dataesr/scientific-paragraphs-categorization huggingface dataset:
We note that the ICAL criterion behaves similarly to the ICL. Over the 100 simulated datasets, ICAL, as ICL, leads to some uncertainty as to whether a three cluster solution (53 times) or a four cluster solution (47 times) is best (Table  2 ). In this case, the annotation u B is not related to the components and has no impact on the resulting clustering, even if the number of annotations in the set is increased to 12, each simulated with the same pattern as u B as displayed in Figure  3B. Finally, for the mixed annotation u C with d annot = 0. 05, ICAL most frequently selects three clusters (67 times) or four clusters (33 times). Because the annotation u C is mixed, there is less evidence to merge the two clusters on the left than in the case of the informative annotation u A. Using the three types of annotations at the same time (u A , u B , u C ), the ICAL criterion almost systematically selects three clusters (Table  2 ).
Generally speaking, we note that when the number of annotations in the set increases, the evidence to merge clusters is stronger; for a higher density of annotations, a smaller number of annotations in the set is needed to merge clusters. In particular, for a lower density of annotations (d annot = 0. 05), increasing the number of associated low density annotations in the set (from one to twelve) also increases the level of confidence to merge clusters; clusters 1 and 2 are merged 72% of the time for a single annotation, and systematically merged in all simulated datasets for a set of twelve annotations (Table  2 ). On the other hand, for a higher density of annotations (d annot = 0. 25), clusters 1 and 2 are systematically merged even for a set of one single annotation (Table  3 ). If the annotation density is high (i.
e. where more than half of the genes in clusters 3 and 4 are annotated), multiple unassociated annotations can lead to overly parsimonious solutions, such as the two cluster solutions in Table  3. Mach et al. (2014)  analyzed transcriptome differences in the small intestine of healthy piglets to better understand their immune response. The expression of 24924 genes across 12 samples was measured using RNA-seq, corresponding to three different tissues (the duodenum, the jejunum and the ileum), each sequenced for four different healthy piglets. The raw data are available at NCBI's SRA repository (PRJNA221286 BioProject; accessions SRR1006118 to SRR1006133), and sequencing reads were pre-processed (i. e. quality control, alignment, and estimation of gene expression) as described in  Mach et al.
(2014). We performed a differential analysis using a negative binomial generalized linear model as implemented in the edgeR package version 3. 4. 2 (Robinson et al. , 2010). We identified 4021 genes as differentially expressed among any of the tissues after controlling the false discovery rate (FDR) below the level 0. 05 with the approach of  Benjamini and Hochberg (1995). For the following co-expression analysis, we restrict our attention to this set of differentially expressed genes.
Presentation of functional annotation data The Molecular Signatures Database  (Liberzon et al. , 2011)  was built by the Brain Institute and provides collections of annotated gene sets for use with the Gene Set Enrichment Analysis software  (Subramanian et al. , 2005). The Molecular Signatures Database (MSigDB) contains collections of gene sets from several sources: positional gene sets, curated gene sets from online pathway databases, motif gene sets, computational gene sets, GO gene sets, oncogenic canonical pathways and immunologic signatures. We used the Canonical Pathways (CP) gene sets collection, compiling 1320 canonical representations of biological processes curated by domain experts from online metabolic and signaling pathways databases such as the KEGG (http://www. genome. jp/kegg), BioCarta (http://www.
biocarta. com) and Reactome databases (http://www. reactome. org). Among the 1320 CP in the database, 1131 are represented among the 4021 differentially expressed genes. We select the CPs for which annotated genes are overrepresented in the set of differentially expressed genes The model most commonly selected for each criterion is highlighted in bold. Brought to you by | INRA Institut National de la Recherche Agronomique Authenticated Download Date | 12/4/15 10:02 AM with respect to the set of non-null genes using a Fisher's exact test. Since a test is performed for every possible annotation (i. e. each CP), we select those whose adjusted p-value is less than 0. 05, after applying a Bonferroni correction for multiple testing. This procedure yields 10 CPs of interest, as described in Table  4.
Since we do not include annotations in the clustering model, we detect associations between annotations and clusters with a stronger evidence than if we had included the external annotations in the clustering model. In particular, the ICAL criterion is a good way to include prior biological expertise without according it too much importance, which is a good balance between what can be observed in the data and what experts expect to see in the data. As illustrated in numerical simulations, the model selected by ICAL depends on the quality of the annotation information provided. Selecting the appropriate annotations to include in ICAL is an important step and should be done based on expert knowledge. We also suggest the use of gene annotation databases that are curated manually by experts, such as the gene sets collection from the MSigDB database  (Liberzon et al.
