Publications

Stellar members of binary systems are formed from the same material, therefore they should be chemically identical. However, recent high-precision studies have unveiled chemical differences between the two members of binary pairs composed by Sun-like stars. The very existence of these chemically inhomogeneous binaries represents one of the most contradictory examples in stellar astrophysics and source of tension between theory and observations. It is still unclear whether the abundance variations are the result of chemical inhomogeneities in the protostellar gas clouds or instead if they are due to planet engulfment events occurred after the stellar formation. While the former scenario would undermine the belief that the chemical makeup of a star provides the fossil information of the environment where it formed, a key assumption made by several studies of our Galaxy, the second scenario would shed light on the possible evolutionary paths of planetary systems. Here, we perform a statistical study on 107 binary systems composed by Sun-like stars to provide - for the first time - unambiguous evidence in favour of the planet engulfment scenario. We also establish that planet engulfment events occur in stars similar to our own Sun with a probability ranging between 20 and 35%. This implies that a significant fraction of planetary systems undergo very dynamical evolutionary paths that can critically modify their architectures, unlike our Solar System which has preserved its planets on nearly circular orbits. This study also opens to the possibility of using chemical abundances of stars to identify which ones are the most likely to host analogues of the calm Solar System.

The Portable Extensible Toolkit for Scientific computation (PETSc) library delivers scalable solvers for nonlinear time-dependent differential and algebraic equations and for numerical optimization. The PETSc design for performance portability addresses fundamental GPU accelerator challenges and stresses flexibility and extensibility by separating the programming model used by the application from that used by the library, and it enables application developers to use their preferred programming model, such as Kokkos, RAJA, SYCL, HIP, CUDA, or OpenCL, on upcoming exascale systems. A blueprint for using GPUs from PETSc-based codes is provided, and case studies emphasize the flexibility and high performance achieved on current GPU-based systems.

The mRNA-1273 vaccine is effective against SARS-CoV-2 and was granted emergency use authorization by the FDA. Clinical studies, however, cannot provide the controlled response to infection and complex immunological insight that are only possible with preclinical studies. Hamsters are the only model that reliably exhibits severe SARS-CoV-2 disease similar to that in hospitalized patients, making them pertinent for vaccine evaluation. We demonstrate that prime or prime-boost administration of mRNA-1273 in hamsters elicited robust neutralizing antibodies, ameliorated weight loss, suppressed SARS-CoV-2 replication in the airways, and better protected against disease at the highest prime-boost dose. Unlike in mice and nonhuman primates, low-level virus replication in mRNA-1273–vaccinated hamsters coincided with an anamnestic response. Single-cell RNA sequencing of lung tissue permitted high-resolution analysis that is not possible in vaccinated humans. mRNA-1273 prevented inflammatory cell infiltration and the reduction of lymphocyte proportions, but enabled antiviral responses conducive to lung homeostasis. Surprisingly, infection triggered transcriptome programs in some types of immune cells from vaccinated hamsters that were shared, albeit attenuated, with mock-vaccinated hamsters. Our results support the use of mRNA-1273 in a 2-dose schedule and provide insight into the potential responses within the lungs of vaccinated humans who are exposed to SARS-CoV-2.

Braiding of topological structures in complex matter fields provides a robust framework for encoding and processing information, and it has been extensively studied in the context of topological quantum computation. In living systems, topological defects are crucial for the localization and organization of biochemical signaling waves, but their braiding dynamics remain unexplored. Here, we show that the spiral wave cores, which organize the Rho-GTP protein signaling dynamics and force generation on the membrane of starfish egg cells, undergo spontaneous braiding dynamics. Experimentally measured world line braiding exponents and topological entropy correlate with cellular activity and agree with predictions from a generic field theory. Our analysis further reveals the creation and annihilation of virtual quasi-particle excitations during defect scattering events, suggesting phenomenological parallels between quantum and living matter.

We present `latentcor`, an R package for correlation estimation from data with mixed variable types. Mixed variables types, including continuous, binary, ordinal, zero-inflated, or truncated data are routinely collected in many areas of science. Accurate estimation of correlations among such variables is often the first critical step in statistical analysis workflows. Pearson correlation as the default choice is not well suited for mixed data types as the underlying normality assumption is violated. The concept of semi-parametric latent Gaussian copula models, on the other hand, provides a unifying way to estimate correlations between mixed data types. The R package `latentcor` comprises a comprehensive list of these models, enabling the estimation of correlations between any of continuous/binary/ternary/zero-inflated (truncated) variable types. The underlying implementation takes advantage of a fast multi-linear interpolation scheme with an efficient choice of interpolation grid points, thus giving the package a small memory footprint without compromising estimation accuracy. This makes latent correlation estimation readily available for modern high-throughput data analysis.

