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Reaction of ultramafic rocks with seawater and subsequent serpentinization has been considered one of the most important factors controlling the formation of ultramafic-hosted seafloor massive sulfide (UM-SMS) deposits. However, the mineralization processes responsible for these deposits remain poorly understood, in particular because they are less abundant as compared with their basaltic counterparts. In this work, serpentinites with different alteration grades collected at the Tianzuo hydrothermal field (THF), Southwest Indian Ridge, were studied. Mineralogical and chemical analyses were performed in the secondary opaque minerals resulting from serpentinization to understand the role of this process during the formation of UM-SMS deposits. Our results show that these opaque minerals mainly consist of magnetite, hematite, pentlandite, and minor pyrite, suggestive of high but varying oxygen and sulfur fugacities. The hematite is characterized by an enrichment in Mg, Si, Ni, and Co as compared with magnetite. Pentlandite associated with hematite has elevated and consistent Ni contents as compared with that associated with magnetite. These results indicate that breakdown and decomposition of primary silicate and sulfide minerals during serpentinization has controlled the sources of ore-forming materials. Concentrations of Te are variable and show a positive correlation with Ni in pentlandite associated with magnetite or hematite, suggesting that gabbroic intrusions provided additional material to the hydrothermal system. Oxidation and sulfidation conditions are ideal for the formation of trisulfur ion S3− in THF, which can significantly improve the capability of hydrothermal fluids for leaching ore-forming metals from the wall rocks, promoting the formation of THF. In addition of reduced systems, hydrothermal fluids with high oxygen and sulfur fugacities triggered by extensive seawater infiltration can most likely also develop in ultramafic-hosted systems. These results suggest that the areas with well-developed fractures are promising candidates for further exploration of UM-SMS deposits along mid-oceanic ridges.

The recently explored inactive Tianzuo hydrothermal field, in the amagmatic segment of the ultraslow-spreading Southwest Indian Ridge (SWIR), is closely associated with detachment faults. In this site, sulfide minerals are hosted by serpentine-bearing ultramafic rocks and include high-temperature (isocubanite, sphalerite, and minor pyrrhotite) and low-temperature (pyrite I, marcasite, pyrite II, and covellite) phases. In this study, trace-element concentrations of isocubanite and pyrite II were used to elucidate mineralization processes in ultramafic rocks hosting sulfides. Results show that isocubanite is enriched in metals such as Cu, Co, Sn, Te, Zn, Se, Pb, Bi, Cd, Ag, In, and Mn, and pyrite II is enriched in Mo and Tl. The marked enrichment in Te, Cu, Co, and In in isocubanite (compared with Se, Zn, Ni, and Sn, respectively) is most likely due to the contribution of magmatic fluids from gabbroic intrusions beneath the hydrothermal field. The intrusion of gabbroic magmas would have enhanced serpentinization reactions and provided a relatively oxidizing environment through the dissolution of anhydrite precipitated previously in the reaction zone, within high temperature and low pH conditions. This might have facilitated the extraction of metals by initial hydrothermal fluids, leading to the general enrichment of most metals in isocubanite. Metals in pyrite II have compositions similar to those of isocubanite, except for strong depletion in magmatically derived Te, Cu, Co, and In. This means that serpentinization processes had a dominating role in pyrite II precipitation as well. The enrichment of pyrite II in Mo and Tl is also indicative of seawater contribution in its composition. The study concludes that serpentinization reactions contribute effectively both to high- and low-temperature sulfide mineralization at Tianzuo hydrothermal field, with gabbroic intrusions further promoting high-temperature sulfide mineralization, providing additional metals, fluids and heat. In contrast, low-temperature sulfide mineralization occurred during the cooling of gabbroic intrusions, with decreasing rates of serpentinization reactions and a significant influence of seawater.

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.

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.

