Highlights What are the main findings? A systematic comparison of four machine learning models identified RF as the best approach for estimating GPP, ER, and NEP, demonstrating superior accuracy and robustness in regional carbon-flux upscaling along the eastern coast of China. The eastern coast of China acted as a persistent and strengthening carbon sink. Forests remained the dominant contributor, whereas wetlands, despite their high per-unit-area carbon sequestration potential, experienced a continued decline in total carbon-sink capacity due to area loss. What are the implications of the main findings? The study provides an effective and scalable approach for quantifying multiple carbon-flux components, thereby improving the understanding of regional carbon dynamics in the context of environmental change. The results offer direct scientific support for carbon-sink management along the eastern coast of China by emphasizing the roles of forest conservation, cropland optimization, and wetland restoration in achieving carbon neutrality goals.Highlights What are the main findings? A systematic comparison of four machine learning models identified RF as the best approach for estimating GPP, ER, and NEP, demonstrating superior accuracy and robustness in regional carbon-flux upscaling along the eastern coast of China. The eastern coast of China acted as a persistent and strengthening carbon sink. Forests remained the dominant contributor, whereas wetlands, despite their high per-unit-area carbon sequestration potential, experienced a continued decline in total carbon-sink capacity due to area loss. What are the implications of the main findings? The study provides an effective and scalable approach for quantifying multiple carbon-flux components, thereby improving the understanding of regional carbon dynamics in the context of environmental change. The results offer direct scientific support for carbon-sink management along the eastern coast of China by emphasizing the roles of forest conservation, cropland optimization, and wetland restoration in achieving carbon neutrality goals.Abstract The eastern coast of China, characterized by a pronounced climatic gradient and diverse ecosystems, is an ideal region for exploring the spatiotemporal dynamics of carbon fluxes and their drivers. Based on observations from eight flux tower sites, together with meteorological, remote sensing, and ecohydrological variables from 2001 to 2022, this study developed Back Propagation (BP), Support Vector Regression (SVR), Extreme Gradient Boosting (XGBoost), and Random Forest (RF) models to estimate regional gross primary productivity (GPP), ecosystem respiration (ER), and net ecosystem productivity (NEP). Among them, RF performed best, achieving validation R2 values of 0.92, 0.84, and 0.83 for GPP, ER, and NEP, respectively, and was therefore selected for regional upscaling. The regional mean GPP, ER, and NEP were 1578.38, 1286.05, and 334.56 g C m-2 yr-1, respectively, indicating that the region functioned as a net carbon sink during the study period. GPP, ER, and NEP exhibited a clear spatial gradient, with higher values in the south and lower values in the north. Total regional NEP increased from 344.12 Tg C in 2001 to 517.73 Tg C in 2022, reflecting a continuous strengthening of terrestrial carbon sink strength. Forests contributed most to the regional carbon sink, while the ecosystem-level NEP contribution of croplands increased over time; by contrast, the total carbon sink of wetlands declined because of area loss. These results suggest that ecological restoration, vegetation greening, and land cover optimization jointly enhanced the carbon sink along the eastern coast of China. These findings have important implications for ecological management and green low-carbon development along the eastern coast of China.

Traditional assessments of ecological sensitivity rely on static variables, failing to capture the pronounced seasonal dynamics of temperate mountain ecosystems, which leads to a mismatch between management and actual risks during critical seasons. Taking Mount Tai, China, as a case study, we established a dynamic evaluation framework. We processed Sentinel-2 imagery (2018–2024) on the Google Earth Engine (GEE) to derive seasonal NDVI and NDWI as key biophysical proxies. These were combined with static factors (topography, land use) and integrated using an AHP model validated by Random Forest (RF). The results reveal a significant “summer–winter dual-core driving” mechanism. In summer, patterns are vegetation-dominated (NDVI > 0.6), with high-sensitivity areas (HSA) accounting for 17.79

