A comprehensive review of livestock development: insights into domestication, phylogenetics, diversity, and genomic advances.

Livestock plays an essential role in sustaining human livelihoods, offering a diverse range of species integral to food security, economic stability, and cultural traditions. The domestication of livestock, which began over 10,000 years ago, has driven significant genetic changes in species such as cattle, buffaloes, sheep, goats, and pigs. Recent advancements in genomic technologies, including next-generation sequencing (NGS), genome-wide association studies (GWAS), and genomic selection, have dramatically enhanced our understanding of these genetic developments. This review brings together key research on the domestication process, phylogenetics, genetic diversity, and selection signatures within major livestock species. It emphasizes the importance of admixture studies and evolutionary forces like natural selection, genetic drift, and gene flow in shaping livestock populations. Additionally, the integration of machine learning with genomic data offers new perspectives on the functional roles of genes in adaptation and evolution. By exploring these genomic advancements, this review provides insights into genetic variation and evolutionary processes that could inform future approaches to improving livestock management and adaptation to environmental challenges, including climate change.

Elucidation of population stratifying markers and selective sweeps in crossbred Landlly pig population using genome-wide SNP data.

The present study was aimed at the identification of population stratifying markers from the commercial porcine SNP 60K array and elucidate the genome-wide selective sweeps in the crossbred Landlly pig population. Original genotyping data, generated on Landlly pigs, was merged in various combinations with global suid breeds that were grouped as exotic (global pig breeds excluding Indian and Chinese), Chinese (Chinese pig breeds only), and outgroup pig populations. Post quality control, the genome-wide SNPs were ranked for their stratifying power within each dataset in TRES (using three different criteria) and FIFS programs and top-ranked SNPs (0.5K, 1K, 2K, 3K, and 4K densities) were selected. PCA plots were used to assess the stratification power of low-density panels. Selective sweeps were elucidated in the Landlly population using intra- and inter-population haplotype statistics. Additionally, Tajima's D-statistics were calculated to determine the status of balancing selection in the Landlly population. PCA plots showed 0.5K marker density to effectively stratify Landlly from other pig populations. The A-score in DAPC program revealed the Delta statistic of marker selection to outperform other methods (informativeness and FST methods) and that 3000-marker density was suitable for stratification of Landlly animals from exotic pig populations. The results from selective sweep analysis revealed the Landlly population to be under selection for mammary (NAV2), reproductive efficiency (JMY, SERGEF, and MAP3K20), body conformation (FHIT, WNT2, ASRB, DMGDH, and BHMT), feed efficiency (CSRNP1 and ADRA1A), and immunity (U6, MYO3B, RBMS3, and FAM78B) traits. More than two methods suggested sweeps for immunity and feed efficiency traits, thus giving a strong indication for selection in this direction. The study is the first of its kind in Indian pig breeds with a comparison against global breeds. In conclusion, 500 markers were able to effectively stratify the breeds. Different traits under selective sweeps (natural or artificial selection) can be exploited for further improvement.

Genomic patterns of selection in morphometric traits across diverse Indian cattle breeds.

This study seeks a comprehensive exploration of genome-wide selective processes impacting morphometric traits across diverse cattle breeds, utilizing an array of statistical methods. Morphometric traits, encompassing both qualitative and quantitative variables, play a pivotal role in characterizing and selecting livestock breeds based on their external appearance, size, and physical attributes. While qualitative traits, such as color, horn structure, and coat type, contribute to adaptive features and breed identification, quantitative traits like body weight and conformation measurements bear a closer correlation with production characteristics. This study employs advanced genotyping technologies, including the Illumina BovineSNP50 Bead Chip and next-generation sequencing methods like Reduced Representation sequencing, to identify genomic signatures associated with these traits. We applied four intra-population methods to find evidence of selection, such as Tajima's D, CLR, iHS, and ROH. We found a total of 40 genes under the selection signature, that were associated with morphometric traits in five cattle breeds (Kankrej, Tharparkar, Nelore, Sahiwal, and Gir). Crucial genes such as ADIPDQ, DPP6, INSIG1, SLC35D2 in Kankrej, LPL, ATP6V1B2, CDC14B in Tharparkar, HPSE2, PLAG1 in Nelore, PCSK1, PRKD1 in Sahiwal, and GNAQ, HPCAL1 in Gir were identified in our study. This approach provides valuable insights into the genetic basis of variations in body weight and conformation traits, facilitating informed selection processes and offering a deeper understanding of the evolutionary and domestication processes in diverse cattle breeds.

