| 1. |
- Thomas, HS, et al.
(författare)
-
- 2019
-
swepub:Mat__t
|
|
| 2. |
- Bouckaert, R., et al.
(författare)
-
BEAST 2.5: An advanced software platform for Bayesian evolutionary analysis
- 2019
-
Ingår i: Plos Computational Biology. - : Public Library of Science (PLoS). - 1553-7358. ; 15:4
-
Tidskriftsartikel (refereegranskat)abstract
- Elaboration of Bayesian phylogenetic inference methods has continued at pace in recent years with major new advances in nearly all aspects of the joint modelling of evolutionary data. It is increasingly appreciated that some evolutionary questions can only be adequately answered by combining evidence from multiple independent sources of data, including genome sequences, sampling dates, phenotypic data, radiocarbon dates, fossil occurrences, and biogeographic range information among others. Including all relevant data into a single joint model is very challenging both conceptually and computationally. Advanced computational software packages that allow robust development of compatible (sub-)models which can be composed into a full model hierarchy have played a key role in these developments. Developing such software frameworks is increasingly a major scientific activity in its own right, and comes with specific challenges, from practical software design, development and engineering challenges to statistical and conceptual modelling challenges. BEAST 2 is one such computational software platform, and was first announced over 4 years ago. Here we describe a series of major new developments in the BEAST 2 core platform and model hierarchy that have occurred since the first release of the software, culminating in the recent 2.5 release. Author summary Bayesian phylogenetic inference methods have undergone considerable development in recent years, and joint modelling of rich evolutionary data, including genomes, phenotypes and fossil occurrences is increasingly common. Advanced computational software packages that allow robust development of compatible (sub-)models which can be composed into a full model hierarchy have played a key role in these developments. Developing scientific software is increasingly crucial to advancement in many fields of biology. The challenges range from practical software development and engineering, distributed team coordination, conceptual development and statistical modelling, to validation and testing. BEAST 2 is one such computational software platform for phylogenetics, population genetics and phylodynamics, and was first announced over 4 years ago. Here we describe the full range of new tools and models available on the BEAST 2.5 platform, which expand joint evolutionary inference in many new directions, especially for joint inference over multiple data types, non-tree models and complex phylodynamics.
|
|
| 3. |
- Duong, M., et al.
(författare)
-
Mortality and cardiovascular and respiratory morbidity in individuals with impaired FEV 1 (PURE): an international, community-based cohort study
- 2019
-
Ingår i: The Lancet Global Health. - 2214-109X. ; 7:5
-
Tidskriftsartikel (refereegranskat)abstract
- Background: The associations between the extent of forced expiratory volume in 1 s (FEV 1 ) impairment and mortality, incident cardiovascular disease, and respiratory hospitalisations are unclear, and how these associations might vary across populations is unknown. Methods: In this international, community-based cohort study, we prospectively enrolled adults aged 35–70 years who had no intention of moving residences for 4 years from rural and urban communities across 17 countries. A portable spirometer was used to assess FEV 1 . FEV 1 values were standardised within countries for height, age, and sex, and expressed as a percentage of the country-specific predicted FEV 1 value (FEV 1 %). FEV 1 % was categorised as no impairment (FEV 1 % ≥0 SD from country-specific mean), mild impairment (FEV 1 % <0 SD to −1 SD), moderate impairment (FEV 1 % <–1 SD to −2 SDs), and severe impairment (FEV 1 % <–2 SDs [ie, clinically abnormal range]). Follow-up was done every 3 years to collect information on mortality, cardiovascular disease outcomes (including myocardial infarction, stroke, sudden death, or congestive heart failure), and respiratory hospitalisations (from chronic