Anti cancer molecular mechanism of Actinidia chinensis Planch in gastric cancer based on network pharmacology and molecular docking

Purpose: To determine the anti-tumor effects of Actinidia chinensis Planch (ACP) root extract as well as its mechanism of action against gastric cancer (GC) using network pharmacology.Methods: The bioactive compounds and targets of ACP, as well as GC-related genes were identified from a series of public databases. Functional enrichment analysis was conducted to find relevant biological processes and pathways. The survival analysis was conducted using GEPIA tool. Autodock was used to carry out molecular docking between the ingredients and their targets.Results: A total of 20 bioactive compounds with 209 corresponding targets were identified for ACP, and a total of 871 GC-related genes were obtained. Forty-nine (49) targets of ACP were identified as candidate genes for the prevention of GC, and the PPI network with 584 interactions among these genes was constructed. The data demonstrated that the candidate targets were involved in multiple biological processes such as oxidative stress response, apoptosis, and proliferation. Moreover, these candidate targets were significantly associated with cancer-related pathways and signal transduction pathways. The compound-target-pathway network containing 16 bioactive compounds, 49 targets and 10 pathways was constructed and visualized, and the top 3 targets with a higher degree value were AKT1, MYC, and JUN, respectively. Survival analysis revealed significant associations between GC prognosis and several targets (PREP, PTGS1, AR, and PTGS2). Molecular docking further revealed good binding affinities between bioactive compounds and the prognosis-related targets, indicating the potential roles of these ingredient-target interactions in GC protection.Conclusion: Taken together, this study has provided novel clues for the determination of the antigastric cancer mechanism of ACP

VS-CAM: Vertex Semantic Class Activation Mapping to Interpret Vision Graph Neural Network

Graph convolutional neural network (GCN) has drawn increasing attention and attained good performance in various computer vision tasks, however, there lacks a clear interpretation of GCN's inner mechanism. For standard convolutional neural networks (CNNs), class activation mapping (CAM) methods are commonly used to visualize the connection between CNN's decision and image region by generating a heatmap. Nonetheless, such heatmap usually exhibits semantic-chaos when these CAMs are applied to GCN directly. In this paper, we proposed a novel visualization method particularly applicable to GCN, Vertex Semantic Class Activation Mapping (VS-CAM). VS-CAM includes two independent pipelines to produce a set of semantic-probe maps and a semantic-base map, respectively. Semantic-probe maps are used to detect the semantic information from semantic-base map to aggregate a semantic-aware heatmap. Qualitative results show that VS-CAM can obtain heatmaps where the highlighted regions match the objects much more precisely than CNN-based CAM. The quantitative evaluation further demonstrates the superiority of VS-CAM.Comment: 10 pages, 10 figure

Protective Effect of RNase on Unilateral Nephrectomy-Induced Postoperative Cognitive Dysfunction in Aged Mice

Postoperative cognitive dysfunction (POCD) is a common complication after surgery, especially for elderly patients. Administration of RNase has been reported to exhibit neuroprotective effects in acute stroke. However, the potential role of RNase on POCD is unknown. Therefore, we sought to investigate whether RNase treatment could mitigate unilateral nephrectomy induced-cognitive deficit in aged mice. In the present study, twelve-month-old mice were administered RNase or an equal amount of normal saline perioperatively. All mice underwent Morris Water Maze (MWM) training 3 times per day for 7 days to acclimatize them to the water maze before surgical operation, and testing on days 1, 3 and 7 after surgery. We found that perioperative administration of RNase: 1) attenuated unilateral nephrectomy-induced cognitive impairment at day 3 after surgery; 2) reduced the hippocampal cytokines mRNA production and serum cytokines protein production at day 1 and day 7 (for MCP-1) after surgery, and; 3) inhibited hippocampal apoptosis as indicated by cleaved caspase-3 western blot and TUNEL staining at day 1 after surgery. In addition, a trend decrease of total serum RNA levels was detected in the RNase treated group after surgery compared with the untreated group. Further, our protocol of RNase administration had no impact on the arterial blood gas analysis right after surgery, kidney function and mortality rate at the observed days postoperatively. In conclusion, perioperative RNase treatment attenuated unilateral nephrectomy-induced cognitive impairment in aged mice

An overview of data fusion techniques for internet of things enabled physical activity recognition and measure

Due to importantly beneficial effects on physical and mental health and strong association with many rehabilitation programs, Physical Activity Recognition and Measure (PARM) has been widely recognised as a key paradigm for a variety of smart healthcare applications. Traditional methods for PARM relies on designing and utilising Data fusion or machine learning techniques in processing ambient and wearable sensing data for classifying types of physical activity and removing their uncertainties. Yet they mostly focus on controlled environments with the aim of increasing types of identifiable activity subjects, improved recognition accuracy and measure robustness. The emergence of the Internet of Things (IoT) enabling technology is transferring PARM studies to an open and dynamic uncontrolled ecosystem by connecting heterogeneous cost-effective wearable devices and mobile apps and various groups of users. Little is currently known about whether traditional Data fusion techniques can tackle new challenges of IoT environments and how to effectively harness and improve these technologies. In an effort to understand potential use and opportunities of Data fusion techniques in IoT enabled PARM applications, this paper will give a systematic review, critically examining PARM studies from a perspective of a novel 3D dynamic IoT based physical activity collection and validation model. It summarized traditional state-of-the-art data fusion techniques from three plane domains in the 3D dynamic IoT model: devices, persons and timeline. The paper goes on to identify some new research trends and challenges of data fusion techniques in the IoT enabled PARM studies, and discusses some key enabling techniques for tackling them

