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1579 Publications visible to you, out of a total of 1579
Multitarget, Selective Compound Design Yields Potent Inhibitors of a Kinetoplastid Pteridine Reductase 1

Abstract

Not specified

Authors: Ina Pöhner, Antonio Quotadamo, Joanna Panecka-Hofman, Rosaria Luciani, Matteo Santucci, Pasquale Linciano, Giacomo Landi, Flavio Di Pisa, Lucia Dello Iacono, Cecilia Pozzi, Stefano Mangani, Sheraz Gul, Gesa Witt, Bernhard Ellinger, Maria Kuzikov, Nuno Santarem, Anabela Cordeiro-da-Silva, Maria P. Costi, Alberto Venturelli, Rebecca C. Wade

Date Published: 14th Jul 2022

Publication Type: Journal

Fine-tuning BERT Models for Summarizing German Radiology Findings

Abstract (Expand)

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.

Authors: Siting Liang, Klaus Kades, Matthias Fink, Peter Full, Tim Weber, Jens Kleesiek, Michael Strube, Klaus Maier-Hein

Date Published: 14th Jul 2022

Publication Type: InProceedings

The Free Lunch is not over yet – Systematic Exploration of Numerical Thresholds in Phylogenetic Inference

Abstract

Not specified

Authors: Julia Haag, Lukas Hübner, Alexey M. Kozlov, Alexandros Stamatakis

Date Published: 14th Jul 2022

Publication Type: Journal

Incorporating Centering Theory into Neural Coreference Resolution

Abstract (Expand)

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.

Authors: Haixia Chai, Michael Strube

Date Published: 10th Jul 2022

Publication Type: InProceedings

Combining hypothesis- and data-driven neuroscience modeling in FAIR workflows

Abstract (Expand)

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 …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.

Authors: Olivia Eriksson, Upinder Singh Bhalla, Kim T Blackwell, Sharon M Crook, Daniel Keller, Andrei Kramer, Marja-Leena Linne, Ausra Saudargienė, Rebecca C Wade, Jeanette Hellgren Kotaleski

Date Published: 6th Jul 2022

Publication Type: Journal

Long-term Evolution of Postexplosion Helium-star Companions of Type Iax Supernovae

Abstract

Not specified

Authors: Yaotian Zeng, Zheng-Wei Liu, Alexander Heger, Curtis McCully, Friedrich K. Röpke, Zhanwen Han

Date Published: 5th Jul 2022

Publication Type: Journal

Cholenic acid derivative UniPR1331 impairs tumor angiogenesis via blockade of VEGF/VEGFR2 in addition to Eph/ephrin

Abstract (Expand)

Abstract Angiogenesis, the formation of new blood vessels from preexisting ones, is crucial for tumor growth and metastatization, and is considered a promising therapeutic target. Unfortunately, drugs … therapeutic target. Unfortunately, drugs directed against a specific proangiogenic growth factor or receptor turned out to be of limited benefit for oncology patients, likely due to the high biochemical redundancy of the neovascularization process. In this scenario, multitarget compounds that are able to simultaneously tackle different proangiogenic pathways are eagerly awaited. UniPR1331 is a 3β-hydroxy-Δ 5 -cholenic acid derivative, which is already known to inhibit Eph–ephrin interaction. Here, we employed an analysis pipeline consisting of molecular modeling and simulation, surface plasmon resonance spectrometry, biochemical assays, and endothelial cell models to demonstrate that UniPR1331 directly interacts with the vascular endothelial growth factor receptor 2 (VEGFR2) too. The binding of UniPR1331 to VEGFR2 prevents its interaction with the natural ligand vascular endothelial growth factor and subsequent autophosphorylation, signal transduction, and in vitro proangiogenic activation of endothelial cells. In vivo, UniPR1331 inhibits tumor cell-driven angiogenesis in zebrafish. Taken together, these data shed light on the pleiotropic pharmacological effect of UniPR1331, and point to Δ 5 -cholenic acid as a promising molecular scaffold for the development of multitarget antiangiogenic compounds.

Authors: Marco Rusnati, Giulia Paiardi, Chiara Tobia, Chiara Urbinati, Alessio Lodola, Pasqualina D’Ursi, Miriam Corrado, Riccardo Castelli, Rebecca C. Wade, Massimiliano Tognolini, Paola Chiodelli

Date Published: 1st Jul 2022

Publication Type: Journal

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