Network Mapping Research Articles

OSTF1 knockdown mitigates IL-1β-induced chondrocyte injury via inhibiting the NF-κB signaling pathway

Osteoarthritis (OA) is an age-related joint disease characterized by progressive heterogeneous changes in articular cartilage and subchondral bone. Osteoclast stimulating factor 1 (OSTF1) is a small intracellular protein involved in bone formation and bone resorption. However, to our best knowledge, its role in OA is still unclear. In this study, an OA rat model was established by anterior cruciate ligament transection (ALCT). OSTF1 was increased in the cartilage tissues of OA patients and OA rats. Next, the role of OSTF1 in interleukin-1β (IL-1β)-induced chondrocyte apoptosis, inflammation and extracellular matrix degradation was explored through loss of function assays. Strikingly, OSTF1 knockdown relieved IL-1β-induced chondrocyte apoptosis, with decreased cleaved caspase-3 and cleaved PARP levels. Besides, OSTF1 knockdown restrained IL-1β-induced inflammation and degradation of extracellular matrix of chondrocytes. Subsequently, the molecular mechanism of OSTF1 was explored. Transcriptomic analysis revealed the potential gene network map regulated by OSTF1 knockdown. Some differentially expressed genes (DEGs) were involved in regulating the NF-κB signaling pathway. Furthermore, our results demonstrated that OSTF1 knockdown inhibited IL-1β-activated the NF-κB signaling pathway. Ultimately, we analyzed the potential gene network map regulated by OSTF1 and its downstream NF-κB. Bioinformatics analysis showed that 18 DEGs in OSTF1-silenced chondrocytes overlapped with the NF-κB downstream targets. Collectively, our findings indicate that OSTF1 knockdown mitigates IL-1β-induced chondrocyte injury via inhibiting the NF-κB signaling pathway.

Analysis of the Mechanism of Intervention of Fangxiangxiaozhi Prescription on Dyslipidemia Based on Network Pharmacology and Molecular Docking

Objective: To explore the mechanism of intervention of Fangxiangxiaozhi prescription on dyslipidemia by using network pharmacology and molecular docking. Methods: The traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform (TCMSP), Pubchem, Uniprot, and other databases were adopted to screen the active ingredients and the corresponding targets of Fangxiangxiaozhi prescription. Dyslipidemia-related targets were identified using the databases of Disgenet and GeneCards. Then, the intersection target of drugs and diseases was demonstrated via a Venn diagram. Cytoscape3.7.2 was used to construct a “drugs-active ingredients-intersection targets” network map and the key active ingredients with the top 7-degree values were determined. The protein interaction network and topology analysis of the intersection target genes were carried out by combining STRING11.0 and Cytoscape3.7.2. Moreover, the gene ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis of the intersection target genes were carried out using the Metascape database. Lastly, the key active ingredients and targets were molecular docked by AutoDockTools, Pymol, and other software. Results: There were 51 active components and 509 target genes of which 74 intersect with dyslipidemia. The key targets included tumor necrosis factor (TNF), interleukin-6 (IL-6), AKT1, PPAR gamma (PPARG), VEGFA, and PPARα. GO enrichment analysis obtained 1040 biological processes, 33 cell components, and 84 molecular functions; KEGG enrichment analysis obtained 148 pathways. The molecular docking results showed that the key targets and compounds exhibited good binding force. Conclusion: The active ingredients of Fangxiangxiaozhi prescription regulated several pathways through multiple targets to intervene in dyslipidemia. This study can serve as a foundation for further research.

Clinical Application of MRI in Coronavirus Disease 2019: A Bibliometric Analysis.