A 3-D Surface Reconstruction with Shadow Processing for Optical Tactile Sensors

An optical tactile sensor technique with 3-dimension (3-D) surface reconstruction is proposed for robotic fingers. The hardware of the tactile sensor consists of a surface deformation sensing layer, an image sensor and four individually controlled flashing light emitting diodes (LEDs). The image sensor records the deformation images when the robotic finger touches an object. For each object, four deformation images are taken with the LEDs providing different illumination directions. Before the 3-D reconstruction, the look-up tables are built to map the intensity distribution to the image gradient data. The possible image shadow will be detected and amended. Then the 3-D depth distribution of the object surface can be reconstructed from the 2-D gradient obtained using the look-up tables. The architecture of the tactile sensor and the proposed signal processing flow have been presented in details. A prototype tactile sensor has been built. Both the simulation and experimental results have validated the effectiveness of the proposed 3-D surface reconstruction method for the optical tactile sensors. The proposed 3-D surface reconstruction method has the unique feature of image shadow detection and compensation, which differentiates itself from those in the literature

Stress Analysis of the Radius and Ulna in Tennis at Different Flexion Angles of the Elbow

In this paper, based on the finite element method, the stresses of the radius and ulna are analyzed at different flexion angles of the elbow when playing tennis. The finite element model is presented for the elbow position with flexion angles of 0°, 25°, 60°, and 80° according to the normal human arm bone. In this model, the whole arm with metacarpals, radius, ulna, humerus and scapula is considered. The calculation is simplified by setting the scapula and metacarpals as rigid bodies and using Tie binding constraints between the humerus and the radius and ulna. This model is discretized using the 10-node second-order tetrahedral element (C3D10). This model contains 109,765 nodes and 68,075 elements. The hitting forces applied to the metacarpal bone are 100 N and 300 N, respectively. The numerical results show that the highest principal stresses are at the points of 1/4 of the radius, the elbow joint, and the points of 1/10 of the ulna. The results of the maximum principal stress show that the external pressures are more pronounced as the elbow flexion angle increases and that the magnitude of the hitting force does not affect the principal stress distribution pattern. Elbow injuries to the radius can be reduced by using a stroke with less elbow flexion, and it is advisable to wear a reinforced arm cuff on the dorsal 1/4 of the hand, a radial/dorsal hand wrist, and an elbow guard to prevent radial ulnar injuries

Landscape Ecological Risk Assessment Based on Land Use Change in the Yellow River Basin of Shaanxi, China

The Yellow River Basin in Shaanxi (YRBS) has a relatively fragile ecological environment, with severe soil erosion and a high incidence of natural and geological disasters. In this study, a river basin landscape ecological risk assessment model was constructed using landscape ecology principles to investigate the temporal and spatial evolution, as well as the spatial autocorrelation characteristics of landscape ecological risks in the YRBS over a 20-year period. The main findings from the YRBS were that the land use types changed significantly over the span of 20 years, there was spatial heterogeneity of the landscape pattern, and the ecological risk value was positively correlated. The threat of landscape ecological risks in YRBS is easing, but the pressure on the ecological environment is considerable. This study provides theoretical support administrative policies for future ecological risk assessment and protection, restoration measures, and control in the Yellow River Basin of Shaanxi Province

Harmonic Elimination and Magnetic Resonance Sounding Signal Extraction Based on Matching Pursuit Algorithm

Magnetic resonance sounding (MRS) is a non-invasive, direct, and quantitative geophysical method for detecting groundwater, and has been widely used in groundwater survey, water resource assessment, and disaster water source forecasting. However, the MRS signal is weak (nV level) and highly susceptible to environmental noise, such as random noise and power-line harmonics, resulting in reduced quality of received data. Achieving reliable extraction of MRS signals under strong noise is difficult. To solve this problem, we propose a matching pursuit algorithm based on sparse decomposition theory for data noise suppression and MRS signal extraction. In accordance with the characteristics of the signal and noise, an oscillating atomic library is constructed as a sparse dictionary to realize signal sparse decomposition. A two-step denoising strategy is proposed to reconstruct the power-line harmonics and then extract the MRS signal. We simulated synthetic data with different signal-to-noise ratios (SNRs), relaxation times, and Larmor frequencies. Our results show that the proposed algorithm can effectively remove power-line harmonics and reduce random noise. SNR is significantly improved by up to 35.6 dB after denoising. The effectiveness and superiority of the proposed algorithm are further verified by the measured data and through comparison with the singular spectrum analysis algorithm and harmonic modeling cancellation algorithm