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Compared with traditional mine detection methods, UAV-based measures are more suitable for the rapid detection of large areas of scatterable landmines, and a multispectral fusion strategy based on a deep learning model is proposed to facilitate mine detection. Using the UAV-borne multispectral cruise platform, we establish a multispectral dataset of scatterable mines, with mine-spreading areas of the ground vegetation considered. In order to achieve the robust detection of occluded landmines, first, we employ an active learning strategy to refine the labeling of the multispectral dataset. Then, we propose an image fusion architecture driven by detection, in which we use YOLOv5 for the detection part, to improve the detection performance instructively while enhancing the quality of the fused image. Specifically, a simple and lightweight fusion network is designed to sufficiently aggregate texture details and semantic information of the source images and obtain a higher fusion speed. Moreover, we leverage detection loss as well as a joint-training algorithm to allow the semantic information to dynamically flow back into the fusion network. Extensive qualitative and quantitative experiments demonstrate that the detection-driven fusion (DDF) that we propose can effectively increase the recall rate, especially for occluded landmines, and verify the feasibility of multispectral data through reasonable processing.

Image registration is the basis for the joint interpretation of synthetic aperture radar (SAR) and optical images. However, the significant nonlinear radiation difference (NRD) and the geometric imaging model difference render the registration quite challenging. To solve this problem, both traditional and deep learning methods are used to extract structural information with dense descriptions of the images, but they ignore that structural information of the image pair is coupled and often process images separately. In this paper, a deep learning-based registration method with a co-attention matching module (CAMM) for SAR and optical images is proposed, which integrates structural feature maps of the image pair to extract keypoints of a single image. First, joint feature detection and description are carried out densely in both images, for which the features are robust to radiation and geometric variation. Then, a CAMM is used to integrate both images’ structural features and generate the final keypoint feature maps so that the extracted keypoints are more distinctive and repeatable, which is beneficial to global registration. Finally, considering the difference in the imaging mechanism between SAR and optical images, this paper proposes a new sampling strategy that selects positive samples from the ground-truth position’s neighborhood and augments negative samples by randomly sampling distractors in the corresponding image, which makes positive samples more accurate and negative samples more abundant. The experimental results show that the proposed method can significantly improve the accuracy of SAR–optical image registration. Compared to the existing conventional and deep learning methods, the proposed method yields a detector with better repeatability and a descriptor with stronger modality-invariant feature representation.

Ground Penetrating Radar (GPR) has become a widely used technology in geophysical prospecting. The Variational Mode Decomposition (VMD) method is a fully non-recursive signal decomposition method with noise robustness for GPR data processing. The VMD algorithm determines the central frequency and bandwidth of each Intrinsic Mode Function (IMF) by iteratively searching for the optimal solution of the variational mode and is capable of adaptively and effectively dividing the signal in the frequency domain into the many IMFs. However, the penalty parameter α and the number of IMFs K in VMD processing are determined depending on manual experience, which are important parameters affecting the decomposition results. In this paper, we propose a method to automatically search the parameters α and K optimally by Particle Swarm Optimization (PSO) algorithm. Then the signal-to-noise ratio (SNR) and root-mean-square error (RMSE) are used to judge the best superposition of the IMFs for data reconstruction, and the process is data-driven without human subjective intervention. The proposed method is used to process the field data, and the reconstruction data show that this innovative VMD processing can effectively improve the SNR and highlight the target reflections, even some targets not found in pre-processing are also revealed.

BackgroundAsthma represents a significant global health challenge, exhibiting considerable variation in prevalence, incidence, mortality and disability-adjusted life years (DALYs) across regions and countries. This study evaluates global, regional and national trends in asthma burden from 1990 to 2021, analysing associations with temporal, geographical and demographical factors.MethodsUsing open data from the Global Burden of Disease (GBD) database (1990–2021), we analysed changes in asthma prevalence, incidence, mortality and DALYs by gender, age and Socio-Demographic Index (SDI) groups. Joinpoint regression analysis calculated the average annual percentage change (AAPC) and annual percentage change (APC).ResultsFrom 1990 to 2021, the age-standardised prevalence and incidence rates of asthma declined by 40.01% and 29.89%, respectively. While asthma deaths increased slightly, the age-standardised mortality rate (ASMR) declined by 46.01%. The highest prevalence was observed in South Asia, East Asia and high-income North America, while low-SDI regions exhibited elevated mortality and DALYs. The age and sex-specific patterns indicated a higher asthma burden among females. The results of the joinpoint analysis indicated a global age-standardised incidence rate increase between 2005 and 2010 for both males and females. The ASMR exhibited a statistically significant decline from 1990 to 2021.ConclusionsThe global age-standardised rate of asthma burden declined from 1990 to 2021. However, asthma remains a significant public health issue, particularly in regions with lower socioeconomic development. Understanding global and regional trends in asthma can inform future policies and interventions, aiming to promote more equitable prevention, diagnosis and treatment worldwide.

