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- COVID-19: nothing is normal in this pandemicPublication . Gonçalves, Luzia; Turkman; Brás-Geraldes, Carlos; Marques, Tiago A.; SOUSA, LISETEThis manuscript brings attention to inaccurate epidemiological concepts that emerged during the COVID-19 pandemic. In social media and scientific journals, some wrong references were given to a "normal epidemic curve" and also to a "log-normal curve/distribution". For many years, textbooks and courses of reputable institutions and scientific journals have disseminated misleading concepts. For example, calling histogram to plots of epidemic curves or using epidemic data to introduce the concept of a Gaussian distribution, ignoring its temporal indexing. Although an epidemic curve may look like a Gaussian curve and be eventually modelled by a Gauss function, it is not a normal distribution or a log-normal, as some authors claim. A pandemic produces highly-complex data and to tackle it effectively statistical and mathematical modelling need to go beyond the "onesize-fits-all solution". Classical textbooks need to be updated since pandemics happen and epidemiology needs to provide reliable information to policy recommendations and actions.
- Use of chemometrics in the selection of a Saccharomyces cerevisiae expression system for recombinant cyprosin B productionPublication . Sampaio, P. N.; SOUSA, LISETE; Calado, Cecília; Pais, M. S.; Fonseca\, L. P.Two multivariate statistical methods, factor analysis (FA) and hierarchical cluster analysis (HCA), were applied to experimental data set to evaluate their usefulness in selecting the adequate expression system and optimal growth parameters for recombinant cyprosin B production. Using FA, the large data set was reduced to two factors representing 73.4% of variability. Factor 1, with 53.5% of variability, corresponds to recombinant cyprosin B expression and efficient secretion, while factor 2, accounting for 19.9% of variability, represents cell growth and physiological characteristics. FA and HCA allowed the establishment of correlations among different variables and the clusters obtained providing clear identification of the experimental parameters related to cyprosin B production, which results on more accurate scientific output and time saving when selection of an adequate expression system is concerned.
- Optimization of the culture medium composition using response surface methodology for new recombinant cyprosin B production in bioreactor for cheese productionPublication . Sampaio, Pedro; Calado, Cecília; SOUSA, LISETE; Bressler, David C.; Pais, Maria Salomé; Fonseca, Luís P. P.The optimization of culture medium composition was carried out for improvement the recombinant cyprosin B production, an enzyme with high milk-clotting activity. Response surface methodology (RSM) was applied to evaluate the effect of variables namely glucose, yeast extract (YE) and bactopeptone present in the culture medium, used for recombinant cyprosin B production by transformed Saccharomyces cerevisiae BJ1991 strain in shake-flask and bioreactor culture conditions. The central composite experimental design (CCD) was adopted to derive a statistical model for optimizing the composition of the fermentation medium. The optimal concentration estimated for each variable related to a theoretical maximum of cyprosin B activity (488 U mL-1 ) was 30 g L-1 glucose, 15 g L-1 YE and 27 g L-1 bactopeptone. The optimized medium composition, based on empirical model, led to a cyprosin B activity of 519 U mL-1 , which corresponds to an increase of 46%. The fermentation using optimized culture medium in a 5-L bioreactor allowed a significant increase in biomass (82%) and recombinant cyprosin B production (139%). The improvement in the recombinant cyprosin B production after optimization process can be considered adequate for large-scale applications, and the clotting activity of cyprosin B account for their use in industrial cheese making.