Abstract Resonance lines encode rich information about astrophysical sources and their environments, yet fully analytic treatments of multi-line radiative transfer remain almost entirely unexplored. We present exact, closed-form solutions for steady-state resonant-line radiative transfer in “V-shaped” atomic networks, where a single ground state couples to multiple transitions. Starting from the full angle-dependent transfer equation, we generalise absorption and emission coefficients to an arbitrary number of lines, derive a modified Fokker–Planck expansion of the frequency-redistribution integral, and use a judicious change of variables to reduce the problem to a Helmholtz equation with pointlike sources in frequency space. This transformation admits analytic solutions for arbitrary sets of lines with fixed frequency offsets and strengths in both slab and spherical geometries. We implement V-shaped line networks in the colt Monte Carlo radiative transfer code and find excellent agreement with the analytic predictions across a wide range of line separations, optical depths, and damping parameters, establishing our solutions as stringent validation benchmarks. For concrete applications related to the Lyman-alpha (Ly α ) transition of neutral hydrogen, we examine how fine-structure splitting and deuterium injection modify the emergent spectra, internal radiation field, and radiative force multiplier. We show that these effects leave previous conclusions about Ly α feedback in the early universe essentially unchanged. Even when direct observational diagnostics are subtle, our framework provides novel analytic and numerical insights into coupled resonance-line transport and facilitates progress in general modelling of multi-line radiative transfer in diverse astrophysical settings.

The cell surface glycocalyx is a complex and dynamic network of glycoproteins and proteoglycans that plays a pivotal role in life activities. Its three-dimensional architecture is composed of various glycosaminoglycans (GAGs) mediating various biological functions. Exploring the structure of GAGs and its interaction with proteins or the GAGs code is of great significance for revealing the molecular mechanisms of biological processes. However, the structural complexity of the glycocalyx at both cellular and tissue scales poses challenges for accurate representation, while conventional planar sensors inadequately capture its multiscale spatial characteristics, thereby limiting precise analysis of dynamic GAG-protein interactions. In this study, a three-dimensional ordered interference substrate with surface-modified heparin was constructed to simulate the fine topological structure of the glycocalyx. On this ordered porous layer interferometry (OPLI) platform, combined with experimental and computer simulation methods, the effects of heparin density, spatial distribution, and chain length on the binding behavior of SARS-CoV-2 spike protein were systematically investigated. The experimental results show that a medium heparin density can maximize the binding strength of the spike protein. The affinity of heparin for spike protein can be enhanced by increasing the density of the three-dimensional spatial distribution. Molecular docking and thermodynamic experiments suggest that hydrogen bonds rather than electrostatic interactions play a crucial role in the binding strength. This study recreates the glycocalyx microenvironment, providing a highly biomimetic platform that not only deepens the molecular understanding of viral infection but also lays a methodological foundation for GAG code analysis and drug development.

Sub-meter-resolution Polarimetric Synthetic Aperture Radar (PolSAR) imagery enables precise building footprint extraction but introduces complex scattering correlated with fine spatial structures. This change renders both traditional methods, which rely on simplified scattering models, and existing deep learning approaches, which sacrifice spatial detail through multi-looking, inadequate for high-precision extraction tasks. To address this, we propose an Orthogonal Dual-Resolution Network (ODRNet) for end-to-end, precise segmentation directly from single-look complex (SLC) data. Unlike complex-valued neural networks that suffer from high computational cost and optimization difficulties, our approach decomposes complex-valued data into its orthogonal real and imaginary components, which are then concurrently fed into a Dual-Resolution Branch (DRB) with Bilateral Information Fusion (BIF) to effectively balance the trade-off between semantic and spatial details. Crucially, we introduce an auxiliary Polarization Orientation Angle (POA) regression task to enforce physical consistency between the orthogonal branches. To tackle the challenge of diverse building scales, we designed a Multi-scale Aggregation Pyramid Pooling Module (MAPPM) to enhance contextual awareness and a Pixel-attention Fusion (PAF) module to adaptively fuse dual-branch features. Furthermore, we have constructed a VHR PolSAR building footprint segmentation dataset to support related research. Experimental results demonstrate that ODRNet achieves 64.3% IoU and 78.27% F1-score on our dataset, and 73.61% IoU with 84.8% F1-score on a large-scale SLC scene, confirming the method’s significant potential and effectiveness in high-precision building extraction directly from SLC.

