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1702 Publications visible to you, out of a total of 1702

Abstract

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

Date Published: 14th Jul 2022

Publication Type: Journal

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

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 toconstructed 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

Abstract

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

Date Published: 5th Jul 2022

Publication Type: Journal

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

Abstract

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Authors: Jakob Stegmann, Fabio Antonini, Fabian R. N. Schneider, Vaibhav Tiwari, Debatri Chattopadhyay

Date Published: 1st Jul 2022

Publication Type: Journal

Abstract

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Authors: Sarah A. Brands, Alex de Koter, Joachim M. Bestenlehner, Paul A. Crowther, Jon O. Sundqvist, Joachim Puls, Saida M. Caballero-Nieves, Michael Abdul-Masih, Florian A. Driessen, Miriam Garcı́a, Sam Geen, Götz Gräfener, Calum Hawcroft, Lex Kaper, Zsolt Keszthelyi, Norbert Langer, Hugues Sana, Fabian R. N. Schneider, Tomer Shenar, Jorick S. Vink

Date Published: 1st Jul 2022

Publication Type: Journal

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