The present investigation was focused to study genomic diversity of Indian swamp buffalo populations through reduced representation approach (ddRAD). The heterozygosity (FST) among the swamp ...buffaloes was 0.11 between Assam and Manipuri; 0.20 between swamp (Manipuri) and riverine buffaloes; 0.30 between swamp (Manipuri) and cattle. The average observed and expected heterozygosity in swamp buffalo populations was 0.254 and 0.221 respectively. The Inbreeding coefficient (FIS) value was 0.02 among the swamp buffaloes. PCA and structure analysis revealed Manipuri swamp buffalo was genetically distinct and closely related to Nagaland swamp buffalo and least to Assam swamp buffalo. Identification of selective sweeps revealed 1087 regions to have undergone selection related to immune response, adaptation and nervous system. A total of 3451 SSRs were identified in the genome of swamp buffaloes. The study evidenced the genomic diversity in the swamp buffalo populations and its uniqueness in comparison with riverine buffalo and cattle.
•Manipur swamp buffalo was found to be genetically distinct and closely related to Nagaland; least to Assam swamp buffalo.•A total of 1087 regions were found to have undergone selection related to immune response, adaptation and nervous system.•A total of 3451 SSRs were identified in the genome of swamp buffaloes.•The study evidenced genomic diversity in the swamp buffalo and its uniqueness in comparison with riverine buffalo and cattle.
The present study was carried out to identify and annotate the genome wide SNPs in Murrah buffalo genome. A total of 21.2 million raw reads from 4 pooled female Murrah buffalo samples were obtained ...using restriction enzyme digestion followed by sequencing with Illumina Hiseq 2000. After quality filtration, the reads were aligned to Murrah buffalo genome (ICAR-NBAGR) and Water buffalo genome (UMD_CASPUR_WB_2.0) which resulted in 99.37% and 99.67% of the reads aligning, respectively. A total of 130,688 high quality SNPs along with 35,110 indels were identified versus the Murrah bufffalo genome. Similarly 219,856 high quality SNPs along with 15,201 indels were identified versus the Water buffalo genome. We report 483 SNPs in 66 genes affecting Milk Production, 436 SNPs in 38 genes affecting fertility and 559 SNPs in 72 genes affecting other major traits. The average genome coverage was 13.4% and 14.8% versus the Murrah and Water buffalo genomes, respectively.
•Genome wide identification and annotation of SNPs was carried out in Murrah buffalo.•A total of 130,688 and 219,856 high quality SNPs were identified versus the Murrah and Water buffalo genomes respectively.•A total of 483, 436 and 559 SNPs affecting milk production, fertility and other major traits respectively were identified.•The average genome coverage was found to be 13.4% and 14.8% versus the Murrah and Water buffalo genomes, respectively.
The present study focused upon identification of genome-wide SNPs through the reduced representation approach and to study the genomic divergence of the Indian yak populations. A total of 80 samples ...belonging to Arunachali yak (N = 20), Himachali yak (N = 20), Ladakhi yak (N = 20) and Sikkimi yak (N = 20) of India were used in the study. The results of the study revealed a total of 579575 high quality SNPs along with 50319 INDELs in the Indian yaks. The observed heterozygosity was found to be high in Himachali yak, followed by Arunachali yak, Ladakhi yak and Sikkimi yaks. The Sikkimi yaks was found to be genetically distant, followed by Ladakhi yaks which was observed to have some few individuals from Arunachali and Himachali yaks. Arunachali and Himachali yaks are found to get clustered together and are genetically similar. The study provides evidence about the genomic diversity in the Indian yak populations and information generated in the present study may help to formulate a suitable breeding plan for endangered Indian yaks. Moreover, the unique yak populations identified in the study will further help to focus attention for future characterization and prioritization of the animals for conservation purposes through the ddRAD approach.
The present study was carried out to identify genome-wide genetic markers and variants in candidate genes for production and reproduction traits in Sahiwal cattle using a cost-effective reduced ...representation sequencing method. A total of 258,231 genome-wide SNPs were identified in Sahiwal cattle with reference to
Bos indicus
genome, of which 150,231 were novel SNPs. Among the high-confidence SNPs identified, 91.86% and 27.30% were genotyped in 50% and 100% of the samples. Mapping of the identified SNPs revealed 525 SNPs in candidate genes related to production traits while 333 SNPs were mapped to candidate genes related to reproduction traits. The SNPs identified in this study will facilitate further insights on tropical adaptation, domestication history and population structure of indigenous cattle. The variants in candidate genes identified in this study will serve as useful genetic tools, in the quest for phenotype modifying nucleotide change and help in designing appropriate genetic improvement programs.
