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Human arousal is essential to survival and mediated by the ascending arousal network (AAN) and its connections. It spans from the brainstem to the diencephalon, basal forebrain, and cerebral cortex. Despite advances in mapping the AAN in adults, it is unexplored in fetal and early infant life, especially with high‐resolution magnetic resonance imaging techniques. In this study, we conducted—for the first time—high‐resolution ex vivo diffusion MRI‐based analysis of the AAN in seven fetal, infant, and adult brains, incorporating probabilistic tractography and quantifying connectivity using graph theory. We observed that AAN structural connectivity becomes increasingly integrated during development, progressively reaching rostrally during the first postconceptional year. We quantitatively identified the dorsal raphe (DR) nucleus and ventral tegmental area (VTA) as AAN connectivity hubs already in the fetus persisting into adulthood. The DR appears to form a local hub of short‐range connectivities, while the VTA evolves as a long‐range global hub. The identified connectivity maps advance our understanding of AAN architecture changes due to normative human brain development, as well as disorders of arousal, such as coma and sudden infant death syndrome. We used high‐resolution ex vivo diffusion MRI and graph theory to analyze ascending arousal network development in fetal to adult brains, revealing increasing rostral integration postnatally and identifying the dorsal raphe nucleus and ventral tegmental area as persistent connectivity hubs from fetal stages through adulthood.

Background Intervertebral disc (IVD) degeneration is a major contributor to low back pain, yet its initiating factors remain unclear. While the individual effects of pro‐inflammatory cytokines and mechanical loading on IVDs have been studied, their combined impact is poorly understood. This study investigated how dynamic compression and torsion interact with interleukin‐1 beta (IL‐1β) and its inhibitor, interleukin‐1 receptor antagonist (IL‐1Ra), using bovine IVDs in an ex vivo organ culture system. Methods Whole bovine caudal IVDs were cultured for one week in a custom bioreactor applying diurnal dynamic compression (0.1–0.5 MPa) and torsion (±6°) under three media conditions: physiological, catabolic (10 ng/mL IL‐1β), and inhibitory (10 ng/mL IL‐1Ra). Static compression (0.1 MPa) served as control. 3 T magnetic resonance imaging (MRI) was used pre‐ and post‐culture for imaging and segmentation using 3DSlicer. Subject‐personalized finite element (FE) models were generated via morphing algorithms and coupled with a parallel network (PN) model to analyze metabolite transport and its impact on gene expression. Outcomes included disc height, glycosaminoglycan (GAG) content, qPCR, and cell metabolic activity. Results & Conclusions Degenerative changes were detected in all treatment groups. Results of decreased disc height, hydration, and ACAN expression, alongside increased MMP‐13, indicated that the applied loading was supraphysiological and induced catabolic responses. IL‐1Ra, at the given dose, did not counteract degeneration. MRI‐based FE modeling effectively captured patterns of tissue consolidation and degeneration, providing valuable insights into IVD responses under combined mechanical and inflammatory stress. This integrative platform highlights the importance of modeling complex IVD environments and may inform the design of improved anti‐catabolic therapies. This study investigated the combined effects of dynamic compression and torsion with major pro‐catabolic cytokine interleukin‐1 beta (IL‐1β) and its inhibitor: interleukin‐1 receptor antagonist (IL‐1Ra), on bovine IVD in an ex vivo organ culture system and further evaluated the mechanical effects using a subject‐personalized finite element model based on MRI.

Power quality is a research field related to the proper operation of devices and technological equipment in industry, service, and domestic activities. The level of power quality is determined by variations in voltage, frequency, and waveforms with respect to reference values. These variations correspond to different types of disturbances, including power fluctuations, interruptions, and transients. Several studies have been focused on analysing power quality issues. However, there is a lack of studies on the analysis of both the trending topics and the scientific collaboration network underlying the field of power quality. To address these aspects, an advanced model is used to retrieve data from publications related to power quality and analyse this information using a graph visualisation software and statistical tools. The results suggest that research interests are mainly focused on the analysis of power quality problems and mitigation techniques. Furthermore, they are observed important collaboration networks between researchers within and across countries.

