Week 08.04.2024 – 14.04.2024

The self-organization of turbulence is a remarkable property of flows with two sign-definite conserved quantities. When such flows are forced at small scales, a coherent flow called a condensate emerges, and is sustained by turbulence. The organizational principle for the condensate is that it should occupy the entire domain, respect its symmetries and be independent of small-scale details. One class of flows where condensation occurs is a rapidly rotating shallow fluid layer under the influence of gravity. This family of two-dimensional flows is characterized by a single parameter, the Rossby deformation radius R, which determines the range of influence of a flow perturbation. When R is much larger than the domain size, the flow reduces to two-dimensional Navier-Stokes. In the opposite limit of vanishing R, a regime termed LQG, interactions between fluid elements become strictly local. We uncover an unexpected organizational principle in the latter: the condensate area is determined by the ratio between the forcing scale and the UV cutoff. In particular, the large-scale flow can take different configurations depending on this ratio, including regions of bi-stability of configurations and spontaneous symmetry breaking in the thermodynamic limit (increasing system size). We explain how this behavior arises from the spatial distribution of fluxes of the conserved quantities in the system.

In this talk I will present diffRBM, an approach
based on transfer learning and Restricted Boltzmann Machines, to build sequence-based predictive models of protein-protein interactions underlying effective immune responses. In particular, the protein-protein interaction we focus on is the binding between protein fragments of viral origin (antigens) and the surface receptors of immune cells (T-cell receptors), which mediates the recognition by the immune system of ongoing infections. DiffRBM is designed to learn the distinctive patterns in amino-­acid composition that, on the one hand, underlie the antigen’s probability of triggering a response, and
on the other hand the T-­cell receptor’s ability to bind to a given antigen.
We show that diffRBM reaches performances that compare favorably to existing sequence-­based predictors of antigen-receptor binding specificity, and that the patterns learnt by diffRBM allow us to predict putative contact sites of the antigen-­receptor structural complex.