Figure1: Illustration of a simulated dataset and three annotation patterns. For each figure, the 200 observations are drawn from a mixture of Gaussian bivariate components whose parameters are defined in Table1: circles, triangles, inverted triangles and diamonds correspond to components 1-4. The three figures correspond to three annotation patterns: associated annotation u A (A), unassociated annotation u B (B) and mixed annotations u C (C). For each annotation, the 20 annotated genes are represented by colored bold crosses.
SIMULATION RESULTS In this section, the performances of the amplitude and angle shift estimators are accessed through simulations using Python/Numpy. The phasor parameters are set to d 0 = 1, d 1 = 1. 2, d 2 = 0. 2, φ 0 = 0, φ 1 = 2. a[n] = 1 + 0. 1 cos(2πf m n/F e ) (27a) φ[n] = 2πf 0 n/F e + 0. 1 cos(2πf m n/F e -π) (27b) where f m = 5Hz, f 0 = 50Hz, F e = 1000Hz. For each estimator, the Mean Square Error (MSE) is evaluated through 10000 Monte Carlo simulations. The MSE is analyzed and compared (asymptotically) with the Cramér Rao bounds for different data lengths and signal-to-noise ratios (SNRs), where the SNR is defined as SN R = 10 log trace(A(θ)SS T A T (θ)) 3N σ 2. ( 28 ) In the following simulations, the SNR ranges from -20 to 50dB. It should be mentioned that low values of the SNR are unlikely in power system applications.
bicolor VOCs (Fig. 2d-f ). These data suggest that RHD2-dependent O 2 À. production contributes to LR and root hair growth stimulation by L. bicolor. We then questioned whether L. bicolor VOCs could induce ROS accumulation in Arabidopsis roots. Superoxide anion radicals were detected by staining roots with nitroblue tetrazolium (NBT). Indeed, compared with control plants, 70% (n ¼ 15) of roots exposed to L. bicolor VOCs displayed stronger O 2 À staining in the root tip than the control plants (Fig. 2g ). Laccaria VOCs do not alter auxin signalling in roots. The phytohormone auxin controls several aspects of plant development, including LR development  21. In earlier studies, an increase in the auxin response was visualized with the auxin-sensitive DR5::GFP reporter fusion in Arabidopsis root tips growing in contact with L.
The DR5::GFP signal was recorded using an AZ-C1 Macro Laser Confocal Microscope (Nikon GmbH, Du ¨sseldorf, Germany). GFP was excited using the 488-nm laser line in conjunction with a 505-530-nm band-pass filter. Nikon EZ-C1 software was used to quantify the GFP signal. Eight roots per treatment were analysed. Fungal VOCs were collected after 10 days. To measure the background VOC emissions, 10 plates containing the media and the cellophane membrane without fungal colonies were kept under the same growth conditions. Lovastatin treatment. Lovastatin (97%; Alfa Aesar GmbH & Co KG, Karlsuhe, Germany), a specific inhibitor of the mevalonate pathway  68  , was dissolved in ethanol (10 mg ml À 1 ), sterile-filtered and added to the P20 media at a final concentration of 2 mg ml À 1. The plants and L.
The treatment was performed once at the beginning of the experiment. The Petri dishes were immediately sealed with Parafilm and the plants were grown under controlled environmental conditions as described before. The LRs were quantified after 0, 4, 7 and 10 days of treatment. VOCs were collected for 6 h after 3, 7 and 10 days. Because no significant changes of the VOCs patterns were observed, only data for 10 d. h. c. c. are shown. Furthermore, Arabidopsis seedlings or poplar plantlets were grown in bi-compartmented plates with or without L. bicolor with lovastatin, as described above. The compartment with the inhibited fungus was supplemented with 30 ml of 100 p. p. b. (-)-thujopsene solution or n-pentane as the control. The LRs were quantified after 10 days (n ¼ 5-10 plates per treatment).