Birds appear to be equipped with a light-dependent, radical-pair-based magnetic compass that relies on truly quantum processes. While the identity of the sensory protein has remained speculative, cryptochrome 4 has recently been identified as the most auspicious candidate. Here, we report on all-atom molecular dynamics (MD) simulations addressing the structural reorganisations that accompany the photoreduction of the flavin cofactor in the European robin cryptochrome 4 (ErCry4). Extensive MD simulations reveal that the photo-activation of ErCry4 induces large-scale conformational changes on short (hundreds of nanoseconds) timescales. Specifically, the photo-reduction is accompanied with the release of the C-terminal tail, structural rearrangements in the vicinity of the FAD-binding site, and the noteworthy formation of an -helical segment at the N-terminal part. Some of these rearrangements appear to expose potential phosphorylation sites. We describe the conformational dynamics of the protein using a graph-based approach that is informed by the adjacency of residues and the correlation of their local motions. This approach reveals densely coupled reorganisation entities, i.e. graph communities, which could facilitate an efficient signal transduction due to a high density of hubs. These communities are interconnected by a small number of highly important residues. The network approach clearly identifies the sites restructuring upon photo-activation, which appear as protrusions or delicate bridges in the reorganisation network. We also find that, unlike in the homologous cryptochrome from D. melanogaster, the release of the C-terminal domain does not appear to be correlated with the transposition of a histidine residue close to the FAD cofactor.

The complexity of cancer diseases demands bioinformatic techniques and translational research based on big data and personalized medicine. Open data enables researchers to accelerate cancer studies, save resources and foster collaboration. Several tools and programming approaches are available for analyzing data, including annotation, clustering, comparison and extrapolation, merging, enrichment, functional association and statistics. We exploit openly available data via cancer gene expression analysis, we apply refinement as well as enrichment analysis via gene ontology and conclude with graph-based visualization of involved protein interaction networks as a basis for signaling. The different databases allowed for the construction of huge networks or specified ones consisting of high-confidence interactions only. Several genes associated to glioma were isolated via a network analysis from top hub nodes as well as from an outlier analysis. The latter approach highlights a mitogen-activated protein kinase next to a member of histondeacetylases and a protein phosphatase as genes uncommonly associated with glioma. Cluster analysis from top hub nodes lists several identified glioma-associated gene products to function within protein complexes, including epidermal growth factors as well as cell cycle proteins or RAS proto-oncogenes. By using selected exemplary tools and open-access resources for cancer research and differential network analysis, we highlight disturbed signaling components in brain cancer subtypes of glioma.

Graph-based spatial analysis formalizes relations among spatial units, but the formation of these units and their boundary correspondences remains under-specified. This study defines the structural stage preceding relational abstraction and establishes the conditions under which spatial units and boundary correspondences become analytically determinate. It then develops a layered spatial articulation procedure that derives spatial objects from plan-encoded architectural information by differentiating topographic substrate, building frame, spatial enclosure, and relational boundary conditions. These are organized into a base spatial graph. The topology of this graph is fixed by articulation, and its edges encode admissible relational mode combinations. Using traditional Korean housing (hanok) as an illustrative reference for the proposed methodology, the study shows that heterogeneous spatial conditions can be consistently articulated into a unified structural domain prior to relational abstraction. The resulting base spatial graph defines a finite but combinatorially extensive space of admissible relational configurations. Within this domain, graph-domain operations act without expanding the articulated structure, while certain operations may reduce it through structural transformation. The study shows that spatial units cannot be treated as pre-given entities but must be structurally constituted. By formalizing this prior stage, the study establishes explicit structural preconditions for graph-based spatial analysis and provides a consistent analytical domain for subsequent spatial interpretation.

