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Abstract Background Hypertension is a common disease, often overlooked in its early stages due to mild symptoms. And persistent elevated blood pressure can lead to adverse outcomes such as coronary heart disease, stroke, and kidney disease. There are many risk factors that lead to hypertension, including various environmental chemicals that humans are exposed to, which are believed to be modifiable risk factors for hypertension. Objective To investigate the role of environmental chemical exposures in predicting hypertension. Methods A total of 11,039 eligible participants were obtained from NHANES 2003–2016, and multiple imputation was used to process the missing data, resulting in 5 imputed datasets. 8 Machine learning algorithms were applied to the 5 imputed datasets to establish hypertension prediction models, and the average accuracy score, precision score, recall score, and F1 score were calculated. A generalized linear model was also built to predict the systolic and diastolic blood pressure levels. Results All 8 algorithms had good predictions for hypertension, with Support Vector Machine (SVM) being the best, with accuracy, precision, recall, F1 scores and area under the curve (AUC) of 0.751, 0.699, 0.717, 0.708 and 0.822, respectively. The R2 of the linear model on the training and test sets was 0.28, 0.25 for systolic and 0.06, 0.05 for diastolic blood pressure. Conclusions In this study, relatively accurate prediction of hypertension was achieved using environmental chemicals with machine learning algorithms, demonstrating the predictive value of environmental chemicals for hypertension.

Abstract Weather forecasting is crucial for scientific research and society. Recently, deep learning (DL) methods have achieved significant advancements in medium-range weather forecasting. However, they generally depend on the initial fields generated by the computationally expensive four-dimensional variational (4DVar) data assimilation (DA) technique, which limits their real-time applicability in multivariate three-dimensional (3D) weather forecasting. Here we propose 4DVarFormer by exploring the potential of integrating the 4DVar constraint into an attention-based neural network. 4DVarFormer eliminates the need for background error covariance statistics and the complex adjoint model development. It can generate multivariate 3D weather states within 0.37 s. Moreover, 4DVarFormer can capture inter-variable relationships, allowing the assimilation of observed variables to correct unobserved variables. Hence, medium-range forecasts initiated by 4DVarFormer outperform those of DL-based DA methods and achieve performance comparable to the forecasts initiated by ERA5 reanalyses. These promising findings contribute to future advancements in integrated end-to-end DL weather forecasting systems.

Significant Wave Height (SWH) is a crucial parameter in oceanographic research, essential for understanding various marine and atmospheric processes. Traditional methods for obtaining SWH, such as ship-based and buoy measurements, face limitations like limited spatial coverage and high operational costs. With the advancement of Global Navigation Satellite Systems reflectometry (GNSS-R) technology, a new method for retrieving SWH has emerged, demonstrating promising results. This study utilizes Radio occultation sounder (GNOS) data from the FY-3E satellite and incorporates the latest Vision Transformer (ViT) technology to investigate GNSS-R-based SWH retrieval. We designed and evaluated various deep learning models, including ANN-Wave, CNN-Wave, Hybrid-Wave, Trans-Wave, and ViT-Wave. Through comparative training using ERA5 data, the ViT-Wave model was identified as the optimal retrieval model. The ViT-Wave model achieved a Root Mean Square Error (RMSE) accuracy of 0.4052 m and Mean Absolute Error (MAE) accuracy of 0.2700 m, significantly outperforming both traditional methods and newer deep learning approaches utilizing Cyclone Global Navigation Satellite Systems (CYGNSS) data. These results underscore the potential of integrating GNSS-R technology with advanced deep-learning models to enhance SWH retrieval accuracy and reliability in oceanographic research.

