Search Results (1,621)

Background: Aging is the strongest risk factor for prostate cancer (PCa). It is accompanied by progressive epigenomic divergence, known as epigenetic drift, particularly affecting DNA methylation at regulatory regions. However, the extent to which age-associated promoter methylation contributes to coordinated microRNA (miRNA) expression changes in PCa remains incompletely characterized. Methods: We conducted an integrative in silico analysis of 449 primary tumors from the TCGA-PRAD cohort. Age was modeled as a continuous variable. Age-related miRNA expression changes were estimated from miRNA-seq data using DESeq2. Promoter DNA methylation changes (±2 kb from transcription start sites) were assessed using Illumina 450K arrays and linear regression. MiRNAs showing significant age-associated alterations at both expression and methylation levels were classified as concordant or discordant based on directionality and prioritized using an effect size-based concordance score. We analyzed experimentally validated targets of prioritized miRNAs through functional enrichment and network-based approaches to identify convergent regulatory pathways. Results: Initially, we identified 105 age-associated miRNAs. After filtering, 65 candidates remained. Of these, we found 37 miRNAs with significant age-associated changes at both layers, including 20 concordant and 17 discordant miRNAs. These comprised well-characterized cancer-associated miRNAs and lesser-studied candidates enriched in CpG-rich regulatory regions. Network analyses revealed a limited set of genes under convergent regulation by multiple age-associated miRNAs. These implicated pathways are related to cell cycle control, apoptosis, stress response, and epigenetic regulation. Conclusions: Our findings support a model in which age-dependent promoter methylation drift contributes to coordinated miRNA deregulation in PCa. This convergence highlights biologically plausible miRNA biomarkers and age-sensitive epigenetic circuits relevant to prostate carcinogenesis. Full article

Chemotherapy resistance remains a major obstacle to durable cancer control, yet its underlying mechanisms cannot be fully explained by genetic mutations alone. Increasing evidence suggests that therapeutic stress induces dynamic adaptive programs that reshape tumor phenotypic landscapes. Here, we propose a systems-level framework in which chemotherapy resistance emerges from the stabilization of interconnected stress-response circuits integrating redox signaling, metabolic reprogramming, and transcriptional plasticity. In this model, cytotoxic therapies function as state-generating perturbations that elevate oxidative stress and activate adaptive buffering systems, including NADPH-dependent redox homeostasis, replication stress tolerance, and integrated stress response (ISR)-mediated translational reprogramming. These adaptive modules collectively expand the accessibility of therapy-tolerant phenotypic states within tumor cell populations. Importantly, these circuits coordinate mitochondrial redox homeostasis, metabolic NADPH regeneration, and epigenetic–transcriptional plasticity to sustain cellular survival under persistent oxidative pressure. Such adaptive redox networks not only stabilize stress-tolerant phenotypes but also create vulnerabilities that can be therapeutically exploited. From a translational perspective, this framework suggests that effective strategies to overcome chemotherapy resistance should move beyond single-target inhibition and instead focus on circuit-guided therapeutic interventions that simultaneously destabilize redox buffering systems, constrain phenotypic plasticity, and disrupt metabolic stress adaptation. By conceptualizing therapy resistance as a dynamic redox-regulated state-space phenomenon, this model provides a mechanistic foundation for the development of evolution-aware and plasticity-constraining therapeutic strategies. Targeting the coordinated redox–metabolic–translational circuits that maintain tumor adaptability may therefore represent a promising direction for next-generation redox therapeutics in cancer. Full article