25 mm; Agilent J&W 122-5562G, DB-5MS þ 10 m DG). The carrier gas was helium, with a constant flow rate of 1. 2 ml min À 1. Further technical and chromatographic conditions have been described in detail  15. The chromatograms were analysed by the enhanced ChemStation software (MSD ChemStation E. 02. 01. 1177, 1989-2010 Agilent Technologies), as described earlier  13. The VOCs previously reported in the literature for L. bicolor, C. geophilum  15, 21  and A. thaliana  70  were manually screened in the total ion counts (TICs) of all chromatograms. Compounds with an identification quality above 70% in the Wiley data library and that were present in more than three samples were checked for their m/z ratio. Only compounds with an m/z ratio that matched those of the Wiley data library were retained in the final data set.
In total, 43 compounds were removed from the data set, because they were already present in the background odour of the plates and experimental set-up (Supplementary Table  2 ). The TIC of each VOC in the final data set was recalculated from the absolute abundance of the first representative m/z to eliminate noise. For quantification, the following external standards were used: linalool for oxygenated MTs, transcaryophyllene for SQTs and nerolidol for oxygenated SQTs. OVOCs were quantified relative to toluene, which was used as the external standard. For the comparison of fungal VOC profiles, the TICs were normalized to the fungal mycelium area. The plates were scanned and the mycelium area was measured using ImageJ software (http://rsbweb. nih. gov/ij/download. html). In addition, plates with 30 ml of 100 p.
p. b. (-)-thujopsene in n-pentane and n-pentane were analysed for 6 h. Statistical analysis. Statistical analyses were conducted with R (R Core Team 2012) and Graphpad prism (GraphPad Software, San Diego, CA). Data violating the assumption of normality were log-transformed before statistical analysis. Mean values were compared by the Student's t-test or analysis of variance, followed by the post hoc Tukey's Honest Significant Difference test. A generalized linear model with quasi-Poisson distribution was fitted and analysed by analysis of deviance. P values r0. 05 indicated significant differences.
Acknowledgements We thank the  German Science Foundation  ( DFG Po362/20-1 ,  SCHN653/5-1  and  SFB 746 ), the  Excellence Initiative of the German Federal and State Government  ( EXC 294 ), and the  German Ministry for Education and Research (BMBF; Joint Research Projects 'Probiopa' and 'Systec')  for financial support. This work was supported by the  European Commission  within the  Seventh Framework Program for Research, Project Energypoplar  (grant no. FP7-211917 ), by mobility grants from the  German-French University and Egide/Procope , and by the  French Ministry for Higher Education and Research  (PhD scholarship to J. F. ). M. Fastenrath  and  C. Scheru  ¨bl provided for expert technical assistance.
ACKNOWLEDGMENTS We thank  Z. Gurard-Levin  and  Laura Attardi  for critical reading of the manuscript,  A. Corpet  for help with statistical analysis,  C. Billaudeau  for modeling FRAP experiments, and the  PICT-IBISA@Pasteur Imaging facility of the Institut Curie and the Nikon Imaging Centre@Institut Curie-CNRS  for the BioStation. We also thank  C. Maison  for helpful discussions and the gift of purified glutathione S-transferase (GST) and  GST-HP1a  proteins. K. R. received support from  Ministere de l'Enseignement Superieur et de la Recherche/Universite Ṕierre et Marie Curie and la Ligue Nationale contre le Cancer. This work was supported by  la Ligue Nationale contre le Cancer (Equipe labellisee Ligue) ,  PIC Programs , the  European Commission Network of Excellence EpiGeneSys  ( HEALTH-F4-2010-257082 ),  ERC  Advanced Grant  2009-AdG_20090506.
Collections Specimens examined in this study were obtained by rearing adults from leaf mines and by collecting adults flying by day around Salix bushes, mainly S. glabra Scop. , 1772 and S. waldsteiniana Willd. , 1806, but also a few S. appendiculata Villars, 1789 and S. hastata L. , 1753. Some adults were collected at light trap or flew in the early morning hours. Data for all specimens studied morphologically and genetically can be found in the Suppl. material 1: Table  S1.
Morphology We examined the morphology of 135 dried, pinned and mostly set specimens belonging to C. coffeella s. l. , the majority originating from the Alps, and half a dozen from Scandinavia. Pinned specimens were photographed with an Olympus E 3 digital camera and an Olympus SZX 10 binocular microscope, and processed with Helicon Focus 4. 3 software, resulting in multiple images. Images were later edited by using Adobe Photoshop Lightroom 2. 3 software. Genitalia were photographed with an Olympus E1 digital camera through an Olympus BH2 microscope. Genitalia dissections and slide mounts followed  Robinson (1976). Morphometric analysis was carried out on genital preparations of 16 adult males (5 from the southeastern alpine populations and 11 from Northern and Central alpine populations).
