Search Results (286)

The objective of this paper is to propose a sociological and interdisciplinary framework for analyzing the digitization of emotions in adolescence. This contribution aims to promote theoretical reflection and inform educational and political interventions in the digital age, framing adolescents’ digital experiences as emotionally embodied and socially integrated processes. These aspects are of paramount importance due to the rapid proliferation of digital technologies and artificial intelligence, which have precipitated a profound transformation in the emotional, relational, and educational experiences of adolescents. The role of digital and AI-based environments in mediating communication is expanding beyond the scope of simple facilitation. These environments are increasingly implicated in the production, modulation, and regulation of emotions, thereby influencing developmental trajectories and identity formation processes. This phenomenon is theorized as a socio-technical process, wherein emotions are embodied, narrated, and governed within digital environments. The article introduces the concept of digital emotional embodiment, drawing on the sociology of emotions, theories of embodiment, and critical perspectives on artificial intelligence. Specifically, the concept refers to the manner in which adolescents experience and express emotions through avatars, images, emojis, algorithmic feedback, and AI-mediated interactions. Therefore, it is imperative to underscore the evolution of empathy, which is progressively configured as a virtualized and datafied process, diverging from the tradition established by Hume and characterized by sympathy. In contemporary processes, shaped by the logic of platforms, recommendation systems, and emotionally reactive technologies, conventional emotional concepts have undergone deconstruction, and digital constructs are undergoing a gradual restructuring. In this context, AI systems do not merely reflect adolescents’ emotions but rather actively contribute to the construction of emotional narratives, influencing emotional regulation, social connection, and future orientation. Digital environments have been shown to encourage emotional expressiveness, experimentation, and inclusivity. Conversely, they have the capacity to encourage emotional standardization, dependency, and forms of affective vulnerability, particularly during a sensitive developmental stage such as adolescence. Full article

Digital trade rules have proliferated rapidly, yet the literature still treats institutional environments and firm behavior in a comparative-static manner, overlooking the feedback loops and stock-like accumulation dynamics through which regulatory openness shapes firm capabilities over time. Drawing on general systems theory and system dynamics, this paper models the digital trade rule regime as an “institutional system” and the overseas subsidiary network of digital MNEs as an “enterprise system,” linked through three capability stocks (market, production, knowledge), cross-subsystem coupling, absorptive capacity modulation, and five internal feedback loops. We derive a reduced-form dynamic panel equation mapping structural parameters onto estimable coefficients, and test its static counterpart using data on 6850 subsidiaries of UNCTAD’s top 100 digital MNEs (2000–2024) matched with the TAPED database. Three findings emerge. First, institutional openness—measured by rule depth and breadth—exerts a positive causal effect on subsidiary ROA, surviving IV estimation and multiple robustness checks. Second, the effect transmits through market expansion, production efficiency, and knowledge accumulation channels, confirmed by Baron–Kenny mediation with Sobel tests. Third, the New Digital Economy (NDE) module displays point estimates 4–8 times larger than other modules, and joint Wald tests reject coefficient equality, providing qualified support for Meadows’ leverage-point hierarchy. Our contribution lies in bridging system dynamics modeling with econometric causal identification, and in unifying transaction cost theory, the OLI paradigm, and the knowledge-based view within a single open-system framework. Full article

Attitude control of unmanned aerial vehicles is a problem that needs to be solved in a reliable manner. The research presented in this paper examines a systematic approach to the design of an LQR state feedback controller for the three-DOF hover system. The state space model is used to derive the feedback gain K, with the diagonal elements of the weighting matrices Q and R used as design variables. A multi-objective grey wolf optimizer is used to obtain Q–R matrices based on closed-loop simulations under representative roll, pitch and yaw reference commands. There are four separate multi-objective optimization runs, each using one of four standard error indices which are the integral of absolute error (IAE), the integral of time-weighted absolute error (ITAE), the integral of squared error (ISE) and the integral of time-weighted squared error (ITSE). Each index is used to track roll, pitch and yaw errors at the same time and the resulting non-dominated solution sets are post-processed using TOPSIS to select a compromise knee-point design. The simulation results show that the adjusted LQR parameters lead to feasible tracking performance. The proposed framework provides a systematic and replicable method for LQR weight selection in hover-type attitude control problems under the considered simulation settings. Full article

