dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgements We thank the  International Institute of Tropical Forestry (IITF) ,  USDA Forest Service  for maintaining facilities of the  Center for Forest Mycology Research (CFMR)  in  Puerto Rico , and the  Forest Products Laboratory  for maintaining facilities and support at CFMR on the  University of Wisconsin campus in Madison, WI. Dentinger and Ainsworth were partly supported by grants from  Defra ,  Natural England  and the  Scottish Natural Heritage. A Long-Term Ecological Research  grant  DEB 0620910  from the  US National Science Foundation (NSF)  to the  University of Puerto Rico -Rio Piedras  in collaboration with IITF,  USDA FS  augmented laboratory equipment used in this research. The  USDA Forest Service , CFMR, provided most of the support. This work was not directly supported by grants, but the following grants were essential in obtaining collections and some sequences used in this work:  US NSF Biodiversity Surveys and Inventories Program  grants to the  Research Foundation of the State University of New York, College at Cortland  ( DEB-9525902  and  DEB-0103621 ), in collaboration with the  USDA-Forest Service, Center for Forest Mycology Research, Forest Products Laboratory in Madison  supported collecting in Belize, the Dominican Republic and  Puerto Rico.
Lindner ,  P. B. Matheny ,  J. -M. Moncalvo  and  M. Padamsee ,  A. Vizzini ,  E. Ercole ) were augmented by sequences and analyses by  P. Baymon ,  B. Dentinger ,  K. K. Nakasone , and  D. Rizzo. Dentinger  provided initial and final ITS analyses and M. Ainsworth re-determined collections deposited at Kew from an unpublished manuscript. Andrew Rodriguez  assisted Aime and Padamsee in preparing sequin files for GenBank submission. In addition to advice from co-authors ( R. Courtecuisse ,  A. Minnis ,  L. Norvell ,  S. Redhead ),  S. Pennycook  provided invaluable advice on nomenclature,  J. David  advised on proper name endings in Latin and Greek, and R. H. Petersen provided sage advice on taxonomy and systematics. We thank curators of the Index Fungorum,  P. M. Kirk , and  Mycobank ,  J. Stalpers  and  A.
de Cock  for correcting and updating records in their databases. We thank the following pre-reviewers of manuscript sections: pigment chemistry by  A. Bresinsky  and  N. Arnold , and introduction, ecology and discussion by  D. Hibbett  and  B. Seitzman. We especially thank  K. K. Nakasone ,  M. J. Richardson  and  J. Glaeser  for full manuscript pre-review, and anonymous journal referees.
Agaricus ovinus Bull. , Herbier de la France 13: 592 and plate 580 (1793)] Lectotype here designated as fig. M in Bulliard, Herbier de la France 13: plate 580 (1793)]; Epitype here designated GEDC0877, coll. Griffith, Ffriddoedd Garndolbenmaen, Wales, UK, 19 Oct 2006, K(M)187568, GenBank sequences KF291228, KF291229, KF291230. Neohygrocybe Herink, Sb. , Severocesk. Mus. , Prír. Vedy 1: 71 (1959). Type species: Neohygrocbye ovina (Bull. : Fr. ) Herink, Sb. Severocesk. Mus. , Prír. Vedy 1: 72 (1959) ≡ Hygrophorus ovinus (Bull. : Fr. ) Fr. , Epicr. syst. mycol. (Upsaliae): 328 (1838) [1836-1838], ≡.
2002). Variable fractions of the RNA spots were found to colocalize with the SMN protein. Quantitative analysis was performed with the Imaris software as described in Materials and Methods. Results showed that the colocalization with SMN was statistically nonrandom with mean values of 22. 1% ± 1. 9% (mean ± SEM; n = 9) for Anxa2, 19. 0% ± 2. 3% (n = 5) for Cox4i2, 16. 4% ± 0. 8% (n = 11) for SepW1, and 11. 8% ± 1. 6% (n = 5) for Vim mRNAs (Fig. 5B ). This indicates that SMN-containing complexes may assist the assembly of specific mRNAs into mRNP particles and their targeting to the neuronal transport system along neurites. To determine whether similar colocalization also occurred with the ectopic SMN protein, we also performed FISH and IF analyses in differentiated NSC-34 cells expressing the 3xFlag-SMN protein (Fig.
