Abstract: Hate speech on social media reproduces norms of inequality and gender stereotypes, disproportionately affecting women. This study proposes a hybrid approach that integrates emotional tone classification with explicit hostility detection to strengthen preventive moderation. We constructed a corpus from three open datasets (1,236,371 records; 1,003,991 after ETL) and represented the text using TF-IDF and contextual RoBERTa embeddings. We trained individual models (RoBERTa fine-tuned, Random Forest, and XGBoost) and a stacking metamodel (Gradient Boosting) that combines their probabilities. On the test set, the ensemble outperformed the base classifiers, achieving accuracy of 0.93 in hate detection and 0.90 in emotion classification, with an AUC of 0.98 for emotion classification. We implemented a RESTful API and a web client to validate the moderation flow before publication, along with an administration panel for auditing. Performance tests showed viability under moderate loads and concurrency limitations starting at 300 users, associated with deployment via an Ngrok tunnel. In general, the results indicate that incorporating emotional tone analysis improves the model's ability to identify implicit hostility and offers a practical way to promote safer digital environments. The probabilistic results obtained by the ensemble model were subsequently analyzed using the Bayesian Calibration and Optimal Design under Asymmetric Risk (BACON-AR) framework, which serves as a mathematical post-hoc validation layer to optimize the decision threshold under unequal costs. This framework does not modify the trained architecture but adjusts the estimated probabilities and selects the threshold that minimizes the total expected risk. By combining TF-IDF and RoBERTa embeddings with a stacked metamodel, the ensemble's decision function was optimized via regularization, improving generalizability and the stability of predictions. The incorporation of the BACON-AR framework strengthened the system's probabilistic consistency, ensuring that final decisions were aligned with the actual consequences of errors under an asymmetric risk scheme.

Abstract: CubeSats are used as a platform in modern space missions due to their standardized form factor, reduced development cost, and shortened launch timelines. Earth observation, space weather monitoring and even re-entry applications make use of the CubeSat standard. Despite their advantages, CubeSats are constrained by limited onboard resources, with electrical power availability being one of the most critical bottlenecks. This work presents a dynamic, hybrid offline/online task scheduling and power management algorithm for a re-entry CubeSat, combining pre-computed schedules with real-time adaptation to changing flight conditions. The algorithm employs a heuristic-based approach, ranking tasks by parameters including priority, execution delay, duration, and power consumption. It adapts to varying flight conditions and system failures. In critical battery State of Charge (SoC) scenarios, only high-priority tasks above a defined threshold are executed, conserving power. A simulation suite was developed to evaluate performance under realistic mission profiles and stress tests with high loads and numerous tasks. Metrics included average and maximum task delay and average power consumption. Results show that appropriate heuristic weight selection can yield significant improvements in reliability and efficiency. The proposed algorithm offers a flexible, scalable solution for CubeSat power management, capable of maintaining operational reliability under dynamic conditions.

Abstract: This paper introduces a novel Transformer-Driven Pipeline that seamlessly integrates acoustic hearing, automated speech transcription, and writing synthesis into a unified end-to-end framework powered by advanced transformer architectures. Beginning with raw acoustic inputs captured via microphones, the pipeline preprocesses audio signals into spectrogram representations, leveraging stacked transformer encoders with multi-head self-attention to extract contextualized phonetic and prosodic features. These features feed into a sequence-to-sequence transcription module, where cross-attention mechanisms align auditory patterns with linguistic tokens, achieving robust speech-to-text conversion even in noisy environments or with diverse accents. Extending beyond transcription, the system employs a generative decoder to synthesize structured written outputs, such as summaries, reports, or formatted notes, by refining transcripts through autoregressive language modelling while preserving semantic fidelity and stylistic nuances derived from the original speech. Experimental validation on benchmark datasets like LibriSpeech and Common Voice demonstrates superior performance, with word error rates reduced by up to 25% compared to RNN baselines and enhanced fluency in synthesis metrics like BLEU scores. The pipeline's parallelizable design ensures real-time efficiency, making it ideal for applications in assistive technologies, live captioning, and automated documentation. This work highlights transformer's versatility in bridging auditory perception and textual production, paving the way for scalable multimodal AI systems.