Cerebral ischemia–reperfusion (I/R) injury is a major pathological contributor to neurological deterioration following ischemic stroke (IS) and remains a critical barrier to effective neuroprotection. Accumulating evidence indicates that cerebral I/R injury is driven not by isolated stress responses but by coordinated and dynamic interactions among multiple cellular pathways. Among these, the bidirectional crosstalk between mitophagy and oxidative stress has emerged as a central regulatory axis. Moderate oxidative stress can function as an adaptive signal, activating protective mitophagy through key pathways such as AMPK/ULK1 signaling and cardiolipin externalization, thereby facilitating mitochondrial quality control and maintaining cellular homeostasis. Conversely, appropriately regulated mitophagy limits excessive reactive oxygen species (ROS) production by removing dysfunctional mitochondria, forming a negative feedback mechanism. However, dysregulation or excessive activation of either process disrupts this balance, leading to a self-amplifying cycle of mitochondrial dysfunction and oxidative damage that exacerbates neuronal injury. This review systematically summarizes the molecular mechanisms governing the oxidative stress–mitophagy crosstalk in cerebral I/R injury, highlighting key signaling nodes and regulatory pathways that determine protective versus detrimental outcomes. Furthermore, we discuss emerging therapeutic strategies aimed at precisely modulating this axis in a spatiotemporal- and intensity-dependent manner. By integrating mechanistic insights with translational perspectives, this review provides a conceptual framework for developing targeted neuroprotective interventions based on coordinated regulation of mitochondrial quality control and redox homeostasis. Full article

Tubule-derived pro-fibrotic mediators are central for the development of kidney fibrosis. We previously showed that fibrotic stimuli activate and elevate GEF-H1 (ARHGEF2) in tubular cells, leading to RhoA-dependent fibrotic reprogramming. In search of new mechanisms of GEF-H1 regulation, here we used immunoprecipitation and proximity ligation assay to show interaction between GEF-H1 and Myotonic Dystrophy Kinase-related Cdc42-binding kinase (MRCK)α in tubular cells. MRCKα silencing elevated GEF-H1 activity, and induced GEF-H1-dependent RhoA activation, stress fibre formation and myosin light chain phosphorylation. MRCKα depletion also elevated phospho-cofilin levels in a RhoA-dependent manner. The fibrogenic cytokine TGFβ1 rapidly increased binding between GEF-H1 and MRCKα, while MRCKα silencing augmented TGFβ1-induced GEF-H1 activation, suggesting a negative feedback loop. An mRNA array detecting fibrogenic genes revealed increase in a subset of basal and TGFβ1-induced genes following MRCKα depletion. MRCKα silencing promoted nuclear translocation of the profibrotic transcriptional co-activator Myocardin-Related Transcription Factor (MRTF), and MRTF-A+B depletion prevented increase in ACTA2 (α-smooth muscle actin), a key marker of fibrotic reprogramming. Finally, total MRCKα mRNA was reduced in a murine kidney fibrosis model, and immunohistochemistry revealed a decrease in tubular MRCKα. Taken together, we identified MRCKα as a new suppressor of GEF-H1/RhoA/MRTF signaling. Reduced MRCKα expression in kidney fibrosis may promote tubular fibrotic gene expression. Full article

