Search Results (481)

Marine ecosystems are characterized by an immense biodiversity and represent a rich source of biological compounds with promising potential for the development of novel therapeutic drugs. This review describes the most promising marine-derived neuroprotective compounds with strong potential for the treatment of neurodegenerative disorders. We focus specifically on the retina and brain—two key components of the central nervous system—as primary targets for therapeutic interventions against neurodegeneration. Alzheimer’s disease and retinal degeneration diseases are used here as a representative model of neurodegenerative disorders, where complex molecular processes such as protein misfolding, oxidative stress, and neuroinflammation drive disease progression. We also examine gene therapy approaches inspired by marine biology, with particular attention to their application in retinal diseases, aimed at preserving or restoring photoreceptor function and vision. Full article

Synthetic nanopores were recently demonstrated with memristive and nonlinear voltage-current behaviors, akin to ion channels in a cell membrane. Such ionic devices are considered a promising candidate for the development of brain-inspired neuromorphic computing techniques. In this work, we show the composite behavior of nanopore-array large memristors, formed with different membrane materials, pore sizes, electrolytes, and device arrangements. Anodic aluminum oxide (AAO) membranes with 5 nm and 20 nm diameter pores and track-etched polycarbonate (PCTE) membranes with 10 nm diameter pores are tested and shown to demonstrate memristive and nonlinear behaviors with approximately 10⁷–10¹⁰ pores in parallel when electrolyte concentration across the membranes is asymmetric. Ion diffusion through the large number of channels induces time-dependent electrolyte asymmetry that drives the system through different memristive states. The behaviors of series composite memristors with different configurations are also presented. In addition to helping understand fluidic devices and circuits for neuromorphic computing, the results also shed light on the development of field-assisted ion-selection-membrane filtration techniques as well as the investigations of large neurons and giant synapses. Further work is needed to de-embed parasitic components of the measurement setup to obtain intrinsic large memristor properties. Full article

This paper studies deep reinforcement learning (DRL)-based joint resource allocation and three-dimensional (3D) trajectory optimization for unmanned aerial vehicle (UAV)–ground access point (GAP) cooperative non-orthogonal multiple access (NOMA) communication in Industrial Internet of Things (IIoT) systems. Cooperative and non-cooperative users adopt different signal transmission strategies to meet diverse, task-oriented, quality-of-service requirements. Specifically, the DRL framework based on the Soft Actor–Critic algorithm is proposed to jointly optimize user scheduling, power allocation, and UAV trajectory in continuous action spaces. Closed-form power allocation and maximum weight bipartite matching are integrated to enable efficient user pairing and resource management. Simulation results show that the proposed scheme significantly enhances system performance in terms of throughput, spectral efficiency, and interference management, while enabling robustness against channel uncertainties in dynamic IIoT environments. The findings indicate that combining model-free reinforcement learning with conventional optimization provides a viable solution for adaptive resource management in dynamic UAV-GAP cooperative communication scenarios. Full article

Background/Objectives: Digital food-related videos significantly influence cravings, appetite, and weight outcomes; however, the dynamic neural mechanisms underlying appetite fluctuations during naturalistic viewing remain unclear. This study aimed to identify neural activity patterns associated with moment-to-moment appetite changes during naturalistic food-cue video viewing and to examine their relationships with cravings and weight-related outcomes. Methods: Functional magnetic resonance imaging (fMRI) data were collected from 58 healthy female participants as they viewed naturalistic food-cue videos. Participants concurrently provided continuous ratings of their appetite levels throughout video viewing. Hidden Markov Modeling (HMM), combined with machine learning regression techniques, was employed to identify distinct neural states reflecting dynamic appetite fluctuations. Findings were independently validated using a shorter-duration food-cue video viewing task. Results: Distinct neural states characterized by heightened activation in default mode and frontoparietal networks consistently corresponded with increases in appetite ratings. Importantly, the higher expression of these appetite-related neural states correlated positively with participants’ Body Mass Index (BMI) and post-viewing food cravings. Furthermore, these neural states mediated the relationship between BMI and food craving levels. Longitudinal analyses revealed that the expression levels of appetite-related neural states predicted participants’ BMI trajectories over a subsequent six-month period. Participants experiencing BMI increases exhibited a significantly greater expression of these neural states compared to those whose BMI remained stable. Conclusions: Our findings elucidate how digital food cues dynamically modulate neural processes associated with appetite. These neural markers may serve as early indicators of obesity risk, offering valuable insights into the psychological and neurobiological mechanisms linking everyday media exposure to food cravings and weight management. Full article

