dataesr/scientific-paragraphs-categorization huggingface dataset:
In cold regions the precipitation uncertainty derived from the available datasets is small compared to the uncertainty of the multi-model simulations. This suggests that the model cold processes are an important factor in this multi-model disagreement. However, in these regions there are no satellite estimates and a limited number of rain gauges, which means that the current global datasets most probably underestimate the precipitation uncertainty in those regions. The ability of the multi-model ensemble to model total water storage dynamics at the scale of the GRACE data is generally good although models predict the peak in total water storage earlier in all regions. The fact that the phase difference is largest in the cold zones also indicates that there are difficulties in modelling the snow pack.
This is in line with the observation of  Beck et al. (2016a)  that the models tend to produce the snowmelt runoff peak too early. Getting SWE right is difficult for the models, and the results of the verification with the GlobSnow data strengthen the results of the SNR analysis (and the TWSA analysis), which point to cold regions as regions with low inter-model agreement. It is one of the few cases where the multi-model performance against the reference dataset is markedly lower than the performance of the best model. Table  7. Mean evaporation and runoff for the whole period compared to the change in storage of the total moisture component of each model. Mean precipitation for the whole period using the common land surface mask was 863 (kg m -2 yr -1 ). Surface water storage and storage in snow and glaciers is not taken into account.
Model Total evaporation Runoff (kg m -2 yr -1 ) storage (kg m -2 yr -1 ) (km 3 yr  Although the scores indicate a good performance of the ensemble, the evapotranspiration estimates are higher than those by the benchmark datasets. This, combined with the large spread within the ensemble itself, indicates that the ET estimates have a large uncertainly and further work is needed to improve the results. It also shows that in future versions of the dataset potential ET (PET) and net radiation should also be reported by all models as the choice of PET calculation method and net radiation estimate may be large contributors to the recorded spread in ET estimates. The current study shows a wide spread in runoff into the oceans derived from the set of models used. The large range stems from a combination of different total evaporation values and different storage dynamics in the models due to the different concepts and parameterization of runoff generation.
At the regional level the picture is less clear. This means that although the ensemble mean can be regarded as a best first estimate, a look at the regional results is required for basin-scale applications of the current results. Nevertheless, the above demonstrates that the ensemble mean of the present dataset could be used to evaluate water resources. ) for all models. The solid line represents the input precipitation minus runoff. For this line we used 863 kg m -2 yr -1 , which is the average calculated over all grid cells that have values for each model. The above shows a couple of areas of importance for further development of global models and datasets and the current set in particular: precipitation estimates in the tropics, cold weather processes and evapotranspiration losses. This does not mean that other processes are already properly represented in the global models and that the influence of these processes is not important or not reflected in the current results.
, 2010). We plan to combine this in a future version of the multi-model ensemble that includes uncertainty envelopes  (Nikolopoulos et al. , 2010)  and error estimates for runoff and other hydrological variables. One way of making the forcing data and model results more relevant for basin-scale studies is by including higherresolution model runs. Several of the models will be running at a higher resolution in a future set of runs and the common resolution will be increased to 0. 25 • resolution. In addition, the WaterWorld model (a web-based hydrological model based entirely on provided global datasets;  Mulligan, 2012)  will be run at 10 km resolution at the global scale and will form part of the continuing model inter-comparisons beyond this paper. A literature search reveals that the data we produced have already been used by several other researchers including a study investigating vegetation-atmosphere coupling  (Zscheischler et al.
reanalysis datasets (ERA-Interim: Dee et al. , 2011, JRA55: Kobayashi et al. , 2015, MERRA: Rienecker et al. , 2011), two datasets based on rain gauges (GPCC: Schneider et al. , 2011, CRU TS3. 10: Harris et al. , 2014a) and two datasets derived from remote sensing data (GPCP: Adler et al. , 2003, CMAP: Xie and Arkin, 1997).
The Supplement related to this article is available online at https://doi. org/10. 5194/essd-9-389-2017-supplement. Acknowledgements. This research received funding from the  European Union Seventh Framework Programme  ( FP7/2007-2013)  under grant agreement no. 603608 ,  "Global Earth Observation  for integrated water resource assessment": eartH2Observe. The National Snow and Ice Data Center is acknowledged for providing the IMS snow cover data. We thank  Tim Stockdale  for the suggestions on the use of the SNR analysis. Edited by: David Carlson Reviewed by: two anonymous referees.