Milk exosomal microRNA profiling identified miR-375 and miR-199-5p for regulation of immune response during subclinical mastitis of crossbred cattle.

BACKGROUND: The dairy industry has experienced significant economic losses as a result of mastitis, an inflammatory disease of cows, including both subclinical and clinical cases. Milk exosome microRNAs have gained attention due to their stable and selective wrapping nature, offering potential for the prognosis and diagnosis of bovine mastitis, the most common pathological condition of the mammary gland. METHODS AND RESULTS:  In the present investigation, the microRNA profile of milk exosomes was explored using high-throughput small RNA sequencing data in sub-clinical mastitic and healthy crossbred Vrindavani cattle. In both groups, 349 microRNAs were identified, with 238 (68.19%) microRNAs co-expressed; however, 35 and 76 distinct microRNAs were found in subclinical mastitic and healthy cattle, respectively. Differential expression analysis revealed 11 microRNAs upregulated, and 18 microRNAs were downregulated in sub-clinical mastitic cattle. The functional annotation of the target genes of differentially expressed known and novel microRNAs including bta-miR-375, bta-miR-199-5p and bta-miR-12030 reveals their involvement in the regulation of immune response and inflammatory mechanisms and could be involved in development of mastitis. CONCLUSIONS: The analysis of milk exosomal miRNAs cargos hold great promise as an approach to study the underlying molecular mechanisms associated with mastitis in high milk producing dairy cattle. Concurrently, the significantly downregulated miR-375 may upregulate key target genes, including CTLA4, IHH, IRF1, and IL7R. These genes are negative regulators of immune response pathways, which could be associated with impaired inflammatory mechanisms in mammary cells. According to the findings, bta-miR-375 could be a promising biomarker for the development of mastitis in dairy cattle.

Awareness and adoption of farm biosecurity practices in commercial dairy, pig and poultry farms of Uttar Pradesh (India).

India's livestock sector has been facing significant losses due to episodes of disease outbreaks since time immemorial. Hence, biosecurity measures are very important to maintain and improve animal health along with prevention of disease outbreak. Keeping these facts into consideration, the study was proposed with an objective to assess the existing biosecurity practices adopted by the commercial dairy, pig and poultry farms. The current study was undertaken in the state of Uttar Pradesh as it is the leading state in milk and meat production. A total of 120 farmers were selected randomly including 40 each practicing commercial dairy, pig and poultry farming. An ex-post facto research methodology was used with face-to-face interview and observation to collect data. The biosecurity practices were assessed under seven dimensions such as, location and design of farm, restricted access, isolation and quarantine, cleaning and disinfection, management of feed and water, disposal of carcass, manure and waste, and health management. Results elicited that about 50% of the farmers had medium level of adoption who adopted 18-34 practices out of 51 practices. The average overall adoption score was 34.17 out of 51 (67%) which makes an overall adoption gap of 33%. Maximum adoption gap was seen in case of restricted access (43%) whereas minimum gap in adoption was seen in case of management of feed and water (27%). Pig and poultry farmers showed significantly higher biosecurity measures than dairy farmers (p < 0.05). The more significant contributors to the adoption of biosecurity measures were the level of knowledge of the farmers (p < 0.01). Other factors such as education, income, herd/flock size, Information and Communication Technology utilization, number of trainings also had a significant contribution (p < 0.05) in actual implementation of biosecurity. Hence, better understanding of these measures among the farmers must be ensured by hands on training along with proper demonstration of various procedures involved in maintaining farm biosecurity is need of the hour.