Recent deep learning models, including GPT-4, have achieved remarkable performance using the back-propagation (BP) algorithm. However, the mechanism of BP is fundamentally different from how the human brain processes learning. To address this discrepancy, the Forward-Forward (FF) algorithm was introduced. Although FF enables deep learning without backward passes, it suffers from instability, dependence on artificial input construction, and limited generalizability. To overcome these challenges, we propose Local Back-Propagation (LBP), a method that integrates layer-wise unsupervised learning with standard inputs and conventional loss functions. Specifically, LBP demonstrates high training stability and competitive accuracy, significantly outperforming FF-based training methods. Moreover, LBP reduces memory usage by up to 48% compared to convolutional neural networks trained with back-propagation, making it particularly suitable for resource-constrained environments such as federated learning. These results suggest that LBP is a promising biologically inspired training method for decentralized deep learning. Full article

Memristors with resistive switching capabilities are vital for information storage and brain-inspired computing, making them a key focus in current research. This study demonstrates non-volatile analog resistive switching behavior in Al/TiO_x/TiN/Si(n⁺⁺)/Al memristive devices. Analog resistive switching offers gradual, controllable conductance changes, which are essential for mimicking brain-like synaptic behavior, unlike digital/abrupt switching. The amorphous titanium oxide (TiO_x) active layer was deposited using the pulsed-DC reactive magnetron sputtering technique. The impact of increasing the oxide thickness on the electrical performance of the memristors was investigated. Electrical characterizations revealed stable, forming-free analog resistive switching, achieving endurance beyond 300 DC cycles. The charge conduction mechanisms underlying the current–voltage (I–V) characteristics are analyzed in detail, revealing the presence of ohmic behavior, Schottky emission, and space-charge-limited conduction (SCLC). Experimental results indicate that increasing the TiO_x film thickness from 31 to 44 nm leads to a notable change in the current conduction mechanism. The results confirm that the memristors have good stability (>1500 s) and are capable of exhibiting excellent long-term potentiation (LTP) and long-term depression (LTD) properties. The analog switching driven by oxygen vacancy-induced barrier modulation in the TiO_x/TiN interface is explained in detail, supported by a proposed model. The remarkable switching characteristics exhibited by the TiO_x-based memristive devices make them highly suitable for artificial synapse applications in neuromorphic computing systems. Full article

Threshold-switching memristors (TSMs) are emerging as key enablers for hardware spiking neural networks, offering intrinsic spiking dynamics, sub-pJ energy consumption, and nanoscale footprints ideal for brain-inspired computing at the edge. This review provides a comprehensive examination of how TSMs emulate diverse spiking behaviors—including oscillatory, leaky integrate-and-fire (LIF), Hodgkin–Huxley (H-H), and stochastic dynamics—and how these features enable compact, energy-efficient neuromorphic systems. We analyze the physical switching mechanisms of redox and Mott-type TSMs, discuss their voltage-dependent dynamics, and assess their suitability for spike generation. We review memristor-based neuron circuits regarding architectures, materials, and key performance metrics. At the system level, we summarize bio-inspired neuromorphic platforms integrating TSM neurons with visual, tactile, thermal, and olfactory sensors, achieving real-time edge computation with high accuracy and low power. Finally, we critically examine key challenges—such as stochastic switching origins, device variability, and endurance limits—and propose future directions toward reconfigurable, robust, and scalable memristive neuromorphic architectures. Full article

This narrative review explores the intricate relationship between the plasminogen activator system (PAS), comprising urokinase-type plasminogen activator (uPA) and tissue-type plasminogen activator (tPA), and a range of neuropsychiatric disorders, including depression and anxiety. By synthesizing existing preclinical and clinical evidence, we clarify the roles of uPA and tPA in the pathogenesis and potential treatments of these conditions. This narrative review emphasizes their involvement in modulating neuronal plasticity, synaptic remodeling, and neurotransmitter systems, which are pivotal in maintaining brain function and behavior. Additionally, this review highlights key mechanisms by which these activators influence the neurobiological processes underlying mood and cognitive dysfunction. Critical analysis identifies areas of consensus, such as the role of plasminogen activators in neuroinflammation and stress responses, while also addressing gaps and controversies in the literature. The findings underscore the therapeutic potential of targeting the uPA/tPA system for innovative interventions. By offering a nuanced understanding of their contributions to mood disorders, this review aims to inspire future research toward developing novel, mechanism-based treatment strategies that harness the PAS’ capacity to restore neural homeostasis and improve patient outcomes. Full article