Currently, coronavirus disease 2019 (COVID-19) continues to remain in the pandemic stage, leading to severe challenges in the global public healthcare system. Magnetic resonance imaging (MRI) methods have played an important role in the diagnosis of COVID-19 and the structural evaluation of the affected organs. Reviewing and summarizing the application of MRI has significant clinical implications for COVID-19. The study aimed to analyze literature related to the application of MRI in COVID-19 using bibliometric tools, to explore the research status, hotspots, and developmental trends in this field, and to provide a reference for the application of MRI in the clinical diagnosis and evaluation of COVID-19. We used the Web of Science Core Collection database to search and collect relevant literature on the use of MRI in COVID-19. The authors, institutes, countries, journals, and keyword modules of the bibliometric analysis software CiteSpace and VOSviewer were used to analyze and plot the network map. A total of 1506 relevant articles were shortlisted through the search; the earliest study was published in 2019, showing an overall upward trend every year. The research was mainly presented as published articles. Clinical neurology was found to be the primary discipline. The United States had the highest publication volume and influence in this field. Countries around the world cooperated more closely. The Cureus Journal of Medical Science was the main periodical to publish articles. Institutes, such as Harvard Medical School, Mayo Clinic, and Massachusetts General Hospital, have published a large number of papers. Some of the high-frequency keywords were "COVID-19", "SARS-CoV-2", "magnetic resonance", "myocarditis", and "cardiac magnetic resonance imaging". The keyword clustering study showed that the current research mainly focuses on five "hot" directions. There is a need to strengthen cross-teamwork and multidisciplinary collaboration in the future to completely explore the positive role of MRI in COVID-19 and to discover breakthroughs for the challenges in the clinical diagnosis and treatment of COVID-19.

Building Shortcuts between Distant Nodes with Biaffine Mapping for Graph Convolutional Networks

Multiple recent studies show a paradox in graph convolutional networks (GCNs)—that is, shallow architectures limit the capability of learning information from high-order neighbors, whereas deep architectures suffer from over-smoothing or over-squashing. To enjoy the simplicity of shallow architectures and overcome their limits of neighborhood extension, in this work we introduce a biaffine technique to improve the expressiveness of GCNs with a shallow architecture. The core design of our method is to learn direct dependency on long-distance neighbors for nodes, with which only 1-hop message passing is capable of capturing rich information for node representation. Besides, we propose a multi-view contrastive learning method to exploit the representations learned from long-distance dependencies. Extensive experiments on nine graph benchmark datasets suggest that the shallow biaffine graph convolutional networks (BAGCN) significantly outperform state-of-the-art GCNs (with deep or shallow architectures) on semi-supervised node classification. We further verify the effectiveness of biaffine design in node representation learning and the performance consistency on different sizes of training data.

A robust vessel-labeling pipeline with high tissue clearing compatibility for 3D mapping of vascular networks

The combination of vessel-labeling, tissue-clearing, and light-sheet imaging techniques provides a potent tool for accurately mapping vascular networks, enabling the assessment of vascular remodeling in vascular-related disorders. However, most vascular labeling methods face challenges such as inadequate labeling efficiency or poor compatibility with current tissue clearing technology, which significantly undermines the image quality. To address this limitation, we introduce a vessel-labeling pipeline, termed Ultralabel, which relies on a specially designed dye hydrogel containing lysine-fixable dextran and gelatins for double enhancement. Ultralabel demonstrates not only excellent vessel-labeling capability but also strong compatibility with all tissue clearing methods tested, which outperforms other vessel-labeling methods. Consequently, Ultralabel enables fine mapping of vascular networks in diverse organs, as well as multi-color labeling alongside other labeling techniques. Ultralabel should provide a robust and user-friendly method for obtaining 3D vascular networks in different biomedical applications.

Infrastructure Impact Assessment through Multi-Hazard Analysis at Different Scales: The 26 November 2022 Flood Event on the Island of Ischia and Debris Management

A multi-hazard analysis (seismic, landslide, flood) is conducted to verify the impact on the road network. The ENEA CIPCast platform is an innovative Decision Support System (DSS) that is used to implement the analyses using GIS. Using analytical and geoprocessing tools, the hazards were assessed and mapped. The overlapping of different geospatial layers allowed the implementation of a specific hazard map for the road network. Multi-hazard values were obtained using an appropriate matrix of single values, which were classified, and then summarized into four classes of values. The analyses were conducted at the regional (Campania region), provincial (Metropolitan City of Naples), and local scales (island of Ischia and municipality of Casamicciola Terme). In particular, the landslide event that struck Ischia island on 26 November 2022 and the municipality of Casamicciola Terme was considered as a case study to determine the impact on the road network, infrastructures, buildings, and jeopardizing inter-municipal connections. The results are mainly visualized through map processing and statistical summaries of the data. The management of the landslide debris, which can contain a multitude of fractions (waste, biomass and vegetation, sludge, soil, and rocks transported downstream by water), was also explored. This is a frontier issue for which international manuals and guidelines, as well as national and emergency acts, have been examined. A specific protocol for the sustainable management of the debris generated by floods and landslides is needed, and discussed in the present paper, to overcome emergencies after catastrophic events.