obstructive pulmonary disease, asthma, pneumonia, tuberculosis, or other pulmonary conditions). Fully adjusted hazard ratios (HRs) were calculated by multilevel Cox regression. Findings: Among 126 359 adults with acceptable spirometry data available, during a median 7·8 years (IQR 5·6–9·5) of follow-up, 5488 (4·3%) deaths, 5734 (4·5%) cardiovascular disease events, and 1948 (1·5%) respiratory hospitalisation events occurred. Relative to the no impairment group, mild to severe FEV 1 % impairments were associated with graded increases in mortality (HR 1·27 [95% CI 1·18–1·36] for mild, 1·74 [1·60–1·90] for moderate, and 2·54 [2·26–2·86] for severe impairment), cardiovascular disease (1·18 [1·10–1·26], 1·39 [1·28–1·51], 2·02 [1·75–2·32]), and respiratory hospitalisation (1·39 [1·24–1·56], 2·02 [1·75–2·32], 2·97 [2·45–3·60]), and this pattern persisted in subgroup analyses considering country income level and various baseline risk factors. Population-attributable risk for mortality (adjusted for age, sex, and country income) from mildly to moderately reduced FEV 1 % (24·7% [22·2–27·2]) was larger than that from severely reduced FEV 1 % (3·7% [2·1–5·2]) and from tobacco use (19·7% [17·2–22·3]), previous cardiovascular disease (5·5% [4·5–6·5]), and hypertension (17·1% [14·6–19·6]). Population-attributable risk for cardiovascular disease from mildly to moderately reduced FEV 1 was 17·3% (14·8–19·7), second only to the contribution of hypertension (30·1% [27·6–32·5]). Interpretation: FEV 1 is an independent and generalisable predictor of mortality, cardiovascular disease, and respiratory hospitalisation, even across the clinically normal range (mild to moderate impairment). Funding: Population Health Research Institute, the Canadian Institutes of Health Research, Heart and Stroke Foundation of Ontario, Ontario Ministry of Health and Long-Term Care, AstraZeneca, Sanofi-Aventis, Boehringer Ingelheim, Servier, and GlaxoSmithKline, Novartis, and King Pharma. Additional funders are listed in the appendix. © 2019 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY-NC-ND 4.0 license
|
|
| 4. |
- Swart, Sebastiaan, 1983, et al.
(författare)
-
Constraining Southern ocean air-sea-ice fluxes through enhanced observations
- 2019
-
Ingår i: Frontiers in Marine Science. - : Frontiers Media SA. - 2296-7745. ; 6
-
Forskningsöversikt (refereegranskat)abstract
- © 2019 Swart, Gille, Delille, Josey, Mazloff, Newman, Thompson, Thomson, Ward, Du Plessis, Kent, Girton, Gregor, H, Hyder, Pezzi, De Souza, Tamsitt, Weller and Zappa. Air-sea and air-sea-ice fluxes in the Southern Ocean play a critical role in global climate through their impact on the overturning circulation and oceanic heat and carbon uptake. The challenging conditions in the Southern Ocean have led to sparse spatial and temporal coverage of observations. This has led to a 'knowledge gap' that increases uncertainty in atmosphere and ocean dynamics and boundary-layer thermodynamic processes, impeding improvements in weather and climate models. Improvements will require both process-based research to understand the mechanisms governing air-sea exchange and a significant expansion of the observing system. This will improve flux parameterizations and reduce uncertainty associated with bulk formulae and satellite observations. Improved estimates spanning the full Southern Ocean will need to take advantage of ships, surface moorings, and the growing capabilities of autonomous platforms with robust and miniaturized sensors. A key challenge is to identify observing system sampling requirements. This requires models, Observing System Simulation Experiments (OSSEs), and assessments of the specific spatial-temporal accuracy and resolution required for priority science and assessment of observational uncertainties of the mean state and direct flux measurements. Year-round, high-quality, quasi-continuous in situ flux measurements and observations of extreme events are needed to validate, improve and characterize uncertainties in blended reanalysis products and satellite data as well as to improve parameterizations. Building a robust observing system will require community consensus on observational methodologies, observational priorities, and effective strategies for data management and discovery.
|
|
