Search Results (2,248)

Yellow seabream (Acanthopagrus latus), a species of significant economic importance, predominantly inhabits the warm waters of the Indo-Western Pacific. While previous studies have explored the genetic diversity of A. latus using microsatellites and other nuclear markers, a comprehensive understanding of its genetic characteristics and adaptive evolution using whole-genome resequencing (WGR) remains limited. In this study, we collected 60 individuals from six distinct geographic locations and performed WGR, achieving an average sequencing depth of 12.59×, which resulted in the identification of 19,488,059 high-quality single-nucleotide polymorphisms (SNPs). The nucleotide polymorphism (π_θ) across all populations was consistent, ranging from 0.003042 to 0.003155, indicating low genetic differentiation among populations. Comparative analyses revealed that populations other than that in Xiamen (XM) have undergone adaptive evolution, potentially linked to traits such as growth and development, feeding, immunity, and movement. This study explores the population genetics and adaptive evolutionary patterns of Acanthopagrus latus at the genomic level, providing an essential foundation for the conservation and management of this economically important species in the future. Full article

Machine learning-based computer vision techniques using depth cameras have shown potential in physiotherapy movement assessment. However, a comprehensive understanding of their implementation, effectiveness, and limitations remains needed. Following PRISMA guidelines, we systematically reviewed studies from 2020 to 2024 across Web of Science, Scopus, PubMed, and Astrophysics Data System to explore recent advancements. From 371 initially identified publications, 18 met the inclusion criteria for detailed analysis. The analysis revealed three primary implementation scenarios: local (50%), clinical (33.4%), and remote (22.3%). Depth cameras, particularly the Kinect series (65.4%), dominated data collection methods. Data processing approaches primarily utilized RGB-D (55.6%) and skeletal data (27.8%), with algorithms split between traditional machine learning (44.4%) and deep learning (41.7%). Key challenges included limited real-world validation, insufficient dataset diversity, and algorithm generalization issues, while machine learning-based computer vision systems demonstrated effectiveness in movement assessment tasks, further research is needed to address validation in clinical settings and improve algorithm generalization. This review provides a foundation for enhancing computer vision-based assessment tools in physiotherapy practice. Full article

Football is a sport in which athletes perform many movements using one of their legs, which is characterized as dominant. This differentiation in usage often creates asymmetries between the limbs, which has been noted to be related to the occurrence of injuries as well as negative effects on performance. To assess these asymmetries, vertical jump tests, as well as tests using an isokinetic dynamometer, were employed. The aim of this study was to examine the relationship between the asymmetries observed in the application of the single-leg countermovement jump (SLCMJ) and those observed through isokinetic strength assessment at 60°/s, 180°/s, and 300°/s in high-level youth football players. The study involved 63 elite youth football players. For statistical analysis, correlation analysis and linear regression analysis were used. The results showed a significant positive correlation between the CMJ Bilateral Asymmetry Index and the BAI Anterior Quadriceps (60°/s) (r = 0.262, p = 0.038, 95% CI [0.016, 0.479]), indicating a small-to-moderate effect size (Fisher’s z = 0.269, SE = 0.129). Additionally, a significant negative correlation was identified between the CMJ Bilateral Asymmetry Index and the BAI Posterior Hamstrings (60°/s) (r = −0.319, p = 0.011, 95% CI [−0.525, −0.077]), demonstrating a moderate inverse relationship (Fisher’s z = −0.331, SE = 0.129). The overall regression model was significant, F(6,56) = 2.42, p = 0.038, R² = 0.206, indicating that the predictors collectively explained approximately 20.6% of the variance in the CMJ Bilateral Asymmetry Index. From the findings of this study, we conclude that the SLCMJ asymmetry index cannot replace the asymmetry index from isokinetic strength tests. Full article

The recent development of algorithms through artificial intelligence and the ability to measure the human body through soft textile sensors has enabled the provision of meaningful information to the wearer. In this study, a sensor sleeve using a textile elbow angle sensor that can measure the bending and relaxation of the elbow was manufactured and measured. In addition, biomechanical data from Biomechanical of Bodies (BoB)-4, a software capable of inverse dynamics that can optimally calculate the load on human joints and segments during exercise, was collected. A continuous system of resistance angle and angle biomechanical data was designed with an artificial intelligence multilayer perceptron (MLP) algorithm, and the accuracy and output results were checked. Consequently, the accuracy of MLP1 and MLP2 is exceedingly high, at approximately 0.80 and 1.00, respectively. The biomechanical data of the system is comparable to that of BoB, rendering it suitable for providing reliable information to the wearer. Based on this study, it is possible to develop algorithms and systems that can perform biomechanical analysis for various exercise movements in the future. Full article

