Respiratory and muscular systems must integrate ventilation, oxygen delivery, and muscle activation to meet exercise demands. While decades of research have provided understanding of how these systems function individually, the principles regulating their dynamic coupling as a network remain unexplored. Our goal was to investigate how respiratory dynamics synchronize and integrate as a network with the activity of muscles during exercise, and assess how it responds to fatigue. Nine adults performed two graded cycling tests until exhaustion, starting at 0 W with 25 W·min-1 increments. Continuous synchronous recordings included electromyography (EMG) from right and left vastus lateralis and erector spinae, and respiration waveform via chest belt. Respiratory-muscular coupling was measured using the amplitude-amplitude cross-frequency coupling (ACFC) method. First, breathing rate was extracted from the respiration waveform. Second, EMG signals were decomposed into ten frequency bands [F1-F10], representing distinct neuromuscular processes. Last, cross-correlation coefficients (C) were computed as the ACFC outcome. We uncover novel network maps of respiratory-muscular dynamic interactions. We find that respiratory-muscular networks exhibit a complex hierarchical structure which depends on the role muscles play during exercise. Further, cross-correlations significantly increase with fatigue accumulation during exercise, indicating stronger integration between breathing and muscle activation under rising metabolic demand. This network-level adaptation shows that physiological responses to exercise arise not only from isolated systems, but also from their dynamic interactions as an integrated network. The network physiology approach utilized here contributes to the development of a new class of network-based markers to quantify multisystem interactions underlying human function during exercise.

Network mapping of personality-creativity dynamics: Domain-general and domain-specific associations across genders.

Functional and protein interaction analysis of Nop7 in trypanosomatid parasites.

NIMA-related kinase 4 (NEK4) is a serine/threonine kinase implicated in microtubule stabilization, cilia function, and DNA damage response (DDR), with emerging roles in cancer progression through context-dependent effects on proliferation, epithelial-to-mesenchymal transition (EMT), and metastasis. Despite its significance, site-specific phosphorylation dynamics of NEK4 remain underexplored. Here, we conducted a comprehensive computational phosphoproteomic analysis by curating Class-1 phosphosites from over 3800 public datasets, identifying NEK4 phosphosites, including four predominant sites (S563, S661, S461, S639) outside the kinase domain that exhibit high detection frequencies and differential regulation. Coregulation analysis revealed phosphosites in other proteins (PsOPs) that coordinate with these NEK4 sites, linking them to DDR pathways (e.g., via interactions with DNA-PK complex components), EMT signaling, microtubule organization, and mitochondrial function. Network mapping integrated predicted upstream kinases (e.g., CDK13, RPS6KA1/3), downstream substrates (e.g., MKI67, INCENP), and binary interactors (e.g., TMPO, RRP1B), highlighting NEK4's integration into cancer-associated networks involving cell cycle regulation, apoptosis, and autophagy. Functional enrichment underscored NEK4's potential in modulating genotoxic stress responses and tumorigenic reprogramming. These findings provide a phospho-centric framework for NEK4 signaling, positioning it as a therapeutic target in DDR-defective and EMT-driven cancers, and lay the groundwork for experimental validation of its site-specific roles.

The cultivation of rice (Oryza sativa L.) has become increasingly challenging due to various abiotic stresses such as elevated salinity and drought conditions, and these challenges will only worsen with climate change. This review highlights recent advancements in CRISPR/Cas9 genome editing technology as applied to developing rice for greater tolerance of abiotic stresses. Functional genomics analysis (including transcriptomic, proteomic, and metabolomic data) has identified and validated key regulatory genes and networks that affect plant response to abiotic stress. Researchers using CRISPR/Cas9 technology have made changes to both negative regulators (e.g., OsRR22 for salinity; OsDST for drought/salt) and positive (e.g., OsSAPK2 for drought) regulatory genes, producing transgene-free mutants displaying enhanced ion homeostasis and ROS scavenging as well as stomatal regulation and stability of yield under stress. Results from functional genomics indicate that polygenic regulatory networks, including the transcriptional regulators (NAC, WRKY, bHLH) and abscisic acid (ABA) signalling systems, are suitable for multiplex applications to produce a plant with broad-based stress resilience. Although functional genomic methods have been successful in the lab, there are still major issues with performance under field conditions when multiple stresses are present (e.g., genotype dependence, japonica bias, pleiotropic effects, and regulatory issues). The future integration of prime-editing, network mapping, and multi-site trial work will assist in the development of sustainable, superior quality indica varieties to help fulfill the UN's Sustainable Development Goals (SDGs) for food security.

