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Unlocking plant regeneration: The role for glutathione
No full textIn this issue of Developmental Cell, Lee et al. identify a pivotal role for glutathione (GSH) in plant regeneration, a vital biological process enabling plants to regrow tissues and organs after injury. Applying single-cell RNA sequencing (scRNA-seq) and live imaging, the authors demonstrate that GSH, released upon tissue damage, accelerates cell-cycle transitions, particularly shortening the G1 phase, thereby facilitating efficient organ regeneration
An open problem: Why are motif-avoidant attractors so rare in asynchronous Boolean networks?
No full textAsynchronous Boolean networks are a type of discrete dynamical system in which each variable can take one of two states, and a single variable state is updated in each time step according to pre-selected rules. Boolean networks are popular in systems biology due to their ability to model long-term biological phenotypes within a qualitative, predictive framework. Boolean networks model phenotypes as attractors, which are closely linked to minimal trap spaces (inescapable hypercubes in the system’s state space). In biological applications, attractors and minimal trap spaces are typically in one-to-one correspondence. However, this correspondence is not guaranteed: motif-avoidant attractors (MAAs) that lie outside minimal trap spaces are possible. MAAs are rare and poorly understood, despite recent efforts. In this contribution to the BMB & JMB Special Collection “Problems, Progress and Perspectives in Mathematical and Computational Biology”, we summarize the current state of knowledge regarding MAAs and present several novel observations regarding their response to node deletion reductions and linear extensions of edges. We conduct large-scale computational studies on an ensemble of 14 000 models derived from published Boolean models of biological systems, and more than 100 million Random Boolean Networks. Our findings quantify the rarity of MAAs; in particular, we only observed MAAs in biological models after applying standard simplification methods, highlighting the role of network reduction in introducing MAAs into the dynamics. We also show that MAAs are fragile to linear extensions: in sparse networks, even a single linear node can disrupt virtually all MAAs. Motivated by this observation, we improve the upper bound on the number of delays needed to disrupt a motif-avoidant attractor
Precoloring extension in planar near-Eulerian-triangulations
No full textWe consider the 4-precoloring extension problem in planar near-Eulerian- triangulations, i.e., plane graphs where all faces except possibly for the outer one have length three, all vertices not incident with the outer face have even degree, and exactly the vertices incident with the outer face are precolored. We give a necessary topological condition for the precoloring to extend, and give a complete characterization when the outer face has length at most five and when all vertices of the outer face have odd degree and are colored using only three colors
zELDA II: Reconstruction of galactic Lyman-alpha spectra attenuated by the intergalactic medium using neural networks
No full textContext. The observed Lyman-alpha (Lyα) line profile is a convolution of the complex Lyα radiative transfer taking place in the interstellar, circumgalactic, and intergalactic media (ISM, CGM, and IGM, respectively). Discerning the different components of the Lyα line is crucial in order to use it as a probe of galaxy formation or the evolution of the IGM.
Aims. We aim to present the second version of zELDA (redshift Estimator for Line profiles of Distant Lyman-Alpha emitters), an open-source Python module focused on modelling and fitting observed Lyα line profiles. This new version of zELDA focuses on disentangling the galactic from the IGM effects.
Methods. We built realistic Lyα line profiles that include the ISM and IGM contributions by combining the Monte Carlo radiative-transfer simulations for the so-called shell model (ISM) and IGM transmission curves generated from TNG100. We used these mock line profiles to train different artificial neural networks. These use the observed spectrum as input and the outflow parameters of the best fitting ‘shell model’ as output along with the redshift and Lyα emission IGM escape fraction of the source.
Results. We measured the accuracy of zELDA on mock Lyα line profiles. We find that zELDA is capable of reconstructing the ISM emerging Lyα line profile with high levels of accuracy (Kolmogórov-Smirnov<0.1) for 95% of the cases for HST/COS-like observations and 80% for MUSE-WIDE-like observations. zELDA is able to measure the IGM transmission with typical uncertainties below 10% for HST/COS and MUSE-WIDE data.
