Publications

There is a shortage of multiwavelength and spectroscopic follow-up capabilities given the number of transient and variable astrophysical events discovered through wide-field optical surveys such as the upcoming Vera C. Rubin Observatory and its associated Legacy Survey of Space and Time. From the haystack of potential science targets, astronomers must allocate scarce resources to study a selection of needles in real time. Here we present a variational recurrent autoencoder neural network to encode simulated Rubin Observatory extragalactic transient events using 1% of the PLAsTiCC data set to train the autoencoder. Our unsupervised method uniquely works with unlabeled, real-time, multivariate, and aperiodic data. We rank 1,129,184 events based on an anomaly score estimated using an isolation forest. We find that our pipeline successfully ranks rarer classes of transients as more anomalous. Using simple cuts in anomaly score and uncertainty, we identify a pure (≈95% pure) sample of rare transients (i.e., transients other than Type Ia, Type II, and Type Ibc supernovae), including superluminous and pair-instability supernovae. Finally, our algorithm is able to identify these transients as anomalous well before peak, enabling real-time follow-up studies in the era of the Rubin Observatory.

Mixtures of microtubules and molecular motors form active materials with diverse dynamical behaviors that vary based on their constituents' molecular properties. We map the non-equilibrium phase diagram of microtubules and tip-accumulating kinesin-4 molecular motors. We find that kinesin-4 can drive either global contractions or turbulent-like extensile dynamics, depending on the concentrations of both microtubules and a bundling agent. We also observe a range of spatially heterogeneous non-equilibrium phases, including finite-sized radial asters, 1D wormlike chains, extended 2D bilayers, and system-spanning 3D active foams. Finally, we describe intricate kinetic pathways that yield microphase separated structures and arise from the inherent frustration between the orientational order of filamentous microtubules and the positional order of tip-accumulating molecular motors. Our work shows that the form of active stresses and phases in cytoskeletal networks are not solely dictated by the properties of individual motors and filaments, but are also contingent on the constituent's concentrations and spatial arrangement.

Motile cilia are slender, hair-like cellular appendages that spontaneously oscillate under the action of internal molecular motors and are typically found in dense arrays. These active filaments coordinate their beating to generate metachronal waves that drive long-range fluid transport and locomotion. Until now, our understanding of their collective behavior largely comes from the study of minimal models that coarse-grain the relevant biophysics and the hydrodynamics of slender structures. Here we build on a detailed biophysical model to elucidate the emergence of metachronal waves on millimeter scales from nanometer scale motor activity inside individual cilia. Our study of a 1D lattice of cilia in the presence of hydrodynamic and steric interactions reveals how metachronal waves are formed and maintained. We find that in homogeneous beds of cilia these interactions lead to multiple attracting states, all of which are characterized by an integer charge that is conserved. This even allows us to design initial conditions that lead to predictable emergent states. Finally, and very importantly, we show that in nonuniform ciliary tissues, boundaries and inhomogeneities provide a robust route to metachronal waves.

Tubulin is a well-validated target for herbicides, fungicides, anti-parasitic, and anti-tumor drugs. Many of the non-cancer tubulin drugs bind to its colchicine site but no colchicine-site anticancer drug is available. The colchicine site is composed of three interconnected sub-pockets that fit their ligands and modify others’ preference, making the design of molecular hybrids (that bind to more than one sub-pocket) a difficult task. Taking advantage of the more than eighty published X-ray structures of tubulin in complex with ligands bound to the colchicine site, we generated an ensemble of pharmacophore representations that flexibly sample the interactional space between the ligands and target. We searched the ZINC database for scaffolds able to fit several of the subpockets, such as tetrazoles, sulfonamides and diarylmethanes, selected roughly 8000 compounds with favorable predicted properties. A Flexi-pharma virtual screening, based on ensemble pharmacophore, was performed by two different methodologies. Combining the scaffolds that best fit the ensemble pharmacophore-representation, we designed a new family of ligands, resulting in a novel tubulin modulator. We synthesized tetrazole 5 and tested it as a tubulin inhibitor in vitro. In good agreement with the design principles, it demonstrated micromolar activity against in vitro tubulin polymerization and nanomolar anti-proliferative effect against human epithelioid carcinoma HeLa cells through microtubule disruption, as shown by immunofluorescence confocal microscopy. The integrative methodology succedes in the design of new scaffolds for flexible proteins with structural coupling between pockets, thus expanding the way in which computational methods can be used as significant tools in the drug design process.

Interpreting the effects of genetic variants is key to understanding individual susceptibility to disease and designing personalized therapeutic approaches. Modern experimental technologies are enabling the generation of massive compendia of human genome sequence data and associated molecular and phenotypic traits, together with genome-scale expression, epigenomics and other functional genomic data. Integrative computational models can leverage these data to understand variant impact, elucidate the effect of dysregulated genes on biological pathways in specific disease and tissue contexts, and interpret disease risk beyond what is feasible with experiments alone. In this Review, we discuss recent developments in machine learning algorithms for genome interpretation and for integrative molecular-level modelling of cells, tissues and organs relevant to disease. More specifically, we highlight existing methods and key challenges and opportunities in identifying specific disease-causing genetic variants and linking them to molecular pathways and, ultimately, to disease phenotypes.