Epigenetic dysregulation is a central driver of cancer progression, therapeutic resistance, and phenotypic plasticity. Among epigenetic mechanisms, microRNAs (miRNAs) and DNA methyltransferases (DNMTs) engage in reciprocal regulatory interactions that extend beyond transient gene control. Emerging evidence indicates that DNMT–miRNA feedback loops function as epigenetic memory units, stabilizing malignant cell states and enabling durable phenotypic inheritance even after removal of initiating stimuli under conditions shaped by persistent redox and stress signaling cues. In this review, we synthesize mechanistic, computational, and translational studies demonstrating how double-negative DNMT–miRNA feedback architectures generate bistable regulatory circuits that lock cancer cells into epithelial–mesenchymal transition, stem-like, and therapy-resistant states through redox-sensitive regulatory thresholds rather than static epigenetic alterations. This framework provides a unifying explanation for why transient environmental or therapeutic cues can induce long-lasting epigenetic reprogramming and why conventional single-target epigenetic inhibitors often fail to achieve durable clinical responses. Building on this concept, we propose that herbal medicines and plant-derived phytochemicals act as epigenetic reset signals capable of destabilizing pathological epigenetic attractor states encoded by DNMT–miRNA memory circuits by modulating intracellular redox balance and redox-responsive signaling pathways. Owing to their multi-component and systems-level regulatory properties, herbal interventions modulate miRNA expression, DNMT activity, and upstream stress-responsive pathways in a coordinated manner, facilitating transitions from memory-dominated states toward renewed epigenetic plasticity. We further discuss the translational implications of combining miRNA-based therapies with herbal medicine as a strategy for epigenetic reprogramming rather than transient suppression within a redox-guided therapeutic framework. Finally, we address key challenges and clinical feasibility considerations, including delivery, heterogeneity, and safety, and outline future directions for biomarker-guided and systems-informed epigenetic therapies that incorporate redox state as a functional determinant of epigenetic responsiveness. By reframing DNMT–miRNA interactions through the lens of epigenetic memory, this review highlights miRNA–herbal combination strategies as a forward-looking approach for overcoming therapeutic resistance and achieving durable reprogramming in cancer through selective manipulation of redox-sensitive epigenetic signaling circuits. Full article

In molecular communication via diffusion (MCvD) uplinks where multiple nano-sensors report concurrently to a fusion center (FC), the long channel memory and the near–far imbalance jointly create strong multiple access interference (MAI) coupled with residual inter-symbol/inter-chip effects. This paper studies an oversampling-enabled time-domain reception for an uplink molecular code-division multiple-access (MoCDMA) system employing bipolar molecular signalling. By exploiting intra-chip oversampling at the FC, three linear detectors following the principles of maximum ratio combining (MRC), zero-forcing (ZF), and minimum mean-square error (MMSE) are developed and further enhanced through a feedback-assisted interference subtraction (FAIS) scheme that combines single-tap ISI feedback equalization with near-to-far successive MAI subtraction. Owing to the complementary structure of bipolar molecular emissions, the signal-dependent counting noise corresponding to the two molecule types can be jointly modeled in a symmetric and information-independent manner to support unified linear detection and FAIS processing. Numerical results demonstrate that oversampling effectively improves detection reliability, while increasing the molecular emission budget alone is insufficient to mitigate near–far effects. Moreover, FAIS provides significant performance gains, particularly for far NSs. Full article

Phosphatidylinositol and its derivatives are essential components of cell membranes and play pivotal roles in growth signaling pathways. In the human primordial placenta, phosphatidylinositol synthesis is catalyzed by phosphatidylinositol synthase (PIS) and the phosphatidylinositol-exchange enzyme (IE), both of which require divalent cations. We investigated whether GTP-binding proteins modulate this biosynthetic process. Incorporation of [³H]inositol into phosphatidylinositol was measured in trophoblast tissue and microsomes from 8 to 10-week placentas. Our results demonstrate that Mn²⁺ strongly enhances phosphatidylinositol synthesis, and stimulation with AlF₄⁻ further increases incorporation rates by up to 2.5-fold. In contrast, Mg²⁺ combined with the non-hydrolyzable GTP analog GIDP elevated synthesis by 58%, whereas Mn²⁺ plus GIDP reduced incorporation by 30%. Complementary in silico protein–protein interaction analyses suggest that G-proteins may directly associate with inositol-exchange enzymes, providing a potential mechanism for the observed regulatory effects. These findings indicate that phosphatidylinositol synthesis is modulated in a manner consistent with G-protein involvement, with distinct effects depending on the prevailing enzymatic pathway. We propose that rapid trophoblast proliferation may involve feedback mechanisms mediated by distinct G-protein subtypes acting on early steps of the phosphatidylinositol cycle. Full article

