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Knee osteoarthritis (KOA) is a chronic degenerative joint disease that occurs mostly in the elderly over the age of 60. Exercise therapy, represented by Tai Chi Chuan, has been proven to have good effective in relieving chronic pain caused by KOA. Long-term practice of Tai Chi Chuan can effectively improve knee and ankle proprioception, enhance dynamic stability and reduce the risk of falls in patients with KOA. However, there is no unified consensus on the specific mechanism of Tai Chi Chuan. Patients with KOA have obvious changes in their biomechanical characteristics. The changes in biomechanical parameters are the initial factors and pathological basis of KOA. This study explores the mechanical effects of Tai Chi Chuan on the knee joint of patients with KOA based on sports biomechanics and analyzes the potential mechanism of Tai Chi Chuan in improving KOA, in order to provide a theoretical basis for Tai Chi Chuan intervention in KOA. The theory of sports biomechanics involved in Tai Chi Chuan includes dynamic part, static part and kinematic part. From the perspective of sports biomechanics, the potential mechanism of Tai Chi Chuan to improve KOA includes four aspects: improving balance ability, improving stability (improving quadriceps muscle strength, coordinating muscle groups around the knee joint and reducing the medial load of the knee joint in patients with KOA), balancing intra-articular stress (reducing the peak stress of meniscus and cartilage, improving lower limb muscle strength and lower limb alignment) and reducing movement reaction force. However, there are still some inadequacies in Tai Chi Chuan practice, which may affect the analysis of sports biomechanics, specifically manifest in the following four aspects: (1) The training method for Tai Chi Chuan beginners may not be appropriate, which may increase the probability of muscle, ligament and joint injuries. (2) Tai Chi Chuan training movements are difficult. Subjects tend to have nonstandard movements when they learn in a short time, which easily affects the accuracy of research data. (3) The gradual increase of dropout rate is not conducive to data statistics. (4) Complete Tai Chi Chuan is time-consuming and difficult to analyze individually. At present, representative movements are mainly analyzed, and research on non-representative movements is scarce.

BackgroundAnti-N-methyl-D-aspartate receptor (NMDAR) encephalitis is a recently discovered autoimmune syndrome associated with psychosis, dyskinesia, and seizures. However, the underlying mechanisms of this disease remain unclear, in part because of a lack of suitable animal models.MethodsThis study describes a novel female C57BL/6 mouse model of anti-NMDAR encephalitis that was induced by active immunization against NMDARs using an amino terminal domain (ATD) peptide from the GluN1 subunit (GluN1356-385).ResultsTwelve weeks after immunization, the immunized mice showed significant memory loss. Furthermore, antibodies from the cerebrospinal fluid of immunized mice decreased the surface NMDAR cluster density in hippocampal neurons which was similar to the effect induced by the anti-NMDAR encephalitis patients' antibodies. Immunization also impaired long-term potentiation at Schaffer collateral-CA1 synapses and reduced NMDAR-induced calcium influx.ConclusionWe established a novel anti-NMDAR encephalitis model using active immunization with peptide GluN1356-385 targeting the ATD of GluN1. This novel model may allow further research into the pathogenesis of anti-NMDAR encephalitis and aid in the development of new therapies for this disease.

Objective To analyze the role of preoperative peripheral blood inflammatory parameters and postoperative lymph-node ratio (LNR) in the prognosis of gastric cancer patients treated with chemotherapy. Methods The neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), systemic immune inflammatory response index (SII), lymphocyte-to-monocyte ratio (LMR), Onodera prognostic nutritional index (OPNI), and LNR of 108 patients with gastric cancer were classified into high and low groups to analyze their prognostic value on the overall survival (OS) of gastric cancer patients treated with chemotherapy. The independent prognostic indicators were plotted in columns to predict the survival rate of gastric cancer patients. Results NLR was statistically significant in the prognostic assessment of gastric cancer patients treated with chemotherapy (P < 0.001). Moreover, the high PLR group, high SII group, high LNR group, N3 stage, TNM (Ⅲ-Ⅳ) stage, nerve invasion, and carcinoembryonic antigen (CEA) were independent risk factors for the prognosis of gastric cancer (all P < 0.05). These findings indicated that in predicting the prognosis of gastric cancer, the combination of LNR, PLR, and SII (AUC=0.875) was better than that of LNR, PLR, and SII alone or in pairwise combination. Conclusion Elevated NLR, LNR, PLR, and SII are associated with significantly reduced survival of patients. LNR, PLR, and SII could be used as independent risk factors for the prognosis of gastric cancer patients treated with chemotherapy, and their combination can predict the survival of gastric cancer patients.

