hydrodynamic simulation of the merger between a massive (0.4 M_⊙) helium white dwarf (He WD) and a low-mass (0.6 M_⊙) carbon-oxygen white dwarf (CO WD). Despite the low mass of the primary, the merger triggers a thermonuclear explosion as a result of a double detonation, producing a faint transient and leaving no remnant behind. This type of event could also take place during common-envelope mergers whenever the companion is a CO WD and the core of the giant star has a sufficiently large He mass. The spectra show strong Ca lines throughout the first few weeks after the explosion. The explosion only yields <0.01 M_⊙of ^56Ni, resulting in a low-luminosity SN Ia-like lightcurve that resembles the Ca-rich transients within this broad class of objects, with a peak magnitude of M_\mathrmbol ≈-15.7 mag and a rather slow decline rate of ∆m_15^\mathrmbol≈1.5 mag. Both, its lightcurve-shape and spectral appearance, resemble the appearance of Ca-rich transients, suggesting such mergers as a possible progenitor scenario for this class of events.

From Easy to Hopeless—Predicting the Difficulty of Phylogenetic Analyses - Haag, Julia; Höhler, Dimitri; Bettisworth, Ben; Stamatakis, Alexandros (Molecular Biology and Evolution, 2022)
Abstract Phylogenetic analyzes under the Maximum-Likelihood (ML) model are time and resource intensive. To adequately capture the vastness of tree space, one needs to infer multiple independent trees. On some datasets, multiple tree inferences converge to similar tree topologies, on others to multiple, topologically highly distinct yet statistically indistinguishable topologies. At present, no method exists to quantify and predict this behavior. We introduce a method to quantify the degree of difficulty for analyzing a dataset and present Pythia, a Random Forest Regressor that accurately predicts this difficulty. Pythia predicts the degree of difficulty of analyzing a dataset prior to initiating ML-based tree inferences. Pythia can be used to increase user awareness with respect to the amount of signal and uncertainty to be expected in phylogenetic analyzes, and hence inform an appropriate (post-)analysis setup. Further, it can be used to select appropriate search algorithms for easy-, intermediate-, and hard-to-analyze datasets.

A representative Performance Assessment of Maximum Likelihood based Phylogenetic Inference Tools - Höhler, Dimitri; Haag, Julia; Kozlov, Alexey; Stamatakis, Alexandros (2022)
Abstract

Spatial and temporal heterogeneity in human mobility patterns in Holocene Southwest Asia and the East Mediterranean - Koptekin, Dilek; Yüncü, Eren; Rodríguez-Varela, Ricardo; Altınışık, N. Ezgi; Psonis, Nikolaos; Kashuba, Natalia; Yorulmaz, Sevgi; George, Robert; Kazancı, Duygu Deniz; Kaptan, Damla; Gürün, Kanat; Vural, Kıvılcım Başak; et al. (Current Biology, 2023)

ReStore: In-Memory REplicated STORagE for Rapid Recovery in Fault-Tolerant Algorithms - Hübner, Lukas; Hespe, Demian; Sanders, Peter; Stamatakis, Alexandros (2022 IEEE/ACM 12th Workshop on Fault Tolerance for HPC at eXtreme Scale (FTXS), 2022)

Asteroid: a new algorithm to infer species trees from gene trees under high proportions of missing data - Morel, Benoit; Williams, Tom A; Stamatakis, Alexandros (Bioinformatics, 2023)
Abstract

The power and limitations of species tree-aware phylogenetics - Williams, Tom A.; Davin, Adrian A.; Morel, Benoit; Szánthó, Lénárd L.; Spang, Anja; Stamatakis, Alexandros; Hugenholtz, Philip; Szöllősi, Gergely J. (2023)
Abstract Species tree-aware phylogenetic methods model how gene trees are generated along the species tree by a series of evolutionary events, including the duplication, transfer and loss of genes. Over the past ten years these methods have emerged as a powerful tool for inferring and rooting gene and species trees, inferring ancestral gene repertoires, and studying the processes of gene and genome evolution. However, these methods are complex and can be more difficult to use than traditional phylogenetic approaches. Method development is rapid, and it can be difficult to decide between approaches and interpret results. Here, we review ALE and GeneRax, two popular packages for reconciling gene and species trees, explaining how they work, how results can be interpreted, and providing a tutorial for practical analysis. It was recently suggested that reconciliation-based estimates of duplication and transfer frequencies are unreliable. We evaluate this criticism and find that, provided parameters are estimated from the data rather than being fixed based on prior assumptions, reconciliation-based inferences are in good agreement with the literature, recovering variation in gene duplication and transfer frequencies across lineages consistent with the known biology of studied clades. For example, published datasets support the view that transfers greatly outnumber duplications in most prokaryotic lineages. We conclude by discussing some limitations of current models and prospects for future progress.

Parallel Inference of Phylogenetic Stands with Gentrius - Togkousidis, Anastasis; Chernomor, Olga; Stamatakis, Alexandros (2023 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW), 2023)

Simulations of sequence evolution: how (un)realistic they are and why - Trost, Johanna; Haag, Julia; Höhler, Dimitri; Jacob, Laurent; Stamatakis, Alexandros; Boussau, Bastien (2023)
Abstract

Interpreting phylogenetic placements for taxonomic assignment of environmental DNA - Ewers, Isabelle; Rajter, Lubomír; Czech, Lucas; Mahé, Frédéric; Stamatakis, Alexandros; Dunthorn, Micah (Journal of Eukaryotic Microbiology, 2023)
Abstract Taxonomic assignment of operational taxonomic units (OTUs) is an important bioinformatics step in analyzing environmental sequencing data. Pairwise alignment and phylogenetic‐placement methods represent two alternative approaches to taxonomic assignments, but their results can differ. Here we used available colpodean ciliate OTUs from forest soils to compare the taxonomic assignments of VSEARCH (which performs pairwise alignments) and EPA‐ng (which performs phylogenetic placements). We showed that when there are differences in taxonomic assignments between pairwise alignments and phylogenetic placements at the subtaxon level, there is a low pairwise similarity of the OTUs to the reference database. We then showcase how the output of EPA‐ng can be further evaluated using GAPPA to assess the taxonomic assignments when there exist multiple equally likely placements of an OTU, by taking into account the sum over the likelihood weights of the OTU placements within a subtaxon, and the branch distances between equally likely placement locations. We also inferred the evolutionary and ecological characteristics of the colpodean OTUs using their placements within subtaxa. This study demonstrates how to fully analyze the output of EPA‐ng, by using GAPPA in conjunction with knowledge of the taxonomic diversity of the clade of interest.

The Free Lunch is not over yet—systematic exploration of numerical thresholds in maximum likelihood phylogenetic inference - Haag, Julia; Hübner, Lukas; Kozlov, Alexey; Stamatakis, Alexandros (Bioinformatics Advances, 2023)
Abstract

Adaptive RAxML-NG: Accelerating Phylogenetic Inference under Maximum Likelihood using Dataset Difficulty - Togkousidis, Anastasis; Kozlov, Alexey; Haag, Julia; Höhler, Dimitri; Stamatakis, Alexandros (Molecular Biology and Evolution, 2023)
Abstract Phylogenetic inferences under the maximum likelihood criterion deploy heuristic tree search strategies to explore the vast search space. Depending on the input dataset, searches from different starting trees might all converge to a single tree topology. Often, though, distinct searches infer multiple topologies with large log-likelihood score differences or yield topologically highly distinct, yet almost equally likely, trees. Recently, Haag et al. introduced an approach to quantify, and implemented machine learning methods to predict, the dataset difficulty with respect to phylogenetic inference. Easy multiple sequence alignments (MSAs) exhibit a single likelihood peak on their likelihood surface, associated with a single tree topology to which most, if not all, independent searches rapidly converge. As difficulty increases, multiple locally optimal likelihood peaks emerge, yet from highly distinct topologies. To make use of this information, we introduce and implement an adaptive tree search heuristic in RAxML-NG, which modifies the thoroughness of the tree search strategy as a function of the predicted difficulty. Our adaptive strategy is based upon three observations. First, on easy datasets, searches converge rapidly and can hence be terminated at an earlier stage. Second, overanalyzing difficult datasets is hopeless, and thus it suffices to quickly infer only one of the numerous almost equally likely topologies to reduce overall execution time. Third, more extensive searches are justified and required on datasets with intermediate difficulty. While the likelihood surface exhibits multiple locally optimal peaks in this case, a small proportion of them is significantly better. Our experimental results for the adaptive heuristic on 9,515 empirical and 5,000 simulated datasets with varying difficulty exhibit substantial speedups, especially on easy and difficult datasets (53% of total MSAs), where we observe average speedups of more than 10×. Further, approximately 94% of the inferred trees using the adaptive strategy are statistically indistinguishable from the trees inferred under the standard strategy (RAxML-NG).

AleRax: A tool for gene and species tree co-estimation and reconciliation under a probabilistic model of gene duplication, transfer, and loss - Morel, Benoit; Williams, Tom A.; Stamatakis, Alexandros; Szöllősi, Gergely J. (2023)
ABSTRACT

Identification of the 18 World War II Executed Citizens of Adele, Rethymnon, Crete Using an Ancient DNA Approach and Low Coverage Genomes - Psonis, Nikolaos; Vassou, Despoina; Nafplioti, Argyro; Tabakaki, Eugenia; Pavlidis, Pavlos; Stamatakis, Alexandros; Poulakakis, Nikos (2023)

The young massive SMC cluster NGC 330 seen by MUSE. III. Stellar parameters and rotational velocities - Bodensteiner, J.; Sana, H.; Dufton, P. L.; Wang, C.; Langer, N.; Banyard, G.; Mahy, L.; de Koter, A.; de Mink, S. E.; Evans, C. J.; Götberg, Y.; Hénault-Brunet, V.; et al. (\aap, 2023)

Bimodal Black Hole Mass Distribution and Chirp Masses of Binary Black Hole Mergers - Schneider, Fabian R. N.; Podsiadlowski, Philipp; Laplace, Eva (\apjl, 2023)

Gravitational wave emission from dynamical stellar interactions - Morán-Fraile, Javier; Schneider, Fabian R. N.; Röpke, Friedrich K.; Ohlmann, Sebastian T.; Pakmor, Rüdiger; Soultanis, Theodoros; Bauswein, Andreas (\aap, 2023)

3D simulations of strongly magnetized non-rotating supernovae: explosion dynamics and remnant properties - Varma, Vishnu; Müller, Bernhard; Schneider, Fabian R. N. (\mnras, 2023)

The VLT-FLAMES Tarantula Survey. Observational evidence for two distinct populations of massive runaway stars in 30 Doradus - Sana, H.; Ramı́rez-Agudelo, O. H.; Hénault-Brunet, V.; Mahy, L.; Almeida, L. A.; de Koter, A.; Bestenlehner, J. M.; Evans, C. J.; Langer, N.; Schneider, F. R. N.; Crowther, P. A.; de Mink, S. E.; et al. (\aap, 2022)

From 3D hydrodynamic simulations of common-envelope interaction to gravitational-wave mergers - Moreno, Melvin M.; Schneider, Fabian R. N.; Röpke, Friedrich K.; Ohlmann, Sebastian T.; Pakmor, Rüdiger; Podsiadlowski, Philipp; Sand, Christian (\aap, 2022)

Computational Models of Hen Egg White Lysozyme Physisorption to Silica- and Mica-like Surfaces - Nießner, Jakob (2023)
Protein-surface adsorption phenomena play a crucial role in a variety of fields, including medicine, molecular and cell biology, biotechnology, phar- maceutical sciences, and biophysics. It is therefore desirable to develop accu- rate models to describe them. Hen-Egg-White-Lysozyme (HEWL) adsorption to silica- and mica-like surfaces entails minimal conformational changes, rendering it an ideal system for rigid-body Brownian dynamics simulations. Experimen- tal studies revealed that the fluorescence attached to HEWL exhibits a sharp overshoot followed by a trough as a function of time. This work identifies two inconsistencies in the explanation previously used by the authors to interpret the experiment. A reorientation does not seem to be the main driving factor for the overshoot. Furthermore, I examine previously used electrostatic potential models based on the Poisson-Boltzmann equation (PBE) used by Romanowska et al. and Reinhardt et al.. It is found that even though different orientations of HEWL- FITC on the surface are possible, the required amount of adsorbed proteins and corresponding reorientation is attained only with the linearized PBE-potential. The study provides a possible explanation why reaching the critical overshoot value with non-linearized PBE-based models poses difficulties. Overcoming this problem is an indispensable step in developing Brownian-dynamics-based atomic- detail multi-molecule models for protein-surface adsorption.

