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Spiral arms are metal freeways: azimuthal gas-phase metallicity variations in flocculent discs in the FIRE-2 cosmological zoom-in simulations
No full textWe examine the azimuthal variations in gas-phase metallicity profiles in simulated Milky Way-mass disc galaxies from the Feedback in Realistic Environments (FIRE-2) cosmological zoom-in simulation suite, which includes a sub-grid turbulent metal mixing model. We produce spatially resolved maps of the discs at z ≈ 0 with pixel sizes ranging from 250 to 750 pc, analogous to modern integral field unit galaxy surveys, mapping the gas-phase metallicities in both the cold and dense gas and the ionized gas correlated with H ɪɪ regions. We report that the spiral arms alternate in a pattern of metal rich and metal poor relative to the median metallicity of the order of ≲0.1 dex, appearing generally in this sample of flocculent spirals. The pattern persists even in a simulation with different strengths of metal mixing, indicating that the pattern emerges from physics above the sub-grid scale. Local enrichment does not appear to be the dominant source of the azimuthal metallicity variations at z ≈ 0: there is no correlation with local star formation on these spatial scales. Rather, the arms are moving radially inwards and outwards relative to each other, carrying their local metallicity gradients with them radially before mixing into the larger-scale interstellar medium. We propose that the arms act as freeways channeling relatively metal poor gas radially inwards, and relatively enriched gas radially outwards
Machine learning model to predict obesity using gut metabolite and brain microstructure data
No full textA growing body of preclinical and clinical literature suggests that brain-gut-microbiota interactions may contribute to obesity pathogenesis. In this study, we use a machine learning approach to leverage the enormous amount of microstructural neuroimaging and fecal metabolomic data to better understand key drivers of the obese compared to overweight phenotype. Our findings reveal that although gut-derived factors play a role in this distinction, it is primarily brain-directed changes that differentiate obese from overweight individuals. Of the key gut metabolites that emerged from our model, many are likely at least in part derived or influenced by the gut-microbiota, including some amino-acid derivatives. Remarkably, key regions outside of the central nervous system extended reward network emerged as important differentiators, suggesting a role for previously unexplored neural pathways in the pathogenesis of obesity
Transport and noise of hot electrons in GaAs using a semianalytical model of two-phonon polar optical phonon scattering
No full textRecent ab initio studies of electron transport in GaAs have reported that electron-phonon (e-ph) interactions beyond the lowest order play a fundamental role in charge transport and noise phenomena. Inclusion of the next-leading-order process in which an electron scatters with two phonons was found to yield good agreement for the high-field drift velocity, but the characteristic nonmonotonic trend of the power spectral density of current fluctuations (PSD) with electric field was not predicted. The high computational cost of the ab initio approach necessitated various approximations to the two-phonon scattering term, which were suggested as possible origins of the discrepancy. Here we report a semianalytical transport model of two-phonon electron scattering via the Fröhlich mechanism, allowing a number of the approximations in the ab initio treatment to be lifted while retaining the accuracy to within a few percent. We compare the calculated and experimental transport and noise properties as well as scattering rates measured by photoluminescence experiments. We find quantitative agreement within 15% for the drift velocity and 25% for the Γ valley scattering rates, and agreement with the Γ−L intervalley scattering rates within a factor of two. Considering these results and prior studies of current noise in GaAs, we conclude that the most probable origin of the nonmonotonic PSD trend versus electric field is the formation of space-charge domains rather than intervalley scattering as has been assumed
The chemical ecology of coumarins and phenazines affects iron acquisition by pseudomonads
No full textSecondary metabolites are important facilitators of plant–microbe interactions in the rhizosphere, contributing to communication, competition, and nutrient acquisition. However, at first glance, the rhizosphere seems full of metabolites with overlapping functions, and we have a limited understanding of basic principles governing metabolite use. Increasing access to the essential nutrient iron is one important, but seemingly redundant role performed by both plant and microbial Redox-Active Metabolites (RAMs). We used coumarins, RAMs made by the model plant Arabidopsis thaliana, and phenazines, RAMs made by soil-dwelling pseudomonads, to ask whether plant and microbial RAMs might each have distinct functions under different environmental conditions. We show that variations in oxygen and pH lead to predictable differences in the capacity of coumarins vs phenazines to increase the growth of iron-limited pseudomonads and that these effects depend on whether pseudomonads are grown on glucose, succinate, or pyruvate: carbon sources commonly found in root exudates. Our results are explained by the chemical reactivities of these metabolites and the redox state of phenazines as altered by microbial metabolism. This work shows that variations in the chemical microenvironment can profoundly affect secondary metabolite function and suggests plants may tune the utility of microbial secondary metabolites by altering the carbon released in root exudates. Together, these findings suggest that RAM diversity may be less overwhelming when viewed through a chemical ecological lens: Distinct molecules can be expected to be more or less important to certain ecosystem functions, such as iron acquisition, depending on the local chemical microenvironments in which they reside
Stem cell-derived synthetic embryos self-assemble by exploiting cadherin codes and cortical tension
No full textMammalian embryos sequentially differentiate into trophectoderm and an inner cell mass, the latter of which differentiates into primitive endoderm and epiblast. Trophoblast stem (TS), extraembryonic endoderm (XEN) and embryonic stem (ES) cells derived from these three lineages can self-assemble into synthetic embryos, but the mechanisms remain unknown. Here, we show that a stem cell-specific cadherin code drives synthetic embryogenesis. The XEN cell cadherin code enables XEN cell sorting into a layer below ES cells, recapitulating the sorting of epiblast and primitive endoderm before implantation. The TS cell cadherin code enables TS cell sorting above ES cells, resembling extraembryonic ectoderm clustering above epiblast following implantation. Whereas differential cadherin expression drives initial cell sorting, cortical tension consolidates tissue organization. By optimizing cadherin code expression in different stem cell lines, we tripled the frequency of correctly formed synthetic embryos. Thus, by exploiting cadherin codes from different stages of development, lineage-specific stem cells bypass the preimplantation structure to directly assemble a postimplantation embryo
