Search Results (497)

Animal locomotion exhibits highly structured temporal dynamics despite significant inter-species biomechanical and anatomical discrepancies. While wearable inertial measurement unit (IMU) sensors and deep learning have advanced animal activity recognition, existing systems remain largely species-dependent, requiring large-scale labeled datasets for each deployment. To address this, we propose a biomimetic cross-species behavioral representation learning framework that extracts transferable locomotor structures from heterogeneous IMU signals. The proposed methodology integrates behavioral ontology harmonization, imbalance-aware augmentation, and semi-supervised domain-adversarial adaptation to reduce inter-species distributional discrepancies. Unlike conventional classification models, our dual-head architecture enables simultaneous processing of multi-label and single-label behavioral structures across anatomically diverse species. Extensive experiments conducted on dog, goat, and horse datasets demonstrate that the proposed framework significantly improves cross-species transferability, achieving a mean Macro-F1 score of 0.711 compared to 0.345 for direct transfer learning. Furthermore, we show that sparse target supervision is critical for stabilizing adversarial adaptation. K-Means head adaptation partially mitigated fully unsupervised negative transfer. However, its performance remained below semi-supervised Domain-Adversarial Neural Network (DANN), indicating that sparse target supervision is still necessary for stable cross-species adaptation. These findings suggest that biologically motivated locomotor similarity can support cross-species behavioral transfer, although external validation on additional species, sensor placements, and deployment conditions is still required. Full article

Urban service robots require route planners that are not only collision-free but also consistent with the energetic behavior of differential drive locomotion. Conventional grid planners such as A* are efficient and reliable for geometric navigation, but their usual cost structure prioritizes path length and does not explicitly account for heading changes, concentrated turns, or localization risk near obstacles. This study presents an energy-aware route-planning formulation for differential-drive mobile robots operating in dense polygonal urban environments. The path is encoded through twelve continuous internal waypoints and is evaluated using an interpretable energy proxy that combines translational distance, cumulative absolute rotation, squared rotation, and a clearance-dependent localization risk term. Collision avoidance, boundary compliance, and maximum turn feasibility are handled through a feasibility-first dominance rule, and the resulting constrained problem is solved using a Genetic Algorithm and Particle Swarm Optimization. A* with clearance inflated occupancy grids is included as a deterministic baseline. The final experiments used a dense urban scenario with sixteen polygonal obstacles, an A* grid resolution of 0.10 m, a robot radius of 0.20 m, a safety clearance of 0.10 m, 80 GA individuals, 80 PSO particles, 100 iterations, and 10 independent runs. All methods achieved a 100% feasibility rate. PSO obtained the lowest average energy proxy, 18.505, compared with 18.645 for A* and 18.645 for GA, and reduced average rotation from 4.136 rad to 4.079 rad. However, A* remained much faster, 5.860 s on average, compared with 174.574 s for GA and 175.869 s for PSO. The ranking analysis shows that GA produced the best aggregate score when route quality, time, clearance, and feasibility were weighted equally, while PSO produced the best route quality. External perturbation tests indicate that open-loop execution in narrow corridors is sensitive to bias and waypoint noise, which motivates closed-loop tracking, online replanning, and physical validation in future work. Full article

Existing robotic control frameworks often rely on complex hierarchical state machines or middleware infrastructures, which may be unsuitable for resource-constrained embedded systems and difficult to map directly to low-level code. This work presents a complete state-machine model for the reactive control layer of a differential-drive mobile robot. Although mobile-robot controllers often exhibit considerable complexity—particularly at lower levels, where numerous hardware-dependent operations occur—this work shows that a network of concurrent state machines provides a clear and lightweight method for specifying and implementing control behavior. The proposed approach decomposes the reactive controller into two concurrent extended finite-state machines (CEFSMs) responsible for locomotion and lidar operation, connected through simple and predictable protocols. This structure enables a direct mapping from model diagrams to procedural code in languages such as C++ and Lua. The method has been used extensively in an undergraduate Embedded Systems course since 2011, supporting both physical robots (Arduino-based) and their digital twins in CoppeliaSim. We also introduce a discrete control strategy that approximates continuous behavior and incorporates a simplified proportional–integral–derivative (PID) controller for pose correction. The approach reduces development effort, increases model clarity, and yields reusable code across hardware and simulation platforms. Quantitative evaluation shows that the proposed PID-based control strategy can improve positioning accuracy by up to an order of magnitude compared to a baseline on–off controller, without increasing the computational cost. In addition, the control cycle time has been significantly reduced (from 31 ms to 12 ms) in order to ensure a stable execution period; this, in turn, results in a more predictable controller output and improved trajectory consistency. Full article

