dataesr/scientific-paragraphs-categorization huggingface dataset:
25 (the full gene distance could be grossly calculated by dividing times 1. 4). This was the general picture, since evolution- ary rates might differ among slow-and fast-evolving lineages. Remarkably, many of the arbitrarily defined environmental ribogroups (MALV-III, MALV-V, RAD B and all MAST clades) were consistent with this maximum distance, indicating that they were congruent with a taxonomic rank equivalent to the classical class. Once the dataset was manually curated and all sequences assigned to one of the 65 taxonomic groups, we started to analyze the diversity of the whole dataset of marine microeukaryotes. Overall, we detected 3,677 OTUs at 0. 01 distance, mostly within Alveolata (63% of OTUs), Stramenopiles (15%), Rhizaria (9%) and CCTH (6%). Almost half of these OTUs belonged to taxonomically undefined ribogroups.
Despite the dataset analyzed here most likely captures the general architecture of protist diversity in terms of main phylogenetic lineages, it is clear that a better estimation of diversity extent requires deeper sequencing efforts as provided by HTS. When observing how the clustering threshold affected OTU numbers, Alveolata still dominated at all levels, whereas classes like Labyrinthulida, Diplonemea and Kinetoplastea had an exceptionally high diversity. The last one exhibited the highest maximum corrected distance, probably due to a massive accumulation of sequence mutations  [31]. Whereas the clustering pattern (Fig. 4 ) allowed quantifying the degree of genetic diversity of the groups at present time, the LTT plots (Fig. 5  and Fig. S4 ) used the tree topology to infer the cladogenesis events during the entire evolutionary history of different groups.
In summary, a good approximation to the evolutionary history of a given group could be reached by combining LTT plots and trunk lengths. This provided an overview of when most diversification occurred and what was the uniqueness of each group. The phylogenetic structure enriched and complemented the picture drawn by clustering pattern, which allowed reasonable comparisons among groups in terms of OTU numbers and maximum distances. Together, these two structural features gave a reasonable characterization of the diversity of the main microeukaryote clades. New sequencing technologies (pyrosequencing, Illumina) are already providing a huge amount of sequences, and a good phylogenetic and clustering pattern overview based on a robust technique is required to ensure a solid backbone for interpreting and manipulating future high-throughput datasets.
Sequence dataset and classification into taxonomic groups The initial set of 163,975 sequences derived from molecular surveys of 18S rDNA genes published in GenBank until January 2010 (see Table  S1 ) plus a few (,5%) unpublished sequences obtained at the Station Biologique de Roscoff (France). The database was filtered to keep sequences longer than 500 bp from marine planktonic protists (excluding sequences retrieved in freshwaters and sediments, or affiliating to metazoans and fungi). In addition, the sequence quality of the dataset was refined by keeping only sequences derived from clone libraries, having few unidentified bases (if any), and that passed a chimera check done with the application KeyDNATools (http://www. keydnatools. com) (Fig. S1 ). The resultant 13,270 sequences were taxonomically classified with KeyDNATools (Fig.
S2 ). Sequences ambiguously classified (less than 5 keys, keys in one region of the sequence only, or few keys from different groups [non-obvious chimeras]) were checked with BLAST  [37]  and assigned to a given group if they were $90% similar to a well-identified reference sequence. In some cases, BLAST with different parts of the sequence was done to double-check they were not chimeras. The initial dataset was distributed into 65 taxonomic groups (basically based in the ''Second rank'' level of Adl et al. [38] ), including classical taxa mostly at the ''Class'' level plus new ribogroups. Sequences within each group were aligned with the FFT-NS-i strategy of MAFFT  [39]. The alignment was cut manually in Seaview 3. 2  [40]  to keep a dataset of ,500 bp that covered the V4-V5 regions of the 18S rDNA.
This software was also used to estimate the length of the branch at the base of each monophyletic group, which was named ''trunk'', and the average intergroup phylogenetic distance (the mean of all pair-wise distances between sequences from different groups). Phylogenetic trees representing the different taxonomic groups were extracted from the Stramenopiles tree using Dendroscope  [46]. Trees were transformed to ultrametric, and used to calculate the evolution of the lineages through time (LTT). Relative time was considered, ranging from 21 (the origin of the lineage) to 0 (present time), and the number of lineages was standardized (percentage of the maximum number) to compare LTT plots among groups. For each plot, the c-statistic was calculated as a descriptor of the evolutionary trends  [32].
