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Mangroves are a unique group of plants, which offer a great variety of goods and services to the ecosystem and to the society. Regrettably, they have been globally threatened by urbanization and industrialization, among others, triggering overexploitation of the world’s mangrove forests despite their ecological and economic importance. As a result, mangroves are often under pollution stress as sinks or receivers for numerous man-made pollutants such as pesticides, which are the main focus of this thesis. One of the most widely applied chemicals in the word are the organochlorine pesticides (OCPs) that even after their supposedly worldwide ban between 1950s-1990s, they can still be detected in the environment. Numerous studies have been done in phytoremediation of pollutants by mangroves, but little attention has been given to the role of mangroves in the remediation of OCPs. For this reason, part of this thesis will focus on the occurrence and distribution of OCPs in intertidal tropical and sub-tropical areas around the world with and without mangroves. As a first goal (I), we evaluate —in a theoretical way— if the presence of mangroves affects or modifies the levels of OCPs in the surrounding environment. For this purpose, data from different matrices, such as water, sediment, benthic fauna and plants were included and discussed in this work. Moreover, and considering Macao’s location, we also quantified OCPs from surface waters of this region from areas with and without mangroves and included in this task. Besides this theoretical approach, this thesis also included some laboratoy and field work specifically focused on dicofol and 4,4’-dichlorobenzophenone (4,4’-DCBP, its main metabolite). Dicofol is an OCP strongly related to dichlorodiphenyltrichloroethane (DDT), which has been extensively used in China and more specifically, in the Pearl River Delta (PRD), a region under anthropogenic pressure. However, due to dicofol’s instability (i.e., sensitive to low pH, light exposure and high temperature), we expected to quantify 4,4′-DCBP (which is also common to DDT) as the main form present in the environment. As a second goal (II), we conducted a monitoring study in surface waters from Macao and Hong Kong, to evaluate the contamination status and water quality of these regions. Concentrations of 4,4’-DCBP, nutrients and physicochemical parameters were measured during transition and wet season, and at high and low tide. In addition, since the toxicity of this metabolite was totally unknown, we assessed it via two biological models: Daphnia magna and Artemia salina. Since 4,4’-DCBP was detected and quantified in both regions (2.8-30.0 ng/L), this thesis also includes experimental work focused on the assimilation and depuration pattern by a marine organism. For that purpose and as a final goal (III), we selected the common edible bivalve Meretrix as a model to evaluate the dynamics of accumulation and depuration of the pesticide dicofol. The Vietnamese clams were exposed during 15 days under two different concentrations of dicofol, and decontaminated for the same period of time. Quantification of 4,4’-DCBP was done during both phases (uptake and depuration) and at different sampling times. In summary, all these different works helped us to conclude that: I.1) As expected, vegetated areas with mangroves presented lower concentrations of OCPs for all the matrices, and also better quality in terms of pesticide pollution for water and sediments. Results obtained from Macao’s waters also revealed the same pattern, with mangroves areas having lower levels of contamination. Although the gathered data presented methodological variability (i.e. different quantification methods, extraction protocols, equipment used), the same pattern was observed among matrices, showing how robust and solid the results herein obtained are. II.1). Hong Kong presented higher concentrations of 4,4’-DCBP than Macao, which may be due to the use of dicofol as a pesticide and the use of antifouling-paint for ships. Moreover, concentrations of 4,4’-DCBP during wet season were below limits of quantification, demonstrating a seasonal pattern and a dilution effect due to higher river discharges during this period. II.2). Both regions showed possible eutrophication problems due to the high nutrient concentrations. These levels presented also a seasonal variability, with dissolved inorganic nitrogen and total dissolved solids higher during transition; and dissolved inorganic phosphorous, total suspended solids and chlorophyll a higher during wet season. II.3). Toxicity of 4,4’-DCBP was lower than the parent compound dicofol, and the levels quantified indicated a low environmental risk. However, it is important to pay attention to this compound since interaction with other contaminants could enhance their toxicity, or processes such as biomagnification or bioaccumulation could make low concentrations a threat for the environment. III.1). Different concentrations of dicofol presented different uptake and depuration kinetics. Animals exposed to higher concentrations (500 ng/L), had levels above limits of quantification (LOQ) after 24h exposure, unlike the ones exposed to lower concentrations (50 ng/L), which had levels <LOQ after the same period. The first ones also, presented lower uptake rates, and this could indicate that high dicofol concentrations in the system could affect the respiration rates of the organism. In addition, this work also showed that animals exposed to high concentrations of dicofol will need more than 15 days to depurate in order to reach safe levels for human consumption. The compilation of the work done in this thesis allowed us to better understand the role of mangroves ecosystems on the accumulation of OCPs and to provide solid information that could create strategies for mangroves management and conservation. Moreover, and as a first attempt, we were able to quantify this pesticide metabolite in the PRD (one of the most seriously contaminated areas in China), to determine its toxicity and to define its kinetics in an important organism such as the edible bivalve M. meretrix. We intend that this thesis will be helpful for the scientific community providing new insights regarding metabolite interactions (within and with other molecules) and toxicity (LC50 and theoretical risk assessment), which were unknown until now
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The pathophysiological mechanisms of arterial hypertension during hemodialysis (HD) in patients with end-stage renal disease (ESRD) are still poorly understood. The aim of this study is to investigate physiological, cardiovascular and neuroendocrine changes in patients with ESRD and its correlation with changes in blood pressure (BP) during the HD session. The present study included 21 patients with ESRD undergoing chronic HD treatment. Group A (study) consisted of patients who had BP increase and group B (control) consisted of those who had BP reduction during HD session. Echocardiograms were performed during the HD session to evaluate cardiac output (CO) and systemic vascular resistance (SVR). Before and after the HD session, blood samples were collected to measure brain natriuretic peptide (BNP), catecholamines, endothelin-1 (ET-1), nitric oxide (NO), electrolytes, hematocrit, albumin and nitrogen substances. The mean age of the studied patients was 43±4.9 years, and 54.6% were males. SVR significantly increased in group A (P<0.001). There were no differences in the values of BNP, NO, adrenalin, dopamin and noradrenalin, before and after dialysis, between the two groups. The mean value of ET-1, post HD, was 25.9 pg ml−1 in group A and 13.3 pg ml−1 in group B (P=<0.001). Patients with ESRD showed different hemodynamic patterns during the HD session, with significant BP increase in group A, caused by an increase in SVR possibly due to endothelial dysfunction, evidenced by an increase in serum ET-1 levels.
