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With climate change, there will be higher requirements for monitoring storm surges (SSs) in nearshore areas. However, this capability is limited by the sparseness of tide gauge (TG) stations. Establishing and maintaining a permanent, high‐spatial coverage, in situ TG network is complex and expensive. Here, we propose a joint modeling method developed from the all‐site modeling data‐driven framework by importing temporary TGs into coastal regions with insufficient permanent TG stations. The assessments show that this method can significantly optimize the capability of extreme SS monitoring during typhoons and hurricanes. Moreover, the evaluation based on Coupled Model Intercomparison Project Phase 6 data indicates that it will monitor extreme SSs more effectively during 2025–2050 compared with only using existing permanent in situ TGs (reducing root mean square error and absolute mean bias by ∼50%). The joint modeling method provides an applicable and sustainable solution for optimizing the SS monitoring capability in coastal areas. Plain Language Summary Each year, storm surges (SSs) generated by typhoons and hurricanes cause loss of life and property in coastal areas. With global warming, the destruction from extreme events will increase in the future, thus posing challenges to nearshore SS monitoring. High spatiotemporal resolution and high‐precision monitoring data are crucial for early warning and forecasting, which can better prepare coastal communities for incoming storms. However, as the only means to observe continuous and high‐frequency sea levels, tide gauges often provide limited information due to insufficient spatial resolution. In addition, establishing and maintaining a permanent tide gauge (TG) monitoring network with high spatial coverage is expensive and unrealistic. A joint modeling method based on artificial intelligence technology through importing temporary tide gauges established during extreme events to the existing TG networks can significantly optimize the future SS monitoring capability, providing a valuable and applicable reference for regions affected by powerful typhoons and hurricanes and thereby improving the nearshore SS monitoring system. Key Points A storm surge (SS) joint modeling method developed from the all‐site modeling data‐driven framework Temporary tide gauge (TG) stations can reduce the cost of establishing and maintaining permanent TGs Improve extreme SS monitoring precision by ∼50% from 2025 to 2050 compared with only using permanent TGs

Ankylosing spondylitis (AS) is a systemic, chronic, and inflammatory autoimmune disease associated with the disorder of intestinal microbiota. Unfortunately, effective therapies for AS are lacking. Recent evidence has indicated that indole-3-acetic acid (IAA), an important microbial tryptophan metabolite, can modulate intestinal homeostasis and suppress inflammatory responses. However, reports have not examined the in vivo protective effects of IAA against AS. In this study, we investigated the protective effects and underlying mechanisms through which IAA acts against AS. We constructed a proteoglycan (PG)-induced AS mouse model and administered IAA (50 mg/kg body weight) by intraperitoneal injection daily for 4 weeks. The effects of IAA on AS mice were evaluated by examining disease severity, intestinal barrier function, aryl hydrocarbon receptor (AhR) pathway, T-helper 17 (Th17)/T regulatory (Treg) balance, and inflammatory cytokine levels. The intestinal microbiota compositions were profiled through whole-genome sequencing. We observed that IAA decreased the incidence and severity of AS in mice, inhibited the production of pro-inflammatory cytokines (tumor necrosis factor α [TNF-α], interleukin [IL]-6, IL-17A, and IL-23), promoted the production of the anti-inflammatory cytokine IL-10, and reduced the ratios of pro-/anti- inflammatory cytokines. IAA ameliorated pathological changes in the ileum and improved intestinal mucosal barrier function. IAA also activated the AhR pathway, upregulated the transcription factor forehead box protein P3 (FoxP3) and increased Treg cells, and downregulated the transcription factors retinoic acid receptor–related orphan receptor gamma t (RORγt) and signal transducer and activator of transcription 3 (STAT3) and decreased Th17 cells. Furthermore, IAA altered the composition of the intestinal microbiota composition by increasing Bacteroides and decreasing Proteobacteria and Firmicutes, in addition to increasing the abundances of Bifidobacterium pseudolongum and Mucispirillum schaedleri . In conclusion, IAA exerted several protective effects against PG-induced AS in mice, which was mediated by the restoration of balance among the intestinal microbial community, activating the AhR pathway, and inhibiting inflammation. IAA might represent a novel therapeutic approach for AS.

