The impact of climate change has been alarming for the crop growth. The extreme weather conditions can stress the crops and reduce the yield of major crops belonging to Poaceae family too, that sustains 50% of the world’s food calorie and 20% of protein intake. Computational approaches, such as artificial intelligence-based techniques have become the forefront of prediction-based data interpretation and plant stress responses. In this study, we proposed a novel activation function, namely, Gaussian Error Linear Unit with Sigmoid (SIELU) which was implemented in the development of a Deep Learning (DL) model along with other hyper parameters for classification of unknown abiotic stress protein sequences from crops of Poaceae family. To develop this models, data pertaining to four different abiotic stress (namely, cold, drought, heat and salinity) responsive proteins of the crops belonging to poaceae family were retrieved from public domain. It was observed that efficiency of the DL models with our proposed novel SIELU activation function outperformed the models as compared to GeLU activation function, SVM and RF with 95.11%, 80.78%, 94.97%, and 81.69% accuracy for cold, drought, heat and salinity, respectively. Also, a web-based tool, named DeepAProt (http://login1.cabgrid.res.in:5500/) was developed using flask API, along with its mobile app. This server/App will provide researchers a convenient tool, which is rapid and economical in identification of proteins for abiotic stress management in crops Poaceae family, in endeavour of higher production for food security and combating hunger, ensuring UN SDG goal 2.0.

This current research set out to characterize Halari donkey milk by investigating its nutritional constituents, including its proximate analysis, water activity, titratable acidity, energy, and microbiological analysis. A comprehensive profiling of vitamins, minerals, and amino acids was also carried out. It was found that the composition of Halari donkey milk was consistent with previously published donkey milk literature and was comparable to that of human milk. Halari donkey milk has low 0.86 ± 0.04% fat content, 2.03 ± 0.03% protein content, 0.51 ± 0.05% ash content, and high 5.75 ± 0.15% lactose content making it sweet and palatable. The energy content of Halari donkey milk was 40.39 ± 0.31 kcal/100 g, and the water activity ranged from 0.973 to 0.975. Titratable acidity was 0.03 ± 0.01%. Halari donkey milk can be considered acceptable and microbiologically safe, having low total plate count and yeast and mould counts. Mineral testing revealed that Halari donkey milk included significant amounts of magnesium, sodium, calcium, potassium, phosphorus, and zinc. The concentration of different vitamins and amino acids such as isoleucine and valine also contribute to the nutritional value of Halari donkey milk.

Although feed cost is the greatest concern in aquaculture, the inclusion of carbohydrates in the fish diet, and their assimilation, are still not well understood in aquaculture species. We identified molecular events that occur due to the inclusion of high carbohydrate levels in the diets of genetically improved ‘Jayanti rohu’ Labeo rohita. To reveal transcriptional changes in the liver of rohu, a feeding experiment was conducted with three doses of gelatinized starch (20% (control), 40%, and 60%). Transcriptome sequencing revealed totals of 15,232 (4464 up- and 4343 down-regulated) and 15,360 (4478 up- and 4171 down-regulated) differentially expressed genes. Up-regulated transcripts associated with glucose metabolisms, such as hexokinase, PHK, glycogen synthase and PGK, were found in fish fed diets with high starch levels. Interestingly, a de novo lipogenesis mechanism was found to be enriched in the livers of treated fish due to up-regulated transcripts such as FAS, ACCα, and PPARγ. The insulin signaling pathways with enriched PPAR and mTOR were identified by Kyoto Encyclopedia of Genes and Genome (KEGG) as a result of high carbohydrates. This work revealed for the first time the atypical regulation transcripts associated with glucose metabolism and lipogenesis in the livers of Jayanti rohu due to the inclusion of high carbohydrate levels in the diet. This study also encourages the exploration of early nutritional programming for enhancing glucose efficiency in carp species, for sustainable and cost-effective aquaculture production.

