dataesr/scientific-paragraphs-categorization huggingface dataset:
Then, we compared the intra-hemispheric κ of the left vs. the right hemisphere using as reference the mean between left and right metric values for each session of HCP database independently. We also tested across sessions the reliability of the left intra-hemispheric connectivity and of the right intra-hemispheric connectivity, separately. As reference, we used the mean between both sessions of the left hemisphere and the mean between both sessions of the right hemisphere, respectively. To evaluate the significance of these differences, statistical tests were performed using Wilcoxon rank-sum test (p < 0. 05). In case of no laterality effect in the intra-hemispheric connectivity in controls, we consider that the contralesional intra-hemispheric connectivity in stroke could be pooled together, independently of the side of the lesion.
Comparison of κ in Patients and Controls Using the HERMES dataset, we studied the differences between controls and patients at different costs by computing the κ index for the five graph metrics introduced above. We performed two types of analysis: in the first one, we pooled all the patients (n = 20), whatever the side of the lesion, and compared the κ of the contralesional hemisphere against the mean of left and right hemispheres in controls, without taking into account the inter-hemispheric connectivity. In the second analysis, we explored each sub-group of stroke patients according to the side of the lesion (n = 9 and n = 11 for right and left side lesions, respectively). Therefore, we compared 9 left contralesional hemispheres against 20 left hemispheres of controls and 11 right contralesional hemispheres against 20 right hemispheres of controls.
Robustness of κ Results in Patients For rigorous purpose, we replicated the experiment explained above on healthy subjects from the HCP database using bootstrap techniques. Due to the small number of patients and controls, 20 in each group, we wanted to check whether the statistically significant differences obtained when comparing both groups were reproducible in a group of healthy controls of the same size. For each bootstrap iteration, we randomly selected 20 subjects which played the role of reference group and another 20 subjects that played the role of test group. As in the previous section, we performed two different analysis: in the first one, for the reference group, we computed the mean between left and right hemispheres (same way we did with the HERMES study); for the test group, we selected 9 left hemispheres and 11 right hemispheres and pooled them together in the same group.
5%. Results are given for subgroups of 20, 40, 60, and finally, 100 subjects using the database of the HCP project. First column, p-values of ICC (y-axis) as a function of ICC values (x-axis) for different number of subjects. Second column, values of ICC (y-axis) as a function of the cost (x-axis) for different number of subjects. Third column, ICC associated p-values (y-axis) as a function of the cost (x-axis) for different number of subjects. LEFT refers to the graph built from the left intra-hemispheric connections, RIGHT for the right intra-hemispheric connections. ALL refers to the graph built from connections of the whole brain. In addition, we found that κ D is more reliable than classical metrics. We observed similar behaviors with other metrics (compare Figure  S1  and Figure  S2  in Supplementary Material).
For the sake of comparison, under the same experimental conditions (same database, graph methodology, permutation, and bootstrap sampling), the ICC(E g ) was lower considering the whole brain (ranging between 0. 30 at 20% cost and 0. 40 at 40% cost) and also, the intra-hemispheric connectivity (Figure  S2  in Supplementary Material). These results show that κ E g is more reliable than the average E g metric obtained by averaging all the nodes.
However, it should be noticed that resting state data were acquired with eyes open in controls, and that patients with severe subacute stroke were not all capable to keep their eyes opened during the whole session. Reorganization of the functional network in the eye-opened state compared to the eye-closed state has been reported in previous works, with decreased or increased efficiency at the nodes related to the default mode network and the visual network  (Xu et al. , 2014). Therefore, a question that remains to be addressed is how eye closure may have influenced the changes observed in the brain network.
ACKNOWLEDGMENTS MT is supported by a grant from the  Région Rhône-Alpes, France. SA was partly funded by a grant from la  Région Rhône-Alpes  and a grant from  AGIR-PEPS ,  Université Grenoble Alpes-CNRS. Stroke patients and matched controls MRI data acquisition was performed at IRMaGe, a platform of  France Life Imaging  network partly funded by the grant - ANR-11-INSB-0006. IRMaGe MRI facility was partly funded by the  French program  " Investissement d'Avenir " run by the  "Agence Nationale pour la Recherche" ; grant " Infrastructure d'avenir en Biologie Santé " - ANR-11-INBS-0006. MRI data acquisition was supported by national grant of PHRC 2010 HERMES (Heuristic multiparameter MRI study to assess autologous MEsenchymatous stem cells therapy in subacute stroke),  University Hospital of Grenoble, France.
