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Nexus file for phylogenetic analysis. (ZIP 7 kb)

Additional file 3: Table S2. Summary of the Illumina sequencing data.

Additional file 19: Supplementary Movie 12. Description: Time-lapse imaging of GFP-0N4R reporter cells seeded with S1 brain fractions including pathogenic tau derived from aged TgTauP301L mice with neurological signs. Images were obtained for 16 hours (10 min/frame for 96 frames). Scale bars, 10 μm.

This dataset contains key characteristics about the data described in the Data Descriptor Geolocation of unpublished archaeological sites in the Peruvian Amazon. Contents: 1. human readable metadata summary table in CSV format 2. machine readable metadata file in JSON format

A, Western blot showing AMY3 transfected HEK293 cells demonstrate a marked increase in level of expression of CTR and RAMP3 proteins compared to wild-type (WT) HEK cells. B, in BV2 cells, RAMP3 protein expression shows a marked decreased after RAMP3 siRNA transfection compared to the control non-transfected cells. (JPEG 1495 kb)

Gene Ontology (GO) process category enrichments for the NQP and prion prediction data sets from human (the same sets that are analyzed in Tables 1, 2, 3 and 4). These are derived using the website GOrilla [52]. (TXT 3 kb)

Cox models are commonly used in the analysis of time to event data. One advantage of Cox models is the ability to include time-varying covariates, often a binary covariate that codes for the occurrence of an event that affects an individual subject. A common assumption in this case is that the effect of the event on the outcome of interest is constant and permanent for each subject. In this paper we propose a modification to the Cox model to allow the influence of an event to exponentially decay over time. Methods for generating data using the inverse cumulative density function for the proposed model are developed. Likelihood ratio tests and AIC are investigated as methods for comparing the proposed model to the commonly used permanent exposure model. A more general model proposed by Cox and Oakes [1] is also discussed. A simulation study is performed and three different data sets are presented as examples.

Complete set of 739 unique cyclodipeptide synthases identified in a global analysis of publicly available prokaryotic genomes. (XLSX 144Â kb)

Additional file 4: Figure S2. Evolutionary trajectories and apparent selection of all mutation hotspots identified by population sequencing. Mutations arose either in the MATα (left) or MATa (right) as indicated immediately to the left of each mutation. On the vertical axis are the names of the mutations, giving the closest gene, coordinates with respect to that gene and the nature of the nucleotide substitution. On the horizontal axis are each of the six evolutionary time points (UV, R1, R2, R3, R4, R5) and the mean allele frequency change (M). Frequencies of the mutant alleles are represented by shades of green. Mean allele frequency changes are represented in shades of red (M 1, increasing frequency). Mutations linked by connectors and marked with an asterisk indicate pairs with significantly similar initial frequency (binomial test, p > 0.05).

Recent studies have reported beneficial carryover effects of juvenile development that predict interspecific survival differences at independence. Yet, traits relating to body size (i.e. morphological traits) have proven to be unreliable predictors of juvenile survival within species. Exploring individual variation of growth trajectories and how they covary with physiology could reveal species-specific developmental modes which have implications for our assessments of juvenile quality. Here, we investigated morphological development of European starlings (Sturnus vulgaris) approaching fledging in relation to three components of physiological condition at independence: aerobic capacity, energy state and oxidative status. We found evidence of flexible mass and wing growth which independently covaried with fledgling energy state and aerobic capacity, respectively. By comparison, tarsus and wing length at fledging were unrelated to any physiological trait, while mass was positively associated with principal component scores that comprised aerobic capacity and energy state. Thus, flexible growth trajectories were consistent with ‘developmental plasticity’: adaptive pre-fledging mass recession and compensatory wing growth, which seemingly came at a physiological cost, while fledgling body mass positively reflected overall physiological condition. This highlights how patterns of growth and absolute size may differently reflect fledgling physiology, potentially leading to variable relationships between morphological traits and juvenile fitness.