Modern MT: A New Open-Source Machine Translation Platform for the Translation Industry

Modern MT (www.modernmt.eu) is a three-year Horizon 2020 innovation action (2015–2017) to develop new open-source machine translation technology for use in translation production environments, both fully automatic and as a back-end in interactive post-editing scenarios. Led by Translated srl, the project consortium also includes the Fondazione Bruno Kessler (FBK), the University of Edinburgh, and TAUS B.V. Modern MT has received funding from the European Union’s Horizon 2020 research and innovation programme under Grant Agreement No 645487 (call ICT-17-2014)

The algorithm used for the calculation of gas exchange affects the estimation of O2 uptake kinetics at the onset of moderate-intensity exercise

Abstract: At the start of a moderate-intensity square-wave exercise, after a short delay, breath-by-breath O2 uptake at the mouth is approximated to a mono-exponential function, whose time constant is considered matched to that of the O2 uptake of the working muscles. We compared the kinetic parameters obtained from the breath-by-breath gas exchange data yielded by the ‘Independent-breath’ algorithm (IND), which accounts for the changes in lung gas stores, with those obtained with the classical ‘Expiration-only’ algorithm (EXP). The two algorithms were applied on the same flow and gas fraction traces acquired on 10 healthy volunteers, performing 10 times the same moderate-intensity exercise transition. Repeated O2 uptake responses were stacked together and the kinetic parameters of a mono-exponential function were estimated by non-linear regression, removing the data pertaining to 1-s progressively longer initial periods (ΔTr). Independently of ΔTr, the mean response time (time constant + time delay) obtained for the IND data was faster compared to the EXP data (∼43 s vs. ∼47 s, P 0.07). The different decrease in the time constant, together with the different mean response time, suggests that the data yielded by the two algorithms provide a different picture of the phenomena occurring at the beginning of the exercise. New Findings: What is the central question of this study? Is the kinetics of breath-by-breath gas exchange at the start of a square-wave moderate intensity exercise faster when accounting for the changes in lung gas stores? What is the main finding and its importance? Accounting for the changes in lung gas stores, the time constant of the (Formula presented.) kinetics changed less when the data pertaining to the initial period were progressively removed; time delays and mean response times were faster. Consequently, using different gas exchange calculation algorithms, the physiological phenomena occurring at the beginning of moderate-intensity exercise are characterized differently