dataesr/scientific-paragraphs-categorization huggingface dataset:
Monsonia species exhibit two dispersal syndromes (wind and trypanocarpic) that have favoured the colonization of different types of habitats (Figs 6 and S7, Tables  S6  and S7 ). These syndromes are associated with different dispersal abilities and related to distinct ecological niche patterns. Tripanocarpy is more common in semi-arid or wet habitats, permitting the seeds germinate near the parent plants, while anemochory is advantageous in arid habitats by favouring long distance dispersals  42. Although BayesTraits reconstruction of the fruit type suggests that trypanocarpic is the ancestral state of Monsonia (PP = 0. 630), this software attributes some probability to it being anemochorous (PP = 0. 370). Contrarily, Maximum likelihood analyses in Mesquite, recovered the ancestral state as anemochorous, although with poor confidence.
Methods Study group and taxon sampling. To clarify phylogenetic relationships within Monsonia, 34 species out of a total of 39 were sampled (Table  S8 ), representing 87. 2% of all species currently included in the genus. We included representatives of all other genera of Geraniaceae as outgroups, and Hypseocharis (Hypseocharitaceae) as a more distant outgroup  [45] [46] [47]. We compiled an extensive dataset of distribution records, from the entire distributional range of Monsonia using data from literature, herbarium specimens, and our own fieldwork (Table  S8 ).
DNA sequencing. Total DNA was extracted from silica gel-dried plant tissue using the 'DNeasy Plant Mini Kit' (QIAGEN Inc. , California, USA) according to manufacturer's instructions. To reconstruct phylogenetic relationships within Monsonia, four variable cpDNA regions were sequenced: trnL-trnF, trnS-trnG, matK5F-C2MR, and rbcL1F-rbcL724R producing a total of 107 new sequences (Table  S8 ). We also sequenced the nuclear ribosomal marker ITS, producing a total of 11 new sequences. For details on PCR amplification and sequence alignment see Supplementary Information and Table  S9. Sources of the material examined, location of vouchers, GenBank accession numbers, and full references are listed in Table  S7. Phylogenetic dating, diversification analyses, and biogeographic inference. Phylogenetic relationships and divergence times were estimated with relaxed molecular clocks implemented in the software BEAST v.
1. 7. 5  25. Based on the dating analyses of Sytsma et al. 47  , we used two secondary age constraints to calibrate: a) the split between Hypseocharis bilobata and the other taxa (i. e. , the root node), and b) the split between Pelargonium and Monsonia. We used normal prior distributions representing the mean and 95% high posterior density (HPD) credibility intervals: (48 ± 2. 5 Ma, and 36 ± 3. 0 Ma, respectively) to account for the uncertainty in the original estimates. To identify shifts in diversification rate in the genus, we used BAMM v. 2. 2. 0 (Bayesian Analysis of Macroevolutionary Mixtures)  48. We accounted for incomplete taxon sampling in our dataset by providing global sampling proportions detailing a general proportion of missing taxa of 0. 1, and used a conservative 0. 1 value for the Compound Process Prior.
Our input tree was the MCC chronogram generated from BEAST. This method has been criticized recently for incorrect modelling of rate shifts at unobserved/extinct lineages and improper priors, so results should be taken with caution. Spatio-temporal evolution was inferred using the Dispersal-Extinction-Cladogenesis model (DEC) 49 implemented in the R package BioGeoBEARS 50. We defined four geographic areas as operational units: (1 + 2) Namib-Succulent Karoo plus Cape Floristic Region; (3) Natal-Drakensberg (southeastern Africa); (4) East Africa; (5) Sahara Desert + Arabian Peninsula (Fig. 2 ). Species distribution modelling, niche quantification and climatic niche evolution. Species distribution modelling (SDM) was performed to estimate the potential distributions of Monsonia under current climatic conditions.
The occurrence dataset included 1118 localities of 34 species of Monsonia, compiled from herbarium specimens and our own collections (Table  S8 ). In order to explore the bias that may be caused by the presence of point endemics beside widespread species with large niche breadths, we followed two approaches: 1) we first analysed the 1118 records covering the entire range of the 34 species, and second, and 2) we analysed only the 800 records of the 24 South African species. Then, we used 20 eco-climatological variables in 30 arc-seconds, which were clipped i) for the whole distribution area of Monsonia, or ii) for species distributed only in South Africa (the detailed data is shown in the supporting information). To assess the role of niche conservatism versus niche evolution and determine the range breadths we tested niche similarity between all pairs of species using the metric D 51 as implemented in the program ENMtools v.
