Estimation of Genetic Variations and Selection of Superior Lines from Diallel Crosses in Layer Chicken (산란계종의 잡종강세 이용을 위한 유전학적 기초연구와 우량교배조합 선발에 관한 연구)
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- Korean Journal of Poultry Science
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- v.13 no.1
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- pp.1-14
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- 1986
The subject of this study was to obtain some genetic information for developing superior layer chickens. Heterosis and combining ability effects were estimated with 5,759 progenies of full diallel crosses of 6 strains in White Leghorn. Fertility, hatchability, brooder-house viability, rearing- house viability, laying-house viability, age at 1st egg laying, body weight at 1st egg laying, average egg weight, hen-day egg production, hen-housed egg production, and feed conversion were investigated and analyzed into heterosis effect, general combining ability, specific combining ability and reciprocal effect by Grilling's model I. The results obtained were summarized as follows; 1. The general performance of each traits was 94.76% in fertility, 74.05% in hatchability, 97.47% in brooder-house viability, 99.72% in rearing-house viability, 93.81% in laying-house viability, 150 day in the age at 1st egg laying, 1,505g in the body weight at 1st egg laying, 60.08g in average egg weight, 77.11% in hen-day egg production, 269.8 eggs in hen-housed egg Production, and 2.44 in feed conversion. 2. The heterosis effects were estimated to -0.66%, 9.58%, 0.26%, 1.83%, -3.87%, 3.63%, 0.96%, 4.23%, 6.4%, and -0.8%, in fertility, hatchability, brooder-house viability, laying-house viability, the age at 1st egg laying, the body weight at 1st egg laying, average egg weight, hen-day egg Production, hen-housed egg production and feed conversion, respectively. 3. The results obtained from analysis of combining ability were as follows ; 1) Estimates of general combining ability, specific combining ability and reciprocal effects were not high in fertility. It was considered that fertility was mainly affected by environmental factors. In the hatchability, the general combining ability was more important than specific combining ability and reciprocal effects, and the superior strains were K and V which the additive genetic effects were very high. 2) In the brooder-house viability and laying-house viability, specific combining ability and reciprocal effects appeared to be important and the combinations of K
Over a billion people in the world generate new news minute by minute. People forecasts some news but most news are from unexpected events such as natural disasters, accidents, crimes. People spend much time to watch a huge amount of news delivered from many media because they want to understand what is happening now, to predict what might happen in the near future, and to share and discuss on the news. People make better daily decisions through watching and obtaining useful information from news they saw. However, it is difficult that people choose news suitable to them and obtain useful information from the news because there are so many news media such as portal sites, broadcasters, and most news articles consist of gossipy news and breaking news. User interest changes over time and many people have no interest in outdated news. From this fact, applying users' recent interest to personalized news service is also required in news service. It means that personalized news service should dynamically manage user profiles. In this paper, a content-based news recommendation system is proposed to provide the personalized news service. For a personalized service, user's personal information is requisitely required. Social network service is used to extract user information for personalization service. The proposed system constructs dynamic user profile based on recent user information of Facebook, which is one of social network services. User information contains personal information, recent articles, and Facebook Page information. Facebook Pages are used for businesses, organizations and brands to share their contents and connect with people. Facebook users can add Facebook Page to specify their interest in the Page. The proposed system uses this Page information to create user profile, and to match user preferences to news topics. However, some Pages are not directly matched to news topic because Page deals with individual objects and do not provide topic information suitable to news. Freebase, which is a large collaborative database of well-known people, places, things, is used to match Page to news topic by using hierarchy information of its objects. By using recent Page information and articles of Facebook users, the proposed systems can own dynamic user profile. The generated user profile is used to measure user preferences on news. To generate news profile, news category predefined by news media is used and keywords of news articles are extracted after analysis of news contents including title, category, and scripts. TF-IDF technique, which reflects how important a word is to a document in a corpus, is used to identify keywords of each news article. For user profile and news profile, same format is used to efficiently measure similarity between user preferences and news. The proposed system calculates all similarity values between user profiles and news profiles. Existing methods of similarity calculation in vector space model do not cover synonym, hypernym and hyponym because they only handle given words in vector space model. The proposed system applies WordNet to similarity calculation to overcome the limitation. Top-N news articles, which have high similarity value for a target user, are recommended to the user. To evaluate the proposed news recommendation system, user profiles are generated using Facebook account with participants consent, and we implement a Web crawler to extract news information from PBS, which is non-profit public broadcasting television network in the United States, and construct news profiles. We compare the performance of the proposed method with that of benchmark algorithms. One is a traditional method based on TF-IDF. Another is 6Sub-Vectors method that divides the points to get keywords into six parts. Experimental results demonstrate that the proposed system provide useful news to users by applying user's social network information and WordNet functions, in terms of prediction error of recommended news.
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 (