RT-PCR analyses Purified coimmunoprecipitated RNA was treated with RQ1 RNase free DNase (Promega), and cDNA was synthesized from 0. 5 μg of RNA and pd(N)6 random oligonucleotides with a First strand cDNA synthesis kit (GE-Healthcare). For semi-quantitative RT-PCR analyses, one-tenth of the reaction was amplified for 25-35 cycles with GoTaq polymerase (Promega) according to the supplier's recommendations. The PCR products were separated on 1. 5%-2. 5% agarose gels containing ethidium bromide and visualized under UV light. The gel images were digitally captured and analyzed using the Image J software. Quantitative RT-PCR analyses were performed with one-twentieth of the cDNA synthesis reaction using the LightCycler 480 SYBR Green I Master kit (Roche) and the LightCycler 480 system. Primer sequences and PCR regimes are available upon request.
Microarray hybridizations and bioinformatic analyses RNA was purified as mentioned above from independent IPs (three for 3xFlag-SMN and two for mock-transfected NSC-34 cells). Probe labeling was done using the GeneChip WT Double-Stranded cDNA Synthesis Kit (Affymetrix) following the manufacturer's protocol. Labeled probes were hybridized to Mouse GeneChip 1. 0 ST Arrays (Affymetrix) using the manufacturer's protocol. Scanning and data collection were done by GeneChip Scanner 3000 7G (Affymetrix) and GeneChip Operating Software. Microarrays were analyzed with the specific R script (http://www. R-project. org/), which includes methods from the Bioconductor  (Gentleman et al. 2004 ) tools package. Probes intensities were extracted from CEL files with the "expresso" function. According to  Bolstad et al.
Gene Ontology analyses The WebGestalt program (http://bioinfo. vanderbilt. edu/webgestalt) (Zhang et al. 2005 ) was used to determine the frequencies and numbers of genes corresponding to SMN-associated mRNAs in the different KEGG biological pathways and to compare them with those in the mouse genome. A hypergeometric distribution test was used to determine the statistical significance of the observed over the expected frequency. P-values <0. 01 were considered statistically significant, and only Gene Ontology categories with more than three genes in the SMN-associated mRNA data sets were considered.
Image acquisition and processing Images were acquired on a DMRA microscope equipped for epifluorescence, and with a 100× PlanApo objective. Digital images were recorded with a 12-bit C4795-NR CCD camera (Hamamatsu). The camera and microscope were controlled by MetaMorph software. Three-dimensional images were deconvolved with Huygens 2. 3 software (Scientific Volume Imaging) using an MLE algorithm. Maximal image projections of the resulting stacks were then converted to 8-bit images and colorized with PhotoShop (Adobe). For quantitative analyses of the RNA granules and SMN protein colocalization, we used the Spot function of Imaris (Bitplane), which allows us to consider only the signal present in foci on three-dimensional images. This software first applies a Gaussian filter of a given size (0.
4 μm in this case) to remove small objects and then identifies local maxima above a given threshold (the same threshold was used for all images of a given data set). A distance of 0. 4 μm between two spots was arbitrarily selected, and the Spot function was run in each channel. The spots that colocalized or did not colocalize were then counted. Each colocalization analysis was performed on a total of six to 15 neurites representing 60-145 spots. The significance of the colocalization data was determined with the unpaired student t-test (Prism software). Image acquisition and analysis were performed on workstations of the Montpellier RIO Imaging facility of the CNRS campus.
