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ObjectiveThis study aims to investigate the plantar biomechanics of healthy young males as they descend a single transition step from varying heights.MethodsThirty healthy young males participated the experiment using the F-scan insole plantar pressure system in which participants made single transition steps descent from four step heights (5, 15, 25, and 35 cm), leading with their dominant or non-dominant foot. Plantar pressure data were collected for 5 s during the period between landing touchdown and standing on the ground. Landing at each step height was repeated three times, with a five-minute rest between different height trials.ResultsAt 5 cm and 15 cm steps, participants demonstrated a rearfoot landing strategy on both sides. However, forefoot contact was observed at heights of 25 cm and 35 cm. Parameters related to center of plantar pressure (COP) of the leading foot were significantly larger compared to the trailing foot (P < 0.001), increased with higher step heights. Vertical ground reaction forces for the biped, leading and trailing feet decreased with increasing step height (all P < 0.05). The leading foot had a higher proportion of overall and forefoot loads, and a lower proportion of rearfoot load compared to the trailing foot (P < 0.001). The overall load on the dominant side was lower than that on the non-dominant side for both the leading and trailing feet (P < 0.001). For the trailing foot, forefoot load on the dominant side was lower than that on the non-dominant side, however, the opposite result appeared in rearfoot load (P < 0.001). Upon the leading foot landing, forefoot load exceeded the rearfoot load for the dominant (P < 0.001) and non-dominant sides (P < 0.001). Upon the trailing foot landing, forefoot load was lower than the rearfoot load for the dominant (P < 0.001) and non-dominant sides (P = 0.019).ConclusionWhen the characteristics of biomechanical stability are compromised by step height, landing foot, and footedness factors — due to altered foot landing strategies, changing COP, or uneven force distribution — ability to control motion efficiently and respond adaptively to the forces experienced during movement is challenged, increasing the likelihood of loss of dynamic balance, with a consequent increased risk of ankle sprains and falls.

An attention aware edge-node exchange graph neural network (AENN) model was proposed, which used edge-node switched convolutional graph neural network method for graph encoding in a graph structured data representation framework for semi supervised classification and regression analysis.AENN is an universal graph encoding framework for embedding graph nodes and edges into a unified latent feature space.Specifically, based on the original undirected graph, the convolution between edges and nodes was continuously switched, and during the convolution process, attention mechanisms were used to assign weights to different neighbors, thereby achieving feature propagation.Experimental studies on three datasets show that the proposed method has significant performance improvements in semi-supervised classification and regression analysis compared to existing methods.

Progressive growing of generative adversarial networks (PGGAN) is an adversarial network model that can generate high-resolution images.However, when the categories of samples are unbalanced, or the categories of samples are too similar or too dissimilar, it is prone to produce mode collapse, resulting in poor image generation effect.A progressive growing of conditional generative adversarial networks (PGCGAN) model was proposed.The idea of conditional generative adversarial networks (CGAN) was introduced into PGGAN.Using category information as condition, PGGAN was improved in two aspects of network structure and mini-batch standard deviation, and the phenomenon of model collapse in the process of image generation was alleviated.In the experiments on the three data sets, compared with PGGAN, PGCGAN has a greater degree of improvement in inception score and Fréchet inception distance, two evaluation indicators for image generation, and the generated images have higher diversity and authenticity; and PGCGAN multiple unrelated datasets can be trained simultaneously without crashing, and high-quality images can be produced in datasets with imbalanced categories or data that are too similar and dissimilar.

Traditional attention-based recommendation algorithms only use position embeddings to model user behavior sequences, however, ignore specific timestamp information, resulting in poor recommendation performance and overfitting of model training.The multi-task matrix factorization recommendation model based on time attention was proposed, which used the attention mechanism to extract the neighborhood information for the user and item embedding, and used the Ebbinghaus forgetting curve to describe the changing characteristics of user interests over time.The model training process introduced a reinforcement learning strategy of experience replay to simulate the human memory review process.Experimental results on real datasets show that the proposed model has better recommendation performance than existing recommendation models.

Usually the edges of the graph contain important information of the graph.However, most of deep learning models for graph learning, such as graph convolutional network (GCN) and graph attention network (GAT), do not fully utilize the characteristics of multi-dimensional edge features.Another problem is that there may be noise in the graph that affects the performance of graph learning.Multilayer perceptron (MLP) was used to denoise and optimize the graph data, and a multi-channel learning edge feature method was introduced on the basis of GCN.The multi-dimensional edge attributes of the graph were encoded, and the attributes contained in the original graph were modeled as multi-channel.Each channel corresponds to an edge feature attribute to constrain the training of graph nodes, which allows the algorithm to learn multi-dimensional edge features in the graph more reasonably.Experiments based on Cora, Tox21, Freesolv and other datasets had proved the effectiveness of denoising methods and multi-channel methods.

