Structural Information Research Articles

Constructing cospectral hypergraphs

Spectral hypergraph theory mainly concerns using hypergraph spectra to obtain structural information about the given hypergraphs. The study of cospectral hypergraphs is important since it reveals which hypergraph properties cannot be deduced from their spectra. In this paper, we show a new method for constructing cospectral uniform hypergraphs using two well-known hypergraph representations: adjacency tensors and adjacency matrices.

Characterization of the rainbow trout (Oncorhynchus mykiss) mucosal glycosphingolipid repertoire and Aeromonas salmonicida binding to neutral glycosphingolipids.

Infections pose a challenge for the fast growing aquaculture sector. Glycosphingolipids are cell membrane components that pathogens utilize for attachment to the host to initiate infection. Here, we characterized rainbow trout glycosphingolipids from five mucosal tissues using mass spectrometry and nuclear magnetic resonance and investigated binding of radiolabeled Aeromonas salmonicida to the glycosphingolipids on thin-layer chromatograms. 12 neutral and 14 acidic glycosphingolipids were identified. The glycosphingolipids isolated from the stomach and intestine were mainly neutral, whereas glycosphingolipids isolated from the skin, gills and pyloric caeca were largely acidic. Many of the acidic structures were poly-sialylated with shorter glycan structures in the skin compared to the other tissues. The sialic acids found were Neu5Ac and Neu5Gc. Most of the glycosphingolipids had isoglobo and ganglio core chains, or a combination of these. The epitopes on the rainbow trout glycosphingolipid glycans differed between epithelial sites leading to differences in pathogen binding. A major terminal epitope was fucose, that occurred attached to GalNAc in a α1-3 linkage but also in the form of HexNAc-(Fuc-)HexNAc-R. A. salmonicida were shown to bind to neutral glycosphingolipids from the gill and intestine. This study is the first to do a comprehensive investigation of the rainbow trout glycosphingolipids and analyze binding of A. salmonicida to glycosphingolipids. The structural information paves the way for identification of ways of interfering in pathogen colonization processes to protect against infections in aquaculture and contributes towards understanding A. salmonicida infection mechanisms.

Cross-section extraction and model reconstruction of lava tube based on L1-medial skeleton

Lunar lava tube caves, due to their unique geographical structure and potential as shelters, have repeatedly emerged as prime candidates or priority areas during discussions on lunar base site selection. However, owing to current technological limitations and the complexities of lunar exploration, obtaining detailed data directly from the interiors of lunar lava tubes poses a significant challenge. Hence, the study of terrestrial lava tubes assumes critical importance. By delving into Earth’s lava tubes, we can gain insights into crucial aspects such as their formation mechanisms, geological characteristics, and stability, which can then inform and guide research on lava tubes found on extraterrestrial bodies like the Moon and Mars. During the data acquisition process for lava tubes, given their intricate spatial configurations and dim lighting conditions, we have opted to employ LiDAR technology to collect point cloud data, revealing the internal structural information of the lava tubes. Consequently, this paper thoroughly explores a method for extracting lava tube parameters and constructing models using this point cloud data. This approach encompasses four key steps: data preprocessing, axis construction of the lava tube, cross-sectional feature extraction, and 3D model reconstruction. Initially, raw data is refined through the creation of point cloud voxels to mitigate noise interference. Subsequently, an iterative algorithm is utilized to obtain the L1-medial skeleton, enabling precise capture of the lava tube’s axis information. Using this extracted axis, we proceed with sampling at fixed intervals to derive cross-sectional points of the lava tube. These points are then utilized to extract fundamental geometric parameters such as cross-sectional area, length, and width. Ultimately, by integrating all these cross-sectional point cloud data, a comprehensive 3D model of the lava tube is constructed. Remarkably, this method achieves a high degree of accuracy, with 99 % of the extracted cross-sectional points exhibiting an error of less than 0.12 m, and 99 % of the model’s positional errors falling within 0.15 m. This not only enhances the precision of parameter extraction but also provides significant technical support for in-depth studies of extraterrestrial volcanic activities and lava tube formation mechanisms. Furthermore, it offers a valuable tool for parameter extraction and research on other irregular natural tunnels.

