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- Thoma, B, et al.
(författare)
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An international, interprofessional investigation of the self-reported podcast listening habits of emergency clinicians: A METRIQ Study
- 2020
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Ingår i: CJEM. - : Springer Science and Business Media LLC. - 1481-8043 .- 1481-8035. ; 22:1, s. 112-117
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Tidskriftsartikel (refereegranskat)abstract
- ObjectivesPodcasts are increasingly being used for medical education. A deeper understanding of usage patterns would inform both producers and researchers of medical podcasts. We aimed to determine how and why podcasts are used by emergency medicine and critical care clinicians.MethodsAn international interprofessional sample (medical students, residents, physicians, nurses, physician assistants, and paramedics) was recruited through direct contact and a multimodal social media (Twitter and Facebook) campaign. Each participant completed a survey outlining how and why they utilize medical podcasts. Recruitment materials included an infographic and study website.Results390 participants from 33 countries and 4 professions (medicine, nursing, paramedicine, physician assistant) completed the survey. Participants most frequently listened to medical podcasts to review new literature (75.8%), learn core material (75.1%), and refresh memory (71.8%). The majority (62.6%) were aware of the ability to listen at increased speeds, but most (76.9%) listened at 1.0 x (normal) speed. All but 25 (6.4%) participants concurrently performed other tasks while listening. Driving (72.3%), exercising (39.7%), and completing chores (39.2%) were the most common. A minority of participants used active learning techniques such as pausing, rewinding, and replaying segments of the podcast. Very few listened to podcasts multiple times.ConclusionsAn international cohort of emergency clinicians use medical podcasts predominantly for learning. Their listening habits (rarely employing active learning strategies and frequently performing concurrent tasks) may not support this goal. Further exploration of the impact of these activities on learning from podcasts is warranted.
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- Romagnoni, A, et al.
(författare)
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Comparative performances of machine learning methods for classifying Crohn Disease patients using genome-wide genotyping data
- 2019
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Ingår i: Scientific reports. - : Springer Science and Business Media LLC. - 2045-2322. ; 9:1, s. 10351-
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Tidskriftsartikel (refereegranskat)abstract
- Crohn Disease (CD) is a complex genetic disorder for which more than 140 genes have been identified using genome wide association studies (GWAS). However, the genetic architecture of the trait remains largely unknown. The recent development of machine learning (ML) approaches incited us to apply them to classify healthy and diseased people according to their genomic information. The Immunochip dataset containing 18,227 CD patients and 34,050 healthy controls enrolled and genotyped by the international Inflammatory Bowel Disease genetic consortium (IIBDGC) has been re-analyzed using a set of ML methods: penalized logistic regression (LR), gradient boosted trees (GBT) and artificial neural networks (NN). The main score used to compare the methods was the Area Under the ROC Curve (AUC) statistics. The impact of quality control (QC), imputing and coding methods on LR results showed that QC methods and imputation of missing genotypes may artificially increase the scores. At the opposite, neither the patient/control ratio nor marker preselection or coding strategies significantly affected the results. LR methods, including Lasso, Ridge and ElasticNet provided similar results with a maximum AUC of 0.80. GBT methods like XGBoost, LightGBM and CatBoost, together with dense NN with one or more hidden layers, provided similar AUC values, suggesting limited epistatic effects in the genetic architecture of the trait. ML methods detected near all the genetic variants previously identified by GWAS among the best predictors plus additional predictors with lower effects. The robustness and complementarity of the different methods are also studied. Compared to LR, non-linear models such as GBT or NN may provide robust complementary approaches to identify and classify genetic markers.
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- Craddock, Nick, et al.
(författare)
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Genome-wide association study of CNVs in 16,000 cases of eight common diseases and 3,000 shared controls
- 2010
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Ingår i: Nature. - : Springer Science and Business Media LLC. - 0028-0836 .- 1476-4687. ; 464:7289, s. 713-720
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Tidskriftsartikel (refereegranskat)abstract
- Copy number variants (CNVs) account for a major proportion of human genetic polymorphism and have been predicted to have an important role in genetic susceptibility to common disease. To address this we undertook a large, direct genome-wide study of association between CNVs and eight common human diseases. Using a purpose-designed array we typed,19,000 individuals into distinct copy-number classes at 3,432 polymorphic CNVs, including an estimated similar to 50% of all common CNVs larger than 500 base pairs. We identified several biological artefacts that lead to false-positive associations, including systematic CNV differences between DNAs derived from blood and cell lines. Association testing and follow-up replication analyses confirmed three loci where CNVs were associated with disease-IRGM for Crohn's disease, HLA for Crohn's disease, rheumatoid arthritis and type 1 diabetes, and TSPAN8 for type 2 diabetes-although in each case the locus had previously been identified in single nucleotide polymorphism (SNP)-based studies, reflecting our observation that most common CNVs that are well-typed on our array are well tagged by SNPs and so have been indirectly explored through SNP studies. We conclude that common CNVs that can be typed on existing platforms are unlikely to contribute greatly to the genetic basis of common human diseases.
