ST-YOLOA: a Swin-transformer-based YOLO model with an attention mechanism for SAR ship detection under complex background

A synthetic aperture radar (SAR) image is crucial for ship detection in computer vision. Due to the background clutter, pose variations, and scale changes, it is a challenge to construct a SAR ship detection model with low false-alarm rates and high accuracy. Therefore, this paper proposes a novel SAR ship detection model called ST-YOLOA. First, the Swin Transformer network architecture and coordinate attention (CA) model are embedded in the STCNet backbone network to enhance the feature extraction performance and capture global information. Second, we used the PANet path aggregation network with a residual structure to construct the feature pyramid to increase global feature extraction capability. Next, to cope with the local interference and semantic information loss problems, a novel up/down-sampling method is proposed. Finally, the decoupled detection head is used to achieve the predicted output of the target position and the boundary box to improve convergence speed and detection accuracy. To demonstrate the efficiency of the proposed method, we have constructed three SAR ship detection datasets: a norm test set (NTS), a complex test set (CTS), and a merged test set (MTS). The experimental results show that our ST-YOLOA achieved an accuracy of 97.37%, 75.69%, and 88.50% on the three datasets, respectively, superior to the effects of other state-of-the-art methods. Our ST-YOLOA performs favorably in complex scenarios, and the accuracy is 4.83% higher than YOLOX on the CTS. Moreover, ST-YOLOA achieves real-time detection with a speed of 21.4 FPS

PD-L1 aptamer-functionalized degradable hafnium oxide nanoparticles for near infrared-II diagnostic imaging and radiosensitization

Immune checkpoint blockade is now recognized as a paradigm-shifting cancer therapeutic strategy, whereas there remains difficulty in accurately predicting immunotherapy efficacy by PD-L1 expression. In addition, radiotherapy for cancer patients faces the problem of insufficient dose of radiotherapy at the tumor site while which have been not tolerated by normal tissues. In this study, we created PD-L1 aptamer-anchored spherical nucleic acids (SNAs) with a shell made of PD-L1 aptamer and indocyanine green (ICG) embedded in a mesoporous hafnium oxide nanoparticle core (Hf@ICG-Apt). Upon low pH irradiation in the tumor sites, the nano-system enabled the release of ICG in the high PD-L1 expression tumor to develop a high tumor-to-background ratio of 7.97 ± 0.76 and enhanced the ICG tumor retention to more than 48 h. Moreover, Hf@ICG-Apt improved radiation therapy (RT) when combined with radiation. Notably, Hf@ICG-Apt showed scarcely any systemic toxicity in vivo. Overall, this research offered a novel approach for applying reliable monitoring of PD-L1 expression and localization and robust RT sensitization against cancer with good biosafety