Antarctic new particle formation from continental biogenic precursors

Over Antarctica, aerosol particles originate almost entirely from marine areas, with minor contribution from long-range transported dust or anthropogenic material. The Antarctic continent itself, unlike all other continental areas, has been thought to be practically free of aerosol sources. Here we present evidence of local aerosol production associated with melt-water ponds in continental Antarctica. We show that in air masses passing such ponds, new aerosol particles are efficiently formed and these particles grow up to sizes where they may act as cloud condensation nuclei (CCN). The precursor vapours responsible for aerosol formation and growth originate very likely from highly abundant cyanobacteria Nostoc commune (Vaucher) communities of local ponds. This is the first time freshwater vegetation has been identified as an aerosol precursor source. The influence of the new source on clouds and climate may increase in future Antarctica, and possibly elsewhere undergoing accelerating summer melting of semi-permanent snow cover

Cell identity and nucleo-mitochondrial genetic context modulate OXPHOS performance and determine somatic heteroplasmy dynamics

Heteroplasmy, multiple variants of mitochondrial DNA (mtDNA) in the same cytoplasm, may be naturally generated by mutations but is counteracted by a genetic mtDNA bottleneck during oocyte development. Engineered heteroplasmic mice with nonpathological mtDNA variants reveal a nonrandom tissue-specific mtDNA segregation pattern, with few tissues that do not show segregation. The driving force for this dynamic complex pattern has remained unexplained for decades, challenging our understanding of this fundamental biological problem and hindering clinical planning for inherited diseases. Here, we demonstrate that the nonrandom mtDNA segregation is an intracellular process based on organelle selection. This cell type-specific decision arises jointly from the impact of mtDNA haplotypes on the oxidative phosphorylation (OXPHOS) system and the cell metabolic requirements and is strongly sensitive to the nuclear context and to environmental cues

Drosophila Evolution over Space and Time (DEST): A New Population Genomics Resource

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.We thank four reviewers and the handling editor for helpful comments on previous versions of our manuscript. We are grateful to the members of the DrosEU and DrosRTEC consortia for their long-standing support, collaboration, and for discussion. DrosEU was funded by a Special Topic Networks (STN) grant from the European Society for Evolutionary Biology (ESEB). M.K. was supported by the Austrian Science Foundation (grant no. FWF P32275); J.G. by the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (H2020-ERC-2014-CoG-647900) and by the Spanish Ministry of Science and Innovation (BFU-2011-24397); T.F. by the Swiss National Science Foundation (SNSF grants PP00P3_133641, PP00P3_165836, and 31003A_182262) and a Mercator Fellowship from the German Research Foundation (DFG), held as a EvoPAD Visiting Professor at the Institute for Evolution and Biodiversity, University of Münster; AOB by the National Institutes of Health (R35 GM119686); M.K. by Academy of Finland grant 322980; V.L. by Danish Natural Science Research Council (FNU) (grant no. 4002-00113B); FS Deutsche Forschungsgemeinschaft (DFG) (grant no. STA1154/4-1), Project 408908608; J.P. by the Deutsche Forschungsgemeinschaft Projects 274388701 and 347368302; A.U. by FPI fellowship (BES-2012-052999); ET Israel Science Foundation (ISF) (grant no. 1737/17); M.S.V., M.S.R. and M.J. by a grant from the Ministry of Education, Science and Technological Development of the Republic of Serbia (451-03-68/2020-14/200178); A.P., K.E. and M.T. by a grant from the Ministry of Education, Science and Technological Development of the Republic of Serbia (451-03-68/2020-14/200007); and TM NSERC grant RGPIN-2018-05551. The authors acknowledge Research Computing at The University of Virginia for providing computational resources and technical support that have contributed to the results reported within this publication (https://rc.virginia.edu, last accessed September 6, 2021)

Drosophila evolution over space and time (DEST):A new population genomics resource

Drosophila melanogaster is a leading model in population genetics and genomics, and a growing number of whole-genome datasets from natural populations of this species have been published over the last years. A major challenge is the integration of disparate datasets, 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 dataset, which we call Drosophila Evolution over Space and Time (DEST), is coupled with sampling and environmental meta-data. A web-based genome browser and web portal provide easy access to the SNP dataset. 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 dataset. Our resource will enable population geneticists to analyze spatio-temporal genetic patterns and evolutionary dynamics of D. melanogaster populations in unprecedented detail.DrosEU is funded by a Special Topic Networks (STN) grant from the European Society for Evolutionary Biology (ESEB). MK (M. Kapun) was supported by the Austrian Science Foundation (grant no. FWF P32275); JG by the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (H2020-ERC-2014-CoG-647900) and by the Spanish Ministry of Science and Innovation (BFU-2011-24397); TF by the Swiss National Science Foundation (SNSF grants PP00P3_133641, PP00P3_165836, and 31003A_182262) and a Mercator Fellowship from the German Research Foundation (DFG), held as a EvoPAD Visiting Professor at the Institute for Evolution and Biodiversity, University of Münster; AOB by the National Institutes of Health (R35 GM119686); MK (M. Kankare) by Academy of Finland grant 322980; VL by Danish Natural Science Research Council (FNU) grant 4002-00113B; FS Deutsche Forschungsgemeinschaft (DFG) grant STA1154/4-1, Project 408908608; JP by the Deutsche Forschungsgemeinschaft Projects 274388701 and 347368302; AU by FPI fellowship (BES-2012-052999); ET Israel Science Foundation (ISF) grant 1737/17; MSV, MSR and MJ by a grant from the Ministry of Education, Science and Technological Development of the Republic of Serbia (451-03-68/2020-14/200178); AP, KE and MT by a grant from the Ministry of Education, Science and Technological Development of the Republic of Serbia (451-03-68/2020-14/200007); and TM NSERC grant RGPIN-2018-05551.Peer reviewe