Modeling of mechanistic effects of neurotrophin modulators - Athanasiou, Christina (2023)
Neurotrophins (NTs) are growth factors that are expressed in the central and peripheral nervous systems. They are implicated in different phases of the development and maintenance of the nervous system and they can regulate neuronal survival, development, function, plasticity, as well as neuronal apoptosis. NTs can be used as therapeutics for the treatment of neurodegenerative disorders. However, their poor pharmacokinetic properties and their invasive administration to patients renders them inefficient for use as pharmaceuticals. A solution to this can be offered by small molecule NT mimetics, which can elicit NT mechanisms through binding to NT receptors, which are transmembrane (TM) glycoproteins. The mechanism of activation of NT receptors remains elusive and thus, in this thesis, I have investigated the mechanism of action of NT receptors and mimetics through molecular modeling and molecular dynamics (MD) simulations. I modeled and simulated the glycosylated state of the full extracellular (EC) domains of Tropomyosin receptor kinases A and B (TrkA, TrkB) NT receptors, which revealed that the glycans can shield the accessible surface area of the receptors and participate in the contact area between receptor and NT. Most importantly, glycosylation promoted the extended conformations of the EC domains, which might facilitate NT binding. Then, I performed coarse-grained MD simulations to study the possible arrangements of the TM helical homodimers of TrkA and TrkB receptors in micelles. The results revealed arrangements that could correspond to the active state of the receptors, while metadynamics simulations indicated a stronger binding for the TrkA helices by 10 kJ/mol compared to TrkB. Next, I modeled the full-length structures of the TrkA and TrkB receptors in their homodimeric, glycosylated state bound to their NTs. I embedded the receptors in a realistic model of a neuronal asymmetric membrane. I verified the proper behavior of the membrane and proteins with smaller systems comprising of the TM and intracellular monomers of the receptors. These test simulations revealed interactions between positively charged residues of the kinase domain of TrkA with negatively charged lipids of the inner leaflet of the membrane. These interactions were also formed in the full-length system, and they might stabilize the two kinase domains of the receptor dimer in an orientation that promotes activation, even though the kinase domains were not activated during the simulations. Also, in the full-length systems, the EC domains approached and lay down on the neuronal membrane, while interacting with membrane lipids, such as gangliosides, which are able to activate the NT receptors. Finally, I investigated the binding of small-molecule NT mimetics to the EC and TM domains of TrkA and TrkB receptors, with molecular docking and MD simulations. While plausible poses were 8 obtained, they were not able to explain the selectivity of the compounds for the receptors and the simulations showed that binding was weak. Due to the cholesterol core of the NT mimetics, I tested the ability of the compounds to enter the cell membrane with MD simulations. The compounds were able to spontaneously penetrate the membranes, indicating that their binding site could also lie in the TM region of the receptors. However, simulations with the compounds bound or close to the TM helices in the membrane environment, showed no specific binding. Further experimental exploration of the binding mechanism of these compounds is required. Overall, this thesis sheds light on the dynamic behavior of TrkA and TrkB NT receptors in membranes and the mechanistic insights provide a basis for future studies to develop NT mimetics.

Optimization of a Drug: Polymer Formulation Exploiting a Computational Approach based on Molecular Dynamics Simulations - Tarnow, Michel (2023)

SUMO's intrinsically disordered N-terminus is an intramolecular inhibitor of SUMO - SIM interactions - Richter, Martin; Jin, Fan; Ritterhoff, Tobias; Fergin, Aleksandra; Maurer, Eric; Frank, Andrea; Hajnal, Alex; Klevit, Rachel; Gräter, Frauke; Flotho, Annette; Melchior, Frauke (bioRxiv, 2024)
Small Ubiquitin-related modifiers of the SUMO family regulate thousands of proteins in eukaryotic cells. Many SUMO substrates, effectors and enzymes carry short motifs (SIMs) that mediate low affinity interactions with SUMO proteins. This raises the question how specificity is achieved in target selection, SUMO paralogue choice and SUMO-dependent interactions. A unique but poorly understood feature of SUMO proteins is their intrinsically disordered N-terminus. We reveal a function for N-termini of human, C. elegans, and yeast SUMO proteins as intramolecular inhibitors of SUMO-SIM interactions. Mutational analyses, NMR spectroscopy, and Molecular Dynamics simulations indicate that SUMO's N-terminus can inhibit SIM binding by fast and fuzzy interactions with SUMO‘s core. Deletion of the C. elegans SUMO1 N-terminus leads to p53-dependent apoptosis during germline development, indicating an important role of SUMO's N-termini in DNA damage repair. Our findings reveal a mechanism of disorder-based autoinhibition that contributes to the specificity of SUMOylation and SUMO-dependent interactions.

Resource-aware Research on Universe and Matter: Call-to-Action in Digital Transformation - Bruers, Ben; Cruces, Marilyn; Demleitner, Markus; Duckeck, Guenter; Düren, Michael; Eich, Niclas; Enßlin, Torsten; Erdmann, Johannes; Erdmann, Martin; Fackeldey, Peter; Felder, Christian; Fischer, Benjamin; et al. (arXiv,physics.comp-ph,2311.01169, 2023)

The contribution of binary star formation via core fragmentation on protostellar multiplicity - Kuruwita, Rajika; Haugbølle, Troels (Astronomy & Astrophysics, 2023)

Experimental and Computational Study on the Effects of High Pressure on the Crystal Structure of Boron Nitrilotriacetate - Montisci, Fabio; Ernst, Michelle; Macchi, Piero (Crystal Growth & Design, 2023)
Herein, we report the structural changes occurring to the molecule of boron nitrilotriacetate and its interactions when the crystal is under compression. A special focus is on the intermolecular interactions involving the carbonyl groups. In fact, these short-range contacts may anticipate cooperative addition reactions that could eventually lead to a polymer. However, X-ray diffraction experiments do not evidence any polymerization at least up to 16 GPa, due to competing interactions. In this work, we use and illustrate several theoretical tools to investigate the variable nature of the intermolecular interactions and their changes upon compression.

Nucleosynthesis of Binary-stripped Stars - Farmer, R.; Laplace, E.; Ma, Jing-ze; de Mink, S. E.; Justham, S. (The Astrophysical Journal, 2023)
The cosmic origin of the elements, the fundamental chemical building blocks of the universe, is still uncertain. Binary interactions play a key role in the evolution of many massive stars, yet their impact on chemical yields is poorly understood. Using the MESA stellar evolution code, we predict the chemical yields ejected in wind mass loss and the supernovae of single and binary-stripped stars. We do this with a large 162-isotope nuclear network at solar metallicity. We find that binary-stripped stars are more effective producers of the elements than single stars, due to their increased mass loss and an increased chance to eject their envelopes during a supernova. This increased production by binaries varies across the periodic table, with F and K being more significantly produced by binary-stripped stars than single stars. We find that the 12C/13C could be used as an indicator of the conservativeness of mass transfer, as 13C is preferentially ejected during mass transfer while 12C is preferentially ejected during wind mass loss. We identify a number of gamma-ray-emitting radioactive isotopes that may be used to help constrain progenitor and explosion models of core-collapse supernovae with next-generation gamma-ray detectors. For single stars we find that 44V and 52Mn are strong probes of the explosion model, while for binary-stripped stars it is 48Cr. Our findings highlight that binary-stripped stars are not equivalent to two single stars and that detailed stellar modeling is needed to predict their final nucleosynthetic yields.

Bringing Stellar Evolution and Feedback Together: Summary of Proposals from the Lorentz Center Workshop - Geen, Sam; Agrawal, Poojan; Crowther, Paul A.; Keller, B. W.; de Koter, Alex; Keszthelyi, Zsolt; van de Voort, Freeke; Ali, Ahmad A.; Backs, Frank; Bonne, Lars; Brugaletta, Vittoria; Derkink, Annelotte; et al. (Publications of the Astronomical Society of the Pacific, 2023)
Stars strongly impact their environment, and shape structures on all scales throughout the universe, in a process known as "feedback." Due to the complexity of both stellar evolution and the physics of larger astrophysical structures, there remain many unanswered questions about how feedback operates and what we can learn about stars by studying their imprint on the wider universe. In this white paper, we summarize discussions from the Lorentz Center meeting "Bringing Stellar Evolution and Feedback Together" in 2022 April and identify key areas where further dialog can bring about radical changes in how we view the relationship between stars and the universe they live in.

X-Shooting ULLYSES: Massive stars at low metallicity. I. Project description - Vink, Jorick S.; Mehner, A.; Crowther, P. A.; Fullerton, A.; Garcia, M.; Martins, F.; Morrell, N.; Oskinova, L. M.; St-Louis, N.; ud-Doula, A.; Sander, A. A. C.; Sana, H.; et al. (Astronomy and Astrophysics, 2023)
Observations of individual massive stars, super-luminous supernovae, gamma-ray bursts, and gravitational wave events involving spectacular black hole mergers indicate that the low-metallicity Universe is fundamentally different from our own Galaxy. Many transient phenomena will remain enigmatic until we achieve a firm understanding of the physics and evolution of massive stars at low metallicity (Z). The Hubble Space Telescope has devoted 500 orbits to observing ∼250 massive stars at low Z in the ultraviolet (UV) with the COS and STIS spectrographs under the ULLYSES programme. The complementary X-Shooting ULLYSES (XShootU) project provides an enhanced legacy value with high-quality optical and near-infrared spectra obtained with the wide-wavelength coverage X-shooter spectrograph at ESO’s Very Large Telescope. We present an overview of the XShootU project, showing that combining ULLYSES UV and XShootU optical spectra is critical for the uniform determination of stellar parameters such as effective temperature, surface gravity, luminosity, and abundances, as well as wind properties such as mass-loss rates as a function of Z. As uncertainties in stellar and wind parameters percolate into many adjacent areas of astrophysics, the data and modelling of the XShootU project is expected to be a game changer for our physical understanding of massive stars at low Z. To be able to confidently interpret James Webb Space Telescope spectra of the first stellar generations, the individual spectra of low-Z stars need to be understood, which is exactly where XShootU can deliver. Table B.1 and full Table B.2 are available at the CDS via anonymous ftp to cdsarc.cds.unistra.fr (ftp://130.79.128.5) or via https://cdsarc.cds.unistra.fr/viz-bin/cat/J/A+A/675/A154 Based on observations collected at the European Southern Observatory under ESO programme 106.211Z.001.

Substituting density functional theory in reaction barrier calculations for hydrogen atom transfer in proteins - Riedmiller, Kai; Reiser, Patrick; Bobkova, Elizaveta; Maltsev, Kiril; Gryn'ova, Ganna; Friederich, Pascal; Gräter, Frauke (Chemical Science, 2024)
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.

Observations of high-order multiplicity in a high-mass stellar protocluster - Li, Shanghuo; Sanhueza, Patricio; Beuther, Henrik; Chen, Huei-Ru Vivien; Kuiper, Rolf; Olguin, Fernando A.; Pudritz, Ralph E.; Stephens, Ian W.; Zhang, Qizhou; Nakamura, Fumitaka; Lu, Xing; Kuruwita, Rajika; et al. (Nature Astronomy, 2024)
Abstract

Accelerated chemical science with AI - Back, Seoin; Aspuru-Guzik, Alán; Ceriotti, Michele; Gryn'ova, Ganna; Grzybowski, Bartosz; Gu, Geun Ho; Hein, Jason; Hippalgaonkar, Kedar; Hormázabal, Rodrigo; Jung, Yousung; Kim, Seonah; Kim, Woo Youn; et al. (Digital Discovery, 2024)
The ASLLA Symposium focused on accelerating chemical science with AI. Discussions on data, new applications, algorithms, and education were summarized. Recommendations for researchers, educators, and academic bodies were provided.

Asteroseismic Inversions for Internal Sound Speed Profiles of Main-sequence Stars with Radiative Cores - Buchele, Lynn; Bellinger, Earl P.; Hekker, Saskia; Basu, Sarbani; Ball, Warrick; Christensen-Dalsgaard, Jørgen (The Astrophysical Journal, 2024)
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.

Bioschemas Resource Index for Chem and Plants - Arend, Daniel; Del Conte, Alessio; Feser, Manuel; Gadiya, Yojana; Gaignard, Alban; Castro, Leyla Jael; Mičetić, Ivan; Moretti, Sebastien; Neumann, Steffen; Rayya, Noura; Tsueng, Ginger; Willighagen, Egon; et al. (2024)
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 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.

Metadata schema of the NFDI4Health and the NFDI4Health Task Force COVID-19 (V3_1) - Abaza, Haitham; Shutsko, A.; Golebiewski, Martin; Klopfenstein, Sophie; Schmidt, C. O.; Vorisek, Carina; COVID-19, NFDI4Health Task Force; NFDI4Health,; Brünings-Kuppe, C.; Clemens, V.; Darms, J.; Hanß, S.; et al. (2023)
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_1 of the Metadata Schema, which introduces two new resource types, namely registries and secondary data sources. Accordingly, the metadata set describing studies, which was part of the core module in previous versions, has been split into a separate module and adapted to also apply to registries and secondary data sources. An additional use case-specific module has also been added, including metadata specific to record linkage. The undertaken changes are described within the document.

Metadata schema of the NFDI4Health and the NFDI4Health Task Force COVID-19 (V3_2) - Abaza, Haitham; Shutsko, A.; Golebiewski, Martin; Klopfenstein, Sophie; Schmidt, Carsten Oliver; Vorisek, Carina; COVID-19, NFDI4Health Task Force; NFDI4Health,; Brünings-Kuppe, C.; Clemens, V.; Darms, J.; Hanß, S.; et al. (2023)
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.

