Search Results (566)

The efficiency with which solanaceous crops are pollinated is determined by pollinator behavioral preferences. Although Bombus terrestris typically outperforms Apis mellifera in this respect, the chemo-ecological mechanisms underlying their divergent olfactory responses remain insufficiently determined. We combined behavioral assays, gas chromatography–mass spectrometry, and electroantennogram recordings to compare the responses of these bee species to the volatile organic compounds (VOCs) emitted by tomato, pepper, and eggplant flowers. Whereas B. terrestris showed a strong foraging preference for all three crop plants, A. mellifera displayed distinct avoidance. Chemical analyses identified 82, 63, and 60 VOCs in tomato, pepper, and eggplant flowers, respectively. Among the 14 VOCs commonly emitted by all three crops, linalool, nerol, (E,E)-2,4-decadienal, 2-hexenal, tridecanal, and (E,Z)-2,6-nonadienal elicited significantly different electrophysiological responses in the two bee species, and are, thus, considered key compounds mediating their behavioral differences. Moreover, in behavioral assays, A. mellifera and B. terrestris showed significantly different responses to four concentration levels of linalool, nerol, (E,E)-2,4-decadienal, and tridecanal. This study elucidates the plant–pollinator olfactory interactions that contribute to determining the different foraging behaviors of two bee species in pollinating solanaceous crops, thereby providing a theoretical basis for optimizing pollinator attraction strategies. Full article

High-density microelectrode arrays (HDMEAs) have become increasingly important tools in neuroscience and biomedical engineering because of their high spatial and temporal resolution for recording and modulating electrical activity across diverse biological systems. Initially developed for in vitro studies of cultured cells, HDMEAs are now being applied to increasingly complex models, including organoids, animal systems, and even human neural systems. These advancements enable a deeper investigation of cellular interactions, network dynamics, and disease mechanisms, as well as providing novel therapeutic and diagnostic tools for neurological disorders. This review explores the evolution of HDMEAs, emphasizing recent innovations in their design, fabrication, and functionalization. We discuss their applications across cellular models, organoid systems, animal studies, and human electrophysiology, and highlight current challenges such as biocompatibility, long-term stability, scalability, and translational deployment. Finally, we outline future directions for advancing HDMEA technologies in both research and clinical settings. Full article

Background/Objectives: Mental fatigue during driving can arise under different task conditions and typically progresses from mild to severe states. Active fatigue is usually linked to cognitively demanding driving, whereas passive fatigue is associated with prolonged monotonous driving. However, studies on multilevel mental fatigue remain scarce, and direct comparisons of prefrontal multimodal physiological responses to active and passive fatigue are still limited. The objective of this study is to characterize and compare the prefrontal multimodal physiological signatures across three fatigue levels under two simulated driving paradigms designed to induce active and passive fatigue. Methods: Eleven healthy participants completed two simulated driving tasks designed to induce active and passive fatigue. Physiological data were recorded using a self-developed prefrontal EEG-fNIRS system, and pulse-related signals were derived from the hemodynamic measurements. Based on subjective and objective indicators, fatigue was classified into non-fatigue (NonF), moderate fatigue (ModF), and severe fatigue (SevF). Results: In the active-fatigue-inducing paradigm, significant changes in prefrontal EEG and hemodynamic already emerged from NonF to ModF; for example, the EEG β/(θ + α) power ratio increased from 0.973 to 1.157 (p < 0.001) and the normalized mean deoxyhemoglobin feature increased from −0.06 to 0.09 (p < 0.001). In the passive-fatigue-inducing paradigm, EEG changes became prominent mainly from ModF to SevF, with β/(θ + α) power ratio decreasing from 0.806 to 0.761 (p < 0.05). Pulse rate variability showed increasing trends in both paradigms. Conclusions: These findings suggest that the two simulated driving paradigms were associated with distinct prefrontal electrophysiological, hemodynamic, and autonomic evolution patterns across three fatigue levels, supporting graded fatigue assessment and multimodal fatigue monitoring in driving. Full article

