Aiping Liu

University of Science and Technology of China

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Xun Chen

University of Science and Technology of China

102

Martin J. McKeown

University of British Columbia

Z. Jane Wang

Beijing Zhongke Science and Technology (China)

Ruobing Qian

University of Science and Technology of China

Xueyang Fu

University of Science and Technology of China

Angela Gibson

University of Wisconsin–Madison

Peiyu Wang

Peking University People's Hospital

Le Wu

Hefei University of Technology

Chang Li

Hefei University of Technology

Xu Zhang

University of Science and Technology of China

Cooperative Institutions

University of Science and Technology of China

144

Chinese Academy of Sciences

100

Sun Yat-sen University

Peking University

Capital Medical University

Fudan University

University of Wisconsin–Madison

Chinese Academy of Medical Sciences & Peking Union Medical College

Central South University

Sichuan University

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Influences of temperature and humidity conditions on flight capacity and main utilization of energy substances in Oedaleus asiaticus

Caoye Kexue (2016)

Gao Shujing Han Haibin Ning Wang Xu Linbo Aiping Liu

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Emotion Recognition From Multi-Channel EEG via Deep Forest

IEEE Journal of Biomedical and Health Informatics (2020)

Juan Cheng Meiyao Chen Chang Li Yü Liu Rencheng Song

Recently, deep neural networks (DNNs) have been applied to emotion recognition tasks based on electroencephalography (EEG), and have achieved better performance than traditional algorithms. However, DNNs still have the disadvantages of too many hyperparameters and lots of training data. To overcome these shortcomings, in this article, we propose a method for multi-channel EEG-based emotion recognition using deep forest. First, we consider the effect of baseline signal to preprocess the raw artifact-eliminated EEG signal with baseline removal. Secondly, we construct 2$D$ frame sequences by taking the spatial position relationship across channels into account. Finally, 2$D$ frame sequences are input into the classification model constructed by deep forest that can mine the spatial and temporal information of EEG signals to classify EEG emotions. The proposed method can eliminate the need for feature extraction in traditional methods and the classification model is insensitive to hyperparameter settings, which greatly reduce the complexity of emotion recognition. To verify the feasibility of the proposed model, experiments were conducted on two public DEAP and DREAMER databases. On the DEAP database, the average accuracies reach to 97.69% and 97.53% for valence and arousal, respectively; on the DREAMER database, the average accuracies reach to 89.03%, 90.41%, and 89.89% for valence, arousal and dominance, respectively. These results show that the proposed method exhibits higher accuracy than the state-of-art methods.

Hyperparameter

Artifact (error)

10.1109/jbhi.2020.2995767

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A Self-Interpretable Deep Learning Model for Seizure Prediction Using a Multi-Scale Prototypical Part Network

IEEE Transactions on Neural Systems and Rehabilitation Engineering (2023)

Yikai Gao Aiping Liu Lanlan Wang Ruobing Qian Xun Chen

The epileptic seizure prediction (ESP) method aims to timely forecast the occurrence of seizures, which is crucial to improving patients' quality of life. Many deep learning-based methods have been developed to tackle this issue and achieve significant progress in recent years. However, the "black-box" nature of deep learning models makes the clinician mistrust the prediction results, severely limiting its clinical application. For this purpose, in this study, we propose a self-interpretable deep learning model for patient-specific epileptic seizure prediction: Multi-Scale Prototypical Part Network (MSPPNet). This model attempts to measure the similarity between the inputs and prototypes (learned during training) as evidence to make final predictions, which could provide a transparent reasoning process and decision basis (e.g., significant prototypes for inputs and corresponding similarity score). Furthermore, we assign different sizes to the prototypes in latent space to capture the multi-scale features of EEG signals. To the best of our knowledge, this is the first study that develops a self-interpretable deep learning model for seizure prediction, other than the existing post hoc interpretation studies. Our proposed model is evaluated on two public epileptic EEG datasets (CHB-MIT: 16 patients with a total of 85 seizures, Kaggle: 5 dogs with a total of 42 seizures), with a sensitivity of 93.8% and a false prediction rate of 0.054/h in the CHB-MIT dataset and a sensitivity of 88.6% and a false prediction rate of 0.146/h in the Kaggle dataset, achieving the current state-of-the-art performance with self-interpretable evidence.