This paper presents ANAID (Autonomous Naturalistic obstacle-Avoidance Interaction Dataset), a new multimodal dataset designed to support research on autonomous driving, particularly with regard to obstacle avoidance and naturalistic driver–vehicle interaction. Data were collected using a Hyundai Tucson Hybrid equipped with a Comma-3X autonomous-driving development kit, combining high-resolution front-facing video with detailed CAN-bus telemetry. The dataset comprises four data collection campaigns, each corresponding to a single continuous driving session, yielding a total of 208 videos and 240,014 synchronized frames. In addition to the video data, the dataset provides vehicle state measurements (speed, acceleration, steering, pedal positions, turn signals, etc.) and an additional annotation layer identifying evasive maneuvers derived from steering-related signals. Data were recorded across four driving campaigns on an urban circuit at Universidad Europea de Madrid, capturing diverse real-world scenarios such as roundabouts, intersections, pedestrian areas, and segments requiring obstacle avoidance. A multi-stage processing pipeline aligns telemetry and visual data, extracts frames at 20 FPS, and detects evasive maneuvers using threshold-based time-series analysis. ANAID provides a fully aligned and non-destructive representation of naturalistic driving behavior, enabling research on control prediction, driver modeling, anomaly detection, and human–autonomy interaction in realistic traffic conditions. Full article

To address the limitations of conventional LC resonant battery equalization circuits, including slow balancing speed under small voltage differences, limited scalability in multi-cell configurations, and the risk of over-equalization, this paper proposes a dual-layer LC resonant equalization topology integrated with a Boost-assisted mechanism and a state-of-charge (SOC)-based dual closed-loop current control strategy. In the proposed topology, a Boost converter is introduced to actively enhance the effective voltage difference between cells, thereby improving the equalization current amplitude and accelerating the balancing process. A switched-inductor structure is further adopted to enable scalable inter-group energy transfer in multi-cell battery systems. To improve control accuracy, SOC is selected as the balancing variable, and a dual closed-loop control framework is designed, where the outer loop regulates SOC deviation, and the inner loop controls the equalization current via proportional–integral (PI) controllers. A MATLAB/Simulink model is established to evaluate the proposed method under multiple operating conditions, including idle, charging, and discharging states. The results show that the proposed topology significantly reduces the equalization time compared with conventional LC resonant circuits and improves balancing speed by approximately 49% under the dual closed-loop control strategy. In addition, the system maintains stable performance across different operating conditions. It should be noted that this study focuses on topology design and control strategy validation through simulation. Due to the focus on topology validation and control mechanism analysis, this study is limited to simulation-based verification. Experimental implementation will be conducted in future work. Full article

Matrix Product States (MPSs) have become an indispensable symmetry-based representation for simulating quantum systems on near-term hardware by constraining entanglement entropy through a fixed bond dimension $\mathsf{\chi }$. This study identifies a critical “rank explosion” phenomenon that destabilizes this low-rank manifold during conditional quantum diffusion processes. We empirically demonstrate that the introduction of conditional guidance—essential for semantic control—injects global correlations that drive the effective Schmidt rank to increase by $4\times$ (from $\mathsf{\chi }=4$ to $16$), saturating the simulation limits and necessitating quantum circuits with approximately $1.8\times {10}^3$ Controlled-NOT (CNOT) gates. Such circuit depths fundamentally exceed the operational coherence budgets of Noisy Intermediate-Scale Quantum (NISQ) devices. To mitigate this structural instability, we propose Rank-Aware Conditional Synthesis (RACS), a sampling framework that maintains the latent trajectory within a prescribed MPS manifold through step-wise manifold projection and time-shift error correction. Experimental results on real-world semantic data reveal that RACS reduces reconstruction error, or Mean Squared Error (MSE) by 30.8% and enhances latent trajectory smoothness by 36.8% compared to conventional post hoc truncation. At a fixed hardware-efficient rank of $\mathsf{\chi }=4$, RACS achieves a +4.8% fidelity gain and exhibits superior robustness against depolarizing noise. By resolving the tension between conditional expressivity and entanglement constraints, RACS provides a principled, hardware-aware methodology for high-fidelity quantum generative modeling. Full article