Against the dual backdrop of the rural revitalization strategy and the pursuit of high-quality, balanced urban–rural education, optimizing rural campus spaces has emerged as an important lever for addressing educational resource disparities and improving pedagogical quality. However, conventional evaluation of campus space optimization faces two systemic dilemmas. First, top-down decision-making often neglects the authentic needs of diverse stakeholders and place-based knowledge, resulting in spatial interventions that lose regional distinctiveness. Second, routine public participation is constrained by geographical barriers, time costs, and sample-size limitations, which can amplify professional cognitive bias and impede comprehensive feedback formation. The compounded effect of these challenges contributes to a disconnect between spatial optimization outcomes and perceived needs, thereby constraining the distinctive development of rural educational spaces. To address these constraints, this study proposes a novel method that integrates regional spatial feature recognition with digital media-based public perception assessment. At the data collection and ethical governance level, the study strictly adheres to platform compliance and academic ethics. A total of 12,800 preliminary comments were scraped from major social media platforms (e.g., Douyin, Dianping, and Xiaohongshu) and processed through a three-stage screening workflow—keyword screening–rule-based filtering–manual verification—to yield 8616 valid records covering diverse public groups across China. All user-identifying information was fully anonymized to ensure lawful use and privacy protection. At the analytical modeling level, we develop a Transformer-based deep learning system that leverages multi-head attention mechanisms to capture implicit spatial-sentiment features and metaphorical expressions embedded in review texts. Evaluation on an independent test set indicates a classification accuracy of 89.2%, aligning with balanced and stable scoring performance. Robustness is further strengthened by introducing an equal-weight alternative strategy and conducting stability checks to indicate the consistency of model outputs across weighting assumptions. At the scenario interpretation level, we combine grounded-theory coding with semantic network analysis to establish a three-tier spatial analysis framework—macro (landscape pattern/hydro-topological patterns), meso (architectural interface), and micro (teaching scenes/pedagogical scenarios)—and incorporate an interpretive stakeholder typology (tourists, residents, parents, and professional groups) to systematically identify and quantify key features shaping public spatial perception. Findings show that, at the macro level, naturally integrated scenarios—such as “campus–farmland integration” and “mountain–water embeddedness”—exhibit high affective association, aligning with the “mountain-water-field-village” spatial sequence logic and suggesting broad public endorsement of ecological campus concepts, whereas vernacular settlement-pattern scenarios receive relatively low attention due to cognitive discontinuities. At the meso level, innovative corridor strategies (e.g., framed vistas and expanded corridor spaces) strengthen the building–nature interaction and suggest latent value in stimulating exploratory spatial experience. At the micro level, place-based practice-oriented teaching scenes (e.g., intangible cultural heritage handcraft and creative workshops) achieve higher scores, aligning with the compatibility of vernacular education’s “differential esthetics,” while urban convergence-oriented interdisciplinary curriculum scenes suggest an interpretive gap relative to public expectations. These results indicate an embedded relationship between public perception and regional spatial features, which is further shaped by a multi-actor governance process—characterized by “Government + Influencers + Field Study”—that mediates how rural educational spaces are produced, communicated, and interpreted in digital environments. The study’s innovative value lies in integrating sociological theories (e.g., embeddedness) with deep learning techniques to fill the regional and multi-actor perspective gap in rural campus POE and to promote a methodological shift from “experience-based induction” toward a “data-theory” dual-drive model. The findings provide inferential evidence for rural campus renewal and optimization; the methodological pipeline is transferable to small-scale rural primary schools with media exposure and salient regional ecological characteristics, and it offers a new pathway for incorporating digital media-driven public perception feedback into planning and design practice. The research methodology of this study consists of four sequential stages, which are implemented in a systematic and progressive manner: First, data collection was conducted: Python and the Octopus Collector were used to crawl online comment data related to Fuwen Township Central Primary School, strictly complying with the user agreements of the Douyin, Dianping, and Xiaohongshu platforms. Second, semantic preprocessing was performed: The evaluation content was segmented to generate word frequency statistics and semantic networks; qualitative analysis was conducted using Origin software, and quantitative translation was realized via Sankey diagrams. Third, spatial scene coding was carried out: Combined with a spatial characteristic identification system, a macro–meso–micro three-tier classification system for spatial scene characteristics was constructed to encode and quantitatively express the textual content. Finally, sentiment quantification and correlation analysis was implemented: A deep learning model based on the Transformer framework was employed to perform sentiment quantification scoring for each comment; Sankey diagrams were used to quantitatively correlate spatial scenes with sentiment tendencies, thereby exploring the public’s perceptual associations with the architectural spatial environment of rural campuses. Full article