Deciphering climate resilience in Indian cattle breeds by selection signature analyses.

The signature of selection is a crucial concept in evolutionary biology that refers to the pattern of genetic variation which arises in a population due to natural selection. In the context of climate adaptation, the signature of selection can reveal the genetic basis of adaptive traits that enable organisms to survive and thrive in changing environmental conditions. Breeds living in diverse agroecological zones exhibit genetic "footprints" within their genomes that mirror the influence of climate-induced selective pressures, subsequently impacting phenotypic variance. It is assumed that the genomes of animals residing in these regions have been altered through selection for various climatic adaptations. These regions are known as signatures of selection and can be identified using various summary statistics. We examined genotypic data from eight different cattle breeds (Gir, Hariana, Kankrej, Nelore, Ongole, Red Sindhi, Sahiwal, and Tharparkar) that are adapted to diverse regional climates. To identify selection signature regions in this investigation, we used four intra-population statistics: Tajima's D, CLR, iHS, and ROH. In this study, we utilized Bovine 50 K chip data and four genome scan techniques to assess the genetic regions of positive selection for high-temperature adaptation. We have also performed a genome-wide investigation of genetic diversity, inbreeding, and effective population size in our target dataset. We identified potential regions for selection that are likely to be caused by adverse climatic conditions. We observed many adaptation genes in several potential selection signature areas. These include genes like HSPB2, HSPB3, HSP20, HSP90AB1, HSF4, HSPA1B, CLPB, GAP43, MITF, and MCHR1 which have been reported in the cattle populations that live in varied climatic regions. The findings demonstrated that genes involved in disease resistance and thermotolerance were subjected to intense selection. The findings have implications for marker-assisted breeding, understanding the genetic landscape of climate-induced adaptation, putting breeding and conservation programs into action.

Read-depth based approach on whole genome resequencing data reveals important insights into the copy number variation (CNV) map of major global buffalo breeds.

BACKGROUND: Elucidating genome-wide structural variants including copy number variations (CNVs) have gained increased significance in recent times owing to their contribution to genetic diversity and association with important pathophysiological states. The present study aimed to elucidate the high-resolution CNV map of six different global buffalo breeds using whole genome resequencing data at two coverages (10X and 30X). Post-quality control, the sequence reads were aligned to the latest draft release of the Bubaline genome. The genome-wide CNVs were elucidated using a read-depth approach in CNVnator with different bin sizes. Adjacent CNVs were concatenated into copy number variation regions (CNVRs) in different breeds and their genomic coverage was elucidated. RESULTS: Overall, the average size of CNVR was lower at 30X coverage, providing finer details. Most of the CNVRs were either deletion or duplication type while the occurrence of mixed events was lesser in number on a comparative basis in all breeds. The average CNVR size was lower at 30X coverage (0.201 Mb) as compared to 10X (0.013 Mb) with the finest variants in Banni buffaloes. The maximum number of CNVs was observed in Murrah (2627) and Pandharpuri (25,688) at 10X and 30X coverages, respectively. Whereas the minimum number of CNVs were scored in Surti at both coverages (2092 and 17,373). On the other hand, the highest and lowest number of CNVRs were scored in Jaffarabadi (833 and 10,179 events) and Surti (783 and 7553 events) at both coverages. Deletion events overnumbered duplications in all breeds at both coverages. Gene profiling of common overlapped genes and longest CNVRs provided important insights into the evolutionary history of these breeds and indicate the genomic regions under selection in respective breeds. CONCLUSION: The present study is the first of its kind to elucidate the high-resolution CNV map in major buffalo populations using a read-depth approach on whole genome resequencing data. The results revealed important insights into the divergence of major global buffalo breeds along the evolutionary timescale.

Comprehensive selection signature analyses in dairy cattle exploiting purebred and crossbred genomic data.

