Multiple linear regression models to simulate spore yields of Bacillus amyloliquefaciens BS13 through optimization of medium composition.

Bacillus amyloliquefaciens is a food spoilage spore-forming bacterium. Its spores are useful for multiple biotechnological applications. Nevertheless, few reports are available regarding the achievement of a high cell density and good sporulation effectiveness under fermentation conditions. Therefore, the current study was designed to optimize a low-cost fermentation medium allowing the highest sporulation yield by B. amyloliquefaciens strain BS13. Our data revealed that tryptone and starch were the best carbon and energy sources. In addition, two nitrogen sources namely, corn steep liquor (CSL) and yeast extract (YE), allowed a significant enhancement of spore production and they were both retained for further optimization. A combination of CaCl2 , MgSO4 , and MnSO4 showed a positive impact on spores' production. The composition of the optimized medium was (in g/L); tryptone 3, starch 15, CSL 13.5, YE 1.5, CaCl2 0.1, MgSO4 ·7H2 O 0.012, and MnSO4 ·7H2 O 0.0012. Such medium was further validated in a 400-L fermentor. The spore yield by B. amyloliquefaciens strain BS13 was enhanced from 3.0 × 1010 spores/mL under flask culture conditions to 6.2 × 1010 spores/mL when cultures were performed on large scale. Therefore, strain BS13 spore preparation could be proposed as a promising probiotic and a biocontrol agent useful for plants, animals, and humans.

Identification and characterization of single nucleotide polymorphism markers in FADS2 gene associated with olive oil fatty acids composition.

BACKGROUND: Genotyping of the FAD2.1 and FAD2.3 polymorphisms in the fatty acid desaturase 2 gene (FADS2) shows that they are associated with the fatty acids composition of olive oil samples. However, these associations require further confirmation in the Tunisian olive oil cultivars, and little is known about the effect of polymorphisms in fatty acid-related genes on olive oil mono- and poly- unsaturated fatty acids distribution. METHODS: A set of olive oils from 12 Tunisian cultivars was chosen. The fatty acid composition of each olive oil sample was determined by gas chromatography. Statistical and modeling Bayesian analyses were used to assess whether the FAD2.1 and FAD2.3 genotypes were associated with fatty acids composition. RESULTS: The TT-FAD2.1 and the GG-FAD2.3 genotypes were found to be associated with a lower proportion of oleic acid (C18:1) (r = -0.778, p = 0.003; r = -0.781, p= 0.003) as well as higher proportion of linoleic (C18:2) (r = 0.693, p = 0.012; r = -0.759, p= 0.004) and palmitic acids (C16:0) (r = 0.643, p = 0.024; r = -0.503, p= 0.095), making varieties with this haplotype (i.e. Chemlali Sfax and Meski) producing more saturated (C16: 0) and polyunsaturated acids than oleic acid. The latter plays a major role in preventing several diseases. CONCLUSION: The two associations FADS2 FAD2.1 and FADS2 FAD2.3 with the fatty acid compositions of olive oil samples were identified among the studied olive cultivars. These associations differed between studied cultivars, which might explain variability in lipidic composition among them and consequently reflecting genetic diversity through differences in gene expression and biochemical pathways. FADS2 locus would constitute thus a good marker for detecting interesting lipidic chemotypes among commercial olive oils.

Application of data fusion modeling for the prediction of auxin response elements in Zea mays for food security purposes.

Food security will be affected by climate change worldwide, particularly in the developingworld, where the most important food products originate from plants. Plants are often exposed to environmental stresses that may affect their growth, development, yield, and foodquality. Auxin is a hormone that plays a critical role in improving plants' tolerance of environmental conditions. Auxin controls the expression of many stress-responsive genes inplants by interacting with specific cis-regulatory elements called auxin-responsive elements (AuxREs). In this work, we performed an in silico prediction of AuxREs in promotersof five auxin-responsive genes in Zea mays. We applied a data fusion approach based onthe combined use of Dempster-Shafer evidence theory and fuzzy sets. Auxin has a directimpact on cell membrane proteins. The short-term auxin response may be represented bythe regulation of transmembrane gene expression. The detection of an AuxRE in the promoter of prolyl oligopeptidase (POP) in Z. mays and the 3-fold overexpression of this geneunder auxin treatment for 30 min indicated the role of POP in maize auxin response. POP isregulated by auxin to perform stress adaptation. In addition, the detection of two AuxRETGTCTC motifs in the upstream sequence of the bx1 gene suggests that bx1 can be regulated by auxin. Auxin may also be involved in the regulation of dehydration-responsive element-binding and some members of the protein kinase superfamily.

