Curvature formulas related to a family of stable Higgs bundles - Hu, Zhi; Huang, Pengfei (Comm. Math. Phys., 2021)

A flat torus theorem for convex co-compact actions of projective linear groups - Islam, Mitul; Zimmer, Andrew (J. Lond. Math. Soc. (2), 2021)

The sum of Lagrange numbers - Gaster, Jonah; Loustau, Brice (Proc. Amer. Math. Soc., 2021)

A collar lemma for partially hyperconvex surface group representations - Beyrer, Jonas; Pozzetti, Beatrice (Trans. Amer. Math. Soc., 2021)

Symmetric Spaces for Graph Embeddings: A Finsler-Riemannian Approach - López, Federico; Pozzetti, Beatrice; Trettel, Steve; Strube, Michael; Wienhard, Anna (2021)

Cusp excursion in hyperbolic manifolds and singularity of harmonic measure - Randecker, Anja; Tiozzo, Giulio (J. Mod. Dyn., 2021)

An Extension of the Three Gap Theorem to Interval Exchange Transformations - Taha, Diaaeldin (International Mathematics Research Notices, 2021)
The three gap theorem asserts that for any real \\\α\ and any positive integer \\N\ the fractional parts of the sequence \\0, \α, 2\α, \⋯, (N-1)\α\ have at most three distinct gap lengths. In this note, we extend the three gap theorem to arbitrary orbits of interval exchange transformations, and in the process, we generalize the three gap theorem and a result by Boshernitzan.

Finding self-similar behavior in quantum many-body dynamics via persistent homology - Spitz, Daniel; Berges, Jürgen; Oberthaler, Markus; Wienhard, Anna (SciPost Phys., 2021)

Primary and Recurrent Repair of Incisional Hernia Based on Biomechanical Considerations to Avoid Mesh-Related Complications - Nessel, Regine; Löffler, Thorsten; Rinn, Johannes; Lösel, Philipp; Voss, Samuel; Heuveline, Vincent; Vollmer, Matthias; Görich, Johannes; Ludwig, Yannique-Maximilian; Al-Hileh, Luai; Kallinowski, Friedrich (Frontiers in Surgery, 2021)

Biomechanical Influences on Mesh-Related Complications in Incisional Hernia Repair - Kallinowski, Friedrich; Ludwig, Yannique; Gutjahr, Dominik; Gerhard, Christian; Schulte-Hörmann, Hannah; Krimmel, Lena; Lesch, Carolin; Uhr, Katharina; Lösel, Philipp; Voß, Samuel; Heuveline, Vincent; Vollmer, Matthias; et al. (Frontiers in Surgery, 2021)

Slow White Dwarf Mergers as a New Galactic Source of Trans-Iron Elements - Battino, Umberto; Lederer-Woods, Claudia; Travaglio, Claudia; Röpke, Friedrich; Gibson, Brad (EPJ Web Conf., 2022)

Development and Biological Characterization of a Novel Selective TrkA Agonist with Neuroprotective Properties against Amyloid Toxicity - Rogdakis, Thanasis; Charou, Despoina; Latorrata, Alessia; Papadimitriou, Eleni; Tsengenes, Alexandros; Athanasiou, Christina; Papadopoulou, Marianna; Chalikiopoulou, Constantina; Katsila, Theodora; Ramos, Isbaal; Prousis, Kyriakos C.; Wade, Rebecca C.; et al. (Biomedicines, 2022)
Neurotrophins are growth factors that exert important neuroprotective effects by preventing neuronal death and synaptic loss. Nerve Growth Factor (NGF) acts through the activation of its high-affinity, pro-survival TrkA and low-affinity, pro-apoptotic p75NTR receptors. NGF has been shown to slow or prevent neurodegenerative signals in Alzheimer’s Disease (AD) progression. However, its low bioavailability and its blood–brain-barrier impermeability limit the use of NGF as a potential therapeutic agent against AD. Based on our previous findings on synthetic dehydroepiandrosterone derivatives, we identified a novel NGF mimetic, named ENT-A013, which selectively activates TrkA and exerts neuroprotective, anti-amyloid-β actions. We now report the chemical synthesis, in silico modelling, metabolic stability, CYP-mediated reaction phenotyping and biological characterization of ENT-A013 under physiological and neurodegenerative conditions. We show that ENT-A013 selectively activates the TrkA receptor and its downstream kinases Akt and Erk1/2 in PC12 cells, protecting these cells from serum deprivation-induced cell death. Moreover, ENT-A013 promotes survival of primary Dorsal Root Ganglion (DRG) neurons upon NGF withdrawal and protects hippocampal neurons against Amyloid β-induced apoptosis and synaptic loss. Furthermore, this neurotrophin mimetic partially restores LTP impairment. In conclusion, ENT-A013 represents a promising new lead molecule for developing therapeutics against neurodegenerative disorders, such as Alzheimer’s Disease, selectively targeting TrkA-mediated pro-survival signals.

Entity-based Neural Local Coherence Modeling - Jeon, Sungho; Strube, Michael (2022)
In this paper, we propose an entity-based neural local coherence model which is linguistically more sound than previously proposed neural coherence models. Recent neural coherence models encode the input document using large-scale pretrained language models. Hence their basis for computing local coherence are words and even sub-words. The analysis of their output shows that these models frequently compute coherence on the basis of connections between (sub-)words which, from a linguistic perspective, should not play a role. Still, these models achieve state-of-the-art performance in several end applications. In contrast to these models, we compute coherence on the basis of entities by constraining the input to noun phrases and proper names. This provides us with an explicit representation of the most important items in sentences leading to the notion of focus. This brings our model linguistically in line with pre-neural models of computing coherence. It also gives us better insight into the behaviour of the model thus leading to better explainability. Our approach is also in accord with a recent study (O’Connor and Andreas, 2021), which shows that most usable information is captured by nouns and verbs in transformer-based language models. We evaluate our model on three downstream tasks showing that it is not only linguistically more sound than previous models but also that it outperforms them in end applications.

Dynamics in a stellar convective layer and at its boundary: Comparison of five 3D hydrodynamics codes - Andrássy, Róbert; Higl, Johann; Mao, H.; Mocák, M.; Vlaykov, D. G.; Arnett, W. D.; Baraffe, I.; Campbell, S. W.; Constantino, T.; Edelmann, P. V. F.; Goffrey, T.; Guillet, T.; et al. (Astronomy & Astrophysics, 2022)

From Photometric Redshifts to Improved Weather Forecasts: machine learning and proper scoring rules as a basis for interdisciplinary work - Polsterer, Kai; Lerch, Sebastian; D'Isanto, Antonio (2021)
The amount, size, and complexity of astronomical data-sets and databases are growing rapidly in the last decades, due to new technologies and dedicated survey telescopes. Besides dealing with poly-structured and complex data, sparse data has become a field of growing scientific interest. A specific field of Astroinformatics research is the estimation of redshifts of extra-galactic sources by using sparse photometric observations. Many techniques have been developed to produce those estimates with increasing precision. In recent years, models have been favored which instead of providing a point estimate only, are able to generate probabilistic density functions (PDFs) in order to characterize and quantify the uncertainties of their estimates. Crucial to the development of those models is a proper, mathematically principled way to evaluate and characterize their performances, based on scoring functions as well as on tools for assessing calibration. Still, in literature inappropriate methods are being used to express the quality of the estimates that are often not sufficient and can potentially generate misleading interpretations. In this work we summarize how to correctly evaluate errors and forecast quality when dealing with PDFs. We describe the use of the log-likelihood, the continuous ranked probability score (CRPS) and the probability integral transform (PIT) to characterize the calibration as well as the sharpness of predicted PDFs. We present what we achieved when using proper scoring rules to train deep neural networks as well as to evaluate the model estimates and how this work led from well calibrated redshift estimates to improvements in probabilistic weather forecasting. The presented work is an example of interdisciplinarity in data-science and illustrates how methods can help to bridge gaps between different fields of application.

Evaluation of individual and ensemble probabilistic forecasts of COVID-19 mortality in the United States - Cramer, E. Y.; Ray, E. L.; Lopez, V. K.; Bracher, Johannes; Brennen, A.; Castro Rivadeneira, A. J.; Gerding, A.; Gneiting, Tilmann; (282 further coauthors),; Walker, J. W.; Slayton, R. B.; Johansson, M. A.; et al. (Proceedings of the National Academy of Sciences, 2022)

Strength and Nature of Host‐Guest Interactions in Metal‐Organic Frameworks from a Quantum‐Chemical Perspective - Ernst, Michelle; Gryn'ova, Ganna (ChemPhysChem, 2022)
Metal-organic frameworks (MOFs) offer a convenient means for capturing, transporting, and releasing small molecules. Their rational design requires an in-depth understanding of the underlying non-covalent host-guest interactions, and the ability to easily and rapidly pre-screen candidate architectures in silico. In this work, we devised a recipe for computing the strength and analysing the nature of the host-guest interactions in MOFs. By assessing a range of density functional theory methods across periodic and finite supramolecular cluster scale we find that appropriately constructed clusters readily reproduce the key interactions occurring in periodic models at a fraction of the computational cost. Host-guest interaction energies can be reliably computed with dispersion-corrected density functional theory methods; however, decoding their precise nature demands insights from energy decomposition schemes and quantum-chemical tools for bonding analysis such as the quantum theory of atoms in molecules, the non-covalent interactions index or the density overlap regions indicator.

New mechanism for autoxidation of polyolefins: kinetic Monte Carlo modelling of the role of short-chain branches, molecular oxygen and unsaturated moieties - De Keer, Lies; Van Steenberge, Paul; Reyniers, Marie-Françoise; Gryn'ova, Ganna; Aitken, Heather M.; Coote, Michelle L. (Polymer Chemistry, 2022)
In this work, a bivariate kinetic Monte Carlo (kMC) model is constructed to study autoxidation, which is the degradation of polymers in the presence of oxygen. The use of computational methods for the determination of rate coefficients as input for the model is illustrated. Focus is on the presence of short-chain branches (SCB) and unsaturated moieties and their role in the fate of alkyl, alkoxyl and alkylperoxyl radicals in the autoxidation mechanism. The autoxidation kinetics are studied for three model polymers, namely poly(ethylene) (reference case), poly(butadiene) (presence of allylic hydrogens), and poly(isobutylene) (presence of quaternary carbon atoms). Using the kMC model, reaction path analysis shows that the autoxidation mechanism for each of the polymer types follows a chain reaction mechanism, but that the presence of branches/unsaturated moieties influences the dominant reaction pathway in the autoxidation mechanism, and thus also the autoxidation rate. It is shown that the influence of varying oxygen concentration and initiation rate coefficient (e.g. to simulate variable ultraviolet (UV) light intensity) on the dominant pathway is small as their role is mainly situated in the first steps of the chain mechanism.

Martini 3 coarse-grained force field for poly(para-phenylene ethynylene)s. - Brosz, M.; Michelarakis, N.; Bunz, U. H. F.; Aponte-Santamaria, C.; Grater, F. (Phys Chem Chem Phys, 2022)
Poly(para-phenylene ethynylene)s, or short PPEs, are a class of conjugated and semi-flexible polymers with a strongly delocalized pi electron system and increased chain stiffness. Due to this, PPEs have a wide range of technological applications. Although the material properties of single-chains or mixtures of few PPE chains have been studied in detail, the properties of large assemblies remain to be fully explored. Here, we developed a coarse-grained model for PPEs with the Martini 3 force field to enable computational studies of PPEs in large-scale assembly. We used an optimization geometrical approach to take the shape of the pi conjugated backbone into account and also applied an additional angular potential to tune the mechanical bending stiffness of the polymer. Our Martini 3 model reproduces key structural and thermodynamic observables of single PPE chains and mixtures, such as persistence length, density, packing and stacking. We show that chain entanglement increases with the expense of nematic ordering with growing PPE chain length. With the Martini 3 PPE model at hand, we are now able to cover large spatio-temporal scales and thereby to uncover key aspects for the structural organization of PPE bulk systems. The model is also predicted to be of high applicability to investigate out-of-equilibrium behavior of PPEs under mechanical force.
Keywords: *Polymers/chemistry; Thermodynamics

Analytic models of the spectral properties of gravitational waves from neutron star merger remnants - Soultanis, Theodoros; Bauswein, Andreas; Stergioulas, Nikolaos (Physical Review D, 2022)

Probabilistic photometric redshift estimation in massive digital sky surveys via machine learning - D'Isanto, Antonio (2019)
The problem of photometric redshift estimation is a major subject in astronomy, since the need of estimating distances for a huge number of sources, as required by the data deluge of the recent years. The ability to estimate redshifts through spectroscopy does not scale with this avalanche of data. Photometric redshifts provide the required redshift estimates at the cost of some precision. The success of several forthcoming missions is highly dependent on the availability of photometric redshifts. The purpose of this thesis is to provide innovative methods for photometric redshift estimation. Two models are proposed. The first is fully-automatized, based on the combination of a convolutional neural network with a mixture density network, to predict probabilistic multimodal redshifts directly from images. The second model is features-based, performing a massive combination of photometric parameters to apply a forward selection in a huge feature space. The proposed models perform very efficiently compared to some of the most common models used in the literature. An important part of the work is dedicated to the correct estimation of the errors and prediction quality. The proposed models are very general and can be applied to different topics in astronomy and beyond.

Learning Features via Transformer Networks for Cardiomyocyte Profiling - Plier, Jan; Zisler, Matthias; Furkel, Jennifer; Knoll, Maximilian; Marx, Alexander; Fischer, Alena; Polsterer, Kai; Konstandin, Mathias H.; Petra, Stefania (Bildverarbeitung für die Medizin 2022, 2022)

Integration of Distributed Parallel Simulation Environment with Cloud-Infrastructures - Shcherbakov, O.; Polsterer, Kai L.; Svyatnyy, V. A. (2020)
This article describes an integration of the subsystems of the distributed parallel simulation environment with cloud infrastructures. A complex support for simulation of the dynamic network object with distributed parameters on Amazon AWS cloud is provided. As well as a tool helping to significantly save running costs for cloud simulations.

Convolutional autoencoders for spatially-informed ensemble post-processing - Lerch, Sebastian; Polsterer, Kai L. (AI for Earth and Space Science Workshop, ICLR, 2022)
Ensemble weather predictions typically show systematic errors that have to be corrected via post-processing. Even state-of-the-art post-processing methods based on neural networks often solely rely on location-specific predictors that require an interpolation of the physical weather model's spatial forecast fields to the target locations. However, potentially useful predictability information contained in large-scale spatial structures within the input fields is potentially lost in this interpolation step. Therefore, we propose the use of convolutional autoencoders to learn compact representations of spatial input fields which can then be used to augment location-specific information as additional inputs to post-processing models. The benefits of including this spatial information is demonstrated in a case study of 2-m temperature forecasts at surface stations in Germany.

Convective boundary mixing in a post-He core burning massive star model - Davis, A; Jones, S; Herwig, F (Monthly Notices of the Royal Astronomical Society, 2019)

Convective–reactive nucleosynthesis of K, Sc, Cl and p-process isotopes in O–C shell mergers - Ritter, C; Andrassy, R; Côté, B; Herwig, F; Woodward, P R; Pignatari, M; Jones, S (Monthly Notices of the Royal Astronomical Society: Letters, 2018)

NuGrid stellar data set – II. Stellar yields from H to Bi for stellar models with MZAMS = 1–25 M⊙ and Z = 0.0001–0.02 - Ritter, C; Herwig, F; Jones, S; Pignatari, M; Fryer, C; Hirschi, R (Monthly Notices of the Royal Astronomical Society, 2018)

JINA-NuGrid Galactic Chemical Evolution Pipeline - Côté, Benoit; Ritter, Christian; Herwig, Falk; O’Shea, Brian W.; Pignatari, Marco; Silvia, Devin; Jones, Samuel; Fryer, Chris L. (Proceedings of the 14th International Symposium on Nuclei in the Cosmos (NIC2016), 2017)

Modelling the ionization state of Type Ia supernovae in the nebular phase - Shingles, Luke J; Flörs, Andreas; Sim, Stuart A; Collins, Christine E; Röpke, Friedrich; Seitenzahl, Ivo R; Shen, Ken J (Monthly Notices of the Royal Astronomical Society, 2022)

Signatures of a Surviving Helium-star Companion in Type Ia Supernovae and Constraints on the Progenitor Companion of SN 2011fe - Liu, Zheng-Wei; Röpke, Friedrich; Zeng, Yaotian (The Astrophysical Journal, 2022)

Bipolar planetary nebulae from common-envelope evolution of binary stars - Ondratschek, Patrick A.; Röpke, Friedrich; Schneider, Fabian; Fendt, Christian; Sand, Christian; Ohlmann, Sebastian T.; Pakmor, Rüdiger; Springel, Volker (Astronomy & Astrophysics, 2022)

Models of pulsationally assisted gravitationally confined detonations with different ignition conditions - Lach, Florian; Callan, F. P.; Sim, S. A.; Röpke, Friedrich (Astronomy & Astrophysics, 2022)

Type Iax supernovae from deflagrations in Chandrasekhar mass white dwarfs - Lach, Florian; Callan, F. P.; Bubeck, David; Röpke, Friedrich; Sim, S. A.; Schrauth, M.; Ohlmann, S. T.; Kromer, M. (Astronomy & Astrophysics, 2022)

Multiscale molecular simulations to investigate adenylyl cyclase‐based signaling in the brain - van Keulen, Siri C.; Martin, Juliette; Colizzi, Francesco; Frezza, Elisa; Trpevski, Daniel; Diaz, Nuria Cirauqui; Vidossich, Pietro; Rothlisberger, Ursula; Hellgren Kotaleski, Jeanette; Wade, Rebecca C.; Carloni, Paolo (WIREs Computational Molecular Science, 2023)
Abstract Adenylyl cyclases (ACs) play a key role in many signaling cascades. ACs catalyze the production of cyclic AMP from ATP and this function is stimulated or inhibited by the binding of their cognate stimulatory or inhibitory Gα subunits, respectively. Here we used simulation tools to uncover the molecular and subcellular mechanisms of AC function, with a focus on the AC5 isoform, extensively studied experimentally. First, quantum mechanical/molecular mechanical free energy simulations were used to investigate the enzymatic reaction and its changes upon point mutations. Next, molecular dynamics simulations were employed to assess the catalytic state in the presence or absence of Gα subunits. This led to the identification of an inactive state of the enzyme that is present whenever an inhibitory Gα is associated, independent of the presence of a stimulatory Gα. In addition, the use of coevolution‐guided multiscale simulations revealed that the binding of Gα subunits reshapes the free‐energy landscape of the AC5 enzyme by following the classical population‐shift paradigm. Finally, Brownian dynamics simulations provided forward rate constants for the binding of Gα subunits to AC5, consistent with the ability of the protein to perform coincidence detection effectively. Our calculations also pointed to strong similarities between AC5 and other AC isoforms, including AC1 and AC6. Findings from the molecular simulations were used along with experimental data as constraints for systems biology modeling of a specific AC5‐triggered neuronal cascade to investigate how the dynamics of downstream signaling depend on initial receptor activation.

