The influence of resistance exercise and aerobic exercise on type 2 diabetes: a meta-analysis.

INTRODUCTION: Diabetes is a worldwide chronic disease. The incidence rate of this disease is high, and it is a common disease in clinics. At present, the incidence rate of diabetes patients is increasing year by year due to the increasing work pressure, the accelerated pace of life, the change of diet, the reduction of labor, and the acceleration of aging. EVIDENCE ACQUISITION: The computer retrieves four databases to obtain random controlled trials on the influence of resistance exercise and aerobic exercise on type 2 diabetes. After a rigorous literature quality evaluation, data analysis was performed using RevMan 5.3 software. EVIDENCE SYNTHESIS: Ten studies were ultimately included in this meta-analysis. 10 studies reported the HbA1c of the test group and the control group, which was no significant statistical significance (SMD: -0.01; 95% CI: -0.20,0.19; P=0.959) than the control group, HOMA-IR (SMD: 0.02; 95% CI: -0.65,0.69; P=0.954), SBP (SMD: 3.92; 95% CI: -0.92,8.75; P=0.112), DBP (SMD: 0.67; 95% CI: -3.66,5.01; P=0.761), HDL (SMD: -0.08; 95% CI: -2.79,2.64; P=0.955), TG (SMD: -7.51; 95% CI: -21.25,6.22; P=0.284) and TC (SMD: 9.10; 95% CI: -13.43,31.62; P=0.428). CONCLUSIONS: The results of this study suggest that both resistance exercise and aerobic exercise may be effective on patients with type 2 diabetes, as evidenced by HbA1c, HOMA-IR, SBP, DBP, HDL, TG and TC. There is no significant difference in their impact on type 2 diabetes patients, and the above conclusions need to be verified by more high-quality studies.

Modeling of the control logic of a UASS based on coefficient of variation spraying distribution analysis in an indoor flight simulator.

Introduction: In the past decade, unmanned aerial spraying systems (UASS) have emerged as an effective crop treatment platform option, competing with other ground vehicle treatments. The development of this platform has provided an effective spraying system that can be used on all crop types and in all weather conditions. However, related research has not been able to develop a UASS that can be operated in windy conditions with a low drift percentage. Methods: In this research, spraying was simulated in an indoor flight simulator by considering flight speed, altitude, wind speed, wind direction, rotor rotation, interval, spraying pattern, and nozzle type, which were used as the parameters affecting the output value of the coefficient of variation (CV) of spraying. These parameters were referenced as properties that occur in the field, and using machine learning methods, the CV value was used as a dataset to develop a model that can execute pump opening by controlling the flow rate. There are four machine learning methods used, i.e. random forest regression, gradient boosting, ada boost, and automatic relevance determination regression which are compared with simple linear regression and ridge regression as linear regression. Results: The results revealed that the random forest regression model was the most accurate, with R2 of 0.96 and root mean square error (RMSE) of 0.04%. The developed model was used to simulate spraying with pump opening A, which connects two nozzles in front, and pump opening AB, which connects all four nozzles. Discussion: Using the logic based on CV value and pesticide quantity, the model can execute the pump opening against the environment and UASS operation.

Improved Position Estimation Algorithm of Agricultural Mobile Robots Based on Multisensor Fusion and Autoencoder Neural Network.

High-precision position estimations of agricultural mobile robots (AMRs) are crucial for implementing control instructions. Although the global navigation satellite system (GNSS) and real-time kinematic GNSS (RTK-GNSS) provide high-precision positioning, the AMR accuracy decreases when the signals interfere with buildings or trees. An improved position estimation algorithm based on multisensor fusion and autoencoder neural network is proposed. The multisensor, RTK-GNSS, inertial-measurement-unit, and dual-rotary-encoder data are fused with Extended Kalman filter (EKF). To optimize the EKF noise matrix, the autoencoder and radial basis function (ARBF) neural network was used for modeling the state equation noise and EKF measurement equation. A multisensor AMR test platform was constructed for static experiments to estimate the circular error probability and twice-the-distance root-mean-squared criteria. Dynamic experiments were conducted on road, grass, and field environments. To validate the robustness of the proposed algorithm, abnormal working conditions of the sensors were tested on the road. The results showed that the positioning estimation accuracy was improved compared to the RTK-GNSS in all three environments. When the RTK-GNSS signal experienced interference or rotary encoders failed, the system could still improve the position estimation accuracy. The proposed system and optimization algorithm are thus significant for improving AMR position prediction performance.

Development of Multiple UAV Collaborative Driving Systems for Improving Field Phenotyping.

