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Yuan J, Li Q, Sun Y, Wang Y, Li Y, You Z, Ni A, Zong Y, Ma H, Chen J. Multi-tissue transcriptome profiling linked the association between tissue-specific circRNAs and the heterosis for feed intake and efficiency in chicken. Poult Sci 2024; 103:103783. [PMID: 38713987 PMCID: PMC11091503 DOI: 10.1016/j.psj.2024.103783] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2024] [Revised: 04/11/2024] [Accepted: 04/17/2024] [Indexed: 05/09/2024] Open
Abstract
Heterosis has been widely utilized in chickens. The nonadditive inheritance of genes contributes to this biological phenomenon. However, the role of circRNAs played in the heterosis is poorly determined. In this study, we observed divergent heterosis for residual feed intake (RFI) between 2 crossbreds derived from a reciprocal cross between White Leghorns and Beijing You chickens. Then, circRNA landscape for 120 samples covering the hypothalamus, liver, duodenum mucosa and ovary were profiled to elucidate the regulatory mechanisms of heterosis. We detected that a small proportion of circRNAs (7.83-20.35%) were additively and non-additively expressed, in which non-additivity was a major inheritance of circRNAs in the crossbreds. Tissue-specific expression of circRNAs was prevalent across 4 tissues. Weighted gene co-expression network analysis revealed circRNA-mRNA co-expression modules associated with feed intake and RFI in the hypothalamus and liver, and the co-expressed genes were enriched in oxidative phosphorylation pathway. We further identified 8 nonadditive circRNAs highly correlated with 16 nonadditive genes regulating negative heterosis for RFI in the 2 tissues. Circ-ITSN2 was validated in the liver tissue for its significantly positive correlation with PGPEP1L. Moreover, the bioinformatic analysis indicated that candidate circRNAs might be functioned by binding the microRNAs and interacting with the RNA binding proteins. The integration of multi-tissue transcriptome firstly linked the association between tissue-specific circRNAs and the heterosis for feed intake and efficiency in chicken, which provide novel insights into the molecular mechanism underlying heterosis for feed efficiency. The validated circRNAs can act as potential biomarkers for predicting RFI and its heterosis.
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Affiliation(s)
- Jingwei Yuan
- Key Laboratory of Animal (Poultry) Genetics Breeding and Reproduction, Ministry of Agriculture and Rural Affairs, Institute of Animal Sciences, Chinese Academy of Agricultural Sciences, Beijing 100193, China
| | - Qin Li
- Key Laboratory of Animal (Poultry) Genetics Breeding and Reproduction, Ministry of Agriculture and Rural Affairs, Institute of Animal Sciences, Chinese Academy of Agricultural Sciences, Beijing 100193, China
| | - Yanyan Sun
- Key Laboratory of Animal (Poultry) Genetics Breeding and Reproduction, Ministry of Agriculture and Rural Affairs, Institute of Animal Sciences, Chinese Academy of Agricultural Sciences, Beijing 100193, China
| | - Yuanmei Wang
- Key Laboratory of Animal (Poultry) Genetics Breeding and Reproduction, Ministry of Agriculture and Rural Affairs, Institute of Animal Sciences, Chinese Academy of Agricultural Sciences, Beijing 100193, China
| | - Yunlei Li
- Key Laboratory of Animal (Poultry) Genetics Breeding and Reproduction, Ministry of Agriculture and Rural Affairs, Institute of Animal Sciences, Chinese Academy of Agricultural Sciences, Beijing 100193, China
| | - Zhangjing You
- Key Laboratory of Animal (Poultry) Genetics Breeding and Reproduction, Ministry of Agriculture and Rural Affairs, Institute of Animal Sciences, Chinese Academy of Agricultural Sciences, Beijing 100193, China
| | - Aixin Ni
- Key Laboratory of Animal (Poultry) Genetics Breeding and Reproduction, Ministry of Agriculture and Rural Affairs, Institute of Animal Sciences, Chinese Academy of Agricultural Sciences, Beijing 100193, China
| | - Yunhe Zong
- Key Laboratory of Animal (Poultry) Genetics Breeding and Reproduction, Ministry of Agriculture and Rural Affairs, Institute of Animal Sciences, Chinese Academy of Agricultural Sciences, Beijing 100193, China
| | - Hui Ma
- Key Laboratory of Animal (Poultry) Genetics Breeding and Reproduction, Ministry of Agriculture and Rural Affairs, Institute of Animal Sciences, Chinese Academy of Agricultural Sciences, Beijing 100193, China
| | - Jilan Chen
- Key Laboratory of Animal (Poultry) Genetics Breeding and Reproduction, Ministry of Agriculture and Rural Affairs, Institute of Animal Sciences, Chinese Academy of Agricultural Sciences, Beijing 100193, China.