Seven parameters were measured: phallus, valva, saccus, anellus and anellus process lengths, valva width and valva constriction. All measurements were done on a Leica M 165C stereomicroscope by P. Triberti and expressed in mm. The dataset resulting from these measurements was analyzed using a multivariate approach -one-way ANOVA  (Montgomery 2001) , with species as a single categorical independent variable and the seven dependent measurement length variables mentioned above. Significance of each genital parameter was analyzed using a non-parametric Mann-Whitney test (MWT). Since our sampling size was rather small, particularly for southern populations, MWT was used because it does not require the normality of the data and allows tied values  (Hollander and Wolfe 1999). With MWT, we tested the null hypothesis of no morphological differences.
basistrigella collected on 14. VII. 2013, 2 C. basistrigella on 27. VII. 2013, about 30 leaf mines on Salix glabra on 07. IX. 2013), 4 specimens were identified based on both morphology and genetic data as C. coffeella and 5 specimens were identified as C. basistrigella. In addition, 7 of 9 samples (i. e. 3 specimens of C. coffeella and 4 of C. basistrigella) were reared from the same host plant -Salix glabra. Furthermore old records confirm this sympatry in the nearby Italian Carnic Alps, in the surroundings of Sappada (1 C. coffeella and 1 C. basistrigella were collected in Passo Siera, 1600 m, 04. VII. 1933; 1 C. coffeella and 4 C. basistrigella -in L. d'Olbe, 2000 m, 02. VII. 1933 ) (Fig. 19B ). No evidence of genetic admixture was detected in the contact zone.
Biogeography and speciation. The distribution of C. basistrigella as currently known is shared by several other endemic Lepidoptera. The south-eastern Alps is considered as one of the major areas of endemism in the region  (Huemer 1998). However, most of the taxa restricted to this area have been defined only by morphological characters so far and their taxonomy has to be re-assessed using molecular data. The specific distinctness of Udea murinalis (Fischer von Röslerstamm, 1842) and the allopatric south-eastern alpine Udea carniolica  Huemer & Tarmann, 1989 (Lepidoptera, Crambidae)  both separated by moderate morphological differences, was recently well supported by molecular datasets  (Mally and Nuss 2011). Another alleged set of sister taxa include Dichrorampha bugnionana bugnionana (Duponchel, 1843) and the south-eastern alpine subspecies D.
2012a-c) , in regions that are thought to be well studied. Beatrice Courtial (Orléans, France) for assistance in the molecular analyses; Vladimir Shishov (Krasnoyarsk, Russia) for providing valuable comments on statistics. We acknowledge the team at the Biodiversity Institute of Ontario, University of Guelph, Ontario, Canada for their assistance in the production of DNA barcodes. Funding for DNA barcoding was partly provided by the government of Canada through Genome Canada and the Ontario Genomics Institute in support of the International Barcode of Life project, and by NSERC. Sequence analysis was enabled by a grant from the government of Canada through Genome Canada and the Ontario Genomics Institute in support of the International Barcode of Life Project. Our work was also aided by the BOLD informatics platform whose development is funded by the Ontario Ministry of Economic Development and Innovation.
Acknowledgements We are grateful to  Marko Mutanen  ( Oulu, Finland ),  Peter Lichtmannecker  ( Adlkofen, Germany ) and  Andreas Segerer  ( Munich, Germany ) for kindly giving us an access to unpublished barcode data of Callisto; to  Jurij Rekelj  (Kranj, Slovenia),  Jurate and Willy De Prins (London, UK) ,  Stanislav Gomboc  (Kranj, Slovenia),  Melania Massaro and Paolo Pantini (Bergamo, Italy) ,  Jürg Schmid  ( Illanz, Switzerland ),  Christian Wieser  ( Klagenfurt, Austria ) and  Josef Wimmer  ( Steyr, Austria ) for sending specimens for DNA barcoding;  Stefan Heim  ( Innsbruck, Austria ) for photographs of the adults and genitalia;  Christelle Robinet  ( Orléans, France ) for help with mapping;  Emmanuelle Magnoux  and.
