dataesr/scientific-paragraphs-categorization huggingface dataset:
Discussion We investigated the large-scale topographical organization of cortical RSNs networks, and we analyzed their correlations with TBNs, BAs, and fiber tracts between areas. We extracted the 32 most representative RSNs in a large group of subjects and verified that these RSNs were the same in another independent group. Then we investigated the RSNs topographical organization by measuring their overlap with BAs and inferred their functional specialization by measuring their overlap with TBNs. We used this approach because the TBNs extracted from large databases and the BAs are well-defined, independently of the RSNs. The overlap between RSNs and TBNs (provided by the RSN6TBN matrix) revealed clusters that correspond to the major specializations of visual, auditory, somatomotor, and also bimodal functions.
This conjecture will have to be validated by future studies, using methods that can extend the coverage of extracted RSNs. Likewise, it would be of interest to consider the relationships between the dual ring architecture and subcortical systems. Since subcortical-cortical pathways have a topographical organization (e. g. , thalamic projections), we can predict that the VSA and PTF rings should project towards separate regions of the thalamus, basal ganglia, and cerebellum. We have limited our analysis to the cerebral cortex for two reasons. First, subcortical TBNs were not available in the TBNs database. Second, the RSNs extracted from subcortical structures were not robustly linked to regions of the cerebral cortex and were not included in the analysis. These two limitations might be overcome by using new tools for the extraction of cortico-subcortical RSNs and by extending the TBNs database to subcortical structures.
Resting-state database Resting-state fMRI data were acquired from 198 healthy volunteers (age: 18-30) (from FCP Classic-Cambridge,). Another similar database comprising 170 healthy volunteers (age: 18-26) (from INDI Beijing enhanced) was used to confirm the results obtained from the first onset of volunteers. Both databases are freely available from the NITRC (http://fcon_1000. projects. nitrc. org)  [85] [86] [87]. Acquisition parameters for the Cambridge databases were: TR = 3000 ms, 47 contiguous slices, 119 volumes, matrix size = 72|72, voxel size = 3|3|3mm 3 ; and for the Beijing databases were: TR = 2000 ms, 33 contiguous slices, 225 volumes, matrix size = 72|72, voxel size = 3|3|3mm 3 ; For each individual in both populations, a T1-weighted sagittal MP-RAGE structural image was obtained from the same databases.
Pre-processing Individual time-series were corrected for slice-timing, within run motion and smoothed (FWHM = 5 mm) using the FSL software (http://www. fmrib. ox. ac. uk/fsl/). A nonlinear transformation, used to register the fMRI volumes into the MNI standard space, was calculated as the combination of a linear transformation (using FLIRT), between the averaged fMRI volume and the T1 volume in the subject space and a nonlinear transformation (using FNIRT) was used between the T1 volume in the individual space and the T1 template in the MNI standard space. Then, the FA and ADC images were registered on a 1|1|1mm 3 ; standard space image (MNI152 space), using a nonlinear registration procedure. Functional network identification by spatial Independent Component Analysis (NEDICA) Group functional networks were identified using a two-step procedure, the NEDICA method  [26].
RSNs specialization: overlap with TBNs We characterized the functional properties of the 32 RSNs by computing their overlaps with the TBNs extracted from large databases of fMRI activation, during sensorimotor and cognitive tasks. This analysis was carried out using the following measurement of topological similarity: D(RSN i ,TBN j )~R SN i \TBN j RSN i |TBN j , where RSN i are the binary thresholded T-map of the RSN i and the masks of TBN j  [14]. We then constructed a similarity matrix between each RSN and each TBN. Each RSN was then characterized by its overlap with the TBNs specialized in cognitive functions (see  [13, 14] ). In order to find RSNs clusters with similar functional specializations, the similarity matrix was reorganized by applying an Expectation Maximization (EM) algorithm as described in  [25].
