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The first all-sky, high-resolution, 3D map of the optical extinction curve of the Milky Way (Zhang & Green 2024) revealed an unexpected steepening of the extinction curve in the moderate-density, "translucent" interstellar medium (ISM). We argue that this trend is driven by growth of polycyclic aromatic hydrocarbons (PAHs) through gas-phase accretion. We find a strong anti-correlation between the slope of the optical extinction curve -- parameterized by $R(V)$ -- and maps of PAH abundance -- parameterized by $q_{\rm PAH}$ -- derived from infrared emission. The range of observed $q_{\rm PAH}$ indicates PAH growth by a factor of $\sim$2 between $A_V \simeq 1$ and 3. This implies a factor-of-two stronger 2175 Angstrom feature, which is sufficient to lower $R(V)$ by the observed amount. This level of PAH growth is possible given rapid accretion timescales and the depletion of carbon in the translucent ISM. Spectral observations by JWST would provide a definitive test of this proposed explanation of $R(V)$ variation., Comment: 11 pages, 6 figures, submitted on October 30, 2024

The dust extinction curve is typically parameterized by a single variable, R(V), in optical and near-infrared wavelengths. R(V) controls the slope of the extinction-vs.-wavelength curve, and is thought to reflect the grain-size distribution and composition of dust. Low-resolution, flux-calibrated BP/RP spectra from Gaia have allowed the determination of the extinction curve along sightlines to 130 million stars in the Milky Way and Magellanic Clouds. We show that these extinction curves contain more than a single degree of freedom - that is, that they are not simply described by R(V). We identify a number of components that are orthogonal to R(V) variation, and show that these components vary across the sky in coherent patterns that resemble interstellar medium structure. These components encode variation in the 770 nm extinction feature, intermediate-scale and very broad structure, and a newly identified feature at 850 nm, and likely trace both dust composition and local conditions in the interstellar medium. Correlations of the 770 nm and 850 nm features with R(V) suggest that their carriers become more abundant as the carrier of the 2175 Angstrom feature is destroyed. Our 24 million extinction-curve decompositions and feature equivalent-width measurements are publicly available at https://dx.doi.org/10.5281/zenodo.14005028., Comment: 19 pages, 13 figures; data available at DOI:10.5281/zenodo.14005028

Two-dimensional (2D) 1T-VSe$_2$ has prompted significant interest due to the discrepancies regarding alleged ferromagnetism (FM) at room temperature, charge density wave (CDW) states and the interplay between the two. We employed a combined Diffusion Monte Carlo (DMC) and density functional theory (DFT) approach to accurately investigate the magnetic properties and response of strain of monolayer 1T-VSe$_2$. Our calculations show the delicate competition between various phases, revealing critical insights into the relationship between their energetic and structural properties. We went on to perform Classical Monte Carlo simulations informed by our DMC and DFT results, and found the magnetic transition temperature ($T_c$) of the undistorted (non-CDW) FM phase to be 228 K and the distorted (CDW) phase to be 68 K. Additionally, we studied the response of biaxial strain on the energetic stability and magnetic properties of various phases of 2D 1T-VSe$_2$ and found that small amounts of strain can enhance the $T_c$, suggesting a promising route for engineering and enhancing magnetic behavior. Finally, we synthesized 1T-VSe$_2$ and performed Raman spectroscopy measurements, which were in close agreement with our calculated results. Our work emphasizes the role of highly accurate DMC methods in advancing the understanding of monolayer 1T-VSe$_2$ and provides a robust framework for future studies of 2D magnetic materials.

We present a direct measurement of extinction curves using corrected $Gaia$ XP spectra of the common sources in $Gaia$ DR3 and LAMOST DR7. Our analysis of approximately 370 thousand high-quality samples yielded a high-precision average extinction curve for the Milky Way. After incorporating infrared photometric data from 2MASS and WISE, the extinction curve spans wavelengths from 0.336 to 4.6 $\mu$m. We determine an average $R_{55}$ of $2.730 \pm 0.007$, corresponding to $R_V= 3.073 \pm 0.009$, and a near-infrared power-law index $\alpha$ of $1.935 \pm 0.037$. Our study confirmed some intermediate-scale structures within the optical range. Two new features were identified at 540 and 769 nm, and their intensities exhibited a correlation with extinction and $R_V$. This extinction curve can be used to investigate the characteristics of dust and enhance the extinction correction of Milky Way stars. A Python package for this extinction curve is available., Comment: 11 pages, 5 figures, accepted for publication in ApJ