1. 0  52  , and a measure derived from the Hellinger distance called I  53. We included the identity test I to determine if the distribution models of two species differ in their niche by pooling the locality data for both species and sampling randomly from the pooled occurrences to create pseudoreplicate datasets (100 pseudoreplicates in our analyses) of equal size. The niche breadth was measured using the 'inverse concentration' metric in ENMTools. We assessed the evolution of the species' climatic variables through ancestral state reconstruction methods implemented in the R package phytools v. 0. 3-93  54. The MCC tree obtained from the concatenate dataset was used to study the evolution of the climatic niche with four variables: the annual mean temperature, the annual mean precipitation, the aridity index and the highest weekly moisture index.
We chose these variables or climatic traits because they retained an important part of the variance in the principal component analyses (PCAs). We calculated the mean value for each of the four variables per species and conducted maximal likelihood (ML) ancestral state inference with four models of continuous trait evolution: BM (Brownian model, random drift), OU, (Ornstein-Uhlenbeck model, a selective-adaptive model), White Noise (lack of phylogenetic signal), and Early Burst (deceleration of BM variance). To infer the evolution of the occupation of habitat types, we also used the aridity index (aridity index = mean annual precipitation/mean annual potential evapotranspiration, data and methods available at: http://www. cgiar-csi. org/data/global-aridity-and-pet-database). Models were compared by using the corrected Akaike information Criterion (AIC c ) for small sample sizes, as implemented in the R package geiger v.
We implemented this analysis with the package geiger because it allows the possibility of choosing the evolution model (for instance BM or OU). Photosynthetic metabolism: carbon isotope ratios (δ 13 C). To detect evidence for carbon concentrating mechanisms, we conducted a broad screening of carbon isotope ratios (δ 13 C) in Monsonia. The two major carbon fixing enzymes RuBP carboxylase and PEP carboxylase fractionate the stable isotopes of carbon, 12 and 13, differently  63  , resulting in ranges of to -22‰ to -33‰ 64 are typical of C 3 species and -9‰ to -20‰ of C 4 species and obligate CAM species  65. Intermediate δ 13 C values (-18‰ to -22‰) might indicate a significant proportion CO 2 fixation at night  65  or simply long term stomatal closure  27. The carbon isotope ratio of ca. 2 mg samples from adult leaves was measured using isotope ratio mass spectrometry  63.
Acknowledgements This research was supported by the  Spanish government  through projects  CGL2009-13322-C03-01 ,  CGL2012-40129-C02-01  and  CGL2015-67849-P  ( MINECO/FEDER)  to  I. Sanmartín , and  CGL2009-10031  to  P. Vargas  and GREB. We would like to thank  F. Albers ,  K. Müller  and  J. Röschenbleck  ( University of Münster ),  Kirstenboch Botanical Garden (RSA) , and the herbaria  COI, G, LE, MA, MJG, MO, MSUN, P, PRE, STEU, W and WU  for providing material of various species of Monsonia, as well as to  L. Sáez ,  B. Estébanez ,  A. Herrero ,  S. Nisa  for providing us with Geraniaceae samples. For laboratory assistance, we thank  E. Cano  and  F. Durán  ( RJB-CSIC ). We are especially grateful to  J. L. García  for his photographies and help in the Namibian and South African sampling trip.
The ade4 package provides tools for multivariate analyses. Whereas new statistical methods have been added regularly in the package since its first release in 2002, the graphical functions, that are used to display the main outputs of an analysis, have not benefited from such enhancements. In this context, the adegraphics package, available on CRAN since 2015, is a complete reimplementation of the ade4 graphical functionalities but with large improvements. The package uses the S4 object system (each graph is an object) and is based on the graphical framework provided by lattice and grid. We give a brief description of the package and illustrate some important functionalities to build elegant graphs.
> library(ade4) > library(adegraphics) The adegraphics package can be installed from CRAN. A development release is available on GitHub (https://github. com/sdray/adegraphics) and a detailled description of the package is given in the vignette available at https://cran. r-project. org/web/packages/adegraphics/vignettes/ adegraphics. html or in any R session by typing vignette("adegraphics"). A reproducible version of the code in this paper is available online at http://lbbe-shiny. univ-lyon1. fr/Reproducible_.