An Evidence Theory and Fuzzy Logic Combined Approach for the Prediction of Potential ARF-Regulated Genes in Quinoa.

Quinoa constitutes among the tolerant plants to the challenging and harmful abiotic environmental factors. Quinoa was selected as among the model crops destined for bio-saline agriculture that could contribute to the staple food security for an ever-growing worldwide population under various climate change scenarios. The auxin response factors (ARFs) constitute the main contributors in the plant adaptation to severe environmental conditions. Thus, the determination of the ARF-binding sites represents the major step that could provide promising insights helping in plant breeding programs and improving agronomic traits. Hence, determining the ARF-binding sites is a challenging task, particularly in species with large genome sizes. In this report, we present a data fusion approach based on Dempster-Shafer evidence theory and fuzzy set theory to predict the ARF-binding sites. We then performed an "In-silico" identification of the ARF-binding sites in Chenopodium quinoa. The characterization of some known pathways implicated in the auxin signaling in other higher plants confirms our prediction reliability. Furthermore, several pathways with no or little available information about their functions were identified to play important roles in the adaptation of quinoa to environmental conditions. The predictive auxin response genes associated with the detected ARF-binding sites may certainly help to explore the biological roles of some unknown genes newly identified in quinoa.

Using Coordinated Visual and Verbal Cues in Complex Multimedia Materials to Improve Tactical Learning in Soccer.

This study aimed to explore whether the use of coordinated visual and verbal cues in narrated diagrams would support novices in learning soccer scenes. Eighty female university students (Mage = 20 years, SD = 1.2) in physical education (PE) were randomly exposed to four multimedia material versions: (a) simple without cues, (b) simple with cues, (c) complex without cues, and (d) complex with cues. In the non-cued versions, students learned the evolution of soccer scenes via arrow-based diagrams accompanied by oral explanations. In the cued versions, game actions in narrations were spoken with a louder accent (verbal cueing), while at the same time, the corresponding arrows turned red in diagrams (visual cueing). After studying one of the four versions, participants were asked to complete immediate and delayed recall-reconstruction tests, and to indicate their attitudes towards instructional materials. The results demonstrate the instructional benefits of using coordinated dual-modality cues in the complex multimedia material, in terms of immediate (p = 0.03, d = 0.53) and delayed (p = 0.02, d = 0.85) recall performances. The findings encourage soccer teachers to verbalize game actions with a louder accent, while simultaneously coloring the corresponding arrows in the diagram when explaining complex game situations for novices.

Geographical Discrimination of Virgin Olive Oils from the Tunisian Coasts by Combining Fatty Acids and Phenolic Acids Profiles within a Multivariate Analysis.

Virgin olive oils extracted from three principal Tunisian olive cultivars (Chemlali, Chetoui and Zarrazi) and coming from four different regions (Sfax, Beja, Gabes and Medenine) along the Tunisian costs were analysed. The quality indices as well as fatty acids and phenolic acids content of oil samples were examined using univariate and multivariate statistical analysis. The finding demonstrated that significant differences (p < 0.05) were found in quality indices between the different cultivars and that fatty acid content is the most informative in discriminating olive oils from production sites that are different by geographical and climatic parameters. In fact, southern cultivars (Zarrazi Gabes and Zarrazi Medenine) have the best fatty acid combination according to their oxidative effect. Besides, phenolic acids content was not useful in discriminating olive oil samples and could depend not only on geographic location but also on olive variety and agronomic practices. Nevertheless, Principal Component Analysis allowed us to highlight the Chemlali Beja olive oil for its interesting oxidative stability, fatty acid composition and its richness in phenolic acids content.

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 MgSO4and 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, KH2PO4and 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.