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ObjectivesTo understand the sex difference in age at adiposity rebound (AR), integrate the prevalence of early AR (EAR), and provide a quantitative association between early age at AR and overweight/obesity.MethodsLiterature review was conducted in different databases, including the Web of Science, PubMed, EMBASE, Wiley, Chinese National Knowledge Infrastructure, and ScienceDirect databases up to August 2021. Studies that reported data related to AR were considered for inclusion. Pooled effect sizes and their respective 95% confidence intervals (CIs) were calculated using random effects models, depending on the size of heterogeneity. Heterogeneity was tested by using the I2 statistics.Results28 studies with a combined sample size of 106,397 people were included in the final meta-analysis. Girls had a significantly earlier age of AR than boys (mean difference = 3.38 months; 95% CI 2.14–4.63). The overall prevalence of EAR was 40% (95% CI 31% to 50%), and the prevalence in girls was 5% higher than that in boys based on the definition of age at AR < 5.0–5.1 years. The overall pooled prevalence of EAR showed an increasing trend by child’s birth year [1934–1973]: 29% (95% CI 22% to 37%), 1991–2001: 35% (95% CI 26% to 44%), and 2002–2009: 52% (95% CI 40–63%). Early age at AR (age at AR < 5.0–5.1 years) was associated with a significantly increased risk of overweight/obesity (OR = 5.07; 95% CI 3.60–7.12), overweight (OR = 3.10; 95% CI 1.69–5.70), and obesity (OR = 6.97; 95% CI 4.32–11.26) from the preschool period to adulthood.ConclusionsThe overall prevalence of EAR is increasing, and girls experience AR earlier than boys. The early age at AR in children may be an early and effective marker of obesity.

Remote sensing technology has become an indispensable core means for Earth observation. As two of the most commonly used remote sensing modalities, the fusion of optical and synthetic aperture radar (SAR) (OPT-SAR fusion) can effectively overcome the limitations of a single data source, achieve information complementarity and synergistic enhancement, thereby significantly improving the interpretation capability of multi-source remote sensing data. This paper first discusses the necessity of OPT-SAR fusion, systematically reviews the historical development of fusion technologies, and summarizes open-source resources for various tasks, aiming to provide a reference for related research. Finally, building upon recent advances in OPT-SAR fusion research and cutting-edge developments in deep learning, this paper proposes that future fusion technologies should develop in the following directions: interpretable fusion models driven by both data and knowledge, general fusion perception driven by multimodal large models, and lightweight architectures with efficient deployment strategies.

Vascular covered stents play a significant therapeutic role in cardiovascular diseases. However, the poor compliance and biological inertness of commercial materials cause post-implantation complications. Silk fibroin (SF), as a biomaterial, possesses satisfactory hemocompatibility and tissue compatibility. In this study, we developed a silk film for use in covered stents by employing a layer-by-layer self-assembly strategy with regenerated SF on silk braiding fabric. We investigated the effects on the mechanical properties of the silk films in detail, which were closely correlated with fabric parameters and layer-by-layer self-assembly. The results showed that there was a significant relationship between these factors and both the compliance and mechanical strength. The 1 × 2/90°/100/SF 6 film exhibited excellent mechanical properties. Notably, compliance reached 2.6%/100 mmHg, matching that of the human saphenous vein. Thus, this strategy shows promise in developing a novel covered stent, with biocompatible and comprehensive mechanical properties, and significant potential for clinical applications.

[This corrects the article DOI: 10.1371/journal.pone.0242523.].

Artificial neural networks (ANN) which include deep learning neural networks (DNN) have problems such as the local minimal problem of Back propagation neural network (BPNN), the unstable problem of Radial basis function neural network (RBFNN) and the limited maximum precision problem of Convolutional neural network (CNN). Performance (training speed, precision, etc.) of BPNN, RBFNN and CNN are expected to be improved. Main works are as follows: Firstly, based on existing BPNN and RBFNN, Wavelet neural network (WNN) is implemented in order to get better performance for further improving CNN. WNN adopts the network structure of BPNN in order to get faster training speed. WNN adopts the wavelet function as an activation function, whose form is similar to the radial basis function of RBFNN, in order to solve the local minimum problem. Secondly, WNN-based Convolutional wavelet neural network (CWNN) method is proposed, in which the fully connected layers (FCL) of CNN is replaced by WNN. Thirdly, comparative simulations based on MNIST and CIFAR-10 datasets among the discussed methods of BPNN, RBFNN, CNN and CWNN are implemented and analyzed. Fourthly, the wavelet-based Convolutional Neural Network (WCNN) is proposed, where the wavelet transformation is adopted as the activation function in Convolutional Pool Neural Network (CPNN) of CNN. Fifthly, simulations based on CWNN are implemented and analyzed on the MNIST dataset. Effects are as follows: Firstly, WNN can solve the problems of BPNN and RBFNN and have better performance. Secondly, the proposed CWNN can reduce the mean square error and the error rate of CNN, which means CWNN has better maximum precision than CNN. Thirdly, the proposed WCNN can reduce the mean square error and the error rate of CWNN, which means WCNN has better maximum precision than CWNN.