Deep fair clustering with multi-level decorrelation

Fair clustering aims to prevent sensitive attributes (e.g., race or gender) from dominating the clustering process. However, real-world datasets, often characterized by low quality and high dimensionality, restrict existing fair clustering methods from achieving satisfactory outcomes. Typically, these sensitive attributes are intricately intertwined with other attributes in high-dimensional continuous data, forming backgrounds or entities within the data. The integration results in a significant correlation of features and samples across different clusters, thereby hindering the model's ability to explore the intrinsic structure. To address these issues, we propose a novel fair clustering method that incorporates multi-level decorrelation constraints. Our goal is to extract inherent fair structural information under the interference of sensitive attributes, enhancing both the validity and fairness of the model. Specifically, we introduce a new cluster-wise similarity metric based on the partition correlation coefficient, which facilitates cluster-level decorrelation and captures cluster-discriminative properties. Furthermore, by incorporating softmax-formulated decorrelation at the sample-level and feature-level, we concurrently explore representations that favor fairness. These three components are seamlessly integrated into our clustering framework, yielding a more robust and confident data partition. Experiments conducted on six commonly-used datasets demonstrate the effectiveness of our proposed method.

Resonance Raman spectral analysis of the heme site structure of cytochrome c oxidase with its positive regulator CHCHD2

Cytochrome c oxidase (CcO) reduces O2, pumps protons in the mitochondrial respiratory chain, and is essential for oxygen consumption in the cell. The coiled-coil-helix-coiled-coil-helix domain-containing 2 (CHCHD2; also known as mitochondrial nuclear retrograde regulator 1 [MNRR1], Parkinson's disease 22 [PARK22] and aging-associated gene 10 protein [AAG10]) is a protein that binds to CcO from the intermembrane space and positively regulates the activity of CcO. Despite the importance of CHCHD2 in mitochondrial function, the mechanism of action of CHCHD2 and structural information regarding its binding to CcO remain unknown. Here, we utilized visible resonance Raman spectroscopy to investigate the structural changes around the hemes in CcO in the reduced and CO-bound states upon CHCHD2 binding. We found that CHCHD2 has a significant impact on the structure of CcO in the reduced state. Mapping of the heme peripheries that result in Raman spectral changes in the structure of CcO highlighted helices IX and X near the hemes as sites where CHCHD2 takes action. Part of helix IX is exposed in the intermembrane space, whereas helix X, located between both hemes, may play a key role in proton uptake to a proton-loading site in the reduced state for proton pumping. Taken together, our results suggested that CHCHD2 binds near helix IX and induces a structural change in helix X, accelerating proton uptake.

Review article: advances in the diagnosis and management of anorectal motility disorders.

Anorectal motility disorders such as dyssynergic defecation (DD), faecal incontinence (FI) and anorectal pain affect 40% of the population and are a frequent reason for gastroenterology consultation. They significantly affect the quality of life and lead to psychological distress. Lack of understanding of these problems compounded by a lack of availability and knowledge of diagnostic tools in most medical centres and/or trained physicians has significantly hampered this field. To discuss the latest advances in pathophysiology, diagnostic tests and therapeutic options for these disorders using an evidence-based approach. We reviewed the published literature over the past 20 years on DD, FI and anorectal pain and distilled these into a narrative review. A detailed history, prospective stool diary and digital rectal exam, together with diagnostic tests such as anorectal manometry, balloon expulsion test, translumbosacral anorectal magnetic stimulation test for assessing neuropathy, defecography and anal ultrasound, can provide detailed mechanistic and structural information. Such knowledge can pave the way for a meaningful and pathophysiologic-based management approach. This could include biofeedback therapy for DD or FI, sensory training for rectal hyposensitivity or sensory adaptation training for rectal hypersensitivity or sphincter bulking agents or neuromodulation therapies. These treatments are effective and safe. Anorectal motility disorders are common, but either less well recognized or poorly managed by most gastroenterologists. Equipped with the practical and up-to-date knowledge provided in this review, physicians could provide improved health care for these patients.