The East China Sea represents a critical coastal wetland region, characterized by complex geomorphology, heterogeneous land-cover composition, and diverse wetland types. Accurate delineation of coastal wetland extent is essential for ecosystem service assessment and sustainable coastal management, directly contributing to wetland-related Sustainable Development Goals (SDGs), particularly SDG 15, on ecosystem conservation and biodiversity protection. However, pronounced spectral similarity and structural heterogeneity among wetland classes pose substantial challenges to reliable classification. To address these challenges, this study developed a hierarchical classification framework integrating Random Forest, K-means clustering, and a decision tree classifier based on multi-source Sentinel-1 and Sentinel-2 imagery. Spectral, polarimetric, texture, and morphological features were systematically constructed to enhance class separability. Using this framework, a 10 m resolution coastal wetland map of the East China Sea was generated for 2023. The proposed approach achieved an overall accuracy of 91.32% and improved the discrimination of spectrally similar wetland types. Feature fusion reduced confusion among water-related classes, while object-based clustering improved the extraction of linear riverine wetlands. The resulting 10 m wetland map provides updated spatial information for ecological assessment and coastal management in the East China Sea.

Coastal provinces in eastern China are experiencing rapid urbanization that challenges ecosystem services and low-carbon development. In this study, Zhejiang, Fujian, and Guangdong Provinces were selected, and the influence of land use/land cover change (LUCC) on carbon storage and its spatial heterogeneity was quantified. LUCC datasets for 2000, 2005, 2010, 2015, and 2020 were compiled to describe land-use dynamics over 2000-2020. Carbon storage was estimated with the InVEST model. Land-use patterns for 2035 were simulated using the PLUS model under three scenarios: natural development, ecological protection, and development priority. Spatial autocorrelation analysis and multiscale geographically weighted regression (MGWR) were then used to determine the key drivers of spatial variability in carbon storage. Between 2000 and 2020, farmland, forest, grassland, and unused land showed an overall decline, while water bodies and tt-up land expanded; together, these changes corresponded to a carbon-storage loss of 121.19 Tg. Carbon density exhibited pronounced spatial clustering, with higher values concentrated in mountainous and less urbanized areas; built-up expansion and forest degradation were the primary contributors to carbon loss. By 2035, total carbon storage is projected to decrease by 74.67 Tg under natural development and by 108.54 Tg under development priority, whereas ecological protection is projected to yield the smallest decline (35.71 Tg). These results underscore the importance of sustainable coastal land-use planning and integrated coastal zone management, which balance development and ecosystem services by prioritizing ecological protection, curbing built-up expansion, and promoting forest restoration. Such nature-based solutions can enhance carbon sequestration, strengthen climate resilience, and support China's low-carbon transition toward its dual-carbon targets.

With global climate change intensifying, the carbon cycle and its related processes have become a central topic in ecological research. In this study, a large-scale carbon flux estimation model for the U.S. East Coast was developed based on long-term eddy covariance observations. Through this model, carbon flux characteristics and their spatiotemporal patterns across different ecosystem types in the region were analyzed over the past two decades. By integrating correlation analysis and the Geodetector method, the roles of multiple environmental drivers in carbon flux estimation were elucidated. Subsequently, models for gross primary productivity (GPP), ecosystem respiration (ER), and net ecosystem productivity (NEP) were constructed using four machine learning algorithms: random forest (RF), artificial neural network (ANN), support vector regression (SVR), and extreme gradient boosting (XGBoost). The results indicate that: (1) The combination of eight factors, including T2M, VPD, SSRD, EVI, LSWI, LAI H, EVAVT, and DEM, exhibited the most accurate and stable performance in carbon flux estimation. Under identical input combinations, the RF model achieved the highest accuracy for GPP, NEP, and ER estimation. (2) The verification accuracy of GPP, ER, and NEP achieved R 2 values of 0.88, 0.81, and 0.55, respectively. These accuracies markedly outperform those of existing carbon-flux products such as FLUXCOM, which show lower R 2 values of 0.61, 0.57, and 0.28. (3) The analysis of environmental variable importance reveals that EVI is the most important variations in carbon fluxes across all ecosystems, underscoring that vegetation growth status is the most critical driver of carbon exchange processes.