Funding has been provided by projects  FLAME  ( CGL2010-16304 ,  MICINN, Spain)  and  BioMarKs  ( 2008-6530 ,  ERA-net Biodiversa, EU)  to RM, by project  GEMMA  ( CTM2007-63753-C02-01/MAR ,  MEC, Spain)  to  Carlos Pedro  ´s-375 Alio, by a  FPI fellowship  from the  Spanish Ministry of Education and Science  to MCP, and by a  Marie Curie Intra-European Fellowship  grant  PIEF-GA-2009-235365  to RL. Original sequence database was built under the frame of the  French ANR-Biodiversite  ´project AQUAPARADOX. High-performance computing resources were provided by the  Barcelona Supercomputing Center at the MareNostrum  (grants  BCV-2011-2-0003 & BCV-2011-3-0005 ) to RM and RL. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
The computation resources are measured as the CPU time per scan, obtained for a Matlab © implementation of both methods on a 3. 16 GHz Intel Xeon machine. Note that from the results in Table  2 , the computation resources consumed by both algorithms are approximately the same, thus ensuring a fair comparison between both methods. We first compare the proposed and benchmark TBD algorithms in terms of detection performance for the target parameters in Table  1. Detection performance is measured in terms of mean-time-to-detect (MTTD) and probability of detection, P d. The MTTD is the average time delay between the onset of a target and its actual detection. The results in Table  2  show that both algorithms have approximately the same MTTD. Also, Figure  3  illustrates the evolution of the detection probability versus time, beginning at the onset of the target.
Study Population Fifteen male or female volunteers (aged 18 years or older) without known cardiovascular disease were recruited in this study. Each subject gave his/her written informed consent prior to participation. This study received local institutional review board approval from the comitede protection des personnes (CPP) Ouest II, conformed to the Declaration of Helsinki, and was registered to the American National Institutes of Health database under reference Nu: NCT01664572. The first subject was included July 2, 2012.
Microvascular Recordings All microvascular tests were performed with subjects resting supine in a temperature-controlled room (2361uC). Tests were performed at least 2 hours after a meal. All tests were performed in the morning and at a same hour for each subject. All subjects had an acclimatization period longer than 15 min before the beginning of the cutaneous blood flow (CBF) acquisitions. LDF recordings. The LDF relies on the Doppler effect. When photons of a laser light encounter moving particles (mainly red blood cells) in the studied tissues, the Doppler effect appears (modification of photon frequency). For LDF recordings, we used a laser Doppler flowmeter (Periflux PF 5000, Perimed, Ja ¨rfa ¨lla, Sweden). Data of the CBF acquired by LDF were expressed as arbitrary units (a. u. ) and recorded on a computer via an analogue to digital converter (Biopac System, Inc.
This blurring depends on the speed and volume of moving particles in the tissues: low contrast images come from a high amount of blood flow, and inversely, high contrast images come from a low amount of blood flow  [28, 29]. LSCI recordings of the forearm CBF were performed using a 70-mW system (PeriCam PSI System H, Perimed, Ja ¨rfa ¨lla, Sweden) having a laser wavelength of 785 nm  [30]. The sampling frequency was 18 Hz. The distance between the laser head and skin surface was fixed at 15 cm, according to our previous publication  [31]. Images were stored on a computer and analyzed off-line. The signal amplitudes backscattered from the skin were calculated using the manufacturer's software (PimSoft5. 1 H; Perimed, Ja ¨rfa ¨lla, Sweden) before being exported to an Excel spreadsheet (Excel 2002 V3 H, Microsoft USA).
Cardiovascular and Temperature Recordings Blood pressure was recorded from the left middle finger (Nexfin, Bmeye, Amsterdam, Netherlands) with mean arterial pressure (MAP) obtained from the continuous blood pressure signal. The MAP signal was recorded at a sampling rate of 18 Hz using an analog to digital converter (MP150, Biopac sys, Goleta, USA) and analyzed offline using the Acknowledge H software V3. 5. 4 (Biopac sys, Goleta, USA). Local cutaneous temperature was measured on the right forearm using a surface thermocouple probe connected to an.
Data Analysis The region of interest (ROI) is defined as the skin area of interest. The size of ROI was 20 mm 2. Time of interest (TOI) is defined as the duration in seconds over which data (pixel values) are averaged  [21]. It was set to 5 s for the maximal amplitude of ACh induced vasodilatation for LDF and LSCI. Baseline (absolute) corresponds to the CBF measured before the first current stimulation and is expressed in a. u. when LDF was used and in laser speckle perfusion units (LSPU) when LSCI was used. Baseline (CVC) corresponds to the CBF measured before the first current stimulation and expressed in Cutaneous Vascular Conductance (CVC). CVC is calculated as the laser signal value divided by the mean arterial pressure when the measurement is performed. CVC is expressed either in au/mmHg (for LDF) or in LSPU/mmHg (for LSCI).
