A dynamic network analysis: Temporal interaction of individual differences and its effect on oral language development

Linear system analysis and optimal control of natural gas dynamics in pipeline networks

The proliferation of social networks has transformed global communication, yet it has concurrently facilitated the rapid and widespread dissemination of harmful content. This content, encompassing disinformation, hate speech, extremism, and cyberbullying, poses significant threats to individual well-being, social cohesion, and democratic processes. The sheer volume and velocity of user-generated data render manual moderation untenable, necessitating the development of sophisticated automated detection systems - therefore, the social networks are one of the best environments for swindlers, cheaters, and other inadequate people to spread off harmful and dangerous content. This review systematically analyzes scientific literature to map the landscape of existing solutions and identify critical gaps for future research. A thorough literature review was conducted following formal protocol. Key scholarly databases such as IEEE Xplore, ACM Digital Library, Scopus, and arXiv were searched using a comprehensive set of keywords related to malicious content, social media, and detection methodologies. The review focused on peer-reviewed articles, conference proceedings, and scholarly books published in the last five years, supplemented by foundational works. The analysis reveals a clear evolution in detection methodologies, from traditional machine learning (ML) models reliant on manual feature engineering to advanced deep learning architectures. A taxonomy of harmful content is established, clarifying the distinctions between phenomena such as disinformation, extremism, and cyberbullying. The review critically examines the dominant detection paradigms: content-based methods using Natural Language Processing (NLP), structure-based methods leveraging Graph Neural Networks (GNNs) to analyze propagation patterns, and emerging hybrid and multi-modal approaches. Despite significant progress, all current methods face fundamental limitations, including a critical lack of contextual understanding, susceptibility to algorithmic bias, vulnerability to adversarial attacks, and a pervasive lack of transparency and explainability. Current methodologies for harmful content detection, while increasingly sophisticated, remain largely reactive and operate on static snapshots of data. They are insufficient for the early, robust, and context-aware identification of evolving threats. A significant research gap exists for a new generation of systems that deeply integrate dynamic content and network analysis. Future research should focus on developing proactive solutions founded on temporal graph learning and Complex Event Processing (CEP), with explainability integrated by design, to effectively model, detect, and mitigate harmful scenarios as they unfold in real-time

This study extends a traditional neural network model to an asymmetric Hopfield model incorporating triple time delays through a combined qualitative and quantitative analytical approach. The system is first linearized using Taylor series expansion, followed by the determination of bifurcation points in the resulting system of equations containing quadratic transcendental terms. An improved Adams-Bashforth-Moulton predictor-corrector method is employed for discretization analysis. Theoretical findings are validated through three numerical case studies, with further examination of how time delays influence the system’s dynamic behavior.

Access to outdoor space for dry cows is strongly influenced by environmental conditions, feed availability, and social dynamics within the barn. Understanding the interplay among these factors can support precision herd management strategies aimed at enhancing cow welfare and health. The aim of this study was to assess how environmental conditions, grazing forage availability and social dynamics influence the behavior of a dairy cow herd, based on their entry and exit from outdoor spaces monitored using the new technologies. The experiment was conducted for 154 days on a commercial dairy farm located in Mantua (Italy) with 35 Holstein cows during the dry period. The availability of outdoor forage was monitored by mowing on several test plots, while meteorological conditions were monitored using a weather station. The cows were equipped with radio frequency identification (RFID) tags to record the free access the pasture. Social interactions were monitored based on the temporal association of cow passages through the gate. To identify a leader-follower relationship, a k-means algorithm was applied to log the frequency of time intervals between successive passages. Correlation analysis results between the number of daily passes and rumination and feeding time revealed strong correlations, with R² values of 0.71*** and 0.65***, respectively. Temperature-humidity index (THI) was the most influent parameter, while RH, solar radiation and rainfall appeared to have less significant impact. However, wind speed during the day and humidity at night has greatest negative influence. The dynamic social network analysis (SNA) showed that the 42% of passages were associated with a leader-follower relationship and three key findings were observed: i) the cow established preferential relationships with specific members of the herd independently of animal's status (heifer or dry), age, and number of calvings; ii) some individuals are more skilled at establishing connections, while others tend to be more solitary; iii) when a significant bond is lost, it is often replaced by another.

