Search Results (384)

In the study of biomechanical models, balance represents a complex problem due to the issue of indeterminate forces while standing. In order to solve this problem, it is essential to measure the ground reaction forces (GRFs) applied to each foot independently. The present work proposes a methodology for determining the independent GRF applied to each foot while standing when only one forceplate is available. For this purpose, an analytical method is proposed to determine the distribution of vertical GRFs and the position of the independent center of pressure (CoP) in each foot. Concurrently, several neural network (NN) models are trained to improve the results obtained. This hypothesis is experimentally validated by a self-developed device that allows one to simultaneously obtain the vertical GRF and CoP location of each foot at the same time that the GRF and the global CoP location are obtained from a single forceplate. The results obtained achieve a CoP position error of less than 8% and a vertical force error of 2%. The analytical hypothesis is demonstrated to offer a satisfactory level of precision, while the NN is shown to result in considerable improvement in some cases. Full article

Background: This study was performed to validate the addition of capacitive-based pressure sensors to an existing smart sock developed by the research team. This study focused on evaluating the accuracy of soft robotic sensor (SRS) pressure data and its relationship with laboratory-grade Kistler force plates in collecting ground force reaction data. Methods: Nineteen participants performed walking trials while wearing the smart sock with and without shoes. Data was collected simultaneously with the sock and the force plates for each gait phase including foot-flat, heel-off, and midstance. The correlation between the smart sock and force plates was analyzed using Pearson’s correlation coefficient and R-squared values. Results: Overall, the strength of the relationship between the smart sock’s SRS data and the vertical ground reaction force (GRF) data from the force plates showed a strong correlation, with a Pearson’s correlation coefficient of 0.85 ± 0.1; 86% of the trials had a value higher than 0.75. The linear regression models also showed a strong correlation, with an R-squared value of 0.88 ± 0.12, which improved to 0.90 ± 0.07 when including a stretch-SRS for measuring ankle flexion. Conclusions: With these strong correlation results, there is potential for capacitive pressure sensors to be integrated into the proposed device and utilized in telehealth and sports performance applications. Full article

Background: Residual deficits after early treatment of developmental dysplasia of the hip (DDH) using osteotomy often led to asymmetrical gait deviations with increased repetitive rates of ground reaction force (GRF) in both hips, resulting in a higher risk of early osteoarthritis. This study investigated lower limb inter-joint coordination and swing foot control during level walking in adolescents with early-treated unilateral DDH. Methods: Eleven female adolescents treated early for DDH using Pemberton osteotomy were compared with 11 age-matched healthy controls. The joint angles and angular velocities of the hip, knee, and ankle were measured, and the corresponding phase angles and continuous relative phase (CRP) for hip–knee and knee–ankle coordination were obtained. The variability of inter-joint coordination was quantified using the deviation phase values obtained as the time-averaged standard deviations of the CRP curves over multiple trials. Results: The DDH group exhibited a flexed posture with increased variability in knee–ankle coordination of the affected limb throughout the gait cycle compared to the control group. In contrast, the unaffected limb compensated for the kinematic alterations of the affected limb with reduced peak angular velocities but increased knee–ankle CRP over double-limb support and trajectory variability over the swing phase. Conclusions: The identified changes in inter-joint coordination in adolescents with early treated DDH provide a plausible explanation for the previously reported increased GRF loading rates in the unaffected limb, a risk factor of premature OA. Full article