The NFDI4Health Metadata Schema (V3_3) - Abaza, Haitham; Shutsko, A.; Golebiewski, Martin; Klopfenstein, Sophie; Schmidt, C. O.; Vorisek, Carina; Brünings-Kuppe, C.; Clemens, V.; Darms, J.; Hanß, S.; Intemann, T.; Jannasch, F.; et al. (2023)
Originally developed by the NFDI4Health Task Force COVID-19, the Metadata Schema of the NFDI4Health 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_3 of the Metadata Schema, which improves the modules of the previous version via refined display names, description and additional information texts, and short input helps and examples. The new version also introduces a new url item in the data sharing section for linking to data request applications. The undertaken changes are described within the document.

Substituting density functional theory in reaction barrier calculations for hydrogen atom transfer in proteins - Riedmiller, Kai; Reiser, Patrick; Bobkova, Elizaveta; Maltsev, Kiril; Gryn'ova, Ganna; Friederich, Pascal; Gräter, Frauke (Chem. Sci., 2024)
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.

Easy uncertainty quantification (EasyUQ): Generating predictive distributions from single-valued model output - Walz, Eva-Maria; Henzi, Alexander; Ziegel, Johanna; Gneiting, Tilmann (SIAM Review, 2024)

T-FINDER: A highly sensitive, pan-HLA platform for functional T cell receptor and ligand discovery - Cetin, Miray; Pinamonti, Veronica; Schmid, Theresa; Boschert, Tamara; Mellado Fuentes, Ana; Kromer, Kristina; Lerner, Taga; Zhang, Jing; Herzig, Yonatan; Ehlert, Christopher; Hernandez-Hernandez, Miguel; Samaras, Georgios; et al. (Science Advances, 2024)
Effective, unbiased, high-throughput methods to functionally identify both class II and class I HLA–presented T cell epitopes and their cognate T cell receptors (TCRs) are essential for and prerequisite to diagnostic and therapeutic applications, yet remain underdeveloped. Here, we present T-FINDER [T cell Functional Identification and (Neo)-antigen Discovery of Epitopes and Receptors], a system to rapidly deconvolute CD4 and CD8 TCRs and targets physiologically processed and presented by an individual’s unmanipulated, complete human leukocyte antigen (HLA) haplotype. Combining a highly sensitive TCR signaling reporter with an antigen processing system to overcome previously undescribed limitations to target expression, T-FINDER both robustly identifies unknown peptide:HLA ligands from antigen libraries and rapidly screens and functionally validates the specificity of large TCR libraries against known or predicted targets. To demonstrate its capabilities, we apply the platform to multiple TCR-based applications, including diffuse midline glioma, celiac disease, and rheumatoid arthritis, providing unique biological insights and showcasing T-FINDER’s potency and versatility.

An artificial neural network model to predict structure-based protein–protein free energy of binding from Rosetta-calculated properties - Ferraz, Matheus V. F.; Neto, José C. S.; Lins, Roberto D.; Teixeira, Erico S. (Physical Chemistry Chemical Physics, 2023)
An artificial neural network protocol to compute protein–protein free energy of binding.

Enhanced sampling simulations and machine learning to address infectious diseases - Ferraz, Matheus (2023)

Testing quantile forecast optimality - Fosten, Jack; Gutknecht, Daniel; Pohle, Marc-Oliver (Journal of Business & Economic Statistics, 2024)

Mechanical control of the adhesion of malaria-infected erythrocytes to the placenta - Roessner, Rita; Michelarakis, Nicholas; Gräter, Frauke; Aponte-Santamaría, Camilo (Biophysical Journal, 2024)
Pathogens use sophisticated adhesion mechanisms to remain attached to the host’s surfaces. A key example of this is the adhesion of erythrocytes infected with Plasmodium, the parasite which causes malaria, to the microvasculature. Remarkably, in the case of pregnancy-associated malaria, the adherence of parasitized erythrocytes to the placenta is enhanced by the shear of the flowing blood, suggesting a catch-bond adhesion mechanism. The adhesion is mediated by a parasite protein called VAR2CSA which anchors such infected erythrocytes to the proteoglycan matrix of the placenta. In this work, by using extensive equilibrium and force-probe molecular dynamics simulations, we elucidate the—so far unknown—molecular mechanism governing the adhesive function of VAR2CSA. We demonstrate that the elongation tension that arises from the shear of the flowing blood opens VAR2CSA into two structurally-intact domains, thereby exposing cryptic sugar binding sites. The orientation of VAR2CSA with respect to the pulling direction as well as strong sugar-protein shearing interactions favor this mode of opening. Accordingly, as the basis for a catch bond, we propose that mechanical forces strengthen the adhesion of infected erythrocytes, by increasing the number of sugar binding sites in VAR2CSA and not the bond lifetime as it would be canonically thought for a catch bond. This constitutes a new intriguing hypothesis which is of high relevance for our understanding of malaria infection and for the design of vaccines. More generally, our results put forward force-induced multivalency of mechano-responsive proteins as a key new concept for pathogen-host interactions.

Graph-based clustering for detecting semantic change across time and languages - Ma, Xianghe; Strube, Michael; Zhao, Wei (2024)

Attention or convolution: Transformer encoders in audio language models for inference efficiency - Jeon, Sungho; Yeh, Ching-Feng; Inan, Hakan; Hsu, Wei-Ning; Rungta, Rashi; Mehdad, Yashar; Bikel, Daniel (2024)

Scalable stellar evolution forecasting - Deep learning emulation versus hierarchical nearest-neighbor interpolation - Maltsev, Kiril; Schneider, Fabian; Röpke, Friedrich; Jordan, Alexander I.; Qadir, G. A.; Kerzendorf, W. E.; Riedmiller, Kai; Smagt, P. van der (A&A, 2024)
Many astrophysical applications require efficient yet reliable forecasts of stellar evolution tracks. One example is population synthesis, which generates forward predictions of models for comparison with observations. The majority of state-of-the-art rapid population synthesis methods are based on analytic fitting formulae to stellar evolution tracks that are computationally cheap to sample statistically over a continuous parameter range. The computational costs of running detailed stellar evolution codes, such as MESA, over wide and densely sampled parameter grids are prohibitive, while stellar-age based interpolation in-between sparsely sampled grid points leads to intolerably large systematic prediction errors. In this work, we provide two solutions for automated interpolation methods that offer satisfactory trade-off points between cost-efficiency and accuracy. We construct a timescale-adapted evolutionary coordinate and use it in a two-step interpolation scheme that traces the evolution of stars from zero age main sequence all the way to the end of core helium burning while covering a mass range from 0.65 to 300 M⊙. The feedforward neural network regression model (first solution) that we train to predict stellar surface variables can make millions of predictions, sufficiently accurate over the entire parameter space, within tens of seconds on a 4-core CPU. The hierarchical nearest-neighbor interpolation algorithm (second solution) that we hard-code to the same end achieves even higher predictive accuracy, the same algorithm remains applicable to all stellar variables evolved over time, but it is two orders of magnitude slower. Our methodological framework is demonstrated to work on the MESA Isochrones and Stellar Tracks (Choi et al. 2016) data set, but is independent of the input stellar catalog. Finally, we discuss the prospective applications of these methods and provide guidelines for generalizing them to higher dimensional parameter spaces.

Substituting density functional theory in reaction barrier calculations for hydrogen atom transfer in proteins - Riedmiller, Kai; Reiser, Patrick; Bobkova, Elizaveta; Maltsev, Kiril; Gryn’ova, Ganna; Friederich, Pascal; Gräter, Frauke (Chem. Sci., 2024)
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.

Data Publication for Personalised Health Data - Fluck, Juliane; Golebiewski, Martin; Darms, Johannes (Proceedings of the Conference on Research Data Infrastructure, 2023)
Health data collected in clinical trials and epidemiological as well as public health studies cannot be freely published, but are valuable datasets whose subsequent use is of high importance for health research. The National Research Data Infrastructure for Personal Health Data (NFDI4Health) aims to promote the publication of such health data without compromising privacy. Based on existing international standards, NFDI4Health has established a generic information model for the description and preservation of high-level metadata describing health-related studies, covering both clinical and epidemiological studies. As an infrastructure for publishing such preservation metadata as well as more detailed representation information of study data (e.g. questionaries and data dictionaries), NFDI4Health has developed the German Central Health Study Hub. Content is either harvested from existing distributed sources or entered directly via a user interface. This metadata makes health studies more discoverable, and researchers can use the published metadata to evaluate the content of data collections, learn about access conditions and how and where to request data access. The goal of NFDI4Health is to establish interoperable and internationally accepted standards and processes for the publication of health data sets to make health data FAIR.

NFDI4Health Local Data Hubs for Finding and Accessing Health Data - Meineke, Frank; Golebiewski, Martin; Hu, Xiaoming; Kirsten, Toralf; Löbe, Matthias; Klammt, Sebastian; Sax, Ulrich; Müller, Wolfgang (Proceedings of the Conference on Research Data Infrastructure, 2023)
To support federated data structuring and sharing for sensitive health data from clinical trial, epidemiological and public health studies in the context of the German National Research Data Infrastructure for Personal Health Data (NFDI4Health), we have developed Local Data Hubs (LDHs) based on the FAIRDOM-SEEK platform. Those LDHs connect to the German Central Health Study Hub (CSH) to make the health data searchable and findable. This decentralised approach supports researchers to make health studies with their data FAIR (Findable, Accessible, Interoperable and Reusable), and at the same time fully preserves data protection for sensitive data.

Shot noise reduction in radiographic and tomographic multi-channel imaging with self-supervised deep learning - Zharov, Yaroslav; Ametova, Evelina; Spiecker, Rebecca; Baumbach, Tilo; Burca, Genoveva; Heuveline, Vincent (Optics Express, 2023)

Deep Learning and Explainable Artificial Intelligence for Improving Specificity and Detecting Metabolic Patterns in Newborn Screening - Zaunseder, Elaine; Mütze, Ulrike; Garbade, Sven F.; Haupt, Saskia; Kölker, Stefan; Heuveline, Vincent (2023 IEEE Symposium Series on Computational Intelligence (SSCI), 2023)

Personalised metabolic whole-body models for newborns and infants predict growth and biomarkers of inherited metabolic diseases - Zaunseder, Elaine; Mütze, Ulrike; Okun, Jürgen G.; Hoffmann, Georg F.; Kölker, Stefan; Heuveline, Vincent; Thiele, Ines (2023)
Abstract Extensive whole-body models (WBMs) accounting for organ-specific dynamics have been developed to simulate adult metabolism. However, there is currently a lack of models representing infant metabolism taking into consideration its special requirements in energy balance, nutrition, and growth. Here, we present a resource of organ-resolved, sex-specific, anatomically accurate models of newborn and infant metabolism, referred to as infant-whole-body models (infant-WBMs), spanning the first 180 days of life. These infant-WBMs were parameterised to represent the distinct metabolic characteristics of newborns and infants accurately. In particular, we adjusted the changes in organ weights, the energy requirements of brain development, heart function, and thermoregulation, as well as dietary requirements and energy requirements for physical activity. Subsequently, we validated the accuracy of the infant-WBMs by showing that the predicted neonatal and infant growth was consistent with the recommended growth by the World Health Organisation. We assessed the infant-WBMs’ reliability and capabilities for personalisation by simulating 10,000 newborn models, personalised with blood concentration measurements from newborn screening and birth weight. Moreover, we demonstrate that the models can accurately predict changes over time in known blood biomarkers in inherited metabolic diseases. By this, the infant-WBM resource can provide valuable insights into infant metabolism on an organ-resolved level and enable a holistic view of the metabolic processes occurring in infants, considering the unique energy and dietary requirements as well as growth patterns specific to this population. As such, the infant-WBM resource holds promise for personalised medicine, as the infant-WBMs could be a first step to digital metabolic twins for newborn and infant metabolism for personalised systematic simulations and treatment planning.

VTABLE HIJACKING: OBJECT TYPE INTEGRITY FOR RUN- TIME TYPE INFORMATION - Schröder, Marco; Machmeier, Stefan; Heuveline, Vincent (Preprint Series of the Engineering Mathematics and Computing Lab, 2023)
Attackers try to hijack the control-flow of a victim’s process by exploiting a run-time vulnerability. Vtable hijacking is a state-of-the-art technique adversaries use to conduct control-flow hijacking attacks. It abuses the reliance of language constructs related to polymorphism on dynamic type information. The Control Flow Integrity (CFI) security policy is a well-established solution designed to prevent attacks that corrupt the control-flow. Deployed defense mechanisms based on CFI are often generic, which means that they do not consider high-level programming language semantics. This makes them vulnerable to vtable hijacking attacks. Object Type Integrity (OTI) is an orthogonal security policy that specifically addresses vtable hijacking. CFIXX is a Clang compiler extension that enforces OTI in the context of dynamic dispatch, which prevents vtable hijacking in this setting. However, this extension does not enforce OTI in context of polymorphism. The contribution of this work is a practical implementation to enable OTI in the context of C++’s run-time type information for the dynamic_cast expressions and the typeid operator.

