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Maize is a dynamic cereal of world’s agriculture community. It is grown in spring and autumn seasons in Pakistan and both pistillate and staminate flowers experience severe temperature stress during spring sowing (February sowing) at reproductive phase, which ultimately results in poor seed setting because of increased silk dryness and pollen desiccation. In-vivo haploid induction was performed by using 2nd generation tropically adapted maize haploid inducer allocated from CIMMYT. Indigenously developed maize double haploid (DH) lines using local segregating population as source were screened out against high temperature stress. DH lines were evaluated and selected by using various indicators cell membrane thermostability (CMT), leaf temperature (LT), pollen production potential (PPP), pollen germination %age (PG%), pollen tube length (PTL), grain yield per plant (GY/P), plant height (PH), ear height (EH), days to tasseling (DT), days to silking (DS), number of grains per row (NGPR) and number of rows per cob (NRPC). Significant differences were noted at allelic level among the DH lines for high temperature tolerance related indicators, which provided wide range of option for selection, opportunity to improve tolerance level against high temperature stress and usage of these DH lines in different future maize breeding programs. Correlation coefficient analysis depicted PH, NGPR, HGW, PPP, PTL, and PG as suitable selection criteria for GY/P. The most effective physiological benchmarks for evaluating germplasm resistance to high temperature stress are CMT, PG%, and PTL. According to principal component analysis (PCA) DH lines viz. 5 (PBG-5), 11 (PBG-11), 15 (PBG-15), 19 (PBG-19), 24(PBG-24), 30(PBG-30), 33(PBG-33), 39 (PBG-39), 46 (PBG-46) and 52 (PBG-52) showed high level of diversity and performed well under the normal and high temperature stress conditions and DH lines viz. 10 (PBG-10), 13 (PBG-13), 26 (PBG-26), 37 (PBG-37) and 51 (PBG-51) performed poor under the normal and high temperature stress conditions. To develop maize varieties that can thrive in high-temperature conditions, it is necessary to cultivate DH lines that exhibit strong adaptability when exposed to induced stress. These lines could serve as a valuable genetic resource, as they likely contain a diverse array of genes that contribute to high temperature tolerance. [ABSTRACT FROM AUTHOR]

During the processing of maize, Stigma maydis, also known as corn silk, is normally discarded as waste. Phytochemical research was carried out on the S. maydis to use it as a valuable source of bioactive components. This research aimed to maximize the recovery of free and bound phenolic compounds from corn silk under optimal experimental conditions. Response surface design was operated to optimize the alkaline hydrolysis extraction of bound phytochemicals from corn silk based on total phenolic content and DPPH radical scavenging activity. The optimum conditions (i.e., NaOH concentration 2 M, digestion time 135 min, digestion temperature of 37.5°C, the solid‐to‐solvent ratio of 1:17.5, and acetone) were obtained. The optimum parameters were used to extract the corn silk. The structures of two compounds isolated from ethyl acetate extracts were then identified as friedelin (1) and (E)‐4‐(4‐hydroxy‐3‐methoxyphenyl) but‐3‐en‐2‐one (2). The DPPH, H2O2, and ABTS % inhibition of the compounds is as follows: compound (1) 74.81%, 76.8%, 70.33% and compound (2) 70.37%, 56.70% and 57.46%, respectively. The current study has opened previously unexplored perspectives of the composition of bound compounds in corn silk and established the foundations for more effective processing and utilization of corn waste. Practical Application: Bound phenolic compounds from corn silk under optimal experimental conditions were obtained. Corn silk can be utilized as a type of medicinal herb as well as a source of inexpensive natural antioxidants. [ABSTRACT FROM AUTHOR]

The availability of favorable genetic diversity is a thriving vitality for the success of a breeding program. It provides a firm basis of selecting superior breeding lines for the development of high yielding crop genotypes. In this context, present investigation aimed to generate information on genetic divergence and character association in a diversity panel of 123 local and exotic soybean genotypes. Analysis of variance revealed significant response of the evaluated genotypes based on studied attributes. It depicted the probability of selecting desirable soybean genotypes by focusing on character association studies and genetic diversity analysis. Correlation analysis revealed that seed yield per plant showed high positive correlation with 100-seed weight followed by pods per plant and plant height. Furthermore, path coefficient analysis exposed that pods per plant had maximum direct contribution in seed yield per plant followed by 100-seed weight, days to flowering and SPAD measurement. Genotype named "G-10" showed maximum yield per plant followed by 24607, G-52, 24593, Arisoy, 24566, 17426, A-3127, 24570 and 24567. Genetic diversity analysis grouped the evaluated germplasm into 17 clusters. All clusters showed zero intra-cluster variability; while inter-cluster divergence ranged from 9.00 to 91.11. Cluster V showed maximum inter-cluster distance with cluster XII followed by that of between V and VIII. Moreover, cluster IV with superior genotypes (G-10, 24607, 24593 and 24566), VI (17426 and 24567), XIII (24570) and X (Arisoy and G-52) showed above mean values for most of the studied characters. Overall, the results of hybridization between the superior genotypes of these cluster pairs might be useful for soybean breeding with improving agronomic traits and adaptability. [ABSTRACT FROM AUTHOR]

Promoters are the genomic regions upstream of genes that RNA polymerase binds in order to initiate gene transcription. Understanding the regulation of gene expression depends on being able to identify promoters, because they are the most important component of gene expression. Agrobacterium tumefaciens (A. tumefaciens) strain C58 was the subject of this study with the goal of creating a machine learning-based model to predict promoters. In this study, nucleotide density (ND), k-mer, and one-hot were used to encode the promoter sequence. Support vector machine (SVM) on fivefold cross-validation with incremental feature selection (IFS) was used to optimize the generated features. These improved characteristics were then used to distinguish promoter sequences by feeding them into the random forest (RF) classifier. Tenfold cross-validation (CV) analysis revealed that the projected model has the ability to produce an accuracy of 84.22%., (© 2024. The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature.)