Conventional ground reaction force (GRF) and load rate (LR) analyses may overlook temporal and waveform characteristics that reflect injury status and acuity. This study used an alternative GRF processing methodology to characterize GRF waveforms among runners with symptomatic medial tibial stress fractures (MTSS) and those recovering from tibial stress fractures (TSF; both unilateral [UL] and bilateral [BL]). This cross-sectional analysis of runners (n = 66) included four groups: symptomatic MTSS, recovering from UL or BL TSF, or uninjured case-matched controls. Participants ran at self-selected speed on an instrumented treadmill. Kinematics were collected with a 3D optical motion analysis system. Double-Gaussian models described the biphasic loading pattern of running gait (initial impact, active phases). Gaussian parameters described relative differences in the GRF waveform by injury condition. LR was calculated using the central difference numerical derivative of the raw normalized net force data. During the impact phase (0–20% of stance), controls and BL TSF produced higher GRF amplitudes than UL TSF and MTSS (p < 0.05). BL TSF and controls had greater maximal positive LR and minimum LR than UL TSF and MTSS. Peak medial GRF was 18–43% higher in the BL TSF group than in MTSS and UL TSF (p < 0.05). Correlations existed between tibial pain severity and early stance net GRF (r = 0.512; p = 0.016) and between pain severity and the duration since diagnosis for LR values during the impact phase (r values = 0.389–0.522; all p < 0.05). Collectively, these data suggest that this waveform modeling approach can differentiate injury status and pain acuity in runners. Early stance GRF and LR may offer novel insight into the management of running-related injuries. Full article

Background/Objectives: This study investigated the relationship between glycemic control and increased glomerular filtration rate (eGFR), as assessed by serum creatinine and the CKiD equation in children and adolescents with T1DM. Methods: This cross-sectional observational study involved 80 T1DM patients (4–19 years) attending the Interdisciplinary Center for Diabetes. Biochemical, anthropometric, and skeletal muscle mass parameters were evaluated. The GFR was estimated using the CKiD equation expressed in mL/min/1.73 m². Results: Our results showed that nearly 19.0% of the included patients presented increased values for eGFR, and most had poor glycemic control. Patients with HbA1c levels above 8% presented eGRF > 130. There was a positive correlation between hyperglycemia, elevated HbA1c, and fat percentage with higher eGRF values. In addition, the reduction in lean mass and skeletal muscle mass was related to elevated eGRF. Conclusions: Our study indicates that children and adolescents with T1DM who have elevated HbA1c, lower lean mass, and less than five years of diagnosis of diabetes are more likely to present higher eGRF values. Full article

Flavonoids are key bioactive compounds in plants that play important defense roles against abiotic stress and are involved in plant growth and development. Methyl jasmonate (MeJA) is a significant growth regulator that promotes the accumulation of flavonoids in a variety of plants, but the effect of MeJA in Hedera helix remains poorly understood. In the present study, the flavonoid content was significantly increased after MeJA treatment and peaked at 6 h post-treatment. A total of 31,931 genes were identified using transcriptome, and 6484 DEGs were identified at 6 h post-treatment. Through GO and KEGG enrichment analysis, it was shown that DEGs were primarily enriched in phenylpropanoid biosynthesis pathways. Based on the putative transcription factors derived from DEGs, growth-regulating factor (GRF), a transcription factor potentially linking MeJA signaling to flavonoid accumulation and participating in plant growth and stress responses, was further identified. A total of 20 Hh-GRFd genes were identified on the whole genome level and clustered into five phylogenetic groups with conserved subfamily characteristics. Abundant MeJA-responsive cis-elements were presented in the promoter regions of HhGRF1-HhGRF20. They exhibited a tissue-specific expression variation, and HhGRF10 was dominantly expressed in leaves of H. helix. Notably, HhGRF10 exhibited MeJA-induced expression that correlated temporally with flavonoid accumulation, suggesting that HhGRF10 might play a potential role in promoting flavonoid biosynthesis, and overexpression and knockout assay substantiated this conclusion. The finding provides the first transcriptome-wide resource for flavonoid biosynthesis in H. helix and identifies the candidate GRF-mediated regulator for flavonoid accumulation. Full article