Neuroblastoma (NB) represents a paradigmatic developmental malignancy in which lineage specification, oncogenic signalling, and epigenetic regulation converge to define tumour behaviour. Among the molecular axes shaping NB heterogeneity, neurotrophin receptors of the tropomyosin receptor kinase (Trk) family (TrkA, TrkB, and TrkC) and the p75NTR occupy a central position at the intersection between neuronal differentiation programs and malignant plasticity. While high TrkA and TrkC expression is associated with adrenergic identity, differentiation competence, and favourable clinical outcome, TrkB, frequently sustained by BDNF-driven autocrine loops, characterises mesenchymal-like, therapy-resistant states enriched in metabolic and inflammatory adaptations. Importantly, in NB, the dysregulation of neurotrophin signalling rarely arises from recurrent genetic alterations of neurotrophic tyrosine receptor kinase (NTRK) loci. Instead, Trk receptor expression is dynamically shaped by promoter methylation, polycomb repressive complex 2/Enhancer of Zeste homolog 2 (PRC2/EZH2)-dependent chromatin repression, MYCN-driven transcriptional silencing, enhancer rewiring, and microRNA-mediated control. These epigenetic mechanisms govern reversible transitions along the adrenergic–mesenchymal (ADRN–MES) continuum, enabling tumour cells to adapt to microenvironmental and therapeutic stress. Single-cell and spatial multi-omics approaches have further revealed that Trk-associated phenotypes are embedded within complex regulatory circuits integrating receptor tyrosine kinase (RTK) networks, cytokine signalling, metabolic remodelling, and stromal reinforcement. Here, we provide a comprehensive synthesis of the epigenetic and microenvironmental mechanisms regulating neurotrophin receptors in NB, with particular emphasis on how chromatin plasticity and cell-state transitions reshape Trk-dependent signalling outputs. We discuss advanced three-dimensional and organoid-based models that recapitulate niche-specific regulation of the Trk axis and evaluate emerging therapeutic strategies combining epigenetic modulators, differentiation-inducing agents, and RTK-targeted compounds. Understanding the temporal and spatial dynamics of Trk signalling may open new opportunities to therapeutically stabilise differentiation states and disrupt adaptive resistance programs in high-risk NB. Full article

The series resonant converter (SRC) is widely used in power conversion systems that require high efficiency and high-power density. However, under light-load conditions, the resonant current decreases, and a higher switching frequency is often required to regulate the output voltage, which leads to efficiency degradation. To mitigate this issue, phase-shift control can be applied to the SRC, and an appropriate small-signal model is essential for accurate dynamic analysis and controller design. Conventional extended describing function (EDF)-based small-signal models provide high accuracy, but their complex equivalent circuits make analytical derivation of the transfer functions difficult and limit intuitive physical interpretation. To overcome this limitation, this paper proposes a non-coupled third-order equivalent-circuit model for the phase-shift SRC. The proposed model reduces the complexity of the conventional EDF-based fifth-order model while preserving the essential low-frequency dynamic characteristics. By employing approximations based on the relationship between the modulation frequency and the switching frequency, together with the superposition principle and equivalent transformations, the model removes the coupling among state variables and enables analytical derivation of the transfer functions. The proposed model is verified through comparisons of the low-frequency small-signal frequency responses with the conventional fifth-order model, PLECS simulations, and experimental measurements. Full article

