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The sustainability of livestock systems is widely acknowledged to be threatened by climate change on a worldwide scale. There are worries about the effects this phenomenon may have on the productivity and performance of native livestock species due to its influence on environmental stresses, such as the frequency and severity of unfavorable weather occurrences and the ongoing changes in the agro-ecological landscape. Among the most climatically tolerant livestock animals, goats can survive in a range of environments, from deserts to alpine areas. The domestic goat has undergone significant phenotypic changes in terms of shape, behavior, physiological adaptation, reproduction, and production over their evolutionary journey. It will be possible to better understand the genetic mechanisms underlying successful domestication and the practical breeding strategies leading to the improvement in productivity and resilience to environmental challenges by identifying the genes underlying these modifications. This review explores current knowledge on goat adaptation strategies, emphasizing gene expression patterns, epigenetic modifications, and whole-genome selection signatures. It examines how these molecular mechanisms enable goats to endure heat stress, hypoxia, and other environmental challenges. Furthermore, the review highlights the potential of epigenetic markers and selection signatures in developing climate-resilient goat breeds through marker-assisted selection and genome editing, offering actionable insights into sustainable goat production in the context of global climate change.

Donkeys ( Equus asinus ) have played a vital role in agriculture, transportation, and companionship, particularly in developing regions where they are indispensable working animals. The domestication of donkeys marked a significant turning point in human history, as they became essential for transportation, agriculture, and trade, especially in arid and semi-arid areas where their resilience and endurance were highly valued. In modern society, donkeys are indispensable due to their diversified applications, including meat, dairy, medicine, and functional bioproducts, supporting economic, cultural, and medical industries. Despite their critical importance, research on donkeys has historically been overshadowed with studies on horses. However, recent advancements in high-throughput sequencing and bioinformatics have significantly deepened our understanding of the molecular landscape of donkey genome, uncovering their unique adaptations, genetic diversity, and potential therapeutic applications. Microsatellite and mitochondrial DNA (mtDNA) markers have proven effective in assessing the genetic diversity of donkeys across various regions of the world. Additionally, significant strides have been made in characterizing differentially abundant genes, proteins, and metabolic profiles in donkey milk, meat, and skin, and in identifying specific genes/proteins/metabolites associated with sperm quality, motility, and reproduction. Advanced genomic technologies, such as genome-wide association studies and the identification of selection signatures, have also been instrumental in delineating genomic regions associated with phenotypic and adaptive traits. This review integrates data from diverse studies, including those on genetic diversity, transcriptomics, whole genome sequencing, protein analysis, and metabolic profiling, to provide a comprehensive overview of donkey biology. It underscores the unique characteristics of donkeys and emphasizes the importance of continued research to improve their genetic management, conservation, and agricultural use, ensuring their ongoing contribution to human societies.

Camels, known as the “Ship of the Desert,” play a vital role in the ecosystems and economies of arid and semi-arid regions. They provide meat, milk, transportation, and other essential services, and their resilience to harsh environments makes them invaluable. Despite their similarities, camel breeds exhibit notable differences in size, color, and structure, with over 40 million camels worldwide. This number is projected to increase, underscoring their growing significance. Economically, camels are crucial for food production, tourism, and trade, with camel racing being particularly significant in Arab countries. Their unique physiological traits, such as low disease susceptibility and efficient water conservation, further enhance their value. Camel products, especially meat and milk, offer substantial nutritional and therapeutic benefits, contributing to their high demand. Genetic diversity studies have advanced our understanding of camels’ adaptation to extreme environments. Functional genomics and whole-genome sequencing have identified genes responsible for these adaptations, aiding breeding programs and conservation efforts. High-throughput sequencing has revealed genetic markers linked to traits like milk production and disease resistance. The development of SNP chips has revolutionized genetic studies by providing a cost-effective alternative to whole-genome sequencing. These tools facilitate large-scale genotyping, essential for conserving genetic diversity and improving breeding strategies. To prevent the depletion of camel genetic diversity, it is crucial to streamline in situ and ex situ conservation efforts to maintain their ecological and economic value. A comprehensive approach to camel conservation and genetic preservation, involving advanced genomic technologies, reproductive biotechniques, and sustainable management practices, will ensure their continued contribution to human societies.