Spatial updating—the ability to continuously revise spatial representations during locomotion—is fundamental to adaptive navigation and depends on flexible reference frames. Although previous research has established independent effects of field cognitive style and scene layout on spatial performance, their interaction and underlying neural substrates remain poorly understood. The present study examined how field dependence–independence and environment geometry jointly modulate spatial updating by combing the judgment of relative direction (JRD) paradigm with functional near-infrared spectroscopy (fNIRS). Forty participants were recruited and assigned to two groups (20 field-independent [FI] and 20 field-dependent [FD]) based on Embedded Figures Test scores. They completed directional pointing tasks in two virtual environments: a geometrically structured rectangular spaces affording explicit orthogonal reference axes and an ambiguous oval environments devoid of stable global geometric anchors. Behaviorally, FI individuals exhibited shorter response time in rectangular layouts yet superior accuracy in oval layouts relative to FD individuals. Neurally, the middle frontal gyrus (MFG) emerged as a critical locus exhibiting a significant interaction effect between cognitive style and environmental layout. Significant main effects of field cognitive style were observed in the precentral gyrus, superior parietal lobule, and paracentral lobule, with FI individuals showing greater oxyhemoglobin (HbO) elevation than FD participants. Collectively, these findings may tentatively suggest an interpretation that FI individuals flexibly alternate between internal egocentric and external allocentric reference frames during spatial information processing, whereas FD individuals predominantly rely on inherent structural cues embedded in the external environment. These findings may reflect cortical hemodynamic correlates of field cognitive style differences during spatial processing, and may offer empirical references for relevant cognitive neuroscience research and subsequent exploratory applications. Full article

Trematodes are a class of parasitic flatworms with complex life cycles involving multiple hosts. They can influence the physiology and behavior of their intermediate hosts to improve the efficiency of transmission to definitive hosts, including by increasing host mortality. However, the mechanisms of their effect on the crustacean nervous system leading to neuronal dysregulation and its consequences have not been sufficiently elucidated. This study investigated the effects of infection by metacercariae of the trematode Cercaria fluviocinguli on the serotonin (5-HT) immunoreactivity, inflammation, and pathological processes in the nervous system of the shore crab Hemigrapsus sanguineus. The regions in the brain, the ventral nerve cords (VNC), and the nerves targeted by this parasite were examined by immunohistochemistry and confocal laser scanning microscopy methods. The cysts and the enclosed flukes caused compaction, compression, and distortion of many nerve fibers of the VNC and also impaired conduction in the mixed nerves of the thoracic ganglia controlling locomotion. The infection by C. fluviocinguli induced a decrease in 5-HT-like immunoreactivity in neurons and neuropils of the VNC, damage to nerve fibers and neurons of the VNC, inflammatory reactions, and apoptosis. In heavily infected crabs, the increased content of TUNEL-positive cells in the VNC was correlated with granuloma accumulation. Further investigations into host–parasite interaction mechanisms under controlled experimental conditions are needed to clarify the actual effects of encapsulated and non-encapsulated metacercariae on host behavior and their role in transmission to the next host. Full article

