Search Results (2,736)

Prenatal alcohol exposure (PAE) is recognized as a major public health concern due to its profound and lasting effects on the central nervous system (CNS) and its ability to induce fetal alcohol spectrum disorders (FASD), which encompass a wide range of cognitive, behavioural, and neuropsychiatric disorders that persist throughout life. Experimental and clinical studies have identified several mechanisms underlying ethanol impairing brain development, including apoptosis, oxidative stress, disruption of morphogen and growth factor signalling pathways, impaired neuronal proliferation and migration, neurotransmitter systems’ dysfunction, glial cells damage associated with deficient myelination, vascular and blood–brain barrier (BBB) alterations, and lasting epigenetic reprogramming. However, to date no widely accepted integrative framework explaining how these impairments underline the heterogeneous phenotype observed in FASD is available. The present brings together developmental neurobiology and computational neuroscience to conceptualize PAE as a disorder of emerging neural and functional architecture. Here, we summarize the pharmacokinetics of ethanol in pregnancy, critical windows of vulnerability, and the classical pathways of alcohol teratogenesis affecting neuronal survival, migration, synaptogenesis, myelination, and gene regulation. We have also reviewed MRI, diffusion imaging, and EEG/MEG evidence showing altered brain volumes, white matter microstructure, functional connectivity, and network organization in individuals with PAE. Finally, we propose a systems-level model that conceptualizes PAE as a disorder of emerging neuro-computational architecture, in which ethanol-induced cellular and molecular perturbations collectively alter the building blocks and self-organization rules of brain network assembly. Full article

This work proposes an Adaptive Neural Control for the asymptotic stabilization of a chain of integrators at the origin. The proposed approach addresses the stabilization of the integrator chain by means of a control law whose applied signal is structurally bounded to $(-1,1)$ by the hyperbolic tangent architecture, i.e., $u\left(t\right)=tanh\left(z\right)$, where z represents a weighted linear combination of the system states and a bias term. Furthermore, an adaptation law for the weights is proposed, based on the classical backpropagation algorithm for neural networks. The stability analysis is conducted using singular perturbation theory, demonstrating that, under a sufficiently high learning rate, the closed-loop system exhibits a Standard Singular Perturbation Form. This formulation allows for the analysis of the system across two distinct time scales: the adaptation dynamics (fast subsystem) and the state dynamics (slow subsystem). Based on this formulation, explicit conditions on the learning rate and the initial conditions are derived to guarantee local asymptotic stability using Tikhonov’s theorem. These conditions characterize the region of attraction and ensure that the adaptive neural controller stabilizes the system. Numerical simulations were carried out to evaluate the controller’s performance under three different scenarios: ideal conditions, initialization outside the region of attraction, and a low learning rate. These scenarios illustrate the closed-loop system behavior and validate the theoretical conditions required for asymptotic stability. Furthermore, comparative numerical simulations were conducted on an Inverted Pendulum on a Cart system to benchmark the proposed Adaptive Neural Control against Linear Quadratic Regulator, Sliding Mode Control, and Nested Saturation Function controllers. Based on the Integral of Time-weighted Squared Error performance index, the Adaptive Neural Control demonstrated a significant reduction in control effort, achieving performance improvements of up to 95.02% compared to the aforementioned strategies. Full article

Centrifugal compressors are critical assets in the oil and gas, petrochemical, and power generation industries, where unplanned downtime results in severe economic and safety consequences. Despite the application of digital twin technology for predictive maintenance, existing approaches struggle to combine accurate degradation modeling with formal assurance evidence that regulators and operators demand before trusting machine learning-augmented systems. This paper proposes a hybrid digital twin framework whose architecture is structured around a formal safety case template, addressing both the accuracy and the trustworthiness challenges simultaneously. The methodology couples a first-principles thermodynamic model with a neural-network residual learner, and the complete system is organized through a design-stage safety case constructed in Goal Structuring Notation. The design stage identifies the requirements for operational deployment. Validation through a simulation study on a one-year synthetic operational dataset shows that the hybrid model reduces root-mean-square prediction error by over 50% for both pressure ratio and polytropic efficiency compared to the physics-only baseline. The anomaly detection module, presented here as a proof of concept, achieves 92% recall in identifying injected faults, and a composite health index tracks the progression of fouling, erosion, and seal wear over the simulated service life. This study is purely theoretical, with no experimental measurements conducted. It demonstrates the structural viability and coherence of the proposed framework within a controlled environment, providing a solid theoretical and computational foundation for future physical validation efforts. These findings provide preliminary evidence that embedding a structured safety argument into the design of a hybrid digital twin is technically feasible and beneficial for building the confidence needed to deploy such systems in safety-critical industrial environments. Full article

