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

Environmental Pollution 246 (2019) 381e389

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Environmental Pollution journal homepage: www.elsevier.com/locate/envpol

Using artificial neural network to investigate physiological changes and cerium oxide nanoparticles and cadmium uptake by Brassica napus plants* Lorenzo Rossi a, b, c, *, 1, Majid Bagheri c, 1, Weilan Zhang a, Zehua Chen d, Joel G. Burken c, Xingmao Ma a a

Zachry Department of Civil Engineering, Texas A&M University, TAMU 3136, College Station, TX, 77843-3136, USA Department of Horticultural Sciences, University of Florida, Institute of Food and Agricultural Sciences, Indian River Research and Education Center, Fort Pierce, FL, 34945, USA c Department of Civil, Architectural and Environmental Engineering, Missouri University of Science & Technology, Rolla, MO, 65409-0030, USA d College of Big Data, Taiyuan University of Technology, JinZhong, Shanxi Province, 030600, PR China b

a r t i c l e i n f o

a b s t r a c t

Article history: Received 10 September 2018 Received in revised form 27 October 2018 Accepted 11 December 2018 Available online 12 December 2018

Heavy metals and emerging engineered nanoparticles (ENPs) are two current environmental concerns that have attracted considerable attention. Cerium oxide nanoparticles (CeO2NPs) are now used in a plethora of industrial products, while cadmium (Cd) is a great environmental concern because of its toxicity to animals and humans. Up to now, the interactions between heavy metals, nanoparticles and plants have not been extensively studied. The main objectives of this study were (i) to determine the synergistic effects of Cd and CeO2NPs on the physiological parameters of Brassica and their accumulation in plant tissues and (ii) to explore the underlying physiological/phenotypical effects that drive these specific changes in plant accumulation using Artificial Neural Network (ANN) as an alternative methodology to modeling and simulating plant uptake of Ce and Cd. The combinations of three cadmium levels (0 [control] and 0.25 and 1 mg/kg of dry soil) and two CeO2NPs concentrations (0 [control] and 500 mg/kg of dry soil) were investigated. The results showed high interactions of co-existing CeO2NPs and Cd on plant uptake of these metal elements and their interactive effects on plant physiology. ANN also identified key physiological factors affecting plant uptake of co-occurring Cd and CeO2NPs. Specifically, the results showed that root fresh weight and the net photosynthesis rate are parameters governing Ce uptake in plant leaves and roots while root fresh weight and Fv/Fm ratio are parameters affecting Cd uptake in leaves and roots. Overall, ANN is a capable approach to model plant uptake of cooccurring CeO2NPs and Cd. © 2018 Elsevier Ltd. All rights reserved.

Keywords: Cerium oxide nanoparticles Cadmium Artificial neural network Brassica napus Canola

1. Introduction Engineered nanoparticles (ENPs) and heavy metals are two big environmental concerns that, in the last decade, have attracted attention and scrutiny in the environmental science and engineering community. Nanotechnology industry is rapidly expanding

*

This paper has been recommended for acceptance by Baoshan Xing. * Corresponding author. Department of Horticultural Sciences, University of Florida, Institute of Food and Agricultural Sciences, Indian River Research and Education Center, Fort Pierce, FL, 34945, USA. E-mail address: l.rossi@ufl.edu (L. Rossi). 1 Both authors contributed equally to this manuscript. https://doi.org/10.1016/j.envpol.2018.12.029 0269-7491/© 2018 Elsevier Ltd. All rights reserved.

and its growth is forecasted to reach $3 trillion in final goods by 2020 (Roco, 2011), leading to significant increases in the synthesis and also increasing disposal and unintended releases of ENPs into the environment (Pradhan and Mailapalli, 2017). In the meanwhile, geologic and anthropogenic activities increase the concentration of heavy metals to levels that are harmful to both plants and animals (Alloway, 1990; Alloway, 1995; Alloway et al., 1990). Among the numerous nanoparticles available today, cerium oxide nanoparticles (CeO2NPs) attracted great attention from environmental scientists. These nanoparticles are used in a plethora of industrial products (e.g., catalysts, sunscreen creams, microelectronics and polishing agents), thanks to their unique catalytic and optic properties (EPA, 2009). Their use as a diesel fuel

