Cell aging preserves cellular immortality in the presence of lethal levels of damage.

Cellular aging, a progressive functional decline driven by damage accumulation, often culminates in the mortality of a cell lineage. Certain lineages, however, are able to sustain long-lasting immortality, as prominently exemplified by stem cells. Here, we show that Escherichia coli cell lineages exhibit comparable patterns of mortality and immortality. Through single-cell microscopy and microfluidic techniques, we find that these patterns are explained by the dynamics of damage accumulation and asymmetric partitioning between daughter cells. At low damage accumulation rates, both aging and rejuvenating lineages retain immortality by reaching their respective states of physiological equilibrium. We show that both asymmetry and equilibrium are present in repair mutants lacking certain repair chaperones, suggesting that intact repair capacity is not essential for immortal proliferation. We show that this growth equilibrium, however, is displaced by extrinsic damage in a dosage-dependent response. Moreover, we demonstrate that aging lineages become mortal when damage accumulation rates surpass a threshold, whereas rejuvenating lineages within the same population remain immortal. Thus, the processes of damage accumulation and partitioning through asymmetric cell division are essential in the determination of proliferative mortality and immortality in bacterial populations. This study provides further evidence for the characterization of cellular aging as a general process, affecting prokaryotes and eukaryotes alike and according to similar evolutionary constraints

Finite speed of quantum information in models of interacting bosons at finite density

We prove that quantum information propagates with a finite velocity in any model of interacting bosons whose (possibly time-dependent) Hamiltonian contains spatially local single-boson hopping terms along with arbitrary local density-dependent interactions. More precisely, with density matrix $\rho \propto \exp[-\mu N]$ (with $N$ the total boson number), ensemble averaged correlators of the form $\langle [A_0,B_r(t)]\rangle$, along with out-of-time-ordered correlators, must vanish as the distance $r$ between two local operators grows, unless $t \ge r/v$ for some finite speed $v$. In one dimensional models, we give a useful extension of this result that demonstrates the smallness of all matrix elements of the commutator $[A_0,B_r(t)]$ between finite density states if $t/r$ is sufficiently small. Our bounds are relevant for physically realistic initial conditions in experimentally realized models of interacting bosons. In particular, we prove that $v$ can scale no faster than linear in number density in the Bose-Hubbard model: this scaling matches previous results in the high density limit. The quantum walk formalism underlying our proof provides an alternative method for bounding quantum dynamics in models with unbounded operators and infinite-dimensional Hilbert spaces, where Lieb-Robinson bounds have been notoriously challenging to prove.Comment: 23 pages, 1 figur

Prethermalization and the local robustness of gapped systems

We prove that prethermalization is a generic property of gapped local many-body quantum systems, subjected to small perturbations, in any spatial dimension. More precisely, let $H_0$ be a Hamiltonian, spatially local in $d$ spatial dimensions, with a gap $\Delta$ in the many-body spectrum; let $V$ be a spatially local Hamiltonian consisting of a sum of local terms, each of which is bounded by $\epsilon \ll \Delta$. Then, the approximation that quantum dynamics is restricted to the low-energy subspace of $H_0$ is accurate, in the correlation functions of local operators, for stretched exponential time scale $\tau \sim \exp[(\Delta/\epsilon)^a]$ for any $a<1/(2d-1)$. This result does not depend on whether the perturbation closes the gap. It significantly extends previous rigorous results on prethermalization in models where $H_0$ had an integer-valued spectrum. We infer the robustness of quantum simulation in low-energy subspaces, the existence of ``scarring" (strongly athermal correlation functions) in gapped systems subject to generic perturbations, and the robustness of quantum information in non-frustration-free gapped phases with topological order.Comment: 5+33 pages; 1+4 figure

Polynomial-time classical sampling of high-temperature quantum Gibbs states

The computational complexity of simulating quantum many-body systems generally scales exponentially with the number of particles. This enormous computational cost prohibits first principles simulations of many important problems throughout science, ranging from simulating quantum chemistry to discovering the thermodynamic phase diagram of quantum materials or high-density neutron stars. We present a classical algorithm that samples from a high-temperature quantum Gibbs state in a computational (product state) basis. The runtime grows polynomially with the number of particles, while error vanishes polynomially. This algorithm provides an alternative strategy to existing quantum Monte Carlo methods for overcoming the sign problem. Our result implies that measurement-based quantum computation on a Gibbs state can provide exponential speed up only at sufficiently low temperature, and further constrains what tasks can be exponentially faster on quantum computers.Comment: 4+4 pages; 0+1 figur

Heisenberg-limited metrology with perturbing interactions

We show that it is possible to perform Heisenberg-limited metrology on GHZ-like states, in the presence of generic spatially local interactions during the measurement process. An explicit protocol, which relies on measurements and feedback based on polynomial-time classical computation, achieves the Heisenberg limit. In one dimension, matrix product state methods can be used to perform this classical calculation, while in higher dimensions the cluster expansion underlies the efficient calculations. The latter approach is based on an efficient classical sampling algorithm for short-time quantum dynamics, which may be of independent interest.Comment: 24+5 pages, 3+0 figure

Olfaction Contributes to Pelagic Navigation in a Coastal Shark.