Unmanned aerial vehicle-based remote sensing technology has recently been widely applied to crop monitoring due to the rapid development of unmanned aerial vehicles, and these technologies have considerable potential in smart agriculture applications. Field phenotyping using remote sensing is mostly performed using unmanned aerial vehicles equipped with RGB cameras or multispectral cameras. For accurate field phenotyping for precision agriculture, images taken from multiple perspectives need to be simultaneously collected, and phenotypic measurement errors may occur due to the movement of the drone and plants during flight. In this study, to minimize measurement error and improve the digital surface model, we proposed a collaborative driving system that allows multiple UAVs to simultaneously acquire images from different viewpoints. An integrated navigation system based on MAVSDK is configured for the attitude control and position control of unmanned aerial vehicles. Based on the leader-follower-based swarm driving algorithm and a long-range wireless network system, the follower drone cooperates with the leader drone to maintain a constant speed, direction, and image overlap ratio, and to maintain a rank to improve their phenotyping. A collision avoidance algorithm was developed because different UAVs can collide due to external disturbance (wind) when driving in groups while maintaining a rank. To verify and optimize the flight algorithm developed in this study in a virtual environment, a GAZEBO-based simulation environment was established. Based on the algorithm that has been verified and optimized in the previous simulation environment, some unmanned aerial vehicles were flown in the same flight path in a real field, and the simulation and the real field were compared. As a result of the comparative experiment, the simulated flight accuracy (RMSE) was 0.36 m and the actual field flight accuracy was 0.46 m, showing flight accuracy like that of a commercial program.

A model for phenotyping crop fractional vegetation cover using imagery from unmanned aerial vehicles.

Fractional vegetation cover (FVC) is the key trait of interest for characterizing crop growth status in crop breeding and precision management. Accurate quantification of FVC among different breeding lines, cultivars, and growth environments is challenging, especially because of the large spatiotemporal variability in complex field conditions. This study presents an ensemble modeling strategy for phenotyping crop FVC from unmanned aerial vehicle (UAV)-based multispectral images by coupling the PROSAIL model with a gap probability model (PROSAIL-GP). Seven field experiments for four main crops were conducted, and canopy images were acquired using a UAV platform equipped with RGB and multispectral cameras. The PROSAIL-GP model successfully retrieved FVC in oilseed rape (Brassica napus L.) with coefficient of determination, root mean square error (RMSE), and relative RMSE (rRMSE) of 0.79, 0.09, and 18%, respectively. The robustness of the proposed method was further examined in rice (Oryza sativa L.), wheat (Triticum aestivum L.), and cotton (Gossypium hirsutum L.), and a high accuracy of FVC retrieval was obtained, with rRMSEs of 12%, 6%, and 6%, respectively. Our findings suggest that the proposed method can efficiently retrieve crop FVC from UAV images at a high spatiotemporal domain, which should be a promising tool for precision crop breeding.

Field-Based Calibration of Unmanned Aerial Vehicle Thermal Infrared Imagery with Temperature-Controlled References.

Accurate and reliable calibration methods are required when applying unmanned aerial vehicle (UAV)-based thermal remote sensing in precision agriculture for crop stress monitoring, irrigation planning, and harvesting. The primary objective of this study was to improve the calibration accuracies of UAV-based thermal images using temperature-controlled ground references. Two temperature-controlled ground references were installed in the field to serve as high- and low-temperature references, approximately spanning the expected range of crop surface temperatures during the growing season. Our results showed that the proposed method using temperature-controlled references was able to reduce errors due to ambient conditions from 9.29 to 1.68 °C, when tested with validation panels. There was a significant improvement in crop temperature estimation from the thermal image mosaic, as the error reduced from 14.0 °C in the un-calibrated image to 1.01 °C in the calibrated image. Furthermore, a multiple linear regression model (R2 = 0.78; p-value < 0.001; relative RMSE = 2.42%) was established to quantify soil moisture content based on canopy surface temperature and soil type, using UAV-based thermal image data and soil electrical conductivity (ECa) data as the predictor variables.

Multifunctional Ground Control Points with a Wireless Network for Communication with a UAV.

Ground control points (GCPs) are commonly used for georeferencing in remote sensing. Precise position measurement of the GCPs typically requires careful ground surveying, which is time-consuming and labor-intensive and thus excessively costly if it needs to be repeated multiple times in a season. A system of multifunctional GCPs and a wireless network for communication with an unmanned aerial vehicle (UAV) was developed to improve the speed of GCP setup and provide GCP data collection in real-time during the flight. While testing the system, a single-board computer on a fixed-wing UAV used in the study successfully recorded position data from all the GCPs during the flight. The multifunctional GCPs were also tested for use as references for calibration of reflectance and height for field objects like crops. The test of radiometric calibration resulted in an average reflectance error of 2.0% and a strong relationship (R2 = 0.99) between UAV-based estimates and ground reflectance. Furthermore, the average height difference between UAV-based height estimates and ground measurements was within 10 cm.

Measurement and Calibration of Plant-Height from Fixed-Wing UAV Images.