Intercellular communication is crucial for the coordination of skeletal muscle development. However, the intricate signaling networks that regulate chicken myogenesis are not yet fully elucidated. In this study, we utilized CellChat analysis on single-cell and single-nucleus RNA sequencing data to systematically delineate cell–cell communication patterns across five critical developmental stages of chicken skeletal muscle: embryonic day 4 (E4), day 6 (E6), day 12 (E12), day 18 (E18), and post-hatch day 30 (P30). Our findings indicate that communication architectures are highly stage-specific, with mesenchymal cells acting as the predominant signaling hub during the early embryonic stages (E4–E6), whereas fibro-adipogenic progenitors become the principal communicators during mid-to-late embryogenesis (E12–E18). At E4, the communication network was relatively simple, comprising 51 ligand–receptor pairs primarily involving the neural cell adhesion molecule, slit guidance ligand, and midkine (MK) signaling pathways between myogenic progenitors and mesenchymal cells. By E6, the network had expanded significantly, encompassing 6237 ligand–receptor pairs across 51 signaling pathways, which coincided with the emergence of multiple myogenic lineages. Peak communication complexity was observed at E12, characterized by 11,675 ligand–receptor pairs and 61 signaling pathways, reflecting the secondary wave of myogenesis. Comparative analysis across developmental stages revealed key signaling transitions: the pleiotrophin and MK pathways were predominantly active during the early phase of myogenic commitment (E4–E6), whereas the collagen, laminin, and adhesion G protein-coupled receptor L pathways were more prominent during the secondary myogenesis phase (E6–E12). Notably, a significant shift in communication patterns was observed from E12 to E18, marked by a reduction in developmental pathway signaling and an increase in immune-related communications. By P30, the communication network had stabilized into a homeostatic state, centered on interactions among myofibers, stromal cells, and the vascular system. This comprehensive atlas of intercellular communication offers novel insights into the signaling dynamics underpinning chicken skeletal muscle development. Full article

The reliable operation of grid-connected distributed generation converters is challenged by severe unbalanced conditions and stringent fault ride-through requirements. To address these issues, this paper presents a sensorless flexible predictive direct power control (SF-PDPC) strategy for converters operating under severe asymmetrical grid faults. The proposed approach combines a frequency-adaptive neural network quadrature signal generator (FANN-QSG)-based virtual-flux estimator with a flexible power-reference generation scheme, enabling predictive control without grid-voltage sensors, conventional synchronization units, or cascaded filtering stages. The key feature of the proposed method lies in a flexible power-reference formulation that exploits the degrees of freedom associated with positive- and negative-sequence power components, allowing continuous regulation of the trade-off among current quality, active-power oscillations, and reactive-power oscillations under unbalanced grid conditions. This enables a unified control framework adaptable to different grid support objectives. The effectiveness of the proposed strategy is validated under a severe type-C voltage sag, grid frequency deviation, and harmonic distortion. Compared with the conventional PDPC, the proposed method reduces current total harmonic distortion from 57.78% to 0.44% while maintaining satisfactory active power tracking performance. Furthermore, the FANN-QSG-based estimator and the overall control structure demonstrate strong robustness under highly disturbed operating conditions. The proposed SF-PDPC enhances the operational flexibility of predictive power control for grid-connected converters operating under highly disturbed and unbalanced grid conditions. Full article

