Publications

A novel optimization formulation is provided for holographic phase retrieval with data subjected to Poisson shot noise in the low-photon regime. This formulation, based on regularized maximum likelihood estimation, consistently provides improved image reconstruction.

AU Microscopii (AU Mic) is the second closest pre main sequence star, at a distance of 9.79 parsecs and with an age of 22 million years. AU Mic possesses a relatively rare and spatially resolved3 edge-on debris disk extending from about 35 to 210 astronomical units from the star, and with clumps exhibiting non-Keplerian motion. Detection of newly formed planets around such a star is challenged by the presence of spots, plage, flares and other manifestations of magnetic activity on the star. Here we report observations of a planet transiting AU Mic. The transiting planet, AU Mic b, has an orbital period of 8.46 days, an orbital distance of 0.07 astronomical units, a radius of 0.4 Jupiter radii, and a mass of less than 0.18 Jupiter masses at 3 sigma confidence. Our observations of a planet co-existing with a debris disk offer the opportunity to test the predictions of current models of planet formation and evolution.

Microbial diversity in the cystic fibrosis (CF) lung decreases over decades as pathogenic bacteria such as Pseudomonas aeruginosa take over. The dynamics of the CF microbiome and metabolome over shorter time frames, however, remain poorly studied. Here, we analyze paired microbiome and metabolome data from 594 sputum samples collected over 401 days from six adult CF subjects (subject mean = 179 days) through periods of clinical stability and 11 CF pulmonary exacerbations (CFPE). While microbiome profiles were personalized (permutational multivariate analysis of variance [PERMANOVA] r2 = 0.79, P < 0.001), we observed significant intraindividual temporal variation that was highest during clinical stability (linear mixed-effects [LME] model, P = 0.002). This included periods where the microbiomes of different subjects became highly similar (UniFrac distance, <0.05). There was a linear increase in the microbiome alpha-diversity and in the log ratio of anaerobes to pathogens with time (n = 14 days) during the development of a CFPE (LME P = 0.0045 and P = 0.029, respectively). Collectively, comparing samples across disease states showed there was a reduction of these two measures during antibiotic treatment (LME P = 0.0096 and P = 0.014, respectively), but the stability data and CFPE data were not significantly different from each other. Metabolome alpha-diversity was higher during CFPE than during stability (LME P = 0.0085), but no consistent metabolite signatures of CFPE across subjects were identified. Virulence-associated metabolites from P. aeruginosa were temporally dynamic but were not associated with any disease state. One subject died during the collection period, enabling a detailed look at changes in the 194 days prior to death. This subject had over 90% Pseudomonas in the microbiome at the beginning of sampling, and that level gradually increased to over 99% prior to death. This study revealed that the CF microbiome and metabolome of some subjects are dynamic through time. Future work is needed to understand what drives these temporal dynamics and if reduction of anaerobes correlate to clinical response to CFPE therapy.

How bulk cytoplasm generates forces to separate post-anaphase microtubule (MT) asters in Xenopus laevis and other large eggs remains unclear. Previous models proposed dynein-based organelle transport generates length-dependent forces on astral MTs that pull centrosomes through the cytoplasm, away from the midplane. In Xenopus egg extracts, we co-imaged MTs, endoplasmic reticulum (ER), mitochondria, acidic organelles, F-actin, keratin, and fluorescein in moving and stationary asters. In asters that were moving in response to dynein and actomyosin forces, we observed that all cytoplasmic components moved together, i.e., as a continuum. Dynein-mediated organelle transport was restricted by interior MTs and F-actin. Organelles exhibited a burst of dynein-dependent inward movement at the growing aster surface, then mostly halted inside the aster. Dynein-coated beads were slowed by F-actin, but in contrast to organelles, beads did not halt inside asters. These observations call for new models of aster positioning based on surface forces and internal stresses.

The measurement precision of modern quantum simulators is intrinsically constrained by the limited set of measurements that can be efficiently implemented on hardware. This fundamental limitation is particularly severe for quantum algorithms where complex quantum observables are to be precisely evaluated. To achieve precise estimates with current methods, prohibitively large amounts of sample statistics are required in experiments. Here, we propose to reduce the measurement overhead by integrating artificial neural networks with quantum simulation platforms. We show that unsupervised learning of single-qubit data allows the trained networks to accommodate measurements of complex observables, otherwise costly using traditional post-processing techniques. The effectiveness of this hybrid measurement protocol is demonstrated for quantum chemistry Hamiltonians using both synthetic and experimental data. Neural-network estimators attain high-precision measurements with a drastic reduction in the amount of sample statistics, without requiring additional quantum resources.

