DeepGenomeScan of 15 Worldwide Bovine Populations Detects Spatially Varying Positive Selection Signals.

Identifying genomic regions under selection is essential for understanding the genetic mechanisms driving species evolution and adaptation. Traditional methods often fall short in detecting complex, spatially varying selection signals. Recent advances in deep learning, however, present promising new approaches for uncovering subtle selection signals that traditional methods might miss. In this study, we utilized the deep learning framework DeepGenomeScan to detect spatially varying selection signatures across 15 bovine populations worldwide. Our analysis uncovered novel insights into selective sweep hotspots within the bovine genome, revealing key genes associated with physiological and adaptive traits that were previously undetected. We identified significant quantitative trait loci linked to milk protein and fat percentages. By comparing the selection signatures identified in this study with those reported in the Bovine Genome Variation Database, we discovered 38 novel genes under selection that were not identified through traditional methods. These genes are primarily associated with milk and meat yield and quality. Our findings enhance our understanding of spatially varying selection's impact on bovine genomic diversity, laying a foundation for future research in genetic improvement and conservation. This is the first deep learning-based study of selection signatures in cattle, offering new insights for evolutionary and livestock genomics research.

Machine Learning-Aided Ultra-Low-Density Single Nucleotide Polymorphism Panel Helps to Identify the Tharparkar Cattle Breed: Lessons for Digital Transformation in Livestock Genomics.

Cattle breed identification is crucial for livestock research and sustainable food systems, and advances in genomics and artificial intelligence present new opportunities to address these challenges. This study investigates the identification of the Tharparkar cattle breed using genomics tools combined with machine learning (ML) techniques. By leveraging data from the Bovine SNP 50K chip, we developed a breed-specific panel of single nucleotide polymorphisms (SNPs) for Tharparkar cattle and integrated data from seven other Indian cattle populations to enhance panel robustness. Genome-wide association studies (GWAS) and principal component analysis were employed to identify 500 SNPs, which were then refined using ML models-AdaBoost, bagging tree, gradient boosting machines, and random forest-to determine the minimal number of SNPs needed for accurate breed identification. Panels of 23 and 48 SNPs achieved accuracy rates of 95.2-98.4%. Importantly, the identified SNPs were associated with key productive and adaptive traits, thus attesting to the value and potentials of digital transformation in livestock genomics. The ML-aided ultra-low-density SNP panel approach reported here not only facilitates breed identification but also contributes to preserving genetic diversity and guiding future breeding programs.

Hepatocyte-specific Epidermal Growth Factor Receptor Deletion Promotes Fibrosis but has no Effect on Steatosis in Fast-food Diet Model of Metabolic Dysfunction-associated Steatotic Liver Disease.

BACKGROUND & AIMS: Metabolic dysfunction-associated steatotic liver disease (MASLD) has become the most prevalent chronic liver disorder, with no approved treatment. Our previous work demonstrated the efficacy of a pan-ErbB inhibitor, Canertinib, in reducing steatosis and fibrosis in a murine fast-food diet (FFD) model of MASLD. The current study explores the effects of hepatocyte-specific ErbB1 (ie, epidermal growth factor receptor [EGFR]) deletion in the FFD model. METHODS: EGFRflox/flox mice, treated with AAV8-TBG-CRE to delete EGFR specifically in hepatocytes (EGFR-KO), were fed either a chow-diet or FFD for 2 or 5 months. RESULTS: Hepatocyte-specific EGFR deletion reduced serum triglyceride levels but did not prevent steatosis. Surprisingly, hepatic fibrosis was increased in EGFR-KO mice in the long-term study, which correlated with activation of transforming growth factor-ß/fibrosis signaling pathways. Further, nuclear levels of some of the major MASLD regulating transcription factors (SREBP1, PPARγ, PPARα, and HNF4α) were altered in FFD-fed EGFR-KO mice. Transcriptomic analysis revealed significant alteration of lipid metabolism pathways in EGFR-KO mice with changes in several relevant genes, including downregulation of fatty-acid synthase and induction of lipolysis gene, Pnpla2, without impacting overall steatosis. Interestingly, EGFR downstream signaling mediators, including AKT, remain activated in EGFR-KO mice, which correlated with increased activity pattern of other receptor tyrosine kinases, including ErbB3/MET, in transcriptomic analysis. Lastly, Canertinib treatment in EGFR-KO mice, which inhibits all ErbB receptors, successfully reduced steatosis, suggesting the compensatory roles of other ErbB receptors in supporting MASLD without EGFR. CONCLUSIONS: Hepatocyte-specific EGFR-KO did not impact steatosis, but enhanced fibrosis in the FFD model of MASLD. Gene networks associated with lipid metabolism were greatly altered in EGFR-KO, but phenotypic effects might be compensated by alternate signaling pathways.