Medium Density Fiberboard (MDF) has been one of the most rapidly growing composite panel products to enter world market in recent years. Optimization of drilling parameters for Medium Density ...Fiberboard (MDF) panel with multiple performance characteristics using Grey Relational Analysis (GRA) method was done. Drilling parameters, such as feed rate, spindle speed, drill diameters were considered for the study. By analyzing grey grade matrix, the degree of influence for each controllable process factor on to individual targets can be found. An optimal parameter combination of drilling operation was obtained via GRA. The feed rate was identified to be the most influencing factor on surface roughness and delamination. Additionally, ANOVA (Analysis of Variance) is used to find the interaction between parameters and the possible error in the experiments
Salinity stress adversely affects plant growth and causes considerable losses in cereal crops. Salinity stress tolerance is a complex phenomenon, imparted by the interaction of compounds involved in ...various biochemical and physiological processes. Conventional breeding for salt stress tolerance has had limited success. However, the availability of molecular marker-based high-density linkage maps in the last two decades boosted genomics-based quantitative trait loci (QTL) mapping and QTL-seq approaches for fine mapping important major QTL for salinity stress tolerance in rice, wheat, and maize. For example, in rice, 'Saltol' QTL was successfully introgressed for tolerance to salt stress, particularly at the seedling stage. Transcriptomics, proteomics and metabolomics also offer opportunities to decipher and understand the molecular basis of stress tolerance. The use of proteomics and metabolomics-based metabolite markers can serve as an efficient selection tool as a substitute for phenotype-based selection. This review covers the molecular mechanisms for salinity stress tolerance, recent progress in mapping and introgressing major gene/QTL (genomics), transcriptomics, proteomics, and metabolomics in major cereals, viz., rice, wheat and maize.
The presence and longevity of nanomaterials in the ecosystem, as well as their properties, account for environmental toxicity. When nanomaterials in terrestrial and aquatic systems are exposed to the ...prevailing environmental conditions, they undergo various transformations such as dissociation, dissolution, and aggregation, which affects the food chain. The toxicity of nanomaterials is influenced by a variety of factors, including environmental factors and its physico-chemical characteristics. Bioaccumulation, biotransformation, and biomagnification are the mechanisms that have been identified for determining the fate of nanomaterials. The route taken by nanomaterials to reach living cells provides us with information about their toxicity profile. This review discusses the recent advances in the transport, transformation, and fate of nanomaterials after they are released into the environment. The review also discusses how nanoparticles affect lower trophic organisms through direct contact, the impact of nanoparticles on higher trophic organisms, and the possible options for remediation.
•Ecotoxicological effect greatly depends on factors such size, shape, aggregation, and environmental factors.•A series of nano/bio interfaces formation occur when nanoparticles encounter cell, tissue, and organisms.•Uptake of nanomaterials is facilitated by process like phagocytosis, clathrin-mediated endocytosis, caveolae-dependent endocytosis, clathrin/caveolae independent endocytosis, and micropinocytosis.•Trophic transfer is crucial as nanomaterials consumed by lower-level organisms end up accumulating in higher organisms such as human beings.
Carbon Fiber Reinforced Plastic (CFRP) is a very strong and light composite material. During drilling of these composite panels, the layers of fibers peel out and get detached from its adjacent ...layers posing severe threat to the strength of the material and quality of the drilled hole which is referred as delamination. In this paper, the concept of delamination factor Fd, is used to analyze and compare easily the delamination degree in the drilling of carbon fiber reinforced plastic (CFRP) composite laminates. Delamination is measured by infrared thermo graphic technique and analysed. Experiments were conducted by varying the drilling parameters and the output response delamination is modelled mathematically. The adequacy of the mathematical model is analyzed statistically using ANOVA which gives a high degree of correlation between the input parameters and output response. It is evident that point angle during drilling is the most influencing factor to delamination.
Fake news is the purposeful circulation of misleading or untrue matter presented as news. It is becoming a growing problem in the digital era since it is so easy to make and spread information ...online. Fake news can harm society by misleading people, creating conflict, and weakening trust in institutions. Effective fake news detection systems are in high demand. These models can assist in identifying and flagging fake news portions, allowing people to be aware of them and avoid being misled. There are various methods for identifying fake news. Some systems apply machine learning algorithms to detect patterns in false news content, For instance, the use of particular terms or words, as well as links to other fake news articles. Other methods rely on human fact-checkers to ensure that news stories are accurate. There are multiple roadblocks for detecting fake news. The first problem is that false news sources are frequently written to appear credible. They may use genuine names and locations, as well as quote trustworthy sources. Another issue is that the fake news articles can morph quickly when new information becomes available or the news cycle evolves. This research study proposes a novel stacked ensemble model for differentiating fake news from real news. The proposed model is built using three state-of-the-art classification models Random Forest, the Support Vector Machine (SVM), and the Logistic Regression classifier as base classifiers. XGBoost classifier is used as a meta-classifier. In addition, this study also implemented the base classifiers individually and compared the results. The models are evaluated using metrics that include accuracy, precision, Fl-score, and recall.
A multilevel inverter (MLI) is an ingenious technology in generating a sinusoidal output voltage for AC applications. Higher output voltage levels improve the waveform quality but render increased ...size and power loss due to higher component count. In this paper, a new MLI topology with reduced device count is proposed which generates different numbers of output voltage levels according to the source management without altering the proposed circuit configuration. This MLI consists of ten semiconductor switches and two isolated DC sources. The asymmetric source management of 1:3 generates seventeen levels in the output voltage waveform and nine levels with symmetric (1:1) and thirteen levels with binary-asymmetric (1:2) source selection. This work projects the application of the proposed 17-level MLI in a constant power standalone solar energy conversion system. Minimum switching transitions and fundamental-voltage-reference-based switching angle calculation are adopted to achieve sinusoidal output voltage with least switching loss. As compared with the established 17-level MLI topologies, the proposed configuration has a lower component count level (CCL), lesser total blocking voltage (TBV), lesser THD, low switching frequency, no electromagnetic interference, more efficient, and cost-effective. This manuscript incorporates the design, simulation, and experimental validation of the proposed system for different PV input and load conditions.
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