ACKNOWLEDGMENTS This work was supported by the  CNRS  and by grants of the AFM to J. S. ,  SMA Europe  to  R. B. , and  ANR RNAtrans  ( ANR-10-Blanc Inter SVSE 4-001 ) to E. B. and F. R. N. B. was supported by a fellowship of the  French Research Ministry. Thanks are due to  Dr. S. Salinas  for precious help with microfluidic chambers and to  Dr. R. A. Hipskind  for critical reading of the manuscript. Affymetrix microarrays were processed in the  Microarray Core Facility of the Institute of Research on Biotherapy, CHRU-INSERM-UM1 Montpellier  (http://irb. chu-montpellier. fr/). This work is dedicated to the memory of our friend and colleague  Henry Neel.
42 - Genscan chromosome GENSCAN00000039588 a 3. 41 Npm1 Nucleophosmin 1 NM_008722 3. 41 Zscan4c Zinc finger and SCAN domain containing 4C NM_001013765 3. 39 Gm6274 Pseudogene chromosome ENSMUST00000118173 a 3. 39 P4ha2 Procollagen-proline, 2-oxoglutarate 4-dioxygenase NM_001136076 3. 38 2700094K13Rik RIKEN cDNA 2700094K13 gene NM_001033166 a From Ensembl database. All other accession numbers are from GenBank database.
Photometric studies by  Herbst & Shevchenko (1999)  showed strong variations of two magnitudes in the V band. Yudin & Evans (1998)  found that the wavelength dependence of polarization toward KK Oph is different from interstellar polarization, indicating a possible intrinsic property of the polarizing material. In this paper, we present the first near-infrared interferometric observations of the UX Ori-type star KK Oph. In Sect. 2, we describe our observations and the data reduction. In Sect. 2 ). Bottom: observed visibilities of the circumstellar environment (at four different PAs; color-coded) and visibility cuts (four lines; same color-coding as the observations) through the visibilities of our two-dimensional, elliptical Gaussian model. We fitted this two-dimensional Gaussian model to all visibilities instead of fitting one-dimensional visibility models to the visibilities corresponding to a single PA.
Discussion The geometric modeling of the KK Oph data showed that the intensity distribution has a strong elongation and that an additional extended component is required to reproduce the visibilities. The best-fit two-dimensional geometric ring model consists of a ring-shaped intensity distribution with an inclination angle of ∼71 • (i. e. , almost edge-on) located at a distance of ∼0. 4 AU from the central star with an extended component contributing to 0. 41 of the total near-infrared flux. To compare KK Oph with other Herbig Ae/Be stars, we plotted its inclination-corrected K-band ring-fit radius into the sizeluminosity relation for Herbig Ae/Be stars (see Fig. 3 ). We show (1) the radius obtained with a model of a circumstellar environment consisting of just an inclined ring and (2) the radius of the model consisting of an inclined ring and an additional extended envelope component.
Acknowledgements. A. Kreplin was supported for this research through a stipend from the  International Max Planck Research School (IMPRS)  for  Astronomy and Astrophysics at the Universities of Bonn and Cologne. V. G. and L. T. were supported in part by the grant of the Presidium of RAS P 21 and grant  NSh. -1625. 2012. 2. They also thank the  Max-Planck-Society  for the support during their stay in Bonn. This research has made use of  NASA's Astrophysics Data System Bibliographic Services.
Phylogenetic analysis Phylogenetic trees were generated using the Neighbor-Joining method of ClustalX (ftp://ftp. ebi. ac. uk/pub/software/clustalw2/). Bootstrap mode (1000 replications) was used for estimating the level  of confidence assigned to the particular nodes in the tree. The tree was visualized with the help of Treeviewer 1. 6. 6 (http://taxonomy. zoology. gla. ac. uk/rod/treeview. html) and rooted using ClustalW (http://www. bioinformatics. nl/tools/clustalw. html). Alignments were coloured using Boxshade (http://www. ch. embnet. org).