The molecular mechanisms underlying the adaptation to freshwater habitats in fish of marine origin remain unclear. Grenadier anchovies, such as Coilia nasus, originate from marine environments and include both anadromous and freshwater-resident conspecifics, making them ideal for studying adaptive evolution from marine to freshwater habitats. We conducted a comparative population genomic and transcriptome analysis of two distinct C. nasus lineages, one anadromous and the other freshwater-resident, collected from mainstream and estuarine regions of the Yangtze River, China. By genome-wide genotyping of the anadromous and the freshwater-resident populations, we observed significant divergence in osmoregulation, energy metabolism, and immune response pathways associated with ecological adaptation and energy expenditure for migration. Some ion transport genes such as CAMK1, ATP1α3, KCNJ1 and SLC30A2 were identified that may contribute to freshwater adaptation. Notably, numerous mineralocorticoid signalling genes (e.g., NR3C2, SGK1, ATP1α3, KCNJ1) exhibit dynamic change between the anadromous and freshwater populations, suggesting an important role for the hormone cortisol in regulating salinity acclimation in euryhaline fish. Among these genes, the ion channel ATP1α3 experienced adaptive amino acid substitutions (Val317Ile and Thr329Ser), which appear to be evolutionary hotspots across migratory species based on ortholog comparisons. These variants may facilitate sodium/potassium transport and highlight salinity tolerance as a key driver of divergence in anadromous fish transitioning to freshwater. These results enhance our understanding of the genetic basis underlying freshwater adaptation for an anadromous fish across osmotic boundaries.

To mitigate the negative impacts of unregulated aquaculture development and promote sustainable industry growth, it is essential to quickly and accurately identify and extract aquaculture ponds. These ponds are distinctive grid-like water bodies segmented by dikes and roads, making their accurate extraction challenging with a single spectral feature. This study addresses this challenge by using data from Sentinel-1 and Sentinel-2, incorporating a comprehensive analysis of spectral, shape, polarization, environmental and temporal features. We propose a two-stage hierarchical decision tree - random forest (HDT-RF) framework: the first stage extracts water bodies using polarization and vegetation indices to suppress non-water interference, while the second stage employs a random forest classifier that fuses multi-source features to refine pond identification. The study results indicate that: 1) HDT-RF achieves an overall accuracy of 95.31%. 2) Compared to traditional methods, the inclusion of environmental and temporal characteristics improves classification accuracy by 6% and enhances the ability to identify water bodies with shapes and structures similar to aquaculture ponds. 3) The introduction of VV and VH polarization features and the NDVI effectively mitigates the impacts of building shadows, non-water dark surfaces, and vegetation, improving the accuracy of water body extraction. 4) HDT-RF enables the automated extraction of aquaculture ponds in different study areas, demonstrating strong portability and high extraction accuracy. This method provides a valuable reference for large-scale pond extraction and offers technical support for fisheries management and sustainable development.