The main objective of the current research was to locate, annotate, and highlight specific areas of the bovine genome that are undergoing intense positive selection. Here, we are analyzing selection signatures in crossbred (Bos taurus X Bos indicus), taurine (Bos taurus), and indicine (Bos indicus) cattle breeds. Indicine cattle breeds found throughout India are known for their higher heat tolerance and disease resilience. More breeds and more methods can provide a better understanding of the selection signature. So, we have worked on nine distinct cattle breeds utilizing seven different summary statistics, which is a fairly extensive approach. In this study, we carried out a thorough genome-wide investigation of selection signatures using bovine 50K SNP data. We have included the genotyped data of two taurine, two crossbreds, and five indicine cattle breeds, for a total of 320 animals. During the 1950s, these indicine (cebuine) cattle breeds were exported with the aim of enhancing the resilience of taurine breeds in Western countries. For this study, we employed seven summary statistics, including intra-population, i.e., Tajima's D, CLR, iHS, and ROH and inter-population statistics, i.e., FST, XP-EHH, and Rsb. The NCBI database, PANTHER 17.0, and CattleQTL database were used for annotation after finding the important areas under selection. Some genes, including EPHA6, CTNNA2, NPFFR2, HS6ST3, NPR3, KCNIP4, LIPK, SDCBP, CYP7A1, NSMAF, UBXN2B, UGDH, UBE2K, and DAB1, were shown to be shared by three or more different approaches. Therefore, it gives evidence of the most intense selection in these areas. These genes are mostly linked to milk production and adaptability traits. This study also reveals selection regions that contain genes which are crucial to numerous biological functions, including those associated with milk production, coat color, glucose metabolism, oxidative stress response, immunity and circadian rhythms.

Selection signatures for fiber production in commercial species: A review.

Natural fibers derived from diverse animal species have gained increased attention in recent years due to their favorable environmental effects, long-term sustainability benefits, and remarkable physical and mechanical properties that make them valuable raw materials used for textile and non-textile production. Domestication and selective breeding for the economically significant fiber traits play an imperative role in shaping the genomes and, thus, positively impact the overall productivity of the various fiber-producing species. These selection pressures leave unique footprints on the genome due to alteration in the allelic frequencies at specific loci, characterizing selective sweeps. Recent advances in genomics have enabled the discovery of selection signatures across the genome using a variety of methods. The increased demand for 'green products' manufactured from natural fibers necessitates a detailed investigation of the genomes of the various fiber-producing plant and animal species to identify the candidate genes associated with important fiber attributes such as fiber diameter/fineness, color, length, and strength, among others. The objective of this review is to present a comprehensive overview of the concept of selection signature and selective sweeps, discuss the main methods used for its detection, and address the selection signature studies conducted so far in the diverse fiber-producing animal species.

Landmarks in the history of selective sweeps.

Half a century ago, a seminal article on the hitchhiking effect by Smith and Haigh inaugurated the concept of the selection signature. Selective sweeps are characterised by the rapid spread of an advantageous genetic variant through a population and hence play an important role in shaping evolution and research on genetic diversity. The process by which a beneficial allele arises and becomes fixed in a population, leading to a increase in the frequency of other linked alleles, is known as genetic hitchhiking or genetic draft. Kimura's neutral theory and hitchhiking theory are complementary, with Kimura's neutral evolution as the 'null model' and positive selection as the 'signal'. Both are widely accepted in evolution, especially with genomics enabling precise measurements. Significant advances in genomic technologies, such as next-generation sequencing, high-density SNP arrays and powerful bioinformatics tools, have made it possible to systematically investigate selection signatures in a variety of species. Although the history of selection signatures is relatively recent, progress has been made in the last two decades, owing to the increasing availability of large-scale genomic data and the development of computational methods. In this review, we embark on a journey through the history of research on selective sweeps, ranging from early theoretical work to recent empirical studies that utilise genomic data.

Molecular and phylogenetic analysis of MHC class I exons 7-8 in a variety of cattle and buffalo breeds.

