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Marker-assisted selection (MAS) has been widely used in the last few decades in plant breeding programs for the mapping and introgression of genes for economically important traits, which has enabled the development of a number of superior cultivars in different crops. In sugarcane, which is the most important source for sugar and bioethanol, marker development work was initiated long ago; however, marker-assisted breeding in sugarcane has been lagging, mainly due to its large complex genome, high levels of polyploidy and heterozygosity, varied number of chromosomes, and use of low/medium-density markers. Genomic selection (GS) is a proven technology in animal breeding and has recently been incorporated in plant breeding programs. GS is a potential tool for the rapid selection of superior genotypes and accelerating breeding cycle. However, its full potential could be realized by an integrated approach combining high-throughput phenotyping, genotyping, machine learning, and speed breeding with genomic selection. For better understanding of GS integration, we comprehensively discuss the concept of genetic gain through the breeder’s equation, GS methodology, prediction models, current status of GS in sugarcane, challenges of prediction accuracy, challenges of GS in sugarcane, integrated GS, high-throughput phenotyping (HTP), high-throughput genotyping (HTG), machine learning, and speed breeding followed by its prospective applications in sugarcane improvement.

Nodal culture is a powerful plant tissue culture technique addressing critical challenges such as desiccation, microbial contamination, and the limited viability of explants, particularly in recalcitrant horticultural crops like Garcinia mangostana, Artocarpus heterophyllus, Cucumis melo, Citrus limon, Kinnow mandarin, and Coffea arabica. This method utilizes sterilized immature nodal explants, with regeneration induced through the precise application of growth regulators, primarily auxins and cytokinins, to media such as Driver-Kuniyuki (DKW), Woody Plant Media (WPM), and Murashige and Skoog (MS) under controlled conditions. These regulators significantly enhance both shoot and root regeneration, thus reducing the generation time for difficult-to-regenerate species. Reactive oxygen species (ROS) play a pivotal role in regulating cell division and hormone signaling during regeneration. Additionally, transcription factors such as wound-induced dedifferentiation 1 (WIND1), WUSCHEL (WUS), Enhancer of Shoot Regeneration 1 (ESR1), Cup-shaped Cotyledon 1 and 2 (CUC1, CUC2), and Lateral Organ Boundaries Domain 16 (LBD16) are integral to callus induction and organogenesis. Genetic variation observed in regenerated populations reflects the complexity of these regulatory networks and underscores the need for further investigation. Notably, nodal culture provides a promising alternative to conventional tissue culture methods, particularly in facilitating CRISPR/Cas9-mediated genetic modifications in recalcitrant crops. This technique enhances the efficient regeneration of transgenic horticultural crops, overcoming significant barriers to transformation. Future research should focus on refining nodal culture protocols across a broader spectrum of horticultural species, improving gene editing efficiency, and integrating this approach with advanced breeding technologies for targeted trait development and sustainable crop improvement.

The two most critical economic factors for sugar beet farmers are root yield and sugar content, both of which are heavily influenced by environmental factors such as weather and soil conditions. Water management is critical for sugar beet growth, with both the timing and amount of water being key factors for success. Together, these diseases and pests create significant obstacles to successful sugar beet cultivation, making pest and disease management essential for maintaining yield and quality. [...]the incidence of pests such as sugar beet weevils, flea beetles, and caterpillars has been observed to increase as climate conditions shift.

The armyworm, Spodoptera litura Fabricius, 1775 (Noctuidae: Lepidoptera) is a serious and emerging insect pest of sugar beet in India, resulting in significant yield losses. A phylogenetic tree was constructed using the CLUSTAL W and neighbour joining technique, and a neighbour joining haplotype network was formed using PopArt to assess the relationships between S. litura haplotypes. The evolutionary divergence of different strains of Indian-origin S. litura was calculated using the p-distance method in MEGA 11. Neutrality indices, including Tajima’s D, Fu, and Li’s F, was calculated to test the hypothesis of selective neutrality using DnaSPv6. Larval identification relied on the morphological characteristics, while the molecular characterization utilized the mitochondrial cytochrome oxidase I gene with universal primers (LCO1490 and HCO2198). A DNA fragment of approximately 700 bp from mitochondrial COI revealed two different strains (OP420870 and OP117231) infesting sugar beet crops under Indian subtropical conditions. The amplified barcode sequences exhibited variations in both strains, with genetic divergence ranging from 0.0 to 0.79. The strains OP420870 and OP117231 displayed maximum divergence at 0.74 and 0.73, respectively. Interpopulation nucleotide differences (Kxy) and the average number of nucleotide substitutions per site between populations (Dxy) in different states of India were calculated at 336.42 and 0.61, respectively. The pairwise Fst value was 0.63, with an Nm value of 0.15. One of the identified strains of S. litura in this study was also found to be a haplotype. This study provides valuable insights into the genetic characterization of S. litura infesting sugar beet crops in Indian subtropical conditions, contributing to the understanding of its population structure and diversity. The findings enhance our knowledge of S. litura infestations and can aid in the development of effective strategies for pest management and crop protection in sugar beet.

