Search Results (1,195)

Unmanned aerial vehicles open new possibilities for developing technologies that support more sustainable and efficient agriculture. This paper presents a non-invasive method for repelling rodents from crop fields using ultrasound. The proposed system is implemented as a spherical-cap ultrasound loudspeaker array consisting of eight transducers, mounted on a drone that overflies the field while emitting sound in the 20–70 kHz range. The hardware design includes both the loudspeaker array and a custom printed circuit board hosting power amplifiers and a signal generator tailored to drive multiple ultrasonic transducers. In parallel, a genetic algorithm is used to compute flight paths that maximize coverage and increase the probability of driving rodents away from the protected area. As part of the validation phase, artificial intelligence models for rodent detection using a thermal camera are developed to provide quantitative feedback on system performance. The complete prototype is evaluated through a series of experiments conducted both in controlled laboratory conditions and in the field. Field trials highlight which parts of the concept are already effective and identify open challenges that need to be addressed in future work to move from a research prototype toward a deployable product. Full article

Corticosteroid receptor signaling in primary afferent neurons of the dorsal root ganglion (DRG) has emerged as a potential target to modulate nociception via genomic and nongenomic mechanisms shown in animal pain models. However, the expression landscape of glucocorticoid receptors (GRs) relative to mineralocorticoid receptors (MRs) in human DRG, their association with pain-related markers, and their functional relevance remain incompletely defined. We analyzed human and rat DRG by mRNA profiling and immunofluorescence confocal microscopy to assess GR/MR expression and complemented these studies with a clinical evaluation of neuraxial corticosteroid delivery. Here, GR transcripts in human DRG were the most abundant among corticosteroid receptor-related genes examined (including MR) and were observed alongside transcripts of pain-signaling molecules. Human DRG immunofluorescence analysis revealed substantial colocalization of GR with calcitonin gene-related peptide (CGRP), a marker of nociceptive unmyelinated C-fibers and thinly myelinated Aδ-fibers, as well as with gial fibrillary acidic protein (GFAP), a marker of satellite glial cells (SGCs), but minimal expression in myelinated neurofilament 200 (RT-200) immunoreactive (IR) human DRG neurons. In addition, GR immunoreactivity was primarily distributed to medium-diameter neurons (40–65 µm). Functionally, preclinical experiments showed that GR activation and MR blockade attenuate inflammatory pain via rapid, nongenomic neuronal mechanisms that counter an intrinsic mineralocorticoid receptor-mediated pronociceptive drive. Consistently, clinical analgesia over at least 3 months after transforaminal plus caudal epidural delivery of GR agonists in chronic radicular pain supports a functional role for neuronal GR signaling within spinal cord and DRG circuits. Together, these molecular, functional, and clinical findings identify GR as a key modulator of sensory neuron excitability and pain, highlight MR as a pronociceptive counterpart, and suggest that selectively enhancing GR signaling or inhibiting MR signaling may offer a potential strategy for improving corticosteroid-based analgesic therapies. Full article

As the brain’s resident macrophages, microglia on the one side are increasingly recognized as essential players in discrete developmental stages, where immune, metabolic, and activity-derived signals are coordinately integrated to guide brain development. On the other side, the precise temporal and molecular coordination of microglial maturation is imperative for the structural and functional integrity of the developing central nervous system (CNS). In this review, we synthesize recent data that reposition microglia from a uniform population of immune sentinels to temporally programmed and regionally specialized regulators of circuit maturation. This involves dissecting the embryonic origins and migratory pathways of microglial progenitors in mouse and human systems and illustrating how gradual transcriptional and morphological maturation aligns the biology of microglia with progressive phases of neurogenesis, synaptic fine-tuning, myelination, and vascular stabilization. In addition, we discuss how individual gene mutations, inflammatory insults during perinatal life, and environmental disturbances intersect with these temporal programs to alter microglial phenotypes and compromise circuit formation. With a special emphasis on epilepsy and autism spectrum disorder, often sharing the common etiology, we illustrate how early malfunction of microglia may drive neural network dysfunction. Full article

Acute respiratory distress syndrome (ARDS) is a major cause of morbidity and mortality despite decades of progress in ventilatory support. Mechanical ventilation, while essential for oxygenation, may exacerbate lung injury through excessive mechanical power delivery, even when using lung-protective strategies. Extracorporeal carbon dioxide removal (ECCO₂R) was conceived to enable “ultra-protective” ventilation, allowing for further reductions in tidal volume and respiratory rate by selectively removing CO₂ at low extracorporeal blood flows, typically between 0.3 and 1.0 L/min. This physiological decoupling of ventilation and gas exchange aims to mitigate ventilator-induced lung injury (VILI) while maintaining adequate acid–base homeostasis. Although early physiological studies demonstrated feasibility, large, randomized trials have failed to show a survival benefit and have raised concerns about bleeding and technical complications. Recent evidence suggests that these neutral outcomes may stem from the biological and physiological heterogeneity of ARDS rather than from inefficacy of the intervention itself. Patients with high driving pressures, poor compliance, or hyperinflammatory phenotypes may derive greater benefit from ECCO₂R-mediated mechanical unloading. Ongoing technological improvements, including circuit miniaturization, enhanced biocompatibility, and integration with renal replacement therapy, have improved safety and feasibility, yet the procedure remains complex and resource-intensive. Future research should focus on phenotype-enriched trials and the integration of ECCO₂R into precision ventilation frameworks. Ultimately, ECCO₂R should be regarded not as a universal therapy for ARDS but as a targeted physiological tool for selected patients in experienced centers. Full article