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- Kattge, Jens, et al.
(författare)
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TRY plant trait database - enhanced coverage and open access
- 2020
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Ingår i: Global Change Biology. - : Wiley-Blackwell. - 1354-1013 .- 1365-2486. ; 26:1, s. 119-188
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Tidskriftsartikel (refereegranskat)abstract
- Plant traits-the morphological, anatomical, physiological, biochemical and phenological characteristics of plants-determine how plants respond to environmental factors, affect other trophic levels, and influence ecosystem properties and their benefits and detriments to people. Plant trait data thus represent the basis for a vast area of research spanning from evolutionary biology, community and functional ecology, to biodiversity conservation, ecosystem and landscape management, restoration, biogeography and earth system modelling. Since its foundation in 2007, the TRY database of plant traits has grown continuously. It now provides unprecedented data coverage under an open access data policy and is the main plant trait database used by the research community worldwide. Increasingly, the TRY database also supports new frontiers of trait-based plant research, including the identification of data gaps and the subsequent mobilization or measurement of new data. To support this development, in this article we evaluate the extent of the trait data compiled in TRY and analyse emerging patterns of data coverage and representativeness. Best species coverage is achieved for categorical traits-almost complete coverage for 'plant growth form'. However, most traits relevant for ecology and vegetation modelling are characterized by continuous intraspecific variation and trait-environmental relationships. These traits have to be measured on individual plants in their respective environment. Despite unprecedented data coverage, we observe a humbling lack of completeness and representativeness of these continuous traits in many aspects. We, therefore, conclude that reducing data gaps and biases in the TRY database remains a key challenge and requires a coordinated approach to data mobilization and trait measurements. This can only be achieved in collaboration with other initiatives.
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- Bower, K. N., et al.
(författare)
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ACE-2 HILLCLOUD. An overview of the ACE-2 ground-based cloud experiment
- 2000
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Ingår i: Tellus. Series B: Chemical and Physical Meteorology. - : Stockholm University Press. - 0280-6509. ; 52:2, s. 750-778
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Tidskriftsartikel (refereegranskat)abstract
- The ACE-2 HILLCLOUD experiment was carried out on the island of Tenerife in June-July 1997 to investigate the interaction of the boundary layer aerosol with a hill cap cloud forming over a ridge to the north-east of the island. The cloud was used as a natural flow through reactor to investigate the dependence of the cloud microphysics and chemistry on the characteristics of the aerosols and trace gases entering cloud, and to simultaneously study the influence of the physical and chemical processes occurring within the cloud on the size distribution, chemical and hygroscopic properties of the aerosol exiting cloud. 5 major ground base sites were used, measuring trace gases and aerosols upwind and downwind of the cloud, and cloud microphysics and chemistry and interstitial aerosol and gases within the cloud on the hill. 8 intensive measurement periods or runs were undertaken during cloud events, (nocturnally for seven of the eight runs) and were carried out in a wide range of airmass conditions from clean maritime to polluted continental. Polluted air was characterised by higher than average concentrations of ozone (> 50 ppbv), fine and accumulation mode aerosols (> 3000 and > 1500 cm -3 , respectively) and higher aerosol mass loadings. Cloud droplet number concentrations N, increased from 50 cm -3 in background maritime air to > 2500 cm -3 in aged polluted continental air, a concentration much higher than had previously been detected. Surprisingly, N was seen to vary almost linearly with aerosol number across this range. The droplet aerosol analyser (DAA) measured higher droplet numbers than the corrected forward scattering spectrometer probe (FSSP) in the most polluted air, but at other times there was good agreement (FSSP = 0.95 DAA with an r 2 = 0.89 for N < 1200 cm -3 ). Background ammonia gas concentrations were around 0.3 ppbv even in air originating over the ocean, another unexpected but important result for the region. NO 2 was present in background concentrations of typically 15 pptv to 100 pptv and NO 3 . (the nitrate radical) was observed at night throughout. Calculations suggest NO 3 . losses were mainly by reaction with DMS to produce nitric acid. Low concentrations of SO 2 (~30 pptv), HNO 3 and HCl were always present. HNO 3 concentrations were higher in polluted episodes and calculations implied that these exceeded those which could be accounted for by NO 2 oxidation. It is presumed that nitric and hydrochloric acids were present as a result of outgassing from aerosol, the HNO 3 from nitrate rich aerosol transported into the region from upwind of Tenerife, and HCl from sea salt aerosol newly formed at the sea surface. The oxidants hydrogen peroxide and ozone were abundant (i.e., were well in excess over SO 2 throughout the experiment). Occasions of significant aerosol growth following cloud processing were observed, particularly in cleaner cases. Observations and modelling suggested this was due mainly to the take up of nitric acid, hydrochloric acid and ammonia by the smallest activated aerosol particles. On a few occasions a small contribution was made by the in-cloud oxidation of S(IV). The implications of these results from HILLCLOUD for the climatologically more important stratocumulus Marine Boundary Layer (MBL) clouds are considered.
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