Dominantly inherited micro-satellite instable cancer – the four Lynch syndromes - an EHTG, PLSD position statement - Møller, Pal; Seppälä, Toni T.; Ahadova, Aysel; Crosbie, Emma J.; Holinski-Feder, Elke; Scott, Rodney; Haupt, Saskia; Möslein, Gabriela; Winship, Ingrid; Broeke, Sanne W. Bajwa-ten; Kohut, Kelly E.; Ryan, Neil; et al. (Hereditary Cancer in Clinical Practice, 2023)
Abstract

Implementation Techniques for SPMD Kernels on CPUs - Meyer, Joachim; Alpay, Aksel; Hack, Sebastian; Fröning, Holger; Heuveline, Vincent (International Workshop on OpenCL, 2023)

A Generalizable Approach for Network Flow Image Representation for Deep Learning - Machmeier, Stefan; Trageser, Manuel; Buchwald, Marcus; Heuveline, Vincent (2023 7th Cyber Security in Networking Conference (CSNet), 2023)

Explainable Artificial Intelligence for Improving a Session-Based Malware Traffic Classification with Deep Learning - Machmeier, Stefan; Hoecker, Maximilian; Heuveline, Vincent (2023 IEEE Symposium Series on Computational Intelligence (SSCI), 2023)

Finite element approximation of dielectrophoretic force driven flow problems - Gerstner, Philipp; Heuveline, Vincent (ESAIM: Mathematical Modelling and Numerical Analysis, 2023)

ACSEG: AUTOMATED 3D CYTOPLASM SEGMENTATION IN SOFT X-RAY TOMOGRAPHY - Erozan, Ayse; Lösel, Philipp; Heuveline, Vincent; Weinhardt, Venera (Preprint Series of the Engineering Mathematics and Computing Lab, 2023)
The structure of cells is a key to understanding cellular function, diagnosis of pathological conditions, and development of new treatments. Soft X-ray tomography (SXT) is a unique tool to image cellular structure without fixation or labeling at high spatial resolution and throughput. Ongoing improvements in faster acquisition times increase demand for accelerated image analysis. Currently, the automatic segmentation of cellular structures is a major bottleneck in the SXT data analysis pipeline. In this paper, we introduce an automated 3D cytoplasm segmentation model - ACSeg - by use of semi-automatically segmented labels and 3D U-Net, implemented in the online platform Biomedisa. The segmentation model is trained on semi-automatically labeled datasets and shows rapid convergence to high-accuracy segmentation, therefore reducing time and labor. ACSeg trained on 43 SXT tomograms of human immune T cells, the model successfully segmented unseen SXT tomograms of human hepatocyte-derived carcinoma cells, mouse microglia, and embryonic fibroblast cells. Furthermore, we could diversify the model by adding only 6 specific SXT tomograms, showing the potential for the development of an optimal experimental design. The ACSeg is published on the open image segmentation platform Biomedisa, enabling high-throughput analysis of cell volume and structure of cytoplasm in diverse cell types. The approach can be expanded for automatic segmentation of other organelles visualized by SXT, providing means for structural analysis of cell remodeling under different pathogens at statistically significant sizes, therefore enabling the development of novel drug treatments.

One Pass to Bind Them: The First Single-Pass SYCL Compiler with Unified Code Representation Across Backends - Alpay, Aksel; Heuveline, Vincent (International Workshop on OpenCL, 2023)

Multi-view-AE: A Python package for multi-view autoencoder models - Aguila, Ana Lawry; Jayme, Alejandra; Montaña-Brown, Nina; Heuveline, Vincent; Altmann, Andre (Journal of Open Source Software, 2023)

Direction augmentation in the evaluation of armed conflict predictions - Bracher, Johannes; Rüter, Lotta; Krüger, Fabian; Lerch, Sebastian; Schienle, Melanie (International Interactions, 2023)

Why are different estimates of the effective reproductive number so different? A case study on COVID-19 in Germany - Brockhaus, Elisabeth K.; Wolffram, Daniel; Stadler, Tanja; Osthege, Michael; Mitra, Tanmay; Littek, Jonas M.; Krymova, Ekaterina; Klesen, Anna J.; Huisman, Jana S.; Heyder, Stefan; Helleckes, Laura M.; an der Heiden, Matthias; et al. (PLOS Computational Biology, 2023)
tive reproductive number Rt has taken a central role in the scientific, political, and public discussion during the COVID-19 pandemic, with numerous real-time estimates of this quantity routinely published. Disagreement between estimates can be substantial and may lead to confusion among decision-makers and the general public. In this work, we compare different estimates of the national-level effective reproductive number of COVID-19 in Germany in 2020 and 2021. We consider the agreement between estimates from the same method but published at different time points (within-method agreement) as well as retrospective agreement across eight different approaches (between-method agreement). Concerning the former, estimates from some methods are very stable over time and hardly subject to revisions, while others display considerable fluctuations. To evaluate between-method agreement, we reproduce the estimates generated by different groups using a variety of statistical approaches, standardizing analytical choices to assess how they contribute to the observed disagreement. These analytical choices include the data source, data pre-processing, assumed generation time distribution, statistical tuning parameters, and various delay distributions. We find that in practice, these auxiliary choices in the estimation of Rt may affect results at least as strongly as the selection of the statistical approach. They should thus be communicated transparently along with the estimates.

Analysis of pesticide and persistent organic pollutant residues in German bats - Schanzer, Sonja; Koch, Martin; Kiefer, Andreas; Jentke, Thalia; Veith, Michael; Bracher, Franz; Bracher, Johannes; Müller, Christoph (Chemosphere, 2022)

Regression diagnostics meets forecast evaluation: Conditional calibration, reliability diagrams, and coefficient of determination - Gneiting, Tilmann; Resin, Johannes (Electronic Journal of Statistics, 2023)

Contact tracing of binary stars: Pathways to stellar mergers - Henneco, Jan; Schneider, Fabian; Laplace, Eva (Astronomy & Astrophysics, 2024)
Stellar mergers are responsible for a wide variety of phenomena such as rejuvenated blue stragglers, highly magnetised stars, spectacular transients, iconic nebulae, and stars with peculiar surface chemical abundances and rotation rates. Before stars merge, they enter a contact phase. Here, we investigate which initial binary-star configurations lead to contact and classical common-envelope (CE) phases and assess the likelihood of a subsequent merger. To this end, we computed a grid of about 6000 detailed 1D binary evolution models with initial component masses of 0.5 − 20.0 M⊙ at solar metallicity. Both components were evolved, and rotation and tides were taken into account. We identified five mechanisms that lead to contact and mergers: runaway mass transfer, mass loss through the outer Lagrange point L2, expansion of the accretor, orbital decay because of tides, and non-conservative mass transfer. At least 40% of mass-transferring binaries with initial primary-star masses of 5 − 20 M⊙ evolve into a contact phase; > 12% and > 19% likely merge and evolve into a CE phase, respectively. Because of the non-conservative mass transfer in our models, classical CE evolution from late Case-B and Case-C binaries is only found for initial mass ratios qi < 0.15 − 0.35. For larger mass ratios, we find stable mass transfer. In early Case-B binaries, contact occurs for initial mass ratios qi < 0.15 − 0.35, while in Case-A mass transfer, this is the case for all qi in binaries with the initially closest orbits and qi < 0.35 for initially wider binaries. Our models predict that most Case-A binaries with mass ratios of q < 0.5 upon contact mainly get into contact because of runaway mass transfer and accretor expansion on a thermal timescale, with subsequent L2-overflow in more than half of the cases. Thus, these binaries likely merge quickly after establishing contact or remain in contact only for a thermal timescale. On the contrary, Case-A contact binaries with higher mass ratios form through accretor expansion on a nuclear timescale and can thus give rise to long-lived contact phases before a possible merger. Observationally, massive contact binaries are almost exclusively found with mass ratios q > 0.5, confirming our model expectations. Because of non-conservative mass transfer with mass transfer efficiencies of 15 − 65%, 5 − 25%, and 25 − 50% in Case-A, -B, and -C mass transfer, respectively (for primary-star masses above 3 M⊙), our contact, merger, and classical CE incidence rates are conservative lower limits. With more conservative mass transfer, these incidences would increase. Moreover, in most binaries, the non-accreted mass cannot be ejected, raising the question of the further evolution of such systems. The non-accreted mass may settle into circumstellar and circumbinary disks, but could also lead to further contact systems and mergers. Overall, contact binaries are a frequent and fascinating result of binary mass transfer of which the exact outcomes still remain to be understood and explored further.

Understanding and exploiting interfacial interactions between phosphonic acid functional groups and co-evaporated perovskites - Feeney, Thomas; Petry, Julian; Torche, Abderrezak; Hauschild, Dirk; Hacene, Benjamin; Wansorra, Constantin; Diercks, Alexander; Ernst, Michelle; Weinhardt, Lothar; Heske, Clemens; Gryn'ova, Ganna; Paetzold, Ulrich W.; et al. (Matter, 2024)
Interfacial engineering has fueled recent development of p-i-n perovskite solar cells (PSCs), with self-assembled monolayer-based hole-transport layers (SAM-HTLs) enabling almost lossless contacts for solution-processed PSCs, resulting in the highest achieved power conversion efficiency (PCE) to date. Substrate interfaces are particularly crucial for the growth and quality of co-evaporated PSCs. However, adoption of SAM-HTLs for co-evaporated perovskite absorbers is complicated by the underexplored interaction of such perovskites with phosphonic acid functional groups. In this work, we highlight how exposed phosphonic acid functional groups impact the initial phase and final bulk crystal structures of co-evaporated perovskites and their resultant PCE. The explored surface interaction is mediated by hydrogen bonding with interfacial iodine, leading to increased formamidinium iodide adsorption, persistent changes in perovskite structure, and stabilization of bulk α-FAPbI3, hypothesized as being due to kinetic trapping. Our results highlight the potential of exploiting substrates to increase control of co-evaporated perovskite growth.

Standardization landscape, needs and gaps for the virtual human twin (VHT) - Golebiewski, Martin (2024)
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.

EDITH standards implementation guide (IG) - Golebiewski, Gerhard Mayer, Martin (2024)
This document provides a guideline for using and implementing standards, terminologies, and metadata guidelines when setting up, executing, and archiving virtual human twins.

Vision for the Virtual Human Twin and Roadmap Outline - Golebiewski, Gerhard Mayer, Martin (2023)
The present document is the first written presentation of the Virtual Human Twin (VHT) vision as it has been prepared by the EDITH consortium and discussed with select representatives of the wider ecosystem. After a brief statement on the genesis of the vision, the document is composed of two main parts: the outline of the VHT roadmap and the elaboration of the vision for the integrated Virtual Human Twin.

First Draft of the VHT Roadmap - Golebiewski, Gerhard Mayer, Martin (2023)
The VHT Roadmap is due – in its final version – by the end of the EDITH Coordination and Support Action (i.e., September 2024). The present document is the first draft of the Roadmap, This preliminary version of the Roadmap was planned in EDITH’s Grant Agreement as an initial contribution to the internal decision-making process of the European Commission. It has the declared purpose of allowing the Commission to start specifying already at an early stage by what steps the goal of pursuing the development of a VHT-based healthcare will be likely to trigger an effective engagement of Europe’s researchers, clinicians, industries, and regulators.

KineticNet: Deep learning a transferable kinetic energy functional for orbital-free density functional theory - Remme, R.; Kaczun, T.; Scheurer, M.; Dreuw, A.; Hamprecht, F. A. (The Journal of Chemical Physics, 2023)
Orbital-free density functional theory (OF-DFT) holds promise to compute ground state molecular properties at minimal cost. However, it has been held back by our inability to compute the kinetic energy as a functional of electron density alone. Here, we set out to learn the kinetic energy functional from ground truth provided by the more expensive Kohn–Sham density functional theory. Such learning is confronted with two key challenges: Giving the model sufficient expressivity and spatial context while limiting the memory footprint to afford computations on a GPU and creating a sufficiently broad distribution of training data to enable iterative density optimization even when starting from a poor initial guess. In response, we introduce KineticNet, an equivariant deep neural network architecture based on point convolutions adapted to the prediction of quantities on molecular quadrature grids. Important contributions include convolution filters with sufficient spatial resolution in the vicinity of nuclear cusp, an atom-centric sparse but expressive architecture that relays information across multiple bond lengths, and a new strategy to generate varied training data by finding ground state densities in the face of perturbations by a random external potential. KineticNet achieves, for the first time, chemical accuracy of the learned functionals across input densities and geometries of tiny molecules. For two-electron systems, we additionally demonstrate OF-DFT density optimization with chemical accuracy.

Towards a self-consistent model of the convective core boundary in upper main sequence stars - Andrássy, Róbert; Leidi, Giovanni; Higl, Johann; Edelmann, P. V. F.; Schneider, Fabian; Röpke, Friedrich (Astronomy & Astrophysics, 2024)
There is strong observational evidence that the convective cores of intermediate-mass and massive main sequence stars are substantially larger than those predicted by standard stellar-evolution models. However, it is unclear what physical processes cause this phenomenon or how to predict the extent and stratification of stellar convective boundary layers. Convective penetration is a thermal-timescale process that is likely to be particularly relevant during the slow evolution on the main sequence. We use our low-Mach-number SEVEN-LEAGUE HYDRO code to study this process in 2.5D and 3D geometries. Starting with a chemically homogeneous model of a 15  M⊙ zero-age main sequence star, we construct a series of simulations with the luminosity increased and opacity decreased by the same factor, ranging from 10^3 to 10^6. After reaching thermal equilibrium, all of our models show a clear penetration layer; its thickness becomes statistically constant in time and it is shown to converge upon grid refinement. The penetration layer becomes nearly adiabatic with a steep transition to a radiative stratification in simulations at the lower end of our luminosity range. This structure corresponds to the adiabatic ‘step overshoot’ model often employed in stellar-evolution calculations. The simulations with the highest and lowest luminosity differ by less than a factor of two in the penetration distance. The high computational cost of 3D simulations makes our current 3D data set rather sparse. Depending on how we extrapolate the 3D data to the actual luminosity of the initial stellar model, we obtain penetration distances ranging from 0.09 to 0.44 pressure scale heights, which is broadly compatible with observations.