Objective: The plantar pressure analysis technique was used to explore the static balance ability and stability of healthy adult males under the influence of visual and step height factors during bipedal and unipedal stances.Methods: Thirty healthy adult males volunteered for the study. Experiments used the F-scan plantar pressure analysis insoles to carry out with eyes open (EO) and eyes closed (EC) at four different step heights. The plantar pressure data were recorded for 10 s and pre-processed to derive kinematic and dynamic parameters.Results: For unipedal stance, most of kinematic parameters of the subjects’ right and left feet were significantly greater when the eyes were closed compared to the EO condition and increased with step height. The differences in toe load between right and left feet, open and closed eyes were extremely statistically significant (p < 0.001). The differences in midfoot load between the EO and EC conditions were statistically significant (p = 0.024) and extremely statistically significant between the right and left feet (p < 0.001). The difference in rearfoot load between EO and EC conditions was extremely statistically significant (p < 0.001) and statistically significant (p = 0.002) between the right and left feet. For bipedal stance, most of kinematic parameters of the subjects’ EO and EC conditions were statistically significant between the right and left feet and increased with step height. The overall load’s difference between EO and EC states was statistically significant (p = 0.003) for both feet. The overall load’s difference between the right and left feet was extremely statistically significant (p < 0.001) in the EC state. The differences between the right and left feet of the forefoot and rearfoot load with EO and EC suggested that the right foot had a smaller forefoot load, but a larger rearfoot load than the left foot (p < 0.001). The differences between the forefoot and rearfoot load of the subjects’ both feet with EO and EC were extremely statistically significant (p < 0.001).Conclusion: Both visual input and step height factors, even the dominant foot, act on kinematic and dynamic parameters that affect the maintenance of static balance ability.

SARS-CoV-2 causes a spectrum of clinical symptoms from respiratory damage to gastrointestinal disorders. Intestinal infection of SARS-CoV-2 triggers immune response. However, the cellular mechanism that how SARS-CoV-2 initiates and induces intestinal immunity is not understood. Here, we exploited SARS-CoV-2-GFP/ΔN trVLP pseudo-virus system and demonstrated that RIG-I and DHX15 are required for sensing SARS-CoV-2 and inducing cellular immune response through MAVS signaling in intestinal epithelial cells (IECs) upon SARS-CoV-2 infection. NLRP6 also engages in the regulation of SARS-CoV-2 immunity by producing IL-18. Furthermore, primary cellular immune response provoked by SARS-CoV-2 in IECs further cascades activation of MAIT cells and produces cytotoxic cytokines including IFN-γ, granzyme B via an IL-18 dependent mechanism. These findings taken together unveil molecular basis of immune recognition in IECs in response to SARS-CoV-2, and provide insights that intestinal immune cross-talk with other immune cells triggers amplified immunity and probably contributes to immunopathogenesis of COVID-19.

As an effective recommendation method，collaborative filtering typically has the data sparsity and cold-start problems. It was proposed that using multiple data sources of social network to overcome the above problems. First of a11，both the rating similarity and the social trust between users were considered to resolve the data sparsity problem. Then a simple and effective trust reasoning method was proposed to identify the implicit trust relationship between users. In order to solve the cold-start problem，information of the category of items and domain experts was used for joint recommendation. Experimental results show that the proposed algorithm has significantly better precision and reca11 than existing methods.

We investigate the production of Xb in the process Υ(5S,6S)→γXb, where Xb is assumed to be the counterpart of X(3872) in the bottomonium sector as BB¯⁎ molecular state. We use the effective Lagrangian based on the heavy quark symmetry to explore the rescattering mechanism and calculate their production ratios. Our results have shown that the production ratios for Υ(5S,6S)→γXb are orders of 10-5 with reasonable cutoff parameter range α≃2~3. The sizeable production ratios may be accessible at the future experiments like forthcoming BelleII, which will provide important clues to the inner structures of the exotic state Xb.

Etiologic diagnoses of lower respiratory tract infections (LRTI) have been relying primarily on bacterial cultures that often fail to return useful results in time. Although DNA-based assays are more sensitive than bacterial cultures in detecting pathogens, the molecular results are often inconsistent and challenged by doubts on false positives, such as those due to system- and environment-derived contaminations. Here we report a nationwide cohort study on 2986 suspected LRTI patients across P. R. China. We compared the performance of a DNA-based assay qLAMP (quantitative Loop-mediated isothermal AMPlification) with that of standard bacterial cultures in detecting a panel of eight common respiratory bacterial pathogens from sputum samples. Our qLAMP assay detects the panel of pathogens in 1047(69.28%) patients from 1533 qualified patients at the end. We found that the bacterial titer quantified based on qLAMP is a predictor of probability that the bacterium in the sample can be detected in culture assay. The relatedness of the two assays fits a logistic regression curve. We used a piecewise linear function to define breakpoints where latent pathogen abruptly change its competitive relationship with others in the panel. These breakpoints, where pathogens start to propagate abnormally, are used as cutoffs to eliminate the influence of contaminations from normal flora. With help of the cutoffs derived from statistical analysis, we are able to identify causative pathogens in 750 (48.92%) patients from qualified patients. In conclusion, qLAMP is a reliable method in quantifying bacterial titer. Despite the fact that there are always latent bacteria contaminated in sputum samples, we can identify causative pathogens based on cutoffs derived from statistical analysis of competitive relationship.ClinicalTrials.gov NCT00567827.