Structural Information Research Articles

Visualizing the virus world inside the cell by cryo-electron tomography.

Structural studies on purified virus have revealed intricate architectures, but there is little structural information on how viruses interact with host cells in situ. Cryo-focused ion beam (FIB) milling and cryo-electron tomography (cryo-ET) have emerged as revolutionary tools in structural biology to visualize the dynamic conformational of viral particles and their interactions with host factors within infected cells. Here, we review the state-of-the-art cryo-ET technique for in situ viral structure studies and highlight exemplary studies that showcase the remarkable capabilities of cryo-ET in capturing the dynamic virus-host interaction, advancing our understanding of viral infection and pathogenesis.

Photothermal optical coherence microscopy for studying lipid architecture in human carotid arteries

Photothermal optical coherence microscopy (PT-OCM) combines the high-resolution, label-free morphological imaging of OCM with the ability to discriminate tissue composition through phase-sensitive photothermal imaging. In this study, we perform 2D imaging of human carotid endarterectomies to spectrally determine lipid distribution, with verification via histologically stained samples. The structural information from OCM is combined with the spectral information gained from measuring the resulting sample surface displacement from thermoelastic expansion, following light irradiation. PT-OCM is thus demonstrated as a potential tool in the investigation of atherosclerotic plaque lipids, contributing towards the understanding of plaque instability.

MIPPIS: protein–protein interaction site prediction network with multi-information fusion

BackgroundThe prediction of protein–protein interaction sites plays a crucial role in biochemical processes. Investigating the interaction between viruses and receptor proteins through biological techniques aids in understanding disease mechanisms and guides the development of corresponding drugs. While various methods have been proposed in the past, they often suffer from drawbacks such as long processing times, high costs, and low accuracy.ResultsAddressing these challenges, we propose a novel protein–protein interaction site prediction network based on multi-information fusion. In our approach, the initial amino acid features are depicted by the position-specific scoring matrix, hidden Markov model, dictionary of protein secondary structure, and one-hot encoding. Simultaneously, we adopt a multi-channel approach to extract deep-level amino acids features from different perspectives. The graph convolutional network channel effectively extracts spatial structural information. The bidirectional long short-term memory channel treats the amino acid sequence as natural language, capturing the protein’s primary structure information. The ProtT5 protein large language model channel outputs a more comprehensive amino acid embedding representation, providing a robust complement to the two aforementioned channels. Finally, the obtained amino acid features are fed into the prediction layer for the final prediction.ConclusionCompared with six protein structure-based methods and six protein sequence-based methods, our model achieves optimal performance across evaluation metrics, including accuracy, precision, F1, Matthews correlation coefficient, and area under the precision recall curve, which demonstrates the superiority of our model.

RecGOBD: Accurate Recognition of Gene Ontology Related Brain Development Protein Functions through Multi-Feature Fusion and Attention Mechanisms

Abstract Motivation Protein function prediction is important in bioinformatics, driven by the increasing availability of protein sequence data from high-throughput sequencing technologies. Traditional methods for determining protein functions are costly and time-consuming, highlighting the need for computational approaches. Deep learning models are powerful tools in this area, but many are not optimized for brain development-related datasets. Understanding protein functions in brain development is essential for studying neurodevelopmental disorders. To address this, we developed RecGOBD (Recognition of Gene Ontology-related Brain Development protein function), designed to predict protein functions related to key brain development processes. Result RecGOBD focuses on ten critical Gene Ontology (GO) terms associated with brain development, extracting and embedding the protein sequences linked to these terms. Using advanced pre-trained models, we generated embeddings that capture both sequence and structural information, and applied attention mechanisms to align them with relevant GO terms. RecGOBD’s category attention layer improves prediction accuracy for brain development-related terms. Evaluated against five models using AUROC, AUPR, and Fmax metrics, RecGOBD outperformed in all measures. We also applied the model to predict protein functions related to autism spectrum disorder and analyzed how protein site mutations affect GO term changes. These results highlight RecGOBD’s potential for advancing protein function prediction, particularly in brain development, offering valuable insights into neurodevelopmental disorder research. Availability and implementation All Python codes associated with this study are available at https://github.com/ZL-Xia/RECGOBD.git. Supplementary information Supplementary data are available at Bioinformatics Advance online

Intransitive clause word order in Neo-Aramaic: information structure, pragmatics and word order shifts

Intransitive clause word order in Neo-Aramaic: information structure, pragmatics and word order shifts

