Using artificial neural network to investigate physiological changes and cerium oxide nanoparticles and cadmium uptake by Brassica napus plants

Uptake of zinc from the soil to the wheat grain: Nonlinear process prediction based on artificial neural network and geochemical data

Trace elements in plants primarily derive from soils, subsequently influencing human health through the food chain. Therefore, it is essential to understand the relationship of trace elements between plants and soils. Since trace elements from soils absorbed by plants is a nonlinear process, traditional multiple linear regression (MLR) models failed to provide accurate predictions. Zinc (Zn) was chosen as the objective element in this case. Using soil geochemical data, artificial neural networks (ANN) were utilized to develop predictive models that accurately estimated Zn content within wheat grains. A total of 4036 topsoil samples and 73 paired rhizosphere soil-wheat samples were collected for the simulation study. Through Pearson correlation analysis, the total content of elements (TCEs) of Fe, Mn, Zn, and P, as well as the available content of elements (ACEs) of B, Mo, N, and Fe, were significantly correlated with the Zn bioaccumulation factor (BAF). Upon comparison, ANN models outperformed MLR models in terms of prediction accuracy. Notably, the predictive performance using ACEs as input factors was better than that using TCEs. To improve the accuracy, a two-step model was established through multiple testing. Firstly, ACEs in the soil were predicted using TCEs and properties of the rhizosphere soil as input factors. Secondly, the Zn BAF in grains was predicted using ACE as input factors. Consequently, the content of Zn in wheat grains corresponding to 4036 topsoil samples was predicted. Results showed that 85.69 % of the land was suitable for cultivating Zn-rich wheat. This finding offers a more accurate method to predict the uptake of trace elements from soils to grains, which helps to warn about abnormal levels in grains and prevent potential health risks.

Transcriptomics, proteomics, and metabolomics interventions prompt crop improvement against metal(loid) toxicity

The escalating challenges posed by metal(loid) toxicity in agricultural ecosystems, exacerbated by rapid climate change and anthropogenic pressures, demand urgent attention. Soil contamination is a critical issue because it significantly impacts crop productivity. The widespread threat of metal(loid) toxicity can jeopardize global food security due to contaminated food supplies and pose environmental risks, contributing to soil and water pollution and thus impacting the whole ecosystem. In this context, plants have evolved complex mechanisms to combat metal(loid) stress. Amid the array of innovative approaches, omics, notably transcriptomics, proteomics, and metabolomics, have emerged as transformative tools, shedding light on the genes, proteins, and key metabolites involved in metal(loid) stress responses and tolerance mechanisms. These identified candidates hold promise for developing high-yielding crops with desirable agronomic traits. Computational biology tools like bioinformatics, biological databases, and analytical pipelines support these omics approaches by harnessing diverse information and facilitating the mapping of genotype-to-phenotype relationships under stress conditions. This review explores: (1) the multifaceted strategies that plants use to adapt to metal(loid) toxicity in their environment; (2) the latest findings in metal(loid)-mediated transcriptomics, proteomics, and metabolomics studies across various plant species; (3) the integration of omics data with artificial intelligence and high-throughput phenotyping; (4) the latest bioinformatics databases, tools and pipelines for single and/or multi-omics data integration; (5) the latest insights into stress adaptations and tolerance mechanisms for future outlooks; and (6) the capacity of omics advances for creating sustainable and resilient crop plants that can thrive in metal(loid)-contaminated environments.

In silico interactions and deep neural network modeling for toxicity profile of methyl methanesulfonate

In this study, the toxicity induced by the alkylating agent methyl methanesulfonate (MMS) in Allium cepa L. was investigated. For this aim, bulbs were divided into 4 groups as control and application (100, 500 and 4000 µM MMS) and germinated for 72 h at 22-24 °C. At the end of the germination period root tips were collected and made ready for analysis by applying traditional preparation methods. Germination, root elongation, weight, mitotic index (MI) values, micronucleus (MN) and chromosomal abnormality (CAs) numbers, malondialdehyde (MDA) levels, superoxide dismutase (SOD) and catalase (CAT) activities and anatomical structures of bulbs were used as indicators to determine toxicity. Moreover the extent of DNA fragmentation induced by MMS was determined by comet assay. To confirm the DNA fragmentation induced by MMS, the DNA-MMS interaction was examined with molecular docking. Correlation and principal component analyses (PCA) were performed to examine the relationship between all parameters and understand the underlying structure and relationships among these parameters. In the present study, a deep neural network (DNN) with two hidden layers implemented in Matlab has been developed for the comparison of the estimated data with the real data. The effect of MDA levels, SOD and CAT activities at 4 different endpoints resulting from administration of various concentrations of MMS, including MN, MI, CAs and DNA damage, was attempted to be estimated by DNN model. It is assumed that the predicted results are in close agreement with the actual data. The effectiveness of the model was evaluated using 4 different metrics, MAE, MAPE, RMSE and R2, which together show that the model performs commendably. As a result, the highest germination, root elongation, weight gain and MI were measured in the control group. MMS application caused a decrease in all physiological parameters and an increase in cytogenetic (except MI) and biochemical parameters. MMS application caused an increase in antioxidant enzyme levels (SOD and CAT) up to a concentration of 500 µM and a decrease at 4000 µM. MMS application induced different types of CAs and anatomical damages in root meristem cells. The results of the comet assay showed that the severity of DNA fragmentation increased with increasing MMS concentration. Molecular docking analysis showed a strong DNA-MMS interaction. The results of correlation and PCA revealed significant positive and negative interactions between the studied parameters and confirmed the interactions of these parameters with MMS. It has been shown that the DNN model developed in this study is a valuable resource for predicting genotoxicity due to oxidative stress and lipid peroxidation. In addition, this model has the potential to help evaluate the genotoxicity status of various chemical compounds. At the end of the study, it was concluded that MMS strongly supports a versatile toxicity in plant cells and the selected parameters are suitable indicators for determining this toxicity.

