In this work, we compared the unique artificial neural networks (ANNs) technology with the usual statistical analysis to establish its utility as an alternative methodology in plant research. For this purpose, we selected a simple in vitro proliferation experiment with the aim of evaluating the effects of light intensity and sucrose concentration on the success of the explant proliferation and finally, of optimizing the process taking into account any influencing factors. After data analysis, the traditional statistical procedure and ANNs technology both indicated that low light treatments and high sucrose concentrations are required for the highest kiwifruit microshoot proliferation under experimental conditions. However, this particular ANNs software is able to model and optimize the process to estimate the best conditions and does not need an extremely specialized background. The potential of the ANNs approach for analyzing plant biology processes, in this case, plant tissue culture data, is discussed.

Mesenchymal stem cells (MSCs) are present in all organs and tissues, playing a well-known function in tissue regeneration. However, there is also evidence indicating a broader role of MSCs in tissue homeostasis. In vivo studies have shown MSC paracrine mechanisms displaying proliferative, immunoregulatory, anti-oxidative, or angiogenic activity. In addition, recent studies also demonstrate that depletion and/or dysfunction of MSCs are associated with several systemic diseases, such as lupus, diabetes, psoriasis, and rheumatoid arthritis, as well as with aging and frailty syndrome. In this review, we hypothesize about the role of MSCs as keepers of tissue homeostasis as well as modulators in a variety of inflammatory and degenerative systemic diseases. This scenario opens the possibility for the use of secretome-derived products from MSCs as new therapeutic agents in order to restore tissue homeostasis, instead of the classical paradigm “one disease, one drug”.

BackgroundPlant acclimation is a highly complex process, which cannot be fully understood by analysis at any one specific level (i.e. subcellular, cellular or whole plant scale). Various soft-computing techniques, such as neural networks or fuzzy logic, were designed to analyze complex multivariate data sets and might be used to model large such multiscale data sets in plant biology.Methodology and Principal FindingsIn this study we assessed the effectiveness of applying neuro-fuzzy logic to modeling the effects of light intensities and sucrose content/concentration in the in vitro culture of kiwifruit on plant acclimation, by modeling multivariate data from 14 parameters at different biological scales of organization. The model provides insights through application of 14 sets of straightforward rules and indicates that plants with lower stomatal aperture areas and higher photoinhibition and photoprotective status score best for acclimation. The model suggests the best condition for obtaining higher quality acclimatized plantlets is the combination of 2.3% sucrose and photonflux of 122–130 µmol m−2 s−1.ConclusionsOur results demonstrate that artificial intelligence models are not only successful in identifying complex non-linear interactions among variables, by integrating large-scale data sets from different levels of biological organization in a holistic plant systems-biology approach, but can also be used successfully for inferring new results without further experimental work.

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We combined machine learning and plant in vitro culture methodologies as a novel approach for unraveling the phytochemical potential of unexploited medicinal plants. In order to induce phenolic compound biosynthesis, the in vitro culture of three different species of Bryophyllum under nutritional stress was established. To optimize phenolic extraction, four solvents with different MeOH proportions were used, and total phenolic content (TPC), flavonoid content (FC) and radical-scavenging activity (RSA) were determined. All results were subjected to data modeling with the application of artificial neural networks to provide insight into the significant factors that influence such multifactorial processes. Our findings suggest that aerial parts accumulate a higher proportion of phenolic compounds and flavonoids in comparison to roots. TPC was increased under ammonium concentrations below 15 mM, and their extraction was maximum when using solvents with intermediate methanol proportions (55–85%). The same behavior was reported for RSA, and, conversely, FC was independent of culture media composition, and their extraction was enhanced using solvents with high methanol proportions (>85%). These findings confer a wide perspective about the relationship between abiotic stress and secondary metabolism and could serve as the starting point for the optimization of bioactive compound production at a biotechnological scale.