Accession numbers Sequence data from this article can be found in the GenBank data library under accession numbers KC633285 (PhDWARF7), KC633286, (PhCPD), KC633287 (PhCYP85), KC633288 (PhDWARF5), KC633289 (PhDET3), KC633290 (PhSMT2), KC633291 (PhROT3), KC633292 (PhDIM), KC633293 (PhDWARF4), KC633294 (PhDET2), KC633295 (PhBRI1), KC633296 (PhPSK9), KC633297 (PhPSK8), KC633298 (PhBEH2), KC633299 (PROTEIN INTERACTING with BEH2 1 (PIB1), KC633300 (PIB8), KC633301 (PIB16), KC633302 (PIB16), KC633303 (PIB25), KC633304 (PIB41), KC633305 (PIB60), KC633306 (PIB67), KC633307 (PIB81), KC633308 [PROTEIN INTERACTING with GSK3 1 (PIG1)], KC633309 (PIG3), KC633310 PIG4), KC633311 (PIG16), KC633312 (PIG21), KC633313 (PIG28) and KC633314 (PIG93).
cd2 and cd3 encode the petunia CYP90A1 and CYP85A1 homologues, respectively The biochemical analysis of BR content in cd mutants in some cases provided clues on the identity of the affected gene. Using a combination of database mining and primer design based on sequences of Solanaceae species (e. g. tomato, tobacco, potato, etc. ; Supplementary Table S1 at JXB online), it was possible to clone gene and/or cDNA fragments of petunia homologues of the Arabidopsis BR biosynthesis genes CYP90A1, DWF5, CYP85A1, DIM, DWF7, SMT2, DETIOLATED2 (DET2), DWARF4 (DWF4), and CYP90C1 (ROT3), and a number of signalling genes (see below). The obtained sequence data of petunia BR biosynthesis genes were used to determine in each cd mutant if any of these known genes is disrupted by a dTph1 insertion. When DNA isolated from cd2 plants (Fig.
3F ). The identity of cd3 P2014 was revealed by mining a 3D indexed petunia insertion database generated from the ENZA population  (Vandenbussche et al. , 2008). A transposon-flanking sequence unique for family P2014 showed homology to CYP85A1  (Bishop et al. , 1999). The dTph1 element was identified in exon 6 of the petunia CYP85A1 homologue (Fig. 3E ). In progeny of cd3 P2014 , a stable mutant was shown to carry a mutant allele containing a 7 bp footprint (cd3 S2007 ). In plants carrying the cd3 D2213 allele, a transposable element could not be identified in the petunia CYP85A1 homologue. However, sequencing of the CYP85A1 coding sequence from one of the progeny (family cd3-K2066) showed an 8 bp footprint in exon 1, indicating the previous existence of a transposable element at this site (Fig.
3E, F ). Analysis of cd1 and cd9 mutants did not reveal transposon insertions in the putative petunia homologues of DWARF5, DET3, DIM, DWARF7, SMT2, DET2, DWARF4, or ROT3. It must be said that the complete sequences of these genes are currently unknown. As 3' sequences (DIM), 5' sequences (DWARF5, CYP85, DWARF7, SMT2), or both (DET3, DET2, DWARF4, ROT3) are unknown, it cannot be excluded that there are dTph1 insertions present in these parts of the gene. In the 3D indexed petunia insertion database  (Vandenbussche et al. , 2008) , transposon insertions were identified in petunia homologues of DIM and DET2, both without phenotypic consequences, suggesting that these genes are redundant in petunia (see the Supplementary data at JXB online).