Grape pomace extract (GPE) from Vitis vinifera L. cv. Aglianico is rich in polyphenols with recognized cardioprotective properties, yet its direct electrophysiological effects on spontaneous cardiac activity have not been previously investigated. Here, we examined the chronotropic effects of GPE using two complementary models: HL-1 cardiomyocytes, assessed by whole-cell patch-clamp and intracellular Ca²⁺ imaging, and the Drosophila melanogaster larval heart tube, evaluated by optical recording. In HL-1 cells, chronic treatment with 25 µg/mL GPE for 48 h significantly reduced potential spontaneous action frequency and selectively prolonged the diastolic depolarization phase without altering action potential morphology, depolarization-activated currents, or cytosolic Ca²⁺ homeostasis. GPE reduced the hyperpolarization-activated funny current (I_f) density without shifting its voltage dependence. GPE-treated cells retained cAMP sensitivity, as both isoproterenol and intracellular 8-Br-cAMP significantly increased I_f amplitude, while ELISA quantification confirmed that global cAMP levels were unaffected by GPE. In Drosophila larvae, a cAMP-independent myogenic preparation, GPE administered in the diet significantly reduced heart rate. These findings demonstrate that Aglianico GPE exerts a negative chronotropic effect through a mechanism that reduces functional I_f density without altering cAMP availability or HCN channel voltage dependence, and reveal a cAMP-independent component of action conserved across phylogenetically distant species. Full article

Latrophilin 1 (LPHN1/ADGRL1), an adhesion G-protein-coupled receptor (GPCR), is the principal receptor for α-latrotoxin (αLTX), a toxin that triggers massive neurotransmitter release. However, its endogenous signaling mechanism remains elusive. Here, we dissect the LPHN1 signaling pathway at the vertebrate neuromuscular junction, using the pore-deficient αLTX mutant LTX^N4C as a selective agonist. Combining electrophysiological recordings from LPHN1 knockout mice with pharmacological inhibitors, calcium imaging, and biochemical assays, we delineate the cascade from receptor activation to spontaneous quantal acetylcholine release. We demonstrate that LPHN1 is specifically localized to the presynaptic membrane and mediates LTX^N4C-evoked release. Upon activation, LPHN1 engages the G_αq–phospholipase C pathway to generate inositol 1,4,5-trisphosphate (IP₃), triggering Ca²⁺ release from intracellular stores via IP₃ receptors. This store depletion activates store-operated Ca²⁺ entry (SOCE), providing sustained Ca²⁺ required for LTX^N4C-induced burst-like exocytosis. We uncover distinct roles for Ca_V2.1 and Ca_V1 channels in initiating and sustaining this response. These findings establish LPHN1 as a GPCR that harnesses intracellular stores and SOCE to drive spontaneous neurotransmission, revealing a novel signaling paradigm for adhesion GPCRs in presynaptic function. Full article

Plant electrophysiological signals can rapidly reflect the dynamic responses of plants to external stimuli, giving them strong potential for nondestructive monitoring and early state recognition. However, differences among plant organs, as well as spatial heterogeneity within the same organ, may substantially affect signal quality and stability because of variations in tissue structure and local physiological activity. To address this issue, this study used Clivia as an experimental model and established a controlled local leaf-surface salt-treatment paradigm to systematically evaluate the relative discriminative ability of electrophysiological signals recorded from different spatial positions on leaves. First, stepwise screening of longitudinal leaf regions and leaf hierarchy was performed using 0 mM and 100 mM NaCl agarose gel treatments to determine the optimal signal acquisition position. Then, based on the selected position, a five-level NaCl treatment recognition task was constructed, and LRPNet, a residual network integrating PoolFormer and an efficient channel attention mechanism, was proposed for multi-gradient classification of plant electrophysiological signals. The results showed that, within the current experimental framework, the basal region of the top leaf exhibited the highest relative separability and the best overall recognition performance. In the five-gradient recognition task, LRPNet achieved the highest mean Accuracy of 92.21% among the compared models. These findings indicated that plant electrophysiological signals exhibited pronounced spatial heterogeneity and that optimization of the recording location was not merely an experimental detail, but an important upstream factor that affected downstream recognition performance. This study provides a methodological basis for optimizing signal acquisition positions and improving electrophysiological signal recognition in plants. However, the present conclusions are mainly applicable to the controlled local salt-treatment paradigm established in this study and still require further validation through more rigorous physiological verification, cross-scenario testing, and more independent data-partitioning strategies. Full article

