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Department of

Biosciences

A wide range of advanced biological research activities are performed in the Department of Biosciences: from curiosity-driven “basic science” disciplines to investigations into the molecular basis of life. The scientific interests of the Department are directed towards understanding the organization and evolution of biodiversity, from the structure and function of biomolecules, to the cellular, ..

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Seminar Elena GUZZOLINO 23 January 2020

Consiglio Nazionale delle Ricerche di Pisa, Istituto di Fisiologia Clinica - “The role of miR-182 in zebrafish heart development: a TBX5-controlled miRNA in Holt-Oram Syndrome”

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Seminar Cristina ARRIGONI 24 January 2020

Cardiovascular Research Institute, UCSF, San Francisco - "In quest of a thermosensor in ion channels - Unfolding of a temperature-sensitive domain controls voltage-gated channel activation"

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Seminar Thomas R. SCHNEIDER 31 January 2020

Group Leader and Senior Scientist Joint Head of Research Infrastructures EMBL c/o DESY, Hamburg - “Crystallographic Enzymology: EMBL beamlines on PETRA III for high resolution in space and time”

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Harris & Benedict.. the original sin

Prediction (regression) equations are widely used, but their reliability as predictive tools is questionable as they provide contradicting results. The key point is that values calculated by regression equations are not precisely defined numbers but lie within a range of possible values in the standard deviation interval, none of which can be considered as the most probable. Ignoring this point leads to illicit/improper calculations, generating wrong results, which may have adverse consequences for human health. To demonstrate this, we applied the equations of Harris and Benedict in a reverse method, i.e. calculating (predicting) the daily energy expenditure in the same subjects used to obtain the equations and comparing values with the original measured data. We used the Bland-Altman and frequency distribution analyses. We found large differences in both individual data and population characteristics, showing that prediction equations are not predictive tools.

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