Detailed Structure Research Articles

PST-Diff: Achieving High-Consistency Stain Transfer by Diffusion Models With Pathological and Structural Constraints.

Histopathological examinations heavily rely on hematoxylin and eosin (HE) and immunohistochemistry (IHC) staining. IHC staining can offer more accurate diagnostic details but it brings significant financial and time costs. Furthermore, either re-staining HE-stained slides or using adjacent slides for IHC may compromise the accuracy of pathological diagnosis due to information loss. To address these challenges, we develop PST-Diff, a method for generating virtual IHC images from HE images based on diffusion models, which allows pathologists to simultaneously view multiple staining results from the same tissue slide. To maintain the pathological consistency of the stain transfer, we propose the asymmetric attention mechanism (AAM) and latent transfer (LT) module in PST-Diff. Specifically, the AAM can retain more local pathological information of the source domain images, while ensuring the model's flexibility in generating virtual stained images that highly confirm to the target domain. Subsequently, the LT module transfers the implicit representations across different domains, effectively alleviating the bias introduced by direct connection and further enhancing the pathological consistency of PST-Diff. Furthermore, to maintain the structural consistency of the stain transfer, the conditional frequency guidance (CFG) module is proposed to precisely control image generation and preserve structural details according to the frequency recovery process. To conclude, the pathological and structural consistency constraints provide PST-Diff with effectiveness and superior generalization in generating stable and functionally pathological IHC images with the best evaluation score. In general, PST-Diff offers prospective application in clinical virtual staining and pathological image analysis.

Passive Source Reverse Time Migration Based on the Spectral Element Method

AbstractIncreasing deployment of dense arrays has facilitated detailed structure imaging for tectonic investigation, hazard assessment and resource exploration. Strong velocity heterogeneity and topographic changes have to be considered during passive source imaging. However, it is quite challenging for ray‐based methods, such as Kirchhoff migration or the widely used teleseismic receiver function, to handle these problems. In this study, we propose a 3‐D passive source reverse time migration strategy based on the spectral element method. It is realized by decomposing the time reversal full elastic wavefield into amplitude‐preserved vector P and S wavefields by solving the corresponding weak‐form solutions, followed by a dot‐product imaging condition to get images for the subsurface structures. It enables us to use regional 3‐D migration velocity models and take topographic variations into account, helping us to locate reflectors at more accurate positions than traditional 1‐D model‐based methods, like teleseismic receiver functions. Two synthetic tests are used to demonstrate the advantages of the proposed method to handle topographic variations and complex velocity heterogeneities. Furthermore, applications to the Laramie array data using both teleseismic P and S waves enable us to identify several south‐dipping structures beneath the Laramie basin in southeast Wyoming, which are interpreted as the Cheyenne Belt suture zone and agree with, and improve upon previous geological interpretations.

Morphological properties of two-dimensional and three-dimensional bedforms in open channel flow: A flume experiments study

Bedforms are formed by sediment particles under the action of flow, which in turn affect flow and sediment movement. However, the fundamental mechanisms behind the formation and development of bedforms under varying flow conditions, the interconnection between sediment particle movement and bed morphology development, as well as the influence of bedforms on flow and sediment transport, are still not well understood. In the current study, the moveable bed load transport flume experiments under different flow conditions were done, and the development processes of two-dimensional and three-dimensional dunes formed under different flow intensities were simulated. The detailed structure of the bedforms was measured by laser scanning technology, the characteristics of the bedforms were analyzed in detail, and the relations between the formation of the bedforms and the movement of sediment particles are discussed. The results found that the correlation coefficients of the longitudinal profile curve can serve as a quick reference for distinguishing two-dimensional and three-dimensional dunes. When the crestline ratio is used as the criterion for two-dimensional and three-dimensional dunes, the relative amplitude of the dune crestline and the included angle with the flow direction should also be comprehensively considered. The lee side angles of dunes calculated using the triangle generalization method and local section tangent method are compared and show that the values obtained by the former method are only about half of that of the latter. It is also found that the lee side angles of dunes are related to the dune height. The superimposed dunes generally exist in the downstream area of the stoss side of the dunes. The local bed slopes on the stoss side of the dunes show reverse slopes. The superimposed dunes improve the local bed height and further increase the reverse slope degree of the stoss side. A streamwise ridge is an important form located in the upstream area of the dunes stoss side, and they are symmetrically distributed on both sides. Multiple streamwise ridges divide the stoss side of the dunes into relatively independent movement areas, restricting the movement of sediment particles to specific regions. According to the distribution characteristics of bed morphologies, the effects of dunes on sediment particle movement and flow energy consumption are analyzed.