Virtual fencing is increasingly used in grazing systems as a flexible alternative to physical fencing, yet detailed assessments of cattle behavior within such systems remain limited. This study investigates the use of collar-mounted tri-axial accelerometers combined with supervised machine learning to characterize cattle behavior in a virtual fencing system. Seven free-ranging Angus cattle were monitored using accelerometers mounted on a virtual fencing system, GNSS positioning, and virtual fence warning logs. A random forest classifier was developed and trained to identify key behaviors (grazing/feeding, ruminating, lying, standing and locomotion) using features derived from tri-axial accelerometer data. The model achieved high classification performance for grazing/feeding, ruminating, and lying (mean accuracy = 0.87, range = 0.83–0.90), enabling estimation of individual behavioral time budgets. Daily activity patterns were generally stable over time and across individuals. Spatial analyses revealed significant differences in behavior between areas near the virtual fence boundary and interior pasture locations, with increased grazing and reduced ruminating near the boundary, potentially reflecting spatial variation in habitat type or forage availability. In the virtual fencing system, cattle are equipped with collars that emit an auditory warning when they approach a virtual boundary, followed by a low-energy electrical impulse when the warning is ignored over a directional distance of 5–10 m. Event-based analyses showed no consistent short-term changes in either movement intensity and direction nor locomotion following auditory warning events, indicating that cattle habituated to the system did not exhibit uniform behavioral disturbance in response to warnings. These results suggest that accelerometer-based behavior classification can provide fine-scale, non-invasive insights into spatio-temporal cattle behavior in virtual fencing systems. The finding indicates that, in a habituated herd, virtual fencing was not associated with pronounced disruption to the measured behavioral patterns, while highlighting the potential of embedded sensor data for animal-based behavioral monitoring. Full article

Background: Adolescence is a sensitive period for brain maturation during which exposure to Δ⁹-tetrahydrocannabinol (THC) can induce long-lasting neurobehavioral alterations. Yet, preclinical and clinical studies report inconsistent long-term outcomes of adolescent THC exposure, ranging from clear impairments to apparently normalizing effects. We hypothesize that these discrepancies reflect stable individual differences in stress-coping abilities. Methods: To test this, selectively bred Dominant (Dom; stress-resilient, risk-prone) and Submissive (Sub; stress-vulnerable, depressive-like) Sabra mice received THC or vehicle during adolescence and were assessed in adulthood. Results: Anxiety-like and exploratory behavior, measured in the hole-board test, were differentially affected by THC as a function of stress vulnerability: in Sub mice, THC increased exploration and reduced anxiety-like behavior, whereas in Dom mice THC produced the opposite pattern. Recognition memory, evaluated by the novel object recognition test, showed modest, line-dependent alterations. Sensitivity to N-methyl-D-aspartate (NMDA) receptor hypofunction, a widely used index of vulnerability to schizophrenia-like symptoms, was examined using MK-801-induced locomotion. Adolescent THC potentiated MK-801-evoked hyperlocomotion in Dom mice but attenuated it in Sub mice. Conclusions: In the context of increasing medical and recreational cannabis exposure, these in vivo findings suggest that stress-vulnerability-related traits may be an important variable to consider in future preclinical and translational studies of adolescent THC exposure. Full article

Background/Objectives: Multiple sclerosis (MS) is a chronic autoimmune disease of the central nervous system characterized by inflammation, demyelination, and axonal loss. Despite available therapies, there is currently no effective cure for MS. Ibudilast (IBD), a phosphodiesterase inhibitor, and 6-shogaol (SH), a bioactive compound from ginger, have independently shown therapeutic potential in MS models. This study aimed to evaluate the therapeutic efficacy of combining SH and IBD in modulating neuroinflammation and improving functional recovery in a cuprizone (CPZ) mouse model of MS. Methods: Male SWR/J mice were exposed to 0.3% CPZ for 5 weeks to induce demyelination, followed by 4 weeks of spontaneous remyelination after CPZ withdrawal. During remyelination, the CPZ group was subdivided into four groups: no therapy, SH (25 mg/kg), IBD (10 mg/kg), and SH + IBD. Behavioral tests were used to assess locomotion, muscle strength, coordination, and memory. Gene expression of proinflammatory and anti-inflammatory cytokines was analyzed in brain tissue. Results: The combined treatment significantly improved locomotor activity, muscle strength, and memory during remyelination phases while suppressing proinflammatory gene expression and enhancing anti-inflammatory pathways in the brain. Conclusions: SH and IBD combination therapy provides enhanced anti-inflammatory and functional benefits compared with monotherapies, supporting its potential as a promising multi-target therapeutic strategy for improving functional recovery and modulating neuroinflammation during the spontaneous remyelination phase following CPZ withdrawal. Full article