The demonstration of superconductivity in nickelate analogues of high Tc cuprates provides new perspectives on the physics of correlated electron materials. The degree to which the nickelate electronic structure is similar to that of cuprates is an important open question. This paper presents results of a comparative study of the many-body electronic structure and theoretical phase diagram of the isostructural materials CaCuO2 and NdNiO2. Important differences include the proximity of the oxygen 2p bands to the Fermi level, the bandwidth of the transition metal-derived 3d bands, and the presence, in NdNiO2, of both Nd-derived 5d states crossing the Fermi level and a van Hove singularity that crosses the Fermi level as the out of plane momentum is varied. The low energy physics of NdNiO2 is found to be that of a single Ni-derived correlated band, with additional accompanying weakly correlated bands of Nd-derived states that dope the Ni-derived band. The effective correlation strength of the Ni-derived d-band crossing the Fermi level in NdNiO2 is found to be greater than that of the Cu-derived d-band in CaCuO2, but the predicted magnetic transition temperature of NdNiO2 is substantially lower than that of CaCuO2 because of the smaller bandwidth.

A mechanistic understanding of the SARS-CoV-2 viral replication cycle is essential to develop new therapies for the COVID-19 global health crisis. In this study, we show that the SARS-CoV-2 nucleocapsid protein (N-protein) undergoes liquid-liquid phase separation (LLPS) with the viral genome, and propose a model of viral packaging through LLPS. N-protein condenses with specific RNA sequences in the first 1000 nts (5’-End) under physiological conditions and is enhanced at human upper airway temperatures. N-protein condensates exclude non-packaged RNA sequences. We comprehensively map sites bound by N-protein in the 5’-End and find preferences for single-stranded RNA flanked by stable structured elements. Liquid-like N-protein condensates form in mammalian cells in a concentration-dependent manner and can be altered by small molecules. Condensation of N-protein is sequence and structure specific, sensitive to human body temperature, and manipulatable with small molecules thus presenting screenable processes for identifying antiviral compounds effective against SARS-CoV-2.

Evolved stars near the tip of the red giant branch (TRGB) show solar-like oscillations with periods spanning hours to months and amplitudes ranging from ∼1 mmag to ∼100 mmag. The systematic detection of the resulting photometric variations with ground-based telescopes would enable the application of asteroseismology to a much larger and more distant sample of stars than is currently accessible with space-based telescopes such as \textit{Kepler} or the ongoing Transiting Exoplanet Survey Satellite (\textit{TESS}) mission. We present an asteroseismic analysis of 493 M giants using data from two ground-based surveys: the Asteroid Terrestrial-impact Last Alert System (ATLAS) and the All-Sky Automated Survey for Supernovae (ASAS-SN). By comparing the extracted frequencies with constraints from \textit{Kepler}, the Sloan Digital Sky Survey Apache Point Observatory Galaxy Evolution Experiment (APOGEE), and Gaia we demonstrate that ground-based transient surveys allow accurate distance measurements to oscillating M giants with a precision of ∼15%. Using stellar population synthesis models we predict that ATLAS and ASAS-SN can provide asteroseismic distances to ∼2×106 galactic M giants out to typical distances of 20−50kpc, vastly improving the reach of Gaia and providing critical constraints for Galactic archaeology and galactic dynamics.

The large number of available sequences and the diversity of protein functions challenge current experimental and computational approaches to determining and predicting protein function. We present a deep learning Graph Convolutional Network (GCN) for predicting protein functions and concurrently identifying functionally important residues. This model is initially trained using experimentally determined structures from the Protein Data Bank (PDB) but has significant de-noising capability, with only a minor drop in performance observed when structure predictions are used. We take advantage of this denoising property to train the model on > 200,000 protein structures, including many homology-predicted structures, greatly expanding the reach and applications of the method. Our model learns general structure-function relationships by robustly predicting functions of proteins with ≤ 40% sequence identity to the training set. We show that our GCN architecture predicts functions more accurately than Convolutional Neural Networks trained on sequence data alone and previous competing methods. Using class activation mapping, we automatically identify structural regions at the residue-level that lead to each function prediction for every confidently predicted protein, advancing site-specific function prediction. We use our method to annotate PDB and SWISS-MODEL proteins, making several new confident function predictions spanning both fold and function classifications.

We present a neural network architecture that is fully equivariant with respect to transformations under the Lorentz group, a fundamental symmetry of space and time in physics. The architecture is based on the theory of the finite-dimensional representations of the Lorentz group and the equivariant nonlinearity involves the tensor product. For classification tasks in particle physics, we show that such an equivariant architecture leads to drastically simpler models that have relatively few learnable parameters and are much more physically interpretable than leading approaches that use CNNs and point cloud approaches. The performance of the network is tested on a public classification dataset [https://zenodo.org/record/2603256] for tagging top quark decays given energy-momenta of jet constituents produced in proton-proton collisions.