dataesr/scientific-paragraphs-categorization huggingface dataset:
The traces in the WHISPER spectrograms are correctly assigned to some characteristic frequencies in plasma, from where electron density can be inferred. In these circumstances, the WHISPER data can be taken as reference. The first two assumptions greatly simplify the calibration task, basically implying that only two coefficients (one for each sensitivity side) are needed to correct the HIA efficiency. It also means that the on-board moments are accurate up to a multiplication factor determined through calibration, allowing thus to take advantage of the HIA highest temporal, directional and energy resolution. Indeed, e. g. in magnetospheric modes the on-board moments are computed every spin period based on uncompressed data accumulated in 32 energy channels and 16 elevation × 32 azimuth solid angles  (Di Lelis and Formisano, 2000) , whereas the ground-computed moments are based on the reduced distribution function transmitted to the ground, having typically 31 energy × 88 solid angle bins and poorer time resolution.
duced by the on-board processing software in order to comply with the limited capacity of the telemetry; in that process the counts registered by individual anodes are binned in 8 angular sectors. Regarding the second assumption, i. e. constancy of the coefficients describing the efficiency energy dependence, there are some indications that this might not be completely valid (see e. g. the discussion about the HIA measurements in the plasma sheet environment in Sect. 6) but so far no careful study addressing this problem has been carried out. However, it seems that a change in the efficiency energy dependence is a second order effect, at least in the plasma environments where the two HIA sides are calibrated (see the statistical studies presented in Sect. 5). One particular aspect that might be of concern is the role of ion composition in the HIA-WHISPER data comparison.
The last value in the set is assumed to be valid until the beginning of the next MS season. Intervals for calibration are carefully selected to meet several criteria listed below: the HIA energy spectrogram suggests that, presumably, the vast majority of ions are detected, i. e. no indication of a significant ion population below or above the detector energy range exists. the evolution of HIA density is regular, useful for revealing potential instrument artifacts. This condition is also important because HIA and WHISPER could provide different values when a steep boundary is encountered (due to Larmor radius effects). It is desirable as well to select intervals where the density values span over a wide range, for a better comparison. preferably the same intervals for both C1 and C3 are used, to allow for an inter- For the selected intervals, the WHISPER density data are requested from the instrument team, that either decides to re-generate it for the purpose of HIA calibration or to re-validate the already available CAA data set.
The sixth panel checks the plasma gyrotropy as measured by the quantity (p ⊥2p ⊥1 )/[(p ⊥2 + p ⊥1 )/2], with p ⊥1 and p ⊥2 being the plasma thermal pressure along two orthogonal directions in the plane perpendicular to the magnetic field. The relatively small deviations from gyrotropy, e. g. around or below 5 %, provides supporting evidence of HIA symmetric anode response for this interval. The bottom panel shows the N HIA /N WHI density ratio (blue) and its average value for this interval (magenta straight line). The result of comparison can also be shown in the form of HIA vs. WHISPER density plot, presented in Fig. 4 , where the left part refers to the event described above. The points are clearly scattered along the regression line (in red) forced to cross the origin; In spite of the careful selection, it can happen that the HIA-WHISPER data comparison brings inconsistent results on some intervals.
Calibration of HIA LS section With some specific differences, the calibration of the HIA low sensitivity section follows a similar procedure. One uses solar wind (SW) data and compares the HIA and WHISPER density on carefully selected intervals. The selection is based on the type of plot presented in Fig. 5. The top panel shows HIA (red) and WHISPER (black) raw density data, A number of sudden HIA density drops (instrument artifacts), can be seen here as well. In the second panel the energy spectrogram based on data accumulated on the LS and is shown. For the purpose of calibration, it is desirable to select events with low presence of α particles, (seen in the second panel as the faint green line, at around twice the peak proton energy). Since an accurate calibration of the LS section requires no significant counts accumulated on the HS section (blocked when facing the SW direction, see Sect.
2), the third and the fourth panel present the plasma density and the energy spectrogram based on data accumulated on this side. The bottom panel compares the LS (red) and HS (black) count rates. After the selection of SW intervals suitable for calibration, the raw HIA and WHISPER data are processed in a manner similar to that described in the previous section. For the event presented above, the result of the comparison is showed in the right part of inferred from different intervals, a value is obtained that will be used in the calibration files update.