The whole-angle hemispherical resonator gyroscope (WA-HRG) is critical to high-precision attitude control and navigational positioning, boasting significant deployment potential in both highly dynamic inertial navigation systems and industrial instrumentation. This paper presents a mechanistic analysis of quantization error inherent to the HRG’s hardware detection and driving circuits, focusing specifically on its impact on parameter calculation and driving control in whole-angle mode. Furthermore, a simulation platform was constructed to verify and elucidate the correlations between the effects of quantization error and key resonator parameters, such as the major axis amplitude and the standing wave azimuth. Compared to existing HRG error studies which frame quantization error as isolated circuit noise, this work uniquely uncovers the azimuth-modulated periodic behavior of quantization error within the WA-HRG. It also formalizes a quantitative relationship between quantization error and the resonator’s key parameters, laying a critical theoretical foundation for suppressing quantization error and enhancing accuracy in high-performance WA-HRGs. Full article

With the increasing integration of renewable energy into power grids, voltage source converter-based high-voltage direct current (VSC-HVDC) stations often adopt hybrid grid-following (GFL) and grid-forming (GFM) control strategies to improve adaptability to varying grid strengths. In many existing schemes, the hybrid coefficient changes abruptly, which may produce large transient current overshoots and compromise the safe and stable operation of converters. An adaptive hybrid GFL-GFM control framework equipped with a hybrid coefficient transition regulation is proposed. Small-signal state–space models are established and eigenvalue analysis confirms stability over the considered short-circuit ratio (SCR) range. The regulating method is activated only during coefficient transitions and is inactive in steady-state, thereby preserving the operating-point eigenvalue properties. Dynamic equations of the converter current change rate are derived to reveal the key role of the hybrid-coefficient change rate in driving transient current overshoots, based on which a real-time hybrid coefficient regulating method is developed to shape coefficient transitions. Simulations on a 500 kV/2100 MW VSC-HVDC project demonstrate reduced transient current overshoot and power oscillations during SCR variations, with robustness under moderate parameter deviations as well as representative SCR assessment error and update delay. Full article

Mitochondrial Ca²⁺ signaling is increasingly recognized as a key integrator of synaptic activity, metabolism, and redox balance within the tripartite synapse. At excitatory synapses, Ca²⁺ influx through ionotropic glutamate receptors and voltage-gated channels is sensed and transduced by strategically positioned mitochondria, whose Ca²⁺ uptake and release tune tricarboxylic acid cycle activity, adenosine triphosphate synthesis, and reactive oxygen species (ROS) generation. Through these Ca²⁺-dependent processes, mitochondria are proposed to help set the threshold at which glutamatergic activity supports synaptic plasticity and homeostasis or, instead, drives hyperexcitability and excitotoxic stress. Here, we synthesize how mitochondrial Ca²⁺ dynamics in presynaptic terminals, postsynaptic spines, and perisynaptic astrocytic processes regulate glutamate uptake, recycling, and release, and how subtle impairments in these pathways may prime synapses for failure well before overt energetic collapse. We further examine the reciprocal interplay between Ca²⁺-dependent metabolic adaptations and glutamate homeostasis, the crosstalk between mitochondrial Ca²⁺ and ROS signals, and the distinct vulnerabilities of neuronal and astrocytic mitochondria. Finally, we discuss how disruption of this Ca²⁺-centered mitochondria–glutamatergic axis contributes to synaptic dysfunction and circuit vulnerability in neurodegenerative diseases, with a particular focus on Alzheimer’s disease. Full article

