Language ; Pathology ; Speech ; Speech-language Pathology ; Weights And Measures

Human ; Research ; Information Dissemination ; Reference Standards

Tujia ethnic group medicine Xuetong is derived from Kadsura heteroclita, the stem of which has the medicinal value for anti-rheumatoid arthritis, liver protection, anti-tumor, anti-oxidation effects, and has been widely used in Hunan and Guangdong in China. The selection of reliable and stable reference genes is the basis for subsequent molecular research on K. heteroclita. In this study, GAPDH, TUA, Actin, UBQ, EF-1α, 18S-rRNA, CYP, UBC, TUB, H2A, and RPL were selected as candidate reference genes in Kadsura heteroclita. The gene expression levels of the 11 candidate reference genes of K. heteroclita in its 6 different parts(stem-inside of the cambium, stem-outside of the cambium, fruit, flower, root, and leaf) and under different intervention conditions [drought stress, salt stress, and methyl jasmonate(MeJA) treatment] were detected by quantitative real-time polymerase chain reaction(qRT-PCR). The expression stability of the 11 candidate reference genes was comprehensively analyzed and evaluated by geNorm, NormFinder, ΔCT algorithm, and RefFinder software. The results showed that the expression of UBC and RPL was relatively stable in 6 different parts, and UBC and GAPDH genes were relatively stable under different intervention conditions. To verify the reliability of reference genes for K. heteroclita, this study further examined the relative expression levels of KhFPS, KhIDI, KhCAS, KhSQE, KhSQS, KhSQS-2, KhHMGS, KhHMGR, KhMVD, KhMVK, KhDXR, KhDXS, KhPMVK, and KhGGPS in different parts and under different intervention conditions, which might relate to the synthesis of the main component(Xuetongsu) of K. heteroclita. The results showed that with UBC and RPL or UBC and GAPDH as the reference genes, the expression trends of these 14 genes were basically consistent in different parts or under different intervention conditions for K. heteroclita. In conclusion, UBC can be used as a reference gene of K. heteroclita for its different parts and different intervention conditions, which lays a foundation for further research on the biosynthetic pathway of main components in K. heteroclita.
Real-Time Polymerase Chain Reaction/methods* ; Reference Standards ; Gene Expression Regulation, Plant ; Gene Expression Profiling ; Plant Proteins/metabolism* ; Drugs, Chinese Herbal

Meditation aims to guide individuals into a state of deep calm and focused attention, and in recent years, it has shown promising potential in the field of medical treatment. Numerous studies have demonstrated that electroencephalogram (EEG) patterns change during meditation, suggesting the feasibility of using deep learning techniques to monitor meditation states. However, significant inter-subject differences in EEG signals poses challenges to the performance of such monitoring systems. To address this issue, this study proposed a novel model-calibrated multi-source adversarial adaptation network (CMAAN). The model first trained multiple domain-adversarial neural networks in a pairwise manner between various source-domain individuals and the target-domain individual. These networks were then integrated through a calibration process using a small amount of labeled data from the target domain to enhance performance. We evaluated the proposed model on an EEG dataset collected from 18 subjects undergoing methamphetamine rehabilitation. The model achieved a classification accuracy of 73.09%. Additionally, based on the learned model, we analyzed the key EEG frequency bands and brain regions involved in the meditation process. The proposed multi-source domain adaptation framework improves both the performance and robustness of EEG-based meditation monitoring and holds great promise for applications in biomedical informatics and clinical practice.
Humans ; Electroencephalography/methods* ; Meditation ; Calibration ; Neural Networks, Computer ; Brain/physiology* ; Rest/physiology* ; Deep Learning ; Signal Processing, Computer-Assisted

In animal navigation, head direction is encoded by head direction cells within the olfactory-hippocampal structures of the brain. Even in darkness or unfamiliar environments, animals can estimate their head direction by integrating self-motion cues, though this process accumulates errors over time and undermines navigational accuracy. Traditional strategies rely on visual input to correct head direction, but visual scenes combined with self-motion information offer only partially accurate estimates. This study proposed an innovative calibration mechanism that dynamically adjusts the association between visual scenes and head direction based on the historical firing rates of head direction cells, without relying on specific landmarks. It also introduced a method to fine-tune error correction by modulating the strength of self-motion input to control the movement speed of the head direction cell activity bump. Experimental results showed that this approach effectively reduced the accumulation of self-motion-related errors and significantly enhanced the accuracy and robustness of the navigation system. These findings offer a new perspective for biologically inspired robotic navigation systems and underscore the potential of neural mechanisms in enabling efficient and reliable autonomous navigation.
Animals ; Neural Networks, Computer ; Calibration ; Spatial Navigation/physiology* ; Head Movements/physiology* ; Neurons/physiology* ; Models, Neurological ; Head/physiology* ; Action Potentials/physiology*

Medical robotic arm often encounters multi-source and nonlinear errors during the calibration process, making it difficult for traditional mathematical modeling methods to fully characterize system error features, thereby limiting further improvement in calibration accuracy. In this study, a robotic arm parameter error identification model is established, and a calibration method based on an adaptive long short-term memory (ALSTM) neural network is proposed. The method incorporates a particle swarm optimization (PSO) algorithm to optimize the weights of each layer of the LSTM neural network, enabling more effective fitting of robotic arm kinematic errors and ultimately yielding more accurate Denavit-Hartenberg (D-H) parameters. To validate the proposed approach, 110 sets of experimental data are collected using the HSR-JR680 robotic arm calibration system. Experimental results demonstrate that the ALSTM model reduces the root mean square error (RMSE) by 23.07%-80.39% compared to traditional calibration methods, and shortens the convergence time by 32.44% compared to a standard LSTM model. The optimized D-H parameters obtained meet the high-precision calibration requirements of medical robotic arm, confirming the effectiveness of the proposed method.
Calibration ; Neural Networks, Computer ; Algorithms ; Robotics ; Robotic Surgical Procedures ; Models, Theoretical

