Scientific Results

This catalogue is obtained by conducting a systematic literature review of scientific studies and reviews related to monitoring, forecasting, and simulating the inland water cycle. The analysis maps scientific expertise across research groups and classifies findings by the type of inland water studied, application focus, and geographical scope. A gap analysis will identify missing research areas and assess their relevance to policymaking.

ID â–˛ Type Year Authors Title Venue/Journal DOI Research type Water System Technical Focus Abstract Link with Projects Link with Tools Related policies ID
publications-2231 Peer reviewed articles 2019 Carlos Kamienski, Juha-Pekka Soininen, Markus Taumberger, Ramide Dantas, Attilio Toscano, Tullio Salmon Cinotti, Rodrigo Filev Maia, André Torre Neto Smart Water Management Platform: IoT-Based Precision Irrigation for Agriculture Sensors 10.3390/s19020276 Hydrological modeling River Basins The smart management of freshwater for precision irrigation in agriculture is essential for increasing crop yield and decreasing costs, while contributing to environmental sustainability. The intense use of technologies offers a means for providing the exact amount of water needed by plants. The Internet of Things (IoT) is the natural choice for smart water management applications, even though the integration of different technologies required for making it work seamlessly in practice is still not fully accomplished. The SWAMP project develops an IoT-based smart water management platform for precision irrigation in agriculture with a hands-on approach based on four pilots in Brazil and Europe. This paper presents the SWAMP architecture, platform, and system deployments that highlight the replicability of the platform, and, as scalability is a major concern for IoT applications, it includes a performance analysis of FIWARE components used in the Platform. Results show that it is able to provide adequate performance for the SWAMP pilots, but requires specially designed configurations and the re-engineering of some components to provide higher scalability using less computational resources. 777112
publications-2232 Peer reviewed articles 2020 Fabrizio F. Borelli, Gabriela O. Biondi, Carlos A. Kamienski BIoTA: A Buildout IoT Application Language IEEE Access 10.1109/access.2020.3003694 Hydrological modeling Natural Water Bodies No abstract available 777112
publications-2233 Peer reviewed articles 2020 Kirsty T. T. Kwok, Myrna M. T. de Rooij, Aniek B. Messink, Inge M. Wouters, Marion P. G. Koopmans, My V. T. Phan Genome Sequences of Seven Megrivirus Strains from Chickens in The Netherlands Microbiology Resource Announcements 10.1128/mra.01207-20 Hydrological modeling River Basins We report seven chicken megrivirus genome sequences identified in chicken fecal samples from a broiler farm in the Netherlands. The sequences were determined using metagenomic sequencing and would expand our understanding of the genome diversity of megriviruses. 799417
publications-2234 Peer reviewed articles 2020 David F. Nieuwenhuijse, Bas B. Oude Munnink, My V. T. Phan, Patrick Munk, Shweta Venkatakrishnan, Frank M. Aarestrup, Matthew Cotten, Marion P. G. Koopmans Setting a baseline for global urban virome surveillance in sewage Scientific Reports 10.1038/s41598-020-69869-0 Hydrological modeling River Basins AbstractThe rapid development of megacities, and their growing connectedness across the world is becoming a distinct driver for emerging disease outbreaks. Early detection of unusual disease emergence and spread should therefore include such cities as part of risk-based surveillance. A catch-all metagenomic sequencing approach of urban sewage could potentially provide an unbiased insight into the dynamics of viral pathogens circulating in a community irrespective of access to care, a potential which already has been proven for the surveillance of poliovirus. Here, we present a detailed characterization of sewage viromes from a snapshot of 81 high density urban areas across the globe, including in-depth assessment of potential biases, as a proof of concept for catch-all viral pathogen surveillance. We show the ability to detect a wide range of viruses and geographical and seasonal differences for specific viral groups. Our findings offer a cross-sectional baseline for further research in viral surveillance from urban sewage samples and place previous studies in a global perspective. 799417
publications-2235 Peer reviewed articles 2019 Sofia Strubbia, Julien Schaeffer, Bas B. Oude Munnink, Alban Besnard, My V. T. Phan, David F. Nieuwenhuijse, Miranda de Graaf, Claudia M. E. Schapendonk, Candice Wacrenier, Matthew Cotten, Marion P. G. Koopmans, Françoise S. Le Guyader Metavirome Sequencing to Evaluate Norovirus Diversity in Sewage and Related Bioaccumulated Oysters Frontiers in Microbiology 10.3389/fmicb.2019.02394 Simulation & Modeling Groundwater No abstract available 799417
publications-2236 Peer reviewed articles 2019 Sofia Strubbia, My V. T. Phan, Julien Schaeffer, Marion Koopmans, Matthew Cotten, Françoise S. Le Guyader Characterization of Norovirus and Other Human Enteric Viruses in Sewage and Stool Samples Through Next-Generation Sequencing Food and Environmental Virology 10.1007/s12560-019-09402-3 Simulation & Modeling River Basins No abstract available 799417