Antibodies against low-molecular-weight compounds exhibit cross-reactivities (CRs) with their structural analogs, varying by orders of magnitude for different substances. This variability limits the informativeness of antibody applications as analytical reagents and for other aims when samples contain several members of the same family, their derivatives, or partial degradation products. Therefore, there is a demand to find some criteria for understanding the relationships between the structural characteristics of antigens of a given chemical class and their immunochemical activity. This study presents an experimental and theoretical investigation of the properties of a monoclonal antibody (MAb) against the S-stereoisomer of gatifloxacin, a member of the widely used (fluoro)quinolone (FQ) family of antibiotics, characterized by high structural diversity. The aim was to determine FQs that form complexes with MAb and suggest a methodology to predict their CRs in silico. For this, the interaction of MAb with 26 FQs was studied using the enzyme-linked immunosorbent assay and presented as CR values to the target antigen. The most pronounced CRs were observed for lomefloxacin, sarafloxacin, and ciprofloxacin. Molecular dynamics (MD) simulations were performed to identify differences in analyte interactions at the MAb antigen-binding site, which determines binding affinity. It has been shown that molecular docking fails to discriminate cross-reactive from non-cross-reactive compounds because FQs have similar cores. Therefore, advanced analysis of MD trajectories was carried out. It allowed for clarification of the dynamic features of analyte–antibody interactions responsible for binding. It was shown by the dynamical network analysis that the sum of betweenness centrality between a node corresponding to the quinolone ring and nodes representing MAb amino acids is higher for cross-reactive haptens. The found regularities can be transferred to other analyte–antibody systems as a binary classifier that discriminates cross-reactive and non-cross-reactive compounds.

Visual arts education has been linked to cognitive and neural benefits, yet the neural mechanisms associated with creativity remain unclear. This study examined how long-term engagement in design-related visual arts education relates to creative performance and brain function. Using a quasi-experimental design with propensity score matching, we compared design majors to matched non-design majors. Participants completed visual art creative tasks (product and book cover design) and divergent thinking tasks (AUT, TTCT-figural) during fNIRS recording. The design group outperformed peers across tasks and showed greater left dorsolateral prefrontal activation during early idea generation, while non-design peers relied more on sensory and motor regions. Functional connectivity revealed reduced coupling within task-relevant circuits, indicating greater neural efficiency. Dynamic network analysis showed design majors spent more time in efficient states and switched between states more flexibly. These findings suggest that design-related visual arts education may support creativity through efficient and flexible brain network engagement.

Cancer research has seen transformative advances with single-cell RNA sequencing, yet challenges persist in accurately classifying cancers with similar gene expression profiles. Addressing this, we developed an integrated computational framework leveraging single-cell RNA sequencing data from 10x Genomics Datasets, mathematical analysis, and Seurat-based clustering to classify and predict cancer types. A key methodological innovation involves the application of a mathematical approach that employs the Hausdorff distance matrix and norm analysis across a range of gene correlation thresholds. By generating stacked line plot patterns of the computed norms, the method captures distinct trends that differentiate between similar and dissimilar cancer types, thereby enabling effective classification and prediction. Key findings include robust classification accuracy for breast and lung cancers, derived from dynamic gene network analyses, while colorectal and ovarian cancers presented challenges linked to higher intratumoral heterogeneity. Our results revealed unique norm patterns reflective of distinct transcriptional architectures, including the dynamic immune landscapes in breast cancer and linear transcriptional progression in lung cancer. Validation on independent datasets underscored the method's reliability in categorizing unseen cancer data, providing statistical confidence for breast and lung cancer classifications. Beyond classification, the study advances understanding of cancer gene correlation networks, offering novel insights into transcriptional diversity and tumor microenvironment interactions. This framework bridges gaps in current methodologies, combining precision with scalability for diverse datasets. By integrating mathematical tools with single-cell RNA sequencing data, this study establishes a foundation for transformative applications in cancer diagnostics and treatment.

