Clinical profile of Parkinson's disease in the Gumei community of Minhang district, Shanghai

OBJECTIVE: We examined the demographic and clinical profiles of Parkinson's disease in Shanghai, China, to assist in disease management and provide comparative data on Parkinson's disease prevalence, phenotype, and progression among different regions and ethnic groups. METHODS: A door-to-door survey and follow-up clinical examinations identified 180 community-dwelling Han-Chinese Parkinson's disease patients (104 males, 76 females). RESULTS: The average age at onset was 65.16±9.60 years. The most common initial symptom was tremor (112 patients, 62.22%), followed by rigidity (38, 21.11%), bradykinesia (28, 15.56%) and tremor plus rigidity (2, 1.11%). Tremor as the initial symptom usually began in a single limb (83.04% of patients). The average duration from onset to mild Parkinson's disease (Hoehn-Yahr phase 1-2) was 52.74±45.64 months. Progression from mild to moderate/severe Parkinson's disease (phase≥3) was significantly slower (87.07±58.72 months;

Time-frequency analysis framework for understanding non-stationary and multi-scale characteristics of sea-level dynamics

Rising sea level caused by global climate change may increase extreme sea level events, flood low-lying coastal areas, change the ecological and hydrological environment of coastal areas, and bring severe challenges to the survival and development of coastal cities. Hong Kong is a typical economically and socially developed coastal area. However, in such an important coastal city, the mechanisms of local sea-level dynamics and their relationship with climate teleconnections are not well explained. In this paper, Hong Kong tide gauge data spanning 68 years was documented to study the historical sea-level dynamics. Through the analysis framework based on Wavelet Transform and Hilbert Huang Transform, non-stationary and multi-scale features in sea-level dynamics in Hong Kong are revealed. The results show that the relative sea level (RSL) in Hong Kong has experienced roughly 2.5 cycles of high-to-low sea-level transition in the past half-century. The periodic amplitude variation of tides is related to Pacific Decadal Oscillation (PDO) and El Niño-Southern Oscillation (ENSO). RSL rise and fall in eastern Hong Kong often occur in La Niña and El Niño years, respectively. The response of RSL to the PDO and ENSO displays a time lag and spatial heterogeneity in Hong Kong. Hong Kong's eastern coastal waters are more strongly affected by the Pacific climate and current systems than the west. This study dissects the non-stationary and multi-scale characteristics of relative sea-level change and helps to better understand the response of RSL to the global climate system

Identification of key candidate genes and biological pathways in bladder cancer

Background Bladder cancer is a malignant tumor in the urinary system with high mortality and recurrence rates. However, the causes and recurrence mechanism of bladder cancer are not fully understood. In this study, we used integrated bioinformatics to screen for key genes associated with the development of bladder cancer and reveal their potential molecular mechanisms. Methods The GSE7476, GSE13507, GSE37815 and GSE65635 expression profiles were downloaded from the Gene Expression Omnibus database, and these datasets contain 304 tissue samples, including 81 normal bladder tissue samples and 223 bladder cancer samples. The RobustRankAggreg (RRA) method was utilized to integrate and analyze the four datasets to obtain integrated differentially expressed genes (DEGs), and the gene ontology (GO) functional annotation and Kyoto encyclopedia of genes and genomes (KEGG) pathway analysis were performed. Protein-protein interaction (PPI) network and module analyses were performed using Cytoscape software. The OncoLnc online tool was utilized to analyze the relationship between the expression of hub genes and the prognosis of bladder cancer. Results In total, 343 DEGs, including 111 upregulated and 232 downregulated genes, were identified from the four datasets. GO analysis showed that the upregulated genes were mainly involved in mitotic nuclear division, the spindle and protein binding. The downregulated genes were mainly involved in cell adhesion, extracellular exosomes and calcium ion binding. The top five enriched pathways obtained in the KEGG pathway analysis were focal adhesion (FA), PI3K-Akt signaling pathway, proteoglycans in cancer, extracellular matrix (ECM)-receptor interaction and vascular smooth muscle contraction. The top 10 hub genes identified from the PPI network were vascular endothelial growth factor A (VEGFA), TOP2A, CCNB1, Cell division cycle 20 (CDC20), aurora kinase B, ACTA2, Aurora kinase A, UBE2C, CEP55 and CCNB2. Survival analysis revealed that the expression levels of ACTA2, CCNB1, CDC20 and VEGFA were related to the prognosis of patients with bladder cancer. In addition, a KEGG pathway analysis of the top 2 modules identified from the PPI network revealed that Module 1 mainly involved the cell cycle and oocyte meiosis, while the analysis in Module 2 mainly involved the complement and coagulation cascades, vascular smooth muscle contraction and FA. Conclusions This study identified key genes and pathways in bladder cancer, which will improve our understanding of the molecular mechanisms underlying the development and progression of bladder cancer. These key genes might be potential therapeutic targets and biomarkers for the treatment of bladder cancer