Metabolic dysfunction-associated steatotic liver disease (MASLD) represents a progressive spectrum of metabolic liver injury in which immune activation, metabolic stress, and stromal remodeling evolve in a tightly interdependent manner. Although early disease stages are dominated by metabolic overload, accumulating evidence indicates that immunometabolic rewiring and fibro-inflammatory amplification critically shape the transition toward metabolic dysfunction-associated steatohepatitis (MASH) and advanced fibrosis. This review synthesizes emerging insights into how hepatocyte stress responses, innate and adaptive immune circuits, and extracellular matrix-producing stromal populations interact to form a dynamic, feed-forward network driving disease progression. Particular emphasis is placed on the deterministic role of immune–fibrotic coupling in shaping clinical phenotypes, disease trajectory, and therapeutic responsiveness. Rather than focusing on individual molecular layers, we highlight how integrated clinical, imaging, and biomarker-informed frameworks can capture immune–fibrotic signatures relevant to risk stratification and precision intervention. Building on this systems-level perspective, we outline next-generation therapeutic strategies targeting immunometabolic circuits, cross-organ communication, and multi-system dysfunction. Finally, we discuss how future precision medicine—supported by integrative biomarker profiling and dynamic physiological assessment—may reshape MASLD management and improve long-term hepatic and cardiometabolic outcomes. Full article

An equivalent circuit model (ECM) is a highly practical tool for simulating Li-ion battery behavior. There are many relevant studies which compare different ECM variants or suggest algorithms to extract model parameters from the experimental data. However, little attention has been given to the battery tests used for identification of the ECM parameters. Therefore, here the influence of experimental test pulse characteristics on the parameterized ECM accuracy was systematically studied. The test pulse duration was varied in a wide range from 9 s to about 2.5 min. The portion of the relaxation phase data used by the parameter optimization algorithm was also varied in an even wider range. Total 168 ECM parameter sets were obtained. Each parameter set was validated using nine diverse current profiles representing different battery operation conditions, including one based on Urban Dynamometer Driving Schedule (UDDS). The validation results prove that the impact of the test pulse choice on the parameterized ECM accuracy is great to the point that it can overshadow the use of a higher-order Thevenin model. By choosing the optimal parameter set, the simulated voltage root mean square error (RMSE) was reduced to as low as 3.0 mV and 1.2 mV for first- and second-order ECM, respectively, while the second-order model based on arbitrary chosen test pulse on average yields RMSE value above 5 mV. Full article

Berberine (BBR), a protoberberine alkaloid with a long history of medicinal use, has consistently demonstrated benefits in glucose–lipid metabolism and inflammatory balance across both preclinical and human studies. These diverse effects are not mediated by a single molecular target but by BBR’s capacity to restore network coordination among metabolic, immune, and microbial systems. At the core of this regulation is an AMP-activated Protein Kinase (AMPK)-centered mechanistic hub, integrating signals from insulin and nutrient sensing, Sirtuin 1/3 (SIRT1/3)-mediated mitochondrial adaptation, and inflammatory pathways such as nuclear Factor Kappa-light-chain-enhancer of Activated B cells (NF-κB) and NOD-, LRR- and Pyrin Domain-containing Protein 3 (NLRP3). This hub is dynamically regulated by system-level inputs from the gut, mitochondria, and epigenome, which in turn strengthen intestinal barrier function, reshape microbial and bile-acid metabolites, improve redox balance, and potentially reverse the epigenetic imprint of metabolic stress. These interactions propagate through multi-organ axes, linking the gut, liver, adipose, and vascular systems, thus aligning local metabolic adjustments with systemic homeostasis. Within this framework, BBR functions as a negentropic modulator, reducing metabolic entropy by fostering a coordinated balance among these interconnected systems, thereby restoring physiological order. Combination strategies, such as pairing BBR with metformin, Sodium-Glucose Cotransporter 2 (SGLT2) inhibitors, and agents targeting the microbiome or inflammation, have shown enhanced efficacy and substantial translational potential. Berberine ursodeoxycholate (HTD1801), an ionic-salt derivative of BBR currently in Phase III trials and directly compared with dapagliflozin, exemplifies the therapeutic promise of such approaches. Within the hub–axis paradigm, BBR emerges as a systems-level modulator that recouples energy, immune, and microbial circuits to drive multi-organ remodeling. Full article

Diarrhea-predominant irritable bowel syndrome (IBS-D) is a common chronic disorder of gut–brain interaction characterized by intestinal dysmotility. Central sensitization has a proposed role in intestinal dysmotility, yet the precise neural circuits and mechanisms remain poorly understood. In this study, we established a neonatal maternal deprivation plus restraint stress (NMD + RS) mouse model that recapitulates key diarrhea-like phenotypes. Neural activation mapping revealed a significant upregulation of c-Fos expression within the medial prefrontal cortex (mPFC) and locus coeruleus (LC), which was predominantly localized to glutamatergic neurons. Chemogenetic inhibition of mPFC glutamatergic neurons suppressed intestinal dysmotility, whereas the activation of mPFC glutamatergic neurons evoked intestinal dysmotility in control mice. Furthermore, viral tracing revealed direct projections from mPFC neurons to glutamatergic neurons in the LC. Subsequent chemogenetic manipulation of these LC glutamatergic neurons receiving projection from mPFC neurons similarly regulated intestinal motility, demonstrating a functional downstream node. Critically, selective activation of the mPFC-LC glutamatergic circuit significantly induced intestinal dysmotility in CON mice. In contrast, inhibition of the mPFC-LC glutamatergic circuit significantly ameliorated intestinal dysmotility in NMD + RS mice. Our findings proved that the enhanced activity of the mPFC-LC circuit led to intestinal dysmotility in NMD + RS mice, hopefully providing new mechanistic perspectives and a potential neuromodulatory target for clinical management of IBS. Full article

