dataesr/scientific-paragraphs-categorization huggingface dataset:
949 to 9. 049 keV with a resolution of 0. 5 eV. We averaged three consecutive spectra in ATHENA software to increase signal to noise ratio. 47 TR-FTIR. ReactIR15 with a 6. 5 mm AgX DiComp Fiber Conduit probe was used for in situ ATR-FTIR measurements. The spectrum of preheated benzyl alcohol was used as background. The spectra were recorded every 2 minutes for the synthesis at 140 °C and every 15 seconds for the synthesis at 170 °C. Due to deposition of particles on the probe, the measurements were paused to clean the probe: (a) at 140 °C, only in the time period between 2996 and 3067 minutes; (b) at 170 °C, starting after 30 minutes of the reaction and then every 15 minutes. Control experiment. The decomposition of pure benzyl alcohol upon heating was measured every 5 minutes (140 °C) or every 15 seconds (170 °C).
Data handling Extended X-ray Absorption Fine Structure (EXAFS). We performed the EXAFS data reduction using the ATHENA software. The normalization parameters (pre-edge ranges, postedge ranges and E 0 ) are given in ESI † Table  S1. The resulting χĲk)-function was k 3 weighted and Fourier transformed (FT) using a Hanning window function. ATR-FTIR data. We applied a baseline offset correction and shifted the in situ spectra so that the absorbance value was always 0 at 1800 cm -1. In addition, due to the particle deposition, we divided the analyzed spectra into two sets. At 140 °C the first and second sets contained spectra before and after particle deposition on the probe, respectively. For more information please refer to ESI † Fig. S1. At 170 °C the first and second sets contained spectra recorded before and after the cleaning of the probe, respectively.
Multivariate Curve Resolution-Alternative Least Squares (MCR-ALS). In situ ATR-FTIR and XANES data were analyzed with the MCR-ALS, implemented under MATLAB environment and discussed by De Juan and Tauler. 37, 48 The underlying idea behind MCR-ALS is the recovery of spectral components and their correlated fraction profiles from time evolving data by bilinear decomposition of experimental data, represented by matrix D, according to eqn (1): D = CS T + E (1) Rows of matrix D are spectra acquired during measurement, columns of matrix C and rows in matrix S T are concentration profiles and spectra of resolved components, respectively. Matrix E contains residuals not explained by the model. Eqn (1), generally valid for spectroscopic data governed by the Lambert-Beer law like XANES or FTIR, is solved by ALS algorithm, which iteratively calculates the C and S T matrices that fits best the experimental data.
The optimization of C and S T is carried out for a proposed number of components. Initial estimates of C and S T are obtained by using SIMPLe-to-use Interactive Self-Modeling Algorithm (SIMPLISMA), which finds the most different spectra within the dataset that are further used as an input for ALS optimization. The number of components was selected on the basis of Singular Value Decomposition (SVD). The initial spectra of the components were estimated with the PURE algorithm with the noise level set to 3% (XANES) and 4% (ATR-FTIR). Constraints applied for ALS calculation were as follows: non-negativity of spectra and concentration (XANES), nonnegativity of concentration (XANES and ATR-FTIR), unimodality of concentration (XANES and ATR-FTIR), convergence criterion: 0. 1 (XAS and ATR-FTIR). For more information please refer to ESI † Fig.
The spectra of CuĲacac) 2 in benzyl alcohol at 180 °C shows, with respect to RT, an increase of the intensity of the 1s → 4p transition. We do not observe characteristic features either of the CuO or of the CuĲOH) 2. Thus, we can conclude that no solid CuO or the CuOH 2 species are formed before formation of Cu 2 O. Cu reduction at 180 °C in benzyl alcohol. To track the changes from CuĲacac) 2 to Cu during the reaction at 180 °C we analyze the in situ XANES data by MCR-ALS as shown in Fig. 4. According to SVD results, three components suffice to fit the data. We use SIMPLISMA based algorithm to find the spectra that are used as initial estimates for MCR-ALS. We show the spectra of powder references recorded at room temperature in transmission mode only as guidance, since the XANES spectra of nanoparticles strongly depend on their size, species adsorbed at their surface and temperature at which the spectrum was measured.
