Integrated Calendar

Zoom Link: https://weizmann.zoom.us/j/97324532197?pwd=MGoxSGhJODNWQ2ZGT1p4elJjMG9lZz09

Cell-cell communication is essential for growth, development and function. Cells can communicate mechanically by responding to mechanical deformations generated by their neighbors in the extracellular matrix (ECM). The ECM is a non-linear viscoelastic material and therefore mechanical communication is expected to be frequency-dependent. In my talk, I will describe our work on the characteristics and implications of mechanical communication over the ECM.

Global climate models represent small-scale processes, such as clouds and convection, using subgrid models known as parameterizations. Traditional parameterizations are usually based on simplified physical models, and inaccuracies in these parameterizations are a main cause for the large uncertainty in climate projections. One alternative to traditional parameterizations is to use machine learning to learn new parameterizations which are data driven. However, machine-learning parameterizations might violate physical principles and often lead to instabilities when coupled to an atmospheric model. I will show how machine learning algorithms, such as neural networks and random forests, can be used to learn new parameterizations from the output of a three-dimensional high-resolution atmospheric model, while obeying physical constraints such as energy conservation. Implementing these parameterizations in the atmospheric model at coarse resolution leads to stable simulations that replicate the climate of the high-resolution simulation, and capture important statistics such as precipitation extremes. I will also discuss how machine-learning parameterizations can give further insights into the parameterization problem. Specifically, I will show that failures of machine-learning parameterizations can be used to better understand the relationship between large-scale fields and subgrid processes.

Zoom LInk:
https://weizmann.zoom.us/j/95881429481?pwd=VkxwUmg1Z2ErZmhpZDJqMTZwellGZz09

Lifestyle dietary interventions are an essential practice in treating obesity, hence neural factors that may assist in predicting individual treatment success are of great significance. Here, in a prospective, open-label, three arms study, we examined the correlation between brain resting-state functional connectivity measured at baseline and weight loss following 6 months of lifestyle intervention in 92 overweight participants. We report a robust subnetwork composed mainly of sensory and motor cortical regions, whose edges correlated with future weight loss. This effect was found regardless of intervention group. Importantly, this main finding was further corroborated using a stringent connectivity-based prediction model assessed with cross-validation thus attesting to its robustness. The engagement of senso-motor regions in this subnetwork is consistent with the over-sensitivity to food cues theory of weight regulation. Finally, we tested an additional hypothesis regarding the role of brain-gastric interaction in this subnetwork, considering recent findings of a cortical network synchronized with gastric activity. Accordingly, we found a significant spatial overlap with the subnetwork reported in the present study. Moreover, power in the gastric basal electric frequency within our reported subnetwork negatively correlated with future weight loss. This finding was specific to the weight loss related subnetwork and to the gastric basal frequency. These findings should be further corroborated by combining direct recordings of gastric activity in future studies. Taken together, these intriguing results may have important implications for our understanding of the etiology of obesity and the mechanism of response to dietary intervention as well as to interoceptive perception.
Zoom link to join:
https://weizmann.zoom.us/j/96608033618?pwd=SEdJUkR2ZzRBZ3laUUdGbWR1VFJTdz09

Meeting ID: 966 0803 3618
Password: 564068

Host: Dr. Rita Schmidt rita.schmidt@weizmann.ac.il tel: 9070

I will present several applications of the Dirac inequality to the determination of unitary representations of p-adic groups and associated "spectral gaps". The method works particularly well in order to attach irreducible unitary representations to the large nilpotent orbits (e.g., regular, subregular) in the Langlands dual complex Lie algebra. These results can be viewed as a p-adic analogue of Salamanca-Riba's classification of irreducible unitary (g,K)-modules with strongly regular infinitesimal character.

Controlling the properties of chemical bonds by an external stimulus is a central goal in chemistry. At the level of individual bonds, such control was achieved using light, current, electrochemical potential and electric field. In my talk, I will show that the size and direction of applied magnetic fields can affect bond stability, interatomic distance, and bond-formation probability. This behavior is demonstrated in a variety of atomic wires and single-molecule junctions. The revealed magneto-structural phenomena show that the influence of magnetic interactions on chemical bonds can be dramatic in nanoscale systems.

Zoom Lecture: https://weizmann.zoom.us/j/98819686427?pwd=algvMEJUNHdvaFppNS9xVzlTUkhYQT09
Passcode: 551107

Many functions of RNA depend on rearrangements in secondary structure that are triggered by external factors, such as protein or small molecule binding. These transitions can feature on one hand localized structural changes in base-pairs or can be presented by a change in chemical identity of e.g. a nucleo-base tautomer. We use and develop R1ρ-relaxation-dispersion NMR methods for characterizing transient structures of RNA that exist in low abundance (populations <10%) and that are sampled on timescales spanning three orders of magnitude (µs to s).
The characterization of three different types of transient structures is going to be presented. 1) The HIV-1 dimerization initiation site (DIS) undergoes large secondary structure rearrangements that provide the basis for a molecular zipper, which can be crucial for genome packaging (Nature 2012). 2) The GU wobble base-pair undergoes a change from standard wobble GU geometry to appear like a Watson-Crick base-pair stabilized by Keto-Enol tautomerization (Nature 2015). 3) a microRNA – mRNA complex changes conformation to activate the RISC complex (Nature 2020).
I will furthermore give an outlook on recent efforts to measure in-cell NMR of nucleic acids in functional complexes and ribosome dynamics.
www.petzoldlab.com