Counting fetal movements is essential for assessing fetal health, but manually recording these movements can be challenging and inconvenient for pregnant women. This study presents a wearable device designed to detect fetal movements across various settings, both within and outside medical facilities. The device integrates accelerometer and gyroscope sensors with Internet of Things (IoT) technology to accurately differentiate between fetal and non-fetal movements. Data were collected from 35 pregnant women at Suranaree University of Technology (SUT) Hospital. This study evaluated ten signal extraction methods, six machine learning algorithms, and four feature selection techniques to enhance classification performance. The device utilized Particle Swarm Optimization (PSO) for feature selection and Extreme Gradient Boosting (XGB) with PSO hyper-tuning. It achieved a sensitivity of 90.00%, precision of 87.46%, and an F1-score of 88.56%, reflecting commendable results. The IoT-enabled technology facilitated continuous monitoring with an average latency of 423.6 ms. It ensured complete data integrity and successful transmission, with the capability to operate continuously for up to 48 h on a single charge. The findings substantiate the efficacy of the proposed approach in detecting fetal movements, thereby demonstrating a practical and valuable technology for fetal movement detection applications. Full article

This study introduces a novel approach to chess analysis based on center-of-mass dynamics and discrete fractal derivatives, offering an alternative framework for evaluating gameplay strategies. Unlike conventional methods that rely on exhaustive search and statistical simulations, our model provides a macroscopic perspective by analyzing the collective motion of pieces over time. By representing chess positions as a dynamic system in ${\mathbb{R}}^2$, we identify key movement patterns—such as oblique, parallel, and orthogonal trends—revealing strategic tendencies throughout the game. Additionally, fractal derivatives enable the detection of subtle momentum shifts and long-term imbalances, enhancing the understanding of decision-making processes. This approach is computationally efficient and adaptable, extending beyond chess to applications in sports analytics and real-time strategy games. These findings highlight the potential of interdisciplinary techniques in capturing complex strategic behavior within dynamic environments. Full article

A human gait recognition method based on the PSO-ELM algorithm is proposed in order to achieve coordinated movement between humans and lower limb exoskeletons. Ground reaction force (GRF) from the foot, and motion capture data (MCD) from two joints were collected through the exoskeleton device. The sample data were obtained through multiple experiments in different action scenarios, including standing still, walking on the flat, climbing up and down stairs, traveling up and down slopes, in addition to squatting down and standing up. The algorithm utilizes short-term posture data to recognize different posture movement patterns, with two advantages: (1) A user-friendly wearable device was constructed based on multi-source sensors distributed throughout the body, addressing multiple subjects with varying weights and heights, while being cost-effective and reliably and easily collecting data. (2) The PSO-ELM algorithm identifies key features of gait data, achieving a higher recognition accuracy than other advanced recognition methods, especially during arbitrary gait transition duration. Full article

Investigations carried out on 72 fluid samples from 59 sites spread over the area surrounding the Sea of Marmara show that their geochemical and isotopic features are related to different segment settings of the North Anatolian Fault Zone (NAFZ). We collected fluids from thermal and mineral waters including bubbling and dissolved gases. The outlet temperatures of the collected waters ranged from 14 to 97 °C with no temperature-related geochemical features. The free and dissolved gases are a mixture of shallow and mantle-derived components. The large variety of geochemical features comes from intense gas–water (GWI) and water–rock (WRI) interactions besides other processes occurring at relatively shallow depths. CO₂ contents ranging from 0 to 98.1% and helium isotopic ratios from 0.11 to 4.43 Ra indicate contributions, variable from site to site, of mantle-derived volatiles in full agreement with former studies on the NAFZ. We propose that the widespread presence of mantle-derived volatiles cannot be related only to the lithospheric character of the NAFZ branches and magma intrusions have to be considered. Changes in the vertical permeability induced by fault movements and stress accumulation during seismogenesis, however, modify the shallow/deep ratio of the released fluids accordingly, laying the foundations for future monitoring activities. Full article