The extraction of 21 insecticides and 5 metabolites was performed using an optimized and validated QuEChERS protocol that was further used for the quantification (GC–MS/MS) in several seafood matrices (crustaceans, bivalves, and fish-mudskippers). Seven species, acquired from Hong Kong and Macao wet markets (a region so far poorly monitored), were selected based on their commercial importance in the Indo-Pacific region, market abundance, and affordable price. Among them, mussels from Hong Kong, together with mudskippers from Macao, presented the highest insecticide concentrations (median values of 30.33 and 23.90 ng/g WW, respectively). Residual levels of fenobucarb, DDTs, HCHs, and heptachlors were above the established threshold (10 ng/g WW) for human consumption according to the European and Chinese legislations: for example, in fish-mudskippers, DDTs, fenobucarb, and heptachlors (5-, 20- and tenfold, respectively), and in bivalves, HCHs (fourfold) had higher levels than the threshold. Risk assessment revealed potential human health effects (e.g., neurotoxicity), especially through fish and bivalve consumption (non-carcinogenic risk; ΣHQLT > 1), and a potential concern of lifetime cancer risk development through the consumption of fish, bivalves, and crustaceans collected from these markets (carcinogenic risk; ΣTCR > 10–4). Since these results indicate polluted regions, where the seafood is collected/produced, a strict monitoring framework should be implemented in those areas to improve food quality and safety of seafood products.

Mangroves are a unique group of plants growing along tropical and sub-tropical coastlines, with the ability to remove several types of contaminants such as heavy metals and other persistent organic compounds in coastal waters. However, little attention has been given to the possible role of mangroves in the removal of organochlorinated pesticides (OCPs) from the environment. Used worldwide, these pesticides were banned in the late 80s, withal they can still be quantified in aquatic environments due to their high stability. Moreover, as persistent and lipophilic compounds, OCPs are known for their tendency to bioaccumulate and biomagnify through the food chain, affecting local ecosystems, and potentially human health. This work aimed to investigate the potential benefits of mangrove ecosystems as OCP phytoremediators. For this purpose, a total of seventy-three articles from non-mangrove and mangrove areas around the world were gathered, integrated and re-analysed as a whole. These data include information from four different matrices (water, sediment, benthic fauna and mangrove plants). A common trend of less pesticide contamination in mangrove areas was observed for all the selected matrices. As a complement, average concentrations were discussed considering International Directives, such as the European legislation 2013/39/EU for water policy and the Dutch List together with the International Sediment Quality Guideline, for sediments. Additionally, theoretical risk assessments were also included. Since information regarding OCPs in mangroves ecosystem is very scarce compared to non-mangrove areas, this review provides valuable insights regarding these environments, and the importance of preserving them as a relevant remediation unit.

Uptake and depuration kinetics of 4,4′-dichlorobenzophenone (main metabolite of dicofol) in the edible clam Meretrix meretrix were evaluated through a mesocosm experiment. M. meretrix was exposed to different dicofol concentrations (environmental concentration, D1 = 50 ng/L; supra-environmental concentration, D2 = 500 ng/L) for 15 days, followed by the same depuration period. To accomplish this goal, an analytical method was successfully optimized for 4,4′-DCBP using QuEChERS as extraction method with a range of concentrations 0.3–76.8 ng/g ww quantified by gas chromatography coupled to tandem mass spectrometry. Our results demonstrated different kinetics of accumulation depending on the two dicofol treatments. For D1, the uptake kinetic was best fitted using a plateau followed by one phase association kinetic model, while for D2 a one phase association kinetic model suited better. Similar bioconcentration factors were obtained for both concentrations but only animals exposed to D2, showed 4,4′-DCBP levels above the limits of quantification after 24 h exposure. These animals also showed lower uptake rate (ku) than organisms exposed to D1. During the depuration period, only organisms exposed to D1 successfully depurated after 24 h. On the other hand, although animals exposed to D2 presented higher elimination factor, they did not reach the original levels after depuration. Moreover, values detected in these clams were higher than the Maximum Residue Level (10 ng/g) established by the European legislation. This indicates that longer periods of depuration time than the ones used in this study, may be needed in order to reach safe levels for human consumption. This work also demonstrated that studies on metabolite kinetics during uptake/depuration experiments, could be a new alternative to understand the impact and metabolism of pesticides in the marine environment.