Turbulent dynamo action and its effects on the mixing at the convective boundary of an idealized oxygen-burning shell - Leidi, Giovanni; Andrássy, Róbert; Higl, Johann; Edelmann, P. V. F.; Röpke, Friedrich (Astronomy & Astrophysics, 2023)

Deep learning for postprocessing global probabilistic forecasts on subseasonal time scales - Horat, Nina; Lerch, Sebastian (Monthly Weather Review, 2024)

Non-crossing quantile regression neural network as a calibration tool for ensemble weather forecasts - Song, Mengmeng; Yang, Dazhi; Lerch, Sebastian; Xia, Xiang’ao; Yagli, Gokhan Mert; Bright, Jamie M.; Shen, Yanbo; Liu, Bai; Liu, Xingli; Mayer, Martin János (Advances in Atmospheric Sciences, 2024)

Effect of nuclear reactions rates and core boundary mixing on the seismology of red clump stars - Noll, Anthony; Basu, Sarbani; Hekker, Saskia (Astronomy & Astrophysics, 2024)
Context: Modeling of the stars in the red clump (RC), that is, core helium-burning stars that have gone through a He flash, is challenging because of the uncertainties associated with the physical processes in their core and during the helium flash. By probing the internal stellar structure, asteroseismology allows us to constrain the core properties of RC stars and eventually, to improve our understanding of this evolutionary phase. Aims: We aim to quantify the impact on the seismic properties of the RC stars of the two main core modeling uncertainties: core boundary mixing, and helium-burning nuclear reaction rates. Methods: Using the MESA stellar evolution code, we computed models with different core boundary mixing as well as different 3α and 12C(α, γ)16O nuclear reaction rates. We investigated the impact of these parameters on the period spacing ΔΠ, which is a probe of the region around the core. Results: We find that different core boundary mixing schemes yield significantly different period spacings, with differences of 30 s between the maximum ΔΠ value computed with semiconvection and maximal overshoot. We show that an increased rate of 12C(α, γ)16O lengthens the core helium-burning phase, which extends the range of ΔΠ covered by the models during their evolution. This results in a difference of 10 s between the models computed with a nominal rate and a rate multiplied by 2, which exceeds the observational uncertainties. The effect of changing the 3α reaction rate is comparatively small. Conclusions: The core boundary mixing is the main source of uncertainty in the seismic modeling of RC stars. Moreover, the effect of the 12C(α, γ)16O is non-negligible, even though it is difficult to distinguish from the effect of the mixing. This degeneracy could be seen more frequently in the future in the new seismic data from the PLATO mission and through theoretical constraints from numerical simulations.

Grappa – A Machine Learned Molecular Mechanics Force Field - Seute, Leif; Hartmann, Eric; Stühmer, Jan; Gräter, Frauke (arXiv,physics.chem-ph,2404.00050, 2024)

A research data management (RDM) community for ELIXIR - D'Anna, Flora; Jareborg, Niclas; Jetten, Mijke; Ahokas, Minna; Alper, Pinar; Andrews, Robert; Bösl, Korbinian; D’Altri, Teresa; Faria, Daniel; Fatima, Nazeefa; Fuchs, Siiri; Garrard, Clare; et al. (F1000Research, 2024)
Research data management (RDM) is central to the implementation of the FAIR (Findable Accessible, Interoperable, Reusable) and Open Science principles. Recognising the importance of RDM, ELIXIR Platforms and Nodes have invested in RDM and launched various projects and initiatives to ensure good data management practices for scientific excellence. These projects have resulted in a rich set of tools and resources highly valuable for FAIR data management. However, these resources remain scattered across projects and ELIXIR structures, making their dissemination and application challenging. Therefore, it becomes imminent to coordinate these efforts for sustainable and harmonised RDM practices with dedicated forums for RDM professionals to exchange knowledge and share resources. The proposed ELIXIR RDM Community will bring together RDM experts to develop ELIXIR’s vision and coordinate its activities, taking advantage of the available assets. It aims to coordinate RDM best practices and illustrate how to use the existing ELIXIR RDM services. The Community will be built around three integral pillars, namely, a network of RDM professionals, RDM knowledge management and RDM training expertise and resources. It will also engage with external stakeholders to leverage benefits and provide a forum to RDM professionals for regular knowledge exchange, capacity building and development of harmonised RDM practices, keeping in line with the overall scope of the RDM Community. In the short term, the Community aims to build upon the existing resources and ensure that the content of these remain up to date and fit for purpose. In the long run, the Community will aim to strengthen the skills and knowledge of its RDM professionals to support the emerging needs of the scientific community. The Community will also devise an effective strategy to engage with other ELIXIR structures and international stakeholders to influence and align with developments and solutions in the RDM field.

What causes the failure of explicit to implicit discourse relation recognition? - Liu, Wei; Wan, Stephen; Strube, Michael (2024)

Connectivity optimized nested line graph networks for crystal structures - Ruff, Robin; Reiser, Patrick; Stühmer, Jan; Friederich, Pascal (Digital Discovery, 2024)

Complexity of avian evolution revealed by family-level genomes - Stiller, Josefin; Feng, Shaohong; Chowdhury, Al-Aabid; Rivas-González, Iker; Duchêne, David A.; Fang, Qi; Deng, Yuan; Kozlov, Alexey; Stamatakis, Alexandros; Claramunt, Santiago; Nguyen, Jacqueline M. T.; Ho, Simon Y. W.; et al. (Nature, 2024)
Despite tremendous efforts in the past decades, relationships among main avian lineages remain heavily debated without a clear resolution. Discrepancies have been attributed to diversity of species sampled, phylogenetic method, and the choice of genomic regions 1–3. Here, we address these issues by analyzing genomes of 363 bird species 4 (218 taxonomic families, 92% of total). Using intergenic regions and coalescent methods, we present a well-supported tree but also a remarkable degree of discordance. The tree confirms that Neoaves experienced rapid radiation at or near the Cretaceous–Paleogene (K–Pg) boundary. Sufficient loci rather than extensive taxon sampling were more effective in resolving difficult nodes. Remaining recalcitrant nodes involve species that challenge modeling due to extreme GC content, variable substitution rates, incomplete lineage sorting, or complex evolutionary events such as ancient hybridization. Assessment of the impacts of different genomic partitions showed high heterogeneity across the genome. We discovered sharp increases in effective population size, substitution rates, and relative brain size following the K–Pg extinction event, supporting the hypothesis that emerging ecological opportunities catalyzed the diversification of modern birds. The resulting phylogenetic estimate offers novel insights into the rapid radiation of modern birds and provides a taxon-rich backbone tree for future comparative studies.

Pandora: A Tool to Estimate Dimensionality Reduction Stability of Genotype Data - Haag, Julia; Jordan, Alexander I.; Stamatakis, Alexandros (2024)
Motivation: Genotype datasets typically contain a large number of single nucleotide polymorphisms for a comparatively small number of individuals. To identify similarities between individuals and to infer an individual’s origin or membership to a cultural group, dimensionality reduction techniques are routinely deployed. However, inherent (technical) difficulties such as missing or noisy data need to be accounted for when analyzing a lower dimensional representation of genotype data, and the uncertainty of such an analysis should be reported in all studies. However, to date, there exists no stability estimation technique for genotype data that can estimate this uncertainty.

Predicting Phylogenetic Bootstrap Values via Machine Learning - Wiegert, Julius; Höhler, Dimitri; Haag, Julia; Stamatakis, Alexandros (2024)
Estimating the statistical robustness of the inferred tree(s) constitutes an integral part of most phylogenetic analyses. Commonly, one computes and assigns a branch support value to each inner branch of the inferred phylogeny. The most widely used method for calculating branch support on trees inferred under Maximum Likelihood (ML) is the Standard, non-parametric Felsenstein Bootstrap Support (SBS). Due to the high computational cost of the SBS, a plethora of methods has been developed to approximate it, for instance, via the Rapid Bootstrap (RB) algorithm. There have also been attempts to devise faster, alternative support measures, such as the SH-aLRT (Shimodaira–Hasegawalike approximate Likelihood Ratio Test) or the UltraFast Bootstrap 2 (UFBoot2) method. Those faster alternatives exhibit some limitations, such as the need to assess model violations (UFBoot2) or meaningless low branch support intervals (SH-aLRT). Here, we present the Educated Bootstrap Guesser (EBG), a machine learning-based tool that predicts SBS branch support values for a given input phylogeny. EBG is on average 9.4 (σ = 5.5) times faster than UFBoot2. EBG-based SBS estimates exhibit a median absolute error of 5 when predicting SBS values between 0 and 100. Furthermore, EBG also provides uncertainty measures for all per-branch SBS predictions and thereby allows for a more rigorous and careful interpretation. EBG can predict SBS support values on a phylogeny comprising 1654 SARS-CoV2 genome sequences within 3 hours on a mid-class laptop. EBG is available under GNU GPL3.

Towards explainable evaluation metrics for machine translation - Leiter, Christoph; Lertvittayakumjorn, Piyawat; Fomicheva, Marina; Zhao, Wei; Gao, Yang; Eger, Steffen (Journal of Machine Learning Research, 2024)
Unlike classical lexical overlap metrics such as BLEU, most current evaluation metrics for machine translation (for example, COMET or BERTScore) are based on black-box large language models. They often achieve strong correlations with human judgments, but recent research indicates that the lower-quality classical metrics remain dominant, one of the potential reasons being that their decision processes are more transparent. To foster more widespread acceptance of novel high-quality metrics, explainability thus becomes crucial. In this concept paper, we identify key properties as well as key goals of explainable machine translation metrics and provide a comprehensive synthesis of recent techniques, relating them to our established goals and properties. In this context, we also discuss the latest state-of-the-art approaches to explainable metrics based on generative models such as ChatGPT and GPT4. Finally, we contribute a vision of next-generation approaches, including natural language explanations. We hope that our work can help catalyze and guide future research on explainable evaluation metrics and, mediately, also contribute to better and more transparent machine translation systems.

Expansion of Accreting Main-sequence Stars during Rapid Mass Transfer - Lau, Mike; Hirai, Ryosuke; Mandel, Ilya; Tout, Christopher A. (The Astrophysical Journal Letters, 2024)
Accreting main-sequence stars expand significantly when the mass accretion timescale is much shorter than their thermal timescales. This occurs during mass transfer from an evolved giant star onto a main-sequence companion in a binary system and is an important phase in the formation of compact binaries including X-ray binaries, cataclysmic variables, and gravitational-wave sources. In this study, we compute 1D stellar models of main-sequence accretors with different initial masses and accretion rates. The calculations are used to derive semianalytical approximations to the maximum expansion radius. We assume that mass transfer remains fully conservative as long as the inflated accretor fits within its Roche lobe, leading stars to behave like hamsters, stuffing excess material behind their expanding cheeks. We suggest a physically motivated prescription for the mass growth of such "hamstars," which can be used to determine mass-transfer efficiency in rapid binary population synthesis models. With this prescription, we estimate that progenitors of high-mass X-ray binaries and gravitational-wave sources may have experienced highly nonconservative mass transfer. In contrast, for low-mass accretors, the accretion timescale can exceed the thermal timescale by a larger factor without causing significant radial expansion.

Provenance Core Data Set: A Minimal Information Model for Data Provenance in Biomedical Research - Sax, Ulrich; Henke, Christian; Dräger, Christian; Bender, Theresa; Kuntz, Alessandra; Golebiewski, Martin; Ulrich, Hannes; Löbe, Mattias (Proceedings of the Conference on Research Data Infrastructure, 2023)
The exchange, dissemination, and reuse of biological specimens and data have become essentialfor life sciences research. This requires standards that enable cross-organizational documentation, traceability, and tracking of data and its corresponding metadata. Thus, data provenance, or the lineage of data, is an important aspect of data management in any information system integrating data from different sources [1]. It provides crucial information about the origin, transformation, and accountability of data, which is essential for ensuring trustworthiness, transparency, and quality of healthcare data [2]. For biological material and derived data, a novel ISO standard was recently introduced that specifies a general concept for a provenance information model for biological material and data and requirements for provenance data interoperability and serialization [3,4]. However, a specific standard for health data provenance is currently missing. In recent years, there has been a growing need for developing a minimal core data set for representing provenance information in health information systems. This paper presents a Provenance Core Data Set (PCDS), a generalized data model that aims to provide a set of attributes for describing data provenance in health information systems and beyond.