Neuroinflammation is a key hallmark of both neurodegenerative and neurospecific autoimmune diseases, including multiple sclerosis (MS), where immune dysregulation contributes to cellular stress, autophagy, and disease progression in Alzheimer’s disease (AD), Parkinson’s disease (PD), and MS. Emerging evidence suggests a shared mechanism behind MS, AD, and PD, driven by chronic interaction between the peripheral immune system and the central nervous system (CNS). While MS was traditionally viewed as a primary autoimmune condition, recent research indicated that all three disorders involve a breakdown of the blood–brain barrier (BBB). This structural failure enables peripheral immune cells and cytokines to enter the brain, causing sustained neuroinflammation and accelerating disease progression. Here, we propose an end-to-end framework for identification of the diagnostic and therapeutic cell-specific protein markers commonly regulated in mild–moderate AD (MMAD), early-stage PD (ESPD), and MS within peripheral blood mononuclear cells (PBMCs). PBMC markers were first identified based on shared differential protein expression, followed by filtering for BBB permeability. Subsequently, sorted cell markers were mapped to disease-specific neural cell types. Our analysis suggests that PBMC-derived cells, including astrocyte- and monocyte-like populations, share overlapping transcriptional signatures and functional similarity with macrophages and neuroglial cells, indicating potential transcriptional similarity or functional convergence. Furthermore, intra- and inter-cellular pathway analysis suggested both shared and disease-specific signaling mechanisms, with kinase–integrin interactions emerging as key regulatory factors. Selected potential seed markers, primarily kinases and immunoglobulins, were further analyzed through evolutionary sequence–structure space to identify druggable structural features. Next, protein moonlighting possibilities were tested to enhance the temporal functional trajectory of the markers for precise therapeutic impact. Hence, the framework provides a robust strategy to identify immune-based disease-specificcandidate diagnostic andpotential therapeutic targets. Full article

This study develops and analyzes a four-state nonlinear policy–feedback dynamical system that couples a system stressor, an accumulated burden, a signed mitigation–response variable, and a signed policy-pressure variable. The proposed model represents governance response through a smooth threshold-centered feedback mechanism, in which the policy-pressure dynamics depend continuously on the deviation of the stressor from a prescribed reference threshold. Unlike reduced-order formulations with purely exogenous interventions, the present framework generates endogenous interactions among stress accumulation, burden evolution, mitigation response, and policy adjustment. The qualitative analysis establishes local well-posedness in the admissible phase domain, conditional nonnegativity of the accumulated burden, and boundedness of trajectories on admissible intervals. An autonomous effective system is then derived to characterize quasi-stationary mean behavior of the periodically forced dynamics. For this effective system, local stability is investigated using Gershgorin estimates and Routh–Hurwitz criteria, leading to explicit analytical conditions for local asymptotic stability and a critical policy-responsiveness threshold associated with possible Hopf-type oscillatory transitions. The analysis highlights the stabilizing role of mitigation damping and cubic saturation in regulating the feedback loop. To approximate the nonlinear system, a Physics-Informed Neural Network (PINN) surrogate is constructed by embedding the governing equations into a differentiable residual loss while enforcing the initial conditions analytically. The accumulated burden is represented through an admissible neural-network ansatz to preserve the well-definedness of the logarithmic coupling term, while the mitigation–response and policy-pressure variables remain signed in accordance with the model formulation. Numerical validation against reference ode45 solutions across two governance regimes shows maximum absolute errors of order ${10}^{-3}$, indicating that the PINN provides a reliable differentiable surrogate for the coupled policy–feedback dynamics. The resulting framework offers a foundation for future inverse modeling, parameter estimation, and data-assimilation studies involving policy responsiveness, intervention thresholds, and burden- suppression effects. Full article

