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One of the first organizing processes during animal development is the assembly of embryonic cells into epithelia. Common features unite epithelialization across select bilaterians, however, we know less about the molecular and cellular mechanisms that drive epithelial emergence in early branching nonbilaterians. In sea anemones, epithelia emerge both during embryonic development and after cell aggregation of dissociated tissues. Although adhesion is required to keep cells together, it is not clear whether cell polarization plays a role as epithelia emerge from disordered aggregates. Here, we use the embryos of the sea anemoneNematostella vectensisto investigate the evolutionary origins of epithelial development. We demonstrate that lateral cell polarization is essential for epithelial organization in both embryos and aggregates. With disrupted lateral polarization, cell contact in the aggregate is not sufficient to trigger epithelialization and further tissue development. Specifically, knockdown of the conserved lateral polarity and tumor suppressor protein Lethal giant larvae (Lgl) disrupts epithelia in developing embryos and impairs the capacity of dissociated cells to epithelialize from aggregates. In contrast to other systems, cells inNematostella lglknockdown embryos do not undergo excessive proliferation. Cells with reduced Lgl levels lose their columnar shape and proper positioning of their mitotic spindles and basal bodies. Due to misoriented divisions and aberrant shapes, cells arrange nonuniformly without forming a monolayer. Together our data show that, inNematostella,Lgl drives epithelialization in embryos and cell aggregates through its effect on cell shape and organelle localization. 

ABSTRACT Accounting for uncertainty in model selection is crucial for statistical inference and data‐driven decision‐making, particularly with high‐dimensional data. While multiple studies have focused on constructing model confidence sets, a practical and informative visualization tool to assist in decision‐making under such uncertainty has been lacking. This paper introduces an intuitive visualization tool, the graph of ranking from solution paths (GRASP), designed to provide instant insights into model selection uncertainty. Additionally, GRASP accounts for the uncertainty of variable importance, enabling decision‐makers to assess each variable under uncertainty. Based on an innovative selection procedure that utilizes the entire solution path, a feature importance score and bootstrap techniques, GRASP effectively visualizes the uncertainty of model selection, as demonstrated by our numerical examples. Furthermore, we propose a novel measure of uncertainty based on GRASP, providing a single‐number summary of selection uncertainty. This measure incorporates the concept of the flat norm, traditionally used in geometry and physics. Our simulation studies and numerical examples confirm that this measure accurately and robustly quantifies uncertainty. 

Abstract Big datasets are gathered daily from different remote sensing platforms. Recently, statistical co‐kriging models, with the help of scalable techniques, have been able to combine such datasets by using spatially varying bias corrections. The associated Bayesian inference for these models is usually facilitated via Markov chain Monte Carlo (MCMC) methods which present (sometimes prohibitively) slow mixing and convergence because they require the simulation of high‐dimensional random effect vectors from their posteriors given large datasets. To enable fast inference in big data spatial problems, we propose the recursive nearest neighbor co‐kriging (RNNC) model. Based on this model, we develop two computationally efficient inferential procedures: (a) the collapsed RNNC which reduces the posterior sampling space by integrating out the latent processes, and (b) the conjugate RNNC, an MCMC free inference which significantly reduces the computational time without sacrificing prediction accuracy. An important highlight of conjugate RNNC is that it enables fast inference in massive multifidelity data sets by avoiding expensive integration algorithms. The efficient computational and good predictive performances of our proposed algorithms are demonstrated on benchmark examples and the analysis of the High‐resolution Infrared Radiation Sounder data gathered from two NOAA polar orbiting satellites in which we managed to reduce the computational time from multiple hours to just a few minutes. 

Allostery is an inherent feature of proteins, but it remains challenging to reveal the mechanisms by which allosteric signals propagate. A clearer understanding of this intrinsic circuitry would afford new opportunities to modulate protein function. Here, we have identified allosteric sites in protein tyrosine phosphatase 1B (PTP1B) by combining multiple-temperature X-ray crystallography experiments and structure determination from hundreds of individual small-molecule fragment soaks. New modeling approaches reveal 'hidden' low-occupancy conformational states for protein and ligands. Our results converge on allosteric sites that are conformationally coupled to the active-site WPD loop and are hotspots for fragment binding. Targeting one of these sites with covalently tethered molecules or mutations allosterically inhibits enzyme activity. Overall, this work demonstrates how the ensemble nature of macromolecular structure, revealed here by multitemperature crystallography, can elucidate allosteric mechanisms and open new doors for long-range control of protein function.