MGlu4R, mGlu7R, and mGlu8R allosteric modulation for treating acute and chronic neurodegenerative disorders.

Neuroprotection, defined as safeguarding neurons from damage and death by inhibiting diverse pathological mechanisms, continues to be a promising approach for managing a range of central nervous system (CNS) disorders, including acute conditions such as ischemic stroke and traumatic brain injury (TBI) and chronic neurodegenerative diseases like Parkinson's disease (PD), Alzheimer's disease (AD), and multiple sclerosis (MS). These pathophysiological conditions involve excessive glutamatergic (Glu) transmission activity, which can lead to excitotoxicity. Inhibiting this excessive Glu transmission has been proposed as a potential therapeutic strategy for treating the CNS disorders mentioned. In particular, ligands of G protein-coupled receptors (GPCRs), including metabotropic glutamatergic receptors (mGluRs), have been recognized as promising options for inhibiting excessive Glu transmission. This review discusses the complex interactions of mGlu receptors with their subtypes, including the formation of homo- and heterodimers, which may vary in function and pharmacology depending on their protomer composition. Understanding these intricate details of mGlu receptor structure and function enhances researchers' ability to develop targeted pharmacological interventions, potentially offering new therapeutic avenues for neurological and psychiatric disorders. This review also summarizes the current knowledge of the neuroprotective potential of ligands targeting group III mGluRs in preclinical cellular (in vitro) and animal (in vivo) models of ischemic stroke, TBI, PD, AD, and MS. In recent years, experiments have shown that compounds, especially those activating mGlu4 or mGlu7 receptors, exhibit protective effects in experimental ischemia models. The discovery of allosteric ligands for specific mGluR subtypes has led to reports suggesting that group III mGluRs may be promising targets for neuroprotective therapy in PD (mGlu4R), TBI (mGlu7R), and MS (mGlu8R).

Pan-genome analysis of GT64 gene family and expression response to Verticillium wilt in cotton

BackgroundThe GT64 subfamily, belonging to the glycosyltransferase family, plays a critical function in plant adaptation to stress conditions and the modulation of plant growth, development, and organogenesis processes. However, a comprehensive identification and systematic analysis of GT64 in cotton are still lacking.ResultsThis study used bioinformatics techniques to conduct a detailed investigation on the GT64 gene family members of eight cotton species for the first time. A total of 39 GT64 genes were detected, which could be classified into five subfamilies according to the phylogenetic tree. Among them, six genes were found in upland cotton. Furthermore, investigated the precise chromosomal positions of these genes and visually represented their gene structure details. Moreover, forecasted cis-regulatory elements in GhGT64s and ascertained the duplication type of the GT64 in the eight cotton species. Evaluation of the Ka/Ks ratio for similar gene pairs among the eight cotton species provided insights into the selective pressures acting on these homologous genes. Additionally, analyzed the expression profiles of the GT64 gene family. Overexpressing GhGT64_4 in tobacco improved its disease resistance. Subsequently, VIGS experiments conducted in cotton demonstrated reduced disease resistance upon silencing of the GhGT64_4, may indicate its involvement in affecting lignin and jasmonic acid biosynthesis pathways, thus impacting cotton resistance. Weighted Gene Co-expression Network Analysis (WGCNA) revealed an early immune response against Verticillium dahliae in G. barbadense compared to G. hirsutum. Quantitative Reverse Transcription Polymerase Chain Reaction (qRT-PCR) analysis indicated that some GT64 genes might play a role under various biotic and abiotic stress conditions.ConclusionsThese discoveries enhance our knowledge of GT64 family members and lay the groundwork for future investigations into the disease resistance mechanisms of this gene in cotton.