Lesion Network Mapping of New-onset Aggression/Agitation/Irritability After Focal Brain Injury Identifies Distinct Populations with Distinct Network Injury Patterns (P1-9.004)

Lesion Network Mapping of New-onset Aggression/Agitation/Irritability After Focal Brain Injury Identifies Distinct Populations with Distinct Network Injury Patterns (P1-9.004)

Expanding the Lesion Network Mapping of Delusions (P10-4.004)

Expanding the Lesion Network Mapping of Delusions (P10-4.004)

Functional and structural reorganization in brain tumors: a machine learning approach using desynchronized functional oscillations

Neuroimaging studies have allowed for non-invasive mapping of brain networks in brain tumors. Although tumor core and edema are easily identifiable using standard MRI acquisitions, imaging studies often neglect signals, structures, and functions within their presence. Therefore, both functional and diffusion signals, as well as their relationship with global patterns of connectivity reorganization, are poorly understood. Here, we explore the functional activity and the structure of white matter fibers considering the contribution of the whole tumor in a surgical context. First, we find intertwined alterations in the frequency domain of local and spatially distributed resting-state functional signals, potentially arising within the tumor. Second, we propose a fiber tracking pipeline capable of using anatomical information while still reconstructing bundles in tumoral and peritumoral tissue. Finally, using machine learning and healthy anatomical information, we predict structural rearrangement after surgery given the preoperative brain network. The generative model also disentangles complex patterns of connectivity reorganization for different types of tumors. Overall, we show the importance of carefully designing studies including MR signals within damaged brain tissues, as they exhibit and relate to non-trivial patterns of both structural and functional (dis-)connections or activity.

Abstract SY35-02: Spatial mapping of mitochondrial networks in lung cancer

Abstract Mitochondria are critical to the governance of metabolism and bioenergetics in cancer cells. The mitochondria form highly organized networks, in which their outer and inner membrane structures define their bioenergetic capacity. However, in vivo studies delineating the relationship between the structural organization of mitochondrial networks and their bioenergetic activity have been limited. Here we present an in vivo structural and functional profiling of mitochondrial networks and bioenergetic phenotypes in non-small cell lung cancer (NSCLC) using an integrated platform comprised of positron emission tomography (PET) imaging, respirometry and 3-dimensional scanning block-face electron microscopy (3D SBEM). Interestingly, the diverse bioenergetic phenotypes and metabolic dependencies we identified in NSCLC tumors align with distinct structural organization of mitochondrial networks present. Further, we discovered that mitochondrial networks are organized into distinct compartments within tumor cells. In tumors with high rates of oxidative phosphorylation (OXPHOSHI) and fatty acid oxidation we identified peri-droplet mitochondrial networks wherein mitochondria contact and surround lipid droplets. In contrast, we discovered that in tumors with low rates of OXPHOS (OXPHOSLO), high glucose flux regulated i) peri-nuclear localization of mitochondria, ii) structural remodeling of cristae and iii) mitochondrial respiratory capacity. Our findings suggest that in NSCLC, mitochondrial networks are compartmentalized into distinct subpopulations that govern the bioenergetic capacity of tumors. Structure-and-function studies defining the relationship between mitochondrial architecture and metabolic dependencies may hold promise as strategies to profile metabolic liabilities unique to lung cancer subtypes. We anticipate that coupling PET imaging with 3D SBEM will have dynamic applications beyond that of lung cancer and enrich our understanding of how mitochondrial bioenergetics impact human disease. Citation Format: David B. Shackelford. Spatial mapping of mitochondrial networks in lung cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 2 (Late-Breaking, Clinical Trial, and Invited Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(7_Suppl):Abstract nr SY35-02.