Geosynchronous (GEO) spaceborne–airborne very high-frequency ultra-wideband bistatic synthetic aperture radar (VHF UWB BiSAR) can conduct high-resolution and wide-swath imaging for ocean scenes. However, GEO spaceborne–airborne VHF UWB BiSAR imaging faces some challenges such as the geometric configuration, huge amount of echo data, serious range–azimuth coupling, large spatial variance, and complex motion error, which increases the difficulty of the high-efficiency and high-precision imaging. In this paper, we present an improved bistatic fast factorization backprojection (FFBP) algorithm for ocean scene imaging using the GEO satellite-unmanned aerial vehicle (GEO-UAV) VHF UWB BiSAR, which can solve the above issues with high efficiency and high precision. This method reconstructs the subimages in the orthogonal elliptical polar (OEP) coordinate system based on the GEO satellite and UAV trajectories as well as the location of the imaged scene, which can further reduce the computational burden. First, the imaging geometry and signal model of the GEO-UAV VHF UWB BiSAR are established, and the construction of the OEP coordinate system and the subaperture imaging method are proposed. Moreover, the Nyquist sampling requirements for the subimages in the OEP coordinate system are derived from the range error perspective, which can offer a near-optimum tradeoff between precision and efficiency. In addition, the superiority of the OEP coordinate system is analyzed, which demonstrates that the angular dimensional sampling rate of the subimages is significantly reduced. Finally, the implementation processes and computational burden of the proposed algorithm are provided, and the speed-up factor of the proposed FFBP algorithm compared with the BP algorithm is derived and discussed. Experimental results of ideal point targets and natural ocean scenes demonstrate the correctness and effectiveness of the proposed algorithm, which can achieve near-optimal imaging performance with a low computational burden.

The cGAS-STING signaling pathway is a crucial component of the innate immune system that detects aberrant cytosolic DNA, such as that derived from viruses or damaged cells, to activate downstream immune responses. Within this pathway, cyclic guanosine monophosphate–adenosine monophosphate (cGAMP) serves as the essential second messenger linking DNA sensing to immune activation. Upon recognition of cytosolic DNA, cGAS synthesizes cGAMP, whose unique \"mixed linkage\" structure enables efficient binding to and activation of the STING protein on the endoplasmic reticulum, thereby inducing type I interferons and inflammatory cytokines. This review details cGAMP’s biosynthesis, structural characteristics, and transport mechanisms, including efflux via ABCC1 and uptake by SLC19A1, underscoring its role as an intercellular \"immune messenger.\" It also explores the dual functions of cGAMP in antiviral and antitumor immunity as well as in autoimmune and aging-related diseases, where it can either enhance immune defense or promote chronic inflammation. Therapeutically, cGAMP has been investigated as a vaccine adjuvant, a target for synthesis or degradation enzymes, and in nanoparticle-based delivery systems. However, challenges regarding its stability, delivery efficiency, and immunotoxicity remain, and future research should focus on real-time monitoring and tissue-specific modulation to advance cGAMP-based precision immunotherapeutics.

For the full waveform inversion, it is necessary to provide an accurate source wavelet for forwarding modeling in the iteration. The source wavelet estimation method based on deconvolution technology can solve this problem to some extent, but we find that the estimated source wavelet is not accurate and needs to be manually corrected repeatedly in the iteration. This process is highly operator-intensive, and the update process is time-consuming and increases the potential for errors. We propose a source-independent waveform inversion (SIEWI) scheme for cross-hole GPR data, and use the envelope objective function combined with this method to effectively reduce the nonlinearity of inversion. The residual field used by SIEWI to construct the gradient inherits the characteristics of the envelope wavefield. Compared with full waveform inversion (FWI), SIEWI is more robust and less sensitive to frequency components and inaccurate source wavelet. To avoid cycle jumping, the multi-scale strategy effectively utilizes the properties of convolutional wavefields. In one iteration, the wavefield is decomposed into multiple frequency bands through multiple convolutions in the time domain to construct a multi-scale inversion strategy that preferentially inverts low-frequency information.

