Neural Network Based on Multi-Scale Saliency Fusion for Traffic Signs Detection

Aiming at recognizing small-scale and complex traffic signs in the driving environment, a traffic sign detection algorithm YOLO-FAM based on YOLOv5 is proposed. Firstly, a new backbone network, ShuffleNet-v2, is used to reduce the algorithm’s parameters, realize lightweight detection, and improve detection speed. Secondly, the Bidirectional Feature Pyramid Network (BiFPN) structure is introduced to capture multi-scale context information, so as to obtain more feature information and improve detection accuracy. Finally, location information is added to the channel attention using the Coordinated Attention (CA) mechanism, thus enhancing the feature expression. The experimental results show that compared with YOLOv5, the mAP value of this method increased by 2.27%. Our approach can be effectively applied to recognizing traffic signs in complex scenes. At road intersections, traffic planners can better plan traffic and avoid traffic jams

Neural Network Based on Multi-Scale Saliency Fusion for Traffic Signs Detection

Aiming at recognizing small-scale and complex traffic signs in the driving environment, a traffic sign detection algorithm YOLO-FAM based on YOLOv5 is proposed. Firstly, a new backbone network, ShuffleNet-v2, is used to reduce the algorithm’s parameters, realize lightweight detection, and improve detection speed. Secondly, the Bidirectional Feature Pyramid Network (BiFPN) structure is introduced to capture multi-scale context information, so as to obtain more feature information and improve detection accuracy. Finally, location information is added to the channel attention using the Coordinated Attention (CA) mechanism, thus enhancing the feature expression. The experimental results show that compared with YOLOv5, the mAP value of this method increased by 2.27%. Our approach can be effectively applied to recognizing traffic signs in complex scenes. At road intersections, traffic planners can better plan traffic and avoid traffic jams

Effect of melatonin on regeneration of cortical neurons in rats with traumatic brain injury

Purpose: To investigate the effect of melatonin on regeneration of cortical neurons in rats with traumatic brain injury (TBI). Methods: Sprague-Dawley rats (n=36) were randomly divided into sham, TBI+vehicle and TBI+melatonin groups. Cerebral blood flow and cognitive function were observed via laser Doppler flowmetry and by Morris water maze testing, respectively. The serum malondialdehyde (MDA) and superoxide dismutase (SOD) levels were used to assess oxidative stress. Immunofluorescence and terminal deoxynucleotidyl transferase dUTP nick end labelling assay was used to observe the newborn neurons and apoptotic cells. Results: Cerebral blood flow in the TBI+melatonin group was higher than that of the TBI+vehicle group at one, 12, 24 and 48 h post-injury, but the difference was not statistically significant (P>0.05). The cognitive function of the rats was better in the TBI+melatonin group than the TBI+vehicle group (

Estimation of Aerosol Optical Depth at 30 m Resolution Using Landsat Imagery and Machine Learning

Current remote sensing-based aerosol optical depth (AOD) products have coarse spatial resolutions, which are useful for studies at continental and global scales, but unsatisfactory for local scale applications, such as urban air pollution monitoring. In this study, we investigated the possibility of using Landsat imagery to develop high-resolution AOD estimations at 30 m based on machine learning algorithms. We assessed the performance of six machine learning algorithms, including Extreme Gradient Boosting, Random Forest, Cascade Random Forest, Gradient Boosted Decision Trees, Extremely Randomized Trees, and Multiple Linear Regression. To obtain accurate AOD estimations, we used prior knowledge from multiple sources as inputs to the machine learning models, including the Global Land Surface Satellite (GLASS) albedo, the 1-km AOD product from MODIS data using the Multi-Angle Implementation of Atmospheric Correction (MAIAC) algorithm, and meteorological and surface elevation data. A total of 13,624 AOD measurements from Aerosol Robotic Network (AERONET) sites were used for model training and validation. We found that all six algorithms exhibited good performance, with R2 values ranging from 0.73 to 0.78 and AOD root-mean-square errors (RMSE) ranging from 0.089 to 0.098. The extremely randomized trees algorithm, however, demonstrated marginally superior performance as compared to the other algorithms; hence, it was used to produce AOD estimates at a 30 m resolution for one Landsat scene coving Beijing in 2013–2019. Through a comparison with overlapping AERONET observations, a high level of accuracy was achieved, with an R2 = 0.889 and an RMSE = 0.156. Our method can be potentially used to generate a global high-resolution AOD dataset based on Landsat imagery

Assessing the upper elevational limits of vegetation growth in global high-mountains