Anatomical Fiber tracting We compared the topography of RSNs, made of non-adjacent cortical regions, with underlying cortical fiber tracts. The data used in the preparation of this work were obtained from the Human Connectome Project (HCP) database https://ida. loni. ucla. edu/login. jsp. The HCP project (Principal Investigators: Bruce Rosen, M. D. , Ph. D. , Martinos Center at the Massachusetts General Hospital; Arthur W. Toga, Ph. D. , University of California, Los Angeles, Van J. Weeden, MD, Martinos Center at the Massachusetts General Hospital) is supported by the National Institute of Dental and Craniofacial Research (NIDCR), the National Institute of Mental Health (NIMH) and the National Institute of Neurological Disorders and Stroke (NINDS). HCP is the result of contributions from co-investigators from the University of California, Los Angeles, the Martinos Center for Biomedical Imaging at Massachusetts General Hospital (MGH), the Washington University and the University of Minnesota.
Acknowledgments We would like to thank the ''1000  Functional Connectomes Project INDI '' community for permitting data sharing among neuroimaging scientists. We thank the  Beijing Normal University  and the  State Key Laboratory of Cognitive Neuroscience and Learning  for sharing their data and Cambridge University and especially  Dr. R Bukner , for sharing their data. We would also like to thank the  Human Connectome  Project (HCP; Principal Investigators:  Bruce Rosen ,  Arthur W. Toga ,  Van J. Weeden ). HCP data were disseminated by the  Laboratory of Neuro Imaging at the University of California, Los Angeles. We also thank the  Institute of Complex Systems of Paris (ISC-PIF ) and  Dr. R. Reuillon  for facilitating the access to the computing server which was used to calculate the results presented in this paper.
05 overall, Figure  2A ). Additionally, using intracellular cytokine staining we determined that following CHDMAPP and IL-2 co-treatment a higher number of c9 + T cells expressed IFN-c in comparison with marmosets treated with IL-2 only (P,0. 05, Figure  2B ). However, this increase was not represented in the proportion of c9 + T cells that were IFN-c + (data not shown), showing that this increase was proportional of the increase in total numbers of c9 + T cells. Furthermore, we calculated the number of c9 + T cells that were clustered cells (two or more cells attached to each other determined by event aspect ratio, by flow cytometry). We observed a sharp increase in the number of c9 + clustered cells 2 days post CHDMAPP and IL-2 co-treatment in comparison with IL-2 treated animals (P,0. 05, Figure  2C ).
0. 05, Figure  6C ). Infection increased the proportion of c9 + T cells expressing IFN-c (P,0. 001) which was not affected by different treatment regimens (P. 0. 05, Figure  6C ). We also found that the infection increased the number of c9 2 T cells expressing IFN-c (P,0. 05, Figure  6D ). Additionally, when compared to the IL-2 only treated group, c9 2 CD3 + T cells in the blood of CHDMAPP and IL-2 co-treated animals post mortem had increased IFN-c expression (P,0. 05, Figure  6D ). The expression of CD27 was also measured on T cells; however no measurable changes in its expression, on c9 + T cells or c9 2 CD3 + T cells, was observed (P. 0. 05). A number of cytokines were investigated in post-mortem samples (data not shown). While we found that the treatments had no measureable effect on cytokine concentration (P.
Both sham treated animals had liver pathology scores of 3. Non-parametric comparison of CHDMAPP and IL-2 co-treated animals in comparison with IL-2 only treated animals did not elude to a treatment-based decrease in liver pathology (P. 0. 05). The infection study generated 43 metrics in which variation was observed. These included; time-to-death, pathology in the liver, estimated challenge dose, 6 cytokine concentrations across 4 locations (lung, liver, spleen and blood), proportions of c9 + T cells across 4 locations, proportions of IFN-c + c9 + T cells and c9 2 T cells across 4 locations, viable bacteria across 4 locations and bacterial genomes across 4 locations. A correlation matrix was generated from this data to further understand the relationships between these factors. In short, we found that: as survival increased, liver pathology increased and higher liver pathology was associated with higher inflammatory cytokines in the liver and lower c9 + T cells (Figure  8 ).
