dataesr/scientific-paragraphs-categorization huggingface dataset:
Y2H screens The coding sequence for full-length MEL-26 (GenBank accession no. gi:212645231) was PCR amplified and cloned into the pB27 plasmid as a C-terminal fusion to LexA (N-LexA-MEL-26-C). The construct was checked by sequencing the entire insert and used as a bait to screen separately two random-primed C. elegans cDNA libraries constructed into the pP6 plasmid. The first library was constructed by combining equimolar amounts of mRNA from four different N2 samples: (1) males and hermaphrodites (all stages including embryos), (2) starved mixed stage culture, (3) heat-shocked mixed stage culture, and (4) dauer stage. The second library was prepared mixed stage embryos isolated by bleaching. pB27 and pP6 derive from the original pBTM116  (Vojtek and Hollenberg, 1995)  and pGADGH disassemble the meiotic spindle.
Acquisition parameters were controlled by MetaMorph software (Molecular Devices). In all cases a 60×, 1. 4 NA PlanApochromat lens with 2 × 2 binning was used, and four z-sections were collected at 2-µm intervals every 20 s. In Fig. 3 , each image shown results from the sum of three adjacent focal planes (performed with ImageJ/Fiji software).
The interacting fragments recovered in the positive colonies were amplified by PCR, sequenced at both their 5 and 3 ends, and identified in GenBank. Finally, each interaction was assigned a confidence score as previously described  (Formstecher et al. , 2005). Mixed stage library (pool of three preparations) Unstressed mixed stage worms. 30 N2 hermaphrodites were mated to an excess of males on large nematode growth media (NGM) plates. Worms were grown at 20°C for 4-5 d, harvested before food was exhausted, washed in water twice, and rapidly frozen in liquid nitrogen. "Stressed" mixed stage worms. 30 N2 hermaphrodites were mated to an excess of males on large NGM plates. Worms were grown at 20°C for 4-5 d and then submitted to the following treatments: (a) starvation: worms were washed off food in M9 medium and incubated for 7 h at room temperature with agitation; (b) heat shock: worms were heat shocked for 1 h at 35°C and then allowed to recover at 20°C for 6 h.
Data submission to external databases The protein interactions from this publication have been submitted to the IMEx (http://www. imexconsortium. org/) consortium through IntAct and assigned the identifier IM-20907. The entire results of both MEL-26 Y2H screens can be browsed using the PIMRider software at http://pimr. hybrigenics. com. Online supplemental material Fig. S1  shows ASPM-1 and PPFR-1 protein localization in meiotic embryos. Fig. S2  shows wild-type MI versus MII divisions. Table  S1  is a list of partners identified by the Y2H screens performed using MEL-26 as bait. Online supplemental material is available at http://www. jcb. org/cgi/content/ full/jcb. 201304174/DC1. Additional data are available in the JCB Data-Viewer at http://dx. doi. org/10. 1083/jcb. 201304174. dv.
We thank  B. Bowerman  for providing the mei-1(or646ts) allele before publication and for critical reading of the manuscript and  S. Van den Heuvel  and the  Caenorhabditis Genetics Center  (funded by the  National Institutes of Health Center for Research Resources ) for strains and reagents. We thank members of the  Institut Jacques Monod Imaging facility (ImagoSeine), Thomas Moncion, and all Hybrigenics  staff for their assistance. J. E. Gomes was supported by a post-doctoral fellowship from the  Association pour la Recherche sur le Cancer. L. Pintard was supported by an  ATIP+ grant  from the  Centre National de la Recherche Scientifique  and the  City of Paris. Submitted: 26 April 2013 Accepted: 1 July 2013.
The presence of lignin in secondary cell walls (SCW) is a major factor preventing hydrolytic enzymes from gaining access to cellulose, thereby limiting the saccharification potential of plant biomass. To understand how lignification is regulated is a prerequisite for selecting plant biomass better adapted to bioethanol production. Because transcriptional regulation is a major mechanism controlling the expression of genes involved in lignin biosynthesis, our aim was to identify novel transcription factors (TFs) dictating lignin profiles in the model plant Arabidopsis. To this end, we have developed a post-genomic approach by combining four independent in-house SCW-related transcriptome datasets obtained from (1) the fiber cell wall-deficient wat1 Arabidopsis mutant, (2) Arabidopsis lines over-expressing either the master regulatory activator EgMYB2 or (3) the repressor EgMYB1 and finally (4) Arabidopsis orthologs of Eucalyptus xylem-expressed genes.