Design, Synthesis, Characterization, Molecular Docking Studies, In vitro and In vivo Cervical Cancer Activity of Novel N-Substituted Pyrazole Derivatives

The aim of this study is to design, synthesize, characterize few novel N-substituted pyrazole derivatives and evaluated for their anticancer capabilities; these compounds were selected based on their ability to inhibit the HPV E6 protein. The IR, 1H & 13C NMR, mass spectral and elemental analysis were used to determine the structural details of the newly synthesized substances. The molecular docking was performed using Auto Dock Vina, and the protein-ligand interaction was analyzed using Discovery Studio. The sulforhodamine B (SRB) assay was used to determine the in vitro anticancer activity against the HeLa cell line. Body weight analysis, mean survival time and % increase in life span approaches were used to assess in vivo anticancer effectiveness in Swiss albino mice bearing Dalton’s Lymphoma Ascites (DAL) tumor model. Synthesized compounds have excellent docking energies, according to docking experiments. Compounds I, II and III were selected for anticancer activity in vivo DAL-bearing mice based on the findings of their in vitro and SRB assays exhibiting the antiproliferative activity. Pyrazole derivatives, compounds I and III, demonstrated significant potential as anticancer agents.

Shape-Guided Detection: A joint network combining object detection and underwater image enhancement together

Most of the existing underwater image object detection methods involve pre-processing, such as using underwater image enhancement, to improve the accuracy of object detection. However, pre-processing methods are designed to improve the subjective perception of the human eye, which does not necessarily improve the object detection performance and consumes a large amount of computational resources. Therefore, in this paper, we creatively combine these two tasks and propose a Shape-Guided Detection network (SGD) to simultaneously optimize underwater image enhancement and object detection. In the SGD network, we innovatively incorporate the prior shape features as a learnable module embedded in it to fully explore the shape characteristics and structural details of the target object. To ensure that the prior knowledge can be effectively fused into the global network structure, we design a Shape Prior Enhancement module, which aims to realize the deep integration of the prior information with the local details. In order to optimize the stability of model training and enhance its convergence performance, a dual strategy of explicit and implicit constraints is ingeniously proposed in our method. We conduct extensive experiments on public datasets and the results show that the combination of our method with different detectors significantly improves the performance. The object detection performance reaches up to 0.491 mAP for optical images and 0.576 mAP for sonar images, and improves the preprocessing speed by 0.1 s.

2D Sedimentary structures at the southeast margin of the Tarim Basin, China, constrained by love wave ambient noise tomography

Summary Imaging the detailed structure of sedimentary basins is crucial for natural resource exploration and essential for better analysis and correction of sediment responses when studying deeper interior earth structures using seismic data. In the Tarim Basin, previous studies on the sedimentary structures are mostly obtained by active-source seismic surveys, which can provide high-resolution underground interface information but are highly costly or environmentally unfriendly. In this paper, ambient noise tomography, an efficient and economical method based on background vibration, is employed to construct the sedimentary velocity structure at the southeast margin of the Tarim Basin. Based on ambient noise data collected from a linear dense short-period seismic array, we extract Love wave signals from T-T component cross-correlation functions (CCFs) and measure Love wave dispersion curves at a period band of ∼0.3–11.5 s. Then, we utilize a one-step direct surface wave tomography method to image a fine 2D sedimentary shear wave velocity structure with a depth reaching 10 km. Our results reveal a clear layered sedimentary structure, the paleo Tadong uplift, and the thrust Cherchen fault. Our study provides reference sedimentary velocity models for the southeastern Tarim Basin, focusing on depths shallower than 10 km. This model is intended to serve as crucial input for studies requiring detailed shallow sedimentary velocity data. Moreover, our research demonstrates that the application of the ambient noise tomography method with dense arrays has great potential for enhancing resource exploration efforts in sedimentary basins.

