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  • 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.

  • Employees work long hours in an environment where the ambient air quality is poor, directly affecting their work efficiency. The concentration of particulate matters (PM) produced by the interior renovation of shopping malls has not received particular attention in Macao. Therefore, this study will investigate the indoor air quality (IAQ), in particular of PM2.5, in large-scale shopping mall renovation projects. This study collected on-site PM data with low-cost portable monitoring equipment placed temporarily at specific locations to examine whether the current control measures are appropriate and propose some improvements. Prior to this study, there were no measures being implemented, and on-site monitoring to assess the levels of PM2.5 concentrations was non-existent. The results show the highest level of PM2.5 recorded in this study was 559.00 μg/m3. Moreover, this study may provide a reference for decision-makers, management, construction teams, design consultant teams, and renovation teams of large-scale projects. In addition, the monitoring of IAQ can ensure a comfortable environment for employees and customers. This study concluded that the levels of PM2.5 concentration have no correlation with the number of on-site workers, but rather were largely influenced by the processes being performed on-site.

  • Parental nutrient reserves are directly related to reproductive performance in sea cucumbers. This study focused on the lipid requirements of male and female sea cucumbers Apostichopus japonicus during the reproductive stage and analyzed their physiological responses to a high-fat diet (HFD). The intestinal lipid metabolites and microbiome profile changed significantly in animals fed with the HFD, as given by an upregulation of metabolites related to lipid metabolism and an increase in the predominance of Proteobacteria in the microbiome, respectively. The metabolic responses of male and female sea cucumbers to the HFD differed, which in turn could have triggered sex-related differences in the intestinal microbiome. These results suggest that the lipid content in diets can be differentially adjusted for male and female sea cucumbers to improve nutrition and promote reproduction. This data contributes to a better understanding of the reproductive biology and sex differences of sea cucumbers.

  • Reproduction of the sea cucumber Apostichopus japonicus is critical for aquaculture production. Gonadal development is the basis of reproduction, and lipids, which are among the main nutrients required for gonadal development, directly affect reproduction. We investigated whether gonadal and intestinal lipid metabolism differed between male and female A. japonicus. Transcriptome analysis of the intestines of sexually mature male and female wild-caught individuals revealed differences in gene expression, with 27 and 39 genes being up-regulated in females and males, respectively. In particular, the expression of the fatty acid synthase gene was higher in males than in females. Metabolome analysis of the gonads identified 141 metabolites that were up-regulated and 175 metabolites that were down-regulated in the testes compared with the ovaries in the positive/negative mode of an LC-MS/MS analysis. A variety of polyunsaturated fatty acids were found at higher concentrations in the testes than in the ovaries. 16 s rDNA sequencing analysis showed that the composition and structure of the intestinal microbiota were similar between males and females. These results suggest that sex differences in intestinal metabolism of A. japonicus are not due to differences in the microbiota, and we speculate that gonadal metabolism may be related to intestinal morphology. This information might be useful in improving the reproductive efficiency of sea cucumbers in captivity.

  • 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.

  • Air pollution in Macau has become a serious problem following the Pearl River Delta’s (PRD) rapid industrialization that began in the 1990s. With this in mind, Macau needs an air quality forecast system that accurately predicts pollutant concentration during the occurrence of pollution episodes to warn the public ahead of time. Five different state-of-the-art machine learning (ML) algorithms were applied to create predictive models to forecast PM2.5, PM10, and CO concentrations for the next 24 and 48 h, which included artificial neural networks (ANN), random forest (RF), extreme gradient boosting (XGBoost), support vector machine (SVM), and multiple linear regression (MLR), to determine the best ML algorithms for the respective pollutants and time scale. The diurnal measurements of air quality data in Macau from 2016 to 2021 were obtained for this work. The 2020 and 2021 datasets were used for model testing, while the four-year data before 2020 and 2021 were used to build and train the ML models. Results show that the ANN, RF, XGBoost, SVM, and MLR models were able to provide good performance in building up a 24-h forecast with a higher coefficient of determination (R2) and lower root mean square error (RMSE), mean absolute error (MAE), and biases (BIAS). Meanwhile, all the ML models in the 48-h forecasting performance were satisfactory enough to be accepted as a two-day continuous forecast even if the R2 value was lower than the 24-h forecast. The 48-h forecasting model could be further improved by proper feature selection based on the 24-h dataset, using the Shapley Additive Explanations (SHAP) value test and the adjusted R2 value of the 48-h forecasting model. In conclusion, the above five ML algorithms were able to successfully forecast the 24 and 48 h of pollutant concentration in Macau, with the RF and SVM models performing the best in the prediction of PM2.5 and PM10, and CO in both 24 and 48-h forecasts.