Averaging Strategy for Interpretable Machine Learning on Small Datasets to Understand Element Uptake after Seed Nanotreatment

Understanding plant uptake and translocation of nanomaterials is crucial for ensuring the successful and sustainable applications of seed nanotreatment. Here, we collect a dataset with 280 instances from experiments for predicting the relative metal/metalloid concentration (RMC) in maize seedlings after seed priming by various metal and metalloid oxide nanoparticles. To obtain unbiased predictions and explanations on small datasets, we present an averaging strategy and add a dimension for interpretable machine learning. The findings in post-hoc interpretations of sophisticated LightGBM models demonstrate that solubility is highly correlated with model performance. Surface area, concentration, zeta potential, and hydrodynamic diameter of nanoparticles and seedling part and relative weight of plants are dominant factors affecting RMC, and their effects and interactions are explained. Furthermore, self-interpretable models using the RuleFit algorithm are established to successfully predict RMC only based on six important features identified by post-hoc explanations. We then develop a visualization tool called RuleGrid to depict feature effects and interactions in numerous generated rules. Consistent parameter-RMC relationships are obtained by different methods. This study offers a promising interpretable data-driven approach to expand the knowledge of nanoparticle fate in plants and may profoundly contribute to the safety-by-design of nanomaterials in agricultural and environmental applications.

Artificial neural network approach for predicting the sesame (Sesamum indicum L.) leaf area: A non-destructive and accurate method

The estimative of the leaf area using a nondestructive method is paramount for successive evaluations in the same plant with precision and speed, not requiring high-cost equipment. Thus, the objective of this work was to construct models to estimate leaf area using artificial neural network models (ANN) and regression and to compare which model is the most effective model for predicting leaf area in sesame culture. A total of 11,000 leaves of four sesame cultivars were collected. Then, the length (L) and leaf width (W), and the actual leaf area (LA) were quantified. For the ANN model, the parameters of the length and width of the leaf were used as input variables of the network, with hidden layers and leaf area as the desired output parameter. For the linear regression models, leaf dimensions were considered independent variables, and the actual leaf area was the dependent variable. The criteria for choosing the best models were: the lowest root of the mean squared error (RMSE), mean absolute error (MAE), and absolute mean percentage error (MAPE), and higher coefficients of determination (R²). Among the linear regression models, the equation yˆ=0.515+0.584*LW was considered the most indicated to estimate the leaf area of the sesame. In modeling with ANNs, the best results were found for model 2-3-1, with two input variables (L and W), three hidden variables, and an output variable (LA). The ANN model was more accurate than the regression models, recording the lowest errors and higher R² in the training phase (RMSE: 0.0040; MAE: 0.0027; MAPE: 0.0587; and R²: 0.9834) and in the test phase (RMSE: 0.0106; MAE: 0.0029; MAPE: 0.0611; and R²: 0.9828). Thus, the ANN method is the most indicated and accurate for predicting the leaf area of the sesame.

Artificial intelligence-based approaches to evaluate and optimize phytoremediation potential of in vitro regenerated aquatic macrophyte Ceratophyllum demersum L

Water bodies or aquatic ecosystem are susceptible to heavy metal accumulation and can adversely affect the environment and human health especially in underdeveloped nations. Phytoremediation techniques of water bodies using aquatic plants or macrophytes are well established and are recognized as eco-friendly world over. Phytoremediation of heavy metals and other pollutants in aquatic environments can be achieved by using Ceratophyllum demersum L. - a well-known floating macrophyte. In vitro regenerated plants of C. demersum (7.5 g/L) were exposed to 24, 72, and 120 h to 0, 0.5, 1.0, 2.0, and 4.0 mg/L of cadmium (CdSO₄·8H₂O) in water. Results revealed significantly different relationship in terms of Cd in water, Cd uptake by plants, bioconcentration factor (BCF), and Cd removal (%) from water. The study showed that Cd uptake by plants and BCF values increased significantly with exposure time. The highest BCF value (3776.50) was recorded for plant samples exposed to 2 mg/L Cd for 72 h. Application of all Cd concentrations and various exposure duration yielded Cd removal (%) between the ranges of 93.8 and 98.7%. These results were predicted through artificial intelligence-based models, namely, random forest (RF), extreme gradient boosting (XGBoost), and multilayer perceptron (MLP). The tested models predicted the results accurately, and the attained results were further validated via three different performance metrics. The optimal regression coefficient (R²) for the models was recorded as 0.7970 (Cd water, mg/L), 0.9661 (Cd plants, mg/kg), 0.9797 bioconcentration factor (BCF), and 0.9996 (Cd removal, %), respectively. These achieved results suggest that in vitro regenerated C. demersum can be efficaciously used for phytoremediation of Cd-contaminated aquatic environments. Likewise, the proposed modeling of phytoremediation studies can further be employed more comprehensively in future studies aimed at data prediction and optimization.

Response characteristics of root to moisture change at seedling stage of Kengyilia hirsuta

Kengyilia hirsuta is an important pioneer plant distributed on the desertified grassland of the Qinghai-Tibet Plateau. It has strong adaptability to alpine desert habitats, so it can be used as a sand-fixing plant on sandy alpine land. To study the response mechanisms of root morphological and physiological characteristics of K. hirsuta to sandy soil moisture, 10%, 25% and 40% moisture levels were set up through potted weighing water control method. The biomass, root-shoot ratio, root architecture parameters, and biochemical parameters malondialdehyde, free proline, soluble protein, indole-3-acetic acid, abscisic acid, cytokinin, gibberellin, relative conductivity and antioxidant enzyme activities were measured in the trefoil stage, and the response mechanisms of roots at different moisture levels were analyzed. The results showed that with the increase of soil moisture, root morphological indexes such as root biomass, total root length, total root volume and total root surface increased, while the root topological index decreased continuously. The malondialdehyde content, relative conductivity, superoxide dismutase activity, peroxidase activity, catalase activity, free proline content, soluble protein content, abscisic acid content and cytokinin content at the 25% and 40% moisture levels were significantly decreased compared with the 10% level (P< 0.05). Thus, the root growth of K. hirsuta was restricted by the 10% moisture level, but supported by the 25% and 40% moisture levels. An artificial neural network revealed that total root length, total root surface area, root link average length, relative conductivity, soluble protein, free proline and moisture level were the key factors affecting root development. These research results could contribute to future agricultural sustainability.