Similarity (geometry)

Epileptic seizure

Transfer of learning

10.1109/tnsre.2023.3260845

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[Detection of human telomerase reverse transcriptase mRNA of cells in pleural fluid by two-step in situ hybridization using digoxin-labeled oligonucleotide probes].

Jun Shu Gengyun Sun Aiping Liu Jing Liu

To establish an method for detecting human telomerase reverse transcriptase (hTERT) mRNA of cells in pleural fluid by in situ hybridization (ISH), and to evaluate preliminarily the efficacy of this new method in recognizing neoplastic cells in the pleural fluid.Fresh pleural fluid specimens were collected in 23 patients with pleural effusions and cell smears were prepared and after pretreatment, ISH between hTERT mRNA of the cells and digoxin-labelled oligonucleotide probes was performed. The signals of hTERT mRNA were detected by the sequential treatment with antigen, antibody, enzyme, and staining. With strict negative and positive controls, the experimental study was performed independent of clinical diagnosis and treatment.Positive results were seen in 9 cases of malignant pleural effusions, where dark staining of the cytoplasm and a few stained spots in the cell nuclei could be observed microscopically irrespective of the cytomorphological findings. Seven of the 9 cases had positive results of cytomorphological examination, in which neoplasmic cell with obvious morphological abnormalities were also spotted. In the other 2 cases where cytomorphological findings were negative but pleural biopsy confirmed the diagnosis of malignant pleural effusions, cytoplasm dark staining with stained spots in the cell nuclei were seen under microscope in spite of the absence of obvious abnormality in cell morphology. In contrast, the 14 cases with benign pleural effusions all had negative results, in which neither staining of the cells was identified microscopically, nor was evidently abnormal cell morphology.As a new method of clinical cytopathology, detection of hTERT mRNA of cells in the pleural fluid by ISH may provide a new means for cytomorphological examination for differential diagnosis between benign and malignant pleural effusions and for classification of the identified tumor cells.

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Preparation of CIT-protein conjugates using a novel citrinin derivative as hapten

European Food Research and Technology (2010)

Aiping Liu Youxiang Zhou Yongxia Zhang Fusheng Chen Xiaohong Wang

Citrinin

Conjugate

Hapten

Derivative (finance)

Mannich reaction

Molar ratio

10.1007/s00217-010-1385-5

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38 Combined ICG and PpIX Fluorescence Imaging of Necrotic and Hypoxic Tissue in Murine Burn Wounds

Journal of Burn Care & Research (2024)

Aiping Liu Marien Iliza Ochoa Mendoza Matthew S. Reed Brian W. Pogue Angela Gibson

Abstract Introduction Hypoxia is known to play a role in the pathophysiology of burn wound progression and healing. However, the localization of the hypoxic tissue in burns has not been clearly delineated in vivo. Recently, delayed fluorescence signal from Protoporphyrin IX (PpIX), an endogenous fluorescence molecule, has been shown to be a unique reporter of the local oxygen partial pressure in tissue. We have shown that Second-Window Indocyanine Green (SWIG) fluorescence identifies necrotic tissue in burn wounds in both murine models and human subjects. This pilot study aimed to study the feasibility of applying both PpIX and SWIG imaging to identify the necrotic and hypoxic regions of burn wounds, and to determine whether PpIX precursor 5-aminolevulinic acid (5-ALA) enhanced PpIX signals are necessary to identify the hypoxic region of burn early after injury and during healing. Methods Nude mice received a full-thickness contact burn on the hind leg with a customized burn device. 24 hours (hr) after ICG administration (@5 mg/kg), two groups of mice (n = 4 per group) with/without 5-ALA injection (@ 250 mg/kg, i.v.) were imaged using an ICG imager and a time-gated PpIX imager at various times post burn. To determine the effect of burn age on fluorescence signals, additional mice were injected with 5-ALA or ICG at 0, 24, or 48 hours post burn (hpb). After imaging, the burned tissues were cryosectioned for microscopic imaging to localize ICG and PpIX. The SWIG signal-to-background ratio (SBR) was calculated as the ratio between the averaged ICG intensity in the burn region and that of its contralateral non-burn region. Results SWIG and PpIX signals localized to the burn region with different dynamics. When ICG was administered immediately after burn injury, SWIG SBR was highest (2) at 24 hr after injection and remained elevated (>1.5) three days post injection. When ICG was administered 48 hpb, SWIG SBR 24 hr after injection was less than 1.5. Endogenous PpIX signals in the burns were visible at 24 hpb and peaked 96 hpb. Delayed endogenous PpIX revealed the burn wound was hypoxic 72 hpb. 5-ALA injection at 45 hpb enhanced PpIX signals and allowed the delayed PpIX to reveal hypoxia in the burn wounds at 48 hpb. If injected immediately after burn, 5-ALA enhanced the capability of delayed PpIX to identify hypoxia regions as early as 6 hpb. Microscopically, co-localization of SWIG and PpIX signals was confirmed in the burn wounds. Conclusions The pilot study has demonstrated the potential of a novel combined ICG and PpIX imaging to identify the necrotic and hypoxic tissues in burn wounds in vivo. Like ICG, we show that endogenous PpIX is sufficient to identify the burn region as early as 24 hpb and to identify hypoxia in the healing phase. 5-ALA enhanced delayed PpIX signals to reveal hypoxia in burns in the early injury phase. Applicability of Research to Practice Novel non-contact methods to identify necrotic and hypoxic regions in burn wounds in vivo may enhance our understanding of burn wound progression and healing.

