Publications

Surface tension at cavity walls can play havoc with the mechanical properties of perforated soft solids when the cavities are filled with a fluid. This study is an investigation of the macroscopic elastic properties of elastomers embedding spherical cavities filled with a pressurized liquid in the presence of surface tension, starting with the linearization of the fully nonlinear model and ending with the enhancement properties of the linearized model when many such liquid filled cavities are present.

Surface tension at cavity walls can play havoc with the mechanical properties of perforated soft solids when the cavities are filled with a fluid. This study is an investigation of the macroscopic elastic properties of elastomers embedding spherical cavities filled with a pressurized liquid in the presence of surface tension, starting with the linearization of the fully nonlinear model and ending with the enhancement properties of the linearized model when many such liquid filled cavities are present.

Incomplete labels are common in multi-task learning for biomedical applications due to several practical difficulties, e.g., expensive annotation efforts by experts, limit of data collection, different sources of data. A naive approach to enable joint learning for partially labeled data is adding self-supervised learning for tasks without ground truths by augmenting an input image and forcing the multi-task model to return the same outputs for both the input and augmented images. However, the partially labeled setting can result in imbalanced learning of tasks since not all tasks are trainable with ground truth supervisions for each data sample. In this work, we propose a multi-task curriculum learning method tailored for partially labeled data. For balanced learning of tasks, our multitask curriculum prioritizes less performing tasks during training by setting different supervised learning frequencies for each task. We demonstrate that our method outperforms standard approaches on one biomedical and two natural image datasets. Furthermore, our learning method with partially labeled data performs better than the standard multi-task learning methods with fully labeled data for the same number of annotations.

Blastomere instance segmentation is important for analyzing embryos’ abnormality. To measure the accurate shapes and sizes of blastomeres, their amodal segmentation is necessary. Amodal instance segmentation aims to recover an object’s complete silhouette even when the object is not fully visible. For each detected object, previous methods directly regress the target mask from input features. However, images of an object under different amounts of occlusion should have the same amodal mask output, making it harder to train the regression model. To alleviate the problem, we propose to classify input features into intermediate shape codes and recover complete object shapes. First, we pre-train the Vector Quantized Variational Autoencoder (VQ-VAE) model to learn these discrete shape codes from ground truth amodal masks. Then, we incorporate the VQ-VAE model into the amodal instance segmentation pipeline with an additional refinement module. We also detect an occlusion map to integrate occlusion information with a backbone feature. As such, our network faithfully detects bounding boxes of amodal objects. On an internal embryo cell image benchmark, the proposed method outperforms previous state-of-the-art methods. To show generalizability, we show segmentation results on the public KINS natural image benchmark. Our method would enable accurate measurement of blastomeres in In Vitro Fertilization (IVF) clinics, potentially increasing the IVF success rate.

Sensation of light is essential for all organisms. The eye-less nematode Caenorhabditis elegans detects UV and blue light to evoke escape behavior. The photosensor LITE-1 absorbs UV photons with an unusually high extinction coefficient, involving essential tryptophans. Here, we modeled the structure and dynamics of LITE-1 using AlphaFold2-multimer and molecular dynamics (MD) simulations and performed mutational and behavioral assays in C. elegans to characterize its function. LITE-1 resembles olfactory and gustatory receptors from insects, recently shown to be tetrameric ion channels. We identified residues required for channel gating, light absorption, and mechanisms of photo-oxidation, involving a likely binding site for the peroxiredoxin PRDX-2. Furthermore, we identified the binding pocket for a putative chromophore. Several residues lining this pocket have previously been established as essential for LITE-1 function. A newly identified critical cysteine pointing into the pocket represents a likely chromophore attachment site. We derived a model for how photon absorption, via a network of tryptophans and other aromatic amino acids, induces an excited state that is transferred to the chromophore. This evokes conformational changes in the protein, possibly leading to a state receptive to oxidation of cysteines and, jointly, to channel gating. Electrophysiological data support the idea that LITE-1 is a photon and H2O2-coincidence detector. Other proteins with similarity to LITE-1, specifically C. elegans GUR-3, likely use a similar mechanism for photon detection. Thus, a common protein fold and assembly, used for chemoreception in insects, possibly by binding of a particular compound, may have evolved into a light-activated ion channel.

