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Animals
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Application of Data Science in Reproduction of Domestic Animals
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Application of Data Science in Reproduction of Domestic Animals

A special issue of Animals (ISSN 2076-2615). This special issue belongs to the section "Animal Reproduction".

Deadline for manuscript submissions: 4 August 2024 | Viewed by 2395

Special Issue Editors

Dr. Sang-Hee Lee

E-Mail Website
Guest Editor
Department of Animal Science, College of Animal Life Sciences, Kangwon National University, Chuncheon 24341, Republic of Korea
Interests: sperm; oocyte; embryo; artificial intelligence; deep learning; computer vision
Dr. Seunghyung Lee

E-Mail Website
Guest Editor
Department of Animal Industry Convergence, College of Animal Life Sciences, Kangwon National University, Chuncheon 24341, Republic of Korea
Interests: ovary; corpus luteum; endocrinology; reproduction; cell biology

Special Issue Information

Dear Colleagues, 

The general reproductive characteristics of animals are evaluated through visual assessment. Specifically, professional experience and knowledge are essential for analyzing the reproductive characteristics of domestic animals. However, the subjective decisions and tedious work of experts can affect the accuracy of the examination. Recently, data science-based computer science has rapidly developed, with text analysis, computer vision, and other technologies applied in various industries, such as engineering, agriculture, and medicine. Currently, data science-based computer science is being applied in domestic animals, including in the prediction of physiological phenomena, automatic detection of behavior, body shape, and weight, and smart poultry farming. However, there are few studies on domestic animal reproduction.

This Special Issue, "Application of Data Science in Reproduction of Domestic Animals", welcomes original research and review papers that aim to provide research on all reproductive phenomena, including reproductive behavior, reproductive cell analysis based on data science, machine learning, general statistical analysis, and deep learning in domestic animals.

Dr. Sang-Hee Lee
Dr. Seunghyung Lee
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Animals is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • data science
  • big data
  • machine learning
  • sperm
  • oocyte
  • embryo
  • reproductive phenomenon
  • reproductive behavior
 

Published Papers (2 papers)

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Research

18 pages, 3742 KiB  
Article
Proteomic Analysis Identifies Distinct Protein Patterns for High Ovulation in FecB Mutant Small Tail Han Sheep Granulosa Cells
by Xiangyu Wang, Xiaofei Guo, Xiaoyun He, Ran Di, Xiaosheng Zhang, Jinlong Zhang and Mingxing Chu
Animals 2024, 14(1), 11; https://doi.org/10.3390/ani14010011 - 19 Dec 2023
Viewed by 866
Abstract
The Booroola fecundity (FecB) mutation in the bone morphogenetic protein receptor type 1B (BMPR1B) gene increases ovulation in sheep. However, its effect on follicular maturation is not fully understood. Therefore, we collected granulosa cells (GCs) at a critical stage of follicle [...] Read more.
The Booroola fecundity (FecB) mutation in the bone morphogenetic protein receptor type 1B (BMPR1B) gene increases ovulation in sheep. However, its effect on follicular maturation is not fully understood. Therefore, we collected granulosa cells (GCs) at a critical stage of follicle maturation from nine wild-type (WW), nine heterozygous FecB mutant (WB), and nine homozygous FecB mutant (BB) Small Tail Han sheep. The GCs of three ewes were selected at random from each genotype and consolidated into a single group, yielding a total of nine groups (three groups per genotype) for proteomic analysis. The tandem mass tag technique was utilized to ascertain the specific proteins linked to multiple ovulation in the various FecB genotypes. Using a general linear model, we identified 199 proteins significantly affected by the FecB mutation with the LIMMA package (p < 0.05). The differential abundance of proteins was enriched in pathways related to cholesterol metabolism, carbohydrate metabolism, amino acid biosynthesis, and glutathione metabolism. These pathways are involved in important processes for GC-regulated ‘conservation’ of oocyte maturation. Further, the sparse partial least-squares discriminant analysis and the Fuzzy-C-mean clustering method were combined to estimate weights and cluster differential abundance proteins according to ovulation to screen important ovulation-related proteins. Among them, ZP2 and ZP3 were found to be enriched in the cellular component catalog term “egg coat”, as well as some apolipoproteins, such as APOA1, APOA2, and APOA4, enriched in several Gene Ontology terms related to cholesterol metabolism and lipoprotein transport. A higher abundance of these essential proteins for oocyte maturation was observed in BB and WB genotypes compared with WW ewes. These proteins had a high weight in the model for discriminating sheep with different FecB genotypes. These findings provide new insight that the FecB mutant in GCs improves nutrient metabolism, leading to better oocyte maturation by altering the abundance of important proteins (ZP2, ZP3, and APOA1) in favor of increased ovulation or better oocyte quality. Full article
(This article belongs to the Special Issue Application of Data Science in Reproduction of Domestic Animals)
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18 pages, 12918 KiB  
Article
Deep Learning-Based Precision Analysis for Acrosome Reaction by Modification of Plasma Membrane in Boar Sperm
by Mira Park, Heemoon Yoon, Byeong Ho Kang, Hayoung Lee, Jisoon An, Taehyun Lee, Hee-Tae Cheong and Sang-Hee Lee
Animals 2023, 13(16), 2622; https://doi.org/10.3390/ani13162622 - 14 Aug 2023
Cited by 1 | Viewed by 1007
Abstract
The analysis of AR is widely used to detect loss of acrosome in sperm, but the subjective decisions of experts affect the accuracy of the examination. Therefore, we develop an ARCS for objectivity and consistency of analysis using convolutional neural networks (CNNs) trained [...] Read more.
The analysis of AR is widely used to detect loss of acrosome in sperm, but the subjective decisions of experts affect the accuracy of the examination. Therefore, we develop an ARCS for objectivity and consistency of analysis using convolutional neural networks (CNNs) trained with various magnification images. Our models were trained on 215 microscopic images at 400× and 438 images at 1000× magnification using the ResNet 50 and Inception–ResNet v2 architectures. These models distinctly recognized micro-changes in the PM of AR sperms. Moreover, the Inception–ResNet v2-based ARCS achieved a mean average precision of over 97%. Our system’s calculation of the AR ratio on the test dataset produced results similar to the work of the three experts and could do so more quickly. Our model streamlines sperm detection and AR status determination using a CNN-based approach, replacing laborious tasks and expert assessments. The ARCS offers consistent AR sperm detection, reduced human error, and decreased working time. In conclusion, our study suggests the feasibility and benefits of using a sperm diagnosis artificial intelligence assistance system in routine practice scenarios. Full article
(This article belongs to the Special Issue Application of Data Science in Reproduction of Domestic Animals)
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