Preparing Chaperone–Client Protein Complexes for Biophysical and Structural Studies - Sučec, I.; Schanda, Paul (Biophysics of Molecular Chaperones)
Understanding the mechanisms of chaperones at the atomic level generally requires producing chaperone–client complexes in vitro. This task comes with significant challenges, because one needs to find conditions in which the client protein is presented to the chaperone in a state that binds and at the same time avoid the pitfalls of protein aggregation that are often inherent to such states. The strategy differs significantly for different client proteins and chaperones, but there are common underlying principles. Here, we discuss these principles and deduce the strategies that can be successfully applied for different chaperone–client complexes. We review successful biochemical strategies applied to making the client protein “binding competent” and illustrate the different strategies with examples of recent biophysical and biochemical studies.

Probing Single Chaperone Substrates - Wruck, F.; Avellaneda Sarrió, Mario; Naqvi, M. M.; Koers, E. J.; Till, K.; Gross, L.; Moayed, F.; Roland, A.; Heling, L. W. H. J.; Mashaghi, A.; Tans, S. J. (Biophysics of Molecular Chaperones)
Regulating protein states is considered the core function of chaperones. However, despite their importance to all major cellular processes, the conformational changes that chaperones impart on polypeptide chains are difficult to study directly due to their heterogeneous, dynamic, and multi-step nature. Here, we review recent advances towards this aim using single-molecule manipulation methods, which are rapidly revealing new mechanisms of conformational control and helping to define a different perspective on the chaperone function.

On the rightmost eigenvalue of non-Hermitian random matrices - Cipolloni, Giorgio; Erdös, László; Schröder, Dominik J; Xu, Yuanyuan (The Annals of Probability)
We establish a precise three-term asymptotic expansion, with an optimal estimate of the error term, for the rightmost eigenvalue of an n×n random matrix with independent identically distributed complex entries as n tends to infinity. All terms in the expansion are universal.
Keywords: Statistics; Probability and Uncertainty; Statistics and Probability

Mechanisms for the Self‐Organization of Tropical Deep Convection - Haerter, Jan O.; Muller, Caroline J (Clouds and Their Climatic Impacts)
Organization – or departure from a random pattern – in tropical deep convection is heavily studied due to its immediate relevance to climate sensitivity and extremes. Low-latitude convection has motivated numerical model idealizations, where the Coriolis force is removed and boundary conditions are simplified spatially and temporally. One of the most stunning aspects of such idealized simulated cloud organization is the spontaneous clumping of convection that can occur without any predetermining external perturbation, such as inhomogeneous surface boundary conditions or large-scale waves. Whereas individual convective rain cells measure only few kilometers in horizontal diameter, the clusters they form can often span hundreds or even thousands of kilometers. Hence, organization may emerge from the very small scales but can show effects at the synoptic scale. We refer to such emergent organization as convective self-organization. Convective self-organization thus features characteristics of emergence, such as non-trivial system-scale pattern formation or hysteresis. We summarize observational evidence for large-scale organization and briefly recap classical idealized modeling studies that yield convective self-aggregation – emergent organization under strongly idealized boundary conditions. We then focus on developing research, where temporal variation, such as the diurnal cycle, or two-way interactive surface properties yield distinct organizational modes. Convectively generated cold pools and mesoscale convective systems, both ubiquitous in nature, are thereby found to potentially play key roles in promoting – rather than suppressing – sustained system-scale organization.

Ubiquity of bound states for the strongly coupled polaron - Mitrouskas, David Johannes; Seiringer, Robert (Pure and Applied Analysis)
We study the spectrum of the Fröhlich Hamiltonian for the polaron at fixed total momentum. We prove the existence of excited eigenvalues between the ground state energy and the essential spectrum at strong coupling. In fact, our main result shows that the number of excited energy bands diverges in the strong coupling limit. To prove this we derive upper bounds for the min-max values of the corresponding fiber Hamiltonians and compare them with the bottom of the essential spectrum, a lower bound on which was recently obtained by Brooks and Seiringer (Comm. Math. Phys. 404:1 (2023), 287–337). The upper bounds are given in terms of the ground state energy band shifted by momentum-independent excitation energies determined by an effective Hamiltonian of Bogoliubov type.
Keywords: General Medicine

Cover Picture: The rigid core and flexible surface of amyloid fibrils probed by Magic‐Angle‐Spinning NMR spectroscopy of aromatic residues - Becker, Lea Marie; Berbon, Mélanie; Vallet, Alicia; Grelard, Axelle; Morvan, Estelle; Bardiaux, Benjamin; Lichtenecker, Roman; Ernst, Matthias; Loquet, Antoine; Schanda, Paul (Angewandte Chemie International Edition)
Cover Page
Keywords: General Chemistry; Catalysis

Complex vaccination strategies prevent the emergence of vaccine resistance - Rella, Simon; Kulikova, Y; Minnegalieva, Aygul; Kondrashov, Fyodor (European Journal of Public Health)
Keywords: Public Health; Environmental and Occupational Health