Over the past two decades, epitaxial semiconductor quantum dots (QDs) have demonstrated very promising properties as sources of single and entangled photons on-demand. Among different growth methods, droplet etching epitaxy has allowed the growth of almost strain-free QDs, with low and controllable surface densities, small excitonic fine structure splitting (FSS), and fast radiative decays. Here, we extend the technique to In(Ga)As QDs in AlGaAs, thereby increasing the achievable emission wavelength range beyond that accessible to GaAs/AlGaAs QDs while preserving some of the key advantages of this growth method. We observe QD densities of ∼0.25 μm-2, FSS values as small as 3 μeV, and short radiative lifetimes of ∼300 ps, while extending the achievable emission wavelength to ∼900 nm at cryogenic temperatures. We envision these QDs to be particularly suitable for integrated quantum photonics applications.

Electronic computer-based neural networks can recover high-resolution quantitative phase images from inline holograms to reveal the fine structure of samples, which may face issues such as intensive computing, energy consumption, and time delay. In contrast, optical diffraction computing not only has super-fast parallel processing capabilities and extremely low energy consumption but also is naturally compatible with the physical process of holographic imaging. However, since existing optical diffraction computing models are not able to preserve the phase detail in phase reconstruction, they focus on intensity recovery of holograms. To address this problem, this paper proposes an all-optical residual diffraction neural network (RDNN) architecture to obtain a high-precision phase distribution of inline holograms. In this method, residual optical structures and cascaded diffraction layer designs are combined to effectively reduce the loss of phase information during reconstruction, thereby ensuring a high degree of consistency between the reconstructed phase and the original phase. A series of numerical simulation results shows that the proposed architecture can be generalized to the phase reconstruction task of three-dimensional biological samples. In phase reconstruction of human erythrocytes, the average SSIM and PSNR reach 0.82 and 28, respectively. Additionally, we assess the network’s performance with noisy holograms, demonstrating that, unlike fully connected diffraction neural networks, this approach significantly enhances phase reconstruction quality, even under random Gaussian noise. These results indicate that the proposed RDNN has the potential to replace the electronic neural network to achieve accurate phase reconstruction of inline holograms and provide an efficient solution to observe the living cell dynamics.

The potential energy curves and spectroscopic constants of the ground and several low-lying excited states of the Caq+-Kr (q = 0, 1, 2) van der Waals complexes were investigated using one- and two-electron pseudopotential approaches. This treatment effectively reduces the number of active electrons in Caq+-Kr to a single valence electron for q = 1 and two valence electrons for q = 0, allowing the use of large and flexible basis sets for both Ca and Kr atoms. Within this work, potential energy curves (PECs) were calculated at the SCF level for the Ca+-Kr system, while both SCF and full configuration interaction (FCI) calculations were performed for the neutral Ca-Kr. Spin–orbit coupling effects were explicitly included in all calculations to accurately describe the fine-structure splitting of the asymptotic atomic states. The short-range core–core interaction for Ca2+-Kr was obtained using high-level CCSD(T) calculations. Spectroscopic constants were derived from the computed PECs and compared with available theoretical and experimental results, showing consistent trends. Furthermore, the transition dipole moments (TDM) were evaluated as a function of internuclear distances, including spin–orbit effects, to provide a comprehensive description of the electronic structure and radiative properties of these weakly bound systems.