The present study was conducted on the MHC class I (BoLA-A/BuLA-A) gene in Sahiwal, Jersey, Hariana, and Tharparkar breeds of cattle and Murrah, Mehsana, and Bhadawari breeds of buffalo to study the polymorphism. Exons 7-8 of the MHC class I gene was first characterized for polymorphism study in buffalo and the results reveal that this gene has a higher level of nucleotide changes than the cattle. Genes were investigated for polymorphisms in 285 animals of cattle and buffalo breeds. Molecular characterization of the MHC class I (BoLa-A/Bula-A) gene reveals a higher degree of polymorphism at the nucleotide level in cattle and buffalo. Results revealed this region has a higher level of polymorphisms in buffalo as campared to the cattle. Alul restriction patterns were monomorphic except for three different patterns but it was able to illustrate the differences in buffalo and cattle. SSCP analysis of exons 7-8 showed remarkable differences in cattle and buffalo. Sequence analysis revealed more closeness of Murrah breed with crossbred and indigenous cattle than Holstein Friesian. Exon 8 had more deletion and stop codon as compared to exon 7. The investigation confirmed that MHC class I BoLa-A/Bula-A exons 7-8 is highly polymorphic in buffalo as compared to cattle.

Characterization of BoLA class II DQA and DQB by PCR-RFLP, cloning, and sequencing reveals sequence diversity in crossbred cattle.

The BoLA class II DQA and DQB genes in crossbred cattle were studied using PCR-RFLP, cloning, and sequencing techniques. Seventy-two crossbred cattle (Vrindavani) were used in the current study. HaeIII and XbaI restriction enzymes digested DQA exon 2-3, revealing seven (HaeIII-A-G) and three (XbaI A-C) motifs, respectively. The BoLA-DQB gene was analyzed using PCR-RFLP with PstI and TaqI restriction enzymes, yielding five restriction motifs for each restriction enzyme (PstI-A-E and TaqI-A-E). In crossbred cattle, addition, deletion, and substitutions were observed in distinct sequences, resulting in variations in overall gene length. Changes in nucleotides at positions 64-80, 110-200, and 207-264 were largely responsible for polymorphism in DQA exon 2. The phylogenetic analysis predicted a high degree of nucleotide and amino acid changes in DQA exon 2-3 and DQB exon 2. DQA genes had a nucleotide dissimilarity of 0.3-25.4 percent, while DQB genes had a nucleotide dissimilarity of 1.5-14.3 percent. We cloned and sequenced 20 genotypes based on PCR-RFLP of the DQA and DQB genes. The current study observed variation in the DQA and DQB genes and will serve as a foundation for future research on the BoLA DQA and DQB genes.

Progress and future perspectives of livestock genomics in India: a mini review.

The field of genetics has evolved a lot after the emergence of molecular and advanced genomic technologies. The advent of Next Generation Sequencing, SNP genotyping platforms and simultaneous reduction in the cost of sequencing had opened the door to genomic research in farm animals. There are various applications of genomics in livestock, such as the use of genomic data: (i) to investigate genetic diversity and breed composition/population structure (ii) to identify genetic variants and QTLs related to economically important and ecological traits, genome-wide association studies (GWAS) and genomic signatures of selection; (iii) to enhance breeding programs by genomic selection. Compared to traditional methods, genomic selection is expected to improve selection response by increasing selection accuracy and reducing the generation interval due to early selection. Genomic selection (GS) in developed countries has led to rapid genetic gains, especially in dairy cattle, due to a well-established genetic evaluation system. Indian livestock system is still lagging behind developed nations in adopting these technologies. This review discusses the current status, challenges, and future perspectives of livestock genomics in India.

Genome wide copy number variations using Porcine 60K SNP Beadchip in Landlly pigs.