Study on the accurate detection method of full‐polarimetric ground‐penetrating radar faults in mines based on modified Yamaguchi decomposition

AbstractObtaining information on tectonic tendencies is a prerequisite for intelligent and accurate mining in mines. In the special mine environment, the co‐polarized ground‐penetrating radar can only identify the spatial location of faults, and it is difficult to analyse the inclination information of fault structures. This paper proposes a mine full‐polarimetric ground‐penetrating radar fault tendency detection method based on this. First, based on the stacking characteristics of the coal depositional, this paper analyses the propagation law of the pulse electromagnetic wave in the coal seam and puts forward the assumption of the overlapping echo reflection of the fault structure. The reasonableness of the fault reflection assumption is verified through a numerical simulation study. Second, based on the cutting relationship of the fault to the coal seam, we divided the reflection structure of the fault structure into plane scattering and dihedral angle scattering. We realized the mingled echoes’ decomposition using the improved Yamaguchi decomposition technique. To analyse the applicability of the modified Yamaguchi and Freeman decomposition methods in the identification of fault inclination, we use the upright fault simulation data for the discussion, and we find that the improved Yamaguchi decomposition method is more advantageous in the identification of fault inclination in the mine. The decomposition results based on the simulation data of fault models with different dip angles found that when the dip angle of the fault is less than 90°, the scattering of the fault structure is dominated by planar scattering and dihedral angle scattering; when the dip angle of the fault is greater than or equal to 90°, the scattering of the fault structure is dominated by planar scattering, and the scattering power of the dihedral angle model is zero. By analysing the effect of fault strike on the decomposition results, it is found that the fault strike angle has little effect on the identification of fault tendency. Finally, the application potential of this paper's method is tested by constructing complex numerical models and probing experiments. Therefore, the method proposed in this paper can solve the fault tendency identification under a thick coal seam.

Learning face super-resolution through identity features and distilling facial prior knowledge

Deep learning techniques in electronic surveillance have shown impressive performance for super-resolution (SR) of captured low-quality face images. Most of these techniques adopt facial priors to improve the feature details in the resultant super-resolved images. However, the estimation of facial priors from the captured low-quality images is often inaccurate in real-life situations because of their tiny, noisy, and blurry nature. Thus, the fusion of such priors badly affects the performance of these models. Therefore, this work presents a teacher–student-based face SR framework that efficiently preserves the personal facial structure information in the super-resolved faces. In the proposed framework, the teacher network exploits the facial heatmap-based ground-truth-prior to learn the facial structure that is utilized by the student network. The student network is trained with the identity feature loss for maintaining the identity and facial structure information in reconstructed high-resolution (HR) face images. The performance of the proposed framework is evaluated by conducting the experimental study on standard datasets namely CelebA-HQ and LFW face. The experimental results reveal that the proposed technique conquers the existing methods for the face SR task.

Dark-field radiography for the detection of bone microstructure changes in osteoporotic human lumbar spine specimens

BackgroundDark-field radiography imaging exploits the wave character of x-rays to measure small-angle scattering on material interfaces, providing structural information with low radiation exposure. We explored the potential of dark-field imaging of bone microstructure to improve the assessment of bone strength in osteoporosis.MethodsWe prospectively examined 14 osteoporotic/osteopenic and 21 non-osteoporotic/osteopenic human cadaveric vertebrae (L2–L4) with a clinical dark-field radiography system, micro-computed tomography (CT), and spectral CT. Dark-field images were obtained in both vertical and horizontal sample positions. Bone microstructural parameters (trabecular number, Tb.N; trabecular thickness, Tb.Th; bone volume fraction, BV/TV; degree of anisotropy, DA) were measured using standard ex vivo micro-CT, while hydroxyapatite density was measured using spectral CT. Correlations were assessed using Spearman rank correlation coefficients.ResultsThe measured dark-field signal was lower in osteoporotic/osteopenic vertebrae (vertical position, 0.23 ± 0.05 versus 0.29 ± 0.04, p < 0.001; horizontal position, 0.28 ± 0.06 versus 0.34 ± 0.04, p = 0.003). The dark-field signal from the vertical position correlated significantly with Tb.N (ρ = 0.46, p = 0.005), BV/TV (ρ = 0.45, p = 0.007), DA (ρ = -0.43, p = 0.010), and hydroxyapatite density (ρ = 0.53, p = 0.010). The calculated ratio of vertical/horizontal dark-field signal correlated significantly with Tb.N (ρ = 0.43, p = 0.011), BV/TV (ρ = 0.36, p = 0.032), DA (ρ = -0.51, p = 0.002), and hydroxyapatite density (ρ = 0.42, p = 0.049).ConclusionDark-field radiography is a feasible modality for drawing conclusions on bone microarchitecture in human cadaveric vertebral bone.Relevance statementGaining knowledge of the microarchitecture of bone contributes crucially to predicting bone strength in osteoporosis. This novel radiographic approach based on dark-field x-rays provides insights into bone microstructure at a lower radiation exposure than that of CT modalities.Key PointsDark-field radiography can give information on bone microstructure with low radiation exposure.The dark-field signal correlated positively with bone microstructure parameters.Dark-field signal correlated negatively with the degree of anisotropy.Dark-field radiography helps to determine the directionality of trabecular loss.Graphical

