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MIDIS: Unveiling the star formation history in massive galaxies at 1 < z < 4.5 with spectro-photometric analysis
No full textContext. This paper investigates the star formation histories (SFHs) of a sample of massive galaxies (M⋆ ≥ 1010 M⊙) in the redshift range 1 < z < 4.5.
Methods. We analyzed spectro-photometric data, combining broadband photometry from HST and JWST with low-resolution grism spectroscopy from JWST/NIRISS, obtained as part of the MIRI Deep Imaging Survey program. SFHs were derived through spectral energy distribution fitting using two independent codes, BAGPIPES and synthesizer, under various SFH assumptions. This approach enables a comprehensive assessment of the biases introduced by different modeling choices.
Results. The inclusion of NIRISS spectroscopy, even with its low resolution, significantly improves constraints on key physical parameters, such as the mass-weighted stellar age (tM) and formation redshift (zform), by narrowing their posterior distributions. The massive galaxies in our sample exhibit rapid stellar mass assembly, forming 50% of their mass between 3 ≤ z ≤ 9. The highest inferred formation redshifts are compatible with elevated star formation efficiencies (ϵ) at early epochs. Nonparametric SFHs generally imply an earlier and slower mass assembly compared to parametric forms, highlighting the sensitivity of inferred formation timescales to the chosen SFH model–particularly for galaxies at z < 2. We find that quiescent galaxies are, on average, older (tM ∼ 1.1 Gyr) and assembled more rapidly at earlier times than their star-forming counterparts. These findings support the “downsizing” scenario, in which more massive and passive systems form earlier and more efficiently
Generating synthetic task-based brain fingerprints for population neuroscience using deep learning
No full textTask-based functional magnetic resonance imaging (fMRI) reveals individual differences in neural correlates of cognition but faces scalability challenges due to cognitive demands, protocol variability, and limited task coverage in large datasets. Here, we propose DeepTaskGen, a deep-learning approach that synthesizes non-acquired task-based contrast maps from resting-state (rs-) fMRI. We validate this approach using the Human Connectome Project lifespan data, then generate 47 contrast maps from 7 different cognitive tasks for over 20,000 individuals from UK Biobank. DeepTaskGen outperforms several benchmarks in generating synthetic task-contrast maps, achieving superior reconstruction performance while retaining inter-individual variation essential for biomarker development. We further show comparable or superior predictive performance of synthetic maps relative to actual maps and rs-connectomes across diverse demographic, cognitive, and clinical variables. This approach facilitates the study of individual differences and the generation of task-related biomarkers by enabling the generation of arbitrary functional cognitive tasks from readily available rs-fMRI data
Competition of light-and phonon-dressing in microwave-dressed Bose polarons
No full textWe theoretically investigate the stationary properties of a spin-1/2 impurity immersed in a one-dimensional confined Bose gas. In particular, we consider coherently coupled spin states with an external field, where only one spin component interacts with the bath, enabling light dressing of the impurity and spin-dependent bath-impurity interactions. Through detailed comparisons with ab-initio many-body simulations, we demonstrate that the composite system is accurately described by a simplified effective Hamiltonian. The latter builds upon previously developed effective potential approaches in the absence of light dressing. It can be used to extract the impurity energy, residue, effective mass, and anharmonicity induced by the phononic dressing. Light-dressing is shown to increase the polaron residue, undressing the impurity from phononic excitations because of strong spin coupling. For strong repulsions, previously shown to trigger dynamical Bose polaron decay (a phenomenon called temporal orthogonality catastrophe), it is explained that strong light-dressing stabilizes a repulsive polaron-dressed state. Our results establish the effective Hamiltonian framework as a powerful tool for exploring strongly interacting polaronic systems and corroborating forthcoming experimental realizations
Antagonistic SnRK2 and PID kinases' action on auxin transport-mediated root gravitropism