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additive is particularly concerning and has raised serious environmental concerns (Cassee et al., 2011; Johnson and Park, 2012). Similarly, cadmium (Cd) is of great concern in the environment because of its toxicity to animals and humans. The increase of Cd in agricultural soils is mostly due to anthropogenic activities (Singh and McLaughlin, 1999). For example, smelting and sintering of non-ferrous metals have resulted in Cd contamination of the nearby environment (Page et al., 1987). Similarly, sewage sludge from municipalities frequently applied to agricultural soils as a source of plant nutrients always exceed the Cd concentrations normally found in soils. Interestingly, phosphorus fertilizers also often contain greater concentrations of Cd than typically found in soils (Page et al., 1987). Even though Cd in soil may not exert overt toxicity to plants, animals consuming the plants may experience toxicity over time (Kirkham, 2006). Humans are more susceptible to the toxic effects of Cd than other animals due to their longevity (Tudoreanu and Phillips, 2004). Cd uptake from food consumption has been a primary source of human exposure due to its highly efficient soil-to-plant transfer compared with other heavy metals (Clemens et al., 2013). A World Health Organization (WHO) study estimated that food accounts of about 90% of Cd exposure in the general non-smoking population globally (WHO, 2010). Emerging evidences have shown that ENPs and heavy metals interact closely to alter their fate and transport in a plant system (Mustafa and Komatsu, 2016). For instance, two recent publications showed that co-existing CeO2NPs and Cd mutually affect their uptake and accumulation by soybean seedlings (Glycine max (L.) Merr.) and CeO2NPs appeared to enhance plant physiology in the presence of Cd (Rossi et al., 2018; Rossi et al., 2017). The enhancive effect of CeO2NPs on plant physiological parameters such as net photosynthesis and water use efficiency have also been reported (Cao et al., 2017). However, a comprehensive understanding on correlations between the phenotypical/physiological changes (i.e., plant biomass, photosystem II fluorescence, stomatal conductance and net photosynthesis rate) with plant Ce and Cd uptake in cultivated crops is lacking. In recent years, artificial neural networks (ANNs) have been successfully applied to various modeling, function approximation, and classification problems (Bagheri et al., 2016; Movafeghi et al., 2016; Papantoniou and Kolokotsa, 2016). Modeling and prediction have been useful approaches to deal with various problems in several fields of science and engineering (Sofizadeh et al., 2016; Trapp, 2004). Agricultural systems, such as the environmenteplant system, are highly complex and diverse, and can be considered as
zquez-Cruz et al., 2014). This systems are, in ill-defined systems (Va fact, characterized by nonlinearities, time-varying properties, climatic interactions, and many other factors. Therefore, quantifying complex relationships between the input and the output of a system based on analytical methods is quite challenging and difficult
zquez-Cruz et al., 2014). (Va ANNs are an alternative methodology for modeling and simulating plant biophysical properties. ANN models were primarily designed for dynamic nonlinear systems (Rahimikhoob, 2010) and are now used in many computer-based applications to identify patterns or “learn” relationships between a set of input and output variables (Danson et al., 2003). During the last decade, a significant increase in agronomic ANN applications has occurred (Huang et al., 2010), including crop development modeling, crop yield prediction, and biotic and abiotic stress detection (Dai et al., 2011; Pantazi et al., 2017; Rahimikhoob, 2010). The applications of ANNs in the fate and impact of ENPs in plant systems are rare even though ANNs have been used to model the plant uptake of heavy metals. (Ahmad et al., 2014; Bagheri et al., 2017; Fan et al., 2017). ANNs are data driven non-parametric models, which use interconnected mathematical nodes to create an intelligent