Introduction In many fields, data consists in tables containing measurements of several variables for a number of samples. In this context, multivariate analyses provide tools to summarize the main structures of multivariate data. After a dimension reduction step, these methods provide a graphical display of the primary relationships between variables and similarities between samples. Multivariate methods are routinely used in various fields including marketing, psychometry or ecology and have been implemented in several R packages (e. g. vegan  (Oksanen et al. , 2017) , MASS  (Venables and Ripley, 2002) , FactoMineR  (Lê et al. , 2008) , see the Multivariate Task View of CRAN). The ade4 package  (Dray and Dufour, 2007)  is an alternative, distributed on CRAN since 2002, that implements more than 30 different methods, mainly oriented to the analysis of ecological data, and around 40 graphical functions to display the results of analyses.
For users, adegraphics provides a flexible environment to produce, edit and easily manipulate graphs. To ensure continuity with the graphical functions of ade4, functions and parameters names have been largely preserved in adegraphics, so that regular users can easily migrate to the new package. Moreover, the graphical functions of ade4 are not removed to preserve the functioning of other R packages and scripts that depend on ade4. The use of the new adegraphics functions is preferred and encouraged for future developments. Keeping the old version of the functions in ade4 has some practical effects. In a classical case, when multivariate analyses are performed with ade4, it is imperative to load ade4 before adegraphics. This solves the issues of conflicting names between functions by ensuring that the versions from adegraphics will be used.
A simple example We illustrate some principles and functionalities of adegraphics by considering the analysis of the mafragh data set available in ade4. It contains a phyto-ecological survey collected in an Algerian plain, called La Mafragh  (de Bélair and Bencheikh-Lehocine, 1987; Pavoine et al. , 2011). > data("mafragh") > names(mafragh) [1] "xy" "flo" "neig" "env" [5] "partition" "area" "tre" "traits" [9] "nb" "Spatial" "spenames" "Spatial. contour" The aim of the original study  (de Bélair and Bencheikh-Lehocine, 1987)  was to propose an integrated management project of the Mafragh marshy coastal plain. mafragh$flo is a data frame giving the abundance of 56 plant species (columns) in 97 sites (rows) distributed in the plain. mafragh$env is a data frame with 97 rows and 11 columns describing the physico-chemical and topographic characteristics, and the granulometry of soil samples taken at the same sampling sites.
Representation of spatial information In ade4, classes were implemented to manage spatial information: the "area" class for geographic maps and the "neig" class to store spatial neighborhood objects. The adegraphics package abandoned The R Journal Vol. 9/2, December 2017 ISSN 2073-4859 these outdated implementations and adopted more efficient classes. Maps are now considered as objects inherited from classes of the sp package  (Pebesma and Bivand, 2005; Bivand et al. , 2013b). "nb" and "listw" objects, from the spdep package  (Bivand and Piras, 2015; Bivand et al. , 2013a) , are used to manage spatial neighborhood graphs. Note that only "ADEg. S2" objects (bidimensional plot of xy coordinates, implemented in s. * functions) of adegraphics support the representation of spatial objects by the use of arguments Sp and nb.
contour) [1] "SpatialPolygons" attr(,"package") [1] "sp" > library(RColorBrewer) > valuecolors <-rev(brewer. pal(6, "RdBu")) > g_sv1 <-s. value(mafragh$xy, z = pca1$li[, 1], Sp = mafragh$Spatial. contour, method = "color", symbol = "circle", col = valuecolors, pgrid. draw = FALSE, ppoints. cex = 0. 4) A similar graph (called g_sp1) can easily be built (Figure  3 , right) using the s. Spatial function and a "SpatialPolygonsDataFrame" object (from the sp package). The spobj object couples a "SpatialPolygons" object with the scores of sites on the first PCA axis. > library(sp) > spobj <-SpatialPolygonsDataFrame(Sr = mafragh$Spatial, data = pca1$li[, 1, drop = FALSE], match. ID = FALSE) > class(spobj) [1] "SpatialPolygonsDataFrame" attr(,"package") [1] "sp" > g_sp1 <-s. Spatial(spobj, col = valuecolors) The R Journal Vol.