Statistic comparison between HIA and WHISPER data To validate the results of calibration methodology presented in Sect. 4, two statistical studies have been carried out, each based on data provided by one HIA sensitivity side. All MS and SW intervals observed on Cluster 1 data between 15 December 2006 and 15 February 2007 have been analyzed in order to compare calibrated HIA and WHISPER density data. Stable detector efficiency is expected in that period since the previous MCP high voltage increase occurred in January2006, the next one being performed on 16 February 2007, i. e. just after the chosen interval. Using selection criteria similar to those presented in Sect. 4, a total of 54 MS intervals sampled by HIA HS section have been identified, comprising around 55 h of data. For the LS section, 64 SW intervals have been identified, covering around 96 hours of data.
Summary and future work The first two panels in Fig. 8 , showing the evolution of HS and LS detection efficiency in the course of the mission, summarize the results of HIA in-flight calibration activity. Our comments below will refer mainly to the period starting from October 2005, subject to the calibration methodology presented in this paper. The top and middle panels in Fig. 8  refer to the HS and LS section, respectively. The shown detector efficiency, obtained by comparing the HIA and WHISPER data densities, are relative to the beginning of the mission. Black lines present the C1 evolution while the red lines the evolution for C3. Each value of the efficiency is shown by a horizontal segment, with the length indicating its validity period. There are no HIA data on C3 after November 2009 and on C1 LS side after June 2011 (see the end paragraph of Sect.
to calibrate the He ++ data provided by HIA in the SW. Related to the third item above, there is evidence suggesting the need for correction of the efficiency energy dependence. The situation presented in Fig. 9  is typical for the plasmasheet. The HIA-CODIF data discrepancy in this environment could be explained by changes in the efficiency energy dependence, since the average plasma energy in the plasmasheet is higher than in the magnetosheath, where the instrument was calibrated. It means that a correction that results in lower efficiency for the high energy channels would provide a plasma density correction in the right direction (i. e. an increase in density and, mostly, in pressure). The same type of correction has been suggested by the instrument response to the penetrating radiation during perigee passes (see  Ganushkina et al.
Acknowledgements. The authors would like to thank the WHISPER team (Principal Investigator,  Jean Louis Rauch ; former Principal Investigators,  Pierrette M. E. Décréau ,  Jean Gabriel Trotignon ) for their effort to ensure reliable electron density data for the HIA calibration. The calibration routine package are based on the cl (credit to  Emmanuel Penou ) and CCAT (credit to  Jim McFadden ,  Peter Schroeder  and  Chris Mouikis ) programs for CIS data analysis. The work has been supported through the project  VALS  (contract  45/19. 11. 2012 ) of the  Romanian Space Agency STAR program , and by the  OTKA  Grant  K75640  of the  Hungarian Scientific Research Fund.
ChIP-chip DNA was immunoprecipitated as described earlier using 10 µl of anti-CENP-A Cnp1 antibody per 100 µl chromatin extract  [36]. For analysis on GeneChip S. pombe Tiling 1. 0FR Arrays (Affymetrix) 5 mM dUTP was added to the second round of DNA amplification. Fragmentation, labelling and hybridization were performed by the Affymetrix core facility at Karolinska Institiutet (BEA) using standard protocols (http:// www. affymetrix. com). Raw data from Affymetrix (. CEL format) were normalized with Affymetrix Tiling Analysis Software (TAS) v 1. 1 and analyzed and visualized using Integrated Genome Browser (IGB  [48] ,. Normalized signal of each probe (25nt) were obtained from Tiling Arrays Software (TAS; Affymetrix) according to  [49]. For Figure  2 , probe values were averaged to give one value for one genomic region.
Cytology Immunolocalization was performed as described  [50]. Cells were grown in PMG liquid medium (with thiamine) at 25 °C and shifted to 36 °C for 24 h to allow CENP-A Cnp1 expression before fixation. Cells were fixed for 7 min in 3. 7% formaldehyde. The following antibodies were used: sheep CENP-A Cnp1 antiserum  (1:2000)  and Alexa-488-coupled donkey anti-sheep secondary antibody (1:1000) (Invitrogen -Life Technologies). Microscopy was performed as described  [50]  using a Zeiss Imaging 2 microscope (Zeiss, http://www. zeiss. com). Image acquisition was controlled using Metamorph software (Universal Imaging Corporation, http://www. moleculardevices. com). For comparison of cells expressing CENP-A Cnp1 from different promoters, identical exposures are used for imaging of all cells stained for CENP-A Cnp1 in any given set.
