Since the inception of global industrialization, steroidal estrogens have become an emerging and serious concern. Worldwide, steroid estrogens including estrone, estradiol and estriol, pose serious ...threats to soil, plants, water resources and humans. Indeed, estrogens have gained notable attention in recent years, due to their rapidly increasing concentrations in soil and water all over the world. Concern has been expressed regarding the entry of estrogens into the human food chain which in turn relates to how plants take up and metabolism estrogens.
In this review we explore the environmental fate of estrogens highlighting their release through effluent sources, their uptake, partitioning and physiological effects in the ecological system. We draw attention to the potential risk of intensive modern agriculture and waste disposal systems on estrogen release and their effects on human health. We also highlight their uptake and metabolism in plants.
We use MEDLINE and other search data bases for estrogens in the environment from 2005 to the present, with the majority of our sources spanning the past five years. Published acceptable daily intake of estrogens (μg/L) and predicted no effect concentrations (μg/L) are listed from published sources and used as thresholds to discuss reported levels of estrogens in the aquatic and terrestrial environments. Global levels of estrogens from river sources and from Waste Water Treatment Facilities have been mapped, together with transport pathways of estrogens in plants.
Estrogens at polluting levels have been detected at sites close to waste water treatment facilities and in groundwater at various sites globally. Estrogens at pollutant levels have been linked with breast cancer in women and prostate cancer in men. Estrogens also perturb fish physiology and can affect reproductive development in both domestic and wild animals. Treatment of plants with steroid estrogen hormones or their precursors can affect root and shoot development, flowering and germination. However, estrogens can ameliorate the effects of other environmental stresses on the plant.
There is published evidence to establish a causal relationship between estrogens in the environment and breast cancer. However, there are serious gaps in our knowledge about estrogen levels in the environment and a call is required for a world wide effort to provide more data on many more samples sites. Of the data available, the synthetic estrogen, ethinyl estradiol, is more persistent in the environment than natural estrogens and may be a greater cause for environmental concern. Finally, we believe that there is an urgent requirement for inter-disciplinary studies of estrogens in order to better understand their ecological and environmental impact.
With the launch of space-borne satellites, more synthetic aperture radar (SAR) images are available than ever before, thus making dynamic ship monitoring possible. Object detectors in deep learning ...achieve top performance, benefitting from a free public dataset. Unfortunately, due to the lack of a large volume of labeled datasets, object detectors for SAR ship detection have developed slowly. To boost the development of object detectors in SAR images, a SAR dataset is constructed. This dataset labeled by SAR experts was created using 102 Chinese Gaofen-3 images and 108 Sentinel-1 images. It consists of 43,819 ship chips of 256 pixels in both range and azimuth. These ships mainly have distinct scales and backgrounds. Moreover, modified state-of-the-art object detectors from natural images are trained and can be used as baselines. Experimental results reveal that object detectors achieve higher mean average precision (mAP) on the test dataset and have high generalization performance on new SAR imagery without land-ocean segmentation, demonstrating the benefits of the dataset we constructed.
This paper focuses on how the cultural industry affects economic growth through the theory and model of cultural economics. Based on the development of the economic growth model endogenized by the ...cultural industry, it explains how the arts and entertainment sector and the digital economy play a crucial role in the long-term economic growth after examining the logic and mechanism of the cultural industry impacting economic growth through induction and theoretical derivation. To create a quantitative analytical model of the digital economy and cultural industry and investigate the relationship between them, multiple regression analysis is employed. The impact of individual aspects of each condition variable on the economic performance composition of the cultural industry is evaluated using group effects. The results of the NCA test demonstrate that the digital economy, the scale of the cultural industry, public participation, and resources are all necessary for the sector to perform economically. The effect sizes of these factors are (0.525, 0.540), (0.362, 0.347), (0.315, 0.363), and (0.271, 0.188). The digital economy’s regression coefficients, which are usually positive, are0.470, 0.461, 0.428, 0.422, 0.383, and 0.381, according to the regression study. According to this study, the cultural sector’s growth at a high standard is positively impacted by the digital economy.
Cancer is a severe public health issue that is a leading cause of mortality globally. It is also an impediment to improving life expectancy worldwide. Furthermore, the global burden of cancer ...incidence and death is continuously growing. Current therapeutic options are insufficient for patients, and tumor complexity and heterogeneity necessitate customized medicine or targeted therapy. It is critical to identify potential cancer therapeutic targets. Aberrant activation of the PI3K/AKT/mTOR pathway has a significant role in carcinogenesis. This review summarized oncogenic PI3K/Akt/mTOR pathway alterations in cancer and various cancer hallmarks associated with the PI3K/AKT/mTOR pathway, such as cell proliferation, autophagy, apoptosis, angiogenesis, epithelial-to-mesenchymal transition (EMT), and chemoresistance. Importantly, this review provided recent advances in PI3K/AKT/mTOR inhibitor research. Overall, an in-depth understanding of the association between the PI3K/AKT/mTOR pathway and tumorigenesis and the development of therapies targeting the PI3K/AKT/mTOR pathway will help make clinical decisions.
