The management of variety collections can be supported by integrating molecular data into Distinctness, Uniformity, and Stability (DUS) testing. DurdusTools is a genetic distance (GD) calculation tool that supports planning field trials in durum wheat. A commercially available single-nucleotide polymorphism (SNP) wheat microarray is used to profile varieties and candidate varieties. Their molecular profiles are stored in a database. SNP markers selected based on quality parameters are used to calculate the pairwise genetic distance (GD) between the varieties. Combining molecular and variety information creates a downloadable, user-friendly Excel-based output. The file shows the pairwise GD of the varieties most similar to the candidate variety of interest and selected variety information. After the first year of field trials, the DUS experts use the phenotypic assessment data together with the GD information to select genotypes to be grown in the field for side-by-side comparisons. The principles of DurdusTools allow for an easy integration of molecular data into DUS testing. Using the tool requires neither specific infrastructure nor molecular expert knowledge and was developed by its users for DUS testing purposes. This makes DurdusTools an easily accessible and user-friendly tool that supports variety selection for DUS field trials through an improved data basis.

Wastewater data can be very useful for epidemic control during a disease outbreak and proper synthesis of different sources of information can be integrated towards an alerting system, that can be used for decision support. Wastewater data are considered to be of high quality, since they do not depend on testing and can take into account asymptomatic cases. However, little effort has been given into utilizing such information in statistical process control procedures, usually aimed at industrial problems. In this article, we demonstrate how wastewater data can be utilized in health surveillance and propose a statistical framework that can act as a decision support tool. Specifically, we analyze SARS-CoV-2 wastewater data from Austria, constructing summary variables to implicitly describe the Covid-19 prevalence and, based on them, we assess the effectiveness of the current sampling strategy of Austria. We propose a framework of a statistical process monitoring system to aid epidemic management procedures in case SARS-CoV-2 concentration gets dangerously high.