  • Hydrothermal activity on mid-ocean ridges is an important mechanism for the delivery of Zn from the mantle to the surface environment. Zinc isotopic fractionation during hydrothermal activity is mainly controlled by the precipitation of Zn-bearing sulfide minerals, in which isotopically light Zn is preferentially retained in solid phases rather than in solution during mineral precipitation. Thus, seafloor hydrothermal activity is expected to supply isotopically heavy Zn to the ocean. Here, we studied sulfide-rich samples from the Duanqiao-1 hydrothermal field, located on the Southwest Indian Ridge. We report that, at the hand-specimen scale, late-stage conduit sulfide material has lower δ66Zn values (−0.05 ± 0.15 ‰; n = 19) than early-stage material (+0.13 ± 0.15 ‰; n = 10). These lower values correlate with enrichments in Pb, As, Cd, and Ag, and elevated δ34S values. We attribute the low δ66Zn values to the remobilization of earlier sub-seafloor Zn-rich mineralization. Based on endmember mass balance calculations, and an assumption of a fractionation factor (αZnS-Sol.) of about 0.9997 between sphalerite and its parent solution, the remobilized Zn was found consist of about 1/3 to 2/3 of the total Zn in the fluid that formed the conduit samples. Our study suggests that late-stage subsurface hydrothermal remobilization may release isotopically-light Zn to the ocean, and that this process may be common along mid-ocean ridges, thus increasing the size of the previously identified isotopically light Zn sink in the ocean.

  • The physiological mechanisms underlying variation in aggression in fish remain poorly understood. One possibly confounding variable is the lack of standardization in the type of stimuli used to elicit aggression. The presentation of controlled stimuli in videos, a.k.a. video playback, can provide better control of the fight components. However, this technique has produced conflicting results in animal behaviour studies and needs to be carefully validated. For this, a similar response to the video and an equivalent live stimulus needs to be demonstrated. Further, different physiological responses may be triggered by live and video stimuli and it is important to demonstrate that video images elicit appropriate physiological reactions. Here, the behavioural and endocrine response of male Siamese fighting fish Betta splendens to a matched for size conspecific fighting behind a one-way mirror, presented live or through video playback, was compared. The video playback and live stimulus elicited a strong and similar aggressive response by the focal fish, with a fight structure that started with stereotypical threat displays and progressed to overt attacks. Post-fight plasma levels of the androgen 11-ketotestosterone were elevated as compared to controls, regardless of the type of stimuli. Cortisol also increased in response to the video images, as previously described for live fights in this species. These results show that the interactive component of a fight, and its resolution, are not needed to trigger an endocrine response to aggression in this species. The study also demonstrates for the first time in a fish a robust endocrine response to video stimuli and supports the use of this technique for researching aggressive behaviour in B. splendens.

  • 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.