Sulfonamides are widely used to treat infectious diseases during pregnancy. However, the safety of maternal exposure to sulfonamides is controversial. This study aims to systematically review the available studies and examine the effect of maternal sulfonamides use on adverse pregnancy outcomes. We searched PubMed, Science Direct, Web of Science, ClinicalTrials.gov, CNKI and Wanfang Database (in Chinese). The meta-analysis used random effects model or fixed effects model to obtain the total odds ratio (OR) for each outcome through Stata11.0 software. Study on the relationship between sulfonamide exposure during pregnancy and adverse pregnancy outcomes. The study design covered randomized controlled trials, cohort studies and case-control studies. The study protocol was registered in PROSPERO with protocol number CRD42020178687. A total of 10 studies, and 1096350 participants were included for systematic review. Maternal exposure to sulfonamides was found to be possibly associated with increased risk of congenital malformations (OR = 1.21, 95% CI 1.07-1.37). The use of sulfonamides in the first trimester of pregnancy and during the entire pregnancy might be associated with congenital malformations. Maternal exposure to sulfonamides may be associated with offspring' s congenital malformations. Prescription of sulfonamides for pregnant women is suggested to be carefully censored.

This study analyzes the transient axial force of a centrifugal pump under variable operating conditions using wavelet analysis and a novel technique called windowed multi-resolution dynamic mode decomposition (wmrDMD). Numerically simulating the sampled time series allows the reconstruction of the impeller’s axial force information, providing validation for this innovative data-driven analysis technique. The comparison between the reconstructed results and the original axial force data demonstrates a remarkable agreement, as all data points exhibit error values below 2.49%. The wmrDMD technique systematically decomposes the impeller’s axial force field into dynamically significant modes across various time scales. Removing the mean flow field in this study resolves the transient motion of the impeller’s axial force, facilitating the identification of positions with high-frequency axial force oscillations and fluctuations in intensity amplitude. The high-frequency axial force of the impeller exhibits stable periodic variations within the operating range of 1.0nr-1.0Qr, whereas the changes are insignificant within the range of 0.4nr-0.4Qr. However, within the operating range of 1.0nr-0.4Qr, both the position and intensity amplitude of the axial force exhibit significant variations without a stable trend. Furthermore, cross-wavelet and wavelet coherence analyses reveal that within the operating range of 0.4nr-0.4Qr, the axial forces on the front and rear cover plates show the strongest correlation at the periodic scale. Within the operating range of 1.0nr-1.0Qr, the next highest correlation is observed, whereas the correlation is lowest within the 1.0nr-0.4Qr operating range.

This study looks into the parameters that affect the heat transfer coefficient (h2) on the wet surfaces of vertical tube indirect evaporative coolers (VTIEC). An experimental platform was used to investigate the impact of secondary-to-primary airflow ratios (AFR) and spray water density on the HTC. The findings show that raising the primary air temperature drop, expanding the outside dry-bulb and wet-bulb temperature differences, and decreasing the air-to-water ratio improve heat transmission. The HTC of the wet sides ranged from 34.79 to 924.5 W/(m2·°C) throughout testing. To achieve optimal performance, aim for a spray water density of 2.07 to 3.46 m3/(m2·h), an AFR of 0.5 to 0.6, and a primary air temperature drop of at least 6 °C. These factors help keep the h2 above 350 W/(m2·°C).

Hospitalised, unwell patients have vital signs such as heart rate (HR), oxygen saturation (SpO2) and temperature measured multiple times a day to detect clinical deterioration and monitor health trajectories. Advancements in contact-free (ambient) sensors (AS) to measure vital signs can help mitigate risks due to skin injury and infection transmission seen in conventional hospital equipment. This prospective, observational clinical study aims to validate vital sign measurements from a multi-channel AS compared to conventional equipment in three cohorts: patients in a hospital ward, patients at home within a Hospital-at-Home service, and healthy volunteers. Data analysis of 571 paired measurements from 29 participants indicates that heart rate measurements via AS were accurate, though they lacked precision, with the clinical agreement range between 6.38 and 6.49 beats per minute. Temperature and oxygen saturation measurements showed less agreement when compared with the reference standard. In conclusion, ambient sensors show promising utility in measuring vital signs, with this study amongst the first of its kind to utilise this in measuring vital signs in hospitalised patient cohorts in both ward and home environments.