Objective:To establish normal reference value ranges for dynamic balance function parameters in healthy Chinese adults aged 20-69 years. Methods:A total of 100 healthy subjects were selected and evenly divided into five age groups: 20-29, 30-39, 40-49, 50-59, and 60-69 years, with equal gender distribution in each group. Balance function was assessed using the EquiTest system （NeuroCom）, with following tests performed Sensory Organization Test （SOT）, Motor Control Test （MCT）, Adaptation Test （ADT）, and Limits of Stability （LOS） test. All parameters were statistically analyzed and expressed as ±S. Results:The normal reference ranges for SOT, MCT, ADT, and LOS parameters were established for each age group. Multiple balance function parameters demonstrated a gradual decline with advancing age, with more pronounced deterioration observed after the age of 60. Specific findings included decreased vestibular ratio and reduced visual preference in SOT, as well as prolonged reaction time, impaired directional control, and reduced maximum excursion in the backward direction during LOS testing. Conclusion:This study is the first to establish age-specific reference ranges for dynamic balance function parameters in a healthy Chinese population aged 20-69 years, providing localized and objective criteria for the assessment of balance function and supporting clinical diagnosis of balance-related disorders in China.
Humans ; Middle Aged ; Adult ; Postural Balance/physiology* ; Reference Values ; Aged ; Male ; Female ; Young Adult ; Healthy Volunteers

OBJECTIVES: We propose a new melanoma segmentation model, SG-UNet, to enhance the precision of melanoma segmentation in dermascopy images to facilitate early melanoma detection. METHODS: We utilized a U-shaped convolutional neural network, UNet, and made improvements to its backbone, skip connections, and downsampling pooling sections. In the backbone, with reference to the structure of VGG, we increased the number of convolutions from 10 to 13 in the downsampling part of UNet to achieve a deepened network hierarchy that allowed capture of more refined feature representations. To further enhance feature extraction and detail recognition, we replaced the traditional convolution the backbone section with self-calibrated convolution to enhance the model's ability to capture both spatial and channel dimensional features. In the pooling part, the original pooling layer was replaced by Haar wavelet downsampling to achieve more effective multi-scale feature fusion and reduce the spatial resolution of the feature map. The global attention mechanism was then incorporated into the skip connections at each layer to enhance the understanding of contextual information of the image. RESULTS: The experimental results showed that the SG-UNet model achieved significantly improved segmentation accuracy on ISIC 2017 and ISIC 2018 datasets as compared with other current state-of-the-art segmentation models, with Dice reached 92.41% and 86.62% and IoU reaching 92.31% and 86.48% on the two datasets, respectively. CONCLUSIONS The proposed model is capable of effective and accurate segmentation of melanoma from dermoscopy images.
Melanoma/diagnosis* ; Humans ; Neural Networks, Computer ; Dermoscopy/methods* ; Skin Neoplasms ; Image Processing, Computer-Assisted/methods* ; Calibration ; Algorithms

The construction method and simulation parameter settings for the discrete element model of Jianwei Xiaoshi Granules, as the primary material of Jianwei Xiaoshi Tablets, are not yet clear. The accuracy of the simulation model significantly influences the dynamic response characteristics between granules. Therefore, it is necessary to calibrate the parameters to improve the accuracy of the simulation parameters. Using the repose angle of Jianwei Xiaoshi Granules as the response value, the response surface methodology was employed to optimize and calibrate the discrete element parameters. Physical experiments were conducted to determine the physical properties of Jianwei Xiaoshi Granules. Based on the Hertz-Mindlin with Johnson-Kendall-Roberts(JKR) V2 model and virtual simulation methods, a repose angle determination model was constructed in EDEM software. The repose angle was measured using image analysis and numerical fitting methods. The Plackett-Burman experiment was used to screen the initial parameters for significance in the discrete element simulation. The significant parameters were then subjected to a steepest ascent experiment to determine the optimal parameter range. Furthermore, based on the Box-Behnken experiment, a second-order regression equation between significant parameters and repose angle was established, with the repose angle of 37.64° in the physical experiment as the target value. The regression equation was optimized and solved. The significance screening experiment revealed that the granule-granule static friction coefficient, granule-granule rolling friction, and granule-steel plate rolling friction of Jianwei Xiaoshi Granules significantly influenced the simulated repose angle. The optimal parameter combination was found to be 0.330, 0.222, and 0.229. The simulation results with this optimal parameter combination showed that there was no significant difference between the simulated repose angle and the repose angle obtained in the physical experiment, with a relative error of 0.05%, which further validated the reliability of the calibrated discrete element parameters for Jianwei Xiaoshi Granules.
Drugs, Chinese Herbal/chemistry* ; Calibration ; Computer Simulation

Humans ; Transcatheter Aortic Valve Replacement ; Consensus ; Aortic Valve Stenosis/surgery* ; Aortic Valve/surgery* ; Reference Standards ; Treatment Outcome ; Heart Valve Prosthesis