Spatial-temporal variability and influence factors of Cd in soils of Guangxi, China

In this study, the regional spatial-temporal variability of cadmium (Cd) in the topsoil of Guangxi, China from 2010 to 2016 was studied from data obtained from the China Geochemical Baseline Project (CGB Ⅰ and CGB Ⅱ). The driving forces of natural and anthropogenic variables were quantitatively analyzed using a geographically and temporally weighted regression model. The results showed that 1) soil Cd was highly enriched in 2010 and in soils of Hechi city in northwest Guangxi, a non-ferrous metal mining and metallurgy area, ~17% of the samples exceeded the soil contamination risk limit. In contrast, in 2016, the topsoil Cd content decreased significantly, with 7% of sites exceeding the soil risk limit. 2) Multiple factors jointly influenced the regional spatial variability of Cd. pH and organic carbon were found to be the main factors influencing Cd content and were strongly spatially correlated with Cd. Anthropogenic activities, including mining and industrial emissions, resulted in significant Cd enrichment in local areas, whereas agricultural and domestic pollutants were relatively weakly correlated with Cd. The weathering products of carbonates were significantly enriched in Cd; thus, the geological background played a significant role in the spatial variability of Cd. Soil-forming factors, including temperature, precipitation, and elevation influenced the spatial distribution of Cd, especially in the Cd background area. 3) Anthropogenic activities were the key factors influencing temporal changes in Cd. Mining caused significant enrichment of Cd in CGB Ⅰ, while industrial emissions were the primary factor for Cd enrichment in CGB Ⅱ. In addition, natural factors also played an important role; the increased Normalized Difference Vegetation Index suggested reduced desertification and reduction of soil erosion in the watershed and in pollutants transported from upstream.

Assessing plant uptake of organic contaminants by food crops tomato, wheat, and corn through sap concentration factor

This study investigated uptake of two organic compounds including hexahydro-1,3,5-trinitro-1,3,5-triazine (RDX) and exogenous caffeine by tomato (Solanum lycopersicum L.), corn (Zea mays L.), and wheat (Triticum aestivum L.). The plants were grown in a growth chamber under recommended conditions and then were exposed to these compounds for 19 days. The uptake of the compounds was measured by sap concentration factor. The plant samples (stem transpiration stream) and solution in the exposure media were taken and analyzed by high performance liquid chromatography-tandem mass spectrometry. The plant stem samples were analyzed after a freeze-thaw centrifugation process. The average sap concentration factor for the RDX by tomato, wheat, and corn was 0.71, 0.67, and 0.65. The average sap concentration factor for the exogenous caffeine by tomato, wheat, and corn was 0.72, 0.50, and 0.34. These relatively high sap concentration factor values were expected as available predictive models offer high sap concentration factor values for moderately hydrophobic and hydrophilic compounds. The generated sap concentration factor values for the RDX and exogenous caffeine are important for improving the accuracy of previously developed machine learning models predicting the uptake and translocation of emerging contaminants.

Physiochemical responses of earthworms (Eisenia fetida) under exposure to lanthanum and cerium alone or in combination in artificial and contaminated soils

Rare earth elements inevitably release into the soil due to their widespread application. However, it is unclear how they affect the soil animals. The study surveyed the growth and physiological responses of earthworm (Eisenia fetida) exposed into artificial soils spiked with La, Ce, and their mixture, and actual mine soil collected from an abandoned La-Ce mining area (Mianning, Sichuan). The results showed that the 1000-1200 mg/kg combined exposure in two soils induced significant histopathological and phenotypic changes of earthworms. Concentration significantly affected the superoxide dismutase (SOD), peroxidase (POD), malondialdehyde (MDA), and protein of E. fetida and the effects differentiated with the prolonging duration. These indicators were negatively affected under the La stress ≥800 mg/kg (SOD, POD, and protein), the 1200 mg/kg (SOD), Ce stress ≥1000 mg/kg (protein), and the combination ≥800 mg/kg (SOD, POD) and ≥1000 mg/kg (protein). Artificial combination had -15.04% (SOD), 8.87% (POD), 5.64% (MDA), and -8.34% (protein) difference compared with the contamination soil, respectively. Overall, E. fetida respond sensitively under the La and Ce stress, the antioxidant defense system and the lipid peroxidation were stimulated, and the artificial soil might overestimate eco-toxicological effect.

Application of a novel nanocomposite containing micro-nutrient solubilizing bacterial strains and CeO2 nanocomposite as bio-fertilizer

Chemical fertilizers are used in modern agricultural practice to increase plant output. They possess anthropogenic compounds which are hazardous, result in poor soil quality, poor crop nutrition and pollutes the water table. Currently, food crops that lack in micro-nutrients (Zn, silicates and Se) can be enriched with micronutrients by use of fertilizers. Eco-friendly bio-fertilizers have been proved to provide a known population of microorganisms that create a mutual benefit to the plants & the rhizosphere soil. Nanomaterials are often used in plant fertilizer formulation, allowing for controlled release and targeted delivery of beneficial nanoscale components, as well as to boost plant production and reduce environmental pollutants. In the present study we identified a multipotent micronutrient solubilizing bacterium (MSB) - Pseudomonas gessardi and Pseudomonas azotoformans as a bio-fertiliser. Comparative study of the formulated MSB, with nanocomposite prepared with the soya chunks as natural carrier material and chemically synthesized cerium oxide was performed on the growth of fenugreek for its effectiveness. The SEM images of nanocomposite showed the non-uniform distribution of CeO₂ in bio-inoculant with an average size of 25.24 nm. The current study deals with increase in the shoot and root length of the fenugreek plant with only 75 ppm of CeO₂ in nanocomposite, thereby preventing bioaccumulation of Ce in soil. This work gives a potential use of CeO2 nanocomposite with MSB bio-inoculants which could be applied to soil deficient with the micronutrients that can enhance the crop yield.