Karnal bunt (KB) of wheat (Triticum aestivum L.), known as partial bunt has its origin in Karnal, India and is caused by Tilletia indica (Ti). Its incidence had grown drastically since late 1960s from northwestern India to northern India in early 1970s. It is a seed, air and soil borne pathogen mainly affecting common wheat, durum wheat, triticale and other related species. The seeds become inedible, inviable and infertile with the precedence of trimethylamine secreted by teliospores in the infected seeds. Initially the causal pathogen was named Tilletia indica but was later renamed Neovossia indica. The black powdered smelly spores remain viable for years in soil, wheat straw and farmyard manure as primary sources of inoculum. The losses reported were as high as 40% in India and also the cumulative reduction of national farm income in USA was USD 5.3 billion due to KB. The present review utilizes information from literature of the past 100 years, since 1909, to provide a comprehensive and updated understanding of KB, its causal pathogen, biology, epidemiology, pathogenesis, etc. Next generation sequencing (NGS) is gaining popularity in revolutionizing KB genomics for understanding and improving agronomic traits like yield, disease tolerance and disease resistance. Genetic resistance is the best way to manage KB, which may be achieved through detection of genes/quantitative trait loci (QTLs). The genome-wide association studies can be applied to reveal the association mapping panel for understanding and obtaining the KB resistance locus on the wheat genome, which can be crossed with elite wheat cultivars globally for a diverse wheat breeding program. The review discusses the current NGS-based genomic studies, assembly, annotations, resistant QTLs, GWAS, technology landscape of diagnostics and management of KB. The compiled exhaustive information can be beneficial to the wheat breeders for better understanding of incidence of disease in endeavor of quality production of the crop.

Black pepper (Piper nigrum), the “King of Spices,” is an economically important spice in India and is known for its medicinal and cultural values. SSRs, the tandem repeats of small DNA sequences, are often polymorphic in nature with diverse applications. For population structure, QTL/gene discovery, MAS, and diversity analysis, it is imperative to have their location specificity. The existing PinigSSRdb catalogs ~70K putative SSR markers but these are anonymous (unknown chromosomal location), based on 916 scaffolds rather than 26 chromosomes. Under this study, we generated ddRAD sequence data of 29 black pepper genotypes from all over India, being low-cost and most efficient technique for the identification of polymorphic markers. The major limitation of ddRAD with compromised/non-uniform coverage has been successfully overcome by taking advantage of chromosome-wise data availability. The latest black pepper genome assembly was used to extract genome-wide SSRs. A total of 276,230 genomic SSRs were mined distributed over 26 chromosomes, with relative density of 362.88 SSRs/Mb and average distance of 2.76 Kb between two SSRs. This assembly was also used to find the polymorphic SSRs in the generated GBS data of 29 black pepper genotypes utilizing rapid and cost-effective method giving 3,176 polymorphic SSRs, out of which 2015 were found to be hypervariable. The developed web-genomic resource, BlackP2MSATdb (http://webtom.cabgrid.res.in/blackp2msatdb/), is the largest and first reported web resource for genomic and polymorphic SSRs of black pepper, which is useful to develop varietal signature, coreset, physical map, QTL/gene identification, and MAS in endeavor of black pepper production.

Identification and diversity analysis of fungi is greatly challenging. Though internal transcribed spacer (ITS), region-based DNA fingerprinting works as a “gold standard” for most of the fungal species group, it cannot differentiate between all the groups and cryptic species. Therefore, it is of paramount importance to find an alternative approach for strain differentiation. Availability of whole genome sequence data of nearly 2000 fungal species are a promising solution to such requirement. We present whole genome sequence-based world’s largest microsatellite database, FungSatDB having >19M loci obtained from >1900 fungal species/strains using >4000 assemblies across globe. Genotyping efficacy of FungSatDB has been evaluated by both in-silico and in-vitro PCR. By in silico PCR, 66 strains of 8 countries representing four continents were successfully differentiated. Genotyping efficacy was also evaluated by in vitro PCR in four fungal species. This approach overcomes limitation of ITS in species, strain signature, and diversity analysis. It can accelerate fungal genomic research endeavors in agriculture, industrial, and environmental management.