Comprehensive landscape of neutralizing antibody and cell-mediated response elicited by the 1/5 fractional dose of 17DD-YF primary vaccination in adults

The present study aimed at evaluating the YF-specific neutralizing antibody profile besides a multiparametric analysis of phenotypic/functional features of cell-mediated response elicited by the 1/5 fractional dose of 17DD-YF vaccine, administered as a single subcutaneous injection. The immunological parameters of each volunteer was monitored at two time points, referred as: before (Day 0) [Non-Vaccinated, NV(D0)] and after vaccination (Day 30–45) [Primary Vaccinees, PV(D30–45)]. Data demonstrated high levels of neutralizing antibodies for PV(D30–45) leading to a seropositivity rate of 93%. A broad increase of systemic soluble mediators with a mixed profile was also observed for PV(D30–45), with IFN-γ and TNF-α presenting the highest baseline fold changes. Integrative network mapping of soluble mediators showed increased correlation numbers in PV(D30–45) as compared to NV(D0) (532vs398). Moreover, PV(D30–45) exhibited increased levels of Terminal Effector (CD45RA+CCR7−) CD4+ and CD8+ T-cells and Non-Classical memory B-cells (IgD+CD27+). Dimensionality reduction of Mass Cytometry data further support these findings. A polyfunctional cytokine profile (TNF-α/IFN-γ/IL-10/IL-17/IL-2) of T and B-cells was observed upon in vitro antigen recall. Mapping and kinetics timeline of soluble mediator signatures for PV(D30–45) further confirmed the polyfunctional profile upon long-term in vitro culture, mediated by increased levels of IFN-γ and TNF-α along with decreased production of IL-10. These findings suggest novel insights of correlates of protection elicited by the 1/5 fractional dose of 17DD-YF vaccine.

Showcasing a ‘Long-COVID (LC) Workforce Toolkit’ for Health and Social Care Providers: Providing resources, supports, and a mapping of networks that support rehabilitative efforts

Showcasing a ‘Long-COVID (LC) Workforce Toolkit’ for Health and Social Care Providers: Providing resources, supports, and a mapping of networks that support rehabilitative efforts

RESEARCH ON TEACHING MATERIALS IN SCIENCE EDUCATION: A BIBLIOMETRIC ANALYSIS FROM 2013 TO 2023

The purpose of this study was to investigate the bibliometric properties of teaching materials in science education from 2013 to 2023. The bibliographic references were found in the Scopus databases by utilizing the keywords "teaching materials" AND "science education" (search date: February 22, 2024). For information analysis, the VOSviewer software program and the RStudio package were utilized. The findings verified that 111 journals and 170 papers covering 45 nations had been scanned. There is an increase in the publication of articles on the topic of teaching materials published in 2013-2023. Germany, the United States of America, and Indonesia were the top three nations in this area. The majority of the publications were released in the Conference Series: Journal of Physics. Barendsen E is the author who has contributed the most. Network map and visualization results related to the relationship between topics discussed there are 4 in addition to the main topics namely teaching, education, curricula, and secondary schools.

Prediction of LncRNA-Protein Interactions Based on Kernel Combinations and Graph Convolutional Networks.

The complexes of long non-coding RNAs bound to proteins can be involved in regulating life activities at various stages of organisms. However, in the face of the growing number of lncRNAs and proteins, verifying LncRNA-Protein Interactions(LPI) based on traditional biological experiments is time-consuming and laborious. Therefore, with the improvement of computing power, predicting LPI has met new development opportunity. In virtue of the state-of-the-art works, a framework called LncRNA-Protein Interactions based on Kernel Combinations and Graph Convolutional Networks (LPI-KCGCN) has been proposed in this article. We first construct kernel matrices by taking advantage of extracting both the lncRNAs and protein concerning the sequence features, sequence similarity features, expression features, and gene ontology. Then reconstruct the existent kernel matrices as the input of the next step. Combined with known LPI interactions, the generated similarity matrices, which can be used as features of the topology map of the LPI network, are exploited in extracting potential representations in the lncRNA and protein space using a two-layer Graph Convolutional Network. The predicted matrix can be finally obtained by training the network to produce scoring matrices w.r.t. lncRNAs and proteins. Different LPI-KCGCN variants are ensemble to derive the final prediction results and testify on balanced and unbalanced datasets. The 5-fold cross-validation shows that the optimal feature information combination on a dataset with 15.5% positive samples has an AUC value of 0.9714 and an AUPR value of 0.9216. On another highly unbalanced dataset with only 5% positive samples, LPI-KCGCN also has outperformed the state-of-the-art works, which achieved an AUC value of 0.9907 and an AUPR value of 0.9267. The code and dataset can be downloaded from https://github.com/6gbluewind/LPI-KCGCN.