Motivation Predicting the properties of molecules is a fundamental problem in drug design and discovery, while how to learn effective feature representations lies at the core of modern deep-learning-based prediction methods. Recent progress shows expressive power of graph neural networks (GNNs) in capturing structural information for molecular graphs. However, we find that most molecular graphs exhibit low clustering along with dominating chains. Such topological characteristics can induce feature squashing during message passing and thus impair the expressivity of conventional GNNs. Results Aiming at improving node features' expressiveness, we develop a novel chain-aware graph neural network model, wherein the chain structures are captured by learning the representation of the center node along the shortest paths starting from it, and the redundancy between layers are mitigated via initial residual difference connection (IRDC). Then the molecular graph is represented by attentive pooling of all node representations. Compared to standard graph convolution, our chain-aware learning scheme offers a more straightforward feature interaction between distant nodes, thus it is able to capture the information about long-range dependency. We provide extensive empirical analysis on real-world datasets to show the outperformance of the proposed method. Availability and implementation The MolPath code is publicly available at https://github.com/Assassinswhh/Molpath. [ABSTRACT FROM AUTHOR]

Aims: This study evaluated millet germplasms in Liaoning Province to support the collection, preservation and innovation of millet germplasm resources. Methods: The study was conducted from 2018 to 2020, involved the selection of 105 millet germplasm resources from the Germplasm Bank of the Liaoning Academy of Agricultural Sciences (LAAS), the observation and recording of 31 traits, and the application of multivariate analysis methods to assess phenotypic diversity. Results: From the diversity analysis and correlation analysis, it was found that the tested traits had abundant diversity and complex correlations among them. Principal component analysis (PCA) comprehensively analyzed all quantitative traits and extracted seven principal components. Grey relational analysis (GRA) highlighted the varied contributions of different traits to yield. Through systematic cluster analysis (SCA), the resources were categorized into six groups at Euclidean distance of 17.09. K-mean cluster analysis determined the distribution interval and central value of each trait, then identified resources with desirable traits. Conclusion: The results revealed resources that possess characteristics such as upthrow seedling leaves, more tillers and branches, larger and well-formed ears, and lodging resistance prefer to higher grain yield. It was also discovered that the subear internode length (SIL) could be an indicator for maturity selection. Four specific resources, namely, Dungu No. 1, Xiao-li-xiang, Basen Shengu, and Yuhuanggu No. 1, were identified for further breeding and practical applications. [ABSTRACT FROM AUTHOR]

The weight of the primary mirror increases as the aperture of ground-based telescopes increases, making it more challenging to maintain the positional stability and surface accuracy of the solid primary mirror. Consequently, a 2 m lightweight silicon carbide (SiC) mirror and an optimization method were proposed in this study. The relationship between the gravitational deformation of the mirror and its thickness and number of supports was derived based on force analysis of the mirror; the thickness of the mirror and the appropriate number of supports were obtained as initial parameters for optimization. The back structure of the mirror was designed in a lotus pattern to improve its rigidity. Numerous structural parameters were classified into major and non-major parameters based on the results of a sensitivity analysis. The non-major and major structural parameters were optimized using a Latin hypercube design method and a non-dominated sorting genetic algorithm, respectively. The optimized 2 m lightweight SiC mirror had a mass of 119 kg and an areal density of 38.7 kg/m2. The surface figure error root-mean-square (RMS) in the vertical state of the optical axis and the first modal resonance of the mirror assembly calculated using finite element analysis were 11.3 nm and 76.5 Hz, respectively. Modal tests of the mirror assembly were conducted using the hammering method, achieving a maximum relative frequency error of 7.4% compared with the simulation results. The optimized 2 m SiC mirror was over 50% lighter than traditional passive Zerodur mirrors of the same size. [ABSTRACT FROM AUTHOR]