dataesr/scientific-paragraphs-categorization huggingface dataset:
Discussion A validated 'voice localizer' for individual localization of higher-level auditory cortical fields We present and make available to the community a 10-min 'voice localizer' protocol for identification of voice-sensitive cortex in the human brain. Here we 'validate' this localizer showing that the TVA can be identified at the single-subject level in 94% of cases with a high test-retest correlation (N 0. 9). The experiment (stimuli and script), raw data and core analysis scripts at available on-line (http://dx. doi. org/10. 7488/ds/280). Similalry the probabilistic and cluster maps of the TVA are available at: http://vnl. psy. gla. ac. uk/resources. php and all the results maps are on Neurovault (http://neurovault. org/collections/33/). The short duration of the voice localizer makes it suitable as an addition to specific auditory protocols as a means of individually identifying areas of higher-level auditory cortex in a standardized way: whereas procedures exist to identify core regions of auditory cortex based on the known tonotopic organization of these fields  (Formisano et al.
Recently, a new approach  [21]  has been developed to measure transient phaseamplitude coupling directly in an event-related manner. Whereas ideally, their approach of analyzing phase-amplitude relations with respect to the stimulus onset should avoid some eventrelated artifacts, it is questionable whether the marker actually works as intended (see Supplementary results:Phase-amplitude coupling for event-related potentials). Ultimately solving these questions requires a formal causal analysis between the spectral variables of different bands (see also Supplementary discussion: Causality methods). Analysis of between-channel phase-amplitude coupling  [22, 23]  is less likely to be the result of a driving input to a single area. In this research intracranial human data was used to identify the spatial maps of the low frequency (phase providing) and high frequency (amplitude providing) components.
An obvious first step is to adopt stricter standards and canonical procedures for CFC analysis. To this end we suggested a -probably incomplete -list of controls that should be routinely checked. We have also attempted to organize the current modeling/statistical approaches to CFC in order to better identify their respective advantages and pitfalls and to point out where further methodological advances are required. We close by suggesting to always use the term "cross frequency correlation" instead of "coupling", unless coupling is unequivocally demonstrated. before being digitized at 32 kHz. For the analysis, data were low-pass filtered with 240 Hz, down sampled to 500 Hz and cut into 60 trials with 50 s each. Visual stimulation. A sequence of red LED light pulses with random duration (uniform distribution between 1 ms and 2 s) and random inter pulse interval (uniform distribution between 1 ms and 5 s) was triggered via the parallel port using MATLAB and the Psychophysics Toolbox extension  [5, 6].
A light guide projected the full field flashes onto the retina. Data collection for the Supplementary Figure  4  Recordings. We analyzed electrocorticograms from 2 subjects with pharmacoresistant epilepsy who had implanted strip electrodes (AD-Tech) on their visual cortex for diagnostic purposes. The electrodes were referenced to linked mastoids, amplified (Schwarzer GmbH), and recorded at a sampling rate of 1000 Hz. The location of electrode contacts was ascertained by MRI. Visual stimulation. The subjects were presented with brief (150 ms) noisy images with or without a person in them. The subjects had to indicate via a button press whether they had perceived a person or not and whether the person was male or female. The pre-stimulus time window ranged from -1000 ms to 0 relative to stimulus onset.
We evaluated this locking precision by comparing the jitter between the highfrequency component and stimulus onset with the jitter between the high-frequency component and the low-frequency component. More precisely, this jitter was computed as the variance over trials of the difference between the time of maximal high frequency amplitude and stimulus onset, and between the time of maximal high-frequency amplitude and any phase of the lower frequency component, respectively. For our recorded data it turned out that the jitter between the highfrequency component and stimulus onset was always smaller than the jitter between the highfrequency component and any phase of the low-frequency component (Supplementary Figure  3F ). This suggests that the high-frequency component is more affected by the stimulus than by the phase of any low-frequency component.
Acknowledgements This work was supported by the  Max Planck Society. RV, VP and MW were supported by  LOEWE grant  " Neuronale Koordination Forschunsgschwerpunkt Frankfurt (NeFF) ". RV also acknowledges the support from the  Hertie Foundation  and the projects PUT438 and SF0180008s12 of the  Estonian Research Council. VP received financial support from the  German Ministry for Education and Research (BMBF)  via the  Bernstein Center for Computational Neuroscience (BCCN) Göttingen  under Grant No. 01GQ1005B.