Continuing population growth will result in increasing global demand for food and fiber for the foreseeable future. During the growing season, variability in the height of crops provides important information on plant health, growth, and response to environmental effects. This paper indicates the feasibility of using structure from motion (SfM) on images collected from 120 m above ground level (AGL) with a fixed-wing unmanned aerial vehicle (UAV) to estimate sorghum plant height with reasonable accuracy on a relatively large farm field. Correlations between UAV-based estimates and ground truth were strong on all dates (R² > 0.80) but are clearly better on some dates than others. Furthermore, a new method for improving UAV-based plant height estimates with multi-level ground control points (GCPs) was found to lower the root mean square error (RMSE) by about 20%. These results indicate that GCP-based height calibration has a potential for future application where accuracy is particularly important. Lastly, the image blur appeared to have a significant impact on the accuracy of plant height estimation. A strong correlation (R² = 0.85) was observed between image quality and plant height RMSE and the influence of wind was a challenge in obtaining high-quality plant height data. A strong relationship (R² = 0.99) existed between wind speed and image blurriness.

Anti-inflammatory action of Athyrium multidentatum extract suppresses the LPS-induced TLR4 signaling pathway.

ETHNOPHARMACOLOGICAL RELEVANCE: The aerial part of Athyrium multidentatum (Doll.) Ching (AM) is widely used in the northeastern region of China as an edible wild herb, but its medicinal value, especially its anti-inflammatory effect, has not been fully explored. AIM OF THE STUDY: To investigate the anti-inflammatory activity of AM and clarify the anti-inflammatory mechanism involving the TLR4 signaling pathway using a lipopolysaccharide (LPS)-induced inflammatory model. MATERIALS AND METHODS: AM ethanol extract was used as the experimental material to investigate the effect that the extract has on the production of pro-inflammatory mediators (NO, PGE2, TNF-α, IL-1ß and IL-6); changes in LPS-induced peritoneal macrophages (PMs); and TLR4-mediated intracellular events, including MAPKs (ERK, JNK, and p38) and IκB-α in the MyD88-dependant pathway and IRF3, STAT1, and STAT3 in the TRIF-dependent pathway. In in vivo experiments, we established an LPS-induced acute lung injury (ALI) model and investigated the cell count and cytokine (TNF-α, IL-1ß and IL-6) levels in bronchoalvelar lavage fluid (BALF) of C57BL6 mice. Histological changes in the lung tissues were observed with H&E staining. RESULTS: AM extract inhibited NO and PGE2 by suppressing their synthetase (iNOS and COX-2) gene expression in LPS-induced PMs; the secretion of IL-6, IL-1ß, and TNF-α also deceased via the down-regulation of mRNA levels. Furthermore, the TLR4-mediated intracellular events involved the phosphorylated forms of MAPKs (ERK, JNK) and IκB-α in the MyD88-dependent pathway and the TRIF-dependent pathway (IRF3, STAT1, STAT3), and the relevant proteins were expressed at low levels in the AM extract groups. In in vivo experiments, the cell count and cytokine (TNF-α, IL-1ß and IL-6) levels in BALF decreased significantly in a dose-dependent manner in the AM extract groups. The lung tissue structure exhibited dramatic damage in the LPS group, and the damaged area decreased in the AM extract groups; in particular, the effect of 10 mg/kg extract was similar to that of the positive control dexamethasone (DEX). CONCLUSION: The findings demonstrate that AM protects against LPS-induced acute lung injury by suppressing TLR4 signaling, provide scientific evidence to support further study of the safety of anti-inflammatory drugs and indicate that AM can be used as an anti-inflammatory and anti-injury agent to prevent pneumonia caused by microbial infection.

Metabolite profiling of ginsenosides in rat plasma, urine and feces by LC-MS/MS and its application to a pharmacokinetic study after oral administration of Panax ginseng extract.

Panax ginseng is widely consumed as a functional food in the form of tea, powder, capsules, among others, and possesses a range of pharmacological activities including adaptogenic, immune-modulatory, anti-tumor, anti-aging and anti-inflammatory effects. The aim of this study was to identify and quantify the major ginsenosides and their metabolites in rat plasma, urine and feces after administration of P. ginseng extract using LC-MS/MS. We collected rat plasma samples at 0.5, 1, 2, 4, 8, 12, 24 and 48 h, and the amounts of urine and fecal samples accumulated in 24 h. Fourteen major ginsenosides and their metabolites were observed in fecal samples at high levels; however, low levels of 11 ginsenosides were detected in urine samples. The pharmacokinetics of the major ginsenosides and their metabolites was investigated in plasma. The results indicated that the maximum plasma concentration, time to maximum concentration and area under the curve of compound K were significantly greater than those of other ginsenosides. This study thus provides valuable information for drug development and clinical application of P. ginseng.