Publications

1370 Publications visible to you, out of a total of 1370

Abstract (Expand)

Abstract Mitochondrial fatty acid synthesis (mtFAS) is essential for respiratory function. MtFAS generates the octanoic acid precursor for lipoic acid synthesis, but the role of longer fatty acidthesis, but the role of longer fatty acid products has remained unclear. The structurally well-characterized component of mtFAS, human 2E -enoyl-ACP reductase (MECR) rescues respiratory growth and lipoylation defects of a Saccharomyces cerevisiae Δ etr1 strain lacking native mtFAS enoyl reductase. To address the role of longer products of mtFAS, we employed in silico molecular simulations to design a MECR variant with a shortened substrate binding cavity. Our in vitro and in vivo analyses indicate that the MECR G165Q variant allows synthesis of octanoyl groups but not long chain fatty acids, confirming the validity of our computational approach to engineer substrate length specificity. Furthermore, our data imply that restoring lipoylation in mtFAS deficient yeast strains is not sufficient to support respiration and that long chain acyl-ACPs generated by mtFAS are required for mitochondrial function.

Authors: M. Tanvir Rahman, M. Kristian Koski, Joanna Panecka-Hofman, Werner Schmitz, Alexander J. Kastaniotis, Rebecca C. Wade, Rik K. Wierenga, J. Kalervo Hiltunen, Kaija J. Autio

Date Published: 1st Dec 2023

Publication Type: Journal

Abstract (Expand)

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.

Authors: Wei Zhao, Michael Strube, Steffen Eger

Date Published: 2nd May 2023

Publication Type: InProceedings

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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.

Authors: S. G. Djorgovski, Ashish Mahabal, M. J. Graham, Kai L. Polsterer, Alberto Krone-Martins

Date Published: 1st Apr 2023

Publication Type: InCollection

Abstract

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Authors: Aysel Ahadova, Albrecht Stenzinger, Toni Seppälä, Robert Hüneburg, Matthias Kloor, Hendrik Bläker, Jan-Niklas Wittemann, Volker Endris, Leonie Gerling, Veit Bertram, Marie Theres Neumuth, Johannes Witt, Sebastian Graf, Glen Kristiansen, Oliver Hommerding, Saskia Haupt, Alexander Zeilmann, Vincent Heuveline, Daniel Kazdal, Johannes Gebert, Magnus von Knebel Doeberitz, Jukka-Pekka Mecklin, Jacob Nattermann

Date Published: 11th Mar 2023

Publication Type: Journal

Abstract

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Authors: Tilmann Gneiting, Daniel Wolffram, Johannes Resin, Kristof Kraus, Johannes Bracher, Timo Dimitriadis, Veit Hagenmeyer, Alexander I. Jordan, Sebastian Lerch, Kaleb Phipps, Melanie Schienle

Date Published: 10th Mar 2023

Publication Type: Journal

Abstract

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Authors: Serge Perez, Olga Makshakova, Jesus Angulo, Emiliano Bedini, Antonella Bisio, Jose Luis de Paz, Elisa Fadda, Marco Guerrini, Michal Hricovini, Milos Hricovini, Frederique Lisacek, Pedro M. Nieto, Kevin Pagel, Giulia Pairardi, Ralf Richter, Sergey A. Samsonov, Romain A. Vivès, Dragana Nikitovic, Sylvie Ricard Blum

Date Published: 2nd Mar 2023

Publication Type: Journal

Abstract

Not specified

Authors: Tilmann Gneiting, Sebastian Lerch, Benedikt Schulz

Date Published: 1st Mar 2023

Publication Type: Journal

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