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  1. Context. Because of its proximity and the large size of its black hole, M 87 is one of the best targets for studying the launching mechanism of active galactic nucleus jets. Currently, magnetic fields are considered to be an essential factor in the launching and accelerating of the jet. However, current observational estimates of the magnetic field strength of the M 87 jet are limited to the innermost part of the jet (≲100 r s ) or to HST-1 (∼10 5   r s ). No attempt has yet been made to measure the magnetic field strength in between. Aims. We aim to infer the magnetic field strength of the M 87 jet out to a distance of several thousand r s by tracking the distance-dependent changes in the synchrotron spectrum of the jet from high-resolution very long baseline interferometry observations. Methods. In order to obtain high-quality spectral index maps, quasi-simultaneous observations at 22 and 43 GHz were conducted using the KVN and VERA Array (KaVA) and the Very Long Baseline Array (VLBA). We compared the spectral index distributions obtained from the observations with a model and placed limits on the magnetic field strengths as a function of distance. Results. The overall spectral morphology is broadly consistent over the course of these observations. The observed synchrotron spectrum rapidly steepens from α 22 − 43 GHz  ∼ −0.7 at ∼2 mas to α 22 − 43 GHz  ∼ −2.5 at ∼6 mas. In the KaVA observations, the spectral index remains unchanged until ∼10 mas, but this trend is unclear in the VLBA observations. A spectral index model in which nonthermal electron injections inside the jet decrease with distance can adequately reproduce the observed trend. This suggests the magnetic field strength of the jet at a distance of 2−10 mas (∼900 r s  − ∼4500 r s in the deprojected distance) has a range of B  = (0.3−1.0 G)( z /2mas) −0.73 . Extrapolating to the Event Horizon Telescope scale yields consistent results, suggesting that the majority of the magnetic flux of the jet near the black hole is preserved out to ∼4500 r s without significant dissipation. 
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    Free, publicly-accessible full text available May 1, 2024
  2. null (Ed.)
    We investigate the circulation of nano- and micro-particles, including spherical particles and filamentous nanoworms, with red blood cells (RBCs) suspension in a constricted channel that mimics a stenosed microvessel. Through three-dimensional simulations using the immersed boundary-based Lattice Boltzmann method, the influence of channel geometries, such as the length and ratio of the constriction, on the accumulation of particles is systematically studied. Firstly, we find that the accumulation of spherical particles with 1 μm diameter in the constriction increases with the increases of both the length and ratio of the constriction. This is attributed to the interaction between spheres and RBCs. The RBCs “carry” the spheres and they accumulate inside the constriction together, due to the altered local hydrodynamics induced by the existence of the constriction. Secondly, nanoworms demonstrate higher accumulation than that of spheres inside the constriction, which is associated with the escape of nanoworms from RBC clusters and their accumulation near the wall of main channel. The accumulated near-wall nanoworms will eventually enter the constriction, thus enhancing their concentration inside the constriction. However, an exceptional case occurs in the case of constrictions with large ratio and long length. In such circumstances, the RBCs aggregate together tightly and concentrate at the center of the channel, which makes the nanoworms hardly able to escape from RBC clusters, leading to a similar accumulation of nanoworms and spheres inside the constriction. This study may provide theoretical guidance for the design of nano- and micro-particles for biomedical engineering applications, such as drug delivery systems for patients with stenosed microvessels. 
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  3. Abstract

    We report the discoveries of a nuclear ring of diameter 10″ (∼1.5 kpc) and a potential low-luminosity active galactic nucleus (LLAGN) in the radio continuum emission map of the edge-on barred spiral galaxy NGC 5792. These discoveries are based on the Continuum Halos in Nearby Galaxies—an Expanded Very Large Array (VLA) Survey, as well as subsequent VLA observations of subarcsecond resolution. Using a mixture of Hαand 24μm calibrations, we disentangle the thermal and nonthermal radio emission of the nuclear region and derive a star formation rate (SFR) of ∼0.4Myr−1. We find that the nuclear ring is dominated by nonthermal synchrotron emission. The synchrotron-based SFR is about three times the mixture-based SFR. This result indicates that the nuclear ring underwent more intense star-forming activity in the past, and now its star formation is in the low state. The subarcsecond VLA images resolve six individual knots on the nuclear ring. The equipartition magnetic field strengthBeqof the knots varies from 77 to 88μG. The radio ring surrounds a point-like faint radio core ofS6 GHz= (16 ± 4)μJy with polarized lobes at the center of NGC 5792, which suggests an LLAGN with an Eddington ratio of ∼10−5. This radio nuclear ring is reminiscent of the Central Molecular Zone of the Galaxy. Both of them consist of a nuclear ring and LLAGN.