  • Antimicrobial resistance has become a critical global health problem due to the abuse of conventional antibiotics and the rise of multi-drug-resistant microbes. Antimicrobial peptides (AMPs) are a group of natural peptides that show promise as next-generation antibiotics due to their low toxicity to the host, broad spectrum of biological activity, including antibacterial, antifungal, antiviral, and anti-parasitic activities, and great therapeutic potential, such as anticancer, anti-inflammatory, etc. Most importantly, AMPs kill bacteria by damaging cell membranes using multiple mechanisms of action rather than targeting a single molecule or pathway, making it difficult for bacterial drug resistance to develop. However, experimental approaches used to discover and design new AMPs are very expensive and time-consuming. In recent years, there has been considerable interest in using in silico methods, including traditional machine learning (ML) and deep learning (DL) approaches, to drug discovery. While there are a few papers summarizing computational AMP prediction methods, none of them focused on DL methods. In this review, we aim to survey the latest AMP prediction methods achieved by DL approaches. First, the biology background of AMP is introduced, then various feature encoding methods used to represent the features of peptide sequences are presented. We explain the most popular DL techniques and highlight the recent works based on them to classify AMPs and design novel peptide sequences. Finally, we discuss the limitations and challenges of AMP prediction.

  • Anthropogenic noise of variable temporal patterns is increasing in aquatic environments, causing physiological stress and sensory impairment. However, scarce information exists on exposure effects to continuous versus intermittent disturbances, which is critical for noise sustainable management. We tested the effects of different noise regimes on the auditory system and behaviour in the zebrafish (Danio rerio). Adult zebrafish were exposed for 24 h to either white noise (150 ± 10 dB re 1 μPa) or silent control. Acoustic playbacks varied in temporal patterns—continuous, fast and slow regular intermittent, and irregular intermittent. Auditory sensitivity was assessed with Auditory Evoked Potential recordings, revealing hearing loss and increased response latency in all noise-treated groups. The highest mean threshold shifts (c. 13 dB) were registered in continuous and fast intermittent treatments, and no differences were found between regular and irregular regimes. Inner ear saccule did not reveal significant hair cell loss but showed a decrease in presynaptic Ribeye b protein especially after continuous exposure. Behavioural assessment using the standardized Novel Tank Diving assay showed that all noise-treated fish spent > 98% time in the bottom within the first minute compared to 82% in control, indicating noise-induced anxiety/stress. We provide first data on how different noise time regimes impact a reference fish model, suggesting that overall acoustic energy is more important than regularity when predicting noise effects.

  • Approximately 50 million people are suffering from epilepsy worldwide. Corals have been used for treating epilepsy in traditional Chinese medicine, but the mechanism of this treatment is unknown. In this study, we analyzed the transcriptome of the branching coral Acropora digitifera and obtained its Kyoto Encyclopedia of Genes and Genomes (KEGG), EuKaryotic Orthologous Groups (KOG) and Gene Ontology (GO) annotation. Combined with multiple sequence alignment and phylogenetic analysis, we discovered three polypeptides, we named them AdKuz1, AdKuz2 and AdKuz3, from A. digitifera that showed a close relationship to Kunitz-type peptides. Molecular docking and molecular dynamics simulation indicated that AdKuz1 to 3 could interact with GABAA receptor but AdKuz2–GABAA remained more stable than others. The biological experiments showed that AdKuz1 and AdKuz2 exhibited an anti-inflammatory effect by decreasing the aberrant level of nitric oxide (NO), IL-6, TNF-α and IL-1β induced by LPS in BV-2 cells. In addition, the pentylenetetrazol (PTZ)-induced epileptic effect on zebrafish was remarkably suppressed by AdKuz1 and AdKuz2. AdKuz2 particularly showed superior anti-epileptic effects compared to the other two peptides. Furthermore, AdKuz2 significantly decreased the expression of c-fos and npas4a, which were up-regulated by PTZ treatment. In addition, AdKuz2 reduced the synthesis of glutamate and enhanced the biosynthesis of gamma-aminobutyric acid (GABA). In conclusion, the results indicated that AdKuz2 may affect the synthesis of glutamate and GABA and enhance the activity of the GABAA receptor to inhibit the symptoms of epilepsy. We believe, AdKuz2 could be a promising anti-epileptic agent and its mechanism of action should be further investigated.