Organic species forming during synthesiscomplementary in situ ATR-FTIR and GC-MS studies Simultaneously with UV-vis, we measure in situ FTIR to track the organic species forming in solution during the synthesis of Cu-based nanoparticles at 140 and 170 °C as shown in ESI † Fig. S2a-b. In the following, we first qualitatively describe the in situ FTIR results. Then, the GC-MS studies give complementary information about species forming in the solution below the detection limit of FTIR. Finally, we determine the lower detection limit and calibration curve for the main organic species, which allows us to estimate their quantities and correlate the organic and the inorganic compounds. (a) Qualitative results. Differently than for the XANES data, MCR-ALS analysis of the FTIR data does not recover the spectra of individual chemical species.
Acknowledgements We acknowledge the  Swiss National Science Foundation  ( 200021_137637 ) for the financial support and  SNX Council of SNBL, ESRF  for the beam time allocation. We thank the  Scientific Center for Optical and Electron Microscopy (ScopeM) of ETH Zurich  for the use of the SEM. We thank  G. Grassi  and  F. Arc  ( Laboratory of Physical Chemistry, ETH Zurich ) for the assistance with GC-MS measurements. MS thanks  Dr. I. Bilecka  and  Dr. F. Heiligtag  for their help in the initial phase of the project.
Hence, the demographic structure is paramount to understand the impact of mating systems. Monogamy may be more likely to approach a balanced OSR than polygyny, but anthropogenic disturbance such as selective hunting may render polygynous species more extinction prone  [33]. Moreover, polygyny has the potential to result in higher demographic variance  [34]. One study in birds found no effect of mating system on the extinction risk  [33] , whereas a study based on Mammals assessed that monogamous and polygynous species living in small groups were more at risk  [35]. Several studies have also assumed an influence of sexual selection in species extinction risk  [36, 37] , but its real impact remains hard to demonstrate. A comparative analyse using an extensive database on bird introductions concluded that sexual selection was negatively correlated to introduction success  [38].
Data collection Conservation status. We chose the IUCN status from the IUCN Red List (2013) as a proxy of extinction risk (downloaded from the IUCN Red List website, http://www. iucnredlist. org/). The IUCN status of threat downloaded from the IUCN Red List are part of a qualitative nominal variable with five modalities: "Least Concern", "Near Threatened", "Vulnerable", "Endangered" and "Critical" (LC, NT, VU, EN and CR). For our analysis we transformed the IUCN status into an ordinal variable (0, 1, 2, 3, 4, respectively). This type of transformation is commonly used in other studies using the IUCN status of threat as a proxy of species extinction risk  [8, 16, 35, 44]. Extrinsic factors of vulnerability. We chose the human footprint index (version 2: 1995-2004) as an indicator of the strength of the extrinsic factors of vulnerability.
Additionally, accessibility to Primates by roads may lead to an increase of hunting intensity  [46]. Second, the human footprint index is available for the distribution range of all Primate species. Finally, to our knowledge, the influence of human footprint on Primate extinction risk has never been studied at a global scale, despite its potential important effect on Primates population dynamics. In this study, we focused on the mean human footprint in species distribution range. Data on the worldwide human footprint was downloaded from the NASA SocioEconomic Data and Applications Center (SEDAC) website (http://sedac. ciesin. columbia. edu/). Polygons representing Primate species distribution range were downloaded from the IUCN website, as ESRI shapefiles format. Polygons result from the liaison of occurrence points of the species, expended to take into account species habitat preference, where unsuitable habitats are removed.
Data concerning these climatic variables were collated on the NASA website. The annual temperature and precipitation ranges were calculated as the difference between the mean highest annual temperatures/precipitations over the distribution area with the mean lowest annual temperatures/precipitations among this same area. The mean annual precipitations were measured as the mean of all mean annual precipitation pixels among the distribution area. Data were collated from peer-reviewed articles and online scientific databases. In order to minimise the bias due to multiple sources of information, data for each variable was collected from comparative analysis published in scientific articles and scientific databases (All the world's Primates  [54] ; panTHERIA  [55]. Final dataset comprised 93 Primate species for each we disposed of information on all factors.
Acknowledgments We would like to acknowledge the biodiversity research group at  ESE  for all their comments and enriching stimulus. The comments and support of  Luis Verde Arregotia ,  Céline Bellard ,  Gloria Luque  and  Elsa Bonnaud  were invaluable, as was the help of  Adèle de Baudouin  regarding data collection. A particular thanks goes to  Franck Courchamp  and  Nathalie Frascaria-Lacoste , without whom this study would not had been possible. The authors were supported by a grant from the  "Agence Nationale pour la Recherche" (ANR) , for the project " Research on Allee and Rarity Effects " (RARE; 2009PEXT01001). http://www. agence-nationale-recherche. fr/.