cd10 is mutated in the petunia BRI homologue In Arabidopsis, BRs are perceived by the leucine-rich repeat (LRR) receptor-like kinase BRI1  (Li and Chory, 1997). From the P. hybrida expressed sequence tag (EST) collection, a sequence that showed a high degree of similarity to the 3' end of BRI1 (GenBank accession no. CV294320) was retrieved. The full-length mRNA sequence of W138 PhBRI1 was obtained via RACE-PCR and SOTI-PCR (see the Materials and methods). Comparison of the derived amino acid sequence of PhBRI1 with AtBRI1, OsBRI1, LeBRI1, and NbBRI1 revealed 63. 3, 53. 4, 84. 1, and 91. 3% identity, respectively (Fig. 4A ; Supplementary Fig. S1  at JXB online). Similar to the other identified BRIs, PhBRI1 has an LRR domain, a putative leucine-zipper motif, a transmembrane domain to anchor the protein in the plasma membrane, and a cytoplasmic kinase domain (Fig.
Acknowledgements We thank  Allan Awanijan  and  Longinus Igbojionu  for their assistance during some of the cloning experiments,  Iris Bijker  and  Serena Della Pina  for their help with the BiFC experiment, and  Maartje Kuijpers  for plant care. NV was supported by a grant from the  Netherlands Organisation for Scientific Research (NWO). TY was supported by the  MEXT-Supported Program for the Strategic Research Foundation at Private Universities , 2008Universities, -2012.
The CluePedia Cytoscape plugin is a search tool for new markers potentially associated to pathways. CluePedia calculates linear and non-linear statistical dependencies from experimental data. Genes, proteins and miRNAs can be connected based on in silico and/or experimental information and integrated into a ClueGO network of terms/pathways. Interrelations within each pathway can be investigated, and new potential associations may be revealed through gene/protein/miRNA enrichments. A pathway-like visualization can be created using the Cerebral plugin layout. Combining all these features is essential for data interpretation and the generation of new hypotheses. The CluePedia Cytoscape plugin is user-friendly and has an expressive and intuitive visualization.
Pathway interactions from custom and in silico data CluePedia calculates statistical dependencies (correlation) for markers of interest from experimental data. Four tests for investigating linear and non-linear dependencies between variables are implemented: Pearson correlation, Spearman's rank, Distance correlation  (Szekely and Rizzo, 2009)  and the recently described Maximal Information Coefficient (MIC)  (Reshef et al. , 2011). These tests can be applied simultaneously or individually to analyze whole-input file (e. g. Affymetrix datasets), selected markers versus the entire dataset or among selected markers only. The resulting file is added to CluePedia as an additional resource for further analysis. Experimental data can be normalized and visualized next to the nodes using adjustable thresholds.
enrichment, and the network will be updated with the specified number of top scored interactions meeting all threshold criteria. Customizable filters allow the visualization of highest common/ specific interaction scores within the data sources selected. The user can easily modify the action type displayed (e. g. activation, inhibition), as well as the color of nodes and edges. Importantly, this enrichment can be used within ClueGO networks of pathways. Newly enriched genes already known to be associated with a pathway will be automatically linked to it. Another original feature of CluePedia expands ClueGO terms into nested networks. Like this, a pathway can be investigated in detail to reveal how known gene interrelations are modulated within the experimental context used, and which could be the newly associated genes/miRNAs.
CASE STUDY T helper 1-related genes  (Mlecnik et al. , 2010)  and their corresponding pathways are simultaneously visualized in a CluePedia network (Fig. 1a ). The 'Regulation of IFNG signaling' pathway is investigated as a nested network. In silico interactions within the pathway (Fig. 1b ) can be compared with linear and nonlinear interrelations derived from expression data from normal colon and colon tumors [E-MTAB-57  (Ancona et al. , 2006 ), E-GEOD-37892, ArrayExpress  (Parkinson et al. , 2011) ]. Normalized gene expression data are visualized next to the node (Fig. 1c ). The network can be switched to a pathway-like view (Fig. 1d ), displaying the cellular location of genes. The pathway is enriched with correlating genes from normal and tumor data as well as with predicted miRNAs that could play a crucial role in modulating interferon gamma (IFNG) signaling regulation (Fig.