The molecular dynamics method is used to further reveal, from the molecular point of view, the mechanisms of salt inhibiting the hydration of Na-MMT. The interaction between water molecules, salt molecules, and montmorillonite are calculated by establishing the adsorption models. According to the simulation results, the adsorption conformation, interlayer concentration distribution, self-diffusion coefficient, ion hydration parameters, and other data are compared and analyzed. The simulation results show that the volume and basal spacing increase in a stepwise manner with the increase of water content, and water molecules have different hydration mechanisms. The addition of salt will enhance the hydration properties of compensating cations of montmorillonite and affect the mobility of particles. The addition of inorganic salts mainly reduces the adsorption tightness between water molecules and crystal surfaces, thereby reducing the thickness of water molecules layer, while the organic salts can better inhibit migration by controlling interlayer water molecules. The results of molecular dynamics simulations reveal the microscopic distribution of particles and the influence mechanism when the swelling properties of montmorillonite are modified by chemical reagents.

Analyzing the features of extreme events and estimating their probabilities robustly require high spatial coverage, high temporal resolution, and sufficiently long storm surge (SS) records. However, in situ observations cannot always meet these demands due to spatiotemporal sparseness. Here, we proposed a novel regional all‐site modeling framework based on a machine learning method, the extreme gradient boosting tree. This framework can reconstruct long SS records simply and quickly and can estimate storm surges simultaneously at both gauged and ungauged locations. Compared to in situ observations, the distribution patterns of SS variations during extreme events can be recognized easily from the reconstructed hourly SS data set. Since its available record is longer than 60 years (1959–2020), the estimation uncertainties of extreme event probabilities are significantly decreased. Noticeably high extreme SS return levels were found along the coast of the northern Gulf of Mexico, which should be given great attention. Plain Language Summary High spatial‐coverage/temporal‐resolution and long storm surge records are crucial for coastal protection because they are the basis for analyzing the spatiotemporal characteristics of extreme sea‐level events and estimating their occurrence probabilities robustly. Due to spatiotemporal sparseness, tide gauge observations cannot always meet these demands. A novel regional all‐site modeling framework based on artificial intelligence technology can address this challenge. The novel framework allows the data‐driven model to reconstruct high‐precision and long‐term hourly storm surges at both gauged and ungauged locations, which can provide valuable information for disaster prevention in coastal areas. Key Points A novel regional all‐site storm surge (SS) modeling framework based on machine learning Over 60‐year reconstructed hourly SS records with high spatial coverage More clear spatiotemporal characteristics of extreme sea‐level events and lower estimation uncertainty of their probabilities

In storm surge (SS) simulation, data‐driven methods can establish the relationship between predictor variables and the predictand, enabling long‐term SS level reconstructions. Here, using the U.S. East Coast as an example, we explored the capabilities of four machine learning algorithms, namely Artificial Neural Networks (ANN), Long Short‐Term Memory (LSTM), Light Gradient Boosting Machine (LightGBM), and Extreme Gradient Boosting (XGBoost) in reconstructing hourly SS levels from 1979 to 2018 under an all‐site modeling framework. Four atmospheric parameters, time index, and tide gauge coordinates from 51 tide gauges are used as predictors. The model performance was evaluated at both the tide gauge and coastal scales. Results indicate that LightGBM and XGBoost models outperform ANN and LSTM in SS reconstructions, with XGBoost showing better overall performance, especially for extreme SSs and historical extreme events. XGBoost can capture the temporal evolution of SSs with higher accuracy, producing reconstructions comparable to observations under the all‐site modeling framework. The model interpretability analysis focusing on XGBoost reveals that the spatial distribution of feature importance varies for each predictor. Mean sea level pressure and the 10 m eastward wind component are the two most important predictors, followed by time index, latitude, and longitude under the all‐site modeling framework and selected stations. These results indicate that data‐driven models under this framework have the potential to capture region‐specific and physically reasonable relationships between SS levels and atmospheric drivers. Key Points Explore the capabilities of four machine learning algorithms in storm surge (SS) reconstruction with high spatiotemporal resolution Extreme Gradient Boosting performs best overall in SS reconstruction, especially for extreme surges and historical extreme events The all‐site modeling framework can capture region‐specific relationships between SS and atmospheric drivers

