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 (Kca) is essential for assessing the ecological risk of organic pollutants and elucidating their partitioning mechanisms. The current work collected 255 measured Kca values from 25 plant species and 106 compounds (dataset (I)) and averaged them to establish a dataset (dataset (II)) containing Kca 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 Kca. The results showed that the developed models had a high goodness of fit, as well as good robustness and predictive performance. The GBDT-2 model (Radj2 = 0.925, QLOO2 = 0.756, QBOOT2 = 0.864, Rext2 = 0.837, Qext2 = 0.811, and CCC = 0.891) is recommended as the best model for predicting Kca due to its superior performance. Moreover, interpreting the GBDT-1 and GBDT-2 models based on the Shapley additive explanations (SHAP) method elucidated how molecular properties, such as molecular size, polarizability, and molecular complexity, affected the capacity of plant cuticles to adsorb organic pollutants in the air. The satisfactory performance of the developed models suggests that they have the potential for extensive applications in guiding the environmental fate of organic pollutants and promoting the progress of eco-friendly and sustainable chemical engineering.

A de novo evolved gene contributes to rice grain shape difference between indica and japonica.

The role of de novo evolved genes from non-coding sequences in regulating morphological differentiation between species/subspecies remains largely unknown. Here, we show that a rice de novo gene GSE9 contributes to grain shape difference between indica/xian and japonica/geng varieties. GSE9 evolves from a previous non-coding region of wild rice Oryza rufipogon through the acquisition of start codon. This gene is inherited by most japonica varieties, while the original sequence (absence of start codon, gse9) is present in majority of indica varieties. Knockout of GSE9 in japonica varieties leads to slender grains, whereas introgression to indica background results in round grains. Population evolutionary analyses reveal that gse9 and GSE9 are derived from wild rice Or-I and Or-III groups, respectively. Our findings uncover that the de novo GSE9 gene contributes to the genetic and morphological divergence between indica and japonica subspecies, and provide a target for precise manipulation of rice grain shape.

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. Compared to models with variables including MACCS fingerprints or solitary physicochemical properties, the XGBoost-6 model developed by the variable combination of MD, QCD and three physicochemical properties achieved the most remarkable performance, with R2 of 0.977. Model interpretation achieved by permutation variable importance and partial dependence plots revealed the vital importance of HOCs lipophilicity, lipid content of plant roots, soil organic matter content, the overall deformability and the molecular dispersive ability of HOCs for regulating RCF. The integration of MD and QCD with physicochemical properties could improve our knowledge of underlying mechanisms regarding HOCs accumulation in plant roots from innovative structural perspectives. Multiple variables combination-oriented performance improvement of model can be extended to other parameters prediction in environmental risk assessment field.

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 (Klip/w) is a key parameter to evaluate the bioaccumulation potential of pollutants. Considering that it is difficult to determine the Klip/w 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. To remedy the defect of single parameters in a traditional model comparison, the TOPSIS evaluation method, based on entropy weight, was first proposed. We use this method to comprehensively evaluate models from multiple angles in this study. Thirty QSPR models, including 3 descriptor dimension reduction methods and 10 algorithms (belonging to 4 tribes), were used to predict Klip/w and verify the effectiveness of the comprehensive assessment method. The results showed that RF (descriptor dimension reduction method), symbolism (tribes) and RF (algorithm) exhibited significant advantages in establishing the Klip/w value prediction model. At present, the application of TOPSIS in environmental model evaluations is almost absent. We hope that the proposed TOPSIS evaluation method can be applied to more chemical datasets and provide a more systematic and comprehensive basis for the application of the QSPR model in environmental studies and other fields.

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. The regulation of rice GN is coordinately controlled by panicle architecture and branch differentiation, and many GN-associated genes showed pleiotropic effect in regulating tillering, grain size, flowering time, and other domestication-related traits. It is also revealed that GN determination is closely related to vascular development and the metabolism of some phytohormones. In this review, we summarize the recent findings in rice GN determination and discuss the genetic and molecular mechanisms of GN regulators.