Abstract: Telemedicine, driven by the Internet of Things (IoT) and next-generation mobile networks, is essential for managing cardiovascular diseases, where hypertension remains the primary risk factor. In preclinical research, rabbits are superior biological models compared to rodents due to their human-like lipid metabolism. However, conventional blood pressure monitoring in this species is hindered by significant limitations: existing systems are non-portable, lack real-time capabilities, and often necessitate terminal procedures (euthanasia). To address these challenges, this study presents a portable, minimally invasive monitoring system utilizing a pressure transducer in the central auricular artery. The device integrates IoT technology for digital signal processing and seamless wireless data transmission to cloud platforms. This development enables continuous, real-time hemodynamic tracking throughout the experimental period without requiring permanent tethering to desktop hardware. By reducing invasiveness and enhancing data mobility, this system provides a robust framework for the preclinical evaluation of antihypertensive agents and cardiovascular mechanisms, bridging the gap between edge computing and remote clinical diagnostics.

Abstract: Background: Age-related macular degeneration (AMD) represents the leading cause of irreversible vision loss in elderly populations globally. While metformin has emerged as a promising candidate for AMD prevention based on multiple observational studies, the causal relationship remains uncertain due to inherent limitations of observational research designs.Objective: This comprehensive review critically evaluates the current evidence base for metformin in AMD prevention and treatment, with particular emphasis on methodological approaches that address causal inference, including target trial emulation frameworks, propensity score methods, and emerging applications of causal artificial intelligence.Methods: We conducted a systematic review of recent literature (2019-2025) focusing on studies employing advanced causal inference methodologies. Particular attention was given to the largest meta-analysis to date (2.68 million participants) and studies utilizing target trial emulation, propensity score matching, instrumental variable analysis, and causal AI approaches.Results: Recent meta-analytic evidence demonstrates a statistically significant protective association (pooled OR = 0.86, 95% CI: 0.79–0.93, p < 0.001) between metformin use and AMD development across 18 observational studies. However, substantial heterogeneity (I² = 90%) and inherent biases in observational designs—including immortal time bias, disease latency bias, and confounding by indication—limit causal interpretation. Studies employing propensity score matching and dose-response analyses reveal protective effects primarily at low-to-moderate cumulative doses (270-600g over 2 years). Critically, no adequately powered randomized controlled trial has yet definitively established causality.Conclusions: While observational evidence suggests potential benefit, the causal effect of metformin on AMD prevention remains unproven. Rigorous application of target trial emulation frameworks, coupled with advanced causal AI methodologies, offers a pathway to strengthen causal inference from existing observational data. However, definitive evidence requires prospective randomized trials specifically designed to test metformin's efficacy in non-diabetic populations at risk for AMD.

Abstract: In recent years, artificial intelligence has been systematically integrated into public environmental decision-making. It increasingly influences risk classification, the distribution of resources, and the exercise of regulatory authority. While policy attention often focuses on predictive performance and ethical principles, less scrutiny has been directed toward the institutional conditions under which algorithmic outputs acquire decision relevance. This policy review addresses that gap by framing environmental artificial intelligence as decision-making infrastructure rather than as neutral analytical software. It introduces the concept of algorithmic sustainability, defined not as a technical property of algorithms but as a governance condition that aligns lifecycle environmental impacts, enforceable accountability, and procedural legitimacy. Drawing on international policy frameworks and regulatory developments, the review shows how current governance instruments insufficiently integrate lifecycle environmental footprints into decision justification. To operationalize algorithmic sustainability, this paper proposes environmental algorithmic impact assessment as a gatekeeping and renewal mechanism for artificial intelligence used in environmental governance. The review concludes that aligning algorithmic deployment with sustainability and the rule of law depends on institutional design choices made before and during system use rather than on technical optimization alone.