Ancestral Gene Order Reconstruction Ancestral gene order within CARs was inferred by a generalization of the method implemented in ANGES software  (Jones et al. 2012) , adapted for possible massive gene losses  (Gavranovic et al. 2011). We implemented the tools described above according to three different principles, according to the nature of the ancestor sought: An ancestor preceding a speciation, preceding a WGD, or preceding the ancestral hexaploidization (see below). (1) We used gene orthology relationships between 1R genomes to reconstruct the order of genes in ancestors preceding a speciation (e. g. , the malvid ancestor, and the common ancestor of malvids and fabids). An ancestral marker was defined as an informative family of genes found to be orthologous between species and an adjacency of ancestral markers was defined as a pair of ancestral markers found to be contiguous in at least two informative species.
2011). No parameters were used because the initial markers were the genes themselves, and no synteny blocks were constructed, and the definition of adjacencies and common intervals were strict, allowing no flexibility. (2) We reconstructed the genomes of ancestors preceding a WGD (i. e. , for Malpighiales, Rosaceae, Papilionoideae, and soybean), by applying the "double conserved synteny" (DCS) principle used, for example, by  Kellis et al. (2004) , and by  Ouangraoua et al. (2011)  for the analysis of synteny relationships in yeasts in the context of WGDs. We used the software of  Ouangraoua et al. (2011) , with all flexibility parameters set to 0. The input for this software is a list of genes orthologous between a 1R genome and a 2R genome, or between a 2R genome and a 3R genome. The output is all segments of contiguous genes of the 1R (or 2R) genome for which two orthologous gene segments are present in the 2R (or 3R) genome.
Conserved and Duplicated Genes in Rosids The synteny of rosids-as exemplified by grape, poplar, Arabidopsis, soybean, lotus, apple, strawberry, cacao, and papaya, representing the Vitales, the fabid and malvid subfamilies, in which genome size may vary by a factor of up to 10 (fig. 1A  and table 1 )-was reassessed by defining conserved/duplicated gene pairs (on the basis of alignment parameters and statistical tests) and block pairs (using Closeup software), as described by  Salse, Abrouk, Murat, et al. (2009)  and illustrated in figure  2  (green and blue panels, respectively). Orthologs and paralogs were selected on the basis of a K s filtering procedure, such that the pairs selected corresponded to known speciation and polyploidization events (r, a, b, g; fig. 1A , speciation and duplication panels).
Reconstruction of ARKs We used ANGES software  (Jones et al. 2012 ), together with the strategy described by  Gavranovic et al. (2011) , to propose a gene order for the ancestral genomes of the Caricaceae, Brassicaceae, Malvaceae, Fabaceae, Rosaceae, Salicaceae, and Vitaceae, including the pretriplication rosid ancestor (n = 7 pre-g). No published method has ever been reported to reconstruct the gene order of an ancestral genome in the context of hexaploidization. We used DCS to unmask synteny signals in the context of WGDs (through integration of the previously identified paralogous and orthologous blocks), and the "matrix sandwich" method  (Gavranovic et al. 2011; Jones et al. 2012) , to order genes within protochromosomes (see Ancestral Gene Order Inference; fig. 2 , purple and brown panels).
The Rosid synteny viewer tool is available from http://urgi. versailles. inra. fr/synteny-dicot (last accessed February 13, 2015) and can be used to navigate between rosid genomes through gene/species queries and use of the different setting parameters. criteria, 2) the identification of synteny groups with Closeup software, 3) the reconstruction of CARs based on conserved gene adjacencies with ANGES software, and 4) the inference of preduplication CARs on the basis of DCS detection. In this method, threshold parameters are introduced only when defining orthologous and paralogous pairs/blocks. They are not required for ancestral genome reconstruction, which is based solely on the conservation of gene adjacencies within CARs. We used this new method to determine the chromosomal structure of the rosid ancestor (ARK) and of the ancestral Salicaceae, Rosaceae, Papilionoideae, Malvales, Brassicaceae, and Caricaceae intermediate karyotypes.
Acknowledgments This work was supported by grants from the  Agence Nationale de la Recherche  ( ANR-09-JCJC-0058-01 ,  ANR-08-GENM-036-01 ,  ANR-08-EMER-011-03 ,  ANR-2011-BSV6-00801_PAGE ) and from the  INRA-INRIA  2010 call for projects.