The potential health hazards caused by microwave exposure have attracted increasing attention. Microwave radiation has been reported to induce oxidative stress in neural tissues, which is considered one of the primary mechanisms underlying its adverse effects on central nervous system function. The hippocampus is sensitive to microwave radiation, whereas underlying cellular and molecular mechanisms remain incompletely understood. In this study, microwave-exposed mice exhibited significantly impaired performance in the Go/No-go, Y-maze, and novel object recognition tests at 6 h and 7 days post-exposure, indicating deficits in hippocampus-dependent working memory. Single-cell RNA sequencing of hippocampal tissues from control and microwave-exposed mice yielded 94,088 high-quality cells across eight major cell types. Astrocyte sub-clustering identified five transcriptionally distinct subpopulations, with Astrocyte_S100a6 and Astrocyte_Son proportions increased and Astrocyte_Serpinf1 decreased in the radiation group. Analysis of astrocyte transcriptional state transitions showed microwave-exposed astrocytes were preferentially distributed toward terminal reactive states with depletion at early homeostatic nodes. Cell–cell communication analysis revealed increased total interactions and interaction strength following radiation. Astrocyte outgoing signaling was increased for pathways associated with vascular remodeling, phagocytic regulation, and neuroinflammation, while pathways related to trophic support were decreased. Incoming signaling showed increased activity in pathways linked to phagocytic recruitment and inflammatory mediation. Taken together, these findings indicate that microwave exposure is associated with hippocampus-dependent working memory deficits accompanied by transcriptional remodeling of astrocyte subpopulation composition, directional astrocyte state transitions toward reactive phenotypes, and broad alterations in astrocyte-centered intercellular communication, providing a cellular and molecular framework for understanding astrocyte involvement in microwave radiation-associated hippocampal dysfunction. Full article

Fine particulate matter (PM_2.5) exposure increases the risk of neurodevelopmental abnormalities by disrupting neural stem cell (NSC) proliferation and cell cycle homeostasis, which are critical for normal neurodevelopment. This study investigated the impact of fine particulate matter (PM_2.5) on NSC proliferation and cell cycle using a three-dimensional (3D) graphene oxide (GO) scaffold that mimics the NSC microenvironment. PM_2.5 exposure led to concentration-dependent decreases in NSC viability and induced G0/G1 phase arrest via the marked downregulation of Cyclin D1-Cdk4 and Cyclin E-Cdk2, which critically impact G1/S transition. NSCs in 3D GO scaffolds maintained higher expression of key cell cycle regulators (Cyclin A, Cdk1/Cdk2, APC, and Cdc20) and superior cell viability when suffering PM_2.5 exposure, demonstrating the 3D culture environment was beneficial for NSC proliferation. We speculate that the 3D culture environment is more favorable and protective for cell proliferation. Therefore, these findings highlight the utility of the 3D GO scaffold for studying PM_2.5 effects on growing neural stem cells. This work provides a physiologically relevant in vitro platform that captures microenvironment-dependent neurotoxic responses, consequently offering valuable mechanistic insights into PM_2.5-induced developmental neurotoxicity. Full article

The prevention and treatment of cardiovascular disease (CVD) are undergoing a paradigm shift from a lipid-centric approach to a holistic metabolic perspective. Central to this evolution is the gut–fat–heart axis, a sophisticated three-dimensional communication network that integrates neural, endocrine, and immunometabolic signaling to regulate systemic lipid homeostasis. This manuscript systematically explores how the gut microbiota acts as a “metabolic organ” to remotely control host health through the production of bioactive metabolites and the modulation of molecular communication networks. At the physiological level, microbial products such as short-chain fatty acids (SCFAs) and modified bile acids regulate energy balance and lipid synthesis via the FXR-FGF15/19 axis and G protein-coupled receptors. Furthermore, gut hormones like GLP-1 and neuro-reflex pathways involving the vagus nerve provide rapid control over postprandial lipid clearance and feeding behavior. Conversely, pathological dysbiosis triggers the accumulation of harmful metabolites, such as trimethylamine N-oxide (TMAO) and lipopolysaccharides (LPS), which drive lipotoxicity, vascular inflammation, and “dysfunctional HDL” formation. These processes accelerate the progression of atherosclerosis, heart failure, and metabolic syndrome. Finally, the article outlines promising clinical translation strategies, including the development of TMA lyase inhibitors, next-generation probiotics, and the use of phytochemicals to reshape the microbial landscape. By decoding the molecular dialogues within the gut–fat–heart axis, this research provides a novel strategic vantage point for the integrated management of cardiovascular–kidney–metabolic (CKM) syndrome. Full article

