• Clinical and Experimental Reproductive Medicine
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• 제34권4호
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• pp.207-217
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• 2007

• 한국자기학회:학술대회 개요집
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• 한국자기학회 2008년도 Asian Magnetics Conference
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• pp.127.2-127.2
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• 2008

• 한국정보과학회:학술대회논문집
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• 한국정보과학회 2007년도 가을 학술발표논문집 Vol.34 No.2 (A)
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• pp.62-63
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• 2007

• 한국자기학회:학술대회 개요집
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• 한국자기학회 2008년도 Asian Magnetics Conference
• /
• pp.234.1-234.1
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• 2008

• 한국자기학회:학술대회 개요집
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• 한국자기학회 2008년도 Asian Magnetics Conference
• /
• pp.128.1-128.1
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• 2008

• 한국전산유체공학회:학술대회논문집
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• 한국전산유체공학회 2008년도 12th Asian Congress of Fluid Mechanics
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• pp.49.1-49.1
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• 2008

• 대한약학회:학술대회논문집
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• 대한약학회 2005년도 Proceedings of the Convention of the Pharmaceutical Society of Korea Vol.2
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• pp.241-244
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• 2005

• Environmental Engineering Research
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• 제24권4호
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• pp.690-698
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• 2019

Air pollution has attracted ever-increasing attention because of its substantial influence on air quality and human health. To better understand the characteristics of long-range transported pollution, the single particle chemical composition and size were investigated by the single particle aerosol mass spectrometry in Fuzhou, China from 17th to 22nd January, 2016. The results showed that the haze was mainly caused by the transport of cold air mass under higher wind speed (10 m·s^-1) from the Yangtze River Delta region to Fuzhou. The number concentration elevated from 1,000 to 4,500 #·h^-1, and the composition of mobile source and secondary aerosol increased from 24.3% to 30.9% and from 16.0% to 22.5%, respectively. Then, the haze was eliminated by the clean air mass from the sea as indicated by a sharp decrease of particle number concentration from 4,500 to 1,000 #·h^-1. The composition of secondary aerosol and mobile sources decreased from 29.3% to 23.5% and from 30.9% to 23.1%, respectively. The particles with the size ranging from 0.5 to 1.5 ㎛ were mainly in the accumulation mode. The stationary source, mobile source, and secondary aerosol contributed to over 70% of the potential sources. These results will help to understand the physical and chemical characteristics of long- range transported pollutants.

• Genes and Genomics
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• 제40권11호
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• pp.1149-1155
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• 2018

Epileptic encephalopathies are genetically heterogeneous disorders which leads to epilepsy and cause neurological disorders. Seizure threshold 2 (SZT2) gene located on chromosome 1p34.2 encodes protein mainly expressed predominantly in the parietal and frontal cortex and dorsal root ganglia in the brain. Previous studies in mice showed that mutation in this gene can confers low seizure threshold, enhance epileptogenesis and in human may leads to facial dysmorphism, intellectual disability, seizure and macrocephaly. Objective of this study was to find out novel gene or novel mutation related to the gene phenotype. We have identified a large consanguineous Saudi family segregating developmental delay, intellectual disability, epilepsy, high forehead and macrocephaly. Exome sequencing was performed in affected siblings of the family to study the novel mutation. Whole exome sequencing data analysis, confirmed by subsequent Sanger sequencing validation study. Our results showed a novel homozygous mutation (c.9368G>A) in a substitution of a conserved glycine residue into a glutamic acid in the exon 67 of SZT2 gene. The mutation was ruled out in 100 unrelated healthy controls. The missense variant has not yet been reported as pathogenic in literature or variant databases. In conclusion, the here detected homozygous SZT2 variant might be the causative mutation that further explain epilepsy and developmental delay in this Saudi family.

• International Journal of Computer Science & Network Security
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• 제24권7호
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• pp.143-147
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• 2024

Image noise classification is a classical problem in the field of image processing, machine learning, deep learning and computer vision. In this paper, image noise classification is performed using deep learning. Keras deep learning library of TensorFlow is used for this purpose. 6900 images images are selected from the Kaggle database for the classification purpose. Dataset for labeled noisy images of multiple type was generated with the help of Matlab from a dataset of non-noisy images. Labeled dataset comprised of Salt & Pepper, Gaussian and Sinusoidal noise. Different training and tests sets were partitioned to train and test the model for image classification. In deep neural networks CNN (Convolutional Neural Network) is used due to its in-depth and hidden patterns and features learning in the images to be classified. This deep learning of features and patterns in images make CNN outperform the other classical methods in many classification problems.