The mechanical architecture of microtubules (MTs) is crucial for modulating their functions within cells; however, the effect of varying the number of protofilaments (PFs) on the propagation of mechanical signals remains largely unexplored. Nevertheless, MTs assembled in vitro exhibit diverse PF numbers depending on the specific tubulin composition, stabilizing agents and cellular context, suggesting a regulated architectural adaptation. Here, we performed a multiscale computational study integrating molecular dynamics, dynamical network analysis and elastic network modelling to investigate the influence of the MT architecture on structural communication and mechanics. Our results highlight that an increase in PF number alters tubulin-tubulin contact patterns, reshapes lateral surface hydrophobicity and modulates the dynamics of a specific unstructured region known as the M-loop. Remarkably, we identified a correlation between the PF number, vibrational path length and bending stiffness, revealing that MTs with larger architectures propagate mechanical information less efficiently, but offer increased structural support. These findings suggest that MT architecture may serve as a design parameter influencing the propagation of mechanical signals across scales. Moreover, they may contribute to the emerging field of neuromechanobiology, where MTs are considered potential integrators of mechanical and informational processes within neurons.

Combining static and dynamic brain network analysis with machine learning for enhanced diagnosis of major depressive disorder.

Genetic signatures predict social-cognitive trajectories in ultra-high-risk psychosis: A 24-month longitudinal study.

Integrated time-course metabolomics and dynamic network analysis reveals stage-specific regulation of arachidonic acid and bile acid metabolism in influenza-associated pneumonitis and enteritis.

With the rapid development of generative AI technology, AI-generated images pose significant challenges for authenticity verification and originality validation. This paper proposes SCADET, a novel detection framework that integrates Dynamic Frequency Attention Network (DFAN) and Contrastive Spectral Analysis Network (CSAN). DFAN adaptively analyzes image frequency domain features and dynamically adjusts attention for different artistic styles, while CSAN establishes discriminative feature spaces through contrastive learning to enhance cross-model generalization capabilities. Comprehensive experiments on the AI-ArtBench dataset demonstrate that SCADET achieves AUC values of 0.962 and 0.801 in full image and local image detection tasks respectively, representing substantial improvements of 30.5% and 34.4% over baseline methods. Cross-model evaluation shows that the framework maintains stable performance across various generation techniques, with an average accuracy of 0.81 and low variance. Ablation studies validate the effectiveness of both DFAN and CSAN components. These results advance the field of AI-generated content detection and provide valuable insights for addressing authenticity challenges in digital media applications.

BackgroundThe amygdala-hippocampal complex (AHC) plays a central role in the neural mechanisms underlying Internet Gaming Disorder (IGD), particularly in emotional regulation, memory processing, and reward-related functions. However, the dynamic interactions between the AHC and large-scale brain networks, and their relationship with cognitive performance in IGD, remain poorly understood.MethodsA total of 123 adolescents (66 with IGD and 57 healthy controls) underwent resting-state functional magnetic resonance imaging (fMRI). Temporal fluctuations in AHC connectivity were assessed using dynamic functional network connectivity (dFNC) analysis. Correlation and mediation analyses were conducted to investigate the relationship between aberrant AHC-related dFNC and cognitive function.ResultsThree distinct connectivity states were identified, each characterized by unique network configurations. In State 2, dFNC strength between the AHC and both the attentional network (ATN) and visual network (VN) was positively correlated with T scores of the MATRICS Consensus Cognitive Battery (MCCB). Further mediation analysis revealed that weakened dFNC between the AHC and VN regions, particularly the calcarine sulcus and cuneus, served as a mediator linking cognitive impairment to the internet addiction severity of IGD.ConclusionThese findings suggest that aberrant dynamic connectivity of the AHC, particularly its disrupted interaction with VN, may underlie the cognitive impairments in adolescents with IGD. This study provides novel insights into the neurobiological basis of behavioral addiction and highlights the importance of dynamic network analysis in elucidating its underlying pathology.

