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Host–guest interactions in framework materials: Insight from modeling

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

Authors: Michelle Ernst, Jack D. Evans, Ganna Gryn'ova

Date Published: 1st Dec 2023

Publication Type: Journal

The young massive SMC cluster NGC 330 seen by MUSE. III. Stellar parameters and rotational velocities

Abstract

Not specified

Authors: J. Bodensteiner, H. Sana, P. L. Dufton, C. Wang, N. Langer, G. Banyard, L. Mahy, A. de Koter, S. E. de Mink, C. J. Evans, Y. Götberg, V. Hénault-Brunet, L. R. Patrick, F. R. N. Schneider

Date Published: 1st Dec 2023

Publication Type: Journal

Dominantly inherited micro-satellite instable cancer – the four Lynch syndromes - an EHTG, PLSD position statement

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Abstract The recognition of dominantly inherited micro-satellite instable (MSI) cancers caused by pathogenic variants in one of the four mismatch repair ( MMR ) genes MSH2, MLH1, MSH6 and PMS2 has … MMR ) genes MSH2, MLH1, MSH6 and PMS2 has modified our understanding of carcinogenesis. Inherited loss of function variants in each of these MMR genes cause four dominantly inherited cancer syndromes with different penetrance and expressivities: the four Lynch syndromes. No person has an “average sex “or a pathogenic variant in an “average Lynch syndrome gene” and results that are not stratified by gene and sex will be valid for no one. Carcinogenesis may be a linear process from increased cellular division to localized cancer to metastasis. In addition, in the Lynch syndromes (LS) we now recognize a dynamic balance between two stochastic processes: MSI producing abnormal cells, and the host’s adaptive immune system’s ability to remove them. The latter may explain why colonoscopy surveillance does not reduce the incidence of colorectal cancer in LS, while it may improve the prognosis. Most early onset colon, endometrial and ovarian cancers in LS are now cured and most cancer related deaths are after subsequent cancers in other organs. Aspirin reduces the incidence of colorectal and other cancers in LS. Immunotherapy increases the host immune system’s capability to destroy MSI cancers. Colonoscopy surveillance, aspirin prevention and immunotherapy represent major steps forward in personalized precision medicine to prevent and cure inherited MSI cancer.

Authors: Pal Møller, Toni T. Seppälä, Aysel Ahadova, Emma J. Crosbie, Elke Holinski-Feder, Rodney Scott, Saskia Haupt, Gabriela Möslein, Ingrid Winship, Sanne W. Bajwa-ten Broeke, Kelly E. Kohut, Neil Ryan, Peter Bauerfeind, Laura E. Thomas, D. Gareth Evans, Stefan Aretz, Rolf H. Sijmons, Elizabeth Half, Karl Heinimann, Karoline Horisberger, Kevin Monahan, Christoph Engel, Giulia Martina Cavestro, Robert Fruscio, Naim Abu-Freha, Levi Zohar, Luigi Laghi, Lucio Bertario, Bernardo Bonanni, Maria Grazia Tibiletti, Leonardo S. Lino-Silva, Carlos Vaccaro, Adriana Della Valle, Benedito Mauro Rossi, Leandro Apolinário da Silva, Ivana Lucia de Oliveira Nascimento, Norma Teresa Rossi, Tadeusz Dębniak, Jukka-Pekka Mecklin, Inge Bernstein, Annika Lindblom, Lone Sunde, Sigve Nakken, Vincent Heuveline, John Burn, Eivind Hovig, Matthias Kloor, Julian R. Sampson, Mev Dominguez-Valentin

Date Published: 1st Dec 2023

Publication Type: Journal

In situ or accreted? Using deep learning to infer the origin of extragalactic globular clusters from observables

Abstract (Expand)

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

Authors: Sebastian Trujillo-Gomez, J M Diederik Kruijssen, Joel Pfeffer, Marta Reina-Campos, Robert A Crain, Nate Bastian, Ivan Cabrera-Ziri

Date Published: 1st Dec 2023

Publication Type: Journal

Approximating Phylogenetic Tree Distributions with Distance-Based Methods

Abstract (Expand)

Accurately reconstructing the evolutionary history of a group of organism is a complex task. Current state-of-the-art tools produce phylogenetic tree distributions with Markov chain Monte-Carlo (MCMC) … methods by sampling the posterior tree distribution under a given model to reflect uncertainties in the underlying models and data. While these distributions offer very good insight into the phylogenetic history, they are very compute intensive. In this thesis we present and evaluate multiple heuristics to approximate these distributions with distance-based methods. To judge the quality of our heuristics, we compare our distribution against a reference MCMC-based distribution with split and frequency-based metrics. We show that our method works well for some types of data, but not all, compared to other tools, and that further information about the data needs to be incorporated to make this viable in practice. Our most successful method is characterized by the use of pair-wise distance distributions to apply likelihood-supported perturbation to the input distances for the Neighbor Joining algorithm. Because this ignores the interdependencies between distances, we need to add parsimony filtering as a post-processing step to eliminate unlikely trees from our distributions, which significantly improves the results. Finally, we also discuss the shortcomings and future potential of our heuristics to more accurately estimate pair-wise distances and their interdependencies, which should lead to more competitive results.

Authors: Noah Wahl, Benoit Morel, Alexandros Stamatakis

Date Published: 1st Dec 2023

Publication Type: Master's Thesis

Why are different estimates of the effective reproductive number so different? A case study on COVID-19 in Germany

Abstract (Expand)

tive reproductive number Rt has taken a central role in the scientific, political, and public discussion during the COVID-19 pandemic, with numerous real-time estimates of this quantity routinely … published. Disagreement between estimates can be substantial and may lead to confusion among decision-makers and the general public. In this work, we compare different estimates of the national-level effective reproductive number of COVID-19 in Germany in 2020 and 2021. We consider the agreement between estimates from the same method but published at different time points (within-method agreement) as well as retrospective agreement across eight different approaches (between-method agreement). Concerning the former, estimates from some methods are very stable over time and hardly subject to revisions, while others display considerable fluctuations. To evaluate between-method agreement, we reproduce the estimates generated by different groups using a variety of statistical approaches, standardizing analytical choices to assess how they contribute to the observed disagreement. These analytical choices include the data source, data pre-processing, assumed generation time distribution, statistical tuning parameters, and various delay distributions. We find that in practice, these auxiliary choices in the estimation of Rt may affect results at least as strongly as the selection of the statistical approach. They should thus be communicated transparently along with the estimates.

Authors: Elisabeth K. Brockhaus, Daniel Wolffram, Tanja Stadler, Michael Osthege, Tanmay Mitra, Jonas M. Littek, Ekaterina Krymova, Anna J. Klesen, Jana S. Huisman, Stefan Heyder, Laura M. Helleckes, Matthias an der Heiden, Sebastian Funk, Sam Abbott, Johannes Bracher

Date Published: 27th Nov 2023

Publication Type: Journal

Wird KI neue Medikamente gegen Herzkrankheiten hervorbringen?

Abstract (Expand)

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

Authors: Manuel Glaser, Julia Ritterhof, Patrick Most, Rebecca C. Wade

Date Published: 20th Nov 2023

Publication Type: Journal

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