network. The intelligent algorithm then is able to discern complex relationships between inputs-outputs of a given system through experience and available data (Ma and Zhang, 2016). The successful application and accuracy of ANNs in various problems have outperformed many traditional modeling approaches. Brassica napus L. (canola) emerged as the third most important source of vegetable oil in the world, after soybean and palm oil. In addition, canola has shown high resistance to abiotic stresses (Elferjani and Soolanayakanahally, 2018). Given the chances of ENPs and Cd accumulation in agricultural soils, a comprehensive study examining the synergistic effects of these two conditions is needed. Particularly, considering the extent of CeO2NPs effects on plants and its unique redox chemistry on the surface, it is intriguing to investigate how these nanoparticles will affect plant responses to external abiotic stresses (e.g., Cd stress) and their accumulation. The main objectives of this study were (i) to determine the synergistic effects Cd and CeO2NPs on the physiological parameters of Brassica and their accumulation in plant tissues and (ii) to explore the underlying physiological/phenotypical effects that drive these specific changes in plant accumulation using ANNs. 2. Materials and methods 2.1. CeO2NPs and Cd The dispersion of CeO2NPs coated with polyvinylpyrrolidone (PVP) was purchased from the US Research Nanomaterials, Inc. (Houston, TX). The average size of the CeO2NPs was 55.6 nm. The size and size distribution, and the Transmission Electron Microscopy (TEM) image of CeO2NPs from the same batch as used in this study were reported in (Rossi et al., 2016). CeO2NPs concentrations (500 CeO2 mg kg
1 dry sand) were chosen because the majority of the previous studies on the toxicity of CeO2NPs to terrestrial plants fell in the range of 1e1000 mg/kg
1, (Holden et al., 2014). Cd sulfate (CdSO4) was purchased from Fisher Scientific Int. (Pittsburgh, PA) and was dissolved in the Hoagland nutrient solution (Hoagland and Arnon, 1950) (Phyto Technology Lab, Shawnee Mission, KS) to reach the targeted concentrations (0.25 and 1.0 mg/ kg dry soil) at the beginning of the experiment. Cd concentrations were chosen because they represented the background Cd concentrations in many agricultural soils (ICdA, 2013). 2.2. Plant species and growth conditions Brassica napus (canola) cv. ‘Dwarf Essex’ seeds were purchased from Johnny's Selected Seeds (Winslow, ME). The seeds were sterilized in 2.7% Clorox bleach for 10 min and washed three times with deionized (DI) water. They were then placed in 100 mm diameter  15 mm depth polystyrene petri dishes with a thin layer of DI water above the supporting filter paper. After germination, young seedlings were individually transplanted into 9.5 cm diameter  12 cm height plastic pots filled with 500 g of topsoil (Scotts Company, Marysville, OH) saturated with 25% Hoagland solution (Hoagland and Arnon, 1950) (Phyto Technology Lab, Shawnee Mission, KS). 80 mL of CeO2NPs and/or Cd sulfate (CdSO4) anhydrous (Thermo Fisher Scientific, Waltham, MA) were introduced into the soil and mixed vigorously before transplant to achieve a concentration of 500 mg/kg CeO2NPs and/or 0.25 and 1.00 mg kg
1 Cdþ dry soil. Five plants for each treatment were grown at room temperature under fluorescent bulbs providing 250 mmol m2 s
1 photosynthetic photon flux density (16 h light e 8 h dark photoperiod). Plants were fert-irrigated with full strength Hoagland for 60 days. Plants grown in soils without the addition of CdSO4 and CeO2NPs were used as controls.

