dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgements. This work, which is a contribution to the  North Greenland Ice Core Project  ( NGRIP ), was supported by the  University of Bern , the  Swiss National Science Foundation , and the  Prince Albert II of Monaco Foundation. Additional support was provided by the  LEFE program  of  CNRS/INSU. NGRIP is coordinated by the  Department of Geophysics at the Niels Bohr Institute for Astronomy, Physics and Geophysics, University of Copenhagen. It is supported by  Funding Agencies in Denmark (SHF) ,  Belgium (FNRS-CFB) ,  France  ( IPEV  and  INSU/CNRS) ,  Germany (AWI) ,  Iceland (RannIs), Japan (MEXT), Sweden (SPRS) ,  Switzerland (SNF)  and the  United States of America (NSF, Office of Polar Programs). The research leading to these results has received funding from the  European Union 's  Seventh Framework program  ( FP7/2007-2013)  under grant agreement no.
Floodplain vegetation Three LPX simulations were performed at 0. 5 • resolution with different sets of values for fldd, WTP max , t inund and M add (Table  1 ). For each simulation, the Climate Research Unit (CRU) TS 3. 0 climate data set  (Mitchell and Jones, 2005)  was used with monthly input data for surface air temperature, total precipitation, sunshine hours from fractional cloud cover, and number of wet days. The used soil data set was the Harmonized World Soil Database (HWSD, version 1. 0. , FAO/IIASA/ISRIC/ISSCAS/JRC, 2009). The following procedure was used for the LPX simulations: first, a spin-up of 1500 years was conducted, by recycling the input Note that the climate data sets used to force LPX and PCR-GLOBWB are not the same. The correction of ERA-Interim to CRU TS 2. 1, described in the previous section, accounts largely for differences in the long-term mean.
The vegetation cover for both LPX and GLC2000 data sets are plotted. For LPX, the different vegetation covers are approximated by the sum of FPC over all PFTs corresponding to this vegetation cover. A mean ratio y/x is given for each estimate (in brackets in the legend) and its corresponding symbol is displayed in red. Two distinct regions of the Amazon Basin (Llaonos de Mojos in blue; rest of the Amazon Basin in black) are identified in the GLC2000 data sets. The vegetation covers simulated by three LPX simulations, which differ through the used (fldd, t inund ,WTP max , M add ) set of parameters (see Table  1 ), are plotted. (bottom) ecosystems. It shows the improved agreement between LPX and MODIS on both LUs when the a NPP scaling factor is applied. The improvement for the non-floodplain is trivial since a NPP was calibrated using non-floodplain ecosystems.
On-site evaluation of CH 4 flux density The evaluation of LPX CH 4 emissions with site data is hampered by the scarcity of available measurements as well as by the mismatch between the spatial coverage of such measurements and the spatial resolution of Land Surface Models. Figure  5  displays a comparison between the CH 4 flux densities simulated by LPX and measurements at Cuini, Itu and Araca (sites 1-3) where information is available at a yearly timescale. For sites 4 (Isla Marchentaria) and 5 (Marrecao), information is available at a monthly timescale (Fig. 6 ). In the following, the two groups of sites are treated separately. Regarding LPX, we rely on a set of sensitivity simulations with different parameter settings and vegetation cover (Table  3 ). Sites 1-3 concern interfluvial wetlands in the Negro River basin.
Trait-based approaches incorporating more plant strategy components  (Van Bodegom et al. , 2012)  might also help. Also, a more realistic and mechanistic representation of flooding stress (e. g. , by separating the effects of partial submergence and soil oxygen demand on plant physiology;  van Bodegom et al. , 2008 ) may be considered. Such representations do, however, require experimental data on adaptation to these stresses representative for tropical PFTs and require accounting for seasonality in vegetation submergence. Through the big-leaf approach for grasses commonly used in GVMs, LPX cannot provide any information about the length of grass shoots in the course of the year. This prevents varying WTP max in time and an alternative expression needs to be found. Also, a better representation of plant adaptation to flooded conditions is required.