Fast Green FCF Improves Depiction of Extracellular Matrix in Ex Vivo Fluorescence Confocal Microscopy.

Rapid microscopic analysis of tissue is an essential diagnostic tool in oncological surgery. The gold standard for intraoperative histological tissue evaluation is frozen sections. However, frozen sections are prone to a variety of artefacts and require skilled staff and specialized lab equipment. A potential method for rapid intraoperative tissue evaluation that does not require fixation, freezing, or sectioning of the tissue is ex vivo fluorescence confocal microscopy (FCM). The visualization of the structurally important extracellular matrix (ECM) in conventional ex vivo FCM lags behind the standards of conventional histology. The objective of this study was to find a stain that would improve the depiction of the ECM to resemble FFPE H&E sections as closely as possible. Eleven different tissue stains were tested on 122 tissue samples submitted to the Department of Pathology at the Medical University of Vienna. This study was conducted on the RS-G4 Upright (Caliber I.D. Rochester, NY, USA, distributed in Europe by MAVIG GmbH, Munich, Germany). Fast Green FCF (FGFCF) in combination with acridine orange as a nuclear stain improved the visibility of the structural details of the ECM. Morphological details in FCM were equivalent or even superior to frozen sections in most analyzed categories. The addition of FGFCF to the conventional staining protocol improves the assessment of the ECM and analysis of fibrosis. The rapid staining protocol is compatible with an application in intraoperative microscopy.

DCW-YOLO: An Improved Method for Surface Damage Detection of Wind Turbine Blades

Wind turbine blades (WTBs) are prone to damage from their working environment, including surface peeling and cracks. Early and effective detection of surface defects on WTBs can avoid complex and costly repairs and serious safety hazards. Traditional object detection methods have disadvantages of insufficient detection capabilities, extended model inference times, low recognition accuracy for small objects, and elongated strip defects within WTB datasets. In light of these challenges, a novel model named DCW-YOLO for surface damage detection of WTBs is proposed in this research, which leverages image data collected by unmanned aerial vehicles (UAVs) and the YOLOv8 algorithm for image analysis. Firstly, Dynamic Separable Convolution (DSConv) is introduced into the C2f module of YOLOv8, allowing the model to more effectively focus on the geometric structural details associated with damage on WTBs. Secondly, the upsampling method is replaced with the content-aware reassembly of features (CARAFE), which significantly minimizes the degradation of image characteristics throughout the upsampling process and boosts the network’s ability to extract features. Finally, the loss function is substituted with the WIoU (Wise-IoU) strategy. This strategy allows for a more accurate regression of the target bounding boxes and helps to improve the reliability in the localization of WTBs damages, especially for low-quality examples. This model demonstrates a notable superiority in surface damage detection of WTBs compared to the original YOLOv8n and has achieved a substantial improvement in the mAP@0.5 metric, rising from 91.4% to 93.8%. Furthermore, in the more rigorous mAP@0.5–0.95 metric, it has also seen an increase from 68.9% to 71.2%.

Breaking Down the Bottlebrush: Atomically Detailed Structural Dynamics of Mucins.

Mucins, the biomolecular components of mucus, are glycoproteins that form a thick physical barrier at all tissue-air interfaces, forming a first line of defense against pathogens. Structural features of mucins and their interactions with other biomolecules remain largely unexplored due to the challenges associated with their high-resolution characterization. Combining limited mass spectrometry glycomics and protein sequencing data, we present all-atom, explicitly solvated molecular dynamics simulations of a major respiratory mucin, MUC5B. We detail key forces and degrees of freedom imposed by the extensive O-glycosylation, which imbue the canonically observed bottlebrush-like structures to these otherwise intrinsically disordered protein backbones. We compare our simulation results to static structures observed in recent scanning tunneling microscopy experiments as well as other published experimental efforts. Our work represents the demonstration of a workflow applied to a mucin example, which we hope will be employed by other groups to investigate the dynamics and interactions of other mucins, which can inform on structural details currently inaccessible to experimental techniques.