  • Despite the levels of air pollution in Macao continuing to improve over recent years, there are still days with high-pollution episodes that cause great health concerns to the local community. Therefore, it is very important to accurately forecast air quality in Macao. Machine learning methods such as random forest (RF), gradient boosting (GB), support vector regression (SVR), and multiple linear regression (MLR) were applied to predict the levels of particulate matter (PM10 and PM2.5) concentrations in Macao. The forecast models were built and trained using the meteorological and air quality data from 2013 to 2018, and the air quality data from 2019 to 2021 were used for validation. Our results show that there is no significant difference between the performance of the four methods in predicting the air quality data for 2019 (before the COVID-19 pandemic) and 2021 (the new normal period). However, RF performed significantly better than the other methods for 2020 (amid the pandemic) with a higher coefficient of determination (R2) and lower RMSE, MAE, and BIAS. The reduced performance of the statistical MLR and other ML models was presumably due to the unprecedented low levels of PM10 and PM2.5 concentrations in 2020. Therefore, this study suggests that RF is the most reliable prediction method for pollutant concentrations, especially in the event of drastic air quality changes due to unexpected circumstances, such as a lockdown caused by a widespread infectious disease.

  • Ligand peptides that have high affinity for ion channels are critical for regulating ion flux across the plasma membrane. These peptides are now being considered as potential drug candidates for many diseases, such as cardiovascular disease and cancers. In this work, we developed Multi-Branch-CNN, a CNN method with multiple input branches for identifying three types of ion channel peptide binders (sodium, potassium, and calcium) from intra- and inter-feature types. As for its real-world applications, prediction models that are able to recognize novel sequences having high or low similarities to training sequences are required. To this end, we tested our models on two test sets: a general test set including sequences spanning different similarity levels to those of the training set, and a novel-test set consisting of only sequences that bear little resemblance to sequences from the training set. Our experiments showed that the Multi-Branch-CNN method performs better than thirteen traditional ML algorithms (TML13), yielding an improvement in accuracy of 3.2%, 1.2%, and 2.3% on the test sets as well as 8.8%, 14.3%, and 14.6% on the novel-test sets for sodium, potassium, and calcium ion channels, respectively. We confirmed the effectiveness of Multi-Branch-CNN by comparing it to the standard CNN method with one input branch (Single-Branch-CNN) and an ensemble method (TML13-Stack). The data sets, script files to reproduce the experiments, and the final predictive models are freely available at https://github.com/jieluyan/Multi-Branch-CNN.

  • The geochemistry and mineralogy of sediments provide relevant information for the understanding of the origin and metallogenic mechanism of ferromanganese nodules and crusts. At present, there are still few studies on the sediment origin of the Clarion–Clipperton Zone (CCZ) of the east Pacific, particularly on the systematic origin of sediments with a longer history/length. Here, bulk sediment geochemistry and clay mineral compositions were analyzed on a 5.7 m gravity core (GC04) obtained at the CCZ, an area rich in polymetallic nodules. The results indicate that the average total content of rare earth elements (REE), including yttrium (REY), in sediments is 454.7 ppm and the REEs distribution patterns normalized by the North American Shale Composite of samples are highly consistent, with all showing negative Ce anomalies and more obvious enrichment in heavy REE (HREE) than that of light REE (LREE). Montmorillonite/illite ratio, discriminant functions and smear slide identification indicate multiple origins for the material, and are strongly influenced by contributions from marine biomass, while terrestrial materials, seamount basalts and their alteration products and authigenic source also make certain contributions. The REY characteristics of the sediments in the study area are different from those of marginal oceanic and back-arc basins, and more similar to pelagic deep-sea sediments. Based on LREE/HREE-1/δCe and LREE/HREE-Y/Ho diagrams, we conclude that samples from the study area had pelagic sedimentary properties which suffered from a strong “seawater effect”.