The higher mappability of the P. cynomolgi blood stage and liver schizont RNA-seq libraries resulted in higher coverage with less than 200 genes having zero reads in all of these replicative stages combined (Figure  S2a ,b; Table  S2 ). In contrast, because of the lower number of reads mapping to the P. cynomolgi genome, we observed lower genome-wide coverage for the hypnozoite sample: more than 2,500 genes did not have any mapped reads in at least one of the two replicates (Figure  S2a ,b; Table  S2 ). Nonetheless, in all three stages, 50-60% of the reads mapped to annotated P. cynomolgi exonic regions with a significant percentage mapping to intergenic regions (Figure  S2c ). These regions likely correspond to meaningful, unannotated coding or noncoding transcripts that our dataset could help uncover.
In contrast, the top 25th percentile of the blood stage transcriptome was enriched for GO terms such as hemoglobin metabolism and digestive vacuole (Figure  2a ; see doughnut chart). Given that our blood stage datasets correlated to patient samples CM012 (63% asexual and 37% gametocyte stages) and CM013 (83% asexual stages and 11% gametocytes)   (Westenberger et al. , 2010)  with a Pearson correlation coefficient of. 5 (Figure  S4b ), our analysis supports the use of P. cynomolgi transcriptomic analysis to obtain insights into P. vivax biology. To further contrast the metabolic state of the dividing liver and blood stage forms of P. cynomolgi, we performed differential expression analysis using DESeq2  (Love, Huber, & Anders, 2014)  of 4,801 genes detected at ≥1 CPM in at least one of the two samples of each stage (Figure  S5a  and Table  S3 ).
| Laser capture microdissection P. cynomolgi-infected hepatocytes were microdissected at day 7 postinfection. The schizont forms were visualized using a modified Cresyl violet (Sigma) staining protocol and hypnozoites were visualized using a rapid immunostaining method optimized for RNA preservation (Figure  S1b ). See supplementary information for additional details. LCM was carried out using the PALM microlaser system (Zeiss) and the PALM-Robo software, which permits the selection, posterior cut and catapulting of regions of interest into a collection tube. 1, 29 or 69 schizont stage parasites and 45 or 59 hypnozoites were captured and pooled for RNA preparation as described below.
(c) The mean expression of genes in the schizont and blood stage datasets (x-axis) was compared to the log 2 (fold change) of gene expression in schizonts relative to the blood stages (y-axis) and represented as an MA-plot using the DESeq2 package. Genes with an FDR < 5% are represented with a blue dot and the names of those with a log 2 (fold change) >2 are shown.
ACKNOWLEDGMENTS This work was supported by an  ANR  grant "HypEpicC to D. M. , a  European Research Council   Advanced Grant  ( PlasmoSilencing 670301 ) awarded to A. S. , and the  French Parasitology consortium grant ParaFrap  ( ANR-11-LABX0024 ) to D. M. and A. S. S. S. V. was supported by the  Carnot-Pasteur-Maladies Infectieuse  fellowship.
(c) The mean expression of genes in the hypnozoite and schizont stage datasets (x-axis) was compared to the log 2 (fold change) of gene expression in hypnozoites relative to schizonts (y-axis) and represented as an MA-plot using DESeq2. Genes with an FDR < 5% are represented with a blue dot and the names of those with a log 2 (fold change) >2 are shown. Genes of interest are highlighted in red.
Phylogenetic Comparative Analysis Species Used. To test whether hosts of brood parasites have evolved different clutch sizes depending on the costs of brood parasitism, we collected a data set that included nonhosts and hosts of both highly virulent parasites (such as cuckoos and honeyguides) and nonvirulent parasites (such as Clamator cuckoos and cowbirds). We chose 104 Australian passerine species based on  Brooker and Brooker's (1989)  classification of (i) species that are unsuitable as hosts and (ii) biological hosts of eight species of Australian cuckoos (see appendix). We excluded introduced species and species recorded as occasional hosts because we cannot infer the extent of coevolution with cuckoos in these species. For American species (n p 89), we used the published data by  Soler (2014)  and  Friedmann and Kiff (1985)  of hosts and The range of parameters refers to values used to collect 10,000 examples per scenario, with a uniform distribution within the range unless otherwise specified.