Theoretical investigation of [formula omitted]-type doping modulated interlayer magnetic and potential topological phases transition in MnSb[formula omitted]Te[formula omitted] based systems

MnSb2Te4, as an important member of intrinsic magnetic topological insulator MnBi2Te4 family, shows promise for realizing quantum anomalous Hall effect (QAHE). However, interlayer A-type antiferromagnetic coupling has been a challenge to successfully observe this novel physical phenomenon in MnSb2Te4 system. Based on density functional theory, our calculations demonstrated that doping p-type non-magnetic elements into 2 SLs and 3 SLs of MnSb2Te4 can induce a magnetic phase transition from interlayer antiferromagnetic to ferromagnetic states with a Curie temperature up to 52.2 K. Moreover, our calculations present that interlayer ferromagnetic coupling and band gap in Ca-doped 2 SLs MnSb2Te4 systems can be further tuned by applying compressive strain. In addition, the interlayer ferromagnetic coupling in MnSb2Te4/Sb2Te3/MnSb2Te4 sandwich heterostructure by p-type doping can be enhanced, The detailed electronic structure analysis show that realization of interlayer ferromagnetic coupling is mainly related to a redistribution of d-electron orbital occupancy in Mn atoms of various SL layers. These findings not only advance the understanding of interlayer ferromagnetic order in ultrathin MnSb2Te4 layers, but also provide new way to realize the QAHE in MnSb2Te4 based systems without introducing extraneous magnetic disorder.

Detection of Guanine Quadruplexes by Raman Optical Activity and Quantum-Chemical Interpretation of the Spectra.

Quadruplexes formed by guanine derivatives or guanine-rich nucleic acids are involved in metabolism and genetic storage of many living organisms, they are used in DNA nanotechnologies or as biosensors. Since many quadruplex geometries are possible the determination of their structures in aqueous solutions is difficult. Raman optical activity (ROA) can make it easier: For guanosine monophosphate (GMP), we observed a distinct change of the spectra upon its condensation and quadruplex formation. The vibrational bands become more numerous, stronger, and narrower. In particular, a huge ROA signal appears below 200 cm-1. The aggregation can be induced by high concentration, low temperature, or by a metal ion. We focused on well-defined quadruplexes stabilized by potassium, where using molecular dynamics and density functional theory the spectra and particular features related to GMP geometric parameters could be understood. The simulations explain the main experimental trends and confirm that the ROA spectroscopy is sensitive to fine structural details, including guanine base twist in the quadruplex helix.

Luminescent Ln-MOFs for Chemical Sensing Application on Biomolecules.

At present, the application of rare-earth organic frameworks (Ln-MOFs) in fluorescence sensing has entered rapid development and shown great potential in various analytical fields, such as environmental analysis, food analysis, drug analysis, and biological and clinical analysis by utilizing their internal porosity, tunable structural size, and energy transfer between rare-earth ions, ligands, and photosensitizer molecules. In addition, because the luminescence properties of rare-earth ions are highly dependent on the structural details of the coordination environment surrounding the rare-earth ions, and although their excitation lifetimes are long, they are usually not burst by oxygen and can provide an effective platform for chemical sensing. In order to further promote the development of fluorescence sensing technology based on Ln-MOFs, we summarize and review in detail the latest progress of the construction of Ln-MOF materials for fluorescence sensing applications and related sensor components, including design strategies, preparation methods, and modification considerations and initially propose the future development prospects and prospects.

