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

Revealing the synergistic spatial effects in soil heavy metal pollution with explainable machine learning models

The identification of factors that affect changes in the heavy metal content of soil is the basis for reducing or preventing soil heavy metal pollution. In this research, 16 environmental factors were selected, and the influences of soil heavy metal spatial distribution factors and the synergy amongst space factors were evaluated using a geographic detector (GD) and the extreme gradient boosting (XGBoost)-Shapley additive explanations (SHAP) model. Three heavy metal elements, namely, Cd, Cu and Pb, in the study region were examined. The following results were obtained. (1) XGBoost demonstrated high accuracy in predicting the spatial distributions of soil heavy metals, with each heavy metal having an R2 value of over 0.6. (2) Geological type map (Geomap) and enterprise density considerably affected the concentrations of Cd, Cu and Pb in soil in the GD and XGBoost-SHAP models. In addition, cross-detection revealed strong explanatory power when natural and human factors were combined. (3) Under the same geological background, the different trends of gross domestic product effects on heavy metals indicated that pollution control measures were effective in economically developed areas, and the economy and the environment could be balanced. Meanwhile, the interaction between the normalised difference vegetation index and enterprise density showed that vegetation could alleviate heavy metal pollution in the region. This study supports strategic decision-making, serving as a reference for the global management of soil heavy metal contamination, sustainable ecological development and assurance of people's health and well-being.

Genotype-specific responses to in vitro drought stress in myrtle (Myrtus communis L.): integrating machine learning techniques

Background

Myrtle (Myrtus communis L.), native to the Mediterranean region of Türkiye, is a valuable plant with applications in traditional medicine, pharmaceuticals, and culinary practices. Understanding how myrtle responds to water stress is essential for sustainable cultivation as climate change exacerbates drought conditions.

Methods

This study investigated the performance of selected myrtle genotypes under in vitro drought stress by employing tissue culture techniques, rooting trials, and acclimatization processes. Genotypes were tested under varying polyethylene glycol (PEG) concentrations (1%, 2%, 4%, and 6%). Machine learning (ML) algorithms, including Gaussian process (GP), support vector machine (SVM), Random Forest (RF), and Extreme Gradient Boosting (XGBoost), were utilized to model and predict micropropagation and rooting efficiency.

Results

The research revealed a genotype-dependent response to drought stress. Black-fruited genotypes exhibited higher micropropagation rates compared to white-fruited ones under stress conditions. The application of ML models successfully predicted micropropagation and rooting efficiency, providing insights into genotype performance.

Conclusions

The findings suggest that selecting drought-tolerant genotypes is crucial for enhancing myrtle cultivation. The results underscore the importance of genotype selection and optimization of cultivation practices to address climate change impacts. Future research should explore the molecular mechanisms of stress responses to refine breeding strategies and improve resilience in myrtle and similar economically important crops.

Phytobial remediation advances and application of omics and artificial intelligence: a review

Industrialization and urbanization increased the use of chemicals in agriculture, vehicular emissions, etc., and spoiled all environmental sectors. It causes various problems among living beings at multiple levels and concentrations. Phytoremediation and microbial association are emerging as a potential method for removing heavy metals and other contaminants from soil. The treatment uses plant physiology and metabolism to remove or clean up various soil contaminants efficiently. In recent years, omics and artificial intelligence have been seen as powerful techniques for phytobial remediation. Recently, AI and modeling are used to analyze large data generated by omics technologies. Machine learning algorithms can be used to develop predictive models that can help guide the selection of the most appropriate plant and plant growth-promoting rhizobacteria combination that is most effective at remediation. In this review, emphasis is given to the phytoremediation techniques being explored worldwide in soil contamination.

Effectiveness of light-emitting diodes for arsenic and mercury accumulation by Ceratophyllum demersum L.: An innovative advancement in phytoremediation technology

Light Emitting Diodes (LEDs) have emerged as a tool with great potential in the field of phytoremediation, offering a novel approach to enhance the efficiency of plant-based remediation techniques. In this work investigated the influence of LEDs on the phytoremediation of arsenic (As) and mercury (Hg) by Ceratophyllum demersum L., propagated using tissue culture methods. In addition, the biochemical properties of the plants exposed to metal toxicity were examined. Phytoremediation experiments employed concentrations of As (0.01-1.0 mg/L) and Hg (0.002-0.2 mg/L), with application periods set at 1, 7, 14, and 21 days. In addition to white, red and blue LEDs, white fluorescent light was used for control purposes in the investigations. A positive correlation was observed between higher metal concentrations, extended exposure times, and increased metal accumulation in the plants. Red LED light yielded the highest level of heavy metal accumulation, while white fluorescent light resulted in the lowest accumulation level. Examination of the biochemical parameters of the plants, including photosynthetic pigment levels, protein quantities, and lipid peroxidation, revealed a pronouncedly enhanced performance in specimens subjected to red and blue LED illumination, surpassing outcomes observed in other light treatments. The findings of this study introduce innovative avenues for the effective utilization of red and blue LED lights in the realm of phytoremediation research. Thus, the interaction between LEDs, tissue culture, and the phytoremediation process could lead to synergistic effects that contribute to more effective and sustainable remediation strategies.