Abstract Advances in data assimilation (DA) methods and the increasing amount of observations have continuously improved the accuracy of initial fields in numerical weather prediction during the last decades. Meanwhile, in order to effectively utilize the rapidly increasing data, Earth scientists must further improve DA methods. Recent studies have introduced machine learning (ML) methods to assist the DA process. In this paper, we explore the potential of a four‐dimensional variational (4DVar) constrained neural network (NN) method for accurate DA. Our NN is trained to approximate the solution of the variational problem, thereby avoiding the need for expensive online optimization when generating the initial fields. In the context that the full‐field system truths are unavailable, our approach embeds the system's kinetic features described by a series of analysis fields into the NN through a 4DVar‐form loss function. Numerical experiments on the Lorenz96 physical model demonstrate that our method can generate better initial fields than most traditional DA methods at a low computational cost, and is robust when assimilating observations with higher error outside of the distributions where it is trained. Furthermore, our NN‐based DA model is effective against Lorenz96 physical models with larger variable numbers. Our approach exemplifies how ML methods can be leveraged to improve both the efficiency and accuracy of DA techniques.

Background With the establishment of the cardiac-gut axis concept, increasing evidence has suggested the involvement and important regulatory role of the gut microbiota (GM) and short chain fatty acid (SCFA) in cardiovascular diseases. However, the relationship between GM and atrial fibrillation (AF) is still poorly understood. Objectives The aim of this study was to investigate whether there were differences in GM and SCFA between AF patients and healthy controls. Methods In this study, we enrolled 30 hospitalized patients with AF and 30 matched patients with sinus rhythm (SR). GM species in fecal samples were evaluated through amplicon sequencing targeting the 16Sribosomal RNA gene. The feces SCFAs were describe step by step the quantitative analysis using gas chromatography-mass spectrometry (GC-MS). GM species richness, diversity, differential abundance of individual taxa between AF and SR were analyzed. Results AF patients showed decreased species richness and α-diversity compared to SR patients, but there was no statistical difference. The phylogenetic diversity was significant decreased in AF group. The β-diversity indexes revealed significant differences in GM community structure between the AF group and the SR group. After investigated the individual taxa, AF group showed altered relative abundance in several taxa compared to the SR group. linear discriminant analysis (LDA) effect size (LEfSe) analysis revealed, a significant decrease in Bifidobacterium and a greater abundance of Lactobacillus, Fusobacterium, Haemophilus in AF group compared with the SR group. The abundance of haemophilus was negative correlated with isovaleric acid and isobutyric acid. Conclusions In AF patients, the GM phylogenetic diversity and β-diversity decreased, the relative abundance altered in several taxa and the bacterial community structure changed as well as the SCFA level. GM and SCFA dysbiosis might play a crucial part in the occurrence and development of AF.

Accurate knowledge of wind directions plays a critical role in ocean surface wind retrieval and tropical cyclone (TC) research. Under TC conditions, apparent wind streaks induced by marine atmospheric boundary layer rolls can be detected in VV- and VH-polarized synthetic aperture radar (SAR) images. It suggests that though relatively noisy, VH signals may help enhance wind streak orientation magnitudes contained in VV signals and thus to achieve a more accurate wind direction estimation. The study proposes a new method for wind direction retrieval from TC SAR images. Unlike conventional approaches, which calculate wind directions from single-polarization imagery, the method combines VV and VH signals to obtain continuous wind direction maps across moderate and extreme wind speed regimes. The technique is developed based on the histogram of oriented gradient descriptor and Hann window function, accounting for the contribution of neighboring wind streak information (weighted by separation distances). As a case study, the wind directions over four TCs (Karl, Maria, Douglas, and Larry) are derived and verified by estimates from simultaneous dropsonde, ASCAT and ECMWF winds, showing a promising consistency. Furthermore, a more comprehensive statistical analysis is carried out with 14 SAR images, revealing that obtained wind directions have a correlation coefficient of 0.98, a bias of −6.07$^{\circ }$ and a RMSD of 20.24$^{\circ }$, superior to estimates from VV (0.97, −7.84$^{\circ }$, and 24.23$^{\circ }$, resp.) and VH signals (0.96, −10.46$^{\circ }$, and 29.53$^{\circ }$, resp.). The encouraging results prove the feasibility of the technique in SAR wind direction retrieval.