Acknowledgements We want to thank the two anonymous reviewers that helped to significantly improve the manuscript and clarify its message. This work was funded by the  International Climate Initiative (ICI)  of the  German Federal Ministry for the Environment, Nature Conservation Building and Nuclear Safety (BMUB) , as part of the  CASCADE  project (" Ecosystem-based Adaptation for Smallholder Subsistence and Coffee Farming Communities in Central America "). The  German Federal Ministry for the Environment, Nature Conservation, Building and Nuclear Safety (BMUB)  supports this initiative on the basis of a decision adopted by the  German Bundestag. This work was also supported by the  Agroforestry Systems with Perennial Crop Scientific Partnership Platform  led by  CIRAD  and  CATIE. Contributions by  C.
Indonesia IV 10 --/-- --/-- UQRS283 T38 Solanum lycopersicum Indonesia IV 10 --/-- --/-- UQRS627 1712075KkB-PWR Musa spp. Indonesia IV 10 --/-- --/-- UQRS633 240808RB-MND Musa spp. Indonesia IV 10 --/-- --/-- ACH732 CIP357 Solanum lycopersicum Australia IV 11 --/-- --/-- LMG5942T Ralstonia pickettii --/-- --/-- LMG21421 Ralstonia insidiosa --/-- --/-- LMG6866T Ralstonia mannitolytica --/-- --/-- LMG1199T Ralstonia eutropha --/-- --/-- (Continued) Duplex PCR assay development Marker selection was performed by conducting genome comparisons using the Gene Phyloprofile Exploration tool (default parameters) of the MicroScope platform  [23]  (Genoscope, Evry, France) of both the Moko and IIB-4NPB strains versus the non-target strains from the Rssc and non-Rssc databases (available online: https://www.
The accuracy evaluation was performed using the duplex PCR assay with 111 strains (Table  1 ; n = 40 target strains; n = 71 non-target strains) at a concentration of 10 8 CFU/mL in molecular biology-grade water. The inclusivity and the exclusivity were assessed for three replicates and were calculated using the ratio of agreement (true positives) relative to the total repetitions performed within the experiment (agreements and disagreements). Accuracy was calculated following the same exact methods as for the inclusivity and exclusivity (Table  1 ). Binomial exact proportion confidence interval was calculated for all the three parameters, namely inclusivity, exclusivity, and accuracy scores (R statistical softwarev3. 1. 2 [29] , package "STATS" function "binom. test"). PCR was performed on pure cultured strains.
Duplex PCR assay development A genomic comparison was performed in two steps due to the high phylogenetic proximity of strains from phylotype IIB-4NPB to the Moko phylotype IIB-4. The first step compared Moko strains and variant IIB-4NPB strains to all the other strains of the Ralstonia solanacearum species complex in the Prokaryotic Genome DataBase (PkGDB) of the MicroScope platform  [23] , and the second step compared variant IIB-4NPB strains to all the other strains of the Ralstonia solanacearum species complex in the PkGDB of the MicroScope platform. Following extensive basic local alignment search tool (BLAST) searches against the NCBI genomic database to screen specific matches, two coding sequences were selected as markers. The first coding sequence was RALUWv1_4260003 (1,038 bp; GenBank accession KM387307), a putative KfrA protein (functional assignment based on the presence of a conserved amino acid motif, structural feature or limited homology) related to DNA binding proteins and a transcriptional modulator found in the UW181 genome and shared by both the Moko and NPB strains (92% pairwise identity).
For this sequence, the primers 93F (5' CGC TGC GCG GCC GTT TCA C 3') and 93R (5' CGG TCG CGG CAT GGG CTT GG 3') were designed; they produce a 477-bp amplicon. The second coding sequence was RAL70v1_1150031 (1,140 bp; GenBank accession KM387308), a chemotaxis-related protein found in the CFBP7014 genome and only shared by NPB strains (96% pairwise identity). For this sequence, the primers 5F (5' GCG CGC GAG GCT GGT GAT GT 3') and 5R (5' TGG GTT CGC AGG CGG ACA GC 3') were designed; they produce a 661-bp amplicon. 1 '+': positive result; '-': negative result; two platings were performed on the modified Sequeira semi-selective medium per sample (separated by a slash) 2 '+': positive result; '-': negative result; two PCR reactions per sample were repeated twice (separated by a slash) 3 Results given by the duplex-PCR 93F/R & 5F/R were in accordance with the type of strain: Moko or IIB-4NPB.
