Letters In Animal Biology 2024-03-01T14:09:19+00:00 Editor-In-Chief: Dr. Jubeda Begum Open Journal Systems <p><strong><em>Letters in Animal Biology</em></strong> (<em>LIAB</em>) is an open access, peer-reviewed international journal that publishes the results of original research pertaining to animal biology. <em>LIAB</em> encompasses a broad range of topics on animal production, health, and welfare along with the fundamental aspects of genetics, physiology, nutrition, medicine, microbiology, biotechnology, biochemistry, reproduction, and animal products. Articles published in <em>LIAB</em> cover research topics on all domestic animals, birds, and companion animals; however, the topics on wildlife species, laboratory animals, and other aquatic species will be considered for publication as long as they have direct or indirect implications on animal production, health, or other biological aspects. <em>LIAB</em> publishes high quality research articles, reviews, short communications, and case reports.</p> Genetic diversity analysis by using Heterologous Microsatellite markers among cattle and buffalo breeds 2024-03-01T14:09:19+00:00 Manpreet Kaur Simarjeet Kaur Puneet Malhotra Chandra Shekhar Mukhopadhyay <div class="page" title="Page 1"> <div class="section"> <div class="layoutArea"> <div class="column"> <div class="page" title="Page 1"> <div class="section"> <div class="layoutArea"> <div class="column"> <p>Microsatellite markers have become a reliable technique for genetic diversity studies, parentage analysis, and breed characterization in animals. The 18 amplified heterologous microsatellite markers out of 20 microsatellites were used for studying the genetic variation among cattle and buffalo. The estimated mean allelic diversity for cattle breeds was 12.50 for Sahiwal, 10.94 alleles in HF crossbred, and 10.444 and 10.944 alleles for Murrah and Nili Ravi breeds of buffalo, respectively. The Sahiwal breed had the highest allelic diversity compared to other studied breeds. A high level of genetic variability was observed for the observed heterozygosity (0.857±0.027) and expected heterozygosity (0.811±0.017) between the Sahiwal and HF crossbred breed of cattle. A low level of genetic variability was observed between the Murrahand Nili Ravi breeds of buffalo. The FIS values -0.156 to 0.065 depicted low inbreeding in the breeds. The Nei's genetic distance was measured for all the breeds which showed the genetic distance/divergence between the HF crossbred and Sahiwal was 1.070. The genetic difference based on Nei's genetic distance between the cattle HF crossbred and Nili Ravi breed of buffalo was 2.456. The genetic difference between the Nili Ravi breed of buffalo and the HF crossbred was the highest. The principal component analysis accurately reflected genetic distances, forming distinct groups for HF crossbred, Sahiwal, Murrah, and Nili Ravi. The HF crossbred and Sahiwal appeared in different coordinates, indicating the notable genetic distance between these breeds, while Nili Ravi and Murrah clustered together in a single coordinate. These groups showcased clear genetic distinctiveness. The bottleneck analysis exhibited the typical L-shaped pattern, implying that all breeds did not undergo a recent bottleneck and were not at risk of potential extinction.</p> </div> </div> </div> </div> </div> </div> </div> </div> 2024-04-21T00:00:00+00:00 Copyright (c) 2024 All the papers accepted for publication in LIAB will be published as open access. Advancing vector-borne disease prediction through functional classifier integration: A novel approach for enhanced modeling 2024-01-07T13:58:27+00:00 Mokammel Hossain Tito Most Hoor E Jannat Mst Tachhlima Aktar Barshon Saha Puja Das Md. Kawser Md. Arafat Hossain Md. Neyamul Islam Muhammad Shahzad Chohan Shahzad Khan <div class="page" title="Page 1"> <div class="section"> <div class="layoutArea"> <div class="column"> <p>This paper evaluates various machine learning models for predicting vector-borne diseases, focusing on performance metrics that reveal insights into their efficacy. The Multilayer Perceptron (MLP) model demonstrated the highest accuracy at 92%, surpassing the Simple Logistic (SL) and Support Vector Machine (SVM) models, which achieved 88% and 90.87% accuracy, respectively. Notably, the MLP model excelled in precision, recall, and F-Measure, indicating superior classification accuracy. Conversely, the SVM model exhibited noteworthy computational efficiency with the lowest processing time at 0.3 seconds, emphasizing its potential for real-time applications in public health interventions. In contrast, the Radial Basis Function Network (RBFN) lagged in accuracy and other metrics. The results underscore the trade-offs between accuracy and computational efficiency, emphasizing the need for a nuanced model selection. Considering the holistic evaluation, the SVM model emerged as a compelling choice, balancing high accuracy and efficient processing, making it promising for real-time public health applications. This study contributes valuable insights into machine learning model performance, emphasizing the importance of selecting models tailored to the specific needs of vector-borne disease prediction. As we confront emerging infectious diseases, the SVM model stands as an indispensable tool, supporting a proactive and data-driven approach to mitigate the global health impact of vector- borne diseases.