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2.3. Growth analyses and plant partitioning After 60 days of treatment, five plants per treatment were carefully removed from the growth media and rinsed with DI water, divided into roots and leaves and tapped dry with a paper towel. Plant biomass were divided into leaves and roots only because stems were not fully developed at termination. The root and leaf tissues from each replicate were dried in an oven at 70  C for 7 days to determine the dry biomass (DW), and concentrations of Cd and Ce in these tissues. Detailed descriptions on the analytical procedures are provided below. 2.4. Net photosynthesis and stomatal conductance Net photosynthesis and stomatal conductance were measured using a portable Infra-Red (IR) gas analyzer (LI-6400xt; LI-COR, Lincoln, NE) equipped with a standard LI-6400xt, 2  3 cm leaf chamber and a red/blue light source (6400e02B, LI-COR). At the time of measurement, leaf temperature ranged between 25 and 30  C and chamber temperature was kept constant at 25  C. Chamber CO2 concentration was kept constant at 400 mg L
1 and light levels were obtained using the red/blue light source connected to the gas analyzer. Measurements were performed on top leaves. Leaves were allowed to adapt to each light level for 2 min before each point was recorded. Net photosynthesis and stomatal conductance were measured every 30 days from the beginning of the experiment: D0 (Day 0), D30 (Day 30), and D60 (Day 60). 2.5. Chlorophyll fluorescence Leaf chlorophyll fluorescence measurements were carried out each week using a continuous excitation chlorophyll fluorescence analyzer (OS1p, Opti-Sciences, Hudson, NH). Leaves were acclimated to the dark using lightweight leaf clips for at least 30 min before measurements were taken (Maxwell and Johnson, 2000). Baseline (F0) and maximum (Fm) fluorescence were measured and variable (Fv ¼ Fm
F0) fluorescence and the ratio of variable fluorescence to maximum fluorescence (Fv/Fm) were calculated. Chlorophyll fluorescence was measured every 30 days from the beginning of the experiment as indicated above. 2.6. Cadmium and cerium contents analyses An aliquot of 0.5 g of dry plant tissues were digested using a DigiPREP MS hot block digester (SCP science, Clark Graham, Canada), following the EPA method 3050b (USEPA, 1996). Dry leaves and roots of three replicates were ground and mixed with 4 mL of 70% (v/v) nitric acid. The mixture was maintained at room temperature overnight for predigestion, and then was digested in the hot block at 95  C for 4 h. After cooling down to room temperature, 2 mL of 30% (w/v) H2O2 was added to the mixture, further heated in the hot block at 95  C for 2 h. Finally, the Ce and Cd in the digestate was quantified by an inductively coupled plasma mass spectrometry (ICP-MS, Perkin Elmer mod. DRCII, Waltham, MA). 2.7. Artificial neural networks (ANNs) programing ANNs are machine learning methods that mimic biological neurons to simulate functions, depending on a number of input parameters. ANNs relate the inputs of a system to its outputs using connected computational neurons operating in parallel (Bagheri et al., 2017). More details regarding neural networks and the connection of neurons can be found in (Demuth et al., 2014). For large and very complex systems, one layer of connected neurons is not enough to discern the relationship between inputs

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and outputs. Thus, the complex relationship between inputs and outputs is learned by increasing the number of layers. Multilayer perceptron ANN (MLPANN) normally has three layers and is capable of learning the most complex relationships (Mateo et al., 2011). The first layer is the input layer, the second layer is the hidden layer with a number of computational neurons, and the third layer is the output layer (Biglarijoo et al., 2017). The MLPANN allocates a portion of input dataset (usually 70% of data) for learning the relationship between inputs and outputs, and then sets a model between inputs and outputs (Li et al., 2010). While algorithm is learning the input-output relationship, 15% of data is used by algorithm to validate the model (Validation dataset). Following the learning or training procedure, the MLPANN is able to make accurate predictions when presented with unseen or test datasets (usually 15% of data). In order to simulate the uptake of Ce and Cd using MLPANN, seven variables including leaf fresh weight, root fresh weight, leaf dry weight, root dry weight, net photosynthesis rate at day 60, stomatal conductance at day 60, and Fv/Fm at day 60 were used as inputs of the network. The accuracy of modeling was measured using mean squared error (MSE), and correlation of coefficient (R). MATLAB R2014a was used to perform MLPANN model for predicting the uptake of Ce and Cd based on plant physiological parameters. 2.8. Variable selection analysis Selection of effective variables is an important aspect of ANN modeling (Mollalo et al., 2014). The variable selection analysis aims to find the most important variables among all inputs for the study of a given system. Forward selection is a simple selection method to determine the significant variables. The forward selection method determines the significance of variables by t-test and p-value. More details regarding the variable selection method can be found in (Wang, 2009). The forward selection method was applied in this study and was performed using IBM SPSS 16 software. 2.9. Statistical analysis Experimental results were analyzed by a two-way ANOVA analysis, with CeO2NPs and Cd as the two variables. In addition, one-way ANOVA was conducted, and mean separations between treatments was obtained by the Tukey's test. These analyses were carried out using the Minitab 17 Statistical Software (Minitab Inc., State College, PA). 3. Results and discussion Previously, we studied the impact of co-existing CeO2NPs and Cd on the plant physiology and root anatomy of soil-grown soybean (Rossi et al., 2018) and found that CeO2NPs did not affect Cd accumulation in soybean but led to higher Fv/Fm ratio. However, Cd significantly increased the accumulation of Ce in both root and leaf organs, and the altered in-planta Ce distribution was partially associated with the formation of root apoplastic barriers in the copresence of Cd and CeO2NPs. Similarly, we investigated the impact of salt stress on the accumulation of Ce by Brassica for 40 days. We found that in the presence of 100 mM NaCl, the concentrations of Ce in both Brassica roots and leaves were drastically increased compared with plants exposed to the same concentration of CeO2NPs but without salt stress (Rossi et al., 2016). Generally, most previous work in this specific area have been focused on the study of the plant physiological, anatomical and molecular reactions to different nanoparticles and/or heavy metals (Cao et al., 2017; Chen et al., 2003; Lin and Aarts, 2012; Tumburu