Driver models, which provide mathematical or computational representations of human driving behavior, are crucial for intelligent driving systems by enabling stable and repeatable operations. However, existing models typically employ fixed weighting parameters to simulate preview delay, failing to reflect individual driver differences and real-time dynamic behaviors. This paper proposes a Brain-Memory Driver Model (BMDM) that emulates human brain memory mechanisms to dynamically adjust preview weights by integrating global path curvature, real-time vehicle speed, and steering torque. This emulation involves a three-stage process: capturing data in an Instantaneous Memory (IM) region, filtering data via a forgetting mechanism in a Short-Time Memory (STM) region to reduce scale, and retaining data based on correlation strength in a Long-Time Memory (LTM) region for persistent mining. By deploying a trained behavioral memory database, the model dynamically calibrates preview weights based on the driver’s state and real-time curvature variations under different road conditions. This enables the model to more accurately simulate authentic preview characteristics and improves its adaptability. Simulation results from an automated steering case study demonstrate that the improved model exhibits control performance closer to the real driving process, reproducing authentic steering behavior within the human–vehicle–road closed-loop system from an intelligent biomimetic perspective. Full article

We developed the Global–Local Integrated Topology inverse design algorithm (denoted as the GLINT algorithm), which employs a trajectory-based optimization strategy with waveguide–substrate material-flipping structural modifications, enabling the direct optimization of discrete waveguide–substrate binary structures. Compared to the conventional Direct Binary Search (DBS), the GLINT algorithm not only significantly enhances computational efficiency through its global search–local refinement framework but also achieves a superior 20 nm × 20 nm optimization resolution while maintaining its optimization speed—substantially advancing the design capability. Utilizing this algorithm, we designed and experimentally demonstrated a 3.5 µm × 3.5 µm dual-port wavelength division multiplexer (WDM), achieving a minimum crosstalk of −11.3 dB and a 2 µm × 2 µm 90-degree bending waveguide exhibiting a 0.31–0.52 dB insertion loss over the 1528–1600 nm wavelength range, both fabricated on silicon-on-insulator (SOI) wafers. Additionally, a 4.5 µm × 4.5 µm three-port WDM structure was also designed and simulated, demonstrating crosstalk as low as −36.5 dB. Full article

Background/Objectives: Knowledge Graphs (KGs) are often incomplete, which can significantly impact the performance of downstream applications. Manual completion of KGs is time-consuming and costly, emphasizing the importance of developing automated methods for KGC. Link prediction serves as a fundamental task in this domain. The semantic correlation among entity features plays a crucial role in determining the effectiveness of link-prediction models. Notably, the human brain can often infer information using a limited set of salient features. Methods: Inspired by this cognitive principle, this paper proposes a lightweight Bi-level routing attention mechanism specifically designed for link-prediction tasks. This proposed module explores a theoretically grounded and lightweight structural design aimed at enhancing the semantic recognition capability of language models without altering their core parameters. The proposed module enhances the model’s ability to attend to feature regions with high semantic relevance. With only a marginal increase of approximately one million parameters, the mechanism effectively captures the most semantically informative features. Result: It replaces the original feature-extraction module within the KGML framework and is evaluated on the publicly available WN18RR and FB15K-237 dataset. Conclusions: Experimental results demonstrate consistent improvements in standard evaluation metrics, including Mean Rank (MR), Mean Reciprocal Rank (MRR), and Hits@10, thereby confirming the effectiveness of the proposed approach. Full article

This work presents a high-performance novel photodetector based on two-dimensional boron nitride (BN) nanosheets functionalized with gold nanoparticles (Au NPs), offering ultra-broadband photoresponse from 0.25 to 5.9 μm. Operating in both photovoltaic and photoconductive modes, the device features rapid response times (<0.5 ms), high responsivity (up to 1015 mA/W at 250 nm and 2.5 V bias), and thermal stability up to 100 °C. The synthesis process involved CO₂ laser exfoliation of hexagonal boron nitride, followed by gold NP deposition via RF sputtering and thermal annealing. Structural and compositional analyses confirmed the formation of a three-dimensional network of atomically thin BN nanosheets decorated with uniformly distributed gold nanoparticles. This architecture facilitates plasmon-enhanced absorption and efficient charge separation via heterojunction interfaces, significantly boosting photocurrent generation across the deep ultraviolet (DUV), visible, near-infrared (NIR), and mid-infrared (MIR) spectral regions. First-principles calculations support the observed broadband response, confirming bandgap narrowing induced by defects in h-BN and functionalization by gold nanoparticles. The device’s self-driven operation, wide spectral response, and durability under elevated temperatures underscore its strong potential for next-generation broadband, self-powered, and wearable sensing and optoelectronic applications. Full article