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.

Effect of selective fermentation on nutritional parameters and techno-functional characteristics of fermented millet-based probiotic dairy product.

The primary goal of this study was to assess the effect of selective fermentation on the nutritional and techno-functional characteristics of fermented millet-skim milk-based product. The product was made with HHB-311 biofortified pearl millet (PM) flour, skim milk powder, and isolated cultures (either alone or in combination) of Limosilactobacillus fermentum MS005 (LF) and Lactobacillus rhamnosus GG 347 (LGG). To optimize fermentation time, time intervals 8, 16, and 24 h were explored, while the temperature was kept 37 °C. Results of protein digestibility showed that LF (16 h) and LGG (24 h) fermented samples had significantly higher (P < 0.05) protein digestibility of 90.75 ± 1.6% and 93.76 ± 3.4%, respectively, than that of control (62.60 ± 2.6%). Further, 16 h fermentation with LF showed enhanced iron (39%) and zinc (14%) bioavailability. The results suggested that LF with 16 h fermentation is most suitable for making millet-based fermented products with superior techno-functional attributes and micronutrient bioavailability.

The evolution of contemporary livestock species: Insights from mitochondrial genome.

The domestication of animals marks a pivotal moment in human history, profoundly influencing our demographic and cultural progress. This process has led to significant genetic, behavioral, and physical changes in livestock species compared to their wild ancestors. Understanding the evolutionary history and genetic diversity of livestock species is crucial, and mitochondrial DNA (mtDNA) has emerged as a robust marker for investigating molecular diversity in animals. Its highly conserved gene content across animal species, minimal duplications, absence of introns, and short intergenic regions make mtDNA analysis ideal for such studies. Mitochondrial DNA analysis has uncovered distinct cattle domestication events dating back to 8000 years BC in Southwestern Asia. The sequencing of water buffalo mtDNA in 2004 provided important insights into their domestication history. Caprine mtDNA analysis identified three haplogroups, indicating varied maternal origins. Sheep, domesticated 12,000 years ago, exhibit diverse mtDNA lineages, suggesting multiple domestication events. Ovine mtDNA studies revealed clades A, B, C, and a fourth lineage, group D. The origins of domestic pigs were traced to separate European and Asian events followed by interbreeding. In camels, mtDNA elucidated the phylogeographic structure and genetic differentiation between wild and domesticated species. Horses, domesticated around 3500 BC, show significant mtDNA variability, highlighting their diverse origins. Yaks exhibit unique adaptations for high-altitude environments, with mtDNA analysis providing insights into their adaptation. Chicken mtDNA studies supported a monophyletic origin from Southeast Asia's red jungle fowl, with evidence of multiple origins. This review explores livestock evolution and diversity through mtDNA studies, focusing on cattle, water buffalo, goat, sheep, pig, camel, horse, yak and chicken. It highlights mtDNA's significance in unraveling maternal lineages, genetic diversity, and domestication histories, concluding with insights into its potential application in improving livestock production and reproduction dynamics.

Nanotechnology Integrated Innovative Drug Delivery and Therapy for Cancer.

Cancer remains a significant global health challenge, necessitating innovative approaches to enhance the efficacy and specificity of therapeutic interventions while minimizing adverse effects on healthy tissues. Nanotechnology has emerged as a promising avenue in cancer treatment, offering novel strategies for targeted drug delivery. Nanoparticles, liposomes, and polymer-based systems have played pivotal roles in revolutionizing cancer therapy. Nanotechnology possesses unique physicochemical properties, enabling efficient encapsulation of therapeutic agents and controlled and prolonged release at tumour sites. Advancement in formulations using nanotechnology has made it possible to make multifunctional systems that respond to the microenvironment of a tumour by releasing payloads in response to changes in pH, temperature, or enzymes. Stimuli-responsive polymers can release drugs in response to external cues, enabling site-specific drug release and minimizing systemic exposure. This review explores recent studies and preclinical trials that show how nanoparticles, liposomes, and polymerbased systems could be used to treat cancer, discussing challenges such as scalability, regulatory approval, and potential toxicity concerns along with patents published recently.

