Atherosclerosis is a major cause of cardiovascular disease. Photothermal ablation provides a minimally invasive therapeutic approach but remains constrained by the lack of reliable temperature monitoring. Conventional thermometry provides only single-point readings and is easily affected by contact and flow, limiting spatial accuracy. Although array-based photoacoustic thermometry allows non-contact detection, it remains bulky and unsuitable for catheter integration. This study realizes real-time intravascular temperature imaging and monitoring through dynamic registration-corrected photoacoustic endoscopy (PETI-DRC). The system employs a miniaturized dual-modality catheter achieving photoacoustic/ultrasound imaging and angularly registered temperature mapping compatible with clinical interventions. Experiments on phantoms and ex vivo rabbit aortas quantitatively verified frame registration accuracy and temperature estimation reliability through comparison with thermocouple references and microscopic validation. Results show improved inter-frame consistency (average SSIM = 0.9684) and high temperature accuracy (RMSE $= 0.65~^{\circ }$ C), enabling stable reconstruction of angularly registered temperature images and reliable tracking of thermal dynamics for real-time temperature monitoring. This method provides accurate thermal feedback for safer and more effective photothermal therapy and offers a promising direction for future intravascular interventions.

This paper presents a general efficiency enhancement method for compact broadband rectifiers. First, a simple LCR model is employed to characterize the frequency-dependent parasitic effects of Schottky barrier diodes (SBDs) in broadband. Subsequently, based on the LCR model, a compact rectifier circuit configuration is proposed to minimize impedance mismatch in broadband, thereby significantly improving the rectification efficiency. To validate the proposed approach, two representative rectifier prototypes are designed, fabricated, and measured. The measured results show that Rectifier I achieves a power conversion efficiency (PCE) exceeding 70% across 0.5–2.5 GHz under 20 dBm input power with a${}\,1000{\Omega }$load, while Rectifier II attains a PCE above 64% from 1.1 to 5.4 GHz under 10 dBm input power with the same load. Compared with state-of-the-art designs, the proposed rectifiers exhibit superior PCE performance while achieving a fractional bandwidth (FBW) exceeding 100%. Notably, the proposed efficiency enhancement technique is suitable for most broadband rectifier designs, such as diverse rectifier topologies, SBD types, input power levels, and load conditions.

Sequence labeling serves as the most commonly used scheme for Chinese named entity recognition(NER). However, traditional sequence labeling methods classify tokens within an entity into different classes according to their positions. As a result, different tokens in the same entity may be learned with representations that are isolated and unrelated in target representation space, which could finally negatively affect the subsequent performance of token classification. In this paper, we point out and define this problem as Entity Representation Segmentation in Label-semantics. And then we present NerCo: Named entity recognition with Contrastive learning, a novel NER framework which can better exploit labeled data and avoid the above problem. Following the pretrain-finetune paradigm, NerCo firstly guides the encoder to learn powerful label-semantics based representations by gathering the encoded token representations of the same Semantic Class while pushing apart that of different. Subsequently, NerCo finetunes the learned encoder for final entity prediction. Extensive experiments on several datasets demonstrate that our framework can consistently improve the baseline and achieve state-of-the-art performance.

Single cell sequencing techniques enable researchers view complex bio-tissues from a more precise perspective to identify cell types. However, more and more recent works have been done to find more detailed subtypes within already known cell types. Here, we present MeHi-SCC, a method which utilized meta-learning protocol and brought in multi scRNA-seq datasets’ information in order to assist graph-based hierarchical sub-clustering process. In result, MeHi-SCC outperformed current-prevailing scRNA clustering methods and successfully identified cell subtypes in two large scale cell atlas. Our codes and datasets are available online at https://github.com/biomed-AI/MeHi-SCC

EEG emotion recognition is an essential area of brain-computer interface(BCI). Because of the low signal-to-noise ratio (SNR) and the uncertainty of the relationship between channels, it is arduous to mine the spatial and temporal information of EEG, especially through a single data representation method. Nowadays, several studies have applied knowledge distillation to the field of emotion recognition. However, traditional knowledge distillation requires a more powerful teacher model, which is time-consuming and needs massive storage space. In order to solve the above problems, in this paper, we propose a novel deep spatio-temporal mutual learning architecture named MLBNet for EEG emotion recognition, which is composed of temporal biased feature learner and spatial biased feature learner. The two components can learn well from chain-like data and matrix-like data respectively, and are trained collaboratively to mimic the predicted probability of each other. By the proposed architecture, we can improve the performance of EEG emotion recognition simply and effectively. To evaluate the validity of proposed method, we performed subject-dependent binary-class and four-class emotion identification tasks on DEAP dataset. The average result of the 10-fold cross-validation is considered as the final result. The MLBNet achieves 98.72% accuracy on valence and 98.85% accuracy on arousal respectively, and 98.32% accuracy on four-class classification tasks. To our best knowledge, our model demonstrates a better performance than the state-of-the-art models with the identical settings.