Could there be quantum superpositions of conscious states, as suggested by the Wigner’s friend thought experiment? Mathematical theories of consciousness, notably integrated information theory (IIT), make this question more precise by associating physical systems with both quantitative amounts of consciousness and structural characterizations of conscious states. Motivated by a recent proposal that ties wave-function collapse to integrated information, we construct a simple quantum circuit that would, on that proposal, place a minimal system—a feedback dyad—into a superposition of states that differ in their associated conscious states. This “Schrödinger’s dyad” provides a controlled setting for evaluating a central desideratum of consciousness-based collapse models: that collapse rates depend on how different the experiences in the superposition are. We prove a structural constraint on collapse dynamics of a standard (Lindblad) type: if collapse is governed by too few collapse operators, collapse rates cannot in general be made to depend solely on qualitative differences between conscious states. Avoiding this limitation requires introducing many commuting operators, leading to a rapid proliferation of collapse terms even for very simple systems. This proliferation bears directly on claims that IIT-based collapse theories may be especially experimentally tractable, since the required dynamics becomes highly complex. More generally, the difficulty is not specific to IIT: any Wigner-style collapse theory that distinguishes experiences using rich internal organization (such as neural connectivity in addition to neural state) will face a comparable explosion in dynamical complexity. Full article

The rapid evolution of 6G networks and large-scale Internet of Things (IoT) deployments intensifies security and privacy challenges in embedded SIM (eSIM) Remote SIM Provisioning (RSP), particularly during the bootstrap and profile delivery phases. Traditional perimeter-based and VPN-centric approaches expose static attack surfaces, making provisioning workflows vulnerable to denial-of-service (DoS) attacks, reconnaissance, and profile lock-in risks. This paper presents MTD-SDP-eSIM, a hardware-anchored Zero Trust Architecture that secures eSIM provisioning by integrating the embedded Universal Integrated Circuit Card (eUICC) as a root of trust with Software-Defined Perimeter (SDP), Software-Defined Networking (SDN), and Moving Target Defense (MTD). The framework introduces Hardware-Anchored Single Packet Authorization (ES-SPA), which cryptographically binds initial access to tamper-resistant eUICC credentials and enforces an authenticate-before-connect model. A unified Zero Trust controller dynamically orchestrates SDP access control, SDN-based micro-segmentation, and MTD-driven Network Address Shuffling during high-risk provisioning phases. This framework is validated on a high-fidelity 6G testbed built using ns-3, Open5GS, and P4-programmable switches. Experimental results demonstrate a 90% DoS survival rate during provisioning, a 35% scalability improvement over VPN-based baselines, and a 75% reduction in profile lock-in failures through runtime deletion verification. These findings confirm that anchoring dynamic network defenses in hardware-rooted identity significantly enhances the resilience, scalability, and privacy of eSIM provisioning for massive 6G IoT deployments. Full article

