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.     

Competition at the soybean V6 stage affects root morphology and biochemical composition

• The experiment was conducted in the 2016/17 crop season in a greenhouse at Passo Fundo University, Brazil. We hypothesised that the morphological characteristics and biochemical and anatomical composition of soybean roots and shoots, when competing with weeds during different growth periods, are negatively affected, so current concepts of competition between plants should also consider changes in plant roots.
• The soybean cultivar P 95R51 and horseweed (Conyza bonariensis) were used. The treatments consisted of the presence or absence of weeds during different coexistence periods of soybean with horseweed. The periods were V0–V3, V0–V6, V0–R2, V3–R6, V6–R6 and R2–R6, where V0 was the date of soybean sowing and V3, V6, R2 and R6 were phenological stages of the crop. Two fresh roots were used to examine morphological traits. Four roots were used for quantification of dry matter and secondary metabolites.
• Root length was reduced by 21%, 14% and 20% when competing with a weed in the V0–V3, V0–V6 and R2–R6 coexistence periods, respectively. Total phenol content in the V0–V6 and V0–R2 periods was reduced when plants were in competition with weeds; a similar trend was found for flavonoids in the V0–V6 period.
• Soybean–horseweed competition from crop emergence to the V6 stage, in general, affects shoot and root morphological traits and the biochemical composition of the soybean roots. The presence of horseweed at the V3, V6 and R2 stages does not negatively alter the traits evaluated. Root anatomical composition is not modified during all coexistence periods with horseweed.

     

Spliceosome-targeted therapies trigger an antiviral immune response in triple-negative breast cancer

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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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BET inhibition blocks inflammation-induced cardiac dysfunction and SARS-CoV-2 infection

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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.