Non-exhaust emissions (NEEs), particularly brake wear particles (BWPs), have become a dominant source of traffic-related particulate matter (PM), accounting for approximately 77% of PM10 and 60% of PM2.5 emissions. Accurate quantification of these emissions is essential under increasingly stringent regulations such as Euro 7. However, measurement reliability is strongly influenced by particle transport and sampling losses. This review provides a state-of-the-art analysis of laboratory-scale methodologies for investigating BWP emissions, focusing on pin-on-disc (PoD) tribometers and inertia dynamometer systems. Particular attention is given to chamber design, airflow management, sampling configurations, and the mechanisms governing particle transport efficiency. The literature indicates that PoD systems are often affected by complex and non-uniform flow fields, leading to incomplete particle capture and reduced representativeness, whereas inertia dynamometers, especially when coupled with constant volume sampling (CVS), provide more controlled and reproducible conditions. Key loss mechanisms, including inertial deposition, diffusion, gravitational settling, and non-isokinetic sampling effects, are major contributors to uncertainty. The reviewed studies highlight that aerodynamic limitations in PoD systems, particularly box-shaped chambers, promote flow recirculation and particle losses. Advanced optimization approaches that combine artificial neural networks (ANNs) with computational fluid dynamics (CFD) simulations show strong potential to improve system design and measurement reliability. Full article

Liquid storage tanks are widely used in sectors such as water treatment, oil and gas, food processing, and chemical manufacturing. Knowing the exact amount of liquid in a tank is essential for ensuring safety, preventing spills, and optimizing process control; therefore, the liquid level in a tank must be maintained at a precise reference point. This is where liquid level control for tanks becomes crucial and constitutes a fundamental problem in the industrial sector due to nonlinearities, multivariable coupling, and stochastic disturbances. Given the drawbacks of available control methods, such as classical Model Predictive Control (MPC), which are highly dependent on model accuracy and struggle to reject complex stochastic noise, predicting random disturbances represents a major technological challenge. A new approach is proposed to specifically address the problem and challenge of the four-tank system, where water levels in two lower tanks must be controlled by two pumps, often with varying delays and significant parameter disturbances. To establish a relationship between expected performance and MPC parameters, this approach uses a novel hybrid nonlinear MPC, Extended State Observer, and Physics-Informed Neural State Estimation (NMPC-ESO-PINSE) architecture. A Physics-Informed Neural State Estimation (PINSE) layer, chosen for its learning capacity, is designed to filter sensor noise by applying Bernoulli’s physical laws, while an Extended State Observer (ESO) is integrated to capture and compensate for unmodeled uncertainties in the process. Finally, a proposed hybrid (NMPC-ESO-PINSE) strategy leverages these clean, physically consistent state estimations to solve a non-convex optimization problem via Sequential Quadratic Programming (SQP), computing optimal pump voltages. Extensive numerical simulations demonstrate the superior resilience of this decoupled framework against parametric drifts and continuous noise sequences, yielding a $+27.36\%$ reduction in global Root Mean Square Error (RMSE) compared to standard NMPC, accelerating the closed-loop settling time to $15.2 \mathrm{s}$, and restricting transient overshoot to just $0.18\%$. Full article

Nervonic acid (NA, C24:1 Δ15) is a vital extra-long-chain monounsaturated fatty acid essential for neural development, myelin sheath formation, and neurological health. As the most abundant natural source of NA, Malania oleifera Chun & S.K.Lee has become a key model for studying NA biosynthesis and regulation. This review systematically summarizes the metabolic pathways of nervonic acid biosynthesis in M. oleifera, including plastidial de novo fatty acid synthesis, endoplasmic reticulum (ER)-based very-long-chain fatty acid elongation, and Δ15 desaturation. We focus on the catalytic mechanisms and rate-limiting roles of the elongase complex (KCS, KCR, HCD, ECR) and Δ15 desaturase. Additionally, we integrate recent multi-omics data to analyze key enzyme KCS gene families, their phylogenetic relationships, and syntenic distribution patterns. Furthermore, transcriptional regulatory networks (MYB, bZIP, WRI1, ABI3, FUS3) and epigenetic regulation underlying NA accumulation are also discussed. Finally, we highlight advances, challenges, and prospects in metabolic engineering and synthetic biology for sustainable NA production. This review provides a theoretical basis for the conservation, molecular breeding, and biotechnological utilization of M. oleifera. Full article