Supporting Information Figure S1. CENP-A Cnp1 overexpression in S. pombe cells. (A) CENP-A Cnp1 can be detected when overexpressed from the nmt1 promoter. Adapted from Castillo et al. , 2007 (Figure  S3 ). Western analyses of extracts from wild-type cells overexpressing CENP-A Cnp1 from nmt81 (81xC), nmt41 (41xC) or nmt1 (3xC) promoter to give low, medium and high expression levels compared with cells containing endogenous levels (3x). (B) Western analysis of GFP-CENP-A Cnp1 levels in wt and spt16-18 cells. From Choi et al. , 2012 (Figure  1D ). Western analysis of GFP-CENP-A Cnp1 levels in wt and spt16-18 cells expressing GFP-CENP-A Cnp1 under endogenous, nmt81 or nmt41 promoter (upper panel). The intensities of GFP-CENP-A Cnp1 and TAT-1 (alpha-tubulin) signals were measured using LICOR Odyssey Infrared Imaging System software (Li-COR Bioscience) and the relative intensities of GFP-CENP-A Cnp1 /TAT-1 were quantified (bottom panel).
Acknowledgements We thank members of the  Allshire Lab  for advice and discussion and we are very grateful to  Lakxmi Subramanian  for comments on the manuscript and helpful discussions. We thank  Julie P. Cooper  for providing the ura4 + : Int-Telo strain prior to publication along with taz1∆, ccq1∆, and rif1∆. We also thank  Takeshi Urano  for providing the H3K9m2 antibody (mAb5. 1. 1).
5%) showed a visual developmental phenotype (stem size and diameter, plant architecture, flower-related). The majority of these families showed more than one phenotype. Mutant phenotype data are organised in a database and can be accessed and searched at UTILLdb (http://urgv. evry. inra. fr/UTILLdb). Preliminary screens were also performed for atypical fiber and seed phenotypes. Genomic DNA was extracted from 3,515 M2 families and eight-fold pooled for subsequent mutant detection by ENDO1 nuclease mis-match cleavage. In order to validate the collection for reverse genetics, DNA pools were screened for two genes coding enzymes of the lignin biosynthesis pathway: Coumarate-3-Hydroxylase (C3H) and Cinnamyl Alcohol Dehydrogenase (CAD). We identified 79 and 76 mutations in the C3H and CAD genes, respectively.
5%) and cotyledons (shape and number -1. 8%). A majority (55. 2%) of the families show multiple phenotype modifications (Figure  1 ). Approximately 8. 6% (937) M2 plants were sterile and analyses indicated that sterility varied depending upon EMS concentration ranging from 6. 31% (0. 3% EMS) to 18. 36% (0,75% EMS) (Table  1 ). Data on flax mutant phenotypes were introduced into the UTILLdb database and can be consulted at http://urgv. evry. inra. fr/UTILLdb.
Based on currently available sequence data (http://www. phytozome. net/) provided by whole genome sequencing, three C3H and seventeen CAD genes can be identified in the flax genome. For C3H we decided to screen for mutations in [Phytozome : Lus10033524] the flax ortholog of the Arabidopsis thaliana CYP98A3 [Tair : At2G40890. 1] involved in lignin and flavonoid biosynthesis  [34]. In order to identify the flax CAD gene most likely involved in lignification, we established a phylogenetic tree containing CAD proteins from Arabidopsis thaliana, Populus trichocarpa and Linum usitatissimum (Figure  3 ). CAD proteins can be divided into 5 classes  [35]  with functional proof for the involvement of class 1 proteins in lignification. Four LuCAD genes were present in this class and we therefore decided to screen for mutations in the flax gene The CODDLE program (Codons Optimized to Discover Deleterious Lesions) combined with the PRIMER 3 tool were used to define the region within our two genes, with the most probability of a deleterious G/C to A/T transition and to design PCR primers compatible with the ENDO1 approach.