In silico analyses Alignment of amino acid sequences was performed with Clustal X (www. clustal. org) and Phylogene (acces. ens-lyon. fr/biotic/evolut/phylogene/accueil. htm) softwares. Poliphen 2 (http:// genetics. bwh. harvard. edu/pph2), Predict SNP (http://loschmidt. chemi. muni. cz/predictsnp) and UMD-HTS (www. umd-hts. eu/WHTS9), were used to appraise the potential pathogenicity of each allelic variant.
For these 2 transcription factors, the mechanisms of delayed deficiency remain however unknown  [4]. Our functional studies did not show any deleterious effect of the three allelic variants DelK242, N271S and Q346R. Despite the fact that these variants have not been identified in SNP databases (data not shown), and that the patients were presenting with phenotypes compatible with LHX4 mutations, our results supported their classification as rare polymorphisms (ie. they were not responsible for the phenotype of CPHD). Of note, the delK242 variant has been found in a heterozygous state in 3 individuals (3 out of 6259, i. e. , 0. 05%) from an American population, (see Exome Variant Server website URL: http://evs. gs. washington. edu/EVS/). Other genes coding for transcription factors have been screened, but no clear etiology could be identified.
PATIENTS AND METHODS Patients The primary objective of the study was to compare OS between patients who received azacitidine as front line therapy and patients who received conventional treatment, including best supportive care and intensive AML chemotherapy, in patients with higher-risk MDS. Secondary end points were to examine risk of AML evolution after the different alternatives and to recognize predictive factors for OS and risk of AML progression. For these purposes, this retrospective study included all consecutive higherrisk MDS patients (defined by FAB and WHO morphological criteria and having an IPSS risk score of 1. 5 or more-intermediate-2 and high-risk groups) with sufficient follow-up data reported to the registry of the GESMD between 2000 and 2013. Reporting criteria did not change with time.
Patients with therapy-related MDS were included in the analysis whereas patients receiving an allogeneic hematopoietic cell transplantation were excluded. Patient and disease characteristics recorded included demographic data, such as date of birth and sex, data at diagnosis, including date of MDS diagnosis, hemoglobin level, white blood cell leukocytes, neutrophil, and platelet counts, percentage of blasts in peripheral blood and bone marrow, cytogenetic abnormalities (classified as good, intermediate or poor according to the IPSS classification), serum level of lactate dehydrogenase (LDH), FAB and WHO 7 morphologic classifications, IPSS risk score, type and date of starting treatment, date of AML progression, date of death and date of last follow-up. All data were double-checked for data inconsistency.
A similar scheme to the one previously reported for OS was used for analyzing the subdistribution from diagnosis to AML evolution. Due to the presence of competing risks, risk of progression to AML was analyzed by cumulative incidence methodology; thus, for multivariate analysis of this end point, the Fine and Gray proportional hazards regression method  12  was used. Two sided P-values o0. 05 were considered as statistically significant in all analyses. All the statistical studies and graphs were done using R software (version 3. 0. 1) with the packages 'Survival', 'cmprsk' and 'ggplot2' (R Foundation for Statistical Computing, Vienna, Austria, available at http:// www. R-project. org/). R code is available on request.
ACKNOWLEDGEMENTS We thank all the contributors to the  Spanish Registry of Myelodysplastic Syndromes  for their efforts to keep the database. We also thank  Teresa Cedena ,  Beatriz Arrizabalaga ,  Almudena Fernández ,  Fernando Ramos ,  Nicolas Diaz ,  Rosa Coll ,  María Calbacho ,  Jose Falantes ,  Bernardo González  and  Santiago Bonanad  for their help to collect additional data.
, 2013). After sequencing, peak detection was performed using the MACS software  ([Zhang et al. , 2008]  http:// liulab. dfci. harvard. edu/MACS/). Peaks were then annotated with GPAT  (Krebs et al. , 2008)  using a window of ±10 kb (or as indicated in the figures) relative to the transcription start site of RefSeq transcripts. Global clustering analysis and quantitative comparisons were performed using seqMINER (  [Ye et al. , 2011]  http://bips. u-strasbg. fr/seqminer/) and R (http://www. r-project. org/). The public human foreskin melanocyte H3K27ac data were taken from the Geo entry GSM958157. De novo motif discovery on FASTA sequences corresponding to windowed peaks was performed using MEME-ChIP. Motif correlation matrix was calculated with in-house algorithms using JASPAR database. mRNA preparation, quantitative PCR and RNA-seq mRNA isolation was performed according to standard procedure (Qiagen kit, Venlo, Holland).