Normed spaces for graph embedding - Taha, Diaaeldin; Zhao, Wei; Riestenberg, J. Maxwell; Strube, Michael (Transactions of Machine Learning Research, 2024)

Impact of an irreversible β-galactosylceramidase inhibitor on the lipid profile of zebrafish embryos - Guerra, Jessica; Belleri, Mirella; Paiardi, Giulia; Tobia, Chiara; Capoferri, Davide; Corli, Marzia; Scalvini, Elisa; Ghirimoldi, Marco; Manfredi, Marcello; Wade, Rebecca C.; Presta, Marco; Mignani, Luca (Computational and Structural Biotechnology Journal, 2024)

Editorial: The Journal of Molecular Recognition: Changing of the guard - Wade, Rebecca C. (Journal of Molecular Recognition, 2024)

Myotubularin-related-protein-7 inhibits mutant (G12V) K-RAS by direct interaction - Weidner, Philip; Saar, Daniel; Söhn, Michaela; Schroeder, Torsten; Yu, Yanxiong; Zöllner, Frank G.; Ponelies, Norbert; Zhou, Xiaobo; Zwicky, André; Rohrbacher, Florian N.; Pattabiraman, Vijaya R.; Tanriver, Matthias; et al. (Cancer Letters, 2024)

The role of heparin in spike SARS-CoV-2 infection: From a model for heparan sulfates to a starting structure for antivirals - Paiardi, Giulia; Milanesi, Maria; Ferraz, Matheus; Zimmermann, Liv; Urbinati, Chiara; Oreste, Pasqua; Caccuri, Francesca; Chlanda, Petr; Rusnati, Marco; Wade, Rebecca C. (Biophysical Journal, 2024)

Challenges of COVID-19 case forecasting in the US, 2020–2021 - Lopez, Velma K.; Cramer, Estee Y.; Pagano, Robert; Drake, John M.; O'Dea, Eamon B.; Adee, Madeline; Ayer, Turgay; Chhatwal, Jagpreet; Dalgic, Ozden O.; Ladd, Mary A.; Linas, Benjamin P.; Mueller, Peter P.; et al. (PLOS Computational Biology, 2024)

Physics-based vs. data-driven 24-hour probabilistic forecasts of precipitation for northern tropical Africa - Walz, Eva-Maria; Knippertz, Peter; Fink, Andreas H.; Köhler, Gregor; Gneiting, Tilmann (Monthly Weather Review, 2024)

Thermonuclear explosions as Type II supernovae - Kozyreva, Alexandra; Morán-Fraile, Javier; Holas, Alexander; Bronner, Vincent A.; Röpke, Friedrich; Pavlyuk, Nikolay; Mironov, Alexey; Tsvetkov, Dmitry (\aap, 2024)

1100 days in the life of the supernova 2018ibb. The best pair-instability supernova candidate, to date - Schulze, Steve; Fransson, Claes; Kozyreva, Alexandra; Chen, Ting-Wan; Yaron, Ofer; Jerkstrand, Anders; Gal-Yam, Avishay; Sollerman, Jesper; Yan, Lin; Kangas, Tuomas; Leloudas, Giorgos; Omand, Conor M. B.; et al. (\aap, 2024)

SN 2020pvb: A Type IIn-P supernova with a precursor outburst - Elias-Rosa, N.; Brennan, S. J.; Benetti, S.; Cappellaro, E.; Pastorello, A.; Kozyreva, Alexandra; Lundqvist, P.; Fraser, M.; Anderson, J. P.; Cai, Y. -Z.; Chen, T. -W.; Dennefeld, M.; et al. (\aap, 2024)

Type Ia supernova explosion models are inherently multidimensional - Pakmor, Rüdiger; Seitenzahl, Ivo R.; Ruiter, Ashley J.; Sim, Stuart A.; Röpke, Friedrich K.; Taubenberger, Stefan; Bieri, Rebekka; Blondin, Stéphane (\aap, 2024)

Performance of high-order Godunov-type methods in simulations of astrophysical low Mach number flows - Leidi, G.; Andrassy, R.; Barsukow, W.; Higl, J.; Edelmann, P. V. F.; Röpke, F. K. (\aap, 2024)

Including a luminous central remnant in radiative transfer simulations for Type Iax supernovae - Callan, F. P.; Sim, S. A.; Collins, C. E.; Shingles, L. J.; Lach, F.; Röpke, F. K.; Pakmor, R.; Kromer, M.; Srivastav, S. (\mnras, 2024)

Morphology and Mach Number Dependence of Subsonic Bondi–Hoyle Accretion - Prust, Logan J.; Glanz, Hila; Bildsten, Lars; Perets, Hagai B.; Röpke, Friedrich K. (\apj, 2024)

General relativistic moving-mesh hydrodynamic simulations with AREPO and applications to neutron star mergers - Lioutas, Georgios; Bauswein, Andreas; Soultanis, Theodoros; Pakmor, Rüdiger; Springel, Volker; Röpke, Friedrich K. (\mnras, 2024)

Dust formation in common envelope binary interactions – II: 3D simulations with self-consistent dust formation - Bermúdez-Bustamante, Luis C; De Marco, Orsola; Siess, Lionel; Price, Daniel J; González-Bolívar, Miguel; Lau, Mike; Mu, Chunliang; Hirai, Ryosuke; Danilovich, Taïssa; Kasliwal, Mansi M (Monthly Notices of the Royal Astronomical Society, 2024)
We performed numerical simulations of the common envelope (CE) interaction between thermally-pulsing asymptotic giant branch (AGB) stars of 1.7 M⊙ and 3.7 M⊙, respectively, and a 0.6 M⊙ compact companion. We use tabulated equations of state to take into account recombination energy. For the first time, formation and growth of dust is calculated explicitly, using a carbon dust nucleation network with a C/O abundance ratio of 2.5 (by number). The first dust grains appear within ∼1–3 yrs after the onset of the CE, forming an optically thick shell at ∼10–20 au, growing in thickness and radius to values of ∼400–500 au over ∼40 yrs, with temperatures around 400 K. Most dust is formed in unbound material, having little effect on mass ejection or orbital evolution. By the end of the simulations, the total dust yield is ∼8.4 × 10−3 M⊙ and ∼2.2 × 10−2 M⊙ for the CE with a 1.7 M⊙ and a 3.7 M⊙ AGB star, respectively, corresponding to a nucleation efficiency close to 100%, if no dust destruction mechanism is considered. Despite comparable dust yields to single AGB stars, in CE ejections the dust forms a thousand times faster, over tens of years as opposed to tens of thousands of years. This rapid dust formation may account for the shift in the infrared of the spectral energy distribution of some optical transients known as luminous red novae. Simulated dusty CEs support the idea that extreme carbon stars and ‘water fountains’ may be objects observed after a CE event.

EDITORIAL: Chemical Compound Space Exploration by Multiscale High-Throughput Screening and Machine Learning - Gryn'ova, Ganna; Bereau, Tristan; Müller, Carolin; Friederich, Pascal; Wade, Rebecca C.; Nunes-Alves, Ariane; Soares, Thereza A.; Merz, Kenneth (Journal of Chemical Information and Modeling, 2024)

EDITORIAL: Chemical Compound Space Exploration by Multiscale High-Throughput Screening and Machine Learning - Gryn’ova, Ganna; Bereau, Tristan; Müller, Carolin; Friederich, Pascal; Wade, Rebecca C.; Nunes-Alves, Ariane; Soares, Thereza A.; Merz, Kenneth (Journal of Chemical Information and Modeling, 2024)

Non-adiabatic molecular dynamics simulations provide new insights into the exciton transfer in the Fenna–Matthews–Olson complex - Sokolov, Monja; Hoffmann, David S.; Dohmen, Philipp M.; Kramer, Manuel; Höfener, Sebastian; Kleinekathöfer, Ulrich; Elstner, Marcus (Physical Chemistry Chemical Physics, 2024)
A trajectory surface hopping approach, which uses machine learning to speed up the most time-consuming steps, has been adopted to investigate the exciton transfer in light-harvesting systems. The present neural networks achieve high accuracy in predicting both Coulomb couplings and excitation energies. The latter are predicted taking into account the environment of the pigments. Direct simulation of exciton dynamics through light-harvesting complexes on significant time scales is usually challenging due to the coupled motion of nuclear and electronic degrees of freedom in these rather large systems containing several relatively large pigments. In the present approach, however, we are able to evaluate a statistically significant number of non-adiabatic molecular dynamics trajectories with respect to exciton delocalization and exciton paths. The formalism is applied to the Fenna–Matthews–Olson complex of green sulfur bacteria, which transfers energy from the light-harvesting chlorosome to the reaction center with astonishing efficiency. The system has been studied experimentally and theoretically for decades. In total, we were able to simulate non-adiabatically more than 30 ns, sampling also the relevant space of parameters within their uncertainty. Our simulations show that the driving force supplied by the energy landscape resulting from electrostatic tuning is sufficient to funnel the energy towards site 3, from where it can be transferred to the reaction center. However, the high efficiency of transfer within a picosecond timescale can be attributed to the rather unusual properties of the BChl a molecules, resulting in very low inner and outer-sphere reorganization energies, not matched by any other organic molecule, e.g., used in organic electronics. A comparison with electron and exciton transfer in organic materials is particularly illuminating, suggesting a mechanism to explain the comparably high transfer efficiency.

Nonadiabatic Simulation of Exciton Dynamics in Organic Semiconductors Using Neural Network-Based Frenkel Hamiltonian and Gradients - Ghalami, Farhad; Dohmen, Philipp M.; Krämer, Mila; Elstner, Marcus; Xie, Weiwei (Journal of Chemical Theory and Computation, 2024)

Multiresolution molecular dynamics simulations reveal the interplay between conformational variability and functional interactions in membrane-bound cytochrome 2B4 - Han, Sungho Bosco; Teuffel, Jonathan; Mukherjee, Goutam; Wade, Rebecca C. (2024)
Cytochrome P450 2B4 (CYP 2B4) is one of the best characterized CYPs and serves as a key model system for understanding the mechanisms of microsomal class II CYPs, which metabolize most known drugs. The highly flexible nature of CYP 2B4 is apparent from crystal structures that show the active site with either a wide open or a closed heme binding cavity. Here, we investigated the conformational ensemble of the full-length CYP 2B4 in a phospholipid bilayer, using multiresolution molecular dynamics (MD) simulations. Coarse-grained MD simulations revealed two predominant orientations of CYP 2B4's globular domain with respect to the bilayer. Their refinement by atomistic resolution MD showed adaptation of the enzyme's interaction with the lipid bilayer, leading to open configurations that facilitate ligand access to the heme binding cavity. CAVER analysis of enzyme tunnels, AquaDuct analysis of water routes, and Random Acceleration Molecular Dynamics simulations of ligand dissociation support the conformation-dependent passage of molecules between the active site and the protein surroundings. Furthermore, simulation of the re-entry of the inhibitor bifonazole into the open conformation of CYP 2B4 resulted in binding at a transient hydrophobic pocket within the active site cavity that may play a role in substrate binding or allosteric regulation. Together, these results show how the open conformation of CYP 2B4 facilitates binding of substrates from and release of products to the membrane, whereas the closed conformation prolongs the residence time of substrates or inhibitors and selectively allows the passage of smaller reactants via the solvent and water channels.

Using Health IT Standards to Facilitate Metadata Exchange Between Research Data Management Systems - Löbe, Matthias; Hu, Xiaoming; Klopfenstein, Sophie A.I. (Digital Health and Informatics Innovations for Sustainable Health Care Systems, 2024)
The National Research Data Infrastructure for Personal Health Data (NFDI4Health) uses Local Data Hubs (LDHs) to manage locally research studies, documents and sensitive personal data to support controlled data sharing. While research data management (RDM) systems facilitate the storage and preparation of data and metadata as well as organizational access, they often lack support for interoperability standards of the application domain. To support the exchange with external registries of research studies, we chose 17 attributes to characterize the most relevant aspects of clinical trials (in the following named “metadata profile”). We implemented the metadata profile in the RDM system FAIRDOM SEEK using core attributes and SEEK’s extended metadata feature and created a mapping conforming to the Health Level 7 Fast Healthcare Interoperability Resources (FHIR) standard version R4. Finally, we implemented a prototype application interface for exports in FHIR-JSON format. We plan to extend the interface to serve central registries and support specific FHIR Implementation Guides from various use cases.

Making Epidemiological and Clinical Studies FAIR Using the Example of COVID-19 - Pigeot, Iris; Ahrens, Wolfgang; Darms, Johannes; Fluck, Juliane; Golebiewski, Martin; Hahn, Horst K.; Hu, Xiaoming; Intemann, Timm; Kasbohm, Elisa; Kirsten, Toralf; Klammt, Sebastian; Klopfenstein, Sophie Anne Ines; et al. (Datenbank-Spektrum, 2024)
Abstract FAIRification of personal health data is of utmost importance to improve health research and political as well as medical decision-making, which ultimately contributes to a better health of the general population. Despite the many advances in information technology, several obstacles such as interoperability problems remain and relevant research on the health topic of interest is likely to be missed out due to time-consuming search and access processes. A recent example is the COVID-19 pandemic, where a better understanding of the virus’ transmission dynamics as well as preventive and therapeutic options would have improved public health and medical decision-making. Consequently, the NFDI4Health Task Force COVID-19 was established to foster the FAIRification of German COVID-19 studies. This paper describes the various steps that have been taken to create low barrier workflows for scientists in finding and accessing German COVID-19 research. It provides an overview on the building blocks for FAIR health research within the Task Force COVID-19 and how this initial work was subsequently expanded by the German consortium National Research Data Infrastructure for Personal Health Data (NFDI4Health) to cover a wider range of studies and research areas in epidemiological, public health and clinical research. Lessons learned from the Task Force helped to improve the respective tasks of NFDI4Health.