Health and environmental safety of nanomaterials: O Data, Where Art Thou?

Nanotechnology keeps drawing attention due to the great tunable properties of nanomaterials in comparison to their bulk conventional materials. The growth of nanotechnology in combination with the digitization era has led to an increased need of safety related data. In addition to safety, new data-driven paradigms on safe and sustainable by design materials are stressing the necessity of data even more. Data is a fundamental asset to the scientific community in studying and analysing the entire life-cycle of nanomaterials. Unfortunately, data exist in a scattered fashion, in different sources and formats. To our knowledge, there is no study focusing on aspects of actual data-structure knowledge that exists in literature and databases. The purpose of this review research is to transparently and comprehensively, display to the nanoscience community the datasets readily available for machine learning purposes making it convenient and more efficient for the next users such as modellers or data curators to retrieve information. We systematically recorded the features and descriptors available in the datasets and provide synopsised information on their ranges, forms and metrics in the supplementary material.

Copper accumulation and physiological markers of soybean (Glycine max) grown in agricultural soil amended with copper nanoparticles

Copper-based nanoparticles (NPs) display a strong potential to replace copper salts (e.g., CuSO4) for application in agricultures as antimicrobial agents or nutritional amendments. Yet, their effects on crop quality are still not comprehensively understood. In this study, the Cu contents in soybeans grown in soils amended with Cu NPs and CuSO4 at 100-500 mg Cu/kg and the subsequent effects on the plant physiological markers were determined. The Cu NPs induced 29-89% at the flowering stage (on Day 40) and 100-165% at maturation stage (on Day 100) more Cu accumulation in soybeans than CuSO4. The presence of particle aggregates in the root cells with deformation upon the Cu NP exposure was observed by transmission electron microscopy. The Cu NPs at 100 and 200 mg/kg significantly improved the plant height and biomass, yet significantly inhibited at 500 mg/kg, compared to the control. In leaves chlorophyll-b was more sensitive than chlorophyll-a and carotenoids to the Cu NP effect. The Cu NPs significantly decreased the root nitrogen and phosphorus contents, while they significantly increased the leaf potassium content in comparison with control. Our results imply that cautious use of Cu NPs in agriculture is warranted due to relatively high uptake of Cu and altered nutrient quality in soybeans.

Explicating the cross-talks between nanoparticles, signaling pathways and nutrient homeostasis during environmental stresses and xenobiotic toxicity for sustainable cultivation of cereals

Precision farming using nanoparticles is a cutting-edge technology for safe cultivation of crop plants in marginal areas afflicted with environmental/climatic stresses like salinity, drought, extremes of temperature, ultraviolet B stress or polluted with xenobiotics like toxic heavy metals and fluoride. Major cereal crops like rice, wheat, maize, barley, sorghum and millets which provide the staple food for the entire global population are mainly glycophytes and are extremely susceptible to abiotic stress-induced oxidative injuries. Nanofertilization/exogenous spraying of beneficial nanoparticles alleviates the oxidative damages in cereals by altering the homeostasis of phytohormones like abscisic acid, gibberellins, cytokinins, auxins, salicylic acid, jasmonic acid and melatonin and by triggering the synthesis of gasotransmitter nitric oxide. Signaling cross-talks of nanoparticles with plant growth regulators enable activation of the defence machinery, comprising of antioxidants, thiol-rich compounds and glyoxalases and restrict xenobiotic mobilization by suppressing the expression of associated transporters. Accelerated nutrient uptake and grain biofortification under the influence of nanoparticles result in optimum crop productivity under sub-optimal conditions. However, over-dosing of even beneficial nanoparticles promotes severe phytotoxicity. Hence, the concentration of nanoparticles and mode of administering need to be thoroughly standardized before large-scale field applications, to ensure sustainable cereal cultivation with minimum ecological imbalance.

Machine Learning: New Ideas and Tools in Environmental Science and Engineering

The rapid increase in both the quantity and complexity of data that are being generated daily in the field of environmental science and engineering (ESE) demands accompanied advancement in data analytics. Advanced data analysis approaches, such as machine learning (ML), have become indispensable tools for revealing hidden patterns or deducing correlations for which conventional analytical methods face limitations or challenges. However, ML concepts and practices have not been widely utilized by researchers in ESE. This feature explores the potential of ML to revolutionize data analysis and modeling in the ESE field, and covers the essential knowledge needed for such applications. First, we use five examples to illustrate how ML addresses complex ESE problems. We then summarize four major types of applications of ML in ESE: making predictions; extracting feature importance; detecting anomalies; and discovering new materials or chemicals. Next, we introduce the essential knowledge required and current shortcomings in ML applications in ESE, with a focus on three important but often overlooked components when applying ML: correct model development, proper model interpretation, and sound applicability analysis. Finally, we discuss challenges and future opportunities in the application of ML tools in ESE to highlight the potential of ML in this field.

New Evidence of Model Crop Brassica napus L. in Soil Clean-Up: Comparison of Tolerance and Accumulation of Lead and Cadmium

The potential of the model crop Brassica napus L. (rapeseed) for the phytoremediation of soils polluted with metals was investigated at laboratory scale. The first step consists in the evaluation of the seed germination and growth of the Brassica napus L. plant in a controlled environment, followed by the determination of the photosynthetic pigments content represented by chlorophyll a and b and carotenoids. The degree of metal accumulation in rapeseed has been evaluated by the bioaccumulation factor (BAC), the bioconcentration factor (BCF) and the translocation factor (TF). Phytotoxicity tests were performed in Petri dishes with filter papers moistened with metal solutions in the range of 0 to 300 mg/L Pb(II) or Cd(II). At the highest concentration of the lead and cadmium treatments (300 mg/L), B. napus L. showed the lowest germination degree (56.67% and 43.33%, respectively). According to Tukey test results, Pb(II) concentrations of up to 300 mg/L do not significantly affect the length of the hypocotyls, whereas, in the case of Cd(II), the mean of the radicle and hypocotyl lengths of the seedlings are significantly affected compared to the mean of the control. In soil pot experiments, important changes have been obtained in the pigment content, especially in the case of cadmium. For both metals and for each treatment (100 to 1500 mg/kg Pb(II) and 1 to 30 mg/kg Cd(II)), a TF < 1 indicates an ineffective metal transfer from root to shoot. Finally, rapeseed can be considered a tolerant plant and a suitable candidate for Pb(II) and Cd(II) accumulation and for the phytostabilization of contaminated soil under the experimental conditions adopted in the present study.