Multitarget, Selective Compound Design Yields Potent Inhibitors of a Kinetoplastid Pteridine Reductase 1 - Pöhner, Ina; Quotadamo, Antonio; Panecka-Hofman, Joanna; Luciani, Rosaria; Santucci, Matteo; Linciano, Pasquale; Landi, Giacomo; Di Pisa, Flavio; Dello Iacono, Lucia; Pozzi, Cecilia; Mangani, Stefano; Gul, Sheraz; et al. (Journal of Medicinal Chemistry, 2022)

Combining hypothesis- and data-driven neuroscience modeling in FAIR workflows - Eriksson, Olivia; Bhalla, Upinder Singh; Blackwell, Kim T; Crook, Sharon M; Keller, Daniel; Kramer, Andrei; Linne, Marja-Leena; Saudargienė, Ausra; Wade, Rebecca C.; Hellgren Kotaleski, Jeanette (eLife, 2022)
Modeling in neuroscience occurs at the intersection of different points of view and approaches. Typically, hypothesis-driven modeling brings a question into focus so that a model is constructed to investigate a specific hypothesis about how the system works or why certain phenomena are observed. Data-driven modeling, on the other hand, follows a more unbiased approach, with model construction informed by the computationally intensive use of data. At the same time, researchers employ models at different biological scales and at different levels of abstraction. Combining these models while validating them against experimental data increases understanding of the multiscale brain. However, a lack of interoperability, transparency, and reusability of both models and the workflows used to construct them creates barriers for the integration of models representing different biological scales and built using different modeling philosophies. We argue that the same imperatives that drive resources and policy for data – such as the FAIR (Findable, Accessible, Interoperable, Reusable) principles – also support the integration of different modeling approaches. The FAIR principles require that data be shared in formats that are Findable, Accessible, Interoperable, and Reusable. Applying these principles to models and modeling workflows, as well as the data used to constrain and validate them, would allow researchers to find, reuse, question, validate, and extend published models, regardless of whether they are implemented phenomenologically or mechanistically, as a few equations or as a multiscale, hierarchical system. To illustrate these ideas, we use a classical synaptic plasticity model, the Bienenstock–Cooper–Munro rule, as an example due to its long history, different levels of abstraction, and implementation at many scales.

Incorporating Centering Theory into Neural Coreference Resolution - Chai, Haixia; Strube, Michael (Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, 2022)
In recent years, transformer-based coreference resolution systems have achieved remarkable improvements on the CoNLL dataset. However, how coreference resolvers can benefit from discourse coherence is still an open question. In this paper, we propose to incorporate centering transitions derived from centering theory in the form of a graph into a neural coreference model. Our method improves the performance over the SOTA baselines, especially on pronoun resolution in long documents, formal well-structured text, and clusters with scattered mentions.

Fine-tuning BERT Models for Summarizing German Radiology Findings - Liang, Siting; Kades, Klaus; Fink, Matthias; Full, Peter; Weber, Tim; Kleesiek, Jens; Strube, Michael; Maier-Hein, Klaus (Proceedings of the 4th Clinical Natural Language Processing Workshop, 2022)
Writing the conclusion section of radiology reports is essential for communicating the radiology findings and its assessment to physician in a condensed form. In this work, we employ a transformer-based Seq2Seq model for generating the conclusion section of German radiology reports. The model is initialized with the pretrained parameters of a German BERT model and fine-tuned in our downstream task on our domain data. We proposed two strategies to improve the factual correctness of the model. In the first method, next to the abstractive learning objective, we introduce an extraction learning objective to train the decoder in the model to both generate one summary sequence and extract the key findings from the source input. The second approach is to integrate the pointer mechanism into the transformer-based Seq2Seq model. The pointer network helps the Seq2Seq model to choose between generating tokens from the vocabulary or copying parts from the source input during generation. The results of the automatic and human evaluations show that the enhanced Seq2Seq model is capable of generating human-like radiology conclusions and that the improved models effectively reduce the factual errors in the generations despite the small amount of training data.

The CODI-CRAC 2021 Shared Task on Anaphora, Bridging, and Discourse Deixis in Dialogue - Khosla, Sopan; Yu, Juntao; Manuvinakurike, Ramesh; Ng, Vincent; Poesio, Massimo; Strube, Michael; Rosé, Carolyn (2021)
In this paper, we provide an overview of the CODI-CRAC 2021 Shared-Task: Anaphora Resolution in Dialogue. The shared task focuses on detecting anaphoric relations in different genres of conversations. Using five conversational datasets, four of which have been newly annotated with a wide range of anaphoric relations: identity, bridging references and discourse deixis, we defined multiple subtasks focusing individually on these key relations. We discuss the evaluation scripts used to assess the system performance on these subtasks, and provide a brief summary of the participating systems and the results obtained across ?? runs from 5 teams, with most submissions achieving significantly better results than our baseline methods.

Proceedings of the 2nd Workshop on Computational Approaches to Discourse, Punta Cana, Dominican Repbulich and Online - Braud, Chloé; Hardmeier, Christian; Li, Junyi Jessy; Louis, Annie; Strube, Michael; Zeldes, Amir (2021)

Proceedings of the First Workshop on Computational Approaches to Discourse, Online - Braud, Chloé; Hardmeier, Christian; Li, Junyi Jessy; Louis, Annie; Strube, Michael (2020)

Macromolecular Entropy Can Be Accurately Computed from Force - Hensen, Ulf; Gräter, Frauke; Henchman, Richard H. (Journal of Chemical Theory and Computation, 2014)
A method is presented to evaluate a molecule’s entropy from the atomic forces calculated in a molecular dynamics simulation. Specifically, diagonalization of the mass-weighted force covariance matrix produces eigenvalues which in the harmonic approximation can be related to vibrational frequencies. The harmonic oscillator entropies of each vibrational mode may be summed to give the total entropy. The results for a series of hydrocarbons, dialanine and a β hairpin are found to agree much better with values derived from thermodynamic integration than results calculated using quasiharmonic analysis. Forces are found to follow a harmonic distribution more closely than coordinate displacements and better capture the underlying potential energy surface. The method’s accuracy, simplicity, and computational similarity to quasiharmonic analysis, requiring as input force trajectories instead of coordinate trajectories, makes it readily applicable to a wide range of problems.

A simple algorithm for exact multinomial tests - Resin, Johannes (Journal of Computational and Graphical Statistics, 2023)

Allosteric activation of vinculin by talin - Franz, Florian; Tapia-Rojo, Rafael; Winograd-Katz, Sabina; Boujemaa-Paterski, Rajaa; Li, Wenhong; Unger, Tamar; Albeck, Shira; Aponte-Santamaría, Camilo; Garcia-Manyes, Sergi; Medalia, Ohad; Geiger, Benjamin; Gräter, Frauke (Nature Communications, 2023)
The talin-vinculin axis is a key mechanosensing component of cellular focal adhesions. How talin and vinculin respond to forces and regulate one another remains unclear. By combining single molecule magnetic tweezer experiments, Molecular Dynamics simulations, actin bundling assays, and adhesion assembly experiments in live cells, we here discover a two-ways allosteric network within vinculin as a regulator of the talin-vinculin interaction. We directly observe a maturation process of vinculin upon talin binding which reinforces the binding to talin at a rate of 0.03 s−1. This allosteric transition can compete with force-induced dissociation of vinculin from talin only at 7-10 pN. Mimicking the allosteric activation by mutation yields a vinculin molecule that bundles actin and localizes to focal adhesions in a force-independent manner. Hence, the allosteric switch confines talin-vinculin interactions and focal adhesion build-up to intermediate force levels. The ‘allosteric vinculin mutant’ is a valuable molecular tool to further dissect the mechanical and biochemical signalling circuits at focal adhesions and elsewhere.

Bond dissociation energies of X–H bonds in proteins - Treyde, Authors Wojtek; Riedmiller, Kai; Gräter, Frauke (RSC Advances, 2022)
Knowledge of reliable X−H bond dissociation energies (X = C, N, O, S) for amino acids in proteins is key for studying the radical chemistry of proteins. X−H bond dissociation energies of model dipeptides were computed using the isodesmic reaction method at the BMK/6-31+G(2df,p) and G4(MP2)-6X levels of theory. The density functional theory values agree well with the composite- level calculations. By this high level of theory, combined with a careful choice of reference compounds and peptide model systems, our work provides a highly valuable data set of bond dissociation energies with unprecedented accuracy and comprehensiveness. It will likely prove useful to predict protein biochemistry involving radicals, e.g., by machine learning.

A Conformational Transition of the D’D3 Domain primes von Willebrand Factor for Multimerization - Gruber, Sophia; Löf, Achim; Hausch, Adina; Kutzki, Fabian; Jöhr, Res; Obser, Tobias; König, Gesa; Schneppenheim, Reinhard; Aponte-Santamaría, Camilo; Gräter, Frauke; Brehm, Maria A.; Benoit, Martin; et al. (Blood Advances, 2022)
Von Willebrand factor (VWF) is a multimeric plasma glycoprotein that is critically involved in hemostasis. Biosynthesis of long VWF concatemers in the endoplasmic reticulum and the (trans-)Golgi is still not fully understood. We use the single-molecule force spectroscopy technique magnetic tweezers to analyze a previously hypothesized conformational change in the D’D3 domain crucial for VWF multimerization. We find that the interface formed by submodules C8-3, TIL3, and E3 wrapping around VWD3 can open and expose two buried cysteines, Cys1099 and Cys1142, that are vital for multimerization. By characterizing the conformational change at varying levels of force, we are able to quantify the kinetics of the transition and the stability of the interface. We find a pronounced destabilization of the interface upon lowering the pH from 7.4 to 6.2 and 5.5. This is consistent with initiation of the conformational change that enables VWF multimerization at the D’D3 domain by a decrease in pH in the trans-Golgi network and Weibel-Palade bodies. Furthermore, we find a stabilization of the interface in the presence of coagulation factor VIII (FVIII), providing evidence for a previously hypothesized binding site in submodule C8-3. Our findings highlight the critical role of the D’D3 domain in VWF biosynthesis and function and we anticipate our methodology to be applicable to study other, similar conformational changes in VWF and beyond.

Mechano-redox control of Mac-1 de-adhesion from ICAM-1 by protein disulfide isomerase promotes directional movement of neutrophils under flow - Dupuy, Alexander; Aponte-Santamaría, Camilo; Yeheskel, Adva; Gräter, Frauke; Hogg, Philip J.; Passam, Freda H.; Chiu, Joyce (bioRxiv, 2022)
Macrophage-1 antigen or Mac-1 (CD11b/CD18, αMβ2) is a leukocyte integrin essential for firm adhesion of neutrophils, lymphocytes and monocytes against flow when recruited to the endothelium. To migrate to the site of inflammation, leukocytes require coordinated adhesion and de-adhesion for directional movement. The vascular thiol isomerase, protein disulfide isomerase (PDI), was found by fluorescence microscopy to colocalize with high affinity Mac-1 at the trailing edge of stimulated neutrophils when adhered to ICAM-1 under fluid shear. From differential cysteine alkylation and mass spectrometry studies, PDI cleaves two allosteric disulfide bonds, C169-C176 and C224-C264, in the βI domain of the β2 subunit, and in mutagenesis and cell transfection studies, cleavage of the C224-C264 disulfide bond was shown to selectively control Mac-1 dis-engagement from ICAM-1 under fluid shear. Molecular dynamics simulations and binding of conformation-specific antibodies reveal that cleavage of the C224-C264 bond induces conformational change and mechanical stress in the βI domain that allosterically alters exposure of an αI domain epitope and shifts Mac-1 to a lower affinity state. From studies of neutrophil adherence to ICAM-1 under fluid shear, these molecular events promote neutrophil motility in the direction of flow at high shear stress. In summary, shear-dependent PDI cleavage of neutrophil Mac-1 C224-C264 disulfide bond triggers Mac-1 de-adherence from ICAM-1 at the trailing edge of the cell and enables directional movement of neutrophils during inflammation.

The United States COVID-19 Forecast Hub dataset - Cramer, E. Y.; Huang, Y.; Wang, Yijin; Ray, Evan L.; Cornell, Matthew; Bracher, Johannes; Brennen, A.; Castro Rivadeneira, A. J.; Gerding, A.; House, Katie; Jayawardena, Dasuni; Kanji, Abdul Hannan; et al. (Scientific Data, 2022)

Graphene BioFET sensors for SARS-CoV-2 detection: a multiscale simulation approach - Toral-Lopez, A.; Kokh, D. B.; Marin, E. G.; Wade, Rebecca C.; Godoy, A. (Nanoscale Advances, 2022)
Two-dimensional (2D) materials BioFETs have already demonstrated their potential for detecting low amounts of molecules. Here, we present a multiscale simulation platform in the context of Graphene BioFET for the detection of SARS-CoV-2.

Hydropersulfides inhibit lipid peroxidation and ferroptosis by scavenging radicals - Barayeu, Uladzimir; Schilling, Danny; Eid, Mohammad; Silva, Thamara Nishida Xavier da; Schlicker, Lisa; Mitreska, Nikolina; Zapp, Christopher; Gräter, Frauke; Miller, Aubry K.; Kappl, Reinhard; Schulze, Almut; Angeli, José Pedro Friedmann; et al. (Nature Chemical Biology, 2022)
erroptosis is a type of cell death caused by radical-driven lipid peroxidation, leading to membrane damage and rupture. Here we show that enzymatically produced sulfane sulfur (S0) species, specifically hydropersulfides, scavenge endogenously generated free radicals and, thereby, suppress lipid peroxidation and ferroptosis. By providing sulfur for S0 biosynthesis, cysteine can support ferroptosis resistance independently of the canonical GPX4 pathway. Our results further suggest that hydropersulfides terminate radical chain reactions through the formation and self-recombination of perthiyl radicals. The autocatalytic regeneration of hydropersulfides may explain why low micromolar concentrations of persulfides suffice to produce potent cytoprotective effects on a background of millimolar concentrations of glutathione. We propose that increased S0 biosynthesis is an adaptive cellular response to radical-driven lipid peroxidation, potentially representing a primordial radical protection system.