The upper elevational limits of vegetation growth in global high-mountains have been the focus for monitoring and assessment of climate change impacts on terrestrial ecosystems. However, existing studies have relied on field surveys that do not allow for large-scale analysis. Although remote sensing data have been used for local and regional monitoring of the vegetation upper boundaries, a global synthesis of the treeline and vegetation line (the upper altitudinal threshold for the existence of trees and the transition line from vegetation to bare land or permanent snow cover, respectively) in high-mountain ecosystems is still missing. To fill this gap, we developed two independent methods based on (1) the relationship between a Sentinel-2 vegetation index and elevation and (2) the European Space Agency's 10 m resolution land cover dataset (WorldCover), respectively, to automatically identify the upper elevational limits of treeline and vegetation line for each one-quarter degree grid across the global high-mountain areas. We obtained highly consistent results from the two methods, both of which are spatially consistent with ground surveyed treeline elevations. Our results are in line with the current understanding of the global distributions of tree and vegetation lines, which are observed at the highest elevations in the Tibetan plateau and decreasing for increasing latitudes. We find that the tree and vegetation lines are aspect-dependent, reaching higher elevations on the equatorial-facing slopes than on the polar-facing slopes in high latitudes, and the opposite in the middle latitudes. Our analysis shows that mountain height is the dominant factor in determining the upper elevational limits of tree and vegetation lines across the globe, while climatic conditions and soil properties also play important roles at regional scales. Our study provides a framework for monitoring the tree and vegetation lines in global high-mountains and provides an important benchmark for further examining their long-term changes in response to climate change

Reliability of using vegetation optical depth for estimating decadal and interannual carbon dynamics

Vegetation optical depth (VOD) from satellite passive microwave sensors has enabled monitoring of aboveground biomass carbon dynamics by building a relationship with static carbon maps over space and then applying this relationship to VOD time series. However, uncertainty in this relationship arises from changes in water stress, as VOD is mainly determined by vegetation water content, which varies at diurnal to interannual scales, and depends on changes in both biomass and relative moisture content. Here, we studied the reliability of using VOD from various microwave frequencies and temporal aggregation methods for estimating decadal biomass carbon dynamics at the global scale. We used the VOD diurnal variations to represent the magnitude of vegetation water content buffering caused by climatic variations for a constant amount of dry biomass carbon. This magnitude of VOD diurnal variations was then used to evaluate the likelihood of VOD decadal variations in reflecting decadal dry biomass carbon changes. We found that SMOS-IC L-VOD and LPDR X-VOD can be reliably used to estimate decadal carbon dynamics for 76.7% and 69.9% of the global vegetated land surface, respectively, yet cautious use is warranted for some areas such as the eastern Amazon rainforest. Moreover, the annual VOD aggregated from the 95% percentile of the nighttime VOD retrievals was proved to be the most suitable parameter for estimating decadal biomass carbon dynamics among the temporal aggregation methods. Finally, we validated the use of annual VOD for estimating interannual carbon dynamics by comparing VOD changes between adjacent years against eddy covariance estimations of gross primary production from flux sites over several land cover classes across the globe. Despite the large difference in spatial scales between them, the positive correlation obtained supports the capability of satellite VOD in quantifying interannual carbon dynamics

Effect of Acupuncture on Neuroplasticity of Stroke Patients with Motor Dysfunction: A Meta-Analysis of fMRI Studies

Importance. Acupuncture is an effective treatment for stroke, especially in the aspect of motor deficit. Many brain imaging studies of acupuncture have found significant changes in brain function after acupuncture treatment in order to reveal its underlying mechanisms in regulating neural plasticity. However, no definite consensus has been reached. Objective. To analyze the pattern of intrinsic brain activity variability that is altered by acupuncture compared with conventional treatment in stroke patients with motor dysfunction, thus providing the mechanism of stroke treatment by acupuncture. Methods. Chinese and English articles published up to May 2020 were searched in the PubMed, Web of Science, EMBASE, and Cochrane Library databases, China National Knowledge Infrastructure, Chongqing VIP, and Wanfang Database. We only included randomized controlled trials (RCTs) using resting-state fMRI to observe the effect of acupuncture on stroke patients with motor dysfunction. R software was used to analyze the continuous variables, and Seed-based d Mapping with Permutation of Subject Images (SDM-PSI) was used to perform an analysis of fMRI data. Findings. A total of 7 studies comprising 143 patients in the treatment group and 138 in the control group were included in the meta-analysis. The results suggest that acupuncture treatment helps the healing process of motor dysfunction in stroke patients and exhibits hyperactivation in the bilateral basal ganglia and insula and hypoactivation in motor-related areas (especially bilateral BA6 and left BA4). Conclusion. Acupuncture plays a role in promoting neuroplasticity in subcortical regions that are commonly affected by stroke and cortical motor areas that may compensate for motor deficits, which may provide a possible mechanism underlying the therapeutic effect of acupuncture