Ocean acidification is transforming marine ecosystems at an unprecedented rate, which in turn requires the estimation of sea surface carbon dioxide partial pressure (pCO(2)) as a crucial metric to gauge acidification. This has substantial implications for marine resource assessment and management, marine ecosystems, and global climate change research. This study utilizes SOCAT cruise survey data to assess the accuracy of global sea surface pCO(2) products offered by Copernicus Marine Service and the Chinese Academy of Sciences Ocean Science Research Center. Through the application of a geographic information analysis method-geographical detector-the study quantitatively reveals the significance of environmental influencing factors, such as longitude, latitude, sea surface 10 m wind speed (U-10), total precipitation (TP), evaporation (E), and significant height of combined wind waves and swell (SHWW), in the reconstruction of sea surface pCO(2). Subsequently, various machine learning models, which include convolutional neural network (CNN), back propagation neural network (BP), long short-term memory network (LSTM), extreme learning machine (ELM), support vector regression (SVR), and extreme gradient boosting tree (XGBoost), are used to reconstruct the monthly sea surface pCO(2) data for the Atlantic Ocean from 2001 to 2020 to investigate the potential and suitability of high-precision reconstruction of the sea surface pCO(2) dataset for this sea area. The findings indicate that: (1) The geographical detector effectively quantifies the contribution of various environmental factors used in sea surface pCO(2) reconstruction. Notably, the Copernicus pCO(2) and CODC-GOSD pCO(2) contribute the most, with both contributing similar to 0.72. These are followed by TP, latitude, longitude, SHWW, U-10, and E. (2) After comprehensive data testing, the six machine learning models select the optimal hyperparameters for reconstruction. Among these, the XGBoost model notably improved the quality of the original dataset when using Copernicus pCO(2) and CODC-GOSD pCO(2) products in conjunction with SHWW, U-10, and TP environmental variable data. Compared with SOCAT data, the overall reconstruction accuracy in the Atlantic Ocean reached an impressive 94 %, outperforming the standalone use of either Copernicus pCO(2) or CODC-GOSD pCO(2) products. Furthermore, the XGBoost model demonstrated strong applicability in regions with numerous outliers, maintaining a reconstruction accuracy of >= 95 %. (3) Stability test results reveal that the XGBoost model exhibits low sensitivity to uncertainties in all input variables. This indicates that the model can accommodate environmental data errors induced by abrupt changes in marine environments. Such robustness enhances its reliability in sea surface pCO(2) reconstruction. The reconstruction of the Atlantic sea surface pCO(2) is conducive to the assessment of global ocean acidification and provides a theoretical basis for the sustainable development of the marine environment.

The continuous rise in atmospheric CO2 levels has led to persistent ocean acidification, which negatively impacts marine environments crucial for marine life and alters the chemical composition of seawater. This phenomenon carries significant implications for human society. Utilizing surface seawater pH data from the North Pacific spanning 1995 to 2019, this study investigates the overall and localized spatiotemporal variations in pH within the region, as well as the factors influencing these variations. Additionally, it conducts a quantitative analysis of the different influencing factors. The findings reveal a consistent downward trend in surface seawater pH in the North Pacific, decreasing from 8.073 to 8.029, with notable seasonal variations. The highest pH values are recorded in winter, followed by spring, with lower values in autumn and summer. Spatially, the pH values are higher in the northwest and lower in the southeast, with the most pronounced acidification occurring in the central and western regions, while other areas exhibit more uniform acidification levels. Spatial correlation analysis indicates that surface seawater pH in the North Pacific generally shows a negative correlation with sea surface temperature (SST), salinity (SSS), and chlorophyll-a concentration (chl a) and a positive correlation with dissolved oxygen (DO). Among these factors, SST exerts the greatest influence on seawater pH, followed by DO and SSS. The degree of acidification varies across different regions, and the dominant influencing factors differ accordingly. In the equatorial central region (A), the primary factors are chl a and SST; in the eastern regions of China and Japan (B) and the western region of Canada (C), DO and SSS are the main controlling factors. An interaction analysis of each pair of dominant factors using the geodetector shows that their respective contributions to regions A, B, and C are 70%, 90%, and 50%, respectively. Understanding the primary factors driving acidification in different regions can aid in comprehending the biological and environmental impacts of acidification in those areas and provide valuable insights for mitigating marine acidification.