A Novel Role for the Phosphatidylinositol-4,5-Bisphosphate 3-Kinase Delta Isoform in Hepatocellular Proliferation.

The phosphatidylinositol-4,5-bisphosphate 3-kinase delta isoform (Pik3cd), usually considered immune-specific, was unexpectedly identified as a gene potentially related to either regeneration and/or differentiation in animals lacking hepatocellular Integrin Linked Kinase (ILK). Since a specific inhibitor (Idelalisib, or CAL101) for the catalytic subunit encoded by Pik3cd (p110δ) has reported hepatotoxicity when used for treating chronic lymphocytic leukemia and other lymphomas, the authors aimed to elucidate whether there is a role for p110δ in normal liver function. To determine the effect on normal liver regeneration, partial hepatectomy (PHx) was performed using mice in which p110δ was first inhibited using CAL101. Inhibition led to over a 50% decrease in proliferating hepatocytes in the first 2 days after PHx. This difference correlated with phosphorylation changes in the HGF and EGF receptors (MET and EGFR, respectively) and NF-κB signaling. Ingenuity Pathway Analyses implicated C/EBPß, HGF, and the EGFR heterodimeric partner, ERBB2, as three of the top 20 regulators downstream of p110δ signaling because their pathways were suppressed in the presence of CAL101 at 1 day post-PHx. A regulatory role for p110δ signaling in mouse and rat hepatocytes through MET and EGFR was further verified using hepatocyte primary cultures, in the presence or absence of CAL101. Combined, these data support a role for p110δ as a downstream regulator of normal hepatocytes when stimulated to proliferate.

Advancements in CRISPR-Based Therapies for Genetic Modulation in Neurodegenerative Disorders.

: Neurodegenerative disorders pose significant challenges in the realm of healthcare, as these conditions manifest in complex, multifaceted ways, often attributed to genetic anomalies. With the emergence of CRISPR (Clustered Regularly Interspaced Short Palindromic Repeats) technology, a new frontier has been unveiled in the quest for targeted, precise genetic manipulation. This abstract explores the recent advancements and potential applications of CRISPR-based therapies in addressing genetic components contributing to various neurodegenerative disorders. The review delves into the foundational principles of CRISPR technology, highlighting its unparalleled ability to edit genetic sequences with unprecedented precision. In addition, it talks about the latest progress in using CRISPR to target specific genetic mutations linked to neurodegenerative diseases like Huntington's disease, Alzheimer's disease, amyotrophic lateral sclerosis (ALS), and Parkinson's disease. It talks about the most important studies and trials that show how well and safely CRISPR-based therapies work. This shows how this technology can change genetic variants that cause diseases. Notably, the discussion emphasizes the challenges and ethical considerations associated with the implementation of CRISPR in clinical settings, including off-target effects, delivery methods, and long-term implications. Furthermore, the article explores the prospects and potential hurdles in the widespread application of CRISPR technology for treating neurodegenerative disorders. It touches upon the need for continued research, improved delivery mechanisms, and ethical frameworks to ensure responsible and equitable access to these groundbreaking therapies.

Genomic insights into key genes and QTLs involved in cattle reproduction.

From an economic standpoint, reproductive characteristics are fundamental for sustainable production, particularly for monotocous livestock like cattle. A longer inter-calving interval is indicative of low reproductive capacity. This issue changes the dynamics of current and future lactations since it necessitates more inseminations, veterinary care, and hormone interventions. Various reproductive phenotypes, including ovulation, mating, fertility, pregnancy, embryonic growth, and calving-related traits, are observed in dairy cattle, and these traits have been associated with several QTLs. Calving ease, age at puberty, scrotal circumference, and inseminations per conception have been associated with 4437, 10623, 10498, and 2476 Quantitative Trait Loci (QTLs), respectively. This data offers valuable insights into enhancing and comprehending reproductive traits in livestock breeding. Studying QTLs associated with reproductive traits has far-reaching implications across various fields, from agriculture and animal husbandry to human health, evolutionary biology, and conservation. It provides the foundation for informed breeding practices, advances in biotechnology, and a deeper understanding of the genetic underpinnings of reproduction.