Acknowledgements. The research leading to these results has received funding from the  European Union 's  Seventh Framework Programme  ( FP7/2007-2013)  under grant agreement no. 283080 , project  GEOCARBON. The authors would like to thank  Roxanna Petrescu  for her help with the data from individual sites,  Rita Wania  for developing the first version of the  LPJ-WhyMe ,  Joe Melton  for providing the WETCHIMP model outputs,  Beni Stocker  for his help with the first LPX simulations and  Guillaume Monteil  for his help with Linux. RS and FJ acknowledge support by the  Swiss National Science Foundation  and by the  EU   FP7  project  PAST4FUTURE  (grant no. 243908 ). Edited by:  K. Thonicke.
Each micro-controller is responsible for reading out 22 columns (the two central columns were read by both micro-controllers) sequentially by two batches of 11 columns, as we used two synchronization signals. For the readout, a serial direct communication protocol was used. This protocol scans the pixels in every column serially, whereas the columns are read in parallel via 11 data lines connected to each microcontroller (cf. Figures  4  and 5 ). One of the microcontrollers masters the direct connection protocol and generates the clock and the synchronization signals, which are shared by the other for the sake of synchronization. The microcontrollers themselves communicate via Serial Peripheral Interface (SPI) bus with an external unit, which can be placed on a robot, as well. The communication protocol of the SPI interface was designed such that data can be extracted from different ROIs (regions of interest) of the CurvACE sensor specified by the user.
Acknowledgment We thank  Jean-Christophe Zufferey  and  Jacques Duparré  for conceptual suggestions;  Andreas Mann  for the sensor module preparation; and  Marc Boyron  and  Julien Diperi  for test bench realization. We also thank the referees for their useful comments. The  CurvACE  project acknowledges the financial support of the  Future and Emerging Technologies (FET) program  within the  Seventh Framework Programme for Research  of the  European Commission , under  FET-Open Grant   237940. This work also received financial support from the  Swiss National Centre of Competence in Research Robotics , the  French National Center for Scientific Research (CNRS)  and  Aix-Marseille University , the  French National Research Agency (ANR)  with the Entomoptere Volant Autonome (EVA),  Intelligent Retina for Innovative Sensing (IRIS)  and  Equipex/Robotex  projects ( EVA  project and  IRIS  project under ANR Grant Numbers  ANR608-CORD-007-04  and  ANR-12-INSE-0009 , respectively) and the  German Federal Ministry of Education and Research.
Methodology: We analyzed MALDI-TOF spectra of promastigote pellets from 46 strains cultured in monophasic medium, including 20 short-term cultured isolates from French travelers (19 with CL, 1 with VL). As per routine procedure, clinical isolates were analyzed in parallel with Multilocus Sequence Typing (MLST) at the National Reference Center for Leishmania. Principal Findings: Automatic dendrogram analysis generated a classification of isolates consistent with reference determination of species based on MLST or hsp70 sequencing. A minute analysis of spectra based on a very simple, database-independent analysis of spectra based on the algorithm showed that the mutually exclusive presence of two pairs of peaks discriminated isolates considered by reference methods to belong either to the Viannia or Leishmania subgenus, and that within each subgenus presence or absence of a few peaks allowed discrimination to species complexes level.
Some of these methods can be applied directly to biological samples avoiding the parasite culture step  [17]. However, these molecular methods lack inter-laboratories standardization and require the use of expensive reagents. Matrix-assisted laser desorption ionization-time-of-flight (MALDI-TOF) mass spectrometry (MS) has emerged as a powerful tool for the identification of microorganisms. Using MALDI-TOF MS, the protein spectral ''fingerprint'' of an isolate is compared to a reference spectral database, yielding results within 1 hour  [18]. Although spectrometers are still relatively expensive, the initial investment is justified by a broad use spanning a wide diversity of microbiological samples  [19] , and the cost of reagents is very limited. This approach has been applied with success to bacteria, yeasts and filamentous fungi, but to our knowledge, no study on direct identification of protozoans has been published yet  [20] [21] [22] [23].
Medical care and parasite collection From 2011 through 2013, data and samples were collected each time treatment advice was sought from an expert at our hospital for patients with CL. Diagnosis procedures were not modified by the process, expert treatment advice was part of normal medical care, data and sample collection was in the context of national health surveillance. Patients were informed of the process by their attending physician using a procedure common to all French National Reference Centers (NRC) (http://www. parasitologie. univ-montp1. fr/doc/Declaration_pub_2011. pdf). Data were obtained through the standard reporting form of the NRCL. This form is available online and is anonymous and the anonymisation process is irreversible. The following characteristics are provided on the form: age (children defined as ,16 years), sex, clinical form, and for CL or MCL: number of lesions, the presumed place of infection.
