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学者姓名：胡桓
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Abstract ：
Cervical cancer remains a significant global health concern. Given the disparity between limited medical resources and the requisite professional personnel, the coverage of cervical screening is inadequate, particularly in underdeveloped areas. Computer-assisted liquid-based cytology diagnostic systems offer favorable solutions. Detection of small nuclei within a complex liquid-based environment poses a challenge, exacerbated by the restricted availability of manual annotations. In this study, we propose FuseDLAM, a comprehensive computer-aided diagnostic system, which employs enhanced YOLOv8 with transformers for rapid localization of individual squamous epithelial cells. We leverage artificial intelligence-generated content techniques for data augmentation, effectively reducing the need for costly manual annotations. By integrating multiple deep convolutional neural network models with self-attention mechanisms, the system extracts crucial features from cell nuclei. These features are then fused through a fully connected layer to facilitate robust cell classification. FuseDLAM achieves an F1-score of 99.3% on the public SIPaKMeD dataset, demonstrating comparability with state-of-the-art approaches. It also proves its practical applicability in real-world clinical scenarios, achieving an F1-score of 91.2 % in identifying abnormal cervical squamous cells. Additionally, ablation experiments in both datasets validate the model's effectiveness. This underscores its potential for widespread application in medical imaging tasks.
Keyword ：
Attention mechanism Attention mechanism Cervical cytology Cervical cytology Computer-aided diagnosis Computer-aided diagnosis Deep learning Deep learning Feature fusion Feature fusion
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| GB/T 7714 | Jin, Yuqi , Ma, Jinghang , Lian, Yong et al. Cervical cytology screening using the fused deep learning architecture with attention mechanisms [J]. | APPLIED SOFT COMPUTING , 2024 , 166 . |
| MLA | Jin, Yuqi et al. "Cervical cytology screening using the fused deep learning architecture with attention mechanisms" . | APPLIED SOFT COMPUTING 166 (2024) . |
| APA | Jin, Yuqi , Ma, Jinghang , Lian, Yong , Wang, Fang , Wu, Tunhua , Hu, Huan et al. Cervical cytology screening using the fused deep learning architecture with attention mechanisms . | APPLIED SOFT COMPUTING , 2024 , 166 . |
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Abstract ：
NMC532 (LiNi0.5Mn0.3Co0.2O2) is a cost-effective and structurally stable cathode material that is widely used in batteries. Despite its stability, it undergoes irreversible phase transitions and transition metal (TM) dissolution during long-term cycling, which significantly impacts cell performance. This study pioneered a methodology to investigate the solid electrolyte interface (SEI) layers in depth, employing advanced analytical techniques: time-of-flight secondary ion mass spectrometry (TOF-SIMS) and cluster analysis. By utilizing these techniques, we specifically examined the impact of Mn dissolution on the interface evolution of graphite during calendar degradations. Additionally, we explored the relationship between Mn dissolution and cell performance in a graphite||NMC532 pouch cell. The results demonstrate that the competition between Mn dissolution and electrolyte depletion governs cell degradation. In particular, Mn dissolution accelerates the decomposition of the electrolyte, ultimately causing SEI layer growth. These findings provide a deeper understanding of battery performance and degradation mechanisms.