Engaging with music has been shown to have a positive impact on the quality of life of residents in care homes, who are known to be affected by anxiety, depression and loneliness. Based on the known benefits of in-person singing activities, a new Virtual Reality (VR) choir application was developed to facilitate group singing, aiming to improve residents’ wellbeing and sense of community. Co-designed with Alzheimer Scotland, the intervention was tested in two care homes for functionality and to develop an approach towards assessing feasibility. Residents participated in scheduled sessions over a five-week period, in addition to staff engaging in independent ad hoc use of the experience with residents. Data on reactions to the intervention, the quality of life of participants and preferences about the outcome instruments were collected. The VR intervention proved technically successful, user-friendly, and allowed multiple users to sing together. Participants and staff showed strong enthusiasm for the intervention, with residents actively engaging in singing and movement, although some residents found the headsets uncomfortable. This suggests that VR choirs could be a valuable, scalable activity in care homes, especially when in-person facilitators are unavailable. Preliminary observations indicated that the intervention was not detrimental to participants’ health; however, the sample size was very small and a larger feasibility study is required to examine the intervention’s effectiveness, scalability, and cost-effectiveness. This research highlights the challenges associated with measuring the feasibility of VR interventions in residential care settings, and the value of capturing qualitative data in an ecological setting that represents the intended use of the intervention. Full article

With increasing numbers of dairy farms adopting zero-grazing systems, there is a growing need for indoor environmental enrichment methods. Enrichment is necessary to meet industry requirements and fulfil behavioural motivations, such as oral manipulation. This study evaluated the magnitude with which Holstein Freisen cows would interact with salt lick enrichment blocks based on the mounting design. Holstein Freisen dairy cows (n = 55) were recruited from a UK dairy farm and observed over a 4-week period (n = 20 days). Three different mounting designs were utilized, low non-moveable (LNM), low moveable (LM), and high moveable (HM), and the LNM setup was repeated on week 4. These mounting designs were each observed over a five-day period and then removed for two days in-between. Data were collected by in-person observation and included cow IDs, instances of interaction, and kilograms of salt lick used per setup. The data were analysed through IBM SPSS Statistics via a One-Way Repeated Measures ANOVA and Microsoft Excel to determine significant findings and habituation. The number of new interactions significantly decreased in the HM setup compared to the LM and LNM. The supporting data of kilograms of salt lick used and total percentage of the herd utilizing the blocks, also favoured the LM setups over LNM. The LNM setup was repeated on the final week to assess the level with which cows had habituated to the environmental enrichment. Despite a significant difference between week 1 and week 4, the trends of cow interactions showed individual variability in habituation and overall negligible herd-level habituation. These findings suggest that the use of mineral licks within a dairy herd serves as effective environmental enrichment, even over extended time periods, and when implemented they are best used at low heights with the ability to have free movement. When implemented on a farm, the LM mounting design should increase the herd-level uptake of enrichment leading to a reduction in stereotypies and fulfilment of oral motivation, which is beneficial for overall cow health and welfare. Full article

Grasping objects, from simple tasks to complex fine motor skills, is a key component of our daily activities. Our approach to facilitate the development of advanced prosthetics, robotic hands and human–machine interaction systems consists of collecting and combining surface electromyography (EMG) signals and contextual data of individuals performing manipulation tasks. In this context, the identification of patterns and prediction of hand grasp types is crucial, with cylindrical grasp being one of the most common and functional. Traditional approaches to grasp prediction often rely on unimodal data sources, limiting their ability to capture the complexity of real-world scenarios. In this work, grasp prediction models that integrate both EMG signals and contextual (task- and product-related) information have been explored to improve the prediction of cylindrical grasps during reaching movements. Three model architectures are presented: an EMG processing model based on convolutions that analyzes forearm surface EMG data, a fully connected model for processing contextual information, and a hybrid architecture combining both inputs resulting in a multimodal model. The results show that context has great predictive power. Variables such as object size and weight (product-related) were found to have a greater impact on model performance than task height (task-related). Combining EMG and product context yielded better results than using each data mode separately, confirming the importance of product context in improving EMG-based models of grasping. Full article