Background There are few studies on the changes in lung function after pneumonia in children. This study aims to explore the changes in lung function in children after pneumonia and analyze the risk factors for airway disorder, especially the impact of different pathogen infection on lung function. Methods This study collected data from patients who were hospitalized due to pneumonia in ten Chinese hospitals between January 2023 and December 2024. Pulmonary function tests were performed to assess changes in lung function one week and one month after discharge. Results A total of 566 children were included in this study, with 40.6% of patients still showing airway disorder one week after discharge. Different pathogenic infections had varying effects on pulmonary function. MP (Mycoplasma pneumoniae) infection [OR (95%CI): 1.881(1.268–2.789), P  = 0.001] and RhV (rhinovirus) infection [OR (95%CI): 2.402(1.027–5.621), P  = 0.043] were significant risk factors for the occurrence of SAD (Small Airway Disorder) one week after discharge. Male gender [OR (95%CI): 2.219, P  = 0.001] and MP infection [OR (95%CI): 1.681(1.024–2.761), P  = 0.039] were significant risk factors for the occurrence of LAD (Large Airway Disorder) one week after discharge. No positive pathogen results [OR (95%CI): 0.366(0.168–0.800), P  = 0.011] were significant protective factors for the persistence of SAD one month after discharge, while RhV infection [OR (95%CI): 7.286(0.802, 66.238), P  = 0.077] and lung consolidation [OR (95%CI): 1.753(0.956, 3.214), P  = 0.069] showed mild significance for the persistence of SAD one month after discharge. Male gender [OR (95%CI): 2.246(1.137–4.436), P  = 0.019] and RhV infection [OR (95%CI): 1.967(1.630–237.549), P  = 0.019] were significant risk factors for the persistence of LAD one month after discharge, while no positive pathogen results [OR (95%CI): 0.249(0.092–0.678), P  = 0.006] were a significant protective factor. Conclusions Approximately 40.6% of children after pneumonia still had airway disorder one week after discharge, which was closely related to different pathogenic infections. Patients with RhV pneumonia, in particular, should be closely monitored for changes in lung function after discharge.

Pediatric fever is a prevalent concern, often causing parental anxiety and frequent medical consultations. While large language models (LLMs) such as ChatGPT, Perplexity, and YouChat show promise in enhancing medical communication and education, their efficacy in addressing complex pediatric fever-related questions remains underexplored, particularly from the perspectives of medical professionals and patients' relatives. This study aimed to explore the differences and similarities among four common large language models (ChatGPT3.5, ChatGPT4.0, YouChat, and Perplexity) in answering thirty pediatric fever-related questions and to examine how doctors and pediatric patients' relatives evaluate the LLM-generated answers based on predefined criteria. The study selected thirty fever-related pediatric questions answered by the four models. Twenty doctors rated these responses across four dimensions. To conduct the survey among pediatric patients' relatives, we eliminated certain responses that we deemed to pose safety risks or be misleading. Based on the doctors' questionnaire, the thirty questions were divided into six groups, each evaluated by twenty pediatric relatives. The Tukey test was used to check for significant differences. Some of pediatric relatives was revisited for deeper insights into the results. In the doctors' questionnaire, ChatGPT3.5 and ChatGPT4.0 outperformed YouChat and Perplexity in all dimensions, with no significant difference between ChatGPT3.5 and ChatGPT4.0 or between YouChat and Perplexity. All models scored significantly better in accuracy than other dimensions. In the pediatric relatives' questionnaire, no significant differences were found among the models, with revisits revealing some reasons for these results. Internet searches (YouChat and Perplexity) did not improve the ability of large language models to answer medical questions as expected. Patients lacked the ability to understand and analyze model responses due to a lack of professional knowledge and a lack of central points in model answers. When developing large language models for patient use, it's important to highlight the central points of the answers and ensure they are easily understandable.

Background Noninvasive magnetic resonance thermometry (MRT) at low-field using proton resonance frequency shift (PRFS) is a promising technique for monitoring ablation temperature, since low-field MR scanners with open-configuration are more suitable for interventional procedures than closed systems. In this study, phase-drift correction PRFS with first-order polynomial fitting method was proposed to investigate the feasibility and accuracy of quantitative MR thermography during hyperthermia procedures in a 0.35 T open MR scanner. Methods Unheated phantom and ex vivo porcine liver experiments were performed to evaluate the optimal polynomial order for phase-drift correction PRFS. The temperature estimation approach was tested in brain temperature experiments of three healthy volunteers at room temperature, and in ex vivo porcine liver microwave ablation experiments. The output power of the microwave generator was set at 40 W for 330 s. In the unheated experiments, the temperature root mean square error (RMSE) in the inner region of interest was calculated to assess the best-fitting order for polynomial fit. For ablation experiments, relative temperature difference profile measured by the phase-drift correction PRFS was compared with the temperature changes recorded by fiber optic temperature probe around the microwave ablation antenna within the target thermal region. Results The phase-drift correction PRFS using first-order polynomial fitting could achieve the smallest temperature RMSE in unheated phantom, ex vivo porcine liver and in vivo human brain experiments. In the ex vivo porcine liver microwave ablation procedure, the temperature error between MRT and fiber optic probe of all but six temperature points were less than 2 °C. Overall, the RMSE of all temperature points was 1.49 °C. Conclusions Both in vivo and ex vivo experiments showed that MR thermometry based on the phase-drift correction PRFS with first-order polynomial fitting could be applied to monitor temperature changes during microwave ablation in a low-field open-configuration whole-body MR scanner.