Machine learning-assisted substrate binding pocket engineering based on structural information.

Engineering enzyme-substrate binding pockets is the most efficient approach for modifying catalytic activity, but is limited if the substrate binding sites are indistinct. Here, we developed a 3D convolutional neural network for predicting protein-ligand binding sites. The network was integrated by DenseNet, UNet, and self-attention for extracting features and recovering sample size. We attempted to enlarge the dataset by data augmentation, and the model achieved success rates of 48.4%, 35.5%, and 43.6% at a precision of ≥50% and 52%, 47.6%, and 58.1%. The distance of predicted and real center is ≤4Å, which is based on SC6K, COACH420, and BU48 validation datasets. The substrate binding sites of Klebsiella variicola acid phosphatase (KvAP) and Bacillus anthracis proline 4-hydroxylase (BaP4H) were predicted using DUnet, showing high competitive performance of 53.8% and 56% of the predicted binding sites that critically affected the catalysis of KvAP and BaP4H. Virtual saturation mutagenesis was applied based on the predicted binding sites of KvAP, and the top-ranked 10 single mutations contributed to stronger enzyme-substrate binding varied while the predicted sites were different. The advantage of DUnet for predicting key residues responsible for enzyme activity further promoted the success rate of virtual mutagenesis. This study highlighted the significance of correctly predicting key binding sites for enzyme engineering.

Incomplete Multiview Clustering Based on Consensus Information.

In contrast to traditional single-view clustering methods, multiview clustering (MVC) approaches aim to extract, analyze, and integrate structural information from diverse perspectives, providing a more comprehensive understanding of internal data structures. However, with an increasing number of views, maintaining the integrity of view information becomes challenging, giving rise to incomplete MVC (IMVC) methods. While existing IMVC methods have shown notable performance on many incomplete multiview (IMV) databases, they still grapple with two key shortcomings: 1) they treat the information of each view as a whole, disregarding the differences among samples within each view; and 2) they rely on eigenvalue and eigenvector operations on the view matrix, limiting their scalability for large-scale samples and views. To overcome these limitations, we propose a novel multiview clustering with consistent information (IMVC-CI) of sample points. Our method explores the multiview information set of sample points to extract consensus structural information and subsequently restores unknown information in each view. Importantly, our approach operates independently on individual sample points, eliminating the need for eigenvalue and eigenvector operations on the view information matrix and facilitating parallel computation. This significantly enhances algorithmic efficiency and mitigates challenges associated with dimensionality. Experimental results on various public datasets demonstrate that our algorithm outperforms state-of-the-art IMVC methods in terms of clustering performance and computational efficiency. The code for our article has been uploaded to https://github.com/PhdJiayiTang/IMVC-CI.git.

Structure-based humanization of a therapeutic antibody for multiple myeloma.

The optimal efficacy of xenogeneically generated proteins intended for application in humans requires that their own antigenicity be minimized. This necessary adaptation of antibodies to a humanized version poses challenges since modifications even distant from the binding sites can greatly influence antigen recognition and this is the primary feature that must be maintained during all modifications. Current strategies often rely on grafting and/or randomization/selection to arrive at a humanized variant retaining the binding properties of the original molecule. However, in terms of speed and efficiency, rationally directed approaches can be superior, provided the requisite structural information is available. We present here a humanization procedure based on the high-resolution X-ray structure of a chimaeric IgG against a marker for multiple myeloma. Based on in silico modelling of humanizing amino acid substitutions identified from sequence alignments, we devised a straightforward cloning procedure to rapidly evaluate the proposed sequence changes. Careful inspection of the structure allowed the identification of a potentially problematic amino acid change that indeed disrupted antigen binding. Subsequent optimization of the antigen binding loop sequences resulted in substantial recovery of binding affinity lost in the completely humanized antibody. X-ray structures of the humanized and optimized variants demonstrate that the antigen binding mode is preserved, with surprisingly few direct contacts to antibody atoms. These results underline the importance of structural information for the efficient optimization of protein therapeutics. KEY MESSAGES: Structure-based humanization of an IgG against BCMA, a marker for Multiple Myeloma. Identification of problematic mutations and unexpected modification sites. Structures of the modified IgG-antigen complexes verified predictions. Provision of humanized high-affinity IgGs against BCMA for therapeutic applications.

