| 1. |
- Kapun, Martin, et al.
(författare)
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Drosophila Evolution over Space and Time (DEST) : A New Population Genomics Resource
- 2021
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Ingår i: Molecular biology and evolution. - : Oxford University Press (OUP). - 0737-4038 .- 1537-1719. ; 38:12, s. 5782-5805
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Tidskriftsartikel (refereegranskat)abstract
- Drosophila melanogaster is a leading model in population genetics and genomics, and a growing number of whole-genome data sets from natural populations of this species have been published over the last years. A major challenge is the integration of disparate data sets, often generated using different sequencing technologies and bioinformatic pipelines, which hampers our ability to address questions about the evolution of this species. Here we address these issues by developing a bioinformatics pipeline that maps pooled sequencing (Pool-Seq) reads from D. melanogaster to a hologenome consisting of fly and symbiont genomes and estimates allele frequencies using either a heuristic (PoolSNP) or a probabilistic variant caller (SNAPE-pooled). We use this pipeline to generate the largest data repository of genomic data available for D. melanogaster to date, encompassing 271 previously published and unpublished population samples from over 100 locations in >20 countries on four continents. Several of these locations have been sampled at different seasons across multiple years. This data set, which we call Drosophila Evolution over Space and Time (DEST), is coupled with sampling and environmental metadata. A web-based genome browser and web portal provide easy access to the SNP data set. We further provide guidelines on how to use Pool-Seq data for model-based demographic inference. Our aim is to provide this scalable platform as a community resource which can be easily extended via future efforts for an even more extensive cosmopolitan data set. Our resource will enable population geneticists to analyze spatiotemporal genetic patterns and evolutionary dynamics of D. melanogaster populations in unprecedented detail.
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| 2. |
- Sartelli, Massimo, et al.
(författare)
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Ten golden rules for optimal antibiotic use in hospital settings: the WARNING call to action
- 2023
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Ingår i: WORLD JOURNAL OF EMERGENCY SURGERY. - 1749-7922. ; 18:1
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Forskningsöversikt (refereegranskat)abstract
- Antibiotics are recognized widely for their benefits when used appropriately. However, they are often used inappropriately despite the importance of responsible use within good clinical practice. Effective antibiotic treatment is an essential component of universal healthcare, and it is a global responsibility to ensure appropriate use. Currently, pharmaceutical companies have little incentive to develop new antibiotics due to scientific, regulatory, and financial barriers, further emphasizing the importance of appropriate antibiotic use. To address this issue, the Global Alliance for Infections in Surgery established an international multidisciplinary task force of 295 experts from 115 countries with different backgrounds. The task force developed a position statement called WARNING (Worldwide Antimicrobial Resistance National/International Network Group) aimed at raising awareness of antimicrobial resistance and improving antibiotic prescribing practices worldwide. The statement outlined is 10 axioms, or "golden rules," for the appropriate use of antibiotics that all healthcare workers should consistently adhere in clinical practice.
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| 3. |
- Zechner, Olivia, et al.
(författare)
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NextGen training for medical first responders : advancing mass-casualty incident preparedness through mixed reality technology
- 2023
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Ingår i: Multimodal Technologies and Interaction. - Basel : MDPI. - 2414-4088. ; 7:12
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Tidskriftsartikel (refereegranskat)abstract
- Mixed reality (MR) technology has the potential to enhance the disaster preparedness of medical first responders in mass-casualty incidents through new training methods. In this manuscript, we present an MR training solution based on requirements collected from experienced medical first responders and technical experts, regular end-user feedback received through the iterative design process used to develop a prototype and feedback from two initial field trials. We discuss key features essential for an effective MR training system, including flexible scenario design, added realism through patient simulator manikins and objective performance assessment. Current technological challenges such as the responsiveness of avatars and the complexity of smart scenario control are also addressed, along with the future potential for integrating artificial intelligence. Furthermore, an advanced analytics and statistics tool that incorporates complex data integration, machine learning for data analysis and visualization techniques for performance evaluation is presented.
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