Background: Hormesis is considered a dose–response phenomenon characterized by growth stimulation at low doses and inhibition at high doses. The hormetic response by silver nanoparticles (AgNPs) on in vitro multiplication of sugarcane was evaluated using a temporary immersion system. Methods: Sugarcane shoots were used as explants cultured in Murashige and Skoog medium with AgNPs at concentrations of 0, 25, 50, 100, and 200 mg/L. Shoot multiplication rate and length were used to determine hormetic response. Total content of phenolic compounds of sugarcane, mineral nutrition, and reactive oxygen species (ROS) was determined. Results: Results were presented as a dose–response curve. Stimulation phase growth was observed at 50 mg/L AgNPs, whereas inhibition phase was detected at 200 mg/L AgNPs. Mineral nutrient analysis showed changes in macronutrient and micronutrient contents due to the effect of AgNPs. Moreover, AgNPs induced ROS production and increased total phenolic content, with a dose-dependent effect. Conclusion: Results suggested that the production of ROS and mineral nutrition are key mechanisms of AgNP-induced hormesis and that phenolic accumulation was obtained as a response of the plant to stress produced by high doses of AgNPs. Therefore, small doses of AgNPs in the culture medium could be an efficient strategy for commercial micropropagation.

This open access book offers an introduction to mixed generalized linear models with applications to the biological sciences, basically approached from an applications perspective, without neglecting the rigor of the theory. For this reason, the theory that supports each of the studied methods is addressed and later - through examples - its application is illustrated. In addition, some of the assumptions and shortcomings of linear statistical models in general are also discussed. An alternative to analyse non-normal distributed response variables is the use of generalized linear models (GLM) to describe the response data with an exponential family distribution that perfectly fits the real response. Extending this idea to models with random effects allows the use of Generalized Linear Mixed Models (GLMMs). The use of these complex models was not computationally feasible until the recent past, when computational advances and improvements to statistical analysis programs allowed users to easily, quickly, and accurately apply GLMM to data sets. GLMMs have attracted considerable attention in recent years. The word "Generalized" refers to non-normal distributions for the response variable and the word "Mixed" refers to random effects, in addition to the fixed effects typical of analysis of variance (or regression). With the development of modern statistical packages such as Statistical Analysis System (SAS), R, ASReml, among others, a wide variety of statistical analyzes are available to a wider audience. However, to be able to handle and master more sophisticated models requires proper training and great responsibility on the part of the practitioner to understand how these advanced tools work. GMLM is an analysis methodology used in agriculture and biology that can accommodate complex correlation structures and types of response variables.

The valorization of byproducts from the sugarcane industry represents a potential alternative method with a low energy cost for the production of metabolites that are of commercial and industrial interest. The production of exopolysaccharides (EPSs) was carried out using the yeast Suhomyces kilbournensis isolated from agro-industrial sugarcane, and the products and byproducts of this agro-industrial sugarcane were used as carbon sources for their recovery. The effect of pH, temperature, and carbon and nitrogen sources and their concentration in EPS production by submerged fermentation (SmF) was studied in 170 mL glass containers of uniform geometry at 30 °C with an initial pH of 6.5. The resulting EPSs were characterized with Fourier-transform infrared spectroscopy (FT-IR). The results showed that the highest EPS production yields were 4.26 and 44.33 g/L after 6 h of fermentation using sucrose and molasses as carbon sources, respectively. Finally, an FT-IR analysis of the EPSs produced by S. kilbournensis corresponded to levan, corroborating its origin. It is important to mention that this is the first work that reports the production of levan using this yeast. This is relevant because, currently, most studies are focused on the use of recombinant and genetically modified microorganisms; in this scenario, Suhomyces kilbournensis is a native yeast isolated from the sugar production process, giving it a great advantage in the incorporation of carbon sources into their metabolic processes in order to produce levan sucrose, which uses fructose to polymerize levan.

When a plant scientist wishes to make genomic-enabled predictions of multiple traits measured in multiple individuals in multiple environments, the most common strategy for performing the analysis is to use a single trait at a time taking into account genotype × environment interaction (G × E), because there is a lack of comprehensive models that simultaneously take into account the correlated counting traits and G × E. For this reason, in this study we propose a multiple-trait and multiple-environment model for count data. The proposed model was developed under the Bayesian paradigm for which we developed a Markov Chain Monte Carlo (MCMC) with noninformative priors. This allows obtaining all required full conditional distributions of the parameters leading to an exact Gibbs sampler for the posterior distribution. Our model was tested with simulated data and a real data set. Results show that the proposed multi-trait, multi-environment model is an attractive alternative for modeling multiple count traits measured in multiple environments.