Wetlands play an important role in ecological health and sustainable development, and dynamic monitoring of their spatial distribution is crucial for developing management and conservation measures. The types of coastal wetlands are complex and diverse, natural and artificial wetlands are easily confused, making precise classification more difficult. The coastal wetland of Chongming Island in China, which has diverse types and unique and complex ecological and hydrological characteristics, was deliberately chosen as a challenging case study. The objective of this study was to research effective method of fine classification of coastal wetlands, by constructing feature variables and proposing strategies for multi-level selection and fusion of feature variables. Sentinel-2 data with rich spectral information and high spatial resolution was be used. In this study, firstly, the classification effect of characteristic variables such as vegetation index, water body index, red edge index, and texture index were evaluated. Focusing on the “different objects with same spectra” of the humid planning land and farm growing ponds, the spectral characteristics of them were analyzed and a “water-rich soil index (WRSI)” was established. Subsequently, correlation analysis and J-M distance method were used to multi-level selection for the feature variables and four sets of features combination schemes were established. Finally, random forest (RF) was applied to classify coastal wetlands using different feature combination schemes, and the accuracy of different schemes was compared and verified. The results show the following: 1)Texture features have a promoting effect on improving classification accuracy. The constructed “water rich soil index”(WRSI) has the effectively contribution to identification and classification of farm growing ponds and humid planned land, improving the overall classification accuracy by 6.52%. 2)By multi-level selecting and fusion of feature variable sets, both accuracy and efficiency for classification are improved. For different features combination schemes, the classification accuracy is up to 90.03% by integrating spectral features, spectral index, texture index, and WRSI. This study evaluates the potential of Sentinel-2 data in coastal wetland classification, constructs effective feature parameters, and provides a new idea for wetland information extraction. The resulting classification map can be used for sustainable management, ecological assessment and conservation of the coastal wetland.

The atmosphere over the ocean is an important research field that involves multiple aspects such as climate change, atmospheric pollution, weather forecasting, and marine ecosystems. It is of great significance for global sustainable development. Satellites provide a wide range of measurements of marine aerosol optical properties and are very important to the study of aerosol characteristics over the ocean. In this study, aerosol optical depth (AOD) data from seventeen AERONET (Aerosol Robotic Network) stations were used as benchmark data to comprehensively evaluate the data accuracy of six aerosol optical thickness products from 2013 to 2020, including MODIS (Moderate-resolution Imaging Spectrometer), VIIRS (Visible Infrared Imaging Radiometer Suite), MISR (Multi-Angle Imaging Spectrometer), OMAERO (OMI/Aura Multi-wavelength algorithm), OMAERUV (OMI/Aura Near UV algorithm), and CALIPSO (Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation) in the East Asian Ocean. In the East Asia Sea, VIIRS AOD products generally have a higher correlation coefficient (R), expected error within ratio (EE within), lower root mean square error (RMSE), and median bias (MB) than MODIS AOD products. The retrieval accuracy of AOD data from VIIRS is the highest in spring. MISR showed a higher EE than other products in the East Asian Ocean but also exhibited systematic underestimation. In most cases, the OMAERUV AOD product data are of better quality than OMAERO, and OMAERO overestimates AOD throughout the year. The CALIPSO AOD product showed an apparent underestimation of the AOD in different seasons (EE Below = 58.98%), but when the AOD range is small (0 < AOD < 0.1), the CALIPSO data accuracy is higher compared with other satellite products under small AOD range. In the South China Sea, VIIRS has higher data accuracy than MISR, while in the Bohai-Yellow Sea, East China Sea, Sea of Japan, and the western Pacific Ocean, MISR has the best data accuracy. MODIS and VIIRS show similar trends in R, EE within, MB, and RMSE under the influence of AOD, Angstrom exponent (AE), and precipitable water. The study on the temporal and spatial distribution of AOD in the East Asian Ocean shows that the annual variation of AOD is different in different sea areas, and the ocean in the coastal area is greatly affected by land-based pollution. In contrast, the AOD values in the offshore areas are lower, and the aerosol type is mainly clean marine type aerosol. These findings can help researchers in the East Asian Ocean choose the most accurate and reliable satellite AOD data product to better study atmospheric aerosols’ impact and trends.

为研究冰川融水径流中汞与悬浮颗粒物的变化及其相关关系,于2019年6月～2020年9月在青藏高原东南缘海螺沟冰川融水径流进行为期一年的连续定点采样,测试了样品中汞形态含量和悬浮颗粒物的数量、含量及粒径特征.分析表明,总汞的平均含量为6.96～10.78 ng/L,其中颗粒态汞为4.54～9.14 ng/L,溶解态汞为1.53～2.42 ng/L,与青藏高原及世界其他偏远地区河流汞含量相当.各形态汞与悬浮颗粒物不同特征在不同季节的相关关系差异显著,总汞和颗粒态汞含量与总悬浮颗粒物含量和数量在夏季消融盛期具有突出且一致的正相关关系,但在秋冬季并未显示出正相关,表明前人揭示的冰川补给河流中颗粒物控制汞含量变化的结论具有季节局限性.冰川径流中的汞受水文过程和汞的来源及其在水体中的转化等多因素影响,汞形态含量与悬浮颗粒物不同物理特征在不同季节的差异性可能反映了不同季节汞的来源和传输机制的差异.

