Learning the rules of mitochondrial network dynamics using generative models

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Cell-to-cell communication is fundamental to multicellular life. In plants, plasmodesmata-cytoplasmic channels that connect adjacent cells-enable the transport of molecules between cells. In roots, such transport is thought to play a central role in nutrie ...

Pronunciation is a critical yet challenging aspect of language acquisition, especially for children. In this work, we propose a new teachable agent system designed to support children's pronunciation learning through a Learning-by-Teaching paradigm to addr ...

PSI and North Carolina State University are developing a high-resolution multi-physics core solver for Pressurized Water Reactor (PWR) analysis in Cartesian geometry, using the neutron transport code nTRACER and two Machine Learning (ML) models providing t ...

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The endoplasmic reticulum (ER) produces proteins destined to organelles of the endocytic and secretory pathways, the plasma membrane, and the extracellular space. While native proteins are transported to their intra- or extracellular site of activity, fold ...

Antimicrobial peptides (AMPs) are innate immune effectors first studied for their role in host defence. Recent studies have implicated these peptides in the clearance of aberrant cells and in neurodegenerative syndromes. In Drosophila, many AMPs are produc ...

The process of quantifying and analyzing animal behavior involves translating the naturally occurring descriptive language of their actions into machine-readable code. Yet, codifying behavior analysis is often challenging without deep understanding of anim ...

Nicotinamide riboside kinases (NRKs) control the conversion of dietary Nicotinamide Riboside (NR) to NAD(+), but little is known about their contribution to endogenous NAD(+) turnover and muscle plasticity during skeletal muscle growth and remodeling. Usin ...

Targeting cells specifically based on receptor expression levels remains an area of active research to date. Selective binding of receptors cannot be achieved by increasing the individual binding strength, as this does not account for differing distributio ...

In this paper, we propose a learning algorithm that enables a model to quickly exploit commonalities among related tasks from an unseen task distribution, before quickly adapting to specific tasks from that same distribution. We investigate how learning wi ...

Learning the rules of mitochondrial network dynamics using generative models

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