A Light Spot Humanoid Motion Paradigm Modulated by the Change of Brightness to Recognize the Stride Motion Frequency

The steady-state visual evoked potential (SSVEP)-based brain–computer interface (BCI) usually has the advantages of high information transfer rate (ITR) and no need for training. However, low frequencies, such as the human stride motion frequency, cannot easily induce SSVEP. To solve this problem, a light spot humanoid motion paradigm modulated by the change of brightness was designed in this study. The characteristics of the brain response to the motion paradigm modulated by the change of brightness were analyzed for the first time. The results showed that the designed paradigm could induce not only the high flicker frequency but also the modulation frequencies between the change of brightness and the motion in the primary visual cortex. Thus, the stride motion frequency can be recognized through the modulation frequencies by using the designed paradigm. Also, in an online experiment, this paradigm was employed to control a lower limb robot to achieve same frequency stimulation, which meant that the visual stimulation frequency was the same as the motion frequency of the robot. Also, canonical correlation analysis (CCA) was used to distinguish three different stride motion frequencies. The average accuracies of the classification in three walking speeds using the designed paradigm with the same and different high frequencies reached 87 and 95% respectively. Furthermore, the angles of the knee joint of the robot were obtained to demonstrate the feasibility of the electroencephalograph (EEG)-driven robot with same stimulation

Development and Validation of an Immunotherapy-Related Prognostic Signature Based on Lymph Node Ratio for Gastric Cancer

Background. The long-term prognosis of gastric cancer (GC) remains poor due to postoperative recurrence and metastasis. The increasing evidence show that the lymph node ratio (LNR) serves as an independent prognostic factor in patients with GC. In this study, we aimed to develop a prognostic signature for GC based on LNR. Methods. Survival analysis was conducted by comparing low- and high-LNR groups according to the optimal cutoff value of LNR, which was identified by receiver operating characteristic (ROC) curve analysis. Then, we identified the differentially expressed genes (DEGs) related to LNR in the training cohort of GC. Univariate Cox regression and least absolute shrinkage and selection operator (LASSO) regression were performed to construct the risk score signature. We then evaluated the risk score signature from the viewpoints of survival, clinic-pathological characteristics, tumor microenvironment (TME), tumor mutation burden (TMB), and immunotherapeutic and chemotherapeutic efficacy. Results. High LNR was significantly correlated with poorer prognosis and was an independent predictor of recurrence in patients with GC. Then, an eleven-gene signature that could predict the prognosis of GC patients was developed based on LNR-related DEGs in the training cohort, and the results were further confirmed in external independent cohort. In addition, the high-risk group showed aggressive clinicopathological characteristics, specific TME status, low TMB, and low immunotherapeutic sensitivity. Conclusions. The present study constructed an eleven-gene prognostic signature based on LNR to predict the prognosis of patients with GC and facilitate the development of individualized treatment strategy

Human pose estimation method based on single depth image

Many of current human pose estimation methods based on depth images require training stage. However, the training stage costs huge work on making samples. And many methods for human pose occlusion condition cannot work well. In this study, a novel approach to estimate human pose with a depth image called model‐based recursive matching (MRM) is introduced. A human skeleton model with customised parameters is created based on T‐pose to fit different body types. The authors use depth image and 3D point cloud corresponding to input. In contrast to previous work, the proposed method avoids training step and can give an accurate estimation in the case of the human occlusion condition. They demonstrate the method by comparing to the method Kinect offered by using random forest on 20 human poses. And the ground truth of coordinates of pose joint is made by the motion capture system. The result shows that the proposed method not only works well on the general human pose but also can deal with human occlusion better. And the authors’ method can be also applied to the disabled people and other creatures

Enhanced Cartilage and Subchondral Bone Repair Using Carbon Nanotube-Doped Peptide Hydrogel–Polycaprolactone Composite Scaffolds

A carbon nanotube-doped octapeptide self-assembled hydrogel (FEK/C) and a hydrogel-based polycaprolactone PCL composite scaffold (FEK/C3-S) were developed for cartilage and subchondral bone repair. The composite scaffold demonstrated modulated microstructure, mechanical properties, and conductivity by adjusting CNT concentration. In vitro evaluations showed enhanced cell proliferation, adhesion, and migration of articular cartilage cells, osteoblasts, and bone marrow mesenchymal stem cells. The composite scaffold exhibited good biocompatibility, low haemolysis rate, and high protein absorption capacity. It also promoted osteogenesis and chondrogenesis, with increased mineralization, alkaline phosphatase (ALP) activity, and glycosaminoglycan (GAG) secretion. The composite scaffold facilitated accelerated cartilage and subchondral bone regeneration in a rabbit knee joint defect model. Histological analysis revealed improved cartilage tissue formation and increased subchondral bone density. Notably, the FEK/C3-S composite scaffold exhibited the most significant cartilage and subchondral bone formation. The FEK/C3-S composite scaffold holds great promise for cartilage and subchondral bone repair. It offers enhanced mechanical support, conductivity, and bioactivity, leading to improved tissue regeneration. These findings contribute to the advancement of regenerative strategies for challenging musculoskeletal tissue defects

Generating Salt-Affected Irrigated Cropland Map in an Arid and Semi-Arid Region Using Multi-Sensor Remote Sensing Data

Soil salinization is a widespread environmental hazard and a major abiotic constraint affecting global food production and threatening food security. Salt-affected cropland is widely distributed in China, and the problem of salinization in the Hetao Irrigation District (HID) in the Inner Mongolia Autonomous Region is particularly prominent. The salt-affected soil in Inner Mongolia is 1.75 million hectares, accounting for 14.8% of the total land. Therefore, mapping saline cropland in the irrigation district of Inner Mongolia could evaluate the impacts of cropland soil salinization on the environment and food security. This study hypothesized that a reasonably accurate regional map of salt-affected cropland would result from a ground sampling approach based on PlanetScope images and the methodology developed by Sentinel multi-sensor images employing the machine learning algorithm in the cloud computing platform. Thus, a model was developed to create the salt-affected cropland map of HID in 2021 based on the modified cropland base map, valid saline and non-saline samples through consistency testing, and various spectral parameters, such as reflectance bands, published salinity indices, vegetation indices, and texture information. Additionally, multi-sensor data of Sentinel from dry and wet seasons were used to determine the best solution for mapping saline cropland. The results imply that combining the Sentinel-1 and Sentinel-2 data could map the soil salinity in HID during the dry season with reasonable accuracy and close to real time. Then, the indicators derived from the confusion matrix were used to validate the established model. As a result, the combined dataset, which included reflectance bands, spectral indices, vertical transmit–vertical receive (VV) and vertical transmit–horizontal receive (VH) polarization, and texture information, outperformed the highest overall accuracy at 0.8938, while the F1 scores for saline cropland and non-saline cropland are 0.8687 and 0.9109, respectively. According to the analyses conducted for this study, salt-affected cropland can be detected more accurately during the dry season by using just Sentinel images from March to April. The findings of this study provide a clear explanation of the efficiency and standardization of salt-affected cropland mapping in arid and semi-arid regions, with significant potential for applicability outside the current study area