Simulations were run in Matlab (MathWorks) such that one combination from the above distributions was used to compare nonhost fitness to hosts performing acceptance or rejection behaviors (as exemplified in fig. 2 ). The better host response-either accepting or rejecting-together with its associated clutch size was then compared to the nonhost clutch sizes to yield categories as described in the main text. Clutch Size and Brood Parasitism E115 nonhosts of cowbirds, and we excluded the rare host category, or those species that had less than 10% of their nests parasitized, because it was not possible to infer the extent of selection on these hosts. For analyses of southern African species (n p 635), we used the host listing for all brood parasites in Roberts Birds of Southern Africa  (Hockey et al.
2005). We included all species listed as hosts of honeyguides, cuckoos, and Vidua finches, as well as nonhost species, and excluded those listed as provisional hosts. This list includes nonpasserine hosts, which are parasitized by honeyguides (Indicatoridae), and some hosts that migrate to Europe or are also present in Europe (taken from Soler 2014). Data Collection. Information on the average clutch size for each species was obtained from the supplementary material from  Jetz et al. (2008). Clutch size varies with latitude, nest type, and body size  (Jetz et al. 2008). To control for potentially confounding effects of latitude, we used the R package dismo to download 2,000 random records per species from the Global Biodiversity Information Facility  (Hijmans et al. 2011 ) and calculated the median latitude of the records.
This value represents where the species is more often recorded and possibly where the clutch-size information will be most likely to belong. To control for confounding effects of nest type and body size, we obtained information from the Handbook of the Birds of the World online  (del Hoyo et al. 2014). Nest type was classified as 1 p open nest, 2 p domed nest, and 3 p cavity nester. The data set for this analysis and the R code have been archived in the Dryad Digital Repository: http://dx. doi. org/10. 5061/dryad. s20hm (Medina et al. 2017 ). 1 everal studies in brood parasitism have considered parasitism as a binary trait (e. g. , being either a host or nonhost), but the costs imposed by parasites may vary largely from host to host. First, we divided our analyses according to the virulence of the parasite (e.
Information on the parasite's body size was obtained from del  Hoyo et al. (2014). We repeated the analyses using a subset of the data set, where all nonhost species belonged to genera where hosts are present. This type of comparison reduced the number of species studied (n p 397) but ensured that the differences aside from parasitism status between hosts and nonhosts were as small as possible. Statistical Analyses. To correct for phylogenetic effects, we obtained 1,000 phylogenetic trees for each data set from www. birdtree. org (Jetz et al. 2012 ) and repeated the comparative analysis on each of these trees. These 1,000 trees are samples from a Bayesian estimate of the phylogeny of all birds and serve to represent our certainty and uncertainty about the relationships among the species in our analysis.
By repeating our analyses across all 1,000 trees, we ensure that our biological inferences do not rely on the assumption that a single phylogeny is correct. Rather, our inferences account for the fact that certain parts of the phylogeny of birds are known with more certainty than other parts. This has been shown to be preferable to using a single phylogeny in a wide range of situations  (Duchêne and Lanfear 2015; Hahn and Nakhleh 2015). We used phylogenetic generalized least squares (PGLS) regression models to account for phylogenetic relationships between species. We report the 95% highest posterior density intervals for P value and b estimate for each predictor (not to be confused with the b parameter in the model). Models were run using the Caper package in R  (Orme et al. 2012). We ran a model for each type of parasite virulence (e.
We also provide information on the phylogenetic signal of each model by reporting the value of the l statistic. This value represents to which extent phylogeny is driving the association between traits. When l p 1, it suggests that closely related species are evolving as expected by a Brownian motion model; hence, phylogeny is possibly driving the association we observe. Consequently, a value closer to zero would suggest that closely related species resemble each other less than expected by a Brownian motion model  (Blomberg et al. 2003). Additionally, we used the dredge function in the MuMIn R package  (Barton 2012)  to select the best model from the list of all possible models given the predictor variables, based on the corrected Akaike information criterion (AICc). We report which predictors were present most frequently in the best models when the analyses were repeated on each of the 1,000 phylogenetic trees.