A previous work on the circulation of rabies virus in wild bats in French Guiana allowed us to identify, among nearly 1,000 bats belonging to 30 species, one common vampire bat, Desmodus rotundus, which was positive for RABV  (6). We report here the full-genome sequence of the rabies virus isolate AT6. To generate the complete genome sequence, total RNA was extracted from the heart using the NucliSENS easyMAG bio-robot (bioMérieux). cDNA was prepared with the Super-Script III reverse transcriptase (Invitrogen). Sequences were generated by reverse transcription PCR (RT-PCR) using different combinations of primers. Overlapping amplicons were generated, cloned, and then sequenced by Beckman Coulter Genomics (Takeley, United Kingdom). One contig sequence was assembled using the MEGA5 software  (7).
KM594041) isolated from a D. rotundus bat in Brazil in 2013 with 98. 79% nucleotide identity. These two sequences belong to a clade composed of sequences of desmodine origin from Brazil and French Guiana identified during the last two decades. All these strains belong to a large group of hematophagous bat-related viruses, named lineage II, which widely circulate in the northern Amazon region  (10). Further analysis of RABV genomes detected in the northern Amazon region should enable an understanding of the circulation and transmission of RABV lineages in the bat populations. Nucleotide sequence accession number. The complete genome sequence of RABV AT6 is available in the GenBank database under accession no. KU523255.
FUNDING INFORMATION This work, including the efforts of  Anne Lavergne ,  Benoit de Thoisy , and  Vincent Lacoste , was funded by  ERDF (ViRUSES and CAROLIA). This work, including the efforts of  Anne Lavergne ,  Benoit de Thoisy , and  Vincent Lacoste , was funded by  EC REGPOT  ( REGPOT-CT-2011-285837-STRonGer ). This work, including the efforts of  Anne Lavergne ,  Benoit de Thoisy , and  Vincent Lacoste , was funded by  ANR Investissements d'Avenir (CEBA Ref. ANR-10-LABX-25-01 ). This work was conducted within the ViRUSES and CAROLIA programmes, supported by  European funds (FEDER/ERDF ) and assistance from the  Région Guyane and Direction Régionale pour la Recherche et la Technologie. It has also benefited from a  European Commission   REGPOT-CT-2011-285837-STRonGer  grant within the  FP7  and an  Investissement d'Avenir grant  managed by  Agence Nationale de la Recherche  ( CEBA  ref.
, 2000). Indeed, dense waters form over the continental shelf upon winter cooling by strong Mistral and then spread downslope to the abyssal plain. This cascading of dense waters contributes to open-ocean deep convection but the main mechanism leading to the formation of Western Published by Copernicus Publications on behalf of the European Geosciences Union. ; 1955 and 2012)  from Word Ocean Atlas 2013 (http://odv. awi. de/de/data/ocean/ world_ocean_atlas_2013/) plotted using Ocean Data View  (Brown, 1998). The location of the KSGC-31_GolHo-1B core and other sites discussed in the text are also reported (from West to East): ODP Site 161-976, Alboran Sea  (Martrat et al. , 2014) ; MD95-2043, Alboran Sea  (Cacho et al. , 2001) ; MD 99-2343, Balearic basin  (Frigola et al. , 2007) ; M40/4-SL78, Ionian Sea  (Emeis et al.
Past, 12, 91-101, 2016  www. clim-past. net/12/91/2016/ Table  1. AMS radiocarbon dated levels and their calibrated ages with a 1σ uncertainty for the KSGC-31 gravity core. The analyses were performed at the Laboratoire de Mesure du Carbone 14, Saclay (France) and at the Beta Analytic Radiocarbon Dating Laboratory (Florida; USA). Raw radiocarbon ages were corrected and calibrated to calendar ages using the Calib7. 1 software  (Stuiver and Reimer, 1993)  and the MARINE13 calibration data set  (Reimer et al. , 2013)  a Post-bomb radiocarbon ages, obtained using OxCal 4. 2  (Ramsey and Lee, 2013) , not used for the interpolation. b Reversal date, not used for the interpolation.