05 was considered as statistically significant. Intra-subject reproducibility. The intra-subject reproducibility at a 7-day interval was evaluated by the typical error of the estimate (TEE) of the peak of ACh as well as the coefficient of variations (intra-CV) according to the procedure proposed by Hopkins  [33]. TEE was calculated as standard deviation of the paired differences/!2). For the calculation of intra-CV, data measured for each subject at D1 and D7 were log-transformed as proposed by Hopkins (http://www. sportsci. org/resource/ stats/)  [33]. Then intra-CV was calculated as 100*[e [(Standard deviation of the log-transformed paired differences/!2/100)] 21]. The lower the intra-CV, the better the reproducibility. Intra-subject reproducibility of measurements was also assessed by intra-class correlation coefficients (ICC)  [34].
ICC corresponds to a measure of agreement between test and re-test values of each measurement technique for D1 and D7 (Please refer to http:// sportsci. org/ressource/stats/xrely. xls )  [33]. The ICC value ranges from 0. 00 to 1. 00, with values closer to 1. 00 representing   [35]. Intra-CV and ICC are currently used to assess the reproducibility and represent additional information about the reproducibility of measurements  [33]. Statistical analyses were performed with SPSS v17 (SPSS, Chicago, Illinois, USA). Calculation of number of subjects. Number of subjects was calculated from ICCs by estimating the width of the 95% confidence interval for an expected correlation of 0. 70 (between the lowest ICC value considered as acceptable, i. e. 0. 40, and the highest, i. e. 1. 00). Considering a two-sided interval, two measurements and a distance from correlation to limit v of 0.
Identification of HMG-box genes in P. anserina The Podospora protein database (Materials and Methods) was searched for HMGB proteins using the previously defined HMGbox consensus sequence  [27]  as query in a Blastp search  [48]. P. anserina genome annotation in Fungal Transcription Factor  [49]  and Superfamily databases  [50]  was examined to identify possible missing HMGB genes in our analyses. A total of 12 HMG-box genes were identified. Of these, eight had not been previously characterized and were here named PaHMG2 to PaHMG9 (Table  1 ). CD-Search  [51]  identified five MATA_HMG-box proteins and five HMGB-UBF_HMG-box proteins (Table  1 ), but failed to categorize two HMG-box proteins. These two proteins, PaHMG7 and PaHMG9, contained an atypical charged residue instead of a conserved aliphatic or aromatic amino acid at position 9 (Figure  1 ).
This color scheme is displayed in Table  S1. doi:10. 1371/journal. pgen. 1003642. g001 2 ), which comprised members of the HMGB-UBF_HMG family, except the clade grouping PaHMG7 (Podan6b), NCU06874 (Neucr6b) from N. crassa and NP_595970 (Schpo7b) from S. pombe. These three proteins are characterized by a conserved atypical residue in the HMG-box domain [see the above section for its description in PaHMG7 (Podan6b)] and a SprT-like domain (Interpro accession number IPR006640). The four HMGB-UBF_HMG-box proteins, PaHMG2 (Podan5c), PaHMG4 (Podan5b), mtHMG1 (Podan5d), and PaHMG6 (Podan5a), branched into different clades of group D. PaHMG2 (Podan5c) belonged to a branch that included proteins with a Sterile Alpha Domain (SAM), which is involved in protein-protein and protein-RNA interactions  [60].
Numbers after species names indicate a1 proteins (1), MATA_HMG (2), MAT1-1-3 (3), SOX (4), HMGB-UBF_HMG (5) and other HMG domains  (6) (7) (8) (9). When more than one domain was present for the same species, the suffix a, b or c was used. Units indicate the number of amino acid changes per position. Species codes and accession numbers grouped by evolutionary affinity are listed in Table  S2. doi:10. 1371/journal. pgen. 1003642. g002 observed in the sub-apical portion of a wild-type thallus (Figure  3B ), sub-apical hyphae from the DPahmg9 strain fused with each other, leading to bundles of tightly attached hyphae (Figure  3B ). Moreover, apical hyphae often fused with each other in the DPahmg9 strain (Figure  3B ). We never observed wild-type apical hypha anastomoses, suggesting that the DPahmg9 mutation deregulates cell fusion.