We employ the phospholipid bilayer membranes of giant unilamellar vesicles as a free-standing environment for the growth of membrane-integrated ultrathin phospholipid crystals possessing a variety of shapes with 6-fold symmetry. Crystal growth within vesicle membranes, where more elaborate shapes grow on larger vesicles is dominated by the bending energy of the membrane itself, creating a means to manipulate crystal morphology. Here we demonstrate how cooling rate preconditions the membrane tension before nucleation, in turn regulating nucleation and growth, and directing the morphology of crystals by the time they are large enough to be visualized. The crystals retain their shapes during further growth through the two phase region. Experiments demonstrate this behavior for single crystals growing within the membrane of each vesicle, ultimately comprising up to 13% of the vesicle area and length scales of up to 50 microns. A model for stress evolution, employing only physical property data, reveals how the competition between thermal membrane contraction and water diffusion from tensed vesicles produces a size- and time-dependence of the membrane tension as a result of cooling history. The tension, critical in the contribution of bending energy in the fluid membrane regions, in turn selects for crystal shape for vesicles of a given size. The model reveals unanticipated behaviors including a low steady state tension on small vesicles that allows compact domains to develop, rapid tension development on large vesicles producing flower-shaped domains, and a stress relaxation through water diffusion across the membrane with a time constant scaling as the square of the vesicle radius, consistent with measurable tensions only in the largest vesicles., Comment: Main text 32 pages and 7 figures; SI 4 pages and 4 figures

We study the effect of geometric frustration on dilational mechanical metamaterial membranes. While shape frustrated elastic plates can only accommodate non-zero Gaussian curvature up to size scales that ultimately vanish with their elastic thickness, we show that frustrated {\it metamembranes} accumulate hyperbolic curvatures up to mesoscopic length scales that are ultimately independent of the size of their microscopic constituents. A continuum elastic theory and discrete numerical model describe the size-dependent shape and internal stresses of axisymmetric, trumpet-like frustrated metamembranes, revealing a non-trivial crossover to a much weaker power-law growth in elastic strain energy with size than in frustrated elastic membranes. We study a consequence of this for the self-limiting assembly thermodynamics of frustrated trumpets, showing a several-fold increase the size range of self-limitation of metamembranes relative to elastic membranes., Comment: 6 pages, 3 figures, supplementary text appendices

Vision-Language Models (VLM) can support clinicians by analyzing medical images and engaging in natural language interactions to assist in diagnostic and treatment tasks. However, VLMs often exhibit "hallucinogenic" behavior, generating textual outputs not grounded in contextual multimodal information. This challenge is particularly pronounced in the medical domain, where we do not only require VLM outputs to be accurate in single interactions but also to be consistent with clinical reasoning and diagnostic pathways throughout multi-turn conversations. For this purpose, we propose a new alignment algorithm that uses symbolic representations of clinical reasoning to ground VLMs in medical knowledge. These representations are utilized to (i) generate GPT-4-guided visual instruction tuning data at scale, simulating clinician-VLM conversations with demonstrations of clinical reasoning, and (ii) create an automatic reward function that evaluates the clinical validity of VLM generations throughout clinician-VLM interactions. Our algorithm eliminates the need for human involvement in training data generation or reward model construction, reducing costs compared to standard reinforcement learning with human feedback (RLHF). We apply our alignment algorithm to develop Dr-LLaVA, a conversational VLM finetuned for analyzing bone marrow pathology slides, demonstrating strong performance in multi-turn medical conversations., Comment: Code available at: https://github.com/AlaaLab/Dr-LLaVA