Monitoring C–C coupling in catalytic reactions via machine-learned infrared spectroscopy

Abstract Tracking atomic structural evolution along chemical transformation pathways is essential for optimizing chemical transitions and enhancing control. However, molecule-level knowledge of structural rearrangements during chemical processes remains a great challenge. Here, we couple infrared spectroscopy as a non-invasive method to probe molecular transformations, with a machine-learned protocol to immediately map the spectroscopic fingerprints to atomistic structures. From the theoretical perspective, we demonstrate it here with the example of C–C coupling in catalytic reactions, elucidating various structural conformations along dynamic trajectories. Within the transferable application to the specific CO–CO dimerization reaction, the structural and energetic variations of the critical chemical species could be identified via infrared spectroscopy. This approach extends the power of spectroscopy from fingerprinting chemical configurations to using them for assigning dynamic structural information.

Atomically resolved imaging of the conformations and adsorption geometries of individual β-cyclodextrins with non-contact AFM

Glycans, consisting of covalently linked sugar units, are a major class of biopolymers essential to all known living organisms. To better understand their biological functions and further applications in fields from biomedicine to materials science, detailed knowledge of their structure is essential. However, due to the extraordinary complexity and conformational flexibility of glycans, state-of-the-art glycan analysis methods often fail to provide structural information with atomic precision. Here, we combine electrospray deposition in ultra-high vacuum with non-contact atomic force microscopy and theoretical calculations to unravel the structure of β-cyclodextrin, a cyclic glucose oligomer, with atomic-scale detail. Our results, established on the single-molecule level, reveal the different adsorption geometries and conformations of β-cyclodextrin. The position of individual hydroxy groups and the location of the stabilizing intramolecular H-bonds are deduced from atomically resolved images, enabling the unambiguous assignment of the molecular structure and demonstrating the potential of the method for glycan analysis.

Structural dynamics of the Ca2+-regulated cutinase towards structure-based improvement of PET degradation activity

We previously revealed the structural basis of Ca2+ dependent regulation of a polyethylene terephthalate (PET)-degrading enzyme, Cut190, and proposed a unique reaction cycle in which the enzyme repeatedly binds and releases Ca2+. Here, we report crystal structures of Cut190 mutants with high thermal stability complexed with PET-like ligands that contain aromatic rings. The structural information has allowed us to perform further computational analyses using a PET-trimer bound model. Our multicanonical molecular dynamics simulations and subsequent analyses of the free energy landscapes revealed a novel intermediate form that occurs during the enzymatic reaction cycle. Furthermore, the computational analyses were used to investigate the effect of the point mutations F77L and F81L in the Ca2+-binding site, which showed that the former stabilizes the engaged and open forms to improve transition between the open and active forms, while the latter extremely increases the open form. Subsequent experiments showed that the F77L mutation increased the activity, while the F81L mutation decreased the activity. Our computational analysis has enabled us to explore the dynamics of Cut190 on a completely new level, providing key insights into how the balance between the various conformations influences the reaction cycle and ultimately how to improve the reaction cycle.