INDM: Chiplet-Based Interconnect Network and Dataflow Mapping for DNN Accelerators

INDM: Chiplet-Based Interconnect Network and Dataflow Mapping for DNN Accelerators

BESOINS ET DÉFIS D’INTÉGRATION POUR UNE TRANSFORMATION VERTE ET INTELLIGENTE DE L’INDUSTRIE PORTUAIRE BASÉE SUR DES DONNÉES MULTI-SOURCES

This research explores the integration needs and challenges for achieving green and smart transformation in the port industry, leveraging multi-source data and geographic information technology. The study utilizes comprehensive data, including information from the National Enterprise Credit Information Publicity System, the National Catalogue Service for Geographic Information, and road network maps of Jiangsu Province in China. Geographical analysis methods, such as weighted kernel density estimation and standard deviation ellipse analysis, are employed to identify spatial distribution patterns and changes in the port and shipping service industry. The results indicate a spatial distribution pattern with weak expansion from the center, along the "northwest-southeast" direction. Challenges such as decentralized management and competition for interests hinder port integration for sustainable development. By understanding the spatial dynamics, policymakers can make informed decisions for a greener, smarter, and more efficient port sector.

Global Trends and Hotspots in Pediatric Anesthetic Neurotoxicity Research: A Bibliometric Analysis From 2000 to 2023.

The impact of general anesthetics on brain function development is one of the top frontier issues of public concern. However, little bibliometric analysis has investigated this territory systematically. Our study aimed to visualize the publications between 2000 and 2023 to inspire the trends and hotspots in anesthetic neurodevelopmental toxicity research. Publications from 2000 to 2023 were collected from the Web of Science Core Collection. CiteSpace was utilized to plot and analyze the network maps of countries, institutions, authors, journals, and keywords associated with these publications. A total of 864 publications, consisting of 786 original articles and 78 reviews, were extracted from 2000 to 2023. The annual publications have increased constantly over the past two decades. The USA and the People's Republic of China were the leading driving forces in this field. Harvard University was the most productive institution. Zhang Y published the most related articles, and Jevtovic-Todorovic V was mostly cited in this field. The most prolific journal was Pediatric Anesthesia, and the most frequently co-cited journal was Anesthesiology. Keywords were divided into nine clusters: "apoptosis", "propofol", "developing brain", "cognitive dysfunction", "neuronal cell degeneration", "brain", "neuroinflammation", "local anesthesia", and "oxygen therapy". The strongest citation bursts in earlier years were "learning disability", "cell death", and "cognitive function". The emerging trends in the coming years were "awake regional anesthesia", "behavioral outcome", and "infancy general anesthesia compared to spinal anesthesia". We concludethat anesthetic-induced neurotoxicity has received growing attention in the past two decades. Our findings evaluated the present status and research trends in this area, which may provide help for exploring further potential prospects on hot topics and frontiers.

Nonlinear Quality‐Related Fault Detection Using Neighborhood Embedding Neural Orthogonal Mapping Algorithm for Batch Process

AbstractQuality‐related fault detection has become a hot research topic in recent years. It is not reliable to measure quality‐related relationships only by mutual information among process variables and single‐quality variables. Frequent alarms for quality‐unrelated faults seriously affect the normal operation of industrial production. At the same time, the strong nonlinearity of the process data leads to the difficulty of feature extraction. In this paper, we propose a fault detection algorithm based on nonlinear quality‐related neighborhood embedding neural orthogonal mapping (QR‐NENOM). First, quality‐related and quality‐unrelated variables are selected by Bayesian fusion mutual information, and the weighted method of mutual information is used to enhance the quality‐related information and suppress the quality‐unrelated information. Second, local manifold information is obtained by reconstructing nearest neighbors of process data, and key features are extracted by the nonlinear method composed of neural network and orthogonal mapping. Then, statistical indicators are established to complete fault detection. Finally, the nonlinear feature extraction ability of NENOM is verified by numerical examples, and the QR‐NENOM algorithm proposed in this paper is applied to the penicillin fermentation process. Comparative experiments show that QR‐NENOM has better detection performance for quality‐related faults and fewer alarms for quality‐unrelated faults.