The endocannabinoid system is a ubiquitous neuromodulatory network that links internal physiological state to neural circuit function across the brain. While its roles in memory, reward, pain, and motor control are well established, its contribution to olfactory processing has only recently gained attention. This review synthesizes the current knowledge on the anatomical, cellular, and functional interactions between the endocannabinoid system and the olfactory pathway, from the olfactory epithelium and main olfactory bulb to higher order cortical targets. We highlight how endocannabinoid signaling, primarily via cannabinoid receptor type 1 (CB1), shapes synaptic transmission within olfactory bulb microcircuits, modulates centrifugal feedback, and adjusts sensory gain in a state-dependent manner, particularly in relation to hunger, feeding behavior, stress, and reward. In addition, we review evidence that the endocannabinoid system regulates olfactory neurodevelopment and adult neurogenesis by influencing neural stem cell proliferation, migration, and integration into existing circuits. Emerging links between endocannabinoid signaling, olfactory dysfunction, neuropsychiatric disease, metabolic disorders, and neurodegeneration underscore the translational relevance of this system. We also discuss methodological challenges inherent to studying endocannabinoid signaling and outline future directions, including circuit-specific targeting and intranasal delivery strategies. Together, these findings position the olfactory system as a powerful and accessible model for understanding how endocannabinoids couple internal state to perception and behavior, with important implications for therapeutic development. Full article

We propose a semi-automatic artificial lips control device that allows a human performer to produce sound on a brass instrument without the need to vibrate their own lips. The device integrates position control that presses artificial lips toward the mouthpiece and aperture control via wire traction, together with a pre-calibrated motor table and acoustic feedback for pitch stabilization. In evaluations using a euphonium, we verified timbre, pitch range, and pitch stabilization, including harmonic modes. The results showed that the harmonic structure of tones produced by a human using the device can be comparable to those produced by a human player in the conventional manner. Pitch-range and pitch-stabilization tests confirmed that the system can generate practical musical intervals and achieve reliable harmonic mode changes. Furthermore, real-time acoustic feedback improved pitch stability during performance. These findings demonstrate that, rather than fully automating human performance, the proposed system provides a compact and reproducible framework for controllable brass sound generation and pitch stabilization using only three actuators. Full article

Emerging from millions of years of evolutionary optimization, Natural products (NPs) remain unique, unparalleled sources of bioactive scaffolds. Unlike synthetic molecules engineered around single therapeutic targets, NPs often exhibit multi-target, system-level bioactivity, aligned with the principles of network pharmacology, which modulates pathways in a coordinated, non-disruptive manner. This approach reduces resistance, buffers compensatory feedback loops, and enhances therapeutic resilience. Fungal endophytes represent one of the most chemically diverse and biologically sophisticated NP reservoirs known, producing polyketides, alkaloids, terpenoids, and peptides with intricate three-dimensional architectures and emergent bioactivity patterns that remain exceptionally difficult to design de novo. Advances in artificial intelligence (AI), machine learning, deep learning, and multi-omics integration have redefined the discovery landscape, transforming previously intractable fungal metabolomes and cryptic biosynthetic gene clusters (BGCs) into tractable, predictable, and engineerable systems. AI accelerates genome mining, metabolomic annotation, BGC-metabolite linking, structure prediction, and activation of silent pathways. Generative AI and diffusion models now enable de novo design of NP-inspired scaffolds while preserving biosynthetic feasibility, opening new opportunities for direct evolution, pathway refactoring, and precision biomanufacturing. This review synthesizes the chemical and biosynthetic diversity of major NP classes from fungal endophytes and maps them onto the rapidly expanding ecosystem of AI-driven tools. We outline how AI transforms NP discovery from empirical screening into a predictive, hypothesis-driven discipline with direct industrial implications for drug discovery and synthetic biology. By coupling evolutionarily refined chemistry with modern computational intelligence, the field is poised for a new era in which natural-product leads are not only rediscovered but systematically expanded, engineered, and industrialized to address urgent biomedical and sustainability challenges. Full article

