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Background: Hydrogen peroxide (H2O2), a novel water treatment agent, can be used for disinfection, water quality adjustment, and disease prevention, while excessive H2O2 can injure farm animals, even leading to death. Hydrogen peroxide is a recommended disinfectant and bactericide for treating gill diseases and vibriosis in the greenfin horse-faced filefish Thamnaconus septentrionalis. However, its cumulative effect, toxic molecular mechanism and relevant signal transduction/metabolic networks in marine fishes are largely unknown. Results: We employed a multi-omics approach to investigate the detrimental effects of 50 mg/L H2O2 exposure (2 h/d) on filefish for 2 d, 4 d, and 6 d. Transcriptome sequencing showed that differentially expressed genes (DEGs) were mainly classified into functions such as signal transduction, nervous system, liver and bile acid metabolism, energy metabolism, cell adhesion and communication, inflammation and immune response. Metabolomic analysis found that the significantly changed metabolites (SCMs) were involved in phenylalanine metabolism, inflammatory mediator regulation, linoleic acid metabolism, and necroptosis. The main SCMs were cholic acid, carnitine C12:1, dimethylmalonic acid, glutamic acid, L-lactic acid, shikimic acid, 2-methylsuccinic acid, and others. Moreover, H2O2-induced oxidative stress also disturbs the balance of the gut microbiota, altering the microbial composition and affecting digestive processes. Conclusions: Integrated multiomics analysis revealed that H2O2-induced detrimental impacts include mucosal damage, inflammatory and immune responses, altered energy metabolism, and gut microbiota disorders. These findings offer novel insights into the harmful effects and signal transduction/metabolic pathways triggered by H2O2 exposure in marine fishes. [ABSTRACT FROM AUTHOR]

Climate change is exerting unprecedented impacts on marine habitats, and many sessile invertebrate species, such as the endangered giant clam Tridacna maxima, are particularly sensitive to climate driven changes in their environment. Understanding its spatial distribution and conservation requirements is of crucial significance in formulating effective protection strategies. However, the species has been extensively harvested and depleted in many regions, leading to its listing as endangered species by the International Union for Conservation of Nature (IUCN). While marine protected areas (MPAs) are considered effective conservation tools, it remains uncertain whether existing MPAs adequately protect these vulnerable giant clams. To enhance the management and conservation of this species, we employed a Species Distribution Models (SDMs) approach, integrating occurrence records of T. maxima with environmental variables, to predict its potential distribution based on habitat suitability and capture spatiotemporal changes. Based on geographical and genetic variations, the T. maxima in the Indo‐Pacific core region is primarily divided into two populations: the East Indian Ocean‐South China Sea population (EIOS) and the West Pacific‐Indonesia population (WPI). We first quantified realized niche to reveal significant differences in ecological niche space among different populations. Subsequently, SDMs were constructed at both species and population levels, demonstrating that population‐level SDMs provide more reliable predictions of population distributions due to differential responses to climatic predictor variables. Finally, we conducted an assessment to identify conservation gaps for T. maxima beyond the existing MPAs and proposed recommendations for future establishment of MPAs within the current framework. Based on these findings, appropriate conservation policies have been proposed to effectively protect the habitat of different geographical populations of T. maxima. Additionally, spatiotemporal predictions of habitat suitability provide crucial information for developing adaptive management strategies for T. maxima in response to climate change, including designing new protected areas and adjusting the location and extent of existing protected areas based on their geographical distribution. We employed a Species Distribution Models (SDMs) approach, integrating occurrence records of Tridacna maxima with environmental variables, to predict its potential distribution based on habitat suitability and capture spatiotemporal changes. In addition, we conducted an assessment to identify conservation gaps beyond the existing MPAs and proposed recommendations for the future establishment of MPAs within the current framework. [ABSTRACT FROM AUTHOR]

With the development of recommendation algorithms, researchers are paying increasing attention to fairness issues such as user discrimination in recommendations. To address these issues, existing works often filter users' sensitive information that may cause discrimination during the process of learning user representations. However, these approaches overlook the latent relationship between items' content attributes and users' sensitive information. In this article, we propose DALFRec, a fairness-aware recommendation algorithm based on user-side and item-side adversarial learning to mitigate the effects of sensitive information on both sides of the recommendation process. First, we conduct a statistical analysis to demonstrate the latent relationship between items' information and users' sensitive attributes. Then, we design a dual-side adversarial learning network that simultaneously filters out users' sensitive information on the user and item side. Additionally, we propose a new evaluation strategy that leverages the latent relationship between items' content attributes and users' sensitive attributes to better assess the algorithm's ability to reduce discrimination. Our experiments on three real datasets demonstrate the superiority of our proposed algorithm over state-of-the-art methods. [ABSTRACT FROM AUTHOR]

The Bohai Sea is a semi-enclosed shallow water that is influenced by both natural and anthropogenic stressors. However, the microeukaryotic communities and environmental factors that affect them in different regions remain largely unclear. We investigated microeukaryotic communities in surface sediments from five geographic regions using high-throughput sequencing of the 18S rDNA gene. The Miaodao Archipelago, Yellow River Estuary, and Central Bohai Sea had the highest Shannon and Simpson indices of the eukaryotic communities, while the Yellow River Estuary exhibited the highest Chao1 index. The microeukaryotic communities in surface sediments were mainly composed of Dinoflagellata, Bacillariophyta, Ciliophora, Cercozoa, and Protalveolata. Thalassiosira has a relatively high abundance at the Liaodong Bay and Central Bohai Sea, possessing the proportion of 41.70% and 38.10%, respectively, while Gonyaulax was the most abundant taxa in the Bohai Bay, occupying a proportion of 57.77%. Moreover, a negative correlation between diatoms and dinoflagellates was observed. Phosphorus, nitrogen, salinity, temperature, and silicate were major environmental determinants of microeukaryotic composition. Microeukaryotic communities in the surface sediments, especially for the composition and ratio of diatoms to dinoflagellates, reflected the environmental quality of marine ecosystems. Overall, these microeukaryotic community compositions provide a reliable indicator for monitoring the level of marine eutrophication in the Bohai Sea. [ABSTRACT FROM AUTHOR]

The high speed conversion of signals between the optical and electrical domains is crucial for many key applications of silicon photonics. Electro-optic modulators integrated with electronic drive amplifiers are typically used to convert an electrical signal to the optical domain. Design of these individual elements is important to achieve high performance, however a true optimisation requires careful co-design of the photonic and electronic components considering the properties of each other. Here we present our recent results in this area together with a MOSCAP type modulator with the potential for high speed, high efficiency and highly linear modulation.