Motif analysis Searching of known TF motifs from the Jaspar 2014 motif database at BRG1-bound sites was made using FIMO  (Grant et al. , 2011)  within regions of 200 bp around peak summits, FIMO results were then processed by a custom Perl script which computed the frequency of occurrence of each motif. To assess the enrichment of motifs within the regions of interest, the same analysis was done 100 times on randomly selected regions of the same length as the BRG1 bound regions and the results used to compute an expected distribution of motif occurrence. The significance of the motif occurrence at the BRG1-occupied regions was estimated through the computation of a Z-score (z) with z = (x -μ)/σ, where:x is the observed value (number of motif occurrence), -μ is the mean of the number of occurrences (computed on randomly selected data), -σ is the standard deviation of the number of occurrences of motifs (computed on randomly selected data).
Acknowledgements We thank,  S Gygi  and  R Tomaino  for mass-spectrometry analysis,  A Hamiche  for advice on tandem immunopurification,  L Larue  for the Tyr-Cre mice,  P Chambon  and  D Metzger  for the floxed Smarca4 mice,  E Sviderskaya  and  D Bennet  for the Hermes cells,  B Jost , and all the staff of the IGBMC high throughput sequencing facility, a member of 'France G énomique' consortium ( ANR10-INBS-09-08 ). This work was supported by grants from the  CNRS , the  INSERM , the  Fondation ARC pour la Recherche contre le Cancer , the  Ligue Nationale et Départementale R égion Alsace contre le Cancer  and the  Institut National du Cancer (INCa)   2011-1-PL BIO-03-INSERM 16-1 , the  ANR-10-LABX-0030-INRT French  state fund through the  Agence Nationale de la Recherche  under the frame programme  Investissements d'Avenir labelled   ANR-10-IDEX-0002-02.
MeTHods Methods and any associated references are available in the online version of the paper. Accession codes. The Illumina reads have been submitted to the Sequence Read Archive (SRA) under accession SRA266458. The assembly and annotation are available under accession PRJNA281024. DNA sequences reported in this manuscript have been submitted to GenBank under accessions KT202232-KT202235 and KT428875. Note: Any Supplementary Information and Source Data files are available in the online version of the paper.
Annotation of the region associated with an inversion. A preliminary annotation for the inverted region was generated with the Maker package (version 2. 31-8)  35. We first composed a set of high-confidence reference sequences from UniProt by selecting bird proteins that were classified as full length and supported by either proteomics or transcriptomics (74,138 sequences). As additional input, we collected available 454 sequencing data from the SRA available under accession SRA049313. Reads were assembled with the Trinity package (release 2014-07-07)  36  into 16,746 transcripts. Finally, to improve the accuracy of the annotation process, we modeled new repeat sequences from a preliminary genome assembly and used this library in combination with a curated repeat library for vertebrates included in the RepeatMasker package.
From these data, we generated two complementary gene builds: one based directly on the aligned sequences to most accurately reflect the evidence data ('evidence build') and a second set of gene models seeded from ab initio (de novo) gene predictions generated by the chicken reference profile model, included with the Augustus package (version 2. 7)  37. Both builds were compared and reconciled for the target region using the WebApollo curation platform  38. Functional annotation of candidate transcript models was performed through similarity searches  39  against the UniProt/ SWISS-PROT reference protein set (downloaded May 2014) in combination with the prediction of functional motifs and domains via the InterProScan package (release 5. 7-48. 0)  40. Whole-genome resequencing and SNP calling.
Amplified fragments were either analyzed by agarose gel electrophoresis or subjected to Sanger sequencing using standard methods. DNA sequences were analyzed using CodonCode Aligner 5. 1. 4 software. Four TaqMan SNP genotyping assays (Life Technologies) diagnostic for the Satellite or Faeder haplotype were designed (Supplementary Table  8 ). Standard TaqMan allele discrimination assays were performed using an Applied Biosystems 7900 HT real-time PCR instrument.