Mechanically resilient protein interactions are crucial for biological processes ranging from bacterial adhesion to human tissue formation. Catch-bonds, a unique class of protein interactions that strengthen under force, act like a molecular finger trap, tightening to prevent bond rupture. However, it remains unclear whether catch-bonds form immediately upon force application or require a specific force threshold for stabilization. Here, we employ an in silico single-molecule force spectroscopy approach that combines molecular dynamics (MD) simulations, dynamical network analysis, and AI-based modeling to investigate the XDoc:CohE complex, a hyperstable catch-bond found in cellulose-degrading bacteria. By analyzing amino acid interactions between XDoc and cohesin E, and between XDoc submodules (X-module and Doc), we show that AI regression models can accurately predict rupture forces using only short MD simulations, capturing key mechanostability features despite the binding interface's complexity. Our results reveal that mechanostability signatures emerge early under force load, indicating that catch-bonds activate almost immediately. These findings provide new insights into the molecular principles governing force-dependent protein interactions and highlight the potential of AI-driven approaches for predicting and characterizing mechanostability, with broad implications for bioengineering and drug design.

Background/Objectives: Amnestic mild cognitive impairment (aMCI) represents a transitional stage between normal aging and dementia, constituting a critical intervention window for Alzheimer's disease (AD). As a non-invasive intervention, neurofeedback training (NFT) has demonstrated potential in ameliorating cognitive deficits and clinical symptoms in aMCI patients; however, its mechanistic effects on functional brain connectivity remain inadequately elucidated. Methods: This study employed low- and high-order functional analytical approaches to comprehensively investigate the effects of NFT on dynamic brain functional networks in aMCI. Results: Our findings revealed that following NFT, aMCI patients exhibited enhanced connectivity strength, global efficiency, and nodal characteristics within the delta band, whereas connectivity was generally attenuated in the theta, alpha, and beta bands. Dynamic network analysis indicated increased entropy in short-time windows. Cognitive assessments showed a significant short-term improvement in MoCA scores among 92.9% of participants. Conclusions: These results suggest that NFT effectively remodels brain network activity patterns in aMCI patients, thereby facilitating cognitive improvement. These findings provide preliminary insights into the brain network mechanisms underlying NFT-mediated cognitive enhancement in aMCI.

Therapeutic elimination of high-grade cervical intraepithelial neoplasia (CIN) is widely implemented for cervical cancer prevention. Despite the demonstrated dysbiosis of vaginal microenvironment in high-grade CIN, its post-therapy restorations remain to be poorly understood, especially in functional aspects. This study aimed to characterize temporal changes in both vaginal microbiota (VM) and metabolome (VMeta) following therapeutic elimination of high-grade CIN. We conducted a longitudinal study of 32 HPV-positive women with high-grade CIN who underwent therapeutic procedures. Vaginal swabs were collected at baseline (pre-therapy) and at 6- and 12-month follow-up visits for integrated VM and VMeta analysis. We observed a gradual restoration of Lactobacillus crispatus levels from baseline to 12 months (P < 0.05). Concurrently, we detected significant decreases in dysbiosis-associated bacteria, including Prevotella bivia, Ureaplasma parvum, and Peptoniphilus sp. 6 months post-therapy compared to the baseline. VMeta analysis revealed distinct metabolic shifts across the follow-up periods. The early post-therapy phase (baseline to 6 months) was characterized by enrichment of glycerophospholipids and depletion of nucleotide metabolites, while the later phase (6-12 months) showed increases in flavonoids, lysophospholipids, bioactive amides, and amino acid metabolism. Integration of correlation and dynamic Bayesian network analysis indicated potential regulatory relationships and time-lag effects involving HPV infection, L. crispatus, Bifidobacterium sp., Streptococcus anginosus, Megasphaera sp., U. parvum, and those metabolites. This study enhances our understanding of a sequential restoration process post-therapy in the vaginal microenvironment.IMPORTANCETherapeutic elimination of high-grade CIN is routine, yet functional recovery of the vaginal ecosystem is poorly defined. In a 12-month longitudinal multi-omics study of 32 women, we show stepwise restoration: progressive L. crispatus dominance with sustained decreases in dysbiosis-associated taxa (P. bivia, U. parvum, Peptoniphilus). Metabolically, an early rise in glycerophospholipids and fall in nucleotide metabolites is followed by later enrichment of flavonoids, lysophospholipids, bioactive amides, and amino acid derivatives. Correlation and dynamic Bayesian network analyses reveal putative regulatory links, time-lag effects, and downstream impacts of HPV clearance. These findings deliver a functional roadmap of post-therapy recovery, nominate measurable microbial-metabolite milestones and candidate biomarkers for monitoring, and suggest targets for adjunct interventions to accelerate re-establishment of protective states. This work informs precision follow-up in cervical cancer prevention programs.