Action and rest tremor map to distinct networks within the primary motor cortex.

Vascular cognitive impairment (VCI) is a devastating clinical endpoint of microvascular senescence. However, the mechanisms by which age-related focal vascular insults cause systemic brain network failure and molecular vulnerability remain unknown.To decode the multi-scale neurobiology of VCI, we conducted a systematic review and meta-analysis of whole-brain voxel-based morphometry studies comparing patients with VCI and healthy controls. We used coordinate-based network mapping on a normative functional connectome to identify convergent structural atrophy networks. To decode multi-scale biological substrates, we checked the resulting macroscopic topography against the Allen Human Brain Atlas and 28 positron emission tomography-derived neurotransmitter maps. 18 studies contributed to the analysis, including 682 VCI patients and 643 healthy controls. VCI-related atrophy, despite appearing disparate, functionally converges onto a robust macroscopic architecture that is anchored predominantly in the somatomotor and salience networks. Transcriptomic profiling further showed that this network colocalizes significantly with Layer 6 corticothalamic and subcortical projection neurons. These neuron populations feature exceptionally long axonal projections, a property that heightens their metabolic susceptibility to chronic hypoperfusion.At the neurochemical level, this structural degradation exhibited profound spatial coherence with the macroscopic distribution of dopamine transporter (DAT) and 5-hydroxytryptamine transporter (5‑HTT). These findings suggest that VCI may represent a quintessential "disconnection syndrome" associated with the vulnerability of long-range projection pathways to vascular aging, providing a novel multi-scale neurobiological template to identify network-level targets for intervention.

This bibliometric study analyzed global research trends and the most cited contributions in robotic-assisted hepatobiliary and pancreaticosplenic surgery using Scopus, complemented by structured citation and network mapping. The search strategies i.e. title-abstract-keywords, abstract based and title only were employed. The abstract-based dataset (n = 1,037 publications) were selected for detail analysis. Annual publication dynamics were assessed using per-year output counts, showing progressive growth from 21 publications/year in 2010 to 176 publications/year in 2025, indicating a sustained expansion in research activity over 16 years. Based on the number of publications, the top contributors (authors, universities, countries, sources, and funding sponsors) are presented. Citation performance of the top 100 most cited publications demonstrated 9,640 total citations, with 96.4 citations per paper on average. Network analyses further included author co-authorship, institutional collaboration, country-level cooperation, and keyword co-occurrence mapping. Journal-level analysis incorporated bibliometric indices included In total, 51 journals contributed to the top-cited literature, highlighting a diversified yet high-impact publication distribution. For each, total publications, total citations, citations per paper, h-index, g-index, m-index, CiteScore, SCImago Journal Rank (SJR), Source Normalized Impact per Paper (SNIP), Journal Impact Factor (JIF), and Scopus/Web of Science quartiles were provided. The top 100 most cited publications mainly focused on robotic hepatobiliary and pancreatic surgery, especially hepatectomy, pancreaticoduodenectomy, and distal pancreatectomy. Liver and pancreatic procedures were most frequently studied, reflecting increasing clinical adoption in complex abdominal surgery and outcome evaluation. Key studies also highlighted technological advances, including robotic platforms, fluorescence guidance, and image-assisted techniques, along with training and learning curve research.