All samples for flow cytometric analysis were run on a FACS Canto II (BD Biosciences TM , UK) and analysis was performed on FACS Diva TM software (BD Biosciences TM , UK). The gating strategy is described in the supplemental data (Figure  S2 ). Cytometric bead array (CBA) flex sets for human Macrophage Chemotactic Protein-1 (MCP-1), Interleukin-1b (IL-1b), Interleukin-6 (IL-6) and Transforming growth factor-b (TGF-b) were used in accordance with manufacturer's instructions (BD Biosciences TM , UK). Additionally, a marmoset-compatible IFN-c CBA was generated using antibodies supplied by MabTech. In short, unconjugated CBA bead E4 (BD Biosciences TM , UK) was conjugated to the anti-human IFN-c specific, biotinylated antibody B6-1 (MabTech, Sweden) using the bead conjugation kit according to manufacturer's instructions (BD Biosciences TM , UK).
Statistical analysis Graphs and analysis were performed using Graphpad PRISM V4. 0. All analysis was performed using the statistical program IBM SPSS version 18. 0. All data analysed in this study, with the exception of the pathology scores, was found to be exponential in nature and were log transformed prior to parametric analysis. Repeated measures univariate general linear models were the main analysis method where the repeated measures of individual marmosets were taken as the basis of repeated measures (time or organ). Bonferroni's post tests were performed for more detailed analysis. Spearman's correlations were performed for the liver pathology scores and Pearson's correlations were performed for all other analysis.
RESULTS The draft RAD questionnaire consisted of two questionnaires: a household questionnaire to assess household demographics and socio-economic status -to investigate associations with poverty, for example -and an individual questionnaire to collect data across five sections: 1. Demographics -comprising 25 items including age, gender, nationality, ethnicity, religion, marital status, education (if not completed, the reasons why), literacy, occupation or reasons for not being employed. Individuals are also asked about health conditions, including the type and cause, and self-perceived impact on daily living as well as information on any assistive devices used.
The interviews and focus group were audio recorded, transcribed in Bangla and translated into English. Members of the research team then analysed the transcripts to identify individual statements describing a single element of lived-in experience of people with disability. These statements were entered into Microsoft Excel (Version 2003, Microsoft Corp. ) and coded according to the main topic, using open coding. Individual statements were mapped onto questionnaire item(s) that best captured the meaning of each statement to determine if or how well that theme was addressed by the questionnaire. This helped to identify items that were potentially redundant or irrelevant, and those that needed to be included in the case of themes that may have been missed or were under-represented. The research team then discussed the implications of the findings for the RAD questionnaire, and revised it accordingly.
ACkNOWLEDgEMENT The research project was funded by the  Australian Agency for International Development  through the  Australian Development Research Awards. The research is a collaboration between the  University of Melbourne's Nossal Institute for Global Health and the Centre   for Eye Research Australia (CERA). CERA receives Operational Infrastructure Support from the  Victorian Government. In-country collaborators include  International Centre for Diarrhoeal Disease Research, Bangladesh (ICDDR, B). Gratitude is expressed to the  Centre for Disability in Development  for their collaboration during this study. The authors are especially grateful to the many people who participated in the study, as their involvement made this stage of the development of the RAD questionnaire possible.
Interestingly, none of the strains that did not contain xopAG produced HR-like symptoms on sweet orange, mandarin and grapefruit (Fig. S4 , see Supporting Information, and data not shown). On these hosts, A strains induced typical canker symptoms, whereas A* strains exhibited variable responses: chlorosis, blister-like lesions with water soaking in the infiltrated area or canker-like symptoms, depending on the strain and host species. Xanthomonas citri pv. bilvae strain NCPPB 3213 induced water-soaking lesions on Mexican lime and HR-like symptoms at 3 dpi on mandarin, sweet orange and grapefruit. This strain contains xopAG, but also possesses other T3Es absent from Xci strains, and we cannot conclude that xopAG is responsible for the HR-like symptoms observed for this pathovar.
, 2010). All strains of our collection shared a common T3E repertoire identical to the repertoire of the sequenced Xci A strain IAPAR 306. Although some T3Es could be missed in our PCR screen, because of sequence divergence between Xci and strains from which primers were designed, homology searches of the effector candidate list on 28 Xci draft genome sequences confirmed that T3E repertoires determined in this study correspond to the T3E deduced from the genomic sequences (data not shown). Despite the fact that host rangerestricted Xci A* strains and X. citri pv. aurantifolii display similar host ranges, searches against the whole genome shotgun database revealed several differences between citrus canker-forming strains in their non-TAL effector repertoires. In addition, the 17. 5repeat TAL-effector PthA4 from Xci IAPAR 306 and its homologues PthB in XauB and PthC in XauC have been reported to be functionally interchangeable to induce canker symptoms when transformed in strains A, A*, A W , B and C, without incidence on the host range  (Al-Saadi et al.
