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| 1 | New loci for refractive errors and ocular biometric parameters in young Chinese Han adults显示文摘Myopia has become a major public health issue with an increasing prevalence.There are still individuals who experience similar environmental risk factors and,yet,remain non-myopic.Thus,there might be genetic factors protecting people from myopia.Considering the opposite ocular characteristics of primary angle closure glaucoma(PACG)to myopia and possible common pathway between them,we propose that certain risk genes for PACG might act as a protective factor for myopia.In this study,2,678 young adults were genotyped for 37 targeted single nucleotide polymorphisms.Compared with emmetropia,rs1401999(allele C:OR=0.795,P=0.03;genotype in dominant model:OR=0.759,P=0.02)and rs1258267(allele A:OR=0.824,P=0.03;genotype in dominant model:OR=0.603,P=0.01)were associated with low to moderate myopia and high myopia,respectively.Genotype under recessive model of rs11024102 was correlated with myopia(OR=1.456,P=0.01),low to moderate myopia(OR=1.443,P=0.02)and high myopia(OR=1.453,P=0.02).However,these associations did not survive Bonferroni correction.Moreover,rs1401999,rs1258267,and rs11024102 showed associations with certain ocular biometric parameters in different groups.Our study suggests that ABCC5,CHAT and PLEKHA7 might be associated with refractive errors by contributing to the regulation of ocular biometry,in terms of uncorrected results and their biological functions. | Yunyun Sun Zi-Bing Jin Shifei Wei Hongyan Jia Kai Cao Jianping Hu Caixia Lin Wenzai An Jiyuan Guo He LiJing Fu Shi-Ming Li Ningli Wang the Anyang University Students Eye Study Group the Anyang University Students Eye Study Group | 2022 | Science China(Life Sciences)2022，65，10： | 1 |
| 2 | A denoising-classification neural network for power transformer protection显示文摘Artificial intelligence(AI)can potentially improve the reliability of transformer protection by fusing multiple features.However,owing to the data scarcity of inrush current and internal fault,the existing methods face the problem of poor generalizability.In this paper,a denoising-classification neural network(DCNN)is proposed,one which inte-grates a convolutional auto-encoder(CAE)and a convolutional neural network(CNN),and is used to develop a reli-able transformer protection scheme by identifying the exciting voltage-differential current curve(VICur).In the DCNN,CAE shares its encoder part with the CNN,where the CNN combines the encoder and a classifier.Based on the inter-action of the CAE reconstruction process and the CNN classification process,the CAE regards the saturated features of the VICur as noise and removes them accurately.Consequently,it guides CNN to focus on the unsaturated features of the VICur.The unsaturated part of the VICur approximates an ellipse,and this significantly differentiates between a healthy and faulty transformer.Therefore,the unsaturated features extracted by the CNN help to decrease the data ergodicity requirement of AI and improve the generalizability.Finally,a CNN which is trained well by the DCNN is used to develop a protection scheme.PSCAD simulations and dynamic model experiments verify its superior performance. | Zongbo Li Zaibin Jiao Anyang He Nuo Xu | 2022 | Protection and Control of Modern Power Systems2022，7，1： | 0 |
| 3 | Enhancement of catalytic activity for hydrogenation of nitroaromatic by anionic metal-organic framework显示文摘Nitroaromatic hydrogenation catalysis without precious metals remains a longstanding challenge.The rate of electron transfer is the crucial factor affecting hydrogenation catalysis.Herein,an ionic Cd-based metal-organic framework(I-Cd-MOF)exhibiting a unique structure with one-dimensional(1D)opening nanochannels and good electron transfer ability was synthesized for catalyzing hydrogenation of 4-nitrophenol(4-NP).The catalytic activity of the unique I-Cd-MOF without noble metals is detected,which is higher than most reported noble metal catalysts.Remarkably,the reaction rate of I-Cd-MOF(4.28 min^(-1))is about 47.6 times higher than that of the Cd-based neutral MOF(N-Cd-MOF)with the similar crystalline structure.Liquid chromatograph mass spectrometer(LC-MS)and theoretical results demonstrate that 4-NP and five intermediates are stabilized in the channels of I-Cd-MOF,which increases the possibility of contact with H^*and H_(2)g enerated at the Cd sites.The I-Cd-MOF was extended to other nitroaromatic hydrogenation catalysis,which still displays excellent activity.More importantly,the I-MOF@Filter membrane was successfully constructed for continuous hydrogenation catalytic reactions,which maintains a high catalytic performance after 7 cycles of recycling without washing.This work fills in the application of the I-MOFs in hydrogenation catalytic reactions and provides an effective way for the rapid and green degradation of nitroaromatic compounds. | Qi Wu Anyang Li Ruibo He Yaxi Wu Lei Hou Guoping Yang Wenyan Zhang Yao-Yu Wang | 2024 | Chinese Chemical Letters2024，35，3： | 0 |
| 4 | Two-dimensional mesoporous sensing materials显示文摘Two-dimensional mesoporous materials combing ultrathin nanosheet morphology with well-defined mesoporous structures,are now emerging and becoming increasingly important for their promising applications in energy storage,electronic devices,electrocatalysts and so on.Here,we synthesized a kind of polypyrrole-based two-dimensional mesoporous materials with uniform pore size,ultrathin thickness and high surface area.Serving for electrochemical NH3 sensor,they exhibited a fast response and high sensitivity.Therefore,our study would promote much interest in design of new materials for gas sensor applications. | Yu Wen Facai Wei Wenqian Zhang Anyang Cui Jing Cui Chengbin Jing Zhigao Hu Qingguo He Jianwei Fu Shaohua Liu Jiangong Cheng | 2020 | Chinese Chemical Letters2020，31，2： | 0 |
| 5 | Knowledge-based Convolutional Neural Networks for Transformer Protection显示文摘Deep learning based transformer protection has attracted increasing attention.However,its poor generalization abilities hinder the application of deep learning in the power system owing to the limited training samples.In order to improve its generalization abilities,this paper proposes a knowledge-based convolutional neural network(CNN)for the transformer protection.In general,the power experts can reliably discriminate between faulty transformers and healthy transformers only through the unsaturated parts of equivalent magnetization curve(voltage of magnetizing branch-differential current curve)but deep learning intends to focus on the combined features of saturated and unsaturated parts.Inspired by the identification process of power experts,CNN adopted a specially designed loss function in this paper which is used to identify the running states of power transformers.Specifically,the presented Restrictive Weight Sparsity substitutes a special regularization term for the common LI regularization.The presented Adaptive Sample Weight Adjustment endows the softmax loss of each sample with the optimizable weight the softmax loss of each sample with the optimizable weights to increase the impact of more-difficult-to-identify cases on the training process.With the modified loss function,the knowledge is abstractly introduced into the training process of CNN so as to successfully imitate the identification process of power experts.Accordingly,the proposed knowledge-based CNN will pay more attention to the unsaturated parts of equivalent magnetization curve even if only limited samples are included in the training process.The results of simulations and dynamic model experiments reveal that the knowledge-based CNN exhibits an improved generalization ability and the knowledge-based deep learning algorithm is a promising research direction. | Zongbo Li Zaibin Jiao Anyang He | 2021 | CSEE Journal of Power and Energy Systems2021，7，2： | 0 |
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