Publications

Binary neutron-star mergers will predominantly produce black-hole remnants of mass ∼3−4M⊙, thus populating the putative \emph{low mass gap} between neutron stars and stellar-mass black holes. If these low-mass black holes are in dense astrophysical environments, mass segregation could lead to "second-generation" compact binaries merging within a Hubble time. In this paper, we investigate possible signatures of such low-mass compact binary mergers in gravitational-wave observations. We show that this unique population of objects, if present, will be uncovered by the third-generation gravitational-wave detectors, such as Cosmic Explorer and Einstein Telescope. Future joint measurements of chirp mass  and effective spin χeff could clarify the formation scenario of compact objects in the low mass gap. As a case study, we show that the recent detection of GW190425 (along with GW170817) favors a double Gaussian mass model for neutron stars, under the assumption that the primary in GW190425 is a black hole formed from a previous binary neutron star merger.

An important component of many image alignment methods is the calculation of inner products (correlations) between an image of $n\times n$ pixels and another image translated by some shift and rotated by some angle. For robust alignment of an image pair, the number of considered shifts and angles is typically high, thus the inner product calculation becomes a bottleneck. Existing methods, based on fast Fourier transforms (FFTs), compute all such inner products with computational complexity $\mathcal{O}(n^3 \log n)$ per image pair, which is reduced to $\mathcal{O}(N n^2)$ if only $N$ distinct shifts are needed. We propose to use a factorization of the translation kernel (FTK), an optimal interpolation method which represents images in a Fourier--Bessel basis and uses a rank-$H$ approximation of the translation kernel via an operator singular value decomposition (SVD). Its complexity is $\mathcal{O}(Hn(n + N))$ per image pair. We prove that $H = \mathcal{O}((W + \log(1/\epsilon))^2)$, where $2W$ is the magnitude of the maximum desired shift in pixels and $\epsilon$ is the desired accuracy. For fixed $W$ this leads to an acceleration when $N$ is large, such as when sub-pixel shift grids are considered. Finally, we present numerical results in an electron cryomicroscopy application showing speedup factors of $3$-$10$ with respect to the state of the art.

To understand the effect of f-functions in predicting the right reaction mechanism for hypervalent iodine reagents, we adopt the Ahlrichs basis set family def2-SVP and def2-TZVP to revisit the potential energy surfaces of IBX-mediated oxidation and Togni I's isomerisation. Our results further prove that f-functions (in either Pople, Dunning, or Ahlrichs basis set series) are indispensable to predict the correct rate-determining step of hypervalent iodine reagents. The f-functions have a significant impact on the predicted reaction barriers for processes involving the I-X (X = O, OH, CF3, etc.) bond cleavage and formation, e.g. in the reductive elimination step or the hypervalent twist step. We furthermore explore two hypervalent twist modes that account for the different influences of f-functions for IBX and Togni I. Our findings may be helpful for theoretical chemists to appropriately study the reaction mechanism of hypervalent iodine reagents.

Despite the strong genetic basis of psychiatric disorders, the molecular origins of these diseases are still largely unmapped. RNA-binding proteins (RBPs) are responsible for most post-transcriptional regulation, from splicing to translational to localization. RBPs thus act as key gatekeepers of cellular homeostasis, especially in the brain. Here, we leverage a deep learning approach to interrogate variant effects genome-wide, and discover that the dysregulation of RBP target sites is a principal contributor to psychiatric disorder risk. We show that specific modes of RBP regulation are genetically linked to the heritability of psychiatric disorders, and demonstrate that diverse RBP regulatory functions are reflected in distinct genome-wide negative selection signatures. Notably, RBP dysregulation has a stronger impact on psychiatric disorders than common coding region variants and explains heritability not currently captured by large-scale molecular QTL studies (expression QTLs and splicing QTLs). We share genome-wide profiles of RBP target site dysregulation, which we used to identify DDHD2 as a candidate schizophrenia risk gene, in a public web server. This resource provides a novel analytical framework to connect the full range of RNA regulation to complex disease.

Spike sorting is a crucial step in electrophysiological studies of neuronal activity. While many spike sorting packages are available, there is little consensus about which are most accurate under different experimental conditions. SpikeForest is an open-source and reproducible software suite that benchmarks the performance of automated spike sorting algorithms across an extensive, curated database of ground-truth electrophysiological recordings, displaying results interactively on a continuously-updating website. With contributions from eleven laboratories, our database currently comprises 650 recordings (1.3 TB total size) with around 35,000 ground-truth units. These data include paired intracellular/extracellular recordings and state-of-the-art simulated recordings. Ten of the most popular spike sorting codes are wrapped in a Python package and evaluated on a compute cluster using an automated pipeline. SpikeForest documents community progress in automated spike sorting, and guides neuroscientists to an optimal choice of sorter and parameters for a wide range of probes and brain regions.