Therapeutic Expedition of Luteolin against Brain-related Disorders: An Updated Review.

Brain-related disorders include neuroinflammation, neurodegenerative disorders, and demyelination, which ultimately affect the quality of life of patients. Currently, brain-related disorders represent the most challenging health problem worldwide due to complex pathogenesis and limited availability of drugs for their management. Further, the available pharmacotherapy accompanies serious side effects, therefore, much attention has been directed toward the development of alternative therapy derived from natural sources to treat such disorders. Recently, flavonoids, natural phytochemicals, have been reported as a treatment option for preventing brain aging and disorders related to this. Among these flavonoids, dietary luteolin, a flavone, is found in many plant products such as broccoli, chamomile tea, and honeysuckle bloom having several pharmacological properties including neuroprotective activities. Therefore, the objective of this paper is to compile the available literature regarding the neuroprotective potential of luteolin and its mechanism of action. Luteolin exerts notable anti-inflammatory, antioxidant, and antiapoptotic activity suggesting its therapeutic efficacy in different neurological disorders. Numerous in-vivo and in-vitro experiments have revealed that luteolin exhibits neuroprotective potential via up-regulating the ER/ERK, PI3AKT, Nrf2 pathways and down-regulating the MAPK/JAK2STAT and NFκB pathways. Taking into account of available facts regarding the neuroprotective efficacy of luteolin, the current study highlights the beneficial effects of luteolin for the prevention, management, and treatment of different neurological disorders. Thus, luteolin can be considered an alternative for the development of new pharmacophores against various brain-related disorders.

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.

Seaweed-inspired underwater anti-oil-fouling and anti-fogging coating with mechanical durability.

Ecofriendly fabrication of anti-oil-fouling materials is of interest. Surfaces with underwater superoleophobicity have been fabricated which exhibit limited mechanical durability and water resistance. In this study, we report on a bioinspired bilayer design of a transparent anti-oil-fouling coating. Seaweed surfaces show anti-oil-fouling in the sea due to its high surface hydration ability. Mussels can adhere tightly onto a surface with good stability in the sea by virtue of its levodopa-containing secretions. The surface layer was fabricated using a crosslinked combination of carboxymethyl cellulose (CMC) and sodium alginate (AlgS) inspired by seaweed, with the addition of calcium ions. Polydopamine (PDA), a derivative of levodopa, was used as the underlayer to enhance bonding strength and water resistance. Oil that adhered to the coated surface was spontaneously detached upon immersion in water. The mechanism underlying this anti-oil-fouling effect was elucidated using Gibbs free energy theory. The coating exhibited mechanical durability and water resistance. The coating is transparent and preserves the original color of the substrate. The coated glass showed stable anti-fogging and anti-frost performance. These coatings hold promise for a wide range of anti-oil-fouling applications.

Stroke incidence, mortality, subtypes in rural and urban populations in five geographic areas of India (2018-2019): results from the National Stroke Registry Programme.