Background: The global burden of mental disorders continues to escalate, necessitating scalable, evidence-based interventions. Artificial intelligence (AI)-delivered health promotion programs represent a promising approach to addressing treatment gaps by targeting the neuropsychological mechanisms that underlie mental health outcomes. This meta-analysis synthesizes evidence on the effectiveness of AI-delivered interventions in improving executive function, emotion regulation, and clinical outcomes across diverse populations. Methods: A systematic search identified 186 studies (n = 22,755 participants) published between 2020 and 2025. Random-effects meta-analyses estimated pooled effect sizes (Hedges’ g, calculated as between-group standardized mean differences with small-sample correction [J = 1 − 3/(4df − 1)]) for primary outcomes. Between-study heterogeneity was quantified using I² and τ² statistics. To address dependency among effect sizes from studies reporting multiple outcomes, robust variance estimation (RVE) was employed. Subgroup analyses examined intervention modalities, delivery formats, and clinical populations. Moderator analyses explored sources of heterogeneity, including publication year, sample size, intervention duration, control condition type, risk-of-bias rating, geographic region, and AI sophistication tier, and mediational models tested putative therapeutic mechanisms. Results: AI-delivered interventions demonstrated a significant overall effect on health outcomes (g = 0.68, 95% CI [0.58, 0.78]; τ² = 0.12; I² = 73.4%). Executive function outcomes showed moderate effects (g = 0.61, τ² = 0.08), with working memory improvements being strongest (g = 0.72). Emotion regulation outcomes demonstrated moderate-to-large effects (g = 0.61, 95% CI [0.51, 0.70], τ² = 0.006); formal subgroup pooled estimates by emotion regulation strategy were not calculated due to insufficient studies per strategy (k < 3 per category); individual study effect sizes ranged from g = 0.27 to g = 1.11. Among 41 studies examining neuropsychological mechanisms, convergent patterns suggested involvement of prefrontal neural circuits (DLPFC), enhanced alpha-band activity, and improved heart rate variability; however, formal mediation was tested in only 18 studies (9.7%). Among clinical populations, interventions for cognitive impairment yielded the largest effects (g = 1.02; this finding should be interpreted cautiously given modest cumulative sample size [n = 482], potential small-study effects [Egger’s p = 0.08], and trim-and-fill adjusted estimate of g = 0.85), followed by mental health conditions (g = 0.72), while other clinical populations showed smaller but significant improvements (g = 0.19). Mobile applications (g = 0.78) and chatbot-based interventions (g = 0.74) demonstrated the strongest effects among delivery formats. Among studies testing formal mediation, analyses suggested mindfulness (β = 0.42), decentering (β = 0.38), and cognitive reappraisal (β = 0.45) as processes associated with therapeutic outcomes. Conclusions: AI-delivered health promotion programs demonstrate significant effectiveness across executive function, emotion regulation, and clinical outcomes, though substantial heterogeneity (I² = 45–82%) indicates meaningful variability warranting attention to subgroup-specific effects. Given the diversity of intervention types included (chatbots, mobile apps, VR systems, neuromodulation), pooled estimates should be interpreted as characterizing the average effect across this heterogeneous landscape; subgroup-specific estimates provide more precise guidance for clinical decision-making regarding specific modalities. Effects are associated with convergent patterns of neuropsychological mechanisms, though mechanistic conclusions remain preliminary given that only 22% of studies (41/186) examined neuropsychological mechanisms, and formal mediation analyses were conducted in only 18 studies (9.7%); most of the mechanistic evidence is correlational rather than causal. Future research should establish standardized AI taxonomies, optimize adaptive algorithms, conduct adequately powered replication studies in populations with cognitive impairment, prioritize experimental mediation designs to establish causal pathways, and evaluate long-term maintenance effects with a minimum of 6–12-month follow-up periods. Full article

The regulation of chloride levels is a crucial part of controlling inhibitory signals, but does not occur uniformly throughout the body. Recent data suggest that chloride is regulated within localized “microdomains” which are defined by the interaction of KCC2 and NKCC1, structural restraints on cells due to their internal structure, the metabolic condition of the cell, and the external environment modified by astrocytes. The gradients of chloride concentrations within these compartment-specific microdomains define the local chloride reversal potential, and thereby determine the directionality (i.e., whether excitatory or inhibitory), magnitude, and timing of GABAergic inhibition. The disruption of this organized chloride gradient within microdomains impairs the stability of inhibitory activity at multiple levels of integration, including dendritic input, spike timing, interneuron synchronization, and network oscillation. Disturbances in inhibitory stability have been found in a variety of diseases, including epilepsy, neonatal seizure, neuropathic pain, and schizophrenia-spectrum disorders. This supports the hypothesis that disturbances in chloride homeostasis lead to a loss of stability in cortical circuits. This review will provide a synthesis of the molecular, spatial, and circuit level principles involved in the regulation of chloride and discuss how failures of these mechanisms produce clinically relevant disturbances in inhibitory signal processing. In addition, we will be discussing new therapeutic strategies for the restoration of chloride homeostasis, including KCC2 repair, selective modulation of NKCC1, targeting astrocytes, and microenvironmental engineering. Overall, the studies reviewed here provide a unified model for understanding the pathophysiology of inhibitory dysfunction, and demonstrate that the regulation of chloride microdomains provides a novel and promising area of research for translational intervention. Full article