Erratum to: 36th International Symposium on Intensive Care and Emergency Medicine

[This corrects the article DOI: 10.1186/s13054-016-1208-6.]

Corrigendum to: Drosophila Evolution over Space and Time (DEST): a New Population Genomics Resource

Drosophila melanogaster is a leading model in population genetics and genomics, and a growing number of whole-genome datasets from natural populations of this species have been published over the last years. A major challenge is the integration of disparate datasets, 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 dataset, which we call Drosophila Evolution over Space and Time (DEST), is coupled with sampling and environmental meta-data. A web-based genome browser and web portal provide easy access to the SNP dataset. 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 dataset. Our resource will enable population geneticists to analyze spatio-temporal genetic patterns and evolutionary dynamics of D. melanogaster populations in unprecedented detail.DrosEU is funded by a Special Topic Networks (STN) grant from the European Society for Evolutionary Biology (ESEB). MK (M. Kapun) was supported by the Austrian Science Foundation (grant no. FWF P32275); JG by the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (H2020-ERC-2014-CoG-647900) and by the Spanish Ministry of Science and Innovation (BFU-2011-24397); TF by the Swiss National Science Foundation (SNSF grants PP00P3_133641, PP00P3_165836, and 31003A_182262) and a Mercator Fellowship from the German Research Foundation (DFG), held as a EvoPAD Visiting Professor at the Institute for Evolution and Biodiversity, University of Münster; AOB by the National Institutes of Health (R35 GM119686); MK (M. Kankare) by Academy of Finland grant 322980; VL by Danish Natural Science Research Council (FNU) grant 4002-00113B; FS Deutsche Forschungsgemeinschaft (DFG) grant STA1154/4-1, Project 408908608; JP by the Deutsche Forschungsgemeinschaft Projects 274388701 and 347368302; AU by FPI fellowship (BES-2012-052999); ET Israel Science Foundation (ISF) grant 1737/17; MSV, MSR and MJ by a grant from the Ministry of Education, Science and Technological Development of the Republic of Serbia (451-03-68/2020-14/200178); AP, KE and MT by a grant from the Ministry of Education, Science and Technological Development of the Republic of Serbia (451-03-68/2020-14/200007); and TM NSERC grant RGPIN-2018-05551.Peer reviewe

Nurses' perceptions of aids and obstacles to the provision of optimal end of life care in ICU

Contains fulltext : 172380.pdf (publisher's version ) (Open Access

Prediction of second neurological attack in patients with clinically isolated syndrome using support vector machines

The aim of this study is to predict the conversion from clinically isolated syndrome to clinically definite multiple sclerosis using support vector machines. The two groups of converters and non-converters are classified using features that were calculated from baseline data of 73 patients. The data consists of standard magnetic resonance images, binary lesion masks, and clinical and demographic information. 15 features were calculated and all combinations of them were iteratively tested for their predictive capacity using polynomial kernels and radial basis functions with leave-one-out cross-validation. The accuracy of this prediction is up to 86.4% with a sensitivity and specificity in the same range indicating that this is a feasible approach for the prediction of a second clinical attack in patients with clinically isolated syndromes, and that the chosen features are appropriate. The two features gender and location of onset lesions have been used in all feature combinations leading to a high accuracy suggesting that they are highly predictive. However, it is necessary to add supporting features to maximise the accuracy. © 2013 IEEE

Cell identity and nucleo-mitochondrial genetic context modulate OXPHOS performance and determine somatic heteroplasmy dynamics

Heteroplasmy, multiple variants of mitochondrial DNA (mtDNA) in the same cytoplasm, may be naturally generated by mutations but is counteracted by a genetic mtDNA bottleneck during oocyte development. Engineered heteroplasmic mice with nonpathological mtDNA variants reveal a nonrandom tissue-specific mtDNA segregation pattern, with few tissues that do not show segregation. The driving force for this dynamic complex pattern has remained unexplained for decades, challenging our understanding of this fundamental biological problem and hindering clinical planning for inherited diseases. Here, we demonstrate that the nonrandom mtDNA segregation is an intracellular process based on organelle selection. This cell type–specific decision arises jointly from the impact of mtDNA haplotypes on the oxidative phosphorylation (OXPHOS) system and the cell metabolic requirements and is strongly sensitive to the nuclear context and to environmental cues