BackgroundMalnutrition is a rising global health issue associated with unfavorable outcomes of a variety of disorders. Currently, the prevalence and prognostic significance of malnutrition to patients with acute coronary syndrome (ACS) and chronic kidney disease (CKD) remained largely unclear.MethodsA total of 705 patients diagnosed with ACS and CKD in the First Affiliated Hospital of Wenzhou Medical University between 2013 and 2021 were included in this retrospective cohort study. Malnutrition was assessed by the Controlling Nutritional Status (CONUT), the Geriatric Nutritional Risk Index (GNRI), and the Prognostic Nutritional Index (PNI), respectively. The relationships between malnutrition and all-cause mortality and major cardiovascular events (MACEs) were analyzed.ResultsDuring a median follow-up of 31 months, 153 (21.7%) patients died, and 165 (23.4%) had MACEs. The prevalence of malnutrition was 29.8, 80.6, and 89.8% for the PNI, CONUT, and GNRI, respectively. All the malnutrition indexes were correlated with each other (r = 0.77 between GNRI and PNI, r = −0.72 between GNRI and CONUT, and r = −0.88 between PNI and CONUT, all p

Spaceborne microwave sensors, measuring co-/cross-polarization (VV/VH) normalized radar cross section signals, have been widely used for tropical cyclone (TC) monitoring. However, considerable gaps remain to obtain TC intensity since these satellite data either blur inner-core structures (e.g., scatterometer data) or have limited spatial–temporal coverage (e.g., synthetic aperture radar (SAR) data). This study aims to get more accurate TC intensity estimates from scatterometers, which have good global coverage but relatively low spatial resolution. To overcome the blurring effect in scatterometers, we propose a new technique for guidance on TC intensities, with maximum 1-min sustained winds calculated as a function of decay parameters provided by the parametric Rankine-type model. The technique is employed on advanced scatterometer (ASCAT) data acquired between 2016 and 2017, validated with simultaneous SAR VH geophysical model function measurements and best-track (BT) estimates. When validated with BT estimates, the method enhances the blurred maximum winds, where the standard deviation of difference decreased from 6.3 to 3.49 m/s and the coefficient of determination increased from 0.7 to 0.89. Besides, it is noteworthy that the proposed technique performs slightly better than the Mayers–Ruf method. The promising results indicate that the technique can provide more representative TC maximum 1-min sustained wind estimates from ASCAT data, thus contributing to the further exploitation of scatterometer data for TC warnings.

Wind speed reconciliation across different wind sources is critically needed for extending available satellite wind records in Tropical Cyclones. The deviations between wind references of extremes, such as the moored buoy data and dropsonde wind estimates for guidance on geophysical model function development, are one of the main causes of wind speed differences for wind products, for instance, the overestimation of Synthetic Aperture Radars (SARs) relative to ASCAT winds. The study proposes a new wind speed adjustment to achieve mutual adjustment between ASCAT CMOD7 winds and simultaneous SAR wind speeds. The so-called CMOD7D-v2 adjustment is constructed based on the statistical analysis of SAR and ASCAT Tropical Cyclone acquisitions between 2016 and 2021, showing a satisfactory performance in wind speed reconciliation for winds with speeds higher than 14 m/s. Furthermore, the error characteristics of the CMOD7D-v2 adjustment for Tropical Cyclone winds are analyzed using the Triple Collocation analysis technique. The analysis results show that the proposed wind adjustment can reduce ASCAT wind errors by around 16.0% when adjusting ASCAT winds to SAR wind speeds. In particular, when downscaling SAR winds, the improvement in ASCAT wind errors can be up to 42.3%, effectively alleviating wind speed differences across wind sources. Furthermore, to avoid the impacts of large footprints by ASCAT sensors, wind speeds retrieved from SAR VV signals (acting as a substitute for ASCAT winds) are adjusted accordingly and compared against SAR dual-polarized winds and collocated Stepped Frequency Microwave Radiometer (SFMR) observations. We find that the bias values of adjusted winds are lower than products from other adjustment schemes by around 5 m/s at the most extreme values. These promising results verify the plausibility of the CMOD7D-v2 adjustment, which is conducive to SAR and ASCAT wind speed comparisons and extreme wind analysis in Tropical Cyclone cases.