Abstract: Fish allergy, primarily driven by Parvalbumin (PV), is a global health concern with limited effective mitigation strategies. This study explored Maillard conjugation using chitosan (CS) and various saccharides to modify the structural, functional, and allergenic properties of turbot (Scophthalmus maximus) PV. Structural analyses: Sodium dodecyl sulfate-polyacrylamide gel electrophoresis (SDS-PAGE), Western blotting (WB), Fourier transform infrared (FTIR) spectroscopy, and Circular dichroism (CD) confirmed the successful conjugation and significant alterations in secondary structure, including a loss of α-helical content and an increase in β-sheet/random coil fractions. Glycation markedly enhanced antioxidant activity, with total phenolic content (TPC) increasing up to 10.3-fold and 2,2-diphenyl-1-picrylhydrazyl (DPPH) radical scavenging reaching 74.5% in the CS-xylose-PV conjugate (CXTPV). Indirect Enzyme-linked immunosorbent assay (ELISA) showed significant (p < 0.05), sugar-dependent reductions in IgE-binding capacity (up to ~72% for CXTPV). Rat basophilic Leukemia-2H3 (RBL-2H3) cell line assays demonstrated suppressed β-hexosaminidase release (~75% reduction), decreased Interleukin-6 (IL-6) secretion, and potent inhibition of Interleukin-4 (IL-4) production, indicating attenuated allergenic potential and immunomodulatory effects. CXTPV exhibited the strongest overall performance. These results highlight CS-saccharide Maillard conjugation as an effective strategy developed for hypoallergenic marine-derived ingredients with enhanced bioactive properties.

Abstract: Polyaniline-cadmium sulfide-gold (PANI-CdS-Au) nanocomposites were synthesized with varying Au loadings (0.023, 0.046, 0.092)wt% to enhance antibacterial performance. Structural (FTIR, XRD) and morphological (FE-SEM) analyses confirmed successful formation, strong interactions among components, and homogeneous nanoparticle dispersion. UV–Vis spectra revealed gold surface plasmon resonance and polaronic transitions consistent with PANI emeraldine base. XRD results showed the expected wurtzite CdS and fcc Au phases. Agar well diffusion tests against Escherichia coli (Gram-negative) and Staphylococcus aureus (Gram-positive) demonstrated that the 0.092 wt% of Au composite produced the largest inhibition zones at 100 µg mL⁻¹ (E. coli: 36 mm; S. aureus: 24 mm), with the same trend at 25 µg mL⁻¹. These results position PANI-CdS-Au nanocomposites as promising antibacterial materials; additional cytotoxicity assays are recommended for biomedical translation.

Abstract: Otolith microchemistry was used to investigate habitat use and connectivity of the estuarine catfish Genidens genidens across three estuaries in southeastern Brazil. A total of 58 individuals were analyzed using laser ablation inductively coupled plasma mass spectrometry, focusing on strontium-to-calcium (Sr:Ca) and barium-to-calcium (Ba:Ca) ratios. Variations in elemental ratios along otolith transects allowed the reconstruction of individual ontogenetic trajectories along the estuarine–marine gradient. Most individuals exhibited combined use of estuarine and marine environments, while trajectories restricted to freshwater were rare. The complexity of chemical profiles increased with age, indicating more frequent habitat shifts throughout ontogeny. These patterns reveal high ecological plasticity and partial migration within populations of G. genidens. Strontium-to-calcium ratios were effective indicators of salinity-related habitat transitions, whereas Ba:Ca ratios provided complementary information associated with continental influence. Overall, this study demonstrates the applicability of otolith microchemistry to infer individual life-history pathways and highlights the role of estuaries as key habitats for feeding, growth, and recruitment in G. genidens.