The vast majority of Milky Way stellar halo stars were likely accreted from a small number (≲3) of relatively large dwarf galaxy accretion events. However, the timing of these events is poorly constrained, relying predominantly on indirect dynamical mixing arguments or imprecise age measurements of stars associated with debris structures. Here, we aim to infer robust stellar ages for stars associated with galactic substructures to more directly constrain the merger history of the Galaxy. By combining kinematic, asteroseismic, and spectroscopic data where available, we infer stellar ages for a sample of 10 red giant stars that were kinematically selected to be associated with the stellar halo, a subset of which are associated with the Gaia-Enceladus-Sausage halo substructure, and compare their ages to 3 red giant stars in the Galactic disk. Despite systematic differences in both absolute and relative ages determined by this work, age rankings of stars in this sample are robust. Passing the same observable inputs to multiple stellar age determination packages, we measure a weighted average age for the Gaia-Enceladus-Sausage stars in our sample of 8 ± 3 (stat.) ± 1 (sys.) Gyr. We also determine hierarchical ages for the populations of Gaia-Enceladus-Sausage, in situ halo and disk stars, finding a Gaia-Enceladus-Sausage population age of 8.0+3.2−2.3 Gyr. Although we cannot distinguish hierarchical population ages of halo or disk structures with our limited data and sample of stars, this framework should allow distinct characterization of Galactic substructures using larger stellar samples and additional data available in the near future.

The vast majority of Milky Way stellar halo stars were likely accreted from a small number (≲3) of relatively large dwarf galaxy accretion events. However, the timing of these events is poorly constrained and predominantly relies on indirect dynamical mixing arguments or imprecise age measurements of stars associated with debris structures. Here, we aim to infer robust stellar ages for stars associated with galactic substructures to more directly constrain the merger history of the Galaxy. By combining kinematic, asteroseismic, and spectroscopic data where available, we infer stellar ages for a sample of 10 red giant stars that were kinematically selected to be within the stellar halo, a subset of which are associated with the Gaia–Enceladus–Sausage halo substructure, and compare their ages to 3 red giant stars in the Galactic disk. Despite systematic differences in both absolute and relative ages determined here, age rankings of stars in this sample are robust. Passing the same observable inputs to multiple stellar age determination packages, we measure a weighted average age for the Gaia–Enceladus–Sausage stars in our sample of 8 ± 3 (stat.) ± 1 (sys.) Gyr. We also determine hierarchical ages using isochrones for the populations of Gaia–Enceladus–Sausage, in situ halo and disk stars, finding a Gaia–Enceladus–Sausage population age of 8.0${}_{-2.3}^{+3.2}$ Gyr. Although we cannot distinguish hierarchical population ages of halo or disk structures with our limited data and sample of stars, this framework should allow a distinct characterization of Galactic substructures using larger stellar samples and additional data available in the near future.

In order to ensure that analyses of complex electronic healthcare record (EHR) data are reproducible and generalizable, it is crucial for researchers to use comparable preprocessing, filtering, and imputation strategies. We introduce COP-E-CAT: Cleaning and Organization Pipeline for EHR Computational and Analytic Tasks, an open-source processing and analysis software for MIMIC-IV, a ubiquitous benchmark EHR dataset. COP-E-CAT allows users to select filtering characteristics and preprocess covariates to generate data structures for use in downstream analysis tasks. This user-friendly approach shows promise in facilitating reproducibility and comparability among studies that leverage the MIMIC-IV data, and enhances EHR accessibility to a wider spectrum of researchers than current data processing methods. We demonstrate the versatility of our workflow by describing three use cases: ensemble prediction, reinforcement learning, and dimension reduction. The software is available at: https://github.com/eyeshoe/cop-e-cat.

A remarkable consequence of the Hohenberg-Kohn theorem of density functional theory is the existence of an injective map between the electronic density and any observable of the many electron problem in an external potential. In this work, we study the problem of predicting a particular observable, the band gap of semiconductors and band insulators, from the knowledge of the local electronic density. Using state-of-the-art machine learning techniques, we predict the experimental band gaps from computationally inexpensive density functional theory calculations. We propose a modified Behler-Parrinello (BP) architecture that greatly improves the model capacity while maintaining the symmetry properties of the BP architecture. Using this scheme, we obtain band gaps at a level of accuracy comparable to those obtained with state of the art and computationally intensive hybrid functionals, thus significantly reducing the computational cost of the task.

Twisted bilayer transition metal dichalcogenides have emerged as important model systems for the investigation of correlated electron physics because their interaction strength, carrier concentration, band structure, and inversion symmetry breaking are controllable by device fabrication, twist angle, and most importantly, gate voltage, which can be varied in situ. The low energy physics of some of these materials has been shown to be described by a "moiré Hubbard model" generalized from the usual Hubbard model by the addition of strong, tunable spin orbit coupling and inversion symmetry breaking. In this work, we use a Hartree-Fock approximation to reach a comprehensive understanding of the moiré Hubbard model on the mean field level. We determine the magnetic and metal-insulator phase diagrams, and assess the effects of spin orbit coupling, inversion symmetry breaking, and the tunable van Hove singularity. We also consider the spin and orbital effects of applied magnetic fields. This work provides guidance for experiments and sets the stage for beyond mean-field calculations.