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  1. This work focuses on the representation of model-form uncertainties in phase-field models of brittle fracture. Such uncertainties can arise from the choice of the degradation function for instance, and their consideration has been unaddressed to date. The stochastic modeling framework leverages recent developments related to the analysis of nonlinear dynamical systems and relies on the construction of a stochastic reduced-order model. In the latter, a POD-based reduced-order basis is randomized using Riemannian projection and retraction operators, as well as an information-theoretic formulation enabling proper concentration in the convex hull defined by a set of model proposals. The model thus obtained is mathematically admissible in the almost sure sense and involves a low-dimensional hyperparameter, the calibration of which is facilitated through the formulation of a quadratic programming problem. The relevance of the modeling approach is further assessed on one- and two-dimensional applications. It is shown that model uncertainties can be efficiently captured and propagated to macroscopic quantities of interest. An extension based on localized randomization is also proposed to handle the case where the forward simulation is highly sensitive to sample localization. This work constitutes a methodological development allowing phase-field predictions to be endowed with statistical measures of confidence, accounting for the variability induced by modeling choices. 
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    Free, publicly-accessible full text available January 1, 2025
  2. Free, publicly-accessible full text available December 1, 2024
  3. Abstract Overly restrictive eligibility criteria for clinical trials may limit the generalizability of the trial results to their target real-world patient populations. We developed a novel machine learning approach using large collections of real-world data (RWD) to better inform clinical trial eligibility criteria design. We extracted patients’ clinical events from electronic health records (EHRs), which include demographics, diagnoses, and drugs, and assumed certain compositions of these clinical events within an individual’s EHRs can determine the subphenotypes—homogeneous clusters of patients, where patients within each subgroup share similar clinical characteristics. We introduced an outcome-guided probabilistic model to identify those subphenotypes, such that the patients within the same subgroup not only share similar clinical characteristics but also at similar risk levels of encountering severe adverse events (SAEs). We evaluated our algorithm on two previously conducted clinical trials with EHRs from the OneFlorida+ Clinical Research Consortium. Our model can clearly identify the patient subgroups who are more likely to suffer or not suffer from SAEs as subphenotypes in a transparent and interpretable way. Our approach identified a set of clinical topics and derived novel patient representations based on them. Each clinical topic represents a certain clinical event composition pattern learned from the patient EHRs. Tested on both trials, patient subgroup (#SAE=0) and patient subgroup (#SAE>0) can be well-separated by k-means clustering using the inferred topics. The inferred topics characterized as likely to align with the patient subgroup (#SAE>0) revealed meaningful combinations of clinical features and can provide data-driven recommendations for refining the exclusion criteria of clinical trials. The proposed supervised topic modeling approach can infer the clinical topics from the subphenotypes with or without SAEs. The potential rules for describing the patient subgroups with SAEs can be further derived to inform the design of clinical trial eligibility criteria. 
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    Free, publicly-accessible full text available December 1, 2024
  4. Mitochondrial morphology provides unique insights into their integrity and function. Among fluorescence microscopy techniques, 3D super-resolution microscopy uniquely enables the analysis of mitochondrial morphological features individually. However, there is a lack of tools to extract morphological parameters from super-resolution images of mitochondria. We report a quantitative method to extract mitochondrial morphological metrics, including volume, aspect ratio, and local protein density, from 3D single-molecule localization microscopy images, with single-mitochondrion sensitivity. We validated our approach using simulated ground-truth SMLM images of mitochondria. We further tested our morphological analysis on mitochondria that have been altered functionally and morphologically in controlled manners. This work sets the stage to quantitatively analyze mitochondrial morphological alterations associated with disease progression on an individual basis.

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  5. We developed a multiscale optical imaging workflow, integrating and correlating visible-light optical coherence tomography, confocal laser scanning microscopy, and single-molecule localization microscopy to investigate mouse cornea damage from thein-vivotissue level to the nanoscopic single-molecule level. We used electron microscopy to validate the imaged nanoscopic structures. We imaged wild-type mice and mice with acute ocular hypertension and examined the effects of Rho-kinase inhibitor application. We defined four types of intercellular tight junction structures as healthy, compact, partially-distorted, and fully-distorted types by labeling the zonula occludens-1 protein in the corneal endothelial cell layer. We correlated the statistics of the four types of tight junction structures with cornea thickness and intraocular pressure. We found that the population of fully-distorted tight junctions correlated well with the level of corneal edema, and applying Rho-kinase inhibitor reduced the population of fully-distorted tight junctions under acute ocular hypertension. Together, these data point to the utility of multiscale optical imaging in revealing fundamental biology relevant to disease and therapeutics.

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  6. Free, publicly-accessible full text available December 1, 2024
  7. Abstract

    The search for new elementary particles is one of the most basic pursuits in physics, spanning from subatomic physics to quantum materials. Magnons are the ubiquitous elementary quasiparticle to describe the excitations of fully-ordered magnetic systems. But other possibilities exist, including fractional and multipolar excitations. Here, we demonstrate that strong quantum interactions exist between three flavors of elementary quasiparticles in the uniaxial spin-one magnet FeI2. Using neutron scattering in an applied magnetic field, we observe spontaneous decay between conventional and heavy magnons and the recombination of these quasiparticles into a super-heavy bound-state. Akin to other contemporary problems in quantum materials, the microscopic origin for unusual physics in FeI2is the quasi-flat nature of excitation bands and the presence of Kitaev anisotropic magnetic exchange interactions.

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    Free, publicly-accessible full text available December 1, 2024
  8. Abstract

    Magnetic reconnection is often invoked as a source of high-energy particles, and in relativistic astrophysical systems it is regarded as a prime candidate for powering fast and bright flares. We present a novel analytical model—supported and benchmarked with large-scale three-dimensional kinetic particle-in-cell simulations in electron–positron plasmas—that elucidates the physics governing the generation of power-law energy spectra in relativistic reconnection. Particles with Lorentz factorγ≳ 3σ(here,σis the magnetization) gain most of their energy in the inflow region, while meandering between the two sides of the reconnection layer. Their acceleration time istaccγηrec1ωc120γωc1, whereηrec≃ 0.06 is the inflow speed in units of the speed of light andωc=eB0/mcis the gyrofrequency in the upstream magnetic field. They leave the region of active energization aftertesc, when they get captured by one of the outflowing flux ropes of reconnected plasma. We directly measuretescin our simulations and find thattesctaccforσ≳ few. This leads to a universal (i.e.,σ-independent) power-law spectrumdNfree/dγγ1for the particles undergoing active acceleration, anddN/dγγ2for the overall particle population. Our results help to shed light on the ubiquitous presence of power-law particle and photon spectra in astrophysical nonthermal sources.

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  9. Free, publicly-accessible full text available August 1, 2024
  10. Free, publicly-accessible full text available June 1, 2024