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

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 co-occurring CeO2NPs and Cd.

[1]  R. Gadea,et al.  Multilayer perceptron neural networks and radial-basis function networks as tools to forecast accumulation of deoxynivalenol in barley seeds contaminated with Fusarium culmorum , 2011 .

[2]  Roberto Oberti,et al.  Detection of biotic and abiotic stresses in crops by using hierarchical self organizing classifiers , 2017, Precision Agriculture.

[3]  Christian P Andersen,et al.  Molecular and physiological responses to titanium dioxide and cerium oxide nanoparticles in Arabidopsis , 2017, Environmental toxicology and chemistry.

[4]  Andrew C Johnson,et al.  Predicting contamination by the fuel additive cerium oxide engineered nanoparticles within the United Kingdom and the associated risks , 2012, Environmental toxicology and chemistry.

[5]  S. Trapp.  Plant uptake and transport models for neutral and ionic chemicals , 2004, Environmental science and pollution research international.

[6]  Zaoxiao Zhang,et al.  Contaminant dispersion prediction and source estimation with integrated Gaussian-machine learning network model for point source emission in atmosphere. , 2016, Journal of hazardous materials.

[7]  R. Brennan,et al.  Canola Takes Up More Cadmium and Phosphorus from Soil Than Spring Wheat , 2005 .

[8]  Yubin Lan,et al.  Review: Development of soft computing and applications in agricultural and biological engineering , 2010 .

[9]  M. Kirkham.  CADMIUM IN PLANTS ON POLLUTED SOILS: EFFECTS OF SOIL FACTORS, HYPERACCUMULATION AND AMENDMENTS , 2006 .

[10]  R. R. Pillutla.  Mathematical modeling of biosystems , 1991 .

[11]  Frédéric Baret,et al.  Training a neural network with a canopy reflectance model to estimate crop leaf area index , 2003 .

[12]  Mark G. M. Aarts,et al.  Plant science: the key to preventing slow cadmium poisoning. , 2013, Trends in plant science.

[13]  Ali Rahimikhoob,et al.  Estimation of evapotranspiration based on only air temperature data using artificial neural networks for a subtropical climate in Iran , 2010 .

[14]  Hansheng Wang.  Forward Regression for Ultra-High Dimensional Variable Screening , 2009 .

[15]  Martin T. Hagan,et al.  Neural network design , 1995 .

[16]  Amanat Ali Bhatti,et al.  Application of artificial neural network for the prediction of biosorption capacity of immobilized Bacillus subtilis for the removal of cadmium ions from aqueous solution , 2014 .

[17]  Oswer,et al.  SW-846 Test Method 3050B: Acid Digestion of Sediments, Sludges, and Soils , 2015 .

[18]  Sotiris Papantoniou,et al.  Prediction of outdoor air temperature using neural networks: Application in 4 European cities , 2016 .

[19]  D. R. Mailapalli,et al.  Interaction of Engineered Nanoparticles with the Agri-environment. , 2017, Journal of agricultural and food chemistry.

[20]  S. Komatsu,et al.  Toxicity of heavy metals and metal-containing nanoparticles on plants. , 2016, Biochimica et biophysica acta.

[21]  M. Bagheri,et al.  Assessment of effective parameters in landfill leachate treatment and optimization of the process using neural network, genetic algorithm and response surface methodology , 2017 .

[22]  D. R. Hoagland,et al.  The Water-Culture Method for Growing Plants Without Soil , 2018 .

[23]  Abbas Alimohammadi,et al.  Spatial and statistical analyses of the relations between vegetation cover and incidence of cutaneous leishmaniasis in an endemic province, northeast of Iran , 2014 .

[24]  Xingmao Ma,et al.  Physiological effects of cerium oxide nanoparticles on the photosynthesis and water use efficiency of soybean (Glycine max (L.) Merr.) , 2017 .

[25]  W. Ni,et al.  Effects of Cadmium Stress on Leaf Chlorophyll Fluorescence and Photosynthesis of Elsholtzia argyi—A Cadmium Accumulating Plant , 2015, International journal of phytoremediation.