Acknowledgments We would like to thank  R. Mulder  and  M. Hall  for comments on early versions of this work. We are also grateful to  Y. Michalakis ,  P. Taylor , and two anonymous reviewers for helpful comments and suggestions. H. K. would like to thank the  Swiss National Science Foundation  and the  Finnish Academy. N. E. L. would like to thank the  Australian Research Council  (grant  DP110101966 ).
Our focus is the impact of monetary shocks on price dispersion on the one hand and on the distribution and the signs of price variations on the other hand. We measure price dispersion using the coefficient of variation of the cross section of normalised prices denoted by v t in period t and 4 We generate a new scale-free network for each computational experiment using Stanford Network Analysis Project's Python (Snapy) library. The Snapy function which generates a directional scale-free network takes two parameters: number of nodes (N firms) and mean degree of the distribution (d). Snapy uses the Barabasi-Albert preferential attachment algorithm to generate a scale-free network  (Barabási & Albert 1999). The scale-free network generated by Snapy is non-directional. We transform it to a directional network with randomly assigning directions to links.
The model is developed in object oriented programming language Python. The code is available at bitbucket. org/VipinVeetil/networkeconomy. The code is modular and contains comments. The results of the agent-based computational experiments can be replicated using the model code (Lucius 2017). All computational experiments can be run, data generated, data processed, and plots created with the execution of a single line in the terminal after downloading the code from the bitbucket repository as described in the readMe_cantillon file in the repository.
Genomes and Annotated Sequences Most sequences were retrieved from the NCBI annotated databases (nr and EST, http://www. ncbi. nlm. nih. gov), using NCBI PHI-BLAST as well as BLAST and Annotation search tools available in the Geneious 9. 1. 6 software package (Biomatters, http://www. geneious. com/). For specific searches, additional genome browsers were used (see supplementary table S6, Supplementary Material online). Protein sequences derived from genomes lacking annotations were annotated by searching Pfam, CDD or SMART domain databases using the InterProScan tool, integrated in the Geneious software. The InterProScan tool is freely available on the InterPro resource (http://www. ebi. ac. uk/interpro/).
Sequence Alignments Amino acid sequences were aligned using MAFFT v7. 017, available in the Geneious 9. 1. 6 package  (Katoh et al. 2002). For human Dbl RhoGEFs, alignments were confirmed by using MUSCLE  (Edgar 2004 ) and Promals3D  (Pei et al. 2008) , (http:// prodata. swmed. edu/promals3d/promals3d. php), using the ARHGEF7 (PDB: 1by1) and ARHGEF3 (PDB:2z0q) X-ray 3D structures as guides. MSAs were manually edited for minor corrections and processed by BMGE (Block Mapping and Gathering with Entropy, Criscuolo and Gribaldo 2010) with a 0. 6 cut-off value.
MrBayes consensus trees were generated after two independent runs of four Markov chains for 1,100,000 generations sampled every 200 generations, with sampled trees from the first 100,000 generations discarded as "burnin". At the end of each run, average standard deviations of split frequencies were below 0. 01 and the estimated sample sizes (ESS) were above 200 for all sampled parameters. Minimum ESS values were 787. 01 (fig. 1A ),  247. 52 (fig. 3A), 313. 87 (fig. 3B), 298. 59 (fig. 3C), 347. 39 (fig. 3D), 428. 16 (fig. 6C left panel), and 591. 28 (fig. 6C right panel). For each analysis, 50% majority-rule consensus trees and associated clade PPs were computed from sample trees. Trees were visualized and exported as PDF files with FigTree (v1. 4. 2, http://tree. bio. ed. ac. uk/software/figtree/) then assembled in Adobe Illustrator.