How animals navigate the constantly moving and visually uniform pelagic realm, often along straight paths between distant sites, is an enduring mystery. The mechanisms enabling pelagic navigation in cartilaginous fishes are particularly understudied. We used shoreward navigation by leopard sharks (Triakis semifasciata) as a model system to test whether olfaction contributes to pelagic navigation. Leopard sharks were captured alongshore, transported 9 km offshore, released, and acoustically tracked for approximately 4 h each until the transmitter released. Eleven sharks were rendered anosmic (nares occluded with cotton wool soaked in petroleum jelly); fifteen were sham controls. Mean swimming depth was 28.7 m. On average, tracks of control sharks ended 62.6% closer to shore, following relatively straight paths that were significantly directed over spatial scales exceeding 1600 m. In contrast, tracks of anosmic sharks ended 37.2% closer to shore, following significantly more tortuous paths that approximated correlated random walks. These results held after swimming paths were adjusted for current drift. This is the first study to demonstrate experimentally that olfaction contributes to pelagic navigation in sharks, likely mediated by chemical gradients as has been hypothesized for birds. Given the similarities between the fluid three-dimensional chemical atmosphere and ocean, further research comparing swimming and flying animals may lead to a unifying paradigm explaining their extraordinary navigational abilities

A model to determine the effect of collagen fiber alignment on heart function post myocardial infarction

BACKGROUND: Adverse remodeling of the left ventricle (LV) following myocardial infarction (MI) leads to heart failure. Recent studies have shown that scar anisotropy is a determinant of cardiac function post-MI, however it remains unclear how changes in extracellular matrix (ECM) organization and structure contribute to changes in LV function. The objective of this study is to develop a model to identify potential mechanisms by which collagen structure and organization affect LV function post-MI. METHODS: A four-region, multi-scale, cylindrical model of the post-MI LV was developed. The mechanical properties of the infarct region are governed by a constitutive equation based on the uncrimping of collagen fibers. The parameters of this constitutive equation include collagen orientation, angular dispersion, fiber stiffness, crimp angle, and density. Parametric variation of these parameters was used to elucidate the relationship between collagen properties and LV function. RESULTS: The mathematical model of the LV revealed several factors that influenced cardiac function post-MI. LV function was maximized when collagen fibers were aligned longitudinally. Increased collagen density was also found to improve stroke volume for longitudinal alignments while increased fiber stiffness decreased stroke volume for circumferential alignments. CONCLUSIONS: The results suggest that cardiac function post-MI is best preserved through increased circumferential compliance. Further, this study identifies several collagen fiber-level mechanisms that could potentially regulate both infarct level and organ level mechanics. Improved understanding of the multi-scale relationships between the ECM and LV function will be beneficial in the design of new diagnostic and therapeutic technologies

Hierarchy and Performance of Analyst Teams

We examine the effect of hierarchy on analyst teams’ performance using a large sample of financial analysts from China. Hierarchy, defined as the disparity in power or status within a group, which we operationalise as the difference in experience between the senior and junior analyst in a team, is an important aspect of team structure that could affect team performance. We find that analyst teams with a hierarchy outperform flat teams (teams without a clear hierarchy). Specifically, hierarchical teams issue forecasts with higher accuracy, less optimism bias, less co-movement with the consensus, and stronger investor reactions. The results remain robust after we control for a number of firm and analyst characteristics and fixed effects. Further analysis shows that working in a hierarchical team helps junior analysts improve their individual forecasts for other firms, and senior analysts also benefit from working with junior analysts in hierarchical teams. Our results provide important insights into understanding the effect of team structure on the performance of analyst teams who issue majority of earnings forecasts

Self-Supervised Video Forensics by Audio-Visual Anomaly Detection

Manipulated videos often contain subtle inconsistencies between their visual and audio signals. We propose a video forensics method, based on anomaly detection, that can identify these inconsistencies, and that can be trained solely using real, unlabeled data. We train an autoregressive model to generate sequences of audio-visual features, using feature sets that capture the temporal synchronization between video frames and sound. At test time, we then flag videos that the model assigns low probability. Despite being trained entirely on real videos, our model obtains strong performance on the task of detecting manipulated speech videos. Project site: https://cfeng16.github.io/audio-visual-forensicsComment: CVPR 202