Cancer cells reprogram their metabolism not only through genetic mutations but also via reversible epigenetic modifications that alter gene expression without changing the DNA sequence. AMP-activated protein kinase (AMPK) is a master regulator of cellular energy homeostasis. It plays a crucial role in cancer by modulating key metabolic pathways, including autophagy, lipid biosynthesis, and glucose utilization. Emerging evidence suggests that AMPK is tightly regulated by epigenetic mechanisms, including DNA methylation, histone remodeling, and non-coding RNAs, which influence AMPK gene expression, activation, and post-translational stability. Non-coding RNAs, including long non-coding RNAs, microRNAs, and circular RNAs, often engage in dynamic feedback loops with AMPK, coupling metabolic stress to transcriptional and epigenetic remodeling. In parallel, DNA methylation and histone modifications influence AMPK signaling indirectly through modulation of upstream regulators and directly via chromatin-associated functions of AMPK. Despite extensive characterization of AMPK function in cancer metabolism, the epigenetic mechanism governing its regulation remain comparatively underexplored. Distinct epigenetic signatures associated with AMPK regulation are being explored as a potential therapeutic target. This review provides a comprehensive overview of epigenetic regulation of AMPK in cancer and highlights its potential in the context of metabolic reprogramming and precision oncology.

Homeostatic feedback loops are essential to stabilize the activity of neurons and neuronal networks. It has been hypothesized that, in the context of Amyotrophic Lateral Sclerosis (ALS), an excessive gain in feedback loops might hyper- or hypo-excite motoneurons (MNs) and contribute to the pathogenesis. Here, we investigated how the neuromodulation of MN intrinsic properties is homeostatically controlled in presymptomatic adult SOD1(G93A) mice and in the age-matched control WT mice. First, we determined that Adrb2 and Adrb3 adrenergic receptors, which are Gs-coupled receptors and subject to tight and robust feedback loops, are specifically expressed in spinal MNs of both SOD1 and WT mice at P45. We then demonstrated that these receptors elicit a so-far overlooked neuromodulation of the electrical properties of MNs, in particular the frequency-current gain, a crucial determinant of excitability. These electrical properties are homeostatically regulated following receptor engagement, which triggers ion channel transcriptional changes and downregulates those receptors. These homeostatic feedbacks are not dysregulated in presymptomatic SOD1 mice, and they set the MN excitability upon β-adrenergic neuromodulation.

A self-adapting DNA-crosslinked hydrogel reprograms macrophage polarization and angiogenesis for synergistic repair of diabetic wounds.

Hypothalamic-pituitary-adrenal axis attenuation following child sexual abuse distinguishes posttraumatic stress trajectories from late adolescence to midlife.

NNMT as a therapeutic target in fibrosis: Insights from the heart, liver, kidneys, and lungs.

Digitalization plays a critical role in the transformation of electricity distribution systems, enabling greater efficiency, flexibility, and integration of renewable energy sources. However, Distribution System Operators (DSOs) often face regulatory and economic barriers that hinder investments in digital technologies. This study adopts a qualitative System Dynamics (SD) approach to explore the complex, feedback-driven interactions between regulatory incentive structures and DSOs’ investment decisions in digitalization. Focusing on the Australian regulatory framework, the SD model was developed and validated through expert interviews with representatives from National Regulatory Authorities, academia, and industry. The analysis identifies elevencritical feedback loops that either reinforce or obstruct digital investment decisions. These loops reveal how incentive structures, cost-recovery rules, information asymmetries, and risk perceptions interact to create self-reinforcing dynamics that often favor conventional grid expansion over digital alternatives. The model provides a structured mapping of regulatory feedback mechanisms, and serves as a diagnostic tool to support policy design by highlighting where regulatory interventions can be most effective. The findings show that incentive structures, investment choices, and grid-performance outcomes are linked through feedback dynamics that shape long-term infrastructure trajectories, potentially locking systems into conventional asset expansion or enabling digitally driven modernization. This study lays the groundwork for future research by offering a structured method to trace and compare regulatory-investment dynamics, and by providing actionable insights for designing more coherent incentive schemes that support DSOs’ digitalization efforts in diverse regulatory settings. • Economic incentives to drive power grid digitalization are currently insufficient. • A system dynamics model is used to unveil regulatory factors hindering digitalization. • Impacts of CAPEX-OPEX, performance and innovation schemes on investments are analyzed. • Eleven key feedback loops impacting digitalization are identified and validated. • Regulatory tools are proposed to redirect incentives and support digitalization.

This technical note investigates the stabilization of a continuous-time linear system, whose feedback loop is closed over a digital communication network with bit rate constraints and network delay. To reduce the required bit rate for the stabilization of the concerned system, we propose a novel quantization strategy which can utilize both the quantization bits and the time information to encode the system state. The time information is carried by event-triggering time instants and transmission time instants and can provide extra state information to the controller without consuming any bit rate. The proposed strategy schedules multiple data transmissions in a time-triggered manner after a triggered event. By appropriately co-designing transmission time instants and the corresponding quantization scheme, our time information can produce more state information than existing approaches. We derive a sufficient bit rate condition to guarantee the input-to-state stability of the concerned system, which depends on the unstable eigenvalues of the system matrix and the upper bound of network delay. The derived stabilizing bit rate can be lower than the current minimum stabilizing bit rates due to our enhanced time information. Numerical examples are provided to confirm the effectiveness of the proposed strategy.