Post global financial crisis multiplex financial network structure longevity: Does it predict re-globalization? Empirical investigation for 234 countries

Research background: International capital flows show decreasing trend after each financial crisis as countries become unwilling to invest in risky counterparties. Recent discussions over structural changes within the global financial network suggest that its structure may be still undergoing some changes. Uncovered weaknesses of over-connectedness with unstable un-ally countries during global financial crisis were further highlighted by recent Covid-19 pandemic. However, previous research suggest that important structural changes happened after global financial crisis. Thus, such situation raises a question whether post global financial crisis multiplex network structure is long-term and is it exhibiting re-globalization pattern as suggested by re-globalization megatrend. Purpose of the article: The main objective of the paper is to assess post global financial crisis multiplex financial network structure longevity and to identify whether it exhibits re-globalization pattern. Methods: Multiplex financial network mapping, network topological analysis and structure longevity methods were employed to evaluate network connectedness, topological structural properties of multiplex financial network and its’ structure longevity. 5-layer multiplex financial network was mapped for different types of capital flows for each layer: net direct and portfolio investments in debt, equity and net banking assets for each year in the post global financial crisis period 2009–2020. Each network layer employed bilateral data from up to 234 countries of the world. Next step of research employed a set of network-level measures defining topological features of each network in the period of 2009–2020 in order to capture the trend of structural changes and evidence of re-globalization. The final stage analyzes multiplex financial network structure by layer and country longevity. Findings & value added: Analysis reveals that stock (positions) multiplex financial network aggregate connectedness, i.e., density and value, increased during post- global financial crisis period until Covid-19 pandemic period, thus, suggesting that globalization in terms of stock is continuing. Topological structural properties of multiplex financial network changed as number of strongly and weakly connected countries decreased and more countries became included in network. Longevity analysis of layer and country structure within multiplex financial network reveal that banking and debt investments are increasing since 2018, while equity part is decreasing. Developed countries, i.e., the United Kingdom and Germany, decrease in multiplex financial network structure, while developing, especially from Asia region (i.e., Hong Kong SAR, China and Singapore) increase. Such empirical results support the re-globalization megatrend in terms of investment type and counterparty structure as highlighted by most recent scientific discussion showing that it started early after global financial crisis. Research also uncovers some regional trends of Asia region increasing in investment network structure, while Europe decreasing. As developing countries attract more and more investment and further develop, global cost saving and, thus, returns from global investment into these countries may be decreasing, resulting in lower gain from globalization. Our analysis may facilitate investment strategy decisions by suggesting that even though gains from globalization might be decreasing, instead of localization, countries could follow ally investment strategy to manage the risk of over-dependency from unstable counterparty while preserving globalization wealth.

NETWORK ANALYSIS FOR THE STUDY OF TRANSPORT IN THE METROPOLITAN AREA OF ALGEIRS

Transport in the metropolitan area of Algiers is very complicated, so we used the powerful tools of geographic information systems in the analysis of transport in the study area. Through the utilization of network analysis and GIS capabilities, our study focuses on optimizing transportation networks and enhancing port accessibility in the metropolitan regions of Boumerdes, Blida, Tipaza, and Algiers. Our methodology involves data collection, GIS analysis, and mapping to generate databases and maps of metropolitan networks. Through assessing port accessibility, road connectivity, and population-based hotspots analysis. Our study reveals Algiers as the most accessible municipality, attributed to its strong centralization. The roadbased analysis and population-based hotspot analysis provide valuable insights into port accessibility, road connectivity, and high-density townships, facilitating informed urban planning and infrastructure development.