During the first 2 hours of Drosophila development, precisely orchestrated nuclear cleavages, cytoskeletal rearrangements, and directed membrane growth lead to the formation of an epithelial sheet around the yolk. The newly formed epithelium remains relatively quiescent during the next hour as it is patterned by maternal inductive signals and zygotic gene products. We discovered that this mechanically quiet period is disrupted in embryos with high levels of dNTPs, which have been recently shown to cause abnormally fast nuclear cleavages and interfere with zygotic transcription. High levels of dNTPs are associated with robust onset of oscillatory two-dimensional flows during the third hour of development. Tissue cartography, particle image velocimetry, and dimensionality reduction techniques reveal that these oscillatory flows are low dimensional and are characterized by the presence of spiral vortices. We speculate that these aberrant flows emerge through an instability triggered by deregulated mechanical coupling between the nascent epithelium and three-dimensional yolk. These results highlight an unexplored connection between a core metabolic process and large-scale mechanics in a rapidly developing embryo.

Many problems in contemporary astrophysics---from understanding the formation of black holes to untangling the chemical evolution of galaxies---rely on knowledge about binary stars. This, in turn, depends on discovery and characterization of binary companions for large numbers of different kinds of stars in different chemical and dynamical environments. Current stellar spectroscopic surveys observe hundreds of thousands to millions of stars with (typically) few observational epochs, which allows binary discovery but makes orbital characterization challenging. We use a custom Monte Carlo sampler (The Joker) to perform discovery and characterization of binary systems through radial-velocities, in the regime of sparse, noisy, and poorly sampled multi-epoch data. We use it to generate posterior samplings in Keplerian parameters for 232,531 sources released in APOGEE Data Release 16. Our final catalog contains 19,635 high-confidence close-binary (P < few years, a < few AU) systems that show interesting relationships between binary occurrence rate and location in the color-magnitude diagram. We find notable faint companions at high masses (black-hole candidates), at low masses (substellar candidates), and at very close separations (mass-transfer candidates). We also use the posterior samplings in a (toy) hierarchical inference to measure the long-period binary-star eccentricity distribution. We release the full set of posterior samplings for the entire parent sample of 232,531 stars. This set of samplings involves no heuristic "discovery" threshold and therefore can be used for myriad statistical purposes, including hierarchical inferences about binary-star populations and sub-threshold searches.

The female meiotic spindles of most animals are acentrosomal and undergo drastic morphological changes while transitioning from metaphase to anaphase. The ultra-structure of acentrosomal spindles, and how this enables such dramatic rearrangements remains largely unknown. To address this, we applied light microscopy, large-scale electron tomography and mathematical modeling of female meiotic C. elegans spindles undergoing the transition from metaphase to anaphase. Combining these approaches, we find that meiotic spindles are dynamic arrays of short microtubules that turn over on second time scales. The results show that the transition from metaphase to anaphase correlates with an increase in the number of microtubules and a decrease of their average length. To understand the mechanisms that drive this transition, we developed a mathematical model for the microtubule length distribution that considers microtubule growth, catastrophe, and severing. Using Bayesian inference to compare model predictions and data, we find that microtubule turn-over is the major driver of the observed large-scale reorganizations. Our data suggest that cutting of microtubules occurs, but that most microtubules are not severed before undergoing catastrophe.

We study how to estimate a nearly low-rank Toeplitz covariance matrix T from compressed measurements. Recent work of Qiao and Pal addresses this problem by combining sparse rulers (sparse linear arrays) with frequency finding (sparse Fourier transform) algorithms applied to the Vandermonde decomposition of T. Analytical bounds on the sample complexity are shown, under the assumption of sufficiently large gaps between the frequencies in this decomposition. In this work, we introduce random ultra-sparse rulers and propose an improved approach based on these objects. Our random rulers effectively apply a random permutation to the frequencies in T's Vandermonde decomposition, letting us avoid frequency gap assumptions and leading to improved sample complexity bounds. In the special case when T is circulant, we theoretically analyze the performance of our method when combined with sparse Fourier transform algorithms based on random hashing. We also show experimentally that our ultra-sparse rulers give significantly more robust and sample efficient estimation then baseline methods.

Binary black holes (BBHs) detected by gravitational wave (GW) observations could be broadly divided into two formation channels: those formed through field binary evolution and those assembled dynamically in dense stellar systems. Each of these formation channels, and their sub-channels, populate a distinct region in the effective spin-mass (χeff−M) plane. Depending on the branching ratio of different channels, an ensemble of BBHs could show a trend in this plane. Here we fit a mass-dependent distribution for χeff to the GWTC-1 BBHs from the first and second observing runs of Advanced LIGO and Advanced Virgo. We find a negative correlation between mass and the mean effective spin (χ¯eff), and positive correlation with its dispersion (σχeff) at 75\% and 80\% confidence. This trend is robust against the choice of mass variable, but most pronounced when the mass variable is taken to be the chirp mass of the binary. The result is consistent with significant contributions from both dynamically assembled and field binaries in the GWTC-1 catalog. The upcoming LIGO O3a data release will critically test this interpretation.