Publications

Machine learning based surrogate models offer researchers powerful tools for accelerating simulation-based workflows. However, as standard datasets in this space often cover small classes of physical behavior, it can be difficult to evaluate the efficacy of new approaches. To address this gap, we introduce the Well: a large-scale collection of datasets containing numerical simulations of a wide variety of spatiotemporal physical systems. The Well draws from domain experts and numerical software developers to provide 15TB of data across 16 datasets covering diverse domains such as biological systems, fluid dynamics, acoustic scattering, as well as magneto-hydrodynamic simulations of extra-galactic fluids or supernova explosions. These datasets can be used individually or as part of a broader benchmark suite. To facilitate usage of the Well, we provide a unified PyTorch interface for training and evaluating models. We demonstrate the function of this library by introducing example baselines that highlight the new challenges posed by the complex dynamics of the Well. The code and data is available at https://github.com/PolymathicAI/the_well.

Understanding the behavioral and neural dynamics of social interactions is a goal of contemporary neuroscience. Many machine learning methods have emerged in recent years to make sense of complex video and neurophysiological data that result from these experiments. Less focus has been placed on understanding how animals process acoustic information, including social vocalizations. A critical step to bridge this gap is determining the senders and receivers of acoustic infor- mation in social interactions. While sound source localization (SSL) is a classic problem in signal processing, existing approaches are limited in their ability to localize animal-generated sounds in standard laboratory environments. Advances in deep learning methods for SSL are likely to help address these limitations, however there are currently no publicly available models, datasets, or benchmarks to systematically evaluate SSL algorithms in the domain of bioacoustics. Here, we present the VCL Benchmark: the first large-scale dataset for benchmarking SSL algorithms in rodents. We acquired synchronized video and multi-channel audio recordings of 767,295 sounds with annotated ground truth sources across 9 conditions. The dataset provides benchmarks which evaluate SSL performance on real data, simulated acoustic data, and a mixture of real and simulated data. We intend for this benchmark to facilitate knowledge transfer between the neuroscience and acoustic machine learning communities, which have had limited overlap.

During eukaryotic cell division, a microtubule-based structure called the spindle exerts forces on chromosomes. The best-studied spindle forces, including those responsible for the separation of sister chromatids, are directed parallel to the spindle’s long axis. By contrast, little is known about forces perpendicular to the spindle axis, which determine the metaphase plate configuration and thus the location of chromosomes in the subsequent nucleus. Using live-cell microscopy, we find that metaphase chromosomes are spatially anti-correlated in mouse oocyte spindles, evidence of previously unknown long-range forces acting perpendicular to the spindle axis. We explain this observation by showing that the spindle’s microtubule network behaves as a nematic liquid crystal and that deformation of the nematic field around embedded chromosomes causes long-range repulsion between them.

Viral infections can induce changes in innate immunity that persist after virus clearance. Here, we used blood samples from a human influenza H3N2 challenge study to perform comprehensive multi-omic analyses. We detected remodeling of immune programs in innate immune cells after resolution of the infection that was proportional in magnitude to the level of prior viral load. We found changes associated with suppressed inflammation including decreased cytokine and AP-1 gene expression as well as decreased accessibility at AP-1 targets and interleukin-related gene promoter regions. We also found decreased histone deacetylase gene expression, increased MAP kinase gene expression, and increased accessibility at interferon-related gene promoter regions. Genes involved in inflammation and epigenetic-remodeling showed modulation of gene-chromatin site regulatory circuit activity. These results reveal a coordinated rewiring of the epigenetic landscape in innate immune cells induced by mild influenza virus infection.

Representation of the environment by hippocampal populations is known to drift even within a familiar environment, which could reflect gradual changes in single-cell activity or result from averaging across discrete switches of single neurons. Disambiguating these possibilities is crucial, as they each imply distinct mechanisms. Leveraging change point detection and model comparison, we find that CA1 population vectors decorrelate gradually within a session. In contrast, individual neurons exhibit predominantly step-like emergence and disappearance of place fields or sustained changes in within-field firing. The changes are not restricted to particular parts of the maze or trials and do not require apparent behavioral changes. The same place fields emerge, disappear, and reappear across days, suggesting that the hippocampus reuses pre-existing assemblies, rather than forming new fields de novo. Our results suggest an internally driven perpetual step-like reorganization of the neuronal assemblies.