Helically close-packed states of filaments are common in natural and engineered material systems, ranging from nanoscopic biomolecules to macroscopic structural components. While the simplest models of helical close-packing, described by the ideal rope model, neglect anisotropy perpendicular to the backbone, physical filaments are often quite far from circular in their cross-section. Here, we consider an anisotropic generalization of the ideal rope model and show that cross-section anisotropy has a strongly non-linear impact on the helical close-packing configurations of helical filaments. We show that the topology and composition of the close-packing landscape depends on the cross-sectional aspect ratio and is characterized by several distinct states of self-contact. We characterize the local density of these distinct states based on the notion of confinement within a 'virtual' cylindrical capillary, and show that states of optimal density vary strongly with the degree of anisotropy. While isotropic filaments are densest in a straight configuration, any measure of anisotropy leads to helicity of the maximal density state. We show the maximally dense states exhibit a sequence of transitions in helical geometry and cross-sectional tilt with increasing anisotropy, from spiral tape to spiral screw packings. Furthermore, we show that maximal capillary density saturates in a lower bound for volume fraction of $\pi/4$ in the large-anisotropy, spiral-screw limit. While cross-sectional anisotropy is well-known to impact the mechanical properties of filaments, our study shows its strong effects to shape the configuration space and packing efficiency of this elementary material motif., Comment: 32 pages, 15 figures

Motivated by recent studies of two-phase lipid vesicles possessing 2D solid domains integrated within a fluid bilayer phase, we study the shape equilibria of closed vesicles possessing a single planar, circular inclusion. While 2D solid elasticity tends to expel Gaussian curvature, topology requires closed vesicles to maintain an average, non-zero Gaussian curvature leading to an elementary mechanism of shape frustration that increases with inclusion size. We study elastic ground states of the Helfrich model of the planar-fluid composite vesicles, analytically and computationally, as a function of planar fraction and reduced volume. Notably, we show that incorporation of a planar inclusion of only a few percent dramatically shifts the ground state shapes of vesicles from predominantly {\it prolate} to {\it oblate}, and moreover, shifts the optimal surface to volume ratio far from spherical shapes. We show that for sufficiently small planar inclusions, the elastic ground states break symmetry via a complex variety of asymmetric oblate, prolate, and triaxial shapes, while inclusion sizes above about $8\%$ drive composite vesicles to adopt axisymmetric oblate shapes. These predictions cast useful light on the emergent shape and mechanical responses of fluid-solid composite vesicles., Comment: 15 pages, 13 figures, 3 appendices

Our understanding of organs at risk is progressing to include physical small tissues such as coronary arteries and the radiosensitivities of many small organs and tissues are high. Therefore, the accurate segmentation of small volumes in external radiotherapy is crucial to protect them from over-irradiation. Moreover, with the development of the particle therapy and on-board imaging, the treatment becomes more accurate and precise. The purpose of this work is to optimize organ segmentation algorithms for small organs. We used 50 three-dimensional (3-D) computed tomography (CT) head and neck images from StructSeg2019 challenge to develop a general-purpose V-Net model to segment 20 organs in the head and neck region. We applied specific strategies to improve the segmentation accuracy of the small volumes in this anatomical region, i.e., the lens of the eye. Then, we used 17 additional head images from OSF healthcare to validate the robustness of the V Net model optimized for small-volume segmentation. With the study of the StructSeg2019 images, we found that the optimization of the image normalization range and classification threshold yielded a segmentation improvement of the lens of the eye of approximately 50%, compared to the use of the V-Net not optimized for small volumes. We used the optimized model to segment 17 images acquired using heterogeneous protocols. We obtained comparable Dice coefficient values for the clinical and StructSeg2019 images (0.61 plus/minus 0.07 and 0.58 plus/minus 0.10 for the left and right lens of the eye, respectively)

The steady flow of social-media cases toward the Supreme Court shows a nation reworking its fundamental relationship with technology. The cases raise a host of questions ranging from difficult to impossible: how to nurture a vibrant public square when a few tech giants dominate the flow of information, how social media can be at the same time free from conformist groupthink and also protected against harmful disinformation campaigns, and how government and industry can cooperate on such problems without devolving toward censorship. To such profound questions, this Essay offers a comparatively modest contribution -- what not to do. Always the lawyer's instinct is toward analogy, considering what has come before and how it reveals what should come next. Almost invariably, that is the right choice. The law's cautious evolution protects society from disruptive change. But almost is not always, and, with social media, disruptive change is already upon us. Using social-media laws from Texas and Florida as a case study, this Essay shows how social-media's distinct features render it poorly suited to analysis by analogy and argues that courts should instead shift their attention toward crafting legal doctrines targeted to address social media's unique ills.