The famous Arrhenius equation is well suited to describing the temperature dependence of chemical reactions but has also been used for complicated biological processes. Here, we evaluate how well the simple Arrhenius equation predicts complex multi-step biological processes, using frog and fruit fly embryogenesis as two canonical models. We find that the Arrhenius equation provides a good approximation for the temperature dependence of embryogenesis, even though individual developmental intervals scale differently with temperature. At low and high temperatures, however, we observed significant departures from idealized Arrhenius Law behavior. When we model multi-step reactions of idealized chemical networks, we are unable to generate comparable deviations from linearity. In contrast, we find the two enzymes GAPDH and β-galactosidase show non-linearity in the Arrhenius plot similar to our observations of embryonic development. Thus, we find that complex embryonic development can be well approximated by the simple Arrhenius equation regardless of non-uniform developmental scaling and propose that the observed departure from this law likely results more from non-idealized individual steps rather than from the complexity of the system.

Modern studies of embryogenesis are increasingly quantitative, powered by rapid advances in imaging, sequencing and genome manipulation technologies. Deriving mechanistic insights from the complex datasets generated by these new tools requires systematic approaches for data-driven analysis of the underlying developmental processes. Here, we use data from our work on signal-dependent gene repression in the Drosophila embryo to illustrate how computational models can compactly summarize quantitative results of live imaging, chromatin immunoprecipitation and optogenetic perturbation experiments. The presented computational approach is ideally suited for integrating rapidly accumulating quantitative data and for guiding future studies of embryogenesis.

Our situation as occupants of the Milky Way (MW) Galaxy, bombarded by the Sagittarius dwarf galaxy, provides an intimate view of physical processes that can lead to the dynamical heating of a galactic disc. While this evolution is instigated by Sagittarius, it is also driven by the intertwined influences of the dark matter halo and the disc itself. We analyse an N-body simulation following a Sagittarius-like galaxy interacting with a MW-like host to disentangle these different influences during the stages of a minor merger. The accelerations in the disc plane from each component are calculated for each snapshot in the simulation, and then decomposed into Fourier series on annuli. The analysis maps quantify and compare the scales of the individual contributions over space and through time: (i) accelerations due to the satellite are only important around disc passages; (ii) the influence around these passages is enhanced and extended by the distortion of the dark matter halo; (iii) the interaction drives disc asymmetries within and perpendicular to the plane and the self-gravity of these distortions increase in importance with time eventually leading to the formation of a bar. These results have interesting implications for identifying different influences within our own Galaxy. Currently, Sagittarius is close enough to a plane crossing to search for localized signatures of its effect at intermediate radii, the distortion of the MW's dark matter halo should leave its imprint in the outer disc and the disc's own self-consistent response is sculpting the intermediate and inner disc.

Cortical pyramidal neurons receive inputs from multiple distinct neural populations and integrate these inputs in separate dendritic compartments. We explore the possibility that cortical microcircuits implement canonical correlation analysis (CCA), an unsupervised learning method that projects the inputs onto a common subspace so as to maximize the correlations between the projections. To this end, we seek a multichannel CCA algorithm that can be implemented in a biologically plausible neural network. For biological plausibility, we require that the network operates in the online setting and its synaptic update rules are local. Starting from a novel CCA objective function, we derive an online optimization algorithm whose optimization steps can be implemented in a single-layer neural network with multicompartmental neurons and local non-Hebbian learning rules. We also derive an extension of our online CCA algorithm with adaptive output rank and output whitening. Interestingly, the extension maps onto a neural network whose neural architecture and synaptic updates resemble neural circuitry and non-Hebbian plasticity observed in the cortex.

The simplest scheme for predicting real galaxy properties after performing a dark matter simulation is to rank order the real systems by stellar mass and the simulated systems by halo mass and then simply assume monotonicity - that the more massive halos host the more massive galaxies. This has had some success, but we study here if a better motivated and more accurate matching scheme is easily constructed by looking carefully at how well one could predict the simulated IllustrisTNG galaxy sample from its dark matter computations. We find that using the dark matter rotation curve peak velocity, vmax, for normal galaxies reduces the error of the prediction by 30% (18% for central galaxies and 60% for satellite systems) - following expectations from the physics of monolithic collapse. For massive systems with halo mass > 1012.5 M⊙ hierarchical merger driven formation is the better model and dark matter halo mass remains the best single metric. Using a new single variable that combines these effects, ϕ = vmax/vmax,12.7 + Mpeak/(1012.7 M⊙) allows further improvement and reduces the error, as compared to ranking by dark matter mass at z=0 by another 6% from vmax ranking. Two parameter fits -- including environmental effects produce only minimal further impact.