This allowed us to identify 502 up-or down-regulated TFs. We preferentially selected those present in more than one dataset and further analyzed their in silico expression patterns as an additional selection criteria. This selection process led to 80 candidates. Notably, 16 of them were already proven to regulate SCW formation, thereby validating the overall strategy. Then, we phenotyped 43 corresponding mutant lines focusing on histological observations of xylem and interfascicular fibers. This phenotypic screen revealed six mutant lines exhibiting altered lignification patterns. Two of them [Bel-like HomeoBox6 (blh6) and a zinc finger TF ] presented hypolignified SCW. Three others (myb52, myb-like TF, hb5) showed hyperlignified SCW whereas the last one (hb15) showed ectopic lignification.
A POST-GENOMIC APPROACH TO IDENTIFY NOVEL REGULATORY GENES INVOLVED IN SCW FORMATION In order to identify novel regulatory genes involved in SCW formation, we took advantage of four large scale in house transcriptomic data sets and developed a post-genomic approach. A flow chart of the main steps of this original strategy is described in Figure  1. The first SCW-related in house transcriptome dataset came from the Arabidopsis mutant, wat1 (walls are thin 1), which has little to no SCW in fibers  (Ranocha et al. , 2010). In this mutant, the transcript levels of many genes associated with the regulation and/or the biosynthesis of the different SCW wall polymers were dramatically reduced in keeping with the mutant phenotype. Within the genes up/or down-regulated in the mutant background, we identified 97 TFs including some well-known SCW-regulating TFs such as SND1, SNT1, and MYB46 (Table  S1 ).
The second transcriptome dataset was comprised of 240 TFs (Table  S2 ) that exhibited de-regulated expression in Arabidopsis lines over-expressed the SCW-master activator EgMYB2, which is a Eucalyptus R2R3 MYB TF highly expressing in xylem cells undergoing SCW thickening  (Goicoechea et al. , 2005). EgMYB2 is able to activate the promoters of lignin  (Goicoechea et al. , 2005) , cellulose, and xylan biosynthetic genes  (Zhong and Ye, 2009) , leading to thicker SCW in EgMYB2 over-expressing lines in tobacco  (Goicoechea et al. , 2005; De Micco et al. , 2012). Moreover, the closest orthologs of EgMYB2 in Arabidopsis AtMYB46 and AtMYB83 encode for master regulators capable of activating the whole SCW biosynthetic program  (Zhong et al. , 2007a; McCarthy et al. , 2009) , and EgMYB2 was able to complement the myb46-myb83 double mutant  (Zhong et al.
, 2010). The third transcriptome dataset included 309 TFs (Table  S3 ) deregulated in Arabidopsis lines over-expressing the SCW-repressor, EgMYB1  (Legay et al. , 2010). EgMYB1 over-expressors exhibited fewer lignified fibers particularly in the interfascicular zones and reduced SCW thickenings. Klason lignin content was moderately but significantly reduced and decreased transcript accumulation was observed for genes involved in the biosynthesis of lignins, cellulose, and xylan  (Legay et al. , 2010). Finally, the fourth dataset was composed of 87 TFs (Table  S4 ) that were the Arabidopsis orthologs of Eucalyptus TFs preferentially expressed in differentiating xylem  (Rengel et al. , 2009) , a tissue that is particularly rich in cells undergoing SCW deposition and lignification. Altogether, these four transcriptomic datasets allowed us to identify a total of 502 candidate TFs.
PHENOTYPES OF TF T-DNA MUTANT OR RNAi TRANSGENIC LINES We then collected and characterized publicly available T-DNA mutant lines or RNAi transgenic lines that corresponded to 43 of the 80 candidate genes (Table  S6 ). The information concerning the different lines including T-DNA insertion position and inhouse databases source is presented in Table  S6. Phenotyping was performed on 20 cm-high mutant stems grown in shortday growth conditions. Under these conditions, the basal part of the stem abundantly develops cells undergoing SCW thickening (xylem vessel cells, xylary fiber cells, and interfascicular fiber cells). Histological analyses of SCW were performed using the natural auto fluorescence of phenolic compounds under UV-light as well as phloroglucinol-HCl staining, which is indicative of the lignin content.
CO-EXPRESSION ANALYSIS OF CANDIDATE TFs GENES Since it is known that transcriptionally coordinated genes tend to be functionally related  (Ruprecht and Persson, 2012) , we performed co-expression analyses for the six candidate genes in order to further validate their role in controlling SCW synthesis and get some clues about their function. The co-expression genes lists were generated using the Genevestigator platform (https://www. genevestigator. com), Arabidopsis co-expression data mining tools and GeneCAT. All six candidate TFs were co-expressed with genes related to cell wall formation (Tables  2, 3  and Tables S7-S10 ) albeit to different extents ranging from 10 to 66% of SCW associated genes amongst the 50 first co-expressed genes. The most remarkably high co-expression profiles were found for MYB52 and BLH6.