Abstract: General Relativity (GR) has long been confronted with a fragmentation dilemma regarding black hole singularities and galaxy rotation curves: the former requires undetectable higher-dimensional quantum gravity to circumvent infinite curvature, while the latter similarly relies on undetectable dark matter to provide additional gravitational force. In this paper, we abandon the hypothesis of undetectable entities and reveal that the two challenges may share an intrinsic geometric solution: the universal asymptotic behavior of mainstream dark matter halo models is equivalent to a logarithmically corrected gravitational potential \( \Phi(r)\sim-(\ln{r}+1)/r \), which originates from the self-response of the curvature divergence at the GR singularity (\( R^r_{trt}\propto r^{-3} \)) via Poisson integration. At the microscopic scale, the sign reversal of lnr generates a repulsive effect, thereby avoiding the singularity. The constructed logarithmically corrected Schwarzschild metric is rigorously solved via the Lambert W function, revealing a layered internal structure determined by the black hole mass M (with thickness ∝1/M), which realizes the holographic screen of the renormalization group flow under the AdS/CFT correspondence. On this basis, we present parameter-free a priori predictions for the black hole shadows of Sgr A* and M87* that are consistent with Event Horizon Telescope (EHT) observations, and provide rigid falsifiable predictions for unobserved black holes, especially the crucial discriminative prediction for NGC315. On the galactic scale, the logarithmic term enables the fitting of the rotation curves of the Milky Way, the Andromeda Galaxy, and NGC2974 without the need for additional gravity from dark matter. Meanwhile, the tidal acceleration difference in the Solar System (Δg∼10^-18 m/s²) is far below the current experimental limit, ensuring the validity of the equivalence principle without the need for a screening mechanism. This work demonstrates that gravitational phenomena from black holes to galaxies are governed by the spacetime self-response triggered by the GR singularity. It further reveals that macroscopic gravitational systems may be “holographic projections” of quantum topological structures (quantum vortices). This framework thus pulls quantum gravity research from pure mathematical modeling back to the energy scales accessible to contemporary observations, and provides a new direction for thinking about the unification of General Relativity and quantum mechanics.

Abstract: Background: Exclusive breastfeeding (EBF) is a critical public health intervention for improving maternal and child health outcomes. This study evaluated the knowledge, practices, and challenges regarding EBF among mothers in the Ife East Local Government Area (LGA), Osun State. Methodology: A cross-sectional survey was conducted among 200 respondents. Data were collected via structured questionnaires administered across various residential areas, including Ita Osa (10.5%) and Ifelodun (9.5%). Statistical analysis was performed to determine the relationship between geographic area and access to healthcare. Results: The majority of participants were aged 20–24 years (33.5%), married (79.5%), and of Yoruba ethnicity (80.5%). Approximately 49.0% held tertiary education qualifications. Access to medical facilities varied significantly by area (x2=32.971, p=0.002), with Ita Osa reporting the highest easy access (10.5%). Regarding knowledge, 48.5% of mothers believed EBF should last 3–6 months, while 40.5% correctly identified the 6-month standard. Healthcare providers were the primary source of EBF information (46.0%). The most recognized benefits included boosting the child's immune system (62.5%) and reducing the mother's risk of breast cancer (74.5%). However, significant barriers persist, notably inadequate nutrition supply (37.0%), pain/discomfort (28.0%), and the return to work (27.5%). Respondents identified emotional support (38.5%) and education (35.5%) as the most desired forms of assistance from family and providers. Conclusion: While there is a high level of awareness regarding the benefits of EBF in Ife East LGA, there remains a gap in precise knowledge regarding its recommended duration. Addressing physiological challenges like nutrition and structural barriers, such as workplace re-entry, is essential to improving EBF rates.

Abstract: Human papillomavirus (HPV) E6/E7 oncoproteins perturb host gene regulatory networks, driving oncogenesis. Existing computational methods often struggle to provide interpretable, chain-of-thought mechanistic explanations for observed transcriptomic changes. To address this, we introduce OncoReasoner, a novel framework that integrates biological expression analysis with the advanced reasoning capabilities of large language models (LLMs) and graph neural networks (GNNs). OncoReasoner comprises an Expression Encoder for rich gene embeddings, a Bio-LLM Reasoning Module for context-aware mechanistic explanations, and a Graph Refinement Module leveraging GNNs and prior knowledge for network consistency. Evaluated on diverse datasets, including GEO and TCGA, our framework significantly outperforms traditional statistical methods, GNNs, and other LLM baselines across differential gene expression classification, regulatory network edge prediction, and particularly, functional pathway reasoning. OncoReasoner notably achieves high accuracy in pathway identification and receives excellent expert ratings for its mechanistic explanations, demonstrating its superior ability to provide deep, accurate, and highly interpretable biological insights. An ablation study confirms the critical contribution of each module, and human evaluation further validates the qualitative excellence of its mechanistic explanations, marking a substantial advancement in explainable AI for cancer research.