Pupillometry can be used as a method for monitoring pain. There are experimental conditions under which standard pupillometry equipment cannot be used. Studying the effects of different pulse forms used for transcutaneous spinal cord stimulation (tSCS) is one such task. The aim was to create a system for recording pupil diameter based on a web camera because it can be synchronised with external equipment, which allows the diameter to be recorded simultaneously with other physiological signals. A markerless system for recording and analysing pupil diameter using deep neural networks was developed based on a commercially available web camera. The accuracy of this system was compared with the accuracy of measurements using manual analysis with ImageJ (version 1.54g). For validation, the system was tested in a study of the dependence of tolerance to tSCS on the shape of stimulating pulses, which involved volunteers (n = 12). The results of the developed pupillometry were compared with the pain rating scale traditionally used in such studies. The developed system is accurate in determining the pupil diameter, comparable to human accuracy. The pupillometry results reproduced those obtained using a subjective pain scale. This method was found to be a reliable method for recording nociceptive pupillary responses in electrophysiology. Full article

Background: Three-dimensional (3D) mapping of the left atrium (LA) using multipolar high-density (HD) catheters plays a central role in contemporary LA ablation procedures, as accurate and efficient acquisition of anatomical and electrophysiological information is essential. This study benchmarks workflow efficiency during acquisition of a predefined complete HD LA map across four widely used multipolar HD catheter designs. The analysis focuses on efficiency metrics and does not aim to assess mapping quality, arrhythmia interpretation accuracy, or clinical outcomes. Methods: We analyzed 182 consecutive patients from an ongoing cohort undergoing LA procedures, including pulmonary vein isolation and complex LA ablations, using 3D mapping in accordance with current guideline recommendations. Four multipolar HD catheters were applied according to the respective 3D mapping systems: a basket catheter (Orion, Rhythmia), a grid catheter (HD Grid, EnSite X), a penta-spline catheter (PentaRay, Carto 3), and an octa-spline catheter (OctaRay, Carto 3). For each procedure, the time required for acquisition of a complete 3D LA map and the number of acquired points were systematically recorded. LA HD mapping speed was calculated by relating LA volume to the time required for complete map acquisition. Results: The study population had a mean age of 69 years, with a median CHA₂DS₂-VASc score of 3, indicating a cohort with a moderate thromboembolic risk profile. The median LA volume index (LAVI) was 34 mL/m². Patients were distributed across four HD catheter groups, comprising 44 patients in the basket group, 29 in the grid group, 23 in the penta-spline group, and 86 in the octa-spline group. LA mapping speed differed significantly among the groups, with values of 3 mL/min in the basket group, 2.5 mL/min in the grid group, 3.1 mL/min in the penta-spline group, and the highest mapping speed observed in the octa-spline group at 5.9 mL/min. Conclusions: The octa-spline catheter was associated with a significantly higher LA mapping speed compared with other widely used HD catheters. Full article

SARS-CoV-2 infection is associated with not only acute respiratory symptoms but is also characterized by strong neurotropism which may contribute to the development of the multisystem post-COVID syndrome (PASC). Patients frequently report chronic neurocognitive disorders such as brain fog, significant attention deficits and increased susceptibility to epileptiform discharges. The aim of this review is to systematize the knowledge regarding deviations in quantitative electroencephalography (QEEG) recordings in convalescents and to evaluate the utility of this method as an objective biomarker. This work constitutes a comprehensive literature review integrating the latest data on neuroinflammation, blood-brain barrier damage and changes in cortical oscillatory dynamics induced by the infection. The literature analysis indicates that the virus may induce a pathological excitation and inhibition imbalance (E/I imbalance) in neuronal networks. In QEEG studies this manifests as excessive activity of slow bands (Theta, Delta), a deficit of rhythms responsible for attention and sensorimotor integration (SMR) and a pathologically elevated Theta to Beta ratio (TBR). In conclusion, QEEG can serve as an objective and highly sensitive tool supporting the diagnosis and stratification of patients with neurocognitive complications of Long COVID. The integration of precise electrophysiological phenotyping with targeted behavioral neuromodulation (e.g., EEG-Biofeedback) fits into the paradigm of personalized medicine and offers a prospective strategy for mitigating long-term neurological burdens. Full article

