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- 2017
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Ingår i: Physical Review D. - 2470-0010 .- 2470-0029. ; 96:2
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Tidskriftsartikel (refereegranskat)
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| 6. |
- Flannick, Jason, et al.
(författare)
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Data Descriptor : Sequence data and association statistics from 12,940 type 2 diabetes cases and controls
- 2017
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Ingår i: Scientific Data. - : Springer Science and Business Media LLC. - 2052-4463. ; 4
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Tidskriftsartikel (refereegranskat)abstract
- To investigate the genetic basis of type 2 diabetes (T2D) to high resolution, the GoT2D and T2D-GENES consortia catalogued variation from whole-genome sequencing of 2,657 European individuals and exome sequencing of 12,940 individuals of multiple ancestries. Over 27M SNPs, indels, and structural variants were identified, including 99% of low-frequency (minor allele frequency [MAF] 0.1-5%) non-coding variants in the whole-genome sequenced individuals and 99.7% of low-frequency coding variants in the whole-exome sequenced individuals. Each variant was tested for association with T2D in the sequenced individuals, and, to increase power, most were tested in larger numbers of individuals (> 80% of low-frequency coding variants in similar to ~82 K Europeans via the exome chip, and similar to ~90% of low-frequency non-coding variants in similar to ~44 K Europeans via genotype imputation). The variants, genotypes, and association statistics from these analyses provide the largest reference to date of human genetic information relevant to T2D, for use in activities such as T2D-focused genotype imputation, functional characterization of variants or genes, and other novel analyses to detect associations between sequence variation and T2D.
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- Crous, P.W, et al.
(författare)
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Fungal Planet description sheets: 469–557
- 2016
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Ingår i: Persoonia. - 0031-5850. ; 37, s. 218-403
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Tidskriftsartikel (refereegranskat)abstract
- Novel species of fungi described in this study include those from various countries as follows: Australia: Apiognomonia lasiopetali on Lasiopetalum sp., Blastacervulus eucalyptorum on Eucalyptus adesmophloia, Bul- lanockia australis (incl. Bullanockia gen. nov.) on Kingia australis, Caliciopsis eucalypti on Eucalyptus marginata, Celerioriella petrophiles on Petrophile teretifolia, Coleophoma xanthosiae on Xanthosia rotundifolia, Coniothyrium hakeae on Hakea sp., Diatrypella banksiae on Banksia formosa, Disculoides corymbiae on Corymbia calophylla, Elsinoë eelemani on Melaleuca alternifolia, Elsinoë eucalyptigena on Eucalyptus kingsmillii, Elsinoë preissianae on Eucalyptus preissiana, Eucasphaeria rustici on Eucalyptus creta, Hyweljonesia queenslandica (incl. Hyweljonesia gen. nov.) on the cocoon of an unidentified microlepidoptera, Mycodiella eucalypti (incl. Mycodiella gen. nov.) on Eucalyptus diversicolor, Myrtapenidiella sporadicae on Eucalyptus sporadica, Neocrinula xanthorrhoeae (incl. Neocrinula gen. nov.) on Xanthorrhoea sp., Ophiocordyceps nooreniae on dead ant, Phaeosphaeriopsis agava- cearum on Agave sp., Phlogicylindrium mokarei on Eucalyptus sp., Phyllosticta acaciigena on Acacia suaveolens, Pleurophoma acaciae on Acacia glaucoptera, Pyrenochaeta hakeae on Hakea sp., Readeriella lehmannii on Eucalyptus lehmannii, Saccharata banksiae on