The role of information in auctions

This review discusses the seminal contributions of Engelbrecht-Wiggans et al. (1983) Milgrom and Weber (1982a) to the literature that studies the role of information in auctions. We describe the results in these papers and present several extensions. Much of the earlier literature on auctions takes the information environment as exogenous. The extensions that we present will demonstrate how the insights of Engelbrecht-Wiggans et al. (1983) and Milgrom and Weber (1982a) apply to the more recent literature on flexible information acquisition in auctions where the information structure is endogenously determined in equilibrium.

Structural information awareness-based regularization model for infrared image stripe noise removal

Structural information awareness-based regularization model for infrared image stripe noise removal

CDGT: Constructing diverse graph transformers for emotion recognition from facial videos

Recognizing expressions from dynamic facial videos can find more natural affect states of humans, and it becomes a more challenging task in real-world scenes due to pose variations of face, partial occlusions and subtle dynamic changes of emotion sequences. Existing transformer-based methods often focus on self-attention to model the global relations among spatial features or temporal features, which cannot well focus on important expression-related locality structures from both spatial and temporal features for the in-the-wild expression videos. To this end, we incorporate diverse graph structures into transformers and propose a CDGT method to construct diverse graph transformers for efficient emotion recognition from in-the-wild videos. Specifically, our method contains a spatial dual-graphs transformer and a temporal hyperbolic-graph transformer. The former deploys a dual-graph constrained attention to capture latent emotion-related graph geometry structures among local spatial tokens for efficient feature representation, especially for the video frames with pose variations and partial occlusions. The latter adopts a hyperbolic-graph constrained self-attention that explores important temporal graph structure information under hyperbolic space to model more subtle changes of dynamic emotion. Extensive experimental results on in-the-wild video-based facial expression databases show that our proposed CDGT outperforms other state-of-the-art methods.

A study on the structure-functionality relationship of Solenaia oleivora protein under high-intensity ultrasonication processing

Solenaia oleivora is a valuable freshwater mussel endemic to China with a high content of high-quality proteins, but the lack of structural information and limited functionality of Solenaia oleivora proteins constrained their application in the food industry. This study investigates the changes in structural characteristics and functionality of Solenaia oleivora protein under ultrasound processing at power from 200 to 600 W. The ultrasound treatment caused increased contents of β-turn and α-helix, and the exposure of interior hydrophobic groups, resulting in the increased hydrophobicity by around 3 folds. The ultrasound treatment could significantly decrease particle size and increase surface charges of Solenaia oleivora proteins, facilitating the increase of hydrosolubility from 10.2% to 81.7%. These structural changes and increased hydrosolubility contributed to the enhancement of emulsifying and foaming properties, and in vitro digestibility. The results suggested that the ultrasound-treated Solenaia oleivora proteins possessed the potential as an alternative protein in food applications.