Background: There are few studies on the epidemiology of depression, insomnia, and their association with quality of life (QOL) in older adults living in rural China. This study examined the prevalence of depressive and insomnia symptoms, and their association with QOL in community-dwelling older adults in a rural area in Anhui province, China.Methods: This was a cross-sectional study conducted in the rural areas of four cities (Hefei, Huaibei, Anqing, and Xuancheng) in Anhui province between July and October, 2019 using random sampling method. All community-dwelling residents from the selected villages who met the study entry criteria were invited to participate in this study. Depressive and insomnia symptoms and QOL were assessed with the Chinese version of self-reported Center for Epidemiological Survey Depression Scale (CES-D), the Insomnia Severity Index (ISI) and the 26-item World Health Organization Quality of Life Brief version (WHOQOL-BREF), respectively.Results: A total of 871 older adults were included. The prevalence of overall depressive symptoms, insomnia symptoms, and comorbid depressive and insomnia symptoms were 34.0% [95% confidence intervals (95% CI): 30.8–37.1%], 45.7% (95% CI: 42.4–49.0%) and 20.3% (95% CI: 17.6–23.0%), respectively. Older adults with depressive symptoms, insomnia symptoms, and comorbid depressive and insomnia symptoms had lower scores in QOL compared to those without. Depressive symptoms were positively associated with living with families [Odd Ratio (OR) = 1.82, 95% CI: 1.31–2.54] and negatively associated with current drinking (OR = 0.49, 95% CI: 0.33–0.72). Insomnia symptoms were negatively associated with fair and good financial status (fair: OR = 0.53, 95% CI = 0.38–0.75; good: OR = 0.30, 95% CI = 0.14–0.64) and current drinking (OR = 0.64, 95% CI = 0.45–0.93), and positively associated with more frequent major medical conditions (OR = 1.32, 95% CI = 1.16–1.51). Comorbid depressive and insomnia symptoms were positively associated with living with families (OR = 2.02, 95% CI = 1.36–3.00), and negatively associated with fair and good financial status (fair: OR = 0.61, 95% CI = 0.41–0.89; good: OR = 0.34, 95% CI = 0.12–0.95) and current drinking (OR = 0.57, 95% CI = 0.35–0.92).Conclusion: Depressive and insomnia symptoms were common in older adults living in rural areas in China. Considering the negative health outcomes caused by depressive and insomnia symptoms, regular screening and effective treatments should be developed for this population.

The efficient preparation of electromagnetic wave absorbing materials with low density and excellent electromagnetic wave absorption remains a considerable challenge. In this study, reduced graphene oxide (RGO) wrapped Fe3O4 nanoparticles (NPs) were synthesized based on one-step reaction by the reduction of graphene oxide (GO), and the generation of super-fine Fe3O4 NPs was achieved. The phase structure, chemical composition, micromorphology, and magnetism were characterized by X-ray diffraction (XRD), X-ray photoelectron spectroscope (XPS), scanning electron microscope (SEM), transmission electron microscope (TEM), and vibrating sample magnetometer (VSM), respectively. The electromagnetic characteristics were evaluated on a vector network analyzer by the coaxial line method. The results showed that super-fine Fe3O4 NPs with an average size of 6.18 nm are densely distributed on the surface of graphenes. The RGO/Fe3O4 nanocomposites exhibited excellent microwave absorption properties with a minimum reflection loss (RL) of up to −55.71 dB at 6.78 GHz at 3.5 mm thickness and the highest effective absorption bandwidth with RL values exceeding −10 dB is 4.76 GHz between 13.24 and 18 GHz at 1.7 mm thickness. This work provides a concise method for the development of RGO supported super dense Fe3O4 nanocomposites for high performance electromagnetic absorption applications.