Evaluation of Variability in Tunisian Olea europaea L. Accessions using Morphological Characters and Computational Approaches

The olive trees (Olea europaea L.) have been cultivated for millennia in the Mediterranean basin and its oil has been an important part of human nutrition in the region. In order to distinguish between olive accessions, morphological and biological characters have been widely and commonly used for descriptive purposes and have been used to characterize olive accessions. A comparative study of morphological characters of olive accessions grown in Tunisia was carried out and analyzed using Bayesian Networks (BN) and Principal Components Analysis (PCA). The obtained results showed that averages of fruit and kernel weights were 2.27 grams and 0.41 grams, respectively.  Besides, a relatively moderate level of variation (51.22%) being explained by four Principal components. BN revealed that geographical localisation plays a role in the increase of tree habit, size of lenticels and leaf shape. A dendrogram has been carried out in the aim to classify studied olive accessions. We proposed a novel method of analysis based on the three-step scheme, in which first the data set is clustered, then olive tree features are evaluated. The studied accessions can be divided into four main groups by cutting the dendrogram at a similarity value of 0.645. Different relationships are studied and highlighted, and finally the collected features are subjected to a global principal component analysis. Obtained results confirmed that core surface was negatively correlated with geographical location (r = -0.52, p<0.05) and maturation period r = -0.539, p<0.05). Number of lenticels was positively correlated to lenticels size (r = 0.632, p<0.05). Core shape had a negative correlation with fruit shape (r = -0.759, p<0.05). On the basis of these findings, this research confirmed that morphological markers are a preliminary tool to characterize olive oil accessions

Coping with salinity stress: segmental group 7 chromosome introgressions from halophytic Thinopyrum species greatly enhance tolerance of recipient durum wheat

Increased soil salinization, tightly related to global warming and drought and exacerbated by intensified irrigation supply, implies highly detrimental effects on staple food crops such as wheat. The situation is particularly alarming for durum wheat (DW), better adapted to arid/semi-arid environments yet more sensitive to salt stress than bread wheat (BW). To enhance DW salinity tolerance, we resorted to chromosomally engineered materials with introgressions from allied halophytic Thinopyrum species. “Primary” recombinant lines (RLs), having portions of their 7AL arms distally replaced by 7el1L Th. ponticum segments, and “secondary” RLs, harboring Th. elongatum 7EL insertions “nested” into 7el1L segments, in addition to near-isogenic lines lacking any alien segment (CLs), cv. Om Rabia (OR) as salt tolerant control, and BW introgression lines with either most of 7el1 or the complete 7E chromosome substitution as additional CLs, were subjected to moderate (100 mM) and intense (200 mM) salt (NaCl) stress at early growth stages. The applied stress altered cell cycle progression, determining a general increase of cells in G1 and a reduction in S phase. Assessment of morpho-physiological and biochemical traits overall showed that the presence of Thinopyrum spp. segments was associated with considerably increased salinity tolerance versus its absence. For relative water content, Na+ accumulation and K+ retention in roots and leaves, oxidative stress indicators (malondialdehyde and hydrogen peroxide) and antioxidant enzyme activities, the observed differences between stressed and unstressed RLs versus CLs was of similar magnitude in “primary” and “secondary” types, suggesting that tolerance factors might reside in defined 7el1L shared portion(s). Nonetheless, the incremental contribution of 7EL segments emerged in various instances, greatly mitigating the effects of salt stress on root and leaf growth and on the quantity of photosynthetic pigments, boosting accumulation of compatible solutes and minimizing the decrease of a powerful antioxidant like ascorbate. The seemingly synergistic effect of 7el1L + 7EL segments/genes made “secondary” RLs able to often exceed cv. OR and equal or better perform than BW lines. Thus, transfer of a suite of genes from halophytic germplasm by use of fine chromosome engineering strategies may well be the way forward to enhance salinity tolerance of glycophytes, even the sensitive DW

Experimental design and Bayesian networks for enhancement of delta-endotoxin production by Bacillus thuringiensis

Bacillus thuringiensis (Bt) is a Gram-positive bacterium. The entomopathogenic activity of Bt is related to the existence of the crystal consisting of protoxins, also called delta-endotoxins. In order to optimize and explain the production of delta-endotoxins of Bacillus thuringiensis kurstaki, we studied seven medium components: soybean meal, starch, KH2PO4, K2HPO4, FeSO4, MnSO4, and MgSO4 and their relationships with the concentration of delta-endotoxins using an experimental design (Plackett—Burman design) and Bayesian networks modelling. The effects of the ingredients of the culture medium on delta-endotoxins production were estimated. The developed model showed that different medium components are important for the Bacillus thuringiensis fermentation. The most important factors influenced the production of delta-endotoxins are FeSO4, K2HPO4, starch and soybean meal. Indeed, it was found that soybean meal, K2HPO4, KH2PO4 and starch also showed positive effect on the delta-endotoxins production. However, FeSO4 and MnSO4 expressed opposite effect. The developed model, based on Bayesian techniques, can automatically learn emerging models in data to serve in the prediction of delta-endotoxins concentrations. The constructed model in the present study implies that experimental design (Plackett—Burman design) joined with Bayesian networks method could be used for identification of effect variables on delta-endotoxins variation

