| 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. |
- Flannick, Jason, et al.
(author)
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Data Descriptor : Sequence data and association statistics from 12,940 type 2 diabetes cases and controls
- 2017
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In: Scientific Data. - : Springer Science and Business Media LLC. - 2052-4463. ; 4
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Journal article (peer-reviewed)abstract
- To investigate the genetic basis of type 2 diabetes (T2D) to high resolution, the GoT2D and T2D-GENES consortia catalogued variation from whole-genome sequencing of 2,657 European individuals and exome sequencing of 12,940 individuals of multiple ancestries. Over 27M SNPs, indels, and structural variants were identified, including 99% of low-frequency (minor allele frequency [MAF] 0.1-5%) non-coding variants in the whole-genome sequenced individuals and 99.7% of low-frequency coding variants in the whole-exome sequenced individuals. Each variant was tested for association with T2D in the sequenced individuals, and, to increase power, most were tested in larger numbers of individuals (> 80% of low-frequency coding variants in similar to ~82 K Europeans via the exome chip, and similar to ~90% of low-frequency non-coding variants in similar to ~44 K Europeans via genotype imputation). The variants, genotypes, and association statistics from these analyses provide the largest reference to date of human genetic information relevant to T2D, for use in activities such as T2D-focused genotype imputation, functional characterization of variants or genes, and other novel analyses to detect associations between sequence variation and T2D.
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| 3. |
- Sellberg, Jonas A., et al.
(author)
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How Cubic Can Ice Be?
- 2017
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In: The Journal of Physical Chemistry Letters. - : American Chemical Society (ACS). - 1948-7185. ; 8:14, s. 3216-3222
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Journal article (peer-reviewed)abstract
- Using an X-ray laser, we investigated the crystal structure of ice formed by homogeneous ice nucleation in deeply supercooled water nanodrops (r approximate to 10 nm) at similar to 225 K The nanodrops were formed by condensation of vapor in a supersonic nozzle, and the ice was probed within 100 mu s of freezing using femtosecond wide-angle X-ray scattering at the Linac Coherent Light Source free-electron X-ray laser. The X-ray diffraction spectra indicate that this ice has a metastable, predominantly cubic structure; the shape of the first ice diffraction peak suggests stacking-disordered ice with a cubicity value, chi, in the range of 0.78 +/- 0.05. The cubicity value determined here is higher than those determined in experiments with micron-sized drops but comparable to those found in molecular dynamics simulations. The high cubicity is most likely caused by the extremely low freezing temperatures and by the rapid freezing, which occurs on a similar to 1 mu s time scale in single nanodroplets.
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| 4. |
- Williams, Matt N., et al.
(author)
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Using Bayes Factors to Test Hypotheses in Developmental Research
- 2017
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In: Research in Human Development. - : Informa UK Limited. - 1542-7609 .- 1542-7617. ; 14:4, s. 321-337
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Journal article (peer-reviewed)abstract
- This article discusses the concept of Bayes factors as inferential tools that can serve as an alternative to null hypothesis significance testing in the day-to-day work of developmental researchers. A Bayes factor indicates the degree to which data observed should increase (or decrease) the credibility of one hypothesis in comparison to another. Bayes factor analyses can be used to compare many types of models but are particularly helpful when comparing a point null hypothesis to a directional or nondirectional alternative hypothesis. A key advantage of this approach is that a Bayes factor analysis makes it clear when a set of observed data is more consistent with the null hypothesis than the alternative. Bayes factor alternatives to common tests used by developmental psychologists are available in easy-to-use software. However, we note that analysis using Bayes factors is a less general approach than Bayesian estimation/modeling, and is not the right tool for every research question.
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