Background: Increasing stroke burden in India demands a long-term stroke surveillance framework. Earlier studies in India were urban-based, short term and provided limited data on stroke incidence and its outcomes. This gap is addressed by the establishment of five population-based stroke registries (PBSRs) of the National Stroke Registry Programme, India. This paper describes stroke incidence, mortality and age, sex, and subtypes distribution in the five PBSRs with urban and rural populations. Methods: First-ever incident stroke patients in age group ≥18 years, resident for at least one year in the defined geographic area, identified from health facilities were registered. Death records with stroke as the cause of death from the Civil Registration System (CRS) were included. Transient ischemic attack (TIA) was excluded. Three PBSRs (Cuttack, Tirunelveli, Cachar) included urban and rural populations. PBSRs in Kota and Varanasi were urban areas. The crude and age-standardized incidence rate (ASR) by age, sex, and residence (urban and rural), rate ratios of ASR, case fatality proportions and rates at day 28 after onset of stroke were calculated for years 2018-2019. Findings: A total of 13,820 registered first-ever stroke cases that included 985 death certificate-only cases (DCOs) were analysed. The pooled crude incidence rate was 138.1 per 100,000 population with an age-standardized incidence rate (ASR) of 103.4 (both sexes), 125.7 (males) and 80.8 (females). The risk of stroke among rural residents was one in seven (Cuttack), one in nine (Tirunelveli), and one in 15 (Cachar). Ischemic stroke was the most common type in all PBSRs. Age-standardized case fatality rates (ASCFR) per 100,000 population for pooled PBSRs was 30.0 (males) and 18.8 (females), and the rate ratio (M/F) ranged from 1.2 (Cuttack) to 2.0 (Cachar). Interpretation: Population-based registries have provided a comprehensive stroke surveillance platform to measure stroke burden and outcomes by age, sex, residence and subtype across India. The rural-urban pattern of stroke incidence and mortality shall guide health policy and programme planning to strengthen stroke prevention and treatment measures in India. Funding: The National Stroke Registry Programme is funded through the intramural funding of the Indian Council of Medical Research, Department of Health Research, Ministry of Health and Family Welfare, India.

Precision Genome Editing Techniques in Gene Therapy: Current State and Future Prospects.

Precision genome editing is a rapidly evolving field in gene therapy, allowing for the precise modification of genetic material. The CRISPR and Cas systems, particularly the CRISPRCas9 system, have revolutionized genetic research and therapeutic development by enabling precise changes like single-nucleotide substitutions, insertions, and deletions. This technology has the potential to correct disease-causing mutations at their source, allowing for the treatment of various genetic diseases. Programmable nucleases like CRISPR-Cas9, transcription activator-like effector nucleases (TALENs), and zinc finger nucleases (ZFNs) can be used to restore normal gene function, paving the way for novel therapeutic interventions. However, challenges, such as off-target effects, unintended modifications, and ethical concerns surrounding germline editing, require careful consideration and mitigation strategies. Researchers are exploring innovative solutions, such as enhanced nucleases, refined delivery methods, and improved bioinformatics tools for predicting and minimizing off-target effects. The prospects of precision genome editing in gene therapy are promising, with continued research and innovation expected to refine existing techniques and uncover new therapeutic applications.

Recent Advances in Pharmaceutical Design: Unleashing the Potential of Novel Therapeutics.

Pharmaceutical design has made significant advancements in recent years, leading to the development of novel therapeutics with unprecedented efficacy and safety profiles. This review highlights the potential of these innovations to revolutionize healthcare and improve patient outcomes. The application of cutting-edge technologies like artificial intelligence, machine learning, and data mining in drug discovery and design has made it easier to find potential drug candidates. Combining big data and omics has led to the discovery of new therapeutic targets and personalized medicine strategies. Nanoparticles, liposomes, and microneedles are examples of advanced drug delivery systems that allow precise control over drug release, better bioavailability, and targeted delivery to specific tissues or cells. This improves the effectiveness of the treatment while reducing side effects. Stimuli-responsive materials and smart drug delivery systems enable drugs to be released on demand when specific internal or external signals are sent. Biologics and gene therapies are promising approaches in pharmaceutical design, offering high specificity and potency for treating various diseases like cancer, autoimmune disorders, and infectious diseases. Gene therapies hold tremendous potential for correcting genetic abnormalities, with recent breakthroughs demonstrating successful outcomes in inherited disorders and certain types of cancer. Advancements in nanotechnology and nanomedicine have paved the way for innovative diagnostic tools and therapeutics, such as nanoparticle-based imaging agents, targeted drug delivery systems, gene editing technologies, and regenerative medicine strategies. Finally, the review emphasizes the importance of regulatory considerations, ethical challenges, and future directions in pharmaceutical design. Regulatory agencies are adapting to the rapid advancements in the field, ensuring the safety and efficacy of novel therapeutics while fostering innovation. Ethical considerations regarding the use of emerging technologies, patient privacy, and access to advanced therapies also require careful attention.

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.

Dissecting the genomic regions of selection on the X chromosome in different cattle breeds.