Abstract: Background: Psychological safety—the belief that one can speak up without fear of negative consequences—is fundamental to team learning and performance, yet controlled experimental research is constrained by practical and ethical limitations. This study validates large language model (LLM) agents as a methodological tool for simulating team psychological safety dynamics by comparing AI-simulated teams against human teams across identical experimental scenarios. We conducted parallel experiments with 5,280 AI teams (26,400 agent interactions across 5 LLM architectures) and 249 human teams (1,245 participants; final analytic sample: 247 teams, 1,235 participants after quality screening) using a 2×2 factorial design manipulating leader inclusiveness (High/Low) and error management culture (Learning/Blaming). Teams completed realistic work scenarios while we measured psychological safety perceptions, learning behaviors, team performance, and moderating effects of demographic diversity. A comprehensive validation framework assessed convergent validity (main effects, moderation patterns, mediation pathways), discriminant validity (falsification tests), and measurement properties. AI simulations demonstrated strong convergent validity for main effects: leader inclusiveness effect size (AI: d = 2.21, 95% CI [2.13, 2.29]; Human: d = 1.58, 95% CI [1.42, 1.74]), error culture effect (AI: d = 1.39, 95% CI [1.32, 1.46]; Human: d = 0.97, 95% CI [0.82, 1.12]). AI effects were consistently larger than human effects across all relationship types. Main effects showed calibration ratio = 1.42× (95% CI [1.37×, 1.49×]), with precision-weighted calibration across all 14 effect comparisons = 1.38× (95% CI [1.32×, 1.44×]). This systematic inflation requires effect size adjustment when extrapolating to human teams: multiply main effects by ≈0.70, correlations by ≈0.88, with type-specific calibration detailed for different relationship types.AI effects were consistently larger (mean ratio = 1.40×), suggesting a systematic calibration factor. Mediation pathways showed parallel structure (AI: 77.7% mediated, 95% CI [73.2%, 82.2%]; Human: 90.7%, 95% CI [83.8%, 97.6%]), with bootstrap difference test indicating proportions do not differ significantly (p = .182) despite narrowly non-overlapping individual confidence intervals. Moderator convergence varied: demographic composition effects showed lower pattern correlations (r = .43, 95% CI [.09, .68]) compared to main effects (r = .97, 95% CI [.89, .99]). Eight falsification tests confirmed discriminant validity: AI teams showed theoretically appropriate null effects in control scenarios (8/8 tests supported predictions after theoretical refinement). Cross-model consistency was high (ICC = .79, 95% CI [.73, .84]), with calibration factors stable across architectures (SD = 0.04), indicating systematic rather than model-specific inflation. GPT-4 and Claude-3.5 showed closest absolute alignment to human effect magnitudes. LLM-based simulations offer valid approximations of psychological safety dynamics for theory testing, with predictable calibration requirements (effect size multiplier ≈ 0.70). These tools enable hypothesis testing at scales and experimental control infeasible with human participants, though current limitations in capturing complex moderator interactions and precise effect magnitude warrant continued validation. This methodology significantly expands the experimental toolkit for team science research.

Abstract: The application of quantum machine learning (QML) to security-relevant problems has attracted growing attention, yet its practical behavior in realistic workloads remains insufficiently characterized. This paper investigates the feasibility and limitations of variational quantum classifiers (VQCs) in a representative side-channel analysis (SCA) setting focused on distinguishing real from dummy power traces. A controlled benchmark framework is developed to examine training stability, sensitivity to key design parameters, and resource–performance trade-offs under realistic constraints. Hardware-relevant factors, including finite measurement budgets and device noise, are incorporated to approximate execution beyond idealized simulation and to quantify inference-time robustness. Experimental results indicate that VQCs can extract meaningful discriminative patterns from structured side-channel inputs, while robustness and performance depend on encoding choices, circuit depth, and measurement conditions. These findings provide an empirically grounded perspective on the applicability and limitations of QML in side-channel security and offer practical guidance for future research in this area.

