| 1. |
- Hudson, Lawrence N, et al.
(author)
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The database of the PREDICTS (Projecting Responses of Ecological Diversity In Changing Terrestrial Systems) project
- 2017
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In: Ecology and Evolution. - : John Wiley & Sons. - 2045-7758. ; 7:1, s. 145-188
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Journal article (peer-reviewed)abstract
- The PREDICTS project-Projecting Responses of Ecological Diversity In Changing Terrestrial Systems (www.predicts.org.uk)-has collated from published studies a large, reasonably representative database of comparable samples of biodiversity from multiple sites that differ in the nature or intensity of human impacts relating to land use. We have used this evidence base to develop global and regional statistical models of how local biodiversity responds to these measures. We describe and make freely available this 2016 release of the database, containing more than 3.2 million records sampled at over 26,000 locations and representing over 47,000 species. We outline how the database can help in answering a range of questions in ecology and conservation biology. To our knowledge, this is the largest and most geographically and taxonomically representative database of spatial comparisons of biodiversity that has been collated to date; it will be useful to researchers and international efforts wishing to model and understand the global status of biodiversity.
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| 2. |
- Wang, Li-San, et al.
(author)
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Rarity of the Alzheimer Disease-Protective APP A673T Variant in the United States.
- 2015
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In: JAMA neurology. - : American Medical Association (AMA). - 2168-6157 .- 2168-6149. ; 72:2
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Journal article (peer-reviewed)abstract
- Recently, a rare variant in the amyloid precursor protein gene (APP) was described in a population from Iceland. This variant, in which alanine is replaced by threonine at position 673 (A673T), appears to protect against late-onset Alzheimer disease (AD). We evaluated the frequency of this variant in AD cases and cognitively normal controls to determine whether this variant will significantly contribute to risk assessment in individuals in the United States.
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| 3. |
- Ainsworth, Elizabeth A., et al.
(author)
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Next generation of elevated [CO2] experiments with crops: a critical investment for feeding the future world
- 2008
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In: Plant, Cell and Environment. - : Wiley. - 0140-7791 .- 1365-3040. ; 31:9, s. 1317-1324
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Journal article (peer-reviewed)abstract
- A rising global population and demand for protein-rich diets are increasing pressure to maximize agricultural productivity. Rising atmospheric [CO2] is altering global temperature and precipitation patterns, which challenges agricultural productivity. While rising [CO2] provides a unique opportunity to increase the productivity of C-3 crops, average yield stimulation observed to date is well below potential gains. Thus, there is room for improving productivity. However, only a fraction of available germplasm of crops has been tested for CO2 responsiveness. Yield is a complex phenotypic trait determined by the interactions of a genotype with the environment. Selection of promising genotypes and characterization of response mechanisms will only be effective if crop improvement and systems biology approaches are closely linked to production environments, that is, on the farm within major growing regions. Free air CO2 enrichment (FACE) experiments can provide the platform upon which to conduct genetic screening and elucidate the inheritance and mechanisms that underlie genotypic differences in productivity under elevated [CO2]. We propose a new generation of large-scale, low-cost per unit area FACE experiments to identify the most CO2-responsive genotypes and provide starting lines for future breeding programmes. This is necessary if we are to realize the potential for yield gains in the future.
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| 5. |
- Zanini, Fabio, et al.
(author)
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Northstar enables automatic classification of known and novel cell types from tumor samples
- 2020
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In: Scientific Reports. - : NATURE RESEARCH. - 2045-2322. ; 10:1
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Journal article (peer-reviewed)abstract
- Single cell transcriptomics is revolutionising our understanding of tissue and disease heterogeneity, yet cell type identification remains a partially manual task. Published algorithms for automatic cell annotation are limited to known cell types and fail to capture novel populations, especially cancer cells. We developed northstar, a computational approach to classify thousands of cells based on published data within seconds while simultaneously identifying and highlighting new cell states such as malignancies. We tested northstar on data from glioblastoma, melanoma, and seven different healthy tissues and obtained high accuracy and robustness. We collected eleven pancreatic tumors and identified three shared and five private neoplastic cell populations, offering insight into the origins of neuroendocrine and exocrine tumors. Northstar is a useful tool to assign known and novel cell type and states in the age of cell atlases.
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