Background: The Oxidative Balance Score (OBS), which quantifies the balance between antioxidants and pro-oxidants influenced by diet and lifestyle, is crucial given oxidative stress’s significant role in Chronic Kidney Disease (CKD). This study aims to determine the association between OBS and CKD using data from the National Health and Nutrition Examination Survey (NHANES) 1999-2018.Methods: We analyzed data from the National Health and Nutrition Examination Survey (NHANES) spanning 1999 to 2018. OBS was constructed from a detailed array of 20 factors, including dietary nutrients and lifestyle behaviors. The relationship between OBS and CKD risk was evaluated using weighted logistic regression models, adjusted for potential confounders, with a generalized additive model (GAM) examining non-linear associations. Subgroup analyses and interaction effects across diverse demographic and clinical groups, along with sensitivity analyses, were performed to validate the findings.Results: Among 32,120 participants analyzed, 4,786 were identified with CKD. Fully adjusted weighted logistic regression analysis revealed that each unit increase in OBS was associated with a 2% reduction in CKD prevalence [OR: 0.98 (0.98–0.99), P < 0.001]. Higher OBS quartiles were significantly correlated with a decreased CKD risk [Q4 vs. Q1: OR: 0.82 (0.68–0.98), P = 0.03; P for trend = 0.01]. The GAM and smoothed curve fit indicated a linear relationship between OBS and the risk of CKD. Stratified and sensitivity analyses further substantiated the inverse relationship between OBS and CKD prevalence.Conclusions: Our findings from the NHANES data affirm a significant inverse association between OBS and CKD risk in the U.S. population, underscoring the role of optimizing dietary and lifestyle factors in managing CKD risk. These results advocate for incorporating OBS considerations into CKD prevention and treatment strategies.

Most state-of-the-art object detection methods suffer from poor generalization due to the domain shift between training and testing datasets. To resolve this challenge, unsupervised cross domain object detection is proposed to learn an object detector for an unlabeled target domain by transferring knowledge from an annotated source domain. Promising results have been achieved via Mean Teacher, however, pseudo labeling which is the bottleneck of mutual learning remains to be further explored. In this study, we find that confidence misalignment of the predictions, including category-level overconfidence, instance-level task confidence inconsistency, and image-level confidence misfocusing, leading to the injection of noisy pseudo labels in the training process, will bring suboptimal performance. Considering the above issue, we present a novel general framework termed Multi-Granularity Confidence Alignment Mean Teacher (MGCAMT) for unsupervised cross domain object detection, which alleviates confidence misalignment across category-, instance-, and image-levels simultaneously to refine pseudo labeling for better teacher-student learning. Specifically, to align confidence with accuracy at category level, we propose Classification Confidence Alignment (CCA) to model category uncertainty based on Evidential Deep Learning (EDL) and filter out the category incorrect labels via an uncertainty-aware selection strategy. Furthermore, we design Task Confidence Alignment (TCA) to mitigate the instance-level misalignment between classification and localization by enabling each classification feature to adaptively identify the optimal feature for regression. Finally, we develop imagery Focusing Confidence Alignment (FCA) adopting another way of pseudo label learning, i.e., we use the original outputs from the Mean Teacher network for supervised learning without label assignment to achieve a balanced perception of the image’s spatial layout. When these three procedures are integrated into a single framework, they mutually benefit to improve the final performance from a cooperative learning perspective. Extensive experiments across multiple scenarios demonstrate that our method outperforms large foundational models, and surpasses other state-of-the-art approaches by a large margin.