S2. Although no typical HR could be visualized using the detached leaf assay, defence-like reactions that appeared over time were also recorded (Fig. S1 ). To assess the global response of each tested strain, a DDi was calculated as the area under the disease progression curve (see the 'Data analysis' section below for further details). DDis on grapefruit, combined with the DDis of each of three other species (sweet orange, alemow or Mexican lime), were then plotted to give an overview of Xci pathogenicity using this mode of inoculation.
T3E screening We screened for the presence of 66 Xanthomonas T3Es (Xop) described on the Xanthomonas resource website (http://www. xanthomonas. org/; R. Koebnik, personal communication). The screen was performed using two pairs of primers for each Xop (Table  S4 ). Screening of the 26 Xops found in Xci strain IAPAR 306 was performed using pairs of primers targeting different regions of the T3E genes. One of the two pairs of primers was designed to amplify at least 80% of the open reading frame (ORF), except for xopAD, which is too long (8652 bp) to be amplified in a single reaction, for which we amplified two fragments of 1660 and 608 bp. Screening of xopAG (avrGf1) was performed using primers designed on the sequence of avrGf1 (accession number GI: 82571048, LOCUS DQ275469). In order to screen the 40 remaining effectors absent in Xci IAPAR 306, primers were designed in conserved regions based on the alignments of Xop orthologues from Xanthomonas available sequences to avoid false-negative results caused by point mutations or length differences between orthologues.
DNA sequencing All PCR products to be sequenced were sent to Beckman Coulter Genomics (Stansted, Essex, UK). They consisted of Xop ORF and flanking regions for mutagenesis, and fragments from the screen with unexpected size. Sequence alignments and sequence analyses were performed using Geneious software (v5. 5. 6; Biomatters Ltd, Auckland, New Zealand). Sequences were submitted to the nucleotide BLAST program available online (http://www. ncbi. nlm. nih. gov/blast/Blast. cgi; accessed July 2012) when necessary.
Data analysis Data were analysed using R software (version R 2. 15. 0; R Development Core Team, Vienna, Austria). Evolutionary genome divergence (EGD) values were calculated on the basis of Dice similarity indices  (Mougel et al. , 2002)  and were used as distances to construct a weighted NJ tree with the 'ape' package version 3. 0-2 in R  (Paradis, 2006). The robustness of the tree was assessed by bootstrap (1000 resamplings). MDS was used to represent distances between strains based on the Dice dissimilarity matrix. MDS transforms a distance matrix (which cannot be analysed by eigendecomposition) into a cross-product matrix, and then solves the eigenvector problem to find the coordinates of individuals, so that distortions to the distance matrix are minimized. As in principal component analysis, individuals are projected into n dimensions  (Abdi, 2007).
MDS was performed using the cmd-scale function. Populations in planta were analysed by analysis of variance (ANOVA). Whenever F values were significant (P = 0. 05), mean population sizes were compared using Tukey's contrasts at level 0. 05. The area under the disease progress curve (AUDPC) was calculated using the audpc function from the package agricolae. Hierarchical agglomerative clustering was performed on a simple matching dissimilarity matrix based on a matrix of T3E presence/absence built after homology searches of the T3E candidate list on 28 Xci draft genome sequences and genomes of XauB (accession number ACPX00000000) and XauC (accession number ACPY00000000). We used Ward's method implemented in 'cluster' package version 1. 14. 2 in R  (Maechler et al. , 2012).