Species and Tissue-Wise Gene Expression Analysis We used mRNA-seq data sets, generated from tissues of various species, as described in  (Brawand et al. 2011) and (Barbosa-Morais et al. 2012). As comparison of gene expression levels between species relied on orthology relationships, we established a RhoGEF orthology table (see supplementary table S5, Supplementary Material online) by using Ensembl orthologous gene data  (Flicek et al. 2014) , which we further refined by phylogenetic analysis. For each species, RhoGEF data were retrieved from global mRNA-seq data and expressed as Reads Per Kilobase of transcript per Million reads mapped (RPKM). RhoGEF mRNA-seq data were then clustered using Cluster 3. 0 (http://bonsai. hgc. jp/$mdehoon/software/cluster/; de  Hoon et al. 2004 ). Mean centered log 10 (RPKM) values were normalized and hierarchically clustered (Euclidian distance, complete method).
Acknowledgments We thank  J. Piette  and  P. Lemaire  for giving access to tunicate transcriptomic data and P. Labbefor help in R scripts. We also thank CRBM members for fruitful discussions and  Julian Venables  for help in writing the manuscript. This work was supported by institutional grants from the  Centre National de la Recherche Scientifique  and from  Montpellier Universite.
Data analysis Processing and analysis of RNA sequencing data from islets exposed ex vivo to hyperglycaemia Single-end reads (75 bp) were aligned to human genomic sequence (hg19 assembly) using TopHat and Bowtie2 (version 2. 0. 11 and version 2. 2. 1, respectively; http://ccb. jhu. edu/software. shtml) and using Samtools (version 0. 1. 19; http://samtools. sourceforge. net/) for sorting of alignment files. Read counts per gene were then generated using HTSeq (htseq-count version 0. 5. 4p3; https://htseq. readthedocs. io/) and Ensembl genome annotation 75 (http://feb2014. archive. ensembl. org/ index. html). Differentially expressed genes were detected using either DESeq2 (version 1. 15. 51)  or Limma (version 3. 30. 12) from the Bioconductor software package (https:// bioconductor. org/) in the R statistical programming environment (https://www.
r-project. org/). For DESeq2, single-end reads (75 bp) were aligned to human genomic sequence (hg38 assembly) using GSNAP (version 2017-03-17; http://research-pub. gene. com/gmap/), and Ensembl annotation 87 was used to detect reads spanning splice sites. The uniquely aligned reads were counted with featureCounts (version 1. 5. 2, Bioconductor) and the same Ensembl annotation. The raw counts were normalised based on the library size and testing for differential gene expression between the two conditions, samples treated with glucose vs control, was performed with the DESeq2 R package. For Limma, the raw count data were first filtered for an average of at least five reads in all the samples, normalised to library size using the weighted trimmed mean of M-values (TMM) method in edgeR (version 3.
Normalisation and statistical analysis of microarray data Transcriptomics data were normalised by Robust Multi-array Average (RMA) in Array Studio software (Omicsoft, Cary, NC, USA). Batch correction of microarray data was performed using the Bioconductor package ComBat in R. Elimination of technical outlier samples was performed at two steps of the transcriptomics analysis. The criterion for expression was an intensity value of >75% for ≥25% of the samples in that group. Subsequently, islet samples from organ donors with no previous history of diabetes but with blood fructosamine >285 μmol/l or glucose >11. 1 mmol/l were excluded from the analysis. Islet samples from non-diabetic/type 2 diabetic OD with insulin levels <1 SD from the withingroup mean were also excluded. For comparisons between type 2 diabetic and non-diabetic OD islets, significant differences were defined as a change in expression of ≥1.
5 after correction for multiple hypothesis testing using the Benjamini-Hochberg method (p ≤ 0. 05). Principal component analysis was performed using the prcomp in R. The analysis of all islet sample types and of a single sample type (islets from OD or PPP) was based on the intensities of the probe sets after batch correction. Contamination of islet samples with exocrine pancreatic tissue was determined using selected markers of exocrine and ductal cells, as indicated in ESM Table  5. Bioinformatics The bioinformatics analyses performed with Ingenuity Pathway Analysis software (Qiagen, Hilden, Germany) comprised gene ontology over-representation analysis of differentially expressed genes; determination of enriched genes involved in insulin secretion; and pathway analysis of differentially expressed genes.