Banksia grandis, Saccharata daviesiae on Daviesia pachyphylla, Saccharata eucalyptorum on Eucalyptus bigalerita, Saccharata hakeae on Hakea baxteri, Saccharata hakeicola on Hakea victoria, Saccharata lambertiae on Lambertia ericifolia, Saccharata petrophiles on Petrophile sp., Sac- charata petrophilicola on Petrophile fastigiata, Sphaerellopsis hakeae on Hakea sp., and Teichospora kingiae on Kingia australis. Brazil: Adautomilanezia caesalpiniae (incl. Adautomilanezia gen. nov.) on Caesalpina echinata, Arthrophiala arthrospora (incl. Arthrophiala gen. nov.) on Sagittaria montevidensis, Diaporthe caatingaensis (en- dophyte from Tacinga inamoena), Geastrum ishikawae on sandy soil, Geastrum pusillipilosum on soil, Gymnopus pygmaeus on dead leaves and sticks, Inonotus hymenonitens on decayed angiosperm trunk, Pyricularia urashimae on Urochloa brizantha, and Synnemellisia aurantia on Passiflora edulis. Chile: Tubulicrinis australis on Lophosoria quadripinnata. France: Cercophora squamulosa from submerged wood, and Scedosporium cereisporum from fluids of a wastewater treatment plant. Hawaii: Beltraniella acaciae, Dactylaria acaciae, Rhexodenticula acaciae, Rubikia evansii and Torula acaciae (all on Acacia koa). India: Lepidoderma echinosporum on dead semi-woody stems, and Rhodocybe rubrobrunnea from soil. Iran: Talaromyces kabodanensis from hypersaline soil. La Réunion: Neocordana musarum from leaves of Musa sp. Malaysia: Anungitea eucalyptigena on Eucalyptus grandis × pellita, Camptomeriphila leucaenae (incl. Camptomeriphila gen. nov.) on Leucaena leucocephala, Castanediella communis on Eucalyptus pellita, Eucalyptostroma eucalypti (incl. Eucalyptostroma gen. nov.) on Eucalyptus pellita, Melanco- niella syzygii on Syzygium sp., Mycophilomyces periconiae (incl. Mycophilomyces gen. nov.) as hyperparasite on Periconia on leaves of Albizia falcataria, Synnemadiella eucalypti (incl. Synnemadiella gen. nov.) on Eucalyptus pellita, and Teichospora nephelii on Nephelium lappaceum. Mexico: Aspergillus bicephalus from soil. New Zealand: Aplosporella sophorae on Sophora microphylla, Libertasomyces platani on Platanus sp., Neothyronectria sophorae (incl. Neothyronectria gen. nov.) on Sophora microphylla, Parastagonospora phoenicicola on Phoenix canariensis, Phaeoacremonium pseudopanacis on Pseudopanax crassifolius, Phlyctema phoenicis on Phoenix canariensis, and Pseudoascochyta novae-zelandiae on Cordyline australis. Panama: Chalara panamensis from needle litter of Pinus cf. caribaea. South Africa: Exophiala eucalypti on leaves of Eucalyptus sp., Fantasmomyces hyalinus (incl. Fantasmomyces gen. nov.) on Acacia exuvialis, Paracladophialophora carceris (incl. Paracladophialophora gen. nov.) on Aloe sp., and Umthunziomyces hagahagensis (incl. Umthunziomyces gen. nov.) on Mimusops caffra. Spain: Clavaria griseobrunnea on bare ground in Pteridium aquilinum field, Cyathus ibericus on small fallen branches of Pinus halepensis, Gyroporus pseudolacteus in humus of Pinus pinaster, and Pseudoascochyta pratensis (incl. Pseudoascochyta gen. nov.) from soil. Thailand: Neoascochyta adenii on Adenium obesum, and Ochroconis capsici on Capsicum annuum. UK: Fusicolla melogrammae from dead stromata of Melogramma campylosporum on bark of Carpinus betulus. Uruguay: Myrmecridium pulvericola from house dust. USA: Neoscolecobasidium agapanthi (incl. Neoscolecobasidium gen. nov.) on Agapanthus sp., Polyscytalum purgamentum on leaf litter, Pseudopithomyces diversisporus from human toenail, Saksenaea trapezispora from knee wound of a soldier, and Sirococcus quercus from Quercus sp. Morphological and culture characteristics along with DNA barcodes are provided.
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| 8. |
- Fanourgakis, George S., et al.