Recent insights into the impact, fate and transport of cerium oxide nanoparticles in the plant-soil continuum

The advent of the nanotechnology era offers a unique opportunity for sustainable agriculture provided that the exposure and toxicity are adequately assessed and properly controlled. The global production and application of cerium oxide nanoparticles (CeO₂-NPs) in various industrial sectors have tremendously increased. Most of the nanoparticles end up in water and soil where they interact with soil microorganisms and plants. Investigating the uptake, translocation and accumulation of CeO₂-NPs is critical for its safe application in agriculture. Plant uptake of CeO₂-NPs may lead to their accumulation in different plant tissues and interference with key metabolic processes of plants. Soil microbes can also be affected by increasing CeO₂-NPs in soil, leading to changes in the physiology and enzymatic activity of soil microorganisms. The interactions between CeO₂-NPs, microbes and plants in the agricultural system need systemic research in ecologically relevant conditions. In the present review, The uptake pathways and in-planta translocation of CeO₂-NPs,and their impact on plant morphology, nutritional values, antioxidant enzymes and molecular determinants are presented. The role of CeO₂-NPs in modifying soil microbial community in plant rhizosphere is also discussed. Overall, the review aims to provide a comprehensive account on the behaviour of CeO₂-NPs in soil-plant systems and their potential impacts on the soil microbial community and plant health.

Exposure of cerium oxide nanoparticles to the hyperaccumulator Sedum alfredii decreases the uptake of cadmium via the apoplastic pathway

Cadmium (Cd) is harmful to the environment and threatens human health. With the increasing use of cerium oxide nanoparticles (CeO₂NPs) in extensive industries, investigating the combination of CeO₂NPs and plants has attracted research interests for phytoremediation. Here, we explored the effects of CeO₂NPs on Cd uptake, transport and the consequent Cd accumulation in Sedum alfredii. Exposure of 50 or 500 mg L^-1 CeO₂NPs alone had no apparent damaging effects on plant growth. However, upon Cd condition, the consistent CeO₂NPs decreased Cd concentrations in the roots and shoots by up to 37%. Furthermore, the application of a metabolic inhibitor revealed that CeO₂NPs mainly decreased the Cd uptake in roots by the apoplastic pathway. Simultaneously, CeO₂NPs accelerated the development of Casparian strips (CSs) and suberin, which was further proven by the elevated expression levels of genes associated with their formation, SaCASP, SaGPAT5, SaKCS20 and SaCYP86A1. Compared to CeO₂NPs added alone, the concurrent Cd decreased the Ce contents in the roots and altered its translocation from root to shoot. Taken together, both CeO₂NPs and Cd influence the interactional uptake of both chemicals in roots of S. alfredii mainly via the apoplastic pathway which is primarily regulated by the development of CSs and suberin.

Early Detection of Aphid Infestation and Insect-Plant Interaction Assessment in Wheat Using a Low-Cost Electronic Nose (E-Nose), Near-Infrared Spectroscopy and Machine Learning Modeling

Advances in early insect detection have been reported using digital technologies through camera systems, sensor networks, and remote sensing coupled with machine learning (ML) modeling. However, up to date, there is no cost-effective system to monitor insect presence accurately and insect-plant interactions. This paper presents results on the implementation of near-infrared spectroscopy (NIR) and a low-cost electronic nose (e-nose) coupled with machine learning. Several artificial neural network (ANN) models were developed based on classification to detect the level of infestation and regression to predict insect numbers for both e-nose and NIR inputs, and plant physiological response based on e-nose to predict photosynthesis rate (A), transpiration (E) and stomatal conductance (gs). Results showed high accuracy for classification models ranging within 96.5-99.3% for NIR and between 94.2-99.2% using e-nose data as inputs. For regression models, high correlation coefficients were obtained for physiological parameters (gs, E and A) using e-nose data from all samples as inputs (R = 0.86) and R = 0.94 considering only control plants (no insect presence). Finally, R = 0.97 for NIR and R = 0.99 for e-nose data as inputs were obtained to predict number of insects. Performances for all models developed showed no signs of overfitting. In this paper, a field-based system using unmanned aerial vehicles with the e-nose as payload was proposed and described for deployment of ML models to aid growers in pest management practices.

Elucidating the effects of TiO2 nanoparticles on the toxicity and accumulation of Cu in soybean plants (Glycine max L.)

Copper (Cu) pollution is common in the soil. Due to the widespread application of TiO2 NPs, there is a high propensity for the co-occurrence of TiO2 nanoparticles (NPs) and Cu in agricultural soils. It is therefore imperative to evaluate the joint effects of TiO2 NPs and Cu on crops. In this study, the mutual effects of TiO2 NPs and Cu on their toxicity and accumulation in soybean seedlings and on their fates in a hydroponic system were determined. When Cu was at levels of 1 and 2 mg/L, the co-occurring TiO2 NPs at a non-toxic concentration (10 mg/L) significantly enhanced the toxicity and accumulation of Cu and Ti in soybeans, and inhibited the translocation of Cu from soybean roots to shoots. However, when the Cu concentration for co-exposure was ≥ 5 mg/L, such mutual effects disappeared. The amount of Cu ions adsorbed onto TiO2 NPs after 48 h of co-exposure gradually increased from 31 to 118 mg/g when the Cu concentration was increased from 1 to 20 mg/L. The aggregation and sedimentation of TiO2 NPs were significantly increased after 48 h of co-exposure with the Cu at a concentration higher than 5 mg/L, as compared to the single TiO2 NPs exposure. The increasing aggregation and sedimentation might reduce the bioavailability of TiO2 NPs associated with the adsorbed Cu to soybeans, and consequently alleviate or even neutralize the enhanced toxicity and accumulation of Cu in soybeans exerted by the co-existing TiO2 NPs. Our results thus suggest that consideration of the impact of TiO2 NPs on the phytotoxicity of heavy metals, and specifically Cu, needs to be interpreted with care, and highlight the importance of integrating the interaction and fates of TiO2 NPs and metals into their risk assessment.

