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| 1 | Expression,deleton and mnutation of ρ16 gene in human gastric cancer显示文摘AIM To investigate the relationship between the expression of p16 gene and the gastric carcinogenesis,depth of invasion and lymph node metastases, and to evaluate the deletion and mutation of exon 2 in p16 gene in gastric carcinoma.METHODS The expression of P16 protein was examined by streptavidin-peroxidase conjugated method (S-P); the deletion and mutation of p16 gene were respectively examined by polymerase chain reaction (PCR) and polymerase chain reaction single-strand conformation polymorphism analysis (PCR-SSCP) in gastric carcinoma.RESULTS Expression of P16 protein was detected in 96.25% (77/80) of the normal gastric mucosa, in 92.00% (45/50) of the dysplastic gastric mucosa and in 47.54% (58/122) of the gastric carcinoma. The positive rate of P16 protein expression in gastric carcinoma was significantly lower than that in normal gastric mucosa and dysplastic gastric mucosa (P<0.05). The positive rate of P16 protein expression in mucoid carcinoma 10.00% (1/ 10) was significantly lower than that in poorly differentiated carcinoma 51.22% ( 21/ 41 ),undifferentiated carcinoma 57.69% (15/26) and signet ring cell carcinoma 62.50% (10/ 16) (P<0.05). The positive rate of p16 protein in 30 cases paired primary and lymph node metastatic gastric carcinoma: There was 46.67% (14/30) in primary gastric carcinoma, 16.67% (5/30) in lymph node metastatic gastric carcinoma. The positive rate of lymph node metastatic carcinoma was significantly lower than that of primary carcinoma (P<0.05). There was of p16 gene mutation in exon 2, but 5 cases displayed deletion of p16 gene in exon 2 in the 25 primary gastric carcinomas.CONCLUSIONS The expression loss of P16 protein related to the gastric carcinogenesis, gastric carcinoma histopathological subtypes and lymph metastasis. The mutation of p16 gene in exon 2 may not be involved in gastric carcinogenesis. But the deletion of p16 gene in exon 2 may be involved in gastric carcinogenesis. | Xiu-Sheng He Qi Su Zhu-Chu Chen Xiu-Tao He Zhi-Feng Long Hui Ling Liang-Run Zhang Oncology Institute,Nanhua University,Hengyang 421001,Hunan Province,ChinaOncology Institute,Center South University,Changsha 410078,Hunan Province,China Department of Gastroenterology,First People’s Hospital of Changde City,Changde 415003,Hunan Province,China | 2001 | World Journal of Gastroenterology2001，7，4： | 40 |
| 2 | Design of integrated amorphous-silicon thin-film transistor gate driver显示文摘 | Liao Congwei He Changde | 2013 | Journal of Display Teehnology2013，9，1： | 1 |
| 3 | Multi-Modal Domain Adaptation Variational Autoencoder for EEG-Based Emotion Recognition显示文摘Traditional electroencephalograph(EEG)-based emotion recognition requires a large number of calibration samples to build a model for a specific subject,which restricts the application of the affective brain computer interface(BCI)in practice.We attempt to use the multi-modal data from the past session to realize emotion recognition in the case of a small amount of calibration samples.To solve this problem,we propose a multimodal domain adaptive variational autoencoder(MMDA-VAE)method,which learns shared cross-domain latent representations of the multi-modal data.Our method builds a multi-modal variational autoencoder(MVAE)to project the data of multiple modalities into a common space.Through adversarial learning and cycle-consistency regularization,our method can reduce the distribution difference of each domain on the shared latent representation layer and realize the transfer of knowledge.Extensive experiments are conducted on two public datasets,SEED and SEED-IV,and the results show the superiority of our proposed method.Our work can effectively improve the performance of emotion recognition with a small amount of labelled multi-modal data. | Yixin Wang Shuang Qiu Dan Li Changde Du Bao-Liang Lu Huiguang He | 2022 | IEEE/CAA Journal of Automatica Sinica2022，9，9： | 1 |
| 4 | Self-Floating Efficient Solar Steam Generators Constructed Using Super-Hydrophilic N,O Dual-Doped Carbon Foams from Waste Polyester显示文摘Solar evaporation is recognized as a prospective technique to produce freshwater from non-drinkable water using inexhaustible solar energy.However,it remains a challenge to fabricate low-cost solar evaporators with obviously reduced water evaporation enthalpy to achieve high evaporation rates.Herein,N,O dual-doped carbon foam(NCF)is fabricated from the lowtemperature carbonization of poly(ethylene terephthalate)(PET)waste by melamine/molten salts at 340℃.During carbonization,melamine reacts with carboxylic acids of PET degradation products to yield a crosslinking network,and then molten salts catalyze the decarboxylation and dehydration to construct a stable framework.Owing to rich N,O-containing groups,3D interconnected pores,super-hydrophilicity,and ultra-low thermal conductivity(0.0599 W m^(−1) K^(−1)),NCF not only achieves high light absorbance(ca.99%)and solar-to-thermal conversion,but also promotes the formation of water cluster to reduce water evaporation enthalpy by ca.37%.Consequently,NCF exhibits a high evaporation rate(2.4 kg m^(−2) h^(−1)),surpassing the-state-of-the-art solar evaporators,and presents good antiacid/basic abilities,long-term salt-resistance,and self-cleaning ability.Importantly,a large-scale NCF-based outdoor solar desalination device is developed to produce freshwater.The daily freshwater production amount per unit area(6.3 kg)meets the two adults’daily water consumption.The trash-to-treasure strategy will give impetus to the development of low-cost,advanced solar evaporators from waste plastics for addressing the global freshwater shortage. | Huiying Bai Ning Liu Liang Hao Panpan He Changde Ma Ran Niu Jiang Gong Tao Tang | 2022 | Energy & Environmental Materials2022，5，4： | 1 |