ABSTRACT When industries face systemic shocks, enterprise risks extend beyond the effects of isolated incidents and propagate through intricate competitive networks. Existing research predominantly addresses risk spillovers initiated by isolated events, such as financial scandals and product recalls, while neglecting the risk spillover effects associated with firms embedded in multi‐market competitive networks. This study undertakes an empirical analysis within the context of China's electronic information manufacturing industry. The findings reveal that the broader a firm's product market competition boundary, the more pronounced the peer risk spillover effects it encounters. Furthermore, the degree of product diversification and analyst coverage positively moderate the relationship between competition boundaries and risk spillovers. Firms positioned downstream in the supply chain, those with lower corporate social responsibility performance, and non‐state‐owned enterprises experience more significant impacts of competition boundary breadth on risk spillovers. This study conceptualizes the boundaries of product market competition as a crucial lens for comprehending industry risk spillovers, thereby advancing risk spillover research from static event analysis to dynamic network analysis. The findings indicate that, in the context of systemic shocks, expansive competition boundaries exacerbate risk contagion through two primary channels: direct business linkages and indirect cognitive biases. This study offers a novel decision‐making framework for firms to navigate the balance between market opportunities and risk exposure within intricate competitive environments.

PurposeThis study examines the intersectoral interdependencies of nurse employment, conceptualized as nurse employment spillovers, and their effects on inpatient care quality in Taiwan. It particularly addresses the critical issue that over 40% of Taiwanese nurses report unwillingness to enter or remain in the nursing profession.MethodsThe research design follows a quantitative and observational time-series methodology. A time-series dynamic connectedness network analysis was employed to assess interdependencies in nurse employment across various practice settings. Additionally, a general-to-specific modeling approach was used to examine the relationship between the transmission of nurse employment spillovers and inpatient care quality. The analysis utilized annual data from 1997 to 2023, comprising 27 observations, sourced from publicly available Taiwanese government databases that contain information on nurse employment, inpatient care quality indicator, and relevant control variables.ResultsThe analysis reveals that nurse employment in other healthcare facilities and the non-nursing labor market (hospitals, clinics, and long-term care facilities) serves as a net transmitter (receiver) of nurse employment spillovers. Notably, nurse employment inflows from non-nursing to nursing labor markets are linked to enhancements in inpatient care quality. Five distinct spillover pathways originating from clinics, other medical institutions, and non-nursing sectors into hospitals were identified as having positive effects on inpatient care quality. Furthermore, increases in nurses’ regular wages are associated with a shift in employment toward hospital settings.ConclusionThese findings underscore the importance of strengthening hospital nurse employment to improve inpatient care quality. Policymakers should consider salary increases as a means to attract and retain nurses in the healthcare sector, thereby mitigating shortages of nurses and enhancing care outcomes.

Comparative Study of a Variant Neural Relational Inference Deep Learning Model and Dynamical Network Analysis for p53-DNA Allosteric Interactions.