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This commentary reviews recent research in terms of tourist’s mobilities in terms practices of walking, cycling and driving. It concludes by reflecting on the contemporary lock down of travel in terms of the global pandemic and its consequences for waiting, stillness and immobility – particularly in terms of flying.
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Traditional text classification models have some drawbacks, such as the inability of the model to focus on important parts of the text contextual information in text processing. To solve this problem, we fuse the long and short-term memory network BiGRU with a convolutional neural network to receive text sequence input to reduce the dimensionality of the input sequence and to reduce the loss of text features based on the length and context dependency of the input text sequence. Considering the extraction of important features of the text, we choose the long and short-term memory network BiLSTM to capture the main features of the text and thus reduce the loss of features. Finally, we propose a BiGRU-CNN-BiLSTM model (DCRC model) based on CNN, GRU and LSTM, which is trained and validated on the THUCNews and Toutiao News datasets. The model outperformed the traditional model in terms of accuracy, recall and F1 score after experimental comparison.
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In a context of a new transnational division of labour, temporary international labour mobility is on the rise in Europe. In particular, recent decades have seen considerably more women seeking work experience abroad. Observers have been concerned with how such mobility is related to individualization, and in particular how it may challenge collective institutions, communities and families. The aim of this study is to explore such issues among women and men with international work experience. Using data from European Social Survey, the paper investigates previously mobile workers in terms of their current working and living conditions. Across genders, we consider different forms of individualization that may be associated with transnational labour mobility. While both women and men with transnational work experience generally feature strong strategic individualization, this is most pronounced among men. Hence, men's mobility is among other things associated with increased autonomy in working life, while – in contrast to women – it does not seem to hamper their integration in the sphere of social reproduction.
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Classification of electroencephalogram (EEG) is a key approach to measure the rhythmic oscillations of neural activity, which is one of the core technologies of brain-computer interface systems (BCIs). However, extraction of the features from non-linear and non-stationary EEG signals is still a challenging task in current algorithms. With the development of artificial intelligence, various advanced algorithms have been proposed for signal classification in recent years. Among them, deep neural networks (DNNs) have become the most attractive type of method due to their end-to-end structure and powerful ability of automatic feature extraction. However, it is difficult to collect large-scale datasets in practical applications of BCIs, which may lead to overfitting or weak generalizability of the classifier. To address these issues, a promising technique has been proposed to improve the performance of the decoding model based on data augmentation (DA). In this article, we investigate recent studies and development of various DA strategies for EEG classification based on DNNs. The review consists of three parts: what kind of paradigms of EEG-based on BCIs are used, what types of DA methods are adopted to improve the DNN models, and what kind of accuracy can be obtained. Our survey summarizes the current practices and performance outcomes that aim to promote or guide the deployment of DA to EEG classification in future research and development.
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The key challenge of Unsupervised Domain Adaptation (UDA) for analyzing time series data is to learn domain-invariant representations by capturing complex temporal dependencies. In addition, existing unsupervised domain adaptation methods for time series data are designed to align marginal distribution between source and target domains. However, existing UDA methods (e.g. R-DANN Purushotham et al. (2017), VRADA Purushotham et al. (2017), CoDATS Wilson et al. (2020)) neglect the conditional distribution discrepancy between two domains, leading to misclassification of the target domain. Therefore, to learn domain-invariant representations by capturing the temporal dependencies and to reduce the conditional distribution discrepancy between two domains, a novel Attentive Recurrent Adversarial Domain Adaptation with Top-k time series pseudo-labeling method called ARADA-TK is proposed in this paper. In the experiments, our proposed method was compared with the state-of-the-art UDA methods (R-DANN, VRADA and CoDATS). Experimental results on four benchmark datasets revealed that ARADA-TK achieves superior classification accuracy when it is compared to the competing methods.
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Stock movement prediction is one of the most challenging problems in time series analysis due to the stochastic nature of financial markets. In recent years, a plethora of statistical methods and machine learning algorithms were proposed for stock movement prediction. Specifically, deep learning models are increasingly applied for the prediction of stock movement. The success of deep learning models relies on the assumption that massive training data are available. However, this assumption is impractical for stock movement prediction. In stock markets, a large number of stocks do not have enough historical data, especially for the companies which underwent initial public offering in recent years. In these situations, the accuracy of deep learning models to predict the stock movement could be affected. To address this problem, in this paper, we propose novel instance-based deep transfer learning models with attention mechanism. In the experiments, we compare our proposed methods with state-of-the-art prediction models. Experimental results on three public datasets reveal that our proposed methods significantly improve the performance of deep learning models when limited training data are available.
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