Information structure and syntax of Uralic languages: A foreword

Information structure and syntax of Uralic languages: A foreword

Interaction between grammatical phenomena and information structure in Northern Mansi

Interaction between grammatical phenomena and information structure in Northern Mansi

DAG‐Net: Dual‐Branch Attention‐Guided Network for Multi‐Scale Information Fusion in Lung Nodule Segmentation

ABSTRACTThe development of deep learning has played an increasingly crucial role in assisting medical diagnoses. Lung cancer, as a major disease threatening human health, benefits significantly from the use of auxiliary medical systems to assist in segmenting pulmonary nodules. This approach effectively enhances both the accuracy and speed of diagnosis for physicians, thereby reducing the risk of patient mortality. However, pulmonary nodules are characterized by irregular shapes and a wide range of diameter variations. They often reside amidst blood vessels and various tissue structures, posing significant challenges in designing an automated system for lung nodule segmentation. To address this, we have developed a three‐dimensional dual‐branch attention‐guided network (DAG‐Net) for multi‐scale information fusion, aimed at segmenting lung nodules of various types and sizes. First, a dual‐branch encoding structure is employed to provide the network with prior knowledge about nodule texture information, which aids the network in better identifying different types of lung nodules. Next, we designed a structure to extract global information, which enhances the network's ability to localize lung nodules of different sizes by fusing information from multiple resolutions. Following that, we fused multi‐scale information in a parallel structure and used attention mechanisms to guide the network in suppressing the influence of non‐nodule regions. Finally, we employed an attention‐based structure to guide the network in achieving more accurate segmentation by progressively using high‐level semantic information at each layer. Our proposed network achieved a DSC value of 85.6% on the LUNA16 dataset, outperforming state‐of‐the‐art methods, demonstrating the effectiveness of the network.

Heterogeneous graph representation learning via mutual information estimation for fraud detection

In the fraud detection, fraudsters frequently engage with numerous benign users to disguise their activities. Consequently, the fraud graph exhibits not only homogeneous connections between the fraudsters and the same labelled nodes, but also heterogeneous connections, where fraudsters interact with the legitimate nodes. Heterogeneous graph representation learning aims at extracting the structural and semantic information and embed it into the low-dimensional node representation. Recently, maximizing the mutual information between the local node embedding and the summary representation has achieved the promising results on node classification tasks. However, existing deep graph infomax methods still have the following limitations. Firstly, attribute information of nodes in the graph is not fully utilized for capturing the semantic relationships between nodes. Secondly, the local and global supervision signal are not simultaneously exploited for the node embedding learning. Thirdly, the multiplex heterogeneous relations among nodes are ignored. To address these issues, a heterogeneous graph representation learning model by mutual information estimation (MIE-HetGRL) is proposed in this paper to identify the fraudsters in the fraud review graph. Concretely, a high-order mutual information estimation is proposed to integrate the local and global mutual information as the supervision signal. Then we devise a semantic attention fusion module to aggregate the relation-aware node embeddings into a compact node representation. Finally, a joint contrastive learning is designed for facilitating the training and optimization of model. The experimental results show that our proposed model significantly outperforms state-of-the-art baselines for fraud detection.

A spatio-temporal forecasting method of fracture distribution using dynamically exposed rock images in tunnel: Methodology and application

Forecasting the geometric characteristics of rock fractures in unexcavated tunnel sections is crucial for construction arrangements. This paper presents a method for predicting fracture distribution near the tunnel face from a spatiotemporal perspective. We Innovatively analogize the tunnel excavation mileage as the time series data. Using fractal geometry and geostatistics, we extract geometric features of peripheral rock fractures and establish a spatiotemporal dataset for dynamic prediction. The SCINet model, a spatial-aware recursive neural network, is employed for deterministic-stochastic dynamic prediction. Numerical simulations are conducted to validate the predictive accuracy of the model for various distributions, including trace length, dip angle, and density. Notably, the Mean Absolute Percentage Error (MAPE) for trace length prediction is remarkably low at 5.49 %, and the Kullback-Leibler (KL) divergence is as low as 4.81 %. The method is further applied to an underground oil storage cavern, China, revealing structural surface information. By incorporating continuously exposed fracture data, prediction accuracy is progressively improved, demonstrating the method's potential as a predictive tool for enhancing tunnel construction safety and efficiency.

ACGRHA-Net: Accelerated Multi-Contrast MR Imaging with Adjacency Complementary Graph assisted Residual Hybrid Attention Network