These results demonstrated that PaHMG6, PaHMG8 and KEF1 are required for making the perithecial envelope and that they are dispensable for the hymenium. We also examined the behaviour of the Dmthmg1 strain in a trikaryon mosaic test. As previously described  [44] , most Dmthmg1 cultures died quickly and did not differentiate protoperithecia. A trikaryotic culture involving sexually compatible Dmthmg1 strains and Dmat strain produced perithecia, but no ascospores were formed (data not shown). These results indicate that the Dmat tissue complemented the vegetative requirement for mtHMG1 during protoperithecium formation, while the absence of mtHMG1 in the hymenium led to an arrest of its development. The protein mtHMG1 is therefore required for both protoperithecial and hymenium development.
Conversely, to evaluate whether Dmat PaHMG5 + could restore the male competency of DPahmg5, cultures of the mat2 wild-type strain were fertilized with spermatia issued from a co-culture of mat+ DPahmg5 and Dmat PaHMG5 + strains (Materials and Methods). Two fruit-bodies were observed on 10 Petri dishes for the female sterility restoration assay, while three fruit-bodies were observed on 10 mat2 plates for the male restoration assay. Several thousand perithecia were formed with wild-type strains in similar experiments, indicating that male and female sterility of DPahmg5 strain was inefficiently complemented by the Dmat PaHMG5 + strain. Taken together, these data are consistent with a requirement of PaHMG5 for fertilization or an early stage of hymenium development. The arrest in the development of the hymenium at an early stage precludes any conclusion on the role of PaHMG5 in the development of the maternal perithecial envelope.
Identification of HMG-box genes and structural analyses The P. anserina genome and protein databases are available at http://podospora. igmors. u-psud. fr/. Identification of HMG-box genes was checked using Fungal Transcription Factor (http://ftfd. snu. ac. kr/intro. php) [49]  and the Superfamily databases (http:// supfam. cs. bris. ac. uk/SUPERFAMILY/index. html) [50]. For domain identification, Pfam  [96]  and CD-searches  [51]  were run on http://pfam. sanger. ac. uk/ and http://www. ncbi. nlm. nih. gov/ Structure/cdd/cdd. shtml, respectively. N. crassa HMG-box genes were obtained from http://www. broadinstitute. org/annotation/ genome/neurospora/MultiHome. html. FUNGIpath  [52]  was used to identify systemically orthologous groups of genes (http:// embg. igmors. u-psud. fr/fungipath/).
The normalization genes are listed in Table  S13. RT-qPCR normalization was performed according to the relative quantification method with kinetic PCR efficiency correction. Standard error and 95% confidence interval calculations, and other statistical analyses were performed using REST 2009 software (Qiagen, Hilden, Germany)  [102]. The FC in the expression of a gene of interest was computed as the normalized relative quantity of cDNA in sample relative to that in the control: FC = relative quantity of cDNA for the gene of interest x (geometric mean of relative quantity of cDNA for the normalization genes) 21  The relative quantity (RQ) of a cDNA was: RQ = efficiency of amplification (arithmetic mean for WT strain replicates -arithmetic mean for mutant strain replicates) The efficiency of amplification was above 1.
Color scheme used for Jalview. (DOC)TableS2Code and accession numbers for proteins shown in Figure2. (DOC) Table S3 Relative quantification of HMG-box gene and mating-type target gene transcription in DPahmg5 (DPa_1_13940) and WT strains. (DOC) Table S4 Relative quantification of HMG-box gene and mating-type target gene transcription in DPahmg6 (DPa_1_14230) and WT strains. (DOC) Table S5 Relative quantification of HMG-box gene and mating-type target gene transcription in DPahmg8 (DPa_6_4110) and WT strains. (DOC) Table S6 Relative quantification of HMG-box gene and matingtype target gene transcription in Dkef1 (DPahmg9, DPa_1_7190) and WT strains. (DOC) Table S7 Relative quantification of HMG-box gene and mating-type target gene transcription in Dmthmg1 (DPa_1_13340) and WT strains. (DOC) Table S8 Phenotype of F.
Direct and continuous measurement of water content and water pressure every 10 min during evaporation of the substrate made it possible to calculate the water flux q through the column, using the following equation: q = - Â t ( 9 ) where q is expressed in m s -1 , Â is the water storage variation in the container (m) during time interval t (s). The unsaturated hydraulic conductivity K( ) (expressed in m s -1 ) was calculated by direct measurement from water content and water pressure data during the experiment with the Buckingham-Darcy equation  (Green et al. , 1986) , by dividing the fluxes calculated above by the hydraulic gradient (d ) at the same positions (dz = h 1h 2 ) and times: K( ) = -q (d /dz -1) (10) Air entry point: measurements of air entry values ( ea ) were performed at the global pot scale.
