| 1. |
- 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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| 2. |
- Niemi, MEK, et al.
(författare)
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- 2021
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swepub:Mat__t
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| 3. |
- Almora, Osbel, et al.
(författare)
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Device Performance of Emerging Photovoltaic Materials (Version 1)
- 2020
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Ingår i: Advanced Energy Materials. - : Wiley. - 1614-6832 .- 1614-6840. ; 11:11
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Tidskriftsartikel (refereegranskat)abstract
- Emerging photovoltaics (PVs) focus on a variety of applications complementing large scale electricity generation. Organic, dye-sensitized, and some perovskite solar cells are considered in building integration, greenhouses, wearable, and indoor applications, thereby motivating research on flexible, transparent, semitransparent, and multi-junction PVs. Nevertheless, it can be very time consuming to find or develop an up-to-date overview of the state-of-the-art performance for these systems and applications. Two important resources for recording research cells efficiencies are the National Renewable Energy Laboratory chart and the efficiency tables compiled biannually by Martin Green and colleagues. Both publications provide an effective coverage over the established technologies, bridging research and industry. An alternative approach is proposed here summarizing the best reports in the diverse research subjects for emerging PVs. Best performance parameters are provided as a function of the photovoltaic bandgap energy for each technology and application, and are put into perspective using, e.g., the Shockley–Queisser limit. In all cases, the reported data correspond to published and/or properly described certified results, with enough details provided for prospective data reproduction. Additionally, the stability test energy yield is included as an analysis parameter among state-of-the-art emerging PVs.
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| 4. |
- Jung, Christian, et al.
(författare)
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A comparison of very old patients admitted to intensive care unit after acute versus elective surgery or intervention
- 2019
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Ingår i: Journal of critical care. - : W B SAUNDERS CO-ELSEVIER INC. - 0883-9441 .- 1557-8615. ; 52, s. 141-148
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Tidskriftsartikel (refereegranskat)abstract
- Background: We aimed to evaluate differences in outcome between patients admitted to intensive care unit (ICU) after elective versus acute surgery in a multinational cohort of very old patients (80 years; VIP). Predictors of mortality, with special emphasis on frailty, were assessed.Methods: In total, 5063 VIPs were induded in this analysis, 922 were admitted after elective surgery or intervention, 4141 acutely, with 402 after acute surgery. Differences were calculated using Mann-Whitney-U test and Wilcoxon test. Univariate and multivariable logistic regression were used to assess associations with mortality.Results: Compared patients admitted after acute surgery, patients admitted after elective surgery suffered less often from frailty as defined as CFS (28% vs 46%; p < 0.001), evidenced lower SOFA scores (4 +/- 5 vs 7 +/- 7; p < 0.001). Presence of frailty (CFS >4) was associated with significantly increased mortality both in elective surgery patients (7% vs 12%; p = 0.01), in acute surgery (7% vs 12%; p = 0.02).Conclusions: VIPs admitted to ICU after elective surgery evidenced favorable outcome over patients after acute surgery even after correction for relevant confounders. Frailty might be used to guide clinicians in risk stratification in both patients admitted after elective and acute surgery.
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| 5. |
- Nagaraja, Ch., et al.
(författare)
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Opening remarks
- 2016
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Konferensbidrag (refereegranskat)
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| 6. |
- 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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| 7. |
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