Prediction of Plant Uptake and Translocation of Engineered Metallic Nanoparticles by Machine Learning

Machine learning was applied to predict the plant uptake and transport of engineered nanoparticles (ENPs). A back propagation neural network (BPNN) was used to predict the root concentration factor (RCF) and translocation factor (TF) of ENPs from their essential physicochemical properties (e.g., composition and size) and key external factors (e.g., exposure time and plant species). The relative importance of input variables was determined by sensitivity analysis, and gene-expression programming (GEP) was used to generate predictive equations. The BPNN model satisfactorily predicted the RCF and TF in both hydroponic and soil systems, with an R² higher than 0.8 for all simulations. Inclusion of the initial ENP concentration as an input variable further improved the accuracy of the BPNN for soil systems. Sensitivity analysis indicated that the composition of ENPs (e.g., metals vs metal oxides) is a major factor affecting RCF and TF values in a hydroponic system. However, the soil organic matter and clay contents are more dominant in a soil system. The GEP model (R² = 0.8088 and 0.8959 for RCF and TF values) generated more accurate predictive equations than the conventional regression model (R² = 0.5549 and 0.6664 for RCF and TF values) in a hydroponic system, which could guide the sustainable design of ENPs for agricultural applications.

Nanoparticles potentially mediate salt stress tolerance in plants

In the era of climate change, salt stress is a promising threat to agriculture, limiting crop production via imposing primary effects such as osmotic and ionic, as well as secondary effects such as oxidative stress, perturbance in hormonal homeostasis, and nutrient imbalance. On the other hand, production areas are expanding into the salt affected regions due to excessive pressure for fulfilling food security targets to meet the needs of continuously increasing human population. Accumulating evidences demonstrate that supplementation of nanoparticles to plants can significantly alleviate the injurious effects caused by various harsh conditions including salt stress, and hence, regulate adaptive mechanisms in plants. Various types of NPs and nanofertilizers have shown a promising evidence so far regarding salt stress management. In this review, we recapitulate recent pioneering progress made towards acquiring salt stress tolerance in crop plants utilizing NPs. Finally, future research directions in this domain to explicate the comprehensive roles of nanoparticles in improving salt tolerance in plants are underscored. To ensure social acceptance and safe use of NPs, some conclusive directions have been elaborated in order to achieve sustainable progress in crop production under saline environments.

Application of Nanoparticles Alleviates Heavy Metals Stress and Promotes Plant Growth: An Overview

Nanotechnology is playing a significant role in addressing a vast range of environmental challenges by providing innovative and effective solutions. Heavy metal (HM) contamination has gained considerable attention in recent years due their rapidly increasing concentrations in agricultural soil. Due to their unique physiochemical properties, nanoparticles (NPs) can be effectively applied for stress alleviation. In this review, we explore the current status of the literature regarding nano-enabled agriculture retrieved from the Web of Science databases and published from January 2010 to November 2020, with most of our sources spanning the past five years. We briefly discuss uptake and transport mechanisms, application methods (soil, hydroponic and foliar), exposure concentrations, and their impact on plant growth and development. The current literature contained sufficient information about NPs behavior in plants in the presence of pollutants, highlighting the alleviation mechanism to overcome the HM stress. Furthermore, we present a broad overview of recent advances regarding HM stress and the possible mechanism of interaction between NPs and HM in the agricultural system. Additionally, this review article will be supportive for the understanding of phytoremediation and micro-remediation of contaminated soils and also highlights the future research needs for the combined application of NPs in the soil for sustainable agriculture.

Phytoremediation of Cadmium: Physiological, Biochemical, and Molecular Mechanisms

Cadmium (Cd) is one of the most toxic metals in the environment, and has noxious effects on plant growth and production. Cd-accumulating plants showed reduced growth and productivity. Therefore, remediation of this non-essential and toxic pollutant is a prerequisite. Plant-based phytoremediation methodology is considered as one a secure, environmentally friendly, and cost-effective approach for toxic metal remediation. Phytoremediating plants transport and accumulate Cd inside their roots, shoots, leaves, and vacuoles. Phytoremediation of Cd-contaminated sites through hyperaccumulator plants proves a ground-breaking and profitable choice to combat the contaminants. Moreover, the efficiency of Cd phytoremediation and Cd bioavailability can be improved by using plant growth-promoting bacteria (PGPB). Emerging modern molecular technologies have augmented our insight into the metabolic processes involved in Cd tolerance in regular cultivated crops and hyperaccumulator plants. Plants’ development via genetic engineering tools, like enhanced metal uptake, metal transport, Cd accumulation, and the overall Cd tolerance, unlocks new directions for phytoremediation. In this review, we outline the physiological, biochemical, and molecular mechanisms involved in Cd phytoremediation. Further, a focus on the potential of omics and genetic engineering strategies has been documented for the efficient remediation of a Cd-contaminated environment.