S7 ) before zymogram loading and recovered no polysaccharide priming after migration. Phylogenetic Analysis of the SSIII/SSIV/GlgA2 Glycogen/Starch Synthases Several detailed and recent phylogenies of GT5 ADP-Glc requiring starch synthases have appeared  (Ball et al. , 2013). However, the databases have increased considerably in size since our last analysis and now include a much larger diversity of cyanobacteria. For our phylogenetic analysis, we have selected only the SSIII/SSIV/GlgA2 monophyletic subgroup of enzymes that was defined previously in these phylogenies with very high support and have restricted our alignment to these sequences. The GT5 ADP-Glc-dependent glycosyltransferases represent a distinctively prokaryotic group of enzymes with no representatives within eukaryotes, with the noticeable exception of Archaeplastida.
It is thus reasonable to assume that the green algae and plant sequences summarized in Figure  10  were gained by lateral gene transfer (LGT) from a prokaryotic source. Because the tree is unrooted and because the phylogeny of GT5 glycogen/starch synthases shows many signs of signal erosion, we cannot exclude the unlikely possibility that the sequence was donated to Chlamydiales by the Archaeplastida rather than the reverse. Nevertheless, the phylogeny represented in Figure  10  and detailed in Supplemental Figure  S8  demonstrates, despite the growing databases, not only that the Chlamydiales remain the most plausible donors for the ancestral plant SSIII/SSIV starch synthase gene but also that cyanobacteria can be very confidently rejected as possible donors for the plant enzymes. The phylogeny now suggests, in addition, that GlgA2 may define a very ancient cyanobacterial enzyme as a substantial portion of the cyanobacterial diversity that appears uninterrupted by foreign clades in a large monophyletic group.
Phylogenetic Tree Homologs of GlgA were identified in GenBank or other sources using BLASTp and aligned with MUSCLE (http://www. ebi. ac. uk/Tools/msa/ muscle/). The alignment was manually refined using SeAl (http://tree. bio. ed. ac. uk/software/seal/), and blocks of missing data in some taxa or regions of low identity were manually removed (final alignment of 595 amino acids available from Steven G. Ball). This reduced alignment was analyzed under maximum likelihood. The best-fitting amino acid substitution model was selected according to the Akaike informational criterion with ProtTest using the default values  (Abascal et al. , 2005). The LG  (Le et al. , 2008)  model with heterogenous gamma rate distribution across sites (+G) was selected by ProtTest for this protein data set. The LG model parameter values were used under RAxML version 7.
2012). Again, we found multiple distinct histone acetylation patterns in promoter regions (Supplemental Fig. 4A ). mP1 promoters, which were maximally enriched in H2BK20ac, recapitulated all of the qualitative features of the P1 promoter class identified in GM12878, including cell-type specificity of gene expression and enrichment for ChIP-seq peaks of TFs associated with pluripotency of mESCs (Supplemental Fig. 4C-E ;  Chen et al. 2008; Percharde et al. 2012). Moreover, just as P1 promoters were enriched for B-cellspecific functions, mP1 promoters were maximally enriched for functions specific to mESCs (Supplemental Fig. 4F ). To rule out potential artifacts arising from the specifics of our ChIP-seq assays, we also examined published ChIP-seq data (GEO accession: GSE16256) on human ESCs generated in another laboratory using different antibodies and a different ChIP-seq protocol.
Again, we observed that promoters enriched in H2BK20ac showed the strongest signal of cell-type specific gene expression, in comparison to 15 other histone acetylations and seven methylations (Supplemental Fig. 5B ). We also used externally generated ChIPseq data (GEO accession: GSE16256) to classify promoter histone modification states in IMR90 cells: 16 acetylation marks, plus H3K4me1 and H3K4me3. Again, we observed the same seven promoter classes as discovered in GM12878 and mESCs, and the iP1 promoter class, which was equivalent to P1 in GM12878 cells, again showed the greatest cell-type specificity (Fig. 6A, B ). Notably, three of the other four H2B acetylation marks profiled in IMR90 cells (H2BK12ac, H2BK15ac and H2BK120ac) showed an enrichment pattern similar to H2BK20ac (Fig. 6A ).