BackgroundHigh‐resolution (7T) MRI facilitates in vivo imaging of fine anatomical structures selectively affected in Alzheimer's disease (AD), including medial temporal lobe subregions. However, 7T data is challenging to acquire and largely unavailable in clinical settings. Here, we use deep learning to synthesize 7T resolution T1‐weighted MRI images from lower‐resolution (3T) images.MethodPaired 7T and 3T T1‐weighted images were acquired from 178 participants (134 clinically unimpaired, 48 impaired) from the Swedish BioFINDER‐2 study. To synthesize 7T‐resolution images from 3T images, we trained two models: a specialized U‐Net, and a U‐Net mixed with a generative adversarial network (U‐Net‐GAN) on 80% of the data. We evaluated model performance on the remaining 20%, compared to models from the literature (V‐Net, WATNet), using image‐based performance metrics and by surveying five blinded MRI professionals based on subjective quality. For n = 11 participants, amygdalae were automatically segmented with FastSurfer on 3T and synthetic‐7T images, and compared to a manually segmented “ground truth”. To assess downstream performance, FastSurfer was run on n = 3,168 triplets of matched 3T and AI‐generated synthetic‐7T images, and a multi‐class random forest model classifying clinical diagnosis was trained on both datasets.ResultSynthetic‐7T images were generated for images in the test set (Figure 1A). Image metrics suggested the U‐Net as the top performing model (Figure 1B), though blinded experts qualitatively rated the GAN‐U‐Net as the best looking images, exceeding even real 7T images (Figure 1C). Automated segmentations of amygdalae from the synthetic GAN‐U‐Net model were more similar to manually segmented amygdalae, compared to the original 3T they were synthesized from, in 9/11 images (Figure 2). Classification obtained modest performance (accuracy∼60%) but did not differ across real or synthetic images (Figure 3A). Synthetic image models used slightly different features for classification (Figure 3B).ConclusionSynthetic T1‐weighted images approaching 7T resolution can be generated from 3T images, which may improve image quality and segmentation, without compromising performance in downstream tasks. This approach holds promise for better measurement of deep cortical or subcortical structures relevant to AD. Work is ongoing toward improving performance, generalizability and clinical utility.

The rod-like Au42(SC8H9)32 monolayer-protected cluster (MPC) was studied using two-dimensional electronic spectroscopy (2DES). This study combined analysis of the excitation power, cross-peak specific maps, and time-dependent 2DES signals that resulted from excitation of a longitudinal electronic resonance at 13 500 cm-1. The Au42(SC8H9)32 longitudinal resonance is of interest due to the exceptional photothermal efficiency of this MPC. A traditional plasmonic gold nanorod is used throughout as a point of comparison. The excitation power study and 2DES results obtained from excitation of the longitudinal resonance were distinct from plasmonic excitations, thus implicating it as an exitonic system. The time-dependent signal amplitudes of 2DES cross-peaks showed that the longitudinal mode consisted of multiple electronic fine structure states that internally converted in a state-to-state manner. Taken together, these results point to a manifold of nondegenerate electronic states, rather than a collective plasmon resonance, that comprise the longitudinal mode excitation and dynamics of Au42(SC8H9)32.

L-edge x-ray absorption spectra for first-row transition metal complexes are obtained from relativistic equation-of-motion singles and doubles coupled-cluster (EOM-CCSD) calculations that make use of the core–valence separation scheme, with scalar and spin–orbit relativistic effects modeled within the molecular mean-field exact two-component (X2C) framework. By incorporating relativistic effects variationally at the Dirac–Coulomb–Breit reference level, this method delivers accurate predictions of L-edge features, including energy shifts, intensity ratios, and fine-structure splittings, across a range of molecular systems. Benchmarking against perturbative spin–orbit treatments and relativistic TDDFT highlights the superior performance and robustness of the CVS-DCB-X2C-EOM-CCSD approach, including the reliability of basis set recontraction schemes. While limitations remain in describing high-density spectral regions, our results establish CVS-DCB-X2C-EOM-CCSD as a powerful and broadly applicable tool for relativistic core-excitation spectroscopy.