Genome sequencing was funded by the Knut and Alice Wallenberg foundation (to L. A. ) and by grants of the National Basic Research Program of China (973 Program; 2013CB835204), the Shenzhen municipal government of China (DRC-SZ (2014) 843), the Key Laboratory of Genomics, the Chinese Ministry of Agriculture, the Platform of Whole Genome-Based Molecular Breeding and the ShenZhen Engineering Laboratory for Genomics-Assisted Animal Breeding. The Swedish Research Council funded the field work (grants 1989-2546, 1992-2685 and 2013-5418 to J. H. and grant 2001-6005 to F. W. ). D. S. T. benefited from an Erasmus-Mundus fellowship associated with the European Graduate School of Animal Breeding and Genetics. The SNP&SEQ Technology Platform, supported by Uppsala University and Hospital, the Science for Life Laboratory and the Swedish Research Council (80576801 and 70374401), contributed to genome sequencing.
Features of the Azadinium spinosum genome The 454-shotgun sequencing on a GS Junior System yielded 103,860 high quality genomic reads (37 Mb) (GenBank accession number: SRR1576789). The dataset provided substantial insights into the genomic structure and organization of A. spinosum, although the total coverage was rather low for such a big genome. The distribution of genomic features in A. spinosum based upon sequence analysis with the program RepeatMasker  [34]  is given in Table  1. This analysis revealed the overall genomic GC-content to be 49. 7% which was lower than the transcriptome GC content of 60. 3%. About six percent of the assembled A. spinosum genome consisted of simple repeats 2-10 bp in length. Of these, repeats (ATG)n, (TTG)n and (CAT)n were the most common, accounting for 0.
005% of the total number of bp. Low-complexity repeats, primarily poly-purine/poly-pyrimidine stretches, or regions of extremely high AT or GC content, made up 0. 44% of the A. spinosum genome and, of these, A-rich repeats were most abundant followed by GA-rich and then G-rich repeats (Table  1 ). Annotation of the genomic sequences with blastx against the Swissprot database using the blast2go tool resulted in a low annotation success of the genomic DNA data. From a total of 103,860 genomic DNA reads, 860 (0. 8%) were identified as potential coding sequence. This corresponds to 374,136 bp (0. 99% from a total of 37 Mb; Table  1 ), with an average read length of 472 bp. Combined with a retrovirus content of 0. 17%, the protein coding sequence in A. spinosum therefore accounts for 1. 16% of the total genomic DNA (Table  1 ).
Features of the Azadinium spinosum transcriptome A total of 75,455 contigs were assembled after Illumina RNA sequencing, of which 844 were carrying the complete spliced leader sequence (DCCGUAGCCAUUUUGG-CUCAAG, D = U, A or G). From these contigs, 69,956 protein fragments were predicted, of which 28,357 (40%) could be assigned to proteins in the PFAM database with an e-value <0. 001. The completeness of the transcriptomic dataset was determined using the CEGMA tool, which represents a database of 458 highly conserved eukaryotic core genes present in a wide range of eukaryotic taxa  [35]. In the A. spinosum transcriptome data, 430 (94%) of these conserved gene families were detected. Additionally, we searched for the presence of key metabolic pathways enzymes (for glycolysis, tricarboxylic acid [TCA], pentose phosphate and oxidative phosphorylation pathways) in our transcriptomic data.
Among all enzymes checked, only hexokinase (for glycolysis) was absent; instead, we found glucokinase, an enzyme capable of performing a similar reaction to the conversion of glucose into glucose-6-phosphate. Further transcripts highly similar to all histone encoding genes (H2A, H2B, H3, H4), histone methyl-transferases and histone deacetylase were present (see Additional file 1). No evidence supporting a weaker expression of these genes was detected (median reads per base: histones 3. 1, all other genes 2. 9). Analysis of the translated protein sequences, searched against the PFAM database revealed the most common protein domains in A. spinosum. Among these protein domains, protein kinases and, ion transportersboth common domains for protein-protein interaction and ABC-transporterswere found (Figure  1 , see Additional file 2).