We compared the tag count of bins (200 bp) in regions of positive and negative using the Wilcoxon rank-sum test. The ChIP-seq data sets for which there was significant difference (Wilcoxon rank-sum P-value <0. 01) between tag counts at enhancers and nonenhancers were chosen for further analysis. At this stage, we acquired 20 ChIP-seq data sets related to gene activation  (Wang et al. 2008 ) that had a significant difference between tested enhancers and nonenhancers. Those marks included 14 histone acetylations  (H2BK120ac, H2BK12ac, H2BK20ac, H2BK5ac, H3K18ac, H3K27ac, H3K36ac, H3K4ac, H3K9ac, H4K12ac, H4K16ac, H4K5ac, H4K8ac, H4K91ac) , H2AZ, Pol II, and four histone methylations (H3K4me1, H3K4me2, H3K4me3, H3K9me1). There could be overlapping occurrences and cross-talk between histone modifications.
Hence we sought to find single or double best predictors of enhancers among the 20 ChIP-seq data sets that showed significant difference between tested enhancers and nonenhancers. For this purpose, in the second round, we used a regression model where positives were taken as enhancers (luciferase >1. 5), and negatives were taken as regions which did not lie near promoters, CpG islands, or regions with DNase hypersensitivity or CAGE tag enrichment in CD4 + T cells  (Carninci et al. 2006). At this stage, we tried to remove background artifacts in ChIP-seq profiles due to biases such as copy number variation (CNV) by convolving each ChIP-seq signal with a zero-mean filter (an approach used in DFilter)  (Kumar et al. 2013). For each ChIPseq data set, a zero-mean filter was made using the average profile of binned tag count around the center of DNase hypersensitive sites.
Data sources For CD4 + T cells, ChIP-seq data were adapted from a previously published study  (Barski et al. 2007; Wang et al. 2008). Histone modification data sets (except for H3K27ac and H2BK20ac) for GM12878 cells were adapted from ENCODE (GEO accession GSE26320)  (Ernst et al. 2011). RNA-seq data for human cell lines were downloaded from ENCODE (http://hgdownload. cse. ucsc. edu/goldenPath/hg19/encodeDCC/wgEncodeCaltechRnaSeq/) and from other published sources (GEO accession GSE16190, GSE43070)  (Chepelev et al. 2009; Jin et al. 2013). The ChIP-seq peaks of TFs were downloaded from ENCODE (http:// hgdownload. cse. ucsc. edu/goldenPath/hg19/encodeDCC/). ChIP-seq and RNA-seq data of IMR90 and H1-hESC were downloaded from the NCBI GEO data set (GEO accession: GSE16256) submitted by Bing Ren's group.
The table of Hi-C interactions in IMR90 used here was previously published  (Jin et al. 2013). RNA-seq data sets for several tissues and organs of mouse generated by  Shen et al. (2012)  were used for finding cell-type-specific expression in mESC (GEO accession: GSE29184). For histone modification of ChIP-seq data sets (except for H3K27ac and H2BK20ac) in mESCs, we relied on other published sources (GEO accession: GSE29184, GSE12241)  (Mikkelsen et al. 2007; Shen et al. 2012).
As for the unsteady surface determination, a specific process is implemented like the following: i) the edge detection of the gradient magnitude in the PIV frame, ii) the extraction of the particles by filtering high-intensity large areas related to the bubbles and/or hull reflections, iii) the extraction of the rough region containing these particles and their reflections, iv) the removal of these reflections. The unsteady surface is finally obtained with a fifth-order polynomial interpolation. The resulted free surface is successfully validated from the Fourier analysis and by visualizing selected PIV images containing numerous spurious high intensity areas. This paper demonstrates how this data analysis process leads to PIV images database without reflections and an automatic detection of both the free surface and the rigid body.
The FlowMap software from Dantec Dynamics has been used for PIV image processing. The PIV measurement system has been implemented by using two distinct parameters allowing an investigation of both the near flow field (database 1) and of the flow field in a more extended area (database 2). Database 2 is deduced from the first one by repositioning the camera and repeating the experiments, because of the field width required. Indeed, database 2 allows the visualization of both the bubble sweep-down phenomenon and the flow field in a large area including the free surface and the hull. Note that during the measurements related to database 1, the free surface may be above the measurement area (see Fig. 3 ). The associated parameters are given in Table  1. Furthermore, for each database, we perform four PIV measurements campaigns based on different flow conditions and plane locations (see Fig.