DISCUSSION Functional genomics approaches developed during the last decade have generated numerous candidate genes related to SCW formation in Arabidopsis and other plant species. Whereas these large individual gene lists make difficult the choice of the most promising candidates for the further functional validation, metaanalyses combining multiple transcriptomic data sets offer a new way to reveal some core regulators. By cross-comparing four SCW-related transcriptomic datasets, we selected 186 TFs present in at least two experimental conditions. Since these datasets came from very different backgrounds (mutants and over-expressors of SCW regulatory genes as well as orthologs of Eucalyptus xylem expressed genes), the selection of genes appearing in more than one dataset likely helped us to identify "core regulators" of SCW formation but might also have filtered out some more specific regulators.
e. , cellulose, xylan, and lignin as well as with the master regulator MYB46 and its closest paralog MYB83. Together with the hypolignified phenotype of the mutant and the thinner SCW particularly in the fibers, this further supports a role of BLH6 as an activator of the whole SCW program. AP2 ERF TF (AT3G14230) was identified in all four SCWrelated transcriptomic datasets and exhibited high and preferential expression in xylem, but the corresponding mutant had no detectable cell wall phenotype. Twelve members of the AP2 ERF TF family were highlighted by our in silico approach, seven of which had high and preferential expression in xylem and another (AT5G61590) was strongly induced during in vitro tracheary element formation. Although this family was the second most highly represented TF family just after the MYBs in the 80 candidate list, none of its members have yet been shown to be directly involved in the regulation of SCW formation.
This family therefore deserves more attention especially because it was recently reported that ethylene regulates cambium activity and promotes secondary walled xylem formation  (Love et al. , 2009). Some members of the auxin-dependent TFs Aux/IAA and ARF families have been shown to be involved in vascular tissue formation. For example, loss-of-function in ARF5/MP  (Hardtke and Berleth, 1998)  and gain-of-function in IAA12/BDL  (Hamann et al. , 2002)  resulted in reduced and discontinuous vascular formation. These TF families were also highly represented within the 80 candidates with seven and two members for Aux/IAA and ARF, respectively. IAA9 was a very promising candidate found in the four transcriptomic datasets, highly and preferentially expressed in xylem and during tracheary elements differentiation.
CROSS-COMPARISON OF MICROARRAY DATASETS Four in house microarray datasets were generated in our laboratory. In brief, datasets are from wat1 T-DNA Arabidopsis mutant CATMA microarray  (Ranocha et al. , 2010) ; EgMYB1  (Legay et al. , 2010) , EgMYB2 over-expressed in Arabidopsis (unpublished), and orthologs of Eucalyptus xylem expressed genes  (Rengel et al. , 2009). Publicly available microarray datasets were extracted from Genevestigator (https://www. genevestigator. com) (Hruz et al. , 2008)  by using Arabidopsis ATH1 22k array platform (7392 array datasets).
CO-EXPRESSION ANALYSIS Three co-expression analysis tools were explored using Genevestigator (https://www. genevestigator. com), Arabidopsis co-expression data mining tools (http://www. arabidopsis. leeds. ac. uk/act/), and GeneCAT (http://genecat. mpg. de/). The results were presented using Genevestigator output tables and genes classified according to gene ontology semantic  (Berardini et al. , 2004). We used Genevestigator Arabidopsis ATH1 22k array platform with in absentia parameters that comprise all 7392 qualified datasets and is regardless of the underlying microarray datasets and the bait genes (i. e. , all samples, condition-independent, and no-tissues specific bait genes), 50 was as "cut-off " threshold for co-expressed genes list.
ACKNOWLEDGMENTS This work was supported by grants from the  European FP7  project  RENEWALL  ( FP7-211982 ), the  Centre National pour la Recherche Scientifique (CNRS) , and the  Université Toulouse III Paul Sabatier (UPS). This work was part of the  Laboratoire d'Excellence (LABEX)  project entitled  TULIP  ( ANR-10-LABX-41 ). The authors are grateful to  Prof S. Hawkins  ( Université de Lille, France ) for kindly communicating unpublished data on Arabidopsis lines over-expressing EgMYB2. We also acknowledge  Dr. P. Ranocha  ( LRSV ) for his precious advice and help since the beginning of this work,  Y. Martinez  ( FR3450 ) for assistance with microscopy analysis. Thanks also to PhD student  H. Yu  for her help in quantifying the transcript levels of HB15 and ZINC FINGER TF in their corresponding T-DNA insertional mutants and the internship training students  C.