Intensification mechanisms of tropical cyclones - Polesello, Andrea; Muller, Caroline J; Pasquero, Claudia; Meroni, Agostino N. (EGU General Assembly 2023)

Measuring spontaneous charging of single aerosol particles - Stöllner, Andrea; Lenton, Isaac C; Muller, Caroline J; Waitukaitis, Scott R (EGU General Assembly 2023)

Evaluating memory properties in convection schemes using idealised tests - Hwong, Yi-Ling; Colin, Maxime; Aglas, Philipp; Muller, Caroline J; Sherwood, Steven (EGU General Assembly 2023)

Extreme precipitation in tropical squall lines - Abramian, Sophie; Muller, Caroline J; Risi, Camille (EGU General Assembly 2023)

Constructing Hamilton cycles and perfect matchings efficiently - Anastos, Michael (Proceedings of the 12th European Conference on Combinatorics, Graph Theory and Applications)
Starting with the empty graph on $[n]$, at each round, a set of $K=K(n)$ edges is presented chosen uniformly at random from the ones that have not been presented yet. We are then asked to choose at most one of the presented edges and add it to the current graph. Our goal is to construct a Hamiltonian graph with $(1+o(1))n$ edges within as few rounds as possible. We show that in this process, one can build a Hamiltonian graph of size $(1+o(1))n$ in $(1+o(1))(1+(\log n)/2K) n$ rounds w.h.p. The case $K=1$ implies that w.h.p. one can build a Hamiltonian graph by choosing $(1+o(1))n$ edges in an online fashion as they appear along the first $(0.5+o(1))n\log n$ rounds of the random graph process. This answers a question of Frieze, Krivelevich and Michaeli. Observe that the number of rounds is asymptotically optimal as the first $0.5n\log n$ edges do not span a Hamilton cycle w.h.p. The case $K=Θ(\log n)$ implies that the Hamiltonicity threshold of the corresponding Achlioptas process is at most $(1+o(1))(1+(\log n)/2K) n$. This matches the $(1-o(1))(1+(\log n)/2K) n$ lower bound due to Krivelevich, Lubetzky and Sudakov and resolves the problem of determining the Hamiltonicity threshold of the Achlioptas process with $K=Θ(\log n)$. We also show that in the above process one can construct a graph $G$ that spans a matching of size $\lfloor V(G)/2) \rfloor$ and $(0.5+o(1))n$ edges within $(1+o(1))(0.5+(\log n)/2K) n$ rounds w.h.p. Our proof relies on a robust Hamiltonicity property of the strong $4$-core of the binomial random graph which we use as a black-box. This property allows it to absorb paths covering vertices outside the strong $4$-core into a cycle.

High-precision mapping of nuclear pore-chromatin interactions reveals new principles of genome organization at the nuclear envelope - Tyagi, Swati; Capitanio, Juliana S.; Xu, Jiawei; Chen, Fei; Sharma, Rahul; Huang, Jialiang; HETZER, Martin W (eLife)
The role of nuclear pore complexes (NPCs) in genome organization remains poorly characterized due to technical limitations in probing genome-wide protein-DNA interactions specific to the nuclear periphery. Here, we developed a new sensitive method, NPC-DamID, which combines in vitro reconstitution of nuclear import and DamID technology. The fixation-free method identifies chromatin interactions at the NPCs in intact nuclei from cells and tissues. We found that NPCs are preferentially associated with common and hierarchically arranged super-enhancers (SEs) across multiple cell types. We also uncovered phase-separated condensates at NPCs that compartmentalize and concentrate transcriptional coactivators and structural proteins at SE-regulated genes. Our results support NPCs as anchoring sites for SE regulatory hubs and cell-type-specific transcriptional control.

Entangling microwaves and telecom wavelength light - Sahu, Rishabh; Qiu, Liu; Hease, William J; Arnold, Georg M; Minoguchi, Yuri; Rabl, Peter; Fink, Johannes M (Frontiers in Optics + Laser Science 2023)
We entangled microwave and optical photons for the first time as verified by a measured two-mode vacuum squeezing of 0.7 dB. This electro-optic entanglement is the key resource needed to connect cryogenic quantum circuits.

douglastome/dynamic-engrams: Dynamic and selective engrams emerge with memory consolidation - Feitosa Tomé, Douglas
Code and data necessary to reproduce the simulations and data analyses reported in our manuscript: Tomé, D.F., Zhang, Y., Aida, T., Mosto, O., Lu, Y., Chen, M., Sadeh, S., Roy, D. S., Clopath, C. Dynamic and selective engrams emerge with memory consolidation. 2023.