The first group, corresponding to the most marked phenotype contained 11 individuals belonging to 9 mutant families (8 missense mutations and one codon stop mutation). Characteristics of these mutants are detailed in Table  5. The potential effect of the different missense mutations was then evaluated by using the SIFT software (Sorting Intolerant From Tolerant; http://sift. jcvi. org/) that uses PSI-BLAST alignments, and the PARSESNP software (Project Aligned Related Sequences and Evaluate SNPs; http://www. proweb. org/parsesnp/) to provide a position-specific scoring matrix based on alignment blocks. PSSM scores were obtained by PARESSNP for 5 of the 8 missense mutations (Table  5 ). No scores were obtained for the G176R and P280L mutations, most probably because of a lack of alignment blocks.
Discussion Flax is an ancient crop that has long been cultivated for its fibers and seeds. Current breeding programs aim at improving yield and quality in both fibers and seeds, as well as increasing resistance to different pathogens. The flax genome has recently been sequenced  [13] , physical and genetic maps have been developed and SNP markers identified  [10]. In addition, flax specific microarrays, proteomics and metabolomics  [6, 7, 12, 50, 51]  have also been used to increase our knowledge of flax biology. In this paper we report the development of a flax TILLinG platform for forward and reverse genetics in this economically    UTILLdb is an open phenotypic and genomic mutant database containing information on mutant populations of pea  [37] , Brachypodium  [52] , tomato  [53]  and flax (this paper).
The integration of our data into UTILLdb will enable flax breeders and scientists working on the flax model to search the database for particular mutant phenotypes. For example, detailed characterization of mutants showing altered stem/fiber morphology (Figures  2  and 7 ), and/or flower/seed modifications (Figures  2  and 8 ) will prove particularly interesting for scientists and breeders interested in fiber formation and seed oil/lignan biosynthesis. The database will be updated with information on mutations in specific genes that scientists will be able to access by sequence homology and/or keyword queries. In order to validate the mutant collection as an efficient reverse genetics tool we extracted DNA from 3,515 lines and successfully TILLed 2 genes involved in lignin biosynthesis using the ENDO1 enzyme as previously described  [37, 54].
Although a previous report of CAD down-regulation in flax RNAi plants was associated with reduced lignin, somewhat surprisingly the authors did not report the presence/absence of a brown midrib phenotype  [16]. This might be related to the relatively high (60-80%) residual activity in these plants. Preliminary analyses of our M3 cad mutants (data not shown) indicate that the mutation is heritable, segregates and can be correlated with brown/orange coloured xylem further establishing the link between mutation and phenotype in the flax population. Wet chemistry and Table  4  Frequency of triplet motif centred on all Gs and on mutated Gs. Ratios were obtained by comparing the frequency of a particular triplet centred on the mutated G to the frequency of the same triplet centred on any available G in both amplicons  spectroscopy will allow confirmation of structural modifications in cell wall lignin in these mutants.
PCR amplifications were verified on an agarose gel. Mutations were detected using a LI-COR 4300 DNA analyzer as previously described  [66]. Individual mutations were confirmed and characterized by sequencing of DNA from individual M2 families. final manuscript. RT managed greenhouse/field mutant plant production, participated in plant phenotyping and sample collection and checked the final manuscript. J-PT managed greenhouse/field mutant plant production. BT and OvW participated in plant phenotyping, in the obtention of seed collections, of the phenotyping analysis of all seed banks. MChat. Designed the EMS pilot dose experiments, selected the doses and checked the final manuscript. XG represented Laboulet in the PT-Flax consortium. VB organized the phenotypic data set in the UTILLdb web site.
Acknowledgements Max. Chant. Gratefully acknowledges financial support of the  Région Nord-Pas-de-Calais  and the  University of Lille  1 for the Ph. D. grant. SH gratefully acknowledges the financial support of the  Agence Nationale de la Recherche  ( ANR ) for the  PT-Flax project  ( ANR-09-GENM-020-005 ). Man. Chat. would like to thank  Dr S. K. Samanta ,  Prof Charles Spillane  (NUIG) and  Anish Kumar  for general advice on flax, mutant population creation and pilot EMS dose experiments. The authors would like to thank all the different people who contributed to the collection and phenotyping of the flax plants ( Stephane Fénart ,.