No full textPlants have evolved sophisticated mechanisms to adapt to environmental changes, with root gravitropism playing a pivotal role in nutrient and water acquisition. Our study reveals that SnRK2 kinases (SnRK2.2 and SnRK2.3) are critical regulators of root gravitropism through their direct phosphorylation of the auxin transporter PIN2 at S259. We demonstrate that SnRK2s-mediated phosphorylation modulates both the polar localization and transport activity of PIN2. Importantly, SnRK2s function antagonistically to the AGCVIII kinase PID, which phosphorylates PIN2 at a distinct site (S258), establishing a regulatory balance essential for adaptive root growth. Structural modeling and phosphorylation assays further suggest that SnRK2s-mediated phosphorylation at S259 sterically hinders access of PID to S258, providing a mechanistic basis for their antagonistic relationship. These findings uncover a novel regulatory mechanism, by which plants fine-tune root developmental programs to adapt to environmental stimuli, highlighting the evolutionary significance of multilayered kinase-mediated regulation in plant adaptation
Electrostatics overcome acoustic collapse to assemble, adapt, and activate levitated matter
No full textAcoustic levitation provides a unique method for manipulating small particles as it completely evades effects from gravity, container walls, or physical handling. These advantages make it a tantalizing platform for studying complex phenomena in many-particle systems. In most standing-wave traps, however, particles interact via acoustic scattering forces that cause them to merge into a single dense object. Here, we introduce a complementary approach that combines acoustic levitation with electrostatic charging to assemble, adapt, and activate complex, separated many-particle systems. The key idea is to superimpose electrostatic repulsion on the intrinsic acoustic attraction, rendering a so-called “mermaid” potential where interactions are attractive at short range and repulsive at long range. By controlling the attraction–repulsion balance, we can levitate expanded structures where all particles are separated, collapsed structures where they are in contact, and hybrid ones consisting of both expanded and collapsed components. We find that collapsed and expanded structures are inherently stable, whereas hybrid ones exhibit transient stability governed by acoustically unstable dimers. Furthermore, we show how electrostatics allow us to adapt between configurations on the fly, either by quasistatic discharge or discrete up/down charge steps. Finally, we demonstrate how large structures experience selective energy pumping from the acoustic field—thrusting some particles into motion while others remain stationary—leading to complex dynamics including coupled rotations and oscillations. Our approach establishes a design space beyond acoustic collapse, offering possibilities to study many-particle systems with complex interactions, while suggesting pathways toward scalable integration into materials processing and other applications
Promoters of cooperation in evolutionary games
No full textEvolutionary games provide a flexible mathematical framework for many problems in biology and social evolution. Prisoners’ dilemma, and in particular, the important special case of donation games, represents social dilemmas where cooperation is mutually beneficial, yet defection is preferred by selfish agents. In evolutionary games on networks, the agents interact over a population structure. The existence of population structures that promote cooperative behavior is a fascinating and active research topic. Previous research establishes structures promoting cooperation in the limit of weak selection where the benefit-to-cost ratio β exceeds 1.5. The existence of such structures for medium and strong selection for 1 1, we construct networks promoting cooperation. Second, we present a robustness result with respect to β and selection strength: Our structures promote cooperation for a range of these parameter values rather than specific parameter values. Finally, we supplement our theoretical results with simulation results on small population structures that show the effectiveness of our construction over well-studied population structures
The neural basis of species-specific defensive behaviour in Peromyscus mice
