COVID-19 and “natural” experiments arising from physical distancing: a hypothetical case study from chronobiology

ABSTRACT

With countless “natural” experiments triggered by the COVID-19-associated physical distancing, one key question comes from chronobiology: “When confined to homes, how does the reduced exposure to natural daylight arising from the interruption of usual outdoor activities plus lost temporal organization ordinarily provided from workplaces and schools affect the circadian timing system (the internal 24 h clock) and, consequently, health of children and adults of all ages?” Herein, we discuss some ethical and scientific facets of exploring such natural experiments by offering a hypothetical case study of circadian biology.     

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The antigenic anatomy of SARS-CoV-2 receptor binding domain

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Discovery and functional interrogation of SARS-CoV-2 RNA-host protein interactions

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Accurate method for rapid biomass quantification based on specific absorbance of microalgae species with biofuel importance

The development of microalgae culture technology has been an integral part to produce biomass feedstock to biofuel production. Due to this, numerous attempts have been made to improve some operational parameters of microalgae production. Despite this, specialized research in cell growth monitoring, considered as a fundamental parameter to achieve profitable applications of microalgae for biofuels production, presents some opportunity areas mainly related to the development of specific and accurate methodologies for growth monitoring. In this work, predictive models were developed through statistical tools that correlate a specific micro-algal absorbance with cell density measured by cell count (cells∙per ml), for three species of interest for biofuels production. The results allow the precise prediction of cell density through a logistic model based on spectrophotometry, valid for all the kinetics analysed. The adjusted determination coefficients () for the developed models were 0·993, 0·995 and 0·994 for Dunaliella tertiolecta, Nannochloropsis oculata and Chaetoceros muelleri respectively. The results showed that the equations obtained here can be used with an extremely low error (≤2%) for all the cell growth ranges analysed, with low operational cost and high potential of automation. Finally, a user-friendly software was designed to give practical use to the developed predictive models.