We compared the predicted protein data of A. spinosum (expressed sequence tag, EST data) with EST or whole genome data from 18 other dinoflagellate species (see Additional file 3). For comparison within the Rhizaria Alveolate Stramelopile (RAS) group proteins associated with cellular signal transduction, we conducted a database comparison of three diatoms (Fragilariopsis cylindrus  [36] , Thalassiosira pseudonana  [37]  and Phaeodactylum tricornutum  [38] ), the haptophyte Emiliania huxleyi  [39] , and the cryptomonad Guillardia theta  [40]. In addition, the chlorophyte Chlamydomonas reinhardtii  [41]  served as a representative species of the Plantae. BLAST analysis revealed that 18% of the proteins found in A. spinosum represent orphan genes (proteins found in a given species that lack homologues in other species), 37% have homologues  in other dinoflagellate species, 19% are shared between A.
We identified a number of proteins putatively involved in this pathway, by both BLAST analysis and by accessing the KEGG-database. Among these proteins, KS, KR, AT, ACP, ACPS, TE and MT were identified, whereas ER proteins were absent from our dataset (Table  3 ). Next, we assembled six putative PKS mRNA sequences out of the reads matching PKS sequences (GenBank Accession numbers: KM588916-KM588921; Table  4 ), of which four were complete and two were partial (splice variants). In five of the six cases, BLASTx analysis revealed high similarity to those of another dinoflagellate, A. ostenfeldii, for which three sequences exist in Gen-Bank (Ac0019, Ac0038 and 10-x_J14). The remaining sequence was found to be most similar to another dinoflagellate sequence, Heterocapsa triquetra; strain HTE5908 (Table  4 ).
The 20 most abundant protein domains in the Azadinium spinosum transcriptome. Black bars denote the number of proteins with a certain domain. Black and grey bars together indicate the total number of proteins for the first n domains. The subset and order of domains was iteratively chosen to maximize the total number of proteins (black plus grey bars) for the first n domains. The 20 most abundant domains make up 28% of the total proteome  (28, 357)  that could be matched to the PFAM database (e-value 0. 001). Each of these six transcripts was also analysed in silico, yielding theoretical protein sizes of approximately 100 KDa after translation for each transcript. Additionally, the presence of a single ketide synthase (KS) domain in each individual transcript was supported by comparing these transcripts against the PFAM database.
Chlorophytes In A. spinosum, the mean putative protein length among the six identified PKS transcripts was estimated to be 865 ± 84 aa (± s. e. m. ). The average protein consisted of an N-terminal region (330 ± 64 aa), a central KS domain with an estimated size of 388 ± 29 aa, and a C-terminal region (147 ± 29 aa). A conserved motif was found in the Nterminal region of all transcripts (Figure  4 ), which did not yield any database hits via BLAST and PFAM domain search algorithms. Nevertheless, the motif showed sequence similarity to K. brevis, A. ostenfeldii and H. triqueta sequences in the database. This sequence similarity varied from 28 to 51% between K. brevis and A. spinosum, whereas the maximum sequence similarity between A. spinosum and H. triqueta (HTE 6310), and between A. spinosum and A.
In a final phylogenetic analysis focusing on the PKS Cterminal region, the BLAST algorithm identified 25% (See figure on previous page. ) Figure  2  Transcriptomic data reveals proteins conserved between different dinoflagellates and outgroup species. Comparison between 19 dinoflagellate species including A. spinosum, three diatoms, the haptophyte Emiliania huxleyi, and the cryptomonad Guillardia theta. The size of the pie charts is proportional to the number of sequences analysed. The colour code indicates the hierarchical classification into homology groups defined by descending evolutionary distance, e. g. , if a sequence had BLAST hits in the outgroup species Chlamydomonas reinhardtii, it was classified as "homologues in outgroup" irrespective of potential homologues inside the phylum.
These findings are similar to published datasets from other dinoflagellates, however in our analysis the number of domains involved in metabolism seems to be less dominant within the top 20 domains  [4, 5]. As an interesting exception, hexokinase, the key enzyme of glycolysis, was absent from the A. spinosum transcriptome. The absence of the hexokinase is an indication that enzymatic activity might have been replaced by another   --------------------M   enzymein this case, glycokinase -capable of performing similar enzymatic reactions. This is supported by the fact that the reaction catalyzed by both enzymes is essential for energy metabolism. A similar finding was also observed in another study  [46]. This could hint towards a general phenomenon in dinoflagellates that should be investigated further, because it suggests a major difference in a basic metabolic pathway.