The 14-3-3 proteins play crucial roles in regulating plant growth, development, signal transduction and abiotic stress responses. However, there exists a scarcity of research on the role of 14-3-3 proteins in responding to abiotic stress in apples. In this study, we isolated the MdGRF22 gene from the apple 14-3-3 family. Through the screening of interacting proteins and genetic transformation of Arabidopsis thaliana and apple callus tissues, the function of the MdGRF22 gene under drought stress was verified. The coding sequence (CDS) of MdGRF22 consists of 786 bp and encodes for 261 amino acids. Through sequence alignment, the conserved 14-3-3 domain was identified in MdGRF22 and its homologous genes, which also share similar gene structures and conserved motifs. Subcellular localization revealed that the MdGRF22 protein was predominantly located in the cytoplasm and cell membrane. The yeast two-hybrid (Y2H) analysis demonstrated a possible interaction between MdGRF22 and MdSK. In addition, MdGRF22 transgenic plants generally exhibited lower superoxide dismutase (SOD), catalase (CAT) and peroxidase (POD) activities, higher malondialdehyde (MDA) levels and relative electrolyte leakage under drought conditions compared with wild-type (WT) plants. Our study suggests that MdGRF22 may reduce the drought resistance of transgenic A. thaliana and callus tissues by interacting with MdSK. This study provides a theoretical basis for further exploring the function of 14-3-3 family genes. Full article

The Cerasus humilis, a perennial shrub belonging to the Cerasus genus, is native to China and holds significant ecological and economic importance. Growth regulation factors (GRF) are a family of transcription factors (TF) that play a key role in plant growth and development. This research entailed an in-depth examination of the GRF family in C. humilis, exploring its significance in the evolution of C. humilis. Twelve GRF genes were identified in the C. humilis genome. Named separately as ChGRF1-Chumilis15987.1, ChGRF2-Chumilis25207.1, ChGRF3-Chumilis26233.1, ChGRF4-Chumilis08578.3, ChGRF5-Chumilis18808.1, ChGRF6-Chumilis12052.1, ChGRF7-Chumilis10417.1, ChGRF8-Chumilis01608.1, ChGRF9-Chumilis14057.1, ChGRF10-Chumilis12169.1, ChGRF11-Chumilis14952.1, and ChGRF12-Chumilis07534.1. Phylogenetic analysis divided twelve GRF genes into five subfamilies. The gene structure, pattern, and cis-regulatory components of the GRF gene family were analyzed. In addition, according to collinearity analysis, there are six collinearity with Arabidopsis, twelve collinearity with Malus pumila, eight collinearity with Vitis vinifera, and three collinearity with Oryza sativa. Intraspecific collinearity analysis revealed the presence of three pairs of tandem repeat genes in the dwarf cherry genome. Identifying cis-acting elements revealed the prominent presence of gibberellin reaction elements, which are widely distributed in the promoter region. Cluster heatmap analysis showed that ChGRF2 had the highest expression levels in fruits and stems. ChGRF3 is highly expressed in red fruits of different colors, while ChGRF6 and ChGRF12 are highly expressed in yellow fruits. This study mainly focused on dwarf cherries treated with gibberellin. As the treatment time increased, the ChGRF gene showed different expression levels. ChGRF2, ChGRF3, ChGRF6, and ChGRF12 were up-regulated under gibberellin treatment. These genes all contain hormone-responsive cis-acting elements, indicating tht the ChGRF gene family plays a vital role under gibberellin treatment in C. humilis. The results laid the foundation for further research on the biological functions of the GRF genes in C. humilis. Full article

Vertical ground reaction force (vGRF) plays an important role in the study of running-related injuries (RRIs). This study explores the synchronization method between inertial measurement unit (IMU) and vGRF data of running and develops ANN models to accurately predict vGRF. Fifteen runners participated in this study. Acceleration data and vGRF values of eight rearfoot strikers and seven forefoot strikers running at 12, 14, and 16 km/h were collected by a single IMU and an instrumented treadmill. The sliding time window synchronization (STWS) algorithm was developed to sync IMU data with vGRF data. The wavelet neural network model (WNN) and feed-forward neural network model (FFNN) were adapted to predict vGRF using three-axis or sagittal-axis acceleration data in the stance phase, respectively. One rearfoot striker and one forefoot striker were randomly selected as a test set, while the other participants formed training sets. After synchronization, mean absolute errors for stride time of the IMU and vGRF data were less than 11.2 ms. The coefficient of multiple correlations for vGRF measured curves and predicted curves was more than 0.97. The normalized root mean square errors (NRMSEs) between two curves were 4.6~9.2%, and R² was 0.93~0.99. For peak vGRF, the NRMSEs were 1.6~8.2%, except for rearfoot strike runners at 16 km/h using the FFNN model (10.7% and 11.1%). The Bland–Altman plots indicate that the errors for both the WNN and FFNN models are within acceptable limits. The STWS algorithm can effectively achieve the data synchronization between the IMU and the force plate during running. Both WNN and FFNN models demonstrated good accuracy and agreement in predicting vGRF. Using sagittal-axis acceleration data may be an ideal model with good prediction accuracy and less input data. This work provides direction for developing ANN models of personalized monitoring of lower limb load. Full article

