The STOIC2021 COVID-19 AI challenge: Applying reusable training methodologies to private data.

Challenges drive the state-of-the-art of automated medical image analysis. The quantity of public training data that they provide can limit the performance of their solutions. Public access to the training methodology for these solutions remains absent. This study implements the Type Three (T3) challenge format, which allows for training solutions on private data and guarantees reusable training methodologies. With T3, challenge organizers train a codebase provided by the participants on sequestered training data. T3 was implemented in the STOIC2021 challenge, with the goal of predicting from a computed tomography (CT) scan whether subjects had a severe COVID-19 infection, defined as intubation or death within one month. STOIC2021 consisted of a Qualification phase, where participants developed challenge solutions using 2000 publicly available CT scans, and a Final phase, where participants submitted their training methodologies with which solutions were trained on CT scans of 9724 subjects. The organizers successfully trained six of the eight Final phase submissions. The submitted codebases for training and running inference were released publicly. The winning solution obtained an area under the receiver operating characteristic curve for discerning between severe and non-severe COVID-19 of 0.815. The Final phase solutions of all finalists improved upon their Qualification phase solutions.

Application of Ion Chromatography for the Reliable Quantification of Ammonium in Electrochemical Ammonia Synthesis Experiments: A Practical Guide.

Assessing novel electrocatalysts for the electrochemical ammonia synthesis (EAS) requires reliable quantitative trace analysis of electrochemically produced ammonia to infer activity and selectivity. This study concerns the development of an ion chromatography (IC) method for quantitative trace analysis of ammonium in 0.1 M sulfuric acid electrolyte, which is applied to EAS gas-diffusion electrode (GDE) experiments with commercial chromium nitride as electrocatalyst. The developed IC method is highly sensitive, versatile, and reliable, achieving a limit of quantification (LOQ) of 6 µg l-1 (6 ppbmol ) ammonium. The impacts of the sample matrix, dilution, and neutralization, as well as contamination, on the quantitative analysis by IC are analyzed. Experimental constraints result in an effective LOQ including dilution of 60 µg l-1 for the determination of ammonium in 0.1 M sulfuric acid electrolyte, owing to necessary sample dilution. The practical guide presented herein is intended to be very relevant for the field of EAS as a guideline and applicable to a broad range of catalyst systems and ion chromatography devices.