3D Point Cloud Semantic Segmentation based on Multi-scale Dense Nested Networks

Aiming at the problem that the relationship between geometric features and semantic features is ignored in the point cloud data downsampling process, which leads to inaccurate segmentation of object boundaries and structural details, this paper proposes a 3D point cloud semantic segmentation network based on multi-scale dense nested type. Firstly, a dense nested network architecture is constructed by nesting multiple multi-scale feature fusion modules to fuse multi-scale features of different directions between encoder-decoder paths, so as to effectively propagate local ge-ometric context information and enhance the ability of cross-scale information interaction. Secondly, a local feature aggregation unit is constructed in the multi-scale feature fusion module, which strengthens the structural awareness within the local point set based on graph convolution and attention mechanism, and promotes the complementarity of local geometric features and abstract semantic information. Then, the cross-layer multi-loss supervision module is combined to further optimize the multi-scale feature propagation, which makes the network training more stable and improves the point cloud segmentation accuracy. Finally, this paper verified the proposed network on the S3DIS dataset. The experimental results show that the proposed network has the mean intersection over Union of 71.2% and the overall accuracy of 88.7%, which is 1.2 and 0.7 percentage points higher than RandLA-Net, respectively, which proves that the proposed network can effectively improve the accuracy of 3D point cloud semantic segmentation.

Morphological characters and molecular data reveal ten new forest macrofungi species from Hebei Province, North China

ABSTRACT China has a complex and diverse forest ecological environment, which breeds abundant forest macrofungi, including some edible, medicinal, and poisonous species. During the investigations of macrofungi in the Saihanba National Nature Reserve, North China, we collected abundant specimens of Agaricales and Polyporales within the Agaricomycetes. Based on the morphological characters and molecular evidence of DNA sequences including the internal transcribed spacer (ITS) regions, the large subunit of nuclear ribosomal RNA gene (nLSU), the small subunit of mitochondrial rRNA gene (mtSSU), the small subunit of nuclear ribosomal RNA gene (nuSSU), the largest subunit of RNA polymerase II (RPB1), the second largest subunit of RNA polymerase II gene (RPB2), the β-tubulin gene (TUB), and the translation elongation factor 1-α gene (TEF1), this study identifies ten species of Agaricales and Polyporales new to science, viz. Cyanosporus subpopuli, Gelatinofungus betulina, Lycoperdon pseudoperlatum, Macrocystidia hebeiensis, Mycena subbrunnea, M. subpura, M. variispora, M. violocea-ardesiaca, Picipes griseus, and Pleuroflammula hebeiensis. Detailed morphological descriptions, fruiting bodies, and microscopic structure diagrams of these ten novel species are provided.

Shallow crustal structure of the northern Longmen Shan fault zone revealed by a dense seismic array with ambient noise analysis

The Longmen Shan (LMS) fault zone located in the southwest of China is not only the boundary tectonic zone of the Tibetan Plateau and South China block, but also an important part of the north–south seismic belt of China. To investigate the detailed crustal structure of the LMS fault zone, we deployed a dense seismic array of 151 short-period temporary stations in its northern section from March 16 to April 6, 2023. Continuous vertical component ambient noise waveforms recorded by these stations were cross-correlated for all station pairs, enabling the extraction of Rayleigh wave phase velocity dispersion curves across periods ranging from 0.5 s to 6 s. We then inverted these data to derive the crustal shear wave velocity using direct surface wave tomography. Meanwhile, a linear sub-array consisting of 54 stations and crossing the surface rupture of the 2008 Wenchuan earthquake was used to image the fault structure by applying the Transmitted Surface Wave Reverse Time Migration method (TSW-RTM). The ambient noise tomography results show that strong lateral heterogeneity exists in the shallow crust in the study area, with a high shear velocity in the northwest and a low shear velocity in the southeast. The low shear velocity is generally distributed between the Yingxiu-Beichuan fault (YBF) and the Guanxian-Jiangyou fault (GJF). The results of TSW-RTM indicate that the Wenchuan-Maoxian fault (WMF) and the YBF are both characterized with a steep dip to the northwest. Our results could serve as the important basis for the study of segmentation characteristics of the LMS fault and seismic hazard preparation and mitigation.

