Comparative Genomic Analysis of the Poaceae Cytokinin Response Regulator Gene Family and Functional Characterization of in Drought Stress Tolerance in Rice.

The cytokinin (CK) type B response regulator () gene is involved in the CK signaling pathway and performs a key function for mediating reactions to amounts of abiotic stresses. Nevertheless, the gene family remains to be characterized in Poaceae (also known as Gramineae or grasses). Here, we performed a comprehensive analysis encompassing phylogenetic relationships, evolutionary pressures, and expression patterns of the gene family in six Poaceae species, including rice, , , , maize, and wheat.

Melatonin enhances metal oxide nanoparticles tolerance in rice seedlings through dual regulation of antioxidant defense and photosynthetic efficiency.

The growing utilization of metal oxide nanoparticles (MONPs) presents novel and potential hazards to plants. However, the impacts of MONPs on plants and the mechanisms underlying their tolerance to MONPs remain unclear. In this study, we demonstrated that both CuO and ZnO nanoparticles hindered plant growth and triggered oxidative damage in rice seedlings.

Metabolic marker-assisted genomic prediction improves hybrid breeding.

Hybrid breeding is widely acknowledged as the most effective method for increasing crop yield, particularly in maize and rice. However, a major challenge in hybrid breeding is the selection of desirable combinations from the vast pool of potential crosses. Genomic selection (GS) has emerged as a powerful tool to tackle this challenge, but its success in practical breeding depends on prediction accuracy.

Machine-Learning-Based Prediction of Plant Cuticle-Air Partition Coefficients for Organic Pollutants: Revealing Mechanisms from a Molecular Structure Perspective.

Accurately predicting plant cuticle-air partition coefficients () is essential for assessing the ecological risk of organic pollutants and elucidating their partitioning mechanisms. The current work collected 255 measured values from 25 plant species and 106 compounds (dataset (I)) and averaged them to establish a dataset (dataset (II)) containing values for 106 compounds. Machine-learning algorithms (multiple linear regression (MLR), multi-layer perceptron (MLP), k-nearest neighbors (KNN), and gradient-boosting decision tree (GBDT)) were applied to develop eight QSPR models for predicting .

Contribution of molecular structures and quantum chemistry technique to root concentration factor: An innovative application of interpretable machine learning.

Root concentration factor (RCF) is a significant parameter to characterize uptake and accumulation of hazardous organic contaminants (HOCs) by plant roots. However, complex interactions among chemicals, plant roots and soil make it challenging to identify underlying mechanisms of uptake and accumulation of HOCs. Here, nine machine learning techniques were applied to investigate major factors controlling RCF based on variable combinations of molecular descriptors (MD), MACCS fingerprints, quantum chemistry descriptors (QCD) and three physicochemical properties related to chemical-soil-plant system.

A comprehensive evaluation of liposome/water partition coefficient prediction models based on the Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) method: Challenges from different descriptor dimension reduction methods and machine learning algorithms.

The liposome/water partition coefficient (K) is a key parameter to evaluate the bioaccumulation potential of pollutants. Considering that it is difficult to determine the K values of all pollutants through experiments, researchers gradually developed models to predict it. However, there is currently no research on how to comprehensively evaluate prediction models and recommend a compelling optimal modeling method.

Nucleotide polymorphisms of the maize gene and their association with ear-related traits.

Plant genes, encoding proteins harboring a placenta-specific eight domain, have been suggested to control fruit and grain size through regulating cell division, differentiation, and expansion. Here, we re-sequenced the nucleotide sequences of the maize gene, a member of the family, in 256 elite maize inbred lines, and the associations of nucleotide polymorphisms in this locus with 11 ear-related traits were further detected. A total of 175 variants, including 159 SNPs and 16 InDels, were identified in the locus.

Genetic and molecular factors in determining grain number per panicle of rice.

It was suggested that the most effective way to improve rice grain yield is to increase the grain number per panicle (GN) through the breeding practice in recent decades. GN is a representative quantitative trait affected by multiple genetic and environmental factors. Understanding the mechanisms controlling GN has become an important research field in rice biotechnology and breeding.

Versatile in silico modeling of XAD-air partition coefficients for POPs based on abraham descriptor and temperature.

The concentration of persistent organic pollutants (POPs) makes remarkable difference to environmental fate. In the field of passive sampling, the partition coefficients between polystyrene-divinylbenzene resin (XAD) and air (i.e.

Effect of sewage sludge amendment on heavy metal uptake and yield of ryegrass seedling in a mudflat soil.

Mudflat soil amendment by sewage sludge is a potential way to dispose of solid wastes and increase fertility of mudflat soils for crop growth. The present study aimed to assess the impact of sewage sludge amendment (SSA) on heavy metal accumulation and growth of ryegrass ( L.) in a seedling stage.