ACKNOWLEDGEMENTS This work was supported by funding from the ice2sea project from the  European Union 7th Framework Programme , grant No. 226375. This is ice2sea contribution No. 112. LGGE was granted access to the high-performance computing resources of  CINES (Centre Informatique National de l'Enseignement Superieur, France)  under allocations  2011-016066  and  2012-016066  made by  GENCI  ( Grand Equipement National de Calcul Intensif)  to perform the Elmer/Ice simulations. E. Larour  and  M. Morlighem  are supported by the  NASA   Cryospheric Sciences and Modeling Analysis and Prediction Programs. H. Seroussi  was supported by an appointment to the  NASA   Postdoctoral Program  at the  Jet Propulsion Laboratory , administered by  Oak Ridge Associated Universities  through a contract with  NASA.
Genotyping DNA was extracted from cheek swabs using standard procedures. The genotype of the COMT Val 158 Met (rs4680) single nucleotide polymorphism (SNP) was determined by allelic discrimination assay (C_25746809_50) based on fluorogenic 5 0 nuclease activity: a TaqMan SNP genotyping assay was performed the ABI Prism 7900HT and analyzed with Sequence Detection System software according to the manufacturer's instructions (Applied Biosystems, Warrington, UK). Twenty participants were found to be Val homozygotes and the remaining 71 found to be Met carriers. To determine the genotypes of the SLC6A4 5-HTTLPR insertion/deletion and of the rs25531 G/A SNP, a modified version of the protocol described by  Wendland et al. (2006)  was used. The 5 0 -TCCTCCGCTTTGGCGCCTCTTCC-3 0 forward and 5 0 -TGGGGG TTGCAGGGGAGATCCTG-3 0 reverse primers were used (Operon, Huntsville, AT) that amplify a 469 (short or S allele) and 512 (long or L allele) product.
The top and bottom cameras were black and white, and were used to capture general shape information. The middle camera in each pod was a color camera, used to capture texture and color information. A lamp was placed behind each camera, and luminance kept constant at 21 amps. Video information was recorded by Di3D software on this PC as a series of jpegs at high resolution (2 megapixels). Vocal sound-information was transmitted via a Microtech Geffell GMBH UMT 800 microphone-positioned above the actor-to a second PC outside the booth, and was recorded at 44100 Hz using Adobe Audition (Adobe Systems Incorporated, San Jose, CA). The actors were instructed to express anger and happiness in both the face and the voice. The sound "ah" was chosen as it contains no linguistic information. They were asked to sit as still as possible, in order to keep head movement to a minimum.
Video processing Video output was split into a number of different sequences, where each sequence was made up of a number of jpegs (frames) and each repetition of each emotional expression formed one sequence. Two final sequences were chosen for each actor. Using the Di3D software, 43 landmarks were placed around the face and facial features in the first and last frame of the sequence, forming a landmark-mesh. An existing generic mesh was applied to the beginning and the end of the sequence (i. e. , first and last jpeg), which was then warped to fit the landmark-mesh. The first mesh was then used to estimate the mesh position in the second jpeg, which was then used to estimate the position in the third and so on. This forward tracking/mesh estimation was then carried out in the opposite direction (i.
Audio morphing Auditory stimuli were edited using Adobe Audition 2. 0. In order to generate the auditory components to the "morph-videos" three temporal and three frequency points were identified and landmarks corresponding to these set in the MATLAB-based morphing algorithm STRAIGHT  (Kawahara, 2003) , which were then used to generate a morph continuum between the two affective vocalizations equivalent to the faces. Two continua of voices-one for each actor, and consisting of five different voices ranging from 90% angry to 90% happy in 20% steps-were then generated by resynthesis based on a logarithmic interpolation of the angry and happy voices temporal and frequency anchor templates to a 50% average.
Continuous carry-over experiment In the main experiment, stimuli were presented by way of a continuous carry over design  (Aguirre, 2007). In summary, these are efficiency-optimized, serially balanced fMRI sequences in which every stimulus precedes and follows every other stimulus (i. e. , the "Type 1 Index 1" sequence), which account for stimulus counterbalancing. Carry-over designs are particularly useful when there is more complex variation in a set of stimuli, whose differences can be expressed as parametric changes along a number of different axes (e. g. , face and voice emotion morph). A full description of the continuous carry over design can be found in  Aguirre (2007). Stimuli were presented using the Psychtoolbox in Matlab, via electrostatic headphones (NordicNeuroLab, Norway) at a sound pressure level of 80 dB as measured using a Lutron Sl-4010 sound level meter.