Cardiotonic steroids (CTS) can modulate central nervous system function through their interaction with the Na⁺,K⁺-ATPase, affecting dopaminergic transmission. While the CTS ouabain is known to induce mania-like behavior and oxidative damage, the effects of other CTS are less clear. This study examined the effects of 14-day intracerebroventricular administration of 1.5 μL 100 μM marinobufagenin (MBG) on locomotion, gait, monoamine metabolism, and oxidative stress markers (MDA, SOD, catalase, MAO-B) in C57BL/6 mice. Chronic MBG caused increased locomotor activity and time spent in the center of the open field. Unlike ouabain, chronic MBG did not impair motor function, evaluated via gait analysis. MBG elevated striatal MAO-B activity and reduced prefrontal MDA levels, with no changes in SOD or catalase, indicating that it did not cause oxidative stress. However, it did affect dopamine and serotonin metabolism. Monoamine tissue content evaluation on day 15 showed increased dopamine turnover in the striatum and brain stem, and a decrease in the thalamus. Norepinephrine levels increased in the striatum and hippocampus. Serotonin turnover increased in the prefrontal cortex. These results indicate that chronic MBG increases locomotion and reduces anxiety-like behavior through region-specific modulation of dopaminergic and serotonergic signaling distinct from that caused by ouabain. Full article

Objective: Neuroinflammation contributes to cognitive impairment across neurodegenerative disorders. Prostaglandins (e.g., PGE₂, PGD₂, PGF₂α) and pro-inflammatory cytokines (TNF-α, IL-1β) are key mediators of lipopolysaccharide (LPS)-induced hippocampal dysfunction. Carvacrol (CRV), a monoterpenic phenol with anti-inflammatory and antioxidant properties, may mitigate these effects, but its impact on hippocampal prostaglandin profiles is not well-defined. Methods: BALB/c mice were randomly assigned to Control (PBS; n = 7), LPS (1 mg/kg, i.p.; n = 10), or LPS + CRV (50 mg/kg, p.o.; n = 7). Body weight was tracked daily for 7 days; rectal temperature was measured once before behavioral testing and euthanasia. Locomotion/anxiety were assessed by the open-field test (OFT; average speed, total distance, freezing, mobility rate) using ToxTrac. Spatial recognition memory was evaluated in the Y-maze novel-arm task (novel-arm entries, duration, total entries, discrimination index [DI]). Hippocampal PGE₂, PGD₂, PGF₂α, TNF-α, and IL-1β were quantified by ELISA. Data were analyzed by one-way ANOVA with Sidak’s post hoc test. Results: OFT measures did not differ among groups (p > 0.05), indicating no confounding locomotor deficits. In the Y-maze, LPS reduced novel-arm entries versus the Control (p = 0.0029), while LPS + CRV showed a nonsignificant increase versus LPS (p = 0.2406). Novel-arm duration differed among groups (p = 0.0033); LPS + CRV spent less time than LPS (p = 0.0128). Critically, DI showed a robust treatment effect (p < 0.0001): LPS markedly impaired DI versus the Control (p < 0.0001), and CRV significantly improved DI versus LPS (p < 0.0001). Biochemically, LPS elevated hippocampal PGE₂ (p < 0.0001) and PGF₂α (p = 0.0040); CRV normalized PGE₂ (p < 0.0001) but not PGF₂α (p = 0.2656). PGD₂ was unchanged. LPS increased TNF-α and IL-1β (both p < 0.0001), and CRV significantly reduced both versus LPS (both p < 0.0001). Conclusions: Acute LPS provokes prostaglandin- and cytokine-driven hippocampal inflammation with associated recognition memory deficits. Carvacrol attenuates cognitive impairment and suppresses hippocampal PGE₂, TNF-α, and IL-1β, supporting a mechanism involving modulation of the prostaglandin–cytokine axis. These findings highlight CRV as a candidate adjunct for inflammation-associated cognitive dysfunction. Full article