Prediction of cancer drug sensitivity based on genomic feature distribution alignment and drug structure information

In recent years, precision medicine has demonstrated wide applications in cancer therapy, and the focus of precision medicine lies in accurately predicting the responses of different patients to drug treatment. We propose a model for predicting cancer drug sensitivity based on genomic feature distribution alignment and drug structure information. This model initially aligns the genomic features from cell lines with those from patients and removes noise from gene expression data. Subsequently, it integrates drug structure features and employs multi-task learning to predict the drug sensitivity of patients. The experimental results on the genomics of drug sensitivity in cancer (GDSC) dataset indicates that this method achieved a reduced mean square error of 0.905 2, an increased correlation coefficient of 0.875 4, and an enhanced accuracy rate of 0.836 0 which significantly outperformed the recently published methods. On the cancer genome atlas (TCGA) dataset, this method demonstrates an improved average recall rate of 0.571 4 and an increased F1-score of 0.658 0 in predicting drug sensitivity, exhibiting excellent generalization performance. The result demonstrates the potential of this method to assist in the selection of clinical treatment plans in the future.

Expert System for Extracting Hidden Information from Electronic Documents during Outgoing Control

For confidential and sensitive electronic documents within enterprises and organizations, failure to conduct proper checks before sending can easily lead to incidents such as security degradation. Sensitive information transmission has become one of the main ways of internal data leakage. However, existing methods or systems cannot extract hidden data and do not support mining the binary structure of hidden information in files. In this paper, an expert system for mining electronic document hidden information is designed for various office documents, compressed files, and image files. This system can quickly mine various forms of concealment in more than 40 common types of electronic documents and extract hidden information, such as file type tampering, encryption concealment, structure concealment, redundant data concealment, etc. Additionally, feature information in the binary structure of the document is extracted to form a feature information base. Subsequently, an expert knowledge base is constructed. Finally, a hidden information mining engine is designed using the knowledge base to realize the security control of corresponding outgoing files with good expansibility and integration. By controlling the exit of documents through scanning for sensitive information contained within them, the security level contents can be obtained effectively, avoiding data leakage by technical means while also facilitating forensics. The actual test result proves that this system can quickly mine various means used to conceal information, extract their respective information, and provide a fast, practical diagnostic way for outgoing control over these electronic documents.

Ultrasound and diffuse optical tomography-transformer model for assessing pathological complete response to neoadjuvant chemotherapy in breast cancer.

We evaluate the efficiency of integrating ultrasound (US) and diffuse optical tomography (DOT) images for predicting pathological complete response (pCR) to neoadjuvant chemotherapy (NAC) in breast cancer patients. The ultrasound-diffuse optical tomography (USDOT)-Transformer model represents a significant step toward accurate prediction of pCR, which is critical for personalized treatment planning. We aim to develop and assess the performance of the USDOT-Transformer model, which combines US and DOT images with tumor receptor biomarkers to predict the pCR of breast cancer patients under NAC. We developed the USDOT-Transformer model using a dual-input transformer to process co-registered US and DOT images along with tumor receptor biomarkers. Our dataset comprised imaging data from 60 patients at multiple time points during their chemotherapy treatment. We used fivefold cross-validation to assess the model's performance, comparing its results against a single modality of US or DOT. The USDOT-Transformer model demonstrated excellent predictive performance, with a mean area under the receiving characteristic curve of 0.96 (95%CI: 0.93 to 0.99) across the fivefold cross-validation. The integration of US and DOT images significantly enhanced the model's ability to predict pCR, outperforming models that relied on a single imaging modality (0.87 for US and 0.82 for DOT). This performance indicates the potential of advanced deep learning techniques and multimodal imaging data for improving the accuracy (ACC) of pCR prediction. The USDOT-Transformer model offers a promising non-invasive approach for predicting pCR to NAC in breast cancer patients. By leveraging the structural and functional information from US and DOT images, the model offers a faster and more reliable tool for personalized treatment planning. Future work will focus on expanding the dataset and refining the model to further improve its accuracy and generalizability.

HTA model for laboratory medicine technologies: overview of approaches adopted in some international agencies.