As human activities and wildlife increasingly overlap in the Anthropocene, conventional conservation paradigms focused on land-sparing are shifting toward strategies that support human-wildlife coexistence. However, achieving sustainable coexistence is often hindered by a limited understanding of the dynamic social-ecological processes that drive integrated human-wildlife systems. This Special Issue explores "resilience thinking" as a scientific framework to address these knowledge gaps. By bridging sustainability and wildlife sciences, we examine how concepts such as adaptive capacity, feedback loops, system archetypes, and tipping points can illuminate the conditions that facilitate stable coexistence or lead to persistent conflict. Through diverse global case studies ranging from carnivore management in Europe to primate interactions in Indonesia, contributions highlight how societal perceptions, governance, and co-adaptation shape system trajectories. Ultimately, we argue that fostering durable coexistence requires moving beyond incremental adaptation toward transformative governance that centers on equity, relationality, and proactive management.

Depopulation and healthcare service decline: Spatial evidence of a vicious cycle in South Korea

Crystallization of perovskites is the key process in thin film perovskite solar cells (PSCs). The ability to connect film formation dynamics with device performance remains a central challenge in the development of high-performance PSCs. Here, we develop a deep spatiotemporal attention network that outputs a current density-voltage (J-V) curve for each in-situ photoluminescence (PL) patch. Training follows a weak-supervision paradigm: only the global J-V is observed, and learning is driven by aligning the aggregation of local predictions with this global target. Visual Explanation analysis via gradient-based activation map (GAM) indicates that the model focuses on physically meaningful stages of film evolution—capturing early nucleation dynamics and later crystallization—while emphasizing longer-wavelength PL signals during late stages and shorter-wavelength cues during nucleation. Beyond global prediction, the local J-V outputs enable spatial diagnostics: using Spearman’s rank correlation, we find a statistically significant negative monotonic association between the dispersion of local power conversion efficiency (PCE) and overall PCE, which strengthens on well-estimated samples. Together, these results suggest that spatially uniform local performance is a strong indicator of high-performance device and highlight PL-video-driven deep learning as a scalable data-driven workflow for both performance prediction and process optimization in industry. To demonstrate industrial applicability of solar cells, this AI model integrates into scalable production lines like blade-coating. By rapidly evaluating uniformity and predicting performance, it enables a real-time feedback loop for immediate process adjustments, ensuring consistent, high-quality manufacturing. • A model based on Transformer architecture and weak supervision is developed to enable the rapid and precise prediction of the local performance of a perovskite film. • The results demonstrate a marked improvement in predictive capability: The prediction accuracy improves from 0.472 to 0.504. The prediction error is substantially reduced, with the Mean Absolute Error decreasing from 1.488 to 1.358. • A clear negative correlation emerges: devices with lower global Power Conversion Efficiency (PCE) tend to exhibit higher spatial variability in the predicted local efficiencies, whereas devices with higher global PCEs display more uniform local predictions.

NCOA4-mediated ferroptosis drives cGAS-STING-dependent inflammation in radiation dermatitis: Protective modulation by Cu-ATSM.

Vascular environment-responsive DNA nanoswitch controls the positive feedback system for spatiotemporal coupling of angiogenesis and osteogenesis.

Gallic acid-mediated semiquinone radicals drive a self-sustaining fenton-like system for PAH remediation in contaminated soil.

PEX5 integrates the p38 MAPK signaling pathway and taurine metabolism to regulate senescence in lung fibroblasts.

Regulatory interplay between nitric oxide and heme in redox signaling and inflammation.

ATF5 activates LPAR5 to enhance macrophage pro-inflammatory responses to exacerbate rheumatoid arthritis.

This constructivist grounded theory study investigates how tourism stakeholders adapt to dual digital constraints: simultaneous international sanctions and domestic filtering. Drawing on 91 in-depth interviews with Iranian operators, we theorize the Perpetual Cycle of Digital Adaptation under Dual Siege. Three processes emerged: Forced Navigation (cognitive mapping generating psychological burden), Survival-Driven Bricolage (platform substitution degrading capabilities), and Striving for Digital Viability (maintaining operations while undermining competitiveness). The framework reveals entropic adaptation, where survival paradoxically accelerates decline through asymmetric feedback loops. Unlike single-constraint contexts where alternatives exist, dual siege creates institutional voids eliminating pathways. The study extends technology adoption theory from voluntary choice toward impossibility-based conditions, reconceptualizes resilience as pathological endurance, and introduces managed deterioration as a state between success and failure. • Grounded theory of entropic adaptation to technological siege (sanctions & filtering) • Reveals how Iranian tourism stakeholders navigate institutional voids • Theorizes a Perpetual Cycle of Digital Adaptation with feedback driving decline • Extends technology adoption from voluntary choice to impossibility-based frameworks

The Limb-bud and Heart (LBH) promotes renal fibrosis through endoplasmic reticulum stress-induced pyroptosis and partial epithelial-mesenchymal transition in renal tubular epithelial cells.

Disturbance modeling compensation predictive LADRC of tank gun control system using self-attention mechanism based bi-LSTM network.

ESM1 drives cancer angiogenesis and bevacizumab resistance via trioleate synthesis.