Lymphocyte adhesion to myotubes Human myoblasts were cultured in Ham's F-10 medium  [18]. Differentiation was induced as described  [19]. One million PBMCs from healthy individuals or DMD patients were allowed to adhere onto myotubes for 2 h at 37 °C and washed to remove non-adherent PBMCs. Adherent cells were counted and phenotyped. Unrelated IgG1 or anti-CD49d mAb (1 μg/ml) was added in PBMC suspensions before co-culturing with myotubes. In the case of adhesion blockage by specific antibodies, data are presented as percentages of the values obtained with unrelated antibodies, applied on cells from the same subject.
Acknowledgements I,  Gillian Butler-Browne , acknowledge that all the persons mentioned herein have read and agree with the present submission. We thank  Eduardo Pernambuco  and  Cristiane Mello Vieira  ( Pediatrics Institute, Federal University of Rio de Janeiro ) for obtaining the blood samples from all patients enrolled in this study. We thank  PAPES/Fiocruz ,  CAPES ,  FAPERJ ,  CNPq (Brazil) ,  FOCEM (Mercosur) ,  United Parent's Project for Muscular Dystrophies (UPPMD) ,  Inserm ,  UPMC ,  CNRS ,  Association Française contre les Myopathies (AFM) ,  ANR  ( Genopath INAFIB ), MyoAge (EC 7th FP, contract  223576 ),  Inserm/Fiocruz , and  CNPq/ Inserm Conjoint Programs  for the financial support. This work was developed in the context of the  CNPq/Inserm/Fiocruz/UPMC International Associated Laboratory on Cell Therapy and Immunotherapy.
A single 2D diffraction pattern was collected for each angle using 8 keV X-rays with a 100 second exposure time. A total of 72 unique patterns were collected spanning approximately 111° (-60. 6° to 50. 9°, Supplementary Fig. 1 ), as a tomographic tilt series in agreement with the equally sloped tomography (EST) scheme  35. 37 positive angle patterns and 34 negative were collected; at 0° the incident X-ray beam was approximately normal to the membrane, and data collection at high tilt angles was only limited by the geometry of the membrane support. The total acquisition time of the whole tilt series was approximately 5 hours. Improvements in the automated collection of diffraction patterns may reduce the time required for data collection to 2-3 hours per sample. A 0° pattern was collected multiple times during acquisition of the tomographic tilt series to assess diffraction quality of the sample and estimate radiation damage.
A comparison of two independent 0° projections shows no appreciable difference (Supplementary Fig. 2 ). A rise in background intensity towards the end of our tomographic acquisition scheme reveals potentially high parasitic scattering from contamination or ice buildup near the sample (Supplementary Fig. 1 ). Otherwise, pattern features were generally conserved during data taking and the overall scattering intensity in patterns did not show appreciable decay (Supplementary Figs. 1  and 2 ). Post data processing and assembly of a 3D diffraction pattern. To remove the background scattering, two diffraction patterns were acquired for every tilt angle by moving the cell in and out of the X-ray beam; again stressing the importance of a monodisperse, well isolated, preparation of cells with a clean surrounding background.
The two diffraction patterns were scaled on a per-pixel basis, accounting for non-linearity and flat field correction to the detector  32. The background pattern was then subtracted from the diffraction pattern of the cell to obtain corrected intensities. A portion of the low-resolution region of the resulting diffraction pattern, including the direct beam and the first speckle were blocked by a beam stop. Some of these pixels were recovered based on centro-symmetry, and all other pixels assigned the average of their symmetry counterparts. All 72 projections were processed in this way using a data analysis and quality control pipeline implemented in Matlab  33  (Fig. 3a  and Supplementary Fig. 1 ). By combining the post-processed 2D diffraction patterns, a 3D matrix of diffraction intensities was assembled.
The assignment of intensities within this matrix was achieved via an interpolation procedure using an inverse distance-weighting scheme. 3D phase retrieval. Phase retrieval of the assembled 3D diffraction pattern was achieved using the oversampling smoothness (OSS) algorithm  36. OSS iterates back and forth between real and reciprocal space. In each iteration, a support is used as a constraint in real space and the measured data is enforced in reciprocal space. A cubic support was used in the initial reconstruction. The OSS algorithm exploits the correlation information among voxels in the region outside of the defined support in real space by applying a series of frequency filters in reciprocal space. In doing so, we stabilized our search for a global minimum in solution space. In addition, we broadened our search by conducting 100 independent reconstructions, each of which was monitored by R rec     k e k c j e rec k F k F k F R      ) ( ) ( ) (  (1) where ) (k F e  is the experimental Fourier modulus (i.