Testing a non-local 1-equation turbulent convection model: A solar model - Braun, T. A. M.; Ahlborn, Felix; Weiss, A. (Astronomy & Astrophysics, 2024)
Context: Turbulent convection models treat stellar convection more physically than standard mixing-length theory by including non-local effects. We recently successfully applied the Kuhfuss version to convective cores in main sequence stars. Its usefulness for convective envelopes remains to be tested. Aims: The solar convective envelope constitutes a viable test bed for investigating the usefulness of the 1-equation Kuhfuss turbulent convection model. Methods: We used the one-dimensional stellar evolution code GARSTEC to calculate a standard solar model with the 1-equation Kuhfuss turbulent convection model, and compared it to helioseismic measurements and a solar model using standard mixing-length theory. Additionally, we investigated the influence of the additional free parameters of the convection model on the solar structure. Results: The 1-equation Kuhfuss model reproduces the sound-speed profile and the lower boundary of the convective region less well than the mixing-length model, because the inherent non-local effects overestimate the amount of convective penetration below the Schwarzschild boundary. We trace this back to the coupling of the temperature gradient to the convective flux in the 1-equation version of the Kuhfuss theory. Conclusions: The temperature stratification of the solar convective envelope is not well modelled by the 1-equation Kuhfuss turbulent convection model, and the more complex 3-equation version is needed to improve the modelling of convection in the envelopes of 1D stellar evolution models.

New red giants in NGC 6791 and NGC 6819 using Kepler superstamps - Covelo-Paz, A.; Themeßl, Nathalie; Espinoza-Rojas, F.; Hekker, Saskia (Astronomy & Astrophysics, 2023)
Context: Stars that are members of stellar clusters are assumed to be formed at the same time and place from material with the same initial chemical composition. These additional constraints on the ensemble of cluster stars make these stars suitable as benchmarks. Aims: We aimed (1) to identify previously unknown red giants in the open clusters NGC 6791 and NGC 6819, (2) to extract their asteroseismic parameters, and (3) to determine their cluster membership. Methods: We followed a dedicated method based on difference imaging to extract the light curves of potential red giants in NGC 6791 and NGC 6819 from Kepler superstamp data. We extracted the asteroseismic parameters of the stars that showed solar-like oscillations. We performed an asteroseismic membership study to identify which of these stars are likely to be cluster members. Results: We found 149 red giant stars within the Kepler superstamps, 93 of which are likely cluster members. We were able to find 29 red giants that are not primary targets of Kepler, and therefore, their light curves had not been released previously. Five of these previously unknown red giants have a cluster membership probability greater than 95%.

O-glycans Expand Lubricin and Attenuate Its Viscosity and Shear Thinning - Boushehri, Saber; Holey, Hannes; Brosz, Matthias; Gumbsch, Peter; Pastewka, Lars; Aponte-Santamaría, Camilo; Gräter, Frauke (Biomacromolecules, 2024)

Breaking Strong Alkynyl-Phenyl Bonds: Poly(para-phenylene ethynylene)s under Mechanical Stress - Elter, Maximilian; Brosz, Matthias; Sucerquia, Daniel; Kuzhelev, Andrei; Kiesewetter, Denis C.; Kurth, Markus; Dreuw, Andreas; Prisner, Thomas F.; Freudenberg, Jan; Bunz, Uwe H. F.; Gräter, Frauke (Journal of the American Chemical Society, 2024)

Positive and Scale Invariant Gaussian Process Latent Variable Model for Astronomical Spectra - Gianniotis, Nikos; Polsterer, Kai L.; Cortés Pérez, Iliana Isabel (ESANN 2024 proceesdings, 2024)

Efficient large-scale nonstationary spatial covariance function estimation using convolutional neural networks - Nag, Pratik; Hong, Yiping; Abdulah, Sameh; Qadir, Ghulam A.; Genton, Marc G.; Sun, Ying (Journal of Computational and Graphical Statistics, 2024)

NFDI4Health Local Data Hubs Implementing a Tailored Metadata Schema for Health Data - Hu, Xiaoming; Abaza, Haitham; Hänsel, Rene; Abedi, Masoud; Golebiewski, Martin; Müller, Wolfgang; Meineke, Frank (German Medical Data Sciences 2024, 2024)
Introduction: NFDI4Health is a consortium funded by the German Research Foundation to make structured health data findable and accessible internationally according to the FAIR principles. Its goal is bringing data users and Data Holding Organizations (DHOs) together. It mainly considers DHOs conducting epidemiological and public health studies or clinical trials. Methods: Local data hubs (LDH) are provided for such DHOs to connect decentralized local research data management within their organizations with the option of publishing shareable metadata via centralized NFDI4Health services such as the German central Health Study Hub. The LDH platform is based on FAIRDOM SEEK and provides a complete and flexible, locally controlled data and information management platform for health research data. A tailored NFDI4Health metadata schema for studies and their corresponding resources has been developed which is fully supported by the LDH software, e.g. for metadata transfer to other NFDI4Health services. Results: The SEEK platform has been technically enhanced to support extended metadata structures tailored to the needs of the user communities in addition to the existing metadata structuring of SEEK. Conclusion: With the LDH and the MDS, the NFDI4Health provides all DHOs with a standardized and free and open source research data management platform for the FAIR exchange of structured health data.

Multiresolution molecular dynamics simulations reveal the interplay between conformational variability and functional interactions in membrane‐bound cytochrome P450 2B4 - Han, Sungho Bosco; Teuffel, Jonathan; Mukherjee, Goutam; Wade, Rebecca C. (Protein Science, 2024)
Abstract Cytochrome P450 2B4 (CYP 2B4) is one of the best‐characterized CYPs and serves as a key model system for understanding the mechanisms of microsomal class II CYPs, which metabolize most known drugs. The highly flexible nature of CYP 2B4 is apparent from crystal structures that show the active site with either a wide open or a closed heme binding cavity. Here, we investigated the conformational ensemble of the full‐length CYP 2B4 in a phospholipid bilayer, using multiresolution molecular dynamics (MD) simulations. Coarse‐grained MD simulations revealed two predominant orientations of CYP 2B4's globular domain with respect to the bilayer. Their refinement by atomistic resolution MD showed adaptation of the enzyme's interaction with the lipid bilayer, leading to open configurations that facilitate ligand access to the heme binding cavity. CAVER analysis of enzyme tunnels, AquaDuct analysis of water routes, and Random Acceleration Molecular Dynamics simulations of ligand dissociation support the conformation‐dependent passage of molecules between the active site and the protein surroundings. Furthermore, simulation of the re‐entry of the inhibitor bifonazole into the open conformation of CYP 2B4 resulted in binding at a transient hydrophobic pocket within the active site cavity that may play a role in substrate binding or allosteric regulation. Together, these results show how the open conformation of CYP 2B4 facilitates the binding of substrates from and release of products to the membrane, whereas the closed conformation prolongs the residence time of substrates or inhibitors and selectively allows the passage of smaller reactants via the solvent and water channels.

The accomplices: Heparan sulfates and N-glycans foster SARS-CoV-2 spike:ACE2 receptor binding and virus priming - Paiardi, Giulia; Ferraz, Matheus; Rusnati, Marco; Wade, Rebecca C. (Proceedings of the National Academy of Sciences, 2024)
Although it is well established that the SARS-CoV-2 spike glycoprotein binds to the host cell ACE2 receptor to initiate infection, far less is known about the tissue tropism and host cell susceptibility to the virus. Differential expression across different cell types of heparan sulfate (HS) proteoglycans, with variably sulfated glycosaminoglycans (GAGs), and their synergistic interactions with host and viral N-glycans may contribute to tissue tropism and host cell susceptibility. Nevertheless, their contribution remains unclear since HS and N-glycans evade experimental characterization. We, therefore, carried out microsecond-long all-atom molecular dynamics simulations, followed by random acceleration molecular dynamics simulations, of the fully glycosylated spike:ACE2 complex with and without highly sulfated GAG chains bound. By considering the model GAGs as surrogates for the highly sulfated HS expressed in lung cells, we identified key cell entry mechanisms of spike SARS-CoV-2. We find that HS promotes structural and energetic stabilization of the active conformation of the spike receptor-binding domain (RBD) and reorientation of ACE2 toward the N-terminal domain in the same spike subunit as the RBD. Spike and ACE2 N-glycans exert synergistic effects, promoting better packing, strengthening the protein:protein interaction, and prolonging the residence time of the complex. ACE2 and HS binding trigger rearrangement of the S2’ functional protease cleavage site through allosteric interdomain communication. These results thus show that HS has a multifaceted role in facilitating SARS-CoV-2 infection, and they provide a mechanistic basis for the development of GAG derivatives with anti-SARS-CoV-2 potential.

Structural bioinformatics approach to exploring transient binding pockets in proteins, (Bachelor's Thesis), Biochemistry, Faculty of Biosciences and Faculty of Chemistry and Earth Sciences, Ruprecht-Karls University, Heidelberg (2024)

Load-Balance and Fault-Tolerance for Massively Parallel Phylogenetic Inference - Hübner, Lukas (2020)

Advanced Heuristics for Accelerating PaPaRa - Höhler, Dimitri (2020)

Phylogenetic species tree inference from gene trees despite paralogy - Schade, Paul (2020)

Investigating the relationship between tree distance and model distance (2022)

Much Ado About Nothing: Accelerating Maximum Likelihood Phylogenetic Inference via Early Stopping to evade (Over-)optimization - Togkousidis, Anastasis; Stamatakis, Alexandros; Gascuel, Olivier (2024)

Computational screening of the effects of mutations on protein-protein off-rates and dissociation mechanisms by τRAMD - D'Arrigo, Giulia; Kokh, Daria B.; Nunes-Alves, Ariane; Wade, Rebecca C. (Communications Biology, 2024)
Abstract The dissociation rate, or its reciprocal, the residence time (τ), is a crucial parameter for understanding the duration and biological impact of biomolecular interactions. Accurate prediction of τ is essential for understanding protein-protein interactions (PPIs) and identifying potential drug targets or modulators for tackling diseases. Conventional molecular dynamics simulation techniques are inherently constrained by their limited timescales, making it challenging to estimate residence times, which typically range from minutes to hours. Building upon its successful application in protein-small molecule systems, τ-Random Acceleration Molecular Dynamics (τRAMD) is here investigated for estimating dissociation rates of protein-protein complexes. τRAMD enables the observation of unbinding events on the nanosecond timescale, facilitating rapid and efficient computation of relative residence times. We tested this methodology for three protein-protein complexes and their extensive mutant datasets, achieving good agreement between computed and experimental data. By combining τRAMD with MD-IFP (Interaction Fingerprint) analysis, dissociation mechanisms were characterized and their sensitivity to mutations investigated, enabling the identification of molecular hotspots for selective modulation of dissociation kinetics. In conclusion, our findings underscore the versatility of τRAMD as a simple and computationally efficient approach for computing relative protein-protein dissociation rates and investigating dissociation mechanisms, thereby aiding the design of PPI modulators.

H i discs of L* galaxies as probes of the baryonic physics of galaxy evolution - Gensior, Jindra; Feldmann, Robert; Reina-Campos, Marta; Trujillo Gomez, Sebastian; Mayer, Lucio; Keller, Benjamin W; Wetzel, Andrew; Kruijssen, J M Diederik; Hopkins, Philip F; Moreno, Jorge (Monthly Notices of the Royal Astronomical Society, 2024)
ABSTRACT Understanding what shapes the cold gas component of galaxies, which both provides the fuel for star formation and is strongly affected by the subsequent stellar feedback, is a crucial step towards a better understanding of galaxy evolution. Here, we analyse the H i properties of a sample of 46 Milky Way halo-mass galaxies, drawn from cosmological simulations (EMP-Pathfinder and Firebox). This set of simulations comprises galaxies evolved self-consistently across cosmic time with different baryonic sub-grid physics: three different star formation models [constant star formation efficiency (SFE) with different star formation eligibility criteria, and an environmentally dependent, turbulence-based SFE] and two different feedback prescriptions, where only one sub-sample includes early stellar feedback. We use these simulations to assess the impact of different baryonic physics on the H i content of galaxies. We find that the galaxy-wide H i properties agree with each other and with observations. However, differences appear for small-scale properties. The thin H i discs observed in the local universe are only reproduced with a turbulence-dependent SFE and/or early stellar feedback. Furthermore, we find that the morphology of H i discs is particularly sensitive to the different physics models: galaxies simulated with a turbulence-based SFE have discs that are smoother and more rotationally symmetric, compared to those simulated with a constant SFE; galaxies simulated with early stellar feedback have more regular discs than supernova-feedback-only galaxies. We find that the rotational asymmetry of the H i discs depends most strongly on the underlying physics model, making this a promising observable for understanding the physics responsible for shaping the interstellar medium of galaxies.

