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In this study, Levinson-type inequalities for the class of n-convex (n ≥ 3 ) functions are generalized using new Green functions and the Hermite interpolating polynomial involving two types of data points. Some estimations for novel functionals are derived using f-divergence. Furthermore, different inequalities involving Shannon entropies are presented. [ABSTRACT FROM AUTHOR]

Background: Hexaploid bread wheat underwent a series of polyploidization events through interspecific hybridizations that conferred adaptive plasticity and resulted in duplication and neofunctionalization of major agronomic genes. The genetic architecture of polyploid wheat not only confers adaptive plasticity but also offers huge genetic diversity. However, the contribution of different gene copies (homeologs) encoded from different subgenomes (A, B, D) at different growth stages remained unexplored. Methods: In this study, hybrid of elite cultivars of wheat were developed via reciprocal crosses (cytoplasm swapping) and phenotypically evaluated. We assessed differential expression profiles of yield-related negative regulators in these cultivars and their F1 hybrids and identified various cis-regulatory signatures by employing bioinformatics tools. Furthermore, the preferential expression patterns of the syntenic triads encoded from A, B, and D subgenomes were assessed to decipher their functional redundancy at six different growth stages. Results: Hybrid progenies showed better heterosis such as up to 17% increase in the average number of grains and up to 50% increase in average thousand grains weight as compared to mid-parents. Based on the expression profiling, our results indicated significant dynamic transcriptional expression patterns, portraying the different homeolog-dominance at the same stage in the different cultivars and their hybrids. Albeit belonging to same syntenic triads, a dynamic trend was observed in the regulatory signatures of these genes that might be influencing their expression profiles. Conclusion: These findings can substantially contribute and provide insights for the selective introduction of better cultivars into traditional and hybrid breeding programs which can be harnessed for the improvement of future wheat. [ABSTRACT FROM AUTHOR]

Background: SnRK2 plays vital role in responding to adverse abiotic stimuli. The applicability of TaSnRK2.4 and TaSnRK2.9 was investigated to leverage the potential of these genes in indigenous wheat breeding programs. Methods: Genetic diversity was assessed using pre-existing markers for TaSnRK2.4 and TaSnRK2.9. Furthermore, new markers were also developed to enhance their broader applicability. KASP markers were designed for TaSnRK2.4, while CAPS-based markers were tailored for TaSnRK2.9. Results: Analysis revealed lack of polymorphism in TaSnRK2.4 among Pakistani wheat germplasm under study. To validate this finding, available gel-based markers for TaSnRK2.4 were employed, producing consistent results and offering limited potential for application in marker-assisted wheat breeding with Pakistani wheat material. For TaSnRK2.9-5A, CAPS2.9-5A-1 and CAPS2.9-5A-2 markers were designed to target SNP positions at 308 nt and 1700 nt revealing four distinct haplotypes. Association analysis highlighted the significance of Hap-5A-1 of TaSnRK2.9-5A, which exhibited association with an increased number of productive tillers (NPT), grains per spike (GPS), and reduced plant height (PH) under well-watered (WW) conditions. Moreover, it showed positive influence on NPT under WW conditions, GPS under water-limited (WL) conditions, and PH under both WW and WL conditions. High selection intensity observed for Hap-5A-1 underscores the valuable role it has played in Pakistani wheat breeding programs. Gene expression studies of TaSnRK2.9-5A revealed the involvement of this gene in response to PEG, NaCl, low temperature and ABA treatments. Conclusion: These findings propose that TaSnRK2.9 can be effectively employed for improving wheat through marker-assisted selection in wheat breeding efforts. [ABSTRACT FROM AUTHOR]

Achieving high yield and good quality in crops is essential for human food security and health. However, there is usually disharmony between yield and quality. Seed storage protein (SSP) and starch, the predominant components in cereal grains, determine yield and quality, and their coupled synthesis causes a yield–quality trade‐off. Therefore, dissection of the underlying regulatory mechanism facilitates simultaneous improvement of yield and quality. Here, we summarize current findings about the synergistic molecular machinery underpinning SSP and starch synthesis in the leading staple cereal crops, including maize, rice and wheat. We further evaluate the functional conservation and differentiation of key regulators and specify feasible research approaches to identify additional regulators and expand insights. We also present major strategies to leverage resultant information for simultaneous improvement of yield and quality by molecular breeding. Finally, future perspectives on major challenges are proposed. [ABSTRACT FROM AUTHOR]

Roots are the main sensing organ, initiating multiple signaling pathways in response to abiotic factors, including nutrients, drought, and salt stress. A focus on improving the root system architecture is a key strategy to mitigate these stresses in wheat crop. In the present study, a diversity panel comprising indigenous landraces and historical cultivars from Pakistan was characterized for the root system architecture (RSA) and important loci were identified using a genome‐wide association study (GWAS). RSA of the diversity panel was characterized 30 days after sowing in brunch tubes, and root images were taken. A high‐throughput root imaging analysis using Rhizovision software was performed by setting the scale to extract the eight RSA traits and four plant biomass‐related traits. GWAS identified 323 association signals for 12 root and biomass traits present on all wheat chromosomes, while the most important and reliable genetic loci (based on pleotropic loci and candidate genes) were identified on chromosomes 2A, 2B, 5A, 5D, 6A, 7B, and 7D for RSA. SNP annotation and transcriptome profiling identified nine candidate genes regulating the RSA and plant biomass traits, including ROOTLESS WITH UNDETECTABLE MERISTEM1, MYB TRANSCRIPTION FACTOR4, BRASSINOSTEROID INSENSITIVE1, SLENDER RICE1, AUXIN‐RESPONSIVE FACTOR25, SCARECROW, NARROW LEAF2, PIN‐FORMED1 AND PHOSPHATE TRANSCRIPTION FACTOR1. This study provided pre‐breeding information for deep‐rooting genotypes and associated markers that will accelerate the incorporation of such traits in breeding. [ABSTRACT FROM AUTHOR]

Harnessing cutting-edge technologies to enhance crop productivity is a pivotal goal in modern plant breeding. Artificial intelligence (AI) is renowned for its prowess in big data analysis and pattern recognition, and is revolutionizing numerous scientific domains including plant breeding. We explore the wider potential of AI tools in various facets of breeding, including data collection, unlocking genetic diversity within genebanks, and bridging the genotype-phenotype gap to facilitate crop breeding. This will enable the development of crop cultivars tailored to the projected future environments. Moreover, AI tools also hold promise for refining crop traits by improving the precision of gene-editing systems and predicting the potential effects of gene variants on plant phenotypes. Leveraging AI-enabled precision breeding can augment the efficiency of breeding programs and holds promise for optimizing cropping systems at the grassroots level. This entails identifying optimal inter-cropping and crop-rotation models to enhance agricultural sustainability and productivity in the field., Competing Interests: Declaration of interests The authors declare no competing interests., (Copyright © 2024 The Author(s). Published by Elsevier Ltd.. All rights reserved.)