Background The Changthangi sheep thrive at high altitudes in the cold desert regions of Ladakh, India while Muzaffarnagri sheep are well-suited to the low altitude plains of northern India. This study investigates the molecular mechanisms of pulmonary adaptation to diverse environments by analyzing gene expression profiles of lung tissues through RNA sequencing. Methods and results Four biological replicates of lung tissue from each breed were utilized to generate the transcriptomic data. Differences in gene expression analysis revealed discrete expression profiles in lungs of each breed. In Changthangi sheep, genes related to immune responses, particularly cytokine signaling, were significantly enriched. Pathway analysis highlighted the activation of NF-kB signaling, a key mediator of inflammation and immune response. Additionally, the gene network analysis indicated a strong association between cytokine signaling, hypoxia-inducible factor (HIF) and NF-kB activation, suggesting a coordinated response to hypoxic stress in lungs of Changthangi sheep. In Muzaffarnagri sheep, the gene expression profiles were enriched for pathways related to energy metabolism, homeostasis and lung physiology. Key pathways identified include collagen formation and carbohydrate metabolism, both of which are crucial for maintaining lung function and structural integrity. Gene network analysis further reinforced this by revealing a strong connection between genes associated with lung structure and function. Conclusions Our findings shed light on the valuable insights into gene expression mechanisms that enable these sheep breeds to adapt to their respective environments and contribute to a better understanding of high altitude adaptation in livestock.

Protein is the building block for all organisms. Protein structure prediction is always a complicated task in the field of proteomics. DNA and protein databases can find the primary sequence of the peptide chain and even similar sequences in different proteins. Mainly, there are two methodologies based on the presence or absence of a template for Protein structure prediction. Template-based structure prediction (threading and homology modeling) and Template-free structure prediction (ab initio). Numerous web-based servers that either use templates or do not can help us forecast the structure of proteins. In this current study, ORF7a, a transmembrane protein of the SARS-coronavirus, is predicted using Phyre2, IntFOLD, and Robetta. The protein sequence is straightforwardly entered into the sequence bar on all three web servers. Their findings provided information on the domain, the region with the disorder, the global and local quality score, the predicted structure, and the estimated error plot. Our study presents the structural details of the SARS-CoV protein ORF7a. This immunomodulatory component binds to immune cells and induces severe inflammatory reactions.Competing Interest StatementThe authors have declared no competing interest.

Time series analysis and prediction have attained significant attention from the research community in the past few decades. However, the prediction accuracy of the models highly depends on the models’ learning process. In order to optimize resource usage, a better learning methodology, in terms of accuracy and learning time, is needed. In this context, the current research work proposes EvoLearn, a novel method to improve and optimize the learning process of neural-based models. The presented technique integrates the genetic algorithm with back-propagation to train model weights during the learning process. The fundamental idea behind the proposed work is to select the best components from multiple models during the training process to obtain an adequate model. To demonstrate the applicability of EvoLearn, the method is tested on the state-of-the-art neural models (namely MLP, DNN, CNN, RNN, and GRU), and performances are compared. Furthermore, the presented study aims to forecast two types of time series, i.e. air pollution and energy consumption time series, using the developed framework. In addition, the considered neural models are tested on two datasets of each time series type. From the performance comparison and evaluation of EvoLearn using a one-tailed paired T -test against the conventional back-propagation-based learning approach, it was found that the proposed method significantly improves the prediction accuracy.

To meet the challenges of increasing food production demand globally, extracting insights regarding the persistent agriculture-related problems on a nationwide scale is the need of the hour. Policymakers now have limited possibilities for acquiring a comprehensive knowledge of the difficulties that farmers face on a national level. In this direction, the presented work proposes a new artificial intelligence-based pipeline to gain insights at country level regarding the farmers’ demand for assistance in India. The presented study uses the data from the Kisan Call Centres, a nationwide network of farmer’s helplines, including 28.6 million call-log records, made available by the Ministry of Agriculture & Farmers’ Welfare, Government of India. Additionally, the extracted insights are presented in the form of “Topic-wise Problems’ Trend Clusters” (TPTC), which can be used by policymakers in both the government and private sectors to aid decision-making. The article also introduces a pipeline for designing forecasting models to estimate the monthly frequency of farmer inquiries (in terms of the number of query calls). The seven statistical forecasting models were examined in the study with the TBATP1 (Trigonometric seasonal components with Box-Cox transformation incorporating ARIMA errors and Trend including the Seasonal components) model attaining the lowest error rates in terms of Root Mean Square Error (0.034) and Mean Absolute Error (0.107). The study also explores numerous applications of the derived insights in the real world as well as the future scope of the presented work.