Identification of antiparasitic drug targets using a multi-omics workflow in the acanthocephalan model - Schmidt, Hanno; Mauer, Katharina; Glaser, Manuel; Dezfuli, Bahram Sayyaf; Hellmann, Sören Lukas; Silva Gomes, Ana Lúcia; Butter, Falk; Wade, Rebecca C.; Hankeln, Thomas; Herlyn, Holger (BMC Genomics, 2022)
Abstract

Anti-trypanosomatid structure-based drug design – lessons learned from targeting the folate pathway - Panecka-Hofman, Joanna; Pöhner, Ina; Wade, Rebecca C. (Expert Opinion on Drug Discovery, 2022)

Pushing the Limits of Simple Pipelines for Few-Shot Learning: External Data and Fine-Tuning Make a Difference - Hu, Shell Xu; Li, Da; Stühmer, Jan; Kim, Minyoung; Hospedales, Timothy M (2022)

Meta-learning richer priors for VAEs - Negri, Marcello Massimo; Fortuin, Vincent; Stühmer, Jan (2022)

H2o: Two hands manipulating objects for first person interaction recognition - Kwon, Taein; Tekin, Bugra; Stühmer, Jan; Bogo, Federica; Pollefeys, Marc (2021)

Independent subspace analysis for unsupervised learning of disentangled representations - Stühmer, Jan; Turner, Richard; Nowozin, Sebastian (2020)

Disentangling interpretable generative parameters of random and real-world graphs - Stoehr, Niklas; Yilmaz, Emine; Brockschmidt, Marc; Stühmer, Jan (2019)

Genetic defects in β-spectrin and tau sensitize C. elegans axons to movement-induced damage via torque-tension coupling - Krieg, Michael; Stühmer, Jan; Cueva, Juan G; Fetter, Richard; Spilker, Kerri; Cremers, Daniel; Shen, Kang; Dunn, Alexander R; Goodman, Miriam B (Elife, 2017)

A fast projection method for connectivity constraints in image segmentation - Stühmer, Jan; Cremers, Daniel (2015)

Generalized connectivity constraints for spatio-temporal 3d reconstruction - Oswald, Martin Ralf; Stühmer, Jan; Cremers, Daniel (2014)

Tree shape priors with connectivity constraints using convex relaxation on general graphs - Stühmer, Jan; Schroder, Peter; Cremers, Daniel (2013)

Real-Time Dense Geometry from a Handheld Camera - Stühmer, Jan; Gumhold, Stefan; Cremers, Daniel (2010)

Parallel generalized thresholding scheme for live dense geometry from a handheld camera - Stühmer, Jan; Gumhold, Stefan; Cremers, Daniel (2010)

Tau like proteins reduce torque generation in microtubule bundles - Krieg, Michael; Stühmer, Jan; Cueva, Juan G; Fetter, Richard; Spilker, Kerri; Cremers, Daniel; Shen, Kang; Dunn, Alex R; Goodman, Miriam B (Biophysical Journal, 2017)

Model-based tracking at 300hz using raw time-of-flight observations - Stühmer, Jan; Nowozin, Sebastian; Fitzgibbon, Andrew; Szeliski, Richard; Perry, Travis; Acharya, Sunil; Cremers, Daniel; Shotton, Jamie (2015)

Active online learning for interactive segmentation using sparse Gaussian processes - Triebel, Rudolph; Stühmer, Jan; Souiai, Mohamed; Cremers, Daniel (2014)

Movement directionality in collective migration of germ layer progenitors - Arboleda-Estudillo, Yohanna; Krieg, Michael; Stühmer, Jan; Licata, Nicholas A; Muller, Daniel J; Heisenberg, Carl-Philipp (Current Biology, 2010)

Control of convergent yolk syncytial layer nuclear movement in zebrafish - Carvalho, Lara; Stühmer, Jan; Bois, Justin S; Kalaidzidis, Yannis; Lecaudey, Virginie; Heisenberg, Carl-Philipp (2009)

Digitoxin metabolism by rat liver microsomes. - Schmoldt, A.; Benthe, H. F.; Haberland, G. (Biochem Pharmacol, 1975)
Keywords: Animals; Chromatography, Thin Layer; Digitoxigenin/metabolism; Digitoxin/*metabolism; Hydroxylation; In Vitro Techniques; Male; Microsomes, Liver/*metabolism; NADP/metabolism; Rats; Time Factors

Metagenomic Analysis Using Phylogenetic Placement—A Review of the First Decade - Czech, Lucas; Stamatakis, Alexandros; Dunthorn, Micah; Barbera, Pierre (Frontiers in Bioinformatics, 2022)

Lagrange-NG: The next generation of Lagrange - Bettisworth, Ben; Smith, Stephen A.; Stamatakis, Alexandros (2022)

From Easy to Hopeless - Predicting the Difficulty of Phylogenetic Analyses - Haag, Julia; Höhler, Dimitri; Bettisworth, Ben; Stamatakis, Alexandros (2022)

The Free Lunch is not over yet – Systematic Exploration of Numerical Thresholds in Phylogenetic Inference - Haag, Julia; Hübner, Lukas; Kozlov, Alexey; Stamatakis, Alexandros (2022)

Identification of high-wind features within extratropical cyclones using a probabilistic random forest – Part 1: Method and case studies - Eisenstein, Lea; Schulz, Benedikt; Qadir, Ghulam A.; Pinto, Joaquim G.; Knippertz, Peter (Weather and Climate Dynamics, 2022)

Collagen breaks at weak sacrificial bonds taming its mechanoradicals - Rennekamp, Benedikt; Karfusehr, Christoph; Kurth, Markus; Ünal, Aysecan; Riedmiller, Kai; Gryn'ova, Ganna; Hudson, David M.; Gräter, Frauke (Nature Communications, 2023)
Collagen is a force-bearing, hierarchical structural protein important to all connective tissue. In tendon collagen, high load even below macroscopic failure level creates mechanoradicals by homolytic bond scission, similar to polymers. The location and type of initial rupture sites critically decide on both the mechanical and chemical impact of these micro-ruptures on the tissue, but are yet to be explored. We here use scale-bridging simulations supported by gel electrophoresis and mass spectrometry to determine breakage points in collagen. We find collagen crosslinks, as opposed to the backbone, to harbor the weakest bonds, with one particular bond in trivalent crosslinks as the most dominant rupture site. We identify this bond as sacrificial, rupturing prior to other bonds while maintaining the material’s integrity. Also, collagen’s weak bonds funnel ruptures such that the potentially harmful mechanoradicals are readily stabilized. Our results suggest this unique failure mode of collagen to be tailored towards combatting an early onset of macroscopic failure and material ageing.

The Tarantula Massive Binary Monitoring. VI. Characterisation of hidden companions in 51 single-lined O-type binaries: A flat mass-ratio distribution and black-hole binary candidates - Shenar, T.; Sana, H.; Mahy, L.; Maı́z Apellániz, J.; Crowther, Paul A.; Gromadzki, M.; Herrero, A.; Langer, N.; Marchant, P.; Schneider, F. R. N.; Sen, K.; Soszyński, I.; et al. (Astronomy and Astrophysics, 2022)

Binary black hole mergers from merged stars in the Galactic field - Stegmann, Jakob; Antonini, Fabio; Schneider, Fabian R. N.; Tiwari, Vaibhav; Chattopadhyay, Debatri (Physical Review D, 2022)

An X-ray-quiet black hole born with a negligible kick in a massive binary within the Large Magellanic Cloud - Shenar, Tomer; Sana, Hugues; Mahy, Laurent; El-Badry, Kareem; Marchant, Pablo; Langer, Norbert; Hawcroft, Calum; Fabry, Matthias; Sen, Koushik; Almeida, Leonardo A.; Abdul-Masih, Michael; Bodensteiner, Julia; et al. (Nature Astronomy, 2022)

The R136 star cluster dissected with Hubble Space Telescope/STIS. III. The most massive stars and their clumped winds - Brands, Sarah A.; de Koter, Alex; Bestenlehner, Joachim M.; Crowther, Paul A.; Sundqvist, Jon O.; Puls, Joachim; Caballero-Nieves, Saida M.; Abdul-Masih, Michael; Driessen, Florian A.; Garcı́a, Miriam; Geen, Sam; Gräfener, Götz; et al. (Astronomy and Astrophysics, 2022)

Bipolar planetary nebulae from common-envelope evolution of binary stars - Ondratschek, Patrick A.; Röpke, Friedrich K.; Schneider, Fabian R. N.; Fendt, Christian; Sand, Christian; Ohlmann, Sebastian T.; Pakmor, Rüdiger; Springel, Volker (Astronomy and Astrophysics, 2022)

Testing the evolution theory of massive binary systems - Mahy, L.; Sana, H.; Martins, F.; Rauw, G.; Abdul-Masih, M.; Almeida, L. A.; Langer, N.; Sen, K.; Shenar, T.; de Koter, A.; de Mink, S. E.; Evans, C. J.; et al. (2021)

MatchTope: A tool to predict the cross reactivity of peptides complexed with Major Histocompatibility Complex I - Mendes, Marcus Fabiano de Almeida; de Souza Bragatte, Marcelo; Vianna, Priscila; de Freitas, Martiela Vaz; Pöhner, Ina; Richter, Stefan; Wade, Rebecca C.; Salzano, Francisco Mauro; Vieira, Gustavo Fioravanti (Frontiers in Immunology, 2022)

National and subnational short-term forecasting of COVID-19 in Germany and Poland during early 2021 - Bracher, Johannes; Wolffram, Daniel; Deuschel, Jannik; Görgen, Konstantin; Ketterer, Jakob L.; Ullrich, Alexander; Abbott, Sam; Barbarossa, Maria V.; Bertsimas, Dimitris; Bhatia, Sangeeta; Bodych, Marcin; Bosse, Nikos I.; et al. (Communications Medicine, 2022)

Model diagnostics and forecast evaluation for quantiles - Gneiting, Tilmann; Wolffram, Daniel; Resin, Johannes; Kraus, Kristof; Bracher, Johannes; Dimitriadis, Timo; Hagenmeyer, Veit; Jordan, Alexander I.; Lerch, Sebastian; Phipps, Kaleb; Schienle, Melanie (Annual Review of Statistics and Its Application, 2023)

Stereoinversion of tetrahedral p -block element hydrides - Sigmund, Lukas M.; Ehlert, Christopher; Gryn'ova, Ganna; Greb, Lutz (The Journal of Chemical Physics, 2022)
The potential energy surfaces of 15 tetrahedral p-block element hydrides were screened on the multireference level. It was addressed whether stereoinversion competes against other reactions, such as reductive H2-elimination or hydride loss, and if so, along which pathway the stereomutation occurs. Importantly, stereoinversion transition structures for the ammonium cation (C4v) and the tetrahydridoborate anion (Cs) were identified for the first time. Revisiting methane’s Cs symmetric inversion transition structure with the mHEAT+ protocol revealed an activation enthalpy for stereoinversion, in contrast to all earlier studies, which is 5 kJ mol−1 below the C–H bond dissociation enthalpy. Square planar structures were identified lowest in energy only for the inversion of AlH4−, but a novel stepwise Cs-inversion was discovered for SiH4 or PH4+. Overall, the present contribution delineates essentials of the potential energy surfaces of p-block element hydrides, while structure–energy relations offer design principles for the synthetically emerging field of structurally constrained compounds.

Structure and dynamics of the von Willebrand Factor C6 domain - Chen, Po-Chia; Kutzki, Fabian; Mojzisch, Angelika; Simon, Bernd; Xu, Emma-Ruoqi; Aponte-Santamaría, Camilo; Horny, Kai; Jeffries, Cy; Schneppenheim, Reinhard; Wilmanns, Matthias; Brehm, Maria A.; Gräter, Frauke; et al. (Journal of Structural Biology, 2022)
Von Willebrand disease (VWD) is a bleeding disorder with different levels of severity. VWD-associated mutations are located in the von Willebrand factor (VWF) gene, coding for the large multidomain plasma protein VWF with essential roles in hemostasis and thrombosis. On the one hand, a variety of mutations in the C-domains of VWF are associated with increased bleeding upon vascular injury. On the other hand, VWF gain-of-function (GOF) mutations in the C4 domain have recently been identified, which induce an increased risk of myocardial infarction. Mechanistic insights into how these mutations affect the molecular behavior of VWF are scarce and holistic approaches are challenging due to the multidomain and multimeric character of this large protein. Here, we determine the structure and dynamics of the C6 domain and the single nucleotide polymorphism (SNP) variant G2705R in C6 by combining nuclear magnetic resonance spectroscopy, molecular dynamics simulations and aggregometry. Our findings indicate that this mutation mostly destabilizes VWF by leading to a more pronounced hinging between both subdomains of C6. Hemostatic parameters of variant G2705R are close to normal under static conditions, but the missense mutation results in a gain-of-function under flow conditions, due to decreased VWF stem stability. Together with the fact that two C4 variants also exhibit GOF characteristics, our data underline the importance of the VWF stem region in VWF’s hemostatic activity and the risk of mutation-associated prothrombotic properties in VWF C-domain variants due to altered stem dynamics.

Brownian dynamics simulations of biomolecular diffusional association processes - Muniz Chicharro, Abraham; Votapka, Lane W.; Amaro, Rommie E.; Wade, Rebecca C. (WIREs Computational Molecular Science, 2023)
Abstract Brownian dynamics (BD) is a computational method to simulate molecular diffusion processes. Although the BD method has been developed over several decades and is well established, new methodological developments are improving its accuracy, widening its scope, and increasing its application. In biological applications, BD is used to investigate the diffusive behavior of molecules subject to forces due to intermolecular interactions or interactions with material surfaces. BD can be used to compute rate constants for diffusional association, generate structures of encounter complexes for molecular binding partners, and examine the transport properties of geometrically complex molecules. Often, a series of simulations is performed, for example, for different protein mutants or environmental conditions, so that the effects of the changes on diffusional properties can be estimated. While biomolecules are commonly described at atomic resolution and internal molecular motions are typically neglected, coarse‐graining and the treatment of conformational flexibility are increasingly employed. Software packages for BD simulations of biomolecules are growing in capabilities, with several new packages providing novel features that expand the range of questions that can be addressed. These advances, when used in concert with experiment or other simulation methods, such as molecular dynamics, open new opportunities for application to biochemical and biological systems. Here, we review some of the latest developments in the theory, methods, software, and applications of BD simulations to study biomolecular diffusional association processes and provide a perspective on their future use and application to outstanding challenges in biology, bioengineering, and biomedicine.

Myristoyl's dual role in allosterically regulating and localizing Abl kinase - de Buhr, Svenja; Gräter, Frauke (eLife, 2023)
c-Abl kinase, a key signalling hub in many biological processes ranging from cell development to proliferation, is tightly regulated by two inhibitory Src homology domains. An N-terminal myristoyl-modification can bind to a hydrophobic pocket in the kinase C-lobe, which stabilizes the auto-inhibitory assembly. Activation is triggered by myristoyl release. We used molecular dynamics simulations to show how both myristoyl and the Src homology domains are required to impose the full inhibitory effect on the kinase domain, and reveal the allosteric transmission pathway at residue-level resolution. Importantly, we find myristoyl insertion into a membrane to thermodynamically compete with binding to c-Abl. Myristoyl thus not only localizes the protein to the cellular membrane, but membrane attachment at the same time enhances activation of c-Abl by stabilizing its pre-activated state. Our data put forward a model in which lipidation tightly couples kinase localization and regulation, a scheme that currently appears to be unique for this non-receptor tyrosine kinase.

Destabilizers of the thymidylate synthase homodimer accelerate its proteasomal degradation and inhibit cancer growth. - Costantino, L.; Ferrari, S.; Santucci, M.; Salo-Ahen, O. M. H.; Carosati, E.; Franchini, S.; Lauriola, A.; Pozzi, C.; Trande, M.; Gozzi, G.; Saxena, P.; Cannazza, G.; et al. (Elife, 2022)
Drugs that target human thymidylate synthase (hTS), a dimeric enzyme, are widely used in anticancer therapy. However, treatment with classical substrate-site-directed TS inhibitors induces over-expression of this protein and development of drug resistance. We thus pursued an alternative strategy that led us to the discovery of TS-dimer destabilizers. These compounds bind at the monomer-monomer interface and shift the dimerization equilibrium of both the recombinant and the intracellular protein toward the inactive monomers. A structural, spectroscopic, and kinetic investigation has provided evidence and quantitative information on the effects of the interaction of these small molecules with hTS. Focusing on the best among them, E7, we have shown that it inhibits hTS in cancer cells and accelerates its proteasomal degradation, thus causing a decrease in the enzyme intracellular level. E7 also showed a superior anticancer profile to fluorouracil in a mouse model of human pancreatic and ovarian cancer. Thus, over sixty years after the discovery of the first TS prodrug inhibitor, fluorouracil, E7 breaks the link between TS inhibition and enhanced expression in response, providing a strategy to fight drug-resistant cancers.
Keywords: Female; Animals; Mice; Humans; Binding Sites; *Thymidylate Synthase/chemistry/metabolism; Fluorouracil/pharmacology; *Ovarian Neoplasms/drug therapy; Enzyme Inhibitors/pharmacology

The Effect of an Additional Structured Methods Presentation on Decision-Makers' Reading Time and Opinions on the Helpfulness of the Methods in a Quantitative Report: Nonrandomized Trial. - Koetsenruijter, J.; Wronski, P.; Ghosh, S.; Muller, W.; Wensing, M. (JMIR Med Inform, 2022)
BACKGROUND: Although decision-makers in health care settings need to read and understand the validity of quantitative reports, they do not always carefully read information on research methods. Presenting the methods in a more structured way could improve the time spent reading the methods and increase the perceived relevance of this important report section. OBJECTIVE: To test the effect of a structured summary of the methods used in a quantitative data report on reading behavior with eye-tracking and measure the effect on the perceived importance of this section. METHODS: A nonrandomized pilot trial was performed in a computer laboratory setting with advanced medical students. All participants were asked to read a quantitative data report; an intervention arm was also shown a textbox summarizing key features of the methods used in the report. Three data-collection methods were used to document reading behavior and the views of participants: eye-tracking (during reading), a written questionnaire, and a face-to-face interview. RESULTS: We included 35 participants, 22 in the control arm and 13 in the intervention arm. The overall time spent reading the methods did not differ between the 2 arms. The intervention arm considered the information in the methods section to be less helpful for decision-making than did the control arm (scores for perceived helpfulness were 4.1 and 2.9, respectively, range 1-10). Participants who read the box more intensively tended to spend more time on the methods as a whole (Pearson correlation 0.81, P=.001). CONCLUSIONS: Adding a structured summary of information on research methods attracted attention from most participants, but did not increase the time spent on reading the methods or lead to increased perceptions that the methods section was helpful for decision-making. Participants made use of the summary to quickly judge the methods, but this did not increase the perceived relevance of this section.