Raman Spectroscopic Analysis of Steviol Glycosides: Spectral Database and Quality Control Algorithms

Besides all sharing an extraordinary high (i.e., up to ~450 times) sweetening power as compared to sucrose and while presenting strong similarities in their molecular structures, molecules belonging to the family of diterpene glycosides (i.e., the secondary metabolites of Stevia rebaudiana) differ in specific structural details that strongly impact on their levels of sweetness and bitter aftertaste. Given the nutritional and pharmacological benefits of steviol secondary metabolites as natural dietetic and anti-diabetic remedies, extraction and purification of steviol glycosides from plant material are nowadays widely spread among many countries. However, an unpleasant bitter aftertaste, which is linked to a genetic variation in human bitter taste receptors, hampers the full exploitation of such benefits and calls for a prompt improvement in organoleptic property control of stevia products. A deeper understanding of the molecular structure of different steviol glycosides and the consequent development of promptly measurable criteria for the organoleptic performance of their mixtures will support processing optimization and control of taste profiles within desired yields. The present research aimed at establishing Raman spectroscopic algorithms for quantitative characterizations of raw stevia-based sweetener products. First, a series of twelve high-purity diterpene glycosides were analyzed by high spectrally resolved Raman spectroscopy and their spectra analyzed in order to establish a complete Raman library of molecular structures. Then, quantitative spectroscopic parameters were built up and applied to characterize the organoleptic property of five different commercially available samples including the recently developed Rebaudioside M isoform. Raman spectroscopy was confirmed as a versatile analytical technique that could be used for quantitative quality control tasks on the production line and for prompt in situ characterizations of purchased products.

Analytic approach for computation of topological number of integrable vortex on torus

Detailed structures of vortices on a torus are discovered by performing an analytic method to calculate the vortex number. We focus on analytic vortex solutions to the Chern-Simons-Higgs theory, whose governing equation is the so-called Jackiw-Pi equation. The equation is one of the integrable vortex equations and is reduced to Liouville’s equation. The requirement of continuity of the Higgs field strongly restricts the characteristics and the fundamental domain of the vortices. Also considered are the decompactification limits of the vortices on a torus, in which “flux loss” phenomena occasionally occur.

A unified feature-motion consistency framework for robust image matching

Establishing reliable feature matches between a pair of images in various scenarios is a long-standing open problem in photogrammetry. Attention-based detector-free matching with coarse-to-fine architecture has been a typical pipeline to build matches, but the cross-attention module with global receptive field may compromise the structural local consistency by introducing irrelevant regions (outliers). Motion field can maintain structural local consistency under the assumption that matches for adjacent features should be spatially proximate. However, motion field can only estimate local displacements between consecutive images and struggle with long-range displacements estimation in large-scale variation scenarios without spatial correlation priors. Moreover, large-scale variations may also disrupt the geometric consistency with the application of mutual nearest neighbor criterion in patch-level matching, making it difficult to recover accurate matches. In this paper, we propose a unified feature-motion consistency framework for robust image matching (MOMA), to maintain structural consistency at both global and local granularity in scale-discrepancy scenarios. MOMA devises a motion consistency-guided dependency range strategy (MDR) in cross attention, aggregating highly relevant regions within the motion consensus-restricted neighborhood to favor true matchable regions. Meanwhile, a unified framework with hierarchical attention structure is established to couple local motion field with global feature correspondence. The motion field provides local consistency constraints in feature aggregation, while feature correspondence provides spatial context prior to improve motion field estimation. To alleviate geometric inconsistency caused by hard nearest neighbor criterion, we propose an adaptive neighbor search (soft) strategy to address scale discrepancy. Extensive experiments on three datasets demonstrate that our method outperforms solid baselines, with AUC improvements of 4.73/4.02/3.34 in two-view pose estimation task at thresholds of 5°/10°/20° on Megadepth test, and 5.94% increase of accuracy at threshold of 1px in homography task on HPatches datasets. Furthermore, in the downstream tasks such as 3D mapping, the 3D models reconstructed using our method on the self-collected SYSU UAV datasets exhibit significant improvement in structural completeness and detail richness, manifesting its high applicability in wide downstream tasks. The code is publicly available at https://github.com/BunnyanChou/MOMA.