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  4. null (Ed.)
    Study of the permeability of small organic molecules across lipid membranes plays a significant role in designing potential drugs in the field of drug discovery. Approaches to design promising drug molecules have gone through many stages, from experiment-based trail-and-error approaches, to the well-established avenue of the quantitative structure–activity relationship, and currently to the stage guided by machine learning (ML) and artificial intelligence techniques. In this work, we present a study of the permeability of small drug-like molecules across lipid membranes by two types of ML models, namely the least absolute shrinkage and selection operator (LASSO) and deep neural network (DNN) models. Molecular descriptors and fingerprints are used for featurization of organic molecules. Using molecular descriptors, the LASSO model uncovers that the electro-topological, electrostatic, polarizability, and hydrophobicity/hydrophilicity properties are the most important physical properties to determine the membrane permeability of small drug-like molecules. Additionally, with molecular fingerprints, the LASSO model suggests that certain chemical substructures can significantly affect the permeability of organic molecules, which closely connects to the identified main physical properties. Moreover, the DNN model using molecular fingerprints can help develop a more accurate mapping between molecular structures and their membrane permeability than LASSO models. Our results provide deep understanding of drug–membrane interactions and useful guidance for the inverse molecular design of drug-like molecules. Last but not least, while the current focus is on the permeability of drug-like molecules, the methodology of this work is general and can be applied for other complex physical chemistry problems to gain molecular insights. 
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  5. null (Ed.)
    Building upon our previous studies on interactions of amphiphilic Janus nanoparticles with glass-supported lipid bilayers, we study here how these Janus nanoparticles perturb the structural integrity and induce shape instabilities of membranes of giant unilamellar vesicles (GUVs). We show that 100 nm amphiphilic Janus nanoparticles disrupt GUV membranes at a threshold particle concentration similar to that in supported lipid bilayers, but cause drastically different membrane deformations, including membrane wrinkling, protrusion, poration, and even collapse of entire vesicles. By combining experiments with molecular simulations, we reveal how Janus nanoparticles alter local membrane curvature and collectively compress the membrane to induce shape transformation of vesicles. Our study demonstrates that amphiphilic Janus nanoparticles disrupt vesicle membranes differently and more effectively than uniform amphiphilic particles. 
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  6. null (Ed.)
  7. Organic molecules and polymers have a broad range of applications in biomedical, chemical, and materials science fields. Traditional design approaches for organic molecules and polymers are mainly experimentally-driven, guided by experience, intuition, and conceptual insights. Though they have been successfully applied to discover many important materials, these methods are facing significant challenges due to the tremendous demand of new materials and vast design space of organic molecules and polymers. Accelerated and inverse materials design is an ideal solution to these challenges. With advancements in high-throughput computation, artificial intelligence (especially machining learning, ML), and the growth of materials databases, ML-assisted materials design is emerging as a promising tool to flourish breakthroughs in many areas of materials science and engineering. To date, using ML-assisted approaches, the quantitative structure property/activity relation for material property prediction can be established more accurately and efficiently. In addition, materials design can be revolutionized and accelerated much faster than ever, through ML-enabled molecular generation and inverse molecular design. In this perspective, we review the recent progresses in ML-guided design of organic molecules and polymers, highlight several successful examples, and examine future opportunities in biomedical, chemical, and materials science fields. We further discuss the relevant challenges to solve in order to fully realize the potential of ML-assisted materials design for organic molecules and polymers. In particular, this study summarizes publicly available materials databases, feature representations for organic molecules, open-source tools for feature generation, methods for molecular generation, and ML models for prediction of material properties, which serve as a tutorial for researchers who have little experience with ML before and want to apply ML for various applications. Last but not least, it draws insights into the current limitations of ML-guided design of organic molecules and polymers. We anticipate that ML-assisted materials design for organic molecules and polymers will be the driving force in the near future, to meet the tremendous demand of new materials with tailored properties in different fields. 
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