Laser energy was set just above the threshold for ion production. A mass range between 3,000 and 20,000 m/z (ratio mass/charge) was selected to collect the signals with the AutoXecute tool of flexcontrol acquisition software (Version 2. 4; Bruker-Daltonics). Only peaks with a signal/noise ratio. 3 were considered. Spectra were eligible for further analysis when the peaks had a resolution better than 600. For each cultivation condition, we collected mass spectra from 2 biological replicates and 4 technical replicates.
Data processing Data were processed with Biotyper version 1. 1 (Bruker-Daltonics) and ClinProTools 3. 0 (bruker-Daltonics) as described  [18]. ClinProTools software was used to visualize all spectra as virtual gels and to calculate variability for each of the defined markers. The Biotyper software performs smoothing, normalisation, baseline subtraction, and peak picking using default parameters. Strain comparison was done by principal component analysis (PCA)  [27]. Distance values were calculated using Biotyper to build score-oriented dendrograms. Based on these distance values, a dendrogram was generated using the according function of the statistical toolbox of Matlab 7. 1 (The MathWorks Inc. , USA), which was integrated into Biotyper 1. 1. The clustering approach is based on similarity scores implemented in the software.
Process optimization, growth-stage effect and reproducibility Preliminary tests showed that stable spectra were obtained with 10 6 promastigotes but that an optimal discrimination of peaks was achieved with 10 7 promastigotes. Because the culture medium influences spectra (not shown), isolates growing better in NNN medium were transferred for one cycle (i. e. , 48-96 hours) in Schneider medium before analysis. The growth kinetics of 2 L. tropica isolates was established over two consecutive cycles and spectra were obtained at three stages: exponential (24-72 hours), stationary (72-148 hours) and decay, showing that spectra were reproducible at the exponential and early stationary stages (data not shown). All spectra were then obtained from late exponential/ early stationary stages. The reproducibility was further established by analyzing 16 replicates of the same samples for a L.
(L) infantum and a L. (V) braziliensis isolate (Suppl Fig. S2  & 3), by analyzing samples from the same isolates of L. (L) infantum maintained in culture for several days (Suppl Fig. S4 ), by analyzing samples from the same isolates of L. (V) braziliensis analyzed at day 0 and a second culture of the same isolate frozen and thawed for subculture 6 months later (Suppl Fig. S5 ) and by analyzing 3 L. (L) infantum and 5 L. (V) braziliensis isolates (Suppl Fig. S6  & 7 ). Identification was accurate in all cases. The same approach was also performed with a L. major isolate maintained in culture in duplicate for 5 weeks. Spectra lead to the same species identification at all points (not shown). Simple, database-independent analysis of spectra: Mutually exclusive presence of 2 pairs of peaks discriminated isolates considered by reference methods as belonging to the Viannia or Leishmania subgenera Peaks 11121 (+/27) and 7114 (+/24) were both present in all 18 isolates belonging to the Viannia subgenus -13 L.
Discussion Applied on a set cultured isolates spanning most Leishmania species of medical importance, MALDI-TOF MS generated a classification consistent with results obtained by reference methods (MLST or heat-shock protein 70 gene sequencing). This was achieved using a simple, database independent analysis of MALDI-TOF spectra based on the algorithm. The simplicity of the analytical procedure allowed a minute output of results as soon as fair parasite growth was obtained in monophasic medium. Taken together, these observations suggest that MALDI-TOF may be a useful tool to facilitate treatment decision in cutaneous leishmaniasis. Treatment of CL should indeed be based on species identification  [1, 3, 4, 9, 10, [30] [31] [32]. For example, systemic antimony is generally more effective in patients infected with L.
