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1687 Publications visible to you, out of a total of 1687
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

Binary black hole mergers from merged stars in the Galactic field

Abstract

Not specified

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

Date Published: 1st Jul 2022

Publication Type: Journal

The R136 star cluster dissected with Hubble Space Telescope/STIS. III. The most massive stars and their clumped winds

Abstract

Not specified

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

Innovative Tools Fostered by the HiPS Ecosystem

Abstract (Expand)

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.

Authors: Thomas Boch, Mark Allen, Caroline Bot, Pierre Fernique, Matthieu Baumann, Mihaela Buga, Francois Bonnarel, Daniel Durand, Kai Polsterer

Date Published: 1st Jul 2022

Publication Type: InProceedings

Interactive Exploration Framework for Big Data Sets

Abstract (Expand)

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.

Authors: Fenja Kollasch, Kai Polsterer

Date Published: 1st Jul 2022

Publication Type: InProceedings

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