Sugarcane productivity is being hampered globally under changing environmental scenarios like drought and salinity. The highly complex nature of the plant responses against these stresses is determined by a variety of factors such as genotype, developmental phase of the plant, progression rate and stress, intensity, and duration. These factors influence plant responses and can determine whether mitigation approaches associated with acclimation are implemented. In this review, we attempt to summarize the effects of drought and salinity on sugarcane growth, specifically on the plant’s responses at various levels, viz., physiological, biochemical, and metabolic responses, to these stresses. Furthermore, mitigation strategies for dealing with these stresses have been discussed. Despite sugarcane’s complex genomes, conventional breeding approaches can be utilized in conjunction with molecular breeding and omics technologies to develop drought- and salinity-tolerant cultivars. The significant role of plant growth-promoting bacteria in sustaining sugarcane productivity under drought and salinity cannot be overlooked.

BACKGROUND: Drought is the most severe abiotic stress reducing rice yield in rainfed drought prone ecosystems. Variation in intensity and severity of drought from season to season and place to place requires cultivation of rice varieties with different level of drought tolerance in different areas. Multi environment evaluation of breeding lines helps breeder to identify appropriate genotypes for areas prone to similar level of drought stress. From a set of 129 advanced rice (Oryza sativa L.) breeding lines evaluated under rainfed drought-prone situations at three locations in eastern India from 2005 to 2007, a subset of 39 genotypes that were tested for two or more years was selected to develop a drought yield index (DYI) and mean yield index (MYI) based on yield under irrigated, moderate and severe reproductive-stage drought stress to help breeders select appropriate genotypes for different environments. RESULTS: ARB 8 and IR55419-04 recorded the highest drought yield index (DYI) and are identified as the best drought-tolerant lines. The proposed DYI provides a more effective assessment as it is calculated after accounting for a significant genotype x stress-level interaction across environments. For rainfed areas with variable frequency of drought occurrence, Mean yield index (MYI) along with deviation in performance of genotypes from currently cultivated popular varieties in all situations helps to select genotypes with a superior performance across irrigated, moderate and severe reproductive-stage drought situations. IR74371-70-1-1 and DGI 75 are the two genotypes identified to have shown a superior performance over IR64 and MTU1010 under all situations. CONCLUSION: For highly drought-prone areas, a combination of DYI with deviation in performance of genotypes under irrigated situations can enable breeders to select genotypes with no reduction in yield under favorable environments compared with currently cultivated varieties. For rainfed areas with variable frequency of drought stress, use of MYI together with deviation in performance of genotypes under different situations as compared to presently cultivated varieties will help breeders to select genotypes with superior performance under all situations.

PPIs play a significant function in many biological processes. In many different areas, DL algorithms have delivered excellent results, but PPI prediction is one where they fall short. To offer a sequence-based prediction of PPI, this work employs a deep ensemble model. In the beginning, traits including \"enhanced semantic similarity, features based on gene ontologies, and sequence-based features\" are extracted. A deep ensemble model is introduced that combines models like Deep Convolutional Neural Network (DCNN), Recurrent Neural Network (RNN), Deep Max out (DMO), and Deep Belief Network (DBN)\" is then used to predict the outcomes of the retrieved features. To improve the prediction model, the training is done by the Chaotic Initialized COOT Optimization Algorithm (CI-COA) by optimizing the training weights of DCNN. The performance of the chosen strategy is finally shown through several measures.