publications-2237 Peer reviewed articles 2020 Kirsty T. T. Kwok, David F. Nieuwenhuijse, My V. T. Phan, Marion P. G. Koopmans Virus Metagenomics in Farm Animals: A Systematic Review Viruses 10.3390/v12010107 Uncategorized Uncategorized A majority of emerging infectious diseases are of zoonotic origin. Metagenomic Next-Generation Sequencing (mNGS) has been employed to identify uncommon and novel infectious etiologies and characterize virus diversity in human, animal, and environmental samples. Here, we systematically reviewed studies that performed viral mNGS in common livestock (cattle, small ruminants, poultry, and pigs). We identified 2481 records and 120 records were ultimately included after a first and second screening. Pigs were the most frequently studied livestock and the virus diversity found in samples from poultry was the highest. Known animal viruses, zoonotic viruses, and novel viruses were reported in available literature, demonstrating the capacity of mNGS to identify both known and novel viruses. However, the coverage of metagenomic studies was patchy, with few data on the virome of small ruminants and respiratory virome of studied livestock. Essential metadata such as age of livestock and farm types were rarely mentioned in available literature, and only 10.8% of the datasets were publicly available. Developing a deeper understanding of livestock virome is crucial for detection of potential zoonotic and animal pathogens and One Health preparedness. Metagenomic studies can provide this background but only when combined with essential metadata and following the “FAIR” (Findable, Accessible, Interoperable, and Reusable) data principles. 799417
publications-2238 Peer reviewed articles 2020 Charles Masembe, My V T Phan, David L Robertson, Matthew Cotten Increased resolution of African swine fever virus genome patterns based on profile HMMs of protein domains Virus Evolution 10.1093/ve/veaa044 Simulation & Modeling River Basins Abstract African swine fever virus (ASFV), belonging to the Asfarviridae family, was originally described in Africa almost 100 years ago and is now spreading uncontrolled across Europe and Asia and threatening to destroy the domestic pork industry. Neither effective antiviral drugs nor protective vaccines are currently available. Efforts to understand the basis for viral pathogenicity and the development of attenuated potential vaccine strains are complicated by the large and complex nature of the ASFV genome. We report here a novel alignment-free method of documenting viral diversity based on profile hidden Markov model domains on a genome scale. The method can be used to infer genomic relationships independent of genome alignments and also reveal ASFV genome sequence differences that determine the presence and characteristics of functional protein domains in the virus. We show that the method can quickly identify differences and shared patterns between virulent and attenuated ASFV strains and will be a useful tool for developing much-needed vaccines and antiviral agents to help control this virus. The tool is rapid to run and easy to implement, readily available as a simple Docker image. 799417
publications-2239 Peer reviewed articles 2020 Rosa L Allesøe, Camilla K Lemvigh, My V T Phan, Philip T L C Clausen, Alfred F Florensa, Marion P G Koopmans, Ole Lund, Matthew Cotten Automated download and clean-up of family-specific databases for kmer-based virus identification Bioinformatics 10.1093/bioinformatics/btaa857 Simulation & Modeling River Basins Abstract Summary Here, we present an automated pipeline for Download Of NCBI Entries (DONE) and continuous updating of a local sequence database based on user-specified queries. The database can be created with either protein or nucleotide sequences containing all entries or complete genomes only. The pipeline can automatically clean the database by removing entries with matches to a database of user-specified sequence contaminants. The default contamination entries include sequences from the UniVec database of plasmids, marker genes and sequencing adapters from NCBI, an E.coli genome, rRNA sequences, vectors and satellite sequences. Furthermore, duplicates are removed and the database is automatically screened for sequences from green fluorescent protein, luciferase and antibiotic resistance genes that might be present in some GenBank viral entries, and could lead to false positives in virus identification. For utilizing the database, we present a useful opportunity for dealing with possible human contamination. We show the applicability of DONE by downloading a virus database comprising 37 virus families. We observed an average increase of 16 776 new entries downloaded per month for the 37 families. In addition, we demonstrate the utility of a custom database compared to a standard reference database for classifying both simulated and real sequence data. Availabilityand implementation The DONE pipeline for downloading and cleaning is deposited in a publicly available repository (https://bitbucket.org/genomicepidemiology/done/src/master/). Supplementary information Supplementary data are available at Bioinformatics online. 799417
publications-2240 Peer reviewed articles 2021 Kirsty T. T. Kwok, Myrna M. T. de Rooij, Felisita F. Sinartio, Lidwien A. M. Smit, Marion P. G. Koopmans, My V. T. Phan Genome Sequence of a Minacovirus Strain from a Farmed Mink in The Netherlands Microbiology Resource Announcements 10.1128/mra.01451-20 IoT & Sensors Irrigation Systems We report the genome sequence of a Minacovirus strain identified from a fecal sample from a farmed mink ( Neovison vison ) in The Netherlands that was tested negative for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) using real-time PCR (RT-PCR). The viral genome sequence was obtained using agnostic deep sequencing. 799417