Automatic collections Several user-friendly functionalities are implemented in adegraphics to facilitate the creation of collections of graphs, repeating (without using a for-loop command) a simple graph for different groups of individuals, variables or axes. The building of these collections is very simple for the user (definition of an argument in the call of a function) and leads to the creation of an "ADEgS" object. For instance, as in the ggplot2 package  (Wickham, 2016) , a facets argument is available to split a dataset into groups of individuals and to produce one graph per group. The g_sv2 object (Figure  4 ) displays the scores of sites of the first PCA axis (as in the g_sv1 object, left of Figure  3 ) for each of the 7 ecoregions, called C1,. ,C7, defined in magragh$partition. > g_sv2 <-s.
The g_sv2 object is created by only a call to the s. value function. Seven thematic maps (one for each ecoregion) are displayed to facilitate the read of PCA results. These 7 ecoregions were defined by de Bélair and Bencheikh-Lehocine (1987) from a correspondence analysis of the plant species data table (mafragh$flo) and phytosociological interpretations. Each ecoregion takes into account the distribution of plant species of various floristic groups, but also pedological characteristics of sampling sites, and geographical areas. For example, ecoregion C1 is defined by 6 pedological sampling sites (numbered 43, 44, 45, 46, 49 and 51 in g_sv2$C1@data$dfxy) and the Bolboschoenus maritimus and Schoenoplectus litoralis plant communities (high abundances in mafragh$flo  [c(43, 44, 45, 46, 49, 51 ), ]).
> class(g_sv2) [1] "ADEgS" attr(,"package") [1] "adegraphics" > names(g_sv2) [1] "C1" "C2" "C3" "C4" "C5" "C6" "C7" > class(g_sv2$C1) [1] "S2. value" attr(,"package") [1] "adegraphics" On the other hand, multiple graphs can also be produced by splitting graphs by columns. This is achieved when a data frame with several variables is given as an argument to a function that usually requires a vector. For instance, the abundance of four focused species is easily displayed along the mafragh sites. The g_sv3 object (Figure  5 ) is created with the s. value function, using the mafragh$flo[, selectedspecies] data frame as z parameter. A line surrounding the entire mafragh region (contained in the mafragh$Spatial. contour object) is added thanks to the Sp argument. > selectedspecies  <-c(9, 12, 31, 34)  > floselectedspecies <-mafragh$flo[, selectedspecies] The R Journal Vol.
Boof Plco Figure  5 : Automatic creation of an "ADEgS" object. The g_sv3 object is created by only a call to the s. value function using a multivariate argument z. Four maps of abundance are produced, one for each of four species of interest: Juncus maritimus (Juma), Schoenoplectus litoralis (Scli), Borago officinalis (Boof) and Plantago coronopus (Plco). Step-by-step creation "ADEgS" objects can also be created by the manipulation of several simple graphs. Several functions in adegraphics allow superposition, combination or insertion of two objects: superpose, +, cbindADEg, rbindADEg and insert. Following our example, a Correspondence Analysis (CA, called coa1) is performed on the abundance of the 56 plant species of mafragh (contained in the mafragh$flo data table) with the dudi. coa function of the ade4 package, displayed but not shown with the s.
A complete delineation of miRNA and their target expression during normal PCD is essential to understand the role of miRNAs in plasma cell malignancies. We analyzed the expression profile of miRNAs and mR-NAs during human PCD to infer miRNA-target relationships, as well as in multiple myeloma tumor plasma cells. We developed a method and an R package (miRTarget, https:// github. com/kassambara/miRTarget), that uses miRNA and mRNA expression profiles across PCD cell subpopulations to infer candidate miRNA-target interactions that could be active and functional in PCD. We inferred miRNAtarget relationships from sequence-based prediction, experimentally validated target interactions curated from the literature, published data from miRNA perturbation experiments and inverse expression relationships between miR-NAs and their target mRNAs.
A filtering step was included to select only actively expressed features. Differential expression analysis was performed using the 'limma' R package to identify significant differentially expressed miRNAs during plasma cell differentiation. Predicted and validated miRNA targets were collected from miRecords and miRTarbase databases. Co-expression analysis and identification of potential targets were performed directly on the paired miRNA-mRNA data. For a given miRNA, we kept only targeted genes that are differentially expressed during plasma cell differentiation and which expression is negatively correlated to the miRNA. Functional and pathway analysis of miRNA-targeted genes were performed using the clusterProfiler R package  (35)  and the MSigDB (34) gene set collections.