Improving Urban Land Cover Classification in Cloud-Prone Areas with Polarimetric SAR Images

Urban land cover (ULC) serves as fundamental environmental information for urban studies, while accurate and timely ULC mapping remains challenging due to cloud contamination in tropical and subtropical areas. Synthetic aperture radar (SAR) has excellent all-weather working capability to overcome the challenge, while optical SAR data fusion is often required due to the limited land surface information provided by SAR. However, the mechanism by which SAR can compensate optical images, given the occurrence of clouds, in order to improve the ULC mapping, remains unexplored. To address the issue, this study proposes a framework, through various sampling strategies and three typical supervised classification methods, to quantify the ULC classification accuracy using optical and SAR data with various cloud levels. The land cover confusions were investigated in detail to understand the role of SAR in distinguishing land cover under different types of cloud coverage. Several interesting experimental results were found. First, 50% cloud coverage over the optical images decreased the overall accuracy by 10–20%, while the incorporation of SAR images was able to improve the overall accuracy by approximately 4%, by increasing the recognition of cloud-covered ULC information, particularly the water bodies. Second, if all the training samples were not contaminated by clouds, the cloud coverage had a higher impact with a reduction of 35% in the overall accuracy, whereas the incorporation of SAR data contributed to an increase of approximately 5%. Third, the thickness of clouds also brought about different impacts on the results, with an approximately 10% higher reduction from thick clouds compared with that from thin clouds, indicating that certain spectral information might still be available in the areas covered by thin clouds. These findings provide useful references for the accurate monitoring of ULC over cloud-prone areas, such as tropical and subtropical cities, where cloud contamination is often unavoidable

LaneFormer: Real-Time Lane Exaction and Detection via Transformer

In intelligent driving, lane line detection is a basic but challenging task, especially in complex road conditions. The current detection algorithms based on convolutional neural networks perform well for simple scenes with plenty of light, and the lane lines are clean and unobstructed. Still, they do not perform well for complex scenes such as damaged, blocked, and lack-of-light scenes. In this article, we have exceeded the above restrictions and propose an attractive network: LaneFormer; We use an end-to-end network for up and down sampling three times each, then fuse them in their respective channels to extract the slender lane line structure. At the same time, a correction module is designed to adjust the dimensions of the extracted features using MLP, judging whether the feature is completely extracted through the loss function. Finally, we send the feature into the transformer network, detect the lane line points through the attention mechanism, and design a road and camera model to fit the identified lane line feature points. Our proposed method has been validated in the TuSimple benchmark test, showing the most advanced accuracy with the lightest model and fastest speed

New morphological features for urban tree species identification using LiDAR point clouds

Urban tree species identification is the basis for studying the urban-environment coordination mechanism at the species level. Although the gradual maturity of remote sensing data and related research including light detection and ranging (LiDAR) provides a good foundation for the realization of this technology, multiple reasons such as cost, data openness, study scope limitations, and weakness of traditional morphological features make such data still challenging to apply to subtropical urban trees with heterogeneous canopy structures and high biodiversity. To address the problem, we developed two large-scale LiDAR morphological features in this research by, 1) modifying the rotate image method based on the axisymmetric structure to make it easier to use, and 2) developing an innovative adaptive ellipsoid method to extract the canopy features of the non-axisymmetric structure effectively. We evaluated the ability of these two morphological features to describe 12 common subtropical urban tree (SUT) species in Hong Kong growing in urban parks and streets, obtaining an accuracy of 88%. And the advantages of the proposed method are demonstrated by comparison with existing LiDAR morphological features and mean decrease accuracy (MDA) analysis. Our results illustrated that the rotate image feature based on the axisymmetric structure did not perform as well as the adaptive ellipsoid feature based on the non-axisymmetric structure in SUT, and the combined application of these two new morphological features got further accuracy improvement. The method proposed in this study had significant advantages in terms of accuracy, the number of species included, and generalisation capability compared to existing studies on the identification of subtropical urban trees

