Whole-body PET tumor segmentation remains challenging due to limited training data and substantial tumor heterogeneity, which impact the segmentation accuracy and the clinical utility. Tumor distribution information is usually contained in patient medical records and routinely utilized in medical image interpretation, which can also be used to improve the segmentation accuracy. This study introduces a novel 3D PET/MR tumor segmentation framework which integrates tumor distribution priors extracted from medical records. The proposed Tumor Localization Priors(TLP) are generated based on medical records using Large Language Models (LLMs) and organ localization based on MRI. Furthermore, the Region-Aware Fusion Module (RAFM) is designed to fuse TLP and encoded PET information through attention within the Anatomically-Consistent Multitask Model(ACMM). Moreover, Anatomical Consistency Loss(AC Loss) is introduced, integrating tumor localization and its anatomical distribution to enhance segmentation performance. Our method achieves an 9.30% Dice improvement over the baseline nnU-Net v2, with particularly notable 23.06% gains in precision while maintaining high recall (+6.19%). Clinical evaluations confirm superior detection of both primary and metastatic lesions, alongside reduced physiological uptake artifacts.

At present, deep learning based heart sound diagnosis algorithms are mostly complex and large models for high accuracy, which are difficult to deploy on mobile devices due to the high number of parameters and large computational cost. The current mainstream approach for processing heart sound signals involves utilizing their Mel-frequency cepstral coefficients (MFCC) features. However, most existing methods have overlooked the multi-channel characteristics of MFCC. To address this issue, we propose a Quaternion Dynamic Representation with Joint Learning (QDRJL) neural network for learning MFCC multi-channel features. Our proposed approach combines quaternion dynamic convolution with dynamic weighting and the Quaternion Interior Learning Block (QILB). Finally, we present a global and energy joint learning branch for jointly learning MFCC features. The success of the proposed quaternion network depends on its ability to utilize the internal relations between quaternion-valued input features and the definition of the dynamic weight variables in the augmented quaternion domain. We assessed various state-of-the-art classification algorithms for detecting heart sounds and found that our proposed classifier achieved an accuracy of up to 97.2%, outperforming existing models. Our experimental evaluation, using the 2016 PhysioNet/CinC Challenge dataset, revealed that our model could reduce the number of network parameters to 25% due to quaternion properties.

As one of the representative Delegated Proof-of-Stake (DPoS) blockchain platforms, EOSIO blockchain platform is developing rapidly in recent years due to its excellent features, such as the scalability of transaction speed and support for smart contracts and decentralized applications. However, vulnerabilities in EOSIO smart contracts have caused serious economic losses and moreover vulnerability detection tools for EOSIO contracts are limited. To overcome the above shortcomings, we implement a grey-box fuzzer called GFuzzer based on WebAssembly for smart contracts on the EOSIO platform considering that EOSIO contracts are not open-sourced. In order to generate more test cases for branches that are difficult to cover, GFuzzer selects test cases with the minimum distance to explore uncovered branches for mutation. We evaluate GFuzzer on 3963 real-world smart contracts and the experimental results show that GFuzzer can detect more vulnerabilities in EOSIO contracts than the existing tools EOSFuzzer and EVulHunter, and is efficient in achieving high branch coverage during vulnerability detection.