Investigation on Korean Local Maize Lines V. Variabilities of Plant Characters of Multi-eared and Tillered Lines(MET) (재래종 옥수수 수집종에 대한 특성조사 제5보 다수다벽 재래종 옥수수계통의 특성변이)
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- KOREAN JOURNAL OF CROP SCIENCE
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- v.26 no.1
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- pp.56-68
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- 1981
A maize line was selected in 1979 among 1000 Korean local maize lines collected in 1977. The selected maize line was characterized by having three to four tillers and eight to 10 ears on each individual plant. The line was assumed to have a great potential as a silage crop. The investigation was conducted as one of the serial studies on the Korean maize collected lines to provide basic information on the genetic variabilities of the multi-eared and tillered (MET) line and on other agronomic characters, prior to use the line as material for future breeding works for silage crop. The MET line and Suwon #19, single cross hybrid, as check variety were planted on May 1, 15 and 30, in three different levels of plant populations. The results obtained were summarized as follows: 1. The genetic variabilities of multi-ear and tillering habits were greater than environmental variabilities. 2. Total dry leaf weight of individual plant of MET line was also significantly higher than that of Suwon #19. 3. The mean number of tillers and ears bearing on the individual plant of MET line varied greatly with plant densities. The number of tillers and ears was on the average 2.9 and 7.0, respectively, when planted in 60cm. by 60cm. 4. The total dry matter and dried stem weight of the individual plant on MET line were comparable to those of Suwon #19. 5. The kernel weight from the individual plant of MET line was 5 to 40% less than that of Suwon #19, depending upon the plant densities. 6. The Kernel to stover ratio was higher for Suwon #19 than for the MET line. (41% to 35%). 7. The MET line had shown first tiller two weeks after planted on May 1. The second and third tillers appeared three to five days after the appearance of the first tiller. 8. The MET line was very specific in tillering habits. All the tillers were borne on the first few nodes of main stem below the soil surface. 9. The tillering habits of MET line were vigorous in the early part of the growing season, but less vigorous in the later part of the growing season. The number of efficient tillers bearing useable ears, was around two to three, when planted in 60cm. by 60cm. 10. The difference of plant height between main stem and first few tillers was around 10cm. 11. The ear size of MET line was around one-third of the major corn belt hybrids. The shape of ear of MET line was conical, with different diameter. 12. The kernel of the MET line was flinty with small soft starch patches on the endosperm part. 13. The 100 kernel weight was around 15gr., which is about one half of the major high yielding hybrids. 14. The ear height of MET line was comparatively higher than that of Suwon #19. 15. Significantly high and positive phenotypic correlation coefficients were obtained among major plant characters. 16. The growth rate of MET line was slower than that of Suwon #19. 17. MET line and Suwon #19 were both heavily infected with black streaked mosaic virus.
From January 2020 to October 2021, more than 500,000 academic studies related to COVID-19 (Coronavirus-2, a fatal respiratory syndrome) have been published. The rapid increase in the number of papers related to COVID-19 is putting time and technical constraints on healthcare professionals and policy makers to quickly find important research. Therefore, in this study, we propose a method of extracting useful information from text data of extensive literature using LDA and Word2vec algorithm. Papers related to keywords to be searched were extracted from papers related to COVID-19, and detailed topics were identified. The data used the CORD-19 data set on Kaggle, a free academic resource prepared by major research groups and the White House to respond to the COVID-19 pandemic, updated weekly. The research methods are divided into two main categories. First, 41,062 articles were collected through data filtering and pre-processing of the abstracts of 47,110 academic papers including full text. For this purpose, the number of publications related to COVID-19 by year was analyzed through exploratory data analysis using a Python program, and the top 10 journals under active research were identified. LDA and Word2vec algorithm were used to derive research topics related to COVID-19, and after analyzing related words, similarity was measured. Second, papers containing 'vaccine' and 'treatment' were extracted from among the topics derived from all papers, and a total of 4,555 papers related to 'vaccine' and 5,971 papers related to 'treatment' were extracted. did For each collected paper, detailed topics were analyzed using LDA and Word2vec algorithms, and a clustering method through PCA dimension reduction was applied to visualize groups of papers with similar themes using the t-SNE algorithm. A noteworthy point from the results of this study is that the topics that were not derived from the topics derived for all papers being researched in relation to COVID-19 (