ACKNOWLEDGEMENTS We thank  C. Boyer  and  F. Maillot  for their technical expertise, and  F. Chiroleu  and  P. Lefeuvre  for advice on data analysis. The  European Union (FEDER) ,  Conseil Régional de La Réunion  and  Centre de Coopération Internationale en Recherche Agronomique pour le Développement  provided financial support. Financial support for LDN was provided by the  LABEX TULIP  ( ANR-10-LABX-41 ) and an  Agence Nationale de la Recherche-Jeunes Chercheurs  grant (Xopaque  ANR-10-JCJC-1703-01 ). GenBank accession numbers for original sequences in this work are as follows: ISXac5 from Xci strain NCPPB 3608:  JX566665 ; xopAG from Xci strain NCPPB 3608: JX566667; xopC1 from Xci strain  JK2-10 :  JX566666 ; xopAG from X. citri pv. bilvae strain NCPPB 3213:  JX556152.
Background: Since its inception, the carbohydrate-active enzymes database (CAZy; www. cazy. org) has described the families of enzymes that cleave or build complex carbohydrates, namely the glycoside hydrolases (GH), the polysaccharide lyases (PL), the carbohydrate esterases (CE), the glycosyltransferases (GT) and their appended noncatalytic carbohydrate-binding modules (CBM). The recent discovery that members of families CBM33 and family GH61 are in fact lytic polysaccharide monooxygenases (LPMO), demands a reclassification of these families into a suitable category. Results: Because lignin is invariably found together with polysaccharides in the plant cell wall and because lignin fragments are likely to act in concert with (LPMO), we have decided to join the families of lignin degradation enzymes to the LPMO families and launch a new CAZy class that we name "Auxiliary Activities" in order to accommodate a range of enzyme mechanisms and substrates related to lignocellulose conversion.
Even though the catabolism of each of these individual components can be independently studied, synergy in the breakdown of the various components is commonplace, because the accessibility of each constituent is limited by the presence and nature of the others. Therefore, a global view of the breakdown of all components of the plant cell wall, i. e. cellulose, hemicellulose, pectin and lignin is required to unravel the general mechanisms of lignocellulolysis and to explore the lignocellulolytic capabilities of organisms with sequenced genomes. The assembly and breakdown of carbohydrate polymers and glycoconjugates are carried out by a diverse panel of Carbohydrate-Active enzymes, or 'CAZymes'  [1] [2] [3]. The CAZymes classification system, based on amino acid sequence similarities, protein folds and enzymatic mechanisms, has been integrated and meticulously updated in the CAZy database (www.
Here, we describe the integration of a novel class in the CAZy database. This novel category, broadly termed "Auxiliary Activities" groups together the families of LPMO and the families of redox enzymes involved in lignin breakdown. Like the traditional CAZy families, the new families are based on sequence similarity with one or several biochemically-characterized founding member(s), ensuring that members of a given family share the same threedimensional structure. Combined with the traditional CAZy families, this addition allows a complete description of the main actors involved in the degradation of all plant cell wall components. Like all enzyme classes in CAZy, the novel class features a hierarchical division in families and, where relevant, clans and subfamilies, all freely accessible at http://www.
The distribution of the AA families was compared for each fungal genome, demonstrating that different enzymatic tactics are employed across the fungal kingdom (Figure  3 , Additional files 1 and 2). In order to organize the dataset into meaningful groups and identify coherent patterns, a hierarchical clustering of the number of ligninolytic AA family members was generated. Only ligninolytic families AA1 to AA8 were considered to focus exclusively on the lignin-degrading abilities of the 41 fungi including some important WR and BR representatives. Clustering of the AA dataset identified several major groups and subgroups of species that are discussed below according to phylogenetic origin and nutritional mode (Figure  4 , Additional file 3).
Conclusion Plant cell wall degradation requires an arsenal of catalytic and non-catalytic proteins produced by lignocellulolytic microorganisms. Lignocellulolysis is a complex process, and fungi have evolved different enzymatic strategies to utilize these substrates as carbon sources. The growing number of published genome sequences and the allied "omics" data offers a unique opportunity for gaining deeper insight into the evolution of lignocellulolysis and the families involved in this biological machinery of high biotechnological potential. The novel AA classification provides a complementary insight into enzymatic lignocellulolysis by focusing on oxidative enzyme families. All known lignocellulolytic categories are now present in the continuously updated CAZy database. This expansion of the scope of the database is not restricted to the breakdown of plant cell wall as family AA10 (formerly CBM33) has been shown to act not only on cellulose, but also on another recalcitrant and crystalline polysaccharide, chitin  [15] [16] [17].