[26]  Flemming R Cassee,et al.  Exposure, Health and Ecological Effects Review of Engineered Nanoscale Cerium and Cerium Oxide Associated with its Use as a Fuel Additive , 2011, Critical reviews in toxicology.

[27]  B. J. Alloway,et al.  The accumulation of cadmium by vegetables grown on soils contaminated from a variety of sources. , 1990, The Science of the total environment.

[28]  Z. Moradi,et al.  Biodegradation of direct blue 129 diazo dye by Spirodela polyrrhiza: An artificial neural networks modeling , 2016, International journal of phytoremediation.

[29]  Yunli Luo,et al.  Physiological mechanism of plant roots exposed to cadmium. , 2003, Chemosphere.

[30]  K Maxwell,et al.  Chlorophyll fluorescence--a practical guide. , 2000, Journal of experimental botany.

[31]  A. Mollalo,et al.  Predicting the Distribution of Phlebotomus papatasi (Diptera: Psychodidae), the Primary Vector of Zoonotic Cutaneous Leishmaniasis, in Golestan Province of Iran Using Ecological Niche Modeling: Comparison of MaxEnt and GARP Models , 2016, Journal of Medical Entomology.

[32]  Yuancheng Li,et al.  Image compression scheme based on curvelet transform and support vector machine , 2010, Expert Syst. Appl..

[33]  Xingmao Ma,et al.  The impact of cerium oxide nanoparticles on the salt stress responses of Brassica napus L. , 2016, Environmental pollution.

[34]  S. Komatsu,et al.  Comparative proteome analysis of high and low cadmium accumulating soybeans under cadmium stress , 2012, Amino Acids.

[35]  Mihail C. Roco,et al.  The long view of nanotechnology development: the National Nanotechnology Initiative at 10 years , 2011 .

[36]  R. Soolanayakanahally,et al.  Canola Responses to Drought, Heat, and Combined Stress: Shared and Specific Effects on Carbon Assimilation, Seed Yield, and Oil Composition , 2018, Front. Plant Sci..

[37]  Mark G. M. Aarts,et al.  The molecular mechanism of zinc and cadmium stress response in plants , 2012, Cellular and Molecular Life Sciences.

[38]  O. Björkman,et al.  Photon yield of O2 evolution and chlorophyll fluorescence characteristics at 77 K among vascular plants of diverse origins , 1987, Planta.

[39]  Mingyi Fan,et al.  Artificial Neural Network Modeling and Genetic Algorithm Optimization for Cadmium Removal from Aqueous Solutions by Reduced Graphene Oxide-Supported Nanoscale Zero-Valent Iron (nZVI/rGO) Composites , 2017, Materials.

[40]  H. Ramon,et al.  Automatic detection of ‘yellow rust’ in wheat using reflectance measurements and neural networks , 2004 .

[41]  M. Bagheri,et al.  Application of artificial intelligence for the management of landfill leachate penetration into groundwater, and assessment of its environmental impacts , 2017 .

[42]  Majid Bagheri,et al.  Modeling of effluent quality parameters in a submerged membrane bioreactor with simultaneous upward and downward aeration treating municipal wastewater using hybrid models , 2016 .

[43]  A. P. Schwab,et al.  Uptake, Accumulation, and in Planta Distribution of Coexisting Cerium Oxide Nanoparticles and Cadmium in Glycine max (L.) Merr. . , 2017, Environmental science & technology.

[44]  A. P. Schwab,et al.  Mutual effects and in planta accumulation of co-existing cerium oxide nanoparticles and cadmium in hydroponically grown soybean (Glycine max (L.) Merr.) , 2018 .

[45]  B. J. Alloway,et al.  The origins of heavy metals in soils. , 1990 .

[46]  Jorge L Gardea-Torresdey,et al.  Evaluation of exposure concentrations used in assessing manufactured nanomaterial environmental hazards: are they relevant? , 2014, Environmental science & technology.

[47]  Meiling Liu,et al.  Neural-network model for estimating leaf chlorophyll concentration in rice under stress from heavy metals using four spectral indices , 2010 .

[48]  B. J. Alloway,et al.  Heavy metals in soils , 1990 .

[49]  Zailin Huo,et al.  Simulation for response of crop yield to soil moisture and salinity with artificial neural network , 2011 .