The Portuguese oyster Crassostrea angulata and the Pacific oyster C. gigas are two major Crassostrea species that are naturally distributed along the Northwest Pacific coast and possess great ecological and economic value. Here, we report the construction and comparative analysis of the chromosome-level haplotype-resolved genomes of the two oyster congeners. Based on a trio-binning strategy, the PacBio high-fidelity and Illumina Hi-C reads of the offspring of the hybrid cross C. angulata (♂) × C. gigas (♀) were partitioned and independently assembled to construct two chromosome-level fully phased genomes. The assembly size (contig N50 size, BUSCO completeness) of the two genomes were 582.4 M (12.8 M, 99.1%) and 606.4 M (5.46 M, 98.9%) for C. angulata and C. gigas, respectively, ranking at the top of mollusk genomes with high contiguity and integrity. The general features of the two genomes were highly similar, and 15,475 highly conserved ortholog gene pairs shared identical gene structures and similar genomic locations. Highly similar sequences can be primarily identified in the coding regions, whereas most noncoding regions and introns of genes in the same ortholog group contain substantial small genomic and/or structural variations. Based on population resequencing analysis, a total of 2,756 species-specific single-nucleotide polymorphisms and 1,088 genes possibly under selection were identified. This is the first report of trio-binned fully phased chromosome-level genomes in marine invertebrates. The study provides fundamental resources for the research on mollusk genetics, comparative genomics, and molecular evolution.

Tick-borne encephalitis (TBE) is one of natural foci diseases transmitted by ticks. Its distribution and transmission are closely related to geographic and environmental factors. Identification of environmental determinates of TBE is of great importance to understanding the general distribution of existing and potential TBE natural foci. Hulunbuir, one of the most severe endemic areas of the disease, is selected as the study area. Statistical analysis, global and local spatial autocorrelation analysis, and regression methods were applied to detect the spatiotemporal characteristics, compare the impact degree of associated factors, and model the risk distribution using the heterogeneity. The statistical analysis of gridded geographic and environmental factors and TBE incidence show that the TBE patients mainly occurred during spring and summer and that there is a significant positive spatial autocorrelation between the distribution of TBE cases and environmental characteristics. The impact degree of these factors on TBE risks has the following descending order: temperature, relative humidity, vegetation coverage, precipitation and topography. A high-risk area with a triangle shape was determined in the central part of Hulunbuir; the low-risk area is located in the two belts next to the outside edge of the central triangle. The TBE risk distribution revealed that the impact of the geographic factors changed depending on the heterogeneity.

Satellite remote sensing plays a fundamental role in observing oceanic processes by providing large-scale, long-term, and continuous measurements. With the increasing availability of multisource satellite data, challenges such as data gaps, complex environmental conditions, and the limitations of conventional retrieval methods have become more evident. In recent years, artificial intelligence (AI) has emerged as a practical and effective approach to address these issues. This article reviews the development of AI techniques in satellite ocean remote sensing, focusing on three main application areas: parameter retrieval, data reconstruction, and image-based ocean phenomenon detection. For geophysical variable retrieval, AI models such as convolutional neural networks (CNNs) and Transformer architectures have improved the accuracy of ocean waves, sea surface, salinity, wind, and ocean color estimates, especially under extreme or noisy conditions. In the field of data reconstruction, AI methods enable the completion of missing data in both surface and subsurface ocean layers, offering finer spatial–temporal resolution and better consistency than traditional interpolation approaches. For image interpretation, deep learning (DL) models have been applied to detect and segment dynamic ocean features such as mesoscale eddies, internal waves, sea ice, and tropical cyclones (TCs), achieving high efficiency and precision. This article also highlights the integration of AI with physical knowledge, the use of multisource fusion, and the trend toward near real-time (NRT) applications. These developments indicate that AI will play an increasingly important role in future satellite-based ocean observation and environmental monitoring.

With the continuous development of various types of fixed marine observation equipment, satellite remote sensing technology and computer simulation technology, modern marine scientific research has entered the era of big data. Interactive ocean visualization has become ubiquitous owing to the use of ocean data in studies of marine disasters, global climate change and fisheries. However, the primary challenge in analyzing large amounts of ocean data originates from the complexity of the data themselves. Therefore, an interactive multi-scale, multivariate visualization system with dynamic expansion potential is needed for analyzing larger volumes of ocean data. In this study, a unified visual data service was constructed, and a component-based interactive visualization structure for multi-dimensional, spatiotemporal ocean data is presented in this paper. Based on this structure, users can easily customize the system to visualize other types of scientific data.

Changes in nearshore water quality directly influence ecosystem stability and the sustainability of aquaculture production. Among these factors, rapid fluctuations in dissolved oxygen (DO) can compromise the physiological functions of aquatic organisms, often leading to mass mortality events and significant economic losses. To enhance the predictive capability of DO in marine ranching areas, this study evaluates multiple forecasting approaches, including AutoARIMA, XGBoost, BlockRNN-LSTM, BlockRNN-GRU, TCN, Transformer, and an ensemble model that integrates these methods. Using hourly DO observations from coastal buoys, we performed multi-step rolling forecasts and systematically assessed model performance across multiple evaluation metrics (MAPE, RMSE, and R²), complemented by residual and error distribution analyses. The results show that the ensemble model, based on deep learning techniques, consistently outperforms individual models, achieving higher forecast robustness and more effective variance control, with MAPE values maintained below 4% across all three buoys. Building upon these findings, we further developed and deployed a DO forecasting and early-warning system centered on the ensemble framework. This system enables end-to-end functionality, including automatic data acquisition, real-time prediction, hypoxia risk identification, and alert dissemination. It has already been applied in marine ranching operations, providing 1–3 day forecasts of DO dynamics, facilitating the early detection of hypoxia risks, and significantly improving the scientific support and responsiveness of aquaculture management.

The development of wind wave (i.e., sea state) inside an intense tropical cyclone (TC) is the dominant contributor to the sea surface roughness and thus significantly impacts the air–sea interaction. The sea state is known to vary with TC characteristics (intensity, size, and translation speed); however, comprehensive knowledge of the influence of TC characteristics on the sea state and sea surface roughness is quite limited, largely because of the lack of observations. In this study, numerical experiments are performed to investigate the influence of TC characteristics on the sea state and the sea surface roughness under a range of idealized TCs. The numerical results indicate that the sea states are systematically younger for a more intense or smaller TC, and their azimuthal variation is predominantly determined by the TC translation. The dependence of the sea surface roughness on wind speed shows systematic variations with the TC characteristics, which are most significant for a moderately moving TC.