Impact of root distribution on peat physical properties Peat bulk density increased from 0. 103 g cm -3 at the beginning of the experiment to 0. 113 g cm -3 at the end, whatever the peat Relationship between peat unsaturated hydraulic conductivity and peat water potential K( ) on days 0 and 110, for fine and coarse peat particle sizes. K( ) experimental data were obtained from the instantaneous profile method, and they were fitted with the van Genuchten model (VG).
Acknowledgements This study was carried out within the  EPHor research unit at Agrocampus Ouest, Centre of Angers. The authors would like to thank all of the people who contributed to this work. First, we would like to express our gratitude to  Emmanuel Bozon  for data collection and processing. Second, special thanks to  Klassmann-Deilmann  for providing the peat. Third, we would like to express our gratitude to  Yvette Barraud-Roussel ,  Sylviane Delepine-Bourgeois  and  Claudie Mazzega  who performed the laboratory measurements.
The biological activities of ovalbumin in hen egg are still not defined. Bioinformatical analysis of OVAY and OVAX revealed that both proteins are also ov-serpins, named SERPINB14B and SERPINB14C, respectively. The alignment of their respective reactive site loop with that of inhibitory ov-serpins indicated that both proteins have hinge regions that deviate less from the amino acid consensus sequence for inhibitory ov-serpins than that of non-inhibitory ovalbumin, and some have suggested that OVAY and OVAX may be inhibitory  (10). The protein sequence of OVAX is still not fully defined: it appears as a predicted sequence related to OVAY in the NCBI database: predicted sequence, ovalbumin-related protein Y (Gallus gallus) (XP_418984. 3), whereas only a fragment of the protein is available in the Uniprot database (OVALX_CHICK, P01013).
Réhault-Godbert and J. Gautron (authorizations 7323 and 37-144, respectively). Hens were euthanized, and tissues were harvested from infundibulum, white isthmus, magnum and uterus, liver, kidney, and duodenum. Real-time Quantitative-Polymerase Chain Reaction was performed as described (15) using primers 5Ј-AAGA-CAGCACCAGGACACAGA-3Ј (forward) and 5Ј-TTCTGGC-AGATTGGGTATC-3Ј (reverse) for ovalbumin (amplicon ϭ 212 base pairs) and 5Ј-TCCGTGAACATCCACCTACTCT-3Ј (forward) and 5Ј-GGCTTGGTCTGATGCTGTTT-3Ј (reverse) for OVAX (amplicon ϭ 198 base pairs). Ovalbumin and OVAX mRNA levels were corrected relative to ribosomal 18 S rRNA. The ratio value was calculated for each sample as "OVAX" or ovalbumin/18 S RNA. The log of the ratio was used for statistical analysis using one-way analysis of variance (Stat-View software, version 5, SAS Institute, Inc.
(19). All molecular species (102 m/z) obtained with MALDI and nanoESI were matched automatically to proteins in a non-redundant database using MASCOT software (Matrix Science) against the Chordata section. Enzyme specificity was set to trypsin with four missed cleavages using carbamidomethylcysteine and methionine oxidation as variable modifications. The tolerance of the ions was set to 100 ppm. Protein hits were validated if the protein score was above the MASCOT default significance threshold (p Ͻ 0. 05). Biochemical Analysis of OVAX and Ovalbumin-Four g of OVAX and S-free ovalbumin (Sigma-Aldrich, Saint-Quentin Fallavier, France) were diluted in sample buffer with or without ␤-mercaptoethanol. Samples with ␤-mercaptoethanol were boiled, and all samples were further analyzed by SDS-PAGE.
Deglycosylation of OVAX (10. 2 g) and ovalbumin (10 g) by PNGase F was performed following manufacturer's instructions. Identification of Glycosylation Sites in OVAX Protein-Native OVAX and OVAX deglycosylated by PNGase F were separated by SDS-PAGE under reducing conditions, stained by Coomassie Blue, and in-gel digested by trypsin. Peptide mixtures were analyzed by online nanoflow liquid chromatography tandem high resolution mass spectrometry. All experiments were performed on a dual linear ion trap Fourier transform mass spectrometer LTQ Orbitrap Velos (Thermo Fisher Scientific) coupled to an Ultimate 3000 RSLC chromatographer (Dionex, Amsterdam, The Netherlands) controlled by Chromeleon Software (Dionex). Five l of each sample were injected using l pickup mode and loaded on an LCPackings trap column.