As a major driving force in the current technological revolution, artificial intelligence (AI) has significantly accelerated the intelligence, automation, and informatization of enterprises, thereby inevitably influencing the sustainable development performance (SDP) of manufacturing enterprises. This study takes the “Next-Generation AI Innovation Pilot Zone” policy as a case study and utilizes a multi-period difference-in-differences (DID) model and machine learning techniques to investigate the impact of AI on the SDP of Chinese manufacturing enterprises. The findings indicate that AI contributes to improving the SDP of manufacturing firms. The mechanism analysis reveals that AI enhances SDP via a green innovation effect, cost-saving effect, and digital transformation effect. The moderation analysis further shows that the CEO duality inhibits the positive impact of AI on SDP. The heterogeneity results based on the GRF model indicate that the positive relationship between AI and SDP is pronounced in state-owned enterprises and heavily polluting firms. This study not only enriches the literature on the micro-level environmental effects of AI but also provides valuable insights for governments and businesses seeking to improve SDP. Full article

The response to perturbations in the gait of elderly and young individuals can differ due to various factors, such as age-related changes in sensorimotor function, muscle strength, and balance control. This study aimed to identify and compare compensatory kinematic and kinetic gait strategies in response to sudden treadmill perturbations applied during the Pre-Swing phase in young and older adults. The analysis focused on determining age-related differences in joint behavior and force production under perturbation stress, with implications for fall prevention. Twenty-one young and an equal number of elderly healthy females walked on a treadmill in a virtual environment (GRAIL, Motek). Unexpected perturbations were applied five times. Principal Component Analysis (PCA) and k-means clustering identified three distinct compensatory strategies per limb. Young adults primarily employed Strategies I (42.2%) and II (40%), while older adults most often selected Strategy II (45.5%). Statistical analysis (SPM and Mann-Whitney U test, p = 0.05) showed significant between-group differences in joint angles and torques across the gait cycle. For instance, in Strategy I, young participants had significantly lower ankle plantarflexion angles (p < 0.01) and hip extension torques (p < 0.05) compared to the elderly. Strategy II in older adults showed significantly higher vGRF minimums (p < 0.01) and anterior-posterior GRF peaks (p < 0.001). The elderly adopted strategies compatible with their neuromuscular capacity rather than those minimizing joint load, as observed in the young group. These findings offer novel insights into age-related compensatory mechanisms and highlight the importance of tailored fall-prevention strategies based on biomechanical response patterns. Full article

The foot deformation approach (FDA) estimates the ground reaction force (GRF) and moment (GRM) from kinematic data with practical accuracy, low computational cost, and no requirement for training data. Our previous study demonstrated practical estimation accuracy of the FDA under barefoot conditions. However, since the FDA estimates GRFs and GRMs based on foot deformation under body weight, there are concerns about its applicability to footwear conditions, where the foot deformation characteristics differ from those of bare feet. Following the issue, this study conducted a walking experiment at three different speeds with running shoes and sneakers to investigate the impact of footwear on GRF prediction using the FDA. The results showed that the FDA successfully provided practical accuracy when shoes were worn, comparable to that for a barefoot participant. The FDA offers advantages for estimating GRFs and GRMs for the footwear condition, while eliminating the need for collecting training data and enabling rapid analysis and feedback in clinical settings. Although the FDA cannot fully eliminate the effects of footwear and movement speed on prediction accuracy, it has the potential to serve as a convenient biomechanical-based method for estimating GRFs and GRMs during sports and daily activities with footwear. Full article