Wavelength stability of distributed feedback (DFB) semiconductor lasers in dense wavelength division multiplexing (DWDM) systems hinges on sub-millikelvin temperature regulation, a task complicated by the nonlinear, multi-node dynamics of the thermoelectric cooler (TEC) and the purely reactive nature of conventional proportional–integral–derivative (PID) control. We present a physics-informed neural network (PINN) built around a residual correction architecture for hybrid feedforward–feedback TEC temperature control. Rather than penalizing physics-residual violations in the loss function, the architecture wires a simplified one-node thermal model directly into the network graph as a frozen baseline. A trainable branch then learns only the residual mismatch. Temporal lag features are appended to the input so that the network can reconstruct unmeasured internal thermal states from the cold-side temperature history, which proves essential for overcoming the partial-observability bottleneck inherent in multi-node TEC packages. Ablation experiments on a high-fidelity three-node TEC simulator show that all model variants (PINN, physics-feature-augmented NN, and pure NN) exceed R² = 0.993 when trained on the full dataset, yet the PINN’s advantage becomes pronounced under data scarcity. At a 3% training budget, it reaches R² = 0.966 versus 0.930 for the pure NN, implying an approximately 5.4× reduction in the data needed to reach a given accuracy target. In closed-loop validation, the PINN+PID hybrid settles 60% faster than standalone PID. Tracking RMSE drops by 69%, and peak disturbance deviation falls by 74%, across step, multi-setpoint, and current-perturbation scenarios. All results reported here are obtained in simulations. Experimental validation on physical DFB-TEC hardware is left to future work. Full article

Accumulation of aggregated amyloid beta (Aβ) species is a defining pathological hallmark of Alzheimer’s disease and is associated with extensive neuronal structural abnormalities. Mild cognitive impairment (MCI), a transitional stage between normal aging and the onset of dementia, is thought to represent an early phase of this pathological continuum. Studies at the cellular level suggest that the conditions impair the maintenance of established neuronal processes/networks and restrict their capacity for elongation or re-elongation. They may also attenuate the activation and process extension of quiescent neural progenitor or stem-like cells. These early cellular changes precede overt neurodegeneration in neural tissue and are likely to contribute to cognitive decline. They highlight the importance of in vitro models for identifying molecular targets involved in recovery from disease. In this study, we investigated the effects of aggregated Aβ (25–35) on neuronal process elongation and associated intracellular events in the N1E-115 cell line, a widely used model of neuronal differentiation. Addition of aggregated Aβ to cultured N1E-115 cells attenuated process elongation in a concentration-dependent manner. This morphological impairment was accompanied by decreased expression of neuronal differentiation markers. In contrast, at the half-maximal inhibitory concentration for process elongation, long-term cultured cells did not exhibit apparent process retraction or degenerative morphology. This mild but progressive impairment, without extensive cell death, is consistent with the cellular features of early-stage conditions rather than advanced Alzheimer’s pathologies. Similar results were observed in primary cortical neurons. Aβ also decreased the level of GTP-bound Ras and phosphorylation of the downstream mitogen-activated protein kinase/extracellular signal-regulated kinase (MAPK/ERK). Furthermore, treatment with hesperetin, a bioactive flavonoid compound, recovered the Aβ-induced inhibition of neuronal process elongation. Hesperetin also restored Ras and MAPK/ERK states, suggesting that its effects are associated, at least in part, with modulation of signaling through Ras and MAPK/ERK. Our findings suggest that hesperetin may serve as a useful molecular probe for modulating early cellular responses associated with Alzheimer’s disease-related pathology. This in vitro model might serve as a useful platform for investigating the molecular target candidates involved in recovery from nervous system disorders. Full article