Horizons: nuclear astrophysics in the 2020s and beyond - Schatz, H; Becerril Reyes, A D; Best, A; Brown, E F; Chatziioannou, K; Chipps, K A; Deibel, C M; Ezzeddine, R; Galloway, D K; Hansen, C J; Herwig, F; Ji, A P; et al. (Journal of Physics G: Nuclear and Particle Physics, 2022)
Abstract Nuclear astrophysics is a field at the intersection of nuclear physics and astrophysics, which seeks to understand the nuclear engines of astronomical objects and the origin of the chemical elements. This white paper summarizes progress and status of the field, the new open questions that have emerged, and the tremendous scientific opportunities that have opened up with major advances in capabilities across an ever growing number of disciplines and subfields that need to be integrated. We take a holistic view of the field discussing the unique challenges and opportunities in nuclear astrophysics in regards to science, diversity, education, and the interdisciplinarity and breadth of the field. Clearly nuclear astrophysics is a dynamic field with a bright future that is entering a new era of discovery opportunities.

TULIPS: A Tool for Understanding the Lives, Interiors, and Physics of Stars - Laplace, Eva (Astronomy and Computing, 2022)
Understanding the lives and interior structures of stellar objects is a fundamental objective of astrophysics. Research in this domain often relies on the visualization of astrophysical data, for instance, the results of theoretical simulations. However, the diagrams commonly employed to this effect are usually static, complex, and can sometimes be non-intuitive or even counter-intuitive to newcomers in the field. To address some of these issues, this paper introduces TULIPS, a python package that generates novel diagrams and animations of the structure and evolution of stellar objects. TULIPS visualizes the output of one-dimensional physical simulations and is currently optimized for the MESA stellar evolution code. Utilizing the inherent spherical symmetry of such simulations, TULIPS represents the physical properties of stellar objects as the attributes of circles. This enables an intuitive representation of the evolution, energy generation and loss processes, composition, and interior properties of stellar objects, while retaining quantitative information. Users can interact with the output videos and diagrams. The capabilities of TULIPS are showcased by example applications that include a Sun-like star, a massive star, a low-metallicity star, and an accreting white dwarf. Diagrams generated with TULIPS are compared to the Hertzsprung-Russell diagram and to the Kippenhahn diagram, and their advantages and challenges are discussed. TULIPS is open source and free. Aside from being a research tool, it can be used for preparing teaching and public outreach material.

Computational Models for Clinical Applications in Personalized Medicine-Guidelines and Recommendations for Data Integration and Model Validation. - Collin, C. B.; Gebhardt, T.; Golebiewski, M.; Karaderi, T.; Hillemanns, M.; Khan, F. M.; Salehzadeh-Yazdi, A.; Kirschner, M.; Krobitsch, S.; Eu-Stands Pm, C. o. n. s. o. r. t. i. u. m.; Kuepfer, L. (J Pers Med, 2022)
The future development of personalized medicine depends on a vast exchange of data from different sources, as well as harmonized integrative analysis of large-scale clinical health and sample data. Computational-modelling approaches play a key role in the analysis of the underlying molecular processes and pathways that characterize human biology, but they also lead to a more profound understanding of the mechanisms and factors that drive diseases; hence, they allow personalized treatment strategies that are guided by central clinical questions. However, despite the growing popularity of computational-modelling approaches in different stakeholder communities, there are still many hurdles to overcome for their clinical routine implementation in the future. Especially the integration of heterogeneous data from multiple sources and types are challenging tasks that require clear guidelines that also have to comply with high ethical and legal standards. Here, we discuss the most relevant computational models for personalized medicine in detail that can be considered as best-practice guidelines for application in clinical care. We define specific challenges and provide applicable guidelines and recommendations for study design, data acquisition, and operation as well as for model validation and clinical translation and other research areas.

A versatile and interoperable computational framework for the analysis and modeling of COVID-19 disease mechanisms - Niarakis, Anna; Ostaszewski, Marek; Mazein, Alexander; Kuperstein, Inna; Kutmon, Martina; Gillespie, Marc E.; Funahashi, Akira; Acencio, Marcio Luis; Hemedan, Ahmed; Aichem, Michael; Klein, Karsten; Czauderna, Tobias; et al. (2022)
Abstract The COVID-19 Disease Map project is a large-scale community effort uniting 277 scientists from 130 Institutions around the globe. We use high-quality, mechanistic content describing SARS-CoV-2-host interactions and develop interoperable bioinformatic pipelines for novel target identification and drug repurposing. Community-driven and highly interdisciplinary, the project is collaborative and supports community standards, open access, and the FAIR data principles. The coordination of community work allowed for an impressive step forward in building interfaces between Systems Biology tools and platforms. Our framework links key molecules highlighted from broad omics data analysis and computational modeling to dysregulated pathways in a cell-, tissue- or patient-specific manner. We also employ text mining and AI-assisted analysis to identify potential drugs and drug targets and use topological analysis to reveal interesting structural features of the map. The proposed framework is versatile and expandable, offering a significant upgrade in the arsenal used to understand virus-host interactions and other complex pathologies.

Systems Biology in ELIXIR: modelling in the spotlight - Martins dos Santos, Vitor; Anton, Mihail; Szomolay, Barbara; Ostaszewski, Marek; Arts, Ilja; Benfeitas, Rui; Dominguez Del Angel, Victoria; Ferk, Polonca; Fey, Dirk; Goble, Carole; Golebiewski, Martin; Gruden, Kristina; et al. (F1000Research, 2022)
In this white paper, we describe the founding of a new ELIXIR Community - the Systems Biology Community - and its proposed future contributions to both ELIXIR and the broader community of systems biologists in Europe and worldwide. The Community believes that the infrastructure aspects of systems biology - databases, (modelling) tools and standards development, as well as training and access to cloud infrastructure - are not only appropriate components of the ELIXIR infrastructure, but will prove key components of ELIXIR’s future support of advanced biological applications and personalised medicine. By way of a series of meetings, the Community identified seven key areas for its future activities, reflecting both future needs and previous and current activities within ELIXIR Platforms and Communities. These are: overcoming barriers to the wider uptake of systems biology; linking new and existing data to systems biology models; interoperability of systems biology resources; further development and embedding of systems medicine; provisioning of modelling as a service; building and coordinating capacity building and training resources; and supporting industrial embedding of systems biology. A set of objectives for the Community has been identified under four main headline areas: Standardisation and Interoperability, Technology, Capacity Building and Training, and Industrial Embedding. These are grouped into short-term (3-year), mid-term (6-year) and long-term (10-year) objectives.

ISO 20691:2022 Biotechnology — Requirements for data formatting and description in the life sciences - Golebiewski, Martin (2022)
This document specifies requirements for the consistent formatting and documentation of data and corresponding metadata (i.e. data describing the data and its context) in the life sciences, including biotechnology, and biomedical, as well as non-human biological research and development. It provides guidance on rendering data in the life sciences findable, accessible, interoperable and reusable (F-A-I-R). This document is applicable to manual or computational workflows that systematically capture, record or integrate data and corresponding metadata in the life sciences for other purposes. This document provides formatting requirements for both primary experimental or procedural data obtained manually and machine derived data. This document also describes requirements for storing, sharing, accessing, interoperability and reuse of data and corresponding metadata in the life sciences. This document specifies requirements for large quantities of data systematically obtained from automated high throughput workflows in the life sciences, as well as requirements for large-scale and small-scale data sets obtained by other life science technologies and manual data capture. This document is applicable to many domains in biotechnology and the life sciences including, but not limited to: basic/applied research in all domains of the life sciences, and industrial, medical, agricultural, or environmental biotechnology (excluding for diagnostic or therapeutic purposes), as well as methodology-driven domains, such as genomics (including massive parallel sequencing, metagenomics, epigenomics and functional genomics), transcriptomics, translatomics, proteomics, metabolomics, lipidomics, glycomics, enzymology, immunochemistry, synthetic biology, systems biology, systems medicine and related fields.

DOPA Residues Endow Collagen with Radical Scavenging Capacity - Kurth, Markus; Barayeu, Uladzimir; Gharibi, Hassan; Kuzhelev, Andrei; Riedmiller, Kai; Zilke, Jennifer; Noack, Kasimir; Denysenkov, Vasyl; Kappl, Reinhard; Prisner, Thomas F.; Zubarev, Roman A.; Dick, Tobias P.; et al. (Angewandte Chemie International Edition, 2023)
Here we uncover collagen, the main structural protein of all connective tissues, as a redox-active material. We identify dihydroxyphenylalanine (DOPA) residues, post-translational oxidation products of tyrosine residues, to be common in collagen derived from different connective tissues. We observe that these DOPA residues endow collagen with substantial radical scavenging capacity. When reducing radicals, DOPA residues work as redox relay: they convert to the quinone and generate hydrogen peroxide. In this dual function, DOPA outcompetes its amino acid precursors and ascorbic acid. Our results establish DOPA residues as redox-active side chains of collagens, probably protecting connective tissues against radicals formed under mechanical stress and/or inflammation.

Locating Guest Molecules inside Metal–Organic Framework Pores with a Multilevel Computational Approach - Ernst, Michelle; Poręba, Tomasz; Gnägi, Lars; Gryn'ova, Ganna (The Journal of Physical Chemistry C, 2023)
Molecular docking has traditionally mostly been employed in the field of protein–ligand binding. Here, we extend this method, in combination with DFT-level geometry optimizations, to locate guest molecules inside the pores of metal–organic frameworks. The position and nature of the guest molecules tune the physicochemical properties of the host–guest systems. Therefore, it is essential to be able to reliably locate them to rationally enhance the performance of the known metal–organic frameworks and facilitate new material discovery. The results obtained with this approach are compared to experimental data. We show that the presented method can, in general, accurately locate adsorption sites and structures of the host–guest complexes. We therefore propose our approach as a computational alternative when no experimental structures of guest-loaded MOFs are available. Additional information on the adsorption strength in the studied host–guest systems emerges from the computed interaction energies. Our findings provide the basis for other computational studies on MOF–guest systems and contribute to a better understanding of the structure–interaction–property interplay associated with them.

Metal-Free Molecular Catalysts for the Oxygen Reduction Reaction: Electron Affinity as an Activity Descriptor - Ehlert, Christopher; Piras, Anna; Schleicher, Juliette; Gryn'ova, Ganna (The Journal of Physical Chemistry Letters, 2023)
Heteroatom-doped polyaromatic hydrocarbons (or nanographenes) are promising molecular electrocatalysts for the oxygen reduction reaction (ORR). Here, we use density functional theory to investigate the first step of the ORR pathway (chemisorption) for a set of molecules with experimentally determined catalytic activities. Weak chemisorption is found for only negatively charged catalysts, and a strong correlation is observed between the computed electron affinities and experimental catalytic activities for a range of B- and B,N-doped polyaromatic hydrocarbons. The electron affinity is put forward as a simple activity descriptor of charged (activated) catalysts on an electrode.

Convergence of forecast distributions in a 100,000‐member idealised convective‐scale ensemble - Tempest, Kirsten I.; Craig, George C.; Brehmer, Jonas R. (Quarterly Journal of the Royal Meteorological Society, 2023)

Aspects of spatial postprocessing for global temperature forecasts - Feldmann, Kira (2022)

Probabilistic solar forecasting: Benchmarks, post-processing, verification - Gneiting, Tilmann; Lerch, Sebastian; Schulz, Benedikt (Solar Energy, 2023)

Ultrafast dynamics of 2-thiouracil investigated by time-resolved Auger spectroscopy - Lever, F; Mayer, D; Picconi, D; Metje, J; Alisauskas, S; Calegari, F; Düsterer, S; Ehlert, Christopher; Feifel, R; Niebuhr, M; Manschwetus, B; Kuhlmann, M; et al. (Journal of Physics B: Atomic, Molecular and Optical Physics, 2020)
We present time-resolved ultraviolet-pump x-ray probe Auger spectra of 2-thiouracil. An ultraviolet induced shift towards higher kinetic energies is observed in the sulfur 2p Auger decay. The difference Auger spectra of pumped and unpumped molecules exhibit ultrafast dynamics in the shift amplitude, in which three phases can be recognized. In the first 100 fs, a shift towards higher kinetic energies is observed, followed by a 400 fs shift back to lower kinetic energies and a 1 ps shift again to higher kinetic energies. We use a simple Coulomb-model, aided by quantum chemical calculations of potential energy states, to deduce a C–S bond expansion within the first 100 fs. The bond elongation triggers internal conversion from the photoexcited S2 to the S1 state. Based on timescales, the subsequent dynamics can be interpreted in terms of S1 nuclear relaxation and S1-triplet internal conversion.

Au 21 cage structures and their magic number tri‐cations - Ehlert, Christopher; Liu, Xiaojing J.; Hamilton, Ian P. (International Journal of Quantum Chemistry, 2020)
We examine the stability and properties of three Au21 cage structures, one with D3 symmetry and denoted as Au21 (D3), which is novel, and the other two with C2v symmetry. One, denoted as Au21 (C2v-1), has been previously reported but the other, denoted as Au21 (C2v-2), is novel. As reference Au21 structures, we also examine a sheet isomer and a compact isomer, Au21 (Cs-Tetra), formed by adsorbing an Au atom on Au20 (Td). For all structures, we consider charge ranging from −1 to +4. For the Au21 cage structures, a primary property of interest is their spherical aromaticity, as measured by their nucleus independent chemical shift. Our focus is on charge +3 since each gold atom is assumed to contribute one (6s) valence electron, and 18 is a magic number for shell closing. We find that, although Au21 (D3)+3 has the largest aromaticity, it is not the most stable urn:x-wiley:00207608:media:qua26191:qua26191-math-0001 cage species. Surprisingly, Au21 (C2v-2), which is not even stable as a neutral cage species, is the most stable tri-cation cage species. We also examine the stability of (neutral) Au21Xn cage structures relative to Au21Xn structures derived from Au21 (Cs-Tetra) (with X = F, Cl, Br, I and n = 1, 3, 5). We find that, although Au21F3 derived from Au21 (D3) has the largest aromaticity, it is not the most stable Au21F3 cage structure. Nonetheless, all cage structures are stabilized relative to Au21 (Cs-Tetra) and, remarkably, for the trichlorinated, tribrominated, and triiodineated clusters, at least one cage structure is more stable than its Au21 (Cs-Tetra) counterpart.

Following excited-state chemical shifts in molecular ultrafast x-ray photoelectron spectroscopy - Mayer, D.; Lever, F.; Picconi, D.; Metje, J.; Alisauskas, S.; Calegari, F.; Düsterer, S.; Ehlert, Christopher; Feifel, R.; Niebuhr, M.; Manschwetus, B.; Kuhlmann, M.; et al. (Nature Communications, 2022)
The conversion of photon energy into other energetic forms in molecules is accompanied by charge moving on ultrafast timescales. We directly observe the charge motion at a specific site in an electronically excited molecule using time-resolved x-ray photoelectron spectroscopy (TR-XPS). We extend the concept of static chemical shift from conventional XPS by the excited-state chemical shift (ESCS), which is connected to the charge in the framework of a potential model. This allows us to invert TR-XPS spectra to the dynamic charge at a specific atom. We demonstrate the power of TR-XPS by using sulphur 2p-core-electron-emission probing to study the UV-excited dynamics of 2-thiouracil. The method allows us to discover that a major part of the population relaxes to the molecular ground state within 220–250 fs. In addition, a 250-fs oscillation, visible in the kinetic energy of the TR-XPS, reveals a coherent exchange of population among electronic states.

Why Ultrafast Photoinduced CO Desorption Dominates over Oxidation on Ru(0001) - Tetenoire, Auguste; Ehlert, Christopher; Juaristi, J. I.; Saalfrank, Peter; Alducin, M. (The Journal of Physical Chemistry Letters, 2022)
CO oxidation on Ru(0001) is a long-standing example of a reaction that, being thermally forbidden in ultrahigh vacuum, can be activated by femtosecond laser pulses. In spite of its relevance, the precise dynamics of the photoinduced oxidation process as well as the reasons behind the dominant role of the competing CO photodesorption remain unclear. Here we use ab initio molecular dynamics with electronic friction that account for the highly excited and nonequilibrated system created by the laser to investigate both reactions. Our simulations successfully reproduce the main experimental findings: the existence of photoinduced oxidation and desorption, the large desorption to oxidation branching ratio, and the changes in the O K-edge X-ray absorption spectra attributed to the initial stage of the oxidation process. Now, we are able to monitor in detail the ultrafast CO desorption and CO oxidation occurring in the highly excited system and to disentangle what causes the unexpected inertness to the otherwise energetically favored oxidation.