Modelling assisted phytoremediation of soils contaminated with heavy metals - Main opportunities, limitations, decision making and future prospects

The heavy metals (HMs) soils contamination is a growing concern since HMs are not biodegradable and can accumulate in all living organisms causing a threat to plants and animals, including humans. Phytoremediation is a cost-efficient technology that uses plants to remove, transform or detoxify contaminants. In recent years, phytoremediation is entering the stage of large-scale modelling via various mathematical models. Such models can be useful tools to further our understanding and predicting of the processes that influence the efficiency of phytoremediation and to precisely plan such actions on a large-scale. When dealing with extremely complicated and challenging variables like the interactions between the climate, soil and plants, modelling before starting an operation can significantly reduce the time and cost of such process by granting us an accurate prediction of possible outcomes. Research on the applicability of different modelling approaches is ongoing and presented work compares and discusses available models in order to point out their specific strengths and weaknesses in given scenarios. The main aim of this paper is to critically evaluate the main advantages and limitations of available models for large-scale phytoremediation including, among others, the Decision Support System (DSS), Response Surface Methodology (RSM), BALANS, PLANTIX and various regression models. Study compares their applicability and highlight existing gaps in current knowledge with a special reference to improving the efficiency of large-scale phytoremediation of sites contaminated with heavy-metals. The presented work can serve as a useful tool when choosing the most suitable model for the phytoremediation of contaminated sites.

Modeling and optimizing the removal of cadmium by Sinapis alba L. from contaminated soil via Response Surface Methodology and Artificial Neural Networks during assisted phytoremediation with sewage sludge

The study was aimed to model and optimize the removal of cadmium from contaminated post-industrial soil via Sinapis alba L. by comparing two modeling approaches: Response Surface Methodology (RSM) and Artificial Neural Networks (ANN). The experimental design was done using the Box-Behnken Design method. In the RSM model, the quadratic model was shown to predict the closest results in comparison to our experimental data. For ANN approach, a two-layer Feed-Forward Back-Propagation Neural Network model was designed. The results showed that sewage sludge supplementation increased the efficiency of the Sinapis alba plant in removing Cd from the soil. After 28 days of exposure, the removal rate varied from 10.96% without any supplementation to 65.9% after supplementation with the highest possible (law allowed) dose of sewage sludge. The comparison proved that the prediction capability of the ANN model was much higher than that of the RSM model (adjusted R-square: 0.98, standard error of the Cd prediction removal: 0.85 ± 0.02). Thus, the ANN model could be used for the prediction of heavy metal removal during assisted phytoremediation with sewage sludge. Moreover, such approach could also be used to determinate the dose of sewage sludge that will ensure highest process efficiency.

Analysis of temporally evolved nanoparticle-protein corona highlighted the potential ability of gold nanoparticles to stably interact with proteins and influence the major biochemical pathways in Brassica juncea

Nanoparticles (NPs) are known to adsorb proteins from their surroundings, forming NP-protein corona, which determines their fate, distribution, and effects, yet no information of protein corona (PC) has been gathered in the plants so far. Here we report, the analysis of temporally evolved (2 h-36 h) AuNP-protein coronas formed with Brassica juncea leaf crude protein & nuclear-enriched fraction. Protein coronas were characterized by the techniques including SDS PAGE (Sodium Dodecyl Sulfate Polyacrylamide Gel Electrophoresis), spectrophotometry, dynamic light scattering, zeta potential measurements, and Nano LC-MS/MS. Data analysis revealed the formation of two regimes (Regime I from 2 to 8 h & regime II from 16 to 36 h). Interestingly, coated AuNPs had approx. 30% higher zeta potential than pristine AuNPs after 36 h of interactions. The increase in hydrodynamic radii and adsorbed protein concentrations were consistent with the evolution of zeta potential, indicating the probable role of proteins in providing the better stability of AuNPs. MS analysis identified 97 proteins from regime I (soft corona) and 181 proteins from regime II (hard corona) of crude PC. On the other hand, 282 and 308 proteins were identified from nuclear soft and hard corona respectively, indicating better affinity of nuclear proteins. Besides, the high-affinity proteins (fold change ≥5) were found to be rich in lysine residues showing their involvement in promoting the adsorption. Notably, 27% of regime II corona proteins of the crude protein fraction were from energy-yielding pathways highlighting the potential ability AuNPs to influence the yield in Brassica juncea.

Examining plant uptake and translocation of emerging contaminants using machine learning: Implications to food security

When water and solutes enter the plant root through the epidermis, organic contaminants in solution either cross the root membranes and transport through the vascular pathways to the aerial tissues or accumulate in the plant roots. The accumulation of contaminants in plant roots and edible tissues is measured by root concentration factor (RCF) and fruit concentration factor (FCF). In this paper, 1) a neural network (NN) was applied to model RCF based on physicochemical properties of organic compounds, 2) correlation and significance of physicochemical properties were assessed using statistical analysis, 3) fuzzy logic was used to examine the simultaneous impacts of significant compound properties on RCF and FCF, 4) a clustering algorithm (k-means) was used to identify unique groups and discover hidden relationships within contaminants in various parts of the plants. The physicochemical cutoffs achieved by fuzzy logic for the RCF and the FCF were compared versus the cutoffs for compounds that crossed the plant root membranes and found their way into transpiration stream (measured by transpiration stream concentration factor, TSCF). The NN predicted the RCF with improved accuracy compared to mechanistic models. The analysis indicated that log K_ow, molecular weight, and rotatable bonds are the most important properties for predicting the RCF. These significant compound properties are positively correlated with RCF while they are negatively correlated with TSCF. Comparing the relationships between compound properties in various plant tissues showed that compounds detected in the edible parts have physicochemical cutoffs that are more like the compounds crossing the plant root membranes (into xylem tissues) than the compounds accumulating in the plant roots, with clear relationships to food security. The cluster analysis placed the contaminants into three meaningful groups that were in agreement with the results of fuzzy logic.

