| 1. |
- Kattge, Jens, et al.
(författare)
-
TRY plant trait database - enhanced coverage and open access
- 2020
-
Ingår i: Global Change Biology. - : Wiley-Blackwell. - 1354-1013 .- 1365-2486. ; 26:1, s. 119-188
-
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.
|
|
| 2. |
- Chatzikonstantinou, T, et al.
(författare)
-
COVID-19 severity and mortality in patients with CLL: an update of the international ERIC and Campus CLL study
- 2021
-
Ingår i: Leukemia. - : Springer Science and Business Media LLC. - 1476-5551 .- 0887-6924. ; 35:12, s. 3444-3454
-
Tidskriftsartikel (refereegranskat)abstract
- Patients with chronic lymphocytic leukemia (CLL) may be more susceptible to Coronavirus disease 2019 (COVID-19) due to age, disease, and treatment-related immunosuppression. We aimed to assess risk factors of outcome and elucidate the impact of CLL-directed treatments on the course of COVID-19. We conducted a retrospective, international study, collectively including 941 patients with CLL and confirmed COVID-19. Data from the beginning of the pandemic until March 16, 2021, were collected from 91 centers. The risk factors of case fatality rate (CFR), disease severity, and overall survival (OS) were investigated. OS analysis was restricted to patients with severe COVID-19 (definition: hospitalization with need of oxygen or admission into an intensive care unit). CFR in patients with severe COVID-19 was 38.4%. OS was inferior for patients in all treatment categories compared to untreated (p < 0.001). Untreated patients had a lower risk of death (HR = 0.54, 95% CI:0.41–0.72). The risk of death was higher for older patients and those suffering from cardiac failure (HR = 1.03, 95% CI:1.02–1.04; HR = 1.79, 95% CI:1.04–3.07, respectively). Age, CLL-directed treatment, and cardiac failure were significant risk factors of OS. Untreated patients had a better chance of survival than those on treatment or recently treated.
|
|
| 3. |
- Kim, Anna A., et al.
(författare)
-
Independently paced Ca2+ oscillations in progenitor and differentiated cells in an ex vivo epithelial organ
- 2022
-
Ingår i: Journal of Cell Science. - : The Company of Biologists. - 0021-9533 .- 1477-9137. ; 135:14
-
Tidskriftsartikel (refereegranskat)abstract
- Cytosolic Ca2+ is a highly dynamic, tightly regulated and broadly conserved cellular signal. Ca2+ dynamics have been studied widely in cellular monocultures, yet organs in vivo comprise heterogeneous populations of stem and differentiated cells. Here, we examine Ca2+ dynamics in the adult Drosophila intestine, a self-renewing epithelial organ in which stem cells continuously produce daughters that differentiate into either enteroendocrine cells or enterocytes. Live imaging of whole organs ex vivo reveals that stem-cell daughters adopt strikingly distinct patterns of Ca2+ oscillations after differentiation: enteroendocrine cells exhibit single-cell Ca2+ oscillations, whereas enterocytes exhibit rhythmic, long-range Ca2+ waves. These multicellular waves do not propagate through immature progenitors (stem cells and enteroblasts), of which the oscillation frequency is approximately half that of enteroendocrine cells. Organ-scale inhibition of gap junctions eliminates Ca2+ oscillations in all cell types – even, intriguingly, in progenitor and enteroendocrine cells that are surrounded only by enterocytes. Our findings establish that cells adopt fate-specific modes of Ca2+ dynamics as they terminally differentiate and reveal that the oscillatory dynamics of different cell types in a single, coherent epithelium are paced independently.
|
|
| 4. |
- Marchetti, Giovanni Luca, et al.
(författare)
-
Learning Coarsened Dynamic Graph Representations for Deformable Object Manipulation
- 2021
-
Ingår i: 2021 20Th International Conference On Advanced Robotics (ICAR). - : IEEE. ; , s. 955-960
-
Konferensbidrag (refereegranskat)abstract
- Manipulation of deformable objects has long been a challenging task in robotics. Their high-dimensional configuration space and complex dynamics make them difficult to consider for tasks such as robotic manipulation. In this paper, we address the problem of learning efficient representations of deformable objects which lend themselves better suitable for downstream robotics tasks. In particular, we consider graph-based representations of deformable objects which arise naturally from their point-cloud representation. Through manipulation, we learn to coarsen this graph into a simpler representation which still captures the necessary dynamics of the object. Our model consists of (a) a Cluster Assignment Model which takes the initial graph and coarsens it, (b) a Coarsened Dynamics Model that approximates the dynamics of the coarsened graph and (c) a Forward Prediction Model which predicts the next state of the ground truth graph. After end-to-end training, the Cluster Assignment Model learns to build coarse representations which better capture the dynamics compared to conventional clustering methods such as K-means. We evaluate our method on three sets of experiments: rigid objects, rigid objects with pair-wise interactions and a simulated dataset of a shirt.
|
|
| 5. |
|
|