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Madadizadeh F, Bahariniya S. Tutorial on statistical data reduction methods for exploring dietary patterns. Clin Nutr ESPEN 2023; 58:228-234. [PMID: 38057011 DOI: 10.1016/j.clnesp.2023.09.916] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2023] [Revised: 08/31/2023] [Accepted: 09/17/2023] [Indexed: 12/08/2023]
Abstract
Diet is one of the most important factors affecting human health and it is different for each person. Examining individual foods in the diet does not provide sufficient information to the researcher, so we need food patterns to obtain more complete information. Food pattern analysis is also a complementary approach that is carried out by statistical methods and provides additional evidence in this regard. In this tutorial article, we have tried to briefly explain all statistical analyses which can used for dietary pattern analysis.
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Affiliation(s)
- Farzan Madadizadeh
- Center for Healthcare Data Modeling, Departments of Biostatistics and Epidemiology, School of Public Health, Shahid Sadoughi University of Medical Sciences, Yazd, Iran.
| | - Sajjad Bahariniya
- Health Services Management, School of Public Health, Shahid Sadoughi University of Medical Sciences, Yazd, Iran.
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3
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Xu N, Li Q, Zhu W, Li Q, Finkelman RB, Engle MA, Wang R, Wang Z. Advocating the Use of Bayesian Network in Analyzing the Modes of Occurrence of Elements in Coal. ACS OMEGA 2023; 8:39096-39109. [PMID: 37901523 PMCID: PMC10600927 DOI: 10.1021/acsomega.3c04109] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/10/2023] [Accepted: 09/21/2023] [Indexed: 10/31/2023]
Abstract
Modes of occurrence of elements in coal are important because they can be used not only to understand the origin of inorganic components in coal but also to determine the impact on the environment and human health and the deposition process of coal seams as well. Statistical analysis is one of the commonly used indirect methods used to analyze the modes of occurrence of elements in coal, among which hierarchical clustering is widely used. However, hierarchical clustering may lead to misleading results due to its limitation that it focuses on the clusters of elements rather than a single element. To tackle this issue, we use the first part of a well-known Bayesian network structure learning algorithm, i.e., Peter-Clark (PC) algorithm, to explore the relationships of the coal elemental data and then infer modes of occurrence of elements in coal. A data set containing 95 Late Paleozoic coal samples from the Datanhao and Adaohai mines in Inner Mongolia, China, is used for the performance evaluation. Analytical results show that many instructive and surprising insights can be concluded from the first part of the PC algorithm. Compared with the hierarchical clustering algorithm, the first part of the PC algorithm demonstrates superiority in analyzing the modes of occurrence of elements in coal.
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Affiliation(s)
- Na Xu
- College
of Geoscience and Survey Engineering, China
University of Mining and Technology (Beijing), Beijing 100083, China
| | - Qiang Li
- College
of Geoscience and Survey Engineering, China
University of Mining and Technology (Beijing), Beijing 100083, China
| | - Wei Zhu
- College
of Geoscience and Survey Engineering, China
University of Mining and Technology (Beijing), Beijing 100083, China
| | - Qing Li
- Department
of Computing, Hong Kong Polytechnic University, Hung Hom, Kowloon, HKSAR, Hong Kong, China
| | - Robert B. Finkelman
- College
of Geoscience and Survey Engineering, China
University of Mining and Technology (Beijing), Beijing 100083, China
- University
of Texas at Dallas, Richardson, Texas 75080, United States
| | - Mark A. Engle
- Department
of Earth, Environmental and Resource Sciences, University of Texas at El Paso, 500 West University Avenue, El Paso, Texas 79968, United States
| | - Ru Wang
- College
of Geoscience and Survey Engineering, China
University of Mining and Technology (Beijing), Beijing 100083, China
| | - Zhiwei Wang
- College
of Geoscience and Survey Engineering, China
University of Mining and Technology (Beijing), Beijing 100083, China
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4
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Lin Q, Son J. A close contact identification algorithm using kernel density estimation for the ship passenger health. JOURNAL OF KING SAUD UNIVERSITY. COMPUTER AND INFORMATION SCIENCES 2023; 35:101564. [PMID: 37152893 PMCID: PMC10129340 DOI: 10.1016/j.jksuci.2023.101564] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/13/2023] [Revised: 03/31/2023] [Accepted: 04/19/2023] [Indexed: 05/09/2023]
Abstract
COVID-19 has been spread globally, with ships posing a significant challenge for virus containment due to their close-quartered environments. The most effective method for preventing the spread of the virus currently involves tracking and physically isolating close contacts. In this paper, we propose the Close Contact Identification Algorithm (CCIA). The probability density of user location points may be higher in a certain spatial range such as a cabin where there are more location points. The characteristics of CCIA include using Kernel Density Estimation (KDE) to calculate the probability density of each user location point and seeking the maximum Euclidean distance between location points in each cluster for merging clusters. CCIA is capable of calculating the probability density of each location point, a feature that other clustering algorithms, such as Kmeans, Hierarchical, and DBSCAN, cannot achieve. The contribution of CCIA is using the probability density of each location point to identify close contacts in ship environments. The performance of CCIA shows more accurate clustering compared to Kmeans, Hierarchical, and DBSCAN. CCIA can effectively identify close contacts and enhance the capabilities of user devices in mitigating the spread of COVID-19 within ship environments.
