Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
4.9
2.7
Journals
Animals
Special Issues
Advances in Mammary Gland Biology and Lactation of Ruminants: Second...
share announcement

Advances in Mammary Gland Biology and Lactation of Ruminants: Second Edition

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

Deadline for manuscript submissions: 16 December 2024 | Viewed by 1200

Special Issue Editor

Dr. Sam Peterson

E-Mail Website
Guest Editor
School of Agriculture and Environment, Massey University, Private Bag 11222, Palmerston North 4442, New Zealand
Interests: sheep dairy; lactation in sheep; developmental programming; fetal development; mastitis in sheep and beef cattle
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Apart from its milk-producing function, the mammary gland is a unique mammalian organ in two major ways. First, it is almost invariably sited outside of the body cavity, and second, it grows and regresses several times during the animal’s life. Being isolated from the body helps to protect the body from infections that are common in the mammary gland due to the suitability of the intramammary environment for microorganisms, and the gland has both local and systemic methods of protection to combat mastitis. Being isolated also facilitates the growth and regression of the gland by separating the many growth factors that are involved from other body organs (where they might cause chaos). Add to these functions the mechanism by which the gland produces colostrum to provide offspring with immunological competence as well as with milk, both of which are produced in amounts and compositions that are appropriate for the species and the offspring. This surely indicates that the mammary gland is one of the most complicated and fascinating organs.

It has been seven years since Michael Akers wrote the 100-year review on mammary development and lactation, in which he pointed out that despite great advances in knowledge, the fundamental questions regarding mammary development and lactation have changed little. The more we discover, the more questions are raised, and this has resulted in many groups studying a wide variety of lactation-related topics. If we also consider the many different types of dairy animals and production systems being studied, there is a huge amount of research happening in relation to mammary biology. Thus, the plan for this Special Issue is to highlight research in mammary biology and lactation in ruminants.

Considering the success of our previous Special Issue, we are pleased to launch “Advances in Mammary Gland Biology and Lactation of Ruminants: Second Edition”. We welcome the submission of research articles and literature reviews.

Dr. Sam Peterson
Guest Editor

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

  • mammary biology
  • mammogenesis
  • lactogenesis
  • galactopoiesis
  • mastitis
  • mammary histopathology
  • endocrine signals
  • epigenetic effects
  • lactational physiology
  • milk removal
  • colostrum
  • somatic cells
  • milk conductivity
  • mammary metabolism

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • e-Book format: Special Issues with more than 10 articles can be published as dedicated e-books, ensuring wide and rapid dissemination.

Further information on MDPI's Special Issue polices can be found here.

Related Special Issue

Published Papers (1 paper)

Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

20 pages, 2399 KiB  
Article
The Use of Machine Learning to Predict Prevalence of Subclinical Mastitis in Dairy Sheep Farms
by Yiannis Kiouvrekis, Natalia G. C. Vasileiou, Eleni I. Katsarou, Daphne T. Lianou, Charalambia K. Michael, Sotiris Zikas, Angeliki I. Katsafadou, Maria V. Bourganou, Dimitra V. Liagka, Dimitris C. Chatzopoulos and George C. Fthenakis
Animals 2024, 14(16), 2295; https://doi.org/10.3390/ani14162295 - 6 Aug 2024
Cited by 1 | Viewed by 992
Abstract
The objective of the study was to develop a computational model with which predictions regarding the level of prevalence of mastitis in dairy sheep farms could be performed. Data for the construction of the model were obtained from a large Greece-wide field study [...] Read more.
The objective of the study was to develop a computational model with which predictions regarding the level of prevalence of mastitis in dairy sheep farms could be performed. Data for the construction of the model were obtained from a large Greece-wide field study with 111 farms. Unsupervised learning methodology was applied for clustering data into two clusters based on 18 variables (17 independent variables related to health management practices applied in farms, climatological data at the locations of the farms, and the level of prevalence of subclinical mastitis as the target value). The K-means tool showed the highest significance for the classification of farms into two clusters for the construction of the computational model: median (interquartile range) prevalence of subclinical mastitis among farms was 20.0% (interquartile range: 15.8%) and 30.0% (16.0%) (p = 0.002). Supervised learning tools were subsequently used to predict the level of prevalence of the infection: decision trees, k-NN, neural networks, and Support vector machines. For each of these, combinations of hyperparameters were employed; 83 models were produced, and 4150 assessments were made in total. A computational model obtained by means of Support vector machines (kernel: ‘linear’, regularization parameter C = 3) was selected. Thereafter, the model was assessed through the results of the prevalence of subclinical mastitis in 373 records from sheep flocks unrelated to the ones employed for the selection of the model; the model was used for evaluation of the correct classification of the data in each of 373 sets, each of which included a test (prediction) subset with one record that referred to the farm under assessment. The median prevalence of the infection in farms classified by the model in each of the two categories was 10.4% (5.5%) and 36.3% (9.7%) (p < 0.0001). The overall accuracy of the model for the results presented by the K-means tool was 94.1%; for the estimation of the level of prevalence (<25.0%/≥25.0%) in the farms, it was 96.3%. The findings of this study indicate that machine learning algorithms can be usefully employed in predicting the level of subclinical mastitis in dairy sheep farms. This can facilitate setting up appropriate health management measures for interventions in the farms. Full article
(This article belongs to the Special Issue Advances in Mammary Gland Biology and Lactation of Ruminants: Second Edition)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-1
Back to TopTop