3 ). A description of these conditions is provided in Table  2. In the following, each database will be referenced by using the label detailed in these tables. For instance, the PIV database denoted '2d' corresponds to i) the PIV parameters mentioned in Table  1  for database 2 and ii) the flow conditions and the measurement plane location indicated in Table  2  with label (d). To process a hydrodynamic analysis and for a future investigation of the bubble generation, the dynamic positions of the free surface and of the hull surface in each PIV image must be determined. In the following, we describe the algorithm allowing an instantaneous characterization of an adaptive dynamic mask applied to each PIV vector field. Thus, an instantaneous PIV vector field will be set to zero in the region within the mask hull structure and also over the detected free surface.
Detection of the hull edge contours in the PIV image The removal of the hull surface in the PIV frame is accomplished after determining the edge contours of the hull in the frame. The hull's presence in the PIV image generates quite high-intensity areas related to laser reflections. Some illustrations are given in Fig. 5 , where these reflections and the edge of the hull are clearly indicated. The edge contours of the hull are manually determined in the first PIV image of each measurement campaign. When dealing with flow configurations without hull motion (database 'a' and 'b'), this mask is duplicated for all other PIV images. When dealing with flow configurations with hull motion (databases 'c' and 'd'), knowing the imposed hull motion and the PIV measurement frequency, the mask related to the hull edge contours is directly deduced from the one related to the first PIV image.
This increases the difficulty in determining accurately the free surface. Another difficulty encountered concerns the time-space deformable nature of the free surface. As a first investigation, the classical Canny  [7]  or Prewitt  [22]  edge detection methods are considered to extract directly the free surface from the PIV image. The edge detection algorithms from the Image Processing toolbox in MATLAB are used. Canny's method corresponds to a signal-processing optimization problem allowing the edge determination when the gradient magnitude rises to a maximum in the gradient direction based on two user-defined threshold values (to detect strong and weak edges). This method includes the weak edges in the output only if they are connected to strong edges. Prewitt's method detects horizontal and vertical edges in an image by computing an approximation of the gradient of the image intensity magnitude.
The application of this method requires to also select a user-defined threshold value (called 'thresh' in Matlab). It is related to a specific gradient magnitude, generating an output with only magnitudes higher than this threshold. For the present application, different threshold values have been successively tested for both methods. Both provide quite similar results and fail to directly isolate the free surface, especially in the presence of numerous bubbles and reflections near the free surface, confirming previous investigations about the difficulty in retrieving well the edges of a PIV raw image on different scales  [23]. As the classical surface detection methods are not robust enough for such an investigation  [9] , a new algorithm is developed, taking into account the complexity of the PIV images under consideration.
Indeed, we expect to avoid any detection of too many individual pixels related to the particle or to very small reflections above and close to the free surface. The threshold's values vary from ≈ 0. 03 (for PIV frames without bubbles) to ≈ 0. 06 (for PIV image with bubbles). As Prewitt's method is based on the gradient of the image intensity magnitude, when one pixel corresponds to a high gradient magnitude, it extracts the nine pixels located around the latter. However, in the case of a low gradient magnitude, only one pixel can be recovered. At this edge detection stage, the gray-scale image is now binarized. Step 1-2: Morphological closing algorithm Now that the image is binarized, the morphological closing algorithm (function imclose in MATLAB solver), which consists of morphological dilatation followed by erosion, is applied close to the edges of the detected white area.
7 ). It is located near the bottom of the measurement plane, where a homogeneous seeding distribution is obtained. This area is not supposed to contain any bubbles and is far away from the free surface, avoiding the presence of reflections. The application of a specific threshold value allows the determination of the edges or contours around the high gradient magnitude. Then, we fill the area inside the detected edges or contours. For instance, if only one pixel exhibits a high gradient magnitude, the edge detection method isolates its eight neighbors and then only nine pixels are really extracted and colored in white. For such a filling process, the regionprops command of Matlab is used to obtain properties such as the white areas from the binarized image. It allows determination of the shape of the connected white pixels.