Marchettiite, (NH 4 )C 5 H 3 N 4 O 3 , a new organic mineral from Mount Cervandone, Devero Valley, Western–Central Alps, Italy - Guastoni, Alessandro; Nestola, Fabrizio; Zorzi, Federico; Lanza, Arianna; Ernst, Michelle; Gentile, Paolo; Andò, Sergio; Lorenzetti, Alessandra (Mineralogical Magazine, 2022)
The new mineral marchettiite (IMA2017-066) is the natural equivalent of ammonium hydrogen urate. It has a simple molecular formula C5H7N5O3 and can be alternatively written as (NH4)C5H3N4O3. Marchettiite was found in a cleft at Mount Cervandone, Devero Valley, Piedmont, Italy, where it occurs as aggregates of opaque pale pink to white, platy prismatic crystals. This mineral has a white streak, dull and opaque lustre, it is not fluorescent and has a hardness of 2–2.5 (Mohs’ scale). The tenacity is brittle and crystals have a good cleavage parallel to {001}. The calculated density is 1.69 g/cm3. Marchettiite is biaxial (–) with 2V of 47.24°; the optical properties of marchettiite were determined by periodic-DFT methods providing the following values: α = 1.372, β = 1.681 and γ = 1.768. No twinning was observed. Electron microprobe analyses gave the following chemical formula: C4.99H6.97N4.91O3.00. Although the small crystal size did not allow refinement of structural data by single-crystal diffraction, we were able to refine the structure by powder micro X-ray diffraction. Marchettiite has space group P and the following unit-cell parameters: a = 3.6533(2) Å, b = 10.2046(7) Å, c = 10.5837(7) Å, α = 113.809(5)°, β = 91.313(8)°, γ = 92.44(1)° and V = 360.312 Å3. The strongest lines in the powder diffraction pattern [d in Å (I)(hkl)] are: 9.784(50)(001); 8.663(80)(01); 5.659(100)(011); 3.443(100)(10); 3.241(70)(003) and 3.158(100)(1. Marchettiite is named after Gianfranco Marchetti, the mineral collector who found this mineral.

Pitfalls in the location of guest molecules in metal-organic frameworks - Poręba, Tomasz; Macchi, Piero; Ernst, Michelle (Nature Communications, 2022)

Premelting Anomalies in Pyromellitic Dianhydride: Negative Thermal Expansion, Accelerated Radiation Damage, and Polymorphic Phase Transition - Porȩba, Tomasz; Świa̧tkowski, Marcin; Ernst, Michelle; Confalonieri, Giorgia (The Journal of Physical Chemistry C, 2022)
We have established a comprehensive approach to evaluate the structure–property relationships in solid pyromellitic dianhydride (PMDA) at high temperature. Synchrotron single-crystal X-ray diffraction experiments have yielded structural models for this volatile compound up to 250 °C. PMDA exhibits negative thermal expansion around 145 °C, which is correlated to geometrical changes in the intermolecular carbonyl–carbonyl interactions. A reversible phase transition above ca. 210 °C was detected by differential scanning calorimetry and is associated with the lowering of the molecular symmetry, as indicated by Raman spectroscopy. X-ray powder and single-crystal diffraction data confirm the formation of a new high-temperature monoclinic phase, with two symmetry-independent anhydride groups in the asymmetric unit. The influence of pyromellitic acid impurities on the formation temperature of the new phase has been investigated, and thermodynamic parameters of pure pyromellitic dianhydride have been revaluated. Additionally, the analysis of the temperature- and time-dependent variations in the diffraction patterns allowed us to track the augmented radiation-driven decarboxylation upon heating. Significantly, the formation of a high-temperature low-symmetry phase in PMDA may challenge the solid-state polymerization that aims for highly oriented materials.

Generation and Testing of Boltzmann Generators Trained on Peptide Molecular Dynamics Simulations to Predict Peptide Conformational Behaviour - Emmert, Michelle (2022)

P. falciparum Circumsporozoite protein epitope/paratope interactions: Addressing the key role of molecular flexibility - Hanke, Anton (2022)

Structural Insights into Neurotrophin Receptor Dimerization using Coarse-grained Molecular Dynamics Simulations - Cabezudo, Ainara Claveras (2022)

Computational Investigation of the Structure Kinetics Relationship of Small-Molecule Compounds Binding the Histamine-1-Receptor - Kaeser, Melanie (2022)

An engineered variant of MECR reductase reveals indispensability of long-chain acyl-ACPs for mitochondrial respiration - Tanvir Rahman, M.; Kristian Koski, M.; Panecka-Hofman, Joanna; Schmitz, Werner; Kastaniotis, Alexander J.; Wade, Rebecca C.; Wierenga, Rik K.; Kalervo Hiltunen, J.; Autio, Kaija J. (Nature Communications, 2023)
Abstract

DiscoScore: Evaluating text generation with BERT and discourse coherence - Zhao, Wei; Strube, Michael; Eger, Steffen (2023)
Recently, there has been a growing interest in designing text generation systems from a discourse coherence perspective, e.g., modeling the interdependence between sentences. Still, recent BERT-based evaluation metrics are weak in recognizing coherence, and thus are not reliable in a way to spot the discourse-level improvements of those text generation systems. In this work, we introduce DiscoScore, a parametrized discourse metric, which uses BERT to model discourse coherence from different perspectives, driven by Centering theory. Our experiments encompass 16 non-discourse and discourse metrics, including DiscoScore and popular coherence models, evaluated on summarization and document-level machine translation (MT). We find that (i) the majority of BERT-based metrics correlate much worse with human rated coherence than early discourse metrics, invented a decade ago; (ii) the recent state-of-the-art BARTScore is weak when operated at system level—which is particularly problematic as systems are typically compared in this manner. DiscoScore, in contrast, achieves strong system-level correlation with human ratings, not only in coherence but also in factual consistency and other aspects, and surpasses BARTScore by over 10 correlation points on average. Further, aiming to understand DiscoScore, we provide justifications to the importance of discourse coherence for evaluation metrics, and explain the superiority of one variant over another. Our code is available at https://github.com/AIPHES/DiscoScore.

Proceedings of the 3rd Workshop on Computational Approaches to Discourse - Braud, Chloé; Hardmeier, Christian; Li, Junyi Jessy; Loaciga, Sharid; Strube, Michael; Zeldes, Amir (2022)

Proceedings of the CODI-CRAC 2022 Shared Task on Anaphora, Bridging, and Discourse Deixis in Dialogue - Yu, Juntao; Khosla, Sopan; Manuvinakurike, Ramesh; Levin, Lori; Ng, Vincent; Poesio, Massimo; Strube, Michael; Rosé, Carolyn (2022)

The CODI-CRAC 2022 shared task on anaphora, bridging, and discourse deixis in dialogue - Yu, Juntao; Khosla, Sopan; Manuvinakurike, Ramesh; Levin, Lori; Ng, Vincent; Poesio, Massimo; Strube, Michael; Rosé, Carolyn (2022)
The CODI-CRAC 2022 Shared Task on Anaphora Resolution in Dialogues is the second edition of an initiative focused on detecting different types of anaphoric relations in conversations of different kinds. Using five conversational datasets, four of which have been newly annotated with a wide range of anaphoric relations: identity, bridging references and discourse deixis, we defined multiple tasks focusing individually on these key relations. The second edition of the shared task maintained the focus on these relations and used the same datasets as in 2021, but new test data were annotated, the 2021 data were checked, and new subtasks were added. In this paper, we discuss the annotation schemes, the datasets, the evaluation scripts used to assess the system performance on these tasks, and provide a brief summary of the participating systems and the results obtained across 230 runs from three teams, with most submissions achieving significantly better results than our baseline methods.

Evaluating coreference resolvers on community-based question answering: From rule-based to state of the art - Chai, Haixia; Moosavi, Nafise Sadat; Gurevych, Iryna; Strube, Michael (2022)
Coreference resolution is a key step in natural language understanding. Developments in coreference resolution are mainly focused on improving the performance on standard datasets annotated for coreference resolution. However, coreference resolution is an intermediate step for text understanding and it is not clear how these improvements translate into downstream task performance. In this paper, we perform a thorough investigation on the impact of coreference resolvers in multiple settings of community-based question answering task, i.e., answer selection with long answers. Our settings cover multiple text domains and encompass several answer selection methods. We first inspect extrinsic evaluation of coreference resolvers on answer selection by using coreference relations to decontextualize individual sentences of candidate answers, and then annotate a subset of answers with coreference information for intrinsic evaluation. The results of our extrinsic evaluation show that while there is a significant difference between the performance of the rule-based system vs. state-of-the-art neural model on coreference resolution datasets, we do not observe a considerable difference on their impact on downstream models. Our intrinsic evaluation shows that (i) resolving coreference relations on less-formal text genres is more difficult even for trained annotators, and (ii) the values of linguistic-agnostic coreference evaluation metrics do not correlate with the impact on downstream data.

Improving coreference resolution with word formation - Zhao, Wei; Mathews, Kevin; Chai, Haixia (2022)

A proposal for explicit word formation annotation in discourse corpora - Müller, Mark-Christoph (2022)

Learning where and when to reason in neuro-symbolic inference - Cornelio, Cristina; Stühmer, Jan; Hu, Shell Xu; Hospedales, Timothy (2023)

Constrained density matching and modeling for cross-lingual alignment of contextualized representations - Zhao, Wei; Eger, Steffen (2022)
Multilingual representations pre-trained with monolingual data exhibit considerably unequal task performances across languages. Previous studies address this challenge with resource-intensive contextualized alignment, which assumes the availability of large parallel data, thereby leaving under-represented language communities behind. In this work, we attribute the data hungriness of previous alignment techniques to two limitations: (i) the inability to sufficiently leverage data and (ii) these techniques are not trained properly. To address these issues, we introduce supervised and unsupervised density-based approaches named Real-NVP and GAN-Real-NVP, driven by Normalizing Flow, to perform alignment, both dissecting the alignment of multilingual subspaces into density matching and density modeling. We complement these approaches with our validation criteria in order to guide the training process. Our experiments encompass 16 alignments, including our approaches, evaluated across 6 language pairs, synthetic data and 5 NLP tasks. We demonstrate the effectiveness of our approaches in the scenarios of limited and no parallel data. First, our supervised approach trained on 20k parallel data (sentences) mostly surpasses Joint-Align and InfoXLM trained on over 100k parallel sentences. Second, parallel data can be removed without sacrificing performance when integrating our unsupervised approach in our bootstrapping procedure, which is theoretically motivated to enforce equality of multilingual subspaces. Moreover, we demonstrate the advantages of validation criteria over validation data for guiding supervised training.

Diffusion of small molecule drugs is affected by surface interactions and crowder proteins - Dey, Debabrata; Nunes-Alves, Ariane; Wade, Rebecca C.; Schreiber, Gideon (iScience, 2022)

Double detonations: variations in Type Ia supernovae due to different core and He shell masses - II. Synthetic observables - Collins, Christine E.; Gronow, Sabrina; Sim, Stuart A.; Röpke, Friedrich (\mnras, 2022)

On the fate of the secondary white dwarf in double-degenerate double-detonation Type Ia supernovae - Pakmor, R.; Callan, F. P.; Collins, C. E.; de Mink, S. E.; Holas, Alexander; Kerzendorf, W. E.; Kromer, M.; Neunteufel, P. G.; O’Brien, John T.; Röpke, Friedrich; Ruiter, A. J.; Seitenzahl, I. R.; et al. (\mnras, 2022)

A finite-volume scheme for modeling compressible magnetohydrodynamic flows at low Mach numbers in stellar interiors - Leidi, Giovanni; Birke, C.; Andrássy, Róbert; Higl, Johann; Edelmann, P. V. F.; Wiest, G.; Klingenberg, C.; Röpke, Friedrich (\aap, 2022)

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

Long-term Evolution of Postexplosion Helium-star Companions of Type Iax Supernovae - Zeng, Yaotian; Liu, Zheng-Wei; Heger, Alexander; McCully, Curtis; Röpke, Friedrich; Han, Zhanwen (\apj, 2022)

The Lunar Occultation eXplorer (LOX): MeV Gamma-Ray Astrophysics Across Space and Time - Miller, Richard; Ajello, Marco; Auchettl, Katie; Beacom, John; Bloser, Peter; Burrows, Adam; Frebel, Anna; Fryer, Chris; Hartmann, Dieter; Hoeflich, Peter; Hungerford, Aimee; Leising, Mark; et al. (2022)

Ex Luna, Scientia: The Lunar Occultation eXplorer (LOX) and the Future of MeV \ensuremathγ-Ray Astrophysics - Miller, Richard; Ajello, Marco; Auchettl, Katie; Beacom, John; Bloser, Peter; Burrows, Adam; Frebel, Anna; Fryer, Chris; Hartmann, Dieter; Hoeflich, Peter; Hungerford, Aimee; Leising, Mark; et al. (2022)

Stellar gravitational collapse, singularity formation and theory breakdown - Maltsev, Kiril (The Sixteenth Marcel Grossmann Meeting, 2023)
We review three definitions (missing point(s) unsteadiness, infinite quadratic curvature invariant, and geodesic incompleteness) of what a gravitational singularity is, and argue that prediction of a gravitational singularity is problematic for General Relativity (GR), indicating breakdown of Lorentzian geometry, only insofar as it concerns the infinite curvature singularity characterization. In contrast, the geodesic incompleteness characterization is GR’s innovating hallmark, which is not meaningfully available in Newtonian gravity formulations (locally infinite density field, and locally infinite gravitational force) of what a gravitational singularity is. It is the continuous, non-quantized, nature of Lorentzian geometry which admits gravitational contraction be continued indefinitely. The Oppenheimer-Snyder 1939 analytical solution derives formation of a locally infinite curvature singularity and of incomplete geodesics, while Penrose’s 1965 theorem concerns formation of incomplete (null) geodesics only. We critically examine the main physical arguments against gravitational singularity formation in stellar collapse, with scope restriction to decades spanning in between Schwarzschild’s 1916 solution and Penrose’s 1965 singularity theorem. As the most robust curvature singularity formation counter-argument, we assess Markov’s derivation of an upper bound on the quadratic curvature invariant RμνλδRμνλδ≤1ℓP4 from a ratio of natural constants ħ, c and G, in connection with Wheeler’s grounding of the premise that the Planck scale ℓP is ultimate.

On the energy consumption of online and on-site lectures - Holas, Alexander; Koch, Catherine Y.; Leibold, Joachim; Prendi, Alesia; Schlachta, Tim P.; Schmid, Anna Sophia; Schmitt, Leonard (Environmental Research Communications, 2022)

Modeling Type Ia supernovae with explosions in white dwarfs near and below the Chandrasekhar mass - Röpke, Friedrich; Lach, Florian; Gronow, Sabrina; Sim, Stuart A.; Callan, Fionntan P.; Collins, Christine E. (The Sixteenth Marcel Grossmann Meeting, 2023)
The progenitor evolution and the explosion mechanism of Type Ia supernovae remain unexplained. Nonetheless, substantial progress has been made over the past years with three-dimensional hydrodynamic simulations of different scenarios. Here, we review some recent work pertaining to the leading paradigms of modeling: thermonuclear explosions of white dwarf stars near and below the Chandrasekhar mass limit. We discuss implications of the different explosion channels and their predictions of observables.

Family dispute: do Type IIP supernova siblings agree on their distance? - Csörnyei, Géza; Vogl, Christian; Taubenberger, Stefan; Flörs, Andreas; Blondin, Stéphane; Cudmani, Maria Gabriela; Holas, Alexander; Kressierer, Sabrina; Leibundgut, Bruno; Hillebrandt, Wolfgang (Astronomy and Astrophysics - A&A, 2023)

EnzymeML: seamless data flow and modeling of enzymatic data. - Lauterbach, S.; Dienhart, H.; Range, J.; Malzacher, S.; Sporing, J. D.; Rother, D.; Pinto, M. F.; Martins, P.; Lagerman, C. E.; Bommarius, A. S.; Host, A. V.; Woodley, J. M.; et al. (Nat Methods, 2023)
The design of biocatalytic reaction systems is highly complex owing to the dependency of the estimated kinetic parameters on the enzyme, the reaction conditions, and the modeling method. Consequently, reproducibility of enzymatic experiments and reusability of enzymatic data are challenging. We developed the XML-based markup language EnzymeML to enable storage and exchange of enzymatic data such as reaction conditions, the time course of the substrate and the product, kinetic parameters and the kinetic model, thus making enzymatic data findable, accessible, interoperable and reusable (FAIR). The feasibility and usefulness of the EnzymeML toolbox is demonstrated in six scenarios, for which data and metadata of different enzymatic reactions are collected and analyzed. EnzymeML serves as a seamless communication channel between experimental platforms, electronic lab notebooks, tools for modeling of enzyme kinetics, publication platforms and enzymatic reaction databases. EnzymeML is open and transparent, and invites the community to contribute. All documents and codes are freely available at https://enzymeml.org .
Keywords: Reproducibility of Results; *Data Management; *Metadata; Databases, Factual; Kinetics

aRgus: Multilevel visualization of non-synonymous single nucleotide variants & advanced pathogenicity score modeling for genetic vulnerability assessment - Schröter, Julian; Dattner, Tal; Hüllein, Jennifer; Jayme, Alejandra; Heuveline, Vincent; Hoffmann, Georg F.; Kölker, Stefan; Lenz, Dominic; Opladen, Thomas; Popp, Bernt; Schaaf, Christian P.; Staufner, Christian; et al. (Computational and Structural Biotechnology Journal, 2023)

Review of Deep Learning Methods for Individual Treatment Effect Estimation with Automatic Hyperparameter Optimization - Sirazitdinov, Andrei; Buchwald, Marcus; Hesser, Jürgen; Heuveline, Vincent (2022)
Abstract—Estimation of individual treatment effect (ITE) for different types of treatment is a common challenge in therapy assessments, clinical trials and diagnosis. Deep learning methods, namely representation based, adversarial, and variational, have shown promising potential in ITE estimation. However, it was unclear whether the hyperparameters of the originally proposed methods were well optimized for different benchmark datasets. To solve these problems, we created a public code library containing representation-based, adversarial, and variational methods written in TensorFlow. In order to have a broader collection of ITE estimation methods, we have also included neural network based meta-learners. The code library is made accessible for reproducibility and facilitating future works in the field of causal inference. Our results demonstrate that performance of most methods can be improved using automatic hyperparameter optimization. Additionally, we review the methods and compare the performance of the optimized models from our library on publicly available datasets. The potential of hyperparameter optimization may encourage researchers to focus on this aspect when creating new methods for inferring individual treatment effect.