A unified framework of response surface methodology and coalescing of Firefly with random forest algorithm for enhancing nano-phytoremediation efficiency of chromium via in vitro regenerated aquatic macrophyte coontail (Ceratophyllum demersum L.)

Nano-phytoremediation is a novel green technique to remove toxic pollutants from the environment. In vitro regenerated Ceratophyllum demersum (L.) plants were exposed to different concentrations of chromium (Cr) and exposure times in the presence of titania nanoparticles (TiO2NPs). Response surface methodology was used for multiple statistical analyses like regression analysis and optimizing plots. The supplementation of NPs significantly impacted Cr in water and Cr removal (%), whereas NP × exposure time (T) statistically regulated all output parameters. The Firefly metaheuristic algorithm and the random forest (Firefly-RF) machine learning algorithms were coalesced to optimize hyperparameters, aiming to achieve the highest level of accuracy in predicted models. The R2 scores were recorded as 0.956 for Cr in water, 0.987 for Cr in the plant, 0.992 for bioconcentration factor (BCF), and 0.957 for Cr removal through the Firefly-RF model. The findings illustrated superior prediction performance from the random forest models when compared to the response surface methodology. The conclusion is drawn that metal-based nanoparticles (NPs) can effectively be utilized for nano-phytoremediation of heavy metals. This study has uncovered a promising outlook for the utilization of nanoparticles in nano-phytoremediation. This study is expected to pave the way for future research on the topic, facilitating further exploration of various nanoparticles and a thorough evaluation of their potential in aquatic ecosystems.

Artificial intelligence models for validating and predicting the impact of chemical priming of hydrogen peroxide (H2O2) and light emitting diodes on in vitro grown industrial hemp (Cannabis sativa L.)

Industrial hemp (Cannabis sativa L.) is a highly recalcitrant plant under in vitro conditions that can be overcome by employing external stimuli. Hemp seeds were primed with 2.0-3.0% hydrogen peroxide (H2O2) followed by culture under different Light Emitting Diodes (LEDs) sources. Priming seeds with 2.0% yielded relatively high germination rate, growth, and other biochemical and enzymatic activities. The LED lights exerted a variable impact on Cannabis germination and enzymatic activities. Similarly, variable responses were observed for H2O2 × Blue-LEDs combination. The results were also analyzed by multiple regression analysis, followed by an investigation of the impact of both factors by Pareto chart and normal plots. The results were optimized by contour and surface plots for all parameters. Response surface optimizer optimized 2.0% H2O2 × 918 LUX LEDs for maximum scores of all output parameters. The results were predicted by employing Multilayer Perceptron (MLP), Random Forest (RF), and eXtreme Gradient Boosting (XGBoost) algorithms. Moreover, the validity of these models was assessed by using six different performance metrics. MLP performed better than RF and XGBoost models, considering all six-performance metrics. Despite the differences in scores, the performance indicators for all examined models were quite close to each other. It can easily be concluded that all three models are capable of predicting and validating data for cannabis seeds primed with H2O2 and grown under different LED lights.

Prediction and optimization of indirect shoot regeneration of Passiflora caerulea using machine learning and optimization algorithms

BACKGROUND

Optimization of indirect shoot regeneration protocols is one of the key prerequisites for the development of Agrobacterium-mediated genetic transformation and/or genome editing in Passiflora caerulea. Comprehensive knowledge of indirect shoot regeneration and optimized protocol can be obtained by the application of a combination of machine learning (ML) and optimization algorithms.

MATERIALS AND METHODS

In the present investigation, the indirect shoot regeneration responses (i.e., de novo shoot regeneration rate, the number of de novo shoots, and length of de novo shoots) of P. caerulea were predicted based on different types and concentrations of PGRs (i.e., TDZ, BAP, PUT, KIN, and IBA) as well as callus types (i.e., callus derived from different explants including leaf, node, and internode) using generalized regression neural network (GRNN) and random forest (RF). Moreover, the developed models were integrated into the genetic algorithm (GA) to optimize the concentration of PGRs and callus types for maximizing indirect shoot regeneration responses. Moreover, sensitivity analysis was conducted to assess the importance of each input variable on the studied parameters.

RESULTS

The results showed that both algorithms (RF and GRNN) had high predictive accuracy (R² > 0.86) in both training and testing sets for modeling all studied parameters. Based on the results of optimization process, the highest de novo shoot regeneration rate (100%) would be obtained from callus derived from nodal segments cultured in the medium supplemented with 0.77 mg/L BAP plus 2.41 mg/L PUT plus 0.06 mg/L IBA. The results of the sensitivity analysis showed the explant-dependent impact of exogenous application of PGRs on indirect de novo shoot regeneration.

CONCLUSIONS

A combination of ML (GRNN and RF) and GA can display a forward-thinking aid to optimize and predict in vitro culture systems and consequentially cope with several challenges faced currently in Passiflora tissue culture.