Genotoxic effects of dicofol on the edible clam Meretrix meretrix were investigated through a mesocosm experiment. Individuals of M. meretrix, were exposed to environmental concentration (D1 = 50 ng/L) and supra-environmental concentration (D2 = 500 ng/L) of dicofol for 15 days, followed by the same depuration period. DNA damage (i.e., strand breaks and alkali-labile sites) was evaluated at day 1, 7 and 15, during uptake and depuration, using Comet assay (alkaline version) and nuclear abnormalities (NAs) as genotoxicity biomarkers. The protective effects of dicofol against DNA damage induced by ex vivo hydrogen peroxide (H2O2) exposure were also assessed. Comet assay results revealed no significant DNA damages under dicofol exposure, indicating 1) apparent lack of genotoxicity of dicofol to the tested conditions and/or 2) resistance of the animals due to optimal adaptation to stress conditions. Moreover, ex vivo H2O2 exposure showed an increase in the DNA damage in all the treatments without significant differences between them. However, considering only the DNA damage induced by H2O2 during uptake phase, D1 animals had significantly lower DNA damage than those from other treatments, revealing higher protection against a second stressor. NAs data showed a decrease in the % of cells with polymorphic, kidney shape, notched or lobbed nucleus, along the experiment. The combination of these results supports the idea that the clams used in the experiment were probably collected from a stressful environment (in this case Pearl River Delta region) which could have triggered some degree of adaptation to those environmental conditions, explaining the lack of DNA damages and highlighting the importance of organisms’ origin and the conditions that they were exposed during their lives.

Anthropogenic noise can be hazardous for the auditory system and wellbeing of animals, including humans. However, very limited information is known on how this global environmental pollutant affects auditory function and inner ear sensory receptors in early ontogeny. The zebrafish (Danio rerio) is a valuable model in hearing research, including investigations of developmental processes of the vertebrate inner ear. We tested the effects of chronic exposure to white noise in larval zebrafish on inner ear saccular sensitivity and morphology at 3 and 5 days post-fertilization (dpf), as well as on auditory-evoked swimming responses using the prepulse inhibition (PPI) paradigm at 5 dpf. Noise-exposed larvae showed a significant increase in microphonic potential thresholds at low frequencies, 100 and 200 Hz, while the PPI revealed a hypersensitization effect and a similar threshold shift at 200 Hz. Auditory sensitivity changes were accompanied by a decrease in saccular hair cell number and epithelium area. In aggregate, the results reveal noise-induced effects on inner ear structure–function in a larval fish paralleled by a decrease in auditory-evoked sensorimotor responses. More broadly, this study highlights the importance of investigating the impact of environmental noise on early development of sensory and behavioural responsiveness to acoustic stimuli.

Zebrafish is a well-established model organism in hearing research. Although the acoustic environment is known to shape the structure and sensitivity of auditory systems, there is no information on the natural soundscape of this species. Moreover, zebrafish are typically reared in large-scale housing systems (HS), although their acoustic properties and potential effects on hearing remain unknown. We characterized the soundscape of both zebrafish natural habitats and laboratory captive conditions, and discussed possible impact on auditory sensitivity. Sound recordings were conducted in five distinct zebrafish habitats (Southwest India), from quieter stagnant environments with diverse biological/abiotic sounds to louder watercourses characterized by current and moving substrate sounds. Sound pressure level (SPL) varied between 98 and 126 dB re 1 μPa. Sound spectra presented most energy below 3000 Hz and quieter noise windows were found in the noisiest habitats matching the species best hearing range. Contrastingly, recordings from three zebrafish HS revealed higher SPL (122-143 dB) and most energy below 1000 Hz with more spectral peaks, which might cause significant auditory masking. This study establishes an important ground for future research on the adaptation of zebrafish auditory system to the natural soundscapes, and highlights the importance of controlling noise conditions in captivity.