Colorectal cancer incidences in Lynch syndrome: a comparison of results from the prospective lynch syndrome database and the international mismatch repair consortium - Møller, Pål; Seppälä, Toni; Dowty, James G.; Haupt, Saskia; Dominguez-Valentin, Mev; Sunde, Lone; Bernstein, Inge; Engel, Christoph; Aretz, Stefan; Nielsen, Maartje; Capella, Gabriel; Evans, Dafydd Gareth; et al. (Hereditary Cancer in Clinical Practice, 2022)
Abstract

Natural variability in bee brain size and symmetry revealed by micro-CT imaging and deep learning - Lösel, Philipp; Monchanin, Coline; Lebrun, Renaud; Jayme, Alejandra; Relle, Jacob; Devaud, Jean-Marc; Heuveline, Vincent; Lihoreau, Mathieu (2022)
Abstract Analysing large numbers of brain samples can reveal minor, but statistically and biologically relevant variations in brain morphology that provide critical insights into animal behaviour, ecology and evolution. So far, however, such analyses have required extensive manual effort, which considerably limits the scope for comparative research. Here we used micro-CT imaging and deep learning to perform automated analyses of 3D image data from 187 honey bee and bumblebee brains. We revealed strong inter-individual variations in total brain size that are consistent across colonies and species, and may underpin behavioural variability central to complex social organisations. In addition, the bumblebee dataset showed a significant level of lateralization in optic and antennal lobes, providing a potential explanation for reported variations in visual and olfactory learning. Our fast, robust and user-friendly approach holds considerable promises for carrying out large-scale quantitative neuroanatomical comparisons across a wider range of animals. Ultimately, this will help address fundamental unresolved questions related to the evolution of animal brains and cognition.

Is HLA type a possible cancer risk modifier in Lynch syndrome? - Ahadova, Aysel; Witt, Johannes; Haupt, Saskia; Gallon, Richard; Hüneburg, Robert; Nattermann, Jacob; ten Broeke, Sanne; Bohaumilitzky, Lena; Hernandez‐Sanchez, Alejandro; Santibanez‐Koref, Mauro; Jackson, Michael S.; Ahtiainen, Maarit; et al. (International Journal of Cancer, 2022)

Opportunities and challenges in machine learning‐based newborn screening—A systematic literature review - Zaunseder, Elaine; Haupt, Saskia; Mütze, Ulrike; Garbade, Sven F.; Kölker, Stefan; Heuveline, Vincent (JIMD Reports, 2022)

Applications of AI in Astronomy - Djorgovski, S. G.; Mahabal, Ashish; Graham, M. J.; Polsterer, Kai; Krone-Martins, Alberto (2023)
We provide a brief, and inevitably incomplete overview of the use of Machine Learning (ML) and other AI methods in astronomy, astrophysics, and cosmology. Astronomy entered the big data era with the first digital sky surveys in the early 1990s and the resulting Terascale data sets, which required automating of many data processing and analysis tasks, for example the star-galaxy separation, with billions of feature vectors in hundreds of dimensions. The exponential data growth continued, with the rise of synoptic sky surveys and the Time Domain Astronomy, with the resulting Petascale data streams and the need for a real-time processing, classification, and decision making. A broad variety of classification and clustering methods have been applied for these tasks, and this remains a very active area of research. Over the past decade we have seen an exponential growth of the astronomical literature involving a variety of ML/AI applications of an ever increasing complexity and sophistication. ML and AI are now a standard part of the astronomical toolkit. As the data complexity continues to increase, we anticipate further advances leading towards a collaborative human-AI discovery.

Innovative Tools Fostered by the HiPS Ecosystem - Boch, Thomas; Allen, Mark; Bot, Caroline; Fernique, Pierre; Baumann, Matthieu; Buga, Mihaela; Bonnarel, Francois; Durand, Daniel; Polsterer, Kai (ASTRONOMICAL DATA ANALYSIS SOFTWARE AND SYSTEMS XXX, 532, 2022)
Over the past 10 years HiPS (Hierarchical Progressive Surveys) has evolved from an experiment led by CDS to an ecosystem supported by more than 20 data centers exposing their own HiPS node. This trend has been pushed by advanced and simple clients (Aladin Desktop, Aladin Lite) or portals (ESASky, ESO Science Portal) and thanks to Hipsgen. Today the HiPS ecosystem gathers 900 HiPS datasets published by 20+ HiPS nodes. We describe a selection of different tools and services that benefit from having a large collection of multi-wavelength datasets available in the same format: hips2fits, on-the-fly generation of RGB tiles from pre-existing HiPS, HiPS as a container for 1d and 2d histograms, CatTiler, computation on the HiPS grid, generation of Spectral Energy Distribution from FITS tiles.

Interactive Exploration Framework for Big Data Sets - Kollasch, Fenja; Polsterer, Kai (2022)
Big data issues are the order of the day for many researchers in astronomy. In the past, several machine learning methods were proposed to organize, classify, or condense big data sets. However, this is not the end of the road. In most cases, researchers need to take further analysis by hand on automatically preprocessed data to gather valuable conclusions. To facilitate the pipeline of data analysis, we suggest a generic front-end framework allowing the user not only to process the data automatically, but also to interactively explore and investigate the results of machine learning procedures. A compact visualization gives an initial overview and can be adjusted to point out the parts of interest. By providing abstract accommodation functions such as zooming, scrolling, filtering, and labeling, crucial data fragments can be found and marked in an intuitive way.

On the application of machine learning approaches in astronomy: Exploring novel representations of high-dimensional and complex astronomical data - Kugler, Sven Dennis (2015)
The goal of the presented work is the application of data-driven methods on complex and high- dimensional astronomical databases. The focus of the work is the exploration of novel data representations in order to enable the use of statistical learning approaches in the analysis of data. With the help of diverse science cases, the advantages of the introduced approaches for classication, visualization and regression tasks are shown by applying the developed methodology to astronomical data. In the first part, an alternative approach for estimating redshifts of spectra by using the knowledge about the redshifts provided by the SDSS pipeline is presented. A novel data repre- sentation is employed which contains only information relevant for estimating the redshift and the detection of multiple redshift systems. Subsequently, a novel data representation for regu- larly sampled light curves based on recurrent networks is presented. This allows an explorative investigation of huge databases with unlabeled data. Finally, a new way of representing the static part of irregularly sampled light curves by a mixture of Gaussians is discussed. This represen- tation is more general than the extraction of features, as it allows the inclusion of photometric uncertainties and avoids the introduction of observational biases.

Probabilistic Modelling of Time Series for explorative Big Data Analysis in Astronomy - Schweder, Fenja (2023)

An Absolute Calibration of the Near-infrared Period–Luminosity Relations of Type II Cepheids in the Milky Way and in the Large Magellanic Cloud - Wielgórski, Piotr; Pietrzyński, Grzegorz; Pilecki, Bogumił; Gieren, Wolfgang; Zgirski, Bartłomiej; Górski, Marek; Hajdu, Gergely; Narloch, Weronika; Karczmarek, Paulina; Smolec, Radosław; Kervella, Pierre; Storm, Jesper; et al. (The Astrophysical Journal, 2022)
Abstract

Accretion disks, quasars and cosmology: meandering towards understanding - Czerny, Bożena; Cao, Shulei; Jaiswal, Vikram Kumar; Karas, Vladimír; Khadka, Narayan; Martínez-Aldama, Mary Loli; Naddaf, Mohammad Hassan; Panda, Swayamtrupta; Pozo Nunez, Dr. Francisco; Prince, Raj; Ratra, Bharat; Sniegowska, Marzena; et al. (Astrophysics and Space Science, 2023)
Abstract As Setti and Woltjer noted back in 1973, one can use quasars to construct the Hubble diagram; however, the actual application of the idea was not that straightforward. It took years to implement the proposition successfully. Most ways to employ quasars for cosmology now require an advanced understanding of their structure, step by step. We briefly review this progress, with unavoidable personal biases, and concentrate on bright unobscured sources. We will mention the problem of the gas flow character close to the innermost stable circular orbit near the black hole, as discussed five decades ago. This problem later led to the development of the slim disk scenario and is recently revived in the context of Magnetically Arrested Disks (MAD) and Standard and Normal Evolution (SANE) models. We also discuss the hot or warm corona issue, which is still under debate and complicates the analysis of X-ray reflection. We present the scenario of the formation of the low ionization part of the Broad Line Region as a failed wind powered by radiation pressure acting on dust (Failed Radiatively Driven Dusty Outflow – FRADO). Next, we examine the cosmological constraints currently achievable with quasars, primarily concentrating on light echo methods (continuum time delays and spectral-line time delays to the continuum) that are (or should be) incorporating the progress mentioned above. Finally, we briefly discuss prospects in this lively subject area.

The EURONEAR Lightcurve Survey of Near Earth Asteroids 2017–2020 - Vaduvescu, O.; Aznar Macias, A.; Wilson, T. G.; Zegmott, T.; Pérez Toledo, F. M.; Predatu, M.; Gherase, R.; Pinter, V.; Pozo Nunez, Dr. Francisco; Ulaczyk, K.; Soszyński, I.; Mróz, P.; et al. (Earth, Moon, and Planets, 2022)

Modeling photometric reverberation mapping data for the next generation of big data surveys. Quasar accretion disks sizes with the LSST - Pozo Nunez, Dr. Francisco; Bruckmann, C; Desamutara, S; Czerny, B; Panda, S; Lobban, A P; Pietrzyński, G; Polsterer, Kai (Monthly Notices of the Royal Astronomical Society, 2023)
Abstract Photometric reverberation mapping can detect the radial extent of the accretion disc (AD) in Active Galactic Nuclei by measuring the time delays between light curves observed in different continuum bands. Quantifying the constraints on the efficiency and accuracy of the delay measurements is important for recovering the AD size-luminosity relation, and potentially using quasars as standard candles. We have explored the possibility of determining the AD size of quasars using next-generation Big Data surveys. We focus on the Legacy Survey of Space and Time (LSST) at the Vera C. Rubin Observatory, which will observe several thousand quasars with the Deep Drilling Fields and up to 10 million quasars for the main survey in six broadband filter during its 10-year operational lifetime. We have developed extensive simulations that take into account the characteristics of the LSST survey and the intrinsic properties of the quasars. The simulations are used to characterise the light curves from which AD sizes are determined using various algorithms. We find that the time delays can be recovered with an accuracy of 5 and 15% for light curves with a time sampling of 2 and 5 days, respectively. The results depend strongly on the redshift of the source and the relative contribution of the emission lines to the bandpasses. Assuming an optically thick and geometrically thin AD, the recovered time-delay spectrum is consistent with black hole masses derived with 30% uncertainty.

Machine Learning Methods Improve Specificity in Newborn Screening for Isovaleric Aciduria - Zaunseder, Elaine; Mütze, Ulrike; Garbade, Sven F.; Haupt, Saskia; Feyh, Patrik; Hoffmann, Georg F.; Heuveline, Vincent; Kölker, Stefan (Metabolites, 2023)
Isovaleric aciduria (IVA) is a rare disorder of leucine metabolism and part of newborn screening (NBS) programs worldwide. However, NBS for IVA is hampered by, first, the increased birth prevalence due to the identification of individuals with an attenuated disease variant (so-called “mild” IVA) and, second, an increasing number of false positive screening results due to the use of pivmecillinam contained in the medication. Recently, machine learning (ML) methods have been analyzed, analogous to new biomarkers or second-tier methods, in the context of NBS. In this study, we investigated the application of machine learning classification methods to improve IVA classification using an NBS data set containing 2,106,090 newborns screened in Heidelberg, Germany. Therefore, we propose to combine two methods, linear discriminant analysis, and ridge logistic regression as an additional step, a digital-tier, to traditional NBS. Our results show that this reduces the false positive rate by 69.9% from 103 to 31 while maintaining 100% sensitivity in cross-validation. The ML methods were able to classify mild and classic IVA from normal newborns solely based on the NBS data and revealed that besides isovalerylcarnitine (C5), the metabolite concentration of tryptophan (Trp) is important for improved classification. Overall, applying ML methods to improve the specificity of IVA could have a major impact on newborns, as it could reduce the newborns’ and families’ burden of false positives or over-treatment.

A simple approach for detecting HLA‐A *02 alleles in archival formalin‐fixed paraffin‐embedded tissue samples and an application example for studying cancer immunoediting - Witt, Johannes; Haupt, Saskia; Ahadova, Aysel; Bohaumilitzky, Lena; Fuchs, Vera; Ballhausen, Alexej; Przybilla, Moritz Jakob; Jendrusch, Michael; Seppälä, Toni T.; Fürst, Daniel; Walle, Thomas; Busch, Elena; et al. (HLA, 2022)

GPU-basierte Verfahren zur Segmentierung biomedizinischer Bilddaten - Lösel, Philipp (2022)
Die Analyse medizinischer und biologischer Bilddaten erfordert häufig die Isolierung einzelner Strukturen aus einem 3D-Volumen durch Segmentierung. Trotz einer Vielzahl halb- und vollautomatischer Verfahren erfolgt die Bildsegmentierung oft noch manuell und gilt nach wie vor für viele Szenarien als die arbeitsintensivste und zeitaufwendigste Aufgabe innerhalb der 3D-Bildanalyse. Die herkömmliche manuelle Segmentierung vieler Schichten, gefolgt von einer linearen Interpolation und einer manuellen Korrektur der Ergebnisse verhindert jedoch in vielen Fällen die Analyse einer großen Anzahl von Proben. In dieser Arbeit wird darum ein neues parameterfreies Verfahren zur halbautomatischen Segmentierung großer komplexer 3D-Bilddaten entwickelt, das auf einer Interpolation von wenigen manuell vorsegmentierten Schichten basiert, wobei es den gesamten zugrunde liegenden volumetrischen Bilddatensatz berücksichtigt. Die Interpolation erfolgt durch gewichtete Random Walks, deren Unabhängigkeit voneinander eine Berechnung durch massiv parallele Hardware, wie den Grafikprozessoren (GPUs), ermöglicht. Anhand einer vielfältigen Auswahl von Beispieldatensätzen wird gezeigt, dass dieses GPU-basierte Verfahren, insbesondere bei der Segmentierung sehr großer komplexer Bilddaten, wie sie typischerweise in der Mikro-Computertomographie (µCT) entstehen, dazu in der Lage ist, sowohl den Zeit- und Arbeitsaufwand erheblich zu reduzieren als auch die Qualität der Ergebnisse deutlich zu steigern. Dabei erreicht es eine deutliche Beschleunigung und höhere Genauigkeit gegenüber dem konventionellen Ansatz der fast ausschließlich manuellen Segmentierung und auch gegenüber den am weitesten verbreiteten Segmentierungsalgorithmen. Die GPU-basierten Random Walks sind insbesondere dann geeignet, wenn wenig Vorwissen über das zu segmentierende Objekt vorhanden ist, zum Beispiel bei der Beschreibung einer neu entdeckten Art, oder zum Erstellen von Trainingsdaten für ein anschließendes maschinelles Lernen. Um darüber hinaus bei der Segmentierung vieler ähnlicher Proben eine weitgehend automatische Segmentierung zu ermöglichen, wird hier zusätzlich ein auf künstlichen neuronalen Netzen basierendes Verfahren vorgestellt und anhand von 110 µCT-Scans von Honigbienengehirnen evaluiert. Ergänzend wird auf Basis der entwickelten Algorithmen die neue Online-Segmentierungsplattform Biomedisa präsentiert. Die Plattform ist über einen Webbrowser zugänglich und erfordert keine komplexe und langwierige Konfiguration von Software- und Modellparametern. Sie richtet sich gezielt an die Bedürfnisse von Wissenschaftlern/-innen, die nicht über umfangreiche Computer- und Softwarekenntnisse verfügen. Die integrierten GPU-basierten Methoden ermöglichen eine intuitive Anwendung für verschiedene bildgebende Verfahren innerhalb eines breiten Spektrums wissenschaftlicher Disziplinen. Die Plattform wurde bereits in mehreren Studien erfolgreich eingesetzt. Darüber hinaus ermöglicht ihr modularer Aufbau eine leichte Erweiterung der hier vorgestellten Kernfunktionen um weitere benutzerspezifische Funktionalitäten.

