Relevant Aspects for Sustainable Open Source Pandemic Apps and Platform Deployment with Focus on Community Building.

The global COVID-19 pandemic revealed the necessity for mobile and web-based solutions for a variety of medical processes, e.g., individual risk calculation, communication of health information and contact tracing. Many such solutions are provided in form of open source software. However, there are major obstacles to the sustainable long-term continuation of such projects. As the topic of sustainability strategies is complex, a classification would be useful to help new projects to identify relevant sustainability factors. Based on a literature review a classification for long-term success of open source software was created. This paper presents a classification focusing on five unique categories: (1) structural decision, (2) revenue generation, (3) user focus, (4) openness and (5) community building. It was developed within the NUM-COMPASS project, focusing content-wise on pandemic apps and structure-wise on open-source provision. We provide some insights into the community building dimension by discussing factors that go into building sustainable communities.

Discriminating animal fats and their origins: assessing the potentials of Fourier transform infrared spectroscopy, gas chromatography, immunoassay and polymerase chain reaction techniques.

The objective of the reported study was to assess the abilities of various methods to differentiate the sources of fats used in feedstuff formulations. The main target was the identification of tallow (ruminant fat) and its differentiation from non-ruminant fats. Four different techniques were compared in terms of their suitability for enforcing existing and upcoming legislation on animal by-products: (1) Fourier transform infrared spectroscopy (FT-IR) applied to fat samples, (2) gas chromatography coupled with mass spectrometry (GC-MS) to determine fatty acid profiles, (3) immunoassays focusing on the protein fraction included in the fat, and (4) polymerase chain reaction (PCR) for the detection of bovine-specific DNA. Samples of the different fats and oils as well as mixtures of these fats were probed using these analytical methods. FT-IR and GC-MS differentiated pure fat samples quite well but showed limited ability to identify the animal species or even the animal class the fat(s) belonged to; no single compound or spectral signal that could permit species identification could be found. However, immunoassays and PCR were both able to identify the species or groups of species that the fats originated from, and they were the only techniques able to identify low concentrations of tallow in a mixture of fats prepared by the rendering industry, even when the samples had been sterilised at temperatures >133 degrees C. Fats used in animal nutrition come mainly from the rendering industry, thereby confirming the suitability of PCR and immunoassays for their identification. However, neither of these latter techniques was able to detect "premier jus" tallow, representing the highest quality standard of fat with extremely low protein concentration.