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Fig. 1. Fresh and dry weights of Brassica napus grown for 60 days in the presence of 500 mg/kg dry soil CeO2NPs and/or 0.25 or 1.0 mg/kg dry soil Cd. (A) fresh weight of leaves, (B) fresh weight of roots, (C) dry weight of leaves, (D) dry weight of roots. Values represent mean ± SD (n ¼ 5). Different letters indicate significant statistical differences (at p  0.05) according to one-way ANOVA followed by Tukey's test. Two-way ANOVA p-values are reported in Suppl. Table 1.

et al., 2017). So far, no studies have been reported with regard to the prediction of concentrations of nanomaterial elements and heavy metals by means of simple and non-destructive physiological measurements such as biomass and net photosynthesis rate. Such connection will dramatically improve the capability to predict the food security and safety risks of heavy metals and co-existing nanomaterials. The use of neural systems demonstrated the ability to develop accurate predictions of the complex synergistic response of concurrent exposures to multiple pollutants even though more research is needed to enhance performance of training algorithms to improve the ability of neural systems to learn from physiological data patterns for plant metal uptake predictions for complex systems. The forecasting performance can provide a useful guidance or reference for environmental science estimation. In the present study, Brassica plants have been grown for 60 days and we registered the physiological changes under the co-

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presence of CeO2NPs and Cd. Those data have been used as inputs in the ANN. Overall, CeO2NPs alone reduced the total FW by an average of 30%. In the presence of 0.25 and 1.0 mg kg
1 Cd, when compared to control, the FW were reduced by 36% and 25%, respectively, when exposed to CeO2NPs (Fig. S1A). Similar effects by CeO2NPs and Cd were noted for the FW of both leaves (Fig. 1A) and roots (Fig. 1B). Two-way ANOVA confirmed a high level of interactions between CeO2NPs and Cd on leaves FW (Suppl. Table 1). Interestingly, Cd at 1.0 mg/kg increased root FW by 15%, but in the co-presence of CeO2NPs, the FW decreased by 20% (Cd 1.0 mg/kg) and 35% (Cd 0.25 mg/kg). Two-way ANOVA did not show any significant interactions between CeO2NPs  Cd at root level (FW) or the total FW (Suppl. Table 1). The total dry weight (DW) of Brassica decreased by 35% when treated with CeO2NPs alone (Fig. S1B). Two-way ANOVA confirmed CeO2NPs  Cd interactions in total DW and root DW (Suppl. Table 1). Moreover, CeO2NPs and 1.0 mg kg
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Fig. 2. Net photosynthetic rate (A) and stomatal conductance (B) of Brassica napus grown for 60 days in the presence of 500 mg/kg dry soil CeO2NPs and/or 0.25 or 1.0 mg/kg dry soil Cd. Measures were taken at the beginning of the experiment (D0), after 30 days (D30) and after 60 days (D60). Means followed by different letters are significantly different by Tukey's post-hoc test (p < 0.05). Error bars represent the standard deviation (n ¼ 5). Two-way ANOVA p-values are reported in Suppl. Table 1.