Modeling-based optimization approaches for the development of Anti- Agrobacterium tumefaciens activity using Streptomyces sp TN71

A new aerobic bacterium TN71 was isolated from Tunisian Saharan soil and has been selected for its antimicrobial activity against phytopathogenic bacteria. Based on cellular morphology, physiological characterization and phylogenetic analysis, this isolate has been assigned as Streptomyces sp. TN71 strain. In an attempt to increase its anti-Agrobacterium tumefaciens activity, GYM + S (glucose, yeast extract, malt extract and starch) medium was selected out of five different production media and the medium composition was optimized. Plackett-Burman design (PBD) was used to select starch, malt extract and glucose as parameters having significant effects on antibacterial activity and a Box-Behnken design was applied for further optimization. The analysis revealed that the optimum concentrations for anti-A. tumefaciens activity of the tested variables were 19.49 g/L for starch, 5.06 g/L for malt extract and 2.07 g/L for glucose. Several Artificial Neural Networks (ANN): the Multilayer perceptron (MLP) and the Radial basis function (RBF) were also constructed to predict anti-A. tumefaciens activity. The comparison between experimental with predicted outputs from ANN and Response Surface Methodology (RSM) were studied. ANN model presents an improvement of 12.36% in terms of determination coefficients of anti A. tumefaciens activity. To our knowledge, this is the first work reporting the statistical versus artificial intelligence based modeling for optimization of bioactive molecules against phytopathogen

Artificial Intelligence to Improve the Food and Agriculture Sector

The world population is expected to reach over 9 billion by 2050, which will require an increase in agricultural and food production by 70% to fit the need, a serious challenge for the agri-food industry. Such requirement, in a context of resources scarcity, climate change, COVID-19 pandemic, and very harsh socioeconomic conjecture, is difficult to fulfill without the intervention of computational tools and forecasting strategy. Hereby, we report the importance of artificial intelligence and machine learning as a predictive multidisciplinary approach integration to improve the food and agriculture sector, yet with some limitations that should be considered by stakeholders

Dempster-Shafer Theory for the Prediction of Auxin-Response Elements (AuxREs) in Plant Genomes

Auxin is a major regulator of plant growth and development; its action involves transcriptional activation. The identification of Auxin-response element (AuxRE) is one of the most important issues to understand the Auxin regulation of gene expression. Over the past few years, a large number of motif identification tools have been developed. Despite these considerable efforts provided by computational biologists, building reliable models to predict regulatory elements has still been a difficult challenge. In this context, we propose in this work a data fusion approach for the prediction of AuxRE. Our method is based on the combined use of Dempster-Shafer evidence theory and fuzzy theory. To evaluate our model, we have scanning the DORNRÖSCHEN promoter by our model. All proven AuxRE present in the promoter has been detected. At the 0.9 threshold we have no false positive. The comparison of the results of our model and some previous motifs finding tools shows that our model can predict AuxRE more successfully than the other tools and produce less false positive. The comparison of the results before and after combination shows the importance of Dempster-Shafer combination in the decrease of false positive and to improve the reliability of prediction. For an overall evaluation we have chosen to present the performance of our approach in comparison with other methods. In fact, the results indicated that the data fusion method has the highest degree of sensitivity (Sn) and Positive Predictive Value (PPV)

Contamination Assessment of Durum Wheat and Barley Irrigated with Treated Wastewater through Physiological and Biochemical Effects and Statistical Analyses

The present work focused on the impact of crop irrigation by treated wastewater (TWW) on soil fertility, in germination, and growth of two species of cereals (T. turgidum and H. vulgare). This investigation was conducted at the germination stage (controlled condition) and in pots containing a soil irrigated with wastewater in comparison with controlled soil. Germination rate, vigor index, seedling growth, total fresh mass, chlorophyll content, proline, ascorbate peroxidase (APX), guaiacol peroxidase (GPX), and catalase (CAT) activities were measured. Similar effects were shown on both species which emphasize the important role of antioxidant enzymes in the defense against oxidative stress induced by prolonged reuse of TWW. The disturbing effect of the reuse TWW on soil fertility, germination, and development of young plants (T. turgidum and H. vulgare) was linked to the presence of micropollutants in TWW. Data were analyzed by R language using a nonparametric statistical hypothesis test. These have caused the disorganization of many physiological mechanism targets, especially growth disorders observed under different abiotic stress conditions. In conclusion, high salt and heavy metal concentrations contained in the TWW are the major constraints related to the reuse of TWW. Hence, repetitive irrigation with this water can induce, at long term, soil contamination which can limit plant production and crop contamination