Opportunities from energy-loss near-edge fine structure analysis to track chemical and structural damage in zircon.

Corrigendum to ‘The fine structure and seismogenic mechanism of the Yangjiang intraplate seismic zone in Guangdong Province, South China’ [Tectonophysics 914 (2025) 230904

Raspberry polysaccharide extraction, purification, structural characterization, and biological activity research progress: A review.

Deep spatiotemporal clutter filtering of transthoracic echocardiographic images: Leveraging contextual attention and residual learning.

Structural elucidation, in vivo tracking and pharmacokinetic profiling of a novel polysaccharide with anti-migraine efficacy.

The functional morphology of hawkmoth lamina monopolar cells reveals mechanisms of spatial processing in insect motion vision.

BackgroundNon-destructive testing (NDT) is crucial for the preservation and restoration of ancient wooden structures, with Computed Tomography (CT) increasingly utilized in this field. However, practical CT examinations of these structures-often characterized by complex configurations, large dimensions, and on-site constraints-frequently encounter difficulties in acquiring full-angle projection data. Consequently, images reconstructed under limited-angle conditions suffer from poor quality and severe artifacts, hindering accurate assessment of critical internal features such as mortise-tenon joints and incipient damage.ObjectiveThis study aims to develop a novel algorithm capable of achieving high-quality image reconstruction from incomplete, limited-angle projection data.MethodsWe propose CADRE (Contour-guided Alternating Direction Method of Multipliers-optimized Deep Radon Enhancement), an unsupervised deep learning reconstruction framework. CADRE innovatively integrates the ADMM optimization strategy, the learning paradigm of Deep Radon Prior (DRP) networks, and a geometric contour-guidance mechanism. This approach synergistically enhances reconstruction performance by iteratively optimizing network parameters and input images, without requiring large-scale paired training data, rendering it particularly suitable for cultural heritage applications.ResultsSystematic validation using both a digital dougong simulation model of the Yingxian Wooden Pagoda and a physical wooden dougong model from Foguang Temple demonstrates that, under typical 90° and 120° limited-angle conditions, the CADRE algorithm significantly outperforms traditional FBP, iterative reconstruction algorithms SART and ADMM-TV, and other representative unsupervised deep learning methods (Deep Image Prior, DIP; Residual Back-Projection with DIP, RBP-DIP; DRP). This superiority is evident in quantitative metrics such as PSNR and SSIM, as well as in visual quality, including artifact suppression and preservation of structural details. CADRE exhibits exceptional capability in accurately reproducing internal mortise-tenon configurations and fine features within ancient timber.ConclusionThe CADRE algorithm provides a robust and efficient solution for limited-angle CT image reconstruction of ancient wooden structures. It effectively overcomes the limitations of existing methods in handling incomplete data, significantly enhances the quality of reconstructed images and the characterization of internal fine structures, and offers strong technical support for the scientific understanding, condition assessment, and precise conservation of cultural heritage, thereby holding substantial academic value and promising application prospects.

The ability to synthesize next-generation lanthanide and actinide molecular materials with designer photophysical properties rests squarely on our ability to predict, control, and measure their electronic structure. This is especially true of the crystal field (CF) interactions of the metal, which are the only interactions that can be appreciably tuned by ligand design. Herein we present ultraviolet-visible-near infrared magnetic linear dichroism (MLD) spectroscopy as an underutilized magneto-optical technique that holds immense promise in the elucidation of f-block electronic structure. We use a PrIII polyoxometalate complex with pseudo-D 4d symmetry, [n-Bu4N]3[Pr{Mo5O13(OMe)4(NO)}2] (1·Pr), to demonstrate that acquisition of both magnetic circular dichroism (MCD) and MLD spectra allows definitive assignment of the observed CF levels through the complementary selection rules of these techniques. We provide general MCD and MLD sign patterns that can be applied to any (pseudo)-D 4d PrIII complex to facilitate the assignment of fine structure. Our assignments for 1·Pr allow us to fit its transitions with a phenomenological Hamiltonian, providing insight into its CF splitting and solution geometry along with entirely experimentally-derived wavefunctions for its states without use of density functional theory or multireference computational techniques.