Artificial intelligence (AI) and machine learning (ML) technologies are rapidly transforming telesurgery by enhancing robotic-assisted surgical systems, remote surgical communication, image-guided interventions, and intelligent decision-making. The integration of AI-driven algorithms with telesurgical platforms has accelerated research activity across medicine, robotics, engineering, and computer science. However, the global research landscape, collaborative structure, and emerging thematic trends of AI- and ML-enabled telesurgery remain insufficiently explored. Therefore, the present study aimed to perform a comprehensive bibliometric and knowledge mapping analysis of global research on AI and ML applications in robotic, teleoperated, and remote surgery published between 2015 and 2025. A bibliometric analysis was conducted using the Scopus database on 28 May 2026. Articles published between 2015 and 2025 related to AI, machine learning, robotic surgery, teleoperation, and telesurgery were retrieved using predefined search terms. Only English-language research articles were included. Bibliometric indicators including annual publication trends, citation analysis, leading journals, productive authors, institutions, funding agencies, country collaborations, co-citation analysis, and keyword co-occurrence analysis were evaluated. Visualization and network mapping were performed using VOSviewer software (version 1.6.20). A total of 2,201 publications were identified from 112 countries. Scientific output demonstrated substantial exponential growth, increasing from 85 publications in 2015 to 1,167 publications in 2025. Medicine (29%), computer science (24%), and engineering (20%) represented the dominant research areas. Journal of Robotic Surgery emerged as the leading publication source, while China and the United States were identified as the most influential contributing countries. Keyword co-occurrence analysis highlighted major research themes including robotic surgery, deep learning, machine learning, intelligent robotics, teleoperation, and minimally invasive surgery. Overlay visualization demonstrated a recent shift toward AI-driven autonomous systems, computer vision, surgical workflow analysis, and intelligent robotic platforms. Co-citation analysis further revealed strong interdisciplinary foundations involving surgical sciences, robotics, computer vision, and advanced deep learning methodologies. Research on AI and ML applications in robotic, teleoperated, and remote surgery has grown rapidly over the last decade and is increasingly characterized by strong interdisciplinary collaboration and technological innovation. Emerging trends suggest a transition from conventional robotic-assisted surgery toward intelligent, data-driven, and semi-autonomous telesurgical systems. The findings of this bibliometric study provide valuable insights into the evolving scientific landscape of intelligent telesurgery and may support future research, clinical translation, technological development, and policy planning in robotic-assisted remote surgical care.

Hydrogen sulfide is an endogenous gaseous signalling molecule with recognized roles in vascular regulation, redox homeostasis, and inflammation. In the placenta, H2S is essential for maintaining trophoblast function and promoting healthy vascular remodelling. Impaired H2S signalling has been implicated in placental disorders characterized by oxidative stress, particularly in preeclampsia. One of the principal drivers of oxidative stress in the placenta is H/R injury, which mimics the intermittent perfusion patterns seen in early placental maldevelopment. Although the protective roles of H2S have been described in several ischemia-reperfusion models, its genome-wide transcriptional effects on trophoblasts under hypoxia/reoxygenation-induced oxidative stress remain unknown. HTR-8/SVneo trophoblasts were subjected to H/R injury induced by varying oxygen concentrations to model the fluctuating oxygen environments of early placental development, followed by treatment with an exogenous H2S donor (NaHS). A CSE inhibitor (PAG) treatment was also given. RNA sequencing was performed to characterize global gene expression changes. Differentially expressed genes were analyzed using KEGG and Gene Ontology enrichment, protein-protein interaction network mapping, and transcription factor prediction. H/R induced extensive transcriptional remodelling, with robust activation of HIF-1, PI3K-Akt, MAPK, Rap1/Ras, NF-κB, and focal adhesion pathways. H/R [2/10% O2] triggered pronounced glycolytic, hypoxia-adaptive, anti-apoptotic, and pro-invasive signatures. NaHS modulated these responses in a context-dependent manner: it attenuated early chemokine-driven inflammation, enhanced angiogenic and ECM-remodelling programs, and strengthened metabolic adaptation under a higher hypoxic burden 2/10% H/R paradigm. PAG induced a chronic inflammatory angiogenic signature, indicating endogenous H2S restrains basal inflammatory activation. Integrated regulation of proliferation, migration, apoptosis, morphogenesis, and angiogenesis was observed through biological process analysis, with major changes noticed in NaHS-treated 2/10% H/R conditions. JUN, PTGS2, MAP3K5, DUSP1, SFN, NCF2, THBS2, and GADD45A emerged as the central interconnected hub-gene module through PPI analysis. Among these, JUN and PTGS2 appeared as potential integrators of trophoblast remodelling, redox stress, and inflammatory signalling. Our study provides the first evidence of transcriptomic analysis showing that H2S alters gene networks in trophoblast cells subjected to H/R-induced oxidative stress. The results highlight coordinated regulation of metabolic, angiogenic, and inflammatory pathways, providing fundamental understanding into how H2S may influence trophoblast adaptation to stress.