Conclusions. This work represents a step forward in the high-precision reconstruction of IGM-attenuated Lyα line profiles. zELDA allows the disentanglement of the galactic and IGM contribution shaping the Lyα line shape and thus allows us to use Lyα as a tool to study galaxy and ISM evolution
Investigating photometric and spectroscopic variability in the multiply imaged little red dot A2744-QSO1
No full textJWST observations have uncovered a new population of red, compact objects at high redshifts dubbed “little red dots” (LRDs), which typically show broad emission lines and are thought to be dusty active galactic nuclei (AGNs). Some of their other features, however, challenge the AGN explanation, such as prominent Balmer breaks and extremely faint or even missing metal high-ionization lines, X-ray, or radio emission, including in deep stacks. Time variability is another robust test of AGN activity. Here, we exploit the z = 7.045 multiply imaged LRD A2744-QSO1, which offers a particularly unique test of variability due to lensing-induced time delays between the three images spanning 22 yr (2.7 yr in the rest-frame), to investigate its photometric and spectroscopic variability. We find the equivalent widths (EWs) of the broad Hα and Hβ lines, which are independent of magnification and other systematics, to exhibit significant variations, of up to 18 ± 3% for Hα and up to 22 ± 8% in Hβ, on a timescale of 875 d (2.4 yr) in the rest-frame. This suggests that A2744-QSO1 is indeed an AGN. We find no significant photometric variability beyond the limiting systematic uncertainties, so it currently cannot be determined whether the EW variations are due to line-flux or continuum variability. These results are consistent with a typical damped random walk variability model for an AGN such as A2744-QSO1 (MBH = 4 × 107 M⊙) given the sparse sampling of the light curve with the available data. Our results therefore support the AGN interpretation of this LRD, and highlight the need for further photometric and spectroscopic monitoring in order to build a detailed and reliable light curve
A new varifold solution concept for mean curvature flow: Convergence of the Allen-Cahn equation and weak-strong uniqueness
No full textWe propose a new weak solution concept for (two-phase) mean curvature flow which enjoys both (unconditional) existence and (weak-strong) uniqueness properties. These solutions are evolving varifolds, just as in Brakke's formulation, but are coupled to the phase volumes by a simple transport equation. First, we show that, in the exact same setup as in Ilmanen's proof [J. Differential Geom. 38, 417-461, (1993)], any limit point of solutions to the Allen-Cahn equation is a varifold solution in our sense. Second, we prove that any calibrated flow in the sense of Fischer et al. [arXiv:2003.05478] - and hence any classical solution to mean curvature flow-is unique in the class of our new varifold solutions. This is in sharp contrast to the case of Brakke flows, which a priori may disappear at any given time and are therefore fatally non-unique. Finally, we propose an extension of the solution concept to the multi-phase case which is at least guaranteed to satisfy a weak-strong uniqueness principle
Adeno-associated viral tools to trace neural development and connectivity across amphibians
No full textAmphibians, by virtue of their phylogenetic position, provide invaluable insights on nervous system evolution, development, and remodeling. The genetic toolkit for amphibians, however, remains limited. Recombinant adeno-associated viral vectors (AAVs) are a powerful alternative to transgenesis for labeling and manipulating neurons. Although successful in mammals, AAVs have never been shown to transduce amphibian cells efficiently. We screened AAVs in three amphibian species—the frogs Xenopus laevis and Pelophylax bedriagae and the salamander Pleurodeles waltl—and identified at least two AAV serotypes per species that transduce neurons. In developing amphibians, AAVs labeled groups of neurons generated at the same time during development. In the mature brain, AAVrg retrogradely traced long-range projections. Our study introduces AAVs as a tool for amphibian research, establishes a generalizable workflow for AAV screening in new species, and expands opportunities for cross-species comparisons of nervous system development, function, and evolution
Multiplicative auction algorithm for approximate maximum weight bipartite matching
No full textWe present an auction algorithm using multiplicative instead of constant weight updates to compute a (1-E)-approximate maximum weight matching (MWM) in a bipartite graph with n vertices and m edges in time 0(mE-1), beating the running time of the fastest known approximation algorithm of Duan and Pettie [JACM ’14] that runs in 0(mE-1 log E-1). Our algorithm is very simple and it can be extended to give a dynamic data structure that maintains a (1-E)-approximate maximum weight matching under (1) one-sided vertex deletions (with incident edges) and (2) one-sided vertex insertions (with incident edges sorted by weight) to the other side. The total time time used is 0(mE-1), where m is the sum of the number of initially existing and inserted edges
Accessing semiaddressable self-assembly with efficient structure enumeration
No full textModern experimental methods enable the creation of self-assembly building blocks with tunable interactions, but optimally exploiting this tunability for the self-assembly of desired structures remains an important challenge. Many studies of this inverse problem start with the so-called fully addressable limit, where every particle in a target structure is different. This leads to clear design principles that often result in high assembly yield, but it is not a scalable approach—at some point, one must grapple with “reusing” building blocks, which lowers the degree of addressability and may cause a multitude of off-target structures to form, complicating the design process. Here, we solve a key obstacle preventing robust inverse design in the “semiaddressable regime” by developing a highly efficient algorithm that enumerates all structures that can be formed from a given set of building blocks. By combining this with established partition-function-based yield calculations, we show that it is almost always possible to find economical semiaddressable designs where the entropic gain from reusing building blocks outweighs the presence of off-target structures and even increases the yield of the target. Thus, not only does our enumeration algorithm enable robust and scalable inverse design in the semiaddressable regime, our results demonstrate that it is possible to operate in this regime while maintaining the level of control often associated with full addressability
Sensitivity of self-aggregation and the key role of the free convection distance
No full textRecently, Biagioli and Tompkins (2023, https://doi.org/10.1029/2022ms003231) used a simple stochastic model to derive a dimensionless parameter to predict convective self aggregation (SA) development, which was based on the derivation of the maximum free convective distance () expected in the pre-aggregated, random state. Our goal is to test and further investigate this hypothesis, namely that can predict SA occurrence, using an ensemble of twenty-four distinct combinations of horizontal mixing, planetary boundary layer (PBL), and microphysical parameterizations. We conclude that the key impact of parameterization schemes on SA is through their control of the number of convective cores and their relative spacing, , which itself is impacted by cold-pool (CP) properties and mean updraft core size. SA is more likely when the convective core count is small, while CPs modify convective spacing via suppression in their interiors and triggering by gust-front convergence and collisions. Each parameterization scheme emphasizes a different mechanism. Subgrid-scale horizontal turbulent mixing mainly affects SA through the determination of convective core size and thus spacing. The sensitivity to the microphysics is mainly through rain evaporation and the subsequent impact on CPs, while perturbations to the ice cloud microphysics have a limited effect. Non-local PBL mixing schemes promote SA primarily by increasing convective inhibition through inversion entrainment and altering low cloud amounts, leading to fewer convective cores and larger