Bio-informatic sequence analyses Phylogenetic analyses of CAD proteins was performed using the clustalW program with default parameters (http:// www. clustal. org/clustal2/). The choice of gene regions to be TILLed and amplification primers were designed respectively using the CODDLE tool (Codons Optimized to Discover Deleterious Lesions; http://www. proweb. org/coddle/), primer 3 and OligoCalc system (http:// www. basic. northwestern. edu/biotools/oligocalc. html). Potential effects of missense mutations were evaluated using the SIFT software (Sorting Intolerant From Tolerant; http://sift. jcvi. org/) and the PARSESNP software (Project Aligned Related Sequences and Evaluate SNPs; http:// www. proweb. org/parsesnp/).
Selection of loci and strains Initially, 40 coding DNA sequences (CDS) that correspond to housekeeping genes and are evenly spaced in the Leishmania genome were investigated. To identify only single copy genes, a systematic Blast analysis was performed against three complete Leishmania genomes deposited in GenBank (L. infantum, L. major and L. braziliensis)  [9]. Then, the nucleotide sequences of these three species were compared to map polymorphic and conserved regions within each gene and to eliminate CDS containing indels. Finally, seven loci that are located in the central or telomeric region of six different chromosomes and are considered as independent genetic units were selected. To investigate a possible genetic linkage between loci located on the same chromosome, the loci 31. 0280 and 31.
2610 (separated by 1. 2 Mb) on chromosome XXXI (2. 7 Mb) were chosen. The biological function of the housekeeping genes analyzed was not considered as a criterion for locus selection. In all, 222 Leishmania strains isolated in Eurasia and Africa were analyzed. These strains were selected among 6,000 Leishmania strains deposited in the collection of the French Reference Centre on Leishmaniasis (Montpellier, France). They were mainly isolated from infected patients (n = 176, 79. 3%), but also from mammalian reservoirs (n = 38, 17. 1%) and insect vectors (n = 8, 3. 6%) (Table  S1 ). All samples taken from humans were anonymized. To be representative of the Leishmania genetic diversity, strains were selected on the basis of isoenzymatic and geographic criteria. For each strain, isoenzymatic data for 15 enzymatic systems were available and each zymodeme was represented by at least one strain.
When a zymodeme was present in different countries, a strain from each of these countries was selected, if possible. The 222 strains originated from 43 countries and were representative of 110 zymodemes. According to the isoenzymatic-based taxonomy  [15] , these strains were representative of the 10 different Leishmania species currently described in Eurasia and Africa: L. infantum (n = 90), L. major (n = 42) L. donovani (n = 29), L. tropica (n = 18) L. aethiopica (n = 18), L. archibaldi (n = 9), L. turanica (n = 8), L. gerbilli (n = 4), L. killicki (n = 3) and L. arabica (n = 1). Although recent works have suggested the possibility of genetic exchanges in the Leishmania genus, including inter-specific hybrids, such hybrid strains were not included in our dataset  [7, [16] [17] [18] [19] [20] [21] [22] [23] [24].
Nucleotide polymorphism analysis In all, 1,554 double-strand sequences were aligned and visually checked using the CodonCode aligner software v. 4. 0. 4 (Codon-Code Co. , USA). Sequences were put in phase with the open reading frame. The locus size ranged from 486 to 810 bp and the concatenated sequence was 4,677 bp-long (Table  1 ). The 1554 sequences were deposited in GenBank under the following numbers: KC158588-KC160141. As Leishmania is mainly considered to be a diploid organism, a special attention was paid to the heterozygous positions  [10]. Chromatograms were examined visually in both directions and usually results were easily interpreted as heterozygous when two peaks in a chromatogram overlapped. No tri-allelic site was found. Only one strain (L3538) was cloned to look for multi-clonal populations of parasites and the allelic profiles of the clones were identical.
The MEGA version4 program was used to calculate the number of variable nucleotide sites, the nucleotide diversity (average number of nucleotide differences per site between any two randomly selected sequences) and the transition/transversion ratio (R). Haplotype diversity (Hd) was calculated using the DnaSP software, version5  [25]. The possible selection pressure on these protein-coding sequences was checked using the dN/dS ratio test and the Z-test of selection based on the Nei-Gojobori method (implemented in the DnaSP software, version5, and in the MEGA 4. 0 package, respectively)  [26].