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

Comparative Analysis for the Performance of Variant Calling Pipelines on Detecting the de novo Mutations in Humans

Despite of the low occurrence rate in the entire genomes, de novo mutation is proved to be deleterious and will lead to severe genetic diseases via impacting on the gene function. Considering the fact that the traditional family based linkage approaches and the genome-wide association studies are unsuitable for identifying the de novo mutations, in recent years, several pipelines have been proposed to detect them based on the whole-genome or whole-exome sequencing data and were used for calling them in the rare diseases. However, how the performance of these variant calling pipelines on detecting the de novo mutations is still unexplored. For the purpose of facilitating the appropriate choice of the pipelines and reducing the false positive rate, in this study, we thoroughly evaluated the performance of the commonly used trio calling methods on the detection of the de novo single-nucleotide variants (DNSNVs) by conducting a comparative analysis for the calling results. Our results exhibited that different pipelines have a specific tendency to detect the DNSNVs in the genomic regions with different GC contents. Additionally, to refine the calling results for a single pipeline, our proposed filter achieved satisfied results, indicating that the read coverage at the mutation positions can be used as an effective index to identify the high-confidence DNSNVs. Our findings should be good support for the committees to choose an appropriate way to explore the de novo mutations for the rare diseases

Potassium Application Alleviated Negative Effects of Soil Waterlogging Stress on Photosynthesis and Dry Biomass in Cotton

Soil waterlogging is one of the most serious abiotic stresses on plant growth and crop productivity. In this study, two potassium application levels (0 and 150 kg K2O hm−2) with three types of soil waterlogging treatments (0 d, 3 d and 6 d) were established during cotton flowering and boll-forming stages. The results showed that soil waterlogging markedly reduced RWC (relative water content), gas exchange parameters and cotton biomass. However, potassium application considerably improved the aforementioned parameters. Specifically, 3 d soil waterlogging with potassium increased Pn (net photosynthetic rate), Gs (stomatal conductance), Ci (intercellular CO2 concentration) and Tr (transpiration rate) by 4.55%, 27.27%, 5.74% and 3.82%, respectively, compared with 3 d soil waterlogging under no potassium, while the abscission rate reduced by 2.96%. Additionally, the number of bolls and fruit nodes under 6 d soil waterlogging with potassium increased by 16.17% and 4.38%, compared with 6 d soil waterlogging under no potassium. Therefore, it was concluded that regardless of 3 d or 6 d soil waterlogging, potassium application alleviated the negative effects of waterlogging by regulating the plant water status, photosynthetic capacity and plant growth in cotton. These results are expected to provide theoretical references and practical applications for cotton production to mitigate the damage of soil waterlogging

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

The scheme of wind-storage combined system capacity configuration based on random fuzzy chance constrained bi-level programming

A random fuzzy chance constrained bilevel programming scheme for distributed wind-storage combined system is proposed. The random fuzzy simulation is used to describe the uncertainty of distributed wind power output. The reliability of randomness and ambiguity is taken as the index to evaluate the capacity allocation scheme of the distributed wind-storage combined system. Considering system power balance, opportunity measurement constraint of static security index and active management (AM) measures, the random fuzzy expectation value of maximum annual profit is set as the upper optimization goal, and the minimum random fuzzy expectation value of the distributed wind power active reduction is set as the lower optimization target. The scheme is constructed by judging whether the static security index of the upper goal satisfies the confidence level of the random fuzzy chance constraint and the coordination of the upper and lower goals. Finally, the random fuzzy simulation, the forward pushback power flow calculation and the genetic algorithm (GA) are applied to solve the model. The simulation result of IEEE 14-bus example shows the effectiveness and superiority of the model and scheme

24-Week Exposure to Oxidized Tyrosine Induces Hepatic Fibrosis Involving Activation of the MAPK/TGF- β

Scope. Oxidized tyrosine (O-Tyr) has been widely detected in many consumer protein products. O-Tyr products such as dityrosine (Dityr) and 3-nitrotyrosine (3-NT) are universal biomarkers of protein oxidation and have been demonstrated to be associated with metabolic disorders in biological system. Evaluation of potential intracorporal effects of dietary O-Tyr is important since the mechanism of biological impacts induced by oral oxidized protein products (OPPs) is still limited although we have proved that some dietary OPPs would induce oxidative injury to liver and kidney. Methods and Results. The present study aimed to investigate the dose-dependent hepatic injury caused by oral O-Tyr in rats. 24-week feeding of O-Tyr enhanced aspartate aminotransferase (AST) and alanine aminotransferase (ALT) activities, increased total bilirubin (TBiL) content, and led to oxidative damage in rats liver. Besides, O-Tyr distinctly increased the phosphorylation of p38 and ERK2 MAPKs and enhanced fibrosis-related TGF-β1 and Smad2/3 levels. Higher extracellular matrix (ECM) indexes (ICTP, PIIINP) and histological examination (HE and Masson staining) also supported dose-dependent hepatic fibrosis caused by O-Tyr. Conclusion. These findings reveal that O-Tyr may induce oxidative damage and hepatic fibrosis via MAPK/TGF-β1 signaling pathway, in which ROS together with malondialdehyde (MDA) and OPPs act as the pivotal mediators

The scheme of wind-storage combined system capacity configuration based on random fuzzy chance constrained bi-level programming

A random fuzzy chance constrained bilevel programming scheme for distributed wind-storage combined system is proposed. The random fuzzy simulation is used to describe the uncertainty of distributed wind power output. The reliability of randomness and ambiguity is taken as the index to evaluate the capacity allocation scheme of the distributed wind-storage combined system. Considering system power balance, opportunity measurement constraint of static security index and active management (AM) measures, the random fuzzy expectation value of maximum annual profit is set as the upper optimization goal, and the minimum random fuzzy expectation value of the distributed wind power active reduction is set as the lower optimization target. The scheme is constructed by judging whether the static security index of the upper goal satisfies the confidence level of the random fuzzy chance constraint and the coordination of the upper and lower goals. Finally, the random fuzzy simulation, the forward pushback power flow calculation and the genetic algorithm (GA) are applied to solve the model. The simulation result of IEEE 14-bus example shows the effectiveness and superiority of the model and scheme

Improving urban impervious surface extraction by synergizing hyperspectral and polarimetric radar data using sparse representation

Accurate extraction of urban impervious surface (UIS) is essential for urban planning and environmental monitoring. However, multispectral remote sensing data for UIS extraction suffers from the inter-class spectral confusions, e.g. UIS and bare soil, and intra-class variations of sub-class UIS. Hyperspectral and full/dual-polarization synthetic aperture radar (full/dual PolSAR) data provide opportunities for reducing such confusions and have potential for fine UIS mapping, i.e., roads, buildings, and grounds. In this study, we first investigated the hyperspectral data (Gaofen-5) capability to reduce the intra/inter-class misclassification in comparison with multispectral data (Landsat-8). Then, we explored contributions of synergistically using full and dual PolSAR (ALOS-2 and Sentinel-1) with hyperspectral and multispectral data using optical-SAR sparse representation classification (OSSRC). Results showed that both the hyperspectral and the SAR polarization features helped better delineation between UIS and bare soil, and sub-class UIS (roads and buildings). The relative contribution of PolSAR was higher in multispectral data than in hyperspectral data, with full PolSAR contributed significantly. The combined hyperspectral and full PolSAR data using OSSRC delivered the best result, with an overall accuracy higher than 90%. The results indicate the promising capability of synergizing hyperspectral and full/dual PolSAR data for improving UIS extraction from advanced satellite data