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Affiliation(s)
- Qianfeng Lin
- Department of Computer Engineering, Korea Maritime and Ocean University, 727 Taejong-ro, Yeongdo-Gu, Busan 49112, South Korea
| | - Jooyoung Son
- Division of Marine IT Engineering, Korea Maritime and Ocean University, 727 Taejong-ro, Yeongdo-Gu, Busan 49112, South Korea
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Eckhardt CM, Madjarova SJ, Williams RJ, Ollivier M, Karlsson J, Pareek A, Nwachukwu BU. Unsupervised machine learning methods and emerging applications in healthcare. Knee Surg Sports Traumatol Arthrosc 2023; 31:376-381. [PMID: 36378293 DOI: 10.1007/s00167-022-07233-7] [Citation(s) in RCA: 13] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/17/2022] [Accepted: 11/08/2022] [Indexed: 11/16/2022]
Abstract
Unsupervised machine learning methods are important analytical tools that can facilitate the analysis and interpretation of high-dimensional data. Unsupervised machine learning methods identify latent patterns and hidden structures in high-dimensional data and can help simplify complex datasets. This article provides an overview of key unsupervised machine learning techniques including K-means clustering, hierarchical clustering, principal component analysis, and factor analysis. With a deeper understanding of these analytical tools, unsupervised machine learning methods can be incorporated into health sciences research to identify novel risk factors, improve prevention strategies, and facilitate delivery of personalized therapies and targeted patient care.Level of evidence: I.
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Affiliation(s)
- Christina M Eckhardt
- Division of Pulmonary, Allergy and Critical Care Medicine, Department of Medicine, Columbia University College of Physicians and Surgeons Irving Medical Center, New York, NY, USA
| | - Sophia J Madjarova
- Sports Medicine Fellow and Shoulder Service, Department of Orthopedic Surgery and Sports Medicine, Hospital for Special Surgery, 535 East 70th Street, New York, NY, 10021, USA
| | - Riley J Williams
- Sports Medicine Fellow and Shoulder Service, Department of Orthopedic Surgery and Sports Medicine, Hospital for Special Surgery, 535 East 70th Street, New York, NY, 10021, USA
| | - Mattheu Ollivier
- Institut du Movement et de l'appareil locomoteur, Aix-Marseille Université, Marseille, France
| | - Jón Karlsson
- Department of Orthopaedics, Sahlgrenska University Hospital, Sahlgrenska Academy, Gothenburg University, Gothenburg, Sweden
| | - Ayoosh Pareek
- Sports Medicine Fellow and Shoulder Service, Department of Orthopedic Surgery and Sports Medicine, Hospital for Special Surgery, 535 East 70th Street, New York, NY, 10021, USA.
| | - Benedict U Nwachukwu
- Sports Medicine Fellow and Shoulder Service, Department of Orthopedic Surgery and Sports Medicine, Hospital for Special Surgery, 535 East 70th Street, New York, NY, 10021, USA
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Integration Path Analysis of Traditional Media and New Media Based on Internet of Things Data Mining. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2022; 2022:8193800. [PMID: 35571712 PMCID: PMC9106486 DOI: 10.1155/2022/8193800] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/09/2022] [Revised: 03/12/2022] [Accepted: 04/02/2022] [Indexed: 11/17/2022]
Abstract
With the development of information technology, the influence of traditional media is weakening day by day. In view of this, based on the Internet of things data mining technology, this study improves the k-means algorithm, and designs a new media precision marketing system, which combines new media with traditional media and provides a new marketing model for traditional media. The results show that the accuracy of the improved k-means algorithm finally reaches about 93%, which is much higher than that of similar algorithms. It can be seen that the improved k-means algorithm has better performance. In the application experiment, this study can effectively find the new media activities with the highest user preference, and the impact of the two websites' application of precision marketing system on users has also increased. It can be seen that the precision marketing system designed this time is more effective.
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Identification of Enterprise Financial Risk Based on Clustering Algorithm. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2022; 2022:1086945. [PMID: 35449741 PMCID: PMC9018203 DOI: 10.1155/2022/1086945] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/31/2022] [Revised: 03/11/2022] [Accepted: 03/18/2022] [Indexed: 11/17/2022]
Abstract
In order to solve the problem that corporate financial risks seriously affect the healthy development of enterprises, credit institutions, securities investors, and even the whole of China, the K-means clustering algorithm, the risk screening process, and the Gaussian mixture clustering algorithm, the risk screening process, are proposed; experiments have shown that although the number of high-risk companies selected by the K-means algorithm is small, only 9% of the full sample, the high-risk cluster can contain nearly 30% of the new “special treatment” companies. If the time period is extended to the next 5 years, this proportion will be higher. Finally we found that if the prediction of “special handling” events is used as the criterion for evaluating high-risk clusters, then K-means clustering can effectively screen out those risky companies that need to be treated with caution by investors. The validity of the experiment is verified.
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