(författare)
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Evaluation of global simulations of aerosol particle and cloud condensation nuclei number, with implications for cloud droplet formation
- 2019
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Ingår i: Atmospheric Chemistry and Physics. - : Copernicus GmbH. - 1680-7316 .- 1680-7324. ; 19:13, s. 8591-8617
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Tidskriftsartikel (refereegranskat)abstract
- A total of 16 global chemistry transport models and general circulation models have participated in this study; 14 models have been evaluated with regard to their ability to reproduce the near-surface observed number concentration of aerosol particles and cloud condensation nuclei (CCN), as well as derived cloud droplet number concentration (CDNC). Model results for the period 2011-2015 are compared with aerosol measurements (aerosol particle number, CCN and aerosol particle composition in the submicron fraction) from nine surface stations located in Europe and Japan. The evaluation focuses on the ability of models to simulate the average across time state in diverse environments and on the seasonal and short-term variability in the aerosol properties. There is no single model that systematically performs best across all environments represented by the observations. Models tend to underestimate the observed aerosol particle and CCN number concentrations, with average normalized mean bias (NMB) of all models and for all stations, where data are available, of -24% and -35% for particles with dry diameters >50 and >120nm, as well as -36% and -34% for CCN at supersaturations of 0.2% and 1.0%, respectively. However, they seem to behave differently for particles activating at very low supersaturations (<0.1%) than at higher ones. A total of 15 models have been used to produce ensemble annual median distributions of relevant parameters. The model diversity (defined as the ratio of standard deviation to mean) is up to about 3 for simulated N3 (number concentration of particles with dry diameters larger than 3nm) and up to about 1 for simulated CCN in the extra-polar regions. A global mean reduction of a factor of about 2 is found in the model diversity for CCN at a supersaturation of 0.2% (CCN0.2) compared to that for N3, maximizing over regions where new particle formation is important. An additional model has been used to investigate potential causes of model diversity in CCN and bias compared to the observations by performing a perturbed parameter ensemble (PPE) accounting for uncertainties in 26 aerosol-related model input parameters. This PPE suggests that biogenic secondary organic aerosol formation and the hygroscopic properties of the organic material are likely to be the major sources of CCN uncertainty in summer, with dry deposition and cloud processing being dominant in winter. Models capture the relative amplitude of the seasonal variability of the aerosol particle number concentration for all studied particle sizes with available observations (dry diameters larger than 50, 80 and 120nm). The short-term persistence time (on the order of a few days) of CCN concentrations, which is a measure of aerosol dynamic behavior in the models, is underestimated on average by the models by 40% during winter and 20% in summer. In contrast to the large spread in simulated aerosol particle and CCN number concentrations, the CDNC derived from simulated CCN spectra is less diverse and in better agreement with CDNC estimates consistently derived from the observations (average NMB -13% and -22% for updraft velocities 0.3 and 0.6ms-1, respectively). In addition, simulated CDNC is in slightly better agreement with observationally derived values at lower than at higher updraft velocities (index of agreement 0.64 vs. 0.65). The reduced spread of CDNC compared to that of CCN is attributed to the sublinear response of CDNC to aerosol particle number variations and the negative correlation between the sensitivities of CDNC to aerosol particle number concentration (Nd=Na) and to updraft velocity (Nd=w). Overall, we find that while CCN is controlled by both aerosol particle number and composition, CDNC is sensitive to CCN at low and moderate CCN concentrations and to the updraft velocity when CCN levels are high. Discrepancies are found in sensitivities Nd=Na and Nd=w; models may be predisposed to be too "aerosol sensitive" or "aerosol insensitive" in aerosol-cloud-climate interaction studies, even if they may capture average droplet numbers well. This is a subtle but profound finding that only the sensitivities can clearly reveal and may explain intermodel biases on the aerosol indirect effect.
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| 9. |
- Santoro, Maurizio, et al.
(författare)
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Forest growing stock volume of the northern hemisphere : Spatially explicit estimates for 2010 derived from Envisat ASAR
- 2015
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Ingår i: Remote Sensing of Environment. - : Elsevier BV. - 0034-4257 .- 1879-0704. ; 168, s. 316-334
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Tidskriftsartikel (refereegranskat)abstract
- This paper presents and assesses spatially explicit estimates of forest growing stock volume (GSV) of the northern hemisphere (north of 10 degrees N) from hyper-temporal observations of Envisat Advanced Synthetic Aperture Radar (ASAR) backscattered intensity using the BIOMASAR algorithm. Approximately 70,000 ASAR images at a pixel size of 0.01 degrees were used to estimate GSV representative for the year 2010. The spatial distribution of the GSV across four ecological zones (polar, boreal, temperate, subtropical) was well captured by the ASAR-based estimates. The uncertainty of the retrieved GSV was smallest in boreal and temperate forest (<30% for approximately 80% of the forest area) and largest in subtropical forest. ASAR-derived GSV averages at the level of administrative units were mostly in agreement with inventory-derived estimates. Underestimation occurred in regions of very high GSV (>300 m(3)/ha) and fragmented forest landscapes. For the major forested countries within the study region, the relative RMSE between ASAR-derived GSV averages at provincial level and corresponding values from National Forest Inventory was between 12% and 45% (average: 29%).
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