Mammalian X and Y chromosomes independently evolved from various autosomes approximately 300 million years ago (MYA). To fully understand the relationship between genomic composition and phenotypic diversity arising due to the course of evolution, we have scanned regions of selection signatures on the X chromosome in different cattle breeds. In this study, we have prepared the datasets of 184 individuals of different cattle breeds and explored the complete X chromosome by utilizing four within-population and two between-population methods. There were 23, 25, 30, 17, 17, and 12 outlier regions identified in Tajima's D, CLR, iHS, ROH, FST, and XP-EHH. Bioinformatics analysis showed that these regions harbor important candidate genes like AKAP4 for reproduction in Brown Swiss, MBTS2 for production traits in Brown Swiss and Guernsey, CXCR3 and CITED1 for health traits in Jersey and Nelore, and BMX and CD40LG for regulation of X chromosome inactivation in Nelore and Gir. We identified genes shared among multiple methods, such as TRNAC-GCA and IL1RAPL1, which appeared in Tajima's D, ROH, and iHS analyses. The gene TRNAW-CCA was found in ROH, CLR and iHS analyses. The X chromosome exhibits a distinctive interaction between demographic factors and genetic variations, and these findings may provide new insight into the X-linked selection in different cattle breeds.

Unique footprints of balancing selection in bovine genome.

Balancing selection is the process of selection that preserves various alleles within a population. Studying the areas undergoing balancing selection is essential, because it preserves genetic diversity in a population. Finding genes that exhibit signs of balancing selection during the domestication of cattle is the goal of this study. To identify regions where polymorphism has persisted in the cattle population for millions of years, we examined the genome of cattle. In this study, we used bovine SNP 50 k data to conduct a detailed genome-wide assessment of selection signatures for balancing selection. We have included the genotyped data from 427 animals, including five taurines, two crossbreds, and eight Indian cattle breeds. For this study, we employed Tajima's D approach to identify signature regions undergoing balancing selection. Using the NCBI database, PANTHER 17.0, and CattleQTL database, the annotation was carried out after finding the relevant areas under balancing selection. The number of genomic regions undergoing balancing selection in Ayrshire, Brown-Swiss, Frieswal, Gir, Guernsey, Hariana, Holstein Friesian, Jersey, Kankrej, Nelore, Ongole, Red Sindhi, Sahiwal, Tharparkar, and Vrindavani was 11, 13, 13, 19, 18, 11, 17, 14, 14, 12, 10, 12, 13, 13, and 11, respectively. We have observed multiple immune system-related genes going through balancing selection, including KIT, NFATC2, GBP4, LRRC32, SYT7, RAG1, RAG2, LOC513659, and ZBTB17. In our study, we found that the majority of the immune-related genes and a few genes associated with growth, reproduction, production, and adaptation are undergoing balancing selection.

Ensemble learning based compressive strength prediction of concrete structures through real-time non-destructive testing.

This study conducts an extensive comparative analysis of computational intelligence approaches aimed at predicting the compressive strength (CS) of concrete, utilizing two non-destructive testing (NDT) methods: the rebound hammer (RH) and the ultrasonic pulse velocity (UPV) test. In the ensemble learning approach, the six most popular algorithms (Adaboost, CatBoost, gradient boosting tree (GBT), random forest (RF), stacking, and extreme gradient boosting (XGB)) have been used to develop the prediction models of CS of concrete based on NDT. The ML models have been developed using a total of 721 samples, of which 111 were cast in the laboratory, 134 were obtained from in-situ testing, and the other samples were gathered from the literature. Among the three categories of analytical models-RH models, UPV models, and combined RH and UPV models; seven, ten, and thirteen models have been used respectively. AdaBoost, CatBoost, GBT, RF, Stacking, and XGB models have been used to improve the accuracy and dependability of the analytical models. The RH-M5, UPV-M6, and C-M6 (combined UPV and RH model) models were found with highest performance level amongst all the analytical models. The MAPE value of XGB was observed to be 84.37%, 83.24%, 77.33%, 59.46%, and 81.08% lower than AdaBoost, CatBoost, GBT, RF, and stacking, respectively. The performance of XGB model has been found best than other soft computing techniques and existing traditional predictive models.