Remote sensing target detection faces significant challenges in handling multi-scale targets, with the high similarity in color and shape between targets and backgrounds in complex scenes further complicating the detection task. To address this challenge, we propose a multi-Scale and complex Background Aware network for remote sensing target detection, named YOLO-SBA. Our proposed YOLO-SBA first processes the input through the Multi-Branch Attention Feature Fusion Module (MBAFF) to extract global contextual dependencies and local detail features. It then integrates these features using the Bilateral Attention Feature Mixer (BAFM) for efficient fusion, enhancing the saliency of multi-scale target features to tackle target scale variations. Next, we utilize the Gated Multi-scale Attention Pyramid (GMAP) to perform channel–spatial dual reconstruction and gating fusion encoding on multi-scale feature maps. This enhances target features while finely suppressing spectral redundancy. Additionally, to prevent the loss of effective information extracted by key modules during inference, we improve the downsampling method using Asymmetric Dynamic Downsampling (ADDown), maximizing the retention of image detail information. We achieve the best performance on the DIOR, DOTA, and RSOD datasets. On the DIOR dataset, YOLO-SBA improves mAP by 16.6% and single-category detection AP by 0.8–23.8% compared to the existing state-of-the-art algorithm.

In open and dynamic environments, object detection is affected by rain, fog, snow, and complex lighting conditions, leading to decreased accuracy and posing a threat to driving safety. Infrared images can provide clear images at nighttime or in adverse weather conditions. Combined with the mature development of existing cross-modality object detection technologies, both of them offer support for addressing object detection issues in adverse weather scenarios. This paper establishes a novel dataset named Adverse Weather and Illumination Dataset (AWID) to simulate intricate real-world scenarios and proposes a cross-modal object detection algorithm for adverse weather scenarios in autonomous driving, named CME-YOLO, which is based on RGB and infrared images. It integrates the Cross-Perception Transformer Fusion algorithm, CPTFusion, and the Adaptive upsampling technique, AdSample, to enhance the extraction of detailed information and supplement effective information. CPTFusion fuses features from different modalities through multi-scale feature extraction and optimal fusion strategy computation. AdSample adaptively improves the utilization of key features and the quality of the resulting feature tensor. Experiments on two public datasets and AWID show that CME-YOLO performs optimally, with an mAP50 value on the FLIR dataset 6.8% higher than the state-of-the-art MPFT algorithm, verifying its excellent performance in autonomous driving object detection tasks.

Cross-Domain Few-Shot Semantic Segmentation (CD-FSS) aims to achieve pixel-level segmentation of novel categories across various domains by transferring knowledge from the source domain leveraging limited samples. The main challenge in CD-FSS is bridging the inter-domain gap and addressing the scarcity of labeled samples in the target domain to enhance both generalization and discriminative abilities. Current methods usually resort to additional networks and complex strategy to embrace domain variability, which inevitably increases the training costs. This paper proposes a Dual-Branch Fusion with Style Modulation (DFSM) method to tackle this issues. We specifically deploy a parameter-free Grouped Style Modulation (GSM) layer that captures and adjusts a wide spectrum of potential feature distribution changes, thus improving the model's domain transferability. Additionally, to overcome data limitations and enhance adaptability in the target domain, we develope a Dual-Branch Fusion (DBF) strategy which achieves accurate pixel-level prediction results by combining predicted probability maps through weighted fusion, thereby enhancing the discriminative ability of the model. We evaluate the proposed method on multiple widely-used benchmark datasets, including FSS-1000, ISIC, Chest X-Ray, and Deepglobe, and demonstrate superior performance compared to state-of-the-art methods in CD-FSS tasks.

Despite the evident superiority of event cameras in practical vision applications (e.g., action recognition), owing to their distinctive sensing mechanism, existing event-based action recognition methods rely heavily on large-scale training data. However, the expensive cost of camera deployment and the requirement of data privacy protection make it challenging to collect substantial data in real-world scenarios. To address this limitation, we explore a novel yet practical task, Few-Shot Event-Based Action Recognition (FSEAR), which aims at leveraging a minimal number of intractable event action data for model training and accurately classifying unlabeled data into a specific category. Accordingly, we design a new framework for FSEAR, including a Noise-Aware Event Encoder (NAE) and a Distilled Prototypical Distance Fusion (DPDF). The former efficiently filters noise within the spatiotemporal domain while retaining vital information related to action timing. The latter conducts multi-scale measurements across geometric, directional, and distributional dimensions. These two modules benefit mutually and thus effectively exploit the potential characteristics of event data. Extensive experiments on four distinct event action recognition datasets have demonstrated the significant advantages of our model over other few-shot learning methods. Our code and models will be publicly released.