Abstract: Consumption of Sechium edule var. nigrum spinosum has been shown to have hypoglycemic and antioxidant effects. However, the molecular mechanisms underlying these effects remain unknown, with the sirtuin-mediated signaling cascade among the possible mechanisms, as sirtuins regulate glucose metabolism and respond to various stressors. This study aimed to evaluate the effect of Sechium edule on the gene expression of the sirtuin family (SIRT1-SIRT6) in individuals with type 2 diabetes mellitus (T2DM). A quasi-experimental study was conducted with a convenience sample of 26 older adults diagnosed with T2DM: (i) placebo group (PG; n=12); (ii) experimental group (EG; n=14). Clinical, biochemical, and anthropometric measurements were performed, and total oxidant/antioxidant capacity (TOS/TAS) and mRNA expression of genes encoding sirtuins were determined. All parameters were measured at baseline, three months, and six months after the intervention. In the EG, gene expression levels of SIRT1, SIRT3, SIRT5, and SIRT6 increased by 52%, 69%, 62%, and 69%, respectively, six months after treatment. A 50% decrease in TOS and a 44% increase in TAS were also observed. Our findings suggest that the bioactive components of Sechium edule enhance sirtuin expression and exhibit antioxidant effects in older adults with T2DM.

Abstract: This paper proposes a novel information-theoretic upper bound on the mutual information between the physical position of a user and the observed MIMO channel state information (CSI). Unlike classical Cram’er-Rao bounds or I-MMSE relations, our bound explicitly incorporates the spatial variability of the channel via the Jacobian of the channel with respect to position. We provide a derivation for both local linearized models and global nonlinear bounds, highlighting the dependence on array geometry and multipath structure. The results offer new insight into the intrinsic information available for position estimation and semantic localization in wireless networks.

Abstract: Documentation of immovable cultural heritage is a fundamental prerequisite for its con-servation, restoration, and sustainable management. Recent advances in geospatial tech-nologies have significantly improved the accuracy, efficiency, and completeness of spatial data acquisition for historic structures. This study evaluates the contribution of terrestrial laser scanning (TLS) and close-range photogrammetry based on unmanned aerial vehi-cles (UAVs) to the engineering and architectural documentation of immovable cultural heritage. The Church of St. Petka (Sitovo village, Bulgaria), a 19th-century stone masonry monument, is used as a case study. High-density point clouds were generated using TLS and UAV-based photogrammetry and were georeferenced through classical surveying methods. The resulting datasets were assessed in terms of geometric accuracy, level of de-tail, and applicability for architectural documentation and conservation tasks. Accuracy evaluation based on measured control distances indicates a mean squared error below 1 cm for both methods. The results demonstrate that TLS provides superior precision and reliability for interior documentation, while UAV-based photogrammetry is particularly effective for capturing roof structures and inaccessible exterior elements. The integration of both technologies enables the creation of accurate 3D models and GIS-ready spatial prod-ucts, supporting informed decision-making in cultural heritage conservation.

Abstract: Conjugated linolenic acids (CLnAs) are emerging as promising agents to trigger ferroptosis, a cell death driven by excessive lipid peroxidation, in cancer cells. Given the aggressive nature and treatment resistance of malignant melanoma, exploring CLnAs as therapeutic agents may offer a novel strategy to overcome these challenges. Here, we investigated the toxicity of four CLnA isomers on human (A375, WM266.4) and zebrafish (ZMEL1) melanoma cell lines. We observed a dose-dependent reduction in cell viability across all three tested cell lines. While human melanoma cells were more sensitive to CLnAs than ZMEL1 cells, treatment with ferroptosis inhibitors mitigated cell death in all models, confirming ferroptosis as the consistent primary mechanism of cell death. In addition, chemical inhibitors of ACSL4 and GPX4 modulated CLnA toxicity, further substantiating the ferroptotic mechanism by highlighting the role of these key regulators. Furthermore, fatty acid analysis revealed that CLnAs were effectively incorporated into phospholipids, generating substrates for lethal lipid peroxidation. At the transcriptional level, CLnA treatment significantly upregulated the pro-ferroptotic gene acsl4a in ZMEL1 cells. Overall, our study identifies specific CLnAs as potent ferroptosis inducers in both human and zebrafish melanoma cells and underscores the translational relevance of the zebrafish model based on a shared ferroptotic mechanism.