Neuronal activity and cerebral blood flow are tightly coupled to support the high metabolic demands of the brain. Disruption of neurovascular coupling is a defining feature of many neurodegenerative disorders such as Alzheimer’s disease, stroke, small vessel disease, Parkinson’s disease, and aging. Progress in understanding the mechanisms underlying neurovascular coupling requires experimental approaches that can simultaneously measure neuronal activity and vascular dynamics with high spatial and temporal resolution, while also enabling targeted perturbations of the system. Here, we present a methodological framework that combines chronic electrophysiological recordings with two-photon imaging of cerebral blood flow and optogenetic manipulation of the vasculature in vivo. Using a chronically implanted flexible electrode array, we obtain measurements of the single- and multi-unit spiking activity, as well as local field potentials. Concurrently, two-photon microscopy enables high-resolution measurements of vessel diameter and blood flow within individual vascular segments. In addition, optogenetic control of vascular smooth muscle cells allows for rapid and reversible manipulation of the vessel diameter through the same cranial window while simultaneously recording the neural and vascular activity. We provide detailed protocols for surgical implantation, data acquisition, and analysis, and discuss experimental considerations and limitations. This combined platform offers a powerful tool for mechanistic studies of neurovascular coupling and its dysfunction in disease models. Full article

Background/Objectives: Goal-directed praxis actions (GOPAs) integrate perception, motor planning, and executive control. While neural correlates of single actions are known, less is understood about how complexity conditions and their hierarchical organization into elementary tasks shape neural dynamics during ecologically manual assembly tasks. This study tested how electrophysiological (EEG) activity reflects global complexity and selective engagement of executive and sensorimotor systems across GOPAs. Methods: 38 healthy young adults completed two assembly conditions differing in complexity (basic and advanced) decomposed into four elementary tasks: identification, handling, alignment, and joining. EEG was recorded across five frequency bands (delta, theta, alpha, beta, and gamma) and four regions of interest (ROI): frontal, fronto-central, temporo-central, and parieto-occipital. Results: Neural activity varied significantly depending on different complexity, elementary task, and ROI. The advanced-complexity condition elicited stronger neural responses compared to the basic-complexity condition, reflecting greater cognitive, and sensorimotor demands. A task-related gradient emerged, with joining showing the highest activity, followed by alignment, while identification and handling showed lower activation. Frontal regions, particularly in theta activity, were more involved under higher complexity, suggesting increased executive control. In contrast, beta and gamma activity predominated in temporo-central and parieto-occipital regions, supporting visuomotor and sensorimotor integration. Conclusions: EEG oscillatory dynamics during ecological GOPAs are selectively modulated by complexity condition and hierarchical task organization. Neural activity tracks functional demands of specific action phases rather than general arousal, highlighting dynamic coordination between executive and sensorimotor systems during complex manual behavior. Full article

Galeruca daurica (Joannis) (Coleoptera: Chrysomelidae) is an oligophagous pest in which both adults and larvae prefer to feed on Allium forage grasses of the Liliaceae family. In this study, we identified gustatory receptor (GR) genes based on the transcriptome data of G. daurica; analyzed the expression profiles of these GR genes across different larval instars and various tissues of male and female adults using quantitative real-time PCR (qRT-PCR); detected the electrophysiological responses of the mouthparts of male and female G. daurica adults to flavonoids and carbohydrates using single sensillum recording (SSR); and recorded the changes in food consumption of G. daurica adults after feeding on six host plant-derived metabolites. A total of 26 GR genes were identified from the transcriptome data of adult and larval of G. daurica. Phylogenetic analysis was performed to screen candidate functional gustatory receptor genes, including four sugar receptors (GdauGR7, GdauGR10, GdauGR14 and GdauGR28), seven bitter receptors (GdauGR11, GdauGR16~17, GdauGR22, GdauGR25~26 and GdauGR30), and two CO₂ receptors (GdauGR15 and GdauGR20). Larval expression profiling of GdauGRs in G. daurica revealed that the relative expression levels of 17 genes exhibited dynamic changes during larval growth and development. GdauGRs were expressed to varying degrees in the antennae, mouthparts, brain, gut, and forelegs of adult G. daurica, with sex-specific differences. Notably, the expression levels of GdauGR4, GdauGR9 and GdauGR16 in the gut were extremely significantly higher than those in other tissues. In the SSR test, the six tested flavonoids and one carbohydrate were able to induce robust electrophysiological responses in the gustatory sensilla on the antennae and mouthparts of adult G. daurica at specific concentrations. In addition, the supplementation of several host-derived metabolites altered the food consumption of adult G. daurica. These findings lay a solid foundation for elucidating the molecular mechanisms underlying gustatory recognition and host adaptation in G. daurica. Full article