Multi-contrast magnetic resonance (MR) imaging is an advanced technology used in medical diagnosis, but the long acquisition process can lead to patient discomfort and limit its broader application. Shortening acquisition time by undersampling k-space data introduces noticeable aliasing artifacts. To address this, we propose a method that reconstructs multi-contrast MR images from zero-filled data by utilizing a fully-sampled auxiliary contrast MR image as a prior to learn an adjacency complementary graph. This graph is then combined with a residual hybrid attention network, forming the adjacency complementary graph assisted residual hybrid attention network (ACGRHA-Net) for multi-contrast MR image reconstruction. Specifically, the optimal structural similarity is represented by a graph learned from the fully sampled auxiliary image, where the node features and adjacency matrices are designed to precisely capture structural information among different contrast images. This structural similarity enables effective fusion with the target image, improving the detail reconstruction. Additionally, a residual hybrid attention module is designed in parallel with the graph convolution network, allowing it to effectively capture key features and adaptively emphasize these important features in target contrast MR images. This strategy prioritizes crucial information while preserving shallow features, thereby achieving comprehensive feature fusion at deeper levels to enhance multi-contrast MR image reconstruction. Extensive experiments on the different datasets, using various sampling patterns and accelerated factors demonstrate that the proposed method outperforms the current state-of-the-art reconstruction methods.

Enhanced local multi-windows attention network for lightweight image super-resolution

Since the global self-attention mechanism can capture long-distance dependencies well, Transformer-based methods have achieved remarkable performance in many vision tasks, including single-image super-resolution (SISR). However, there are strong local self-similarities in images, if the global self-attention mechanism is still used for image processing, it may lead to excessive use of computing resources on parts of the image with weak correlation. Especially in the high-resolution large-size image, the global self-attention will lead to a large number of redundant calculations. To solve this problem, we propose the Enhanced Local Multi-windows Attention Network (ELMA), which contains two main designs. First, different from the traditional self-attention based on square window partition, we propose a Multi-windows Self-Attention (M-WSA) which uses a new window partitioning mechanism to obtain different types of local long-distance dependencies. Compared with original self-attention mechanisms commonly used in other SR networks, M-WSA reduces computational complexity and achieves superior performance through analysis and experiments. Secondly, we propose a Spatial Gated Network (SGN) as a feed-forward network, which can effectively overcome the problem of intermediate channel redundancy in traditional MLP, thereby improving the parameter utilization and computational efficiency of the network. Meanwhile, SGN introduces spatial information into the feed-forward network that traditional MLP cannot obtain. It can better understand and use the spatial structure information in the image, and enhances the network performance and generalization ability. Extensive experiments show that ELMA achieves competitive performance compared to state-of-the-art methods while maintaining fewer parameters and computational costs.

Label-Free Non-Contact Vascular Imaging using Photon Absorption Remote Sensing.

Functional vascular imaging is a critical method for early detection and prevention of disease. Established non-contact vascular imaging techniques capture predominantly structural information. In this study, a novel non-contact label-free in vivo Photon Absorption Remote Sensing (PARS) microscope is developed for structural and functional vascular imaging. The presented in vivo PARS microscope captures the endogenous absorption of green (532nm) light to form a complete picture of vasculature and surrounding tissues. Imaging system repeatability is enhanced through robust transient absorption signal extraction, and state-of-the-art real-time alignment methods. Detailed imaging of vascular structure is demonstrated through in vivo microscopy of two established animal models: mouse ear and chicken embryo. Preliminary functional contrast is realized through video rate imaging of red blood cell dynamics in the capillary networks of chicken embryos. The presented in vivo PARS microscope successfully captures detailed structural and functional vascular contrast. This innovative non-contact label-free imaging technique holds promise as a tool for preventative medical care, as functional change often precedes structural change.

CIDGMed: Causal Inference-Driven Medication Recommendation with enhanced dual-granularity learning

Medication recommendation aims to integrate patients’ long-term health records to provide accurate and safe medication combinations for specific health states. Existing methods often fail to deeply explore the true causal relationships between diseases/procedures and medications, resulting in biased recommendations. Additionally, in medication representation learning, the relationships between information at different granularities of medications—coarse-grained (medication itself) and fine-grained (molecular level)—are not effectively integrated, leading to biases in representation learning. To address these limitations, we propose the Causal Inference-driven Dual-Granularity Medication Recommendation method (CIDGMed). Our approach leverages causal inference to uncover the relationships between diseases/procedures and medications, thereby enhancing the rationality and interpretability of recommendations. By integrating coarse-grained medication effects with fine-grained molecular structure information, CIDGMed provides a comprehensive representation of medications. Additionally, we employ a bias correction model during the prediction phase to further refine recommendations, ensuring both accuracy and safety. Through extensive experiments, CIDGMed significantly outperforms current state-of-the-art models across multiple metrics, achieving a 2.54% increase in accuracy, a 3.65% reduction in side effects, and a 39.42% improvement in time efficiency. Additionally, we demonstrate the rationale of CIDGMed through a case study.