| 5 | Robust Design Rule with Definite Purpose Character Based on Fuzzy Probability and Study of its Characteristics显示文摘The design target with definite purpose character of product quality was described in a real fuzzy number (named fuzzy target for short in this paper),and its membership functions in common use were given. According to the fuzzy probability theory and the robust design principle, the robust design rule based on fuzzy probability (named fuzzy robust design rule for short) was put forward and its validity and practicability were analyzed and tested with a design example. The theoretical analysis and the design examples make clear that, while the fuzzy robust design rule was used, the fine design effect can be obtained and the fuzzy robust design rule can be very suitable for the choice of the membership function of the fuzzy target; so it has a particular advantage. | ZHANG Long-ting, HE Zhe-ming, GUO Hui-xin Department of Mechanical Engineering, Changde Teachers University, Hunan 415003, P.R.China | 2003 | International Journal of Plant Engineering and Management2003，8，2： | 0 |
| 6 | Exploring the Brain-like Properties of Deep Neural Networks:A Neural Encoding Perspective显示文摘Nowadays,deep neural networks(DNNs)have been equipped with powerful representation capabilities.The deep convolutional neural networks(CNNs)that draw inspiration from the visual processing mechanism of the primate early visual cortex have outperformed humans on object categorization and have been found to possess many brain-like properties.Recently,vision transformers(ViTs)have been striking paradigms of DNNs and have achieved remarkable improvements on many vision tasks compared to CNNs.It is natural to ask how the brain-like properties of ViTs are.Beyond the model paradigm,we are also interested in the effects of factors,such as model size,multimodality,and temporality,on the ability of networks to model the human visual pathway,especially when considering that existing research has been limited to CNNs.In this paper,we systematically evaluate the brain-like properties of 30 kinds of computer vision models varying from CNNs and ViTs to their hybrids from the perspective of explaining brain activities of the human visual cortex triggered by dynamic stimuli.Experiments on two neural datasets demonstrate that neither CNN nor transformer is the optimal model paradigm for modelling the human visual pathway.ViTs reveal hierarchical correspondences to the visual pathway as CNNs do.Moreover,we find that multi-modal and temporal networks can better explain the neural activities of large parts of the visual cortex,whereas a larger model size is not a sufficient condition for bridging the gap between human vision and artificial networks.Our study sheds light on the design principles for more brain-like networks.The code is available at http://gffzz188fe103f8f1460asqpxn9pqb6fc06qcu.ffgz.tsg.suse.edu.cn/QYiZhou/LWNeuralEncoding. | Qiongyi Zhou Changde Du Huiguang He | 2022 | Machine Intelligence Research2022，19，5： | 0 |
| 7 | Brain Encoding and Decoding in fMRI with Bidirectional Deep Generative Models显示文摘Brain encoding and decoding via functional magnetic resonance imaging(fMRI)are two important aspects of visual perception neuroscience.Although previous researchers have made significant advances in brain encoding and decoding models,existing methods still require improvement using advanced machine learning techniques.For example,traditional methods usually build the encoding and decoding models separately,and are prone to overfitting on a small dataset.In fact,effectively unifying the encoding and decoding procedures may allow for more accurate predictions.In this paper,we first review the existing encoding and decoding methods and discuss the potential advantages of a“bidirectional”modeling strategy.Next,we show that there are correspondences between deep neural networks and human visual streams in terms of the architecture and computational rules.Furthermore,deep generative models(e.g.,variational autoencoders(VAEs)and generative adversarial networks(GANs))have produced promising results in studies on brain encoding and decoding.Finally,we propose that the dual learning method,which was originally designed for machine translation tasks,could help to improve the performance of encoding and decoding models by leveraging large-scale unpaired data. | Changde Du Jinpeng Li Lijie Huang Huiguang He | 2019 | Engineering2019，5，5： | 0 |
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