ExoClock Project. III. 450 New Exoplanet Ephemerides from Ground and Space Observations
No full textThe ExoClock project has been created to increase the efficiency of the Ariel mission. It will achieve this by continuously monitoring and updating the ephemerides of Ariel candidates, in order to produce a consistent catalog of reliable and precise ephemerides. This work presents a homogenous catalog of updated ephemerides for 450 planets, generated by the integration of ∼18,000 data points from multiple sources. These sources include observations from ground-based telescopes (the ExoClock network and the Exoplanet Transit Database), midtime values from the literature, and light curves from space telescopes (Kepler, K2, and TESS). With all the above, we manage to collect observations for half of the postdiscovery years (median), with data that have a median uncertainty less than 1 minute. In comparison with the literature, the ephemerides generated by the project are more precise and less biased. More than 40% of the initial literature ephemerides had to be updated to reach the goals of the project, as they were either of low precision or drifting. Moreover, the integrated approach of the project enables both the monitoring of the majority of the Ariel candidates (95%), and also the identification of missing data. These results highlight the need for continuous monitoring to increase the observing coverage of the candidate planets. Finally, the extended observing coverage of planets allows us to detect trends (transit-timing variations) for a sample of 19 planets. All the products, data, and codes used in this work are open and accessible to the wider scientific community
An infrared transient from a star engulfing a planet
No full textPlanets with short orbital periods (roughly under 10 days) are common around stars like the Sun. Stars expand as they evolve and thus we expect their close planetary companions to be engulfed, possibly powering luminous mass ejections from the host star. However, this phase has never been directly observed. Here we report observations of ZTF SLRN-2020, a short-lived optical outburst in the Galactic disk accompanied by bright and long-lived infrared emission. The resulting light curve and spectra share striking similarities with those of red novae—a class of eruptions now confirmed to arise from mergers of binary stars. Its exceptionally low optical luminosity (approximately 1035 erg s⁻¹) and radiated energy (approximately 6.5 × 10⁴¹ erg) point to the engulfment of a planet of fewer than roughly ten Jupiter masses by its Sun-like host star. We estimate the Galactic rate of such subluminous red novae to be roughly between 0.1 and several per year. Future Galactic plane surveys should routinely identify these, showing the demographics of planetary engulfment and the ultimate fate of planets in the inner Solar System
What can machine learning teach us about habit formation? Evidence from exercise and hygiene
No full textWe apply a machine learning technique to characterize habit formation in two large panel data sets with objective measures of 1) gym attendance (over 12 million observations) and 2) hospital handwashing (over 40 million observations). Our Predicting Context Sensitivity (PCS) approach identifies context variables that best predict behavior for each individual. This approach also creates a time series of overall predictability for each individual. These time series predictability values are used to trace a habit formation curve for each individual, operationalizing the time of habit formation as the asymptotic limit of when behavior becomes highly predictable. Contrary to the popular belief in a “magic number” of days to develop a habit, we find that it typically takes months to form the habit of going to the gym but weeks to develop the habit of handwashing in the hospital. Furthermore, we find that gymgoers who are more predictable are less responsive to an intervention designed to promote more gym attendance, consistent with past experiments showing that habit formation generates insensitivity to reward devaluation
A note on microlocal kernel design for some slow–fast stochastic differential equations with critical transitions and application to EEG signals
No full textThis technical note presents an extension of kernel model decomposition (KMD) for detecting critical transitions in some fast–slow random dynamical systems. The approach rests upon modifying KMD for reconstructing an observable by using a novel data-based time-frequency-phase kernel that allows to approximate signals with critical transitions. In particular, we apply the developed method for approximating the solution and detecting critical transitions in some prototypical slow–fast SDEs with critical transitions. We also apply it to detecting seizures in a multi-scale mesoscale nine-dimensional SDE model of brain activity
Machine learning building-block-flow wall model for large-eddy simulation
No full textA wall model for large-eddy simulation (LES) is proposed by devising the flow as a combination of building blocks. The core assumption of the model is that a finite set of simple canonical flows contains the essential physics to predict the wall shear stress in more complex scenarios. The model is constructed to predict zero/favourable/adverse mean pressure gradient wall turbulence, separation, statistically unsteady turbulence with mean flow three-dimensionality, and laminar flow. The approach is implemented using two types of artificial neural networks: a classifier, which identifies the contribution of each building block in the flow, and a predictor, which estimates the wall shear stress via a combination of the building-block flows. The training data are obtained directly from wall-modelled LES (WMLES) optimised to reproduce the correct mean quantities. This approach guarantees the consistency of the training data with the numerical discretisation and the gridding strategy of the flow solver. The output of the model is accompanied by a confidence score in the prediction that aids the detection of regions where the model underperforms. The model is validated in canonical flows (e.g. laminar/turbulent boundary layers, turbulent channels, turbulent Poiseuille–Couette flow, turbulent pipe) and two realistic aircraft configurations: the NASA Common Research Model High-lift and NASA Juncture Flow experiment. It is shown that the building-block-flow wall model outperforms (or matches) the predictions by an equilibrium wall model. It is also concluded that further improvements in WMLES should incorporate advances in subgrid-scale modelling to minimise error propagation to the wall model