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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.

YouTube has become increasingly popular for marketing purposes. As corporate and user-generated content is widely available on this platform, beauty-related professionals need to understand how to create videos that make their products more appealing and stand out from the clutter. In this study, we examine four factors (i.e., perceived usefulness of the information, perceived credibility of the information, attitude toward the purchase, and perceived video characteristics) that affect the purchase intentions of female consumers. After viewing beauty-related videos, a sample of 204 female consumers was analyzed by structural equation modeling. The findings showed that videos with more views, likes, and comments tend to have a greater effect on the respondents' intentions to purchase. Also, the factors of perceived usefulness of the information, perceived credibility of the information, and attitude toward the purchase exhibited a significant effect on the intention to buy beauty-related products. The result showed that perceived video characteristics (such as quality and visuals) did not significantly influence the purchase intention, however, there is evidence that this factor should not be ignored by content creators. Finally, our research provides insights, strategies, and future directions for industry practitioners and marketers.

This study, focusing on the China's Yao minority community, investigates the feasibility to create a generative computational method to replicate the diversity of the existing Yao traditional wood buildings, addressing the critical issues currently facing computational design methods, in the attempt to adapt genetic-generative algorithms to the study of local ancient architecture. The project develops a computational tool to generate a network of three-dimensional prototypes, or building structures, derived from traditional wood frame village houses. It studies possible housing structures that illustrate some of the key working methods available in digital systems such as ‘generating' and ‘compositing' taking as a starting point computational strategies oriented towards geometry and where a set of local variables play a decisive role: available local technologies, use of raw materials, and the dimensioning of timber components based on data collected from Yao architecture.

Artists are increasingly using blockchain as a tool for trading digital artwork as non-fungible tokens (NFTs); however, some are also beginning to experiment with the blockchain as a medium for generative art, using it as a seed for a generative process or to continuously modify an evolving piece. This paper surveys, reviews, and classifies the state-of-the-art in blockchain-interactive NFTs and presents a liberal-arts critique of the opportunities and threats posed by this technology, whilst addressing existing criticism on the broader topic of art-related NFTs. The paper examines some of the most experimental pieces minted on the Hic et Nunc (HEN) and Teia NFT marketplaces, for which a purpose-built research tool was developed. The survey reveals some reliance on centralised infrastructure, namely blockchain indexers, placing undesired trust on third parties which undermines the potential longevity of the artwork. The paper concludes with recommendations for artists and NFT platform designers for developing more resilient and economically sustainable architectures.

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.

Resumo O fascínio do Ocidente pela dicção poética oriental está atestado em várias latitudes e línguas, e resultou numa profícua produção na área da poesia. Sabe-se que a reinvenção da poesia chinesa da autoria de Pound, em grande medida na origem da sua proposta de revolução do idioma poético, nas primeiras décadas do séc. XX, assentou, na verdade, numa falácia; numa concepção errada da natureza da escrita chinesa (e japonesa) como essencialmente pictográfica e ideogramática, na base de propriedades expressivas reconhecidas na poesia que resultariam numa particular eficácia na apreensão e tradução do real. Pessanha enaltece, em termos similares aos da exaltação poundiana, a escrita da poesia chinesa clássica. Interessa-nos rever alguns inventários dos traços da dicção poética chinesa e japonesa que explicam que ela seja tomada como metonímia e metáfora da poesia, ou como meta e utopia da poesia, para perceber o que terá levado autores muito díspares a tentar a mão nos haikus, processo em que sondaremos algumas formulações poéticas em língua portuguesa. Consideramos também que esse fascínio por uma (sonhada) origem da dicção poética, quando cruzada com o habitar (não metafórico, neste caso) do pequenino enclave de Macau, de autores que nele lançaram raízes, resultou em alguns exercícios poéticos particularmente felizes e singulares. Serão trazidos à colação nesta abordagem poemas de Eugénio de Andrade, Sophia de Mello Breyner Andresen, José Tolentino Mendonça, Yao Feng, Fernanda Dias e Fernando Sales Lopes.

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.

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.

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.

Hydrothermal activities on ultraslow-spreading ridges exhibit diverse characteristics, long histories with multiple participants, and might form large-scale, high-grade sulfide deposits. The Duanqiao hydrothermal field (DHF) is located at the segment with the thickest oceanic crust and a large axial magma chamber on the Southwest Indian Ridge, providing unique perspective of sulfide metallogenesis on ultraslow-spreading ridges. Previous studies revealed that DHF sulfide exhibits distinct features of enrichment of ore-forming elements in comparison with those of hydrothermal fields on sediment-starved mid-ocean ridges. However, the genesis and processes responsible for such differences remain poorly constrained. In this study, mineralogical, geochemical and S and Pb isotopic analyses were performed on relict sulfide mound samples to characterize DHF formation. The samples show clear concentric mineral zonation from the interior to the exterior wall. Assemblages of chalcopyrite, sphalerite, and pyrite are distributed mainly in the interior wall, whereas pyrite and marcasite are distributed mainly in the exterior wall. The low Cu content and Pb isotopic composition of the sulfide indicate that the metals are derived mainly from basement basalts. The δ34S values exhibit positive values distributed over a reasonably narrow range (2.42‰–7.97‰), which suggests approximately 62.1%–88.5% of S with basaltic origin. Compared with most hydrothermal fields along the sediment starved mid-ocean ridges, the DHF sulfide shows particularly high contents of Pb (263–2630 ppm), As (234–726 ppm), Sb (7.32–44.3 ppm), and Ag (35.2 to >100 ppm). The δ34S values exhibit an increasing tendency from the sample exterior to the interior. We propose that these features probably reflect the existence of a subsurface zone refining process. Our results provide new insight into the sulfide formation process and contribute to understanding the metallogenic mechanism of hydrothermal sulfides on ultraslow-spreading ridges.

Background This study aimed to investigate English teachers’ self-efficacy for student engagement, classroom management, instructional strategies and literacy instruction, as well as to discover teacher stress and job satisfaction can play a role in interfering their occupational health (in terms of self-efficacy). In addition, this is one of the first studies to understand the differences in self-efficacy among pre-service, novice and experienced in-service teachers in a Chinese society, where English is positioned as a foreign language. Participants and procedure 271 English teachers (90 pre-service, 181 in-service) with mean teaching experience of 5.57 months for per-service, and 98.51 months for in-service were participated in this quantitative research study, as the targets were not be able to approach randomly, the English teachers were approaching individually though referral sampling, informing that the purpose of the study and receive their consent. Results It discovered both pre-service and novice in-service teachers posses lowest self-efficacy. Moreover, teachers’ stress from classroom predicted their self-efficacy for student engagement and classroom management negatively. On the other hand, teachers’ job satisfaction predicts their self-efficacy for student engagement, instructional strategies and literacy instruction positively. Conclusions Implications (based on the findings) are discussed in order to provide insights for for schools and education departments to strengthen the teachers’ capability of teaching and their occupational health.