Walking is not merely locomotion but a window into the nervous system, integrating cortical, subcortical, cerebellar, spinal, and peripheral networks into a unified motor behavior. Across neurological diseases—including Parkinson’s disease, atypical parkinsonism, cerebellar ataxias, stroke, multiple sclerosis, neuropathies, neuromuscular disorders, and functional gait syndromes—gait disturbances are among the most disabling clinical features, contributing to falls, loss of independence, institutionalization, and premature mortality. Traditional bedside observation remains indispensable, but it lacks the sensitivity and reproducibility needed to capture subtle, episodic, or prodromal abnormalities. Over the past decade, advances in wearable sensors, marker-based and markerless motion capture, pressure-sensitive walkways, force plates, artificial intelligence, and machine learning have positioned digital mobility outcomes as promising, ecologically valid biomarkers of neurological function. These measures can support differential diagnosis, provide prognostic information on falls and survival, and serve as sensitive endpoints in therapeutic trials. They may also detect early abnormalities, such as increased stride-to-stride variability or prolonged double-support time, before overt clinical deterioration becomes evident. Clinical applications are increasingly evident across disorders, including distinguishing Parkinson’s disease from atypical parkinsonism, quantifying treatment response in normal-pressure hydrocephalus, tracking progression in ataxia and multiple sclerosis, predicting functional decline in motor neuron disease, and guiding rehabilitation after stroke. Integration with neuroimaging, electrophysiology, and molecular biomarkers is beginning to reveal the circuits underlying variability, instability, and freezing, positioning gait as a systems-level marker of neural integrity. Nevertheless, methodological heterogeneity, limited disease-specific validation, insufficient longitudinal data, and lack of consensus on clinically meaningful parameters continue to constrain translation. Cognitive, affective, and environmental influences also remain insufficiently represented in digital frameworks, while equity, accessibility, algorithmic bias, and privacy require careful ethical governance. Reconceptualizing gait as a “sixth vital sign” reframes mobility as a multidimensional biomarker of neural and systemic health. With harmonized protocols, robust validation, multimodal integration, and appropriate ethical frameworks, gait analysis could become a cornerstone of precision neurology. Full article

Introduction: Aquatic contaminants can alter fish behavior before overt toxicity becomes evident, making neurobehavioral endpoints relevant for ecosystem protection and for hazard prioritization within a One Health framework. We recently developed a high-throughput visual-acoustic zebrafish larval thigmotaxis assay in which edge preference is interpreted as an anxiety-like behavioral endpoint, thereby adding spatial phenotyping beyond conventional locomotion metrics. Objective: To evaluate assay performance in a multisubstance screening challenge and determine whether it can discriminate distinct behavioral fingerprints without prior knowledge of chemical identity. Methodology: Zebrafish larvae were exposed for 1 h at 120 hpf. For each substance, 24 larvae were tested per condition, with six concentrations per substance, plus positive and negative controls. Larvae were challenged using alternating light/dark and tapping/quiet paradigms. The primary endpoint was the percentage of time spent at the edge as a proxy for anxiety-like behavior, while total distance and mean total velocity when moving were used as contextual locomotor metrics; edge distance and edge velocity were used as supportive spatial metrics. Data from 37 substances were analyzed through a standardized automated workflow. Results: Controls performed as expected and supported assay stability across runs. The chemical screening revealed heterogeneous but reproducible behavioral fingerprints. Seven substances produced weak/minimal acute responses, ten showed predominantly suppressive profiles, three predominantly activating profiles, nine showed prominent thigmotaxis-specific anxiety-like signals not explained by locomotion alone, and eight displayed mixed or stimulus-dependent patterns, including non-monotonic responses. Several substances altered edge preference while distance and velocity changed less, differently, or in the opposite direction, indicating behavioral reorganization rather than simple hypo- or hyperactivity. The multi-stimulus design was critical because some effects were evident only under specific sensory contexts. Conclusions: The multisubstance challenge supports the discriminatory capacity, robustness, and added value of the assay for high-throughput neurobehavioral screening. By capturing anxiety-like behavior through thigmotaxis and complementing it with locomotor context, the method improves phenotypic resolution for aquatic pollution assessment and offers a sensitive fish-based NAM to prioritize chemicals of concern for both environmental and human health-oriented testing strategies. Full article