Recent advances enable the creation of nanoscale building blocks with complex geometries and interaction specificities for self-assembly. This nearly boundless design space necessitates design principles for defining the mutual interactions between multiple particle species to target a user-specified complex structure or pattern. In this article, we develop a symmetry-based method to generate the interaction matrices that specify the assembly of two-dimensional tilings which we illustrate using equilateral triangles. By exploiting the allowed 2D symmetries, we develop an algorithmic approach by which any periodic 2D tiling can be generated from an arbitrarily large number of subunit species, notably addressing an unmet challenge of engineering 2D crystals with periodicities that can be arbitrarily larger than subunit size. To demonstrate the utility of our design approach, we encode specific interactions between triangular subunits synthesized by DNA origami and show that we can guide their self-assembly into tilings with a wide variety of symmetries, using up to 12 unique species of triangles. By conjugating specific triangles with gold nanoparticles, we fabricate gold-nanoparticle supercrystals whose lattice parameter spans up to 300 nm. Finally, to generate economical design rules, we compare the design economy of various tilings. In particular, we show that (1) higher symmetries allow assembly of larger unit cells with fewer subunits and (2) linear supercrystals can be designed more economically using linear primitive unit cells. This work provides a simple algorithmic approach to designing periodic assemblies, which may open new doors to the multiscale assembly of superlattices of nanostructured "metatoms" with engineered plasmonic functions., Comment: Main text 10 pages, 4 figures, SI 27 pages, 19 figures, and 3 tables

Inspired by adhesive bio-filamentous structure, such as bacterial pili, this work details the methods used to fabricate and characterize a surface-anchored array of thin, flexible and shape-responsive mesoscale polymer ribbons with a length-to-thickness aspect ratio of up to 100,000. The resulting structures exhibit geometrically complex and dynamic morphologies consistent with elastocapillary bending that experience an increase in curvature over hours of aging due to creep. We develop a computational image analysis framework to generate 3D reconstructions of these densely crowded geometries and extract quantitative descriptors to demonstrate morphological changes due to aging. We demonstrate the robustness of this quantitative method by characterizing the creep-induced change in an aging ribbon array's shape and develop a scaling relationship to describe the importance of ribbon thickness for shape and dynamical observations. These methods demonstrate an essential baseline to probe morphology-property relationships of mesoscale polymer ribbon arrays fabricated from a variety of materials in numerous environments. Through the introduction of perfluorodecalin droplets, we illustrate the potential of these ribbon arrays towards applications in adhesive, microrobotic, and biomedical devices., Comment: 29 pages, 8 figures, supplemental included at the end

Substance use disorder (SUD) poses a major concern due to its detrimental effects on health and society. SUD identification and treatment depend on a variety of factors such as severity, co-determinants (e.g., withdrawal symptoms), and social determinants of health. Existing diagnostic coding systems used by American insurance providers, like the International Classification of Diseases (ICD-10), lack granularity for certain diagnoses, but clinicians will add this granularity (as that found within the Diagnostic and Statistical Manual of Mental Disorders classification or DSM-5) as supplemental unstructured text in clinical notes. Traditional natural language processing (NLP) methods face limitations in accurately parsing such diverse clinical language. Large Language Models (LLMs) offer promise in overcoming these challenges by adapting to diverse language patterns. This study investigates the application of LLMs for extracting severity-related information for various SUD diagnoses from clinical notes. We propose a workflow employing zero-shot learning of LLMs with carefully crafted prompts and post-processing techniques. Through experimentation with Flan-T5, an open-source LLM, we demonstrate its superior recall compared to the rule-based approach. Focusing on 11 categories of SUD diagnoses, we show the effectiveness of LLMs in extracting severity information, contributing to improved risk assessment and treatment planning for SUD patients., Comment: 10 pages, 4 figures, 2 tables