In the present study, Porcine 60K SNP genotype data from 69 Landlly pigs were used to explore Copy Number Variations (CNVs) across the autosomes. A total of 386 CNVs were identified using Hidden Markov Model (HMM) in PennCNV software, which were subsequently aggregated to 115 CNV regions (CNVRs). Among the total detected CNVRs, 58 gain, 49 were loss type while remaining 8 events were both gain and loss types. Identified CNVRs covered 12.5 Mb (0.55%) of Sus scrofa reference 11.1 genome. Comparison of our results with previous investigations on pigs revealed that approximately 75% CNVRs were novel, which may be due to differences in genetic background, environment and implementation of artificial selection in Landlly pigs. Functional annotation and pathway analysis showed the significant enrichment of 267 well-annotated Sus scrofa genes in CNVRs. These genes were involved in different biological functions like sensory perception, meat quality traits, back fat thickness and immunity. Additionally, KIT and FUT1 were two major genes detected on CNVR in our population. This investigation provided a comprehensive overview of CNV distribution in the Indian porcine genome for the first time, which may be useful for further investigating the association of important quantitative traits in Landlly pigs.Highlights115 CNVRs were identified in 69 Landlly pig population.Approximately 75% detected CNVRs were novel for Landlly population.Significant enrichment of 267 well-annotated Sus scrofa genes observed in these CNVRs.These genes were involved in different biological functions like sensory perception, meat quality traits, back fat thickness and immunity.Comprehensive CNV map in the Indian porcine genome developed for the first time.

Evidence for selective sweeps in the MHC gene repertoire of various cattle breeds.

Major Histocompatibility Complex (MHC) genes are among the immune genes that have been extensively studied in vertebrates and are necessary for adaptive immunity. In the immunological response to infectious diseases, they play several significant roles. This research paper provides the selection signatures in the MHC region of the bovine genome as well as how certain genes related to innate immunity are undergoing a positive selective sweep. Here, we investigated signatures of historical selection on MHC genes in 15 different cattle populations and a total of 427 individuals. To identify the selection signatures, we have used three separate summary statistics. The findings show potential selection signatures in cattle from whom we isolated genes involved in the MHC. The most significant regions related to the bovine MHC are BOLA, non-classical MHC class I antigen (BOLA-NC1), Microneme protein 1 (MIC1) , Cluster of Differentiation 244 (CD244), Gap Junction Alpha-5 Protein (GJA5). It will be possible to gain new insight into immune system evolution by understanding the distinctive characteristics of MHC in cattle.

Genome-wide detection of copy number variations in Tharparkar cattle.

Copy number variations (CNVs) are major forms of genetic variation with an increasing importance in animal genomics. This study used the Illumina BovineSNP 50 K BeadChip to detect the genome-wide CNVs in the Tharparkar cattle. With the aid of PennCNV software, we noticed a total of 447 copy number variation regions (CNVRs) across the autosomal genome, occupying nearly 2.17% of the bovine genome. The average size of detected CNVRs was found to be 122.2 kb, the smallest CNVR being 50.02 kb in size, to the largest being 1,232.87 Kb. Enrichment analyses of the genes in these CNVRs gave significant associations with molecular adaptation-related Gene Ontology (GO) terms. Most CNVR genes were significantly enriched for specific biological functions; signaling pathways, sensory responses to stimuli, and various cellular processes. In addition, QTL analysis of CNVRs described them to be linked with economically essential traits in cattle. The findings here provide crucial information for constructing a more comprehensive CNVR map for the indigenous cattle genome.

Detection of genome-wide copy number variation in Murrah buffaloes.

Riverine Buffaloes, especially the Murrah breed because of their adaptability to harsh climatic conditions, is farmed in many countries to convert low-quality feed into valuable dairy products and meat. Here, we investigated the copy number variations (CNVs) in 296 Murrah buffalo using the Axiom® Buffalo Genotyping Array 90K (Affymetrix, Santa Clara, CA, USA). The CNVs were detected on the autosomes, using the Copy Number Analysis Module (CNAM) using the univariate analysis. 7937 CNVs were detected in 279 Buffaloes, the average length of the CNVs was 119,048.87 bp that ranged between 7800 and 4,561,030 bp. These CNVs were accounting for 10.33% of the buffalo genome, which was comparable to cattle, sheep, and goat CNV analyses. Further, CNVs were merged and 1541 CNVRs were detected using the Bedtools-mergeBed command. 485 genes were annotated within 196 CNVRs that were identified in at least 10 animals of Murrah population. Out of these, 40 CNVRs contained 59 different genes that were associated with 69 different traits. Overall, the study identified a significant number of CNVs and CNVRs in the Murrah breed of buffalo, with a wide range of lengths and frequencies across the autosomes. The identified CNVRs contained genes associated with important traits related to production and reproduction, making them potentially important targets for future breeding and genetic improvement efforts.