Background: Acute carbon monoxide (CO) poisoning can cause delayed neuropsychiatric sequelae (DNS) after a latent period, yet its pathophysiology remains poorly understood because of the lack of reproducible experimental models. Methods: We established a rat model of DNS using acute CO poisoning (6500 ppm for 25 min). Behavioral assessments evaluated cognition, locomotion, sensorimotor function, exploratory behavior, and reward responsiveness. Histopathological analyses assessed brain injury, and regional monoamine concentrations were quantified using high-performance liquid chromatography. Results: CO-exposed rats developed delayed and progressive behavioral abnormalities, including impaired spatial working memory, reduced locomotor activity, sensorimotor dysfunction, and diminished exploratory behavior. At 4 weeks, CO-exposed rats showed reduced Y-maze alternation (49.3% vs. 72.2%, p < 0.0001), complete loss of tape-removal success (0% vs. 100%, p < 0.001), reduced digging behavior (10.1 ± 6.9 vs. 27.4 ± 3.9, p < 0.01), and decreased locomotor activity (330.5 ± 172.1 vs. 730.5 ± 139.5 cm, p < 0.01). In contrast, olfactory discrimination, sucrose preference, and grip strength were preserved. Histopathology demonstrated persistent neuronal and inflammatory alterations. Dopamine concentrations were significantly reduced in the cortex and basal ganglia, whereas thalamic serotonin levels were increased following CO poisoning. Conclusion: Acute CO poisoning induces a reproducible DNS characterized by progressive behavioral impairment, persistent histopathological abnormalities, and regional monoaminergic dysregulation. These findings support the concept that DNS is an evolving neuropathological process and identify dopaminergic pathways as potential therapeutic targets. Full article

Botanical insecticides have re-emerged as promising tools within Integrated Pest Management (IPM) due to their biodegradability, chemical diversity, and potential compatibility with resistance management strategies. Although frequently considered safer alternatives to synthetic pesticides, growing evidence indicates that these compounds may also affect non-target organisms, particularly bees. This review discusses the selectivity of botanical insecticides toward pollinators by integrating historical perspectives, mechanisms of action, ecotoxicological effects, and current limitations in risk assessment approaches. Botanical insecticides may induce both lethal and sublethal effects, including alterations in behavior, locomotion, feeding, development, reproduction, and physiology across different bee groups. We also demonstrate that most available studies remain concentrated on Apis mellifera, adult workers, and acute laboratory assays, while important pollinator groups and chronic exposure scenarios remain poorly explored. Furthermore, current regulatory protocols are still largely based on models developed for synthetic pesticides. Expanding ecotoxicological approaches is therefore essential to improve pollinator safety assessments and support more sustainable pest management strategies. Full article

Quantitative analysis of insect locomotion is important for understanding how environmental variables shape behavioral output and for developing reproducible experimental paradigms in neuroethology and biohybrid research. In this study, locomotor responses of the Madagascar hissing cockroach (Gromphadorhina portentosa) were evaluated across a thermal range from 18 to 38 °C using an integrated low-cost platform combining a spherical treadmill motion-tracking system with synchronized environmental regulation of temperature and illumination. The results suggest a possible non-linear relationship between chamber temperature and locomotor activity. Thermal conditions below the programmed 30 °C chamber temperature were generally associated with higher cumulative activity and elevated peak activity-rate values but not low inactivity proportions. In contrast, higher temperatures were associated with more fragmented locomotor patterns characterized by preserved peak activity but increased inactivity proportions. Complementary repeated-measures illumination assays suggested preliminary UV-associated shifts in locomotor output under the tested treadmill conditions. Together, these findings provide a quantitative framework for studying environmentally modulated locomotion in G. portentosa. Full article