Scaling Protein–Water Interactions in the Martini 3 Coarse-Grained Force Field to Simulate Transmembrane Helix Dimers in Different Lipid Environments - Claveras Cabezudo, Ainara; Athanasiou, Christina; Tsengenes, Alexandros; Wade, Rebecca C. (Journal of Chemical Theory and Computation, 2023)

Glycosaminoglycans: What Remains To Be Deciphered? - Perez, Serge; Makshakova, Olga; Angulo, Jesus; Bedini, Emiliano; Bisio, Antonella; de Paz, Jose Luis; Fadda, Elisa; Guerrini, Marco; Hricovini, Michal; Hricovini, Milos; Lisacek, Frederique; Nieto, Pedro M.; et al. (JACS Au, 2023)

More findability, more interoperability for SABIO-RK, the curated database for biochemical reaction kinetics - Weidemann, Andreas; Dudas, Dorotea; Rey, Maja; Wittig, Ulrike; Müller, Wolfgang (F1000Research, 2022)
SABIO-RK represents a repository for structured, curated, and annotated data on reactions and their kinetics. The data are manually extracted from the scientific literature and stored in a relational database. The content comprises both naturally occurring and alternatively measured biochemical reactions, and the data are made available to the public via a web-based search interface as well as easy-to-use JSON web services for programmatic access. Data are highly interlinked to external databases, ontologies, and controlled vocabularies. This includes cross-references with eg Uniprot, ChEBI, KEGG, BRENDA, Biomodels, and MetaNetX. In the past year we have worked on improving findability of SABIO-RK data as well as interoperability: SABIO-RK was extended to read the additional annotations in the EnzymeML data exchange format to allow the direct import of enzymology data from EnzymeML documents. SABIO-RK is part of the EnzymeML workflow to support the data transfer between experimental platforms, modelling tools and databases (Range et al. FEBS J 2021). In the BMBF-funded project SABIO-VIS we focused on visualizing SABIORK data for the purpose of interactive search and search refinement.

Towards Effortless Navigation of Scientific-Literature Screen Reading for Biocuration - Ghosh, Sucheta; Müller, Wolfgang; Wittig, Ulrike; Rey, Maja (2022)

A “two-in-one hit” model of shortcut carcinogenesis in MLH1 Lynch syndrome carriers - Ahadova, Aysel; Stenzinger, Albrecht; Seppälä, Toni; Hüneburg, Robert; Kloor, Matthias; Bläker, Hendrik; Wittemann, Jan-Niklas; Endris, Volker; Gerling, Leonie; Bertram, Veit; Neumuth, Marie Theres; Witt, Johannes; et al. (Gastroenterology, 2023)

Semi-Automated Morphological Classification of All Radio Galaxies in the FIRST Survey Catalog Including Near-IR Counterparts in UKIDSS - Hopkins, Erica (2019)

Hierarchical Bayesian method for constraining the neutron star equation of state with an ensemble of binary neutron star postmerger remnants - Criswell, Alexander W.; Miller, Jesse; Woldemariam, Noah; Soultanis, Theodoros; Bauswein, Andreas; Chatziioannou, Katerina; Coughlin, Michael W.; Jones, Galin; Mandic, Vuk (Physical Review D, 2023)

Chemical identification and indexing in full-text articles: an overview of the NLM-Chem track at BioCreative VII - Leaman, Robert; Islamaj, Rezarta; Adams, Virginia; Alliheedi, Mohammed A; Almeida, João Rafael; Antunes, Rui; Bevan, Robert; Chang, Yung-Chun; Erdengasileng, Arslan; Hodgskiss, Matthew; Ida, Ryuki; Kim, Hyunjae; et al. (Database, 2023)
Abstract The BioCreative National Library of Medicine (NLM)-Chem track calls for a community effort to fine-tune automated recognition of chemical names in the biomedical literature. Chemicals are one of the most searched biomedical entities in PubMed, and—as highlighted during the coronavirus disease 2019 pandemic—their identification may significantly advance research in multiple biomedical subfields. While previous community challenges focused on identifying chemical names mentioned in titles and abstracts, the full text contains valuable additional detail. We, therefore, organized the BioCreative NLM-Chem track as a community effort to address automated chemical entity recognition in full-text articles. The track consisted of two tasks: (i) chemical identification and (ii) chemical indexing. The chemical identification task required predicting all chemicals mentioned in recently published full-text articles, both span [i.e. named entity recognition (NER)] and normalization (i.e. entity linking), using Medical Subject Headings (MeSH). The chemical indexing task required identifying which chemicals reflect topics for each article and should therefore appear in the listing of MeSH terms for the document in the MEDLINE article indexing. This manuscript summarizes the BioCreative NLM-Chem track and post-challenge experiments. We received a total of 85 submissions from 17 teams worldwide. The highest performance achieved for the chemical identification task was 0.8672 F-score (0.8759 precision and 0.8587 recall) for strict NER performance and 0.8136 F-score (0.8621 precision and 0.7702 recall) for strict normalization performance. The highest performance achieved for the chemical indexing task was 0.6073 F-score (0.7417 precision and 0.5141 recall). This community challenge demonstrated that (i) the current substantial achievements in deep learning technologies can be utilized to improve automated prediction accuracy further and (ii) the chemical indexing task is substantially more challenging. We look forward to further developing biomedical text–mining methods to respond to the rapid growth of biomedical literature. The NLM-Chem track dataset and other challenge materials are publicly available at https://ftp.ncbi.nlm.nih.gov/pub/lu/BC7-NLM-Chem-track/. Database URL https://ftp.ncbi.nlm.nih.gov/pub/lu/BC7-NLM-Chem-track/

Gravitational wave emission from dynamical stellar interactions - Moran-Fraile, J.; Schneider, F.; Roepke, F.; Ohlmann, S.; Pakmor, R.; Soultanis, T.; Bauswein, A. (Astronomy & Astrophysics, 2023)

Mortality by age, gene and gender in carriers of pathogenic mismatch repair gene variants receiving surveillance for early cancer diagnosis and treatment: a report from the prospective Lynch syndrome database - Dominguez-Valentin, Mev; Haupt, Saskia; Seppälä, Toni T.; Sampson, Julian R.; Sunde, Lone; Bernstein, Inge; Jenkins, Mark A.; Engel, Christoph; Aretz, Stefan; Nielsen, Maartje; Capella, Gabriel; Balaguer, Francesc; et al. (eClinicalMedicine, 2023)

FERM domains recruit ample PI(4,5)P2s to form extensive protein-membrane attachments - Ehret, Thomas; Heißenberg, Tim; de Buhr, Svenja; Aponte-Santamaría, Camilo; Steinem, Claudia; Gräter, Frauke (Biophysical Journal, 2023)
The four-point-one ezrin-radixin-moesin homology (FERM) protein domain is a multifunctional protein-lipid binding site, constituting an integral part of numerous membrane-associated proteins. Its interaction with the lipid phosphatidylinositol-4,5-bisphosphate (PIP2), located at the inner leaflet of eukaryotic plasma membranes, is important for localization, anchorage, and activation of FERM-containing proteins. FERM-PIP2 complexes structurally determined so far exclusively feature a 1:1 binding stoichiometry of protein and lipid, with a few basic FERM residues neutralizing the −4 charge of the bound PIP2. Whether this picture from static crystal structures also applies to the dynamic interaction of FERM domains on PIP2 membranes is unknown. We here quantified the stoichiometry of FERM-PIP2 binding in a lipid bilayer using atomistic molecular dynamics simulations and experiments on solid supported membranes for the FERM domains of focal adhesion kinase and ezrin. In contrast to the structural data, we find much higher average stoichiometries of FERM-PIP2 binding, amounting to 1:3 or 1:4 ratios, respectively. In simulations, the full set of basic residues at the membrane interface, 7 and 15 residues for focal adhesion kinase and ezrin, respectively, engages in PIP2 interactions. In addition, Na ions enter the FERM-membrane binding interface, compensating negative PIP2 charges in case of high charge surpluses from bound PIP2. We propose the multivalent binding of FERM domains to PIP2 in lipid bilayers to significantly enhance the stability of FERM-membrane binding and to render the FERM-membrane linkage highly adjustable.

Intrinsically disordered region of talin’s FERM domain functions as an initial PIP2 recognition site - Buhr, Jannik; Franz, Florian; Gräter, Frauke (Biophysical Journal, 2023)
Focal adhesions (FAs) mediate the interaction of the cytoskeleton with the extracellular matrix in a highly dynamic fashion. Talin is a central regulator, adaptor protein, and mechano-sensor of FA complexes. For recruitment and firm attachment at FAs, talin’s N-terminal FERM domain binds to phosphatidylinositol 4,5-bisphosphate (PIP2)-enriched membranes. A newly published autoinhibitory structure of talin-1, where the known PIP2 interaction sites are covered up, lead us to hypothesize that a hitherto less examined loop insertion of the FERM domain acts as an additional and initial site of contact. We evaluated direct interactions of talin-1 with a PIP2 membrane by means of atomistic molecular dynamics simulations. We show that this unstructured, 33-residue-long loop strongly interacts with PIP2 and can facilitate further membrane contacts, including the canonical PIP2 interactions, by serving as a flexible membrane anchor. Under force as present at FAs, the extensible FERM loop ensures talin maintains membrane contacts when pulled away from the membrane by up to 7 nm. We identify key basic residues of the anchor mediating the highly dynamic talin-membrane interaction. Our results put forward an intrinsically disordered loop as a key and highly adaptable PIP2 recognition site of talin and potentially other PIP2-binding mechano-proteins.

Disulfide Bond Reduction and Exchange in von-Willebrand-Factor’s C4-Domain Undermines Platelet Binding - Kutzki, Fabian; Butera, Diego; Lay, Angelina J.; Maag, Denis; Chiu, Joyce; Woon, Heng-Giap; Kubař, Tomáš; Elstner, Marcus; Aponte-Santamaría, Camilo; Hogg, Philip J.; Gräter, Frauke (Journal of Thrombosis and Haemostasis, 2023)

Mechano-Redox Control of Macrophage-1 Antigen De-Adhesion From ICAM-1 (Intercellular Adhesion Molecule 1) by Protein Disulfide Isomerase Promotes Directional Movement Under Flow - Dupuy, Alexander; Santamaría, Camilo Aponte; Yeheskel, Adva; Hortle, Elinor; Oehlers, Stefan H.; Gräter, Frauke; Hogg, Philip J.; Passam, Freda H.; Chiu, Joyce (Circulation Research, 2023)
BACKGROUND: Neutrophil migration is critical to the initiation and resolution of inflammation. Macrophage-1 antigen (Mac-1; CD11b/CD18, αMβ2) is a leukocyte integrin essential for firm adhesion to endothelial ICAM-1 (intercellular adhesion molecule 1) and migration of neutrophils in the shear forces of the circulation. PDI (protein disulfide isomerase) has been reported to influence neutrophil adhesion and migration. We aimed to elucidate the molecular mechanism of PDI control of Mac-1 affinity for ICAM-1 during neutrophil migration under fluid shear.

Amortised Invariance Learning for Contrastive Self-Supervision - Chavhan, Ruchika; Gouk, Henry; Stühmer, Jan; Heggan, Calum; Yaghoobi, Mehrdad; Hospedales, Timothy (2023)

Specifications of standards in systems and synthetic biology: status and developments in 2022 and the COMBINE meeting 2022. - Konig, M.; Gleeson, P.; Golebiewski, M.; Gorochowski, T. E.; Hucka, M.; Keating, S. M.; Myers, C. J.; Nickerson, D. P.; Sommer, B.; Waltemath, D.; Schreiber, F. (J Integr Bioinform, 2023)
This special issue of the Journal of Integrative Bioinformatics contains updated specifications of COMBINE standards in systems and synthetic biology. The 2022 special issue presents three updates to the standards: CellML 2.0.1, SBML Level 3 Package: Spatial Processes, Version 1, Release 1, and Synthetic Biology Open Language (SBOL) Version 3.1.0. This document can also be used to identify the latest specifications for all COMBINE standards. In addition, this editorial provides a brief overview of the COMBINE 2022 meeting in Berlin.
Keywords: *Synthetic Biology; *Computational Biology; Programming Languages; Software

Toward a common standard for data and specimen provenance in life sciences - Wittner, Rudolf; Holub, Petr; Mascia, Cecilia; Frexia, Francesca; Müller, Heimo; Plass, Markus; Allocca, Clare; Betsou, Fay; Burdett, Tony; Cancio, Ibon; Chapman, Adriane; Chapman, Martin; et al. (Learning Health Systems, 2023)
Open and practical exchange, dissemination, and reuse of specimens and data have become a fundamental requirement for life sciences research. The quality of the data obtained and thus the findings and knowledge derived is thus significantly influenced by the quality of the samples, the experimental methods, and the data analysis. Therefore, a comprehensive and precise documentation of the pre-analytical conditions, the analytical procedures, and the data processing are essential to be able to assess the validity of the research results. With the increasing importance of the exchange, reuse, and sharing of data and samples, procedures are required that enable cross-organizational documentation, traceability, and non-repudiation. At present, this information on the provenance of samples and data is mostly either sparse, incomplete, or incoherent. Since there is no uniform framework, this information is usually only provided within the organization and not interoperably. At the same time, the collection and sharing of biological and environmental specimens increasingly require definition and documentation of benefit sharing and compliance to regulatory requirements rather than consideration of pure scientific needs. In this publication, we present an ongoing standardization effort to provide trustworthy machine-actionable documentation of the data lineage and specimens. We would like to invite experts from the biotechnology and biomedical fields to further contribute to the standard.

Improved insights into the SABIO-RK database via visualization. - Dudas, D.; Wittig, U.; Rey, M.; Weidemann, A.; Muller, W. (Database (Oxford), 2023)
SABIO-RK is a database for biochemical reactions and their kinetics. Data in SABIO-RK are inherently multidimensional and complex. The complex relationships between the data are often difficult to follow or even not represented when using standard tabular views. With an increasing number of data points the mismatch between tables and insights becomes more obvious, and getting an overview of the data becomes harder. Such complex data benefit from being presented using specially adapted visual tools. Visualization is a natural and user-friendly way to quickly get an overview of the data and to detect clusters and outliers. Here, we describe the implementation of a variety of visualization concepts into a common interface within the SABIO-RK biochemical reaction kinetics database. For that purpose, we use a heat map, parallel coordinates and scatter plots to allow the interactive visual exploration of general entry-based information of biochemical reactions and specific kinetic parameter values. Database URL https://sabiork.h-its.org/.
Keywords: *Databases, Factual; Kinetics

A Gaussian process cross-correlation approach to time-delay estimation in active galactic nuclei - Pozo Nunez, Dr. Francisco; Gianniotis, Nikos; Polsterer, Kai (Astronomy & Astrophysics, 2023)

Mathematical modeling of Lynch syndrome carcinogenesis - Haupt, Saskia (2023)
Cancer is one of the leading causes of disease-related death worldwide. In recent years, large amounts of data on cancer genetics and molecular characteristics have become available and accumulated with increasing speed. However, the current understanding of cancer as a disease is still limited by the lack of suitable models that allow interpreting these data in proper ways. Thus, the highly interdisciplinary research field of mathematical oncology has evolved to use mathematics, modeling, and simulations to study cancer with the overall goal to improve clinical patient care.

Towards Cognition-Guided Patient-Specific Numerical Simulation for Cardiac Surgery Assistance (2017)
Motivation. Patient-specific, knowledge-based, holistic surgical treatment planning is of utmost importance when dealing with complex surgery. Surgeons need to account for all available medical patient data, keep track of technical developments, and stay on top of current surgical expert knowledge to define a suitable surgical treatment strategy. There is a large potential for computer assistance, also, and in particular, regarding surgery simulation which gives surgeons the opportunity not only to plan but to simulate, too, some steps of an intervention and to forecast relevant surgical situations.

Learning to forecast: The Probabilistic Time Series Forecasting Challenge - Bracher, Johannes; Koster, Nils; Krüger, Fabian; Lerch, Sebastian (The American Statistician, 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. (arXiv e-prints, 2023)
Stars with ZAMS masses between 140 and 260M⊙ are thought to explode as pair-instability supernovae (PISNe). During their thermonuclear runaway, PISNe can produce up to several tens of solar masses of radioactive nickel, resulting in luminous transients similar to some superluminous supernovae (SLSNe). Yet, no unambiguous PISN has been discovered so far. SN2018ibb is a H-poor SLSN at z=0.166 that evolves extremely slowly compared to the hundreds of known SLSNe. Between mid 2018 and early 2022, we monitored its photometric and spectroscopic evolution from the UV to the NIR with 2-10m class telescopes. SN2018ibb radiated >3×1051erg during its evolution, and its bolometric light curve reached >2×1044ergs−1 at peak. The long-lasting rise of >93 rest-frame days implies a long diffusion time, which requires a very high total ejected mass. The PISN mechanism naturally provides both the energy source (56Ni) and the long diffusion time. Theoretical models of PISNe make clear predictions for their photometric and spectroscopic properties. SN2018ibb complies with most tests on the light curves, nebular spectra and host galaxy, potentially all tests with the interpretation we propose. Both the light curve and the spectra require 25-44 M⊙ of freshly nucleosynthesised 56Ni, pointing to the explosion of a metal-poor star with a He-core mass of 120-130 M⊙ at the time of death. This interpretation is also supported by the tentative detection of [Co II]λ1.025μm, which has never been observed in any other PISN candidate or SLSN before. Powering by a central engine, such as a magnetar or a black hole, can be excluded with high confidence. This makes SN2018ibb by far the best candidate for being a PISN, to date.

