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| Tan,You-Wen; Ge,Guo-Hong; Zhao,Wei; Gan,Jian-He; Zhao,Yun; Niu,Zhi-Lin; Zhang,Dong-Jun; Chen,Li; Yu,Xue- Jun; Yang,Li-Jun. |
| OBJECTIVE: This study aimed to determine the natural prevalence of variants of tyrosine-methionine-aspartic acid-aspartic acid (YMDD) motif in patients with chronic hepatitis B (CHB), and to explore its relation with demographic and clinical features, hepatitis B virus (HBV) genotypes, and HBV DNA levels. METHODS: A total of 1,042 antiviral treatment naïve CHB patients (including with lamivudine [LAM]) in the past year were recruited from outpatient and inpatient departments of six centers from December 2008 to June 2010. YMDD variants were analyzed using the HBV drug resistance line probe assay (Inno-Lipa HBV-DR). HBV genotypes were detected with polymerase chain reaction (PCR) microcosmic nucleic acid cross-ELISA, and HBV deoxyribonucleic acid (DNA) was... |
| Tipo: Info:eu-repo/semantics/article |
Palavras-chave: YMDD; Mutations; Chronic hepatitis B; Anti-viral therapy. |
| Ano: 2012 |
URL: http://www.scielo.br/scielo.php?script=sci_arttext&pid=S1413-86702012000300006 |
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| Zhao,Yun; Guindo,Mahamed L.; Xu,Xing; Shi,Xiang; Sun,Miao; He,Yong. |
| ABSTRACT We propose a segmentation algorithm for raisin extraction. The proposed approach consists of the following aspects. Deep learning is used to predict the number of raisins in each connected region, and the shape features such as the roundness, area, X-axis value for the centroid, Y-axis value for the centroid, axis length and perimeter of each region will be used to establish the prediction model. Morphological analysis, based on edge parameters including the polar axis, polar angle and angular velocity, is applied to search for the suitable break points that are useful for identifying the dividing lines between two adjacent raisins. To make our segmentation more accurate, some machine-learning algorithms such as the random forest (RF), support... |
| Tipo: Info:eu-repo/semantics/article |
Palavras-chave: Raisin extraction; Segmentation algorithm; Deep learning; Image analysis; Food quality grading. |
| Ano: 2019 |
URL: http://www.scielo.br/scielo.php?script=sci_arttext&pid=S0100-69162019000500639 |
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