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Fig. 3. Fv/Fm ratio of Brassica napus grown for 60 days in the presence of 500 mg/kg dry soil CeO2NPs and/or 0.25 or 1.0 mg/kg dry soil Cd. Measures were taken at the beginning of the experiment (D0), after 30 days (D30) and after 60 days (D60). Means followed by different letters are significantly different by Tukey's post-hoc test (p < 0.05). Error bars represent the standard deviation (n ¼ 5). Two-way ANOVA pvalues are reported in Suppl. Table 1.

Cd alone or in combination led to significant decrease of the leaves and root DW (Fig. 1C and D). These results indicated that the biomass of Brassica can be hindered by the presence of CeO2NPs after 60 days but not by Cd alone. However, at harvest time, roots exposed to both Cd concentrations demonstrated a physiologic response as roots appeared to have a brown discoloration and necrosis compared to the control and CeO2NPs exposed plants, whose roots looked clearer and healthier. The results confirmed a negative impact of Cd on root health and elongation (Chen et al., 2003). Overall, root FW is one of the variables identified by the forward selection, suggesting that the uptake and accumulation of Ce and Cd in roots and leaves depends on the weight of the fresh roots. The impact of Cd and Ce on plants is also confirmed by the plant

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physiology data. In fact, when Cd was present at both concentrations (alone or in the co-presence of CeO2NPs) the net photosynthesis rate and stomatal conductance were decreased after 30 and 60 days. In contrast, CeO2NPs alone maintained values close to the control plants confirming a non-significant role of CeO2NPs in canola physiology (Rossi et al., 2016). Specifically, net photosynthesis rate at day 60 was one of the variables identified by the forward selection as responsible for Ce uptake in leaves and roots. Two-way ANOVA did not reveal any CeO2NPs  Cd interactions during the first 30 days (D0 and D30) for both the net photosynthesis rate and the stomatal conductance (Fig. 2 and Suppl. Table 1). Interestingly, after 60 days (D60) of exposure, plants treated with only CeO2NPs showed similar performance as the unexposed controls, but plants treated with CeO2NPs and Cd exhibited a significant decrease in both net photosynthesis rate and stomatal conductance. Noted differences were also indicated by the two-way ANOVA analysis which reported significant CeO2NPs  Cd interactions at Day 60. Generally, plants treated with CeO2NPs alone exhibited only minimum changes (non-significant) in term of photosynthetic responses compared to the control plants, consistent with the biomass production results. Significant differences were found in the Fv/Fm ratio. Two-way ANOVA reported strong CeO2NPs  Cd interactions for Fv/Fm ratio on D30 and D60. In general, plants treated with Cd and CeO2NPs alone showed higher Fv/Fm values than plants treated with CeO2NPs þ Cd (Fig. 3). Treatment with CeO2NPs led to higher Fv/Fm ratio than control plants at D60. This parameter represents another key variable identified by the ANN in the Cd uptake and accumulation in roots and leaves. Commonly, the Fv/Fm ratio is used as an indicator of the photosynthetic energy conversion in higher plants (Maxwell and

Fig. 4. Ce and Cd concentrations of Brassica napus grown for 60 days in the presence of 500 mg/kg dry soil CeO2NPs and/or 0.25 or 1.0 mg/kg dry soil Cd. (A) cerium in leaves, (B) cerium in roots, (C) cadmium in leaves, (D) cadmium in roots. Values represent mean ± SD (n ¼ 5). Different letters indicate significant statistical differences (at p  0.05) according to one-way ANOVA followed by Tukey's test. Two-way ANOVA p-values are reported in Suppl. Table 1.