Canine mammary tumors (CMTs) are among the most frequent neoplasms in female dogs, with current therapeutic options being limited and non-standardized, prompting the search for alternative treatments such as fungal secondary metabolites. In this study, the fungal pigment Epipyrone A (Epy A) was first isolated from Epicoccum nigrum and then tested in vitro on two CMT cell lines, P114 and CF33. The compound significantly reduced cell viability in both lines in a concentration- and time-dependent manner (p < 0.05), with the strongest effect observed at 175 µg/mL after 48 h (p < 0.0001), while showing no cytotoxicity in MDCK non-tumor cells. Epy A also inhibited cell migration and increased total antioxidant capacity in P114 cells, accompanied by a significant reduction in ROS levels. Western blot analysis revealed modulation of the PI3K/Akt/mTOR pathway, crucial in CMT biology. Specifically, P114 cells showed downregulation of mTOR and p-Akt, indicating inhibition of proliferative signaling, whereas CF33 cells exhibited increased Akt and p-Akt alongside reduced mTOR, consistent with a compensatory feedback mechanism, probably linked to the changing in oxidative balance after treatment. Overall, these results identified Epy A as a promising natural molecule with potential applications in innovative therapeutic approaches for veterinary and comparative oncology. Full article

During viral infection, NLR family CARD domain-containing protein 5 (NLRC5) participates in innate immunity through multiple mechanisms. These include regulating type I interferon and related immune factor expression, as well as modulating immune cell functions, such as cytotoxic T lymphocytes (CTLs) and macrophages, thereby promoting antiviral defence and maintaining immune homeostasis. Our study demonstrates that (1) Enterovirus 71 (EV71) infection upregulates NLRC5 expression through the RIG-I-IRF3-mediated IFN-β pathway, which in turn promotes MHC-I molecule expression and (2) NLRC5 suppresses EV71 replication and simultaneously restrains excessive inflammatory responses by fine-tuning IFN-β production through a negative feedback loop. This loop operates via two distinct mechanisms, namely, direct downregulation of key IFN-β pathway mediators (e.g., RIG-I and IRF3) and binding to the 5′UTR of the EV71 genome to inhibit viral replication, thereby indirectly dampening the IFN-β signal. Furthermore, we show that EV71 activates the NLRC5-dependent MHC-I response in an IFN-β-dependent manner. Collectively, these results elucidate the dual role of NLRC5 during EV71 infection, offering novel insights into viral pathogenesis and highlighting potential targets for antiviral drug development. Full article

Empathic behavior is increasingly incorporated into socially assistive robots, yet little is known about how older adults’ personality-based self-regulatory processes shape responses to such designs. The present study examined a recognition-based “mirror effect” framework of narcissistic self-regulation, referring to the ways individuals maintain a valued self-image through social feedback and acknowledgment. We focused on two core dimensions: narcissistic admiration, characterized by self-promotion and the pursuit of affirmation, and narcissistic rivalry, characterized by defensiveness, antagonism, and sensitivity to threat. Community-dwelling older adults (N = 527; M_age = 72.73) were randomly assigned to view a video of a socially assistive robot interacting in either an empathic or a cold manner. Participants reported their perceived recognition by the robot, defined as the subjective experience of feeling seen, acknowledged, and valued, as well as multiple robot evaluations (anthropomorphism, likability, perceived intelligence, safety, and intention to use). At the mean level, empathic robot behavior increased perceived recognition, anthropomorphism, and likability but did not improve perceived intelligence, safety, or intention to use. Conditional process analyses revealed that narcissistic admiration was positively associated with perceived recognition, which in turn predicted more favorable robot evaluations, regardless of robot behavior. In contrast, narcissistic rivalry showed a behavior-dependent pattern: rivalry was associated with reduced perceived recognition and less favorable evaluations primarily in the empathic condition, whereas this association reversed in the cold condition. Importantly, once perceived recognition and narcissistic traits were accounted for, the cold robot was evaluated as more intelligent, safer, and more desirable to use than the empathic robot. Studying these processes in older adults is theoretically and practically significant, as later life is marked by shifts in social roles, autonomy concerns, and sensitivity to interpersonal evaluation, which may alter how empathic technologies are experienced. Together, the findings identify perceived recognition as a central psychological mechanism linking personality and robot design and suggest that greater robotic empathy is not universally beneficial, particularly for users high in rivalry-related threat sensitivity. Full article