Background Ankylosing spondylitis (AS) is a chronic inflammatory disease characterized by progressive spinal fusion and systemic inflammation. Recent studies suggest that gut microbiota plays a crucial role in the pathogenesis of AS. Methods This study investigated the therapeutic effects of Limosilactobacillus reuteri (L. reuteri) on AS progression and its underlying mechanisms using a proteoglycan (PG)-induced mouse model. Female BALB/c mice (n = 10/group) were randomized into control group, PG group and PG +  L. reuteri group. Disease severity was assessed via arthritis scores, Micro-CT images, and histopathology. Serum cytokines (IL-1β, IL-18, IL-17A, IL-23) were measured by ELISA. Intestinal barrier integrity was evaluated using FITC-dextran permeability, immunofluorescence (ZO-1, occludin), and colon histology. Gut microbiota (16S rRNA sequencing) and fecal metabolites (untargeted metabolomics) were analyzed. AhR/NLRP3 pathway activity was assessed via qRT-PCR (AhR, CYP1A1, CYP1B1, and NLRP3). Results Our findings demonstrated that L. reuteri significantly alleviated AS progression, as evidenced by reduced joint swelling and erythema, alongside a decreased arthritis index and paw thickness. Furthermore, treatment with L. reuteri resulted in a marked reduction in serum levels of pro-inflammatory cytokines, including IL-1β, IL-18, IL-17A, and IL-23, indicating its potential to modulate systemic inflammation. Additionally, L. reuteri enhanced intestinal mucosal barrier function, as demonstrated by improved histopathological integrity, reduced intestinal permeability, and restored expression of tight junction proteins ZO-1 and occludin. Moreover, L. reuteri treatment restored gut microbiota composition and metabolite profiles, aligning them more closely with control groups. Notably, L. reuteri may exert its effects partially through the AhR/NLRP3 pathway, as evidenced by increased mRNA levels of AhR, CYP1A1, and CYP1B1, along with reduced NLRP3 expression. Conclusion In conclusion, L. reuteri effectively prevents the progression of AS in mice by restoring gut microbiota-metabolism homeostasis and modulating inflammatory pathways, highlighting its potential as a therapeutic agent for AS.

Recently, accumulating evidence has suggested that gut microbiota may be involved in the occurrence and development of ankylosing spondylitis (AS). It has been suggested that rifaximin have the ability to modulate the gut bacterial communities, prevent inflammatory response, and modulate gut barrier function. The goal of this work is to evaluate the protective effects of rifaximin in fighting AS and to elucidate the potential underlying mechanism. Rifaximin were administered to the proteoglycan (PG)-induced AS mice for 4 consecutive weeks. The disease severity was measured with the clinical and histological of arthritis and spondylitis. Intestinal histopathological, pro-inflammatory cytokine levels and the intestinal mucosal barrier were evaluated. Then, western blot was performed to explore the toll-like receptor 4 (TLR-4) signal transducer and NF-κB expression. Stool samples were collected to analyze the differences in the gut microbiota via next-generation sequencing of 16S rDNA. We found that rifaximin significantly reduced the severity of AS and resulted in down-regulation of inflammatory factors, such as TNF-α, IL-6, IL-17A, and IL-23. Meanwhile, rifaximin prevented ileum histological alterations, restored intestinal barrier function and inhibited TLR-4/NF-κB signaling pathway activation. Rifaximin also changed the gut microbiota composition with increased phylum ratio, as well as selectively promoting some probiotic populations, including . Our results suggest that rifaximin suppressed progression of AS and regulated gut microbiota in AS mice. Rifaximin might be useful as a novel treatment for AS.