Background: As attested by the fossil record, Cretaceous environmental changes have significantly impacted the diversification dynamics of several groups of organisms. A major biome turnover that occurred during this period was the rise of angiosperms starting ca. 125 million years ago. Though there is evidence that the latter promoted the diversification of phytophagous insects, the response of other insect groups to Cretaceous environmental changes is still largely unknown. To gain novel insights on this issue, we assess the diversification dynamics of a hyperdiverse family of detritivorous beetles (Tenebrionidae) using molecular dating and diversification analyses. Results: Age estimates reveal an origin after the Triassic-Jurassic mass extinction (older than previously thought), followed by the diversification of major lineages during Pangaean and Gondwanan breakups.
The tenebrionid fossil record highlights an age as old as the Middle Jurassic for the family  [28] , and indicates that Mesozoic Pangaean and Gondwanan breakups may be reflected in the disjunct distribution patterns observed in several groups such as the Pimeliinae or the Adeliini  [25, 29, 30]. However, due to its relative scarcity, the fossil record alone is not sufficient to provide an accurate temporal framework for the family. To gain a better understanding of the age of specific divergence events, we conducted molecular dating analyses using 21 calibration points combined with conservative priors and calibration settings. We then used the resulting timeframe to conduct diversification analyses to investigate the early tempo and mode of diversification of the family in relation to Cretaceous environmental changes.
[25]  for details). Thus if we consider that phylogenetic biome conservatism holds in many groups  [32] , this suggests that distinct tenebrionid lineages were already adapted to arid or tropical environments before the end of the KTR. Results from character optimization analyses support this hypothesis and suggest that the common ancestor of tenebrionid beetles was adapted to arid environments (Figure  2 ). Arid environments were extremely widespread in the Early Jurassic  [33, 34] ; though they progressively receded with the break-up of the Pangaea  [6, 7] , they never completely disappeared, which could explain the persistence of distinct lineages of pimeliine beetles across eastern and western hemispheres  [25]. Interestingly, pimeliine beetles are currently nearly absent from Australia, where aridification began in the Miocene and deserts only appeared very recently  [35, 36].
Dataset We used a molecular dataset of eight gene fragments (four mitochondrial and four nuclear) encompassing 404 species (from the study of Kergoat et al. [49] ; see also Additional file 3: Table  S3 ). This dataset was first introduced in a study that has mostly focused on discussing tenebrionid relationships  [49]. Within the family Tenebrionidae this dataset comprises 250 species (150 of which were collected and sequenced by the authors) belonging to 98 distinct genera. The sampled subfamilies (seven out of nine) represent more than 99% of the tenebrionid species diversity. The sampled tribes (37 tribes out of 96) also encompass most (ca. 77%) of the tenebrionid generic diversity (see Additional file 4: Table  S4 ). For calibration purposes, this dataset includes representatives of seve-ral tenebrionid genera (Bolitophagus, Gonocephalum, Isomira, Lorelus, Platydema, Pentaphyllus and Tribolium), for which several unambiguous fossil representatives are known  [28].
In order to limit the numbers of parameter to estimate, we used a guide tree that corresponds to the best maximum likelihood tree inferred with the same dataset in the study of Kergoat et al. [49]. Fossil constraints were either placed on crown or stem nodes. Fossil constraints put on stems are generally more conservative, especially if the sampling of the group is not enough to provide a relevant representation of the clade diversity  [52, 57]. On the other hand, fossil constraints put on stems can also result in severe biases and underestimate the age of a group if the sister clade is too distantly related  [58, 59]. Since our taxon sampling is far from being complete and given the difficulty to assign a calibration to a precise node, we adopt a conservative way in which all node calibrations were enforced using uniform distributions (hard-bound constraints  [49, 60, 61] ).
Convergence of runs was assessed graphically under Tracer v1. 5  [64]  and by examining the effective sample size (ESS) of parameters. Bayes factors  [65]  were then estimated using the log files of the four distinct calibration procedures, using scripts detailed in Baele et al. [63]. Convergence was indicated by ESS of parameters >200 for the post burn-in trees. Dating analyses based on stem calibrations recovered several age estimates that were not consistent with the fossil record (see Additional file 6: Table  S5 ), and were thus discarded. Out of the two crown calibrations, the "Yule crown" calibration procedure was recovered as the best-fit calibration procedure by the Bayes factor comparison (Additional file 7: Table  S6 ). A median age of 180. 05 Ma (95% HPD: 169. 56-191. 62 ; "Yule crown") was estimated for the Tenebrionidae (178.