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Johnson, 2000). The elevated values suggest that CeO2NPs enhanced the plant light energy use efficiency of the photosystem II (PSII). In a previous publication (Rossi et al., 2016) we reported that CeO2 NPs can increased the uptake of Mg2þ ion, which is an essential element of the chlorophyll molecule and this can be the case of the elevated Fv/Fm. As for Cd, the increase of Fv/Fm values other studies indicated that the efficiency of the photosynthetic apparatus and the size and number of active photosynthetic centers under low and mild cadmium stress are usually not inhibited (Li et al., 2015), particularly under 5e50 mmol L
1 Cd treatments. However, higher levels of cadmium can inhibit the photosynthetic apparatus. Our study showed that canola plants responded well to the two cadmium treatments and did not show symptoms of

damages of the PSII. Usually, if Fv/Fm values are “higher than 0.8“, it € rkman means that the potential efficiency of PSII is not affected (Bjo and Demmig, 1987). In our case the higher values found for Cd are maybe due to a specific leaf physiological response. In fact, canola usually respond well to mild Cd stress (Brennan and Bolland, 2005). Interestingly, when the two experimental conditions (Cd and CeO2 NPs) were associated a synergistic effect occurred and lower levels of the Fv/Fm values were recorded. In a similar experiment conducted on soybean we found that Cd  CeO2NPs interaction led to different plant accumulation of co-existing Cd and CeO2NPs and different Fv/Fm (Rossi et al., 2018; Rossi et al., 2017). The copresence of Cd and CeO2NPs enhanced the excretion of soybean root exudates, lowered the pH values on the root surface and

Fig. 5. Regression lines (n ¼ 5) of the MLPANN model used in Brassica napus grown for 60 days in the presence of 500 mg/kg dry soil CeO2NPs and/or 0.25 or 1.0 mg/kg dry soil Cd. (A) Cd concentration in leaves (B) Cd concentration in roots, (C) Ce concentration in leaves and (D) Ce concentration in roots.

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enhanced CeO2NPs dissolution, and therefore resulted in higher accumulation of Ce in soybean leaves compared to plants exposed to CeO2NPs alone. In the present study, when Cd and CeO2NPs were both present, we recorded a higher accumulation of Ce in roots compared to plants exposed to CeO2NPs alone. This can be a result of a similar root physiological mechanism to the one described in soybean. In this case Brassica may release root exudates which enhance CeO2NPs dissolution, and therefore resulted in higher accumulation of Ce in roots. The different translocation of Ce from roots to leaves may be a result of the lack of specific transporters in brassica, while those transporters are actually present in soybean (Hossain et al., 2012). In our modeling, the Ce and Cd concentrations in Brassica roots and leaves were used as model outputs. The concurrent exposure to CeO2NPs and Cd increased the Ce concentration in roots but lowered the Ce concentration in leaves compared to plants exposed to CeO2NPs alone (Fig. 4). Overall, in the co-presence of CeO2NPs and Cd (at both concentrations), plant uptake of Ce significantly decreased in leaves (Fig. 4A) but increased by 50% in roots (Fig. 4B). Two-way ANOVA analysis confirmed high interactions between CeO2NPs and Cd in terms of discrimination in CeO2NPs uptake and translocation. Conversely, in the co-presence of CeO2NPs and Cd, the total Cd concentration in the whole plant biomass decreased

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sensibly by 82% (Cd 1.0 mg/kg) and by 71% (Cd 0.25 mg/kg), compared to plants treated with Cd only (Fig. S2). A similar trend was observed for the leaves (Fig. 4C) as well as roots (Fig. 4D). Twoway ANOVA revealed significant CeO2NPs  Cd interactions in leaves, root and the whole plant, confirming a role of CeO2NPs in the altered Cd uptake. The results of training procedure for the ANN model showed high ability of the MLPANN in learning the relationship between input variables and the concentrations of Ce and Cd in different parts of plants. The correlation coefficients for training models exceed 0.96. Based on the train dataset, the R values for MLPANN models predicting Cd in leaf and root, and CeO2NPs in leaf and root were 0.992, 0.997, 0.96, and 1, respectively (Fig. 5). The R values for MLPANN models predicting concentrations of Ce in Brassica leaf and root varied from 0.935 to 0.979. The accuracy of ANN models to predict concentrations of Ce and Cd in leaf and root is determined by the training dataset. Based on the test dataset, the R values for MLPANN models predicting Cd in leaf and root, and Ce in leaf and root were 0.967, 0.974, 0.912, and 0.977, respectively. The MSE values for MLPANN models predicting Cd in leaf and in root, and Ce in leaf and root were 0.045, 0.003, 0.004, and 4.55 mg L
1, respectively. The average percentage of error for all MLPANN models was less than 10%.