Noise pollution is increasingly present in aquatic ecosystems, causing detrimental effects on growth, physiology and behaviour of organisms. However, limited information exists on how this stressor affects animals in early ontogeny, a critical period for development and establishment of phenotypic traits. We tested the effects of chronic noise exposure to increasing levels (130 and 150 dB re 1 μPa, continuous white noise) and different temporal regimes on larval zebrafish (Danio rerio), an important vertebrate model in ecotoxicology. The acoustic treatments did not affect general development or hatching but higher noise levels led to increased mortality. The cardiac rate, yolk sac consumption and cortisol levels increased significantly with increasing noise level at both 3 and 5 dpf (days post fertilization). Variation in noise temporal patterns (different random noise periods to simulate shipping activity) suggested that the time regime is more important than the total duration of noise exposure to down-regulate physiological stress. Moreover, 5 dpf larvae exposed to 150 dB continuous noise displayed increased dark avoidance in anxiety-related dark/light preference test and impaired spontaneous alternation behaviour. We provide first evidence of noise-induced physiological stress and behavioural disturbance in larval zebrafish, showing that both noise amplitude and timing negatively impact key developmental endpoints in early ontogeny.

The levels of air pollution in Macao often exceeded the levels recommended by WHO. In order for the population to take precautionary measures and avoid further health risks under high pollutant exposure, it is important to develop a reliable air quality forecast. Statistical models based on multiple regression (MR) analysis were developed successfully for Macao to predict the next day concentrations of PM10, PM2.5, and NO2. All the developed models were statistically significantly valid with a 95% confidence level with high coefficients of determination (from 0.89 to 0.92) for all pollutants. The models utilized meteorological and air quality variables based on five years of historical data, from 2013 to 2017. The data from 2013 to 2016 were used to develop the statistical models and data from 2017 were used for validation purposes. A wide range of meteorological and air quality variables were identified, and only some were selected as significant dependent variables. Meteorological variables were selected from an extensive list of variables, including geopotential height, relative humidity, atmospheric stability, and air temperature at different vertical levels. Air quality variables translate the resilience of the recent past concentrations of each pollutant and usually are maximum and/or the average of latest 24-hour levels. The models were applied in forecasting the next day average daily concentrations for PM10, PM2.5, and NO2 for the air quality monitoring stations. The results are expected to be an operational air quality forecast for Macao.

The levels of air pollution in Macao often exceeded the levels recommended by WHO. In order for the population to take precautionary measures and avoid further health risks under high pollutant exposure, it is important to develop a reliable air quality forecast. Statistical models based on linear multiple regression (MR) and classification and regression trees (CART) analysis were developed successfully, for Macao, to predict the next day concentrations of NO2, PM10, PM2.5, and O3. All the developed models were statistically significantly valid with a 95% confidence level with high coefficients of determination (from 0.78 to 0.93) for all pollutants. The models utilized meteorological and air quality variables based on 5 years of historical data, from 2013 to 2017. Data from 2013 to 2016 were used to develop the statistical models and data from 2017 was used for validation purposes. A wide range of meteorological and air quality variables was identified, and only some were selected as significant independent variables. Meteorological variables were selected from an extensive list of variables, including geopotential height, relative humidity, atmospheric stability, and air temperature at different vertical levels. Air quality variables translate the resilience of the recent past concentrations of each pollutant and usually are maximum and/or the average of latest 24-h levels. The models were applied in forecasting the next day average daily concentrations for NO2 and PM and maximum hourly O3 levels for five air quality monitoring stations. The results are expected to be an operational air quality forecast for Macao.