Quantitative real-time polymerase chain reaction (RT-qPCR) using a stable reference gene is widely used for gene expression research. Barnyard millet (Echinochloa spp.) is an ancient crop in Asia and Africa that is widely cultivated for food and fodder. It thrives well under drought, salinity, cold, and heat environmental conditions, besides adapting to any soil type. To date, there are no gene expression studies performed to identify the potential candidate gene responsible for stress response in barnyard millet, due to lack of reference gene. Here, 10 candidate reference genes, Actin (ACT), α-tubulin (α-TUB), β-tubulin (β-TUB), RNA pol II (RP II), elongation factor-1 alpha (EF-1α), adenine phosphoribosyltransferase (APRT), TATA-binding protein-like factor (TLF), ubiquitin-conjugating enzyme 2 (UBC2), ubiquitin-conjugating enzyme E2L5 (UBC5) and glyceraldehyde-3-phosphate dehydrogenase (GAPDH), were selected from mRNA sequences of E. crus-galli and E. colona var frumentacea. Five statistical algorithms (geNorm, NormFinder, BestKeeper, ΔCt, and RefFinder) were applied to determine the expression stabilities of these genes in barnyard millet grown under four different abiotic stress (drought, salinity, cold and heat) exposed at different time points. The UBC5 and ɑ-TUB in drought, GAPDH in salinity, GAPDH and APRT in cold, and EF-1α and RP II in heat were the most stable reference genes, whereas ß-TUB was the least stable irrespective of stress conditions applied. Further Vn/Vn + 1 analysis revealed two reference genes were sufficient to normalize gene expression across all sample sets. The suitability of identified reference genes was validated with Cu-ZnSOD (SOD1) in the plants exposed to different abiotic stress conditions. The results revealed that the relative quantification of the SOD1 gene varied according to reference genes and the number of reference genes used, thus highlighting the importance of the choice of a reference gene in such experiments. This study provides a foundational framework for standardizing RT-qPCR analyses, enabling accurate gene expression profiling in barnyard millet.

Coconut is widely referred to as the "Tree of Life," holds immense value due to its versatility and significant role in numerous sectors including industry, agriculture, food and health. In recent years, a troubling decline in coconut production has been observed due to factors such as palm senility, pests, diseases and natural calamities which poses serious threats to industries and communities that rely highly on this crop thereby creating a gap between demand and supply. Addressing this gap requires innovative solutions and plant tissue culture techniques offer a promising path forward. Tissue culture techniques such as zygotic embryogenesis (mature embryo culture and sliced embryo culture), organogenesis (axillary bud culture and embryo derived shoot tip culture) and somatic embryogenesis have shown great potential for regenerating coconut plants. Axillary bud culture, offers a reliable alternative for producing elite plants with the added benefit of being free from the risk of somaclonal variation, while somatic embryogenesis, in particular has proven to be highly effective for producing large numbers of high-quality planting materials. However, each technique has its own share of shortcomings. Overcoming these challenges and closing the gap between demand and supply is critical for the mass production of elite coconut seedlings. This review explores the different micropropagation techniques, the hurdles facing coconut tissue culture and the potential for future breakthroughs.

Background: Little millet is an important crop grown by tribal farmers in India. Genetic variability can be exploited to develop new varieties with higher yield. However, yield is complex and depends on multiple interconnected component characters. Diversity analyses, such as D2 analysis, are used to evaluate the diversity among genotypes and determine the traits that contribute the most diversity in a given population. These analyses are crucial for achieving the goal of developing new varieties with increased yield. Methods: In this study, 323 little millet genotypes were evaluated using an Augmented RCBD, focusing on ten quantitative traits. The experiment was conducted during the rabi season of 2020-2021, and good agronomic practices were followed. D square cluster analysis and path analysis were used to analyze the data, with the "R" tool and the "biotools" and "agricolae" packages, respectively. Result: In this study, 323 little millet genotypes classified into thirteen distinct clusters based on Mahalanobis's D2 statistics, reflecting differences in their phenotypic characteristics. The largest cluster (cluster I) included 243 genotypes, while the smallest clusters (Cluster IX, Cluster X, Cluster XI, Cluster XII and Cluster XIII) had only 1 genotype. The inter-cluster distance varied, with the largest value (577.7) between cluster V and XII. This analysis can be useful for identifying desirable genotypes and understanding the population's genetic diversity and structure.