In geometrically frustrated assemblies local inter-subunit misfits propagate to intra-assembly strain gradients, giving rise to anomalous self-limiting assembly thermodynamics. Here, we use theory and coarse-grained simulation to study a recently developed class of ``curvamer'' particles, flexible shell-like particles that exhibit self-limiting assembly due to the build up of curvature deformation in cohesive stacks. To address a generic, yet poorly understood aspect of frustrated assembly, we introduce a model of curvamer assembly that incorporates both {\it intra-particle} shape deformation as well as compliance of {\it inter-particle} cohesive gaps, an effect we can attribute to a {\it finite range of attraction} between particles. We show that the ratio of intra-particle (bending elasticity) to inter-particle stiffness not only controls the regimes of self-limitation but also the nature of frustration propagation through curvamer stacks. We find a transition from uniformly-bound, curvature-focusing stacks at small size to gap-opened, uniformly curved stacks at large size is controlled by a dimensionless measure of inter- versus intra-curvamer stiffness. The finite range of inter-particle attraction determines range of cohesion in stacks are self-limiting, a prediction which is in strong agreement with numerical studies of our coarse-grained colloidal model. These predictions provide critical guidance for experimental realizations of frustrated particle systems designed to exhibit self-limitation at especially large multi-particle scales., Comment: 23 pages, 16 figures, 7 appendices

Amphiphiles self-assemble into a variety of bicontinuous mesophases whose equilibrium structures take the form of high-symmetry cubic networks. Here, we show that the symmetry-breaking distortions in these systems give rise to anomalously large, non-affine collective deformations, which we argue to be a generic consequence of mass equilibration within deformed networks. We propose and study a minimal liquid network model of bicontinuous networks, in which acubic distortions are modeled by the relaxation of residually-stressed mechanical networks with constant-tension bonds. We show that non-affinity is strongly dependent on the valency of the network as well as the degree of strain-softening/stiffening force in the bonds. Taking diblock copolymer melts as a model system, liquid network theory captures quantitative features of two bicontinuous phases based on comparison with self-consistent field theory predictions and direct experimental characterization of acubic distortions, which are likely to be pronounced in soft amphiphilic systems more generally., Comment: 23 pages, 9 figures

Self-assembly is one of the prevalent strategies used by living systems to fabricate ensembles of precision nanometer-scale structures and devices. The push for analogous approaches to create synthetic nanomaterials has led to the development of a large class of programmable crystalline structures. However, many applications require `self-limiting' assemblies, which autonomously terminate growth at a well-defined size and geometry. For example, curved architectures such as tubules, vesicles, or capsids can be designed to self-close at a particular size, symmetry, and topology. But developing synthetic strategies for self-closing assembly has been challenging, in part because such structures are prone to polymorphism that arises from thermal fluctuations of their local curvature, a problem that worsens with increased target size. Here we demonstrate a strategy to eliminate this source of polymorphism in self-closing assembly of tubules by increasing the assembly complexity. In the limit of single-component assembly, we find that thermal fluctuations allow the system to assemble nearby, off-target structures with varying widths, helicities, and chirality. By increasing the number of distinct components, we reduce the density of off-target states, thereby increasing the selectivity of a user-specified target structure to nearly 100%. We further show that by reducing the design constraints by targeting either the pitch or the width of tubules, fewer components are needed to reach complete selectivity. Combining experiments with theory, our results reveal an economical limit, which determines the minimum number of components that are required to create arbitrary assembly sizes with full selectivity. In the future, this approach could be extended to more complex self-limited structures, such as shells or triply periodic surfaces., Comment: Main text 8 pages, 5 figures, SI 22 pages, 12 figures, 5 tables. Slight changes to the text from previous version

The packing geometry of macromolecules in complex mesophases is of key importance to self-organization in synthetic and biological soft materials. While approximate or heuristic models rely on often-untested assumptions about how flexible molecules "fit in" to distinct locations of complex assemblies, physical assemblies derive from ensembles of fluctuating conformations, obscuring the connection between mesophase geometry and the underlying arrangements. Here, we present an approach to extract and analyze features of molecular packing in diblock block copolymer (BCP) melts, a prototypical soft matter system, based on the statistical description of chain conformations in self-consistent field (SCF) theory. We show how average BCP chain trajectories in ordered morphologies can be analyzed from the SCF-derived orientational order parameter of chain segments. We use these extracted trajectories to analyze the features of local packing geometry, including chain bending and tilt, as well as the terminal boundaries that delineate distinct domains in ordered BCP morphologies. We illustrate this analysis by focusing on measurable features of packing frustration in 2D (columnar) and 3D (spherical and bicontinuous) morphologies, notably establishing an explicit link between chain conformations in complex morphologies and their medial geometry., Comment: 19 pages, 15 figures