A miniaturised bionic electronic nose system was developed to solve the problems of expensive equipment and long response time for soil pesticide residue detection. The structure of the bionic electronic nasal cavity is designed based on the spatial structure and olfactory principle of the sturgeon nasal cavity. Through experimental study, the structure of the nasal cavity of the sturgeon was extracted and analyzed. The 3D model of the bionic electronic nasal cavity was constructed and verified by Computational Fluid Dynamics (CFD) simulation. The results show that the gas flow distribution in the bionic chamber is more uniform than that in the ordinary chamber. The airflow velocity near the sensor in the bionic chamber is lower than in the ordinary chamber. The eddy current intensity near the bionic chamber sensor is 2.29 times that of the ordinary chamber, further increasing the contact intensity between odor molecules and the sensor surface and shortening the response time. The 10-fold cross-validation method of K-Nearest Neighbor (K-NN), Random Forest (RF) and Support Vector Machine (SVM) was used to compare the recognition performance of the bionic electronic nasal cavity with that of the ordinary electronic nasal cavity. The results showed that, when the bionic electronic nose detection system identified the concentration of pesticide residues in soil, the recognition rate of the above three recognition algorithms reached 97.3%, significantly higher than that of the comparison chamber. The bionic chamber electronic nose system can improve the detection performance of electronic noses and has a good application prospect in soil pesticide residue detection.

Satellite altimetry and tide gauges are the two main techniques used to measure sea level. Due to the limitations of satellite altimetry, a high-quality unified sea level model from coast to open ocean has traditionally been difficult to achieve. This study proposes a fusion approach of altimetry and tide gauge data based on a deep belief network (DBN) method. Taking the Mediterranean Sea as the case study area, a progressive three-step experiment was designed to compare the fused sea level anomalies from the DBN method with those from the inverse distance weighted (IDW) method, the kriging (KRG) method and the curvature continuous splines in tension (CCS) method for different cases. The results show that the fusion precision varies with the methods and the input measurements. The precision of the DBN method is better than that of the other three methods in most schemes and is reduced by approximately 20% when the limited altimetry along-track data and in-situ tide gauge data are used. In addition, the distribution of satellite altimetry data and tide gauge data has a large effect on the other three methods but less impact on the DBN model. Furthermore, the sea level anomalies in the Mediterranean Sea with a spatial resolution of 0.25° × 0.25° generated by the DBN model contain more spatial distribution information than others, which means the DBN can be applied as a more feasible and robust way to fuse these two kinds of sea levels.

Background: Ankylosing spondylitis (AS) is a chronic inflammatory and autoimmune disease that primarily affects the sacroiliac joints and axial skeleton. While the exact pathogenetic mechanism of AS remains unclear, previous reports have highlighted the involvement of genetic factors, immune responses, and gut microbiota dysregulation in the development of this condition. Short-chain fatty acids (SCFAs), which are microbial fermentation products derived from sugar, protein, and dietary fibers, play a role in maintaining the intestinal barrier function and reducing inflammatory responses. The aim of this study was to investigate the therapeutic potential of butyric acid (BA), an important SCFA, in the treatment of AS. Methods: To evaluate the anti-inflammatory and anti-bone loss effects of BA, a murine AS model was established using proteoglycan and dimethyl dioctadecyl ammonium (DDA) adjuvants. Various techniques, including an enzyme-linked immunosorbent assay (ELISA), magnetic resonance imaging (MRI), micro-CT, histology, quantitative PCR (qPCR) for intestinal tight junction protein expression, and 16S rDNA sequencing to analyze gut microbiota abundance, were employed to assess the inflammation and bone health in the target tissues. Results: The results indicated that BA demonstrated potential in alleviating the inflammatory response in the peripheral joints and the axial spine affected by AS, as evidenced by the reductions in inflammatory infiltration, synovial hyperplasia, and endplate erosion. Furthermore, BA was found to impact the intestinal barrier function positively. Notably, BA was associated with the downregulation of harmful inflammatory factors and the reversal of bone loss, suggesting its protective effects against AS. Conclusions: These beneficial effects were attributed to the modulation of gut microbiota, anti-inflammatory properties, and the maintenance of skeletal metabolic homeostasis. This study contributes new evidence supporting the relationship between gut microbiota and bone health.