Tomato leaf curl New Delhi virus (ToLCNDV) and tomato yellow leaf curl virus (TYLCV) are two important viral pathogens that severely affect Solanaceae and Cucurbitaceae plants. In order to reduce the further spread of these viruses, it is crucial to establish an efficient and reliable method to accurately detect the viruses. In this study, a multiplex PCR assay for the simultaneous detection of TYLCV and ToLCNDV was established. Three primer pairs designed from conserved regions within the coat protein or movement protein-encoding regions of the respective viruses were employed in the assay. The optimization of parameters such as primer concentration was set at 0.15 μM/0.15 μM, 0.25 μM/0.25 μM, and 0.50 μM/0.50 μM for ToLCNDV-DNA-A-F/R, TYLCV-F/R, and ToLCNDV-DNA-B-F/R primer pairs. At optimal primer concentrations, the multiplex PCR method demonstrates effective performance with an annealing temperature ranging from 51 °C to 66 °C. The specificity of the assay evaluated by testing against other begomoviruses showed no evidence of cross-amplification. Further sensitivity analysis performed using a serially diluted plasmid containing viral targets as templates demonstrated high sensitivity with a detection limit of 10³ copies/μL. Field surveys utilizing the multiplex PCR assay successfully identified the infection of TYLCV and ToLCNDV in field-collected samples. Full article

The motor cortex of non-human primates plays a key role in brain–machine interface (BMI) research. In addition to recording cortical neural signals, accurately and efficiently capturing the hand movements of experimental animals under unconstrained conditions remains a key challenge. Addressing this challenge can deepen our understanding and application of BMI behavior from both theoretical and practical perspectives. To address this issue, we developed a deep learning framework that combines Yolov5 and RexNet-ECA to reliably detect the hand joint positions of freely moving primates at different distances using a single camera. The model simplifies the setup procedure while maintaining high accuracy, with an average keypoint detection error of less than three pixels. Our method eliminates the need for physical markers, ensuring non-invasive data collection while preserving the natural behavior of the experimental subjects. The proposed system exhibits high accuracy and ease of use compared to existing methods. By quickly and accurately acquiring spatiotemporal behavioral metrics, the method provides valuable insights into the dynamic interplay between neural and motor functions, further advancing BMI research. Full article

As an anti-predation behavior, shoaling enhances survival among prey species by reducing individual predation risk through mechanisms like the dilution effect and collective vigilance. Zebrafish—a highly social and genetically tractable species—are valuable for studying these behaviors. The present study examined zebrafish’s social preferences in a 3-chamber open-tank free-swim task, assessing whether visual cues alone could distinguish between an intact and an alarmed shoal exposed to the synthetic alarm substance H3NO. Subjects were allowed to freely associate with either shoal while their behaviors were recorded and analyzed. The results reveal a significant preference for proximity to the intact shoal, indicating zebrafish’s ability to visually discern threat levels. Subjects spent nearly twice as much time in the zone near the intact shoal, with reduced freezing and faster movement velocities compared to the alarmed shoal zone. Males exhibited more freezing behavior than females, consistent with sex-specific strategies in threat response. These findings underscore zebrafish’s reliance on visual cues for social responding under predatory threat and highlight sex-based differences in threat perception. This research expands the understanding of zebrafish’s social dynamics and provides a robust framework for future exploration of the neural mechanisms underlying social behavior and threat assessment in zebrafish. Full article

Background/Objectives: Parkinson’s Disease (PD) and other forms of parkinsonism share motor symptoms, including tremor, bradykinesia, and rigidity. The overlap in their clinical presentation creates a diagnostic challenge, as conventional methods rely heavily on clinical expertise, which can be subjective and inconsistent. This highlights the need for objective, data-driven approaches such as machine learning (ML) in this area. However, applying ML to clinical datasets faces challenges such as imbalanced class distributions, small sample sizes for non-PD parkinsonism, and heterogeneity within the non-PD group. Methods: This study analyzed wearable sensor data from 260 PD participants and 18 individuals with etiologically diverse forms of non-PD parkinsonism, which were collected during clinical mobility tasks using a single sensor placed on the lower back. We evaluated the performance of ML models in distinguishing these two groups and identified the most informative mobility tasks for classification. Additionally, we examined the clinical characteristics of misclassified participants and presented case studies of common challenges in clinical practice, including diagnostic uncertainty at the patient’s initial visit and changes in diagnosis over time. We also suggested potential steps to address the dataset challenges which limited the models’ performance. Results: Feature importance analysis revealed the Timed Up and Go (TUG) task as the most informative for classification. When using the TUG test alone, the models’ performance exceeded that of combining all tasks, achieving a balanced accuracy of 78.2%, which is within 0.2% of the balanced diagnostic accuracy of movement disorder experts. We also identified differences in some clinical scores between the participants correctly and falsely classified by our models. Conclusions: These findings demonstrate the feasibility of using ML and wearable sensors for differentiating PD from other parkinsonian disorders, addressing key challenges in its diagnosis and streamlining diagnostic workflows. Full article