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Abstract Magnetic fields of molecular clouds in the central molecular zone (CMZ) have been relatively under-observed at sub-parsec resolution. Here, we report JCMT/POL2 observations of polarized dust emission in the CMZ, which reveal magnetic field structures in dense gas at ∼0.5 pc resolution. The 11 molecular clouds in our sample include two in the western part of the CMZ (Sgr C and a farside cloud candidate), four around the Galactic longitude 0 (the 50 km s⁻¹cloud, CO 0.02−0.02, theStone, and theSticksandStrawamong the Three Little Pigs), and five along the Dust Ridge (G0.253+0.016, clouds b, c, d, and e/f), for each of which we estimate the magnetic field strength using the angular dispersion function method. The morphologies of magnetic fields in the clouds suggest potential imprints of feedback from expanding Hiiregions and young massive star clusters. A moderate correlation between the total viral parameter versus the star formation rate (SFR) and the dense gas fraction of the clouds is found. A weak correlation between the mass-to-flux ratio and the SFR, and a weak anticorrelation between the magnetic field and the dense gas fraction are also found. Comparisons between magnetic fields and other dynamic components in clouds suggest a more dominant role of self-gravity and turbulence in determining the dynamical states of the clouds and affecting star formation at the studied scales. 

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. 

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. 

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. 

The margination and adhesion of micro-particles (MPs) have been extensively investigated separately, due to their important applications in the biomedical field. However, the cascade process from margination to adhesion should play an important role in the transport of MPs in blood flow. To the best of our knowledge, this has not been explored in the past. Here we numerically study the margination behaviour of elastic MPs to blood vessel walls under the interplay of their deformability and adhesion to the vessel wall. We use the lattice Boltzmann method and molecular dynamics to solve the fluid dynamics and particle dynamics (including red blood cells (RBCs) and elastic MPs) in blood flow, respectively. Additionally, a stochastic ligand–receptor binding model is employed to capture the adhesion behaviours of elastic MPs on the vessel wall. Margination probability is used to quantify the localization of elastic MPs at the wall. Two dimensionless numbers are considered to govern the whole process: the capillary number $Ca$ , denoting the ratio of viscous force of fluid flow to elastic interfacial force of MP, and the adhesion number $Ad$ , representing the ratio of adhesion strength to viscous force of fluid flow. We systematically vary them numerically and a margination probability contour is obtained. We find that there exist two optimal regimes favouring high margination probability on the plane $$Ca{-}Ad$$ . The first regime, namely region I, is that with high adhesion strength and moderate particle stiffness; the other one, region II, has moderate adhesion strength and large particle stiffness. We conclude that the existence of optimal regimes is governed by the interplay of particle deformability and adhesion strength. The corresponding underlying mechanism is also discussed in detail. There are three major factors that contribute to the localization of MPs: (i) near-wall hydrodynamic collision between RBCs and MPs; (ii) deformation-induced migration due to the presence of the wall; and (iii) adhesive interaction between MPs and the wall. Mechanisms (i) and (iii) promote margination, while (ii) hampers margination. These three factors perform different roles and compete against each other when MPs are located in different regions of the flow channel, i.e. near-wall region. In optimal region I, adhesion outperforms deformation-induced migration; and in region II, the deformation-induced migration is small compared to the coupling of near-wall hydrodynamic collision and adhesion. The finding of optimal regimes can help the understanding of localization of elastic MPs at the wall under the adhesion effect in blood flow. More importantly, our results suggest that softer MP or stronger adhesion is not always the best choice for the localization of MPs. 

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.