Social networks generate vast amounts of data that can reveal patterns of human behaviour, social attachment, and mental states. This paper explores advanced machine learning techniques to detect and model such patterns, focusing on community structures, influential users, and information diffusion pathways. To address the scale, noise, and heterogeneity of social data, we leverage recent advances in graph theory, natural language processing, and anomaly detection. Our framework combines clustering for community detection, sentiment analysis for emotional state inference, and centrality metrics for influence estimation, while integrating multimodal data—including textual and visual content—for richer behavioural insights. Experimental results demonstrate that the proposed approach effectively extracts actionable knowledge, supporting mental well-being and strengthening digital social ties. Furthermore, we emphasise the role of privacy-preserving methods, such as federated learning, to ensure ethical analysis. These findings lay the groundwork for responsible and effective applications of machine learning in social network analysis.

In today’s volatile business environment, securing a sustainable competitive advantage hinges on retaining and effectively managing talent. While talent turnover is inevitable, strategic internal human resource (HR) transfers offer a solution to prevent talent outflow and supplement skill gaps. However, previous models for identifying internal substitutes often focus solely on individual work capabilities, neglecting the critical role of group interactions and collaborative structure. Drawing on social network theory, transactive memory systems, and person–group fit, this study proposes a graph-based analytical approach that models the organization as a complex system. Our methodology provides a holistic framework that integrates both (1) individual capabilities and (2) group-level characteristics (e.g., work-relationship networks and cluster-level similarity) to identify the most suitable substitutes. At the macroscopic level, we use an inductive graph neural network (GraphSAGE) to learn node embeddings from a work relationship network constructed from process event logs and to quantify group-level similarity. At the microscopic level, we compute dynamic collaboration intensity, frequency, and task similarity between employees over time. To validate the approach, we develop four simulation scenarios using an enriched incident management process event log and implement them in a SimPy-based simulator, benchmarking against an existing method that considers only individual factors. Across all scenarios, the proposed dual-factor model significantly outperforms the baseline in terms of efficiency, accuracy, and suitability. This research provides a practical, validated algorithm that supports evidence-based workforce management and more effective internal talent allocation.

This paper proposes a novel approach to represent the geometry of the corrugated board profile during compression using graphs. Graphs are lighter than images, and the computational time of compression analysis is then significantly reduced compared to using the original image data for the same analysis. The main goal of using such graphs is to gain more knowledge about the mechanical behavior of corrugated boards under compression compared to the current load–deformation curve approach. A node tracking algorithm is applied to characterize the different phases occurring during the compression test in order to predict physical phenomena, including buckling and contact. The main results show that analyzing the nodes provides significant insights into the compression phases, which has not been achieved in the current state of the art. The authors believe that the objective of this research is crucial to better understanding the physics of corrugated boards under compression, and it can also be extended to other engineering structures.

The present work aims to demonstrate the hypothesis that atonal music modifies the topological structure of electroencephalographic (EEG) connectivity networks in relation to tonal music. To this, EEG monopolar records were taken in musicians and non-musicians while listening to tonal, atonal, and pink noise sound excerpts. EEG functional connectivities (FC) among channels assessed by a phase synchronization index previously thresholded using surrogate data test were computed. Sound effects, on the topological structure of graph-based networks assembled with the EEG-FCs at different frequency-bands, were analyzed throughout graph metric and network-based statistic (NBS). Local and global efficiency normalized (vs. random-network) measurements (NLE|NGE) assessing network information exchanges were able to discriminate both music styles irrespective of groups and frequency-bands. During tonal audition, NLE and NGE values in the beta-band network get close to that of a small-world network, while during atonal and even more during noise its structure moved away from small-world. These effects were attributed to the different timbre characteristics (sounds spectral centroid and entropy) and different musical structure. Results from networks topographic maps for strength and NLE of the nodes, and for FC subnets obtained from the NBS, allowed discriminating the musical styles and verifying the different strength, NLE, and FC of musicians compared to non-musicians.