Under a sufficiently large load, a solid material will flow via rearrangements, where particles change neighbors. Such plasticity is most easily described in the athermal, quasistatic limit of zero temperature and infinitesimal loading rate, where rearrangements occur only when the system becomes mechanically unstable. For disordered solids, the instabilities marking the onset of rearrangements have long been believed to be fold instabilities, in which an energy barrier disappears and the frequency of a normal mode of vibration vanishes continuously. Here, we report that there exists another, anomalous, type of instability caused by the breaking of a “stabilizing bond,” whose removal creates an unstable vibrational mode. For commonly studied systems, such as those with harmonic finite-range interparticle interactions, such “discontinuous instabilities” are not only inevitable, they often dominate the modes of failure. Stabilizing bonds are a subset of all the bonds in the system and are prevalent in disordered solids generally. Although they do not trigger discontinuous instabilities in systems with vanishing stiffness at the interaction cutoff, they are, even in those cases, local indicators of incipient mechanical failure. They therefore provide an accurate structural predictor of instabilities not only of the discontinuous type but of the fold type as well.

Reaction-diffusion models with nonlocal constraints naturally arise as limiting cases of coupled bulk-surface models of intracellular signalling. In this paper, a minimal, mass-conserving model of cell-polarization on a curved membrane is analyzed in the limit of slow surface diffusion. Using the tools of formal asymptotics and calculus of variations, we study the characteristic wave-pinning behavior of this system on three dynamical timescales. On the short timescale, generation of an interface separating high- and low-concentration domains is established under suitable conditions. Intermediate timescale dynamics are shown to lead to a uniform growth or shrinking of these domains to sizes that are fixed by global parameters. Finally, the long timescale dynamics reduce to area-preserving geodesic curvature flow that may lead to multi-interface steady state solutions. These results provide a foundation for studying cell polarization and related phenomena in biologically relevant geometries.

Mutations in signal transduction pathways lead to various diseases including cancers. MEK1 kinase, encoded by the human MAP2K1 gene, is one of the central components of the MAPK pathway and more than a hundred somatic mutations in the MAP2K1 gene were identified in various tumors. Germline mutations deregulating MEK1 also lead to congenital abnormalities, such as the cardiofaciocutaneous syndrome and arteriovenous malformation. Evaluating variants associated with a disease is a challenge, and computational genomic approaches aid in this process. Establishing evolutionary history of a gene improves computational prediction of disease-causing mutations; however, the evolutionary history of MEK1 is not well understood. Here, by revealing a precise evolutionary history of MEK1, we construct a well-defined dataset of MEK1 metazoan orthologs, which provides sufficient depth to distinguish between conserved and variable amino acid positions. We matched known and predicted disease-causing and benign mutations to evolutionary changes observed in corresponding amino acid positions and found that all known and many suspected disease-causing mutations are evolutionarily intolerable. We selected several variants that cannot be unambiguously assessed by automated prediction tools but that are confidently identified as “damaging” by our approach, for experimental validation in Drosophila. In all cases, evolutionary intolerant variants caused increased mortality and severe defects in fruit fly embryos confirming their damaging nature. We anticipate that our analysis will serve as a blueprint to help evaluate known and novel missense variants in MEK1 and that our approach will contribute to improving automated tools for disease-associated variant interpretation.

Single-particle cryo-electron microscopy (cryo-EM) is a technique that takes projection images of biomolecules frozen at cryogenic temperatures. A major advantage of this technique is its ability to image single biomolecules in heterogeneous conformations. While this poses a challenge for data analysis, recent algorithmic advances have enabled the recovery of heterogeneous conformations from the noisy imaging data. Here, we review methods for the reconstruction and heterogeneity analysis of cryo-EM images, ranging from linear-transformation-based methods to nonlinear deep generative models. We overview the dimensionality-reduction techniques used in heterogeneous 3D reconstruction methods and specify what information each method can infer from the data. Then, we review the methods that use cryo-EM images to estimate probability distributions over conformations in reduced subspaces or predefined by atomistic simulations. We conclude with the ongoing challenges for the cryo-EM community.

Resolving chromatin-remodeling-linked gene expression changes at cell-type resolution is important for understanding disease states. Here we describe MAGICAL (Multiome Accessibility Gene Integration Calling and Looping), a hierarchical Bayesian approach that leverages paired single-cell RNA sequencing and single-cell transposase-accessible chromatin sequencing from different conditions to map disease-associated transcription factors, chromatin sites, and genes as regulatory circuits. By simultaneously modeling signal variation across cells and conditions in both omics data types, MAGICAL achieved high accuracy on circuit inference. We applied MAGICAL to study Staphylococcus aureus sepsis from peripheral blood mononuclear single-cell data that we generated from subjects with bloodstream infection and uninfected controls. MAGICAL identified sepsis-associated regulatory circuits predominantly in CD14 monocytes, known to be activated by bacterial sepsis. We addressed the challenging problem of distinguishing host regulatory circuit responses to methicillin-resistant and methicillin-susceptible S. aureus infections. Although differential expression analysis failed to show predictive value, MAGICAL identified epigenetic circuit biomarkers that distinguished methicillin-resistant from methicillin-susceptible S. aureus infections.