The forces that orient the spindle in human cells remain poorly understood due to a lack of direct mechanical measurements in mammalian systems. We use magnetic tweezers to measure the force on human mitotic spindles. Combining the spindle’s measured resistance to rotation, the speed at which it rotates after laser ablating astral microtubules, and estimates of the number of ablated microtubules reveals that each microtubule contacting the cell cortex is subject to ∼5 pN of pulling force, suggesting that each is pulled on by an individual dynein motor. We find that the concentration of dynein at the cell cortex and extent of dynein clustering are key determinants of the spindle’s resistance to rotation, with little contribution from cytoplasmic viscosity, which we explain using a biophysically based mathematical model. This work reveals how pulling forces on astral microtubules determine the mechanics of spindle orientation and demonstrates the central role of cortical dynein clustering.

Phosphotriesterases (PTEs) represent a class of enzymes capable of efficient neutralization of organophosphates (OPs), a dangerous class of neurotoxic chemicals. PTEs suffer from low catalytic activity, particularly at higher temperatures, due to low thermostability and low solubility. Supercharging, a protein engineering approach via selective mutation of surface residues to charged residues, has been successfully employed to generate proteins with increased solubility and thermostability by promoting charge–charge repulsion between proteins. We set out to overcome the challenges in improving PTE activity against OPs by employing a computational protein supercharging algorithm in Rosetta. Here, we discover two supercharged PTE variants, one negatively supercharged (with −14 net charge) and one positively supercharged (with +12 net charge) and characterize them for their thermodynamic stability and catalytic activity. We find that positively supercharged PTE possesses slight but significant losses in thermostability, which correlates to losses in catalytic efficiency at all temperatures, whereas negatively supercharged PTE possesses increased catalytic activity across 25°C–55°C while offering similar thermostability characteristic to the parent PTE. The impact of supercharging on catalytic efficiency will inform the design of shelf-stable PTE and criteria for enzyme engineering.

Over the last two decades, several fast, robust, and high-order accurate methods have been developed for solving the Poisson equation in complicated geometry using potential theory. In this approach, rather than discretizing the partial differential equation itself, one first evaluates a volume integral to account for the source distribution within the domain, followed by solving a boundary integral equation to impose the specified boundary conditions. Here, we present a new fast algorithm which is easy to implement and compatible with virtually any discretization technique, including unstructured domain triangulations, such as those used in standard finite element or finite volume methods. Our approach combines earlier work on potential theory for the heat equation, asymptotic analysis, the nonuniform fast Fourier transform (NUFFT), and the dual-space multilevel kernel-splitting (DMK) framework. It is insensitive to flaws in the triangulation, permitting not just nonconforming elements, but arbitrary aspect ratio triangles, gaps and various other degeneracies. On a single CPU core, the scheme computes the solution at a rate comparable to that of the fast Fourier transform (FFT) in work per gridpoint.

Neurons in macaque area V2 respond selectively to higher-order visual features, such as the quasi-periodic structure of natural texture, but it is unknown how selectivity for these features is built from V1 inputs tuned more simply for orientation and spatial frequency. We have recently developed an image-computable two-layer linear-nonlinear network that captures higher-order tuning from a sparse combination of subunits tuned in orientation and scale. This model can be independently trained to predict data from single-unit recordings of V1 and V2 neurons from the awake macaque, responding to a stimulus set comprised of multiple superimposed grating patches that localize oriented contrast energy. These optimized models accurately predict neural responses to other stimuli, including gratings and synthetic textures with higher-order features common to natural images. However, the high-dimensional parameter space of these models makes them difficult to interpret. A systematic comparison of V1 and V2 selectivity is therefore elusive. To address this limitation, we investigate neural tuning across our population of models in silico using local spectral reverse correlation (LSRC; Nishimoto et al., 2006). Here, we present thousands of ternary white noise stimuli to fitted models, computing a response-weighted windowed frequency spectrum across image coordinates. LSRC can effectively characterize the tuning properties of our model neurons, thereby estimating the linear and nonlinear components of model receptive fields. These estimates are qualitatively similar to our direct LSRC measurements made using identical methods from single-unit V1 and V2 recordings collected in separate experiments.

In this work we investigate anharmonic vibrational polaritons formed due to strong light-matter interactions in an optical cavity between radiation modes and anharmonic vibrations beyond the long-wavelength limit. We introduce a conceptually simple description of light-matter interactions, where spatially localized cavity radiation modes couple to localized vibrations. Within this theoretical framework, we employ self-consistent phonon theory and vibrational dynamical mean-field theory to efficiently simulate momentum-resolved vibrational-polariton spectra, including effects of anharmonicity. Numerical simulations in model systems demonstrate the accuracy and applicability of our approach.