放射性碳同位素加速器质谱(ASM14C)测年法是湖泊沉积物定年的主要方法,并且是全新世年代学研究的热点.随着对全新世古气候与环境变化研究的深入,选取不同介质定年可能对年代模型的准确性造成误差,进而影响到气候指标的解译.本文选取贵州东北部梵净山九龙池沉积物中树枝、树叶、树皮、种子等植物残体和全样有机质作为测年介质,利用ASM14C进行定年.结果 表明,相比全样有机质,使用植物残体定年可以在一定程度上避免碳库效应的影响.但相比于原位沉积且生长年限较短的叶片,树枝的定年结果偏老,不是一种理想的定年介质.另外,根据定年结果及总有机碳含量重建了全新世九龙池的碳累积速率,发现碳累积速率可以指示该地区亚洲夏季风强度的变化历史.

以1977-2018年整个西北太平洋台风数据为基础,利用MATLAB、ArcGIS等软件对台风的频数、强度等进行统计特征分析,重点突出多年来台风在我国各地区的登陆情况.结果 显示:1977-2018年西北太平洋台风年频数变化呈波动减少的趋势,特别是1994年以来,台风发生频数显著减少,但近几年频数有所上升.西北太平洋台风强度变化不明显,但存在频数增大强度变小,频数减小强度变大的特点.夏季和秋季发生的台风占总数的85％,秋季的台风强度最高,其最大平均风速可达37 m/s.42年期间,登陆我国台风共计314个,年均约7.5个,其频数和强度由沿海地区向内陆地区逐渐递减.

基于2000-2018年的历史资料,运用离差标准法、AHP层次分析法和GIS技术,以台风致灾、社会防灾减灾和自然承灾为子系统建立评价模型,对中国遭遇台风灾害严重的主要沿海城市进行了灾害风险评估和灾害等级分析.结果显示:沿海城市台风灾害综合风险地域性差异明显,从南到北总体呈现递减趋势.山东省是受台风灾害风险最低的区域,广东省和福建省是受台风灾害影响的高风险区,需要重点加强台风防灾系统建设.

There is a growing recognition of the role of particle-attached (PA) and free-living (FL) microorganisms in marine carbon cycle. However, current understanding of PA and FL microbial communities is largely focused on those in the upper photic zone, and relatively fewer studies have focused on microbial communities of the deep ocean. Moreover, archaeal populations receive even less attention. In this study, we determined bacterial and archaeal community structures of both the PA and FL assemblages at different depths, from the surface to the bathypelagic zone along two water column profiles in the South China Sea. Our results suggest that environmental parameters including depth, seawater age, salinity, particulate organic carbon (POC), dissolved organic carbon (DOC), dissolved oxygen (DO) and silicate play a role in structuring these microbial communities. Generally, the PA microbial communities had relatively low abundance and diversity compared with the FL microbial communities at most depths. Further microbial community analysis revealed that PA and FL fractions generally accommodate significantly divergent microbial compositions at each depth. The PA bacterial communities mainly comprise members of Alphaproteobacteria and Gammaproteobacteria, together with some from Planctomycetes and Deltaproteobacteria, while the FL bacterial lineages are also mostly distributed within Alphaproteobacteria and Gammaproteobacteria, along with other abundant members chiefly from Actinobacteria, Cyanobacteria, Bacteroidetes, Marinimicrobia and Deltaproteobacteria. Moreover, there was an obvious shifting in the dominant PA and FL bacterial compositions along the depth profiles from the surface to the bathypelagic deep. By contrast, both PA and FL archaeal communities dominantly consisted of euryarchaeotal Marine Group II (MGII) and thaumarchaeotal Nitrosopumilales, together with variable amounts of Marine Group III (MGIII), Methanosarcinales, Marine Benthic Group A (MBG-A) and Woesearchaeota. However, the pronounced distinction of archaeal community compositions between PA and FL fractions was observed at a finer taxonomic level. A high proportion of overlap of microbial compositions between PA and FL fractions implies that most microorganisms are potentially generalists with PA and FL dual lifestyles for versatile metabolic flexibility. In addition, microbial distribution along the depth profile indicates a potential vertical connectivity between the surface-specific microbial lineages and those in the deep ocean, likely through microbial attachment to sinking particles.