Anxiety, a common mental disorder, is epidemiologically linked to high dietary sugar intake. However, the underlying neural mechanisms remain poorly understood. Here, using male C57BL/6 mice (n ≥ 10 per group), we show that two-week consumption of sugar-sweetened drinks reliably induced anxiety-like behavior, characterized by reduced center time in the open field test and less open arm exploration in the elevated plus maze. Notably, consumption of sucrose, glucose, or the non-metabolizable glucose analog methyl-α-D-glucopyranoside induced anxiety-like behavior, whereas intake of the artificial sweetener acesulfame potassium (Ace-K) did not. Moreover, after two weeks of exposure to sucrose or glucose but not to Ace-K, c-Fos expression was elevated in glutamatergic neurons of the nucleus of the solitary tract (NTS). Mechanistically, high glucose activated intrinsic excitability and the amplitude of spontaneous excitatory postsynaptic currents in NTS glutamatergic neurons; congruently, selective activation of NTS glutamatergic neurons mimicked anxiety-like behavior in mice, while chemogenetic silencing of these neurons abolished glucose-induced anxiety. Together, our findings demonstrate that NTS glutamatergic neurons activation mediates sugar-induced anxiety. These results suggest that this anxiogenic effect is driven by glucose-related signaling rather than artificial sweet taste perception alone, shedding light on a novel clinical therapy against anxiety. Full article

Background: Postoperative neurocognitive disorders (PND) are frequent complications in the elderly surgical patients, with aging recognized as a major risk factor. This study aimed to identify electrophysiological markers and establish an exploratory machine learning framework for PND-related vulnerability prediction using anesthetic electroencephalography (EEG) features in aged mice. Methods: Young and aged mice underwent laparotomy under isoflurane anesthesia with EEG recording. Neurocognitive performance was quantified by 16 standardized behavioral fractions. A semi-supervised K-means algorithm, anchored on young-surgery mice, stratified aged-surgery mice into PND and non-PND clusters. EEG dynamics during anesthesia maintenance and emergence were analyzed, and machine learning models were trained to predict PND from EEG features. Results: At baseline, neurocognitive function was comparable across groups. After anesthesia/surgery, aged mice exhibited selective spatial and contextual memory impairments, with two-thirds classified as PND. During emergence, PND mice displayed elevated δ power and reduced α and β ratios. A Multi-layer Perceptron classifier showed discriminatory performance for PND classification in one evaluation setting (AUC = 0.94). Conclusions: This study identifies emergence-related EEG features associated with postoperative neurocognitive vulnerability in aged mice and provides an exploratory machine learning framework for preclinical risk stratification. These findings support further mechanistic investigation and warrant future validation in human perioperative EEG datasets. Full article

Objective: Electroencephalogram (EEG) measures electrophysiological activity in the cerebral cortex and is broadly used across diagnostic, research, and clinical contexts. Missing data gaps are a pervasive issue in EEG signal recording, resulting from sensor failures and sensor disconnections, amongst other sources. To preserve a continuous signal describing underlying electrophysiological processes, imputation must be used to reconstruct these gaps. The aim of this review is to examine the methods that have been developed for missing data gap imputation in EEG signals. Methods: A search of five databases was conducted based on the Preferred Reporting Items for Systematic Reviews and Meta-Analysis guidelines. The search question examined existing algorithms for imputation in EEG signals. Results: The initial search yielded 17,490 results (an update included 1913 additional results). This review includes 16 articles presenting EEG gap imputation methods. These imputation methods were characterized as (i) tensor-based, (ii) machine learning and deep learning, and (iii) model-based and classical. Conclusions: Several of these methods achieved strong effectiveness for accurately reconstructing gaps in ‘ground truth’ EEG signals; however, the limited generalizability of many of the studies due to small datasets lacking adequate participant diversity as well as methodological differences made it impossible to describe a single leading method. Further, the reliance on full recordings for segment imputation in some methods could prove prohibitive to real-time imputation. Future study is required to rectify these limitations and to properly investigate computational latency and requirements. Significance: This work provides novel insights into existing methods for EEG gap imputation, as it identifies current shortcomings in the literature and paves a way for a more generalizable solution to be achieved through future work. Full article