The Health Technology Assessment (HTA) Working Group of the Emerging Technology Division of International Federation of Clinical Chemistry and Laboratory Medicine (IFCC) aims to develop a methodological approach for producing structured HTA information for laboratory medicine technologies. This approach seeks to support decision-making processes at the country, regional, and/or hospital levels regarding the introduction of specific technologies. The focus of this model will primarily be ondefining assessment elements within the domains of 'organizational aspects' and 'costs and economic evaluations', potentially differentiated by the type of diagnostic technology (e.g., genetic tests, molecular tests). To achieve this project's goal, a literature review and examination ofwebsites of international HTA agencies have been conducted. The research aims to identify multidisciplinary methodological approaches used to assess laboratory diagnostic technologies and to pinpoint the domains and assessment elements utilized. Wefound 7 methodological articles describing methodological approaches adopted toassess laboratory diagnostic technologies. Among the HTA organizations considered, 23 reports were found, of which 7 were produced by the European Network of HTA (EUnetHTA), 4 by the National Institute for Health and Care Excellence Diagnostic Assessment Program (NICE DAP), and 12 by other HTA agencies. The EUnetHTA reports wererapid collaborative assessments covering various domains, while the NICE DAP reports focused on diagnostic guidances, including descriptions of technologies, clinical need and practice, diagnostic tests, accuracy, effectiveness, and cost-effectiveness. Finally, a survey targeting laboratory professionals will be conducted to introduce assessment elements, differentiated by the type of diagnostic technology, primarily for organizational and economic domains.

High Magnetic Performance in MnGa Nanocomposite Magnets.

In view of their potential applicability in technology fields where magnets are required to operate at higher temperatures, the class of nanocomposite magnets with little or no rare earth (RE) content has been widely researched in the last two decades. Among these nanocomposite magnets, the subclass of magnetic binary systems exhibiting the formation of L10 tetragonal phases is the most illustrious. Some of the most interesting systems are represented by the Mn-based alloys, with addition of Al, Bi, Ga, Ge. Such alloys are interesting as they are less costly than RE magnets and they show promising magnetic properties. The paper tackles the case of MnGa binary alloys with various compositions around the Mn3Ga stoichiometry. Four MnGa magnetic alloys, with Mn content ranging from 70 at% to 75 at% were produced using rapid solidification to form the melt. By combining structural information arising from X-ray diffractometry and transmission electron microscopy with magnetic properties determined by vibrating sample magnetometry, we are able to document the nature and properties of the structural phases formed in the alloys in their as-cast state and upon annealing, the evolution of the phase structure after annealing and its influence on the magnetic behavior of the MnGa alloys. After annealing at 400 °C and 500 °C, MnGa alloys are showing a multiple-phase microstructure, consisting of co-existing crystallites of L10 and D022 tetragonal phase. As a consequence of these structurally and magnetically different phases, co-existing within the microstructure, promising magnetic features are obtained, with both coercive fields and saturation magnetization exceeding values previously reported for both alloys and layers of MnGa.

Trial of Converting Nursing Records into Simplified and Structured Information Utilizing ChatGPT-3.5.

In Japan, the excessive length of time required for nursing records has become a social problem. A shift to concise "bulleted" records is needed to apply speech recognition and to work with foreign caregivers. Therefore, using 96,000 descriptively described anonymized nursing records, we identified typical situations for each information source and attempted to convert them to "bulleted" records using ChatGPT-3.5(For return from the operating room, Status on return, Temperature control, Blood drainage, Stoma care, Monitoring, Respiration and Oxygen, Sensation and pain, etc.). The results showed that ChatGPT-3.5 has some usable functionality as a tool for extracting keywords in "bulleted" records. Furthermore, through the process of converting to a "bulleted" record, it became clear that the transition to a standardized nursing record utilizing the "Standard Terminology for Nursing Observation and Action (STerNOA)" would be facilitated.

Extractions of Diverse Structural Information from vEM Data with CLEM and Deep Learning Workflows to Understand Brain Function

Extractions of Diverse Structural Information from vEM Data with CLEM and Deep Learning Workflows to Understand Brain Function