This paper proposes a millimeter-wave miniature on-chip bandpass filter (BPF) implemented using a 0.18 μm CMOS process. To address the issues of insufficient coupling capability, limited control of transmission zeros, and excessive chip area in traditional on-chip filters, a folded cross-interdigital coupling structure is proposed to enhance coupling efficiency and reduce size. The design incorporates metal–insulator–metal (MIM) capacitors to increase the coupling capacitance between resonators without increasing the area, and utilizes a defected ground structure (DGS) to modify the current distribution at the ground plane, generating additional transmission zeros to improve selectivity. An LC equivalent circuit model was established and verified through full-wave electromagnetic simulation, and the design was validated through chip fabrication and on-wafer measurements. The measurement results show an insertion loss of 3.36 dB and a fractional bandwidth of 49.1% at 32 GHz, with two transmission zeros. The core dimensions are 0.25 mm × 0.18 mm. This design achieves a good balance between miniaturization, selectivity, and insertion loss, making it suitable for millimeter-wave SoC applications. Full article

This paper starts by analysing the equivalent circuit model of a Thermoelectric Module (TEM) with PLECS simulation by using the PLECS thermal block-set. The approach enables the evaluation of power-module losses when mounted on a sandwich assembly of a TEM, heatsink, and cooling fan. An experimental setup was first built using power resistors for controlled heat generation to be absorbed by the cooling system and validated with the simulation model. Experimental investigations were then carried out on a DC/DC converter under four cooling conditions: natural convection and forced convection without a TEM and then natural convection and forced convection with a TEM. The experimental results are validated using PLECS Software (version 4.8). This result demonstrates a reduction in the power-module junction temperature of the DC/DC converter when employing forced convection with a TEM compared to forced convection without a TEM. Furthermore, the results indicate about 32% potential weight and size reduction of the converter magnetic components, along with improved power density, through the integration of TEM-based cooling. Full article

Thin-film lithium niobate electro-optic modulators exhibit outstanding advantages such as large bandwidth, low insertion loss, and low half-wave voltage, demonstrating broad application prospects. However, due to internal defects in lithium niobate crystals, modulators exhibit electro-optic relaxation phenomena, with the relaxation time of thin-film structures being reduced by more than two orders of magnitude compared to bulk materials. In this study, we fitted and simulated the electro-optic relaxation behavior of thin-film lithium niobate modulators based on RC circuit model, effectively explaining their time-domain response characteristics under low-frequency conditions. By comparing thin-film modulators with and without silica cladding structures, the fitting results indicate that the relaxation time of modulators with cladding is approximately 11.9 ms, showing positive DC drift, whereas the relaxation time of modulators without cladding is significantly shortened to about 88.6 μs and exhibits negative DC drift. Additionally, the enhancement of optical intensity alters the photoconductivity of the material, thereby affecting its low-frequency electro-optic response behavior. This research provides important ideas for the design and optimization of next-generation integrated lithium niobate photonic modulators with high stability and controllability. Full article

With the increasing popularity of battery energy storage technology, safety issues have become increasingly important. The battery management system (BMS) is a key device for ensuring the safety of lithium-ion battery systems. While the BMS can effectively prevent faults such as external overheating, overload, or deep discharge, it cannot completely eliminate the possibility of internal short-circuit (ISC) faults—these faults may be caused by multiple factors, such as manufacturing defects. Therefore, reliable ISC detection or mitigation strategies need to be designed within the BMS to reduce the consequences of such faults. This study focuses on the critical role of the BMS in responding to thermal runaway (TR) and thermal propagation (TP) events caused by ISC faults and proposes an active energy-dissipation BMS control strategy. This strategy is compared with existing battery current interrupt device (CID) protection and threshold-type BMS protection schemes. A coupled electro-thermal simulation model was constructed based on thermal runaway test data of 280 Ah lithium iron phosphate batteries, and the proposed strategy was verified within this model. The proposed strategy can effectively suppress thermal propagation and thermal runaway in battery energy storage systems, providing a reference for the safety of battery energy storage systems (BESS). Full article