On the origin of globular clusters in a hierarchical universe - De Lucia, Gabriella; Kruijssen, J M Diederik; Trujillo Gomez, Sebastian; Hirschmann, Michaela; Xie, Lizhi (Monthly Notices of the Royal Astronomical Society, 2024)
ABSTRACT We present an end-to-end description of the formation of globular clusters (GCs) combining a treatment for their formation and dynamical evolution within galaxy haloes with a state-of-the-art semi-analytic simulation of galaxy formation. Our approach allows us to obtain exquisite statistics to study the effect of the environment and assembly history of galaxies, while still allowing a very efficient exploration of the parameter space. Our reference model, including both efficient cluster disruption during galaxy mergers and dynamical friction of GCs within the galactic potential, accurately reproduces the observed correlation between the total mass in GCs and the parent halo mass. A deviation from linearity is predicted at low-halo masses, which is driven by a strong dependence on morphological type: bulge-dominated galaxies tend to host larger masses of GCs than their later-type counterparts. While the significance of the difference might be affected by resolution at the lowest halo masses considered, this is a robust prediction of our model and a natural consequence of the assumption that cluster migration into the halo is triggered by galaxy mergers. Our model requires an environmental dependence of GC radii to reproduce the observed low-mass mass distribution of GCs in our Galaxy. At GC masses $\gt 10^6\, {\rm M}_\odot$, our model predicts fewer GCs than observed, due to an overly aggressive treatment of dynamical friction. Our model reproduces well the metallicity distribution measured for Galactic GCs, even though we predict systematically younger GCs than observed. We argue that this adds further evidence for an anomalously early formation of the stars in our Galaxy.

Comprehensive characterization of the neurogenic and neuroprotective action of a novel TrkB agonist using mouse and human stem cell models of Alzheimer’s disease - Charou, Despoina; Rogdakis, Thanasis; Latorrata, Alessia; Valcarcel, Maria; Papadogiannis, Vasileios; Athanasiou, Christina; Tsengenes, Alexandros; Papadopoulou, Maria Anna; Lypitkas, Dimitrios; Lavigne, Matthieu D.; Katsila, Theodora; Wade, Rebecca C.; et al. (Stem Cell Research & Therapy, 2024)
Abstract

Singularity-EOS: Performance Portable Equations of State and Mixed Cell Closures - Miller, Jonah M.; Holladay, Daniel A.; Peterson, Jeffrey H.; Mauney, Christopher M.; Berger, Richard; Graham, Anna Pietarila; Tsai, Karen C.; Barker, Brandon; Holas, Alexander; Mattsson, Ann E.; Gogilashvili, Mariam; Dolence, Joshua C.; et al. (Journal of Open Source Software, 2024)
We present Singularity-EOS, a new performance-portable library for equations of state and related capabilities. Singularity-EOS provides a large set of analytic equations of state, such as the Gruneisen equation of state, and tabulated equation of state data under a unified interface. It also provides support capabilities around these equations of state, such as Python wrappers, solvers for finding pressure-temperature equilibrium between multiple equations of state, and a unique modifier framework, allowing the user to transform a base equation of state, for example by shifting or scaling the specific internal energy. All capabilities are performance portable, meaning they compile and run on both CPU and GPU for a wide variety of architectures.

In situ or accreted? Using deep learning to infer the origin of extragalactic globular clusters from observables - Trujillo Gomez, Sebastian; Kruijssen, J M Diederik; Pfeffer, Joel; Reina-Campos, Marta; Crain, Robert A; Bastian, Nate; Cabrera-Ziri, Ivan (Monthly Notices of the Royal Astronomical Society, 2023)
ABSTRACT Globular clusters (GCs) are powerful tracers of the galaxy assembly process, and have already been used to obtain a detailed picture of the progenitors of the Milky Way (MW). Using the E-MOSAICS cosmological simulation of a (34.4 Mpc)3 volume that follows the formation and co-evolution of galaxies and their star cluster populations, we develop a method to link the origin of GCs to their observable properties. We capture this complex link using a supervised deep learning algorithm trained on the simulations, and predict the origin of individual GCs (whether they formed in the main progenitor or were accreted from satellites) based solely on extragalactic observables. An artificial neural network classifier trained on ∼50 000 GCs hosted by ∼700 simulated galaxies successfully predicts the origin of GCs in the test set with a mean accuracy of 89 per cent for the objects with $\rm [Fe/H]\lt -0.5$ that have unambiguous classifications. The network relies mostly on the alpha-element abundances, metallicities, projected positions, and projected angular momenta of the clusters to predict their origin. A real-world test using the known progenitor associations of the MW GCs achieves up to 90 per cent accuracy, and successfully identifies as accreted most of the GCs in the inner Galaxy associated to the Kraken progenitor, as well as all the Gaia-Enceladus GCs. We demonstrate that the model is robust to observational uncertainties, and develop a method to predict the classification accuracy across observed galaxies. The classifier can be optimized for available observables (e.g. to improve the accuracy by including GC ages), making it a valuable tool to reconstruct the assembly histories of galaxies in upcoming wide-field surveys.

Merger seismology: Distinguishing massive merger products from genuine single stars using asteroseismology - Henneco, Jan; Schneider, Fabian; Hekker, Saskia; Aerts, C. (Astronomy & Astrophysics, 2024)
Products of stellar mergers are predicted to be common in stellar populations and can potentially explain stars with peculiar properties. When the merger occurs after the initially more massive star has evolved into the Hertzsprung gap, the merger product may remain in the blue part of the Hertzsprung–Russell diagram for millions of years. Such objects could, therefore, explain the overabundance of observed blue stars, such as blue supergiants. However, it is currently not straightforward to distinguish merger products from genuine single stars or other stars with similar surface diagnostics. In this work, we made detailed asteroseismic comparisons between models of massive post-main-sequence merger products and genuine single stars to identify which asteroseismic diagnostics can be used to distinguish them. In doing so, we developed tools for the relatively young field of merger seismology. Genuine single stars in the Hertzsprung gap are fully radiative, while merger products have a convective He-burning core and convective H-burning shell while occupying similar locations in the Hertzsprung–Russell diagram. These major structural differences are reflected in lower asymptotic period spacing values for merger products and the appearance of deep dips in their period spacing patterns. Our genuine single-star models with masses above roughly 11.4 solar masses develop short-lived intermediate convective zones during their Hertzsprung gap evolution. This also leads to deep dips in their period spacing patterns. Because of the lack of a convective core, merger products and genuine single stars can be distinguished based on their asymptotic period spacing value in this mass range. We performed the comparisons with and without the effects of slow rotation included in the pulsation equations and conclude that the two types of stars are seismically distinguishable in both cases. The observability of the distinguishing asteroseismic features of merger products can now be assessed and exploited in practice.

Decompositions of the mean continuous ranked probability score - Arnold, Sebastian; Walz, Eva-Maria; Ziegel, Johanna; Gneiting, Tilmann (Electronic Journal of Statistics, 2024)

Multivariate post‐processing of probabilistic sub‐seasonal weather regime forecasts - Mockert, Fabian; Grams, Christian M.; Lerch, Sebastian; Osman, Marisol; Quinting, Julian (Quarterly Journal of the Royal Meteorological Society, 2024)

Postprocessing of ensemble weather forecasts using permutation-invariant neural networks - Höhlein, Kevin; Schulz, Benedikt; Westermann, Rüdiger; Lerch, Sebastian (Artificial Intelligence for the Earth Systems, 2024)

Enhancing the statistical evaluation of earthquake forecasts—An application to Italy - Brehmer, Jonas R.; Kraus, Kristof; Gneiting, Tilmann; Herrmann, Marcus; Marzocchi, Warner (Seismological Research Letters, 2024)

Dusty Common Envelope Evolution - Siess, Lionel; Bermúdez-Bustamante, Luis C.; De Marco, Orsola; Price, Daniel J.; González-Bolívar, Miguel; Mu, Chunliang; Lau, Mike; Hirai, Ryosuke; Danilovich, Taïssa (Galaxies, 2024)
We present the first hydrodynamical simulations of common envelope evolution that include the formation of dust and the effect of radiation pressure on dust grains. We performed smoothed particle hydrodynamics simulations of the CE evolution for two systems made of a 1.7 M⊙ and 3.7 M⊙ AGB star primary with a 0.6 M⊙ binary companion. The results of our calculations indicate that dust formation has a negligible impact on the gas dynamics essentially because dust forms in the already unbound material. The expansion and cooling of the envelope yield very early and highly efficient production of dust. In our formalism, which does not consider dust destruction, almost 100% of the available carbon that is not locked in CO condensates in dust grains. This massive dust production, thus, strongly depends on the envelope mass and composition, in particular, its C/O ratio, and has a considerable impact on the observational aspect of the object, resulting in a photospheric radius that is approximatively one order of magnitude larger than that of a non-dusty system.

Nature versus nurture: distinguishing effects from stellar processing and chemical evolution on carbon and nitrogen in red giant stars - Roberts, John D; Pinsonneault, Marc H; Johnson, Jennifer A; Zinn, Joel C; Weinberg, David H; Vrard, Mathieu; Tayar, Jamie; Stello, Dennis; Mosser, Benoît; Johnson, James W; Cao, Kaili; Stassun, Keivan G; et al. (Monthly Notices of the Royal Astronomical Society, 2024)
The surface [C/N] ratios of evolved giants are strongly affected by the first dredge-up (FDU) of nuclear-processed material from stellar cores. C and N also have distinct nucleosynthetic origins and serve as diagnostics of mixing and mass-loss. We use subgiants to find strong trends in the birth [C/N] with [Fe/H], which differ between the low-α and high-α populations. We demonstrate that these birth trends have a strong impact on the surface abundances after the FDU. This effect is neglected in current stellar models, which use solar-scaled C and N. We map out the FDU as a function of evolutionary state, mass, and composition using a large and precisely measured asteroseismic data set in first-ascent red giant branch (RGB) and core He-burning, or red clump (RC), stars. We describe the domains where [C/N] is a useful mass diagnostic and find that the RC complements the RGB and extends the range of validity to higher mass. We find evidence for extra mixing on the RGB below [Fe/H] = −0.4, matching literature results, for high-α giants, but there is no clear evidence of mixing in the low-α giants. The predicted signal of mass-loss is weak and difficult to detect in our sample. We discuss implications for stellar physics and stellar population applications.

Merger seismology: Distinguishing massive merger products from genuine single stars using asteroseismology - Henneco, Jan; Schneider, Fabian; Hekker, Saskia; Aerts, C. (Astronomy & Astrophysics, 2024)
Products of stellar mergers are predicted to be common in stellar populations and can potentially explain stars with peculiar properties. When the merger occurs after the initially more massive star has evolved into the Hertzsprung gap, the merger product may remain in the blue part of the Hertzsprung–Russell diagram for millions of years. Such objects could, therefore, explain the overabundance of observed blue stars, such as blue supergiants. However, it is currently not straightforward to distinguish merger products from genuine single stars or other stars with similar surface diagnostics. In this work, we made detailed asteroseismic comparisons between models of massive post-main-sequence merger products and genuine single stars to identify which asteroseismic diagnostics can be used to distinguish them. In doing so, we developed tools for the relatively young field of merger seismology. Genuine single stars in the Hertzsprung gap are fully radiative, while merger products have a convective He-burning core and convective H-burning shell while occupying similar locations in the Hertzsprung–Russell diagram. These major structural differences are reflected in lower asymptotic period spacing values for merger products and the appearance of deep dips in their period spacing patterns. Our genuine single-star models with masses above roughly 11.4 solar masses develop short-lived intermediate convective zones during their Hertzsprung gap evolution. This also leads to deep dips in their period spacing patterns. Because of the lack of a convective core, merger products and genuine single stars can be distinguished based on their asymptotic period spacing value in this mass range. We performed the comparisons with and without the effects of slow rotation included in the pulsation equations and conclude that the two types of stars are seismically distinguishable in both cases. The observability of the distinguishing asteroseismic features of merger products can now be assessed and exploited in practice.

The radial modes of stars with suppressed dipole modes - Coppée, Quentin; Müller, J.; Bazot, M.; Hekker, Saskia (Astronomy & Astrophysics, 2024)
Context. The Kepler space mission provided high-quality light curves for more than 16 000 red giants. The global stellar oscillations extracted from these light curves carry information about the interior of the stars. Several hundred red giants were found to have low amplitudes in their dipole modes (i.e. they are suppressed dipole-mode stars). A number of hypotheses (involving e.g. a magnetic field, binarity, or resonant mode coupling) have been proposed to explain the suppression of the modes, yet none has been confirmed. Aims. We aim to gain insight into the mechanism at play in suppressed dipole-mode stars by investigating the mode properties (linewidths, heights, and amplitudes) of the radial oscillation modes of red giants with suppressed dipole modes. Methods.We selected from the literature suppressed dipole-mode stars and compared the radial-mode properties of these stars to the radial-mode properties of stars in two control samples of stars with typical (i.e. non-suppressed) dipole modes. Results. We find that the radial-mode properties of the suppressed dipole-mode stars are consistent with the ones in our control samples, and hence not affected by the suppression mechanism. Conclusions. From this we conclude that (1) the balance between the excitation and damping in radial modes is unaffected by the suppression, and by extrapolation the excitation of the non-radial modes is not affected either; and (2) the damping of the radial modes induced by the suppression mechanism is significantly less than the damping from turbulent convective motion, suggesting that the additional damping originates from the more central non-convective regions of the star, to which the radial modes are least sensitive.

Investigation of the impact of mutations in Cytochrome P450 reductase on the rate of electron transfer to Cytochrome P450 by molecular dynamics simulations (2024)

Structural bioinformatics approach to exploring transient binding pockets in proteins (2024)