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1579 Publications visible to you, out of a total of 1579

Abstract (Expand)

Metal-organic frameworks (MOF) and covalent organic frameworks (COFs) are promising nanocarriers for targeted drug delivery. Noncovalent interactions between frameworks and drugs play a fundamental role in the therapeutic uptake and release of the latter. However, the scope of framework functionalizations and deliverable drugs remains underexplored. Using a multilevel approach combining molecular docking and density functional theory, we show for a range of drugs and frameworks that experimentally reported release metrics are in good agreement with the in silico computed host–guest interaction energies. Functional groups within the framework significantly impact the strength of these host–guest interactions, while a given framework can serve as an efficient delivery agent for drugs beyond the prototypical few. Our findings identify the interaction energy as a reliable and relatively easy to compute descriptor of organic framework materials for drug delivery, able to facilitate their high-throughput screening and targeted design towards extended-release times.

Authors: Michelle Ernst, Ganna Gryn'ova

Date Published: 26th May 2023

Publication Type: Journal

Abstract (Expand)

Abstract Machine learning (ML) models are widely used in life sciences and medicine; however, they are scattered across various platforms and there are several challenges that hinder their accessibility,r their accessibility, reproducibility and reuse. In this manuscript, we present the formalisation and pilot implementation of community protocol to enable FAIReR (Findable, Accessible, Interoperable, Reusable, and Reproducible) sharing of ML models. The protocol consists of eight steps, including sharing model training code, dataset information, reproduced figures, model evaluation metrics, trained models, Dockerfiles, model metadata, and FAIR dissemination. Applying these measures we aim to build and share a comprehensive public collection of FAIR ML models in the BioModels repository through incentivized community curation. In a pilot implementation, we curated diverse ML models to demonstrate the feasibility of our approach and we discussed the current challenges. Building a FAIReR collection of ML models will directly enhance the reproducibility and reusability of ML models, minimising the effort needed to reimplement models, maximising the impact on the application and significantly accelerating the advancement in the field of life science and medicine.

Authors: Divyang Deep Tiwari, Nils Hoffmann, Kieran Didi, Sumukh Deshpande, Sucheta Ghosh, Tung V. N. Nguyen, Karthik Raman, Henning Hermjakob, Rahuman Sheriff

Date Published: 23rd May 2023

Publication Type: Misc

Abstract (Expand)

The cosmic origin of the elements, the fundamental chemical building blocks of the universe, is still uncertain. Binary interactions play a key role in the evolution of many massive stars, yet their impact on chemical yields is poorly understood. Using the MESA stellar evolution code, we predict the chemical yields ejected in wind mass loss and the supernovae of single and binary-stripped stars. We do this with a large 162-isotope nuclear network at solar metallicity. We find that binary-stripped stars are more effective producers of the elements than single stars, due to their increased mass loss and an increased chance to eject their envelopes during a supernova. This increased production by binaries varies across the periodic table, with F and K being more significantly produced by binary-stripped stars than single stars. We find that the 12C/13C could be used as an indicator of the conservativeness of mass transfer, as 13C is preferentially ejected during mass transfer while 12C is preferentially ejected during wind mass loss. We identify a number of gamma-ray-emitting radioactive isotopes that may be used to help constrain progenitor and explosion models of core-collapse supernovae with next-generation gamma-ray detectors. For single stars we find that 44V and 52Mn are strong probes of the explosion model, while for binary-stripped stars it is 48Cr. Our findings highlight that binary-stripped stars are not equivalent to two single stars and that detailed stellar modeling is needed to predict their final nucleosynthetic yields.

Authors: R. Farmer, E. Laplace, Jing-ze Ma, S. E. de Mink, S. Justham

Date Published: 12th May 2023

Publication Type: Journal

Abstract (Expand)

The Metadata Schema of the NFDI4Health and the NFDI4Health Task Force COVID-19 (Metadata Schema) contains a list of properties that describe a resource to be registered in the German Central Health Study Hub. Currently, two main types of resources are distinguished: a) study descriptions (i.e., metadata set describing a study) and b) study documents. However, due to the generic character of the Metadata Schema, other types of resources may also be described and registered. The metadata properties are divided into mandatory and recommended ones. Along with bibliographic information such as title and description of the resource, the related persons and organizations contributing to the development of the resource can also be specified. The results of studies published in journal articles or other text publications can be linked too. For studies, information about study design and accessibility of the collected data should be additionally provided. The Metadata Schema consists mainly of properties adapted from established standards and models such as DataCite Metadata Schema 4.4, data models of the ClinicalTrials.gov, German Clinical Trials Register, International Clinical Trials Registry, HL7® FHIR, MIABIS, Maelstrom Research cataloguing toolkit and DDI Controlled Vocabularies. This is an updated version V3_1 of the Metadata Schema, which introduces two new resource types, namely registries and secondary data sources. Accordingly, the metadata set describing studies, which was part of the core module in previous versions, has been split into a separate module and adapted to also apply to registries and secondary data sources. An additional use case-specific module has also been added, including metadata specific to record linkage. The undertaken changes are described within the document.

Authors: Haitham Abaza, A. Shutsko, M. Golebiewski, Sophie Klopfenstein, C. O. Schmidt, Carina Vorisek, NFDI4Health Task Force COVID-19, NFDI4Health, C. Brünings-Kuppe, V. Clemens, J. Darms, S. Hanß, T. Intemann, F. Jannasch, E. Kasbohm, B. Lindstadt, M. Lobe, E. Orban, I. Perrar, M. Peters, U. Sax, M. Schulze, C. Schupp, F. Schwarz, C. Schwedhelm, S. Strathmann, D. Waltemath, H. Wünsche, A. A. Zeleke

Date Published: 10th May 2023

Publication Type: Misc

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

Abstract

Not specified

Author: Johannes Resin

Date Published: 1st May 2023

Publication Type: Journal

Abstract (Expand)

Abstract Brownian dynamics (BD) is a computational method to simulate molecular diffusion processes. Although the BD method has been developed over several decades and is well established, newd is well established, new methodological developments are improving its accuracy, widening its scope, and increasing its application. In biological applications, BD is used to investigate the diffusive behavior of molecules subject to forces due to intermolecular interactions or interactions with material surfaces. BD can be used to compute rate constants for diffusional association, generate structures of encounter complexes for molecular binding partners, and examine the transport properties of geometrically complex molecules. Often, a series of simulations is performed, for example, for different protein mutants or environmental conditions, so that the effects of the changes on diffusional properties can be estimated. While biomolecules are commonly described at atomic resolution and internal molecular motions are typically neglected, coarse‐graining and the treatment of conformational flexibility are increasingly employed. Software packages for BD simulations of biomolecules are growing in capabilities, with several new packages providing novel features that expand the range of questions that can be addressed. These advances, when used in concert with experiment or other simulation methods, such as molecular dynamics, open new opportunities for application to biochemical and biological systems. Here, we review some of the latest developments in the theory, methods, software, and applications of BD simulations to study biomolecular diffusional association processes and provide a perspective on their future use and application to outstanding challenges in biology, bioengineering, and biomedicine. This article is categorized under: Structure and Mechanism > Computational Biochemistry and Biophysics Molecular and Statistical Mechanics > Molecular Dynamics and Monte‐Carlo Methods Software > Simulation Methods

Authors: Abraham Muñiz‐Chicharro, Lane W. Votapka, Rommie E. Amaro, Rebecca C. Wade

Date Published: 1st May 2023

Publication Type: Journal

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