ObjectiveWe aimed to perform what is, to our knowledge, the first bibliometric analysis focusing on artificial intelligence (AI) applications in paediatric congenital heart disease (CHD) over a 25-year period (2000-2025). We examined the advancements in research, emerging trends, and principal research topics in this field.Materials and MethodsArticles on AI and CHD published between 2000 and 2025 were retrieved. The data sourced from the Web of Science Core Collection encompassed 423 qualifying studies that were evaluated using Cite Space and VOSviewer to examine the contributions of various countries, institutions, authors, journals, and keywords. These visualisation tools facilitated the mapping of collaboration networks, co-citation patterns, and keyword trends.ResultsThe United States is the main research hotspot in national terms, contributing 40% of the publications in this area, Harvard Medical School is the institution with the most research results, with Pan, Silin being the most prolific researcher. Key research areas include the application of AI in prenatal screening for CHD, diagnosis and treatment of paediatric CHD, long-term management of CHD in children, the role of health professionals, and related risks.ConclusionAs far as we know, this is the first bibliometric analysis dedicated to AI in paediatric CHD. It shows continuous growth and interdisciplinary potential. We emphasise the need for improved collaboration between different fields of study, use of AI in medical practice to assess individual risks (especially regarding medication safety), and policy initiatives to address the equity gap between high-income regions and those with the most CHD cases.

ABSTRACT Location-Based Services (LBS) have become integral to contemporary urban life, with Global Navigation Satellite System (GNSS) modules in smart devices serving as the primary geospatial data source. However, urban canyon scenarios challenge satellite positioning. Signal obstruction and multipath effects caused by high-rise buildings result in substantial positioning errors, potentially misplacing users on the sidewalk across the street and compromising LBS reliability. While Map Matching (MM) techniques have proven effective in enhancing positioning accuracy for vehicular navigation systems, pedestrian map matching encounters two primary challenges. First, unlike vehicular roads with directional constraints, bidirectional pedestrian networks hinder direct direction-based road matching. Second, pedestrian pathways, typically situated closer to surrounding buildings, lead to larger GNSS positioning errors, rendering distance-based matching more error-prone. To address these limitations, this paper introduces an Azimuth-Constrained Hidden Markov Model (AC-HMM) based pedestrian map matching methodology. Initially, a comprehensive pedestrian road network map is constructed using open-source datasets. Subsequently, the proposed AC-HMM algorithm is applied to pedestrian map matching, integrating short-term and long-term azimuth information to enhance MM accuracy in complex urban environments. Experimental validation using real-world pedestrian trajectory data demonstrates that the traditional HMM-MM algorithm decreases mean positioning error by approximately 7.1% compared to raw GNSS results. Notably, the AC-HMM-MM methodology substantially outperforms conventional HMM-based map-matching approaches, achieving a 20.7% improvement in matching accuracy and a 37.3% enhancement in mean positioning precision. The algorithm effectively mitigates opposite sidewalk positioning errors typical of high-density urban canyon environments while maintaining real-time processing capabilities for pedestrian navigation applications. Despite these performance improvements, the method preserves computational efficiency with only a minimal 1.27% increase in processing time.