Advances in Distributed Computing and Artificial Intelligence Jornual Special Issue #1 http://adcaj. usal. es In the first results presented in this paper, we make the assumption that all the agents are cooperative, and no deficient; that is to say that they cannot send wrong knowledge, voluntary, or not (if a sensor has a dysfunction). In our current approach, the trust on a knowledge depends only on the date from which the knowledge has been updated or created. We plan to introduce the notion of trust that depends of the sender; for example, if an agent sends a knowledge that is not coherent with the information from other agents, these latters can decide to put in quarantine the faulty agent, like in  [VERCOUTER & JAMONT, 2011]  for example. In order to decide to integrate new knowledge about object o, an agent should, at first, collect data from multiple sources, to be able then to evaluate their coherence.
A B C Figure  2. Development of orientation maps measured by chronic optical imaging in ferrets. A, Recorded selectivity (red square markers) and stability values (blue round markers) for all eight ferrets, as a function of postnatal day [replotted data from the study by  Chapman et al. (1996), their Figs. 4, 7]. Stability is quantified by the average difference in preference of each orientation map with the final map, as defined by Equation  1. A common selectivity and stability scale is used to allow comparison between ferrets. All values are in arbitrary normalized units, using the lowest recorded value as the zero reference point and the highest recorded value as the maximum (see Materials and Methods). B, The mean selectivity and stability across ferrets as a function of postnatal day, with the Ϯ95% confidence intervals for each day indicated by the shaded area around the mean line.
1, ␣ E ϭ 0. 0, and ␣ I ϭ 0. 3, respectively. The density-specific value used in the equation above is then calculated as ␣ ϭ ␣ A / A , where A is the number of connections per connection field in the afferent projection. To increase computational efficiency, lateral excitatory connections do not learn during development in the simulations presented here. The effect of lateral learning has been explored in detail in previous similar models  (Miikkulainen et al. , 2005) , and the maps generated by the GCAL model are visually indistinguishable regardless of whether or not lateral excitatory connections are plastic (data not shown). Analysis of model maps. Model orientation maps are calculated based on the vector average method  (Blasdel and Salama, 1986; Miikkulainen et al. , 2005). Sine grating inputs that cover the full range of parameter values (combinations of all orientations, frequencies, and phases) are presented, and for each orientation, the peak response of the neuron is recorded.
Apart from the specific models introduced, we showed how pinwheel density can be used to assess the quality of simulated orientation maps. Of course, pinwheel density is just one of many factors that could be measured and compared between maps, but we argue that it is a useful metric for several reasons. First, there is a unique, cross-species reference value, which allows objective decisions about map quality. Having a pinwheel density close to is a necessary, if not always sufficient, criterion for a map to be considered biological. Second, reliable high performance on this measure requires that the maps be smooth (thus implicitly incorporating alternative metrics like LHI;  Nauhaus et al. , 2008)  and periodic (thus implicitly incorporating the ringness in the Fourier power spectrum). Third, the reference value of was measured empirically through careful analysis of the raw imaging data that ensured that pinwheel density is a genuine, structural property of the underlying orientation map organization, not simply an artifact of the particular filtering process used.
, 2005)  for what is possibly the first work on this and  (Khatri et al. , 2012)  for a recent survey]. The simplest idea one may think of is to highlight the set of metabolites identified in the experiment that have significantly changed their concentration, let us call them discriminating compounds, and then to visually analyze their interconnections. This is what is done notably in  Xia et al. (2012). However, like a number of other approaches on metabolite set enrichment analysis, the projection of enriched metabolites is done on pathways instead of the whole network, thereby missing (alternative) pathways not annotated in current databases, or more generally paths traversing several pathways. For genome-scale networks, the metabolism of a whole organism is considered, which may be large  (Thiele and Palsson, 2010) , whereas a metabolic perturbation caused by some stress condition may impact only a small portion of this complex network.