Objective: We developed two neural network models to estimate the three-dimensional ground reaction force (GRF) and center of pressure (COP) based on marker trajectories obtained from a markerless motion capture system. Methods: Gait data were collected using two cameras and three force plates. Each gait dataset contained kinematic data and kinetic data from the stance phase. A multi-layer perceptron (MLP) and convolutional neural network (CNN) were constructed to estimate each component of GRF and COP based on the three-dimensional trajectories of the markers. A total of 100 samples were randomly selected as the test set, and the estimation performance was evaluated using the correlation coefficient (r) and relative root mean square error (rRMSE). Results: The r-values for MLP in each GRF component ranged from 0.918 to 0.989, with rRMSEs between 5.06% and 12.08%. The r-values for CNN in each GRF component ranged from 0.956 to 0.988, with rRMSEs between 6.03–9.44%. For the COP estimation, the r-values for MLP ranged from 0.727 to 0.982, with rRMSEs between 6.43% and 27.64%, while the r-values for CNN ranged from 0.896 to 0.977, with rRMSEs between 6.41% and 7.90%. Conclusions: It is possible to estimate GRF and COP from markerless motion capture data. This approach provides an alternative method for measuring kinetic parameters without force plates during gait analysis. Full article

Small RNAs (sRNAs) are pivotal in regulating gene expression and are involved in a diverse array of biological processes. Among these, microRNAs (miRNAs) and phased small interfering RNAs (phasiRNAs) have been extensively investigated over the past decades. We conducted an in-depth analysis of deep sequencing data from the gymnosperm Ginkgo biloba, encompassing sRNA, transcriptome, and degradome libraries. Our analysis identified a total of 746 miRNAs and 654 phasiRNA precursor (PHAS) loci, with 526 (80%) of the PHAS loci predicted to be triggered by 515 miRNAs (69%). Several miRNA-PHAS modules, particularly the miR159/miR319-PHAS module, were found to potentially regulate reproductive development by targeting GAMYB genes and triggering phasiRNA biogenesis. The miR390-PHAS module appears to be involved in flavonoid biosynthesis by targeting key enzyme genes such as chalcone synthase (CHS) and anthocyanin synthase (ANS). Through target gene identification and coexpression analysis, we uncovered two distinct models of complex regulatory networks: growth-related factors like ARF and GRF seem to be regulated exclusively by miRNAs (Model 1), while certain disease resistance-related genes are predicted to be regulated by both miRNAs and phasiRNAs (Model 2), indicating diverse regulatory mechanisms across different biological processes. Overall, our study provides a comprehensive annotation of miRNA and PHAS loci in G. biloba and elucidates a post-transcriptional regulatory network, offering novel insights into sRNA research in gymnosperms. Full article

Traditional force plate-based systems offer high measurement precision but are limited to laboratory settings, restricting their use in real-world environments. To address this, we propose a method for estimating a three-axis ground reaction force (GRF) and two-axis center of pressure (CoP) using a shoe embedded with three uniaxial load cells. The estimation was conducted under five gait conditions: straight walking, turning, uphill, downhill, and running. Data were collected from 40 healthy young adults. Four deep-learning models—Fully Connected Neural Network (FCNN), Convolutional Neural Network (CNN), Sequence-to-Sequence Long Short-Term Memory (Seq2Seq-LSTM), and Transformer—were evaluated. Among them, Seq2Seq-LSTM and CNN achieved the highest performance in predicting both GRF and CoP. However, the medio-lateral (ML) components showed lower accuracy than the vertical and anterior–posterior directions. In slope conditions, particularly for vertical GRF, relatively higher root mean-square error (RMSE) values were observed. Despite some variation across gait types, predicted values showed high agreement with measurements. Compared with previous studies, the proposed method achieved comparable or better performance with a minimal sensor setup. These findings highlight the feasibility of accurate GRF and CoP estimation in diverse gait scenarios and support the potential for real-world applications. Future work will focus on sensor optimization and broader population validation. Full article