Indocyanine Green

Protoporphyrin IX

Hypoxia

Fluorescence-lifetime imaging microscopy

10.1093/jbcr/irae036.038

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Analysis of infective diarrhea pathogen monitoring during 2014-2015 in Shenzhen Longhua.

Journal of Tropical Medicine (Guangzhou) (2016)

Cai MiaoSen Xu ShaoJian Yihan Li Xiaoling Lai Cai Jian-hui

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The Aesthetic Appreciation of A New English Course

Aiping Liu

A New English Course by Professor Li Guanyi is full of impressive aesthetic values.In the process of teaching,both the teacher and students are endowed with the sense of beauty of the language so as to appreciate the artistic style of the text as well as its aesthetic connotation in the book.

Beauty

Connotation

Course (navigation)

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CCDC 1543402: Experimental Crystal Structure Determination

The Cambridge Structural Database (2020)

Min Li Qixin Lu Aiping Liu Jialing Tan Liping Zhang

10.5517/ccdc.csd.cc1nt15p

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Pediatric Seizure Prediction in Scalp EEG Using a Multi-Scale Neural Network With Dilated Convolutions

IEEE Journal of Translational Engineering in Health and Medicine (2022)

Yikai Gao Xun Chen Aiping Liu Liang Deng Le Wu

Objective: Epileptic seizure prediction based on scalp electroencephalogram (EEG) is of great significance for improving the quality of life of patients with epilepsy. In recent years, a number of studies based on deep learning methods have been proposed to address this issue and achieve excellent performance. However, most studies on epileptic seizure prediction by EEG fail to take full advantage of temporal-spatial multi-scale features of EEG signals, while EEG signals carry information in multiple temporal and spatial scales. To this end, in this study, we proposed an end-to-end framework by using a temporal-spatial multi-scale convolutional neural network with dilated convolutions for patient-specific seizure prediction. Methods: Specifically, the model divides the EEG processing pipeline into two stages: the temporal multi-scale stage and the spatial multi-scale stage. In each stage, we firstly extract the multi-scale features along the corresponding dimension. A dilated convolution block is then conducted on these features to expand our model's receptive fields further and systematically aggregate global information. Furthermore, we adopt a feature-weighted fusion strategy based on an attention mechanism to achieve better feature fusion and eliminate redundancy in the dilated convolution block. Results: The proposed model obtains an average sensitivity of 93.3%, an average false prediction rate of 0.007 per hour, and an average proportion of time-in-warning of 6.3% testing in 16 patients from the CHB-MIT dataset with the leave-one-out method. Conclusion: Our model achieves superior performance in comparison to state-of-the-art methods, providing a promising solution for EEG-based seizure prediction.

Convolution (computer science)

Feature (linguistics)

10.1109/jtehm.2022.3144037

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Citations (48)