These stimuli were removed in our later analysis. Participants were instructed to perform a 2 alternative forced choice emotion classification task (responses: Angry or Happy) using 2 buttons of an MR compatible response pad (NordicNeuroLab, Norway). They were also instructed to pay attention to both the face and voice, but could use the information presented in whatever way they wished to make their decision on emotion. Reaction times (relative to stimulus onset) were collected using Matlab with a response window limited to 2 s.
Imaging parameters Functional images covering the whole brain (slices = 32, field of view = 210 × 210 mm, voxel size = 3 × 3 × 3 mm) were acquired on a 3T Tim Trio Scanner (Siemens) with a 12 channel head coil, using an echoplanar imaging (EPI) sequence (interleaved, TR = 2 s, TE = 30 ms, Flip Angle = 80 • ) were acquired in both the carry-over and localizer experiments. In total, we acquired 1560 EPI image volumes for the carry-over experiment, split into two scanning sessions consisting of 780 EPI volumes. The first 4 s of the functional run consisted of "dummy" gradient and radio frequency pulses to allow for steady state magnetization during which no stimuli were presented and no fMRI data collected. MRI was performed at the Centre for Cognitive Neuroimaging (CCNi) in Glasgow, UK. At the end of each fMRI session, high-resolution T1-weighted structural images were collected in 192 axial slices and isotropic voxels (1 mm 3 ; field of view: 256 × 256 mm 2 , TR = 1900 ms, TE = 2.
cm 23 ) that were in turn converted to influxes (numbers. cm 22. yr 21 ) by multiplying concentrations by sediment accumulation rate (cm. yr 21 ) for each sample, resulting in a regularly spaced time series of pollen or macroremains. This procedure uses the pretreatment method of charcoal records for reconstruction of fires as proposed by Long et al. [29]  and was adapted for the pollen and macroremain records by modifying the interpolation procedure from the freely available CharAnalysis software {https://sites. google. com/site/ charanalysis/;Higuera, 2009 #308}.
Evenness Biodiversity estimates are a combination of the taxa number and the species evenness  [31, 32]. Therefore, we also estimated the evenness of fossil assemblages using Hurlbert's probability of interspecific encounter (D1,  [33] ) such as: D 1 ~N N{1 1{ X S i~1 p 2 i ! , ð3Þ were p i is the proportion of the i th taxon and S the total number of taxa in the sample. Finally, we considered important to distinguish between functional groups in the fossil assemblages, i. e. between tree, shrubs and herbs taxa. This allowed us to highlight the main temporal trends of plant community composition. The pollen data from Lac de Fully can be accessed from the European Pollen Database (http://www. europeanpollendatabase. net/), the macroremain data is available at http://doi. pangaea. de/10. 1594/PANGAEA.
The Test Dataset In order to test the new algorithm, we used the macroremain and pollen records from Lac de Fully (2135 m a. s. l), a small subalpine lake located on a south-facing plateau in the Valais (inner Swiss Alps). A full description of the site, sediment sampling, the age,depth model, sample processing, and identification of plant macroremains (e. g. needles, leaves, seeds, bud scales) and pollen is presented in Finsinger and Tinner  [34]. The pollen and macroremain records from Lac de Fully cover the past ,11,000 years. In total, 84 samples were analyzed for macroremains ([minmax] resolution:  years. sample 21 ) and 62 samples were analyzed for pollen ([50-379] years. sample 21 )  [34]. All ages are expressed in calibrated years before the present (hereafter ''cal BP''; by convention the ''present'' year is  AD 1950).
This research was funded by the  FP7 European Research Area Programme (ANR/ERA-net BiodivERsA ). This work was developed following a FIREMAN Project's Workshop held in Montpellier. Financial support was provided by the  FIREMAN program  ( ANR/ERA-net BiodivERsA ) to CC and by grants from the  Ecole Pratique des Hautes Etudes (EPHE-Paris ) to OB. The research was carried out in the framework of the  International Associated Laboratory (LIA France-Canada )  CNRS-UM2-EPHE-UQAT-UQAM-UQAC  '' MONTABOR: Forets montagnardes et boreales : chrono-ecologie et amenagement ecosystemique durable ''. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.