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The gut microbiota plays a pivotal role in influencing various health outcomes, including immune-mediated conditions. Granulomatosis with Polyangiitis (GPA) is one such condition, and its potential associations with gut microbiota remain underexplored. Using a two-sample Mendelian randomization approach, we investigated the causal links between gut microbiota and GPA. We sourced our data from multiple cohorts and consortiums, including the MiBioGen consortium. Our study design incorporated both direct associations and mediation effects of immune traits on the relationship between gut microbiota and GPA. Our analysis revealed significant associations between 1 phylum, 1 family 9 genus microbiota taxa and GPA. Furthermore, we identified several immune cell traits that mediated the effects of gut microbiota on GPA. For instance, the family Defluviitaleaceae and genus Defluviitaleaceae UCG011 influenced GPA through CD11c in granulocytes. The mediation effect proportions further elucidated the complex dynamics between gut microbiota exposures, immune markers, and their combined influence on GPA. Our findings underscore the intricate relationship between gut microbiota, immune markers, and GPA. The identified associations and mediation effects provide valuable insights into the potential therapeutic avenues targeting gut microbiota to manage GPA.

The South China Sea (SCS) is a critical fishery region facing sustainability challenges due to overexploitation, geopolitical tensions, and inadequate monitoring. Traditional monitoring methods, such as AIS and VMS, have limitations due to data gaps and vessel deactivation. We developed an improved remote sensing algorithm using VIIRS nighttime light observations (2013–2022) to detect and classify lit fishing boats in the SCS. The study introduces a Two-Dimensional Constant False Alarm Rate (2D-CFAR) algorithm integrated with morphological analysis, which enhances boats’ detection accuracy. The classification of fishing boat types was based on light power thresholds derived from spatial entropy analysis, where distinct clustering patterns indicated three operational categories: small interfering lights (<1.2–3.7 kW), small-to-medium-sized lit fishing boats (1.2–3.7 to 28.6–43.2 kW), and large lit fishing boats (>28.6–43.2 kW). Our findings reveal a 4.4-fold dominance of small-to-medium-sized lit fishing boats over large lit fishing boats. China’s summer fishing moratorium effectively reduces large lit fishing boats activity by 85%, yet small-to-medium-sized lit fishing boats, primarily from neighboring countries like Vietnam, persist, exploiting this period illegally. Spatially, small-to-medium-sized lit fishing boats concentrate in the central SCS, southeast Vietnam, and Nansha Islands, while large lit fishing boats target upwelling zones near Hainan and Guangdong. Moreover, a new fishing hotspot emerged in eastern SCS, reflecting intensified resource and geopolitical competition. Light intensity analysis reveals rapid growth in contested areas (10% annually, p < 0.01), underscoring ecological risks. These findings highlight the limitations of unilateral policies and the urgent need for regional cooperation to curb illegal, unreported, and unregulated (IUU) fishing. Our algorithm offers a robust tool for monitoring fishing dynamics, providing quantitative insights into vessel distribution, policy impacts, and resource-driven patterns. This supports evidence-based fisheries management and biodiversity conservation in the SCS, adaptable to other marine regions facing similar challenges.

The abundance of phytoplankton is generally estimated by measuring the chlorophyll-a concentration (Cchla), which is an important factor in photosynthesis and can be used to analyze the density and biomass of phytoplankton in the ecosystem. The band-ratio-based empirical or semi-analytical algorithms are operationally applied to retrieve Cchla in global oceans, which generally experience difficulties from the diversity of optical properties and the complexity of the radiative transfer equations in analytical analyses, respectively. With an attempt to develop an accurate Cchla retrieval model for the optically complex coastal and estuarine waters, this study aimed to explore the deep learning (DL) methods in satellite retrieval of Cchla. A two-stage convolutional neural network (CNN), named Cchla-Net, was proposed, which utilized the spectral information of remote sensing reflectances at MODIS/Aqua’s visible bands. In the first-stage phase, the Cchla-Net was pretrained by a set of remote sensing patches, in which the Cchla was generated from an existing model (OC3M). The pretrained results were than used as the initial values to refine the network with the synthetic oversampled in-situ dataset in the second-stage training phase. Using in-situ samples for training with the new initial values has a higher probability to reach the global optimum. The quantitative analyses showed that the two-stage training was more likely to achieve a global optimum in the optimization than the one-stage training. Matchups of the in-situ Cchla measurements were used to evaluate the retrieval models. Results showed that the proposed Cchla-Net produced obvious better performance than the empirical and semi-analytical algorithms, implying the DL method was more effective for optically complex waters with extremely high Cchla. This study provided an applicable method for remote sensing retrieval of Cchla, which should be helpful for studying the spatial distribution and temporal variability in the productive Pearl River estuary (PRE) waters.