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., KXAD-A) are indispensable to obtain POPs concentration, and the KXAD-A is generally thought to be governed by temperature and molecular structure of POPs. However, experimental determination of KXAD-A is unrealistic for countless and novel chemicals. Herein, the Abraham solute descriptors of poly parameter linear free energy relationship (pp-LFER) and temperature were utilized to develop models, namely pp-LFER-T, for predicting KXAD-A values. Two linear (MLR and LASSO) and four nonlinear (ANN, SVM, kNN and RF) machine learning algorithms were employed to develop models based on a data set of 307 sample points. For the aforementioned six models, R2adj and Q2ext were both beyond 0.90, indicating distinguished goodness-of-fit and robust generalization ability. By comparing the established models, the best model was observed as the RF model with R2adj = 0.991, Q2ext = 0.935, RMSEtra = 0.271 and RMSEext = 0.868. The mechanism interpretation revealed that the temperature, size of molecules and dipole-type interactions were the predominant factors affecting KXAD-A values. Concurrently, the developed models with the broad applicability domain provide available tools to fill the experimental data gap for untested chemicals. In addition, the developed models were helpful to preliminarily evaluate the environmental ecological risk and understand the adsorption behavior of POPs between XAD membrane and air.

Nucleotide polymorphisms of the maize ZmFWL7 gene and their association with ear-related traits.

Plant fw2.2-like (FWL) 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 ZmFWL7 gene, a member of the FWL 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 ZmFWL7 locus. Although the promoter and downstream regions showed higher nucleotide polymorphism, the coding region also possessed 61 SNPs and 6 InDels. Eleven polymorphic sites in the ZmFWL7 locus were found to be significantly associated with eight ear-related traits. Among them, two nonsynonymous SNPs (SNP2370 and SNP2898) showed significant association with hundred kernel weight (HKW), and contributed to 7.11% and 8.62% of the phenotypic variations, respectively. In addition, the SNP2898 was associated with kernel width (KW), and contributed to 7.57% of the phenotypic variations. Notably, the elite allele T of SNP2370 was absent in teosintes and landraces, while its frequency in inbred lines was increased to 12.89%. By contrast, the frequency of the elite allele A of SNP2898 was 3.12% in teosintes, and it was raised to 12.68% and 19.92% in landraces and inbred lines, respectively. Neutral tests show that this locus wasn't artificially chosen during the process of domestication and genetic improvement. Our results revealed that the elite allelic variants in ZmFWL7 might possess potential for the genetic improvement of maize ear-related traits.

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. We investigated the metal availability, plant uptake, and plant yield in response to SSA at rates of 0, 30, 75, 150, and 300 t ha. The SSA increased the metal availability in a mudflat soil and subsequently metal accumulation in ryegrass. The SSA increased the bioavailable fraction of the metals by 4550, 58.8, 898, 189, 35.8, and 84.8% for Zn, Mn, Cu, Ni, Cr, and Cd, respectively, at an SSA rate of 300 t ha as compared to unamended soil. Consequently, the metal concentrations in ryegrass increased by 1130, 12.9, 355, 108, 2230, and 497% in roots and by 431, -4.3, 92.6, 58.3, 890, and 211% in aboveground parts, for Zn, Mn, Cu, Ni, Cr, and Cd, respectively, at the 300 t ha rate as compared to unamended soil. The enhanced metal accumulation, however, did not induce growth inhibition of ryegrass. Fresh weight of aboveground parts and roots of ryegrass at 300 t ha SSA rate increased by 555 and 128%, respectively, as compared to those grown in unamended soil. The study suggests that SSA can promote yield of ryegrass seedlings grown in mudflat soils. None of metal concentrations at all SSA rates was above the Chinese permissible limits. Despite the data at only the seedling stage, our results indicate that SSA in mudflat soils might be a potential way for mudflat soil fertility improvement and sewage sludge disposal. Further study at plants' maturity stage is warranted to fully assess the suitability of sewage sludge amendment on mudflat soils.