The impact of effective matter mixing based on three-dimensional hydrodynamical models on the molecule formation in the ejecta of SN 1987A - Ono, Masaomi; Nozawa, Takaya; Nagataki, Shigehiro; Kozyreva, Alexandra; Orlando, Salvatore; Miceli, Marco; Chen, Ke-Jung (\apjs, 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. (arXiv e-prints, 2023)

Formation of star clusters and enrichment by massive stars in simulations of low-metallicity galaxies with a fully sampled initial stellar mass function - Lahén, Natalia; Naab, Thorsten; Kauffmann, Guinevere; Szécsi, Dorottya; Hislop, Jessica May; Rantala, Antti; Kozyreva, Alexandra; Walch, Stefanie; Hu, Chia-Yu (\mnras, 2023)

SN 2021zny: an early flux excess combined with late-time oxygen emission suggests a double white dwarf merger event - Dimitriadis, Georgios; Maguire, Kate; Karambelkar, Viraj R.; Lebron, Ryan J.; Liu, Chang; Kozyreva, Alexandra; Miller, Adam A.; Ridden-Harper, Ryan; Anderson, Joseph P.; Chen, Ting-Wan; Coughlin, Michael; Della Valle, Massimo; et al. (\mnras, 2023)

ReStoRunT: Simple Recording, Storing, Running and Tracing changes in Spreadsheets - Müller, Wolfgang; Mertova, Lukrecia (BTW 2023, 2023)
In addition to the ubiquitous big data, one key challenge indata processing and management in the life sciences is the diversity ofsmall data. Diverse pieces of small data have to be transformed intostandards-compliant data. Here, the challenge lies not in the difficulty ofsingle steps that need to be performed, but rather in the fact that manytransformation tasks are to be performed once or only a few times. Thislimits the time that can be put into automated approaches, which inturn severely limits the verifiability of such transformations.As much of the data to be processed is stored in spreadsheets, withinthis paper we justify and propose a lightweight recording-based solutionthat works on a wide variety of spreadsheet programs, from MicrosoftExcel to Google Docs.

White Paper: Towards in silico approaches for personalised medicine – Recommendations for verifying and validating predictive computational models in EU collaborative research - Belling, Kirstine; Caldara, Marina; Collin, Catherine Bjerre; Gebhardt, Tom; Golebiewski, Martin; Karaderi, Tugce; Khan, Faiz M.; Kirschner, Marc; Krobitsch, Sylvia; Küpfer, Lars; Moser, Heike; Tschinanu, Flora Musuamba; et al. (2022)
Harmonization of data integration is the key to standardization efforts in personalised medicine, which would also facilitate cross-European studies. Standardization of the models themselves is less essential within a research context, where new models are created and tested in line with research progress, harmonization and/or standardization of input data is both feasible and necessary.

ISO/TS 9491-1:2023 Biotechnology — Predictive computational models in personalized medicine research — Part 1: Constructing, verifying and validating models - Kirschner, Marc; Golebiewski, Martin; Moser, Heike; consortium, EU-STANDS4PM; 5, ISO/TC 276/WG (2023)
This document defines challenges and requirements for predictive computational models constructed for research purposes in personalized medicine. It specifies recommendations and requirements for the setup, formatting, validation, simulation, storing and sharing of such models, as well as their application in clinical trials and other research areas. It summarizes specific challenges regarding data input, as well as verifying and validating of such models that can be considered as best practices for modelling in research and development in the field of personalized medicine. This document also specifies recommendations and requirements for data used to construct or needed for validating models, including rules and requirements for formatting, description, annotation, interoperability, integration, accessing, as well as recording and documenting the provenance of such data. This document does not provide specific rules or requirements for the use of computational models in the clinical routine, or for diagnostic or therapeutic purposes.

Proceedings of the 10th Computational Modeling in Biology Network (COMBINE) Meeting 2019 - Golebiewski, Martin; Waltemath, Dagmar (2019)
These are the conference proceedings of COMBINE 2019, the 10th Computational Modeling in Biology Network (COMBINE) meeting that took place in Heidelberg (Germany) from July 15th to July 19th, 2019.

NFDI4Health – Nationale Forschungsdateninfrastruktur für personenbezogene Gesundheitsdaten - Fluck, Juliane; Lindstädt, Birte; Ahrens, Wolfgang; Beyan, Oya; Buchner, Benedikt; Darms, Johannes; Depping, Ralf; Dierkes, Jens; Neuhausen, Hubertus; Müller, Wolfgang; Zeeb, Hajo; Golebiewski, Martin; et al. (Bausteine Forschungsdatenmanagement, 2021)
Epidemiologische und klinische Studien sind standardisiert und gut dokumentiert, jedoch erfüllen Studienprotokolle, eingesetzte Erhebungsinstrumente und erhobene Daten die Anforderungen der FAIR-Prinzipien nicht in ausreichendem Maße. NFDI4Health wird daher eine Struktur schaffen, die eine zentrale Suche nach existierenden, dezentral verwalteten Datenkörpern und zugehörigen Dokumenten sowie einen FAIRen Zugang zu diesen erleichtert. Dazu werden die Auffindbarkeit und der Zugang zu strukturierten Gesundheitsdaten aus Registern, administrativen Gesundheitsdatenbanken, klinischen und epidemiologischen sowie Public Health-Studien verbessert und die Qualität und Harmonisierung der zugrundeliegenden Daten optimiert. Eine weitere Herausforderung entsteht durch die Verwendung personenbezogener Gesundheitsdaten. Diese sind hoch sensibel, so dass ihre Nutzung restriktive Datenschutzbestimmungen und informierte Einwilligungserklärungen der Studienteilnehmenden erfordert, was jedoch ihre Wiederverwendbarkeit einschränkt. NFDI4Health zielt daher darauf ab, den Austausch und die Verknüpfung von personenbezogenen Gesundheitsdaten sowie verteilte Datenanalysen unter Einhaltung datenschutzrechtlicher und ethischer Bestimmungen zu erleichtern. Um dies möglichst effizient zu erreichen, wird NFDI4Health die Entwicklung neuer, maschinenprozessierbarer Zustimmungsmöglichkeiten sowie innovativer Datenzugriffsservices auf Grundlage der FAIR-Prinzipien vorantreiben und die Interoperabilität von IT-Lösungen für Metadatenrepositorien stärken. Komplementiert wird dies durch die Entwicklung entsprechender Angebote für Training und Ausbildung, um der Herausforderung der Umsetzung der Lösungen in den Universitäten und Forschungseinrichtungen zu begegnen. Schließlich wird durch die gemeinsame Arbeit in der NFDI4Health die Kooperation zwischen klinischer und epidemiologischer/Public Health-Forschung gestärkt.

Publication Policy of the National Research Data Infrastructure for Personal Health Data (NFDI4Health) and the NFDI4Health Task Force COVID-19 - Lindstädt, Birte; Shutsko, Aliaksandra; Golebiewski, Martin; Kipker, Dennis-Kenji; Lettieri, Vanessa; Klopfenstein, Sophie; Vorisek, Carina; Löbe, Matthias; Schmidt, Carsten Oliver (2022)

Ten Simple Rules for FAIR Sharing of Experimental and Clinical Data with the Modeling Community - König, Matthias; Grzegorzewski, Jan; Golebiewski, Martin; Hermjakob, Henning; Hucka, Mike; Olivier, Brett; Keating, Sarah; Nickerson, David; Schreiber, Falk; Sheriff, Rahuman; Waltemath, Dagmar (2021)
Science continues to become more interdisciplinary and to involve increasingly complex data sets. Many projects in the biomedical and health-related sciences follow or aim to follow the principles of FAIR data sharing, which has been demonstrated to foster collaboration, to lead to better research outcomes, and to help ensure reproducibility of results. Data generated in the course of biomedical and health research present specific challenges for FAIR sharing in the sense that they are heterogeneous and highly sensitive to context and the needs of protection and privacy. Data sharing must respect these features without impeding timely dissemination of results, so that they can contribute to time-critical advances in medical therapy and treatment. Modeling and simulation of biomedical processes have become established tools, and a global community has been developing algorithms, methodologies, and standards for applying biomedical simulation models in clinical research. However, it can be difficult for clinician scientists to follow the specific rules and recommendations for FAIR data sharing within this domain. We seek to clarify the standard workflow for sharing experimental and clinical data with the simulation modeling community. By following these recommendations, data sharing will be improved, collaborations will become more effective, and the FAIR publication and subsequent reuse of data will become possible at the level of quality necessary to support biomedical and health-related sciences.

RDA COVID-19 Recommendations and Guidelines - Group, RDA COVID-19 Working (2020)
This is the final version of the Recommendations and Guidelines from the RDA COVID19 Working Group, and has been endorsed through the official RDA process.

EU-wide mapping report on good practice examples for integrating phenotype and large scale data - Golebiewski, Martin; Kirschner, Marc; Krobitsch, Sylvia; consortium, EU-STANDS4PM (2022)
The report focusses on national and EU-case studies (good practice examples) for integrating patient derived data, such as phenotype and large scale data, for in silico modelling in personalized medicine.

Engineering Host–Guest Interactions in Organic Framework Materials for Drug Delivery - Ernst, Michelle; Gryn'ova, Ganna (Helvetica Chimica Acta, 2023)
Metal-organic frameworks (MOF) and covalent organic frameworks (COFs) are promising nanocarriers for targeted drug delivery. Noncovalent interactions between frameworks and drugs play a fundamental role in the therapeutic uptake and release of the latter. However, the scope of framework functionalizations and deliverable drugs remains underexplored. Using a multilevel approach combining molecular docking and density functional theory, we show for a range of drugs and frameworks that experimentally reported release metrics are in good agreement with the in silico computed host–guest interaction energies. Functional groups within the framework significantly impact the strength of these host–guest interactions, while a given framework can serve as an efficient delivery agent for drugs beyond the prototypical few. Our findings identify the interaction energy as a reliable and relatively easy to compute descriptor of organic framework materials for drug delivery, able to facilitate their high-throughput screening and targeted design towards extended-release times.

Energetics and kinetics of membrane permeation of photoresists for bioprinting - Diedrich, Lucas; Brosz, Matthias; Abele, Tobias; Steinke, Salome; Gräter, Frauke; Göpfrich, Kerstin; Aponte-Santamaría, Camilo (bioRxiv, 2023)

Design, synthesis and biological evaluation of antiparasitic dinitroaniline-ether phospholipid hybrids - Roussaki, Marina; Magoulas, George E.; Fotopoulou, Theano; Santarem, Nuno; Barrias, Emile; Pöhner, Ina; Luelmo, Sara; Afroudakis, Pantelis; Georgikopoulou, Kalliopi; Nevado, Paloma Tejera; Eick, Julia; Bifeld, Eugenia; et al. (Bioorganic Chemistry, 2023)

Cross-lingual Science Journalism: Select, Simplify and Rewrite Summaries for Non-expert Readers - Fatima, Mehwish; Strube, Michael (2023)
Automating Cross-lingual Science Journalism (CSJ) aims to generate popular science summaries from English scientific texts for non-expert readers in their local language. We introduce CSJ as a downstream task of text simplification and cross-lingual scientific summarization to facilitate science journalists’ work. We analyze the performance of possible existing solutions as baselines for the CSJ task. Based on these findings, we propose to combine the three components - SELECT, SIMPLIFY and REWRITE (SSR) to produce cross-lingual simplified science summaries for non-expert readers. Our empirical evaluation on the WIKIPEDIA dataset shows that SSR significantly outperforms the baselines for the CSJ task and can serve as a strong baseline for future work. We also perform an ablation study investigating the impact of individual components of SSR. Further, we analyze the performance of SSR on a high-quality, real-world CSJ dataset with human evaluation and in-depth analysis, demonstrating the superior performance of SSR for CSJ.

Modeling Structural Similarities between Documents for Coherence Assessment with Graph Convolutional Networks - Liu, Wei; Fu, Xiyan; Strube, Michael (2023)
Coherence is an important aspect of text quality, and various approaches have been applied to coherence modeling. However, existing methods solely focus on a single document’s coherence patterns, ignoring the underlying correlation between documents. We investigate a GCN-based coherence model that is capable of capturing structural similarities between documents. Our model first identifies the graph structure of each document, from where we mine different sub-graph patterns. We then construct a heterogeneous graph for the training corpus, connecting documents based on their shared subgraphs. Finally, a GCN is applied to the heterogeneous graph to model the connectivity relationships. We evaluate our method on two tasks, assessing discourse coherence and automated essay scoring. Results show that our GCN-based model outperforms baselines, achieving a new state-of-the-art on both tasks.

Annotation-Inspired Implicit Discourse Relation Classification with Auxiliary Discourse Connective Generation - Liu, Wei; Strube, Michael (2023)
Implicit discourse relation classification is a challenging task due to the absence of discourse connectives. To overcome this issue, we design an end-to-end neural model to explicitly generate discourse connectives for the task, inspired by the annotation process of PDTB. Specifically, our model jointly learns to generate discourse connectives between arguments and predict discourse relations based on the arguments and the generated connectives. To prevent our relation classifier from being misled by poor connectives generated at the early stage of training while alleviating the discrepancy between training and inference, we adopt Scheduled Sampling to the joint learning. We evaluate our method on three benchmarks, PDTB 2.0, PDTB 3.0, and PCC. Results show that our joint model significantly outperforms various baselines on three datasets, demonstrating its superiority for the task.

From classification accuracy to proper scoring rules: Elicitability of probabilistic top list predictions - Resin, Johannes (Journal of Machine Learning Research, 2023)

Matrix of orthogonalized atomic orbital coefficients representation for radicals and ions - Llenga, Stiv; Gryn'ova, Ganna (The Journal of Chemical Physics, 2023)
Chemical (molecular, quantum) machine learning relies on representing molecules in unique and informative ways. Here, we present the matrix of orthogonalized atomic orbital coefficients (MAOC) as a quantum-inspired molecular and atomic representation containing both structural (composition and geometry) and electronic (charge and spin multiplicity) information. MAOC is based on a cost-effective localization scheme that represents localized orbitals via a predefined set of atomic orbitals. The latter can be constructed from such small atom-centered basis sets as pcseg-0 and STO-3G in conjunction with guess (non-optimized) electronic configuration of the molecule. Importantly, MAOC is suitable for representing monatomic, molecular, and periodic systems and can distinguish compounds with identical compositions and geometries but distinct charges and spin multiplicities. Using principal component analysis, we constructed a more compact but equally powerful version of MAOC—PCX-MAOC. To test the performance of full and reduced MAOC and several other representations (CM, SOAP, SLATM, and SPAHM), we used a kernel ridge regression machine learning model to predict frontier molecular orbital energy levels and ground state single-point energies for chemically diverse neutral and charged, closed- and open-shell molecules from an extended QM7b dataset, as well as two new datasets, N-HPC-1 (N-heteropolycycles) and REDOX (nitroxyl and phenoxyl radicals, carbonyl, and cyano compounds). MAOC affords accuracy that is either similar or superior to other representations for a range of chemical properties and systems.

PfCRT mutations conferring piperaquine resistance in falciparum malaria shape the kinetics of quinoline drug binding and transport - Gomez, Guillermo M.; D'Arrigo, Giulia; Sanchez, Cecilia P.; Berger, Fiona; Wade, Rebecca C.; Lanzer, Michael (PLOS Pathogens, 2023)

WeLT: Improving Biomedical Fine-tuned Pre-trained Language Models with Cost-sensitive Learning - Mobasher, Ghadeer; Müller, Wolfgang; Krebs, Olga; Gertz, Michael (The 22nd Workshop on Biomedical Natural Language Processing and BioNLP Shared Tasks, 2023)
Fine-tuning biomedical pre-trained language models (BioPLMs) such as BioBERT has become a common practice dominating leaderboards across various natural language processing tasks. Despite their success and wide adoption, prevailing fine-tuning approaches for named entity recognition (NER) naively train BioPLMs on targeted datasets without considering class distributions. This is problematic especially when dealing with imbalanced biomedical gold-standard datasets for NER in which most biomedical entities are underrepresented. In this paper, we address the class imbalance problem and propose WeLT, a cost-sensitive fine-tuning approach based on new re-scaled class weights for the task of biomedical NER. We evaluate WeLT’s fine-tuning performance on mixed-domain and domain-specific BioPLMs using eight biomedical gold-standard datasets. We compare our approach against vanilla fine-tuning and three other existing re-weighting schemes. Our results show the positive impact of handling the class imbalance problem. WeLT outperforms all the vanilla fine-tuned models. Furthermore, our method demonstrates advantages over other existing weighting schemes in most experiments.