Fig. 6. Predictions by MLPANN models according to train, test, validation and all datasets for Brassica napus grown for 60 days in the presence of 500 mg/kg dry soil CeO2NPs and/or 0.25 or 1.0 mg/kg dry soil Cd. (A) Cd concentration in leaves, (B) Cadmium concentration in roots, (C) Cerium concentration in leaves and (D) Cerium concentration in roots.

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The MLPANN is a commonly used predictor (general approximator), which is effective in capturing the peaks and changes. The predictions of Cd in Brassica leaf and root, and Ce in leaf and root by MLPANN model-based training, validation, and test are reported in Fig. 6. The results from the models indicate that MLPANN models effectively captured the general changes and peaks in the concentration of Cd and Ce with low relative error in the resulting predictions. Thus, in addition to their accuracy, the MLPANN models can be considered reliable in the prediction of Cd and Ce uptake by plants. The ability of MLPANN models in capturing peaks and changes is important because the concentrations of different compounds may vary over time. According to the forward selection analysis, the parameters related to Ce uptake in leaves and root were the root FW and the net photosynthesis rate at day 60. While, the parameters related to Cd uptake in leaves and roots were the root FW and the Fv/Fm ratio at day 60. For the significant variables, which are supposed to have the highest impacts on the uptake model, the p values were less than 0.05, and t-test was minimum. The results of the study showed that ANN, MLPANN in particular, is a precise modeling approach to predict the Ce and Cd uptake in different plant organs. Previous studies also reported the ability of ANNs in learning the relationship between variables and the target (Liu et al., 2010; Moshou et al., 2004). In most cases, the ANN models due to their higher accuracy outperform the traditional modeling approaches. The high precision (High R values and low MSE values) of MLPANN models in predicting the concentrations of Ce and Cd in plant tissues is achieved by considering error of previous model when adjusting weights and constants for new models (Demuth et al., 2014). The results of validation and test models prove that the intelligent algorithm kept the error in an acceptable range. The high values of R and low values of MSE for testing models predicting Cd and Ce concentrations indicate that the MLPANN is both precise and reliable for practical predictions. The test dataset is a part of data that the model is not aware of and is a good tool to measure the accuracy of algorithm (Abraham, 2005). The results of this study in terms of accuracy and reliability of ANN models are in a good agreement with the results of previous studies focusing on related environmental problems, which reported correlation coefficient higher than 0.9 and low values for error (Liu et al., 2010; Moshou et al., 2004). 4. Conclusion In conclusion, this study confirmed the effects of concurrent CeO2NPs and Cd exposure on plant uptake of these metal elements and their interactive effects on plant physiology. For the first time, connections between plant physiological responses to co-existing CeO2NPs and Cd and their accumulation in plant tissues were found. Key physiological factors affecting plant uptake of cooccurring Cd and CeO2NPs using ANN were also identified. The ability to predict plant metal uptake using non-destructive parameters can profoundly improve the capability for food safety assessment arising from environmental pollution. It is acknowledged that plant uptake of heavy metals is affected by various factors including the properties of metallic oxide nanoparticles, copresent heavy metals, plant species and plant age as well as the growing environment. Future research should expand the horizon of experiments and provide more datasets for ANN to include the above-mentioned factors in the modeling process. Acknowledgement The assistance provided by Leonardo Lombardini (Department of Horticultural Sciences, Texas A&M University, College Station,

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