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The Small World Initiative (SWI) and Tiny Earth are a consolidated and successful education programs rooted in the USA that tackle the antibiotic crisis by a crowdsourcing strategy. Based on active learning, it challenges young students to discover novel bioactive-producing microorganisms from environmental soil samples. Besides its pedagogical efficiency to impart microbiology content in academic curricula, SWI promotes vocations in research and development in Experimental Sciences and, at the same time, disseminates the antibiotic awareness guidelines of the World Health Organization. We have adapted the SWI program to the Spanish academic environment by a pioneering hierarchic strategy based on service-learning that involves two education levels (higher education and high school) with different degrees of responsibility. Throughout the academic year, 23 SWI teams, each consisting of 3-7 undergraduate students led by one faculty member, coordinated off-campus programs in 22 local high schools, involving 597 high school students as researchers. Post-survey-based evaluation of the program reveals a satisfactory achievement of goals: acquiring scientific abilities and general or personal competences by university students, as well as promoting academic decisions to inspire vocations for science- and technology-oriented degrees in younger students, and successfully communicating scientific culture in antimicrobial resistance to a young stratum of society.

The application of Artificial Neural Networks (ANN) in predictive microbiology is presented in this paper. This technique was used to build up a predictive model of the joint effect of NaCl concentration, pH level and storage temperature on kinetic parameters of the growth curve of Lactobacillus plantarum using ANN and Response Surface Model (RSM). Sigmoid functions were fitted to the data and kinetic parameters were estimated and used to build the models in which the independent variables were the factors mentioned above (NaCl, pH, temperature), and in some models, the values of the optical densities (OD) vs. time of the growth curve were also included in order to improve the error of estimation. The determination of the proper size of an ANN was the first step of the estimation. This study shows the usefulness of an ANN pruning methodology. The pruning of the network is a process consisting of removing unnecessary parameters (weights) and nodes during the training process of the network without losing its generalization capacity. The best architecture has been sought using genetic algorithms (GA) in conjunction with pruning algorithms and regularization methods in which the initial distribution of the parameters (weights) of the network is not uniform. The ANN model has been compared with the response surface model by means of the Standard Error of Prediction (SEP). The best values obtained were 14.04% of SEP for the growth rate and 14.84% for the lag estimation by the best ANN model, which were much better than those obtained by the RSM, 35.63% and 39.30%, respectively. These were very promising results that, in our opinion, open up an extremely important field of research.