Decoding chromatin nanoscale plasticity in situ: Insights from native AFM imaging.

Abstract The sporadic E (Es) layer is an ionospheric layer of very high density in the E region. Prediction of the Es layer generation is not yet possible. To understand the generation mechanisms, the structures of the Es layer and their evolution were studied. Data a ultra-dense global navigation satellite system (GNSS) network over Japan operated by SoftBank, rate-of-total electron content (ROTI) maps with $$0.05^\circ \times 0.05^\circ$$ 0 . 05 ∘ × 0 . 05 ∘ resolutions in the latitude and longitudes (approximately 5 $$\times$$ × 5 km at the E region altitudes) were derived. Complex fine horizontal structures of the sporadic E (Es) layer which occurred over Japan in the daytime on 17 May 2024 were imaged. Fine-scale structures of the Es layer and their temporal evolution from generation to decay were elucidated. The complex structures, chain of small patches, vortices, and ripples were observed. The scale size and lifetime were similar to those predicted by high spatial resolution numerical simulations. Future studies include the statistics of occurrences of different complex shapes, scale sizes of them, area of the Es layer, and lifetime. Graphical Abstract

Magnetoacoustic tomography with magnetic induction (MAT-MI) is a promising noninvasive, radiation-free imaging technique capable of millimeter-level spatial resolution for mapping tissue conductivity. However, its application to luminal tissues (e.g., digestive tract, vasculature) is significantly hindered by low image quality due to acoustic wave attenuation and inherent tissue properties. This study aimed to overcome the image quality limitation in MAT-MI imaging of luminal structures by utilizing liquid metal (LM) as a novel conductive contrast agent and implementing M-sequence coded excitation to enhance signal strength and acquisition efficiency. The LM contrast agent used was a biocompatible Ga67In20.5Sn12.5 alloy (σ=3.1×10⁶ S/m). MAT-MI experiments were conducted on gel phantoms containing LM in various shapes and simulated luminal structures (PVC tubes, ex vivo mouse intestines), ex vivo mouse stomach tissues infused with LM or PBS (control), and in vivo mouse stomachs. The MAT-MI system employed 0.34 T static magnetic field, a pulsed excitation coil (1MHz center frequency), and an ultrasound transducer. We applied M-sequence coded excitation (up to 31-bit) and processed the signals using pulse compression followed by filtered back-projection reconstruction. Safety was assessed via long-term biocompatibility studies, blood elemental analysis, and histological examination (H&E staining) of gastrointestinal tissues. Integrating LM into the imaging target greatly increased the MAT-MI signal intensity. In vivo imaging of mouse stomachs demonstrated an approximate 28dB increase in image quality after LM infusion compared to pre-infusion imaging. LM enabled clear visualization within fine luminal structures (down to 0.5mm inner diameter) in phantoms and ex vivo tissues. M-sequence coding further improved image clarity and reduced total imaging time by approximately 84% relative to single-pulse excitation (from 6360s to 1020s for comparable image quality). High-resolution imaging (approximately 2mm spatial resolution) of LM distribution was achieved. The LM was safely excreted, with no significant toxicity observed in blood analysis or histology over 180 days. LM proved to be an effective and safe contrast agent for MAT-MI, significantly enhancing image quality and enabling high-quality imaging of luminal tissues, including the first successful in vivo visualization of the stomach in a live animal model. Combined with M-sequence coded excitation, this approach overcomes key limitations of conventional MAT-MI and could broaden its diagnostic utility in cardiovascular and gastrointestinal imaging.