An attention U-Net was proposed to reconstruct the missing chlorophyll-a concentration (Cchla) data. The U-Net is a lightweight full convolution neural network architecture consisting of an enccoder-decoder (i.e., down-sampling and up-sampling). The attention gates (AGs) were integrated into the U-Net. Training the U-Net with AGs could implicitly teach it to suppress irrelevant areas and highlight the salient features in the missing data areas, which would increase the network sensitivity and reconstruction accuracy. The neural network uses the satellite-derived Cchla anomalies and its variance as the input, and the reconstructed fields along with their variances as outputs. The trained network was applied to long-term daily MODIS/Aqua Cchla products in the Pearl River estuary (PRE) and adjacent continental shelf area. The model performance was evaluated by using an independent test dataset from both satellite-derived and in-situ measurements. The results showed that the proposed neural network not only had good performance in the reconstruction of valid pixels, but also provided a more reasonable reconstruction compared to the standard U-Net without AGs. This study provided a feasible method for the reconstruction task in the field of ocean color, which should be helpful in producing a creditable dataset to study the ecological effects of extreme weather conditions such as typhoons on the upper ocean in the PRE waters. Based on the reconstructed Cchla products, the footprints of the typhoons were studied. An increase in surface Cchla near the typhoons’ track and a decrease in estuary were found. The composite results illustrated that the Cchla increases occurred for almost the entire area within a radius of 100 km. The time series analysis showed that the Cchla peak appeared on the fifth day after the typhoon’s passage.

The oceanic Kármán vortex street is an important hydrokinetic phenomenon caused by the unsteady separation of sea currents in the wake of an obstacle. This study quantitatively analyzed the characteristics of the small-scale vortex street of Didicas Island in the Luzon Strait based on multimission satellite data. Five groups of vortex streets were captured, and each group was observed by two satellite images within one hour. Based on the displacement and change in the vortex street within one hour, we found that the formation time of a single vortex is longer than 40 min. The vortex propagation speed behind the island is approximately 0.98 m s−1 (ranging from 0.66–1.22 m s−1). The incoming velocity was calculated using the vortex propagation speed and the aspect ratio based on a vortex propagation model, which ranges from 0.72 to 1.47 m s−1 The incoming speed is 1.6–2.3 times the geostrophic speed extracted from AVISO data, implying that the contribution of non-geostrophic flow to the total flow field is comparable to that of geostrophic flow in this region.

We evaluated six empirical and semianalytical models of the diffuse attenuation coefficient at 490 nm (Kd(490)) using an in situ dataset collected in the Pearl River estuary (PRE). A combined model with the most accurate performance (correlation coefficient, R2 = 0.92) was selected and applied for long-term estimation from 2003 to 2017. Physical and biological processes in the PRE over the 14-year period were investigated by applying satellite observations (MODIS/Aqua data) and season-reliant empirical orthogonal function analysis (S-EOF). In winter, the average Kd(490) was significantly higher than in the other three seasons. A slight increasing trend was observed in spring and summer, whereas a decreasing trend was observed in winter. In summer, a tongue with a relatively high Kd(490) was found in southeastern Lingdingyang Bay. In Eastern Guangdong province (GDP), the relatively higher Kd(490) value was found in autumn and winter. Based on the second mode of S-EOF, we found that the higher values in the eastern GDP extended westward and formed a distinguishable tongue in winter. The grey relational analysis revealed that chlorophyll-a concentration (Cchla) and total suspended sediment concentration (Ctsm) were two dominant contributors determining the magnitude of Kd(490) values. The Ctsm-dominated waters were generally located in coastal and estuarine turbid waters; the Cchla-dominated waters were observed in open clear ocean. The distribution of constituents-dominated area was different in the four seasons, which was affected by physical forces, including wind field, river runoff, and sea surface temperature.

The research objective of this study is to use finite element analysis to investigate the impact of anterior cruciate ligament (ACL) injury on medial unicompartmental knee arthroplasty (UKA) and explore whether patients with ACL injuries can undergo UKA. Based on the morphology of the ACL, models of ACL with diameters ranging from 1 to 10mm are created. Finite element models of UKA include ACL absence and ACLs with different diameters. After creating a complete finite element model and validating it, four different types of loads are applied to the knee joint. Statistical analysis is conducted to assess the stress variations in the knee joint structure. A total of 11 finite element models of UKA were established. Regarding the stress on the ACL, as the diameter of the ACL increased, when a vertical load of 750N was applied to the femur, combined with an anterior tibial load of 105N, the stress on the ACL increased from 2.61 MPa to 4.62 MPa, representing a 77.05% increase. Regarding the equivalent stress on the polyethylene gasket, a notable high stress change was observed. The stress on the gasket remained between 12.68 MPa and 14.33 MPa in all models. the stress on the gasket demonstrated a decreasing trend. The equivalent stress in the lateral meniscus and lateral femoral cartilage decreases, reducing from the maximum stress of 4.71 MPa to 2.61 MPa, with a mean value of 3.73 MPa. This represents a reduction of 44.72%, and the statistical significance is (P < 0.05). However, under the other three loads, there was no significant statistical significance (P > 0.05). This study suggests that the integrity of the ACL plays a protective role in performing medial UKA. However, this protective effect is limited when performing medial UKA. When the knee joint only has varying degrees of ACL injury, even ACL rupture, and the remaining structures of the knee joint are intact with anterior-posterior stability in the knee joint, it should not be considered a contraindication for medial UKA.