Diversification analyses Diversification analyses were conducted on chronograms resulting from BRC calibration procedures. Analyses were realized under the R environment software implementing the TreePar  [18]  package. To visualize the tempo and mode of diversification of the group, we first reconstructed lineages-through-time plots. We then assessed whether diversification rates remained constant during the early evolutionary history of darkling beetles. Similar to the way Meredith et al. [14]  examined the diversification rates of mammals and the impact of the K-Pg extinction event, we focused on the time period between the estimated origin of the family and 50 Ma (early Eocene). We did not examine the tempo or mode of diversification of the group after this date because our chronograms included only a small fraction of the standing diversity of crown-group tenebrionids that were likely extant in the Cenozoic.
We are aware of potential biases in diversification rate analyses that use incomplete phylogenies  [66, 67]. However, our aim was not to reveal a precise diversification pattern but instead to get a broad sense of the early diversification dynamics. Several previous studies on diversification rates for highly incomplete phylogenies have revealed interesting evolutionary patterns despite methodological limitations of their trait-dependent analyses (e. g. , Goldberg et al. [68]  and Hugall & Stuart-Fox  [69] ), which are even more sensitive to the missing lineages than are those used in the current study  [67]. The TreePar package was used to assess speciation and extinction rates through time. This method relaxes the assumption of constant rates by allowing rates to change at specific points in time  [18].
Acknowledgements We would like to thank  Alfried Vogler ,  Emily Crow  and four anonymous reviewers for the numerous constructive comments and corrections they made on a preliminary version of the manuscript. Interesting insights on the results of the TreePar diversification analyses were kindly provided by  Tanja Stadler. FLC also wishes to thank  Hélène Morlon  for supervision. Partial funding for this study was obtained through the program " ANR Biodiversité " of the  French National Agency for Research  (Project  BIONEOCAL 2008-2012 ) and by funds from  INRA  for GJK. Part of the sequencing was also supported by the program " Bibliothèque du Vivant " (Project  CIAM ) supported by a joint  CNRS ,  INRA  and  MNHN consortium. Both FLC and JLA were supported by  ANR  grants (Projects  ECOEVOBIO-CHEX2011  and  SPATEVOLEPID-10-PDOC-017-01 , respectively).
Acknowledgments Author sequence follows the "sequence-determinescredit" norm (see Tscharntke et al. 2007). We thank  E. Millard ,  D. Newsom ,  A. Baldi ,  M. Schwartz , and two anonymous reviewers for helpful comments. TT and YC were supported by the  DFG  (CRC 990 EFF or TS) and FDC by the  CGIAR research program on Water Land and Ecosystems.
Systems biology analysis Genes associated with development of Charcot-Marie-Tooth disease were retrieved from OMIM database (Online Mendelian Inheritance in Man database: http://www. ncbi. nlm. nih. gov/omim). The initial identification and analysis of the potentially affected CMT1A-relevant signalling pathways, integrating these genes, was made with the help of Ingenuity (https://analysis. ingenuity. com) and GeneGO (https://portal. genego. com) databases/bioinformatics tools and proprietary bioinformatics programs, with subsequent filtering and manual curation of the data. This initial analysis was complemented by manual updating of the most recent literature findings. The choice of the drugs was made through matching the emerging relevant targets using with the help of DrugBank, Reaxys and Integrity databases (http://www.
Immuno-staining Cells were fixed by a cold solution of ethanol (95%) and acetic acid (5%) for 10 min, permeabilised, and blocked with PBS containing 0. 1% saponin (Sigma) and 10% FBS (Invitrogen) for 15 min. Then, cells were incubated with a rabbit polyclonal antibody specific to Myelin Basic Protein (MBP) (Sigma-Aldrich; M3821). MBP staining was revealed with Alexa Fluor 568 goat anti-rabbit IgG (Molecular Probes). Nuclei were counter-stained with Hoechst (Sigma-Aldrich). 20 pictures per well were taken in the same conditions using the InCell Analyzer? 1000 (GE Healthcare) with 20 ? magnification. Analysis of total length of myelinated axonal segments and total number of nuclei was automatically performed with the InCell Analyzer Software. Total nuclei count showed consistency in all wells and at each time point of the experiment.