</p> </div> </div> </div> </div> 2024-02-26T00:00:00+00:00 Copyright (c) 2024 All the papers accepted for publication in LIAB will be published as open access. Life Cycle Assessment as an evaluation tool- A critical review on carbon footprint in dairy sector 2023-12-14T13:04:27+00:00 Gokul Tamilselvan Nitin Tyagi <div class="page" title="Page 1"> <div class="section"> <div class="layoutArea"> <div class="column"> <p>Global warming, a pressing issue affecting countries worldwide, is primarily driven by greenhouse gases emission from various sources including natural disasters and human activities, including industrial processes, agriculture, livestock farming, and the use of fossil fuels. This review specifically addresses the carbon emissions related with dairy farming for milk. While there are several methods available for assessing the dairy carbon footprint, this review concentrates on the widely accepted Life Cycle Assessment (LCA) method recommended mainly by Intergovernmental Panel on Climatic Change. LCA is favoured globally for its comprehensive coverage of the entire product life cycle. The review delves into the application of the LCA method at the farm level, detailing the stages involved in the life cycle assessment. It also provides an in- depth discussion on carbon footprint up to the farm gate level and extends its analysis to encompass the carbon footprint beyond the farm gate for milk production. A significant portion of the review is dedicated in order to elucidate the carbon footprint of dairy cattle and buffalo farming in various countries, drawing insights from diverse research studies worldwide. The focus is primarily on large ruminants, considering that a substantial portion of enteric methane emissions arises from cattle and buffaloes. The review meticulously presents total carbon footprint values for milk production, derived from the cumulative emissions associated with diverse activities involved in the production of milk. This comprehensive examination leads to understanding of the environmental impact of dairy farming and underscores the need for sustainable practices to mitigate the carbon footprint related with milk production globally.</p> </div> </div> </div> </div> 2024-01-08T00:00:00+00:00 Copyright (c) 2024 All the papers accepted for publication in LIAB will be published as open access. A systematic overview of bovine brucellosis and its implications for public health 2023-12-16T17:45:32+00:00 Hasnain Idrees Javairia Abbas Ali Raza Muhammad Haseeb Qamar Muhammad Ahtsham Waheed Muhammad Bilal Arshad Muhammad Hammad Ali Abdul Jabar Mazhar Farooq Tayyab Rehman Abdul Raheem <div class="page" title="Page 1"> <div class="section"> <div class="layoutArea"> <div class="column"> <p>Comprehensive study reveals the complex nature of bovine brucellosis, with special emphasis on its potential for transmission to humans. By adopting a systemic methodology thorough literature search was done, including publications published during 1897 to 2022, form academic databases, research repositories, and scholarly credible literature sources. The criteria for being included in this study ensured an in-depth investigation of the previous and current facets of the disease. The methodological extraction and processing of data enabled for the categorising of pertinent findings into particular groups, which enhanced the comprehension of characteristics, transmission trends, clinical aspects, diagnostic tools, treatment options, prevention strategies, vaccination, and public health concerns. This study highlights the importance of this zoonotic threat indicating the compelling need of effective solutions of this problem by keeping in view the zoonotic variables and epidemiological views. Understanding disease origins and transmission patterns help design effective control strategies that are tailored to each mode of transmission. A detailed evaluation of symptoms shows that humans and animals need appropriate diagnosis. Comprehensive treatment and preventative measures are supported by the review. The study of vaccination as a preventive measure underlines its importance in eliminating bovine brucellosis. Public health issues require knowledge, surveillance, and economic concerns. In conclusion, this comprehensive study not only explores uncharted research grounds but also serves as a beacon for researchers and policy makers. Through an in-depth analysis of the interaction between bovine brucellosis and public health, findings in this study provide crucial information that is useful to a wide range of stakeholders. This systemic review provides a clear and comprehensive perspective of the public health implication of bovine brucellosis by emphasising the need of specific interventions and preventive measures.</p> </div> </div> </div> </div> 2024-01-07T00:00:00+00:00 Copyright (c) 2024 All the papers accepted for publication in LIAB will be published as open access.