B-cells and plasmablasts have emerged as central organizers of autoimmune pathogenesis, extending far beyond their classical role as antibody-producing cells to orchestrate immune circuits, tissue microenvironments, and therapeutic trajectories. Advances in single-cell technologies, high-dimensional cytometry, and B-cell receptor sequencing have uncovered a dynamic continuum of B-cell differentiation programs that drive clinical heterogeneity across systemic autoimmune diseases. Plasmablasts, in particular, have gained recognition as highly responsive sensors of immune activation: they expand during flares, encode interferon-driven and extrafollicular responses, and correlate with disease severity. Autoantibody profiles, long viewed as static diagnostic signatures, are now understood as durable molecular footprints of distinct B-cell pathways. In this review, we propose an endotype-based framework integrating B-cell circuits with clinical phenotypes, illustrate therapeutic decision-making through mechanistic case vignettes, and outline future strategies combining immunomonitoring, multi-omics, and precision therapeutics. We further address translational challenges and discuss complementary approaches, including T-cell modulation, FcRn inhibition, and antigen-specific tolerization. Full article

Anxiety, depression, and social impairment exhibit high clinical comorbidity, yet their underlying shared neural circuitry remains poorly defined. Using a mouse model of chronic social isolation combined with circuit tracing and chemogenetic tools, we identified a key role for the basolateral amygdala (BLA) in relaying prefrontal cortex (PFC) signals to the bed nucleus of the stria terminalis (BNST) to drive behavioral changes. Further circuit dissection identified two distinct BNST microcircuits segregated by their input sources: one receives indirect PFC input relayed through the BLA (PFC → BLA → BNST), while the other is innervated by direct PFC projections (PFC → BNST). Chemogenetic inhibition of BLA neurons in the indirect pathway ameliorated anxiety-like behavior, depression-like behavior, and social deficits. Within the BNST, however, inhibition of neurons in PFC → BLA → BNST pathway selectively alleviated affective phenotypes without altering social behavior. In contrast, inhibition of neurons in PFC → BNST pathway specifically restored social recognition while leaving emotional behaviors intact. Thus, the BLA integrates PFC-derived signals to broadly modulate behavior, while downstream BNST microcircuits dissociate these influences. The indirect, BLA-relayed pathway within the BNST specifically drives affective symptoms, whereas the direct PFC → BNST pathway selectively governs social recognition. This dissociable circuit model offers a new framework for understanding clinical comorbidity and may inform targeted interventions for distinct symptom dimensions. Full article

The study presents the design and simulation of a pneumatic drive unit intended for energy-efficient vehicle propulsion. The research focuses on developing a MATLAB 23.2/Simulink-based model that accurately represents the dynamic behavior of double-acting pneumatic actuators, including the interaction between pressure, force, torque transmission, and wheel rotation. The model integrates pneumatic circuit parameters with mechanical drivetrain components, allowing a comprehensive evaluation of system performance and compressed-air consumption. The simulation architecture is fully modular and parameterized, enabling rapid reconfiguration for different drive layouts and operating conditions. Results demonstrate that the proposed model provides a realistic representation of the physical processes in pneumatic systems, offering valuable insights for optimizing actuator control, gear ratios, and energy management strategies. Identified challenges include computational complexity and sensitivity to manually defined parameters, which highlight opportunities for further refinement. The developed model serves as a practical design and analysis tool for future engineers engaged in the development of sustainable pneumatic propulsion systems and educational simulations. Future work will address adaptive control algorithms, improved visualization using multibody dynamics, and optimization of air consumption under varying load conditions. Full article

Electric motor drives with a wide variety of applications are usually derived with inverters, where the inverter switches are always prone to different types of faults. Short circuit faults can rapidly shut down systems, and open-circuit ones can lead to secondary damage if they are not detected and tolerated in time. Due to this fact, in this paper, a novel data-driven fault detection and diagnosis (FDD) method has been proposed to detect and locate all types of inverter switch faults. Three deep learning algorithms, including fully connected neural networks (FCNs), convolutional neural networks (CNNs), and bidirectional long short-term memory (BiLSTM), have been implemented and compared. The BiLSTM network with 98.45% accuracy outperforms the others and can detect all types of faults in less than half a fundamental period under different and variable speeds with the existence of noise. The results show that the proposed method is highly effective and is a great candidate for real-time applications. Full article