The Bermuda Testbed Mooring (BTM) and Bay of Bengal Ocean Acidification (BOBOA) mooring measurements were used to identify changes in the partial pressure of CO2 at the sea surface (pCO2sea) and air-sea CO2 fluxes (FCO2) associated with passage of two tropical cyclones (TCs), Florence and Hudhud. TC Florence passed about 165 km off the BTM mooring site with strong wind speeds of 24.8 m s-1 and translation speed of 7.23 m s-1. TC Hudhud passed about 178 km off the BOBOA mooring site with wind speeds of 14.0 m s-1 and translation speed of 2.58 m s-1. The present study examined the effect of temperature, salinity, dissolved inorganic carbon (DIC), total alkalinity (TA), air-sea CO2 flux, and phytoplankton chlorophyll a change on pCO2sea as a response to TCs. Enhanced mixed layer depths were observed due to TCs-induced vertical mixing at both mooring sites. Decreased pCO2sea (-15.16±5.60 μatm) at the BTM mooring site and enhanced pCO2sea (14.81±7.03 μatm) at the BOBOA mooring site were observed after the passage of Florence and Hudhud, respectively. Both DIC and TA are strongly correlated with salinity in the upper layer of the isothermal layer depth (ILD). Strong (weak) vertical gradient in salinity is accompanied by strong (weak) vertical gradients in DIC and TA. Strong vertical salinity gradient in the upper layer of the ILD (0.031 psu m-1), that supply much salinity, dissolved inorganic carbon and total alkalinity from the thermocline was the cause of the increased pCO2sea in the BOBOA mooring water. Weak vertical salinity gradient in the upper layer of the ILD (0.003 psu m-1) was responsible for decreasing pCO2sea in the BTM mooring water. The results of this study showed that the vertical salinity gradient in the upper layer of the ILD is a good indicator of the pCO2sea variation after the passages of TCs.

Knee osteoarthritis (KOA) has a high incidence among the elderly, significantly impacting their quality of life and overall health. Medial unicompartmental knee arthroplasty (UKA) is an excellent choice for treating knee single-compartment lesions, and lower limb alignment has a profound impact on medial UKA. To explore the influence of different lower limb alignments on medial UKA. In this study, we selected MR and CT data of healthy adult male knee joints to establish a complete finite element analysis (FEA) model of the knee joint. After validation, we established a finite element model of medial UKA. Subsequently, we created 60 sets of FEA models with different lower limb alignments to analyze the impact of different lower limb alignments on medial UKA. A vertical load of 1000 N was applied to the FEA models with different lower limb alignments. During the process of shifting the Mikulicz line from the midpoint of the knee joint towards the medial side, the lower limb load was primarily concentrated on the medial compartment. The stress values of the lateral meniscus, tibial cartilage, and femoral cartilage gradually decreased. ROI (region of interest) 1 and ROI 2 showed the maximum principal strain changes, while ROI 3 and ROI 4 exhibited less pronounced fluctuations, with the maximum principal strain roughly proportionally increased. During the process of shifting the Mikulicz line towards the lateral side from the midpoint of the knee joint, the stress on the lateral compartment increased observably. ROI 1, ROI 2, ROI 3, and ROI 4 showed decreased maximum principal strains, approximately inversely proportional changes, but the overall reduction was relatively small. Different lower limb alignments have a profound impact on the short- and long-term joint function after UKA. When the Mikulicz line is 10 mm inside the midpoint of the knee joint or slightly outside, there is a relatively lower risk of tibial component fractures, lower stress on the lateral compartment, and lower load on the prosthesis. During medial UKA, measures such as bone resection and prosthesis selection should be taken to ensure that the Mikulicz line is in the ideal position.

Water transparency, measured with Secchi disk depth (SDD), is an important parameter for describing the optical properties of a water body. This study evaluates variations of SDD and related impact factors in the Bohai and Yellow Seas (BYS). Based on a new mechanistic model proposed by Lee et al. (2015) applied to MODIS remote sensing reflectance data, climatological SDD variation from 2003 to 2019 was estimated. The annual mean images showed an increasing trend from the coastal zone to the deep ocean. Lower values were found in the Bohai Sea (BHS), while higher values observed in the center of the southern Yellow Sea (SYS). Additionally, the entire sea has shown a decreasing temporal tend, with the variation rate lowest in the BHS at 0.003 m y−1, and highest in the SYS at 0.015 m y−1. However, the weak increasing trend that appeared since 2017 suggests that water quality seems to have improved. Further, it displayed seasonal patterns of low in winter and spring and high in summer and autumn. The empirical orthogonal function (EOF) analysis of SDD variations over the BYS, shows that the first SDD EOF mode is the highest, strongly correlated with total suspended matter. With the high correlation coefficients of chromophoric dissolved organic matter, it illustrates that the SDD variation is mainly dominated by the optical components in the seawater, although correlation with chlorophyll-a is the weakest. The second and third EOF modes show that photosynthetically available radiation, sea surface temperature, sea surface salinity, and wind speed are the main covariates that cause SDD changes. Water transparency evaluation on a long-term scale is essential for water quality monitoring and marine ecosystem protection.