利用遥感技术快速提取海岸线是一种重要的技术手段,针对传统分水岭算法在高分辨率多光谱卫星数据处理中存在的过分割和抗干扰能力差的问题,本文提出了一种基于扩展极值变换标记分水岭的算法.首先通过形态学重建、扩展极值变换等方法建立前景和背景标记,初步抑制灰度极小值和极大值区域,然后依据这两类标记对梯度图像进行修正,进而进行分水岭变换,提取岛屿水边线.以南海典型海岛为研究区域,利用2017年GF-2卫星数据对本文提出的方法进行验证和精度评价,研究结果表明:改进的分水岭算法对GF-2数据的人工岸线提取质量在1个像素(4 m)之内高于90％,沙质岸线提取质量在1.5个像素(6 m)之内高于90％,可以用于高分辨率多光谱影像的分割和海岛水边线的提取.

Objective: To investigate the value of 1H-magnetic resonance spectroscopy ((1)H-MRS) in determining the content of liver triglyceride in patients with fatty liver disease (FLD), as well as its influencing factors. Methods: A total of 124 patients with nonalcoholic fatty liver disease (NAFLD), chronic hepatitis B (CHB), or hepatitis B complicated by FLD who underwent liver biopsy in the Affiliated Hospital of Hangzhou Normal University were enrolled, and the clinical data, serological markers, FibroScan results, and (1)H-MRS results were collected. A correlation analysis was performed with the results of liver biopsy as the gold standard, and the influence of factors including hepatitic B virus (HBV) infection and obesity on accuracy was analyzed. A one-way analysis of variance was used for comparison of means between the three groups, and the LSD or SNK test (for homogeneity of variance) or the Tamhane's or Dunnett's test (heterogeneity of variance) was used for comparison between any two groups. The t-test was used for comparison of continuous data between groups, and the chi-square test was used for comparison of categorical data. The MRS-PDFF receiver operating characteristic (ROC) curve was plotted, the area under the ROC curve (AUC) was calculated, the optimal cut-off points for the diagnosis of NAFLD were estimated, and sensitivity and specificity were calculated. Results: The NAFLD group (42 patients) and the CHB + NAFLD group (40 patients) had a significantly higher proton density fat fraction (PDFF, the content of triglyceride in the liver) than the CHB group (42 patients) (16.84±9.76/9.39 ± 5.50 vs 3.45 ± 1.63, P < 0.001). The results were significantly correlated with the degree of steatosis confirmed by liver biopsy (P < 0.001), but it was not significantly correlated with inflammation or fibrosis grade. The correlation analysis showed that the MRS-PDFF value measured by 1H-MRS was significantly correlated with body mass index (BMI), blood lipids, alkaline phosphatase, and blood glucose, while it was not significantly correlated with age, sex, or the presence or absence of hepatitis B. The ROC curve analysis showed that the AUCs of PDFF measured by 1H-MRS were 0.93, 0.974, and 0.976, respectively, for the diagnosis of steatosis S1(≥5%), S2(≥34%), and S3(≥66%), and the corresponding optimal thresholds were 5.14%, 11.16%, and 16.7%, respectively. Conclusion: 1H-MRS has a high diagnostic value in quantitative evaluation of the degree of liver steatosis in patients with FLD and is not affected by the factors such as HBV infection, age, and sex, while it is correlated with BMI and lipid metabolism.