What are the main findings? * We construct a large-scale, high-resolution optical–SAR registration dataset, pairing 3-m SAR imagery from the HongTu-1 satellite with Google Earth optical imagery at zoom level 17, covering the major geographical regions of China, and we release the standardized pipeline—including full-scene pairing, DEM-based terrain correction, geometric refinement, standardized 512 × 512 slicing and multi-stage quality filtering—that was used to build it. * Our proposed Log-domain reformulation of the Total Variation (Log-TV) filter substantially improves SAR image preprocessing by converting the multiplicative speckle noise model into an additive one, thereby enabling effective suppression of speckle while preserving edge structures and providing a much cleaner foundation for subsequent keypoint detection. * Combining a machine learning-based edge filter (Structured Random Forest, SRF) with the hand-crafted phase congruency filter yields a strong synergistic effect for cross-modal optical–SAR edge filtering, producing more stable and consistent shared structural responses than either component alone. We construct a large-scale, high-resolution optical–SAR registration dataset, pairing 3-m SAR imagery from the HongTu-1 satellite with Google Earth optical imagery at zoom level 17, covering the major geographical regions of China, and we release the standardized pipeline—including full-scene pairing, DEM-based terrain correction, geometric refinement, standardized 512 × 512 slicing and multi-stage quality filtering—that was used to build it. Our proposed Log-domain reformulation of the Total Variation (Log-TV) filter substantially improves SAR image preprocessing by converting the multiplicative speckle noise model into an additive one, thereby enabling effective suppression of speckle while preserving edge structures and providing a much cleaner foundation for subsequent keypoint detection. Combining a machine learning-based edge filter (Structured Random Forest, SRF) with the hand-crafted phase congruency filter yields a strong synergistic effect for cross-modal optical–SAR edge filtering, producing more stable and consistent shared structural responses than either component alone. What are the implications of the main findings? * Large-scale, high-resolution optical–SAR datasets are both essential and scarce for registration and other downstream tasks. Only on larger and more complex benchmarks do the robustness and the true relative performance of competing algorithms become evident, making such datasets a necessary foundation for future research in this area. * Different imaging modalities require different filtering strategies: for heavily speckled data such as SAR imagery, regularisation in the logarithmic domain is more appropriate than directly applying denoisers designed for additive noise, highlighting the importance of modality-aware preprocessing in cross-modal registration. * Hybrid pipelines that integrate learning-based components with hand-crafted filters are a promising direction: beyond edge filtering, similar combinations of deep features and classical hand-crafted operators may also benefit cross-modal feature description and matching stages. Large-scale, high-resolution optical–SAR datasets are both essential and scarce for registration and other downstream tasks. Only on larger and more complex benchmarks do the robustness and the true relative performance of competing algorithms become evident, making such datasets a necessary foundation for future research in this area. Different imaging modalities require different filtering strategies: for heavily speckled data such as SAR imagery, regularisation in the logarithmic domain is more appropriate than directly applying denoisers designed for additive noise, highlighting the importance of modality-aware preprocessing in cross-modal registration. Hybrid pipelines that integrate learning-based components with hand-crafted filters are a promising direction: beyond edge filtering, similar combinations of deep features and classical hand-crafted operators may also benefit cross-modal feature description and matching stages. Optical and Synthetic Aperture Radar (SAR) image registration is a fundamental task in remote sensing information fusion, yet it remains challenging due to significant differences in imaging mechanisms, radiation characteristics, and noise properties between the two modalities. Existing public datasets suffer from limited resolution, small scale, and insufficient scene diversity, and these limitations have hindered algorithm development. This paper constructs a large-scale, high-resolution optical–SAR registration dataset based on the HongTu-1 satellite 3-m SAR imagery and Google Earth optical imagery at zoom level 17, covering diverse scenes across China with a standardized pipeline including terrain correction, geometric alignment, standardized slicing, and quality filtering. Building upon this dataset, a hand-crafted keypoint-based cross-modal registration method is proposed, incorporating multi-level edge filtering and hybrid feature detection. Unlike conventional hand-crafted methods such as RIFT, SRIF, and LNIFT, which mainly refine keypoint detection, description, or matching within a SIFT-style pipeline, the core novelty of this work lies in SAR-specific preprocessing and multi-level hybrid filtering. These components are designed to suppress speckle while extracting more stable and discriminative shared edge responses for cross-modal registration. An improved Log-domain Total Variation (Log-TV) denoising model is introduced for SAR preprocessing. A hybrid edge filtering framework combining phase congruency analysis and Structured Random Forest (SRF) edge detection is constructed within a Gaussian scale space. A dual-branch feature detection scheme integrating blob and corner features is designed with a robust orientation assignment strategy. Feature description uses the Gradient Location–Orientation Histogram (GLOH) descriptor with Principal Component Analysis (PCA) reduction, while geometric estimation employs the Fast Sample Consensus (FSC) algorithm. Experiments on the self-constructed HT dataset and on the public OSdataset and SAR2Opt benchmarks show that the proposed method consistently achieves low RMSE and high success rates. It also maintains competitive efficiency among hand-crafted methods while retaining strong robustness to scale and rotation variations.