Detecting Snowfall Events over Mountainous Areas Using Optical Imagery

Snowfall over mountainous areas not only has important implications on the water cycle and the Earth’s radiation balance, but also causes potentially hazardous weather. However, snowfall detection remains one of the most difficult problems in modern hydrometeorology. We present a method for detecting snowfall events from optical satellite data for seasonal snow in mountainous areas. The proposed methodology is based on identifying expanded snow cover or suddenly declined snow grain size using time series images, from which it is possible to detect the location and time of snowfall events. The methodology was tested with Moderate Resolution Imaging Spectroradiometer (MODIS) daily radiance data for an entire hydrologic year from July 2014 to June 2015 in the mountainous area of the Manas River Basin, Northwest China. The study evaluated the recordings of precipitation events at eighteen meteorological stations in the study area prove the effectiveness of the proposed method, showing that there was more liquid precipitation in the second and third quarter, and more solid precipitation in the first and fourth quarter

Urbanization and Its Impacts on Land Surface Temperature in Colombo Metropolitan Area, Sri Lanka, from 1988 to 2016

Urbanization has become one of the most important human activities modifying the Earth’s land surfaces; and its impacts on tropical and subtropical cities (e.g., in South/Southeast Asia) are not fully understood. Colombo; the capital of Sri Lanka; has been urbanized for about 2000 years; due to its strategic position on the east–west sea trade routes. This study aims to investigate the characteristics of urban expansion and its impacts on land surface temperature in Colombo from 1988 to 2016; using a time-series of Landsat images. Urban land cover changes (ULCC) were derived from time-series satellite images with the assistance of machine learning methods. Urban density was selected as a measure of urbanization; derived from both the multi-buffer ring method and a gravity model; which were comparatively adopted to evaluate the impacts of ULCC on the changes in land surface temperature (LST) over the study period. The experimental results indicate that: (1) the urban land cover classification during the study period was conducted with satisfactory accuracy; with more than 80% for the overall accuracy and over 0.73 for the Kappa coefficient; (2) the Colombo Metropolitan Area exhibits a diffusion pattern of urban growth; especially along the west coastal line; from both the multi-buffer ring approach and the gravity model; (3) urban density was identified as having a positive relationship with LST through time; (4) there was a noticeable increase in the mean LST; of 5.24 °C for water surfaces; 5.92 °C for vegetation; 8.62 °C for bare land; and 8.94 °C for urban areas. The results provide a scientific reference for policy makers and urban planners working towards a healthy and sustainable Colombo Metropolitan Area

GF-5 Hyperspectral Data for Species Mapping of Mangrove in Mai Po, Hong Kong

Hyperspectral data has been widely used in species discrimination of plants with rich spectral information in hundreds of spectral bands, while the availability of hyperspectral data has hindered its applications in many specific cases. The successful operation of the Chinese satellite, Gaofen-5 (GF-5), provides potentially promising new hyperspectral dataset with 330 spectral bands in visible and near infrared range. Therefore, there is much demand for assessing the effectiveness and superiority of GF-5 hyperspectral data in plants species mapping, particularly mangrove species mapping, to better support the efficient mangrove management. In this study, mangrove forest in Mai Po Nature Reserve (MPNR), Hong Kong was selected as the study area. Four dominant native mangrove species were investigated in this study according to the field surveys. Two machine learning methods, Random Forests and Support Vector Machines, were employed to classify mangrove species with Landsat 8, Simulated Hyperion and GF-5 data sets. The results showed that 97 more bands of GF-5 over Hyperion brought a higher over accuracy of 87.12%, in comparison with 86.82% from Hyperion and 73.89% from Landsat 8. The higher spectral resolution of 5 nm in GF-5 was identified as making the major contribution, especially for the mapping of Aegiceras corniculatum. Therefore, GF-5 is likely to improve the classification accuracy of mangrove species mapping via enhancing spectral resolution and thus has promising potential to improve mangrove monitoring at species level to support mangrove management