Keyword ：
batterycapacity decay mechanism batterycapacity decay mechanism cluster analysis cluster analysis lithium-ion batteries lithium-ion batteries Mn dissolution mechanism Mn dissolution mechanism time-of-flight secondary ionmass spectrometry time-of-flight secondary ionmass spectrometry
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| GB/T 7714 | Xie, Yuxiang , Li, Wei , Hu, Huan et al. Unraveling the Roles of Dissolved Mn(2+) and Its Impact on the Electrode-Electrolyte Interface under Realistic Conditions [J]. | ACS SUSTAINABLE CHEMISTRY & ENGINEERING , 2024 , 12 (17) : 6529-6538 . |
| MLA | Xie, Yuxiang et al. "Unraveling the Roles of Dissolved Mn(2+) and Its Impact on the Electrode-Electrolyte Interface under Realistic Conditions" . | ACS SUSTAINABLE CHEMISTRY & ENGINEERING 12 . 17 (2024) : 6529-6538 . |
| APA | Xie, Yuxiang , Li, Wei , Hu, Huan , Shi, Chen-Guang , Hong, Yu-Hao , Dai, Peng et al. Unraveling the Roles of Dissolved Mn(2+) and Its Impact on the Electrode-Electrolyte Interface under Realistic Conditions . | ACS SUSTAINABLE CHEMISTRY & ENGINEERING , 2024 , 12 (17) , 6529-6538 . |
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Inferring the developmental potential of single cells from scRNA-Seq data and reconstructing the pseudo-temporal path of cell development are fundamental but challenging tasks in single-cell analysis. Although single-cell transcriptional diversity (SCTD) measured by the number of expressed genes per cell has been widely used as a hallmark of developmental potential, it may lead to incorrect estimation of differentiation states in some cases where gene expression does not decrease monotonously during the development process. In this study, we propose a novel metric called single-cell transcriptional complexity (SCTC), which draws on insights from the economic complexity theory and takes into account the sophisticated structure information of scRNA-Seq count matrix. We show that SCTC characterizes developmental potential more accurately than SCTD, especially in the early stages of development where cells typically have lower diversity but higher complexity than those in the later stages. Based on the SCTC, we provide an unsupervised method for accurate, robust, and transferable inference of single-cell pseudotime. Our findings suggest that the complexity emerging from the interplay between cells and genes determines the developmental potential, providing new insights into the understanding of biological development from the perspective of complexity theory. Graphical Abstract
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| GB/T 7714 | Lin, Hai , Hu, Huan , Feng, Zhen et al. SCTC: inference of developmental potential from single-cell transcriptional complexity [J]. | NUCLEIC ACIDS RESEARCH , 2024 , 52 (11) : 6114-6128 . |
| MLA | Lin, Hai et al. "SCTC: inference of developmental potential from single-cell transcriptional complexity" . | NUCLEIC ACIDS RESEARCH 52 . 11 (2024) : 6114-6128 . |
| APA | Lin, Hai , Hu, Huan , Feng, Zhen , Xu, Fei , Lyu, Jie , Li, Xiang et al. SCTC: inference of developmental potential from single-cell transcriptional complexity . | NUCLEIC ACIDS RESEARCH , 2024 , 52 (11) , 6114-6128 . |
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Abstract ：
In recent years, single-cell RNA sequencing (scRNA-seq) has emerged as a powerful technique for investigating cellular heterogeneity and structure. However, analyzing scRNA-seq data remains challenging, especially in the context of COVID-19 research. Single-cell clustering is a key step in analyzing scRNA-seq data, and deep learning methods have shown great potential in this area. In this work, we propose a novel scRNA-seq analysis framework called scAAGA. Specifically, we utilize an asymmetric autoencoder with a gene attention module to learn important gene features adaptively from scRNA-seq data, with the aim of improving the clustering effect. We apply scAAGA to COVID19 peripheral blood mononuclear cell (PBMC) scRNA-seq data and compare its performance with state-of-the-art methods. Our results consistently demonstrate that scAAGA outperforms existing methods in terms of adjusted rand index (ARI), normalized mutual information (NMI), and adjusted mutual information (AMI) scores, achieving improvements ranging from 2.8% to 27.8% in NMI scores. Additionally, we discuss a data augmentation technology to expand the datasets and improve the accuracy of scAAGA. Overall, scAAGA presents a robust tool for scRNA-seq data analysis, enhancing the accuracy and reliability of clustering results in COVID-19 research.
Keyword ：
COVID-19 COVID-19 Data augmentation Data augmentation Deep learning Deep learning Gene attention Gene attention scRNA-seq scRNA-seq
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| GB/T 7714 | Meng, Rui , Yin, Shuaidong , Sun, Jianqiang et al. scAAGA: Single cell data analysis framework using asymmetric autoencoder with gene attention [J]. | COMPUTERS IN BIOLOGY AND MEDICINE , 2023 , 165 . |
| MLA | Meng, Rui et al. "scAAGA: Single cell data analysis framework using asymmetric autoencoder with gene attention" . | COMPUTERS IN BIOLOGY AND MEDICINE 165 (2023) . |
| APA | Meng, Rui , Yin, Shuaidong , Sun, Jianqiang , Hu, Huan , Zhao, Qi . scAAGA: Single cell data analysis framework using asymmetric autoencoder with gene attention . | COMPUTERS IN BIOLOGY AND MEDICINE , 2023 , 165 . |
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