Cell populations and miRNA/mRNA expression profiling Preplasmablasts (prePBs), plasmablasts (PBs), and plasma cells (PCs) were generated using a 3-step in vitro model starting from peripheral blood memory B cells (MBCs) as reported  (2, 27). We performed three experiments starting from purified memory B cells of three different healthy donors. miRNA and mRNA expression profiling were obtained using Affymetrix microarrays as described in the Supplementary Data. To identify miRNAs associated with multiple myeloma (MM), we have analyzed publicly available miRNA expression data of purified normal bone marrow plasma cells (BMPCs) from healthy donors (n = 3), purified MMCs from newly diagnosed multiple myeloma patients (n = 62) and of human myeloma cell lines (HM-CLs, n = 20) representative of the molecular heterogeneity of the patients (28) (ArrayExpress accession number: E-MTAB-1363)  (25).
Microarray data analysis The workflow of data processing and analysis is outlined in Figure  1. All statistical analyses and data visualization were performed with the R/Bioconductor statistic software packages as described in the Supplementary Data. The miRNA expression data were normalized with Affymetrix miRNA QC tool using the default settings. Affymetrix U133P2 mRNA expression data were preprocessed using RMA algorithm  (29). To decrease the number of uninformative features, we used a filtering step: features, for which the mean expression, in at least one subgroup, did not reach a signal intensity of 6 in a log2 scale, were removed (Supplementary Figure  S2 ). Differential expression analysis of miRNA and mRNA data sets were performed using the limma R/Bioconductor package  (30)  (see Supplementary Data).
Inferring putative miRNA targets using the miRTarget R package We developed the miRTarget R package to automatize and facilitate the procedure of inferring and validating miRNA-mRNA regulatory relationships. miRTarget includes a procedure to easily identify the predicted and validated mRNA targets given one or more miRNAs as input (https: //github. com/kassambara/miRTarget). This is performed based on the union of two popular databases, miRTarBase (a database of experimentally validated miRNA targets, http://mirtarbase. mbc. nctu. edu. tw/, release 6. 0)  (32)  and miRecords (http://c1. accurascience. com/miRecords/, version: 27 April 2013)  (33). The miRecords database includes validated miRNA targets and also an integrated sequencebased miRNA target prediction resource from 11 popular miRNA target prediction programs (diana, microinspector, miranda, mirtarget2, mitarget, nbmirtar, pictar, pita, rna22, rnahybrid, targetscan).
We retained only experimentally validated target genes (in either miRecords and/or miRTarbase databases) as well as those genes predicted by at least 5 of 11 available miRNA target prediction programs (in the miRecords database). The optimal number of target prediction programs can be adjusted in the miR-Target package and the rationale of choosing 5 programs is illustrated in the Supplementary Figure  S1. Of the potential miRNA-target genes, only differentially expressed genes between studied cell subpopulations are kept for further analyses (limma R package, adjusted P-value for multiple testing correction ≤ 5%, fold change ≥ 2). To obtain a high-confidence list of candidate miRNA-target interactions, miRTarget package provides a convenient R function to perform correlation analysis between the expression values of each miRNA and the expression values of its deregulated target genes.
Canonical pathway enrichment analysis Canonical pathway enrichment analysis were performed using the Molecular Signatures Database (MSigDB, v5)  (34)  gene set collections and the clusterProfiler R/Bioconductor package  (35)  as described in the Supplementary Data. Quantitative RT-PCR for miRNA/mRNA expression validation miRNA and mRNA expressions were measured by quantitative RT-PCR as described in the Supplementary Data. The miRNA expression was normalized to endogenous RNU6B levels using the Ct method. The mRNA expression was normalized to beta-2 microglobulin expression. All measurements were performed in triplicates following manufacturer's protocol. The primer references are in the Supplementary Table  S1.
Inferring miRNA targets by prediction and integrative analysis of miRNA and mRNA expression We developed the miRTarget R package to facilitate the procedure of inferring and validating miRNA-mRNA regulatory relationships (https://github. com/kassambara/ miRTarget, see Materials and Methods). It uses the union of two popular databases, miRTarBase  (32) , a database of experimentally validated miRNA targets, and miRecords  (33). The miRecords database includes validated miRNA targets and also an integrated sequence-based miRNA target prediction resource from 11 popular miRNA target prediction programs. We retained only experimentally-validated target genes and/or those genes predicted by at least 5 of 11 available miRNA target prediction programs. A total list of 11 972 unique microRNA-target interactions were identified for the 63 miRNAs, involving 4802 unique genes.