Chelator complexes enhanced Amaranthus hypochondriacus L. phytoremediation efficiency in Cd-contaminated soils

The use of degradable chelating agent to enhance phytoextraction is a promising and low-cost method for remediation of heavy metals-polluted soil. However, very limited information is available regarding the effect of chelating agent combinations on plant growth and its capacity to extract metals. In this study, a pot experiment was conducted to evaluate the applicability of [N, N]-bis glutamic acid (GLDA), nitrilotriacetic acid (NTA), [S, S]- ethylenediamine disuccinic acid (EDDS), and citric acid (CA) alone and in combination to enhance the phytoextraction efficiency of amaranth (Amaranthus hypochondriacus L.) in two Cd-contaminated agricultural soils (S₁ soil 2.12 mg/kg and S₂ soil 2.89 mg/kg; the environmental standard value of Cd in agricultural soils in China is lower than 0.8 mg/kg). The results showed that, except for EDDS, other treatments had no obvious effect on plant biomass, and even promoted biomass increase to reach 1.06 (S1), 2.07 (S2) g/pot. The increase in total Cd extraction amount by 5 mM of single chelators GLDA and NTA reached 3.87 and 2.81 (S1), and 3.28 and 2.50 (S2) times that of the control group, respectively. For complexed chelating agents, G-N (GLDA + NTA) combinations (GLDA = 3 mM, NTA = 2 mM) extracted the highest amount of Cd compared with other treatments, reaching 0.36 and 0.52 mg/pot (4.50 and 3.71 times that of the control group), respectively. The order of extraction amount was G-N > GLDA > NTA > G-E (GLDA + EDDS) > G-C (GLDA + CA) > CA (5 mM total Cd concentration). Moreover, soil enzyme activity of G-N treatment increased significantly compared to that of the control group, indicating the great application potential of a composite chelating agent relative to a single chelating agent. Therefore, degradable chelators, especially the G-N combination, can effectively increase the available Cd content and greatly enhance the ability of plants to absorb and transport Cd in soils.

Uptake, translocation and impact of green synthesized nanoceria on growth and antioxidant enzymes activity of Solanum lycopersicum L

Cerium oxide nanoparticles (nanoceria) were synthesized by a novel, simple green chemistry procedure using Elaeagnus angustifolia leaf extract as a reducing and capping agent. The crystalline nature of nanoceria was confirmed by XRD analysis. FTIR analysis revealed that phytochemicals are present on the surface of nanoceria. SEM and TEM images revealed that the nanoceria are well dispersed, spherical in shape with a particle size range in between 30 and 75 nm. Thereafter, the effects of various concentrations of cerium oxide (CeO₂) and green synthesized nanoceria on growth and metabolism of Solanum lycopersicum (tomato) were investigated. The bio-accumulation of Ce in tomato seedlings was found to be dose dependent and the results showed that with the increase in exposure concentrations, the accumulation of Ce contents in both root and shoots augmented. However, unlike nanoceria treated seedlings, Ce contents in the roots with CeO₂ treatments were negligible than that in the shoots at lower concentrations and this suggested the immobilization of Ce in CeO₂ treatment at lower concentrations. Nanoceria at 500 and 1000 mg/L resulted in inhibitory effect on growth of test plant as compared to CeO₂ component. The exposure of plants to nanoceria and CeO₂ has resulted in significant reduction in pigment content, increased LP, EL and H₂O₂ content. The activities of antioxidant enzymes viz. SOD, CAT, APX and GPX were significantly up regulated on exposure of nanoceria and CeO₂. It is concluded that plant exposure with nanoceria at concentrations of 20 and 100 mg/L were more beneficial for growth and metabolism of tomato plants than that of CeO₂ at equivalent concentrations.

How does aquatic macrophyte Salvinia auriculata respond to nanoceria upon an increased CO2 source? A Fourier transform-infrared photoacoustic spectroscopy and chlorophyll a fluorescence study

With the continued increase of technological uses of cerium oxide nanoparticles (CeO₂ NPs or nanoceria) and their unregulated disposal, the accumulation of nanoceria in the environment is inevitable. Concomitantly, atmospheric carbon dioxide (CO₂) levels continue to rise, increasing the concentrations of bicarbonate ions in aquatic ecosystems. This study investigates the influence of CeO₂ NPs (from 0 to 100 μgL^-1) in the presence and absence of an elevated bicarbonate (HCO₃^-) ion concentration (1 mM), on vibrational biochemical parameters and photosystem II (PSII) activity in leaf discs of Salvinia auriculata. Fourier transform-infrared photoacoustic spectroscopy (FTIR-PAS) was capable of diagnostic use to understand biochemical and metabolic changes in leaves submitted to the CeO₂ NPs and also detected interactive responses between CeO₂ NPs and HCO₃^- exposure at the tissue level. The results showed that the higher CeO₂ NPs levels in the presence of HCO₃^- increased the non-photochemical quenching (NPQ) and coefficient of photochemical quenching in dark (qP_d) compared to the absence of HCO₃. Moreover, the presence of HCO₃^- significantly decreased the NPQ at all levels of CeO₂ NPs demonstrating that HCO₃^- exposure may change the non-radiative process involved in the operation of the photosynthetic apparatus. Overall, the results of this study are useful for providing baseline information on the interactive effects of CeO₂ NPs and elevated HCO₃^- ion concentration on photosynthetic systems.

Effects of cerium oxide on rice seedlings as affected by co-exposure of cadmium and salt

Effects of CeO₂ NPs (200 mg.L^-1) on rice (Oryza sativa L.) alone or co-exposure with cadmium (Cd) and salt (sodium chloride, NaCl) were investigated in hydroponic systems for two weeks. Physiological results show that rice biomass was significantly inhibited when NaCl or CdCl₂ added alone or in co-exposure treatment. CeO₂ NPs significantly relieve the chlorophyll damage under CdCl₂ environmental stress. The presence of CeO₂ NPs alleviated both stressors induced damages to rice as indicated by the reduced proline level. Additionally, CeO₂ NPs triggered the antioxidant defense systems to counteract the oxidative stress caused by NaCl and CdCl₂. The level of 8-OHdG, one of the most important indicators for genotoxicity, in rice suggest that the presence of CeO₂ NPs reduced the DNA damage in NaCl treated rice. Elemental analysis indicated that co-exposure to NaCl and CdCl₂ slightly decreased the Cd content as compared to the one in the CdCl₂ alone treatment, and this co-exposure also significantly reduced the Na content when comparing with the NaCl alone treatment. Taken together, our findings suggest that CeO₂ NPs could alleviate the CdCl₂ and NaCl stresses, but could not completely change the phenotype of both contaminants treated rice.