לוח שנה משולב

Dear Colleagues,As part of the Multidisciplinary Vesicle Program Webinar Series, we are pleased to invite you to a special webinar entitled: "Introduction to Analytical Ultracentrifugation (AUC)" This session will provide an overview of Analytical Ultracentrifugation (AUC) and its applications in the characterization of extracellular vesicles, nanoparticles, macromolecular complexes and other biological systems. The webinar will highlight the principles of sedimentation analysis, methodological considerations and the advantages of AUC as a powerful label free analytical platform for assessing size distribution, heterogeneity, aggregation state and sample purity. The session is intended for researchers interested in advanced biophysical characterization approaches and scalable analytical solutions for EV and nanoparticle research. 

Information Theory views learning as universal prediction under log loss, characterized through regret bounds. We propose a framework that provides non-uniform, model dependent bounds utilizing an effective notion of architecture-based model complexity. This complexity is defined by the probability mass or volume of the set of all models in the vicinity of the target model \theta_0, in an informational distance. This volume might be hard to evaluate, yet by local analysis it is related to spectral properties of the expected Hessian or the Fisher Information Matrix at \theta_0, leading to tractable approximations. We argue that successful architectures possess abroad complexity range, enabling learning in highly over-parameterized model classes. The framework sheds light on the role of inductive biases, the effectiveness of the stochastic gradient descent (SGD)algorithm (but also other algorithms), and phenomena such as flat minima. It unifies online, batch, supervised, and generative settings, and applies across the stochastic-realizable and agnostic regimes. Moreover, it provides insights into the success of modern machine-learning architectures, such as deep neural networks and transformers, suggesting that their broad complexity range naturally arises from their layered structure. These insights open the door to the design of alternative architectures with potentially comparable or even superior performance.

Fundamental understanding of physiological processes that occur at biological membranes, such as membrane fusion, necessitates addressing not only the biochemical aspects, but also biophysical aspects such as membrane tension and curvature. In this talk, I will show how we combine membrane model systems, micropipette aspiration, optical tweezers, and confocal fluorescence microscopy to study membrane shaping and remodelling. I will describe a tool we developed in which membrane bilayers are formed on polystyrene microspheres that can be trapped and manipulated with optical tweezers and brought into contact with micropipette-aspirated vesicles. Using this system, we demonstrated that membrane tension inhibits hemifusion by increasing the energy barrier for stalk formation. (Shendrik et al 2023). We then extended the approach to interact supported membranes with asymmetric GUVs, revealing a preferred direction for fusion in asymmetric membranes (Shendrik et al 2025). Expanding our understanding of how membrane tension affects membrane organization, we also explored the effect of membrane stretching on phase-separated membranes (Perlman-Illouz et al 2026). Finally, I will show how biomimetic models can be used to gain mechanistic insight into the action mechanisms of viral fusion proteins (Yosibash I. et al 2025). Together, these studies demonstrate how combining mechanical tools with biomimetic models advances our mechanistic understanding of cell membranes. FOR THE LATEST UPDATES AND CONTENT ON SOFT MATTER AND BIOLOGICAL PHYSICS AT THE WEIZMANN, VISIT OUR WEBSITE: https://www.bio