Glioblastoma (GBM), a highly malignant brain tumor, arises within a complex microenvironment that plays a critical role in facilitating tumor progression, ensuring survival, and enabling immune evasion, ultimately contributing to therapeutic resistance. Cancer-associated fibrosis is increasingly recognized as a key factor in the tumor pathophysiology, particularly in extracranial cancers, and reported therapeutic strategies in several cancers consist of the current use of the standard-of-care treatment combined with anti-fibrotic drugs. However, it remains unclear how the fibrotic changes associated with the GBM microenvironment contribute to the transformation of GBM from a chemosensitive state to a chemoresistant one. Here, we developed an in vitro model that mimics a fibrosis-like mechanism using the U-87MG GBM cell line. To achieve this, we identified the optimal experimental conditions (i.e., U-87MG cultured in serum-deprivation medium in the presence of recombinant TGF-B1 at 5 ng/mL for 72 h) that effectively induced fibrosis, as suggested by the counter-regulated expression of E- and N-cadherin and sustained levels of α-SMA and collagen I. As expected, U-87MG fibrotic cells were demonstrated to be more resistant to TMZ (predicted EC₅₀ = 35 µM) as compared to the non-fibrotic counterpart (EC₅₀ not achieved here; predicted EC₅₀ = 351 µM). Accordingly, the anti-fibrotic uPAcyclin—a new derivative cyclic compound inspired as a A6 decapeptide drug—showed a significant cytotoxic effect, sensitizing resistant U-87MG fibrotic cells to TMZ. This highlights that targeting fibrosis may help to overcome TMZ resistance in GBM. Full article

This paper introduces an innovative mobile-assisted language-learning (MALL) system that harnesses deep learning technology to analyze pronunciation patterns and deliver real-time, personalized feedback. Drawing inspiration from how the human brain processes speech through neural pathways, our system analyzes multiple speech features using spectrograms, mel-frequency cepstral coefficients (MFCCs), and formant frequencies in a manner that mirrors the auditory cortex’s interpretation of sound. The core of our approach utilizes a convolutional neural network (CNN) to classify pronunciation patterns from user-recorded speech. To enhance the assessment accuracy and provide nuanced feedback, we integrated a fuzzy inference system (FIS) that helps learners identify and correct specific pronunciation errors. The experimental results demonstrate that our multi-feature model achieved 82.41% to 90.52% accuracies in accent classification across diverse linguistic contexts. The user testing revealed statistically significant improvements in pronunciation skills, where learners showed a 5–20% enhancement in accuracy after using the system. The proposed MALL system offers a portable, accessible solution for language learners while establishing a foundation for future research in multilingual functionality and mobile platform optimization. By combining advanced speech analysis with intuitive feedback mechanisms, this system addresses a critical challenge in language acquisition and promotes more effective self-directed learning. Full article

Flight behavior in pigeons is governed by intricate neural mechanisms that regulate movement patterns and flight dynamics. Among various kinematic parameters, flight acceleration provides critical information for the brain to modulate movement intensity, speed, and direction. However, the neural representation mechanisms underlying flight acceleration remain insufficiently understood. To address this, we conducted outdoor free-flight experiments in homing pigeons, during which GPS data, flight posture, and eight-channel local field potentials (LFPs) were synchronously recorded. Our analysis revealed that gamma-band activity in the dorsal intermediate arcopallium (AId) region was more prominent during behaviorally demanding phases of flight. In parallel, local functional network analysis showed that the clustering coefficient of gamma-band activity in the AId followed a nonlinear, U-shaped relationship with flight acceleration—exhibiting the strongest and most widespread connectivity during deceleration, moderate connectivity during acceleration, and the weakest network coupling during steady flight. This pattern likely reflects the increased neural demands associated with flight phase transitions, where greater cognitive and sensorimotor integration is required. Furthermore, using LFP signals from five distinct frequency bands as input, machine learning models were developed to decode flight acceleration, further confirming the role of gamma-band dynamics in motor regulation during natural flight. This study provides the first evidence that gamma-band activity in the avian AId region encodes flight acceleration, offering new insights into the neural representation of motor states in natural flight and implications for bio-inspired flight control systems. Full article