  • 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.

  • Limited special education and related services are available for children with autism spectrum disorder (ASD) in Macau, especially those who are educated in general education classrooms. No intervention study has been conducted on these children. This study was conducted to explore the relationship between a board game play intervention and board game play behaviors and social communication of children with ASD educated in general education classrooms in Macau. A repeated measures design was used and the results of this study showed the mean occurrence of unprompted board game play behaviors per session during intervention was not significantly different from that during pre- or post-intervention. The mean occurrence of social communication per session during intervention was significantly higher than that during pre- and post-intervention. These findings suggest a positive relationship existed between the board game intervention used in this study and social communication of children with ASD.

  • Neuropeptides are a group of neuronal signaling molecules that regulate physiological and behavioral processes in animals. Here, we used in silico mining to predict the polypeptide composition of available transcriptomic data of Turbinaria peltata. In total, 118 transcripts encoding putative peptide precursors were discovered. One neuropeptide Y/F-like peptide, named TpNPY, was identified and selected for in silico structural, in silico binding, and pharmacological studies. In our study, the anti-inflammation effect of TpNPY was evaluated using an LPS-stimulated C8-D1A astrocyte cell model. Our results demonstrated that TpNPY, at 0.75–3 μM, inhibited LPS-induced NO production and reduced the expression of iNOS in a dose-dependent manner. Furthermore, TpNPY reduced the secretion of proinflammatory cytokines. Additionally, treatment with TpNPY reduced LPS-mediated elevation of ROS production and the intracellular calcium concentration. Further investigation revealed that TpNPY downregulated the IKK/IκB/NF-κB signaling pathway and inhibited expression of the NLRP3 inflammasome. Through molecular docking and using an NPY receptor antagonist, TpNPY was shown to have the ability to interact with the NPY Y1 receptor. On the basis of these findings, we concluded that TpNPY might prevent LPS-induced injury in astrocytes through activation of the NPY-Y1R.

  • The Mesozoic gold deposits in the North China Craton (NCC) were hosted by the Precambrian basement and Mesozoic intrusions. Thus, most researchers consider that these gold deposits were genetically linked to the Mesozoic intrusions. However, we suggest that a metamorphic devolatilization model provides an alternative based on a combined Fe and in-situ S isotopes study on auriferous pyrites from the Baiyun gold deposit in the NCC. The Triassic Baiyun gold deposit contains the quartz vein and altered rock ores that were developed in the Paleoproterozoic metavolcanic-sedimentary rocks (the Liaohe Group). Our in-situ S isotopic analyses show that pyrites from the quartz vein ores are characterized by negative δ34S values (-10.7 ∼ -5.5‰), while those from the altered rock ores have two distinct groups of δ34S values, one being positive (+13.5 ∼ +16.2‰) and the other negative (-10.6 ∼ -3.0‰). We suggest that pyrite grains with positive δ34S values should be relicts from the host rocks, because they show comparable δ34S values with those from the host rocks schists (+3.3 ∼ +16.1‰). Thus, only the negative δ34S values of pyrites in ores (-10.7 ∼ -3.0‰) and the Fe isotopes of the quartz vein ores (δ56Fe = +0.30 ∼ +0.48‰) can represent the isotopic characteristics of ore-forming fluids at Baiyun. Our study shows that the sulfur were probably from the pyritic volcanic-sedimentary sequences of the Liaohe Group, rather than from magmas. The calculated δ56Fe values of the ore-forming fluids (-0.78 ∼ -0.37‰; pyrite-fluid isotope fractionation) could be modelled in a metamorphic devolatilization model with Fe-species (pyrite&magnetite) of the Liaohe Group as sources. Therefore, our combined S- and Fe- isotope data indicate that the metamorphic devolatilization of the Liaohe Group could account for the genesis of the Baiyun gold deposit.

Last update from database: 3/28/24, 9:27 PM (UTC)