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Extracting roads from remote sensing images is of significant importance for automatic road network updating, urban planning, and construction. However, various factors in complex scenes (e.g., high vegetation coverage occlusions) may lead to fragmentation in the extracted road networks and also affect the robustness of road extraction methods. This study proposes a multi-scale road extraction method with asymmetric generative adversarial learning (MS-AGAN). First, we design an asymmetric GAN with a multi-scale feature encoder to better utilize the context information in high-resolution remote sensing images (HRSIs). Atrous spatial pyramid pooling (ASPP) and feature fusion are integrated into the asymmetric encoder–decoder structure to avoid feature redundancy caused by multi-level cascading operations and enhance the generator network's ability to extract fine-grained road information at the pixel level. Second, to maintain road connectivity, topologic features are considered in the pixel segmentation process. A linear structural similarity loss ( L S S I M ) is introduced into the loss function of MS-AGAN, which guides MS-AGAN to generate more accurate segmentation results. Finally, to fairly evaluate the performance of deep models under complex backgrounds, the Bayesian error rate (BER) is introduced into the field of road extraction for the first time. Experiments are conducted via Gaofen-2 (GF-2) high-resolution remote sensing images with high vegetation coverage in the Daxing District of Beijing, China, and the public DeepGlobe dataset. The performance of MS-AGAN is compared with a list of advanced models, including RCFSNet, CoANet, UNet, DeepLabV3+, and DiResNet. The final results show that (1) with respect to road extraction performance, the Recall, F1, and IoU values of MS-AGAN on the Daxing dataset are 2.17%, 0.04%, and 2.63% higher than the baselines. On DeepGlobe, the Recall, F1, and IoU of MS-AGAN improve by 1.12%, 0.42%, and 0.25%, respectively. (2) On road connectivity, the Conn index of MS-AGAN from the Daxing dataset is 46.39%, with an improvement of 0.62% over the baselines, and the Conn index of MS-AGAN on DeepGlobe is 70.08%, holding an improvement of 1.73% over CoANet. The quantitative and qualitative analyses both demonstrate the superiority of MS-AGAN in preserving road connectivity. (3) In particular, the BER of MS-AGAN is 20.86% over the Daxing dataset with a 0.22% decrease compared to the best baselines and 11.77% on DeepGlobe with a 0.85% decrease compared to the best baselines. The proposed MS-AGAN provides an efficient, cost-effective, and reliable method for the dynamic updating of road networks via HRSIs. [ABSTRACT FROM AUTHOR]

The intelligent maritime transportation system has emerged as a pivotal component in port management, owing to the rapid advancements in artificial intelligence and big data technology. Its essence lies in the application of digital modeling techniques, which leverage extensive ship data to facilitate efficient operations. In this regard, effective modeling and accurate prediction of the fluctuation patterns of ship traffic in multiple port regions will provide data support for trade analysis, port construction planning, and traffic safety management. In order to better express the potential interdependencies between ports, inspired by graph neural networks, this paper proposes a data-driven approach to construct a multi-port network and designs a spatiotemporal graph neural network model. The model incorporates graph attention networks and a dilated causal convolutional architecture to capture the temporal and spatial dimensions of traffic variation patterns. It also employs a gated-mechanism-based spatiotemporal bi-dimensional feature fusion strategy to handle the potential unequal relationships between the two dimensions of features. Compared to existing methods for port traffic prediction, this model fully considers the network characteristics of the overall port and fills the research gap in multi-port scenarios. In the experiments, real port ship traffic datasets were constructed using data from the Automatic Identification System (AIS) and port geographical information data for model validation. The results demonstrate that the model exhibits outstanding robustness and performs well in predicting traffic in multiple sub-regional port clusters. [ABSTRACT FROM AUTHOR]

Mercury (Hg) is a well-recognized environmental pollutant known by its toxicity of development and neurotoxicity, which results in adverse health outcomes. However, the mechanisms underlying the teratogenic effects of Hg are not well understood. Imprinting genes are emerging regulators for fetal development subjecting to environmental pollutants impacts. In this study, we examined the association between preconceptional Hg exposure and the alteration of DNA methylation of imprinting genes H19, Meg3, and Peg3 in human sperm DNA.A total of 616 men, aged from 22 to 59, were recruited from Reproductive Medicine Clinic of Maternal and Child Care Service Center and the Urologic Surgery Clinic of Shanxi Academy of Medical Sciences during April 2015 and March 2016. Demographic information was collected through questionnaires. Urine was collected and urinary Hg concentrations were measured using a fully-automatic double-channel hydride generation atomic fluorescence spectrometer. Methylation of imprinting genes H19, Meg3 and Peg3 of sperm DNA from 242 participants were examined by bisulfite pyrosequencing. Spearman's rank and multivariate regression analysis were used for correlation analysis between sperm DNA methylation status of imprinting genes and urinary Hg levels.The median concentration of Hg for 616 participants was 9.14μg/l (IQR: 5.56-12.52 μg/l; ranging 0.16-71.35μg/l). A total of 42.7% of the participants are beyond normal level for non-occupational exposure according to the criterion of Hg poisoning (≥10 μg/L). Spearman's rank analysis indicated a negative correlation between urinary Hg concentrations and average DNA methylation levels of imprinted genes H19 (rs = -0.346, p

The Euclidean distance is usually used in heuristic planning of Dyna_Q-learning based on reinforcement learning tasks of goal position. But it is not suitable for these tasks whose state space is not continuous in Euclidean space such as path planning of disaster rescue robot in underground coal mine. For the problem, the paper introduced the Laplacian Eigenmap whose computational complexity is lower in manifold learning, then proposed an improved Dyna_Q-learning algorithm based on manifold distance metric. The proposed algorithm is simulated in grid world that is similar to underground environment. The simulation results verified validity of the algorithm.

Recently, an increasing number of human and animal studies have reported that exposure to benzo(a)pyrene (BaP) induces neurological abnormalities and is also associated with adverse effects, such as tumor formation, immunosuppression, teratogenicity, and hormonal disorders. However, the exact mechanisms underlying BaP-induced impairment of neurological function remain unclear. The aim of this study was to examine the regulating mechanisms underlying the impact of chronic BaP exposure on neurobehavioral performance.C57BL mice received either BaP in different doses (1.0, 2.5, 6.25 mg/kg) or olive oil twice a week for 90 days. Memory and emotional behaviors were evaluated using Y-maze and open-field tests, respectively. Furthermore, levels of mRNA expression were measured by using qPCR, and DNA methylation of NMDA receptor 2B subunit (NR2B) was examined using bisulfate pyrosequencing in the prefrontal cortex and hippocampus.Compared to controls, mice that received BaP (2.5, 6.25 mg/kg) showed deficits in short-term memory and an anxiety-like behavior. These behavioral alterations were associated with a down-regulation of the NR2B gene and a concomitant increase in the level of DNA methylation in the NR2B promoter in the two brain regions.Chronic BaP exposure induces an increase in DNA methylation in the NR2B gene promoter and down-regulates NR2B expression, which may contribute to its neurotoxic effects on behavioral performance. The results suggest that NR2B vulnerability represents a target for environmental toxicants in the brain.