Uncovering genes underlying coat color variation in indigenous cattle breeds through genome-wide positive selection.

The identification of candidate genes related to pigmentation and under selective sweep provides insights into the genetic basis of pigmentation and the evolutionary forces that have shaped this variation. The selective sweep events in the genes responsible for normal coat color in Indian cattle groups are still unknown. To find coat color genes displaying signs of selective sweeps in the indigenous cattle, we compiled a list of candidate genes previously investigated for their association with coat color and pigmentation. After that, we performed a genome-wide scan of positive selection signatures using the BovineSNP50K Bead Chip in 187 individuals of seven indigenous breeds. We applied a wide range of methods to find evidence of selection, such as Tajima's D, CLR, iHS, varLD, ROH, and FST. We found a total of sixteen genes under selective sweep, that were involved in coat color and pigmentation physiology. These genes are CRIM1 in Gir, MC1R in Sahiwal, MYO5A, PMEL and POMC in Tharparkar, TYRP1, ERBB2, and ASIP in Red Sindhi, MITF, LOC789175, PAX3 and TYR in Ongole, and IRF2, SDR165 and, KIT in Nelore, ADAMTS19 in Hariana. These genes are related to melanin synthesis, the biology of melanocytes and melanosomes, and the migration and survival of melanocytes during development.

Genome-wide transcriptome profiling of CSF virus challenged monocyte-derived macrophages provides distinct insights into immune response of Landrace and indigenous Ghurrah pigs.

The present study was undertaken to characterize the distinct immune response in indigenous Ghurrah and exotic Landrace pigs by challenging monocyte-derived macrophages (MDMs) with CSF virus under in-vitro conditions and assessing the variations in the transcriptome profile at 48 h post-infection (hpi). RNA-sequencing was carried out in infected and non-infected MDMs of Ghurrah (n = 3) and Landrace (n = 3) piglets prior- as well as post-stimulation. MDMs of Ghurrah showed greater immune regulation in response to CSF infection with 518 significantly differentially expressed genes (DEG) in infected versus non-infected MDMs, as compared to only 31 DEGs in Landrace MDMs. In Landrace, the principal regulators of inflammation (IL1α, IL1ß and TNF) were upregulated in infected cells while in Ghurrah, these were downregulated. Overall, macrophages from indigenous Ghurrah showed more immunological dysregulation in response to virulent CSF virus infection as compared to the exotic Landrace pigs.

Predicting opinion using deep learning: From burning to sustainable management of organic waste in Indian State of Punjab.

In winter season, the burning of crop residues for ease of sowing the next crop, along with industrial emissions and vehicular pollution leads to settling of a thick layer of smog in northern part of India. Therefore, to understand the opinion of farmers regarding sustainable management of organic waste, the present study was conducted in Ludhiana district of Indian state of Punjab. An ex post facto research design was used and a total of 800 dairy farmers having significant crop area were selected randomly for the study, grouped equally as small and large dairy farmers. Results revealed that majority of farmers had a highly favourable opinion regarding organic waste management due to the fact that they were aware of the ill-effects of undesirable practices like crop residue burning. Further, to predict the farmers' opinion and the effect of independent variables on farmers' opinion, a multi-layer perceptron feed-forward deep neural network was developed with mean squared error of 0.036 and 0.137 for validation and training data sets respectively, marking a novel approach of analysing farmers' behaviour. The neural network highlighted that with increase in the magnitude of input variables, namely, education, experience in dairying, information source utilisation, knowledge regarding organic waste management, etc., the farmers' opinion regarding sustainable waste management increases. The study concluded with the impression that cognitive processes like education, information and knowledge play a significant role in forming the opinion of the farmers. Therefore, efforts focusing on enhancing cognition should be made for sustainable management of organic waste.