Diffuse midline gliomas (DMGs) are near-universally lethal tumours of thechildhood central nervous system1,2. In animal models, DMGs form brain-wide integrated networks through neuron-to-glioma synapses3-6 and glioma-to-glioma gap junctional coupling3. This extensive connectivity robustly promotes the growth and invasion of DMG3-9 and other glial malignancies10-12 through paracrine mechanisms and direct neuron-to-glioma synapses. However, the organization and clinical implications of these connections in the living human brain remain to be elucidated. Here, we develop tumour network mapping to compute the brain-wide connectivity profile of DMG, defining a conserved brain network across pontine and thalamic DMG associated with patient short-term survival (DMG network). Tumour functional connectivity with the DMG network was independently predictive of patient overall survival across two external validation cohorts. Tumour growth mapped to DMGnetwork-specific trajectories and peak in-network neurometabolic changes across development spatiotemporally aligned with the peak age incidence of DMG. Analyses of single-nucleus RNAsequencing dataconfirmed diverse synaptic gene enrichment in high-connectivity DMG. Strikingly, incidental surgical resection of high-connectivity thalamic DMG tissue conferred a significant survival advantage. Collectively, these data define a conserved and prognostically important brain network in children with DMG, consistent with the hypothesis that DMGs exploit otherwise healthy brain circuits to promote tumour growth.

While urbanicity increases the risk of mental health issues, its effects on brain networks are heterogeneous and underexplored in relation to different exposome factors. Using a coordinate network mapping strategy termed exposure network mapping (ENM) across eight datasets, this study first consolidated heterogeneous findings of urbanicity to a significant, replicable network involving the middle frontal gyrus, orbital gyrus, and anterior cingulate gyrus. Afterwards, among the other factors examined (air pollution, noise, income, stress, green space), only stress converged into a distinct common network, highlighting the orbital gyrus, caudate, anterior/middle cingulate gyrus, hippocampus, and middle frontal gyrus.This ENM-stress map exhibited the highest correlationwith both the ENM-urbanicity map (r = 0.77) and a transdiagnostic map (r = 0.72). In addition, sleep-related coordinates also formed a consistent network, involving the middle cingulate gyrus, orbital gyrus, caudate, and putamen, which correlated strongly with urbanicity (r = 0.75), stress (r = 0.80), and the transdiagnostic pattern (r = 0.55). Collectively, this study highlights the potential risks of urbanicity and stress, as well as the protective role of sleep on brain networks, which may offer new insights for preventing mental health issues in urban environments.

Nitrogen-containing chemicals are indispensable to modern society, among which lactams occupy a central position owing to their extensive use as polymer precursors, high-performance solvents, and pharmaceutical building blocks. Conventional petroleum-based production routes, however, are increasingly constrained by environmental and resource concerns, underscoring the urgency of developing sustainable alternatives from renewable biomass. Despite growing research activity, existing reviews on biomass-derived lactam synthesis remain fragmented, typically concentrating on a limited number of established pathways and lacking a systematic mapping of renewable feedstocks, reaction networks, and catalytic strategies. To bridge these gaps, this review provides a systematic overview of representative biomass-based lactams, with emphasis on γ-, δ-, and ε-lactams, by correlating diverse renewable feedstocks with their corresponding synthetic routes under heterogeneous catalysis. This work aims to establish a coherent framework for biomass valorization and to offer conceptual and practical guidance for advancing efficient, selective, and sustainable lactam synthesis.

The DNA damage response (DDR) is a complex network of cellular pathways that ensures the faithful maintenance of our genomes upon a wide array of genomic insults. To elucidate the functional architecture of this network, we conducted unbiased genetic interaction screens using the Cas12a genome editor to disrupt 233 DDR genes frequently mutated in cancer and other genetic diseases, either individually or in pairwise combinations. This approach enabled us to assess the phenotypic effects induced by the disruption of >27,000 DDR gene pair combinations under unperturbed cell growth conditions. From this analysis, we identified over 750 high-confidence positive (buffering) or negative (synthetic lethal/sick) gene-gene interactions, along with multiple connections between previously unlinked DDR pathways and modules, allowing us to define novel aspects of the cellular response to spontaneous, DNA replication-associated DNA damage. Among the identified genetic interactions, we uncovered profound synthetic lethal interactions between genes encoding 1) the translesion polymerase REV1-Pol ζ complex and the MCM8-MCM9-HROB DNA helicase complex; 2) Fanconi Anemia (FA) proteins and the mitotic DNA repair factors GEN1, CIP2A, and RHINO; and 3) the DNA translocase SMARCAL1 and components of the FANCM complex, suggesting novel opportunities for targeted therapies in tumors carrying mutations in these genes. Additionally, we identified robust suppressor interactions between theDCLRE1Bgene encoding the nuclease APOLLO and the core non-homologous end joining (NHEJ) genesXRCC4,LIG4, andNHEJ1, suggesting that NHEJ impairs the fitness of APOLLO-deficient cells. This work provides a functional map of the DDR network and demonstrates the power of Cas12a-based screens for identifying synthetic lethal and buffering interactions with therapeutic potential.