Abstract: Cancer progression is influenced by the dynamic interplay between tumor cells and the surrounding stromal microenvironment. Therapy-induced senescence (TIS) of stromal fibroblasts represents a common outcome of anticancer treatments, contributing to tumor progression through the senescence-associated secretory phenotype (SASP). While SASP cytokines promote cancer malignancy, the contribution of secreted metabolites from senescent cells remains poorly understood. Here, we investigate the role of senescent stromal metabolism in regulating prostate and ovarian cancer cell invasion. Conditioned media (CM) from TIS-induced human prostate (HPFs) and ovarian fibroblasts (HOFs) promote enhanced invasion of cancer cells. Invasion is partially preserved after exposure to boiled, protein depleted CM, suggesting a role for heat-stable metabolic factors. Metabolomic profiling of senescent fibroblasts-derived CM reveals a significant increase in Glutamine (Gln) levels. Exposure of cancer cells to senescent CM increases Gln uptake, together with upregulation of the transporter SLC1A5 and increased intracellular Gln. This metabolic adaptation is associated with increased malignant phenotype including epithelial-to-mesenchymal transition (EMT) and stemness features. Extracellular Gln depletion, pharmacological inhibition of glutaminase-1 (GLS1) in cancer cells or Gln synthetase (GS) silencing in fibroblasts markedly impair senescent fibroblasts CM-induced invasion, EMT markers expression, and stemness features in cancer cells. Mechanistically, stromal-derived Gln promotes cancer cell invasion through activation of a redox-dependent NRF2/ETS1 signaling axis. Analysis of patient-derived transcriptomic datasets further supports chemotherapy-associated upregulation of Gln metabolism and ETS1 expression. These findings identify senescent stromal-derived Gln as a key metabolic driver of prostate and ovarian cancer aggressiveness, and a potential therapeutic vulnerability in the context of TIS.

Abstract: The theory of physical degrees of freedom (DoF) developed by Franceschetti–Migliore– Minero (FMM) establishes a fundamental phase transition in the singular-value spectrum of electromagnetic radiation operators under maximal rotational symmetry. In this work, we revisit this result from a symmetry-explicit operator-theoretic perspective and extend it to scenarios with reduced and controllable symmetries, with particular emphasis on reconfigurable intelligent surfaces (RIS). We model the radiation process as a compact operator acting between admissible source and observation spaces and characterize its symmetry through group equivariance. This formulation enables a systematic decompo- sition of the operator into irreducible representation sectors associated with the effective symmetry group, defined as the intersection of symmetries supported jointly by the source architecture, RIS geometry and programmability, receiver configuration, and propagation environment. We show that the FMM phase transition persists within each symmetry sector and that the total DoF budget is redistributed across sectors according to symmetry constraints. A key outcome of this analysis is the distinction between physical and effective degrees of freedom. While breaking the maximal SO(2) symmetry does not increase the total number of electromagnetic DoF dictated by physics, symmetry reduction modifies their allocation across sectors, potentially lifting degeneracies and increasing the number of degrees of freedom that can be effectively addressed by a given excitation, RIS control, and measurement architecture, even when the total number of physical DoF remains fixed by fundamental limits. This clarifies the role of controlled symmetry breaking as a design mechanism rather than a means to surpass fundamental limits. The proposed framework bridges electromagnetic operator theory, representation theory, and RIS-enabled system design, providing both rigorous symmetry-resolved DoF accounting and actionable in- sights for excitation, surface programmability, and measurement strategies under practical architectural constraints.