No full textEvading imminent predator threat is critical for survival. Effective defensive strategies can vary, even between closely related species. However, the neural basis of such species-specific behaviours is still poorly understood. Here we find that two sister species of deer mice (genus Peromyscus) show different responses to the same looming stimulus: P. maniculatus, which occupies densely vegetated habitats, predominantly escapes, while the open field specialist, P. polionotus, briefly freezes. This difference arises from species-specific escape thresholds, is largely context-independent, and can be triggered by both visual and auditory threat stimuli. Using immunohistochemistry and electrophysiological recordings, we find that although visual threat activates the superior colliculus in both species, the role of the dorsal periaqueductal gray (dPAG) in driving behaviour differs. While dPAG activity scales with running speed in P. maniculatus, neural activity in the dPAG of P. polionotus correlates poorly with movement, including during visually triggered escape. Moreover, optogenetic activation of dPAG neurons elicits acceleration in P. maniculatus but not P. polionotus, while their chemogenetic inhibition during a looming stimulus delays escape onset in P. maniculatus to match that of P. polionotus. Together, we trace species-specific escape thresholds to a central circuit node, downstream of peripheral sensory neurons, localizing an ecologically relevant behavioural difference to a specific region of the mammalian brain
Spatiotemporal patterns differentiate hippocampal sharp-wave ripples from interictal epileptiform discharges in mice and humans
No full textHippocampal sharp-wave ripples (SPW-Rs) are high-frequency oscillations critical for memory consolidation. Despite extensive characterization in rodents, their detection in humans is limited by coarse spatial sampling, interictal epileptiform discharges (IEDs), and a lack of consensus on human ripple localization and morphology. Here, we demonstrate that mouse and human hippocampal ripples share spatial, spectral and temporal features, which are clearly distinct from IEDs. In recordings from male APP/PS1 mice, SPW-Rs were distinguishable from IEDs by multiple criteria. Hippocampal ripples recorded during NREM sleep in female and male surgical epilepsy patients exhibited similar narrowband frequency peaks and multiple ripple cycles in the CA1 and subiculum regions. Conversely, IEDs showed a broad spatial extent and wide-band frequency power. We developed a semi-automated, ripple curation toolbox (ripmap) to separate event waveforms by low-dimensional embedding to reduce false-positive rate in selected ripple channels. Our approach improves ripple detection and provides a firm foundation for future human memory research
Fully dynamic approximate minimum cut in subpolynomial time per operation
No full textDynamically maintaining the minimum cut in a graph G under edge insertions and deletion is a fundamental problem in dynamic graph algorithms for which no conditional lower bound on the time per operation exists. In an n-node graph the best known (1 + o (1))-approximate algorithm takes update time [14]. If the minimum cut is guaranteed to be (log n )o (1), a deterministic exact algorithm with n o (1) update time exists [8].
We present the first fully dynamic algorithm for (1 + o (1))-approximate minimum cut with n o(1) update time. Our main technical contribution is to show that it suffices to consider small-volume cuts in suitably contracted graphs
LIPIcs
No full textA quantitative word automaton (QWA) defines a function from infinite words to values. For example, every infinite run of a limit-average QWA obtains a mean payoff, and every word w ∈ Σ^ω is assigned the maximal mean payoff obtained by nondeterministic runs of over w. We introduce quantitative language automata (QLAs) that define functions from language generators (i.e., implementations) to values, where a language generator can be nonprobabilistic, defining a set of infinite words, or probabilistic, defining a probability measure over infinite words. A QLA consists of a QWA and an aggregator function. For example, given a QWA , the infimum aggregator maps each language L ⊆ Σ^ω to the greatest lower bound assigned by to any word in L. For boolean value sets, QWAs define boolean properties of traces, and QLAs define boolean properties of sets of traces, i.e., hyperproperties. For more general value sets, QLAs serve as a specification language for a generalization of hyperproperties, called quantitative hyperproperties. A nonprobabilistic (resp. probabilistic) quantitative hyperproperty assigns a value to each set (resp. distribution) G of traces, e.g., the minimal (resp. expected) average response time exhibited by the traces in G. We give several examples of quantitative hyperproperties and investigate three paradigmatic problems for QLAs: evaluation, nonemptiness, and universality. In the evaluation problem, given a QLA and an implementation G, we ask for the value that assigns to G. In the nonemptiness (resp. universality) problem, given a QLA and a value k, we ask whether assigns at least k to some (resp. every) language. We provide a comprehensive picture of decidability for these problems for QLAs with common aggregators as well as their restrictions to ω-regular languages and trace distributions generated by finite-state Markov chains