One of the major challenges in ecological research is the elucidation of physiological mechanisms that underlie the demographic traits of wild animals. We have assessed whether a marker of plasma oxidative stress (TBARS) and plasma haptoglobin (protein of the acute inflammatory phase response) measured at time t predict five demographic parameters (survival rate, return rate to the breeding colony, breeding probability, hatching and fledging success) in sexually mature wandering albatrosses over the next four years (Diomedea exulans) using a five-year individual-based dataset. Non-breeder males, but not females, having higher TBARS at time t had reduced future breeding probabilities; haptoglobin was not related to breeding probability. Neither TBARS nor haptoglobin predicted future hatching or fledging success.
However, other studies did not find any relationship between oxidative stress and survival proxies  [6] [7]. Further work has also shown that individuals with lower oxidative stress had higher reproductive success  [1, [7] [8]. What is lacking are studies that track the survival and reproductive history of free-living individuals over multiple years and relate these demographic variations to markers of oxidative status. Here, we have assessed whether a marker of plasma oxidative stress (thiobarbituric acid reactive substances, TBARS) and plasma haptoglobin measured at time t predict a number of demographic parameters (survival rate, return rate to the breeding colony, breeding probability, hatching and fledging success) in the next four years in the wandering albatross (Diomedea exulans) using a longitudinal individual-based dataset (5 years).
The volume of blood drawn never exceeded 0. 05% of the bird's body mass  (8) (9) (10) (11) (12). The blood was centrifuged to separate the plasma (for oxidative damage and inflammation analyses) from the red blood cells (for molecular sexing), which were then stored at -20°C prior to laboratory analyses. We also scored the density of ticks Ixodes uriae found on the head plumage during a 10 minutes restraint in order to test whether haptoglobin reflects an inflammatory response induced by these parasites. Scores ranged from 0 (no parasites) to 4 (very high density of ticks). The colony of albatrosses was then subsequently visited each next year up until 2012. During this period, we collected longitudinal data on survival and return rate, breeding probability, hatching and fledging success.
Because breeding probability differed between males and females and between the previous states of the individuals and because we only sampled 4 female non-breeders, we did not test the effects of TBARS or haptoglobin on breeding probability of females previously in the state non-breeders. We used the 95% confidence interval of the slope parameters and Akaike's Information Criterion corrected for small sample size (AICc  [20] ) for inference, taking into account the minimum recommended AICc difference of 2. We tested the goodness-of-fit (GOF) of the time-dependent MSMR model using U-CARE  [21]. Model selection was based on AICc and all models were run under program E-SURGE 1. 8. 5 enabling us to split transition probabilities between states  [17]. To avoid estimating FBE srβ(1-ω) s r βω(1-γ) s r βωγ sr(1-β) s(1-r) - - * FBC srβ(1-ω) s r βω(1-γ) s r βωγ sr(1-β) s(1-r) - - * SB srβ(1-ω) s r βω(1-γ) s r βωγ sr(1-β) - s(1-r) - * ONB srβ(1-ω) s r βω(1-γ) s r βωγ sr(1-β) - - s(1-r) * PFB srβ(1-ω) s r βω(1-γ) s r βωγ sr(1-β) - - - * PSB srβ(1-ω) s r βω(1-γ) S r βωγ sr(1-β) - - - * PONB srβ(1-ω) s r βω(1-γ) s r βωγ sr(1-β) - - - * † - - - - - - - * †.
This project was supported by the  French Polar Institute  (IPEV, program 109, H. W. ),  Terres Australes et Antarctiques Françaises ,  Zone Atelier de Recherches  sur l' Environnement Antarctique et Subantarctique (CNRS-INEE ). B. F. and G. S. received financial support from the  Agence Nationale de la Recherche  ( ANR STRESS OX & AGE). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
100 million years ago (mya;  [3, 4] ). This presumed gain-of-function evolved only once; however, among the plants with the predisposition, subsequently nitrogen-fixing root nodule symbioses evolved several times independently, with common themes but differences in infection process, nodule structure, nodule physiology and microsymbiont specificity. Using a database of nearly 3500 species within the NFC and the angiosperm phylogeny of Zanne et al. [5] , Werner et al. [4]  tested a series of models and came to the conclusion that the earliest root-nodule symbiosis arose in the common ancestors of actinorhizal Cucurbitales, followed by those of legumes, followed by actinorhizal Casuarinaceae and Rosaceae, and then by the other groups of actinorhizal plants. Based on 16S rDNA phylogeny, the genus Frankia has been divided into four clusters.