Background: Hygrophila Auriculata, a popular herb in traditional remedy recognized for its antioxidant, antitumor and anti-inflammatory properties. Current research suggests that it may help minimize oxidative stress associated with Alzheimer's disease (AD). The particular neuroprotective routes by which Hygrophila Auriculata affects AD are not fully understood. Aim: This study focuses on in silico network pharmacology and molecular docking study to examine how active element of Hygrophila Auriculata might offer protection from Alzheimer's. Method: The extensive literature survey were used for collection of bioactive chemicals from Hygrophila Auriculata and relevant molecular targets were discovered. Targets related with Alzheimer's were found by utilizing queries in the GeneCards databases. Cytoscape 3.8.2 was used to create a network mapping showing connections between active drugs and their targets.By looking at the target genes affected by Hygrophila auriculata in Alzheimer's disease, we constructed a protein-protein interaction network using String database. Shiny Go database was used to undertake functional enrichment analysis including KEGG pathway analyses and Gene Ontology analyses in order to uncover potential biological pathways connected to these targets. The next step was to conduct molecular docking experiments to determine strength of contact among active compounds and key targets. Conclusion: The study revealed that the plant contains numerous bioactive compounds that can interact with important AD targets, implying a multi-target mode of action. Molecular docking results confirmed the network analysis by demonstrating positive binding interactions between selected bioactive chemicals and core AD target

Informatics technologies are transforming biodiversity conservation by enabling large-scale data analysis, predictive modelling, and real-time monitoring in the face of anthropogenic climate change. This study presents a bibliometric analysis of global research on the application of informatics tools - such as machine learning, remote sensing, geographic information systems, and big data analytics - to biodiversity conservation and anthropogenic climate change. Using the Scopus database, we analysed 643 publications from 1993 to 2024 to identify research trends, collaboration networks, and emerging thematic areas. The results reveal a rapid increase in publications over the last decade, with developed countries and China leading research output, while contributions from Africa remain limited. Keyword co-occurrence analysis highlights key research themes, including species distribution modelling, climate change impacts, conservation technology, and ecological informatics. Co-authorship network mapping underscores the interdisciplinary and collaborative nature of biodiversity informatics and anthropogenic climate change research. This bibliometric review provides a quantitative synthesis of knowledge production in this field, offering insights into dominant research trajectories and identifying gaps in geographic representation and thematic coverage. Overall, the review reveals a large but geographically skewed scientific footprint whose future value depends on closing gaps in data-poor, biodiversity-rich regions and explicitly linking biodiversity informatics outputs to climate-resilient policy and practice. The findings inform future research and policy efforts aimed at leveraging informatics technologies for effective and inclusive biodiversity conservation strategies in a changing climate. This study is FAIR-aligned and accompanied by openly shared data and materials with ISO-aligned, machine-readable metadata.

The 2022 global health emergency highlighted the necessity for rapid diagnostic measures in response to Mpox clade IIb outbreaks. This study investigates the Mpox lineages identified in Thailand during this period and explores the transmission dynamics of dominant lineages. Using targeted next-generation sequencing from 162 Mpox-infected individuals, we created a transmission network map by linking high-similarity sequences through molecular-temporal distance matrices. Our findings reveal that the study population aligns with global trends of transmission almost exclusively within the MSM community, and a majority of cases resulting from lineage C viruses as well as eight additional sub-lineages. Within the observed substitutions, a majority of mutations were either G→A (289, 49.74%) or C→T (221, 38.04%) indicating APOBEC-3-driven deaminase activity. We also present a global transmission network for lineage C Mpox genomes, illustrating both domestic and international spread. This research underscores the significance of whole-genome sequencing in diagnostics during outbreaks and the importance of sharing genomic data to enhance our understanding of disease transmission on a global scale.

Focused ultrasound ablation for psychiatric disorders.