Air temperature and near-surface meteorology datasets on three Swiss glaciers - Extreme 2022 Summer - Shaw, Thomas; Buri, Pascal; McCarthy, Michael; Miles, Evan; Pellicciotti, Francesca
GLACIER METEOROLOGICAL DATA SWISS ALPS -2022

Fast symbolic algorithms for mega-regular games under strong transition fairness - Banerjee, Tamajit; Majumdar, Rupak; Mallik, Kaushik; Schmuck, Anne-Kathrin; Soudjani, Sadegh (TheoretiCS)
We consider fixpoint algorithms for two-player games on graphs with $ω$-regular winning conditions, where the environment is constrained by a strong transition fairness assumption. Strong transition fairness is a widely occurring special case of strong fairness, which requires that any execution is strongly fair with respect to a specified set of live edges: whenever the source vertex of a live edge is visited infinitely often along a play, the edge itself is traversed infinitely often along the play as well. We show that, surprisingly, strong transition fairness retains the algorithmic characteristics of the fixpoint algorithms for $ω$-regular games -- the new algorithms have the same alternation depth as the classical algorithms but invoke a new type of predecessor operator. For Rabin games with $k$ pairs, the complexity of the new algorithm is $O(n^{k+2}k!)$ symbolic steps, which is independent of the number of live edges in the strong transition fairness assumption. Further, we show that GR(1) specifications with strong transition fairness assumptions can be solved with a 3-nested fixpoint algorithm, same as the usual algorithm. In contrast, strong fairness necessarily requires increasing the alternation depth depending on the number of fairness assumptions. We get symbolic algorithms for (generalized) Rabin, parity and GR(1) objectives under strong transition fairness assumptions as well as a direct symbolic algorithm for qualitative winning in stochastic $ω$-regular games that runs in $O(n^{k+2}k!)$ symbolic steps, improving the state of the art. Finally, we have implemented a BDD-based synthesis engine based on our algorithm. We show on a set of synthetic and real benchmarks that our algorithm is scalable, parallelizable, and outperforms previous algorithms by orders of magnitude.

Deep neural collapse is provably optimal for the deep unconstrained features model - Súkeník, Peter; Mondelli, Marco; Lampert, Christoph (37th Annual Conference on Neural Information Processing Systems)
Neural collapse (NC) refers to the surprising structure of the last layer of deep neural networks in the terminal phase of gradient descent training. Recently, an increasing amount of experimental evidence has pointed to the propagation of NC to earlier layers of neural networks. However, while the NC in the last layer is well studied theoretically, much less is known about its multi-layered counterpart - deep neural collapse (DNC). In particular, existing work focuses either on linear layers or only on the last two layers at the price of an extra assumption. Our paper fills this gap by generalizing the established analytical framework for NC - the unconstrained features model - to multiple non-linear layers. Our key technical contribution is to show that, in a deep unconstrained features model, the unique global optimum for binary classification exhibits all the properties typical of DNC. This explains the existing experimental evidence of DNC. We also empirically show that (i) by optimizing deep unconstrained features models via gradient descent, the resulting solution agrees well with our theory, and (ii) trained networks recover the unconstrained features suitable for the occurrence of DNC, thus supporting the validity of this modeling principle.

Concentration without independence via information measures - Esposito, Amedeo Roberto; Mondelli, Marco (Proceedings of 2023 IEEE International Symposium on Information Theory)
We propose a novel approach to concentration for non-independent random variables. The main idea is to ``pretend'' that the random variables are independent and pay a multiplicative price measuring how far they are from actually being independent. This price is encapsulated in the Hellinger integral between the joint and the product of the marginals, which is then upper bounded leveraging tensorisation properties. Our bounds represent a natural generalisation of concentration inequalities in the presence of dependence: we recover exactly the classical bounds (McDiarmid's inequality) when the random variables are independent. Furthermore, in a ``large deviations'' regime, we obtain the same decay in the probability as for the independent case, even when the random variables display non-trivial dependencies. To show this, we consider a number of applications of interest. First, we provide a bound for Markov chains with finite state space. Then, we consider the Simple Symmetric Random Walk, which is a non-contracting Markov chain, and a non-Markovian setting in which the stochastic process depends on its entire past. To conclude, we propose an application to Markov Chain Monte Carlo methods, where our approach leads to an improved lower bound on the minimum burn-in period required to reach a certain accuracy. In all of these settings, we provide a regime of parameters in which our bound fares better than what the state of the art can provide.

Mismatched estimation of non-symmetric rank-one matrices corrupted by structured noise - Fu, Teng; Liu, YuHao; Barbier, Jean; Mondelli, Marco; Liang, ShanSuo; Hou, TianQi (Proceedings of 2023 IEEE International Symposium on Information Theory)
We study the performance of a Bayesian statistician who estimates a rank-one signal corrupted by non-symmetric rotationally invariant noise with a generic distribution of singular values. As the signal-to-noise ratio and the noise structure are unknown, a Gaussian setup is incorrectly assumed. We derive the exact analytic expression for the error of the mismatched Bayes estimator and also provide the analysis of an approximate message passing (AMP) algorithm. The first result exploits the asymptotic behavior of spherical integrals for rectangular matrices and of low-rank matrix perturbations; the second one relies on the design and analysis of an auxiliary AMP. The numerical experiments show that there is a performance gap between the AMP and Bayes estimators, which is due to the incorrect estimation of the signal norm.