Independent of daylight and weather conditions, synthetic aperture radar (SAR) imagery is widely applied to detect ships in marine surveillance. The shapes of ships are multi-scale in SAR imagery due ...to multi-resolution imaging modes and their various shapes. Conventional ship detection methods are highly dependent on the statistical models of sea clutter or the extracted features, and their robustness need to be strengthened. Being an automatic learning representation, the RetinaNet object detector, one kind of deep learning model, is proposed to crack this obstacle. Firstly, feature pyramid networks (FPN) are used to extract multi-scale features for both ship classification and location. Then, focal loss is used to address the class imbalance and to increase the importance of the hard examples during training. There are 86 scenes of Chinese Gaofen-3 Imagery at four resolutions, i.e., 3 m, 5 m, 8 m, and 10 m, used to evaluate our approach. Two Gaofen-3 images and one Constellation of Small Satellite for Mediterranean basin Observation (Cosmo-SkyMed) image are used to evaluate the robustness. The experimental results reveal that (1) RetinaNet not only can efficiently detect multi-scale ships but also has a high detection accuracy; (2) compared with other object detectors, RetinaNet achieves more than a 96% mean average precision (mAP). These results demonstrate the effectiveness of our proposed method.
Deep learning-based radiomics (DLR) was developed to extract deep information from multiple modalities of magnetic resonance (MR) images. The performance of DLR for predicting the mutation status of ...isocitrate dehydrogenase 1 (IDH1) was validated in a dataset of 151 patients with low-grade glioma. A modified convolutional neural network (CNN) structure with 6 convolutional layers and a fully connected layer with 4096 neurons was used to segment tumors. Instead of calculating image features from segmented images, as typically performed for normal radiomics approaches, image features were obtained by normalizing the information of the last convolutional layers of the CNN. Fisher vector was used to encode the CNN features from image slices of different sizes. High-throughput features with dimensionality greater than 1.6*10
were obtained from the CNN. Paired t-tests and F-scores were used to select CNN features that were able to discriminate IDH1. With the same dataset, the area under the operating characteristic curve (AUC) of the normal radiomics method was 86% for IDH1 estimation, whereas for DLR the AUC was 92%. The AUC of IDH1 estimation was further improved to 95% using DLR based on multiple-modality MR images. DLR could be a powerful way to extract deep information from medical images.
Urban heat islands (UHIs) reflect the localized impact of human activities on thermal fields. In this study, we assessed the surface UHI and its relationship with types of land, meteorological ...conditions, anthropogenic heat sources and urban areas in the Yangtze River Delta Urban Agglomeration (YRDUA) with the aid of remote sensing data, statistical data and meteorological data. The results showed that the UHI intensity in YRDUA was the strongest (0.84°C) in summer, followed by 0.81°C in autumn, 0.78°C in spring and 0.53°C in winter. The daytime UHI intensity is 0.98°C, which is higher than the nighttime UHI intensity of 0.50°C. Then, the relationship between the UHI intensity and several factors such as meteorological conditions, anthropogenic heat sources and the urban area were analysed. The results indicated that there was an insignificant correlation between population density and the UHI intensity. Energy consumption, average temperature and urban area had a significant positive correlation with UHI intensity. However, the average wind speed and average precipitation were significantly negatively correlated with UHI intensity. This study provides insight into the regional climate characteristics and a scientific basis for city layout.
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•It is necessary to explore the driving factors of UHI to develop mitigating measures and reasonable urban plans.•The UHI intensity in the Yangtze River Delta Urban Agglomeration was the strongest (0.84 °C) in summer, followed by 0.81°C in autumn, 0.78 °C in spring, and 0.53 °C in winter.•The daytime UHI intensity is higher than the nighttime UHI intensity.•There is no significant correlation between population density and UHI intensity in the Yangtze River Delta Urban Agglomeration.•Energy consumption, average temperature, and urban area were significantly positively correlated with UHI intensity.•The average wind speed and average precipitation was significantly negatively correlated with UHI intensity.
With the capability to automatically learn discriminative features, deep learning has experienced great success in natural images but has rarely been explored for ship classification in ...high-resolution SAR images due to the training bottleneck caused by the small datasets. In this paper, convolutional neural networks (CNNs) are applied to ship classification by using SAR images with the small datasets. First, ship chips are constructed from high-resolution SAR images and split into training and validation datasets. Second, a ship classification model is constructed based on very deep convolutional networks (VGG). Then, VGG is pretrained via ImageNet, and fine tuning is utilized to train our model. Six scenes of COSMO-SkyMed images are used to evaluate our proposed model with regard to the classification accuracy. The experimental results reveal that (1) our proposed ship classification model trained by fine tuning achieves more than 95% average classification accuracy, even with 5-cross validation; (2) compared with other models, the ship classification model based on VGG16 achieves at least 2% higher accuracies for classification. These experimental results reveal the effectiveness of our proposed method.
Land-use classification based on spaceborne or aerial remote sensing images has been extensively studied over the past decades. Such classification is usually a patch-wise or pixel-wise labeling over ...the whole image. But for many applications, such as urban population density mapping or urban utility planning, a classification map based on individual buildings is much more informative. However, such semantic classification still poses some fundamental challenges, for example, how to retrieve fine boundaries of individual buildings. In this paper, we proposed a general framework for classifying the functionality of individual buildings. The proposed method is based on Convolutional Neural Networks (CNNs) which classify façade structures from street view images, such as Google StreetView, in addition to remote sensing images which usually only show roof structures. Geographic information was utilized to mask out individual buildings, and to associate the corresponding street view images. We created a benchmark dataset which was used for training and evaluating CNNs. In addition, the method was applied to generate building classification maps on both region and city scales of several cities in Canada and the US.
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